id	sid	tid	token	lemma	pos
fcis-31208	1	1	frontiers	frontier	NOUN
fcis-31208	1	2	in	in	ADP
fcis-31208	1	3	computing	computing	NOUN
fcis-31208	1	4	and	and	CCONJ
fcis-31208	1	5	intelligent	intelligent	ADJ
fcis-31208	1	6	systems	system	NOUN
fcis-31208	1	7	issn	issn	VERB
fcis-31208	1	8	:	:	PUNCT
fcis-31208	1	9	2832	2832	NUM
fcis-31208	1	10	-	-	SYM
fcis-31208	1	11	6024	6024	NUM
fcis-31208	1	12	|	|	NOUN
fcis-31208	1	13	vol	vol	NOUN
fcis-31208	1	14	.	.	PROPN
fcis-31208	2	1	12	12	NUM
fcis-31208	2	2	,	,	PUNCT
fcis-31208	2	3	no	no	INTJ
fcis-31208	2	4	.	.	NOUN
fcis-31208	2	5	3	3	NUM
fcis-31208	2	6	,	,	PUNCT
fcis-31208	2	7	2025	2025	NUM
fcis-31208	2	8	36	36	NUM
fcis-31208	2	9	research	research	NOUN
fcis-31208	2	10	on	on	ADP
fcis-31208	2	11	machine	machine	NOUN
fcis-31208	2	12	learning	learn	VERB
fcis-31208	2	13	optimization	optimization	NOUN
fcis-31208	2	14	algorithm	algorithm	NOUN
fcis-31208	2	15	based	base	VERB
fcis-31208	2	16	on	on	ADP
fcis-31208	2	17	qubo	qubo	PROPN
fcis-31208	2	18	model	model	PROPN
fcis-31208	2	19	minyi	minyi	PROPN
fcis-31208	2	20	ye	ye	PROPN
fcis-31208	2	21	*	*	PROPN
fcis-31208	2	22	,	,	PUNCT
fcis-31208	2	23	lanqing	lanqe	VERB
fcis-31208	2	24	wu	wu	PROPN
fcis-31208	2	25	,	,	PUNCT
fcis-31208	2	26	guanquan	guanquan	PROPN
fcis-31208	2	27	zhu	zhu	PROPN
fcis-31208	2	28	,	,	PUNCT
fcis-31208	2	29	jiaguo	jiaguo	PROPN
fcis-31208	2	30	jiang	jiang	PROPN
fcis-31208	2	31	school	school	PROPN
fcis-31208	2	32	of	of	ADP
fcis-31208	2	33	mathematics	mathematic	NOUN
fcis-31208	2	34	and	and	CCONJ
fcis-31208	2	35	information	information	NOUN
fcis-31208	2	36	science	science	NOUN
fcis-31208	2	37	,	,	PUNCT
fcis-31208	2	38	guangzhou	guangzhou	PROPN
fcis-31208	2	39	university	university	PROPN
fcis-31208	2	40	,	,	PUNCT
fcis-31208	2	41	guangzhou	guangzhou	PROPN
fcis-31208	2	42	guangdong	guangdong	PROPN
fcis-31208	2	43	,	,	PUNCT
fcis-31208	2	44	510006	510006	NUM
fcis-31208	2	45	,	,	PUNCT
fcis-31208	2	46	china	china	PROPN
fcis-31208	2	47	*	*	PUNCT
fcis-31208	2	48	corresponding	correspond	VERB
fcis-31208	2	49	author	author	NOUN
fcis-31208	2	50	:	:	PUNCT
fcis-31208	2	51	minyi	minyi	PROPN
fcis-31208	2	52	ye	ye	PROPN
fcis-31208	2	53	(	(	PUNCT
fcis-31208	2	54	email	email	NOUN
fcis-31208	2	55	:	:	PUNCT
fcis-31208	2	56	32215150090@e.gzhu.edu.cn	32215150090@e.gzhu.edu.cn	NUM
fcis-31208	2	57	)	)	PUNCT
fcis-31208	2	58	abstract	abstract	NOUN
fcis-31208	2	59	:	:	PUNCT
fcis-31208	2	60	this	this	DET
fcis-31208	2	61	paper	paper	NOUN
fcis-31208	2	62	deals	deal	VERB
fcis-31208	2	63	with	with	ADP
fcis-31208	2	64	the	the	DET
fcis-31208	2	65	research	research	NOUN
fcis-31208	2	66	of	of	ADP
fcis-31208	2	67	qubo	qubo	PROPN
fcis-31208	2	68	model	model	NOUN
fcis-31208	2	69	optimization	optimization	NOUN
fcis-31208	2	70	machine	machine	NOUN
fcis-31208	2	71	learning	learn	VERB
fcis-31208	2	72	algorithm	algorithm	NOUN
fcis-31208	2	73	,	,	PUNCT
fcis-31208	2	74	including	include	VERB
fcis-31208	2	75	the	the	DET
fcis-31208	2	76	following	following	NOUN
fcis-31208	2	77	:	:	PUNCT
fcis-31208	2	78	first	first	ADV
fcis-31208	2	79	,	,	PUNCT
fcis-31208	2	80	discretize	discretize	VERB
fcis-31208	2	81	the	the	DET
fcis-31208	2	82	continuous	continuous	ADJ
fcis-31208	2	83	parameters	parameter	NOUN
fcis-31208	2	84	of	of	ADP
fcis-31208	2	85	the	the	DET
fcis-31208	2	86	machine	machine	NOUN
fcis-31208	2	87	learning	learn	VERB
fcis-31208	2	88	model	model	NOUN
fcis-31208	2	89	into	into	ADP
fcis-31208	2	90	binary	binary	ADJ
fcis-31208	2	91	variables	variable	NOUN
fcis-31208	2	92	,	,	PUNCT
fcis-31208	2	93	and	and	CCONJ
fcis-31208	2	94	construct	construct	VERB
fcis-31208	2	95	the	the	DET
fcis-31208	2	96	qubo	qubo	PROPN
fcis-31208	2	97	objective	objective	ADJ
fcis-31208	2	98	function	function	NOUN
fcis-31208	2	99	;	;	PUNCT
fcis-31208	2	100	then	then	ADV
fcis-31208	2	101	optimize	optimize	VERB
fcis-31208	2	102	the	the	DET
fcis-31208	2	103	qubo	qubo	NOUN
fcis-31208	2	104	model	model	NOUN
fcis-31208	2	105	by	by	ADP
fcis-31208	2	106	using	use	VERB
fcis-31208	2	107	the	the	DET
fcis-31208	2	108	quantum	quantum	NOUN
fcis-31208	2	109	annealing	annealing	NOUN
fcis-31208	2	110	algorithm	algorithm	NOUN
fcis-31208	2	111	;	;	PUNCT
fcis-31208	2	112	and	and	CCONJ
fcis-31208	2	113	finally	finally	ADV
fcis-31208	2	114	solve	solve	VERB
fcis-31208	2	115	the	the	DET
fcis-31208	2	116	globally	globally	ADV
fcis-31208	2	117	optimal	optimal	ADJ
fcis-31208	2	118	solution	solution	NOUN
fcis-31208	2	119	by	by	ADP
fcis-31208	2	120	adjusting	adjust	VERB
fcis-31208	2	121	the	the	DET
fcis-31208	2	122	annealing	anneal	VERB
fcis-31208	2	123	parameters	parameter	NOUN
fcis-31208	2	124	,	,	PUNCT
fcis-31208	2	125	which	which	PRON
fcis-31208	2	126	include	include	VERB
fcis-31208	2	127	the	the	DET
fcis-31208	2	128	initial	initial	ADJ
fcis-31208	2	129	temperature	temperature	NOUN
fcis-31208	2	130	,	,	PUNCT
fcis-31208	2	131	the	the	DET
fcis-31208	2	132	coefficient	coefficient	NOUN
fcis-31208	2	133	of	of	ADP
fcis-31208	2	134	temperature	temperature	NOUN
fcis-31208	2	135	reduction	reduction	NOUN
fcis-31208	2	136	,	,	PUNCT
fcis-31208	2	137	and	and	CCONJ
fcis-31208	2	138	the	the	DET
fcis-31208	2	139	number	number	NOUN
fcis-31208	2	140	of	of	ADP
fcis-31208	2	141	iterations	iteration	NOUN
fcis-31208	2	142	.	.	PUNCT
fcis-31208	3	1	qubo	qubo	PROPN
fcis-31208	3	2	model	model	NOUN
fcis-31208	3	3	can	can	AUX
fcis-31208	3	4	discretize	discretize	VERB
fcis-31208	3	5	continuous	continuous	ADJ
fcis-31208	3	6	variables	variable	NOUN
fcis-31208	3	7	(	(	PUNCT
fcis-31208	3	8	including	include	VERB
fcis-31208	3	9	parameters	parameter	NOUN
fcis-31208	3	10	such	such	ADJ
fcis-31208	3	11	as	as	ADP
fcis-31208	3	12	weights	weight	NOUN
fcis-31208	3	13	and	and	CCONJ
fcis-31208	3	14	bias	bias	NOUN
fcis-31208	3	15	)	)	PUNCT
fcis-31208	3	16	in	in	ADP
fcis-31208	3	17	ar	ar	PROPN
fcis-31208	3	18	,	,	PUNCT
fcis-31208	3	19	svm	svm	PROPN
fcis-31208	3	20	,	,	PUNCT
fcis-31208	3	21	cnn	cnn	PROPN
fcis-31208	3	22	and	and	CCONJ
fcis-31208	3	23	other	other	ADJ
fcis-31208	3	24	models	model	NOUN
fcis-31208	3	25	in	in	ADP
fcis-31208	3	26	machine	machine	NOUN
fcis-31208	3	27	learning	learn	VERB
fcis-31208	3	28	into	into	ADP
fcis-31208	3	29	binary	binary	ADJ
fcis-31208	3	30	variables	variable	NOUN
fcis-31208	3	31	,	,	PUNCT
fcis-31208	3	32	so	so	SCONJ
fcis-31208	3	33	as	as	SCONJ
fcis-31208	3	34	to	to	PART
fcis-31208	3	35	better	well	ADV
fcis-31208	3	36	deal	deal	VERB
fcis-31208	3	37	with	with	ADP
fcis-31208	3	38	nonlinear	nonlinear	ADJ
fcis-31208	3	39	relationships	relationship	NOUN
fcis-31208	3	40	and	and	CCONJ
fcis-31208	3	41	reduce	reduce	VERB
fcis-31208	3	42	the	the	DET
fcis-31208	3	43	computational	computational	ADJ
fcis-31208	3	44	complexity	complexity	NOUN
fcis-31208	3	45	in	in	ADP
fcis-31208	3	46	the	the	DET
fcis-31208	3	47	training	training	NOUN
fcis-31208	3	48	process	process	NOUN
fcis-31208	3	49	.	.	PUNCT
fcis-31208	4	1	with	with	ADP
fcis-31208	4	2	the	the	DET
fcis-31208	4	3	qubo	qubo	PROPN
fcis-31208	4	4	model	model	NOUN
fcis-31208	4	5	,	,	PUNCT
fcis-31208	4	6	the	the	DET
fcis-31208	4	7	regularization	regularization	NOUN
fcis-31208	4	8	term	term	NOUN
fcis-31208	4	9	can	can	AUX
fcis-31208	4	10	be	be	AUX
fcis-31208	4	11	better	well	ADV
fcis-31208	4	12	controlled	control	VERB
fcis-31208	4	13	to	to	PART
fcis-31208	4	14	avoid	avoid	VERB
fcis-31208	4	15	the	the	DET
fcis-31208	4	16	overfitting	overfitting	ADJ
fcis-31208	4	17	problem	problem	NOUN
fcis-31208	4	18	.	.	PUNCT
fcis-31208	5	1	meanwhile	meanwhile	ADV
fcis-31208	5	2	,	,	PUNCT
fcis-31208	5	3	the	the	DET
fcis-31208	5	4	qubo	qubo	PROPN
fcis-31208	5	5	model	model	NOUN
fcis-31208	5	6	can	can	AUX
fcis-31208	5	7	be	be	AUX
fcis-31208	5	8	solved	solve	VERB
fcis-31208	5	9	in	in	ADP
fcis-31208	5	10	parallel	parallel	NOUN
fcis-31208	5	11	by	by	ADP
fcis-31208	5	12	quantum	quantum	NOUN
fcis-31208	5	13	computing	computing	NOUN
fcis-31208	5	14	or	or	CCONJ
fcis-31208	5	15	simulated	simulate	VERB
fcis-31208	5	16	annealing	annealing	NOUN
fcis-31208	5	17	algorithm	algorithm	NOUN
fcis-31208	5	18	,	,	PUNCT
fcis-31208	5	19	which	which	PRON
fcis-31208	5	20	significantly	significantly	ADV
fcis-31208	5	21	improves	improve	VERB
fcis-31208	5	22	the	the	DET
fcis-31208	5	23	computational	computational	ADJ
fcis-31208	5	24	efficiency	efficiency	NOUN
fcis-31208	5	25	under	under	ADP
fcis-31208	5	26	large	large	ADJ
fcis-31208	5	27	-	-	PUNCT
fcis-31208	5	28	scale	scale	NOUN
fcis-31208	5	29	data	datum	NOUN
fcis-31208	5	30	.	.	PUNCT
fcis-31208	6	1	keywords	keyword	NOUN
fcis-31208	6	2	:	:	PUNCT
fcis-31208	6	3	qubo	qubo	PROPN
fcis-31208	6	4	;	;	PUNCT
fcis-31208	6	5	ar	ar	PROPN
fcis-31208	6	6	;	;	PUNCT
fcis-31208	6	7	svm	svm	PROPN
fcis-31208	6	8	;	;	PUNCT
fcis-31208	6	9	cnn	cnn	PROPN
fcis-31208	6	10	;	;	PUNCT
fcis-31208	6	11	quantum	quantum	NOUN
fcis-31208	6	12	computing	computing	NOUN
fcis-31208	6	13	;	;	PUNCT
fcis-31208	6	14	simulated	simulate	VERB
fcis-31208	6	15	annealing	anneal	VERB
fcis-31208	6	16	algorithm	algorithm	NOUN
fcis-31208	6	17	.	.	PUNCT
fcis-31208	7	1	1	1	X
fcis-31208	7	2	.	.	X
fcis-31208	7	3	introduction	introduction	NOUN
fcis-31208	7	4	the	the	DET
fcis-31208	7	5	development	development	NOUN
fcis-31208	7	6	of	of	ADP
fcis-31208	7	7	quantum	quantum	ADJ
fcis-31208	7	8	technology	technology	NOUN
fcis-31208	7	9	is	be	AUX
fcis-31208	7	10	of	of	ADP
fcis-31208	7	11	great	great	ADJ
fcis-31208	7	12	scientific	scientific	ADJ
fcis-31208	7	13	significance	significance	NOUN
fcis-31208	7	14	and	and	CCONJ
fcis-31208	7	15	social	social	ADJ
fcis-31208	7	16	value	value	NOUN
fcis-31208	7	17	,	,	PUNCT
fcis-31208	7	18	which	which	PRON
fcis-31208	7	19	is	be	AUX
fcis-31208	7	20	expected	expect	VERB
fcis-31208	7	21	to	to	PART
fcis-31208	7	22	have	have	VERB
fcis-31208	7	23	a	a	DET
fcis-31208	7	24	major	major	ADJ
fcis-31208	7	25	impact	impact	NOUN
fcis-31208	7	26	on	on	ADP
fcis-31208	7	27	traditional	traditional	ADJ
fcis-31208	7	28	technology	technology	NOUN
fcis-31208	7	29	and	and	CCONJ
fcis-31208	7	30	trigger	trigger	NOUN
fcis-31208	7	31	technological	technological	ADJ
fcis-31208	7	32	revolution	revolution	NOUN
fcis-31208	7	33	and	and	CCONJ
fcis-31208	7	34	industrial	industrial	ADJ
fcis-31208	7	35	transformation	transformation	NOUN
fcis-31208	8	1	[	[	X
fcis-31208	8	2	1	1	NUM
fcis-31208	8	3	]	]	PUNCT
fcis-31208	8	4	.	.	PUNCT
fcis-31208	9	1	among	among	ADP
fcis-31208	9	2	them	they	PRON
fcis-31208	9	3	,	,	PUNCT
fcis-31208	9	4	quantum	quantum	PROPN
fcis-31208	9	5	computing	computing	NOUN
fcis-31208	9	6	stands	stand	VERB
fcis-31208	9	7	out	out	ADP
fcis-31208	9	8	in	in	ADP
fcis-31208	9	9	decision	decision	NOUN
fcis-31208	9	10	problems	problem	NOUN
fcis-31208	9	11	,	,	PUNCT
fcis-31208	9	12	classification	classification	NOUN
fcis-31208	9	13	problems	problem	NOUN
fcis-31208	9	14	,	,	PUNCT
fcis-31208	9	15	as	as	ADV
fcis-31208	9	16	well	well	ADV
fcis-31208	9	17	as	as	ADP
fcis-31208	9	18	prediction	prediction	NOUN
fcis-31208	9	19	problems	problem	NOUN
fcis-31208	9	20	.	.	PUNCT
fcis-31208	10	1	in	in	ADP
fcis-31208	10	2	recent	recent	ADJ
fcis-31208	10	3	years	year	NOUN
fcis-31208	10	4	,	,	PUNCT
fcis-31208	10	5	people	people	NOUN
fcis-31208	10	6	have	have	AUX
fcis-31208	10	7	discovered	discover	VERB
fcis-31208	10	8	that	that	SCONJ
fcis-31208	10	9	a	a	DET
fcis-31208	10	10	mathematical	mathematical	ADJ
fcis-31208	10	11	formulation	formulation	NOUN
fcis-31208	10	12	known	know	VERB
fcis-31208	10	13	as	as	ADP
fcis-31208	10	14	qubo	qubo	PROPN
fcis-31208	10	15	,	,	PUNCT
fcis-31208	10	16	an	an	DET
fcis-31208	10	17	acronym	acronym	NOUN
fcis-31208	10	18	for	for	ADP
fcis-31208	10	19	a	a	DET
fcis-31208	10	20	quadratic	quadratic	ADJ
fcis-31208	10	21	unconstrained	unconstraine	VERB
fcis-31208	10	22	binary	binary	ADJ
fcis-31208	10	23	optimization	optimization	NOUN
fcis-31208	10	24	problem	problem	NOUN
fcis-31208	10	25	.	.	PUNCT
fcis-31208	11	1	through	through	ADP
fcis-31208	11	2	special	special	ADJ
fcis-31208	11	3	reformulation	reformulation	NOUN
fcis-31208	11	4	techniques	technique	NOUN
fcis-31208	11	5	that	that	PRON
fcis-31208	11	6	are	be	AUX
fcis-31208	11	7	easy	easy	ADJ
fcis-31208	11	8	to	to	PART
fcis-31208	11	9	apply	apply	VERB
fcis-31208	11	10	,	,	PUNCT
fcis-31208	11	11	the	the	DET
fcis-31208	11	12	power	power	NOUN
fcis-31208	11	13	of	of	ADP
fcis-31208	11	14	qubo	qubo	PROPN
fcis-31208	11	15	solvers	solver	NOUN
fcis-31208	11	16	can	can	AUX
fcis-31208	11	17	be	be	AUX
fcis-31208	11	18	used	use	VERB
fcis-31208	11	19	to	to	PART
fcis-31208	11	20	efficiently	efficiently	ADV
fcis-31208	11	21	solve	solve	VERB
fcis-31208	11	22	many	many	ADJ
fcis-31208	11	23	important	important	ADJ
fcis-31208	11	24	problems	problem	NOUN
fcis-31208	11	25	once	once	SCONJ
fcis-31208	11	26	they	they	PRON
fcis-31208	11	27	are	be	AUX
fcis-31208	11	28	put	put	VERB
fcis-31208	11	29	into	into	ADP
fcis-31208	11	30	the	the	DET
fcis-31208	11	31	qubo	qubo	NOUN
fcis-31208	11	32	framework	framework	NOUN
fcis-31208	12	1	[	[	X
fcis-31208	12	2	2	2	NUM
fcis-31208	12	3	]	]	PUNCT
fcis-31208	12	4	.	.	PUNCT
fcis-31208	13	1	according	accord	VERB
fcis-31208	13	2	to	to	ADP
fcis-31208	13	3	the	the	DET
fcis-31208	13	4	above	above	ADJ
fcis-31208	13	5	description	description	NOUN
fcis-31208	13	6	and	and	CCONJ
fcis-31208	13	7	relevant	relevant	ADJ
fcis-31208	13	8	data	datum	NOUN
fcis-31208	13	9	,	,	PUNCT
fcis-31208	13	10	this	this	DET
fcis-31208	13	11	paper	paper	NOUN
fcis-31208	13	12	built	build	VERB
fcis-31208	13	13	mathematical	mathematical	ADJ
fcis-31208	13	14	models	model	NOUN
fcis-31208	13	15	to	to	PART
fcis-31208	13	16	solve	solve	VERB
fcis-31208	13	17	the	the	DET
fcis-31208	13	18	following	follow	VERB
fcis-31208	13	19	problems	problem	NOUN
fcis-31208	13	20	.	.	PUNCT
fcis-31208	14	1	(	(	PUNCT
fcis-31208	14	2	1	1	X
fcis-31208	14	3	)	)	PUNCT
fcis-31208	14	4	transform	transform	VERB
fcis-31208	14	5	the	the	DET
fcis-31208	14	6	autoregressive	autoregressive	ADJ
fcis-31208	14	7	(	(	PUNCT
fcis-31208	14	8	ar	ar	NOUN
fcis-31208	14	9	)	)	PUNCT
fcis-31208	14	10	model	model	NOUN
fcis-31208	14	11	used	use	VERB
fcis-31208	14	12	in	in	ADP
fcis-31208	14	13	the	the	DET
fcis-31208	14	14	time	time	NOUN
fcis-31208	14	15	series	series	PROPN
fcis-31208	14	16	forecasting	forecasting	NOUN
fcis-31208	14	17	problem	problem	NOUN
fcis-31208	14	18	whose	whose	DET
fcis-31208	14	19	primitive	primitive	ADJ
fcis-31208	14	20	data	datum	NOUN
fcis-31208	14	21	ranging	range	VERB
fcis-31208	14	22	from	from	ADP
fcis-31208	14	23	january	january	PROPN
fcis-31208	14	24	to	to	ADP
fcis-31208	14	25	september	september	PROPN
fcis-31208	14	26	into	into	ADP
fcis-31208	14	27	a	a	DET
fcis-31208	14	28	qubo	qubo	NOUN
fcis-31208	14	29	model	model	NOUN
fcis-31208	14	30	and	and	CCONJ
fcis-31208	14	31	define	define	VERB
fcis-31208	14	32	the	the	DET
fcis-31208	14	33	objective	objective	ADJ
fcis-31208	14	34	function	function	NOUN
fcis-31208	14	35	and	and	CCONJ
fcis-31208	14	36	decision	decision	NOUN
fcis-31208	14	37	variables	variable	NOUN
fcis-31208	14	38	clearly	clearly	ADV
fcis-31208	14	39	.	.	PUNCT
fcis-31208	15	1	based	base	VERB
fcis-31208	15	2	on	on	ADP
fcis-31208	15	3	this	this	PRON
fcis-31208	15	4	,	,	PUNCT
fcis-31208	15	5	use	use	VERB
fcis-31208	15	6	kaiwu	kaiwu	PROPN
fcis-31208	15	7	sdk	sdk	PROPN
fcis-31208	15	8	's	's	PART
fcis-31208	15	9	simulated	simulate	VERB
fcis-31208	15	10	annealing	anneal	VERB
fcis-31208	15	11	algorithm	algorithm	NOUN
fcis-31208	15	12	to	to	PART
fcis-31208	15	13	solve	solve	VERB
fcis-31208	15	14	the	the	DET
fcis-31208	15	15	model	model	NOUN
fcis-31208	15	16	and	and	CCONJ
fcis-31208	15	17	predict	predict	VERB
fcis-31208	15	18	the	the	DET
fcis-31208	15	19	demand	demand	NOUN
fcis-31208	15	20	for	for	ADP
fcis-31208	15	21	october	october	PROPN
fcis-31208	15	22	.	.	PUNCT
fcis-31208	16	1	(	(	PUNCT
fcis-31208	16	2	2	2	X
fcis-31208	16	3	)	)	PUNCT
fcis-31208	16	4	classify	classify	VERB
fcis-31208	16	5	the	the	DET
fcis-31208	16	6	provided	provide	VERB
fcis-31208	16	7	dataset	dataset	NOUN
fcis-31208	16	8	using	use	VERB
fcis-31208	16	9	an	an	DET
fcis-31208	16	10	svm	svm	ADJ
fcis-31208	16	11	model	model	NOUN
fcis-31208	16	12	,	,	PUNCT
fcis-31208	16	13	among	among	ADP
fcis-31208	16	14	which	which	PRON
fcis-31208	16	15	you	you	PRON
fcis-31208	16	16	should	should	AUX
fcis-31208	16	17	transform	transform	VERB
fcis-31208	16	18	the	the	DET
fcis-31208	16	19	optimization	optimization	NOUN
fcis-31208	16	20	problem	problem	NOUN
fcis-31208	16	21	of	of	ADP
fcis-31208	16	22	training	train	VERB
fcis-31208	16	23	an	an	DET
fcis-31208	16	24	svm	svm	ADJ
fcis-31208	16	25	-	-	PUNCT
fcis-31208	16	26	based	base	VERB
fcis-31208	16	27	classification	classification	NOUN
fcis-31208	16	28	model	model	NOUN
fcis-31208	16	29	into	into	ADP
fcis-31208	16	30	a	a	DET
fcis-31208	16	31	qubo	qubo	NOUN
fcis-31208	16	32	model	model	NOUN
fcis-31208	16	33	and	and	CCONJ
fcis-31208	16	34	define	define	VERB
fcis-31208	16	35	the	the	DET
fcis-31208	16	36	objective	objective	ADJ
fcis-31208	16	37	function	function	NOUN
fcis-31208	16	38	and	and	CCONJ
fcis-31208	16	39	decision	decision	NOUN
fcis-31208	16	40	variables	variable	NOUN
fcis-31208	16	41	.	.	PUNCT
fcis-31208	17	1	besides	besides	ADV
fcis-31208	17	2	,	,	PUNCT
fcis-31208	17	3	solve	solve	VERB
fcis-31208	17	4	the	the	DET
fcis-31208	17	5	qubo	qubo	NOUN
fcis-31208	17	6	problem	problem	NOUN
fcis-31208	17	7	using	use	VERB
fcis-31208	17	8	the	the	DET
fcis-31208	17	9	simulated	simulated	ADJ
fcis-31208	17	10	annealing	anneal	VERB
fcis-31208	17	11	algorithm	algorithm	NOUN
fcis-31208	17	12	in	in	ADP
fcis-31208	17	13	the	the	DET
fcis-31208	17	14	kaiwu	kaiwu	PROPN
fcis-31208	17	15	sdk	sdk	PROPN
fcis-31208	17	16	.	.	PUNCT
fcis-31208	18	1	(	(	PUNCT
fcis-31208	18	2	3	3	X
fcis-31208	18	3	)	)	PUNCT
fcis-31208	18	4	explore	explore	VERB
fcis-31208	18	5	the	the	DET
fcis-31208	18	6	integration	integration	NOUN
fcis-31208	18	7	of	of	ADP
fcis-31208	18	8	quantum	quantum	NOUN
fcis-31208	18	9	computing	computing	NOUN
fcis-31208	18	10	and	and	CCONJ
fcis-31208	18	11	deep	deep	ADJ
fcis-31208	18	12	learning	learning	NOUN
fcis-31208	18	13	by	by	ADP
fcis-31208	18	14	selecting	select	VERB
fcis-31208	18	15	a	a	DET
fcis-31208	18	16	specific	specific	ADJ
fcis-31208	18	17	application	application	NOUN
fcis-31208	18	18	scenario	scenario	NOUN
fcis-31208	18	19	like	like	ADP
fcis-31208	18	20	image	image	NOUN
fcis-31208	18	21	classification	classification	NOUN
fcis-31208	18	22	or	or	CCONJ
fcis-31208	18	23	recommendation	recommendation	NOUN
fcis-31208	18	24	systems	system	NOUN
fcis-31208	18	25	and	and	CCONJ
fcis-31208	18	26	design	design	VERB
fcis-31208	18	27	a	a	DET
fcis-31208	18	28	deep	deep	ADJ
fcis-31208	18	29	learning	learning	NOUN
fcis-31208	18	30	model	model	NOUN
fcis-31208	18	31	such	such	ADJ
fcis-31208	18	32	as	as	ADP
fcis-31208	18	33	convolutional	convolutional	ADJ
fcis-31208	18	34	neural	neural	ADJ
fcis-31208	18	35	net	net	NOUN
fcis-31208	18	36	-	-	PUNCT
fcis-31208	18	37	works	work	NOUN
fcis-31208	18	38	(	(	PUNCT
fcis-31208	18	39	cnns	cnns	PROPN
fcis-31208	18	40	)	)	PUNCT
fcis-31208	18	41	,	,	PUNCT
fcis-31208	18	42	then	then	ADV
fcis-31208	18	43	convert	convert	VERB
fcis-31208	18	44	the	the	DET
fcis-31208	18	45	model	model	NOUN
fcis-31208	18	46	's	's	PART
fcis-31208	18	47	training	training	NOUN
fcis-31208	18	48	optimization	optimization	NOUN
fcis-31208	18	49	into	into	ADP
fcis-31208	18	50	a	a	DET
fcis-31208	18	51	qubo	qubo	NOUN
fcis-31208	18	52	form	form	NOUN
fcis-31208	18	53	,	,	PUNCT
fcis-31208	18	54	finally	finally	ADV
fcis-31208	18	55	use	use	VERB
fcis-31208	18	56	the	the	DET
fcis-31208	18	57	simulated	simulate	VERB
fcis-31208	18	58	annealing	anneal	VERB
fcis-31208	18	59	algorithm	algorithm	NOUN
fcis-31208	18	60	to	to	PART
fcis-31208	18	61	solve	solve	VERB
fcis-31208	18	62	this	this	DET
fcis-31208	18	63	optimization	optimization	NOUN
fcis-31208	18	64	problem	problem	NOUN
fcis-31208	18	65	.	.	PUNCT
fcis-31208	19	1	2	2	X
fcis-31208	19	2	.	.	X
fcis-31208	19	3	model	model	NOUN
fcis-31208	19	4	1	1	NUM
fcis-31208	19	5	:	:	PUNCT
fcis-31208	19	6	the	the	DET
fcis-31208	19	7	qubo	qubo	PROPN
fcis-31208	19	8	model	model	NOUN
fcis-31208	19	9	based	base	VERB
fcis-31208	19	10	on	on	ADP
fcis-31208	19	11	time	time	NOUN
fcis-31208	19	12	series	series	PROPN
fcis-31208	19	13	prediction	prediction	NOUN
fcis-31208	19	14	(	(	PUNCT
fcis-31208	19	15	1	1	NUM
fcis-31208	19	16	)	)	PUNCT
fcis-31208	19	17	establishment	establishment	NOUN
fcis-31208	19	18	of	of	ADP
fcis-31208	19	19	qubo	qubo	NOUN
fcis-31208	19	20	resource	resource	NOUN
fcis-31208	19	21	demand	demand	NOUN
fcis-31208	19	22	prediction	prediction	NOUN
fcis-31208	19	23	model	model	NOUN
fcis-31208	19	24	based	base	VERB
fcis-31208	19	25	on	on	ADP
fcis-31208	19	26	ar	ar	PROPN
fcis-31208	19	27	firstly	firstly	ADV
fcis-31208	19	28	,	,	PUNCT
fcis-31208	19	29	based	base	VERB
fcis-31208	19	30	on	on	ADP
fcis-31208	19	31	the	the	DET
fcis-31208	19	32	given	give	VERB
fcis-31208	19	33	problem	problem	NOUN
fcis-31208	19	34	settings	setting	NOUN
fcis-31208	19	35	,	,	PUNCT
fcis-31208	19	36	we	we	PRON
fcis-31208	19	37	conduct	conduct	VERB
fcis-31208	19	38	predictions	prediction	NOUN
fcis-31208	19	39	by	by	ADP
fcis-31208	19	40	employing	employ	VERB
fcis-31208	19	41	the	the	DET
fcis-31208	19	42	autoregressive	autoregressive	ADJ
fcis-31208	19	43	model	model	NOUN
fcis-31208	19	44	.	.	PUNCT
fcis-31208	20	1	autoregressive	autoregressive	ADJ
fcis-31208	20	2	(	(	PUNCT
fcis-31208	20	3	ar	ar	NOUN
fcis-31208	20	4	)	)	PUNCT
fcis-31208	20	5	model	model	NOUN
fcis-31208	20	6	is	be	AUX
fcis-31208	20	7	a	a	DET
fcis-31208	20	8	model	model	NOUN
fcis-31208	20	9	for	for	ADP
fcis-31208	20	10	studying	study	VERB
fcis-31208	20	11	time	time	NOUN
fcis-31208	20	12	series	series	NOUN
fcis-31208	20	13	,	,	PUNCT
fcis-31208	20	14	with	with	ADP
fcis-31208	20	15	wide	wide	ADJ
fcis-31208	20	16	applicability	applicability	NOUN
fcis-31208	20	17	and	and	CCONJ
fcis-31208	20	18	high	high	ADJ
fcis-31208	20	19	accuracy	accuracy	NOUN
fcis-31208	20	20	.	.	PUNCT
fcis-31208	21	1	ar	ar	NOUN
fcis-31208	21	2	model	model	NOUN
fcis-31208	21	3	is	be	AUX
fcis-31208	21	4	mainly	mainly	ADV
fcis-31208	21	5	based	base	VERB
fcis-31208	21	6	on	on	ADP
fcis-31208	21	7	the	the	DET
fcis-31208	21	8	linear	linear	ADJ
fcis-31208	21	9	combination	combination	NOUN
fcis-31208	21	10	of	of	ADP
fcis-31208	21	11	past	past	ADJ
fcis-31208	21	12	observations	observation	NOUN
fcis-31208	21	13	and	and	CCONJ
fcis-31208	21	14	present	present	ADJ
fcis-31208	21	15	disturbance	disturbance	NOUN
fcis-31208	21	16	values	value	NOUN
fcis-31208	21	17	for	for	ADP
fcis-31208	21	18	prediction	prediction	NOUN
fcis-31208	21	19	.	.	PUNCT
fcis-31208	22	1	the	the	DET
fcis-31208	22	2	general	general	ADJ
fcis-31208	22	3	p	p	NOUN
fcis-31208	22	4	-	-	PUNCT
fcis-31208	22	5	order	order	NOUN
fcis-31208	22	6	autoregressive	autoregressive	ADJ
fcis-31208	22	7	process	process	NOUN
fcis-31208	22	8	ar	ar	NOUN
fcis-31208	22	9	(	(	PUNCT
fcis-31208	22	10	p	p	NOUN
fcis-31208	22	11	)	)	PUNCT
fcis-31208	22	12	is[3	is[3	PROPN
fcis-31208	22	13	]	]	X
fcis-31208	22	14	∑	∑	PUNCT
fcis-31208	22	15	(	(	PUNCT
fcis-31208	22	16	1	1	NUM
fcis-31208	22	17	)	)	PUNCT
fcis-31208	22	18	where	where	SCONJ
fcis-31208	22	19	,	,	PUNCT
fcis-31208	22	20	y	y	PROPN
fcis-31208	22	21	indicates	indicate	VERB
fcis-31208	22	22	the	the	DET
fcis-31208	22	23	predicted	predict	VERB
fcis-31208	22	24	value	value	NOUN
fcis-31208	22	25	of	of	ADP
fcis-31208	22	26	resource	resource	NOUN
fcis-31208	22	27	demand	demand	NOUN
fcis-31208	22	28	in	in	ADP
fcis-31208	22	29	month	month	NOUN
fcis-31208	22	30	t	t	PROPN
fcis-31208	22	31	,	,	PUNCT
fcis-31208	22	32	and	and	CCONJ
fcis-31208	22	33	y	y	PROPN
fcis-31208	22	34	indicates	indicate	VERB
fcis-31208	22	35	the	the	DET
fcis-31208	22	36	real	real	ADJ
fcis-31208	22	37	value	value	NOUN
fcis-31208	22	38	of	of	ADP
fcis-31208	22	39	resource	resource	NOUN
fcis-31208	22	40	demand	demand	NOUN
fcis-31208	22	41	in	in	ADP
fcis-31208	22	42	month	month	NOUN
fcis-31208	22	43	(	(	PUNCT
fcis-31208	22	44	t-1	t-1	PROPN
fcis-31208	22	45	)	)	PUNCT
fcis-31208	22	46	;	;	PUNCT
fcis-31208	22	47	c	c	PROPN
fcis-31208	22	48	is	be	AUX
fcis-31208	22	49	a	a	DET
fcis-31208	22	50	constant	constant	ADJ
fcis-31208	22	51	,	,	PUNCT
fcis-31208	22	52	representing	represent	VERB
fcis-31208	22	53	the	the	DET
fcis-31208	22	54	average	average	ADJ
fcis-31208	22	55	level	level	NOUN
fcis-31208	22	56	of	of	ADP
fcis-31208	22	57	the	the	DET
fcis-31208	22	58	time	time	NOUN
fcis-31208	22	59	series	series	NOUN
fcis-31208	22	60	;	;	PUNCT
fcis-31208	22	61	φ	φ	PROPN
fcis-31208	22	62	refers	refer	VERB
fcis-31208	22	63	to	to	ADP
fcis-31208	22	64	the	the	DET
fcis-31208	22	65	autoregressive	autoregressive	ADJ
fcis-31208	22	66	model	model	NOUN
fcis-31208	22	67	parameter	parameter	NOUN
fcis-31208	22	68	,	,	PUNCT
fcis-31208	22	69	representing	represent	VERB
fcis-31208	22	70	the	the	DET
fcis-31208	22	71	effect	effect	NOUN
fcis-31208	22	72	of	of	ADP
fcis-31208	22	73	the	the	DET
fcis-31208	22	74	i	i	PROPN
fcis-31208	22	75	-	-	PUNCT
fcis-31208	22	76	th	th	X
fcis-31208	22	77	lag	lag	NOUN
fcis-31208	22	78	term	term	NOUN
fcis-31208	22	79	on	on	ADP
fcis-31208	22	80	the	the	DET
fcis-31208	22	81	current	current	ADJ
fcis-31208	22	82	value	value	NOUN
fcis-31208	22	83	;	;	PUNCT
fcis-31208	22	84	ε	ε	PROPN
fcis-31208	22	85	refers	refer	VERB
fcis-31208	22	86	to	to	ADP
fcis-31208	22	87	random	random	ADJ
fcis-31208	22	88	noise	noise	NOUN
fcis-31208	22	89	with	with	ADP
fcis-31208	22	90	a	a	DET
fcis-31208	22	91	mean	mean	NOUN
fcis-31208	22	92	of	of	ADP
fcis-31208	22	93	zero	zero	NUM
fcis-31208	22	94	;	;	PUNCT
fcis-31208	22	95	p	p	PRON
fcis-31208	22	96	is	be	AUX
fcis-31208	22	97	the	the	DET
fcis-31208	22	98	order	order	NOUN
fcis-31208	22	99	of	of	ADP
fcis-31208	22	100	the	the	DET
fcis-31208	22	101	ar	ar	PROPN
fcis-31208	22	102	model	model	NOUN
fcis-31208	22	103	,	,	PUNCT
fcis-31208	22	104	indicating	indicate	VERB
fcis-31208	22	105	how	how	SCONJ
fcis-31208	22	106	many	many	ADJ
fcis-31208	22	107	lag	lag	NOUN
fcis-31208	22	108	terms	term	NOUN
fcis-31208	22	109	are	be	AUX
fcis-31208	22	110	used	use	VERB
fcis-31208	22	111	to	to	PART
fcis-31208	22	112	predict	predict	VERB
fcis-31208	22	113	the	the	DET
fcis-31208	22	114	current	current	ADJ
fcis-31208	22	115	value	value	NOUN
fcis-31208	22	116	,	,	PUNCT
fcis-31208	22	117	as	as	SCONJ
fcis-31208	22	118	determined	determine	VERB
fcis-31208	22	119	by	by	ADP
fcis-31208	22	120	the	the	DET
fcis-31208	22	121	akaike	akaike	ADJ
fcis-31208	22	122	information	information	NOUN
fcis-31208	22	123	criterion	criterion	NOUN
fcis-31208	22	124	(	(	PUNCT
fcis-31208	22	125	aic	aic	PROPN
fcis-31208	22	126	)	)	PUNCT
fcis-31208	22	127	and	and	CCONJ
fcis-31208	22	128	p=3	p=3	PROPN
fcis-31208	22	129	.	.	PUNCT
fcis-31208	23	1	on	on	ADP
fcis-31208	23	2	this	this	DET
fcis-31208	23	3	basis	basis	NOUN
fcis-31208	23	4	,	,	PUNCT
fcis-31208	23	5	we	we	PRON
fcis-31208	23	6	transform	transform	VERB
fcis-31208	23	7	the	the	DET
fcis-31208	23	8	above	above	ADJ
fcis-31208	23	9	time	time	NOUN
fcis-31208	23	10	series	series	PROPN
fcis-31208	23	11	prediction	prediction	NOUN
fcis-31208	23	12	problem	problem	NOUN
fcis-31208	23	13	into	into	ADP
fcis-31208	23	14	quadratic	quadratic	ADJ
fcis-31208	23	15	unconstrained	unconstraine	VERB
fcis-31208	23	16	binary	binary	NOUN
fcis-31208	23	17	optimization	optimization	NOUN
fcis-31208	23	18	(	(	PUNCT
fcis-31208	23	19	qubo	qubo	PROPN
fcis-31208	23	20	)	)	PUNCT
fcis-31208	23	21	model	model	NOUN
fcis-31208	23	22	.	.	PUNCT
fcis-31208	24	1	qubo	qubo	PROPN
fcis-31208	24	2	,	,	PUNCT
fcis-31208	24	3	whose	whose	DET
fcis-31208	24	4	minimum	minimum	ADJ
fcis-31208	24	5	energy	energy	NOUN
fcis-31208	24	6	state	state	NOUN
fcis-31208	24	7	is	be	AUX
fcis-31208	24	8	called	call	VERB
fcis-31208	24	9	the	the	DET
fcis-31208	24	10	ground	ground	NOUN
fcis-31208	24	11	state	state	NOUN
fcis-31208	24	12	.	.	PUNCT
fcis-31208	25	1	the	the	DET
fcis-31208	25	2	qubo	qubo	PROPN
fcis-31208	25	3	model	model	NOUN
fcis-31208	25	4	consists	consist	VERB
fcis-31208	25	5	of	of	ADP
fcis-31208	25	6	multiple	multiple	ADJ
fcis-31208	25	7	0	0	NUM
fcis-31208	25	8	-	-	SYM
fcis-31208	25	9	1	1	NUM
fcis-31208	25	10	binary	binary	ADJ
fcis-31208	25	11	variables	variable	NOUN
fcis-31208	25	12	,	,	PUNCT
fcis-31208	25	13	and	and	CCONJ
fcis-31208	25	14	a	a	DET
fcis-31208	25	15	combination	combination	NOUN
fcis-31208	25	16	of	of	ADP
fcis-31208	25	17	which	which	PRON
fcis-31208	25	18	gives	give	VERB
fcis-31208	25	19	one	one	NUM
fcis-31208	25	20	solution	solution	NOUN
fcis-31208	25	21	.	.	PUNCT
fcis-31208	26	1	in	in	ADP
fcis-31208	26	2	particular	particular	ADJ
fcis-31208	26	3	,	,	PUNCT
fcis-31208	26	4	the	the	DET
fcis-31208	26	5	combination	combination	NOUN
fcis-31208	26	6	of	of	ADP
fcis-31208	26	7	binary	binary	ADJ
fcis-31208	26	8	variables	variable	NOUN
fcis-31208	26	9	giving	give	VERB
fcis-31208	26	10	the	the	DET
fcis-31208	26	11	minimum	minimum	ADJ
fcis-31208	26	12	energy	energy	NOUN
fcis-31208	26	13	state	state	NOUN
fcis-31208	26	14	of	of	ADP
fcis-31208	26	15	the	the	DET
fcis-31208	26	16	qubo	qubo	PROPN
fcis-31208	26	17	model	model	NOUN
fcis-31208	26	18	is	be	AUX
fcis-31208	26	19	called	call	VERB
fcis-31208	26	20	the	the	DET
fcis-31208	26	21	ground	ground	NOUN
fcis-31208	26	22	-	-	PUNCT
fcis-31208	26	23	state	state	NOUN
fcis-31208	26	24	solution	solution	NOUN
fcis-31208	26	25	.	.	PUNCT
fcis-31208	27	1	a	a	DET
fcis-31208	27	2	qubo	qubo	NOUN
fcis-31208	27	3	model	model	NOUN
fcis-31208	27	4	is	be	AUX
fcis-31208	27	5	represented	represent	VERB
fcis-31208	27	6	by	by	ADP
fcis-31208	27	7	the	the	DET
fcis-31208	27	8	formula	formula	NOUN
fcis-31208	27	9	below	below	ADP
fcis-31208	27	10	using	use	VERB
fcis-31208	27	11	a	a	DET
fcis-31208	27	12	binary	binary	ADJ
fcis-31208	27	13	variable	variable	NOUN
fcis-31208	27	14	that	that	PRON
fcis-31208	27	15	takes	take	VERB
fcis-31208	27	16	0	0	NUM
fcis-31208	27	17	or	or	CCONJ
fcis-31208	27	18	1	1	NUM
fcis-31208	27	19	(	(	PUNCT
fcis-31208	27	20	0	0	NUM
fcis-31208	27	21	1	1	NUM
fcis-31208	27	22	)	)	PUNCT
fcis-31208	27	23	.	.	PUNCT
fcis-31208	28	1	∑	∑	PUNCT
fcis-31208	28	2	∑	∑	PUNCT
fcis-31208	28	3	(	(	PUNCT
fcis-31208	28	4	2	2	NUM
fcis-31208	28	5	)	)	PUNCT
fcis-31208	28	6	where	where	SCONJ
fcis-31208	28	7	，	，	PUNCT
fcis-31208	28	8	⋯	⋯	PROPN
fcis-31208	28	9	is	be	AUX
fcis-31208	28	10	a	a	DET
fcis-31208	28	11	set	set	NOUN
fcis-31208	28	12	of	of	ADP
fcis-31208	28	13	binary	binary	ADJ
fcis-31208	28	14	variables	variable	NOUN
fcis-31208	28	15	;	;	PUNCT
fcis-31208	28	16	is	be	AUX
fcis-31208	28	17	the	the	DET
fcis-31208	28	18	external	external	ADJ
fcis-31208	28	19	magnetic	magnetic	ADJ
fcis-31208	28	20	field	field	NOUN
fcis-31208	28	21	coefficient	coefficient	NOUN
fcis-31208	28	22	representing	represent	VERB
fcis-31208	28	23	the	the	DET
fcis-31208	28	24	force	force	NOUN
fcis-31208	28	25	on	on	ADP
fcis-31208	28	26	the	the	DET
fcis-31208	28	27	binary	binary	ADJ
fcis-31208	28	28	variable	variable	NOUN
fcis-31208	28	29	;	;	PUNCT
fcis-31208	28	30	and	and	CCONJ
fcis-31208	28	31	is	be	AUX
fcis-31208	28	32	the	the	DET
fcis-31208	28	33	interaction	interaction	NOUN
fcis-31208	28	34	37	37	NUM
fcis-31208	28	35	coefficient	coefficient	NOUN
fcis-31208	28	36	representing	represent	VERB
fcis-31208	28	37	the	the	DET
fcis-31208	28	38	weight	weight	NOUN
fcis-31208	28	39	of	of	ADP
fcis-31208	28	40	the	the	DET
fcis-31208	28	41	connection	connection	NOUN
fcis-31208	28	42	between	between	ADP
fcis-31208	28	43	the	the	DET
fcis-31208	28	44	binary	binary	ADJ
fcis-31208	28	45	variables	variable	NOUN
fcis-31208	28	46	and	and	CCONJ
fcis-31208	28	47	.	.	PUNCT
fcis-31208	29	1	a	a	DET
fcis-31208	29	2	solution	solution	NOUN
fcis-31208	29	3	of	of	ADP
fcis-31208	29	4	a	a	DET
fcis-31208	29	5	qubo	qubo	NOUN
fcis-31208	29	6	model	model	NOUN
fcis-31208	29	7	is	be	AUX
fcis-31208	29	8	one	one	NUM
fcis-31208	29	9	of	of	ADP
fcis-31208	29	10	the	the	DET
fcis-31208	29	11	combinations	combination	NOUN
fcis-31208	29	12	of	of	ADP
fcis-31208	29	13	binary	binary	ADJ
fcis-31208	29	14	variables	variable	NOUN
fcis-31208	29	15	.	.	PUNCT
fcis-31208	30	1	the	the	DET
fcis-31208	30	2	groundstate	groundstate	PROPN
fcis-31208	30	3	solution	solution	NOUN
fcis-31208	30	4	of	of	ADP
fcis-31208	30	5	the	the	DET
fcis-31208	30	6	qubo	qubo	PROPN
fcis-31208	30	7	model	model	NOUN
fcis-31208	30	8	is	be	AUX
fcis-31208	30	9	the	the	DET
fcis-31208	30	10	combination	combination	NOUN
fcis-31208	30	11	of	of	ADP
fcis-31208	30	12	binary	binary	ADJ
fcis-31208	30	13	variables	variable	NOUN
fcis-31208	30	14	that	that	PRON
fcis-31208	30	15	minimizes	minimize	VERB
fcis-31208	30	16	.	.	PUNCT
fcis-31208	31	1	the	the	DET
fcis-31208	31	2	value	value	NOUN
fcis-31208	31	3	of	of	ADP
fcis-31208	31	4	for	for	ADP
fcis-31208	31	5	a	a	DET
fcis-31208	31	6	given	give	VERB
fcis-31208	31	7	combination	combination	NOUN
fcis-31208	31	8	of	of	ADP
fcis-31208	31	9	binary	binary	ADJ
fcis-31208	31	10	variables	variable	NOUN
fcis-31208	31	11	is	be	AUX
fcis-31208	31	12	called	call	VERB
fcis-31208	31	13	the	the	DET
fcis-31208	31	14	objective	objective	ADJ
fcis-31208	31	15	value	value	NOUN
fcis-31208	31	16	.	.	PUNCT
fcis-31208	32	1	remember	remember	VERB
fcis-31208	32	2	that	that	PRON
fcis-31208	32	3	q	q	NOUN
fcis-31208	32	4	is	be	AUX
fcis-31208	32	5	composed	compose	VERB
fcis-31208	32	6	of	of	ADP
fcis-31208	32	7	a	a	DET
fcis-31208	32	8	matrix	matrix	NOUN
fcis-31208	32	9	,	,	PUNCT
fcis-31208	32	10	according	accord	VERB
fcis-31208	32	11	to	to	ADP
fcis-31208	32	12	the	the	DET
fcis-31208	32	13	qubo	qubo	PROPN
fcis-31208	32	14	hamiltonian	hamiltonian	NOUN
fcis-31208	32	15	produced	produce	VERB
fcis-31208	32	16	by	by	ADP
fcis-31208	32	17	it	it	PRON
fcis-31208	32	18	,	,	PUNCT
fcis-31208	32	19	the	the	DET
fcis-31208	32	20	objective	objective	ADJ
fcis-31208	32	21	function	function	NOUN
fcis-31208	32	22	is	be	AUX
fcis-31208	32	23	transformed	transform	VERB
fcis-31208	32	24	into	into	ADP
fcis-31208	32	25	a	a	DET
fcis-31208	32	26	quadratic	quadratic	ADJ
fcis-31208	32	27	form	form	NOUN
fcis-31208	32	28	,	,	PUNCT
fcis-31208	32	29	then	then	ADV
fcis-31208	32	30	formula	formula	NOUN
fcis-31208	32	31	(	(	PUNCT
fcis-31208	32	32	2	2	X
fcis-31208	32	33	)	)	PUNCT
fcis-31208	32	34	can	can	AUX
fcis-31208	32	35	be	be	AUX
fcis-31208	32	36	simplified	simplify	VERB
fcis-31208	32	37	as	as	ADP
fcis-31208	32	38	:	:	PUNCT
fcis-31208	32	39	(	(	PUNCT
fcis-31208	32	40	3	3	X
fcis-31208	32	41	)	)	PUNCT
fcis-31208	32	42	where	where	SCONJ
fcis-31208	32	43	x	x	PRON
fcis-31208	32	44	is	be	AUX
fcis-31208	32	45	the	the	DET
fcis-31208	32	46	binary	binary	ADJ
fcis-31208	32	47	decision	decision	NOUN
fcis-31208	32	48	variable	variable	NOUN
fcis-31208	32	49	,	,	PUNCT
fcis-31208	32	50	represents	represent	VERB
fcis-31208	32	51	the	the	DET
fcis-31208	32	52	transpose	transpose	NOUN
fcis-31208	32	53	of	of	ADP
fcis-31208	32	54	,	,	PUNCT
fcis-31208	32	55	a	a	PRON
fcis-31208	32	56	is	be	AUX
fcis-31208	32	57	the	the	DET
fcis-31208	32	58	linear	linear	ADJ
fcis-31208	32	59	term	term	NOUN
fcis-31208	32	60	coefficient	coefficient	NOUN
fcis-31208	32	61	vector	vector	NOUN
fcis-31208	32	62	,	,	PUNCT
fcis-31208	32	63	q	q	X
fcis-31208	32	64	is	be	AUX
fcis-31208	32	65	the	the	DET
fcis-31208	32	66	quadratic	quadratic	ADJ
fcis-31208	32	67	term	term	NOUN
fcis-31208	32	68	coefficient	coefficient	NOUN
fcis-31208	32	69	matrix	matrix	NOUN
fcis-31208	32	70	of	of	ADP
fcis-31208	32	71	the	the	DET
fcis-31208	32	72	quadratic	quadratic	ADJ
fcis-31208	32	73	unconstrained	unconstrained	ADJ
fcis-31208	32	74	binary	binary	ADJ
fcis-31208	32	75	optimization	optimization	NOUN
fcis-31208	32	76	problem	problem	NOUN
fcis-31208	32	77	,	,	PUNCT
fcis-31208	32	78	and	and	CCONJ
fcis-31208	32	79	it	it	PRON
fcis-31208	32	80	is	be	AUX
fcis-31208	32	81	a	a	DET
fcis-31208	32	82	symmetric	symmetric	ADJ
fcis-31208	32	83	matrix	matrix	NOUN
fcis-31208	32	84	,	,	PUNCT
fcis-31208	32	85	otherwise	otherwise	ADV
fcis-31208	32	86	the	the	DET
fcis-31208	32	87	form	form	NOUN
fcis-31208	32	88	of	of	ADP
fcis-31208	32	89	the	the	DET
fcis-31208	32	90	coefficient	coefficient	NOUN
fcis-31208	32	91	matrix	matrix	NOUN
fcis-31208	32	92	q	q	NOUN
fcis-31208	32	93	can	can	AUX
fcis-31208	32	94	be	be	AUX
fcis-31208	32	95	changed	change	VERB
fcis-31208	32	96	by	by	ADP
fcis-31208	32	97	the	the	DET
fcis-31208	32	98	following	follow	VERB
fcis-31208	32	99	formula	formula	NOUN
fcis-31208	32	100	:	:	PUNCT
fcis-31208	32	101	symmetrical	symmetrical	ADJ
fcis-31208	32	102	form	form	NOUN
fcis-31208	32	103	upper	upper	ADJ
fcis-31208	32	104	triangular	triangular	NOUN
fcis-31208	32	105	form	form	NOUN
fcis-31208	32	106	,	,	PUNCT
fcis-31208	32	107	0	0	NUM
fcis-31208	32	108	,	,	PUNCT
fcis-31208	32	109	(	(	PUNCT
fcis-31208	32	110	4	4	X
fcis-31208	32	111	)	)	PUNCT
fcis-31208	32	112	we	we	PRON
fcis-31208	32	113	take	take	VERB
fcis-31208	32	114	70	70	NUM
fcis-31208	32	115	%	%	NOUN
fcis-31208	32	116	of	of	ADP
fcis-31208	32	117	the	the	DET
fcis-31208	32	118	data	datum	NOUN
fcis-31208	32	119	as	as	ADP
fcis-31208	32	120	the	the	DET
fcis-31208	32	121	training	training	NOUN
fcis-31208	32	122	set	set	NOUN
fcis-31208	32	123	,	,	PUNCT
fcis-31208	32	124	30	30	NUM
fcis-31208	32	125	%	%	NOUN
fcis-31208	32	126	of	of	ADP
fcis-31208	32	127	the	the	DET
fcis-31208	32	128	data	datum	NOUN
fcis-31208	32	129	as	as	ADP
fcis-31208	32	130	the	the	DET
fcis-31208	32	131	test	test	NOUN
fcis-31208	32	132	set	set	NOUN
fcis-31208	32	133	,	,	PUNCT
fcis-31208	32	134	by	by	ADP
fcis-31208	32	135	determining	determine	VERB
fcis-31208	32	136	the	the	DET
fcis-31208	32	137	sum	sum	NOUN
fcis-31208	32	138	of	of	ADP
fcis-31208	32	139	squared	square	VERB
fcis-31208	32	140	errors	error	NOUN
fcis-31208	32	141	(	(	PUNCT
fcis-31208	32	142	sse	sse	NOUN
fcis-31208	32	143	)	)	PUNCT
fcis-31208	32	144	of	of	ADP
fcis-31208	32	145	the	the	DET
fcis-31208	32	146	predicted	predict	VERB
fcis-31208	32	147	value	value	NOUN
fcis-31208	32	148	of	of	ADP
fcis-31208	32	149	the	the	DET
fcis-31208	32	150	minimum	minimum	ADJ
fcis-31208	32	151	autoregressive	autoregressive	ADJ
fcis-31208	32	152	function	function	NOUN
fcis-31208	32	153	,	,	PUNCT
fcis-31208	32	154	predict	predict	VERB
fcis-31208	32	155	the	the	DET
fcis-31208	32	156	computing	computing	NOUN
fcis-31208	32	157	resource	resource	NOUN
fcis-31208	32	158	demand	demand	NOUN
fcis-31208	32	159	in	in	ADP
fcis-31208	32	160	october	october	PROPN
fcis-31208	32	161	.	.	PUNCT
fcis-31208	33	1	in	in	ADP
fcis-31208	33	2	this	this	DET
fcis-31208	33	3	problem	problem	NOUN
fcis-31208	33	4	,	,	PUNCT
fcis-31208	33	5	we	we	PRON
fcis-31208	33	6	aim	aim	VERB
fcis-31208	33	7	to	to	PART
fcis-31208	33	8	minimize	minimize	VERB
fcis-31208	33	9	the	the	DET
fcis-31208	33	10	predicted	predict	VERB
fcis-31208	33	11	value	value	NOUN
fcis-31208	33	12	error	error	NOUN
fcis-31208	33	13	while	while	SCONJ
fcis-31208	33	14	satisfying	satisfy	VERB
fcis-31208	33	15	the	the	DET
fcis-31208	33	16	constraint	constraint	NOUN
fcis-31208	33	17	that	that	PRON
fcis-31208	33	18	demand	demand	NOUN
fcis-31208	33	19	varies	vary	VERB
fcis-31208	33	20	within	within	ADP
fcis-31208	33	21	a	a	DET
fcis-31208	33	22	reasonable	reasonable	ADJ
fcis-31208	33	23	range	range	NOUN
fcis-31208	33	24	.	.	PUNCT
fcis-31208	34	1	therefore	therefore	ADV
fcis-31208	34	2	,	,	PUNCT
fcis-31208	34	3	based	base	VERB
fcis-31208	34	4	on	on	ADP
fcis-31208	34	5	the	the	DET
fcis-31208	34	6	above	above	ADJ
fcis-31208	34	7	analysis	analysis	NOUN
fcis-31208	34	8	,	,	PUNCT
fcis-31208	34	9	an	an	DET
fcis-31208	34	10	objective	objective	ADJ
fcis-31208	34	11	function	function	NOUN
fcis-31208	34	12	d	d	NOUN
fcis-31208	34	13	with	with	ADP
fcis-31208	34	14	minimized	minimized	ADJ
fcis-31208	34	15	predicted	predict	VERB
fcis-31208	34	16	value	value	NOUN
fcis-31208	34	17	is	be	AUX
fcis-31208	34	18	constructed	construct	VERB
fcis-31208	34	19	:	:	PUNCT
fcis-31208	34	20	∑	∑	PUNCT
fcis-31208	34	21	(	(	PUNCT
fcis-31208	34	22	5	5	NUM
fcis-31208	34	23	)	)	PUNCT
fcis-31208	34	24	combined	combine	VERB
fcis-31208	34	25	with	with	ADP
fcis-31208	34	26	formula	formula	NOUN
fcis-31208	34	27	(	(	PUNCT
fcis-31208	34	28	3	3	NUM
fcis-31208	34	29	)	)	PUNCT
fcis-31208	34	30	,	,	PUNCT
fcis-31208	34	31	we	we	PRON
fcis-31208	34	32	can	can	AUX
fcis-31208	34	33	see	see	VERB
fcis-31208	34	34	that	that	SCONJ
fcis-31208	34	35	the	the	DET
fcis-31208	34	36	objective	objective	ADJ
fcis-31208	34	37	function	function	NOUN
fcis-31208	34	38	can	can	AUX
fcis-31208	34	39	be	be	AUX
fcis-31208	34	40	expressed	express	VERB
fcis-31208	34	41	as	as	SCONJ
fcis-31208	34	42	follows	follow	VERB
fcis-31208	34	43	:	:	PUNCT
fcis-31208	34	44	(	(	PUNCT
fcis-31208	34	45	6	6	NUM
fcis-31208	34	46	)	)	PUNCT
fcis-31208	34	47	based	base	VERB
fcis-31208	34	48	on	on	ADP
fcis-31208	34	49	this	this	PRON
fcis-31208	34	50	,	,	PUNCT
fcis-31208	34	51	we	we	PRON
fcis-31208	34	52	define	define	VERB
fcis-31208	34	53	a	a	DET
fcis-31208	34	54	(	(	PUNCT
fcis-31208	34	55	1+p	1+p	NUM
fcis-31208	34	56	)	)	PUNCT
fcis-31208	34	57	·	·	PUNCT
fcis-31208	34	58	n	n	CCONJ
fcis-31208	34	59	-	-	PUNCT
fcis-31208	34	60	dimensional	dimensional	ADJ
fcis-31208	34	61	vector	vector	NOUN
fcis-31208	34	62	x	x	PUNCT
fcis-31208	34	63	to	to	PART
fcis-31208	34	64	discretize	discretize	VERB
fcis-31208	34	65	the	the	DET
fcis-31208	34	66	continuous	continuous	ADJ
fcis-31208	34	67	values	value	NOUN
fcis-31208	34	68	c	c	NOUN
fcis-31208	34	69	and	and	CCONJ
fcis-31208	34	70	to	to	ADP
fcis-31208	34	71	binary	binary	ADJ
fcis-31208	34	72	decision	decision	NOUN
fcis-31208	34	73	variables	variable	NOUN
fcis-31208	34	74	,	,	PUNCT
fcis-31208	34	75	where	where	SCONJ
fcis-31208	34	76	:	:	PUNCT
fcis-31208	34	77	is	be	AUX
fcis-31208	34	78	the	the	DET
fcis-31208	34	79	binary	binary	ADJ
fcis-31208	34	80	representation	representation	NOUN
fcis-31208	34	81	of	of	ADP
fcis-31208	34	82	c	c	PROPN
fcis-31208	34	83	and	and	CCONJ
fcis-31208	34	84	the	the	DET
fcis-31208	34	85	binary	binary	PROPN
fcis-31208	34	86	representation	representation	NOUN
fcis-31208	34	87	of	of	ADP
fcis-31208	34	88	is	be	AUX
fcis-31208	34	89	:	:	PUNCT
fcis-31208	34	90	,	,	PUNCT
fcis-31208	34	91	arranged	arrange	VERB
fcis-31208	34	92	in	in	ADP
fcis-31208	34	93	the	the	DET
fcis-31208	34	94	order	order	NOUN
fcis-31208	34	95	of	of	ADP
fcis-31208	34	96	i	i	PRON
fcis-31208	34	97	:	:	PUNCT
fcis-31208	34	98	,	,	PUNCT
fcis-31208	34	99	,	,	PUNCT
fcis-31208	34	100	⋯	⋯	PROPN
fcis-31208	34	101	,	,	PUNCT
fcis-31208	34	102	,	,	PUNCT
fcis-31208	34	103	,	,	PUNCT
fcis-31208	34	104	⋯	⋯	PROPN
fcis-31208	34	105	,	,	PUNCT
fcis-31208	34	106	,	,	PUNCT
fcis-31208	34	107	⋯	⋯	PROPN
fcis-31208	34	108	(	(	PUNCT
fcis-31208	34	109	7	7	X
fcis-31208	34	110	)	)	PUNCT
fcis-31208	34	111	the	the	DET
fcis-31208	34	112	mathematical	mathematical	ADJ
fcis-31208	34	113	formula	formula	NOUN
fcis-31208	34	114	for	for	ADP
fcis-31208	34	115	the	the	DET
fcis-31208	34	116	specific	specific	ADJ
fcis-31208	34	117	binarization	binarization	NOUN
fcis-31208	34	118	is	be	AUX
fcis-31208	34	119	as	as	SCONJ
fcis-31208	34	120	follows	follow	VERB
fcis-31208	34	121	:	:	PUNCT
fcis-31208	34	122	∑	∑	PROPN
fcis-31208	34	123	2	2	NUM
fcis-31208	34	124	∑	∑	SYM
fcis-31208	34	125	2	2	NUM
fcis-31208	34	126	(	(	PUNCT
fcis-31208	34	127	8)	8)	NUM
fcis-31208	34	128	substituting	substituting	NOUN
fcis-31208	34	129	formula	formula	NOUN
fcis-31208	34	130	(	(	PUNCT
fcis-31208	34	131	8)	8)	NUM
fcis-31208	34	132	into	into	ADP
fcis-31208	34	133	formula	formula	NOUN
fcis-31208	34	134	(	(	PUNCT
fcis-31208	34	135	6	6	NUM
fcis-31208	34	136	)	)	PUNCT
fcis-31208	34	137	can	can	AUX
fcis-31208	34	138	simplify	simplify	VERB
fcis-31208	34	139	and	and	CCONJ
fcis-31208	34	140	expand	expand	VERB
fcis-31208	34	141	the	the	DET
fcis-31208	34	142	objective	objective	ADJ
fcis-31208	34	143	function	function	NOUN
fcis-31208	34	144	:	:	PUNCT
fcis-31208	34	145	∑	∑	PUNCT
fcis-31208	34	146	  	  	SPACE
fcis-31208	34	147	2	2	NUM
fcis-31208	34	148	∑	∑	ADP
fcis-31208	34	149	  	  	SPACE
fcis-31208	34	150	2	2	NUM
fcis-31208	34	151	2	2	NUM
fcis-31208	34	152	∑	∑	NOUN
fcis-31208	34	153	  	  	SPACE
fcis-31208	34	154	∑	∑	ADP
fcis-31208	34	155	  	  	SPACE
fcis-31208	34	156	2	2	NUM
fcis-31208	34	157	∑	∑	ADP
fcis-31208	34	158	  	  	SPACE
fcis-31208	34	159	2	2	NUM
fcis-31208	34	160	2	2	NUM
fcis-31208	34	161	∑	∑	ADP
fcis-31208	34	162	  	  	SPACE
fcis-31208	34	163	2	2	NUM
fcis-31208	34	164	∑	∑	ADP
fcis-31208	34	165	  	  	SPACE
fcis-31208	34	166	∑	∑	ADP
fcis-31208	34	167	  	  	SPACE
fcis-31208	34	168	2	2	NUM
fcis-31208	34	169	∑	∑	ADP
fcis-31208	34	170	  	  	SPACE
fcis-31208	34	171	∑	∑	ADP
fcis-31208	34	172	  	  	SPACE
fcis-31208	34	173	2	2	NUM
fcis-31208	34	174	(	(	PUNCT
fcis-31208	34	175	9	9	NUM
fcis-31208	34	176	)	)	PUNCT
fcis-31208	34	177	and	and	CCONJ
fcis-31208	34	178	since	since	SCONJ
fcis-31208	34	179	the	the	DET
fcis-31208	34	180	constant	constant	ADJ
fcis-31208	34	181	term	term	NOUN
fcis-31208	34	182	has	have	VERB
fcis-31208	34	183	nothing	nothing	PRON
fcis-31208	34	184	to	to	PART
fcis-31208	34	185	do	do	VERB
fcis-31208	34	186	with	with	ADP
fcis-31208	34	187	the	the	DET
fcis-31208	34	188	value	value	NOUN
fcis-31208	34	189	of	of	ADP
fcis-31208	34	190	the	the	DET
fcis-31208	34	191	decision	decision	NOUN
fcis-31208	34	192	variable	variable	NOUN
fcis-31208	34	193	when	when	SCONJ
fcis-31208	34	194	the	the	DET
fcis-31208	34	195	function	function	NOUN
fcis-31208	35	1	d	d	X
fcis-31208	35	2	gets	get	VERB
fcis-31208	35	3	the	the	DET
fcis-31208	35	4	minimum	minimum	ADJ
fcis-31208	35	5	value	value	NOUN
fcis-31208	35	6	,	,	PUNCT
fcis-31208	35	7	for	for	ADP
fcis-31208	35	8	simple	simple	ADJ
fcis-31208	35	9	operation	operation	NOUN
fcis-31208	35	10	,	,	PUNCT
fcis-31208	35	11	we	we	PRON
fcis-31208	35	12	directly	directly	ADV
fcis-31208	35	13	ignore	ignore	VERB
fcis-31208	35	14	the	the	DET
fcis-31208	35	15	constant	constant	ADJ
fcis-31208	35	16	term	term	NOUN
fcis-31208	35	17	,	,	PUNCT
fcis-31208	35	18	formula	formula	NOUN
fcis-31208	35	19	(	(	PUNCT
fcis-31208	35	20	9	9	NUM
fcis-31208	35	21	)	)	PUNCT
fcis-31208	35	22	can	can	AUX
fcis-31208	35	23	be	be	AUX
fcis-31208	35	24	simplified	simplify	VERB
fcis-31208	35	25	to	to	ADP
fcis-31208	35	26	the	the	DET
fcis-31208	35	27	following	follow	VERB
fcis-31208	35	28	mathematical	mathematical	ADJ
fcis-31208	35	29	expression	expression	NOUN
fcis-31208	35	30	:	:	PUNCT
fcis-31208	35	31	∑	∑	PUNCT
fcis-31208	35	32	  	  	SPACE
fcis-31208	35	33	2	2	NUM
fcis-31208	35	34	∑	∑	ADP
fcis-31208	35	35	  	  	SPACE
fcis-31208	35	36	2	2	NUM
fcis-31208	35	37	2	2	NUM
fcis-31208	35	38	∑	∑	NOUN
fcis-31208	35	39	  	  	SPACE
fcis-31208	35	40	∑	∑	ADP
fcis-31208	35	41	  	  	SPACE
fcis-31208	35	42	2	2	NUM
fcis-31208	35	43	∑	∑	ADP
fcis-31208	35	44	  	  	SPACE
fcis-31208	35	45	2	2	NUM
fcis-31208	35	46	2	2	NUM
fcis-31208	35	47	∑	∑	ADP
fcis-31208	35	48	  	  	SPACE
fcis-31208	35	49	2	2	NUM
fcis-31208	35	50	∑	∑	ADP
fcis-31208	35	51	  	  	SPACE
fcis-31208	35	52	∑	∑	ADP
fcis-31208	35	53	  	  	SPACE
fcis-31208	35	54	2	2	NUM
fcis-31208	35	55	∑	∑	ADP
fcis-31208	35	56	  	  	SPACE
fcis-31208	35	57	∑	∑	ADP
fcis-31208	35	58	  	  	SPACE
fcis-31208	35	59	2	2	NUM
fcis-31208	35	60	(	(	PUNCT
fcis-31208	35	61	10	10	NUM
fcis-31208	35	62	)	)	PUNCT
fcis-31208	35	63	in	in	ADP
fcis-31208	35	64	addition	addition	NOUN
fcis-31208	35	65	,	,	PUNCT
fcis-31208	35	66	we	we	PRON
fcis-31208	35	67	constrain	constrain	VERB
fcis-31208	35	68	the	the	DET
fcis-31208	35	69	decision	decision	NOUN
fcis-31208	35	70	variable	variable	NOUN
fcis-31208	35	71	by	by	ADP
fcis-31208	35	72	the	the	DET
fcis-31208	35	73	following	follow	VERB
fcis-31208	35	74	formula	formula	NOUN
fcis-31208	35	75	:	:	PUNCT
fcis-31208	35	76	0	0	NUM
fcis-31208	35	77	2	2	NUM
fcis-31208	35	78	1	1	NUM
fcis-31208	35	79	2	2	NUM
fcis-31208	35	80	1	1	NUM
fcis-31208	35	81	(	(	PUNCT
fcis-31208	35	82	11	11	NUM
fcis-31208	35	83	)	)	PUNCT
fcis-31208	35	84	among	among	ADP
fcis-31208	35	85	them	they	PRON
fcis-31208	35	86	,	,	PUNCT
fcis-31208	35	87	by	by	ADP
fcis-31208	35	88	observing	observe	VERB
fcis-31208	35	89	the	the	DET
fcis-31208	35	90	demand	demand	NOUN
fcis-31208	35	91	data	datum	NOUN
fcis-31208	35	92	from	from	ADP
fcis-31208	35	93	january	january	PROPN
fcis-31208	35	94	to	to	ADP
fcis-31208	35	95	september	september	PROPN
fcis-31208	35	96	,	,	PUNCT
fcis-31208	35	97	we	we	PRON
fcis-31208	35	98	calculate	calculate	VERB
fcis-31208	35	99	and	and	CCONJ
fcis-31208	35	100	find	find	VERB
fcis-31208	35	101	that	that	SCONJ
fcis-31208	35	102	the	the	DET
fcis-31208	35	103	data	datum	NOUN
fcis-31208	35	104	9000	9000	NUM
fcis-31208	35	105	in	in	ADP
fcis-31208	35	106	january	january	PROPN
fcis-31208	35	107	is	be	AUX
fcis-31208	35	108	close	close	ADJ
fcis-31208	35	109	to	to	ADP
fcis-31208	35	110	2	2	NUM
fcis-31208	35	111	1	1	NUM
fcis-31208	35	112	,	,	PUNCT
fcis-31208	35	113	so	so	CCONJ
fcis-31208	35	114	the	the	DET
fcis-31208	35	115	value	value	NOUN
fcis-31208	35	116	range	range	NOUN
fcis-31208	35	117	of	of	ADP
fcis-31208	35	118	c	c	PROPN
fcis-31208	35	119	is	be	AUX
fcis-31208	35	120	set	set	VERB
fcis-31208	35	121	in	in	ADP
fcis-31208	35	122	the	the	DET
fcis-31208	35	123	interval	interval	NOUN
fcis-31208	35	124	0	0	NUM
fcis-31208	35	125	,	,	PUNCT
fcis-31208	35	126	2	2	NUM
fcis-31208	35	127	1	1	NUM
fcis-31208	35	128	;	;	PUNCT
fcis-31208	35	129	similarly	similarly	ADV
fcis-31208	35	130	,	,	PUNCT
fcis-31208	35	131	we	we	PRON
fcis-31208	35	132	find	find	VERB
fcis-31208	35	133	that	that	SCONJ
fcis-31208	35	134	the	the	DET
fcis-31208	35	135	difference	difference	NOUN
fcis-31208	35	136	of	of	ADP
fcis-31208	35	137	data	datum	NOUN
fcis-31208	35	138	multiples	multiple	NOUN
fcis-31208	35	139	is	be	AUX
fcis-31208	35	140	not	not	PART
fcis-31208	35	141	more	more	ADJ
fcis-31208	35	142	than	than	ADP
fcis-31208	35	143	two	two	NUM
fcis-31208	35	144	times	time	NOUN
fcis-31208	35	145	,	,	PUNCT
fcis-31208	35	146	so	so	CCONJ
fcis-31208	35	147	the	the	DET
fcis-31208	35	148	value	value	NOUN
fcis-31208	35	149	range	range	NOUN
fcis-31208	35	150	of	of	ADP
fcis-31208	35	151	is	be	AUX
fcis-31208	35	152	set	set	VERB
fcis-31208	35	153	in	in	ADP
fcis-31208	35	154	the	the	DET
fcis-31208	35	155	interval	interval	NOUN
fcis-31208	35	156	2	2	NUM
fcis-31208	35	157	,	,	PUNCT
fcis-31208	35	158	1	1	NUM
fcis-31208	35	159	.	.	PUNCT
fcis-31208	36	1	among	among	ADP
fcis-31208	36	2	them	they	PRON
fcis-31208	36	3	,	,	PUNCT
fcis-31208	36	4	the	the	DET
fcis-31208	36	5	qubo	qubo	NOUN
fcis-31208	36	6	matrix	matrix	NOUN
fcis-31208	36	7	needs	need	VERB
fcis-31208	36	8	to	to	PART
fcis-31208	36	9	be	be	AUX
fcis-31208	36	10	converted	convert	VERB
fcis-31208	36	11	to	to	ADP
fcis-31208	36	12	ising	ise	VERB
fcis-31208	36	13	variable	variable	NOUN
fcis-31208	36	14	before	before	ADP
fcis-31208	36	15	using	use	VERB
fcis-31208	36	16	kaiwu	kaiwu	PROPN
fcis-31208	36	17	sdk	sdk	PROPN
fcis-31208	36	18	.	.	PUNCT
fcis-31208	37	1	the	the	DET
fcis-31208	37	2	ising	ise	VERB
fcis-31208	37	3	model	model	NOUN
fcis-31208	37	4	is	be	AUX
fcis-31208	37	5	a	a	DET
fcis-31208	37	6	random	random	ADJ
fcis-31208	37	7	process	process	NOUN
fcis-31208	37	8	model	model	NOUN
fcis-31208	37	9	that	that	PRON
fcis-31208	37	10	describes	describe	VERB
fcis-31208	37	11	the	the	DET
fcis-31208	37	12	phase	phase	NOUN
fcis-31208	37	13	transition	transition	NOUN
fcis-31208	37	14	of	of	ADP
fcis-31208	37	15	matter	matter	NOUN
fcis-31208	37	16	,	,	PUNCT
fcis-31208	37	17	which	which	PRON
fcis-31208	37	18	is	be	AUX
fcis-31208	37	19	abstracted	abstract	VERB
fcis-31208	37	20	into	into	ADP
fcis-31208	37	21	the	the	DET
fcis-31208	37	22	following	follow	VERB
fcis-31208	37	23	mathematical	mathematical	ADJ
fcis-31208	37	24	form	form	NOUN
fcis-31208	37	25	:	:	PUNCT
fcis-31208	37	26	∑	∑	PUNCT
fcis-31208	37	27	,	,	PUNCT
fcis-31208	37	28	∑	∑	PROPN
fcis-31208	37	29	(	(	PUNCT
fcis-31208	37	30	12	12	NUM
fcis-31208	37	31	)	)	PUNCT
fcis-31208	37	32	where	where	SCONJ
fcis-31208	37	33	,	,	PUNCT
fcis-31208	37	34	is	be	AUX
fcis-31208	37	35	the	the	DET
fcis-31208	37	36	spin	spin	NOUN
fcis-31208	37	37	variable	variable	NOUN
fcis-31208	37	38	to	to	PART
fcis-31208	37	39	be	be	AUX
fcis-31208	37	40	found	find	VERB
fcis-31208	37	41	,	,	PUNCT
fcis-31208	37	42	the	the	DET
fcis-31208	37	43	value	value	NOUN
fcis-31208	37	44	is	be	AUX
fcis-31208	37	45	{	{	PUNCT
fcis-31208	37	46	1,1	1,1	NUM
fcis-31208	37	47	}	}	PUNCT
fcis-31208	37	48	,	,	PUNCT
fcis-31208	37	49	h	h	NOUN
fcis-31208	37	50	is	be	AUX
fcis-31208	37	51	the	the	DET
fcis-31208	37	52	hamiltonian	hamiltonian	NOUN
fcis-31208	37	53	,	,	PUNCT
fcis-31208	37	54	j	j	PROPN
fcis-31208	37	55	is	be	AUX
fcis-31208	37	56	the	the	DET
fcis-31208	37	57	quadratic	quadratic	ADJ
fcis-31208	37	58	term	term	NOUN
fcis-31208	37	59	coefficient	coefficient	NOUN
fcis-31208	37	60	,	,	PUNCT
fcis-31208	37	61	38	38	NUM
fcis-31208	37	62	and	and	CCONJ
fcis-31208	37	63	h	h	NOUN
fcis-31208	37	64	is	be	AUX
fcis-31208	37	65	the	the	DET
fcis-31208	37	66	linear	linear	ADJ
fcis-31208	37	67	term	term	NOUN
fcis-31208	37	68	coefficient	coefficient	NOUN
fcis-31208	37	69	,	,	PUNCT
fcis-31208	37	70	are	be	AUX
fcis-31208	37	71	known	know	VERB
fcis-31208	37	72	quantities[4	quantities[4	NOUN
fcis-31208	37	73	]	]	X
fcis-31208	37	74	we	we	PRON
fcis-31208	37	75	adopt	adopt	VERB
fcis-31208	37	76	the	the	DET
fcis-31208	37	77	following	follow	VERB
fcis-31208	37	78	formula	formula	NOUN
fcis-31208	37	79	as	as	ADP
fcis-31208	37	80	the	the	DET
fcis-31208	37	81	conversion	conversion	NOUN
fcis-31208	37	82	formula	formula	NOUN
fcis-31208	37	83	between	between	ADP
fcis-31208	37	84	the	the	DET
fcis-31208	37	85	binary	binary	PROPN
fcis-31208	37	86	variable	variable	NOUN
fcis-31208	37	87	and	and	CCONJ
fcis-31208	37	88	the	the	DET
fcis-31208	37	89	ising	ise	VERB
fcis-31208	37	90	variable	variable	NOUN
fcis-31208	37	91	:	:	PUNCT
fcis-31208	37	92	(	(	PUNCT
fcis-31208	37	93	13	13	NUM
fcis-31208	37	94	)	)	PUNCT
fcis-31208	37	95	where	where	SCONJ
fcis-31208	37	96	,	,	PUNCT
fcis-31208	37	97	1	1	NUM
fcis-31208	37	98	correspond	correspond	VERB
fcis-31208	37	99	0	0	NUM
fcis-31208	37	100	,	,	PUNCT
fcis-31208	37	101	1	1	NUM
fcis-31208	37	102	correspond	correspond	VERB
fcis-31208	37	103	1	1	NUM
fcis-31208	37	104	.	.	PUNCT
fcis-31208	38	1	here	here	ADV
fcis-31208	38	2	,	,	PUNCT
fcis-31208	38	3	we	we	PRON
fcis-31208	38	4	summarize	summarize	VERB
fcis-31208	38	5	the	the	DET
fcis-31208	38	6	solution	solution	NOUN
fcis-31208	38	7	of	of	ADP
fcis-31208	38	8	this	this	DET
fcis-31208	38	9	model	model	NOUN
fcis-31208	38	10	:	:	PUNCT
fcis-31208	38	11	figure	figure	NOUN
fcis-31208	38	12	1	1	NUM
fcis-31208	38	13	.	.	PUNCT
fcis-31208	39	1	the	the	DET
fcis-31208	39	2	solution	solution	NOUN
fcis-31208	39	3	of	of	ADP
fcis-31208	39	4	model	model	NOUN
fcis-31208	39	5	1	1	NUM
fcis-31208	39	6	(	(	PUNCT
fcis-31208	39	7	2	2	NUM
fcis-31208	39	8	)	)	PUNCT
fcis-31208	39	9	solution	solution	NOUN
fcis-31208	39	10	of	of	ADP
fcis-31208	39	11	resource	resource	NOUN
fcis-31208	39	12	demand	demand	NOUN
fcis-31208	39	13	prediction	prediction	NOUN
fcis-31208	39	14	model	model	NOUN
fcis-31208	39	15	based	base	VERB
fcis-31208	39	16	on	on	ADP
fcis-31208	39	17	sa	sa	NOUN
fcis-31208	39	18	in	in	ADP
fcis-31208	39	19	order	order	NOUN
fcis-31208	39	20	to	to	PART
fcis-31208	39	21	solve	solve	VERB
fcis-31208	39	22	the	the	DET
fcis-31208	39	23	resource	resource	NOUN
fcis-31208	39	24	demand	demand	NOUN
fcis-31208	39	25	corresponding	correspond	VERB
fcis-31208	39	26	to	to	ADP
fcis-31208	39	27	october	october	PROPN
fcis-31208	39	28	when	when	SCONJ
fcis-31208	39	29	the	the	DET
fcis-31208	39	30	error	error	NOUN
fcis-31208	39	31	of	of	ADP
fcis-31208	39	32	the	the	DET
fcis-31208	39	33	predicted	predict	VERB
fcis-31208	39	34	value	value	NOUN
fcis-31208	39	35	is	be	AUX
fcis-31208	39	36	minimized	minimize	VERB
fcis-31208	39	37	,	,	PUNCT
fcis-31208	39	38	we	we	PRON
fcis-31208	39	39	adopted	adopt	VERB
fcis-31208	39	40	the	the	DET
fcis-31208	39	41	simulated	simulate	VERB
fcis-31208	39	42	annealing	annealing	NOUN
fcis-31208	39	43	solver	solver	ADV
fcis-31208	39	44	built	build	VERB
fcis-31208	39	45	in	in	ADP
fcis-31208	39	46	kaiwu	kaiwu	PROPN
fcis-31208	39	47	sdk	sdk	PROPN
fcis-31208	39	48	to	to	PART
fcis-31208	39	49	solve	solve	VERB
fcis-31208	39	50	it	it	PRON
fcis-31208	39	51	.	.	PUNCT
fcis-31208	40	1	the	the	DET
fcis-31208	40	2	following	follow	VERB
fcis-31208	40	3	is	be	AUX
fcis-31208	40	4	the	the	DET
fcis-31208	40	5	flow	flow	ADJ
fcis-31208	40	6	chart	chart	NOUN
fcis-31208	40	7	of	of	ADP
fcis-31208	40	8	the	the	DET
fcis-31208	40	9	simulated	simulate	VERB
fcis-31208	40	10	annealing	anneal	VERB
fcis-31208	40	11	algorithm	algorithm	NOUN
fcis-31208	40	12	solver	solver	NOUN
fcis-31208	40	13	.	.	PUNCT
fcis-31208	41	1	figure	figure	NOUN
fcis-31208	41	2	2	2	NUM
fcis-31208	41	3	.	.	PUNCT
fcis-31208	41	4	flow	flow	VERB
fcis-31208	41	5	chart	chart	NOUN
fcis-31208	41	6	of	of	ADP
fcis-31208	41	7	qubo	qubo	PROPN
fcis-31208	41	8	model	model	NOUN
fcis-31208	41	9	solved	solve	VERB
fcis-31208	41	10	by	by	ADP
fcis-31208	41	11	simulated	simulate	VERB
fcis-31208	41	12	annealing	annealing	NOUN
fcis-31208	41	13	for	for	ADP
fcis-31208	41	14	the	the	DET
fcis-31208	41	15	parameters	parameter	NOUN
fcis-31208	41	16	in	in	ADP
fcis-31208	41	17	the	the	DET
fcis-31208	41	18	simulated	simulate	VERB
fcis-31208	41	19	annealing	annealing	NOUN
fcis-31208	41	20	solver	solver	ADV
fcis-31208	41	21	in	in	ADP
fcis-31208	41	22	the	the	DET
fcis-31208	41	23	kaiwu	kaiwu	PROPN
fcis-31208	41	24	sdk	sdk	PROPN
fcis-31208	41	25	,	,	PUNCT
fcis-31208	41	26	we	we	PRON
fcis-31208	41	27	set	set	VERB
fcis-31208	41	28	them	they	PRON
fcis-31208	41	29	as	as	SCONJ
fcis-31208	41	30	follows	follow	VERB
fcis-31208	41	31	:	:	PUNCT
fcis-31208	41	32	select	select	VERB
fcis-31208	41	33	the	the	DET
fcis-31208	41	34	initial	initial	ADJ
fcis-31208	41	35	temperature	temperature	NOUN
fcis-31208	41	36	and	and	CCONJ
fcis-31208	41	37	the	the	DET
fcis-31208	41	38	end	end	NOUN
fcis-31208	41	39	temperature	temperature	NOUN
fcis-31208	41	40	,	,	PUNCT
fcis-31208	41	41	and	and	CCONJ
fcis-31208	41	42	select	select	VERB
fcis-31208	41	43	the	the	DET
fcis-31208	41	44	appropriate	appropriate	ADJ
fcis-31208	41	45	cooling	cool	VERB
fcis-31208	41	46	coefficient	coefficient	NOUN
fcis-31208	41	47	during	during	ADP
fcis-31208	41	48	the	the	DET
fcis-31208	41	49	annealing	annealing	NOUN
fcis-31208	41	50	process	process	NOUN
fcis-31208	41	51	,	,	PUNCT
fcis-31208	41	52	so	so	SCONJ
fcis-31208	41	53	that	that	SCONJ
fcis-31208	41	54	the	the	DET
fcis-31208	41	55	iterative	iterative	ADJ
fcis-31208	41	56	depth	depth	NOUN
fcis-31208	41	57	of	of	ADP
fcis-31208	41	58	each	each	DET
fcis-31208	41	59	temperature	temperature	NOUN
fcis-31208	41	60	in	in	ADP
fcis-31208	41	61	the	the	DET
fcis-31208	41	62	annealing	annealing	NOUN
fcis-31208	41	63	process	process	NOUN
fcis-31208	41	64	does	do	AUX
fcis-31208	41	65	not	not	PART
fcis-31208	41	66	exceed	exceed	VERB
fcis-31208	41	67	100	100	NUM
fcis-31208	41	68	.	.	PUNCT
fcis-31208	41	69	table	table	NOUN
fcis-31208	41	70	1	1	NUM
fcis-31208	41	71	.	.	PUNCT
fcis-31208	42	1	simulated	simulate	VERB
fcis-31208	42	2	annealing	anneal	VERB
fcis-31208	42	3	parameter	parameter	NOUN
fcis-31208	42	4	table	table	NOUN
fcis-31208	42	5	parameters	parameter	NOUN
fcis-31208	42	6	select	select	VERB
fcis-31208	42	7	parameter	parameter	NOUN
fcis-31208	42	8	values	value	NOUN
fcis-31208	42	9	initial	initial	ADJ
fcis-31208	42	10	temperature	temperature	NOUN
fcis-31208	42	11	3000	3000	NUM
fcis-31208	42	12	cutoff	cutoff	NOUN
fcis-31208	42	13	temperature	temperature	NOUN
fcis-31208	42	14	10	10	NUM
fcis-31208	42	15	cooling	cool	VERB
fcis-31208	42	16	factor	factor	NOUN
fcis-31208	42	17	0.90	0.90	NUM
fcis-31208	42	18	iterations	iteration	NOUN
fcis-31208	42	19	per	per	ADP
fcis-31208	42	20	temperature	temperature	NOUN
fcis-31208	42	21	100	100	NUM
fcis-31208	42	22	size	size	NOUN
fcis-31208	42	23	limit	limit	NOUN
fcis-31208	42	24	100	100	NUM
fcis-31208	42	25	by	by	ADP
fcis-31208	42	26	solving	solve	VERB
fcis-31208	42	27	the	the	DET
fcis-31208	42	28	simulated	simulate	VERB
fcis-31208	42	29	annealing	annealing	NOUN
fcis-31208	42	30	solver	solver	ADP
fcis-31208	42	31	,	,	PUNCT
fcis-31208	42	32	we	we	PRON
fcis-31208	42	33	get	get	VERB
fcis-31208	42	34	the	the	DET
fcis-31208	42	35	following	follow	VERB
fcis-31208	42	36	results	result	NOUN
fcis-31208	42	37	:	:	PUNCT
fcis-31208	42	38	table	table	NOUN
fcis-31208	42	39	2	2	NUM
fcis-31208	42	40	.	.	X
fcis-31208	42	41	optimal	optimal	ADJ
fcis-31208	42	42	prediction	prediction	NOUN
fcis-31208	42	43	parameter	parameter	NOUN
fcis-31208	42	44	values	value	NOUN
fcis-31208	42	45	table	table	VERB
fcis-31208	42	46	optimal	optimal	ADJ
fcis-31208	42	47	prediction	prediction	NOUN
fcis-31208	42	48	parameters	parameter	NOUN
fcis-31208	42	49	value	value	VERB
fcis-31208	42	50	c	c	PROPN
fcis-31208	42	51	1393	1393	NUM
fcis-31208	42	52	0.6484	0.6484	NUM
fcis-31208	42	53	0.1016	0.1016	NUM
fcis-31208	42	54	0.1351	0.1351	NUM
fcis-31208	42	55	at	at	ADP
fcis-31208	42	56	this	this	DET
fcis-31208	42	57	point	point	NOUN
fcis-31208	42	58	,	,	PUNCT
fcis-31208	42	59	the	the	DET
fcis-31208	42	60	mean	mean	ADJ
fcis-31208	42	61	square	square	ADJ
fcis-31208	42	62	error	error	NOUN
fcis-31208	42	63	has	have	AUX
fcis-31208	42	64	reached	reach	VERB
fcis-31208	42	65	its	its	PRON
fcis-31208	42	66	minimum	minimum	ADJ
fcis-31208	42	67	value	value	NOUN
fcis-31208	42	68	of	of	ADP
fcis-31208	42	69	30,723.78	30,723.78	NUM
fcis-31208	42	70	.	.	PUNCT
fcis-31208	43	1	among	among	ADP
fcis-31208	43	2	them	they	PRON
fcis-31208	43	3	,	,	PUNCT
fcis-31208	43	4	can	can	AUX
fcis-31208	43	5	be	be	AUX
fcis-31208	43	6	regarded	regard	VERB
fcis-31208	43	7	as	as	ADP
fcis-31208	43	8	the	the	DET
fcis-31208	43	9	weight	weight	NOUN
fcis-31208	43	10	of	of	ADP
fcis-31208	43	11	the	the	DET
fcis-31208	43	12	real	real	ADJ
fcis-31208	43	13	value	value	NOUN
fcis-31208	43	14	i	i	PRON
fcis-31208	43	15	before	before	ADP
fcis-31208	43	16	the	the	DET
fcis-31208	43	17	predicted	predict	VERB
fcis-31208	43	18	value	value	NOUN
fcis-31208	43	19	to	to	ADP
fcis-31208	43	20	the	the	DET
fcis-31208	43	21	predicted	predict	VERB
fcis-31208	43	22	value	value	NOUN
fcis-31208	43	23	,	,	PUNCT
fcis-31208	43	24	then	then	ADV
fcis-31208	43	25	the	the	DET
fcis-31208	43	26	phenomenon	phenomenon	NOUN
fcis-31208	43	27	of	of	ADP
fcis-31208	43	28	great	great	ADJ
fcis-31208	43	29	influence	influence	NOUN
fcis-31208	43	30	on	on	ADP
fcis-31208	43	31	the	the	DET
fcis-31208	43	32	predicted	predict	VERB
fcis-31208	43	33	value	value	NOUN
fcis-31208	43	34	is	be	AUX
fcis-31208	43	35	in	in	ADP
fcis-31208	43	36	line	line	NOUN
fcis-31208	43	37	with	with	ADP
fcis-31208	43	38	reality	reality	NOUN
fcis-31208	43	39	,	,	PUNCT
fcis-31208	43	40	which	which	PRON
fcis-31208	43	41	is	be	AUX
fcis-31208	43	42	reasonable	reasonable	ADJ
fcis-31208	43	43	.	.	PUNCT
fcis-31208	44	1	on	on	ADP
fcis-31208	44	2	this	this	DET
fcis-31208	44	3	basis	basis	NOUN
fcis-31208	44	4	,	,	PUNCT
fcis-31208	44	5	we	we	PRON
fcis-31208	44	6	substitute	substitute	VERB
fcis-31208	44	7	the	the	DET
fcis-31208	44	8	formula	formula	NOUN
fcis-31208	44	9	(	(	PUNCT
fcis-31208	44	10	1	1	NUM
fcis-31208	44	11	)	)	PUNCT
fcis-31208	44	12	to	to	PART
fcis-31208	44	13	solve	solve	VERB
fcis-31208	44	14	the	the	DET
fcis-31208	44	15	predicted	predict	VERB
fcis-31208	44	16	value	value	NOUN
fcis-31208	44	17	of	of	ADP
fcis-31208	44	18	each	each	DET
fcis-31208	44	19	month	month	NOUN
fcis-31208	44	20	as	as	SCONJ
fcis-31208	44	21	follows	follow	VERB
fcis-31208	44	22	:	:	PUNCT
fcis-31208	44	23	table	table	NOUN
fcis-31208	44	24	3	3	NUM
fcis-31208	44	25	.	.	PUNCT
fcis-31208	45	1	statistics	statistic	NOUN
fcis-31208	45	2	of	of	ADP
fcis-31208	45	3	predicted	predict	VERB
fcis-31208	45	4	values	value	NOUN
fcis-31208	45	5	for	for	ADP
fcis-31208	45	6	each	each	DET
fcis-31208	45	7	month	month	NOUN
fcis-31208	45	8	month	month	NOUN
fcis-31208	45	9	predicted	predict	VERB
fcis-31208	45	10	value	value	NOUN
fcis-31208	45	11	month	month	NOUN
fcis-31208	45	12	predicted	predict	VERB
fcis-31208	45	13	value	value	NOUN
fcis-31208	45	14	april	april	PROPN
fcis-31208	45	15	9784.98	9784.98	NUM
fcis-31208	45	16	august	august	PROPN
fcis-31208	45	17	10443.38	10443.38	NUM
fcis-31208	45	18	may	may	PROPN
fcis-31208	45	19	10030.58	10030.58	NUM
fcis-31208	45	20	september	september	PROPN
fcis-31208	45	21	10613.51	10613.51	NUM
fcis-31208	45	22	june	june	PROPN
fcis-31208	45	23	10147.90	10147.90	NUM
fcis-31208	45	24	october	october	PROPN
fcis-31208	45	25	10719.36	10719.36	NUM
fcis-31208	45	26	july	july	PROPN
fcis-31208	45	27	10248.88	10248.88	NUM
fcis-31208	45	28	in	in	ADP
fcis-31208	45	29	addition	addition	NOUN
fcis-31208	45	30	,	,	PUNCT
fcis-31208	45	31	we	we	PRON
fcis-31208	45	32	draw	draw	VERB
fcis-31208	45	33	the	the	DET
fcis-31208	45	34	fitting	fitting	ADJ
fcis-31208	45	35	image	image	NOUN
fcis-31208	45	36	as	as	SCONJ
fcis-31208	45	37	follows	follow	VERB
fcis-31208	45	38	:	:	PUNCT
fcis-31208	45	39	it	it	PRON
fcis-31208	45	40	can	can	AUX
fcis-31208	45	41	be	be	AUX
fcis-31208	45	42	seen	see	VERB
fcis-31208	45	43	from	from	ADP
fcis-31208	45	44	the	the	DET
fcis-31208	45	45	above	above	ADJ
fcis-31208	45	46	figure	figure	NOUN
fcis-31208	45	47	that	that	SCONJ
fcis-31208	45	48	the	the	DET
fcis-31208	45	49	predicted	predict	VERB
fcis-31208	45	50	results	result	NOUN
fcis-31208	45	51	have	have	AUX
fcis-31208	45	52	a	a	DET
fcis-31208	45	53	high	high	ADJ
fcis-31208	45	54	goodness	goodness	NOUN
fcis-31208	45	55	of	of	ADP
fcis-31208	45	56	fit	fit	PROPN
fcis-31208	45	57	.	.	PUNCT
fcis-31208	46	1	based	base	VERB
fcis-31208	46	2	on	on	ADP
fcis-31208	46	3	this	this	PRON
fcis-31208	46	4	,	,	PUNCT
fcis-31208	46	5	we	we	PRON
fcis-31208	46	6	calculated	calculate	VERB
fcis-31208	46	7	a	a	DET
fcis-31208	46	8	variety	variety	NOUN
fcis-31208	46	9	of	of	ADP
fcis-31208	46	10	prediction	prediction	NOUN
fcis-31208	46	11	evaluation	evaluation	NOUN
fcis-31208	46	12	indicators	indicator	NOUN
fcis-31208	46	13	to	to	PART
fcis-31208	46	14	help	help	VERB
fcis-31208	46	15	explain	explain	VERB
fcis-31208	46	16	the	the	DET
fcis-31208	46	17	excellent	excellent	ADJ
fcis-31208	46	18	performance	performance	NOUN
fcis-31208	46	19	of	of	ADP
fcis-31208	46	20	the	the	DET
fcis-31208	46	21	results	result	NOUN
fcis-31208	46	22	.	.	PUNCT
fcis-31208	47	1	in	in	ADP
fcis-31208	47	2	addition	addition	NOUN
fcis-31208	47	3	to	to	ADP
fcis-31208	47	4	this	this	PRON
fcis-31208	47	5	,	,	PUNCT
fcis-31208	47	6	we	we	PRON
fcis-31208	47	7	also	also	ADV
fcis-31208	47	8	visualized	visualize	VERB
fcis-31208	47	9	the	the	DET
fcis-31208	47	10	process	process	NOUN
fcis-31208	47	11	of	of	ADP
fcis-31208	47	12	hamiltonian	hamiltonian	ADJ
fcis-31208	47	13	change	change	NOUN
fcis-31208	47	14	over	over	ADP
fcis-31208	47	15	time	time	NOUN
fcis-31208	47	16	in	in	ADP
fcis-31208	47	17	the	the	DET
fcis-31208	47	18	simulated	simulated	ADJ
fcis-31208	47	19	annealing	anneal	VERB
fcis-31208	47	20	algorithm	algorithm	NOUN
fcis-31208	47	21	to	to	PART
fcis-31208	47	22	reflect	reflect	VERB
fcis-31208	47	23	the	the	DET
fcis-31208	47	24	energy	energy	NOUN
fcis-31208	47	25	state	state	NOUN
fcis-31208	47	26	of	of	ADP
fcis-31208	47	27	the	the	DET
fcis-31208	47	28	system	system	NOUN
fcis-31208	47	29	.	.	PUNCT
fcis-31208	48	1	as	as	SCONJ
fcis-31208	48	2	can	can	AUX
fcis-31208	48	3	be	be	AUX
fcis-31208	48	4	seen	see	VERB
fcis-31208	48	5	from	from	ADP
fcis-31208	48	6	the	the	DET
fcis-31208	48	7	figure	figure	NOUN
fcis-31208	48	8	above	above	ADV
fcis-31208	48	9	,	,	PUNCT
fcis-31208	48	10	when	when	SCONJ
fcis-31208	48	11	the	the	DET
fcis-31208	48	12	system	system	NOUN
fcis-31208	48	13	is	be	AUX
fcis-31208	48	14	in	in	ADP
fcis-31208	48	15	the	the	DET
fcis-31208	48	16	initial	initial	ADJ
fcis-31208	48	17	state	state	NOUN
fcis-31208	48	18	,	,	PUNCT
fcis-31208	48	19	the	the	DET
fcis-31208	48	20	energy	energy	NOUN
fcis-31208	48	21	is	be	AUX
fcis-31208	48	22	higher	high	ADJ
fcis-31208	48	23	;	;	PUNCT
fcis-31208	48	24	then	then	ADV
fcis-31208	48	25	,	,	PUNCT
fcis-31208	48	26	the	the	DET
fcis-31208	48	27	hamiltonian	hamiltonian	NOUN
fcis-31208	48	28	decreases	decrease	VERB
fcis-31208	48	29	rapidly	rapidly	ADV
fcis-31208	48	30	,	,	PUNCT
fcis-31208	48	31	and	and	CCONJ
fcis-31208	48	32	the	the	DET
fcis-31208	48	33	system	system	NOUN
fcis-31208	48	34	quickly	quickly	ADV
fcis-31208	48	35	finds	find	VERB
fcis-31208	48	36	a	a	DET
fcis-31208	48	37	lower	low	ADJ
fcis-31208	48	38	energy	energy	NOUN
fcis-31208	48	39	state	state	NOUN
fcis-31208	48	40	in	in	ADP
fcis-31208	48	41	the	the	DET
fcis-31208	48	42	optimization	optimization	NOUN
fcis-31208	48	43	process	process	NOUN
fcis-31208	48	44	;	;	PUNCT
fcis-31208	48	45	when	when	SCONJ
fcis-31208	48	46	the	the	DET
fcis-31208	48	47	hamiltonian	hamiltonian	ADJ
fcis-31208	48	48	approaches	approach	VERB
fcis-31208	48	49	0.03	0.03	NUM
fcis-31208	48	50	seconds	second	NOUN
fcis-31208	48	51	,	,	PUNCT
fcis-31208	48	52	there	there	PRON
fcis-31208	48	53	is	be	VERB
fcis-31208	48	54	a	a	DET
fcis-31208	48	55	small	small	ADJ
fcis-31208	48	56	increase	increase	NOUN
fcis-31208	48	57	,	,	PUNCT
fcis-31208	48	58	which	which	PRON
fcis-31208	48	59	is	be	AUX
fcis-31208	48	60	because	because	SCONJ
fcis-31208	48	61	the	the	DET
fcis-31208	48	62	algorithm	algorithm	NOUN
fcis-31208	48	63	is	be	AUX
fcis-31208	48	64	trying	try	VERB
fcis-31208	48	65	to	to	PART
fcis-31208	48	66	jump	jump	VERB
fcis-31208	48	67	out	out	ADP
fcis-31208	48	68	of	of	ADP
fcis-31208	48	69	the	the	DET
fcis-31208	48	70	local	local	ADJ
fcis-31208	48	71	optimal	optimal	ADJ
fcis-31208	48	72	solution	solution	NOUN
fcis-31208	48	73	.	.	PUNCT
fcis-31208	49	1	after	after	ADP
fcis-31208	49	2	the	the	DET
fcis-31208	49	3	decrease	decrease	NOUN
fcis-31208	49	4	,	,	PUNCT
fcis-31208	49	5	the	the	DET
fcis-31208	49	6	hamiltonian	hamiltonian	NOUN
fcis-31208	49	7	tends	tend	VERB
fcis-31208	49	8	to	to	PART
fcis-31208	49	9	be	be	AUX
fcis-31208	49	10	stable	stable	ADJ
fcis-31208	49	11	,	,	PUNCT
fcis-31208	49	12	indicating	indicate	VERB
fcis-31208	49	13	that	that	SCONJ
fcis-31208	49	14	the	the	DET
fcis-31208	49	15	algorithm	algorithm	NOUN
fcis-31208	49	16	has	have	AUX
fcis-31208	49	17	converged	converge	VERB
fcis-31208	49	18	and	and	CCONJ
fcis-31208	49	19	the	the	DET
fcis-31208	49	20	system	system	NOUN
fcis-31208	49	21	has	have	AUX
fcis-31208	49	22	reached	reach	VERB
fcis-31208	49	23	the	the	DET
fcis-31208	49	24	global	global	ADJ
fcis-31208	49	25	optimal	optimal	ADJ
fcis-31208	49	26	state	state	NOUN
fcis-31208	49	27	.	.	PUNCT
fcis-31208	50	1	solving	solve	VERB
fcis-31208	50	2	time	time	NOUN
fcis-31208	50	3	of	of	ADP
fcis-31208	50	4	the	the	DET
fcis-31208	50	5	qubo	qubo	PROPN
fcis-31208	50	6	model	model	NOUN
fcis-31208	50	7	is	be	AUX
fcis-31208	50	8	approximately	approximately	ADV
fcis-31208	50	9	0.07s	0.07s	NUM
fcis-31208	50	10	.	.	PUNCT
fcis-31208	51	1	3	3	X
fcis-31208	51	2	.	.	X
fcis-31208	51	3	model	model	NOUN
fcis-31208	51	4	2	2	NUM
fcis-31208	51	5	:	:	PUNCT
fcis-31208	51	6	the	the	DET
fcis-31208	51	7	qubo	qubo	PROPN
fcis-31208	51	8	model	model	NOUN
fcis-31208	51	9	based	base	VERB
fcis-31208	51	10	on	on	ADP
fcis-31208	51	11	svm	svm	ADJ
fcis-31208	51	12	classification	classification	NOUN
fcis-31208	51	13	(	(	PUNCT
fcis-31208	51	14	1	1	NUM
fcis-31208	51	15	)	)	PUNCT
fcis-31208	51	16	establishment	establishment	NOUN
fcis-31208	51	17	of	of	ADP
fcis-31208	51	18	qubo	qubo	NOUN
fcis-31208	51	19	-	-	PUNCT
fcis-31208	51	20	classification	classification	NOUN
fcis-31208	51	21	model	model	NOUN
fcis-31208	51	22	based	base	VERB
fcis-31208	51	23	on	on	ADP
fcis-31208	51	24	svm	svm	PROPN
fcis-31208	51	25	39	39	NUM
fcis-31208	51	26	figure	figure	NOUN
fcis-31208	51	27	3	3	NUM
fcis-31208	51	28	.	.	PUNCT
fcis-31208	52	1	the	the	DET
fcis-31208	52	2	predicted	predict	VERB
fcis-31208	52	3	value	value	NOUN
fcis-31208	52	4	compared	compare	VERB
fcis-31208	52	5	with	with	ADP
fcis-31208	52	6	the	the	DET
fcis-31208	52	7	true	true	ADJ
fcis-31208	52	8	value	value	NOUN
fcis-31208	52	9	table	table	NOUN
fcis-31208	52	10	4	4	NUM
fcis-31208	52	11	.	.	PUNCT
fcis-31208	52	12	results	result	NOUN
fcis-31208	52	13	evaluation	evaluation	NOUN
fcis-31208	52	14	index	index	NOUN
fcis-31208	52	15	value	value	NOUN
fcis-31208	52	16	statistics	statistic	NOUN
fcis-31208	52	17	prediction	prediction	NOUN
fcis-31208	52	18	evaluation	evaluation	NOUN
fcis-31208	52	19	indicators	indicator	NOUN
fcis-31208	52	20	value	value	VERB
fcis-31208	52	21	mae	mae	PROPN
fcis-31208	52	22	67.624	67.624	NUM
fcis-31208	52	23	mape	mape	NOUN
fcis-31208	52	24	0.667	0.667	NUM
fcis-31208	52	25	0.933	0.933	NUM
fcis-31208	52	26	figure	figure	NOUN
fcis-31208	52	27	4	4	NUM
fcis-31208	52	28	.	.	PUNCT
fcis-31208	52	29	visualization	visualization	NOUN
fcis-31208	52	30	of	of	ADP
fcis-31208	52	31	hamiltonian	hamiltonian	ADJ
fcis-31208	52	32	value	value	NOUN
fcis-31208	52	33	over	over	ADP
fcis-31208	52	34	time	time	NOUN
fcis-31208	52	35	support	support	NOUN
fcis-31208	52	36	vector	vector	NOUN
fcis-31208	52	37	machine	machine	NOUN
fcis-31208	52	38	(	(	PUNCT
fcis-31208	52	39	svm	svm	PROPN
fcis-31208	52	40	)	)	PUNCT
fcis-31208	52	41	is	be	AUX
fcis-31208	52	42	developed	develop	VERB
fcis-31208	52	43	from	from	ADP
fcis-31208	52	44	the	the	DET
fcis-31208	52	45	optimal	optimal	ADJ
fcis-31208	52	46	classification	classification	NOUN
fcis-31208	52	47	surface	surface	NOUN
fcis-31208	52	48	in	in	ADP
fcis-31208	52	49	the	the	DET
fcis-31208	52	50	case	case	NOUN
fcis-31208	52	51	of	of	ADP
fcis-31208	52	52	linear	linear	PROPN
fcis-31208	52	53	separability	separability	NOUN
fcis-31208	52	54	.	.	PUNCT
fcis-31208	53	1	the	the	DET
fcis-31208	53	2	basic	basic	ADJ
fcis-31208	53	3	idea	idea	NOUN
fcis-31208	53	4	can	can	AUX
fcis-31208	53	5	be	be	AUX
fcis-31208	53	6	illustrated	illustrate	VERB
fcis-31208	53	7	by	by	ADP
fcis-31208	53	8	the	the	DET
fcis-31208	53	9	two	two	NUM
fcis-31208	53	10	-	-	PUNCT
fcis-31208	53	11	dimensional	dimensional	ADJ
fcis-31208	53	12	situation	situation	NOUN
fcis-31208	53	13	in	in	ADP
fcis-31208	53	14	figure	figure	NOUN
fcis-31208	53	15	4	4	NUM
fcis-31208	53	16	.	.	PUNCT
fcis-31208	54	1	in	in	ADP
fcis-31208	54	2	the	the	DET
fcis-31208	54	3	figure	figure	NOUN
fcis-31208	54	4	below	below	ADV
fcis-31208	54	5	,	,	PUNCT
fcis-31208	54	6	the	the	DET
fcis-31208	54	7	solid	solid	ADJ
fcis-31208	54	8	point	point	NOUN
fcis-31208	54	9	and	and	CCONJ
fcis-31208	54	10	the	the	DET
fcis-31208	54	11	hollow	hollow	ADJ
fcis-31208	54	12	point	point	NOUN
fcis-31208	54	13	represent	represent	VERB
fcis-31208	54	14	two	two	NUM
fcis-31208	54	15	types	type	NOUN
fcis-31208	54	16	of	of	ADP
fcis-31208	54	17	samples	sample	NOUN
fcis-31208	54	18	respectively	respectively	ADV
fcis-31208	54	19	.	.	PUNCT
fcis-31208	55	1	if	if	SCONJ
fcis-31208	55	2	they	they	PRON
fcis-31208	55	3	are	be	AUX
fcis-31208	55	4	linearly	linearly	ADV
fcis-31208	55	5	separable	separable	ADJ
fcis-31208	55	6	,	,	PUNCT
fcis-31208	55	7	the	the	DET
fcis-31208	55	8	result	result	NOUN
fcis-31208	55	9	of	of	ADP
fcis-31208	55	10	machine	machine	NOUN
fcis-31208	55	11	learning	learning	NOUN
fcis-31208	55	12	is	be	AUX
fcis-31208	55	13	a	a	DET
fcis-31208	55	14	hyperplane	hyperplane	NOUN
fcis-31208	55	15	(	(	PUNCT
fcis-31208	55	16	also	also	ADV
fcis-31208	55	17	known	know	VERB
fcis-31208	55	18	as	as	ADP
fcis-31208	55	19	the	the	DET
fcis-31208	55	20	discriminant	discriminant	NOUN
fcis-31208	55	21	function	function	PROPN
fcis-31208	55	22	)	)	PUNCT
fcis-31208	55	23	,	,	PUNCT
fcis-31208	55	24	which	which	PRON
fcis-31208	55	25	divides	divide	VERB
fcis-31208	55	26	the	the	DET
fcis-31208	55	27	training	training	NOUN
fcis-31208	55	28	sample	sample	NOUN
fcis-31208	55	29	into	into	ADP
fcis-31208	55	30	positive	positive	ADJ
fcis-31208	55	31	and	and	CCONJ
fcis-31208	55	32	negative	negative	ADJ
fcis-31208	55	33	classes	class	NOUN
fcis-31208	55	34	.	.	PUNCT
fcis-31208	56	1	obviously	obviously	ADV
fcis-31208	56	2	,	,	PUNCT
fcis-31208	56	3	there	there	PRON
fcis-31208	56	4	are	be	VERB
fcis-31208	56	5	infinite	infinite	ADJ
fcis-31208	56	6	number	number	NOUN
fcis-31208	56	7	of	of	ADP
fcis-31208	56	8	such	such	ADJ
fcis-31208	56	9	hyperplanes	hyperplane	NOUN
fcis-31208	56	10	.	.	PUNCT
fcis-31208	57	1	according	accord	VERB
fcis-31208	57	2	to	to	ADP
fcis-31208	57	3	the	the	DET
fcis-31208	57	4	requirement	requirement	NOUN
fcis-31208	57	5	of	of	ADP
fcis-31208	57	6	the	the	DET
fcis-31208	57	7	structure	structure	NOUN
fcis-31208	57	8	risk	risk	NOUN
fcis-31208	57	9	minimization	minimization	NOUN
fcis-31208	57	10	principle	principle	NOUN
fcis-31208	57	11	(	(	PUNCT
fcis-31208	57	12	srm	srm	NOUN
fcis-31208	57	13	)	)	PUNCT
fcis-31208	57	14	,	,	PUNCT
fcis-31208	57	15	the	the	DET
fcis-31208	57	16	result	result	NOUN
fcis-31208	57	17	of	of	ADP
fcis-31208	57	18	machine	machine	NOUN
fcis-31208	57	19	learning	learning	NOUN
fcis-31208	57	20	should	should	AUX
fcis-31208	57	21	be	be	AUX
fcis-31208	57	22	the	the	DET
fcis-31208	57	23	optimal	optimal	ADJ
fcis-31208	57	24	hyperplane	hyperplane	NOUN
fcis-31208	57	25	,	,	PUNCT
fcis-31208	57	26	which	which	PRON
fcis-31208	57	27	can	can	AUX
fcis-31208	57	28	not	not	PART
fcis-31208	57	29	only	only	ADV
fcis-31208	57	30	correctly	correctly	ADV
fcis-31208	57	31	separate	separate	VERB
fcis-31208	57	32	the	the	DET
fcis-31208	57	33	two	two	NUM
fcis-31208	57	34	types	type	NOUN
fcis-31208	57	35	of	of	ADP
fcis-31208	57	36	training	training	NOUN
fcis-31208	57	37	samples	sample	NOUN
fcis-31208	57	38	.	.	PUNCT
fcis-31208	58	1	but	but	CCONJ
fcis-31208	58	2	also	also	ADV
fcis-31208	58	3	maximizes	maximize	VERB
fcis-31208	58	4	the	the	DET
fcis-31208	58	5	classification	classification	NOUN
fcis-31208	58	6	interval	interval	NOUN
fcis-31208	58	7	.	.	PUNCT
fcis-31208	59	1	since	since	SCONJ
fcis-31208	59	2	svm	svm	PROPN
fcis-31208	59	3	is	be	AUX
fcis-31208	59	4	a	a	DET
fcis-31208	59	5	binary	binary	ADJ
fcis-31208	59	6	classification	classification	NOUN
fcis-31208	59	7	model	model	NOUN
fcis-31208	59	8	,	,	PUNCT
fcis-31208	59	9	while	while	SCONJ
fcis-31208	59	10	this	this	PRON
fcis-31208	59	11	is	be	AUX
fcis-31208	59	12	a	a	DET
fcis-31208	59	13	three	three	NUM
fcis-31208	59	14	-	-	PUNCT
fcis-31208	59	15	classification	classification	NOUN
fcis-31208	59	16	problem	problem	NOUN
fcis-31208	59	17	,	,	PUNCT
fcis-31208	59	18	we	we	PRON
fcis-31208	59	19	first	first	ADV
fcis-31208	59	20	split	split	VERB
fcis-31208	59	21	the	the	DET
fcis-31208	59	22	three	three	NUM
fcis-31208	59	23	-	-	PUNCT
fcis-31208	59	24	classification	classification	NOUN
fcis-31208	59	25	problem	problem	NOUN
fcis-31208	59	26	into	into	ADP
fcis-31208	59	27	three	three	NUM
fcis-31208	59	28	different	different	ADJ
fcis-31208	59	29	binary	binary	ADJ
fcis-31208	59	30	classification	classification	NOUN
fcis-31208	59	31	problems	problem	NOUN
fcis-31208	59	32	.	.	PUNCT
fcis-31208	60	1	including	include	VERB
fcis-31208	60	2	distinguishing	distinguish	VERB
fcis-31208	60	3	between	between	ADP
fcis-31208	60	4	setosa	setosa	NOUN
fcis-31208	60	5	and	and	CCONJ
fcis-31208	60	6	versicolor	versicolor	NOUN
fcis-31208	60	7	,	,	PUNCT
fcis-31208	60	8	distinguishing	distinguish	VERB
fcis-31208	60	9	between	between	ADP
fcis-31208	60	10	setosa	setosa	NOUN
fcis-31208	60	11	and	and	CCONJ
fcis-31208	60	12	virginica	virginica	PROPN
fcis-31208	60	13	,	,	PUNCT
fcis-31208	60	14	including	include	VERB
fcis-31208	60	15	distinguishing	distinguish	VERB
fcis-31208	60	16	between	between	ADP
fcis-31208	60	17	setosa	setosa	NOUN
fcis-31208	60	18	and	and	CCONJ
fcis-31208	60	19	versicolor	versicolor	NOUN
fcis-31208	60	20	,	,	PUNCT
fcis-31208	60	21	distinguishing	distinguish	VERB
fcis-31208	60	22	between	between	ADP
fcis-31208	60	23	setosa	setosa	NOUN
fcis-31208	60	24	and	and	CCONJ
fcis-31208	60	25	virginica	virginica	NOUN
fcis-31208	60	26	,	,	PUNCT
fcis-31208	60	27	distinguishing	distinguish	VERB
fcis-31208	60	28	between	between	ADP
fcis-31208	60	29	virginica	virginica	NOUN
fcis-31208	60	30	and	and	CCONJ
fcis-31208	60	31	versicolor	versicolor	NOUN
fcis-31208	60	32	.	.	PUNCT
fcis-31208	61	1	distinguishing	distinguish	VERB
fcis-31208	61	2	between	between	ADP
fcis-31208	61	3	virginica	virginica	NOUN
fcis-31208	61	4	and	and	CCONJ
fcis-31208	61	5	versicolor	versicolor	NOUN
fcis-31208	61	6	.	.	PUNCT
fcis-31208	62	1	by	by	ADP
fcis-31208	62	2	doing	do	VERB
fcis-31208	62	3	this	this	PRON
fcis-31208	62	4	,	,	PUNCT
fcis-31208	62	5	a	a	DET
fcis-31208	62	6	tripartite	tripartite	ADJ
fcis-31208	62	7	classification	classification	NOUN
fcis-31208	62	8	problem	problem	NOUN
fcis-31208	62	9	is	be	AUX
fcis-31208	62	10	transformed	transform	VERB
fcis-31208	62	11	into	into	ADP
fcis-31208	62	12	three	three	NUM
fcis-31208	62	13	independent	independent	ADJ
fcis-31208	62	14	binary	binary	ADJ
fcis-31208	62	15	classification	classification	NOUN
fcis-31208	62	16	problems	problem	NOUN
fcis-31208	62	17	.	.	PUNCT
fcis-31208	63	1	suppose	suppose	VERB
fcis-31208	63	2	an	an	DET
fcis-31208	63	3	m	m	ADJ
fcis-31208	63	4	-	-	ADJ
fcis-31208	63	5	dimensional	dimensional	ADJ
fcis-31208	63	6	hyperplane	hyperplane	NOUN
fcis-31208	63	7	is	be	AUX
fcis-31208	63	8	described	describe	VERB
fcis-31208	63	9	by	by	ADP
fcis-31208	63	10	the	the	DET
fcis-31208	63	11	following	follow	VERB
fcis-31208	63	12	equation	equation	NOUN
fcis-31208	63	13	[	[	X
fcis-31208	63	14	5	5	NUM
fcis-31208	63	15	]	]	PUNCT
fcis-31208	63	16	:	:	PUNCT
fcis-31208	63	17	∙	∙	PROPN
fcis-31208	63	18	0	0	PUNCT
fcis-31208	64	1	∈	∈	PROPN
fcis-31208	64	2	,	,	PUNCT
fcis-31208	64	3	∈	∈	PROPN
fcis-31208	64	4	(	(	PUNCT
fcis-31208	64	5	14	14	NUM
fcis-31208	64	6	)	)	PUNCT
fcis-31208	64	7	40	40	NUM
fcis-31208	64	8	figure	figure	NOUN
fcis-31208	64	9	5	5	NUM
fcis-31208	64	10	.	.	PUNCT
fcis-31208	64	11	classification	classification	NOUN
fcis-31208	64	12	hyperplane	hyperplane	NOUN
fcis-31208	64	13	in	in	ADP
fcis-31208	64	14	the	the	DET
fcis-31208	64	15	linearly	linearly	ADV
fcis-31208	64	16	separable	separable	ADJ
fcis-31208	64	17	case	case	NOUN
fcis-31208	64	18	then	then	ADV
fcis-31208	64	19	the	the	DET
fcis-31208	64	20	optimal	optimal	ADJ
fcis-31208	64	21	hyperplane	hyperplane	NOUN
fcis-31208	64	22	with	with	ADP
fcis-31208	64	23	the	the	DET
fcis-31208	64	24	largest	large	ADJ
fcis-31208	64	25	classification	classification	NOUN
fcis-31208	64	26	interval	interval	NOUN
fcis-31208	64	27	can	can	AUX
fcis-31208	64	28	be	be	AUX
fcis-31208	64	29	obtained	obtain	VERB
fcis-31208	64	30	by	by	ADP
fcis-31208	64	31	finding	find	VERB
fcis-31208	64	32	the	the	DET
fcis-31208	64	33	minimum	minimum	ADJ
fcis-31208	64	34	value	value	NOUN
fcis-31208	64	35	of	of	ADP
fcis-31208	64	36	‖	‖	PROPN
fcis-31208	64	37	‖	‖	PROPN
fcis-31208	64	38	,	,	PUNCT
fcis-31208	64	39	where	where	SCONJ
fcis-31208	64	40	the	the	DET
fcis-31208	64	41	constraints	constraint	NOUN
fcis-31208	64	42	are	be	AUX
fcis-31208	64	43	:	:	PUNCT
fcis-31208	64	44	∙	∙	NOUN
fcis-31208	64	45	1	1	NUM
fcis-31208	64	46	0	0	NUM
fcis-31208	64	47	1,⋯	1,⋯	NUM
fcis-31208	64	48	,	,	PUNCT
fcis-31208	64	49	(	(	PUNCT
fcis-31208	64	50	15	15	NUM
fcis-31208	64	51	)	)	PUNCT
fcis-31208	64	52	where	where	SCONJ
fcis-31208	64	53	,	,	PUNCT
fcis-31208	64	54	∈	∈	PROPN
fcis-31208	64	55	1	1	NUM
fcis-31208	64	56	,	,	PUNCT
fcis-31208	64	57	1	1	NUM
fcis-31208	64	58	,	,	PUNCT
fcis-31208	64	59	is	be	AUX
fcis-31208	64	60	the	the	DET
fcis-31208	64	61	sample	sample	NOUN
fcis-31208	64	62	eigenvector	eigenvector	NOUN
fcis-31208	64	63	.	.	PUNCT
fcis-31208	65	1	the	the	DET
fcis-31208	65	2	constraint	constraint	NOUN
fcis-31208	65	3	conditions	condition	NOUN
fcis-31208	65	4	ensure	ensure	VERB
fcis-31208	65	5	that	that	SCONJ
fcis-31208	65	6	the	the	DET
fcis-31208	65	7	distance	distance	NOUN
fcis-31208	65	8	from	from	ADP
fcis-31208	65	9	the	the	DET
fcis-31208	65	10	data	data	NOUN
fcis-31208	65	11	point	point	NOUN
fcis-31208	65	12	to	to	ADP
fcis-31208	65	13	the	the	DET
fcis-31208	65	14	hyperplane	hyperplane	NOUN
fcis-31208	65	15	is	be	AUX
fcis-31208	65	16	at	at	ADV
fcis-31208	65	17	least	least	ADJ
fcis-31208	65	18	1	1	NUM
fcis-31208	65	19	.	.	PUNCT
fcis-31208	65	20	when	when	SCONJ
fcis-31208	65	21	the	the	DET
fcis-31208	65	22	above	above	ADJ
fcis-31208	65	23	formula	formula	NOUN
fcis-31208	65	24	obtains	obtain	VERB
fcis-31208	65	25	an	an	DET
fcis-31208	65	26	equal	equal	ADJ
fcis-31208	65	27	sign	sign	NOUN
fcis-31208	65	28	,	,	PUNCT
fcis-31208	65	29	it	it	PRON
fcis-31208	65	30	means	mean	VERB
fcis-31208	65	31	that	that	SCONJ
fcis-31208	65	32	the	the	DET
fcis-31208	65	33	data	data	NOUN
fcis-31208	65	34	point	point	NOUN
fcis-31208	65	35	is	be	AUX
fcis-31208	65	36	exactly	exactly	ADV
fcis-31208	65	37	on	on	ADP
fcis-31208	65	38	the	the	DET
fcis-31208	65	39	interval	interval	NOUN
fcis-31208	65	40	boundary	boundary	NOUN
fcis-31208	65	41	,	,	PUNCT
fcis-31208	65	42	and	and	CCONJ
fcis-31208	65	43	these	these	DET
fcis-31208	65	44	data	datum	NOUN
fcis-31208	65	45	points	point	NOUN
fcis-31208	65	46	are	be	AUX
fcis-31208	65	47	called	call	VERB
fcis-31208	65	48	support	support	NOUN
fcis-31208	65	49	vectors	vector	NOUN
fcis-31208	65	50	.	.	PUNCT
fcis-31208	66	1	in	in	ADP
fcis-31208	66	2	the	the	DET
fcis-31208	66	3	case	case	NOUN
fcis-31208	66	4	of	of	ADP
fcis-31208	66	5	linear	linear	PROPN
fcis-31208	66	6	indivisibility	indivisibility	NOUN
fcis-31208	66	7	,	,	PUNCT
fcis-31208	66	8	such	such	ADJ
fcis-31208	66	9	as	as	ADP
fcis-31208	66	10	the	the	DET
fcis-31208	66	11	presence	presence	NOUN
fcis-31208	66	12	of	of	ADP
fcis-31208	66	13	noisy	noisy	ADJ
fcis-31208	66	14	data	datum	NOUN
fcis-31208	66	15	,	,	PUNCT
fcis-31208	66	16	svm	svm	PROPN
fcis-31208	66	17	introduces	introduce	VERB
fcis-31208	66	18	a	a	DET
fcis-31208	66	19	relaxation	relaxation	NOUN
fcis-31208	66	20	term	term	NOUN
fcis-31208	66	21	0	0	NUM
fcis-31208	66	22	in	in	ADP
fcis-31208	66	23	the	the	DET
fcis-31208	66	24	above	above	ADJ
fcis-31208	66	25	equation	equation	NOUN
fcis-31208	66	26	to	to	PART
fcis-31208	66	27	achieve	achieve	VERB
fcis-31208	66	28	soft	soft	ADJ
fcis-31208	66	29	spacing	spacing	NOUN
fcis-31208	66	30	,	,	PUNCT
fcis-31208	66	31	i.e.	i.e.	X
fcis-31208	66	32	:	:	PUNCT
fcis-31208	66	33	∙	∙	NOUN
fcis-31208	66	34	1	1	NUM
fcis-31208	66	35	1,⋯	1,⋯	NUM
fcis-31208	66	36	,	,	PUNCT
fcis-31208	66	37	(	(	PUNCT
fcis-31208	66	38	16	16	NUM
fcis-31208	66	39	)	)	PUNCT
fcis-31208	66	40	based	base	VERB
fcis-31208	66	41	on	on	ADP
fcis-31208	66	42	this	this	PRON
fcis-31208	66	43	,	,	PUNCT
fcis-31208	66	44	the	the	DET
fcis-31208	66	45	objective	objective	ADJ
fcis-31208	66	46	function	function	NOUN
fcis-31208	66	47	is	be	AUX
fcis-31208	66	48	changed	change	VERB
fcis-31208	66	49	into	into	ADP
fcis-31208	66	50	the	the	DET
fcis-31208	66	51	following	follow	VERB
fcis-31208	66	52	formula	formula	NOUN
fcis-31208	66	53	:	:	PUNCT
fcis-31208	66	54	min	min	NOUN
fcis-31208	66	55	,	,	PUNCT
fcis-31208	66	56	∑	∑	PROPN
fcis-31208	66	57	(	(	PUNCT
fcis-31208	66	58	17	17	NUM
fcis-31208	66	59	)	)	PUNCT
fcis-31208	66	60	where	where	SCONJ
fcis-31208	66	61	w	w	NOUN
fcis-31208	66	62	is	be	AUX
fcis-31208	66	63	the	the	DET
fcis-31208	66	64	normal	normal	ADJ
fcis-31208	66	65	vector	vector	NOUN
fcis-31208	66	66	of	of	ADP
fcis-31208	66	67	the	the	DET
fcis-31208	66	68	hyperplane	hyperplane	NOUN
fcis-31208	66	69	,	,	PUNCT
fcis-31208	66	70	b	b	PROPN
fcis-31208	66	71	is	be	AUX
fcis-31208	66	72	the	the	DET
fcis-31208	66	73	bias	bias	NOUN
fcis-31208	66	74	of	of	ADP
fcis-31208	66	75	the	the	DET
fcis-31208	66	76	hyperplane	hyperplane	NOUN
fcis-31208	66	77	and	and	CCONJ
fcis-31208	66	78	is	be	AUX
fcis-31208	66	79	the	the	DET
fcis-31208	66	80	relaxation	relaxation	NOUN
fcis-31208	66	81	variable	variable	NOUN
fcis-31208	66	82	,	,	PUNCT
fcis-31208	66	83	c	c	PROPN
fcis-31208	66	84	is	be	AUX
fcis-31208	66	85	the	the	DET
fcis-31208	66	86	regularization	regularization	NOUN
fcis-31208	66	87	parameter	parameter	NOUN
fcis-31208	66	88	,	,	PUNCT
fcis-31208	66	89	which	which	PRON
fcis-31208	66	90	is	be	AUX
fcis-31208	66	91	used	use	VERB
fcis-31208	66	92	to	to	PART
fcis-31208	66	93	control	control	VERB
fcis-31208	66	94	the	the	DET
fcis-31208	66	95	model	model	NOUN
fcis-31208	66	96	's	's	PART
fcis-31208	66	97	tradeoff	tradeoff	NOUN
fcis-31208	66	98	between	between	ADP
fcis-31208	66	99	classification	classification	NOUN
fcis-31208	66	100	interval	interval	NOUN
fcis-31208	66	101	and	and	CCONJ
fcis-31208	66	102	misclassification	misclassification	NOUN
fcis-31208	66	103	,	,	PUNCT
fcis-31208	66	104	and	and	CCONJ
fcis-31208	66	105	determines	determine	VERB
fcis-31208	66	106	the	the	DET
fcis-31208	66	107	relative	relative	ADJ
fcis-31208	66	108	importance	importance	NOUN
fcis-31208	66	109	of	of	ADP
fcis-31208	66	110	the	the	DET
fcis-31208	66	111	two	two	NUM
fcis-31208	66	112	,	,	PUNCT
fcis-31208	66	113	while	while	SCONJ
fcis-31208	66	114	its	its	PRON
fcis-31208	66	115	value	value	NOUN
fcis-31208	66	116	is	be	AUX
fcis-31208	66	117	selected	select	VERB
fcis-31208	66	118	by	by	ADP
fcis-31208	66	119	cross	cross	NOUN
fcis-31208	66	120	-	-	NOUN
fcis-31208	66	121	validation	validation	ADJ
fcis-31208	66	122	,	,	PUNCT
fcis-31208	66	123	that	that	ADV
fcis-31208	66	124	is	is	ADV
fcis-31208	66	125	,	,	PUNCT
fcis-31208	66	126	adjusting	adjust	VERB
fcis-31208	66	127	c	c	NOUN
fcis-31208	66	128	according	accord	VERB
fcis-31208	66	129	to	to	ADP
fcis-31208	66	130	the	the	DET
fcis-31208	66	131	performance	performance	NOUN
fcis-31208	66	132	on	on	ADP
fcis-31208	66	133	the	the	DET
fcis-31208	66	134	verification	verification	NOUN
fcis-31208	66	135	set	set	NOUN
fcis-31208	66	136	.	.	PUNCT
fcis-31208	67	1	based	base	VERB
fcis-31208	67	2	on	on	ADP
fcis-31208	67	3	the	the	DET
fcis-31208	67	4	above	above	ADJ
fcis-31208	67	5	analysis	analysis	NOUN
fcis-31208	67	6	,	,	PUNCT
fcis-31208	67	7	we	we	PRON
fcis-31208	67	8	transform	transform	VERB
fcis-31208	67	9	svm	svm	NOUN
fcis-31208	67	10	into	into	ADP
fcis-31208	67	11	qubo	qubo	PROPN
fcis-31208	67	12	model	model	NOUN
fcis-31208	67	13	,	,	PUNCT
fcis-31208	67	14	which	which	PRON
fcis-31208	67	15	means	mean	VERB
fcis-31208	67	16	that	that	SCONJ
fcis-31208	67	17	formula	formula	NOUN
fcis-31208	67	18	(	(	PUNCT
fcis-31208	67	19	17	17	NUM
fcis-31208	67	20	)	)	PUNCT
fcis-31208	67	21	is	be	AUX
fcis-31208	67	22	transformed	transform	VERB
fcis-31208	67	23	into	into	ADP
fcis-31208	67	24	formula	formula	NOUN
fcis-31208	67	25	(	(	PUNCT
fcis-31208	67	26	3	3	NUM
fcis-31208	67	27	)	)	PUNCT
fcis-31208	67	28	.	.	PUNCT
fcis-31208	68	1	first	first	ADV
fcis-31208	68	2	,	,	PUNCT
fcis-31208	68	3	we	we	PRON
fcis-31208	68	4	discretized	discretize	VERB
fcis-31208	68	5	w	w	PROPN
fcis-31208	68	6	,	,	PUNCT
fcis-31208	68	7	b	b	NOUN
fcis-31208	68	8	,	,	PUNCT
fcis-31208	68	9	and	and	CCONJ
fcis-31208	68	10	in	in	ADP
fcis-31208	68	11	svm	svm	NOUN
fcis-31208	68	12	by	by	ADP
fcis-31208	68	13	converting	convert	VERB
fcis-31208	68	14	to	to	ADP
fcis-31208	68	15	binary	binary	NOUN
fcis-31208	68	16	.	.	PUNCT
fcis-31208	69	1	suppose	suppose	VERB
fcis-31208	69	2	∈	∈	PROPN
fcis-31208	69	3	,	,	PUNCT
fcis-31208	69	4	,	,	PUNCT
fcis-31208	69	5	represented	represent	VERB
fcis-31208	69	6	by	by	ADP
fcis-31208	69	7	m	m	PROPN
fcis-31208	69	8	binary	binary	ADJ
fcis-31208	69	9	variables	variable	NOUN
fcis-31208	69	10	,	,	PUNCT
fcis-31208	69	11	∈	∈	PROPN
fcis-31208	69	12	0,1	0,1	NUM
fcis-31208	69	13	:	:	PUNCT
fcis-31208	69	14	∆	∆	PROPN
fcis-31208	69	15	∑	∑	PUNCT
fcis-31208	69	16	,	,	PUNCT
fcis-31208	69	17	∙	∙	PROPN
fcis-31208	69	18	2	2	NUM
fcis-31208	69	19	(	(	PUNCT
fcis-31208	69	20	18	18	NUM
fcis-31208	69	21	)	)	PUNCT
fcis-31208	69	22	of	of	ADP
fcis-31208	69	23	which	which	PRON
fcis-31208	69	24	,	,	PUNCT
fcis-31208	69	25	∆	∆	PROPN
fcis-31208	69	26	(	(	PUNCT
fcis-31208	69	27	19	19	NUM
fcis-31208	69	28	)	)	PUNCT
fcis-31208	69	29	then	then	ADV
fcis-31208	69	30	the	the	DET
fcis-31208	69	31	mathematical	mathematical	ADJ
fcis-31208	69	32	expression	expression	NOUN
fcis-31208	69	33	of	of	ADP
fcis-31208	69	34	binary	binary	ADJ
fcis-31208	69	35	conversion	conversion	NOUN
fcis-31208	69	36	of	of	ADP
fcis-31208	69	37	w	w	PROPN
fcis-31208	69	38	binormal	binormal	NOUN
fcis-31208	69	39	is	be	AUX
fcis-31208	69	40	as	as	SCONJ
fcis-31208	69	41	follows	follow	VERB
fcis-31208	69	42	:	:	PUNCT
fcis-31208	70	1	‖	‖	PROPN
fcis-31208	70	2	‖	‖	PROPN
fcis-31208	70	3	∑	∑	PROPN
fcis-31208	70	4	∑	∑	PUNCT
fcis-31208	70	5	∆	∆	PROPN
fcis-31208	70	6	∑	∑	PUNCT
fcis-31208	70	7	,	,	PUNCT
fcis-31208	70	8	∙	∙	PROPN
fcis-31208	70	9	2	2	NUM
fcis-31208	70	10	(	(	PUNCT
fcis-31208	70	11	20	20	NUM
fcis-31208	70	12	)	)	PUNCT
fcis-31208	70	13	in	in	ADP
fcis-31208	70	14	the	the	DET
fcis-31208	70	15	same	same	ADJ
fcis-31208	70	16	way	way	NOUN
fcis-31208	70	17	,	,	PUNCT
fcis-31208	70	18	we	we	PRON
fcis-31208	70	19	assume	assume	VERB
fcis-31208	70	20	that	that	SCONJ
fcis-31208	70	21	∈	∈	PROPN
fcis-31208	70	22	,	,	PUNCT
fcis-31208	70	23	,	,	PUNCT
fcis-31208	70	24	represented	represent	VERB
fcis-31208	70	25	by	by	ADP
fcis-31208	70	26	binary	binary	ADJ
fcis-31208	70	27	variable	variable	NOUN
fcis-31208	70	28	,	,	PUNCT
fcis-31208	70	29	∈	∈	PROPN
fcis-31208	70	30	0,1	0,1	NUM
fcis-31208	70	31	:	:	PUNCT
fcis-31208	70	32	∆	∆	PROPN
fcis-31208	70	33	∑	∑	PUNCT
fcis-31208	70	34	,	,	PUNCT
fcis-31208	70	35	∙	∙	PROPN
fcis-31208	70	36	2	2	NUM
fcis-31208	70	37	(	(	PUNCT
fcis-31208	70	38	21	21	NUM
fcis-31208	70	39	)	)	PUNCT
fcis-31208	70	40	among	among	ADP
fcis-31208	70	41	them	they	PRON
fcis-31208	70	42	,	,	PUNCT
fcis-31208	70	43	∆	∆	PROPN
fcis-31208	70	44	(	(	PUNCT
fcis-31208	70	45	22	22	NUM
fcis-31208	70	46	)	)	PUNCT
fcis-31208	70	47	and	and	CCONJ
fcis-31208	70	48	,	,	PUNCT
fcis-31208	70	49	respectively	respectively	ADV
fcis-31208	70	50	represent	represent	VERB
fcis-31208	70	51	the	the	DET
fcis-31208	70	52	minimum	minimum	NOUN
fcis-31208	70	53	and	and	CCONJ
fcis-31208	70	54	maximum	maximum	NOUN
fcis-31208	70	55	of	of	ADP
fcis-31208	70	56	b.	b.	NOUN
fcis-31208	70	57	to	to	PART
fcis-31208	70	58	convert	convert	VERB
fcis-31208	70	59	formula	formula	NOUN
fcis-31208	70	60	(	(	PUNCT
fcis-31208	70	61	15	15	NUM
fcis-31208	70	62	)	)	PUNCT
fcis-31208	70	63	into	into	ADP
fcis-31208	70	64	qubo	qubo	PROPN
fcis-31208	70	65	penalty	penalty	NOUN
fcis-31208	70	66	term	term	NOUN
fcis-31208	70	67	,	,	PUNCT
fcis-31208	70	68	we	we	PRON
fcis-31208	70	69	have	have	VERB
fcis-31208	70	70	:	:	PUNCT
fcis-31208	70	71	∑	∑	PUNCT
fcis-31208	70	72	max	max	PROPN
fcis-31208	70	73	0,1	0,1	NUM
fcis-31208	70	74	(	(	PUNCT
fcis-31208	70	75	23	23	NUM
fcis-31208	70	76	)	)	PUNCT
fcis-31208	70	77	where	where	SCONJ
fcis-31208	70	78	,	,	PUNCT
fcis-31208	70	79	∑	∑	ADV
fcis-31208	70	80	,	,	PUNCT
fcis-31208	70	81	,	,	PUNCT
fcis-31208	70	82	b	b	PROPN
fcis-31208	70	83	is	be	AUX
fcis-31208	70	84	the	the	DET
fcis-31208	70	85	bias	bias	NOUN
fcis-31208	70	86	and	and	CCONJ
fcis-31208	70	87	is	be	AUX
fcis-31208	70	88	the	the	DET
fcis-31208	70	89	penalty	penalty	NOUN
fcis-31208	70	90	term	term	NOUN
fcis-31208	70	91	weight	weight	NOUN
fcis-31208	70	92	.	.	PUNCT
fcis-31208	71	1	secondly	secondly	ADV
fcis-31208	71	2	,	,	PUNCT
fcis-31208	71	3	we	we	PRON
fcis-31208	71	4	substitute	substitute	VERB
fcis-31208	71	5	the	the	DET
fcis-31208	71	6	binary	binary	ADJ
fcis-31208	71	7	expressions	expression	NOUN
fcis-31208	71	8	of	of	ADP
fcis-31208	71	9	w	w	NOUN
fcis-31208	71	10	and	and	CCONJ
fcis-31208	71	11	b	b	NOUN
fcis-31208	71	12	into	into	ADP
fcis-31208	71	13	the	the	DET
fcis-31208	71	14	above	above	ADJ
fcis-31208	71	15	formula	formula	NOUN
fcis-31208	71	16	respectively	respectively	ADV
fcis-31208	71	17	,	,	PUNCT
fcis-31208	71	18	then	then	ADV
fcis-31208	71	19	we	we	PRON
fcis-31208	71	20	have	have	VERB
fcis-31208	71	21	:	:	PUNCT
fcis-31208	71	22	(	(	PUNCT
fcis-31208	71	23	24	24	NUM
fcis-31208	71	24	)	)	PUNCT
fcis-31208	71	25	where	where	SCONJ
fcis-31208	71	26	,	,	PUNCT
fcis-31208	71	27	∑	∑	ADP
fcis-31208	71	28	  	  	SPACE
fcis-31208	71	29	,	,	PUNCT
fcis-31208	71	30	is	be	AUX
fcis-31208	71	31	the	the	DET
fcis-31208	71	32	constant	constant	ADJ
fcis-31208	71	33	term	term	NOUN
fcis-31208	71	34	,	,	PUNCT
fcis-31208	71	35	∆	∆	PUNCT
fcis-31208	71	36	∑	∑	PUNCT
fcis-31208	71	37	  	  	SPACE
fcis-31208	71	38	∑	∑	ADP
fcis-31208	71	39	  	  	SPACE
fcis-31208	71	40	,	,	PUNCT
fcis-31208	71	41	⋅	⋅	PROPN
fcis-31208	71	42	,	,	PUNCT
fcis-31208	71	43	⋅	⋅	PROPN
fcis-31208	71	44	2	2	NUM
fcis-31208	71	45	∆	∆	PROPN
fcis-31208	71	46	∑	∑	PUNCT
fcis-31208	71	47	  	  	SPACE
fcis-31208	71	48	,	,	PUNCT
fcis-31208	71	49	⋅	⋅	PROPN
fcis-31208	71	50	2	2	NUM
fcis-31208	71	51	is	be	AUX
fcis-31208	71	52	the	the	DET
fcis-31208	71	53	part	part	NOUN
fcis-31208	71	54	about	about	ADP
fcis-31208	71	55	the	the	DET
fcis-31208	71	56	binary	binary	ADJ
fcis-31208	71	57	variable	variable	NOUN
fcis-31208	71	58	.	.	PUNCT
fcis-31208	72	1	finally	finally	ADV
fcis-31208	72	2	,	,	PUNCT
fcis-31208	72	3	substitute	substitute	VERB
fcis-31208	72	4	the	the	DET
fcis-31208	72	5	above	above	ADV
fcis-31208	72	6	into	into	ADP
fcis-31208	72	7	the	the	DET
fcis-31208	72	8	formula	formula	NOUN
fcis-31208	72	9	(	(	PUNCT
fcis-31208	72	10	23	23	NUM
fcis-31208	72	11	):	):	PUNCT
fcis-31208	72	12	(	(	PUNCT
fcis-31208	72	13	25	25	NUM
fcis-31208	72	14	)	)	PUNCT
fcis-31208	72	15	where	where	SCONJ
fcis-31208	72	16	1	1	NUM
fcis-31208	72	17	∑	∑	NOUN
fcis-31208	72	18	  	  	SPACE
fcis-31208	72	19	,	,	PUNCT
fcis-31208	72	20	is	be	AUX
fcis-31208	72	21	the	the	DET
fcis-31208	72	22	constant	constant	ADJ
fcis-31208	72	23	term	term	NOUN
fcis-31208	72	24	,	,	PUNCT
fcis-31208	72	25	∆	∆	PUNCT
fcis-31208	72	26	∑	∑	PUNCT
fcis-31208	72	27	  	  	SPACE
fcis-31208	72	28	∑	∑	ADP
fcis-31208	72	29	  	  	SPACE
fcis-31208	72	30	,	,	PUNCT
fcis-31208	72	31	,	,	PUNCT
fcis-31208	72	32	2	2	NUM
fcis-31208	72	33	∆	∆	X
fcis-31208	72	34	∑	∑	PUNCT
fcis-31208	72	35	  	  	SPACE
fcis-31208	72	36	,	,	PUNCT
fcis-31208	72	37	2	2	NUM
fcis-31208	72	38	is	be	AUX
fcis-31208	72	39	the	the	DET
fcis-31208	72	40	linear	linear	ADJ
fcis-31208	72	41	term	term	NOUN
fcis-31208	72	42	about	about	ADP
fcis-31208	72	43	the	the	DET
fcis-31208	72	44	binary	binary	ADJ
fcis-31208	72	45	variable	variable	NOUN
fcis-31208	72	46	,	,	PUNCT
fcis-31208	72	47	respectively	respectively	ADV
fcis-31208	72	48	remember	remember	VERB
fcis-31208	72	49	the	the	DET
fcis-31208	72	50	constant	constant	ADJ
fcis-31208	72	51	term	term	NOUN
fcis-31208	72	52	is	be	AUX
fcis-31208	72	53	,	,	PUNCT
fcis-31208	72	54	,	,	PUNCT
fcis-31208	72	55	then	then	ADV
fcis-31208	72	56	the	the	DET
fcis-31208	72	57	above	above	ADJ
fcis-31208	72	58	formula	formula	NOUN
fcis-31208	72	59	can	can	AUX
fcis-31208	72	60	be	be	AUX
fcis-31208	72	61	abbreviated	abbreviate	VERB
fcis-31208	72	62	as	as	SCONJ
fcis-31208	72	63	follows	follow	VERB
fcis-31208	72	64	:	:	PUNCT
fcis-31208	72	65	1	1	NUM
fcis-31208	72	66	(	(	PUNCT
fcis-31208	72	67	26	26	NUM
fcis-31208	72	68	)	)	PUNCT
fcis-31208	72	69	then	then	ADV
fcis-31208	72	70	formula	formula	NOUN
fcis-31208	72	71	(	(	PUNCT
fcis-31208	72	72	23	23	NUM
fcis-31208	72	73	)	)	PUNCT
fcis-31208	72	74	can	can	AUX
fcis-31208	72	75	be	be	AUX
fcis-31208	72	76	written	write	VERB
fcis-31208	72	77	as	as	ADP
fcis-31208	72	78	:	:	PUNCT
fcis-31208	72	79	∑	∑	PUNCT
fcis-31208	72	80	max	max	PROPN
fcis-31208	72	81	0	0	NUM
fcis-31208	72	82	,	,	PUNCT
fcis-31208	72	83	(	(	PUNCT
fcis-31208	72	84	27	27	NUM
fcis-31208	72	85	)	)	PUNCT
fcis-31208	72	86	41	41	NUM
fcis-31208	72	87	consequently	consequently	ADV
fcis-31208	72	88	,	,	PUNCT
fcis-31208	72	89	the	the	DET
fcis-31208	72	90	objective	objective	ADJ
fcis-31208	72	91	function	function	NOUN
fcis-31208	72	92	can	can	AUX
fcis-31208	72	93	be	be	AUX
fcis-31208	72	94	integrated	integrate	VERB
fcis-31208	72	95	as	as	SCONJ
fcis-31208	72	96	follows	follow	VERB
fcis-31208	72	97	:	:	PUNCT
fcis-31208	72	98	(	(	PUNCT
fcis-31208	72	99	28	28	NUM
fcis-31208	72	100	)	)	PUNCT
fcis-31208	72	101	(	(	PUNCT
fcis-31208	72	102	2	2	X
fcis-31208	72	103	)	)	PUNCT
fcis-31208	72	104	solving	solve	VERB
fcis-31208	72	105	qubo	qubo	NOUN
fcis-31208	72	106	-	-	PUNCT
fcis-31208	72	107	classification	classification	NOUN
fcis-31208	72	108	model	model	NOUN
fcis-31208	72	109	based	base	VERB
fcis-31208	72	110	on	on	ADP
fcis-31208	72	111	sa	sa	PROPN
fcis-31208	72	112	firstly	firstly	ADV
fcis-31208	72	113	,	,	PUNCT
fcis-31208	72	114	we	we	PRON
fcis-31208	72	115	do	do	VERB
fcis-31208	72	116	data	data	NOUN
fcis-31208	72	117	preprocessing	preprocessing	NOUN
fcis-31208	72	118	,	,	PUNCT
fcis-31208	72	119	which	which	PRON
fcis-31208	72	120	scramps	scramp	VERB
fcis-31208	72	121	the	the	DET
fcis-31208	72	122	original	original	ADJ
fcis-31208	72	123	ordered	order	VERB
fcis-31208	72	124	data	datum	NOUN
fcis-31208	72	125	,	,	PUNCT
fcis-31208	72	126	and	and	CCONJ
fcis-31208	72	127	uses	use	VERB
fcis-31208	72	128	70	70	NUM
fcis-31208	72	129	%	%	NOUN
fcis-31208	72	130	of	of	ADP
fcis-31208	72	131	the	the	DET
fcis-31208	72	132	data	datum	NOUN
fcis-31208	72	133	for	for	ADP
fcis-31208	72	134	data	datum	NOUN
fcis-31208	72	135	training	training	NOUN
fcis-31208	72	136	,	,	PUNCT
fcis-31208	72	137	and	and	CCONJ
fcis-31208	72	138	the	the	DET
fcis-31208	72	139	rest	rest	NOUN
fcis-31208	72	140	of	of	ADP
fcis-31208	72	141	which	which	PRON
fcis-31208	72	142	is	be	AUX
fcis-31208	72	143	used	use	VERB
fcis-31208	72	144	for	for	ADP
fcis-31208	72	145	data	datum	NOUN
fcis-31208	72	146	prediction	prediction	NOUN
fcis-31208	72	147	.	.	PUNCT
fcis-31208	73	1	we	we	PRON
fcis-31208	73	2	still	still	ADV
fcis-31208	73	3	use	use	VERB
fcis-31208	73	4	the	the	DET
fcis-31208	73	5	simulated	simulate	VERB
fcis-31208	73	6	annealing	annealing	NOUN
fcis-31208	73	7	solver	solver	ADV
fcis-31208	73	8	built	build	VERB
fcis-31208	73	9	in	in	ADP
fcis-31208	73	10	kaiwu	kaiwu	PROPN
fcis-31208	73	11	sdk	sdk	PROPN
fcis-31208	73	12	for	for	ADP
fcis-31208	73	13	solving	solve	VERB
fcis-31208	73	14	,	,	PUNCT
fcis-31208	73	15	in	in	ADP
fcis-31208	73	16	which	which	PRON
fcis-31208	73	17	the	the	DET
fcis-31208	73	18	parameters	parameter	NOUN
fcis-31208	73	19	in	in	ADP
fcis-31208	73	20	the	the	DET
fcis-31208	73	21	solver	solver	NOUN
fcis-31208	73	22	are	be	AUX
fcis-31208	73	23	set	set	VERB
fcis-31208	73	24	as	as	SCONJ
fcis-31208	73	25	follows	follow	VERB
fcis-31208	73	26	:	:	PUNCT
fcis-31208	73	27	table	table	NOUN
fcis-31208	73	28	5	5	NUM
fcis-31208	73	29	.	.	PUNCT
fcis-31208	73	30	simulated	simulate	VERB
fcis-31208	73	31	annealing	anneal	VERB
fcis-31208	73	32	parameter	parameter	NOUN
fcis-31208	73	33	table	table	NOUN
fcis-31208	73	34	parameters	parameter	NOUN
fcis-31208	73	35	select	select	VERB
fcis-31208	73	36	parameter	parameter	NOUN
fcis-31208	73	37	values	value	NOUN
fcis-31208	73	38	initial	initial	ADJ
fcis-31208	73	39	temperature	temperature	NOUN
fcis-31208	73	40	100	100	NUM
fcis-31208	73	41	cutoff	cutoff	NOUN
fcis-31208	73	42	temperature	temperature	NOUN
fcis-31208	73	43	10	10	NUM
fcis-31208	73	44	cooling	cool	VERB
fcis-31208	73	45	factor	factor	NOUN
fcis-31208	73	46	0.99	0.99	NUM
fcis-31208	73	47	iterations	iteration	NOUN
fcis-31208	73	48	per	per	ADP
fcis-31208	73	49	temperature	temperature	NOUN
fcis-31208	73	50	10	10	NUM
fcis-31208	73	51	size	size	NOUN
fcis-31208	73	52	limit	limit	NOUN
fcis-31208	73	53	10	10	NUM
fcis-31208	73	54	select	select	ADJ
fcis-31208	73	55	the	the	DET
fcis-31208	73	56	initial	initial	ADJ
fcis-31208	73	57	temperature	temperature	NOUN
fcis-31208	73	58	and	and	CCONJ
fcis-31208	73	59	the	the	DET
fcis-31208	73	60	end	end	NOUN
fcis-31208	73	61	temperature	temperature	NOUN
fcis-31208	73	62	,	,	PUNCT
fcis-31208	73	63	and	and	CCONJ
fcis-31208	73	64	select	select	VERB
fcis-31208	73	65	the	the	DET
fcis-31208	73	66	appropriate	appropriate	ADJ
fcis-31208	73	67	cooling	cool	VERB
fcis-31208	73	68	coefficient	coefficient	NOUN
fcis-31208	73	69	during	during	ADP
fcis-31208	73	70	the	the	DET
fcis-31208	73	71	annealing	annealing	NOUN
fcis-31208	73	72	process	process	NOUN
fcis-31208	73	73	,	,	PUNCT
fcis-31208	73	74	so	so	SCONJ
fcis-31208	73	75	that	that	SCONJ
fcis-31208	73	76	the	the	DET
fcis-31208	73	77	iterative	iterative	ADJ
fcis-31208	73	78	depth	depth	NOUN
fcis-31208	73	79	of	of	ADP
fcis-31208	73	80	each	each	DET
fcis-31208	73	81	temperature	temperature	NOUN
fcis-31208	73	82	in	in	ADP
fcis-31208	73	83	the	the	DET
fcis-31208	73	84	annealing	annealing	NOUN
fcis-31208	73	85	process	process	NOUN
fcis-31208	73	86	does	do	AUX
fcis-31208	73	87	not	not	PART
fcis-31208	73	88	exceed	exceed	VERB
fcis-31208	73	89	10	10	NUM
fcis-31208	73	90	.	.	PUNCT
fcis-31208	74	1	by	by	ADP
fcis-31208	74	2	solving	solve	VERB
fcis-31208	74	3	the	the	DET
fcis-31208	74	4	simulated	simulate	VERB
fcis-31208	74	5	annealing	annealing	NOUN
fcis-31208	74	6	solver	solver	ADP
fcis-31208	74	7	,	,	PUNCT
fcis-31208	74	8	we	we	PRON
fcis-31208	74	9	get	get	VERB
fcis-31208	74	10	3	3	NUM
fcis-31208	74	11	independent	independent	ADJ
fcis-31208	74	12	binary	binary	ADJ
fcis-31208	74	13	classification	classification	NOUN
fcis-31208	74	14	problem	problem	NOUN
fcis-31208	74	15	parameter	parameter	NOUN
fcis-31208	74	16	results	result	NOUN
fcis-31208	74	17	:	:	PUNCT
fcis-31208	74	18	table	table	NOUN
fcis-31208	74	19	6	6	NUM
fcis-31208	74	20	.	.	PUNCT
fcis-31208	74	21	optimal	optimal	ADJ
fcis-31208	74	22	prediction	prediction	NOUN
fcis-31208	74	23	parameter	parameter	NOUN
fcis-31208	74	24	values	value	NOUN
fcis-31208	74	25	(	(	PUNCT
fcis-31208	74	26	iris	iris	NOUN
fcis-31208	74	27	-	-	PUNCT
fcis-31208	74	28	setosa	setosa	NOUN
fcis-31208	74	29	and	and	CCONJ
fcis-31208	74	30	irisversicolor	irisversicolor	NOUN
fcis-31208	74	31	)	)	PUNCT
fcis-31208	74	32	optimal	optimal	ADJ
fcis-31208	74	33	prediction	prediction	NOUN
fcis-31208	74	34	parameters	parameter	NOUN
fcis-31208	74	35	value	value	VERB
fcis-31208	74	36	-0.269	-0.269	NUM
fcis-31208	74	37	0.338	0.338	NUM
fcis-31208	74	38	-0.701	-0.701	SYM
fcis-31208	74	39	-0.749	-0.749	NOUN
fcis-31208	74	40	b	b	X
fcis-31208	74	41	-0.247	-0.247	SYM
fcis-31208	74	42	50	50	NUM
fcis-31208	74	43	at	at	ADP
fcis-31208	74	44	this	this	DET
fcis-31208	74	45	point	point	NOUN
fcis-31208	74	46	,	,	PUNCT
fcis-31208	74	47	the	the	DET
fcis-31208	74	48	objective	objective	ADJ
fcis-31208	74	49	function	function	NOUN
fcis-31208	74	50	q	q	PUNCT
fcis-31208	74	51	reaches	reach	VERB
fcis-31208	74	52	a	a	DET
fcis-31208	74	53	minimum	minimum	NOUN
fcis-31208	74	54	,	,	PUNCT
fcis-31208	74	55	and	and	CCONJ
fcis-31208	74	56	w	w	NOUN
fcis-31208	74	57	is	be	AUX
fcis-31208	74	58	the	the	DET
fcis-31208	74	59	decision	decision	NOUN
fcis-31208	74	60	hyperplane	hyperplane	NOUN
fcis-31208	74	61	normal	normal	ADJ
fcis-31208	74	62	vector	vector	NOUN
fcis-31208	74	63	,	,	PUNCT
fcis-31208	74	64	and	and	CCONJ
fcis-31208	74	65	b	b	NOUN
fcis-31208	74	66	is	be	AUX
fcis-31208	74	67	the	the	DET
fcis-31208	74	68	bias	bias	NOUN
fcis-31208	74	69	and	and	CCONJ
fcis-31208	74	70	is	be	AUX
fcis-31208	74	71	the	the	DET
fcis-31208	74	72	penalty	penalty	NOUN
fcis-31208	74	73	term	term	NOUN
fcis-31208	74	74	weight	weight	NOUN
fcis-31208	74	75	.	.	PUNCT
fcis-31208	75	1	on	on	ADP
fcis-31208	75	2	this	this	DET
fcis-31208	75	3	basis	basis	NOUN
fcis-31208	75	4	,	,	PUNCT
fcis-31208	75	5	we	we	PRON
fcis-31208	75	6	draw	draw	VERB
fcis-31208	75	7	the	the	DET
fcis-31208	75	8	classification	classification	NOUN
fcis-31208	75	9	result	result	NOUN
fcis-31208	75	10	image	image	NOUN
fcis-31208	75	11	as	as	SCONJ
fcis-31208	75	12	follows	follow	VERB
fcis-31208	75	13	:	:	PUNCT
fcis-31208	75	14	it	it	PRON
fcis-31208	75	15	can	can	AUX
fcis-31208	75	16	be	be	AUX
fcis-31208	75	17	seen	see	VERB
fcis-31208	75	18	from	from	ADP
fcis-31208	75	19	the	the	DET
fcis-31208	75	20	above	above	ADJ
fcis-31208	75	21	figure	figure	NOUN
fcis-31208	75	22	that	that	SCONJ
fcis-31208	75	23	qubo	qubo	NOUN
fcis-31208	75	24	-	-	PUNCT
fcis-31208	75	25	svm	svm	ADJ
fcis-31208	75	26	binary	binary	ADJ
fcis-31208	75	27	classification	classification	NOUN
fcis-31208	75	28	results	result	NOUN
fcis-31208	75	29	are	be	AUX
fcis-31208	75	30	more	more	ADV
fcis-31208	75	31	accurate	accurate	ADJ
fcis-31208	75	32	.	.	PUNCT
fcis-31208	76	1	based	base	VERB
fcis-31208	76	2	on	on	ADP
fcis-31208	76	3	this	this	PRON
fcis-31208	76	4	,	,	PUNCT
fcis-31208	76	5	we	we	PRON
fcis-31208	76	6	calculated	calculate	VERB
fcis-31208	76	7	a	a	DET
fcis-31208	76	8	variety	variety	NOUN
fcis-31208	76	9	of	of	ADP
fcis-31208	76	10	prediction	prediction	NOUN
fcis-31208	76	11	evaluation	evaluation	NOUN
fcis-31208	76	12	indicators	indicator	NOUN
fcis-31208	76	13	to	to	PART
fcis-31208	76	14	help	help	VERB
fcis-31208	76	15	explain	explain	VERB
fcis-31208	76	16	the	the	DET
fcis-31208	76	17	excellent	excellent	ADJ
fcis-31208	76	18	results	result	NOUN
fcis-31208	76	19	.	.	PUNCT
fcis-31208	77	1	figure	figure	VERB
fcis-31208	77	2	6	6	NUM
fcis-31208	77	3	.	.	PUNCT
fcis-31208	77	4	comparison	comparison	NOUN
fcis-31208	77	5	of	of	ADP
fcis-31208	77	6	classical	classical	ADJ
fcis-31208	77	7	svm	svm	NOUN
fcis-31208	77	8	and	and	CCONJ
fcis-31208	77	9	qubo	qubo	NOUN
fcis-31208	77	10	-	-	PUNCT
fcis-31208	77	11	svm	svm	NOUN
fcis-31208	77	12	classification	classification	NOUN
fcis-31208	77	13	(	(	PUNCT
fcis-31208	77	14	iris	iris	NOUN
fcis-31208	77	15	-	-	PUNCT
fcis-31208	77	16	setosa	setosa	NOUN
fcis-31208	77	17	and	and	CCONJ
fcis-31208	77	18	iris	iris	NOUN
fcis-31208	77	19	-	-	PUNCT
fcis-31208	77	20	versicolor	versicolor	NOUN
fcis-31208	77	21	)	)	PUNCT
fcis-31208	77	22	table	table	NOUN
fcis-31208	77	23	7	7	NUM
fcis-31208	77	24	.	.	PUNCT
fcis-31208	78	1	statistics	statistic	NOUN
fcis-31208	78	2	of	of	ADP
fcis-31208	78	3	outcome	outcome	NOUN
fcis-31208	78	4	evaluation	evaluation	NOUN
fcis-31208	78	5	indicators	indicator	NOUN
fcis-31208	78	6	(	(	PUNCT
fcis-31208	78	7	iris	iris	NOUN
fcis-31208	78	8	-	-	PUNCT
fcis-31208	78	9	setosa	setosa	NOUN
fcis-31208	78	10	and	and	CCONJ
fcis-31208	78	11	iris	iris	NOUN
fcis-31208	78	12	-	-	PUNCT
fcis-31208	78	13	virginica	virginica	NOUN
fcis-31208	78	14	)	)	PUNCT
fcis-31208	78	15	data	datum	NOUN
fcis-31208	78	16	set	set	VERB
fcis-31208	78	17	recall	recall	NOUN
fcis-31208	78	18	rate	rate	NOUN
fcis-31208	78	19	f1	f1	PROPN
fcis-31208	78	20	accuracy	accuracy	NOUN
fcis-31208	78	21	rate	rate	NOUN
fcis-31208	78	22	precision	precision	NOUN
fcis-31208	78	23	rate	rate	NOUN
fcis-31208	78	24	training	training	NOUN
fcis-31208	78	25	set	set	VERB
fcis-31208	78	26	1	1	NUM
fcis-31208	78	27	1	1	NUM
fcis-31208	78	28	1	1	NUM
fcis-31208	78	29	1	1	NUM
fcis-31208	78	30	test	test	NOUN
fcis-31208	78	31	set	set	VERB
fcis-31208	78	32	1	1	NUM
fcis-31208	78	33	1	1	NUM
fcis-31208	78	34	1	1	NUM
fcis-31208	78	35	1	1	NUM
fcis-31208	78	36	based	base	VERB
fcis-31208	78	37	on	on	ADP
fcis-31208	78	38	this	this	PRON
fcis-31208	78	39	,	,	PUNCT
fcis-31208	78	40	we	we	PRON
fcis-31208	78	41	solve	solve	VERB
fcis-31208	78	42	the	the	DET
fcis-31208	78	43	hyperplane	hyperplane	NOUN
fcis-31208	78	44	parameters	parameter	NOUN
fcis-31208	78	45	of	of	ADP
fcis-31208	78	46	irissetosa	irissetosa	PROPN
fcis-31208	78	47	and	and	CCONJ
fcis-31208	78	48	iris	iris	NOUN
fcis-31208	78	49	-	-	PUNCT
fcis-31208	78	50	virginica	virginica	NOUN
fcis-31208	78	51	:	:	PUNCT
fcis-31208	78	52	42	42	NUM
fcis-31208	78	53	table	table	NOUN
fcis-31208	78	54	8	8	NUM
fcis-31208	78	55	.	.	PUNCT
fcis-31208	79	1	optimal	optimal	ADJ
fcis-31208	79	2	prediction	prediction	NOUN
fcis-31208	79	3	parameter	parameter	NOUN
fcis-31208	79	4	values	value	NOUN
fcis-31208	79	5	(	(	PUNCT
fcis-31208	79	6	iris	iris	NOUN
fcis-31208	79	7	-	-	PUNCT
fcis-31208	79	8	setosa	setosa	NOUN
fcis-31208	79	9	and	and	CCONJ
fcis-31208	79	10	iris	iris	NOUN
fcis-31208	79	11	-	-	PUNCT
fcis-31208	79	12	virginica	virginica	NOUN
fcis-31208	79	13	)	)	PUNCT
fcis-31208	79	14	optimal	optimal	ADJ
fcis-31208	79	15	prediction	prediction	NOUN
fcis-31208	79	16	parameters	parameter	NOUN
fcis-31208	79	17	value	value	NOUN
fcis-31208	79	18	-0.098	-0.098	PUNCT
fcis-31208	79	19	0.138	0.138	NUM
fcis-31208	79	20	-0.663	-0.663	NUM
fcis-31208	79	21	-0.657	-0.657	PUNCT
fcis-31208	79	22	b	b	X
fcis-31208	79	23	-0.139	-0.139	PUNCT
fcis-31208	79	24	24	24	NUM
fcis-31208	79	25	at	at	ADP
fcis-31208	79	26	this	this	DET
fcis-31208	79	27	point	point	NOUN
fcis-31208	79	28	,	,	PUNCT
fcis-31208	79	29	the	the	DET
fcis-31208	79	30	objective	objective	ADJ
fcis-31208	79	31	function	function	NOUN
fcis-31208	79	32	q	q	PUNCT
fcis-31208	79	33	reaches	reach	VERB
fcis-31208	79	34	its	its	PRON
fcis-31208	79	35	minimum	minimum	ADJ
fcis-31208	79	36	value	value	NOUN
fcis-31208	79	37	.	.	PUNCT
fcis-31208	80	1	based	base	VERB
fcis-31208	80	2	on	on	ADP
fcis-31208	80	3	this	this	PRON
fcis-31208	80	4	,	,	PUNCT
fcis-31208	80	5	we	we	PRON
fcis-31208	80	6	draw	draw	VERB
fcis-31208	80	7	the	the	DET
fcis-31208	80	8	classification	classification	NOUN
fcis-31208	80	9	result	result	NOUN
fcis-31208	80	10	image	image	NOUN
fcis-31208	80	11	as	as	SCONJ
fcis-31208	80	12	follows	follow	VERB
fcis-31208	80	13	:	:	PUNCT
fcis-31208	80	14	figure	figure	VERB
fcis-31208	80	15	7	7	NUM
fcis-31208	80	16	.	.	PUNCT
fcis-31208	80	17	comparison	comparison	NOUN
fcis-31208	80	18	of	of	ADP
fcis-31208	80	19	classical	classical	ADJ
fcis-31208	80	20	svm	svm	NOUN
fcis-31208	80	21	and	and	CCONJ
fcis-31208	80	22	qubo	qubo	NOUN
fcis-31208	80	23	-	-	PUNCT
fcis-31208	80	24	svm	svm	NOUN
fcis-31208	80	25	classification	classification	NOUN
fcis-31208	80	26	(	(	PUNCT
fcis-31208	80	27	iris	iris	NOUN
fcis-31208	80	28	-	-	PUNCT
fcis-31208	80	29	setosa	setosa	NOUN
fcis-31208	80	30	and	and	CCONJ
fcis-31208	80	31	iris	iris	NOUN
fcis-31208	80	32	-	-	PUNCT
fcis-31208	80	33	virginica	virginica	NOUN
fcis-31208	80	34	)	)	PUNCT
fcis-31208	80	35	as	as	SCONJ
fcis-31208	80	36	can	can	AUX
fcis-31208	80	37	be	be	AUX
fcis-31208	80	38	seen	see	VERB
fcis-31208	80	39	from	from	ADP
fcis-31208	80	40	the	the	DET
fcis-31208	80	41	above	above	ADJ
fcis-31208	80	42	figure	figure	NOUN
fcis-31208	80	43	,	,	PUNCT
fcis-31208	80	44	qubo	qubo	NOUN
fcis-31208	80	45	-	-	PUNCT
fcis-31208	80	46	svm	svm	ADJ
fcis-31208	80	47	binary	binary	ADJ
fcis-31208	80	48	classification	classification	NOUN
fcis-31208	80	49	results	result	NOUN
fcis-31208	80	50	are	be	AUX
fcis-31208	80	51	more	more	ADV
fcis-31208	80	52	accurate	accurate	ADJ
fcis-31208	80	53	.	.	PUNCT
fcis-31208	81	1	based	base	VERB
fcis-31208	81	2	on	on	ADP
fcis-31208	81	3	this	this	PRON
fcis-31208	81	4	,	,	PUNCT
fcis-31208	81	5	we	we	PRON
fcis-31208	81	6	calculate	calculate	VERB
fcis-31208	81	7	multiple	multiple	ADJ
fcis-31208	81	8	prediction	prediction	NOUN
fcis-31208	81	9	evaluation	evaluation	NOUN
fcis-31208	81	10	indicators	indicator	NOUN
fcis-31208	81	11	to	to	PART
fcis-31208	81	12	assist	assist	VERB
fcis-31208	81	13	in	in	ADP
fcis-31208	81	14	illustrating	illustrate	VERB
fcis-31208	81	15	the	the	DET
fcis-31208	81	16	excellent	excellent	ADJ
fcis-31208	81	17	results	result	NOUN
fcis-31208	81	18	.	.	PUNCT
fcis-31208	82	1	table	table	NOUN
fcis-31208	82	2	9	9	NUM
fcis-31208	82	3	.	.	PUNCT
fcis-31208	83	1	statistics	statistic	NOUN
fcis-31208	83	2	of	of	ADP
fcis-31208	83	3	outcome	outcome	NOUN
fcis-31208	83	4	evaluation	evaluation	NOUN
fcis-31208	83	5	indicators	indicator	NOUN
fcis-31208	83	6	(	(	PUNCT
fcis-31208	83	7	iris	iris	NOUN
fcis-31208	83	8	-	-	PUNCT
fcis-31208	83	9	setosa	setosa	NOUN
fcis-31208	83	10	and	and	CCONJ
fcis-31208	83	11	iris	iris	NOUN
fcis-31208	83	12	-	-	PUNCT
fcis-31208	83	13	virginica	virginica	NOUN
fcis-31208	83	14	)	)	PUNCT
fcis-31208	83	15	data	datum	NOUN
fcis-31208	83	16	set	set	VERB
fcis-31208	83	17	recall	recall	NOUN
fcis-31208	83	18	rates	rate	NOUN
fcis-31208	83	19	f1	f1	PROPN
fcis-31208	83	20	accuracy	accuracy	NOUN
fcis-31208	83	21	rate	rate	NOUN
fcis-31208	83	22	precision	precision	NOUN
fcis-31208	83	23	rate	rate	NOUN
fcis-31208	83	24	training	training	NOUN
fcis-31208	83	25	set	set	VERB
fcis-31208	83	26	1	1	NUM
fcis-31208	83	27	1	1	NUM
fcis-31208	83	28	1	1	NUM
fcis-31208	83	29	1	1	NUM
fcis-31208	83	30	test	test	NOUN
fcis-31208	83	31	set	set	VERB
fcis-31208	83	32	1	1	NUM
fcis-31208	83	33	1	1	NUM
fcis-31208	83	34	1	1	NUM
fcis-31208	83	35	1	1	NUM
fcis-31208	83	36	in	in	ADP
fcis-31208	83	37	the	the	DET
fcis-31208	83	38	same	same	ADJ
fcis-31208	83	39	way	way	NOUN
fcis-31208	83	40	,	,	PUNCT
fcis-31208	83	41	we	we	PRON
fcis-31208	83	42	solve	solve	VERB
fcis-31208	83	43	for	for	ADP
fcis-31208	83	44	the	the	DET
fcis-31208	83	45	hyperplane	hyperplane	NOUN
fcis-31208	83	46	parameters	parameter	NOUN
fcis-31208	83	47	of	of	ADP
fcis-31208	83	48	iris	iris	NOUN
fcis-31208	83	49	-	-	PUNCT
fcis-31208	83	50	versicolor	versicolor	NOUN
fcis-31208	83	51	and	and	CCONJ
fcis-31208	83	52	iris	iris	NOUN
fcis-31208	83	53	-	-	PUNCT
fcis-31208	83	54	virginica	virginica	NOUN
fcis-31208	83	55	:	:	PUNCT
fcis-31208	83	56	table	table	NOUN
fcis-31208	83	57	10	10	NUM
fcis-31208	83	58	.	.	PUNCT
fcis-31208	84	1	optimal	optimal	ADJ
fcis-31208	84	2	prediction	prediction	NOUN
fcis-31208	84	3	parameter	parameter	NOUN
fcis-31208	84	4	values	value	NOUN
fcis-31208	84	5	(	(	PUNCT
fcis-31208	84	6	iris	iris	NOUN
fcis-31208	84	7	-	-	PUNCT
fcis-31208	84	8	versicolor	versicolor	NOUN
fcis-31208	84	9	and	and	CCONJ
fcis-31208	84	10	iris	iris	NOUN
fcis-31208	84	11	-	-	PUNCT
fcis-31208	84	12	virginica	virginica	NOUN
fcis-31208	84	13	)	)	PUNCT
fcis-31208	84	14	optimal	optimal	ADJ
fcis-31208	84	15	prediction	prediction	NOUN
fcis-31208	84	16	parameters	parameter	NOUN
fcis-31208	84	17	value	value	VERB
fcis-31208	84	18	1.026	1.026	NUM
fcis-31208	84	19	2.191	2.191	NUM
fcis-31208	84	20	-5.459	-5.459	NOUN
fcis-31208	84	21	-7.785	-7.785	PUNCT
fcis-31208	84	22	b	b	X
fcis-31208	84	23	0.794	0.794	NUM
fcis-31208	84	24	10	10	NUM
fcis-31208	84	25	at	at	ADP
fcis-31208	84	26	this	this	DET
fcis-31208	84	27	point	point	NOUN
fcis-31208	84	28	,	,	PUNCT
fcis-31208	84	29	the	the	DET
fcis-31208	84	30	objective	objective	ADJ
fcis-31208	84	31	function	function	NOUN
fcis-31208	84	32	q	q	PUNCT
fcis-31208	84	33	reaches	reach	VERB
fcis-31208	84	34	the	the	DET
fcis-31208	84	35	minimum	minimum	ADJ
fcis-31208	84	36	value	value	NOUN
fcis-31208	84	37	,	,	PUNCT
fcis-31208	84	38	where	where	SCONJ
fcis-31208	84	39	,	,	PUNCT
fcis-31208	84	40	due	due	ADP
fcis-31208	84	41	to	to	ADP
fcis-31208	84	42	the	the	DET
fcis-31208	84	43	fusion	fusion	NOUN
fcis-31208	84	44	of	of	ADP
fcis-31208	84	45	data	datum	NOUN
fcis-31208	84	46	points	point	NOUN
fcis-31208	84	47	of	of	ADP
fcis-31208	84	48	iris	iris	NOUN
fcis-31208	84	49	-	-	PUNCT
fcis-31208	84	50	versicolor	versicolor	NOUN
fcis-31208	84	51	and	and	CCONJ
fcis-31208	84	52	iris	iris	NOUN
fcis-31208	84	53	-	-	PUNCT
fcis-31208	84	54	virginica	virginica	NOUN
fcis-31208	84	55	,	,	PUNCT
fcis-31208	84	56	we	we	PRON
fcis-31208	84	57	draw	draw	VERB
fcis-31208	84	58	a	a	DET
fcis-31208	84	59	comparison	comparison	NOUN
fcis-31208	84	60	diagram	diagram	NOUN
fcis-31208	84	61	of	of	ADP
fcis-31208	84	62	classification	classification	NOUN
fcis-31208	84	63	results	result	NOUN
fcis-31208	84	64	of	of	ADP
fcis-31208	84	65	iris	iris	NOUN
fcis-31208	84	66	-	-	PUNCT
fcis-31208	84	67	versicolor	versicolor	NOUN
fcis-31208	84	68	and	and	CCONJ
fcis-31208	84	69	irisvirginica	irisvirginica	NOUN
fcis-31208	84	70	between	between	ADP
fcis-31208	84	71	svm	svm	PROPN
fcis-31208	84	72	and	and	CCONJ
fcis-31208	84	73	qubo	qubo	NOUN
fcis-31208	84	74	-	-	PUNCT
fcis-31208	84	75	svm	svm	NOUN
fcis-31208	84	76	:	:	PUNCT
fcis-31208	84	77	figure	figure	NOUN
fcis-31208	84	78	8	8	NUM
fcis-31208	84	79	.	.	PUNCT
fcis-31208	84	80	comparison	comparison	NOUN
fcis-31208	84	81	of	of	ADP
fcis-31208	84	82	classification	classification	NOUN
fcis-31208	84	83	results	result	NOUN
fcis-31208	84	84	between	between	ADP
fcis-31208	84	85	classical	classical	ADJ
fcis-31208	84	86	svm	svm	NOUN
fcis-31208	84	87	and	and	CCONJ
fcis-31208	84	88	qubo	qubo	NOUN
fcis-31208	84	89	-	-	PUNCT
fcis-31208	84	90	svm	svm	PROPN
fcis-31208	84	91	(	(	PUNCT
fcis-31208	84	92	iris	iris	NOUN
fcis-31208	84	93	-	-	PUNCT
fcis-31208	84	94	versicolor	versicolor	NOUN
fcis-31208	84	95	and	and	CCONJ
fcis-31208	84	96	iris	iris	NOUN
fcis-31208	84	97	-	-	PUNCT
fcis-31208	84	98	virginica	virginica	NOUN
fcis-31208	84	99	)	)	PUNCT
fcis-31208	84	100	in	in	ADP
fcis-31208	84	101	addition	addition	NOUN
fcis-31208	84	102	,	,	PUNCT
fcis-31208	84	103	we	we	PRON
fcis-31208	84	104	calculated	calculate	VERB
fcis-31208	84	105	a	a	DET
fcis-31208	84	106	variety	variety	NOUN
fcis-31208	84	107	of	of	ADP
fcis-31208	84	108	predictive	predictive	ADJ
fcis-31208	84	109	evaluation	evaluation	NOUN
fcis-31208	84	110	indicators	indicator	NOUN
fcis-31208	84	111	to	to	PART
fcis-31208	84	112	assist	assist	VERB
fcis-31208	84	113	in	in	ADP
fcis-31208	84	114	illustrating	illustrate	VERB
fcis-31208	84	115	superior	superior	ADJ
fcis-31208	84	116	classification	classification	NOUN
fcis-31208	84	117	43	43	NUM
fcis-31208	84	118	performance	performance	NOUN
fcis-31208	84	119	:	:	PUNCT
fcis-31208	84	120	table	table	NOUN
fcis-31208	84	121	11	11	NUM
fcis-31208	84	122	.	.	PUNCT
fcis-31208	85	1	statistics	statistic	NOUN
fcis-31208	85	2	of	of	ADP
fcis-31208	85	3	outcome	outcome	NOUN
fcis-31208	85	4	evaluation	evaluation	NOUN
fcis-31208	85	5	indicators	indicator	NOUN
fcis-31208	85	6	(	(	PUNCT
fcis-31208	85	7	iris	iris	NOUN
fcis-31208	85	8	-	-	PUNCT
fcis-31208	85	9	versicolor	versicolor	NOUN
fcis-31208	85	10	and	and	CCONJ
fcis-31208	85	11	iris	iris	NOUN
fcis-31208	85	12	-	-	PUNCT
fcis-31208	85	13	virginica	virginica	NOUN
fcis-31208	85	14	)	)	PUNCT
fcis-31208	85	15	data	datum	NOUN
fcis-31208	85	16	set	set	VERB
fcis-31208	85	17	recall	recall	NOUN
fcis-31208	85	18	rates	rate	NOUN
fcis-31208	85	19	f1	f1	PROPN
fcis-31208	85	20	accuracy	accuracy	NOUN
fcis-31208	85	21	rate	rate	NOUN
fcis-31208	85	22	precision	precision	NOUN
fcis-31208	85	23	rate	rate	NOUN
fcis-31208	85	24	training	training	NOUN
fcis-31208	85	25	set	set	VERB
fcis-31208	85	26	0.957	0.957	NUM
fcis-31208	85	27	0.957	0.957	NUM
fcis-31208	85	28	0.957	0.957	NUM
fcis-31208	85	29	0.958	0.958	NUM
fcis-31208	85	30	test	test	NOUN
fcis-31208	85	31	set	set	VERB
fcis-31208	85	32	0.942	0.942	NUM
fcis-31208	85	33	0.933	0.933	NUM
fcis-31208	85	34	0.933	0.933	NUM
fcis-31208	85	35	0.933	0.933	NUM
fcis-31208	85	36	in	in	ADP
fcis-31208	85	37	addition	addition	NOUN
fcis-31208	85	38	,	,	PUNCT
fcis-31208	85	39	we	we	PRON
fcis-31208	85	40	also	also	ADV
fcis-31208	85	41	visualized	visualize	VERB
fcis-31208	85	42	the	the	DET
fcis-31208	85	43	change	change	NOUN
fcis-31208	85	44	of	of	ADP
fcis-31208	85	45	hamiltonian	hamiltonian	NOUN
fcis-31208	85	46	over	over	ADP
fcis-31208	85	47	time	time	NOUN
fcis-31208	85	48	in	in	ADP
fcis-31208	85	49	the	the	DET
fcis-31208	85	50	simulated	simulated	ADJ
fcis-31208	85	51	annealing	anneal	VERB
fcis-31208	85	52	algorithm	algorithm	NOUN
fcis-31208	85	53	during	during	ADP
fcis-31208	85	54	the	the	DET
fcis-31208	85	55	solving	solving	NOUN
fcis-31208	85	56	of	of	ADP
fcis-31208	85	57	3	3	NUM
fcis-31208	85	58	binary	binary	ADJ
fcis-31208	85	59	classification	classification	NOUN
fcis-31208	85	60	problems	problem	NOUN
fcis-31208	85	61	to	to	PART
fcis-31208	85	62	reflect	reflect	VERB
fcis-31208	85	63	the	the	DET
fcis-31208	85	64	energy	energy	NOUN
fcis-31208	85	65	state	state	NOUN
fcis-31208	85	66	of	of	ADP
fcis-31208	85	67	the	the	DET
fcis-31208	85	68	system	system	NOUN
fcis-31208	85	69	.	.	PUNCT
fcis-31208	86	1	figure	figure	NOUN
fcis-31208	86	2	9	9	NUM
fcis-31208	86	3	.	.	PUNCT
fcis-31208	87	1	visualization	visualization	NOUN
fcis-31208	87	2	of	of	ADP
fcis-31208	87	3	hamiltonian	hamiltonian	ADJ
fcis-31208	87	4	changes	change	NOUN
fcis-31208	87	5	over	over	ADP
fcis-31208	87	6	time	time	NOUN
fcis-31208	87	7	for	for	ADP
fcis-31208	87	8	3	3	NUM
fcis-31208	87	9	binary	binary	ADJ
fcis-31208	87	10	classification	classification	NOUN
fcis-31208	87	11	problems	problem	NOUN
fcis-31208	87	12	from	from	ADP
fcis-31208	87	13	the	the	DET
fcis-31208	87	14	figure	figure	NOUN
fcis-31208	87	15	above	above	ADV
fcis-31208	87	16	,	,	PUNCT
fcis-31208	87	17	we	we	PRON
fcis-31208	87	18	can	can	AUX
fcis-31208	87	19	see	see	VERB
fcis-31208	87	20	that	that	SCONJ
fcis-31208	87	21	the	the	DET
fcis-31208	87	22	hamiltonian	hamiltonian	NOUN
fcis-31208	87	23	drops	drop	VERB
fcis-31208	87	24	rapidly	rapidly	ADV
fcis-31208	87	25	at	at	ADP
fcis-31208	87	26	the	the	DET
fcis-31208	87	27	beginning	beginning	NOUN
fcis-31208	87	28	,	,	PUNCT
fcis-31208	87	29	which	which	PRON
fcis-31208	87	30	indicates	indicate	VERB
fcis-31208	87	31	that	that	SCONJ
fcis-31208	87	32	the	the	DET
fcis-31208	87	33	energy	energy	NOUN
fcis-31208	87	34	of	of	ADP
fcis-31208	87	35	the	the	DET
fcis-31208	87	36	system	system	NOUN
fcis-31208	87	37	decreases	decrease	VERB
fcis-31208	87	38	rapidly	rapidly	ADV
fcis-31208	87	39	,	,	PUNCT
fcis-31208	87	40	and	and	CCONJ
fcis-31208	87	41	then	then	ADV
fcis-31208	87	42	there	there	PRON
fcis-31208	87	43	are	be	VERB
fcis-31208	87	44	some	some	DET
fcis-31208	87	45	fluctuations	fluctuation	NOUN
fcis-31208	87	46	.	.	PUNCT
fcis-31208	88	1	these	these	DET
fcis-31208	88	2	fluctuations	fluctuation	NOUN
fcis-31208	88	3	indicate	indicate	VERB
fcis-31208	88	4	that	that	SCONJ
fcis-31208	88	5	during	during	ADP
fcis-31208	88	6	the	the	DET
fcis-31208	88	7	optimization	optimization	NOUN
fcis-31208	88	8	process	process	NOUN
fcis-31208	88	9	,	,	PUNCT
fcis-31208	88	10	the	the	DET
fcis-31208	88	11	system	system	NOUN
fcis-31208	88	12	is	be	AUX
fcis-31208	88	13	exploring	explore	VERB
fcis-31208	88	14	different	different	ADJ
fcis-31208	88	15	states	state	NOUN
fcis-31208	88	16	in	in	ADP
fcis-31208	88	17	order	order	NOUN
fcis-31208	88	18	to	to	PART
fcis-31208	88	19	find	find	VERB
fcis-31208	88	20	the	the	DET
fcis-31208	88	21	state	state	NOUN
fcis-31208	88	22	with	with	ADP
fcis-31208	88	23	lower	low	ADJ
fcis-31208	88	24	energy	energy	NOUN
fcis-31208	88	25	.	.	PUNCT
fcis-31208	89	1	as	as	SCONJ
fcis-31208	89	2	time	time	NOUN
fcis-31208	89	3	goes	go	VERB
fcis-31208	89	4	on	on	ADP
fcis-31208	89	5	,	,	PUNCT
fcis-31208	89	6	the	the	DET
fcis-31208	89	7	rate	rate	NOUN
fcis-31208	89	8	of	of	ADP
fcis-31208	89	9	decline	decline	NOUN
fcis-31208	89	10	gradually	gradually	ADV
fcis-31208	89	11	slows	slow	VERB
fcis-31208	89	12	down	down	ADP
fcis-31208	89	13	,	,	PUNCT
fcis-31208	89	14	and	and	CCONJ
fcis-31208	89	15	the	the	DET
fcis-31208	89	16	hamiltonian	hamiltonian	NOUN
fcis-31208	89	17	tends	tend	VERB
fcis-31208	89	18	to	to	PART
fcis-31208	89	19	stabilize	stabilize	VERB
fcis-31208	89	20	,	,	PUNCT
fcis-31208	89	21	which	which	PRON
fcis-31208	89	22	may	may	AUX
fcis-31208	89	23	mean	mean	VERB
fcis-31208	89	24	that	that	SCONJ
fcis-31208	89	25	the	the	DET
fcis-31208	89	26	system	system	NOUN
fcis-31208	89	27	has	have	AUX
fcis-31208	89	28	approached	approach	VERB
fcis-31208	89	29	or	or	CCONJ
fcis-31208	89	30	reached	reach	VERB
fcis-31208	89	31	a	a	DET
fcis-31208	89	32	stable	stable	ADJ
fcis-31208	89	33	state	state	NOUN
fcis-31208	89	34	.	.	PUNCT
fcis-31208	90	1	and	and	CCONJ
fcis-31208	90	2	solving	solve	VERB
fcis-31208	90	3	time	time	NOUN
fcis-31208	90	4	of	of	ADP
fcis-31208	90	5	the	the	DET
fcis-31208	90	6	qubo	qubo	PROPN
fcis-31208	90	7	model	model	NOUN
fcis-31208	90	8	is	be	AUX
fcis-31208	90	9	between	between	ADP
fcis-31208	90	10	0.07s	0.07s	PROPN
fcis-31208	90	11	and	and	CCONJ
fcis-31208	90	12	0.09s	0.09s	NUM
fcis-31208	90	13	.	.	PUNCT
fcis-31208	91	1	and	and	CCONJ
fcis-31208	91	2	solving	solve	VERB
fcis-31208	91	3	time	time	NOUN
fcis-31208	91	4	of	of	ADP
fcis-31208	91	5	the	the	DET
fcis-31208	91	6	qubo	qubo	PROPN
fcis-31208	91	7	model	model	NOUN
fcis-31208	91	8	is	be	AUX
fcis-31208	91	9	between	between	ADP
fcis-31208	91	10	0.07s	0.07s	PROPN
fcis-31208	91	11	and	and	CCONJ
fcis-31208	91	12	0.09s	0.09s	NUM
fcis-31208	91	13	.	.	PUNCT
fcis-31208	92	1	4	4	X
fcis-31208	92	2	.	.	X
fcis-31208	92	3	model	model	NOUN
fcis-31208	92	4	3	3	NUM
fcis-31208	92	5	:	:	PUNCT
fcis-31208	92	6	the	the	DET
fcis-31208	92	7	qubo	qubo	PROPN
fcis-31208	92	8	model	model	NOUN
fcis-31208	92	9	based	base	VERB
fcis-31208	92	10	on	on	ADP
fcis-31208	92	11	cnn	cnn	PROPN
fcis-31208	92	12	image	image	NOUN
fcis-31208	92	13	classification	classification	NOUN
fcis-31208	92	14	(	(	PUNCT
fcis-31208	92	15	1	1	NUM
fcis-31208	92	16	)	)	PUNCT
fcis-31208	92	17	establish	establish	VERB
fcis-31208	92	18	qubo	qubo	NOUN
fcis-31208	92	19	model	model	NOUN
fcis-31208	92	20	of	of	ADP
fcis-31208	92	21	cnn	cnn	PROPN
fcis-31208	92	22	for	for	ADP
fcis-31208	92	23	image	image	NOUN
fcis-31208	92	24	classification	classification	NOUN
fcis-31208	92	25	of	of	ADP
fcis-31208	92	26	mnist	mnist	ADJ
fcis-31208	92	27	handwritten	handwritten	ADJ
fcis-31208	92	28	numerals	numeral	NOUN
fcis-31208	92	29	dataset	dataset	VERB
fcis-31208	92	30	so	so	ADV
fcis-31208	92	31	far	far	ADV
fcis-31208	92	32	,	,	PUNCT
fcis-31208	92	33	the	the	DET
fcis-31208	92	34	pattern	pattern	NOUN
fcis-31208	92	35	recognition	recognition	NOUN
fcis-31208	92	36	system	system	NOUN
fcis-31208	92	37	based	base	VERB
fcis-31208	92	38	on	on	ADP
fcis-31208	92	39	convolutional	convolutional	ADJ
fcis-31208	92	40	neural	neural	ADJ
fcis-31208	92	41	network	network	NOUN
fcis-31208	92	42	is	be	AUX
fcis-31208	92	43	one	one	NUM
fcis-31208	92	44	of	of	ADP
fcis-31208	92	45	the	the	DET
fcis-31208	92	46	systems	system	NOUN
fcis-31208	92	47	with	with	ADP
fcis-31208	92	48	the	the	DET
fcis-31208	92	49	best	good	ADJ
fcis-31208	92	50	performance	performance	NOUN
fcis-31208	92	51	,	,	PUNCT
fcis-31208	92	52	especially	especially	ADV
fcis-31208	92	53	in	in	ADP
fcis-31208	92	54	the	the	DET
fcis-31208	92	55	field	field	NOUN
fcis-31208	92	56	of	of	ADP
fcis-31208	92	57	handwritten	handwritten	ADJ
fcis-31208	92	58	character	character	NOUN
fcis-31208	92	59	recognition	recognition	NOUN
fcis-31208	92	60	,	,	PUNCT
fcis-31208	92	61	and	and	CCONJ
fcis-31208	92	62	has	have	AUX
fcis-31208	92	63	been	be	AUX
fcis-31208	92	64	used	use	VERB
fcis-31208	92	65	as	as	ADP
fcis-31208	92	66	the	the	DET
fcis-31208	92	67	evaluation	evaluation	NOUN
fcis-31208	92	68	standard	standard	NOUN
fcis-31208	92	69	for	for	ADP
fcis-31208	92	70	the	the	DET
fcis-31208	92	71	performance	performance	NOUN
fcis-31208	92	72	of	of	ADP
fcis-31208	92	73	machine	machine	NOUN
fcis-31208	92	74	recognition	recognition	NOUN
fcis-31208	92	75	systems	system	NOUN
fcis-31208	92	76	.	.	PUNCT
fcis-31208	93	1	by	by	ADP
fcis-31208	93	2	mining	mine	VERB
fcis-31208	93	3	the	the	DET
fcis-31208	93	4	spatial	spatial	ADJ
fcis-31208	93	5	correlation	correlation	NOUN
fcis-31208	93	6	in	in	ADP
fcis-31208	93	7	the	the	DET
fcis-31208	93	8	data	datum	NOUN
fcis-31208	93	9	,	,	PUNCT
fcis-31208	93	10	convolutional	convolutional	ADJ
fcis-31208	93	11	neural	neural	ADJ
fcis-31208	93	12	network	network	NOUN
fcis-31208	93	13	can	can	AUX
fcis-31208	93	14	reduce	reduce	VERB
fcis-31208	93	15	the	the	DET
fcis-31208	93	16	number	number	NOUN
fcis-31208	93	17	of	of	ADP
fcis-31208	93	18	trainable	trainable	ADJ
fcis-31208	93	19	parameters	parameter	NOUN
fcis-31208	93	20	in	in	ADP
fcis-31208	93	21	the	the	DET
fcis-31208	93	22	network	network	NOUN
fcis-31208	93	23	,	,	PUNCT
fcis-31208	93	24	so	so	SCONJ
fcis-31208	93	25	as	as	SCONJ
fcis-31208	93	26	to	to	PART
fcis-31208	93	27	improve	improve	VERB
fcis-31208	93	28	the	the	DET
fcis-31208	93	29	backpropagation	backpropagation	NOUN
fcis-31208	93	30	algorithm	algorithm	NOUN
fcis-31208	93	31	efficiency	efficiency	NOUN
fcis-31208	93	32	of	of	ADP
fcis-31208	93	33	the	the	DET
fcis-31208	93	34	forward	forward	ADJ
fcis-31208	93	35	propagation	propagation	NOUN
fcis-31208	93	36	network	network	NOUN
fcis-31208	93	37	.	.	PUNCT
fcis-31208	94	1	as	as	SCONJ
fcis-31208	94	2	shown	show	VERB
fcis-31208	94	3	in	in	ADP
fcis-31208	94	4	the	the	DET
fcis-31208	94	5	following	follow	VERB
fcis-31208	94	6	figure	figure	NOUN
fcis-31208	94	7	,	,	PUNCT
fcis-31208	94	8	after	after	SCONJ
fcis-31208	94	9	the	the	DET
fcis-31208	94	10	input	input	NOUN
fcis-31208	94	11	layer	layer	NOUN
fcis-31208	94	12	reads	read	VERB
fcis-31208	94	13	images	image	NOUN
fcis-31208	94	14	of	of	ADP
fcis-31208	94	15	a	a	DET
fcis-31208	94	16	unified	unified	ADJ
fcis-31208	94	17	size	size	NOUN
fcis-31208	94	18	,	,	PUNCT
fcis-31208	94	19	the	the	DET
fcis-31208	94	20	input	input	NOUN
fcis-31208	94	21	image	image	NOUN
fcis-31208	94	22	is	be	AUX
fcis-31208	94	23	convolved	convolve	VERB
fcis-31208	94	24	with	with	ADP
fcis-31208	94	25	three	three	NUM
fcis-31208	94	26	filters	filter	NOUN
fcis-31208	94	27	and	and	CCONJ
fcis-31208	94	28	one	one	NUM
fcis-31208	94	29	additive	additive	ADJ
fcis-31208	94	30	bias	bias	NOUN
fcis-31208	94	31	.	.	PUNCT
fcis-31208	95	1	after	after	ADP
fcis-31208	95	2	convolution	convolution	NOUN
fcis-31208	95	3	,	,	PUNCT
fcis-31208	95	4	three	three	NUM
fcis-31208	95	5	feature	feature	NOUN
fcis-31208	95	6	maps	map	NOUN
fcis-31208	95	7	are	be	AUX
fcis-31208	95	8	generated	generate	VERB
fcis-31208	95	9	in	in	ADP
fcis-31208	95	10	layer	layer	NOUN
fcis-31208	95	11	c1	c1	PROPN
fcis-31208	95	12	.	.	PUNCT
fcis-31208	96	1	then	then	ADV
fcis-31208	96	2	,	,	PUNCT
fcis-31208	96	3	the	the	DET
fcis-31208	96	4	four	four	NUM
fcis-31208	96	5	adjacent	adjacent	ADJ
fcis-31208	96	6	pixels	pixel	NOUN
fcis-31208	96	7	in	in	ADP
fcis-31208	96	8	the	the	DET
fcis-31208	96	9	feature	feature	NOUN
fcis-31208	96	10	map	map	NOUN
fcis-31208	96	11	are	be	AUX
fcis-31208	96	12	grouped	group	VERB
fcis-31208	96	13	together	together	ADV
fcis-31208	96	14	to	to	PART
fcis-31208	96	15	obtain	obtain	VERB
fcis-31208	96	16	the	the	DET
fcis-31208	96	17	average	average	ADJ
fcis-31208	96	18	value	value	NOUN
fcis-31208	96	19	,	,	PUNCT
fcis-31208	96	20	and	and	CCONJ
fcis-31208	96	21	then	then	ADV
fcis-31208	96	22	weighted	weight	VERB
fcis-31208	96	23	value	value	NOUN
fcis-31208	96	24	and	and	CCONJ
fcis-31208	96	25	bias	bias	NOUN
fcis-31208	96	26	are	be	AUX
fcis-31208	96	27	added	add	VERB
fcis-31208	96	28	.	.	PUNCT
fcis-31208	97	1	three	three	NUM
fcis-31208	97	2	feature	feature	NOUN
fcis-31208	97	3	maps	map	NOUN
fcis-31208	97	4	of	of	ADP
fcis-31208	97	5	layer	layer	NOUN
fcis-31208	97	6	s2	s2	PROPN
fcis-31208	97	7	are	be	AUX
fcis-31208	97	8	obtained	obtain	VERB
fcis-31208	97	9	by	by	ADP
fcis-31208	97	10	relu	relu	NOUN
fcis-31208	97	11	activation	activation	NOUN
fcis-31208	97	12	function	function	NOUN
fcis-31208	97	13	.	.	PUNCT
fcis-31208	98	1	among	among	ADP
fcis-31208	98	2	them	they	PRON
fcis-31208	98	3	,	,	PUNCT
fcis-31208	98	4	max	max	PROPN
fcis-31208	98	5	0	0	NUM
fcis-31208	98	6	,	,	PUNCT
fcis-31208	98	7	29	29	NUM
fcis-31208	98	8	these	these	DET
fcis-31208	98	9	maps	map	NOUN
fcis-31208	98	10	are	be	AUX
fcis-31208	98	11	then	then	ADV
fcis-31208	98	12	filtered	filter	VERB
fcis-31208	98	13	accordingly	accordingly	ADV
fcis-31208	98	14	to	to	PART
fcis-31208	98	15	get	get	VERB
fcis-31208	98	16	the	the	DET
fcis-31208	98	17	c3	c3	NOUN
fcis-31208	98	18	layer	layer	NOUN
fcis-31208	98	19	.	.	PUNCT
fcis-31208	99	1	this	this	DET
fcis-31208	99	2	layer	layer	NOUN
fcis-31208	99	3	then	then	ADV
fcis-31208	99	4	produces	produce	VERB
fcis-31208	99	5	s4	s4	PROPN
fcis-31208	99	6	,	,	PUNCT
fcis-31208	99	7	as	as	SCONJ
fcis-31208	99	8	does	do	VERB
fcis-31208	99	9	s2	s2	VERB
fcis-31208	99	10	.	.	PUNCT
fcis-31208	100	1	finally	finally	ADV
fcis-31208	100	2	,	,	PUNCT
fcis-31208	100	3	these	these	DET
fcis-31208	100	4	pixel	pixel	PROPN
fcis-31208	100	5	values	value	NOUN
fcis-31208	100	6	are	be	AUX
fcis-31208	100	7	rasterized	rasterize	VERB
fcis-31208	100	8	,	,	PUNCT
fcis-31208	100	9	connected	connect	VERB
fcis-31208	100	10	into	into	ADP
fcis-31208	100	11	a	a	DET
fcis-31208	100	12	one	one	NUM
fcis-31208	100	13	-	-	PUNCT
fcis-31208	100	14	dimensional	dimensional	ADJ
fcis-31208	100	15	vector	vector	NOUN
fcis-31208	100	16	input	input	NOUN
fcis-31208	100	17	into	into	ADP
fcis-31208	100	18	a	a	DET
fcis-31208	100	19	traditional	traditional	ADJ
fcis-31208	100	20	neural	neural	ADJ
fcis-31208	100	21	network	network	NOUN
fcis-31208	100	22	,	,	PUNCT
fcis-31208	100	23	and	and	CCONJ
fcis-31208	100	24	then	then	ADV
fcis-31208	100	25	enter	enter	VERB
fcis-31208	100	26	the	the	DET
fcis-31208	100	27	fully	fully	ADV
fcis-31208	100	28	connected	connect	VERB
fcis-31208	100	29	layer	layer	NOUN
fcis-31208	100	30	to	to	PART
fcis-31208	100	31	produce	produce	VERB
fcis-31208	100	32	an	an	DET
fcis-31208	100	33	output	output	NOUN
fcis-31208	100	34	.	.	PUNCT
fcis-31208	101	1	figure	figure	NOUN
fcis-31208	101	2	10	10	NUM
fcis-31208	101	3	.	.	PUNCT
fcis-31208	102	1	illustration	illustration	NOUN
fcis-31208	102	2	of	of	ADP
fcis-31208	102	3	convolutional	convolutional	ADJ
fcis-31208	102	4	neural	neural	ADJ
fcis-31208	102	5	networks	network	NOUN
fcis-31208	102	6	44	44	NUM
fcis-31208	102	7	among	among	ADP
fcis-31208	102	8	them	they	PRON
fcis-31208	102	9	,	,	PUNCT
fcis-31208	102	10	layer	layer	NOUN
fcis-31208	102	11	c	c	PROPN
fcis-31208	102	12	is	be	AUX
fcis-31208	102	13	the	the	DET
fcis-31208	102	14	convolutional	convolutional	ADJ
fcis-31208	102	15	layer	layer	NOUN
fcis-31208	102	16	,	,	PUNCT
fcis-31208	102	17	that	that	ADV
fcis-31208	102	18	is	is	ADV
fcis-31208	102	19	,	,	PUNCT
fcis-31208	102	20	the	the	DET
fcis-31208	102	21	feature	feature	NOUN
fcis-31208	102	22	extraction	extraction	NOUN
fcis-31208	102	23	layer	layer	NOUN
fcis-31208	102	24	.	.	PUNCT
fcis-31208	103	1	a	a	DET
fcis-31208	103	2	convolutional	convolutional	ADJ
fcis-31208	103	3	layer	layer	NOUN
fcis-31208	103	4	usually	usually	ADV
fcis-31208	103	5	contains	contain	VERB
fcis-31208	103	6	multiple	multiple	ADJ
fcis-31208	103	7	feature	feature	NOUN
fcis-31208	103	8	graphs	graph	NOUN
fcis-31208	103	9	with	with	ADP
fcis-31208	103	10	different	different	ADJ
fcis-31208	103	11	weight	weight	NOUN
fcis-31208	103	12	vectors	vector	NOUN
fcis-31208	103	13	,	,	PUNCT
fcis-31208	103	14	so	so	SCONJ
fcis-31208	103	15	that	that	SCONJ
fcis-31208	103	16	a	a	DET
fcis-31208	103	17	variety	variety	NOUN
fcis-31208	103	18	of	of	ADP
fcis-31208	103	19	different	different	ADJ
fcis-31208	103	20	features	feature	NOUN
fcis-31208	103	21	can	can	AUX
fcis-31208	103	22	be	be	AUX
fcis-31208	103	23	obtained	obtain	VERB
fcis-31208	103	24	at	at	ADP
fcis-31208	103	25	the	the	DET
fcis-31208	103	26	same	same	ADJ
fcis-31208	103	27	position	position	NOUN
fcis-31208	103	28	.	.	PUNCT
fcis-31208	104	1	layer	layer	NOUN
fcis-31208	104	2	s	s	PART
fcis-31208	104	3	is	be	AUX
fcis-31208	104	4	the	the	DET
fcis-31208	104	5	pooling	pool	VERB
fcis-31208	104	6	layer	layer	NOUN
fcis-31208	104	7	,	,	PUNCT
fcis-31208	104	8	which	which	PRON
fcis-31208	104	9	reduces	reduce	VERB
fcis-31208	104	10	the	the	DET
fcis-31208	104	11	resolution	resolution	NOUN
fcis-31208	104	12	of	of	ADP
fcis-31208	104	13	the	the	DET
fcis-31208	104	14	feature	feature	NOUN
fcis-31208	104	15	map	map	NOUN
fcis-31208	104	16	and	and	CCONJ
fcis-31208	104	17	reduces	reduce	VERB
fcis-31208	104	18	the	the	DET
fcis-31208	104	19	sensitivity	sensitivity	NOUN
fcis-31208	104	20	of	of	ADP
fcis-31208	104	21	the	the	DET
fcis-31208	104	22	network	network	NOUN
fcis-31208	104	23	output	output	NOUN
fcis-31208	104	24	to	to	ADP
fcis-31208	104	25	displacement	displacement	NOUN
fcis-31208	104	26	and	and	CCONJ
fcis-31208	104	27	deformation	deformation	NOUN
fcis-31208	104	28	through	through	ADP
fcis-31208	104	29	local	local	ADJ
fcis-31208	104	30	averaging	averaging	NOUN
fcis-31208	104	31	and	and	CCONJ
fcis-31208	104	32	downsampling	downsample	VERB
fcis-31208	104	33	operations	operation	NOUN
fcis-31208	104	34	.	.	PUNCT
fcis-31208	105	1	subsequent	subsequent	ADJ
fcis-31208	105	2	convolutional	convolutional	ADJ
fcis-31208	105	3	layers	layer	NOUN
fcis-31208	105	4	and	and	CCONJ
fcis-31208	105	5	pooling	pool	VERB
fcis-31208	105	6	layers	layer	NOUN
fcis-31208	105	7	are	be	AUX
fcis-31208	105	8	alternately	alternately	ADV
fcis-31208	105	9	distributed	distribute	VERB
fcis-31208	105	10	and	and	CCONJ
fcis-31208	105	11	connected	connect	VERB
fcis-31208	105	12	,	,	PUNCT
fcis-31208	105	13	forming	form	VERB
fcis-31208	105	14	a	a	DET
fcis-31208	105	15	"	"	PUNCT
fcis-31208	105	16	double	double	ADJ
fcis-31208	105	17	pyramid	pyramid	NOUN
fcis-31208	105	18	"	"	PUNCT
fcis-31208	105	19	structure	structure	NOUN
fcis-31208	105	20	:	:	PUNCT
fcis-31208	105	21	the	the	DET
fcis-31208	105	22	number	number	NOUN
fcis-31208	105	23	of	of	ADP
fcis-31208	105	24	feature	feature	NOUN
fcis-31208	105	25	maps	map	NOUN
fcis-31208	105	26	gradually	gradually	ADV
fcis-31208	105	27	increases	increase	VERB
fcis-31208	105	28	,	,	PUNCT
fcis-31208	105	29	and	and	CCONJ
fcis-31208	105	30	the	the	DET
fcis-31208	105	31	resolution	resolution	NOUN
fcis-31208	105	32	of	of	ADP
fcis-31208	105	33	the	the	DET
fcis-31208	105	34	feature	feature	NOUN
fcis-31208	105	35	maps	map	NOUN
fcis-31208	105	36	gradually	gradually	ADV
fcis-31208	105	37	decreases	decrease	VERB
fcis-31208	105	38	.	.	PUNCT
fcis-31208	106	1	convolutional	convolutional	ADJ
fcis-31208	106	2	layers	layer	NOUN
fcis-31208	106	3	are	be	AUX
fcis-31208	106	4	the	the	DET
fcis-31208	106	5	core	core	NOUN
fcis-31208	106	6	of	of	ADP
fcis-31208	106	7	cnn	cnn	PROPN
fcis-31208	106	8	,	,	PUNCT
fcis-31208	106	9	and	and	CCONJ
fcis-31208	106	10	each	each	DET
fcis-31208	106	11	convolutional	convolutional	ADJ
fcis-31208	106	12	layer	layer	NOUN
fcis-31208	106	13	uses	use	VERB
fcis-31208	106	14	several	several	ADJ
fcis-31208	106	15	convolutional	convolutional	ADJ
fcis-31208	106	16	layers	layer	NOUN
fcis-31208	106	17	to	to	PART
fcis-31208	106	18	scan	scan	VERB
fcis-31208	106	19	the	the	DET
fcis-31208	106	20	input	input	NOUN
fcis-31208	106	21	image	image	NOUN
fcis-31208	106	22	.	.	PUNCT
fcis-31208	107	1	as	as	ADP
fcis-31208	107	2	for	for	ADP
fcis-31208	107	3	every	every	DET
fcis-31208	107	4	convolution	convolution	NOUN
fcis-31208	107	5	kernel	kernel	NOUN
fcis-31208	107	6	,	,	PUNCT
fcis-31208	107	7	the	the	DET
fcis-31208	107	8	convolution	convolution	NOUN
fcis-31208	107	9	operation	operation	NOUN
fcis-31208	107	10	computes	compute	VERB
fcis-31208	107	11	the	the	DET
fcis-31208	107	12	weighted	weighted	ADJ
fcis-31208	107	13	sum	sum	NOUN
fcis-31208	107	14	of	of	ADP
fcis-31208	107	15	the	the	DET
fcis-31208	107	16	local	local	ADJ
fcis-31208	107	17	region	region	NOUN
fcis-31208	107	18	between	between	ADP
fcis-31208	107	19	the	the	DET
fcis-31208	107	20	input	input	NOUN
fcis-31208	107	21	image	image	NOUN
fcis-31208	107	22	and	and	CCONJ
fcis-31208	107	23	the	the	DET
fcis-31208	107	24	convolution	convolution	NOUN
fcis-31208	107	25	kernel	kernel	NOUN
fcis-31208	107	26	.	.	PUNCT
fcis-31208	108	1	assuming	assume	VERB
fcis-31208	108	2	the	the	DET
fcis-31208	108	3	size	size	NOUN
fcis-31208	108	4	of	of	ADP
fcis-31208	108	5	the	the	DET
fcis-31208	108	6	input	input	NOUN
fcis-31208	108	7	image	image	NOUN
fcis-31208	108	8	is	be	AUX
fcis-31208	108	9	(	(	PUNCT
fcis-31208	108	10	height	height	ADJ
fcis-31208	108	11	,	,	PUNCT
fcis-31208	108	12	width	width	ADJ
fcis-31208	108	13	,	,	PUNCT
fcis-31208	108	14	depth	depth	NOUN
fcis-31208	108	15	)	)	PUNCT
fcis-31208	108	16	,	,	PUNCT
fcis-31208	108	17	the	the	DET
fcis-31208	108	18	convolution	convolution	NOUN
fcis-31208	108	19	operation	operation	NOUN
fcis-31208	108	20	uses	use	VERB
fcis-31208	108	21	a	a	DET
fcis-31208	108	22	convolution	convolution	NOUN
fcis-31208	108	23	kernel	kernel	NOUN
fcis-31208	108	24	of	of	ADP
fcis-31208	108	25	size	size	NOUN
fcis-31208	108	26	,	,	PUNCT
fcis-31208	108	27	depth	depth	NOUN
fcis-31208	108	28	d	d	NOUN
fcis-31208	108	29	,	,	PUNCT
fcis-31208	108	30	and	and	CCONJ
fcis-31208	108	31	the	the	DET
fcis-31208	108	32	number	number	NOUN
fcis-31208	108	33	of	of	ADP
fcis-31208	108	34	convolution	convolution	NOUN
fcis-31208	108	35	cores	core	NOUN
fcis-31208	108	36	(	(	PUNCT
fcis-31208	108	37	number	number	NOUN
fcis-31208	108	38	of	of	ADP
fcis-31208	108	39	output	output	NOUN
fcis-31208	108	40	channels	channel	NOUN
fcis-31208	108	41	)	)	PUNCT
fcis-31208	108	42	is	be	AUX
fcis-31208	108	43	f.	f.	PROPN
fcis-31208	108	44	for	for	ADP
fcis-31208	108	45	each	each	DET
fcis-31208	108	46	local	local	ADJ
fcis-31208	108	47	region	region	NOUN
fcis-31208	108	48	and	and	CCONJ
fcis-31208	108	49	convolution	convolution	NOUN
fcis-31208	108	50	kernel	kernel	PROPN
fcis-31208	108	51	w	w	PROPN
fcis-31208	108	52	in	in	ADP
fcis-31208	108	53	the	the	DET
fcis-31208	108	54	input	input	NOUN
fcis-31208	108	55	image	image	NOUN
fcis-31208	108	56	x	x	NOUN
fcis-31208	108	57	,	,	PUNCT
fcis-31208	108	58	the	the	DET
fcis-31208	108	59	convolution	convolution	NOUN
fcis-31208	108	60	operation	operation	NOUN
fcis-31208	108	61	is	be	AUX
fcis-31208	108	62	as	as	SCONJ
fcis-31208	108	63	follows	follow	VERB
fcis-31208	108	64	[	[	X
fcis-31208	108	65	9	9	NUM
fcis-31208	108	66	]	]	PUNCT
fcis-31208	108	67	:	:	PUNCT
fcis-31208	108	68	      	      	SPACE
fcis-31208	108	69	,	,	PUNCT
fcis-31208	108	70	,	,	PUNCT
fcis-31208	108	71	,	,	PUNCT
fcis-31208	108	72	,	,	PUNCT
fcis-31208	108	73	30	30	NUM
fcis-31208	108	74	where	where	SCONJ
fcis-31208	108	75	,	,	PUNCT
fcis-31208	108	76	the	the	DET
fcis-31208	108	77	convolution	convolution	NOUN
fcis-31208	108	78	kernel	kernel	NOUN
fcis-31208	108	79	,	,	PUNCT
fcis-31208	108	80	,	,	PUNCT
fcis-31208	108	81	representing	represent	VERB
fcis-31208	108	82	the	the	DET
fcis-31208	108	83	(	(	PUNCT
fcis-31208	108	84	m	m	PROPN
fcis-31208	108	85	,	,	PUNCT
fcis-31208	108	86	n	n	CCONJ
fcis-31208	108	87	,	,	PUNCT
fcis-31208	108	88	d	d	NOUN
fcis-31208	108	89	)	)	PUNCT
fcis-31208	108	90	element	element	NOUN
fcis-31208	108	91	,	,	PUNCT
fcis-31208	108	92	,	,	PUNCT
fcis-31208	108	93	,	,	PUNCT
fcis-31208	108	94	is	be	AUX
fcis-31208	108	95	the	the	DET
fcis-31208	108	96	(	(	PUNCT
fcis-31208	108	97	i+m-1	i+m-1	PROPN
fcis-31208	108	98	,	,	PUNCT
fcis-31208	108	99	j+n-1	j+n-1	PROPN
fcis-31208	108	100	,	,	PUNCT
fcis-31208	108	101	d	d	X
fcis-31208	108	102	)	)	PUNCT
fcis-31208	108	103	pixel	pixel	NOUN
fcis-31208	108	104	of	of	ADP
fcis-31208	108	105	the	the	DET
fcis-31208	108	106	input	input	NOUN
fcis-31208	108	107	image	image	NOUN
fcis-31208	108	108	,	,	PUNCT
fcis-31208	108	109	and	and	CCONJ
fcis-31208	108	110	b	b	NOUN
fcis-31208	108	111	is	be	AUX
fcis-31208	108	112	the	the	DET
fcis-31208	108	113	offset	offset	ADJ
fcis-31208	108	114	term	term	NOUN
fcis-31208	108	115	.	.	PUNCT
fcis-31208	109	1	the	the	DET
fcis-31208	109	2	pooling	pooling	NOUN
fcis-31208	109	3	layer	layer	NOUN
fcis-31208	109	4	is	be	AUX
fcis-31208	109	5	used	use	VERB
fcis-31208	109	6	to	to	PART
fcis-31208	109	7	downsample	downsample	VERB
fcis-31208	109	8	the	the	DET
fcis-31208	109	9	feature	feature	NOUN
fcis-31208	109	10	map	map	NOUN
fcis-31208	109	11	output	output	NOUN
fcis-31208	109	12	of	of	ADP
fcis-31208	109	13	the	the	DET
fcis-31208	109	14	convolutional	convolutional	ADJ
fcis-31208	109	15	layer	layer	NOUN
fcis-31208	109	16	.	.	PUNCT
fcis-31208	110	1	the	the	DET
fcis-31208	110	2	pooling	pool	VERB
fcis-31208	110	3	operation	operation	NOUN
fcis-31208	110	4	performed	perform	VERB
fcis-31208	110	5	here	here	ADV
fcis-31208	110	6	is	be	AUX
fcis-31208	110	7	the	the	DET
fcis-31208	110	8	maximum	maximum	ADJ
fcis-31208	110	9	pooling	pooling	NOUN
fcis-31208	110	10	,	,	PUNCT
fcis-31208	110	11	that	that	ADV
fcis-31208	110	12	is	is	ADV
fcis-31208	110	13	,	,	PUNCT
fcis-31208	110	14	for	for	ADP
fcis-31208	110	15	each	each	DET
fcis-31208	110	16	region	region	NOUN
fcis-31208	110	17	in	in	ADP
fcis-31208	110	18	the	the	DET
fcis-31208	110	19	feature	feature	NOUN
fcis-31208	110	20	map	map	NOUN
fcis-31208	110	21	,	,	PUNCT
fcis-31208	110	22	the	the	DET
fcis-31208	110	23	maximum	maximum	ADJ
fcis-31208	110	24	value	value	NOUN
fcis-31208	110	25	in	in	ADP
fcis-31208	110	26	the	the	DET
fcis-31208	110	27	region	region	NOUN
fcis-31208	110	28	is	be	AUX
fcis-31208	110	29	selected	select	VERB
fcis-31208	110	30	as	as	ADP
fcis-31208	110	31	the	the	DET
fcis-31208	110	32	output	output	NOUN
fcis-31208	110	33	.	.	PUNCT
fcis-31208	111	1	each	each	DET
fcis-31208	111	2	image	image	NOUN
fcis-31208	111	3	in	in	ADP
fcis-31208	111	4	the	the	DET
fcis-31208	111	5	mnist	mnist	NOUN
fcis-31208	111	6	dataset	dataset	NOUN
fcis-31208	111	7	corresponds	correspond	VERB
fcis-31208	111	8	to	to	ADP
fcis-31208	111	9	a	a	DET
fcis-31208	111	10	digital	digital	ADJ
fcis-31208	111	11	label	label	NOUN
fcis-31208	111	12	from	from	ADP
fcis-31208	111	13	0	0	NUM
fcis-31208	111	14	to	to	ADP
fcis-31208	111	15	9	9	NUM
fcis-31208	111	16	,	,	PUNCT
fcis-31208	111	17	which	which	PRON
fcis-31208	111	18	is	be	AUX
fcis-31208	111	19	composed	compose	VERB
fcis-31208	111	20	of	of	ADP
fcis-31208	111	21	nist	nist	NOUN
fcis-31208	111	22	special	special	ADJ
fcis-31208	111	23	database	database	NOUN
fcis-31208	111	24	1	1	NUM
fcis-31208	111	25	and	and	CCONJ
fcis-31208	111	26	nist	nist	NOUN
fcis-31208	111	27	special	special	ADJ
fcis-31208	111	28	database	database	NOUN
fcis-31208	111	29	3	3	X
fcis-31208	111	30	.	.	PUNCT
fcis-31208	112	1	the	the	DET
fcis-31208	112	2	training	training	NOUN
fcis-31208	112	3	set	set	VERB
fcis-31208	112	4	in	in	ADP
fcis-31208	112	5	mnist	mnist	NOUN
fcis-31208	112	6	selects	select	VERB
fcis-31208	112	7	30,000	30,000	NUM
fcis-31208	112	8	samples	sample	NOUN
fcis-31208	112	9	from	from	ADP
fcis-31208	112	10	sd-3	sd-3	ADJ
fcis-31208	112	11	and	and	CCONJ
fcis-31208	112	12	sd-1	sd-1	X
fcis-31208	112	13	respectively	respectively	ADV
fcis-31208	112	14	.	.	PUNCT
fcis-31208	113	1	the	the	DET
fcis-31208	113	2	60,000	60,000	NUM
fcis-31208	113	3	samples	sample	NOUN
fcis-31208	113	4	were	be	AUX
fcis-31208	113	5	drawn	draw	VERB
fcis-31208	113	6	from	from	ADP
fcis-31208	113	7	handwritten	handwritten	ADJ
fcis-31208	113	8	data	datum	NOUN
fcis-31208	113	9	from	from	ADP
fcis-31208	113	10	approximately	approximately	ADV
fcis-31208	113	11	250	250	NUM
fcis-31208	113	12	different	different	ADJ
fcis-31208	113	13	individuals	individual	NOUN
fcis-31208	113	14	,	,	PUNCT
fcis-31208	113	15	and	and	CCONJ
fcis-31208	113	16	the	the	DET
fcis-31208	113	17	5,000	5,000	NUM
fcis-31208	113	18	samples	sample	NOUN
fcis-31208	113	19	each	each	PRON
fcis-31208	113	20	from	from	ADP
fcis-31208	113	21	sd-3	sd-3	ADV
fcis-31208	114	1	and	and	CCONJ
fcis-31208	114	2	sd-1	sd-1	X
fcis-31208	114	3	were	be	AUX
fcis-31208	114	4	similarly	similarly	ADV
fcis-31208	114	5	selected	select	VERB
fcis-31208	114	6	as	as	ADP
fcis-31208	114	7	the	the	DET
fcis-31208	114	8	test	test	NOUN
fcis-31208	114	9	set	set	VERB
fcis-31208	114	10	.	.	PUNCT
fcis-31208	115	1	the	the	DET
fcis-31208	115	2	images	image	NOUN
fcis-31208	115	3	in	in	ADP
fcis-31208	115	4	the	the	DET
fcis-31208	115	5	mnist	mnist	NOUN
fcis-31208	115	6	dataset	dataset	NOUN
fcis-31208	115	7	are	be	AUX
fcis-31208	115	8	all	all	PRON
fcis-31208	115	9	of	of	ADP
fcis-31208	115	10	size	size	NOUN
fcis-31208	115	11	,	,	PUNCT
fcis-31208	115	12	and	and	CCONJ
fcis-31208	115	13	the	the	DET
fcis-31208	115	14	images	image	NOUN
fcis-31208	115	15	of	of	ADP
fcis-31208	115	16	some	some	DET
fcis-31208	115	17	numbers	number	NOUN
fcis-31208	115	18	in	in	ADP
fcis-31208	115	19	the	the	DET
fcis-31208	115	20	dataset	dataset	ADJ
fcis-31208	115	21	are28	are28	NOUN
fcis-31208	115	22	28[6	28[6	ADJ
fcis-31208	115	23	]	]	X
fcis-31208	115	24	figure	figure	NOUN
fcis-31208	115	25	11	11	NUM
fcis-31208	115	26	.	.	PUNCT
fcis-31208	116	1	images	image	NOUN
fcis-31208	116	2	of	of	ADP
fcis-31208	116	3	the	the	DET
fcis-31208	116	4	first	first	ADJ
fcis-31208	116	5	16	16	NUM
fcis-31208	116	6	numbers	number	NOUN
fcis-31208	116	7	in	in	ADP
fcis-31208	116	8	the	the	DET
fcis-31208	116	9	mnist	mnist	NOUN
fcis-31208	116	10	data	datum	NOUN
fcis-31208	116	11	set	set	VERB
fcis-31208	116	12	in	in	ADP
fcis-31208	116	13	the	the	DET
fcis-31208	116	14	multi	multi	ADJ
fcis-31208	116	15	-	-	ADJ
fcis-31208	116	16	class	class	ADJ
fcis-31208	116	17	classification	classification	NOUN
fcis-31208	116	18	problem	problem	NOUN
fcis-31208	117	1	,	,	PUNCT
fcis-31208	117	2	the	the	DET
fcis-31208	117	3	objective	objective	ADJ
fcis-31208	117	4	function	function	NOUN
fcis-31208	117	5	is	be	AUX
fcis-31208	117	6	minimize	minimize	VERB
fcis-31208	117	7	the	the	DET
fcis-31208	117	8	difference	difference	NOUN
fcis-31208	117	9	between	between	ADP
fcis-31208	117	10	the	the	DET
fcis-31208	117	11	predicted	predict	VERB
fcis-31208	117	12	result	result	NOUN
fcis-31208	117	13	and	and	CCONJ
fcis-31208	117	14	the	the	DET
fcis-31208	117	15	true	true	ADJ
fcis-31208	117	16	label	label	NOUN
fcis-31208	117	17	.	.	PUNCT
fcis-31208	118	1	assuming	assume	VERB
fcis-31208	118	2	that	that	SCONJ
fcis-31208	118	3	the	the	DET
fcis-31208	118	4	model	model	NOUN
fcis-31208	118	5	predicts	predict	VERB
fcis-31208	118	6	the	the	DET
fcis-31208	118	7	result	result	NOUN
fcis-31208	118	8	is	be	AUX
fcis-31208	118	9	and	and	CCONJ
fcis-31208	118	10	that	that	SCONJ
fcis-31208	118	11	the	the	DET
fcis-31208	118	12	actual	actual	ADJ
fcis-31208	118	13	label	label	NOUN
fcis-31208	118	14	is	be	AUX
fcis-31208	118	15	,	,	PUNCT
fcis-31208	118	16	we	we	PRON
fcis-31208	118	17	use	use	VERB
fcis-31208	118	18	the	the	DET
fcis-31208	118	19	cross	cross	NOUN
fcis-31208	118	20	entropy	entropy	PROPN
fcis-31208	118	21	loss	loss	NOUN
fcis-31208	118	22	function	function	NOUN
fcis-31208	118	23	to	to	PART
fcis-31208	118	24	measure	measure	VERB
fcis-31208	118	25	the	the	DET
fcis-31208	118	26	error	error	NOUN
fcis-31208	118	27	[	[	X
fcis-31208	118	28	7	7	NUM
fcis-31208	118	29	]	]	NUM
fcis-31208	118	30	:	:	PUNCT
fcis-31208	118	31	,	,	PUNCT
fcis-31208	118	32	  	  	SPACE
fcis-31208	118	33	log	log	VERB
fcis-31208	118	34	31	31	NUM
fcis-31208	118	35	where	where	SCONJ
fcis-31208	118	36	c	c	PROPN
fcis-31208	118	37	is	be	AUX
fcis-31208	118	38	the	the	DET
fcis-31208	118	39	total	total	ADJ
fcis-31208	118	40	number	number	NOUN
fcis-31208	118	41	of	of	ADP
fcis-31208	118	42	categories	category	NOUN
fcis-31208	118	43	,	,	PUNCT
fcis-31208	118	44	and	and	CCONJ
fcis-31208	118	45	for	for	ADP
fcis-31208	118	46	this	this	DET
fcis-31208	118	47	dataset	dataset	NOUN
fcis-31208	118	48	,	,	PUNCT
fcis-31208	118	49	c	c	PROPN
fcis-31208	118	50	is	be	AUX
fcis-31208	118	51	the	the	DET
fcis-31208	118	52	constant	constant	ADJ
fcis-31208	118	53	10	10	NUM
fcis-31208	118	54	;	;	PUNCT
fcis-31208	118	55	is	be	AUX
fcis-31208	118	56	the	the	DET
fcis-31208	118	57	k	k	PROPN
fcis-31208	118	58	-	-	PUNCT
fcis-31208	118	59	th	th	VERB
fcis-31208	118	60	element	element	NOUN
fcis-31208	118	61	of	of	ADP
fcis-31208	118	62	the	the	DET
fcis-31208	118	63	sample	sample	NOUN
fcis-31208	118	64	's	's	PART
fcis-31208	118	65	true	true	ADJ
fcis-31208	118	66	label	label	NOUN
fcis-31208	118	67	,	,	PUNCT
fcis-31208	118	68	if	if	SCONJ
fcis-31208	118	69	the	the	DET
fcis-31208	118	70	sample	sample	NOUN
fcis-31208	118	71	belongs	belong	VERB
fcis-31208	118	72	to	to	ADP
fcis-31208	118	73	class	class	PROPN
fcis-31208	118	74	k	k	PROPN
fcis-31208	118	75	,	,	PUNCT
fcis-31208	118	76	1	1	NUM
fcis-31208	118	77	,	,	PUNCT
fcis-31208	118	78	then	then	ADV
fcis-31208	118	79	0	0	NUM
fcis-31208	118	80	otherwise	otherwise	ADV
fcis-31208	118	81	;	;	PUNCT
fcis-31208	118	82	is	be	AUX
fcis-31208	118	83	the	the	DET
fcis-31208	118	84	probability	probability	NOUN
fcis-31208	118	85	that	that	SCONJ
fcis-31208	118	86	the	the	DET
fcis-31208	118	87	sample	sample	NOUN
fcis-31208	118	88	predicted	predict	VERB
fcis-31208	118	89	by	by	ADP
fcis-31208	118	90	the	the	DET
fcis-31208	118	91	model	model	NOUN
fcis-31208	118	92	belongs	belong	VERB
fcis-31208	118	93	to	to	ADP
fcis-31208	118	94	class	class	PROPN
fcis-31208	118	95	k.	k.	PROPN
fcis-31208	118	96	the	the	DET
fcis-31208	118	97	output	output	NOUN
fcis-31208	118	98	layer	layer	NOUN
fcis-31208	118	99	of	of	ADP
fcis-31208	118	100	cnn	cnn	PROPN
fcis-31208	118	101	uses	use	VERB
fcis-31208	118	102	softmax	softmax	NOUN
fcis-31208	118	103	activation	activation	NOUN
fcis-31208	118	104	function	function	VERB
fcis-31208	118	105	to	to	PART
fcis-31208	118	106	calculate	calculate	VERB
fcis-31208	118	107	the	the	DET
fcis-31208	118	108	probability	probability	NOUN
fcis-31208	118	109	of	of	ADP
fcis-31208	118	110	each	each	DET
fcis-31208	118	111	class	class	NOUN
fcis-31208	118	112	,	,	PUNCT
fcis-31208	118	113	softmax	softmax	NOUN
fcis-31208	118	114	activation	activation	NOUN
fcis-31208	118	115	function	function	NOUN
fcis-31208	118	116	is	be	AUX
fcis-31208	118	117	the	the	DET
fcis-31208	118	118	mathematical	mathematical	ADJ
fcis-31208	118	119	expression	expression	NOUN
fcis-31208	118	120	as	as	ADP
fcis-31208	118	121	follows[8	follows[8	PROPN
fcis-31208	118	122	]	]	X
fcis-31208	118	123	:	:	PUNCT
fcis-31208	118	124	∑	∑	PUNCT
fcis-31208	118	125	32	32	NUM
fcis-31208	118	126	where	where	SCONJ
fcis-31208	118	127	,	,	PUNCT
fcis-31208	118	128	is	be	AUX
fcis-31208	118	129	the	the	DET
fcis-31208	118	130	raw	raw	ADJ
fcis-31208	118	131	score	score	NOUN
fcis-31208	118	132	of	of	ADP
fcis-31208	118	133	class	class	NOUN
fcis-31208	118	134	k	k	PROPN
fcis-31208	118	135	output	output	NOUN
fcis-31208	118	136	by	by	ADP
fcis-31208	118	137	the	the	DET
fcis-31208	118	138	fully	fully	ADV
fcis-31208	118	139	connected	connect	VERB
fcis-31208	118	140	layer	layer	NOUN
fcis-31208	118	141	,	,	PUNCT
fcis-31208	118	142	is	be	AUX
fcis-31208	118	143	the	the	DET
fcis-31208	118	144	predicted	predict	VERB
fcis-31208	118	145	probability	probability	NOUN
fcis-31208	118	146	of	of	ADP
fcis-31208	118	147	class	class	NOUN
fcis-31208	118	148	k.	k.	PROPN
fcis-31208	118	149	for	for	ADP
fcis-31208	118	150	the	the	DET
fcis-31208	118	151	convolutional	convolutional	ADJ
fcis-31208	118	152	layer	layer	NOUN
fcis-31208	118	153	l	l	NOUN
fcis-31208	118	154	and	and	CCONJ
fcis-31208	118	155	the	the	DET
fcis-31208	118	156	fully	fully	ADV
fcis-31208	118	157	connected	connected	ADJ
fcis-31208	118	158	layer	layer	NOUN
fcis-31208	118	159	,	,	PUNCT
fcis-31208	118	160	the	the	DET
fcis-31208	118	161	output	output	NOUN
fcis-31208	118	162	can	can	AUX
fcis-31208	118	163	be	be	AUX
fcis-31208	118	164	expressed	express	VERB
fcis-31208	118	165	as	as	SCONJ
fcis-31208	118	166	follows	follow	VERB
fcis-31208	118	167	:	:	PUNCT
fcis-31208	118	168	relu	relu	NOUN
fcis-31208	118	169	∗	∗	NOUN
fcis-31208	118	170	33	33	NUM
fcis-31208	118	171	where	where	SCONJ
fcis-31208	118	172	,	,	PUNCT
fcis-31208	118	173	refers	refer	VERB
fcis-31208	118	174	to	to	ADP
fcis-31208	118	175	the	the	DET
fcis-31208	118	176	weight	weight	NOUN
fcis-31208	118	177	matrix	matrix	NOUN
fcis-31208	118	178	of	of	ADP
fcis-31208	118	179	the	the	DET
fcis-31208	118	180	convolution	convolution	NOUN
fcis-31208	118	181	layer	layer	NOUN
fcis-31208	118	182	and	and	CCONJ
fcis-31208	118	183	the	the	DET
fcis-31208	118	184	fully	fully	ADV
fcis-31208	118	185	connected	connected	ADJ
fcis-31208	118	186	layer	layer	NOUN
fcis-31208	118	187	,	,	PUNCT
fcis-31208	118	188	and	and	CCONJ
fcis-31208	118	189	x	x	X
fcis-31208	118	190	is	be	AUX
fcis-31208	118	191	the	the	DET
fcis-31208	118	192	output	output	NOUN
fcis-31208	118	193	(	(	PUNCT
fcis-31208	118	194	or	or	CCONJ
fcis-31208	118	195	input	input	NOUN
fcis-31208	118	196	image	image	NOUN
fcis-31208	118	197	)	)	PUNCT
fcis-31208	118	198	of	of	ADP
fcis-31208	118	199	the	the	DET
fcis-31208	118	200	previous	previous	ADJ
fcis-31208	118	201	layer	layer	NOUN
fcis-31208	118	202	and	and	CCONJ
fcis-31208	118	203	is	be	AUX
fcis-31208	118	204	the	the	DET
fcis-31208	118	205	biased	biased	ADJ
fcis-31208	118	206	term	term	NOUN
fcis-31208	118	207	.	.	PUNCT
fcis-31208	119	1	in	in	ADP
fcis-31208	119	2	addition	addition	NOUN
fcis-31208	119	3	,	,	PUNCT
fcis-31208	119	4	denoted	denote	VERB
fcis-31208	119	5	as	as	ADP
fcis-31208	119	6	the	the	DET
fcis-31208	119	7	target	target	NOUN
fcis-31208	119	8	value	value	NOUN
fcis-31208	119	9	,	,	PUNCT
fcis-31208	119	10	then	then	ADV
fcis-31208	119	11	the	the	DET
fcis-31208	119	12	error	error	NOUN
fcis-31208	119	13	term	term	NOUN
fcis-31208	119	14	of	of	ADP
fcis-31208	119	15	the	the	DET
fcis-31208	119	16	convolution	convolution	NOUN
fcis-31208	119	17	layer	layer	NOUN
fcis-31208	119	18	can	can	AUX
fcis-31208	119	19	be	be	AUX
fcis-31208	119	20	expressed	express	VERB
fcis-31208	119	21	as	as	SCONJ
fcis-31208	119	22	follows	follow	VERB
fcis-31208	119	23	:	:	PUNCT
fcis-31208	119	24	34	34	NUM
fcis-31208	119	25	in	in	ADP
fcis-31208	119	26	order	order	NOUN
fcis-31208	119	27	to	to	PART
fcis-31208	119	28	combine	combine	VERB
fcis-31208	119	29	the	the	DET
fcis-31208	119	30	variables	variable	NOUN
fcis-31208	119	31	involved	involve	VERB
fcis-31208	119	32	in	in	ADP
fcis-31208	119	33	the	the	DET
fcis-31208	119	34	above	above	ADJ
fcis-31208	119	35	theoretical	theoretical	ADJ
fcis-31208	119	36	analysis	analysis	NOUN
fcis-31208	119	37	with	with	ADP
fcis-31208	119	38	qubo	qubo	PROPN
fcis-31208	119	39	,	,	PUNCT
fcis-31208	119	40	we	we	PRON
fcis-31208	119	41	defined	define	VERB
fcis-31208	119	42	as	as	ADP
fcis-31208	119	43	the	the	DET
fcis-31208	119	44	binary	binary	ADJ
fcis-31208	119	45	activation	activation	NOUN
fcis-31208	119	46	variable	variable	NOUN
fcis-31208	119	47	of	of	ADP
fcis-31208	119	48	the	the	DET
fcis-31208	119	49	i	i	PROPN
fcis-31208	119	50	-	-	PUNCT
fcis-31208	119	51	th	th	X
fcis-31208	119	52	neuron	neuron	NOUN
fcis-31208	119	53	,	,	PUNCT
fcis-31208	119	54	indicating	indicate	VERB
fcis-31208	119	55	whether	whether	SCONJ
fcis-31208	119	56	the	the	DET
fcis-31208	119	57	neuron	neuron	NOUN
fcis-31208	119	58	is	be	AUX
fcis-31208	119	59	activated	activate	VERB
fcis-31208	119	60	,	,	PUNCT
fcis-31208	119	61	with	with	ADP
fcis-31208	119	62	the	the	DET
fcis-31208	119	63	value	value	NOUN
fcis-31208	119	64	of	of	ADP
fcis-31208	119	65	0	0	NUM
fcis-31208	119	66	or	or	CCONJ
fcis-31208	119	67	1	1	NUM
fcis-31208	119	68	;	;	PUNCT
fcis-31208	119	69	binary	binary	ADJ
fcis-31208	119	70	variable	variable	NOUN
fcis-31208	119	71	for	for	ADP
fcis-31208	119	72	the	the	DET
fcis-31208	119	73	model	model	NOUN
fcis-31208	119	74	prediction	prediction	NOUN
fcis-31208	119	75	class	class	PROPN
fcis-31208	119	76	k	k	PROPN
fcis-31208	119	77	,	,	PUNCT
fcis-31208	119	78	indicating	indicate	VERB
fcis-31208	119	79	whether	whether	SCONJ
fcis-31208	119	80	the	the	DET
fcis-31208	119	81	sample	sample	NOUN
fcis-31208	119	82	is	be	AUX
fcis-31208	119	83	classified	classify	VERB
fcis-31208	119	84	as	as	ADP
fcis-31208	119	85	class	class	NOUN
fcis-31208	119	86	k	k	PROPN
fcis-31208	119	87	,	,	PUNCT
fcis-31208	119	88	taking	take	VERB
fcis-31208	119	89	the	the	DET
fcis-31208	119	90	value	value	NOUN
fcis-31208	119	91	0	0	NUM
fcis-31208	119	92	or	or	CCONJ
fcis-31208	119	93	1	1	NUM
fcis-31208	119	94	;	;	PUNCT
fcis-31208	119	95	in	in	ADP
fcis-31208	119	96	addition	addition	NOUN
fcis-31208	119	97	,	,	PUNCT
fcis-31208	119	98	we	we	PRON
fcis-31208	119	99	need	need	VERB
fcis-31208	119	100	to	to	PART
fcis-31208	119	101	replace	replace	VERB
fcis-31208	119	102	it	it	PRON
fcis-31208	119	103	with	with	ADP
fcis-31208	119	104	binary	binary	NOUN
fcis-31208	119	105	,	,	PUNCT
fcis-31208	119	106	to	to	ADP
fcis-31208	119	107	expression	expression	NOUN
fcis-31208	119	108	.	.	PUNCT
fcis-31208	120	1	to	to	PART
fcis-31208	120	2	prevent	prevent	VERB
fcis-31208	120	3	overfitting	overfitting	NOUN
fcis-31208	120	4	,	,	PUNCT
fcis-31208	120	5	we	we	PRON
fcis-31208	120	6	add	add	VERB
fcis-31208	120	7	regularization	regularization	NOUN
fcis-31208	120	8	terms	term	NOUN
fcis-31208	120	9	,	,	PUNCT
fcis-31208	120	10	especially	especially	ADV
fcis-31208	120	11	l2	l2	VERB
fcis-31208	120	12	regularization	regularization	NOUN
fcis-31208	120	13	terms	term	NOUN
fcis-31208	120	14	.	.	PUNCT
fcis-31208	121	1	,	,	PUNCT
fcis-31208	121	2	35	35	NUM
fcis-31208	121	3	based	base	VERB
fcis-31208	121	4	on	on	ADP
fcis-31208	121	5	the	the	DET
fcis-31208	121	6	above	above	ADJ
fcis-31208	121	7	analysis	analysis	NOUN
fcis-31208	121	8	,	,	PUNCT
fcis-31208	121	9	we	we	PRON
fcis-31208	121	10	determine	determine	VERB
fcis-31208	121	11	that	that	SCONJ
fcis-31208	121	12	the	the	DET
fcis-31208	121	13	objective	objective	ADJ
fcis-31208	121	14	function	function	NOUN
fcis-31208	121	15	is	be	AUX
fcis-31208	121	16	:	:	PUNCT
fcis-31208	121	17	(	(	PUNCT
fcis-31208	121	18	36	36	NUM
fcis-31208	121	19	)	)	PUNCT
fcis-31208	121	20	where	where	SCONJ
fcis-31208	121	21	,	,	PUNCT
fcis-31208	121	22	,	,	PUNCT
fcis-31208	121	23	,	,	PUNCT
fcis-31208	121	24	is	be	AUX
fcis-31208	121	25	the	the	DET
fcis-31208	121	26	penalty	penalty	NOUN
fcis-31208	121	27	coefficient	coefficient	NOUN
fcis-31208	121	28	that	that	PRON
fcis-31208	121	29	controls	control	VERB
fcis-31208	121	30	the	the	DET
fcis-31208	121	31	importance	importance	NOUN
fcis-31208	121	32	of	of	ADP
fcis-31208	121	33	the	the	DET
fcis-31208	121	34	error	error	NOUN
fcis-31208	121	35	term	term	NOUN
fcis-31208	121	36	.	.	PUNCT
fcis-31208	122	1	on	on	ADP
fcis-31208	122	2	this	this	DET
fcis-31208	122	3	basis	basis	NOUN
fcis-31208	122	4	,	,	PUNCT
fcis-31208	122	5	we	we	PRON
fcis-31208	122	6	determine	determine	VERB
fcis-31208	122	7	the	the	DET
fcis-31208	122	8	constraint	constraint	NOUN
fcis-31208	122	9	conditions	condition	NOUN
fcis-31208	122	10	,	,	PUNCT
fcis-31208	122	11	the	the	DET
fcis-31208	122	12	classification	classification	NOUN
fcis-31208	122	13	results	result	NOUN
fcis-31208	122	14	should	should	AUX
fcis-31208	122	15	meet	meet	VERB
fcis-31208	122	16	"	"	PUNCT
fcis-31208	122	17	only	only	ADV
fcis-31208	122	18	one	one	NUM
fcis-31208	122	19	category	category	NOUN
fcis-31208	122	20	is	be	AUX
fcis-31208	122	21	predicted	predict	VERB
fcis-31208	122	22	"	"	PUNCT
fcis-31208	122	23	,	,	PUNCT
fcis-31208	122	24	the	the	DET
fcis-31208	122	25	activation	activation	NOUN
fcis-31208	122	26	value	value	NOUN
fcis-31208	122	27	of	of	ADP
fcis-31208	122	28	neurons	neuron	NOUN
fcis-31208	122	29	can	can	AUX
fcis-31208	122	30	only	only	ADV
fcis-31208	122	31	be	be	AUX
fcis-31208	122	32	0	0	NUM
fcis-31208	122	33	or	or	CCONJ
fcis-31208	122	34	1	1	NUM
fcis-31208	122	35	,	,	PUNCT
fcis-31208	122	36	the	the	DET
fcis-31208	122	37	discretization	discretization	NOUN
fcis-31208	122	38	into	into	ADP
fcis-31208	122	39	binary	binary	ADJ
fcis-31208	122	40	decision	decision	NOUN
fcis-31208	122	41	variables	variable	NOUN
fcis-31208	122	42	in	in	ADP
fcis-31208	122	43	convolution	convolution	NOUN
fcis-31208	122	44	operation	operation	NOUN
fcis-31208	122	45	,	,	PUNCT
fcis-31208	122	46	the	the	DET
fcis-31208	122	47	calculation	calculation	NOUN
fcis-31208	122	48	rules	rule	NOUN
fcis-31208	122	49	of	of	ADP
fcis-31208	122	50	weighted	weight	VERB
fcis-31208	122	51	sum	sum	NOUN
fcis-31208	122	52	of	of	ADP
fcis-31208	122	53	neurons	neuron	NOUN
fcis-31208	122	54	,	,	PUNCT
fcis-31208	122	55	the	the	DET
fcis-31208	122	56	minimization	minimization	NOUN
fcis-31208	122	57	of	of	ADP
fcis-31208	122	58	cross	cross	PROPN
fcis-31208	122	59	entropy	entropy	PROPN
fcis-31208	122	60	loss	loss	NOUN
fcis-31208	122	61	function	function	NOUN
fcis-31208	122	62	and	and	CCONJ
fcis-31208	122	63	regularization	regularization	NOUN
fcis-31208	122	64	constraints	constraint	NOUN
fcis-31208	122	65	,	,	PUNCT
fcis-31208	122	66	the	the	DET
fcis-31208	122	67	specific	specific	ADJ
fcis-31208	122	68	mathematical	mathematical	ADJ
fcis-31208	122	69	expressions	expression	NOUN
fcis-31208	122	70	are	be	AUX
fcis-31208	122	71	as	as	SCONJ
fcis-31208	122	72	follows	follow	VERB
fcis-31208	122	73	:	:	PUNCT
fcis-31208	122	74	45	45	NUM
fcis-31208	122	75	  	  	SPACE
fcis-31208	122	76	1	1	NUM
fcis-31208	122	77	∈	∈	NOUN
fcis-31208	122	78	0,1	0,1	NUM
fcis-31208	122	79	,	,	PUNCT
fcis-31208	122	80	∀	∀	X
fcis-31208	122	81	conv	conv	PROPN
fcis-31208	122	82	  	  	SPACE
fcis-31208	122	83	fe	fe	PROPN
fcis-31208	122	84	  	  	SPACE
fcis-31208	122	85	fe	fe	X
fcis-31208	122	86	fe	fe	X
fcis-31208	122	87	∈	∈	PROPN
fcis-31208	122	88	,	,	PUNCT
fcis-31208	122	89	,	,	PUNCT
fcis-31208	122	90	…	…	PUNCT
fcis-31208	122	91	,	,	PUNCT
fcis-31208	122	92	,	,	PUNCT
fcis-31208	122	93	∀	∀	X
fcis-31208	122	94	,	,	PUNCT
fcis-31208	122	95	cross	cross	NOUN
fcis-31208	122	96	-	-	ADJ
fcis-31208	122	97	entropy	entropy	ADJ
fcis-31208	122	98	  	  	SPACE
fcis-31208	122	99	log	log	VERB
fcis-31208	122	100	regularization	regularization	NOUN
fcis-31208	122	101	reg	reg	NOUN
fcis-31208	122	102	  	  	SPACE
fcis-31208	122	103	,	,	PUNCT
fcis-31208	122	104	37	37	NUM
fcis-31208	122	105	where	where	SCONJ
fcis-31208	122	106	,	,	PUNCT
fcis-31208	122	107	is	be	AUX
fcis-31208	122	108	the	the	DET
fcis-31208	122	109	predicted	predict	VERB
fcis-31208	122	110	binary	binary	ADJ
fcis-31208	122	111	variable	variable	NOUN
fcis-31208	122	112	of	of	ADP
fcis-31208	122	113	class	class	NOUN
fcis-31208	122	114	k	k	PROPN
fcis-31208	122	115	,	,	PUNCT
fcis-31208	122	116	,	,	PUNCT
fcis-31208	122	117	,	,	PUNCT
fcis-31208	122	118	…	…	PUNCT
fcis-31208	122	119	,	,	PUNCT
fcis-31208	122	120	is	be	AUX
fcis-31208	122	121	the	the	DET
fcis-31208	122	122	weight	weight	NOUN
fcis-31208	122	123	value	value	NOUN
fcis-31208	122	124	after	after	ADP
fcis-31208	122	125	discretization	discretization	NOUN
fcis-31208	122	126	.	.	PUNCT
fcis-31208	123	1	(	(	PUNCT
fcis-31208	123	2	2	2	X
fcis-31208	123	3	)	)	PUNCT
fcis-31208	123	4	solution	solution	NOUN
fcis-31208	123	5	of	of	ADP
fcis-31208	123	6	image	image	NOUN
fcis-31208	123	7	recognition	recognition	NOUN
fcis-31208	123	8	classification	classification	NOUN
fcis-31208	123	9	codel	codel	NOUN
fcis-31208	123	10	based	base	VERB
fcis-31208	123	11	on	on	ADP
fcis-31208	123	12	simulated	simulated	ADJ
fcis-31208	123	13	annealing	anneal	VERB
fcis-31208	123	14	we	we	PRON
fcis-31208	123	15	still	still	ADV
fcis-31208	123	16	use	use	VERB
fcis-31208	123	17	the	the	DET
fcis-31208	123	18	simulated	simulate	VERB
fcis-31208	123	19	annealing	annealing	NOUN
fcis-31208	123	20	solver	solver	ADV
fcis-31208	123	21	built	build	VERB
fcis-31208	123	22	in	in	ADP
fcis-31208	123	23	kaiwu	kaiwu	PROPN
fcis-31208	123	24	sdk	sdk	PROPN
fcis-31208	123	25	for	for	ADP
fcis-31208	123	26	solving	solve	VERB
fcis-31208	123	27	,	,	PUNCT
fcis-31208	123	28	in	in	ADP
fcis-31208	123	29	which	which	PRON
fcis-31208	123	30	the	the	DET
fcis-31208	123	31	parameters	parameter	NOUN
fcis-31208	123	32	in	in	ADP
fcis-31208	123	33	the	the	DET
fcis-31208	123	34	solver	solver	NOUN
fcis-31208	123	35	are	be	AUX
fcis-31208	123	36	set	set	VERB
fcis-31208	123	37	as	as	SCONJ
fcis-31208	123	38	follows	follow	VERB
fcis-31208	123	39	:	:	PUNCT
fcis-31208	123	40	table	table	NOUN
fcis-31208	123	41	12	12	NUM
fcis-31208	123	42	.	.	PUNCT
fcis-31208	124	1	simulated	simulate	VERB
fcis-31208	124	2	annealing	anneal	VERB
fcis-31208	124	3	parameter	parameter	NOUN
fcis-31208	124	4	table	table	NOUN
fcis-31208	124	5	parameters	parameter	NOUN
fcis-31208	124	6	select	select	VERB
fcis-31208	124	7	parameter	parameter	NOUN
fcis-31208	124	8	values	value	NOUN
fcis-31208	124	9	initial	initial	ADJ
fcis-31208	124	10	temperature	temperature	NOUN
fcis-31208	124	11	3000	3000	NUM
fcis-31208	124	12	cutoff	cutoff	NOUN
fcis-31208	124	13	temperature	temperature	NOUN
fcis-31208	124	14	10	10	NUM
fcis-31208	124	15	cooling	cool	VERB
fcis-31208	124	16	factor	factor	NOUN
fcis-31208	124	17	0.90	0.90	NUM
fcis-31208	124	18	iterations	iteration	NOUN
fcis-31208	124	19	per	per	ADP
fcis-31208	124	20	temperature	temperature	NOUN
fcis-31208	124	21	100	100	NUM
fcis-31208	124	22	size	size	NOUN
fcis-31208	124	23	limit	limit	VERB
fcis-31208	124	24	100	100	NUM
fcis-31208	124	25	select	select	ADJ
fcis-31208	124	26	the	the	DET
fcis-31208	124	27	initial	initial	ADJ
fcis-31208	124	28	temperature	temperature	NOUN
fcis-31208	124	29	and	and	CCONJ
fcis-31208	124	30	the	the	DET
fcis-31208	124	31	end	end	NOUN
fcis-31208	124	32	temperature	temperature	NOUN
fcis-31208	124	33	,	,	PUNCT
fcis-31208	124	34	and	and	CCONJ
fcis-31208	124	35	select	select	VERB
fcis-31208	124	36	the	the	DET
fcis-31208	124	37	appropriate	appropriate	ADJ
fcis-31208	124	38	cooling	cool	VERB
fcis-31208	124	39	coefficient	coefficient	NOUN
fcis-31208	124	40	during	during	ADP
fcis-31208	124	41	the	the	DET
fcis-31208	124	42	annealing	annealing	NOUN
fcis-31208	124	43	process	process	NOUN
fcis-31208	124	44	,	,	PUNCT
fcis-31208	124	45	so	so	SCONJ
fcis-31208	124	46	that	that	SCONJ
fcis-31208	124	47	the	the	DET
fcis-31208	124	48	iterative	iterative	ADJ
fcis-31208	124	49	depth	depth	NOUN
fcis-31208	124	50	of	of	ADP
fcis-31208	124	51	each	each	DET
fcis-31208	124	52	temperature	temperature	NOUN
fcis-31208	124	53	in	in	ADP
fcis-31208	124	54	the	the	DET
fcis-31208	124	55	annealing	annealing	NOUN
fcis-31208	124	56	process	process	NOUN
fcis-31208	124	57	does	do	AUX
fcis-31208	124	58	not	not	PART
fcis-31208	124	59	exceed	exceed	VERB
fcis-31208	124	60	100	100	NUM
fcis-31208	124	61	.	.	PUNCT
fcis-31208	125	1	through	through	ADP
fcis-31208	125	2	solving	solve	VERB
fcis-31208	125	3	,	,	PUNCT
fcis-31208	125	4	we	we	PRON
fcis-31208	125	5	captured	capture	VERB
fcis-31208	125	6	part	part	NOUN
fcis-31208	125	7	of	of	ADP
fcis-31208	125	8	the	the	DET
fcis-31208	125	9	images	image	NOUN
fcis-31208	125	10	in	in	ADP
fcis-31208	125	11	the	the	DET
fcis-31208	125	12	mnist	mnist	NOUN
fcis-31208	125	13	dataset	dataset	NOUN
fcis-31208	125	14	and	and	CCONJ
fcis-31208	125	15	their	their	PRON
fcis-31208	125	16	recognition	recognition	NOUN
fcis-31208	125	17	results	result	NOUN
fcis-31208	125	18	,	,	PUNCT
fcis-31208	125	19	as	as	SCONJ
fcis-31208	125	20	shown	show	VERB
fcis-31208	125	21	below	below	ADV
fcis-31208	125	22	:	:	PUNCT
fcis-31208	125	23	figure	figure	VERB
fcis-31208	125	24	12	12	NUM
fcis-31208	125	25	.	.	PUNCT
fcis-31208	126	1	part	part	NOUN
fcis-31208	126	2	of	of	ADP
fcis-31208	126	3	the	the	DET
fcis-31208	126	4	image	image	NOUN
fcis-31208	126	5	recognition	recognition	NOUN
fcis-31208	126	6	results	result	VERB
fcis-31208	126	7	in	in	ADP
fcis-31208	126	8	addition	addition	NOUN
fcis-31208	126	9	to	to	ADP
fcis-31208	126	10	this	this	PRON
fcis-31208	126	11	,	,	PUNCT
fcis-31208	126	12	we	we	PRON
fcis-31208	126	13	visualize	visualize	VERB
fcis-31208	126	14	the	the	DET
fcis-31208	126	15	changes	change	NOUN
fcis-31208	126	16	in	in	ADP
fcis-31208	126	17	the	the	DET
fcis-31208	126	18	loss	loss	NOUN
fcis-31208	126	19	(	(	PUNCT
fcis-31208	126	20	left	left	ADJ
fcis-31208	126	21	)	)	PUNCT
fcis-31208	126	22	and	and	CCONJ
fcis-31208	126	23	accuracy	accuracy	NOUN
fcis-31208	126	24	(	(	PUNCT
fcis-31208	126	25	right	right	NOUN
fcis-31208	126	26	)	)	PUNCT
fcis-31208	126	27	of	of	ADP
fcis-31208	126	28	the	the	DET
fcis-31208	126	29	model	model	NOUN
fcis-31208	126	30	training	training	NOUN
fcis-31208	126	31	process	process	NOUN
fcis-31208	126	32	over	over	ADP
fcis-31208	126	33	the	the	DET
fcis-31208	126	34	training	training	NOUN
fcis-31208	126	35	cycle	cycle	NOUN
fcis-31208	126	36	:	:	PUNCT
fcis-31208	126	37	figure	figure	NOUN
fcis-31208	126	38	13	13	NUM
fcis-31208	126	39	.	.	PUNCT
fcis-31208	127	1	the	the	DET
fcis-31208	127	2	change	change	NOUN
fcis-31208	127	3	of	of	ADP
fcis-31208	127	4	loss	loss	NOUN
fcis-31208	127	5	and	and	CCONJ
fcis-31208	127	6	accuracy	accuracy	NOUN
fcis-31208	127	7	in	in	ADP
fcis-31208	127	8	the	the	DET
fcis-31208	127	9	model	model	NOUN
fcis-31208	127	10	training	training	NOUN
fcis-31208	127	11	process	process	NOUN
fcis-31208	127	12	with	with	ADP
fcis-31208	127	13	the	the	DET
fcis-31208	127	14	training	training	NOUN
fcis-31208	127	15	cycle	cycle	NOUN
fcis-31208	127	16	as	as	SCONJ
fcis-31208	127	17	can	can	AUX
fcis-31208	127	18	be	be	AUX
fcis-31208	127	19	seen	see	VERB
fcis-31208	127	20	from	from	ADP
fcis-31208	127	21	the	the	DET
fcis-31208	127	22	figure	figure	NOUN
fcis-31208	127	23	on	on	ADP
fcis-31208	127	24	the	the	DET
fcis-31208	127	25	left	left	NOUN
fcis-31208	127	26	,	,	PUNCT
fcis-31208	127	27	both	both	DET
fcis-31208	127	28	training	training	NOUN
fcis-31208	127	29	losses	loss	NOUN
fcis-31208	127	30	and	and	CCONJ
fcis-31208	127	31	validation	validation	NOUN
fcis-31208	127	32	losses	loss	NOUN
fcis-31208	127	33	decrease	decrease	NOUN
fcis-31208	127	34	as	as	ADP
fcis-31208	127	35	the	the	DET
fcis-31208	127	36	training	training	NOUN
fcis-31208	127	37	cycle	cycle	NOUN
fcis-31208	127	38	increases	increase	NOUN
fcis-31208	127	39	,	,	PUNCT
fcis-31208	127	40	indicating	indicate	VERB
fcis-31208	127	41	that	that	SCONJ
fcis-31208	127	42	the	the	DET
fcis-31208	127	43	model	model	NOUN
fcis-31208	127	44	is	be	AUX
fcis-31208	127	45	learning	learn	VERB
fcis-31208	127	46	and	and	CCONJ
fcis-31208	127	47	gradually	gradually	ADV
fcis-31208	127	48	reducing	reduce	VERB
fcis-31208	127	49	the	the	DET
fcis-31208	127	50	prediction	prediction	NOUN
fcis-31208	127	51	error	error	NOUN
fcis-31208	127	52	.	.	PUNCT
fcis-31208	128	1	after	after	ADP
fcis-31208	128	2	the	the	DET
fcis-31208	128	3	initial	initial	ADJ
fcis-31208	128	4	decline	decline	NOUN
fcis-31208	128	5	,	,	PUNCT
fcis-31208	128	6	the	the	DET
fcis-31208	128	7	validation	validation	NOUN
fcis-31208	128	8	loss	loss	NOUN
fcis-31208	128	9	levels	level	NOUN
fcis-31208	128	10	off	off	ADP
fcis-31208	128	11	and	and	CCONJ
fcis-31208	128	12	is	be	AUX
fcis-31208	128	13	always	always	ADV
fcis-31208	128	14	lower	low	ADJ
fcis-31208	128	15	than	than	ADP
fcis-31208	128	16	the	the	DET
fcis-31208	128	17	training	training	NOUN
fcis-31208	128	18	loss	loss	NOUN
fcis-31208	128	19	,	,	PUNCT
fcis-31208	128	20	indicating	indicate	VERB
fcis-31208	128	21	that	that	SCONJ
fcis-31208	128	22	the	the	DET
fcis-31208	128	23	model	model	NOUN
fcis-31208	128	24	performs	perform	VERB
fcis-31208	128	25	well	well	ADV
fcis-31208	128	26	on	on	ADP
fcis-31208	128	27	the	the	DET
fcis-31208	128	28	training	training	NOUN
fcis-31208	128	29	set	set	VERB
fcis-31208	128	30	without	without	ADP
fcis-31208	128	31	significant	significant	ADJ
fcis-31208	128	32	overfitting	overfitting	NOUN
fcis-31208	128	33	.	.	PUNCT
fcis-31208	129	1	for	for	ADP
fcis-31208	129	2	the	the	DET
fcis-31208	129	3	figure	figure	NOUN
fcis-31208	129	4	on	on	ADP
fcis-31208	129	5	the	the	DET
fcis-31208	129	6	right	right	NOUN
fcis-31208	129	7	,	,	PUNCT
fcis-31208	129	8	both	both	CCONJ
fcis-31208	129	9	the	the	DET
fcis-31208	129	10	training	training	NOUN
fcis-31208	129	11	accuracy	accuracy	NOUN
fcis-31208	129	12	and	and	CCONJ
fcis-31208	129	13	validation	validation	NOUN
fcis-31208	129	14	accuracy	accuracy	NOUN
fcis-31208	129	15	rise	rise	NOUN
fcis-31208	129	16	as	as	ADP
fcis-31208	129	17	the	the	DET
fcis-31208	129	18	training	training	NOUN
fcis-31208	129	19	cycle	cycle	NOUN
fcis-31208	129	20	increases	increase	NOUN
fcis-31208	129	21	,	,	PUNCT
fcis-31208	129	22	and	and	CCONJ
fcis-31208	129	23	both	both	DET
fcis-31208	129	24	approach	approach	VERB
fcis-31208	129	25	1.0	1.0	NUM
fcis-31208	129	26	,	,	PUNCT
fcis-31208	129	27	indicating	indicate	VERB
fcis-31208	129	28	that	that	SCONJ
fcis-31208	129	29	the	the	DET
fcis-31208	129	30	model	model	NOUN
fcis-31208	129	31	's	's	PART
fcis-31208	129	32	predictions	prediction	NOUN
fcis-31208	129	33	are	be	AUX
fcis-31208	129	34	becoming	become	VERB
fcis-31208	129	35	more	more	ADV
fcis-31208	129	36	accurate	accurate	ADJ
fcis-31208	129	37	.	.	PUNCT
fcis-31208	130	1	and	and	CCONJ
fcis-31208	130	2	the	the	DET
fcis-31208	130	3	training	training	NOUN
fcis-31208	130	4	accuracy	accuracy	NOUN
fcis-31208	130	5	and	and	CCONJ
fcis-31208	130	6	verification	verification	NOUN
fcis-31208	130	7	accuracy	accuracy	NOUN
fcis-31208	130	8	are	be	AUX
fcis-31208	130	9	very	very	ADV
fcis-31208	130	10	close	close	ADJ
fcis-31208	130	11	,	,	PUNCT
fcis-31208	130	12	which	which	PRON
fcis-31208	130	13	further	far	ADV
fcis-31208	130	14	indicates	indicate	VERB
fcis-31208	130	15	that	that	SCONJ
fcis-31208	130	16	the	the	DET
fcis-31208	130	17	model	model	NOUN
fcis-31208	130	18	is	be	AUX
fcis-31208	130	19	not	not	PART
fcis-31208	130	20	overfitting	overfitte	VERB
fcis-31208	130	21	and	and	CCONJ
fcis-31208	130	22	has	have	VERB
fcis-31208	130	23	good	good	ADJ
fcis-31208	130	24	generalization	generalization	NOUN
fcis-31208	130	25	ability	ability	NOUN
fcis-31208	130	26	on	on	ADP
fcis-31208	130	27	both	both	CCONJ
fcis-31208	130	28	the	the	DET
fcis-31208	130	29	training	training	NOUN
fcis-31208	130	30	set	set	NOUN
fcis-31208	130	31	and	and	CCONJ
fcis-31208	130	32	the	the	DET
fcis-31208	130	33	verification	verification	NOUN
fcis-31208	130	34	set	set	NOUN
fcis-31208	130	35	.	.	PUNCT
fcis-31208	131	1	in	in	ADP
fcis-31208	131	2	addition	addition	NOUN
fcis-31208	131	3	,	,	PUNCT
fcis-31208	131	4	we	we	PRON
fcis-31208	131	5	calculated	calculate	VERB
fcis-31208	131	6	a	a	DET
fcis-31208	131	7	variety	variety	NOUN
fcis-31208	131	8	of	of	ADP
fcis-31208	131	9	predictive	predictive	ADJ
fcis-31208	131	10	evaluation	evaluation	NOUN
fcis-31208	131	11	indicators	indicator	NOUN
fcis-31208	131	12	to	to	PART
fcis-31208	131	13	help	help	VERB
fcis-31208	131	14	explain	explain	VERB
fcis-31208	131	15	the	the	DET
fcis-31208	131	16	excellent	excellent	ADJ
fcis-31208	131	17	performance	performance	NOUN
fcis-31208	131	18	of	of	ADP
fcis-31208	131	19	image	image	NOUN
fcis-31208	131	20	recognition	recognition	NOUN
fcis-31208	131	21	classification	classification	NOUN
fcis-31208	131	22	results	result	NOUN
fcis-31208	131	23	:	:	PUNCT
fcis-31208	131	24	table	table	NOUN
fcis-31208	131	25	13	13	NUM
fcis-31208	131	26	.	.	PUNCT
fcis-31208	132	1	results	result	NOUN
fcis-31208	132	2	evaluation	evaluation	NOUN
fcis-31208	132	3	index	index	NOUN
fcis-31208	132	4	value	value	NOUN
fcis-31208	132	5	statistics	statistic	NOUN
fcis-31208	132	6	(	(	PUNCT
fcis-31208	132	7	iris	iris	NOUN
fcis-31208	132	8	-	-	PUNCT
fcis-31208	132	9	versicolor	versicolor	NOUN
fcis-31208	132	10	and	and	CCONJ
fcis-31208	132	11	iris	iris	NOUN
fcis-31208	132	12	-	-	PUNCT
fcis-31208	132	13	virginica	virginica	NOUN
fcis-31208	132	14	)	)	PUNCT
fcis-31208	132	15	data	datum	NOUN
fcis-31208	132	16	set	set	VERB
fcis-31208	132	17	recall	recall	NOUN
fcis-31208	132	18	f1score	f1score	NOUN
fcis-31208	132	19	accuracy	accuracy	NOUN
fcis-31208	132	20	support	support	NOUN
fcis-31208	132	21	macro	macro	ADJ
fcis-31208	132	22	avg	avg	NOUN
fcis-31208	132	23	0.99	0.99	NUM
fcis-31208	132	24	0.99	0.99	NUM
fcis-31208	132	25	0.99	0.99	NUM
fcis-31208	132	26	10000	10000	NUM
fcis-31208	132	27	weighted	weight	VERB
fcis-31208	132	28	avg	avg	NOUN
fcis-31208	132	29	0.99	0.99	NUM
fcis-31208	132	30	0.99	0.99	NUM
fcis-31208	132	31	0.99	0.99	NUM
fcis-31208	132	32	10000	10000	NUM
fcis-31208	132	33	46	46	NUM
fcis-31208	132	34	from	from	ADP
fcis-31208	132	35	the	the	DET
fcis-31208	132	36	table	table	NOUN
fcis-31208	132	37	above	above	ADV
fcis-31208	132	38	,	,	PUNCT
fcis-31208	132	39	we	we	PRON
fcis-31208	132	40	can	can	AUX
fcis-31208	132	41	see	see	VERB
fcis-31208	132	42	that	that	SCONJ
fcis-31208	132	43	the	the	DET
fcis-31208	132	44	accuracy	accuracy	NOUN
fcis-31208	132	45	,	,	PUNCT
fcis-31208	132	46	recall	recall	NOUN
fcis-31208	132	47	rate	rate	NOUN
fcis-31208	132	48	,	,	PUNCT
fcis-31208	132	49	and	and	CCONJ
fcis-31208	132	50	f1	f1	ADJ
fcis-31208	132	51	score	score	NOUN
fcis-31208	132	52	of	of	ADP
fcis-31208	132	53	the	the	DET
fcis-31208	132	54	model	model	NOUN
fcis-31208	132	55	recognition	recognition	NOUN
fcis-31208	132	56	results	result	NOUN
fcis-31208	132	57	are	be	AUX
fcis-31208	132	58	close	close	ADJ
fcis-31208	132	59	to	to	ADP
fcis-31208	132	60	0.99	0.99	NUM
fcis-31208	132	61	or	or	CCONJ
fcis-31208	132	62	1.00	1.00	NUM
fcis-31208	132	63	,	,	PUNCT
fcis-31208	132	64	indicating	indicate	VERB
fcis-31208	132	65	that	that	SCONJ
fcis-31208	132	66	the	the	DET
fcis-31208	132	67	model	model	NOUN
fcis-31208	132	68	performs	perform	VERB
fcis-31208	132	69	very	very	ADV
fcis-31208	132	70	well	well	ADV
fcis-31208	132	71	on	on	ADP
fcis-31208	132	72	handwritten	handwritten	ADJ
fcis-31208	132	73	digit	digit	NOUN
fcis-31208	132	74	recognition	recognition	NOUN
fcis-31208	132	75	tasks	task	NOUN
fcis-31208	132	76	.	.	PUNCT
fcis-31208	133	1	solving	solve	VERB
fcis-31208	133	2	time	time	NOUN
fcis-31208	133	3	of	of	ADP
fcis-31208	133	4	the	the	DET
fcis-31208	133	5	qubo	qubo	PROPN
fcis-31208	133	6	model	model	NOUN
fcis-31208	133	7	is	be	AUX
fcis-31208	133	8	211s	211s	NOUN
fcis-31208	133	9	due	due	ADP
fcis-31208	133	10	to	to	ADP
fcis-31208	133	11	the	the	DET
fcis-31208	133	12	huge	huge	ADJ
fcis-31208	133	13	amount	amount	NOUN
fcis-31208	133	14	of	of	ADP
fcis-31208	133	15	data	datum	NOUN
fcis-31208	133	16	and	and	CCONJ
fcis-31208	133	17	individual	individual	ADJ
fcis-31208	133	18	differences	difference	NOUN
fcis-31208	133	19	in	in	ADP
fcis-31208	133	20	hardware	hardware	NOUN
fcis-31208	133	21	performance	performance	NOUN
fcis-31208	133	22	.	.	PUNCT
fcis-31208	134	1	5	5	X
fcis-31208	134	2	.	.	X
fcis-31208	134	3	conclusion	conclusion	NOUN
fcis-31208	134	4	for	for	ADP
fcis-31208	134	5	model	model	NOUN
fcis-31208	134	6	1	1	NUM
fcis-31208	134	7	,	,	PUNCT
fcis-31208	134	8	we	we	PRON
fcis-31208	134	9	used	use	VERB
fcis-31208	134	10	the	the	DET
fcis-31208	134	11	monthly	monthly	ADJ
fcis-31208	134	12	computational	computational	ADJ
fcis-31208	134	13	resource	resource	NOUN
fcis-31208	134	14	demand	demand	NOUN
fcis-31208	134	15	data	datum	NOUN
fcis-31208	134	16	provided	provide	VERB
fcis-31208	134	17	by	by	ADP
fcis-31208	134	18	the	the	DET
fcis-31208	134	19	problem	problem	NOUN
fcis-31208	134	20	and	and	CCONJ
fcis-31208	134	21	combined	combine	VERB
fcis-31208	134	22	with	with	ADP
fcis-31208	134	23	aic	aic	PROPN
fcis-31208	134	24	criterion	criterion	NOUN
fcis-31208	134	25	to	to	PART
fcis-31208	134	26	establish	establish	VERB
fcis-31208	134	27	an	an	DET
fcis-31208	134	28	autoregressive	autoregressive	ADJ
fcis-31208	134	29	model	model	NOUN
fcis-31208	134	30	of	of	ADP
fcis-31208	134	31	order	order	NOUN
fcis-31208	134	32	3	3	X
fcis-31208	134	33	.	.	PUNCT
fcis-31208	134	34	to	to	PART
fcis-31208	134	35	predict	predict	VERB
fcis-31208	134	36	the	the	DET
fcis-31208	134	37	data	datum	NOUN
fcis-31208	134	38	in	in	ADP
fcis-31208	134	39	october	october	PROPN
fcis-31208	134	40	,	,	PUNCT
fcis-31208	134	41	we	we	PRON
fcis-31208	134	42	took	take	VERB
fcis-31208	134	43	predicted	predict	VERB
fcis-31208	134	44	resource	resource	NOUN
fcis-31208	134	45	demand	demand	NOUN
fcis-31208	134	46	in	in	ADP
fcis-31208	134	47	october	october	PROPN
fcis-31208	134	48	as	as	ADP
fcis-31208	134	49	the	the	DET
fcis-31208	134	50	result	result	NOUN
fcis-31208	134	51	when	when	SCONJ
fcis-31208	134	52	sse	sse	PROPN
fcis-31208	134	53	minimises	minimise	NOUN
fcis-31208	134	54	.	.	PUNCT
fcis-31208	135	1	then	then	ADV
fcis-31208	135	2	ar	ar	PROPN
fcis-31208	135	3	model	model	NOUN
fcis-31208	135	4	is	be	AUX
fcis-31208	135	5	transformed	transform	VERB
fcis-31208	135	6	into	into	ADP
fcis-31208	135	7	qubo	qubo	PROPN
fcis-31208	135	8	,	,	PUNCT
fcis-31208	135	9	and	and	CCONJ
fcis-31208	135	10	the	the	DET
fcis-31208	135	11	parameters	parameter	NOUN
fcis-31208	135	12	in	in	ADP
fcis-31208	135	13	ar	ar	PROPN
fcis-31208	135	14	are	be	AUX
fcis-31208	135	15	replaced	replace	VERB
fcis-31208	135	16	by	by	ADP
fcis-31208	135	17	binary	binary	ADJ
fcis-31208	135	18	expressions	expression	NOUN
fcis-31208	135	19	,	,	PUNCT
fcis-31208	135	20	and	and	CCONJ
fcis-31208	135	21	the	the	DET
fcis-31208	135	22	sse	sse	NOUN
fcis-31208	135	23	function	function	NOUN
fcis-31208	135	24	of	of	ADP
fcis-31208	135	25	binary	binary	ADJ
fcis-31208	135	26	variable	variable	NOUN
fcis-31208	135	27	as	as	SCONJ
fcis-31208	135	28	the	the	DET
fcis-31208	135	29	decision	decision	NOUN
fcis-31208	135	30	variable	variable	NOUN
fcis-31208	135	31	is	be	AUX
fcis-31208	135	32	taken	take	VERB
fcis-31208	135	33	as	as	ADP
fcis-31208	135	34	the	the	DET
fcis-31208	135	35	objective	objective	ADJ
fcis-31208	135	36	function	function	NOUN
fcis-31208	135	37	.	.	PUNCT
fcis-31208	136	1	the	the	DET
fcis-31208	136	2	minimum	minimum	ADJ
fcis-31208	136	3	value	value	NOUN
fcis-31208	136	4	is	be	AUX
fcis-31208	136	5	obtained	obtain	VERB
fcis-31208	136	6	when	when	SCONJ
fcis-31208	136	7	sse	sse	PROPN
fcis-31208	136	8	equals	equal	VERB
fcis-31208	136	9	to	to	ADP
fcis-31208	136	10	30723.78	30723.78	NUM
fcis-31208	136	11	,	,	PUNCT
fcis-31208	136	12	and	and	CCONJ
fcis-31208	136	13	the	the	DET
fcis-31208	136	14	predicted	predict	VERB
fcis-31208	136	15	value	value	NOUN
fcis-31208	136	16	in	in	ADP
fcis-31208	136	17	october	october	PROPN
fcis-31208	136	18	is	be	AUX
fcis-31208	136	19	10719.36	10719.36	NUM
fcis-31208	136	20	.	.	PUNCT
fcis-31208	137	1	in	in	ADP
fcis-31208	137	2	addition	addition	NOUN
fcis-31208	137	3	,	,	PUNCT
fcis-31208	137	4	we	we	PRON
fcis-31208	137	5	calculated	calculate	VERB
fcis-31208	137	6	that	that	SCONJ
fcis-31208	137	7	the	the	DET
fcis-31208	137	8	mape	mape	NOUN
fcis-31208	137	9	of	of	ADP
fcis-31208	137	10	the	the	DET
fcis-31208	137	11	model	model	NOUN
fcis-31208	137	12	is	be	AUX
fcis-31208	137	13	0.709	0.709	NUM
fcis-31208	137	14	,	,	PUNCT
fcis-31208	137	15	and	and	CCONJ
fcis-31208	137	16	the	the	DET
fcis-31208	137	17	r	r	NOUN
fcis-31208	137	18	-	-	PUNCT
fcis-31208	137	19	squared	square	VERB
fcis-31208	137	20	is	be	AUX
fcis-31208	137	21	0.933	0.933	NUM
fcis-31208	137	22	,	,	PUNCT
fcis-31208	137	23	indicating	indicate	VERB
fcis-31208	137	24	that	that	SCONJ
fcis-31208	137	25	the	the	DET
fcis-31208	137	26	prediction	prediction	NOUN
fcis-31208	137	27	result	result	NOUN
fcis-31208	137	28	of	of	ADP
fcis-31208	137	29	the	the	DET
fcis-31208	137	30	model	model	NOUN
fcis-31208	137	31	was	be	AUX
fcis-31208	137	32	excellent	excellent	ADJ
fcis-31208	137	33	.	.	PUNCT
fcis-31208	138	1	by	by	ADP
fcis-31208	138	2	using	use	VERB
fcis-31208	138	3	kaiwu	kaiwu	PROPN
fcis-31208	138	4	sdk	sdk	PROPN
fcis-31208	138	5	,	,	PUNCT
fcis-31208	138	6	the	the	DET
fcis-31208	138	7	solving	solving	NOUN
fcis-31208	138	8	time	time	NOUN
fcis-31208	138	9	of	of	ADP
fcis-31208	138	10	the	the	DET
fcis-31208	138	11	qubo	qubo	PROPN
fcis-31208	138	12	model	model	NOUN
fcis-31208	138	13	was	be	AUX
fcis-31208	138	14	0.07	0.07	NUM
fcis-31208	138	15	seconds	second	NOUN
fcis-31208	138	16	.	.	PUNCT
fcis-31208	139	1	for	for	ADP
fcis-31208	139	2	model	model	NOUN
fcis-31208	139	3	2	2	NUM
fcis-31208	139	4	,	,	PUNCT
fcis-31208	139	5	we	we	PRON
fcis-31208	139	6	decomposed	decompose	VERB
fcis-31208	139	7	the	the	DET
fcis-31208	139	8	tripartite	tripartite	ADJ
fcis-31208	139	9	classification	classification	NOUN
fcis-31208	139	10	into	into	ADP
fcis-31208	139	11	three	three	NUM
fcis-31208	139	12	binary	binary	ADJ
fcis-31208	139	13	classification	classification	NOUN
fcis-31208	139	14	tasks	task	NOUN
fcis-31208	139	15	:	:	PUNCT
fcis-31208	139	16	setosa	setosa	PROPN
fcis-31208	139	17	vs.	vs.	ADP
fcis-31208	139	18	versicolor	versicolor	PROPN
fcis-31208	139	19	,	,	PUNCT
fcis-31208	139	20	setosa	setosa	NOUN
fcis-31208	139	21	vs.	vs.	ADP
fcis-31208	139	22	virginica	virginica	NOUN
fcis-31208	139	23	,	,	PUNCT
fcis-31208	139	24	and	and	CCONJ
fcis-31208	139	25	virginica	virginica	NOUN
fcis-31208	139	26	vs.	vs.	X
fcis-31208	139	27	versicolor	versicolor	NOUN
fcis-31208	139	28	.	.	PUNCT
fcis-31208	140	1	the	the	DET
fcis-31208	140	2	objective	objective	ADJ
fcis-31208	140	3	function	function	NOUN
fcis-31208	140	4	minimizes	minimize	VERB
fcis-31208	140	5	the	the	DET
fcis-31208	140	6	norm	norm	NOUN
fcis-31208	140	7	of	of	ADP
fcis-31208	140	8	the	the	DET
fcis-31208	140	9	normal	normal	ADJ
fcis-31208	140	10	vector	vector	NOUN
fcis-31208	140	11	of	of	ADP
fcis-31208	140	12	the	the	DET
fcis-31208	140	13	svm	svm	ADJ
fcis-31208	140	14	hyperplane	hyperplane	NOUN
fcis-31208	140	15	for	for	ADP
fcis-31208	140	16	each	each	DET
fcis-31208	140	17	task	task	NOUN
fcis-31208	140	18	.	.	PUNCT
fcis-31208	141	1	the	the	DET
fcis-31208	141	2	normal	normal	ADJ
fcis-31208	141	3	vector	vector	NOUN
fcis-31208	141	4	,	,	PUNCT
fcis-31208	141	5	bias	bias	NOUN
fcis-31208	141	6	,	,	PUNCT
fcis-31208	141	7	and	and	CCONJ
fcis-31208	141	8	relaxation	relaxation	NOUN
fcis-31208	141	9	variables	variable	NOUN
fcis-31208	141	10	of	of	ADP
fcis-31208	141	11	the	the	DET
fcis-31208	141	12	hyperplane	hyperplane	NOUN
fcis-31208	141	13	are	be	AUX
fcis-31208	141	14	discretized	discretize	VERB
fcis-31208	141	15	into	into	ADP
fcis-31208	141	16	binary	binary	ADJ
fcis-31208	141	17	variables	variable	NOUN
fcis-31208	141	18	,	,	PUNCT
fcis-31208	141	19	with	with	ADP
fcis-31208	141	20	a	a	DET
fcis-31208	141	21	penalty	penalty	NOUN
fcis-31208	141	22	term	term	NOUN
fcis-31208	141	23	and	and	CCONJ
fcis-31208	141	24	weight	weight	NOUN
fcis-31208	141	25	introduced	introduce	VERB
fcis-31208	141	26	.	.	PUNCT
fcis-31208	142	1	the	the	DET
fcis-31208	142	2	objective	objective	NOUN
fcis-31208	142	3	is	be	AUX
fcis-31208	142	4	then	then	ADV
fcis-31208	142	5	transformed	transform	VERB
fcis-31208	142	6	into	into	ADP
fcis-31208	142	7	a	a	DET
fcis-31208	142	8	qubo	qubo	NOUN
fcis-31208	142	9	,	,	PUNCT
fcis-31208	142	10	solved	solve	VERB
fcis-31208	142	11	using	use	VERB
fcis-31208	142	12	the	the	DET
fcis-31208	142	13	kaiwu	kaiwu	PROPN
fcis-31208	142	14	sdk	sdk	PROPN
fcis-31208	142	15	,	,	PUNCT
fcis-31208	142	16	and	and	CCONJ
fcis-31208	142	17	evaluated	evaluate	VERB
fcis-31208	142	18	.	.	PUNCT
fcis-31208	143	1	here	here	ADV
fcis-31208	143	2	only	only	ADV
fcis-31208	143	3	demonstrate	demonstrate	VERB
fcis-31208	143	4	the	the	DET
fcis-31208	143	5	normal	normal	ADJ
fcis-31208	143	6	vector	vector	NOUN
fcis-31208	143	7	for	for	ADP
fcis-31208	143	8	setosa	setosa	NOUN
fcis-31208	143	9	vs.	vs.	ADP
fcis-31208	143	10	versicolor	versicolor	NOUN
fcis-31208	143	11	,	,	PUNCT
fcis-31208	143	12	it	it	PRON
fcis-31208	143	13	’s	’	VERB
fcis-31208	143	14	[	[	X
fcis-31208	143	15	-0.269	-0.269	ADJ
fcis-31208	143	16	,	,	PUNCT
fcis-31208	143	17	0.338	0.338	NUM
fcis-31208	143	18	,	,	PUNCT
fcis-31208	143	19	0.701	0.701	NUM
fcis-31208	143	20	,	,	PUNCT
fcis-31208	143	21	-0.749	-0.749	NOUN
fcis-31208	143	22	]	]	PUNCT
fcis-31208	143	23	,	,	PUNCT
fcis-31208	143	24	the	the	DET
fcis-31208	143	25	bias	bias	NOUN
fcis-31208	143	26	is	be	AUX
fcis-31208	143	27	-0.247	-0.247	VERB
fcis-31208	143	28	,	,	PUNCT
fcis-31208	143	29	and	and	CCONJ
fcis-31208	143	30	the	the	DET
fcis-31208	143	31	penalty	penalty	NOUN
fcis-31208	143	32	weight	weight	NOUN
fcis-31208	143	33	is	be	AUX
fcis-31208	143	34	50	50	NUM
fcis-31208	143	35	.	.	PUNCT
fcis-31208	144	1	evaluation	evaluation	NOUN
fcis-31208	144	2	metrics	metric	NOUN
fcis-31208	144	3	,	,	PUNCT
fcis-31208	144	4	including	include	VERB
fcis-31208	144	5	recall	recall	NOUN
fcis-31208	144	6	,	,	PUNCT
fcis-31208	144	7	f1	f1	NOUN
fcis-31208	144	8	-	-	PUNCT
fcis-31208	144	9	score	score	NOUN
fcis-31208	144	10	,	,	PUNCT
fcis-31208	144	11	and	and	CCONJ
fcis-31208	144	12	accuracy	accuracy	NOUN
fcis-31208	144	13	,	,	PUNCT
fcis-31208	144	14	all	all	PRON
fcis-31208	144	15	exceeded	exceed	VERB
fcis-31208	144	16	0.93	0.93	NUM
fcis-31208	144	17	,	,	PUNCT
fcis-31208	144	18	indicating	indicate	VERB
fcis-31208	144	19	good	good	ADJ
fcis-31208	144	20	model	model	NOUN
fcis-31208	144	21	performance	performance	NOUN
fcis-31208	144	22	.	.	PUNCT
fcis-31208	145	1	and	and	CCONJ
fcis-31208	145	2	the	the	DET
fcis-31208	145	3	qubo	qubo	PROPN
fcis-31208	145	4	model	model	NOUN
fcis-31208	145	5	solving	solving	NOUN
fcis-31208	145	6	time	time	NOUN
fcis-31208	145	7	ranged	range	VERB
fcis-31208	145	8	from	from	ADP
fcis-31208	145	9	0.07	0.07	NUM
fcis-31208	145	10	to	to	ADP
fcis-31208	145	11	0.09	0.09	NUM
fcis-31208	145	12	seconds	second	NOUN
fcis-31208	145	13	.	.	PUNCT
fcis-31208	146	1	for	for	ADP
fcis-31208	146	2	model	model	NOUN
fcis-31208	146	3	3	3	NUM
fcis-31208	146	4	,	,	PUNCT
fcis-31208	146	5	we	we	PRON
fcis-31208	146	6	used	use	VERB
fcis-31208	146	7	the	the	DET
fcis-31208	146	8	mnist	mnist	NOUN
fcis-31208	146	9	dataset	dataset	VERB
fcis-31208	146	10	for	for	ADP
fcis-31208	146	11	image	image	NOUN
fcis-31208	146	12	classification	classification	NOUN
fcis-31208	146	13	,	,	PUNCT
fcis-31208	146	14	applying	apply	VERB
fcis-31208	146	15	convolutional	convolutional	ADJ
fcis-31208	146	16	neural	neural	ADJ
fcis-31208	146	17	networks	network	NOUN
fcis-31208	146	18	(	(	PUNCT
fcis-31208	146	19	cnns	cnns	PROPN
fcis-31208	146	20	)	)	PUNCT
fcis-31208	146	21	with	with	ADP
fcis-31208	146	22	relu	relu	NOUN
fcis-31208	146	23	activation	activation	NOUN
fcis-31208	146	24	and	and	CCONJ
fcis-31208	146	25	cross	cross	ADJ
fcis-31208	146	26	-	-	ADJ
fcis-31208	146	27	entropy	entropy	ADJ
fcis-31208	146	28	loss	loss	NOUN
fcis-31208	146	29	.	.	PUNCT
fcis-31208	147	1	we	we	PRON
fcis-31208	147	2	introduced	introduce	VERB
fcis-31208	147	3	penalty	penalty	NOUN
fcis-31208	147	4	coefficients	coefficient	NOUN
fcis-31208	147	5	for	for	ADP
fcis-31208	147	6	the	the	DET
fcis-31208	147	7	convolutional	convolutional	ADJ
fcis-31208	147	8	and	and	CCONJ
fcis-31208	147	9	fully	fully	ADV
fcis-31208	147	10	connected	connected	ADJ
fcis-31208	147	11	layers	layer	NOUN
fcis-31208	147	12	,	,	PUNCT
fcis-31208	147	13	as	as	ADV
fcis-31208	147	14	well	well	ADV
fcis-31208	147	15	as	as	ADP
fcis-31208	147	16	cross	cross	NOUN
fcis-31208	147	17	-	-	NOUN
fcis-31208	147	18	entropy	entropy	ADJ
fcis-31208	147	19	,	,	PUNCT
fcis-31208	147	20	and	and	CCONJ
fcis-31208	147	21	minimized	minimize	VERB
fcis-31208	147	22	the	the	DET
fcis-31208	147	23	loss	loss	NOUN
fcis-31208	147	24	for	for	ADP
fcis-31208	147	25	image	image	NOUN
fcis-31208	147	26	recognition	recognition	NOUN
fcis-31208	147	27	.	.	PUNCT
fcis-31208	148	1	to	to	PART
fcis-31208	148	2	prevent	prevent	VERB
fcis-31208	148	3	overfitting	overfitting	NOUN
fcis-31208	148	4	,	,	PUNCT
fcis-31208	148	5	l2	l2	NOUN
fcis-31208	148	6	regularization	regularization	NOUN
fcis-31208	148	7	was	be	AUX
fcis-31208	148	8	added	add	VERB
fcis-31208	148	9	.	.	PUNCT
fcis-31208	149	1	the	the	DET
fcis-31208	149	2	softmax	softmax	NOUN
fcis-31208	149	3	activation	activation	NOUN
fcis-31208	149	4	function	function	NOUN
fcis-31208	149	5	was	be	AUX
fcis-31208	149	6	used	use	VERB
fcis-31208	149	7	in	in	ADP
fcis-31208	149	8	the	the	DET
fcis-31208	149	9	output	output	NOUN
fcis-31208	149	10	layer	layer	NOUN
fcis-31208	149	11	to	to	PART
fcis-31208	149	12	calculate	calculate	VERB
fcis-31208	149	13	category	category	NOUN
fcis-31208	149	14	probabilities	probability	NOUN
fcis-31208	149	15	.	.	PUNCT
fcis-31208	150	1	evaluation	evaluation	NOUN
fcis-31208	150	2	metrics	metric	NOUN
fcis-31208	150	3	,	,	PUNCT
fcis-31208	150	4	including	include	VERB
fcis-31208	150	5	macro	macro	ADJ
fcis-31208	150	6	avg	avg	NOUN
fcis-31208	150	7	and	and	CCONJ
fcis-31208	150	8	weighted	weight	VERB
fcis-31208	150	9	avg	avg	PROPN
fcis-31208	150	10	recall	recall	PROPN
fcis-31208	150	11	,	,	PUNCT
fcis-31208	150	12	f1	f1	NOUN
fcis-31208	150	13	-	-	PUNCT
fcis-31208	150	14	score	score	NOUN
fcis-31208	150	15	,	,	PUNCT
fcis-31208	150	16	and	and	CCONJ
fcis-31208	150	17	accuracy	accuracy	NOUN
fcis-31208	150	18	,	,	PUNCT
fcis-31208	150	19	all	all	PRON
fcis-31208	150	20	exceeded	exceed	VERB
fcis-31208	150	21	0.98	0.98	NUM
fcis-31208	150	22	,	,	PUNCT
fcis-31208	150	23	with	with	ADP
fcis-31208	150	24	a	a	DET
fcis-31208	150	25	support	support	NOUN
fcis-31208	150	26	of	of	ADP
fcis-31208	150	27	10,000	10,000	NUM
fcis-31208	150	28	,	,	PUNCT
fcis-31208	150	29	indicating	indicate	VERB
fcis-31208	150	30	strong	strong	ADJ
fcis-31208	150	31	model	model	NOUN
fcis-31208	150	32	performance	performance	NOUN
fcis-31208	150	33	.	.	PUNCT
fcis-31208	151	1	as	as	SCONJ
fcis-31208	151	2	qubo	qubo	PROPN
fcis-31208	151	3	model	model	NOUN
fcis-31208	151	4	solving	solving	NOUN
fcis-31208	151	5	time	time	NOUN
fcis-31208	151	6	was	be	AUX
fcis-31208	151	7	around	around	ADV
fcis-31208	151	8	211	211	NUM
fcis-31208	151	9	seconds	second	NOUN
fcis-31208	151	10	.	.	PUNCT
fcis-31208	152	1	this	this	DET
fcis-31208	152	2	paper	paper	NOUN
fcis-31208	152	3	fully	fully	ADV
fcis-31208	152	4	demonstrates	demonstrate	VERB
fcis-31208	152	5	the	the	DET
fcis-31208	152	6	application	application	NOUN
fcis-31208	152	7	of	of	ADP
fcis-31208	152	8	the	the	DET
fcis-31208	152	9	qubo	qubo	NOUN
fcis-31208	152	10	model	model	NOUN
fcis-31208	152	11	in	in	ADP
fcis-31208	152	12	optimizing	optimize	VERB
fcis-31208	152	13	machine	machine	NOUN
fcis-31208	152	14	learning	learning	NOUN
fcis-31208	152	15	algorithms	algorithm	NOUN
fcis-31208	152	16	,	,	PUNCT
fcis-31208	152	17	which	which	PRON
fcis-31208	152	18	can	can	AUX
fcis-31208	152	19	be	be	AUX
fcis-31208	152	20	extended	extend	VERB
fcis-31208	152	21	to	to	ADP
fcis-31208	152	22	other	other	ADJ
fcis-31208	152	23	artificial	artificial	ADJ
fcis-31208	152	24	intelligence	intelligence	NOUN
fcis-31208	152	25	algorithms	algorithm	NOUN
fcis-31208	152	26	and	and	CCONJ
fcis-31208	152	27	represents	represent	VERB
fcis-31208	152	28	a	a	DET
fcis-31208	152	29	future	future	ADJ
fcis-31208	152	30	direction	direction	NOUN
fcis-31208	152	31	for	for	ADP
fcis-31208	152	32	research	research	NOUN
fcis-31208	152	33	.	.	PUNCT
fcis-31208	153	1	acknowledgments	acknowledgment	NOUN
fcis-31208	153	2	the	the	DET
fcis-31208	153	3	authors	author	NOUN
fcis-31208	153	4	gratefully	gratefully	ADV
fcis-31208	153	5	acknowledge	acknowledge	VERB
fcis-31208	153	6	the	the	DET
fcis-31208	153	7	financial	financial	ADJ
fcis-31208	153	8	support	support	NOUN
fcis-31208	153	9	from	from	ADP
fcis-31208	153	10	2024	2024	NUM
fcis-31208	153	11	guangzhou	guangzhou	PROPN
fcis-31208	153	12	university	university	PROPN
fcis-31208	153	13	student	student	NOUN
fcis-31208	153	14	innovation	innovation	NOUN
fcis-31208	153	15	training	training	NOUN
fcis-31208	153	16	project	project	NOUN
fcis-31208	153	17	of	of	ADP
fcis-31208	153	18	ministry	ministry	PROPN
fcis-31208	153	19	of	of	ADP
fcis-31208	153	20	education	education	NOUN
fcis-31208	153	21	of	of	ADP
fcis-31208	153	22	p.r.c	p.r.c	NOUN
fcis-31208	153	23	(	(	PUNCT
fcis-31208	153	24	2024110	2024110	NUM
fcis-31208	153	25	78145	78145	NUM
fcis-31208	153	26	)	)	PUNCT
fcis-31208	153	27	.	.	PUNCT
fcis-31208	154	1	references	reference	NOUN
fcis-31208	154	2	[	[	X
fcis-31208	154	3	1	1	NUM
fcis-31208	154	4	]	]	X
fcis-31208	154	5	wen	wen	PROPN
fcis-31208	154	6	,	,	PUNCT
fcis-31208	154	7	j.	j.	PROPN
fcis-31208	154	8	,	,	PUNCT
fcis-31208	154	9	wang	wang	PROPN
fcis-31208	154	10	,	,	PUNCT
fcis-31208	154	11	z.	z.	PROPN
fcis-31208	154	12	,	,	PUNCT
fcis-31208	154	13	huang	huang	PROPN
fcis-31208	154	14	,	,	PUNCT
fcis-31208	154	15	z.	z.	PROPN
fcis-31208	154	16	optical	optical	ADJ
fcis-31208	154	17	experimental	experimental	ADJ
fcis-31208	154	18	solutions	solution	NOUN
fcis-31208	154	19	for	for	ADP
fcis-31208	154	20	multi	multi	ADJ
fcis-31208	154	21	-	-	ADJ
fcis-31208	154	22	channel	channel	ADJ
fcis-31208	154	23	partitioning	partitioning	NOUN
fcis-31208	154	24	problems	problem	NOUN
fcis-31208	154	25	and	and	CCONJ
fcis-31208	154	26	their	their	PRON
fcis-31208	154	27	applications	application	NOUN
fcis-31208	154	28	in	in	ADP
fcis-31208	154	29	computing	compute	VERB
fcis-31208	154	30	power	power	NOUN
fcis-31208	154	31	scheduling	scheduling	NOUN
fcis-31208	154	32	.	.	PUNCT
fcis-31208	155	1	sci	sci	PROPN
fcis-31208	155	2	.	.	PUNCT
fcis-31208	156	1	china	china	PROPN
fcis-31208	156	2	phys	phys	PROPN
fcis-31208	156	3	.	.	PUNCT
fcis-31208	157	1	mech	mech	PROPN
fcis-31208	157	2	.	.	PUNCT
fcis-31208	158	1	astron.66	astron.66	PROPN
fcis-31208	158	2	,	,	PUNCT
fcis-31208	158	3	290313	290313	NUM
fcis-31208	158	4	(	(	PUNCT
fcis-31208	158	5	2023	2023	NUM
fcis-31208	158	6	)	)	PUNCT
fcis-31208	158	7	.	.	PUNCT
fcis-31208	159	1	[	[	X
fcis-31208	159	2	2	2	NUM
fcis-31208	159	3	]	]	X
fcis-31208	159	4	glover	glover	PROPN
fcis-31208	159	5	,	,	PUNCT
fcis-31208	159	6	f.	f.	PROPN
fcis-31208	159	7	,	,	PUNCT
fcis-31208	159	8	kochenberger	kochenberger	PROPN
fcis-31208	159	9	,	,	PUNCT
fcis-31208	159	10	g.	g.	PROPN
fcis-31208	159	11	,	,	PUNCT
fcis-31208	159	12	hennig	hennig	PROPN
fcis-31208	159	13	,	,	PUNCT
fcis-31208	159	14	r.	r.	PROPN
fcis-31208	159	15	,	,	PUNCT
fcis-31208	159	16	et	et	PROPN
fcis-31208	159	17	al	al	PROPN
fcis-31208	159	18	.	.	PUNCT
fcis-31208	159	19	quantum	quantum	PROPN
fcis-31208	159	20	bridge	bridge	PROPN
fcis-31208	159	21	analysis	analysis	NOUN
fcis-31208	159	22	i	i	PRON
fcis-31208	159	23	:	:	PUNCT
fcis-31208	159	24	a	a	DET
fcis-31208	159	25	tutorial	tutorial	NOUN
fcis-31208	159	26	on	on	ADP
fcis-31208	159	27	formulating	formulate	VERB
fcis-31208	159	28	and	and	CCONJ
fcis-31208	159	29	using	use	VERB
fcis-31208	159	30	qubo	qubo	NOUN
fcis-31208	159	31	models	model	NOUN
fcis-31208	159	32	.	.	PUNCT
fcis-31208	160	1	ann	ann	PROPN
fcis-31208	160	2	opera	opera	PROPN
fcis-31208	160	3	studies	study	NOUN
fcis-31208	160	4	314	314	NUM
fcis-31208	160	5	,	,	PUNCT
fcis-31208	160	6	141–183	141–183	NUM
fcis-31208	160	7	(	(	PUNCT
fcis-31208	160	8	2022	2022	NUM
fcis-31208	160	9	)	)	PUNCT
fcis-31208	160	10	.	.	PUNCT
fcis-31208	161	1	[	[	X
fcis-31208	161	2	3	3	X
fcis-31208	161	3	]	]	X
fcis-31208	161	4	chen	chen	PROPN
fcis-31208	161	5	si	si	PROPN
fcis-31208	161	6	.	.	PROPN
fcis-31208	161	7	based	base	VERB
fcis-31208	161	8	on	on	ADP
fcis-31208	161	9	machine	machine	NOUN
fcis-31208	161	10	learning	learn	VERB
fcis-31208	161	11	research	research	NOUN
fcis-31208	161	12	on	on	ADP
fcis-31208	161	13	electricity	electricity	NOUN
fcis-31208	161	14	demand	demand	NOUN
fcis-31208	161	15	forecasting	forecasting	NOUN
fcis-31208	161	16	and	and	CCONJ
fcis-31208	161	17	inventory	inventory	NOUN
fcis-31208	161	18	decisions	decision	NOUN
fcis-31208	161	19	[	[	X
fcis-31208	161	20	d	d	X
fcis-31208	161	21	]	]	X
fcis-31208	161	22	.	.	PUNCT
fcis-31208	162	1	shenzhen	shenzhen	PROPN
fcis-31208	162	2	university	university	PROPN
fcis-31208	162	3	,	,	PUNCT
fcis-31208	162	4	2021	2021	NUM
fcis-31208	162	5	.	.	PUNCT
fcis-31208	163	1	the	the	DET
fcis-31208	163	2	doi	doi	NOUN
fcis-31208	163	3	:	:	PUNCT
fcis-31208	163	4	10.27321	10.27321	NUM
fcis-31208	163	5	/	/	SYM
fcis-31208	163	6	,	,	PUNCT
fcis-31208	163	7	dc	dc	PROPN
fcis-31208	163	8	nki	nki	PROPN
fcis-31208	163	9	.	.	PUNCT
fcis-31208	163	10	gszdu	gszdu	PROPN
fcis-31208	163	11	.	.	PUNCT
fcis-31208	164	1	2020	2020	NUM
fcis-31208	164	2	.	.	PUNCT
fcis-31208	165	1	000186	000186	NUM
fcis-31208	165	2	.	.	PUNCT
fcis-31208	166	1	[	[	X
fcis-31208	166	2	4	4	X
fcis-31208	166	3	]	]	X
fcis-31208	166	4	chang	chang	PROPN
fcis-31208	166	5	yunfeng	yunfeng	PROPN
fcis-31208	166	6	.	.	PUNCT
fcis-31208	166	7	phase	phase	NOUN
fcis-31208	166	8	transition	transition	NOUN
fcis-31208	166	9	and	and	CCONJ
fcis-31208	166	10	transmission	transmission	NOUN
fcis-31208	166	11	dynamics	dynamic	NOUN
fcis-31208	166	12	of	of	ADP
fcis-31208	166	13	ising	ise	VERB
fcis-31208	166	14	model	model	NOUN
fcis-31208	166	15	on	on	ADP
fcis-31208	166	16	complex	complex	ADJ
fcis-31208	166	17	networks	network	NOUN
fcis-31208	166	18	[	[	X
fcis-31208	166	19	d	d	X
fcis-31208	166	20	]	]	X
fcis-31208	166	21	.	.	PUNCT
fcis-31208	167	1	huazhong	huazhong	PROPN
fcis-31208	167	2	normal	normal	ADJ
fcis-31208	167	3	university,2008	university,2008	NOUN
fcis-31208	167	4	.	.	PUNCT
fcis-31208	168	1	[	[	X
fcis-31208	168	2	5	5	NUM
fcis-31208	168	3	]	]	PUNCT
fcis-31208	168	4	qi	qi	PROPN
fcis-31208	168	5	hengnian	hengnian	PROPN
fcis-31208	168	6	.	.	PUNCT
fcis-31208	169	1	overview	overview	NOUN
fcis-31208	169	2	of	of	ADP
fcis-31208	169	3	support	support	NOUN
fcis-31208	169	4	vector	vector	NOUN
fcis-31208	169	5	machines	machine	NOUN
fcis-31208	169	6	and	and	CCONJ
fcis-31208	169	7	their	their	PRON
fcis-31208	169	8	applications	application	NOUN
fcis-31208	170	1	[	[	X
fcis-31208	170	2	j	j	X
fcis-31208	170	3	]	]	X
fcis-31208	170	4	.	.	PUNCT
fcis-31208	171	1	computer	computer	NOUN
fcis-31208	171	2	engineering	engineering	NOUN
fcis-31208	171	3	,	,	PUNCT
fcis-31208	171	4	2004	2004	NUM
fcis-31208	171	5	,	,	PUNCT
fcis-31208	171	6	(	(	PUNCT
fcis-31208	171	7	10):6	10):6	NUM
fcis-31208	171	8	-	-	SYM
fcis-31208	171	9	9	9	NUM
fcis-31208	171	10	.	.	PUNCT
fcis-31208	172	1	[	[	X
fcis-31208	172	2	6	6	NUM
fcis-31208	172	3	]	]	PUNCT
fcis-31208	172	4	xu	xu	PROPN
fcis-31208	172	5	ke	ke	PROPN
fcis-31208	172	6	.	.	PUNCT
fcis-31208	173	1	research	research	NOUN
fcis-31208	173	2	on	on	ADP
fcis-31208	173	3	the	the	DET
fcis-31208	173	4	application	application	NOUN
fcis-31208	173	5	of	of	ADP
fcis-31208	173	6	convolutional	convolutional	ADJ
fcis-31208	173	7	neural	neural	ADJ
fcis-31208	173	8	networks	network	NOUN
fcis-31208	173	9	in	in	ADP
fcis-31208	173	10	image	image	NOUN
fcis-31208	173	11	recognition	recognition	NOUN
fcis-31208	174	1	[	[	X
fcis-31208	174	2	d	d	X
fcis-31208	174	3	]	]	X
fcis-31208	174	4	.	.	PUNCT
fcis-31208	175	1	zhejiang	zhejiang	PROPN
fcis-31208	175	2	university,2012	university,2012	PROPN
fcis-31208	175	3	.	.	PUNCT
fcis-31208	176	1	[	[	X
fcis-31208	176	2	7	7	X
fcis-31208	176	3	]	]	X
fcis-31208	176	4	zhang	zhang	PROPN
fcis-31208	176	5	xiaorong	xiaorong	PROPN
fcis-31208	176	6	.	.	PUNCT
fcis-31208	177	1	research	research	NOUN
fcis-31208	177	2	on	on	ADP
fcis-31208	177	3	deep	deep	ADJ
fcis-31208	177	4	learning	learning	NOUN
fcis-31208	177	5	algorithm	algorithm	NOUN
fcis-31208	177	6	based	base	VERB
fcis-31208	177	7	on	on	ADP
fcis-31208	177	8	convolutional	convolutional	ADJ
fcis-31208	177	9	neural	neural	ADJ
fcis-31208	177	10	network	network	NOUN
fcis-31208	177	11	and	and	CCONJ
fcis-31208	177	12	its	its	PRON
fcis-31208	177	13	application[d	application[d	NOUN
fcis-31208	177	14	]	]	PUNCT
fcis-31208	177	15	.	.	PUNCT
fcis-31208	178	1	xidian	xidian	PROPN
fcis-31208	178	2	university	university	PROPN
fcis-31208	178	3	,	,	PUNCT
fcis-31208	178	4	2015	2015	NUM
fcis-31208	178	5	.	.	PUNCT
fcis-31208	179	1	[	[	X
fcis-31208	179	2	8	8	NUM
fcis-31208	179	3	]	]	X
fcis-31208	179	4	chen	chen	PROPN
fcis-31208	179	5	hongxiang	hongxiang	PROPN
fcis-31208	179	6	.	.	PUNCT
fcis-31208	180	1	semantic	semantic	ADJ
fcis-31208	180	2	image	image	NOUN
fcis-31208	180	3	segmentation	segmentation	NOUN
fcis-31208	180	4	based	base	VERB
fcis-31208	180	5	on	on	ADP
fcis-31208	180	6	convolutional	convolutional	ADJ
fcis-31208	180	7	neural	neural	ADJ
fcis-31208	180	8	networks	network	NOUN
fcis-31208	181	1	[	[	X
fcis-31208	181	2	d	d	X
fcis-31208	181	3	]	]	X
fcis-31208	181	4	.	.	PUNCT
fcis-31208	182	1	zhejiang	zhejiang	PROPN
fcis-31208	182	2	university	university	PROPN
fcis-31208	182	3	,	,	PUNCT
fcis-31208	182	4	2016	2016	NUM
fcis-31208	182	5	.	.	PUNCT
fcis-31208	183	1	[	[	X
fcis-31208	183	2	9	9	NUM
fcis-31208	183	3	]	]	PUNCT
fcis-31208	183	4	yang	yang	PROPN
fcis-31208	183	5	juyu	juyu	PROPN
fcis-31208	183	6	.	.	PUNCT
fcis-31208	184	1	research	research	NOUN
fcis-31208	184	2	and	and	CCONJ
fcis-31208	184	3	implementation	implementation	NOUN
fcis-31208	184	4	of	of	ADP
fcis-31208	184	5	object	object	NOUN
fcis-31208	184	6	recognition	recognition	NOUN
fcis-31208	184	7	based	base	VERB
fcis-31208	184	8	on	on	ADP
fcis-31208	184	9	convolutional	convolutional	ADJ
fcis-31208	184	10	neural	neural	ADJ
fcis-31208	184	11	networks	network	NOUN
fcis-31208	185	1	[	[	X
fcis-31208	185	2	d	d	X
fcis-31208	185	3	]	]	X
fcis-31208	185	4	.	.	PUNCT
fcis-31208	186	1	university	university	NOUN
fcis-31208	186	2	of	of	ADP
fcis-31208	186	3	electronic	electronic	ADJ
fcis-31208	186	4	science	science	NOUN
fcis-31208	186	5	and	and	CCONJ
fcis-31208	186	6	technology	technology	NOUN
fcis-31208	186	7	of	of	ADP
fcis-31208	186	8	china	china	PROPN
fcis-31208	186	9	,	,	PUNCT
fcis-31208	186	10	2016	2016	NUM
fcis-31208	186	11	.	.	PUNCT