id	sid	tid	token	lemma	pos
app01-8104	1	1	acta	acta	PROPN
app01-8104	1	2	polytechnica	polytechnica	PROPN
app01-8104	1	3	ctu	ctu	NOUN
app01-8104	1	4	proceedings	proceeding	NOUN
app01-8104	1	5	https://doi.org/10.14311/app.2022.34.0032	https://doi.org/10.14311/app.2022.34.0032	PROPN
app01-8104	1	6	acta	acta	PROPN
app01-8104	1	7	polytechnica	polytechnica	PROPN
app01-8104	1	8	ctu	ctu	NOUN
app01-8104	1	9	proceedings	proceeding	NOUN
app01-8104	1	10	34:32–37	34:32–37	NUM
app01-8104	1	11	,	,	PUNCT
app01-8104	1	12	2022	2022	NUM
app01-8104	1	13	©	©	ADP
app01-8104	1	14	2022	2022	NUM
app01-8104	1	15	the	the	DET
app01-8104	1	16	author(s	author(s	NOUN
app01-8104	1	17	)	)	PUNCT
app01-8104	1	18	.	.	PUNCT
app01-8104	2	1	licensed	license	VERB
app01-8104	2	2	under	under	ADP
app01-8104	2	3	a	a	DET
app01-8104	2	4	cc	cc	NOUN
app01-8104	2	5	-	-	PUNCT
app01-8104	2	6	by	by	ADP
app01-8104	2	7	4.0	4.0	NUM
app01-8104	2	8	licence	licence	NOUN
app01-8104	2	9	published	publish	VERB
app01-8104	2	10	by	by	ADP
app01-8104	2	11	the	the	DET
app01-8104	2	12	czech	czech	PROPN
app01-8104	2	13	technical	technical	PROPN
app01-8104	2	14	university	university	PROPN
app01-8104	2	15	in	in	ADP
app01-8104	2	16	prague	prague	NOUN
app01-8104	2	17	microstructure	microstructure	ADJ
app01-8104	2	18	reconstruction	reconstruction	NOUN
app01-8104	2	19	via	via	ADP
app01-8104	2	20	artificial	artificial	ADJ
app01-8104	2	21	neural	neural	ADJ
app01-8104	2	22	networks	network	NOUN
app01-8104	2	23	:	:	PUNCT
app01-8104	2	24	a	a	DET
app01-8104	2	25	combination	combination	NOUN
app01-8104	2	26	of	of	ADP
app01-8104	2	27	causal	causal	ADJ
app01-8104	2	28	and	and	CCONJ
app01-8104	2	29	non	non	ADJ
app01-8104	2	30	-	-	ADJ
app01-8104	2	31	causal	causal	ADJ
app01-8104	2	32	approach	approach	NOUN
app01-8104	2	33	kryštof	kryštof	PROPN
app01-8104	2	34	latkaa,∗	latkaa,∗	PROPN
app01-8104	2	35	,	,	PUNCT
app01-8104	2	36	martin	martin	PROPN
app01-8104	2	37	doškářb	doškářb	PROPN
app01-8104	2	38	,	,	PUNCT
app01-8104	2	39	jan	jan	PROPN
app01-8104	2	40	zemanb	zemanb	PROPN
app01-8104	2	41	a	a	DET
app01-8104	2	42	gymmázium	gymmázium	NOUN
app01-8104	2	43	nový	nový	ADJ
app01-8104	2	44	porg	porg	NOUN
app01-8104	2	45	,	,	PUNCT
app01-8104	2	46	pod	pod	NOUN
app01-8104	2	47	krčským	krčským	PROPN
app01-8104	2	48	lesem	lesem	NOUN
app01-8104	2	49	25	25	NUM
app01-8104	2	50	,	,	PUNCT
app01-8104	2	51	142	142	NUM
app01-8104	2	52	00	00	NUM
app01-8104	2	53	prague	prague	PROPN
app01-8104	2	54	4	4	NUM
app01-8104	2	55	,	,	PUNCT
app01-8104	2	56	czech	czech	PROPN
app01-8104	2	57	republic	republic	PROPN
app01-8104	2	58	b	b	PROPN
app01-8104	2	59	czech	czech	PROPN
app01-8104	2	60	technical	technical	PROPN
app01-8104	2	61	university	university	PROPN
app01-8104	2	62	in	in	ADP
app01-8104	2	63	prague	prague	PROPN
app01-8104	2	64	,	,	PUNCT
app01-8104	2	65	faculty	faculty	NOUN
app01-8104	2	66	of	of	ADP
app01-8104	2	67	civil	civil	ADJ
app01-8104	2	68	engineering	engineering	NOUN
app01-8104	2	69	,	,	PUNCT
app01-8104	2	70	department	department	NOUN
app01-8104	2	71	of	of	ADP
app01-8104	2	72	mechanics	mechanic	NOUN
app01-8104	2	73	,	,	PUNCT
app01-8104	2	74	thákurova	thákurova	X
app01-8104	2	75	7	7	NUM
app01-8104	2	76	,	,	PUNCT
app01-8104	2	77	166	166	NUM
app01-8104	2	78	29	29	NUM
app01-8104	2	79	prague	prague	NOUN
app01-8104	2	80	6	6	NUM
app01-8104	2	81	,	,	PUNCT
app01-8104	2	82	czech	czech	PROPN
app01-8104	2	83	republic	republic	NOUN
app01-8104	2	84	∗	∗	NOUN
app01-8104	2	85	corresponding	correspond	VERB
app01-8104	2	86	author	author	NOUN
app01-8104	2	87	:	:	PUNCT
app01-8104	2	88	latka@novyporg.cz	latka@novyporg.cz	ADJ
app01-8104	2	89	abstract	abstract	NOUN
app01-8104	2	90	.	.	PUNCT
app01-8104	3	1	we	we	PRON
app01-8104	3	2	investigate	investigate	VERB
app01-8104	3	3	the	the	DET
app01-8104	3	4	applicability	applicability	NOUN
app01-8104	3	5	of	of	ADP
app01-8104	3	6	artificial	artificial	ADJ
app01-8104	3	7	neural	neural	ADJ
app01-8104	3	8	networks	network	NOUN
app01-8104	3	9	(	(	PUNCT
app01-8104	3	10	anns	anns	PROPN
app01-8104	3	11	)	)	PUNCT
app01-8104	3	12	in	in	ADP
app01-8104	3	13	reconstructing	reconstruct	VERB
app01-8104	3	14	a	a	DET
app01-8104	3	15	sample	sample	NOUN
app01-8104	3	16	image	image	NOUN
app01-8104	3	17	of	of	ADP
app01-8104	3	18	a	a	DET
app01-8104	3	19	sponge	sponge	NOUN
app01-8104	3	20	-	-	PUNCT
app01-8104	3	21	like	like	ADJ
app01-8104	3	22	microstructure	microstructure	NOUN
app01-8104	3	23	.	.	PUNCT
app01-8104	4	1	we	we	PRON
app01-8104	4	2	propose	propose	VERB
app01-8104	4	3	to	to	PART
app01-8104	4	4	reconstruct	reconstruct	VERB
app01-8104	4	5	the	the	DET
app01-8104	4	6	image	image	NOUN
app01-8104	4	7	by	by	ADP
app01-8104	4	8	predicting	predict	VERB
app01-8104	4	9	the	the	DET
app01-8104	4	10	phase	phase	NOUN
app01-8104	4	11	of	of	ADP
app01-8104	4	12	the	the	DET
app01-8104	4	13	current	current	ADJ
app01-8104	4	14	pixel	pixel	NOUN
app01-8104	4	15	based	base	VERB
app01-8104	4	16	on	on	ADP
app01-8104	4	17	its	its	PRON
app01-8104	4	18	causal	causal	ADJ
app01-8104	4	19	neighbourhood	neighbourhood	NOUN
app01-8104	4	20	,	,	PUNCT
app01-8104	4	21	and	and	CCONJ
app01-8104	4	22	subsequently	subsequently	ADV
app01-8104	4	23	,	,	PUNCT
app01-8104	4	24	use	use	VERB
app01-8104	4	25	a	a	DET
app01-8104	4	26	non	non	ADJ
app01-8104	4	27	-	-	ADJ
app01-8104	4	28	causal	causal	ADJ
app01-8104	4	29	ann	ann	PROPN
app01-8104	4	30	model	model	NOUN
app01-8104	4	31	to	to	PART
app01-8104	4	32	smooth	smooth	VERB
app01-8104	4	33	out	out	ADP
app01-8104	4	34	the	the	DET
app01-8104	4	35	reconstructed	reconstructed	ADJ
app01-8104	4	36	image	image	NOUN
app01-8104	4	37	as	as	ADP
app01-8104	4	38	a	a	DET
app01-8104	4	39	form	form	NOUN
app01-8104	4	40	of	of	ADP
app01-8104	4	41	post	post	ADJ
app01-8104	4	42	-	-	ADJ
app01-8104	4	43	processing	processing	NOUN
app01-8104	4	44	.	.	PUNCT
app01-8104	5	1	we	we	PRON
app01-8104	5	2	also	also	ADV
app01-8104	5	3	consider	consider	VERB
app01-8104	5	4	the	the	DET
app01-8104	5	5	impacts	impact	NOUN
app01-8104	5	6	of	of	ADP
app01-8104	5	7	different	different	ADJ
app01-8104	5	8	configurations	configuration	NOUN
app01-8104	5	9	of	of	ADP
app01-8104	5	10	the	the	DET
app01-8104	5	11	ann	ann	PROPN
app01-8104	5	12	model	model	NOUN
app01-8104	5	13	(	(	PUNCT
app01-8104	5	14	e.g.	e.g.	ADV
app01-8104	5	15	,	,	PUNCT
app01-8104	5	16	the	the	DET
app01-8104	5	17	number	number	NOUN
app01-8104	5	18	of	of	ADP
app01-8104	5	19	densely	densely	ADV
app01-8104	5	20	connected	connected	ADJ
app01-8104	5	21	layers	layer	NOUN
app01-8104	5	22	,	,	PUNCT
app01-8104	5	23	the	the	DET
app01-8104	5	24	number	number	NOUN
app01-8104	5	25	of	of	ADP
app01-8104	5	26	neurons	neuron	NOUN
app01-8104	5	27	in	in	ADP
app01-8104	5	28	each	each	DET
app01-8104	5	29	layer	layer	NOUN
app01-8104	5	30	,	,	PUNCT
app01-8104	5	31	the	the	DET
app01-8104	5	32	size	size	NOUN
app01-8104	5	33	of	of	ADP
app01-8104	5	34	both	both	CCONJ
app01-8104	5	35	the	the	DET
app01-8104	5	36	causal	causal	ADJ
app01-8104	5	37	and	and	CCONJ
app01-8104	5	38	non	non	ADJ
app01-8104	5	39	-	-	ADJ
app01-8104	5	40	causal	causal	ADJ
app01-8104	5	41	neighbourhood	neighbourhood	NOUN
app01-8104	5	42	)	)	PUNCT
app01-8104	5	43	on	on	ADP
app01-8104	5	44	the	the	DET
app01-8104	5	45	models	model	NOUN
app01-8104	5	46	’	'	PUNCT
app01-8104	5	47	predictive	predictive	ADJ
app01-8104	5	48	abilities	ability	NOUN
app01-8104	5	49	quantified	quantify	VERB
app01-8104	5	50	by	by	ADP
app01-8104	5	51	the	the	DET
app01-8104	5	52	discrepancy	discrepancy	NOUN
app01-8104	5	53	between	between	ADP
app01-8104	5	54	the	the	DET
app01-8104	5	55	spatial	spatial	ADJ
app01-8104	5	56	statistics	statistic	NOUN
app01-8104	5	57	of	of	ADP
app01-8104	5	58	the	the	DET
app01-8104	5	59	reference	reference	NOUN
app01-8104	5	60	and	and	CCONJ
app01-8104	5	61	the	the	DET
app01-8104	5	62	reconstructed	reconstructed	ADJ
app01-8104	5	63	sample	sample	NOUN
app01-8104	5	64	.	.	PUNCT
app01-8104	6	1	keywords	keyword	NOUN
app01-8104	6	2	:	:	PUNCT
app01-8104	6	3	microstructure	microstructure	ADJ
app01-8104	6	4	reconstruction	reconstruction	NOUN
app01-8104	6	5	,	,	PUNCT
app01-8104	6	6	neural	neural	ADJ
app01-8104	6	7	network	network	NOUN
app01-8104	6	8	,	,	PUNCT
app01-8104	6	9	causal	causal	ADJ
app01-8104	6	10	neighbourhood	neighbourhood	NOUN
app01-8104	6	11	,	,	PUNCT
app01-8104	6	12	non	non	ADJ
app01-8104	6	13	-	-	ADJ
app01-8104	6	14	causal	causal	ADJ
app01-8104	6	15	neighbourhood	neighbourhood	NOUN
app01-8104	6	16	.	.	PUNCT
app01-8104	7	1	1	1	X
app01-8104	7	2	.	.	X
app01-8104	7	3	introduction	introduction	NOUN
app01-8104	7	4	multi	multi	ADJ
app01-8104	7	5	-	-	NOUN
app01-8104	7	6	scale	scale	ADJ
app01-8104	7	7	modelling	modelling	NOUN
app01-8104	7	8	is	be	AUX
app01-8104	7	9	a	a	DET
app01-8104	7	10	powerful	powerful	ADJ
app01-8104	7	11	predictive	predictive	ADJ
app01-8104	7	12	tool	tool	NOUN
app01-8104	7	13	that	that	PRON
app01-8104	7	14	bypasses	bypass	VERB
app01-8104	7	15	the	the	DET
app01-8104	7	16	need	need	NOUN
app01-8104	7	17	for	for	ADP
app01-8104	7	18	complex	complex	ADJ
app01-8104	7	19	constitutive	constitutive	ADJ
app01-8104	7	20	laws	law	NOUN
app01-8104	7	21	by	by	ADP
app01-8104	7	22	performing	perform	VERB
app01-8104	7	23	auxiliary	auxiliary	ADJ
app01-8104	7	24	calculations	calculation	NOUN
app01-8104	7	25	at	at	ADP
app01-8104	7	26	lower	low	ADJ
app01-8104	7	27	scales	scale	NOUN
app01-8104	7	28	,	,	PUNCT
app01-8104	7	29	using	use	VERB
app01-8104	7	30	a	a	DET
app01-8104	7	31	characteristic	characteristic	ADJ
app01-8104	7	32	sample	sample	NOUN
app01-8104	7	33	of	of	ADP
app01-8104	7	34	a	a	DET
app01-8104	7	35	material	material	NOUN
app01-8104	7	36	microstructure	microstructure	NOUN
app01-8104	7	37	[	[	X
app01-8104	7	38	1	1	NUM
app01-8104	7	39	]	]	PUNCT
app01-8104	7	40	.	.	PUNCT
app01-8104	8	1	such	such	DET
app01-8104	8	2	a	a	DET
app01-8104	8	3	sample	sample	NOUN
app01-8104	8	4	can	can	AUX
app01-8104	8	5	be	be	AUX
app01-8104	8	6	easily	easily	ADV
app01-8104	8	7	extracted	extract	VERB
app01-8104	8	8	when	when	SCONJ
app01-8104	8	9	analysing	analyse	VERB
app01-8104	8	10	a	a	DET
app01-8104	8	11	material	material	NOUN
app01-8104	8	12	with	with	ADP
app01-8104	8	13	regular	regular	ADJ
app01-8104	8	14	,	,	PUNCT
app01-8104	8	15	periodic	periodic	ADJ
app01-8104	8	16	arrangement	arrangement	NOUN
app01-8104	8	17	of	of	ADP
app01-8104	8	18	material	material	NOUN
app01-8104	8	19	’s	’s	PART
app01-8104	8	20	phases	phase	NOUN
app01-8104	8	21	;	;	PUNCT
app01-8104	8	22	in	in	ADP
app01-8104	8	23	case	case	NOUN
app01-8104	8	24	of	of	ADP
app01-8104	8	25	a	a	DET
app01-8104	8	26	material	material	NOUN
app01-8104	8	27	with	with	ADP
app01-8104	8	28	stochastic	stochastic	ADJ
app01-8104	8	29	microstructure	microstructure	NOUN
app01-8104	8	30	,	,	PUNCT
app01-8104	8	31	the	the	DET
app01-8104	8	32	representative	representative	ADJ
app01-8104	8	33	sample	sample	NOUN
app01-8104	8	34	is	be	AUX
app01-8104	8	35	typically	typically	ADV
app01-8104	8	36	constructed	construct	VERB
app01-8104	8	37	artificially	artificially	ADV
app01-8104	8	38	such	such	ADJ
app01-8104	8	39	that	that	SCONJ
app01-8104	8	40	it	it	PRON
app01-8104	8	41	matches	match	VERB
app01-8104	8	42	selected	select	VERB
app01-8104	8	43	spatial	spatial	ADJ
app01-8104	8	44	statistics	statistic	NOUN
app01-8104	8	45	of	of	ADP
app01-8104	8	46	the	the	DET
app01-8104	8	47	material	material	NOUN
app01-8104	8	48	microstructure	microstructure	NOUN
app01-8104	9	1	[	[	X
app01-8104	9	2	2	2	NUM
app01-8104	9	3	]	]	PUNCT
app01-8104	9	4	.	.	PUNCT
app01-8104	10	1	while	while	SCONJ
app01-8104	10	2	originally	originally	ADV
app01-8104	10	3	the	the	DET
app01-8104	10	4	representative	representative	ADJ
app01-8104	10	5	samples	sample	NOUN
app01-8104	10	6	were	be	AUX
app01-8104	10	7	generated	generate	VERB
app01-8104	10	8	via	via	ADP
app01-8104	10	9	optimization	optimization	NOUN
app01-8104	10	10	approaches	approach	NOUN
app01-8104	10	11	,	,	PUNCT
app01-8104	10	12	e.g.	e.g.	ADV
app01-8104	10	13	[	[	X
app01-8104	10	14	3	3	NUM
app01-8104	10	15	]	]	PUNCT
app01-8104	10	16	,	,	PUNCT
app01-8104	10	17	recently	recently	ADV
app01-8104	10	18	,	,	PUNCT
app01-8104	10	19	reconstruction	reconstruction	NOUN
app01-8104	10	20	methods	method	NOUN
app01-8104	10	21	relying	rely	VERB
app01-8104	10	22	on	on	ADP
app01-8104	10	23	machine	machine	NOUN
app01-8104	10	24	learning	learning	NOUN
app01-8104	10	25	have	have	AUX
app01-8104	10	26	started	start	VERB
app01-8104	10	27	to	to	PART
app01-8104	10	28	emerge	emerge	VERB
app01-8104	10	29	,	,	PUNCT
app01-8104	10	30	using	use	VERB
app01-8104	10	31	various	various	ADJ
app01-8104	10	32	frameworks	framework	NOUN
app01-8104	10	33	including	include	VERB
app01-8104	10	34	markov	markov	NOUN
app01-8104	10	35	random	random	ADJ
app01-8104	10	36	fields	field	NOUN
app01-8104	10	37	[	[	X
app01-8104	10	38	4	4	NUM
app01-8104	10	39	]	]	PUNCT
app01-8104	10	40	,	,	PUNCT
app01-8104	10	41	deep	deep	ADJ
app01-8104	10	42	adversarial	adversarial	ADJ
app01-8104	10	43	neural	neural	ADJ
app01-8104	10	44	networks	network	NOUN
app01-8104	10	45	[	[	X
app01-8104	10	46	5	5	NUM
app01-8104	10	47	,	,	PUNCT
app01-8104	10	48	6	6	NUM
app01-8104	10	49	]	]	PUNCT
app01-8104	10	50	,	,	PUNCT
app01-8104	10	51	or	or	CCONJ
app01-8104	10	52	supervised	supervise	VERB
app01-8104	10	53	learning	learning	NOUN
app01-8104	10	54	using	use	VERB
app01-8104	10	55	classification	classification	NOUN
app01-8104	10	56	trees	tree	NOUN
app01-8104	10	57	[	[	X
app01-8104	10	58	7	7	NUM
app01-8104	10	59	]	]	PUNCT
app01-8104	10	60	.	.	PUNCT
app01-8104	11	1	several	several	ADJ
app01-8104	11	2	papers	paper	NOUN
app01-8104	11	3	include	include	VERB
app01-8104	11	4	references	reference	NOUN
app01-8104	11	5	to	to	ADP
app01-8104	11	6	causal	causal	ADJ
app01-8104	11	7	and	and	CCONJ
app01-8104	11	8	non	non	ADJ
app01-8104	11	9	-	-	ADJ
app01-8104	11	10	causal	causal	ADJ
app01-8104	11	11	neighbourhood	neighbourhood	NOUN
app01-8104	11	12	which	which	PRON
app01-8104	11	13	both	both	PRON
app01-8104	11	14	prove	prove	VERB
app01-8104	11	15	to	to	PART
app01-8104	11	16	be	be	AUX
app01-8104	11	17	effective	effective	ADJ
app01-8104	11	18	ways	way	NOUN
app01-8104	11	19	of	of	ADP
app01-8104	11	20	extracting	extract	VERB
app01-8104	11	21	input	input	NOUN
app01-8104	11	22	data	datum	NOUN
app01-8104	11	23	for	for	ADP
app01-8104	11	24	the	the	DET
app01-8104	11	25	chosen	choose	VERB
app01-8104	11	26	framework	framework	NOUN
app01-8104	11	27	.	.	PUNCT
app01-8104	12	1	however	however	ADV
app01-8104	12	2	,	,	PUNCT
app01-8104	12	3	the	the	DET
app01-8104	12	4	causal	causal	ADJ
app01-8104	12	5	approach	approach	NOUN
app01-8104	12	6	generally	generally	ADV
app01-8104	12	7	seems	seem	VERB
app01-8104	12	8	to	to	PART
app01-8104	12	9	be	be	AUX
app01-8104	12	10	the	the	DET
app01-8104	12	11	preferred	preferred	ADJ
app01-8104	12	12	one	one	NOUN
app01-8104	12	13	,	,	PUNCT
app01-8104	12	14	with	with	ADP
app01-8104	12	15	one	one	NUM
app01-8104	12	16	of	of	ADP
app01-8104	12	17	these	these	DET
app01-8104	12	18	papers	paper	NOUN
app01-8104	12	19	even	even	ADV
app01-8104	12	20	claiming	claim	VERB
app01-8104	12	21	that	that	SCONJ
app01-8104	12	22	their	their	PRON
app01-8104	12	23	model	model	NOUN
app01-8104	12	24	can	can	AUX
app01-8104	12	25	not	not	PART
app01-8104	12	26	generate	generate	VERB
app01-8104	12	27	valid	valid	ADJ
app01-8104	12	28	results	result	NOUN
app01-8104	12	29	if	if	SCONJ
app01-8104	12	30	based	base	VERB
app01-8104	12	31	on	on	ADP
app01-8104	12	32	a	a	DET
app01-8104	12	33	non	non	ADJ
app01-8104	12	34	-	-	ADJ
app01-8104	12	35	causal	causal	ADJ
app01-8104	12	36	neighbourhood	neighbourhood	NOUN
app01-8104	13	1	[	[	X
app01-8104	13	2	4	4	NUM
app01-8104	13	3	]	]	PUNCT
app01-8104	13	4	.	.	PUNCT
app01-8104	14	1	the	the	DET
app01-8104	14	2	objective	objective	NOUN
app01-8104	14	3	of	of	ADP
app01-8104	14	4	our	our	PRON
app01-8104	14	5	work	work	NOUN
app01-8104	14	6	is	be	AUX
app01-8104	14	7	to	to	PART
app01-8104	14	8	reconstruct	reconstruct	VERB
app01-8104	14	9	an	an	DET
app01-8104	14	10	image	image	NOUN
app01-8104	14	11	of	of	ADP
app01-8104	14	12	a	a	DET
app01-8104	14	13	microstructure	microstructure	NOUN
app01-8104	14	14	from	from	ADP
app01-8104	14	15	an	an	DET
app01-8104	14	16	almost	almost	ADV
app01-8104	14	17	random	random	ADJ
app01-8104	14	18	noise	noise	NOUN
app01-8104	14	19	with	with	ADP
app01-8104	14	20	microscopic	microscopic	ADJ
app01-8104	14	21	properties	property	NOUN
app01-8104	14	22	as	as	ADV
app01-8104	14	23	similar	similar	ADJ
app01-8104	14	24	as	as	ADP
app01-8104	14	25	possible	possible	ADJ
app01-8104	14	26	to	to	ADP
app01-8104	14	27	the	the	DET
app01-8104	14	28	original	original	ADJ
app01-8104	14	29	image	image	NOUN
app01-8104	14	30	.	.	PUNCT
app01-8104	15	1	while	while	SCONJ
app01-8104	15	2	many	many	ADJ
app01-8104	15	3	of	of	ADP
app01-8104	15	4	the	the	DET
app01-8104	15	5	aforementioned	aforementioned	ADJ
app01-8104	15	6	proposed	propose	VERB
app01-8104	15	7	approaches	approach	NOUN
app01-8104	15	8	are	be	AUX
app01-8104	15	9	implementation	implementation	NOUN
app01-8104	15	10	-	-	PUNCT
app01-8104	15	11	complex	complex	NOUN
app01-8104	15	12	,	,	PUNCT
app01-8104	15	13	we	we	PRON
app01-8104	15	14	present	present	VERB
app01-8104	15	15	a	a	DET
app01-8104	15	16	simple	simple	ADJ
app01-8104	15	17	method	method	NOUN
app01-8104	15	18	using	use	VERB
app01-8104	15	19	the	the	DET
app01-8104	15	20	tensorflow	tensorflow	NOUN
app01-8104	15	21	framework	framework	NOUN
app01-8104	15	22	with	with	ADP
app01-8104	15	23	the	the	DET
app01-8104	15	24	keras	keras	PROPN
app01-8104	15	25	sequential	sequential	ADJ
app01-8104	15	26	api	api	NOUN
app01-8104	16	1	[	[	X
app01-8104	16	2	8	8	NUM
app01-8104	16	3	]	]	PUNCT
app01-8104	16	4	.	.	PUNCT
app01-8104	17	1	we	we	PRON
app01-8104	17	2	closely	closely	ADV
app01-8104	17	3	follow	follow	VERB
app01-8104	17	4	the	the	DET
app01-8104	17	5	methodology	methodology	NOUN
app01-8104	17	6	of	of	ADP
app01-8104	17	7	bostanabad	bostanabad	NOUN
app01-8104	17	8	and	and	CCONJ
app01-8104	17	9	coworkers	coworker	NOUN
app01-8104	17	10	,	,	PUNCT
app01-8104	17	11	[	[	X
app01-8104	17	12	7	7	NUM
app01-8104	17	13	]	]	X
app01-8104	17	14	;	;	PUNCT
app01-8104	17	15	however	however	ADV
app01-8104	17	16	,	,	PUNCT
app01-8104	17	17	instead	instead	ADV
app01-8104	17	18	of	of	ADP
app01-8104	17	19	using	use	VERB
app01-8104	17	20	classification	classification	NOUN
app01-8104	17	21	trees	tree	NOUN
app01-8104	17	22	,	,	PUNCT
app01-8104	17	23	we	we	PRON
app01-8104	17	24	use	use	VERB
app01-8104	17	25	two	two	NUM
app01-8104	17	26	distinct	distinct	ADJ
app01-8104	17	27	artificial	artificial	ADJ
app01-8104	17	28	neural	neural	ADJ
app01-8104	17	29	networks	network	NOUN
app01-8104	17	30	(	(	PUNCT
app01-8104	17	31	anns	anns	PROPN
app01-8104	17	32	)	)	PUNCT
app01-8104	17	33	,	,	PUNCT
app01-8104	17	34	where	where	SCONJ
app01-8104	17	35	the	the	DET
app01-8104	17	36	first	first	ADJ
app01-8104	17	37	network	network	NOUN
app01-8104	17	38	reconstructs	reconstruct	VERB
app01-8104	17	39	the	the	DET
app01-8104	17	40	general	general	ADJ
app01-8104	17	41	pattern	pattern	NOUN
app01-8104	17	42	of	of	ADP
app01-8104	17	43	the	the	DET
app01-8104	17	44	microstructure	microstructure	NOUN
app01-8104	17	45	and	and	CCONJ
app01-8104	17	46	the	the	DET
app01-8104	17	47	second	second	ADJ
app01-8104	17	48	network	network	NOUN
app01-8104	17	49	denoises	denoise	NOUN
app01-8104	17	50	and	and	CCONJ
app01-8104	17	51	smoothes	smooth	VERB
app01-8104	17	52	out	out	ADP
app01-8104	17	53	the	the	DET
app01-8104	17	54	previously	previously	ADV
app01-8104	17	55	reconstructed	reconstruct	VERB
app01-8104	17	56	pattern	pattern	NOUN
app01-8104	17	57	.	.	PUNCT
app01-8104	18	1	for	for	ADP
app01-8104	18	2	simplicity	simplicity	NOUN
app01-8104	18	3	,	,	PUNCT
app01-8104	18	4	we	we	PRON
app01-8104	18	5	study	study	VERB
app01-8104	18	6	only	only	ADV
app01-8104	18	7	two	two	NUM
app01-8104	18	8	-	-	PUNCT
app01-8104	18	9	phase	phase	NOUN
app01-8104	18	10	materials	material	NOUN
app01-8104	18	11	,	,	PUNCT
app01-8104	18	12	i.e.	i.e.	X
app01-8104	18	13	we	we	PRON
app01-8104	18	14	test	test	VERB
app01-8104	18	15	the	the	DET
app01-8104	18	16	framework	framework	NOUN
app01-8104	18	17	with	with	ADP
app01-8104	18	18	black	black	ADJ
app01-8104	18	19	and	and	CCONJ
app01-8104	18	20	white	white	ADJ
app01-8104	18	21	images	image	NOUN
app01-8104	18	22	.	.	PUNCT
app01-8104	19	1	our	our	PRON
app01-8104	19	2	implementation	implementation	NOUN
app01-8104	19	3	and	and	CCONJ
app01-8104	19	4	data	datum	NOUN
app01-8104	19	5	are	be	AUX
app01-8104	19	6	publicly	publicly	ADV
app01-8104	19	7	available	available	ADJ
app01-8104	19	8	at	at	ADP
app01-8104	19	9	a	a	DET
app01-8104	19	10	gitlab	gitlab	NOUN
app01-8104	19	11	repository	repository	NOUN
app01-8104	19	12	[	[	X
app01-8104	19	13	9	9	NUM
app01-8104	19	14	]	]	SYM
app01-8104	19	15	.	.	PUNCT
app01-8104	20	1	2	2	X
app01-8104	20	2	.	.	X
app01-8104	20	3	methodology	methodology	NOUN
app01-8104	20	4	the	the	DET
app01-8104	20	5	proposed	propose	VERB
app01-8104	20	6	reconstruction	reconstruction	NOUN
app01-8104	20	7	method	method	NOUN
app01-8104	20	8	comprises	comprise	VERB
app01-8104	20	9	two	two	NUM
app01-8104	20	10	distinct	distinct	ADJ
app01-8104	20	11	steps	step	NOUN
app01-8104	20	12	:	:	PUNCT
app01-8104	20	13	(	(	PUNCT
app01-8104	20	14	1	1	NUM
app01-8104	20	15	.	.	PUNCT
app01-8104	20	16	)	)	PUNCT
app01-8104	21	1	the	the	DET
app01-8104	21	2	reconstruction	reconstruction	NOUN
app01-8104	21	3	of	of	ADP
app01-8104	21	4	a	a	DET
app01-8104	21	5	general	general	ADJ
app01-8104	21	6	shape	shape	NOUN
app01-8104	21	7	of	of	ADP
app01-8104	21	8	the	the	DET
app01-8104	21	9	microstructure	microstructure	NOUN
app01-8104	21	10	from	from	ADP
app01-8104	21	11	a	a	DET
app01-8104	21	12	random	random	ADJ
app01-8104	21	13	noise	noise	NOUN
app01-8104	21	14	with	with	ADP
app01-8104	21	15	margins	margin	NOUN
app01-8104	21	16	of	of	ADP
app01-8104	21	17	the	the	DET
app01-8104	21	18	reference	reference	NOUN
app01-8104	21	19	image	image	NOUN
app01-8104	21	20	used	use	VERB
app01-8104	21	21	as	as	ADP
app01-8104	21	22	a	a	DET
app01-8104	21	23	seed	seed	NOUN
app01-8104	21	24	.	.	PUNCT
app01-8104	22	1	(	(	PUNCT
app01-8104	22	2	2	2	NUM
app01-8104	22	3	.	.	PUNCT
app01-8104	22	4	)	)	PUNCT
app01-8104	23	1	the	the	DET
app01-8104	23	2	smoothing	smooth	VERB
app01-8104	23	3	procedure	procedure	NOUN
app01-8104	23	4	of	of	ADP
app01-8104	23	5	the	the	DET
app01-8104	23	6	reconstructed	reconstructed	ADJ
app01-8104	23	7	image	image	NOUN
app01-8104	23	8	that	that	PRON
app01-8104	23	9	improves	improve	VERB
app01-8104	23	10	the	the	DET
app01-8104	23	11	local	local	ADJ
app01-8104	23	12	features	feature	NOUN
app01-8104	23	13	of	of	ADP
app01-8104	23	14	the	the	DET
app01-8104	23	15	microstructural	microstructural	ADJ
app01-8104	23	16	geometry	geometry	NOUN
app01-8104	23	17	.	.	PUNCT
app01-8104	24	1	2.1	2.1	NUM
app01-8104	24	2	.	.	PUNCT
app01-8104	24	3	reconstructing	reconstruct	VERB
app01-8104	24	4	microstructural	microstructural	ADJ
app01-8104	24	5	geometry	geometry	NOUN
app01-8104	24	6	in	in	ADP
app01-8104	24	7	step	step	NOUN
app01-8104	24	8	1	1	NUM
app01-8104	24	9	,	,	PUNCT
app01-8104	24	10	the	the	DET
app01-8104	24	11	overall	overall	ADJ
app01-8104	24	12	material	material	NOUN
app01-8104	24	13	distribution	distribution	NOUN
app01-8104	24	14	should	should	AUX
app01-8104	24	15	be	be	AUX
app01-8104	24	16	outlined	outline	VERB
app01-8104	24	17	in	in	ADP
app01-8104	24	18	the	the	DET
app01-8104	24	19	reconstructed	reconstructed	ADJ
app01-8104	24	20	sample	sample	NOUN
app01-8104	24	21	,	,	PUNCT
app01-8104	24	22	rendering	render	VERB
app01-8104	24	23	the	the	DET
app01-8104	24	24	key	key	ADJ
app01-8104	24	25	features	feature	NOUN
app01-8104	24	26	of	of	ADP
app01-8104	24	27	the	the	DET
app01-8104	24	28	microstructural	microstructural	ADJ
app01-8104	24	29	geometry	geometry	NOUN
app01-8104	24	30	.	.	PUNCT
app01-8104	25	1	we	we	PRON
app01-8104	25	2	opted	opt	VERB
app01-8104	25	3	for	for	ADP
app01-8104	25	4	a	a	DET
app01-8104	25	5	sequential	sequential	ADJ
app01-8104	25	6	approach	approach	NOUN
app01-8104	25	7	,	,	PUNCT
app01-8104	25	8	in	in	ADP
app01-8104	25	9	which	which	PRON
app01-8104	25	10	new	new	ADJ
app01-8104	25	11	values	value	NOUN
app01-8104	25	12	of	of	ADP
app01-8104	25	13	individual	individual	ADJ
app01-8104	25	14	pixels	pixel	NOUN
app01-8104	25	15	in	in	ADP
app01-8104	25	16	the	the	DET
app01-8104	25	17	discrete	discrete	ADJ
app01-8104	25	18	,	,	PUNCT
app01-8104	25	19	pixel	pixel	ADJ
app01-8104	25	20	-	-	PUNCT
app01-8104	25	21	like	like	ADJ
app01-8104	25	22	representation	representation	NOUN
app01-8104	25	23	of	of	ADP
app01-8104	25	24	the	the	DET
app01-8104	25	25	newly	newly	ADV
app01-8104	25	26	generated	generate	VERB
app01-8104	25	27	microstructural	microstructural	ADJ
app01-8104	25	28	geometry	geometry	NOUN
app01-8104	25	29	are	be	AUX
app01-8104	25	30	predicted	predict	VERB
app01-8104	25	31	from	from	ADP
app01-8104	25	32	the	the	DET
app01-8104	25	33	values	value	NOUN
app01-8104	25	34	previously	previously	ADV
app01-8104	25	35	determined	determine	VERB
app01-8104	25	36	at	at	ADP
app01-8104	25	37	antecedent	antecedent	NOUN
app01-8104	25	38	positions	position	NOUN
app01-8104	25	39	(	(	PUNCT
app01-8104	25	40	thus	thus	ADV
app01-8104	25	41	the	the	DET
app01-8104	25	42	causal	causal	ADJ
app01-8104	25	43	approach	approach	NOUN
app01-8104	25	44	)	)	PUNCT
app01-8104	25	45	,	,	PUNCT
app01-8104	25	46	because	because	SCONJ
app01-8104	25	47	such	such	DET
app01-8104	25	48	an	an	DET
app01-8104	25	49	approach	approach	NOUN
app01-8104	25	50	has	have	AUX
app01-8104	25	51	already	already	ADV
app01-8104	25	52	proven	prove	VERB
app01-8104	25	53	its	its	PRON
app01-8104	25	54	merits	merit	NOUN
app01-8104	25	55	in	in	ADP
app01-8104	25	56	microstructural	microstructural	ADJ
app01-8104	25	57	reconstruction	reconstruction	NOUN
app01-8104	25	58	,	,	PUNCT
app01-8104	25	59	cf	cf	NOUN
app01-8104	25	60	.	.	PUNCT
app01-8104	26	1	[	[	X
app01-8104	26	2	7	7	NUM
app01-8104	26	3	]	]	PUNCT
app01-8104	26	4	.	.	PUNCT
app01-8104	27	1	32	32	NUM
app01-8104	27	2	https://doi.org/10.14311/app.2022.34.0032	https://doi.org/10.14311/app.2022.34.0032	PROPN
app01-8104	27	3	https://creativecommons.org/licenses/by/4.0/	https://creativecommons.org/licenses/by/4.0/	PROPN
app01-8104	27	4	https://www.cvut.cz/en	https://www.cvut.cz/en	NOUN
app01-8104	27	5	vol	vol	NOUN
app01-8104	27	6	.	.	PUNCT
app01-8104	28	1	34/2022	34/2022	NUM
app01-8104	28	2	microstructure	microstructure	ADJ
app01-8104	28	3	reconstruction	reconstruction	NOUN
app01-8104	28	4	via	via	ADP
app01-8104	28	5	ann	ann	PROPN
app01-8104	28	6	hr	hr	PROPN
app01-8104	28	7	figure	figure	NOUN
app01-8104	28	8	1	1	NUM
app01-8104	28	9	.	.	PUNCT
app01-8104	28	10	illustration	illustration	NOUN
app01-8104	28	11	of	of	ADP
app01-8104	28	12	the	the	DET
app01-8104	28	13	causal	causal	ADJ
app01-8104	28	14	neighbourhood	neighbourhood	NOUN
app01-8104	28	15	with	with	ADP
app01-8104	28	16	hr	hr	NOUN
app01-8104	28	17	=	=	SYM
app01-8104	28	18	2	2	NUM
app01-8104	28	19	pixels	pixel	NOUN
app01-8104	28	20	around	around	ADP
app01-8104	28	21	the	the	DET
app01-8104	28	22	central	central	ADJ
app01-8104	28	23	pixel	pixel	NOUN
app01-8104	28	24	highlighted	highlight	VERB
app01-8104	28	25	in	in	ADP
app01-8104	28	26	dark	dark	ADJ
app01-8104	28	27	grey	grey	NOUN
app01-8104	28	28	.	.	PUNCT
app01-8104	29	1	the	the	DET
app01-8104	29	2	red	red	ADJ
app01-8104	29	3	pixels	pixel	NOUN
app01-8104	29	4	represent	represent	VERB
app01-8104	29	5	the	the	DET
app01-8104	29	6	input	input	NOUN
app01-8104	29	7	data	datum	NOUN
app01-8104	29	8	for	for	ADP
app01-8104	29	9	the	the	DET
app01-8104	29	10	ann	ann	PROPN
app01-8104	29	11	trained	train	VERB
app01-8104	29	12	in	in	ADP
app01-8104	29	13	step	step	NOUN
app01-8104	29	14	1	1	NUM
app01-8104	29	15	;	;	PUNCT
app01-8104	29	16	however	however	ADV
app01-8104	29	17	,	,	PUNCT
app01-8104	29	18	in	in	ADP
app01-8104	29	19	order	order	NOUN
app01-8104	29	20	to	to	PART
app01-8104	29	21	extract	extract	VERB
app01-8104	29	22	an	an	DET
app01-8104	29	23	input	input	NOUN
app01-8104	29	24	structure	structure	NOUN
app01-8104	29	25	of	of	ADP
app01-8104	29	26	a	a	DET
app01-8104	29	27	rectangular	rectangular	ADJ
app01-8104	29	28	shape	shape	NOUN
app01-8104	29	29	,	,	PUNCT
app01-8104	29	30	the	the	DET
app01-8104	29	31	black	black	ADJ
app01-8104	29	32	and	and	CCONJ
app01-8104	29	33	yellow	yellow	ADJ
app01-8104	29	34	pixels	pixel	NOUN
app01-8104	29	35	are	be	AUX
app01-8104	29	36	also	also	ADV
app01-8104	29	37	included	include	VERB
app01-8104	29	38	in	in	ADP
app01-8104	29	39	the	the	DET
app01-8104	29	40	inputs	input	NOUN
app01-8104	29	41	but	but	CCONJ
app01-8104	29	42	their	their	PRON
app01-8104	29	43	values	value	NOUN
app01-8104	29	44	are	be	AUX
app01-8104	29	45	discarded	discard	VERB
app01-8104	29	46	and	and	CCONJ
app01-8104	29	47	replaced	replace	VERB
app01-8104	29	48	by	by	ADP
app01-8104	29	49	random	random	ADJ
app01-8104	29	50	binary	binary	ADJ
app01-8104	29	51	values	value	NOUN
app01-8104	29	52	.	.	PUNCT
app01-8104	30	1	to	to	ADP
app01-8104	30	2	this	this	DET
app01-8104	30	3	end	end	NOUN
app01-8104	30	4	,	,	PUNCT
app01-8104	30	5	we	we	PRON
app01-8104	30	6	define	define	VERB
app01-8104	30	7	a	a	DET
app01-8104	30	8	rectangular	rectangular	ADJ
app01-8104	30	9	causal	causal	ADJ
app01-8104	30	10	neighbourhood	neighbourhood	NOUN
app01-8104	30	11	of	of	ADP
app01-8104	30	12	(	(	PUNCT
app01-8104	30	13	2hr+1)×	2hr+1)×	PROPN
app01-8104	30	14	(	(	PUNCT
app01-8104	30	15	hr+1	hr+1	X
app01-8104	30	16	)	)	PUNCT
app01-8104	30	17	pixels	pixel	NOUN
app01-8104	30	18	with	with	ADP
app01-8104	30	19	parameter	parameter	NOUN
app01-8104	30	20	hr	hr	NOUN
app01-8104	30	21	being	be	AUX
app01-8104	30	22	the	the	DET
app01-8104	30	23	given	give	VERB
app01-8104	30	24	neighbourhood	neighbourhood	NOUN
app01-8104	30	25	radius	radius	NOUN
app01-8104	30	26	;	;	PUNCT
app01-8104	30	27	see	see	VERB
app01-8104	30	28	fig	fig	NOUN
app01-8104	30	29	.	.	PUNCT
app01-8104	31	1	1	1	NUM
app01-8104	31	2	for	for	ADP
app01-8104	31	3	an	an	DET
app01-8104	31	4	illustration	illustration	NOUN
app01-8104	31	5	.	.	PUNCT
app01-8104	32	1	note	note	VERB
app01-8104	32	2	that	that	SCONJ
app01-8104	32	3	the	the	DET
app01-8104	32	4	positions	position	NOUN
app01-8104	32	5	highlighted	highlight	VERB
app01-8104	32	6	in	in	ADP
app01-8104	32	7	red	red	PROPN
app01-8104	32	8	constitute	constitute	VERB
app01-8104	32	9	the	the	DET
app01-8104	32	10	real	real	ADJ
app01-8104	32	11	input	input	NOUN
app01-8104	32	12	data	datum	NOUN
app01-8104	32	13	;	;	PUNCT
app01-8104	32	14	the	the	DET
app01-8104	32	15	dark	dark	ADJ
app01-8104	32	16	grey	grey	NOUN
app01-8104	32	17	and	and	CCONJ
app01-8104	32	18	yellow	yellow	ADJ
app01-8104	32	19	pixels	pixel	NOUN
app01-8104	32	20	are	be	AUX
app01-8104	32	21	only	only	ADV
app01-8104	32	22	a	a	DET
app01-8104	32	23	padding	padding	NOUN
app01-8104	32	24	(	(	PUNCT
app01-8104	32	25	without	without	ADP
app01-8104	32	26	value	value	NOUN
app01-8104	32	27	)	)	PUNCT
app01-8104	32	28	such	such	ADJ
app01-8104	32	29	that	that	SCONJ
app01-8104	32	30	the	the	DET
app01-8104	32	31	whole	whole	ADJ
app01-8104	32	32	input	input	NOUN
app01-8104	32	33	features	feature	VERB
app01-8104	32	34	a	a	DET
app01-8104	32	35	regular	regular	ADJ
app01-8104	32	36	2d	2d	NUM
app01-8104	32	37	shape	shape	NOUN
app01-8104	32	38	and	and	CCONJ
app01-8104	32	39	,	,	PUNCT
app01-8104	32	40	consequently	consequently	ADV
app01-8104	32	41	,	,	PUNCT
app01-8104	32	42	pooling	pool	VERB
app01-8104	32	43	layers	layer	NOUN
app01-8104	32	44	can	can	AUX
app01-8104	32	45	be	be	AUX
app01-8104	32	46	easily	easily	ADV
app01-8104	32	47	applied	apply	VERB
app01-8104	32	48	.	.	PUNCT
app01-8104	33	1	the	the	DET
app01-8104	33	2	actual	actual	ADJ
app01-8104	33	3	value	value	NOUN
app01-8104	33	4	of	of	ADP
app01-8104	33	5	the	the	DET
app01-8104	33	6	dark	dark	ADJ
app01-8104	33	7	grey	grey	PROPN
app01-8104	33	8	pixel	pixel	PROPN
app01-8104	33	9	serves	serve	VERB
app01-8104	33	10	as	as	ADP
app01-8104	33	11	a	a	DET
app01-8104	33	12	label	label	NOUN
app01-8104	33	13	during	during	ADP
app01-8104	33	14	an	an	DET
app01-8104	33	15	extraction	extraction	NOUN
app01-8104	33	16	of	of	ADP
app01-8104	33	17	the	the	DET
app01-8104	33	18	training	training	NOUN
app01-8104	33	19	data	datum	NOUN
app01-8104	33	20	from	from	ADP
app01-8104	33	21	a	a	DET
app01-8104	33	22	reference	reference	NOUN
app01-8104	33	23	image	image	NOUN
app01-8104	33	24	.	.	PUNCT
app01-8104	34	1	the	the	DET
app01-8104	34	2	neural	neural	ADJ
app01-8104	34	3	network	network	NOUN
app01-8104	34	4	for	for	ADP
app01-8104	34	5	predicting	predict	VERB
app01-8104	34	6	the	the	DET
app01-8104	34	7	pixel	pixel	PROPN
app01-8104	34	8	values	value	NOUN
app01-8104	34	9	in	in	ADP
app01-8104	34	10	step	step	NOUN
app01-8104	34	11	1	1	NUM
app01-8104	34	12	is	be	AUX
app01-8104	34	13	designed	design	VERB
app01-8104	34	14	such	such	ADJ
app01-8104	34	15	that	that	SCONJ
app01-8104	34	16	the	the	DET
app01-8104	34	17	rectangular	rectangular	ADJ
app01-8104	34	18	input	input	NOUN
app01-8104	34	19	is	be	AUX
app01-8104	34	20	first	first	ADV
app01-8104	34	21	subsampled	subsample	VERB
app01-8104	34	22	using	use	VERB
app01-8104	34	23	a	a	DET
app01-8104	34	24	nrp	nrp	NOUN
app01-8104	34	25	×	×	PROPN
app01-8104	34	26	nrp	nrp	NOUN
app01-8104	34	27	pooling	pool	VERB
app01-8104	34	28	layer	layer	NOUN
app01-8104	34	29	,	,	PUNCT
app01-8104	34	30	flattened	flatten	VERB
app01-8104	34	31	and	and	CCONJ
app01-8104	34	32	passed	pass	VERB
app01-8104	34	33	through	through	ADP
app01-8104	34	34	several	several	ADJ
app01-8104	34	35	densely	densely	ADV
app01-8104	34	36	connected	connect	VERB
app01-8104	34	37	layers	layer	NOUN
app01-8104	34	38	.	.	PUNCT
app01-8104	35	1	based	base	VERB
app01-8104	35	2	on	on	ADP
app01-8104	35	3	our	our	PRON
app01-8104	35	4	numerical	numerical	ADJ
app01-8104	35	5	experiments	experiment	NOUN
app01-8104	35	6	,	,	PUNCT
app01-8104	35	7	the	the	DET
app01-8104	35	8	maximum	maximum	ADJ
app01-8104	35	9	pooling	pool	VERB
app01-8104	35	10	layer	layer	NOUN
app01-8104	35	11	consistently	consistently	ADV
app01-8104	35	12	delivered	deliver	VERB
app01-8104	35	13	better	well	ADJ
app01-8104	35	14	results	result	NOUN
app01-8104	35	15	than	than	ADP
app01-8104	35	16	the	the	DET
app01-8104	35	17	average	average	ADJ
app01-8104	35	18	pooling	pool	VERB
app01-8104	35	19	layer	layer	NOUN
app01-8104	35	20	.	.	PUNCT
app01-8104	36	1	the	the	DET
app01-8104	36	2	setup	setup	NOUN
app01-8104	36	3	of	of	ADP
app01-8104	36	4	the	the	DET
app01-8104	36	5	training	training	NOUN
app01-8104	36	6	procedure	procedure	NOUN
app01-8104	36	7	and	and	CCONJ
app01-8104	36	8	the	the	DET
app01-8104	36	9	effect	effect	NOUN
app01-8104	36	10	of	of	ADP
app01-8104	36	11	the	the	DET
app01-8104	36	12	remaining	remain	VERB
app01-8104	36	13	network	network	NOUN
app01-8104	36	14	parameters	parameter	NOUN
app01-8104	36	15	,	,	PUNCT
app01-8104	36	16	namely	namely	ADV
app01-8104	36	17	the	the	DET
app01-8104	36	18	number	number	NOUN
app01-8104	36	19	of	of	ADP
app01-8104	36	20	densely	densely	ADV
app01-8104	36	21	connected	connected	ADJ
app01-8104	36	22	layers	layer	NOUN
app01-8104	36	23	nr	nr	PRON
app01-8104	36	24	`	`	PUNCT
app01-8104	36	25	,	,	PUNCT
app01-8104	36	26	the	the	DET
app01-8104	36	27	number	number	NOUN
app01-8104	36	28	of	of	ADP
app01-8104	36	29	neurons	neuron	NOUN
app01-8104	36	30	in	in	ADP
app01-8104	36	31	each	each	DET
app01-8104	36	32	layer	layer	NOUN
app01-8104	36	33	nrn	nrn	PROPN
app01-8104	36	34	,	,	PUNCT
app01-8104	36	35	the	the	DET
app01-8104	36	36	size	size	NOUN
app01-8104	36	37	of	of	ADP
app01-8104	36	38	the	the	DET
app01-8104	36	39	pooling	pooling	NOUN
app01-8104	36	40	layer	layer	NOUN
app01-8104	36	41	nrp	nrp	NOUN
app01-8104	36	42	and	and	CCONJ
app01-8104	36	43	the	the	DET
app01-8104	36	44	neighbourhood	neighbourhood	NOUN
app01-8104	36	45	radius	radius	NOUN
app01-8104	36	46	hr	hr	NOUN
app01-8104	36	47	,	,	PUNCT
app01-8104	36	48	are	be	AUX
app01-8104	36	49	discussed	discuss	VERB
app01-8104	36	50	later	later	ADV
app01-8104	36	51	in	in	ADP
app01-8104	36	52	section	section	NOUN
app01-8104	36	53	4	4	NUM
app01-8104	36	54	.	.	PUNCT
app01-8104	37	1	once	once	SCONJ
app01-8104	37	2	the	the	DET
app01-8104	37	3	network	network	NOUN
app01-8104	37	4	is	be	AUX
app01-8104	37	5	trained	train	VERB
app01-8104	37	6	,	,	PUNCT
app01-8104	37	7	a	a	DET
app01-8104	37	8	new	new	ADJ
app01-8104	37	9	microstructural	microstructural	ADJ
app01-8104	37	10	realization	realization	NOUN
app01-8104	37	11	is	be	AUX
app01-8104	37	12	generated	generate	VERB
app01-8104	37	13	by	by	ADP
app01-8104	37	14	iterating	iterate	VERB
app01-8104	37	15	over	over	ADP
app01-8104	37	16	pixels	pixel	NOUN
app01-8104	37	17	of	of	ADP
app01-8104	37	18	a	a	DET
app01-8104	37	19	to	to	PART
app01-8104	37	20	-	-	PUNCT
app01-8104	37	21	be	be	AUX
app01-8104	37	22	-	-	PUNCT
app01-8104	37	23	reconstructed	reconstructed	ADJ
app01-8104	37	24	image	image	NOUN
app01-8104	37	25	in	in	ADP
app01-8104	37	26	a	a	DET
app01-8104	37	27	raster	raster	NOUN
app01-8104	37	28	scan	scan	NOUN
app01-8104	37	29	order	order	NOUN
app01-8104	37	30	.	.	PUNCT
app01-8104	38	1	consequently	consequently	ADV
app01-8104	38	2	,	,	PUNCT
app01-8104	38	3	an	an	DET
app01-8104	38	4	initial	initial	ADJ
app01-8104	38	5	microstructural	microstructural	ADJ
app01-8104	38	6	geometry	geometry	NOUN
app01-8104	38	7	must	must	AUX
app01-8104	38	8	be	be	AUX
app01-8104	38	9	provided	provide	VERB
app01-8104	38	10	in	in	ADP
app01-8104	38	11	a	a	DET
app01-8104	38	12	margin	margin	NOUN
app01-8104	38	13	of	of	ADP
app01-8104	38	14	width	width	ADJ
app01-8104	38	15	hr	hr	NOUN
app01-8104	38	16	at	at	ADP
app01-8104	38	17	the	the	DET
app01-8104	38	18	left	left	ADJ
app01-8104	38	19	,	,	PUNCT
app01-8104	38	20	top	top	ADJ
app01-8104	38	21	,	,	PUNCT
app01-8104	38	22	and	and	CCONJ
app01-8104	38	23	bottom	bottom	ADJ
app01-8104	38	24	edge	edge	NOUN
app01-8104	38	25	of	of	ADP
app01-8104	38	26	the	the	DET
app01-8104	38	27	image	image	NOUN
app01-8104	38	28	;	;	PUNCT
app01-8104	38	29	the	the	DET
app01-8104	38	30	initial	initial	ADJ
app01-8104	38	31	values	value	NOUN
app01-8104	38	32	in	in	ADP
app01-8104	38	33	the	the	DET
app01-8104	38	34	remaining	remain	VERB
app01-8104	38	35	part	part	NOUN
app01-8104	38	36	of	of	ADP
app01-8104	38	37	the	the	DET
app01-8104	38	38	image	image	NOUN
app01-8104	38	39	are	be	AUX
app01-8104	38	40	irrelevant	irrelevant	ADJ
app01-8104	38	41	and	and	CCONJ
app01-8104	38	42	we	we	PRON
app01-8104	38	43	generate	generate	VERB
app01-8104	38	44	them	they	PRON
app01-8104	38	45	as	as	ADP
app01-8104	38	46	a	a	DET
app01-8104	38	47	random	random	ADJ
app01-8104	38	48	binary	binary	NOUN
app01-8104	38	49	noise	noise	NOUN
app01-8104	38	50	;	;	PUNCT
app01-8104	38	51	see	see	VERB
app01-8104	38	52	fig	fig	NOUN
app01-8104	38	53	.	.	PUNCT
app01-8104	39	1	2b	2b	NUM
app01-8104	39	2	.	.	PUNCT
app01-8104	40	1	2.2	2.2	NUM
app01-8104	40	2	.	.	PUNCT
app01-8104	40	3	smoothing	smooth	VERB
app01-8104	40	4	procedure	procedure	NOUN
app01-8104	40	5	outputs	output	NOUN
app01-8104	40	6	of	of	ADP
app01-8104	40	7	the	the	DET
app01-8104	40	8	model	model	NOUN
app01-8104	40	9	trained	train	VERB
app01-8104	40	10	in	in	ADP
app01-8104	40	11	step	step	NOUN
app01-8104	40	12	1	1	NUM
app01-8104	40	13	usually	usually	ADV
app01-8104	40	14	contain	contain	VERB
app01-8104	40	15	the	the	DET
app01-8104	40	16	main	main	ADJ
app01-8104	40	17	features	feature	NOUN
app01-8104	40	18	of	of	ADP
app01-8104	40	19	the	the	DET
app01-8104	40	20	trained	train	VERB
app01-8104	40	21	microstructural	microstructural	ADJ
app01-8104	40	22	geometry	geometry	NOUN
app01-8104	40	23	;	;	PUNCT
app01-8104	40	24	however	however	ADV
app01-8104	40	25	,	,	PUNCT
app01-8104	40	26	local	local	ADJ
app01-8104	40	27	details	detail	NOUN
app01-8104	40	28	are	be	AUX
app01-8104	40	29	typically	typically	ADV
app01-8104	40	30	polluted	pollute	VERB
app01-8104	40	31	by	by	ADP
app01-8104	40	32	random	random	ADJ
app01-8104	40	33	noise	noise	NOUN
app01-8104	40	34	;	;	PUNCT
app01-8104	40	35	compare	compare	VERB
app01-8104	40	36	figs	fig	NOUN
app01-8104	40	37	.	.	PUNCT
app01-8104	41	1	2a	2a	NUM
app01-8104	41	2	and	and	CCONJ
app01-8104	41	3	2c	2c	NUM
app01-8104	41	4	.	.	PUNCT
app01-8104	42	1	for	for	ADP
app01-8104	42	2	step	step	NOUN
app01-8104	42	3	2	2	NUM
app01-8104	42	4	we	we	PRON
app01-8104	42	5	train	train	VERB
app01-8104	42	6	an	an	DET
app01-8104	42	7	additional	additional	ADJ
app01-8104	42	8	neural	neural	ADJ
app01-8104	42	9	network	network	NOUN
app01-8104	42	10	to	to	PART
app01-8104	42	11	smooth	smooth	VERB
app01-8104	42	12	out	out	ADP
app01-8104	42	13	the	the	DET
app01-8104	42	14	image	image	NOUN
app01-8104	42	15	and	and	CCONJ
app01-8104	42	16	correct	correct	ADJ
app01-8104	42	17	irregularities	irregularity	NOUN
app01-8104	42	18	in	in	ADP
app01-8104	42	19	the	the	DET
app01-8104	42	20	image	image	NOUN
app01-8104	42	21	generated	generate	VERB
app01-8104	42	22	in	in	ADP
app01-8104	42	23	the	the	DET
app01-8104	42	24	step	step	NOUN
app01-8104	42	25	1	1	NUM
app01-8104	42	26	.	.	PUNCT
app01-8104	43	1	this	this	DET
app01-8104	43	2	time	time	NOUN
app01-8104	43	3	,	,	PUNCT
app01-8104	43	4	the	the	DET
app01-8104	43	5	model	model	NOUN
app01-8104	43	6	works	work	VERB
app01-8104	43	7	with	with	ADP
app01-8104	43	8	a	a	DET
app01-8104	43	9	complete	complete	ADJ
app01-8104	43	10	,	,	PUNCT
app01-8104	43	11	i.e.	i.e.	X
app01-8104	43	12	non	non	ADJ
app01-8104	43	13	-	-	ADJ
app01-8104	43	14	causal	causal	ADJ
app01-8104	43	15	,	,	PUNCT
app01-8104	43	16	square	square	ADJ
app01-8104	43	17	neighbourhood	neighbourhood	NOUN
app01-8104	43	18	around	around	ADP
app01-8104	43	19	the	the	DET
app01-8104	43	20	central	central	ADJ
app01-8104	43	21	pixel	pixel	NOUN
app01-8104	43	22	(	(	PUNCT
app01-8104	43	23	illustrated	illustrate	VERB
app01-8104	43	24	in	in	ADP
app01-8104	43	25	figure	figure	NOUN
app01-8104	43	26	3	3	NUM
app01-8104	43	27	)	)	PUNCT
app01-8104	43	28	,	,	PUNCT
app01-8104	43	29	usually	usually	ADV
app01-8104	43	30	of	of	ADP
app01-8104	43	31	a	a	DET
app01-8104	43	32	smaller	small	ADJ
app01-8104	43	33	neighbourhood	neighbourhood	NOUN
app01-8104	43	34	radius	radius	NOUN
app01-8104	43	35	hs	hs	INTJ
app01-8104	43	36	than	than	ADP
app01-8104	43	37	in	in	ADP
app01-8104	43	38	the	the	DET
app01-8104	43	39	causal	causal	ADJ
app01-8104	43	40	model	model	NOUN
app01-8104	43	41	in	in	ADP
app01-8104	43	42	step	step	NOUN
app01-8104	43	43	1	1	NUM
app01-8104	43	44	.	.	PUNCT
app01-8104	44	1	again	again	ADV
app01-8104	44	2	,	,	PUNCT
app01-8104	44	3	the	the	DET
app01-8104	44	4	two	two	NUM
app01-8104	44	5	-	-	PUNCT
app01-8104	44	6	dimensional	dimensional	ADJ
app01-8104	44	7	structure	structure	NOUN
app01-8104	44	8	of	of	ADP
app01-8104	44	9	the	the	DET
app01-8104	44	10	input	input	NOUN
app01-8104	44	11	is	be	AUX
app01-8104	44	12	needed	need	VERB
app01-8104	44	13	to	to	PART
app01-8104	44	14	facilitate	facilitate	VERB
app01-8104	44	15	a	a	DET
app01-8104	44	16	subsampling	subsampling	NOUN
app01-8104	44	17	/	/	SYM
app01-8104	44	18	pooling	pool	VERB
app01-8104	44	19	layer	layer	NOUN
app01-8104	44	20	,	,	PUNCT
app01-8104	44	21	which	which	PRON
app01-8104	44	22	is	be	AUX
app01-8104	44	23	then	then	ADV
app01-8104	44	24	followed	follow	VERB
app01-8104	44	25	by	by	ADP
app01-8104	44	26	flattening	flattening	NOUN
app01-8104	44	27	and	and	CCONJ
app01-8104	44	28	passing	pass	VERB
app01-8104	44	29	through	through	ADP
app01-8104	44	30	two	two	NUM
app01-8104	44	31	densely	densely	ADV
app01-8104	44	32	connected	connect	VERB
app01-8104	44	33	layers	layer	NOUN
app01-8104	44	34	.	.	PUNCT
app01-8104	45	1	the	the	DET
app01-8104	45	2	impact	impact	NOUN
app01-8104	45	3	of	of	ADP
app01-8104	45	4	the	the	DET
app01-8104	45	5	actual	actual	ADJ
app01-8104	45	6	choice	choice	NOUN
app01-8104	45	7	(	(	PUNCT
app01-8104	45	8	i.e.	i.e.	X
app01-8104	45	9	average	average	ADJ
app01-8104	45	10	vs	vs	ADP
app01-8104	45	11	maximum	maximum	ADJ
app01-8104	45	12	pooling	pooling	NOUN
app01-8104	45	13	)	)	PUNCT
app01-8104	45	14	of	of	ADP
app01-8104	45	15	the	the	DET
app01-8104	45	16	subsampling	subsample	VERB
app01-8104	45	17	layer	layer	NOUN
app01-8104	45	18	is	be	AUX
app01-8104	45	19	discussed	discuss	VERB
app01-8104	45	20	in	in	ADP
app01-8104	45	21	section	section	NOUN
app01-8104	45	22	4	4	NUM
app01-8104	45	23	)	)	PUNCT
app01-8104	45	24	.	.	PUNCT
app01-8104	46	1	to	to	PART
app01-8104	46	2	increase	increase	VERB
app01-8104	46	3	robustness	robustness	NOUN
app01-8104	46	4	of	of	ADP
app01-8104	46	5	the	the	DET
app01-8104	46	6	trained	train	VERB
app01-8104	46	7	model	model	NOUN
app01-8104	46	8	and	and	CCONJ
app01-8104	46	9	prevent	prevent	VERB
app01-8104	46	10	if	if	SCONJ
app01-8104	46	11	from	from	ADP
app01-8104	46	12	learning	learn	VERB
app01-8104	46	13	to	to	PART
app01-8104	46	14	simply	simply	ADV
app01-8104	46	15	copy	copy	VERB
app01-8104	46	16	the	the	DET
app01-8104	46	17	value	value	NOUN
app01-8104	46	18	of	of	ADP
app01-8104	46	19	the	the	DET
app01-8104	46	20	central	central	ADJ
app01-8104	46	21	pixel	pixel	NOUN
app01-8104	46	22	,	,	PUNCT
app01-8104	46	23	we	we	PRON
app01-8104	46	24	introduce	introduce	VERB
app01-8104	46	25	two	two	NUM
app01-8104	46	26	errors	error	NOUN
app01-8104	46	27	:	:	PUNCT
app01-8104	46	28	(	(	PUNCT
app01-8104	46	29	i	i	NOUN
app01-8104	46	30	)	)	PUNCT
app01-8104	46	31	the	the	DET
app01-8104	46	32	value	value	NOUN
app01-8104	46	33	of	of	ADP
app01-8104	46	34	the	the	DET
app01-8104	46	35	dark	dark	ADJ
app01-8104	46	36	grey	grey	PROPN
app01-8104	46	37	pixel	pixel	PROPN
app01-8104	46	38	,	,	PUNCT
app01-8104	46	39	which	which	PRON
app01-8104	46	40	is	be	AUX
app01-8104	46	41	used	use	VERB
app01-8104	46	42	as	as	ADP
app01-8104	46	43	a	a	DET
app01-8104	46	44	label	label	NOUN
app01-8104	46	45	in	in	ADP
app01-8104	46	46	the	the	DET
app01-8104	46	47	training	training	NOUN
app01-8104	46	48	,	,	PUNCT
app01-8104	46	49	is	be	AUX
app01-8104	46	50	always	always	ADV
app01-8104	46	51	randomized	randomize	VERB
app01-8104	46	52	in	in	ADP
app01-8104	46	53	the	the	DET
app01-8104	46	54	inputs	input	NOUN
app01-8104	46	55	,	,	PUNCT
app01-8104	46	56	and	and	CCONJ
app01-8104	46	57	(	(	PUNCT
app01-8104	46	58	ii	ii	NOUN
app01-8104	46	59	)	)	PUNCT
app01-8104	46	60	we	we	PRON
app01-8104	46	61	also	also	ADV
app01-8104	46	62	randomly	randomly	VERB
app01-8104	46	63	choose	choose	VERB
app01-8104	46	64	value	value	NOUN
app01-8104	46	65	for	for	ADP
app01-8104	46	66	ξ	ξ	PROPN
app01-8104	46	67	×	×	NOUN
app01-8104	46	68	100	100	NUM
app01-8104	46	69	%	%	NOUN
app01-8104	46	70	of	of	ADP
app01-8104	46	71	red	red	ADJ
app01-8104	46	72	pixels	pixel	NOUN
app01-8104	46	73	from	from	ADP
app01-8104	46	74	fig	fig	NOUN
app01-8104	46	75	.	.	PUNCT
app01-8104	47	1	3	3	NUM
app01-8104	47	2	.	.	NOUN
app01-8104	47	3	3	3	NUM
app01-8104	47	4	.	.	NOUN
app01-8104	47	5	error	error	NOUN
app01-8104	47	6	quantification	quantification	NOUN
app01-8104	47	7	in	in	ADP
app01-8104	47	8	order	order	NOUN
app01-8104	47	9	to	to	PART
app01-8104	47	10	assess	assess	VERB
app01-8104	47	11	the	the	DET
app01-8104	47	12	performance	performance	NOUN
app01-8104	47	13	of	of	ADP
app01-8104	47	14	the	the	DET
app01-8104	47	15	proposed	propose	VERB
app01-8104	47	16	models	model	NOUN
app01-8104	47	17	beyond	beyond	ADP
app01-8104	47	18	a	a	DET
app01-8104	47	19	visual	visual	ADJ
app01-8104	47	20	inspection	inspection	NOUN
app01-8104	47	21	,	,	PUNCT
app01-8104	47	22	we	we	PRON
app01-8104	47	23	compare	compare	AUX
app01-8104	47	24	generated	generate	VERB
app01-8104	47	25	microstructural	microstructural	ADJ
app01-8104	47	26	samples	sample	NOUN
app01-8104	47	27	to	to	ADP
app01-8104	47	28	the	the	DET
app01-8104	47	29	reference	reference	NOUN
app01-8104	47	30	images	image	NOUN
app01-8104	47	31	in	in	ADP
app01-8104	47	32	terms	term	NOUN
app01-8104	47	33	of	of	ADP
app01-8104	47	34	spatial	spatial	ADJ
app01-8104	47	35	statistics	statistic	NOUN
app01-8104	47	36	.	.	PUNCT
app01-8104	48	1	the	the	DET
app01-8104	48	2	most	most	ADV
app01-8104	48	3	straightforward	straightforward	ADJ
app01-8104	48	4	spatial	spatial	ADJ
app01-8104	48	5	statistics	statistic	NOUN
app01-8104	48	6	is	be	AUX
app01-8104	48	7	a	a	DET
app01-8104	48	8	volume	volume	NOUN
app01-8104	48	9	fraction	fraction	NOUN
app01-8104	48	10	φ	φ	PROPN
app01-8104	48	11	of	of	ADP
app01-8104	48	12	a	a	DET
app01-8104	48	13	chosen	choose	VERB
app01-8104	48	14	phase	phase	NOUN
app01-8104	48	15	(	(	PUNCT
app01-8104	48	16	the	the	DET
app01-8104	48	17	white	white	ADJ
app01-8104	48	18	phase	phase	NOUN
app01-8104	48	19	,	,	PUNCT
app01-8104	48	20	i.e.	i.e.	X
app01-8104	48	21	pixels	pixel	NOUN
app01-8104	48	22	with	with	ADP
app01-8104	48	23	value	value	NOUN
app01-8104	48	24	1	1	NUM
app01-8104	48	25	,	,	PUNCT
app01-8104	48	26	in	in	ADP
app01-8104	48	27	our	our	PRON
app01-8104	48	28	case	case	NOUN
app01-8104	48	29	)	)	PUNCT
app01-8104	48	30	.	.	PUNCT
app01-8104	49	1	we	we	PRON
app01-8104	49	2	define	define	VERB
app01-8104	49	3	error	error	NOUN
app01-8104	49	4	εφ	εφ	ADP
app01-8104	49	5	as	as	ADP
app01-8104	49	6	an	an	DET
app01-8104	49	7	absolute	absolute	ADJ
app01-8104	49	8	value	value	NOUN
app01-8104	49	9	of	of	ADP
app01-8104	49	10	the	the	DET
app01-8104	49	11	difference	difference	NOUN
app01-8104	49	12	between	between	ADP
app01-8104	49	13	the	the	DET
app01-8104	49	14	volume	volume	NOUN
app01-8104	49	15	fraction	fraction	NOUN
app01-8104	49	16	φref	φref	NOUN
app01-8104	49	17	in	in	ADP
app01-8104	49	18	the	the	DET
app01-8104	49	19	reference	reference	NOUN
app01-8104	49	20	sample	sample	NOUN
app01-8104	49	21	and	and	CCONJ
app01-8104	49	22	the	the	DET
app01-8104	49	23	volume	volume	NOUN
app01-8104	49	24	fraction	fraction	NOUN
app01-8104	49	25	φgen	φgen	PROPN
app01-8104	49	26	in	in	ADP
app01-8104	49	27	the	the	DET
app01-8104	49	28	generated	generate	VERB
app01-8104	49	29	microstructure	microstructure	NOUN
app01-8104	49	30	,	,	PUNCT
app01-8104	49	31	εφ	εφ	ADP
app01-8104	49	32	=	=	SYM
app01-8104	49	33	|φref	|φref	NOUN
app01-8104	49	34	−	−	NOUN
app01-8104	49	35	φgen|	φgen|	X
app01-8104	49	36	.	.	PUNCT
app01-8104	50	1	(	(	PUNCT
app01-8104	50	2	1	1	X
app01-8104	50	3	)	)	PUNCT
app01-8104	50	4	the	the	DET
app01-8104	50	5	second	second	ADJ
app01-8104	50	6	spatial	spatial	ADJ
app01-8104	50	7	statistics	statistic	NOUN
app01-8104	50	8	considered	consider	VERB
app01-8104	50	9	in	in	ADP
app01-8104	50	10	our	our	PRON
app01-8104	50	11	work	work	NOUN
app01-8104	50	12	is	be	AUX
app01-8104	50	13	the	the	DET
app01-8104	50	14	two	two	NUM
app01-8104	50	15	-	-	PUNCT
app01-8104	50	16	point	point	NOUN
app01-8104	50	17	probability	probability	NOUN
app01-8104	50	18	function	function	NOUN
app01-8104	50	19	s2(x	s2(x	PROPN
app01-8104	50	20	)	)	PUNCT
app01-8104	50	21	,	,	PUNCT
app01-8104	50	22	which	which	PRON
app01-8104	50	23	states	state	VERB
app01-8104	50	24	the	the	DET
app01-8104	50	25	probability	probability	NOUN
app01-8104	50	26	of	of	ADP
app01-8104	50	27	finding	find	VERB
app01-8104	50	28	two	two	NUM
app01-8104	50	29	points	point	NOUN
app01-8104	50	30	separated	separate	VERB
app01-8104	50	31	by	by	ADP
app01-8104	50	32	x	x	PUNCT
app01-8104	50	33	in	in	ADP
app01-8104	50	34	a	a	DET
app01-8104	50	35	given	give	VERB
app01-8104	50	36	phase	phase	NOUN
app01-8104	50	37	.	.	PUNCT
app01-8104	51	1	since	since	SCONJ
app01-8104	51	2	all	all	DET
app01-8104	51	3	our	our	PRON
app01-8104	51	4	data	datum	NOUN
app01-8104	51	5	are	be	AUX
app01-8104	51	6	represented	represent	VERB
app01-8104	51	7	as	as	ADP
app01-8104	51	8	a	a	DET
app01-8104	51	9	regular	regular	ADJ
app01-8104	51	10	grid	grid	NOUN
app01-8104	51	11	of	of	ADP
app01-8104	51	12	values	value	NOUN
app01-8104	51	13	,	,	PUNCT
app01-8104	51	14	the	the	DET
app01-8104	51	15	discrete	discrete	ADJ
app01-8104	51	16	version	version	NOUN
app01-8104	51	17	of	of	ADP
app01-8104	51	18	s2(x	s2(x	PROPN
app01-8104	51	19	)	)	PUNCT
app01-8104	51	20	can	can	AUX
app01-8104	51	21	be	be	AUX
app01-8104	51	22	easily	easily	ADV
app01-8104	51	23	computed	compute	VERB
app01-8104	51	24	using	use	VERB
app01-8104	51	25	the	the	DET
app01-8104	51	26	fast	fast	ADJ
app01-8104	51	27	fourier	fourier	NOUN
app01-8104	51	28	transform	transform	NOUN
app01-8104	51	29	[	[	X
app01-8104	51	30	2	2	NUM
app01-8104	51	31	]	]	PUNCT
app01-8104	51	32	.	.	PUNCT
app01-8104	52	1	consequently	consequently	ADV
app01-8104	52	2	,	,	PUNCT
app01-8104	52	3	we	we	PRON
app01-8104	52	4	quantify	quantify	VERB
app01-8104	52	5	the	the	DET
app01-8104	52	6	discrepancy	discrepancy	NOUN
app01-8104	52	7	in	in	ADP
app01-8104	52	8	the	the	DET
app01-8104	52	9	reference	reference	NOUN
app01-8104	52	10	and	and	CCONJ
app01-8104	52	11	a	a	DET
app01-8104	52	12	generated	generate	VERB
app01-8104	52	13	microstructure	microstructure	NOUN
app01-8104	52	14	by	by	ADP
app01-8104	52	15	means	mean	NOUN
app01-8104	52	16	of	of	ADP
app01-8104	52	17	their	their	PRON
app01-8104	52	18	discrete	discrete	ADJ
app01-8104	52	19	two	two	NUM
app01-8104	52	20	-	-	PUNCT
app01-8104	52	21	point	point	NOUN
app01-8104	52	22	probability	probability	NOUN
app01-8104	52	23	functions	function	NOUN
app01-8104	52	24	sref	sref	ADJ
app01-8104	52	25	2	2	NUM
app01-8104	52	26	∈	∈	PROPN
app01-8104	52	27	rni×nj	rni×nj	NOUN
app01-8104	52	28	and	and	CCONJ
app01-8104	52	29	sgen	sgen	PROPN
app01-8104	52	30	2	2	NUM
app01-8104	52	31	∈	∈	PROPN
app01-8104	52	32	rni×nj	rni×nj	NOUN
app01-8104	52	33	as	as	ADP
app01-8104	52	34	εs2	εs2	NOUN
app01-8104	52	35	=	=	SYM
app01-8104	52	36	‖s	‖s	ADJ
app01-8104	52	37	gen	gen	NOUN
app01-8104	52	38	2	2	NUM
app01-8104	52	39	−	−	NOUN
app01-8104	52	40	sref	sref	NOUN
app01-8104	52	41	2	2	NUM
app01-8104	52	42	‖f	‖f	ADP
app01-8104	52	43	ninj	ninj	NOUN
app01-8104	52	44	,	,	PUNCT
app01-8104	52	45	(	(	PUNCT
app01-8104	52	46	2	2	X
app01-8104	52	47	)	)	PUNCT
app01-8104	52	48	where	where	SCONJ
app01-8104	52	49	‖a‖f	‖a‖f	NOUN
app01-8104	52	50	is	be	AUX
app01-8104	52	51	the	the	DET
app01-8104	52	52	frobenius	frobenius	ADJ
app01-8104	52	53	matrix	matrix	NOUN
app01-8104	52	54	norm	norm	NOUN
app01-8104	52	55	[	[	X
app01-8104	52	56	10	10	NUM
app01-8104	52	57	]	]	X
app01-8104	52	58	‖a‖2	‖a‖2	PROPN
app01-8104	52	59	f	f	NOUN
app01-8104	52	60	=	=	PUNCT
app01-8104	52	61	ni∑	ni∑	PROPN
app01-8104	52	62	i=1	i=1	PROPN
app01-8104	52	63	nj∑	nj∑	PROPN
app01-8104	52	64	j=1	j=1	NOUN
app01-8104	52	65	(	(	PUNCT
app01-8104	52	66	ai	ai	PROPN
app01-8104	52	67	,	,	PUNCT
app01-8104	52	68	j)2	j)2	PROPN
app01-8104	52	69	.	.	PUNCT
app01-8104	53	1	(	(	PUNCT
app01-8104	53	2	3	3	X
app01-8104	53	3	)	)	PUNCT
app01-8104	53	4	finally	finally	ADV
app01-8104	53	5	,	,	PUNCT
app01-8104	53	6	to	to	PART
app01-8104	53	7	quantify	quantify	VERB
app01-8104	53	8	the	the	DET
app01-8104	53	9	effect	effect	NOUN
app01-8104	53	10	of	of	ADP
app01-8104	53	11	our	our	PRON
app01-8104	53	12	smoothing	smooth	VERB
app01-8104	53	13	noncausal	noncausal	ADJ
app01-8104	53	14	model	model	NOUN
app01-8104	53	15	,	,	PUNCT
app01-8104	53	16	we	we	PRON
app01-8104	53	17	add	add	VERB
app01-8104	53	18	the	the	DET
app01-8104	53	19	third	third	ADJ
app01-8104	53	20	error	error	NOUN
app01-8104	53	21	metric	metric	NOUN
app01-8104	53	22	εd	εd	ADP
app01-8104	53	23	that	that	PRON
app01-8104	53	24	captures	capture	VERB
app01-8104	53	25	the	the	DET
app01-8104	53	26	level	level	NOUN
app01-8104	53	27	of	of	ADP
app01-8104	53	28	local	local	ADJ
app01-8104	53	29	heterogeneity	heterogeneity	NOUN
app01-8104	53	30	.	.	PUNCT
app01-8104	54	1	assuming	assume	VERB
app01-8104	54	2	a	a	DET
app01-8104	54	3	two	two	NUM
app01-8104	54	4	-	-	PUNCT
app01-8104	54	5	phase	phase	NOUN
app01-8104	54	6	medium	medium	NOUN
app01-8104	54	7	,	,	PUNCT
app01-8104	54	8	a	a	DET
app01-8104	54	9	microstructure	microstructure	NOUN
app01-8104	54	10	can	can	AUX
app01-8104	54	11	be	be	AUX
app01-8104	54	12	represented	represent	VERB
app01-8104	54	13	with	with	ADP
app01-8104	54	14	a	a	DET
app01-8104	54	15	boolean	boolean	ADJ
app01-8104	54	16	matrix	matrix	NOUN
app01-8104	54	17	m	m	NOUN
app01-8104	54	18	∈	∈	NOUN
app01-8104	54	19	{	{	PUNCT
app01-8104	54	20	0	0	NUM
app01-8104	54	21	,	,	PUNCT
app01-8104	54	22	1}ni×nj	1}ni×nj	NUM
app01-8104	54	23	.	.	PUNCT
app01-8104	55	1	for	for	ADP
app01-8104	55	2	each	each	DET
app01-8104	55	3	pixel	pixel	NOUN
app01-8104	55	4	we	we	PRON
app01-8104	55	5	can	can	AUX
app01-8104	55	6	computed	compute	VERB
app01-8104	55	7	a	a	DET
app01-8104	55	8	local	local	ADJ
app01-8104	55	9	quantity	quantity	NOUN
app01-8104	55	10	di	di	NOUN
app01-8104	55	11	,	,	PUNCT
app01-8104	55	12	j	j	PROPN
app01-8104	55	13	as	as	ADP
app01-8104	55	14	a	a	DET
app01-8104	55	15	sum	sum	NOUN
app01-8104	55	16	of	of	ADP
app01-8104	55	17	the	the	DET
app01-8104	55	18	averaged	average	VERB
app01-8104	55	19	absolute	absolute	ADJ
app01-8104	55	20	differences	difference	NOUN
app01-8104	55	21	between	between	ADP
app01-8104	55	22	the	the	DET
app01-8104	55	23	33	33	NUM
app01-8104	55	24	k.	k.	PROPN
app01-8104	55	25	latka	latka	PROPN
app01-8104	55	26	,	,	PUNCT
app01-8104	55	27	m.	m.	NOUN
app01-8104	55	28	doškář	doškář	PROPN
app01-8104	55	29	,	,	PUNCT
app01-8104	55	30	j.	j.	PROPN
app01-8104	55	31	zeman	zeman	PROPN
app01-8104	55	32	acta	acta	PROPN
app01-8104	55	33	polytechnica	polytechnica	PROPN
app01-8104	55	34	ctu	ctu	NOUN
app01-8104	55	35	proceedings	proceeding	NOUN
app01-8104	55	36	(	(	PUNCT
app01-8104	55	37	a	a	X
app01-8104	55	38	)	)	PUNCT
app01-8104	55	39	(	(	PUNCT
app01-8104	55	40	b	b	X
app01-8104	55	41	)	)	PUNCT
app01-8104	55	42	(	(	PUNCT
app01-8104	55	43	c	c	X
app01-8104	55	44	)	)	PUNCT
app01-8104	55	45	figure	figure	NOUN
app01-8104	55	46	2	2	NUM
app01-8104	55	47	.	.	PUNCT
app01-8104	55	48	microstructure	microstructure	ADJ
app01-8104	55	49	reconstruction	reconstruction	NOUN
app01-8104	55	50	process	process	NOUN
app01-8104	55	51	using	use	VERB
app01-8104	55	52	the	the	DET
app01-8104	55	53	causal	causal	ADJ
app01-8104	55	54	model	model	NOUN
app01-8104	55	55	.	.	PUNCT
app01-8104	56	1	after	after	ADP
app01-8104	56	2	training	train	VERB
app01-8104	56	3	the	the	DET
app01-8104	56	4	neural	neural	ADJ
app01-8104	56	5	network	network	NOUN
app01-8104	56	6	model	model	NOUN
app01-8104	56	7	on	on	ADP
app01-8104	56	8	input	input	NOUN
app01-8104	56	9	data	datum	NOUN
app01-8104	56	10	from	from	ADP
app01-8104	56	11	the	the	DET
app01-8104	56	12	initial	initial	ADJ
app01-8104	56	13	microstructure	microstructure	NOUN
app01-8104	56	14	(	(	PUNCT
app01-8104	56	15	a	a	NOUN
app01-8104	56	16	)	)	PUNCT
app01-8104	56	17	,	,	PUNCT
app01-8104	56	18	the	the	DET
app01-8104	56	19	trained	train	VERB
app01-8104	56	20	model	model	NOUN
app01-8104	56	21	is	be	AUX
app01-8104	56	22	used	use	VERB
app01-8104	56	23	to	to	PART
app01-8104	56	24	sequentially	sequentially	ADV
app01-8104	56	25	predict	predict	VERB
app01-8104	56	26	pixel	pixel	PROPN
app01-8104	56	27	values	value	NOUN
app01-8104	56	28	in	in	ADP
app01-8104	56	29	a	a	DET
app01-8104	56	30	raster	raster	NOUN
app01-8104	56	31	scan	scan	NOUN
app01-8104	56	32	order	order	NOUN
app01-8104	56	33	using	use	VERB
app01-8104	56	34	previously	previously	ADV
app01-8104	56	35	predicted	predict	VERB
app01-8104	56	36	pixel	pixel	ADJ
app01-8104	56	37	values	value	NOUN
app01-8104	56	38	from	from	ADP
app01-8104	56	39	the	the	DET
app01-8104	56	40	causal	causal	ADJ
app01-8104	56	41	neighbourhood	neighbourhood	NOUN
app01-8104	56	42	.	.	PUNCT
app01-8104	57	1	hs	hs	PROPN
app01-8104	57	2	figure	figure	VERB
app01-8104	57	3	3	3	NUM
app01-8104	57	4	.	.	PUNCT
app01-8104	57	5	illustration	illustration	NOUN
app01-8104	57	6	of	of	ADP
app01-8104	57	7	the	the	DET
app01-8104	57	8	non	non	ADJ
app01-8104	57	9	-	-	ADJ
app01-8104	57	10	causal	causal	ADJ
app01-8104	57	11	neighbourhood	neighbourhood	NOUN
app01-8104	57	12	of	of	ADP
app01-8104	57	13	a	a	DET
app01-8104	57	14	neighbourhood	neighbourhood	NOUN
app01-8104	57	15	radius	radius	NOUN
app01-8104	57	16	of	of	ADP
app01-8104	57	17	hs	hs	PROPN
app01-8104	58	1	=	=	NOUN
app01-8104	58	2	2	2	NUM
app01-8104	58	3	pixels	pixel	NOUN
app01-8104	58	4	.	.	PUNCT
app01-8104	59	1	the	the	DET
app01-8104	59	2	black	black	ADJ
app01-8104	59	3	pixel	pixel	NOUN
app01-8104	59	4	represents	represent	VERB
app01-8104	59	5	the	the	DET
app01-8104	59	6	central	central	ADJ
app01-8104	59	7	pixel	pixel	NOUN
app01-8104	59	8	and	and	CCONJ
app01-8104	59	9	the	the	DET
app01-8104	59	10	red	red	ADJ
app01-8104	59	11	pixels	pixel	NOUN
app01-8104	59	12	represent	represent	VERB
app01-8104	59	13	non	non	ADJ
app01-8104	59	14	-	-	ADJ
app01-8104	59	15	causal	causal	ADJ
app01-8104	59	16	neighbourhood	neighbourhood	NOUN
app01-8104	59	17	.	.	PUNCT
app01-8104	60	1	when	when	SCONJ
app01-8104	60	2	extracting	extract	VERB
app01-8104	60	3	the	the	DET
app01-8104	60	4	neighbourhood	neighbourhood	NOUN
app01-8104	60	5	,	,	PUNCT
app01-8104	60	6	the	the	DET
app01-8104	60	7	central	central	ADJ
app01-8104	60	8	black	black	ADJ
app01-8104	60	9	pixel	pixel	NOUN
app01-8104	60	10	is	be	AUX
app01-8104	60	11	represented	represent	VERB
app01-8104	60	12	as	as	ADP
app01-8104	60	13	a	a	DET
app01-8104	60	14	random	random	ADJ
app01-8104	60	15	binary	binary	NOUN
app01-8104	60	16	value	value	NOUN
app01-8104	60	17	.	.	PUNCT
app01-8104	61	1	value	value	NOUN
app01-8104	61	2	of	of	ADP
app01-8104	61	3	the	the	DET
app01-8104	61	4	central	central	ADJ
app01-8104	61	5	pixel	pixel	NOUN
app01-8104	61	6	and	and	CCONJ
app01-8104	61	7	its	its	PRON
app01-8104	61	8	neighbouring	neighbouring	ADJ
app01-8104	61	9	eight	eight	NUM
app01-8104	61	10	pixels	pixel	NOUN
app01-8104	61	11	,	,	PUNCT
app01-8104	61	12	di	di	NOUN
app01-8104	61	13	,	,	PUNCT
app01-8104	61	14	j	j	PROPN
app01-8104	61	15	=	=	SYM
app01-8104	61	16	1	1	NUM
app01-8104	61	17	8	8	NUM
app01-8104	61	18	1∑	1∑	NUM
app01-8104	61	19	k	k	NOUN
app01-8104	61	20	,	,	PUNCT
app01-8104	61	21	l=−1	l=−1	ADV
app01-8104	61	22	|mi+k	|mi+k	NUM
app01-8104	61	23	,	,	PUNCT
app01-8104	61	24	j+l	j+l	PROPN
app01-8104	61	25	−mi	−mi	PROPN
app01-8104	61	26	,	,	PUNCT
app01-8104	61	27	j	j	PROPN
app01-8104	61	28	|	|	ADV
app01-8104	61	29	.	.	PUNCT
app01-8104	62	1	(	(	PUNCT
app01-8104	62	2	4	4	X
app01-8104	62	3	)	)	PUNCT
app01-8104	62	4	the	the	DET
app01-8104	62	5	error	error	NOUN
app01-8104	62	6	εd	εd	NOUN
app01-8104	62	7	is	be	AUX
app01-8104	62	8	then	then	ADV
app01-8104	62	9	computed	compute	VERB
app01-8104	62	10	again	again	ADV
app01-8104	62	11	as	as	SCONJ
app01-8104	62	12	an	an	DET
app01-8104	62	13	average	average	NOUN
app01-8104	62	14	over	over	ADP
app01-8104	62	15	the	the	DET
app01-8104	62	16	image	image	NOUN
app01-8104	62	17	excluding	exclude	VERB
app01-8104	62	18	the	the	DET
app01-8104	62	19	one	one	NUM
app01-8104	62	20	-	-	PUNCT
app01-8104	62	21	pixel	pixel	NOUN
app01-8104	62	22	wide	wide	ADJ
app01-8104	62	23	margin	margin	NOUN
app01-8104	62	24	,	,	PUNCT
app01-8104	62	25	i.e.	i.e.	X
app01-8104	62	26	εd	εd	ADP
app01-8104	62	27	=	=	SYM
app01-8104	62	28	1	1	NUM
app01-8104	62	29	(	(	PUNCT
app01-8104	62	30	ni	ni	NOUN
app01-8104	62	31	−	−	PROPN
app01-8104	62	32	2)(nj	2)(nj	NUM
app01-8104	62	33	−	−	NOUN
app01-8104	62	34	2	2	NUM
app01-8104	62	35	)	)	PUNCT
app01-8104	62	36	ni−1∑	ni−1∑	VERB
app01-8104	62	37	i=2	i=2	PROPN
app01-8104	62	38	nj−1∑	nj−1∑	PROPN
app01-8104	62	39	j=2	j=2	PROPN
app01-8104	62	40	di	di	PROPN
app01-8104	62	41	,	,	PUNCT
app01-8104	62	42	j	j	PROPN
app01-8104	62	43	.	.	PUNCT
app01-8104	63	1	(	(	PUNCT
app01-8104	63	2	5	5	X
app01-8104	63	3	)	)	PUNCT
app01-8104	63	4	the	the	DET
app01-8104	63	5	reasoning	reasoning	NOUN
app01-8104	63	6	behind	behind	ADP
app01-8104	63	7	this	this	DET
app01-8104	63	8	error	error	NOUN
app01-8104	63	9	measure	measure	NOUN
app01-8104	63	10	is	be	AUX
app01-8104	63	11	that	that	SCONJ
app01-8104	63	12	if	if	SCONJ
app01-8104	63	13	we	we	PRON
app01-8104	63	14	consider	consider	VERB
app01-8104	63	15	the	the	DET
app01-8104	63	16	phases	phase	NOUN
app01-8104	63	17	of	of	ADP
app01-8104	63	18	pixels	pixel	NOUN
app01-8104	63	19	in	in	ADP
app01-8104	63	20	a	a	DET
app01-8104	63	21	very	very	ADV
app01-8104	63	22	small	small	ADJ
app01-8104	63	23	neighbourhood	neighbourhood	NOUN
app01-8104	63	24	around	around	ADP
app01-8104	63	25	the	the	DET
app01-8104	63	26	central	central	ADJ
app01-8104	63	27	pixel	pixel	NOUN
app01-8104	63	28	,	,	PUNCT
app01-8104	63	29	the	the	DET
app01-8104	63	30	number	number	NOUN
app01-8104	63	31	of	of	ADP
app01-8104	63	32	pixels	pixel	NOUN
app01-8104	63	33	whose	whose	DET
app01-8104	63	34	phase	phase	NOUN
app01-8104	63	35	is	be	AUX
app01-8104	63	36	different	different	ADJ
app01-8104	63	37	to	to	ADP
app01-8104	63	38	that	that	PRON
app01-8104	63	39	of	of	ADP
app01-8104	63	40	the	the	DET
app01-8104	63	41	central	central	ADJ
app01-8104	63	42	pixel	pixel	NOUN
app01-8104	63	43	will	will	AUX
app01-8104	63	44	be	be	AUX
app01-8104	63	45	lower	low	ADJ
app01-8104	63	46	if	if	SCONJ
app01-8104	63	47	the	the	DET
app01-8104	63	48	edges	edge	NOUN
app01-8104	63	49	are	be	AUX
app01-8104	63	50	properly	properly	ADV
app01-8104	63	51	smoothed	smooth	VERB
app01-8104	63	52	out	out	ADP
app01-8104	63	53	.	.	PUNCT
app01-8104	64	1	even	even	ADV
app01-8104	64	2	though	though	SCONJ
app01-8104	64	3	this	this	PRON
app01-8104	64	4	might	might	AUX
app01-8104	64	5	not	not	PART
app01-8104	64	6	necessarily	necessarily	ADV
app01-8104	64	7	be	be	AUX
app01-8104	64	8	true	true	ADJ
app01-8104	64	9	for	for	ADP
app01-8104	64	10	the	the	DET
app01-8104	64	11	pixels	pixel	NOUN
app01-8104	64	12	which	which	PRON
app01-8104	64	13	form	form	VERB
app01-8104	64	14	the	the	DET
app01-8104	64	15	edge	edge	NOUN
app01-8104	64	16	of	of	ADP
app01-8104	64	17	the	the	DET
app01-8104	64	18	reconstructed	reconstructed	ADJ
app01-8104	64	19	pattern	pattern	NOUN
app01-8104	64	20	(	(	PUNCT
app01-8104	64	21	and	and	CCONJ
app01-8104	64	22	thus	thus	ADV
app01-8104	64	23	,	,	PUNCT
app01-8104	64	24	the	the	DET
app01-8104	64	25	black	black	ADJ
app01-8104	64	26	and	and	CCONJ
app01-8104	64	27	white	white	ADJ
app01-8104	64	28	phase	phase	NOUN
app01-8104	64	29	must	must	AUX
app01-8104	64	30	switch	switch	VERB
app01-8104	64	31	)	)	PUNCT
app01-8104	64	32	,	,	PUNCT
app01-8104	64	33	it	it	PRON
app01-8104	64	34	will	will	AUX
app01-8104	64	35	apply	apply	VERB
app01-8104	64	36	on	on	ADP
app01-8104	64	37	a	a	DET
app01-8104	64	38	larger	large	ADJ
app01-8104	64	39	scale	scale	NOUN
app01-8104	64	40	(	(	PUNCT
app01-8104	64	41	hence	hence	ADV
app01-8104	64	42	,	,	PUNCT
app01-8104	64	43	we	we	PRON
app01-8104	64	44	compute	compute	VERB
app01-8104	64	45	the	the	DET
app01-8104	64	46	sum	sum	NOUN
app01-8104	64	47	of	of	ADP
app01-8104	64	48	the	the	DET
app01-8104	64	49	values	value	NOUN
app01-8104	64	50	of	of	ADP
app01-8104	64	51	dij	dij	NOUN
app01-8104	64	52	,	,	PUNCT
app01-8104	64	53	for	for	ADP
app01-8104	64	54	all	all	DET
app01-8104	64	55	pixels	pixel	NOUN
app01-8104	64	56	in	in	ADP
app01-8104	64	57	the	the	DET
app01-8104	64	58	image	image	NOUN
app01-8104	64	59	)	)	PUNCT
app01-8104	64	60	.	.	PUNCT
app01-8104	65	1	therefore	therefore	ADV
app01-8104	65	2	,	,	PUNCT
app01-8104	65	3	in	in	ADP
app01-8104	65	4	theory	theory	NOUN
app01-8104	65	5	,	,	PUNCT
app01-8104	65	6	the	the	PRON
app01-8104	65	7	lower	low	ADJ
app01-8104	65	8	the	the	DET
app01-8104	65	9	value	value	NOUN
app01-8104	65	10	of	of	ADP
app01-8104	65	11	εd	εd	NOUN
app01-8104	65	12	is	be	AUX
app01-8104	65	13	for	for	ADP
app01-8104	65	14	an	an	DET
app01-8104	65	15	image	image	NOUN
app01-8104	65	16	,	,	PUNCT
app01-8104	65	17	the	the	DET
app01-8104	65	18	more	more	ADV
app01-8104	65	19	smoothed	smoothed	ADJ
app01-8104	65	20	out	out	ADP
app01-8104	65	21	the	the	DET
app01-8104	65	22	image	image	NOUN
app01-8104	65	23	should	should	AUX
app01-8104	65	24	be	be	AUX
app01-8104	65	25	.	.	PUNCT
app01-8104	66	1	4	4	X
app01-8104	66	2	.	.	X
app01-8104	66	3	results	result	NOUN
app01-8104	66	4	we	we	PRON
app01-8104	66	5	report	report	VERB
app01-8104	66	6	the	the	DET
app01-8104	66	7	effect	effect	NOUN
app01-8104	66	8	of	of	ADP
app01-8104	66	9	parameters	parameter	NOUN
app01-8104	66	10	on	on	ADP
app01-8104	66	11	the	the	DET
app01-8104	66	12	quality	quality	NOUN
app01-8104	66	13	of	of	ADP
app01-8104	66	14	the	the	DET
app01-8104	66	15	microstructural	microstructural	ADJ
app01-8104	66	16	reconstruction	reconstruction	NOUN
app01-8104	66	17	quantified	quantify	VERB
app01-8104	66	18	with	with	ADP
app01-8104	66	19	the	the	DET
app01-8104	66	20	error	error	NOUN
app01-8104	66	21	measures	measure	NOUN
app01-8104	66	22	introduced	introduce	VERB
app01-8104	66	23	in	in	ADP
app01-8104	66	24	the	the	DET
app01-8104	66	25	previous	previous	ADJ
app01-8104	66	26	chapter	chapter	NOUN
app01-8104	66	27	.	.	PUNCT
app01-8104	67	1	first	first	ADV
app01-8104	67	2	,	,	PUNCT
app01-8104	67	3	we	we	PRON
app01-8104	67	4	focus	focus	VERB
app01-8104	67	5	on	on	ADP
app01-8104	67	6	parameters	parameter	NOUN
app01-8104	67	7	of	of	ADP
app01-8104	67	8	the	the	DET
app01-8104	67	9	reconstruction	reconstruction	NOUN
app01-8104	67	10	model	model	NOUN
app01-8104	67	11	.	.	PUNCT
app01-8104	68	1	tables	table	NOUN
app01-8104	68	2	1	1	NUM
app01-8104	68	3	and	and	CCONJ
app01-8104	68	4	2	2	NUM
app01-8104	68	5	illustrate	illustrate	VERB
app01-8104	68	6	the	the	DET
app01-8104	68	7	impact	impact	NOUN
app01-8104	68	8	of	of	ADP
app01-8104	68	9	altering	alter	VERB
app01-8104	68	10	number	number	NOUN
app01-8104	68	11	of	of	ADP
app01-8104	68	12	neurons	neuron	NOUN
app01-8104	68	13	nrn	nrn	NOUN
app01-8104	68	14	9	9	NUM
app01-8104	68	15	16	16	NUM
app01-8104	68	16	25	25	NUM
app01-8104	68	17	nr	nr	NOUN
app01-8104	68	18	`	`	PUNCT
app01-8104	68	19	=	=	NOUN
app01-8104	68	20	2	2	NUM
app01-8104	68	21	0.046	0.046	NUM
app01-8104	68	22	0.031	0.031	NUM
app01-8104	68	23	0.036	0.036	NUM
app01-8104	68	24	nr	nr	PROPN
app01-8104	68	25	`	`	X
app01-8104	68	26	=	=	NOUN
app01-8104	68	27	3	3	NUM
app01-8104	68	28	0.031	0.031	NUM
app01-8104	68	29	0.049	0.049	NUM
app01-8104	68	30	0.029	0.029	NUM
app01-8104	68	31	nr	nr	PROPN
app01-8104	68	32	`	`	X
app01-8104	68	33	=	=	NOUN
app01-8104	68	34	4	4	NUM
app01-8104	68	35	0.002	0.002	NUM
app01-8104	68	36	0.010	0.010	NUM
app01-8104	68	37	0.021	0.021	NUM
app01-8104	68	38	table	table	NOUN
app01-8104	68	39	1	1	NUM
app01-8104	68	40	.	.	PUNCT
app01-8104	68	41	values	value	NOUN
app01-8104	68	42	of	of	ADP
app01-8104	68	43	the	the	DET
app01-8104	68	44	volume	volume	NOUN
app01-8104	68	45	fraction	fraction	NOUN
app01-8104	68	46	error	error	NOUN
app01-8104	68	47	εφ	εφ	PROPN
app01-8104	68	48	,	,	PUNCT
app01-8104	68	49	depending	depend	VERB
app01-8104	68	50	on	on	ADP
app01-8104	68	51	the	the	DET
app01-8104	68	52	number	number	NOUN
app01-8104	68	53	of	of	ADP
app01-8104	68	54	layers	layer	NOUN
app01-8104	68	55	nr	nr	PROPN
app01-8104	68	56	`	`	PUNCT
app01-8104	68	57	and	and	CCONJ
app01-8104	68	58	the	the	DET
app01-8104	68	59	number	number	NOUN
app01-8104	68	60	of	of	ADP
app01-8104	68	61	neurons	neuron	NOUN
app01-8104	68	62	in	in	ADP
app01-8104	68	63	each	each	DET
app01-8104	68	64	layer	layer	NOUN
app01-8104	68	65	nrn	nrn	PROPN
app01-8104	68	66	.	.	PUNCT
app01-8104	68	67	number	number	NOUN
app01-8104	68	68	of	of	ADP
app01-8104	68	69	neurons	neuron	NOUN
app01-8104	68	70	nrn	nrn	NOUN
app01-8104	68	71	9	9	NUM
app01-8104	68	72	16	16	NUM
app01-8104	68	73	25	25	NUM
app01-8104	68	74	nr	nr	PROPN
app01-8104	68	75	`	`	PUNCT
app01-8104	68	76	=	=	SYM
app01-8104	69	1	2	2	NUM
app01-8104	69	2	1.36×10−4	1.36×10−4	NUM
app01-8104	69	3	9.36×10−5	9.36×10−5	NUM
app01-8104	69	4	1.05×10−4	1.05×10−4	NUM
app01-8104	69	5	nr	nr	NOUN
app01-8104	69	6	`	`	X
app01-8104	69	7	=	=	SYM
app01-8104	69	8	3	3	NUM
app01-8104	69	9	9.32×10−5	9.32×10−5	NUM
app01-8104	69	10	1.46×10−4	1.46×10−4	NUM
app01-8104	69	11	7.57×10−5	7.57×10−5	NUM
app01-8104	69	12	nr	nr	NOUN
app01-8104	69	13	`	`	X
app01-8104	69	14	=	=	NOUN
app01-8104	69	15	4	4	NUM
app01-8104	69	16	2.96×10−5	2.96×10−5	NUM
app01-8104	69	17	3.71×10−5	3.71×10−5	NUM
app01-8104	69	18	6.70×10−5	6.70×10−5	NUM
app01-8104	69	19	table	table	NOUN
app01-8104	69	20	2	2	NUM
app01-8104	69	21	.	.	PUNCT
app01-8104	69	22	values	value	NOUN
app01-8104	69	23	of	of	ADP
app01-8104	69	24	the	the	DET
app01-8104	69	25	two	two	NUM
app01-8104	69	26	-	-	PUNCT
app01-8104	69	27	point	point	NOUN
app01-8104	69	28	probability	probability	NOUN
app01-8104	69	29	error	error	NOUN
app01-8104	69	30	εs2	εs2	NOUN
app01-8104	69	31	,	,	PUNCT
app01-8104	69	32	depending	depend	VERB
app01-8104	69	33	on	on	ADP
app01-8104	69	34	the	the	DET
app01-8104	69	35	number	number	NOUN
app01-8104	69	36	of	of	ADP
app01-8104	69	37	layers	layer	NOUN
app01-8104	69	38	nr	nr	PROPN
app01-8104	69	39	`	`	PUNCT
app01-8104	69	40	and	and	CCONJ
app01-8104	69	41	the	the	DET
app01-8104	69	42	number	number	NOUN
app01-8104	69	43	of	of	ADP
app01-8104	69	44	neurons	neuron	NOUN
app01-8104	69	45	in	in	ADP
app01-8104	69	46	each	each	DET
app01-8104	69	47	layer	layer	NOUN
app01-8104	69	48	nrn	nrn	PROPN
app01-8104	69	49	.	.	PUNCT
app01-8104	70	1	the	the	DET
app01-8104	70	2	number	number	NOUN
app01-8104	70	3	of	of	ADP
app01-8104	70	4	densely	densely	ADV
app01-8104	70	5	connected	connected	ADJ
app01-8104	70	6	layers	layer	NOUN
app01-8104	70	7	nr	nr	PRON
app01-8104	70	8	`	`	PUNCT
app01-8104	70	9	and	and	CCONJ
app01-8104	70	10	the	the	DET
app01-8104	70	11	number	number	NOUN
app01-8104	70	12	of	of	ADP
app01-8104	70	13	neurons	neuron	NOUN
app01-8104	70	14	in	in	ADP
app01-8104	70	15	each	each	DET
app01-8104	70	16	layer	layer	NOUN
app01-8104	70	17	nrn	nrn	NOUN
app01-8104	70	18	,	,	PUNCT
app01-8104	70	19	while	while	SCONJ
app01-8104	70	20	keeping	keep	VERB
app01-8104	70	21	the	the	DET
app01-8104	70	22	neighbourhood	neighbourhood	NOUN
app01-8104	70	23	radius	radius	NOUN
app01-8104	70	24	hr	hr	NOUN
app01-8104	70	25	and	and	CCONJ
app01-8104	70	26	the	the	DET
app01-8104	70	27	pooling	pool	VERB
app01-8104	70	28	size	size	NOUN
app01-8104	70	29	nrp	nrp	NOUN
app01-8104	70	30	constant	constant	PROPN
app01-8104	70	31	,	,	PUNCT
app01-8104	70	32	on	on	ADP
app01-8104	70	33	the	the	DET
app01-8104	70	34	volume	volume	NOUN
app01-8104	70	35	fraction	fraction	NOUN
app01-8104	70	36	error	error	NOUN
app01-8104	70	37	εφ	εφ	ADJ
app01-8104	70	38	and	and	CCONJ
app01-8104	70	39	the	the	DET
app01-8104	70	40	twopoint	twopoint	NOUN
app01-8104	70	41	probability	probability	NOUN
app01-8104	70	42	error	error	NOUN
app01-8104	70	43	εs2	εs2	NOUN
app01-8104	70	44	,	,	PUNCT
app01-8104	70	45	respectively	respectively	ADV
app01-8104	70	46	.	.	PUNCT
app01-8104	71	1	in	in	ADP
app01-8104	71	2	particular	particular	ADJ
app01-8104	71	3	,	,	PUNCT
app01-8104	71	4	we	we	PRON
app01-8104	71	5	set	set	VERB
app01-8104	71	6	hr	hr	NOUN
app01-8104	71	7	=	=	SYM
app01-8104	71	8	15	15	NUM
app01-8104	71	9	and	and	CCONJ
app01-8104	71	10	nrp	nrp	NOUN
app01-8104	71	11	=	=	NOUN
app01-8104	71	12	3	3	NUM
app01-8104	71	13	as	as	SCONJ
app01-8104	71	14	these	these	DET
app01-8104	71	15	values	value	NOUN
app01-8104	71	16	produced	produce	VERB
app01-8104	71	17	the	the	DET
app01-8104	71	18	most	most	ADV
app01-8104	71	19	visually	visually	ADV
app01-8104	71	20	appropriate	appropriate	ADJ
app01-8104	71	21	reconstructions	reconstruction	NOUN
app01-8104	71	22	in	in	ADP
app01-8104	71	23	early	early	ADJ
app01-8104	71	24	tests	test	NOUN
app01-8104	71	25	of	of	ADP
app01-8104	71	26	the	the	DET
app01-8104	71	27	model	model	NOUN
app01-8104	71	28	.	.	PUNCT
app01-8104	72	1	the	the	DET
app01-8104	72	2	next	next	ADJ
app01-8104	72	3	two	two	NUM
app01-8104	72	4	tables	table	NOUN
app01-8104	72	5	,	,	PUNCT
app01-8104	72	6	tabs	tab	NOUN
app01-8104	72	7	.	.	PUNCT
app01-8104	72	8	3	3	NUM
app01-8104	72	9	and	and	CCONJ
app01-8104	72	10	4	4	NUM
app01-8104	72	11	,	,	PUNCT
app01-8104	72	12	summarize	summarize	VERB
app01-8104	72	13	the	the	DET
app01-8104	72	14	sensitivity	sensitivity	NOUN
app01-8104	72	15	study	study	NOUN
app01-8104	72	16	investigating	investigate	VERB
app01-8104	72	17	the	the	DET
app01-8104	72	18	influence	influence	NOUN
app01-8104	72	19	of	of	ADP
app01-8104	72	20	the	the	DET
app01-8104	72	21	neighbourhood	neighbourhood	NOUN
app01-8104	72	22	radius	radius	NOUN
app01-8104	72	23	hr	hr	NOUN
app01-8104	72	24	and	and	CCONJ
app01-8104	72	25	the	the	DET
app01-8104	72	26	pooling	pool	VERB
app01-8104	72	27	size	size	NOUN
app01-8104	72	28	nrp	nrp	NOUN
app01-8104	72	29	on	on	ADP
app01-8104	72	30	the	the	DET
app01-8104	72	31	reconstruction	reconstruction	NOUN
app01-8104	72	32	.	.	PUNCT
app01-8104	73	1	this	this	DET
app01-8104	73	2	time	time	NOUN
app01-8104	73	3	,	,	PUNCT
app01-8104	73	4	all	all	DET
app01-8104	73	5	the	the	DET
app01-8104	73	6	tests	test	NOUN
app01-8104	73	7	were	be	AUX
app01-8104	73	8	performed	perform	VERB
app01-8104	73	9	with	with	ADP
app01-8104	73	10	nr	nr	PRON
app01-8104	73	11	`	`	PUNCT
app01-8104	73	12	=	=	SYM
app01-8104	73	13	2	2	NUM
app01-8104	73	14	densely	densely	ADV
app01-8104	73	15	connected	connect	VERB
app01-8104	73	16	layers	layer	NOUN
app01-8104	73	17	with	with	ADP
app01-8104	73	18	nrn	nrn	NOUN
app01-8104	73	19	=	=	SYM
app01-8104	73	20	16	16	NUM
app01-8104	73	21	neurons	neuron	NOUN
app01-8104	73	22	in	in	ADP
app01-8104	73	23	each	each	DET
app01-8104	73	24	layer	layer	NOUN
app01-8104	73	25	.	.	PUNCT
app01-8104	74	1	34	34	NUM
app01-8104	74	2	vol	vol	NOUN
app01-8104	74	3	.	.	PUNCT
app01-8104	75	1	34/2022	34/2022	NUM
app01-8104	75	2	microstructure	microstructure	ADJ
app01-8104	75	3	reconstruction	reconstruction	NOUN
app01-8104	75	4	via	via	ADP
app01-8104	75	5	ann	ann	PROPN
app01-8104	75	6	(	(	PUNCT
app01-8104	75	7	a	a	NOUN
app01-8104	75	8	)	)	PUNCT
app01-8104	75	9	(	(	PUNCT
app01-8104	75	10	b	b	X
app01-8104	75	11	)	)	PUNCT
app01-8104	75	12	figure	figure	NOUN
app01-8104	75	13	4	4	NUM
app01-8104	75	14	.	.	PUNCT
app01-8104	75	15	effect	effect	NOUN
app01-8104	75	16	of	of	ADP
app01-8104	75	17	the	the	DET
app01-8104	75	18	smoothing	smooth	VERB
app01-8104	75	19	network	network	NOUN
app01-8104	75	20	based	base	VERB
app01-8104	75	21	on	on	ADP
app01-8104	75	22	the	the	DET
app01-8104	75	23	non	non	ADJ
app01-8104	75	24	-	-	ADJ
app01-8104	75	25	causal	causal	ADJ
app01-8104	75	26	model	model	NOUN
app01-8104	75	27	:	:	PUNCT
app01-8104	75	28	(	(	PUNCT
app01-8104	75	29	a	a	X
app01-8104	75	30	)	)	PUNCT
app01-8104	75	31	reconstructed	reconstructed	ADJ
app01-8104	75	32	image	image	NOUN
app01-8104	75	33	serving	serve	VERB
app01-8104	75	34	as	as	ADP
app01-8104	75	35	an	an	DET
app01-8104	75	36	input	input	NOUN
app01-8104	75	37	,	,	PUNCT
app01-8104	75	38	(	(	PUNCT
app01-8104	75	39	b	b	NOUN
app01-8104	75	40	)	)	PUNCT
app01-8104	75	41	microstructure	microstructure	NOUN
app01-8104	75	42	corrected	correct	VERB
app01-8104	75	43	by	by	ADP
app01-8104	75	44	the	the	DET
app01-8104	75	45	smoothing	smooth	VERB
app01-8104	75	46	model	model	NOUN
app01-8104	75	47	.	.	PUNCT
app01-8104	76	1	both	both	DET
app01-8104	76	2	images	image	NOUN
app01-8104	76	3	are	be	AUX
app01-8104	76	4	of	of	ADP
app01-8104	76	5	size	size	NOUN
app01-8104	76	6	200	200	NUM
app01-8104	76	7	x	x	SYM
app01-8104	76	8	200	200	NUM
app01-8104	76	9	pixels	pixel	NOUN
app01-8104	76	10	.	.	PUNCT
app01-8104	77	1	neighbourhood	neighbourhood	NOUN
app01-8104	77	2	radius	radius	NOUN
app01-8104	77	3	hr	hr	NOUN
app01-8104	77	4	10	10	NUM
app01-8104	77	5	12	12	NUM
app01-8104	77	6	15	15	NUM
app01-8104	77	7	nrp	nrp	NOUN
app01-8104	77	8	=	=	NOUN
app01-8104	77	9	2	2	NUM
app01-8104	77	10	0.491	0.491	NUM
app01-8104	77	11	0.298	0.298	NUM
app01-8104	77	12	0.006	0.006	NUM
app01-8104	77	13	nrp	nrp	NOUN
app01-8104	77	14	=	=	NOUN
app01-8104	77	15	3	3	NUM
app01-8104	77	16	0.058	0.058	NUM
app01-8104	77	17	0.046	0.046	NUM
app01-8104	77	18	0.031	0.031	NUM
app01-8104	77	19	table	table	NOUN
app01-8104	77	20	3	3	NUM
app01-8104	77	21	.	.	PUNCT
app01-8104	78	1	values	value	NOUN
app01-8104	78	2	of	of	ADP
app01-8104	78	3	the	the	DET
app01-8104	78	4	volume	volume	NOUN
app01-8104	78	5	fraction	fraction	NOUN
app01-8104	78	6	error	error	NOUN
app01-8104	78	7	εφ	εφ	PROPN
app01-8104	78	8	,	,	PUNCT
app01-8104	78	9	depending	depend	VERB
app01-8104	78	10	on	on	ADP
app01-8104	78	11	neighbourhood	neighbourhood	NOUN
app01-8104	78	12	radius	radius	NOUN
app01-8104	78	13	hr	hr	NOUN
app01-8104	78	14	and	and	CCONJ
app01-8104	78	15	pooling	pool	VERB
app01-8104	78	16	size	size	NOUN
app01-8104	78	17	nrp	nrp	PROPN
app01-8104	78	18	.	.	PUNCT
app01-8104	78	19	neighbourhood	neighbourhood	PROPN
app01-8104	78	20	radius	radius	NOUN
app01-8104	78	21	hr	hr	NOUN
app01-8104	78	22	10	10	NUM
app01-8104	78	23	12	12	NUM
app01-8104	78	24	15	15	NUM
app01-8104	78	25	nrp	nrp	NOUN
app01-8104	78	26	=	=	SYM
app01-8104	78	27	2	2	NUM
app01-8104	78	28	2.50×10−3	2.50×10−3	NUM
app01-8104	78	29	1.24×10−3	1.24×10−3	NUM
app01-8104	78	30	4.05×10−5	4.05×10−5	NUM
app01-8104	78	31	nrp	nrp	NOUN
app01-8104	78	32	=	=	NOUN
app01-8104	78	33	3	3	NUM
app01-8104	78	34	1.81×10−4	1.81×10−4	NUM
app01-8104	78	35	1.36×10−4	1.36×10−4	NUM
app01-8104	78	36	9.36×10−5	9.36×10−5	NUM
app01-8104	78	37	table	table	NOUN
app01-8104	78	38	4	4	NUM
app01-8104	78	39	.	.	PUNCT
app01-8104	79	1	values	value	NOUN
app01-8104	79	2	of	of	ADP
app01-8104	79	3	the	the	DET
app01-8104	79	4	two	two	NUM
app01-8104	79	5	-	-	PUNCT
app01-8104	79	6	point	point	NOUN
app01-8104	79	7	probability	probability	NOUN
app01-8104	79	8	error	error	NOUN
app01-8104	79	9	εs2	εs2	NOUN
app01-8104	79	10	,	,	PUNCT
app01-8104	79	11	depending	depend	VERB
app01-8104	79	12	on	on	ADP
app01-8104	79	13	neighbourhood	neighbourhood	NOUN
app01-8104	79	14	radius	radius	NOUN
app01-8104	79	15	hr	hr	NOUN
app01-8104	79	16	and	and	CCONJ
app01-8104	79	17	pooling	pool	VERB
app01-8104	79	18	size	size	NOUN
app01-8104	79	19	nrp	nrp	NOUN
app01-8104	79	20	.	.	PUNCT
app01-8104	80	1	the	the	DET
app01-8104	80	2	next	next	ADJ
app01-8104	80	3	three	three	NUM
app01-8104	80	4	tables	table	NOUN
app01-8104	80	5	,	,	PUNCT
app01-8104	80	6	i.e.	i.e.	X
app01-8104	80	7	tables	table	NOUN
app01-8104	80	8	5	5	NUM
app01-8104	80	9	,	,	PUNCT
app01-8104	80	10	6	6	NUM
app01-8104	80	11	,	,	PUNCT
app01-8104	80	12	and	and	CCONJ
app01-8104	80	13	7	7	NUM
app01-8104	80	14	,	,	PUNCT
app01-8104	80	15	summarize	summarize	VERB
app01-8104	80	16	the	the	DET
app01-8104	80	17	parametric	parametric	ADJ
app01-8104	80	18	study	study	NOUN
app01-8104	80	19	for	for	ADP
app01-8104	80	20	the	the	DET
app01-8104	80	21	smoothing	smooth	VERB
app01-8104	80	22	model	model	NOUN
app01-8104	80	23	with	with	ADP
app01-8104	80	24	non	non	ADJ
app01-8104	80	25	-	-	ADJ
app01-8104	80	26	causal	causal	ADJ
app01-8104	80	27	neighbourhood	neighbourhood	NOUN
app01-8104	80	28	.	.	PUNCT
app01-8104	81	1	we	we	PRON
app01-8104	81	2	chose	choose	VERB
app01-8104	81	3	the	the	DET
app01-8104	81	4	reconstructed	reconstructed	ADJ
app01-8104	81	5	image	image	NOUN
app01-8104	81	6	obtained	obtain	VERB
app01-8104	81	7	by	by	ADP
app01-8104	81	8	the	the	DET
app01-8104	81	9	first	first	ADJ
app01-8104	81	10	model	model	NOUN
app01-8104	81	11	using	use	VERB
app01-8104	81	12	nr	nr	PRON
app01-8104	81	13	`	`	PUNCT
app01-8104	81	14	=	=	SYM
app01-8104	81	15	2	2	NUM
app01-8104	81	16	layers	layer	NOUN
app01-8104	81	17	,	,	PUNCT
app01-8104	81	18	each	each	PRON
app01-8104	81	19	with	with	ADP
app01-8104	81	20	nrn	nrn	NOUN
app01-8104	81	21	=	=	SYM
app01-8104	81	22	16	16	NUM
app01-8104	81	23	neurons	neuron	NOUN
app01-8104	81	24	,	,	PUNCT
app01-8104	81	25	a	a	DET
app01-8104	81	26	neighbourhood	neighbourhood	NOUN
app01-8104	81	27	radius	radius	NOUN
app01-8104	81	28	of	of	ADP
app01-8104	81	29	hr	hr	NOUN
app01-8104	81	30	=	=	PROPN
app01-8104	81	31	15	15	NUM
app01-8104	81	32	and	and	CCONJ
app01-8104	81	33	a	a	DET
app01-8104	81	34	pooling	pool	VERB
app01-8104	81	35	size	size	NOUN
app01-8104	81	36	of	of	ADP
app01-8104	81	37	nrp	nrp	NOUN
app01-8104	81	38	=	=	NOUN
app01-8104	81	39	3	3	NUM
app01-8104	81	40	as	as	ADP
app01-8104	81	41	an	an	DET
app01-8104	81	42	input	input	NOUN
app01-8104	81	43	to	to	ADP
app01-8104	81	44	the	the	DET
app01-8104	81	45	model	model	NOUN
app01-8104	81	46	and	and	CCONJ
app01-8104	81	47	compared	compare	VERB
app01-8104	81	48	the	the	DET
app01-8104	81	49	resulting	result	VERB
app01-8104	81	50	smoothed	smooth	VERB
app01-8104	81	51	-	-	PUNCT
app01-8104	81	52	out	out	ADP
app01-8104	81	53	image	image	NOUN
app01-8104	81	54	to	to	ADP
app01-8104	81	55	the	the	DET
app01-8104	81	56	original	original	ADJ
app01-8104	81	57	microstructure	microstructure	NOUN
app01-8104	81	58	(	(	PUNCT
app01-8104	81	59	before	before	ADP
app01-8104	81	60	reconstruction	reconstruction	NOUN
app01-8104	81	61	)	)	PUNCT
app01-8104	81	62	,	,	PUNCT
app01-8104	81	63	recall	recall	VERB
app01-8104	81	64	fig	fig	PROPN
app01-8104	81	65	.	.	PUNCT
app01-8104	82	1	2a	2a	NUM
app01-8104	82	2	,	,	PUNCT
app01-8104	82	3	in	in	ADP
app01-8104	82	4	terms	term	NOUN
app01-8104	82	5	of	of	ADP
app01-8104	82	6	the	the	DET
app01-8104	82	7	error	error	NOUN
app01-8104	82	8	measures	measure	NOUN
app01-8104	82	9	introduced	introduce	VERB
app01-8104	82	10	in	in	ADP
app01-8104	82	11	section	section	NOUN
app01-8104	82	12	4	4	NUM
app01-8104	82	13	.	.	PUNCT
app01-8104	83	1	we	we	PRON
app01-8104	83	2	carried	carry	VERB
app01-8104	83	3	out	out	ADP
app01-8104	83	4	two	two	NUM
app01-8104	83	5	sets	set	NOUN
app01-8104	83	6	of	of	ADP
app01-8104	83	7	test	test	NOUN
app01-8104	83	8	:	:	PUNCT
app01-8104	83	9	one	one	NUM
app01-8104	83	10	for	for	ADP
app01-8104	83	11	the	the	DET
app01-8104	83	12	average	average	NOUN
app01-8104	83	13	and	and	CCONJ
app01-8104	83	14	one	one	NUM
app01-8104	83	15	for	for	ADP
app01-8104	83	16	the	the	DET
app01-8104	83	17	maximum	maximum	ADJ
app01-8104	83	18	pooling	pool	VERB
app01-8104	83	19	2d	2d	NUM
app01-8104	83	20	layer	layer	NOUN
app01-8104	83	21	.	.	PUNCT
app01-8104	84	1	in	in	ADP
app01-8104	84	2	each	each	DET
app01-8104	84	3	set	set	NOUN
app01-8104	84	4	,	,	PUNCT
app01-8104	84	5	we	we	PRON
app01-8104	84	6	altered	alter	VERB
app01-8104	84	7	the	the	DET
app01-8104	84	8	radius	radius	NOUN
app01-8104	84	9	of	of	ADP
app01-8104	84	10	the	the	DET
app01-8104	84	11	non	non	ADJ
app01-8104	84	12	-	-	ADJ
app01-8104	84	13	causal	causal	ADJ
app01-8104	84	14	neighbourhood	neighbourhood	NOUN
app01-8104	84	15	hs	hs	X
app01-8104	84	16	as	as	ADV
app01-8104	84	17	well	well	ADV
app01-8104	84	18	as	as	ADP
app01-8104	84	19	the	the	DET
app01-8104	84	20	magnitude	magnitude	NOUN
app01-8104	84	21	of	of	ADP
app01-8104	84	22	the	the	DET
app01-8104	84	23	artificially	artificially	ADV
app01-8104	84	24	introduced	introduce	VERB
app01-8104	84	25	noise	noise	NOUN
app01-8104	84	26	ξ	ξ	PROPN
app01-8104	84	27	.	.	PUNCT
app01-8104	85	1	each	each	DET
app01-8104	85	2	set	set	NOUN
app01-8104	85	3	rendered	render	VERB
app01-8104	85	4	three	three	NUM
app01-8104	85	5	tables	table	NOUN
app01-8104	85	6	as	as	SCONJ
app01-8104	85	7	we	we	PRON
app01-8104	85	8	inspected	inspect	VERB
app01-8104	85	9	also	also	ADV
app01-8104	85	10	the	the	DET
app01-8104	85	11	level	level	NOUN
app01-8104	85	12	of	of	ADP
app01-8104	85	13	local	local	ADJ
app01-8104	85	14	heterogeneity	heterogeneity	NOUN
app01-8104	85	15	εd	εd	PROPN
app01-8104	85	16	,	,	PUNCT
app01-8104	85	17	in	in	ADP
app01-8104	85	18	addition	addition	NOUN
app01-8104	85	19	to	to	ADP
app01-8104	85	20	the	the	DET
app01-8104	85	21	errors	error	NOUN
app01-8104	85	22	εφ	εφ	ADJ
app01-8104	85	23	and	and	CCONJ
app01-8104	85	24	εs2	εs2	PROPN
app01-8104	85	25	already	already	ADV
app01-8104	85	26	reported	report	VERB
app01-8104	85	27	for	for	ADP
app01-8104	85	28	the	the	DET
app01-8104	85	29	generative	generative	ADJ
app01-8104	85	30	model	model	NOUN
app01-8104	85	31	.	.	PUNCT
app01-8104	86	1	5	5	X
app01-8104	86	2	.	.	X
app01-8104	86	3	discussion	discussion	NOUN
app01-8104	86	4	first	first	ADV
app01-8104	86	5	,	,	PUNCT
app01-8104	86	6	we	we	PRON
app01-8104	86	7	add	add	VERB
app01-8104	86	8	an	an	DET
app01-8104	86	9	observation	observation	NOUN
app01-8104	86	10	regarding	regard	VERB
app01-8104	86	11	the	the	DET
app01-8104	86	12	three	three	NUM
app01-8104	86	13	error	error	NOUN
app01-8104	86	14	measures	measure	NOUN
app01-8104	86	15	defined	define	VERB
app01-8104	86	16	in	in	ADP
app01-8104	86	17	section	section	NOUN
app01-8104	86	18	3	3	NUM
app01-8104	86	19	and	and	CCONJ
app01-8104	86	20	our	our	PRON
app01-8104	86	21	visual	visual	ADJ
app01-8104	86	22	perception	perception	NOUN
app01-8104	86	23	of	of	ADP
app01-8104	86	24	the	the	DET
app01-8104	86	25	reconstructed	reconstructed	ADJ
app01-8104	86	26	microstructures	microstructure	NOUN
app01-8104	86	27	.	.	PUNCT
app01-8104	87	1	the	the	DET
app01-8104	87	2	first	first	ADJ
app01-8104	87	3	error	error	NOUN
app01-8104	87	4	measure	measure	NOUN
app01-8104	87	5	,	,	PUNCT
app01-8104	87	6	εφ	εφ	ADV
app01-8104	87	7	,	,	PUNCT
app01-8104	87	8	served	serve	VERB
app01-8104	87	9	as	as	ADP
app01-8104	87	10	a	a	DET
app01-8104	87	11	coarse	coarse	ADJ
app01-8104	87	12	check	check	NOUN
app01-8104	87	13	that	that	PRON
app01-8104	87	14	the	the	DET
app01-8104	87	15	volume	volume	NOUN
app01-8104	87	16	fraction	fraction	NOUN
app01-8104	87	17	in	in	ADP
app01-8104	87	18	the	the	DET
app01-8104	87	19	reconstructed	reconstructed	ADJ
app01-8104	87	20	image	image	NOUN
app01-8104	87	21	is	be	AUX
app01-8104	87	22	similar	similar	ADJ
app01-8104	87	23	neighbourhood	neighbourhood	NOUN
app01-8104	87	24	radius	radius	NOUN
app01-8104	87	25	hs	hs	PROPN
app01-8104	87	26	5	5	NUM
app01-8104	87	27	7	7	NUM
app01-8104	87	28	10	10	NUM
app01-8104	87	29	m	m	NOUN
app01-8104	87	30	ax	ax	NOUN
app01-8104	87	31	ξ	ξ	X
app01-8104	87	32	=	=	NOUN
app01-8104	87	33	0.05	0.05	NUM
app01-8104	87	34	0.041	0.041	NUM
app01-8104	87	35	0.002	0.002	NUM
app01-8104	87	36	0.040	0.040	NUM
app01-8104	87	37	ξ	ξ	X
app01-8104	87	38	=	=	SYM
app01-8104	87	39	0.10	0.10	NUM
app01-8104	87	40	0.004	0.004	NUM
app01-8104	87	41	0.036	0.036	NUM
app01-8104	87	42	0.033	0.033	NUM
app01-8104	87	43	ξ	ξ	X
app01-8104	87	44	=	=	SYM
app01-8104	87	45	0.15	0.15	NUM
app01-8104	87	46	0.051	0.051	NUM
app01-8104	87	47	0.027	0.027	NUM
app01-8104	87	48	0.033	0.033	NUM
app01-8104	87	49	av	av	PROPN
app01-8104	87	50	er	er	INTJ
app01-8104	87	51	ag	ag	PROPN
app01-8104	87	52	e	e	PROPN
app01-8104	87	53	ξ	ξ	X
app01-8104	87	54	=	=	SYM
app01-8104	87	55	0.05	0.05	NUM
app01-8104	87	56	0.058	0.058	NUM
app01-8104	87	57	0.000	0.000	NUM
app01-8104	87	58	0.033	0.033	NUM
app01-8104	87	59	ξ	ξ	X
app01-8104	87	60	=	=	SYM
app01-8104	87	61	0.10	0.10	NUM
app01-8104	87	62	0.037	0.037	NUM
app01-8104	87	63	0.003	0.003	NUM
app01-8104	87	64	0.030	0.030	NUM
app01-8104	87	65	ξ	ξ	X
app01-8104	87	66	=	=	SYM
app01-8104	87	67	0.15	0.15	NUM
app01-8104	87	68	0.044	0.044	NUM
app01-8104	87	69	0.029	0.029	NUM
app01-8104	87	70	0.036	0.036	NUM
app01-8104	87	71	table	table	NOUN
app01-8104	87	72	5	5	NUM
app01-8104	87	73	.	.	PUNCT
app01-8104	87	74	values	value	NOUN
app01-8104	87	75	of	of	ADP
app01-8104	87	76	the	the	DET
app01-8104	87	77	volume	volume	NOUN
app01-8104	87	78	fraction	fraction	NOUN
app01-8104	87	79	error	error	NOUN
app01-8104	87	80	εφ	εφ	PROPN
app01-8104	87	81	,	,	PUNCT
app01-8104	87	82	depending	depend	VERB
app01-8104	87	83	on	on	ADP
app01-8104	87	84	the	the	DET
app01-8104	87	85	magnitude	magnitude	NOUN
app01-8104	87	86	of	of	ADP
app01-8104	87	87	artificially	artificially	ADV
app01-8104	87	88	introduce	introduce	VERB
app01-8104	87	89	noise	noise	NOUN
app01-8104	87	90	ξ	ξ	PROPN
app01-8104	87	91	,	,	PUNCT
app01-8104	87	92	neighbourhood	neighbourhood	NOUN
app01-8104	87	93	radius	radius	NOUN
app01-8104	87	94	hs	hs	PROPN
app01-8104	87	95	,	,	PUNCT
app01-8104	87	96	and	and	CCONJ
app01-8104	87	97	the	the	DET
app01-8104	87	98	type	type	NOUN
app01-8104	87	99	of	of	ADP
app01-8104	87	100	pooling	pool	VERB
app01-8104	87	101	layer	layer	NOUN
app01-8104	87	102	used	use	VERB
app01-8104	87	103	(	(	PUNCT
app01-8104	87	104	max	max	NOUN
app01-8104	87	105	or	or	CCONJ
app01-8104	87	106	average	average	ADJ
app01-8104	87	107	)	)	PUNCT
app01-8104	87	108	.	.	PUNCT
app01-8104	88	1	neighbourhood	neighbourhood	PROPN
app01-8104	88	2	radius	radius	NOUN
app01-8104	88	3	hs	hs	PROPN
app01-8104	88	4	5	5	NUM
app01-8104	88	5	7	7	NUM
app01-8104	88	6	10	10	NUM
app01-8104	88	7	m	m	NOUN
app01-8104	88	8	ax	ax	NOUN
app01-8104	88	9	ξ	ξ	X
app01-8104	88	10	=	=	SYM
app01-8104	88	11	0.05	0.05	NUM
app01-8104	88	12	1.22×10−4	1.22×10−4	NUM
app01-8104	88	13	3.15×10−5	3.15×10−5	NUM
app01-8104	88	14	1.20×10−4	1.20×10−4	NUM
app01-8104	88	15	ξ	ξ	X
app01-8104	88	16	=	=	SYM
app01-8104	88	17	0.10	0.10	NUM
app01-8104	88	18	3.24×10−5	3.24×10−5	NUM
app01-8104	89	1	1.06×10−4	1.06×10−4	NUM
app01-8104	89	2	9.88×10−5	9.88×10−5	NUM
app01-8104	89	3	ξ	ξ	X
app01-8104	89	4	=	=	SYM
app01-8104	89	5	0.15	0.15	NUM
app01-8104	89	6	1.23×10−4	1.23×10−4	NUM
app01-8104	89	7	7.21×10−5	7.21×10−5	NUM
app01-8104	90	1	9.88×10−5	9.88×10−5	NUM
app01-8104	90	2	av	av	INTJ
app01-8104	90	3	er	er	INTJ
app01-8104	90	4	ag	ag	PROPN
app01-8104	90	5	e	e	PROPN
app01-8104	90	6	ξ	ξ	PROPN
app01-8104	90	7	=	=	SYM
app01-8104	90	8	0.05	0.05	NUM
app01-8104	90	9	1.76×10−4	1.76×10−4	NUM
app01-8104	90	10	3.08×10−5	3.08×10−5	NUM
app01-8104	91	1	1.01×10−4	1.01×10−4	NUM
app01-8104	91	2	ξ	ξ	X
app01-8104	91	3	=	=	SYM
app01-8104	91	4	0.10	0.10	NUM
app01-8104	91	5	1.12×10−4	1.12×10−4	NUM
app01-8104	91	6	3.25×10−5	3.25×10−5	NUM
app01-8104	91	7	8.98×10−5	8.98×10−5	NUM
app01-8104	91	8	ξ	ξ	X
app01-8104	91	9	=	=	SYM
app01-8104	91	10	0.15	0.15	NUM
app01-8104	91	11	1.31×10−4	1.31×10−4	NUM
app01-8104	91	12	8.90×10−5	8.90×10−5	NUM
app01-8104	91	13	1.08×10−4	1.08×10−4	NUM
app01-8104	91	14	table	table	NOUN
app01-8104	91	15	6	6	NUM
app01-8104	91	16	.	.	PUNCT
app01-8104	92	1	values	value	NOUN
app01-8104	92	2	of	of	ADP
app01-8104	92	3	the	the	DET
app01-8104	92	4	two	two	NUM
app01-8104	92	5	-	-	PUNCT
app01-8104	92	6	point	point	NOUN
app01-8104	92	7	probability	probability	NOUN
app01-8104	92	8	error	error	NOUN
app01-8104	92	9	εs2	εs2	NOUN
app01-8104	92	10	,	,	PUNCT
app01-8104	92	11	depending	depend	VERB
app01-8104	92	12	on	on	ADP
app01-8104	92	13	the	the	DET
app01-8104	92	14	magnitude	magnitude	NOUN
app01-8104	92	15	of	of	ADP
app01-8104	92	16	artificially	artificially	ADV
app01-8104	92	17	introduce	introduce	VERB
app01-8104	92	18	noise	noise	NOUN
app01-8104	92	19	ξ	ξ	PROPN
app01-8104	92	20	,	,	PUNCT
app01-8104	92	21	neighbourhood	neighbourhood	NOUN
app01-8104	92	22	radius	radius	NOUN
app01-8104	92	23	hs	hs	PROPN
app01-8104	92	24	,	,	PUNCT
app01-8104	92	25	and	and	CCONJ
app01-8104	92	26	the	the	DET
app01-8104	92	27	type	type	NOUN
app01-8104	92	28	of	of	ADP
app01-8104	92	29	pooling	pool	VERB
app01-8104	92	30	layer	layer	NOUN
app01-8104	92	31	used	use	VERB
app01-8104	92	32	(	(	PUNCT
app01-8104	92	33	max	max	NOUN
app01-8104	92	34	or	or	CCONJ
app01-8104	92	35	average	average	ADJ
app01-8104	92	36	)	)	PUNCT
app01-8104	92	37	.	.	PUNCT
app01-8104	93	1	to	to	ADP
app01-8104	93	2	the	the	DET
app01-8104	93	3	original	original	NOUN
app01-8104	93	4	;	;	PUNCT
app01-8104	93	5	however	however	ADV
app01-8104	93	6	,	,	PUNCT
app01-8104	93	7	it	it	PRON
app01-8104	93	8	could	could	AUX
app01-8104	93	9	not	not	PART
app01-8104	93	10	assess	assess	VERB
app01-8104	93	11	how	how	SCONJ
app01-8104	93	12	similar	similar	ADJ
app01-8104	93	13	the	the	DET
app01-8104	93	14	reconstructed	reconstructed	ADJ
app01-8104	93	15	pattern	pattern	NOUN
app01-8104	93	16	is	be	AUX
app01-8104	93	17	to	to	ADP
app01-8104	93	18	the	the	DET
app01-8104	93	19	original	original	NOUN
app01-8104	93	20	.	.	PUNCT
app01-8104	94	1	for	for	ADP
app01-8104	94	2	this	this	DET
app01-8104	94	3	purpose	purpose	NOUN
app01-8104	94	4	,	,	PUNCT
app01-8104	94	5	we	we	PRON
app01-8104	94	6	adopted	adopt	VERB
app01-8104	94	7	εs2	εs2	NOUN
app01-8104	94	8	,	,	PUNCT
app01-8104	94	9	based	base	VERB
app01-8104	94	10	on	on	ADP
app01-8104	94	11	the	the	DET
app01-8104	94	12	two	two	NUM
app01-8104	94	13	-	-	PUNCT
app01-8104	94	14	point	point	NOUN
app01-8104	94	15	correlation	correlation	NOUN
app01-8104	94	16	function	function	NOUN
app01-8104	94	17	,	,	PUNCT
app01-8104	94	18	as	as	SCONJ
app01-8104	94	19	we	we	PRON
app01-8104	94	20	excepted	except	VERB
app01-8104	94	21	it	it	PRON
app01-8104	94	22	ot	ot	AUX
app01-8104	94	23	be	be	AUX
app01-8104	94	24	better	well	ADV
app01-8104	94	25	suited	suited	ADJ
app01-8104	94	26	to	to	PART
app01-8104	94	27	compare	compare	VERB
app01-8104	94	28	the	the	DET
app01-8104	94	29	reconstructed	reconstructed	ADJ
app01-8104	94	30	pattern	pattern	NOUN
app01-8104	94	31	to	to	ADP
app01-8104	94	32	the	the	DET
app01-8104	94	33	original	original	NOUN
app01-8104	94	34	.	.	PUNCT
app01-8104	95	1	nevertheless	nevertheless	ADV
app01-8104	95	2	,	,	PUNCT
app01-8104	95	3	we	we	PRON
app01-8104	95	4	noticed	notice	VERB
app01-8104	95	5	a	a	DET
app01-8104	95	6	significant	significant	ADJ
app01-8104	95	7	discrepancy	discrepancy	NOUN
app01-8104	95	8	between	between	ADP
app01-8104	95	9	the	the	DET
app01-8104	95	10	values	value	NOUN
app01-8104	95	11	of	of	ADP
app01-8104	95	12	εs2	εs2	NOUN
app01-8104	95	13	for	for	ADP
app01-8104	95	14	each	each	DET
app01-8104	95	15	model	model	NOUN
app01-8104	95	16	and	and	CCONJ
app01-8104	95	17	the	the	DET
app01-8104	95	18	visual	visual	ADJ
app01-8104	95	19	similarity	similarity	NOUN
app01-8104	95	20	of	of	ADP
app01-8104	95	21	the	the	DET
app01-8104	95	22	reconstructed	reconstructed	ADJ
app01-8104	95	23	pattern	pattern	NOUN
app01-8104	95	24	to	to	ADP
app01-8104	95	25	the	the	DET
app01-8104	95	26	original	original	ADJ
app01-8104	95	27	one	one	NOUN
app01-8104	95	28	.	.	PUNCT
app01-8104	96	1	for	for	ADP
app01-8104	96	2	example	example	NOUN
app01-8104	96	3	,	,	PUNCT
app01-8104	96	4	the	the	DET
app01-8104	96	5	generative	generative	ADJ
app01-8104	96	6	model	model	NOUN
app01-8104	96	7	using	use	VERB
app01-8104	96	8	nr	nr	PRON
app01-8104	96	9	`	`	PUNCT
app01-8104	96	10	=	=	SYM
app01-8104	96	11	2	2	NUM
app01-8104	96	12	layers	layer	NOUN
app01-8104	96	13	,	,	PUNCT
app01-8104	96	14	each	each	PRON
app01-8104	96	15	with	with	ADP
app01-8104	96	16	nrn	nrn	NOUN
app01-8104	96	17	=	=	SYM
app01-8104	96	18	16	16	NUM
app01-8104	96	19	neurons	neuron	NOUN
app01-8104	96	20	,	,	PUNCT
app01-8104	96	21	neighbourhood	neighbourhood	NOUN
app01-8104	96	22	radius	radius	NOUN
app01-8104	96	23	of	of	ADP
app01-8104	96	24	hr	hr	NOUN
app01-8104	96	25	=	=	PROPN
app01-8104	96	26	15	15	NUM
app01-8104	96	27	and	and	CCONJ
app01-8104	96	28	a	a	DET
app01-8104	96	29	pooling	pool	VERB
app01-8104	96	30	size	size	NOUN
app01-8104	96	31	of	of	ADP
app01-8104	96	32	nrp	nrp	NOUN
app01-8104	96	33	=	=	SYM
app01-8104	96	34	3	3	NUM
app01-8104	96	35	,	,	PUNCT
app01-8104	96	36	could	could	AUX
app01-8104	96	37	probably	probably	ADV
app01-8104	96	38	be	be	AUX
app01-8104	96	39	considered	consider	VERB
app01-8104	96	40	as	as	ADP
app01-8104	96	41	the	the	DET
app01-8104	96	42	most	most	ADV
app01-8104	96	43	accurate	accurate	ADJ
app01-8104	96	44	in	in	ADP
app01-8104	96	45	terms	term	NOUN
app01-8104	96	46	of	of	ADP
app01-8104	96	47	the	the	DET
app01-8104	96	48	visual	visual	ADJ
app01-8104	96	49	pattern	pattern	NOUN
app01-8104	96	50	(	(	PUNCT
app01-8104	96	51	the	the	DET
app01-8104	96	52	image	image	NOUN
app01-8104	96	53	reconstructed	reconstruct	VERB
app01-8104	96	54	using	use	VERB
app01-8104	96	55	this	this	DET
app01-8104	96	56	model	model	NOUN
app01-8104	96	57	is	be	AUX
app01-8104	96	58	in	in	ADP
app01-8104	96	59	figure	figure	NOUN
app01-8104	96	60	2c	2c	NUM
app01-8104	96	61	)	)	PUNCT
app01-8104	96	62	,	,	PUNCT
app01-8104	96	63	but	but	CCONJ
app01-8104	96	64	only	only	ADV
app01-8104	96	65	seventh	seventh	ADJ
app01-8104	96	66	35	35	NUM
app01-8104	96	67	k.	k.	PROPN
app01-8104	96	68	latka	latka	PROPN
app01-8104	96	69	,	,	PUNCT
app01-8104	96	70	m.	m.	NOUN
app01-8104	96	71	doškář	doškář	PROPN
app01-8104	96	72	,	,	PUNCT
app01-8104	96	73	j.	j.	PROPN
app01-8104	96	74	zeman	zeman	PROPN
app01-8104	96	75	acta	acta	PROPN
app01-8104	96	76	polytechnica	polytechnica	PROPN
app01-8104	96	77	ctu	ctu	PROPN
app01-8104	96	78	proceedings	proceeding	NOUN
app01-8104	96	79	neighbourhood	neighbourhood	PROPN
app01-8104	96	80	radius	radius	NOUN
app01-8104	96	81	hs	hs	PROPN
app01-8104	96	82	5	5	NUM
app01-8104	96	83	7	7	NUM
app01-8104	96	84	10	10	NUM
app01-8104	96	85	m	m	NOUN
app01-8104	96	86	ax	ax	NOUN
app01-8104	96	87	ξ	ξ	X
app01-8104	96	88	=	=	NOUN
app01-8104	96	89	0.05	0.05	NUM
app01-8104	96	90	0.130	0.130	NUM
app01-8104	96	91	0.133	0.133	NUM
app01-8104	96	92	0.123	0.123	NUM
app01-8104	96	93	ξ	ξ	X
app01-8104	96	94	=	=	PUNCT
app01-8104	96	95	0.10	0.10	NUM
app01-8104	96	96	0.135	0.135	NUM
app01-8104	96	97	0.129	0.129	NUM
app01-8104	96	98	0.124	0.124	NUM
app01-8104	96	99	ξ	ξ	X
app01-8104	96	100	=	=	PUNCT
app01-8104	96	101	0.15	0.15	NUM
app01-8104	96	102	0.123	0.123	NUM
app01-8104	96	103	0.130	0.130	NUM
app01-8104	96	104	0.127	0.127	NUM
app01-8104	96	105	av	av	PROPN
app01-8104	96	106	er	er	INTJ
app01-8104	96	107	ag	ag	PROPN
app01-8104	96	108	e	e	PROPN
app01-8104	96	109	ξ	ξ	X
app01-8104	96	110	=	=	SYM
app01-8104	96	111	0.05	0.05	NUM
app01-8104	96	112	0.113	0.113	NUM
app01-8104	96	113	0.120	0.120	NUM
app01-8104	96	114	0.112	0.112	NUM
app01-8104	96	115	ξ	ξ	X
app01-8104	96	116	=	=	NOUN
app01-8104	96	117	0.10	0.10	NUM
app01-8104	96	118	0.114	0.114	NUM
app01-8104	96	119	0.119	0.119	NUM
app01-8104	96	120	0.112	0.112	NUM
app01-8104	96	121	ξ	ξ	X
app01-8104	97	1	=	=	PUNCT
app01-8104	97	2	0.15	0.15	NUM
app01-8104	97	3	0.114	0.114	NUM
app01-8104	97	4	0.115	0.115	NUM
app01-8104	97	5	0.113	0.113	NUM
app01-8104	97	6	table	table	NOUN
app01-8104	97	7	7	7	NUM
app01-8104	97	8	.	.	PUNCT
app01-8104	98	1	values	value	NOUN
app01-8104	98	2	of	of	ADP
app01-8104	98	3	the	the	DET
app01-8104	98	4	local	local	ADJ
app01-8104	98	5	heterogeneity	heterogeneity	NOUN
app01-8104	98	6	error	error	NOUN
app01-8104	98	7	εd	εd	PROPN
app01-8104	98	8	,	,	PUNCT
app01-8104	98	9	depending	depend	VERB
app01-8104	98	10	on	on	ADP
app01-8104	98	11	the	the	DET
app01-8104	98	12	magnitude	magnitude	NOUN
app01-8104	98	13	of	of	ADP
app01-8104	98	14	artificially	artificially	ADV
app01-8104	98	15	introduce	introduce	VERB
app01-8104	98	16	noise	noise	NOUN
app01-8104	98	17	ξ	ξ	PROPN
app01-8104	98	18	,	,	PUNCT
app01-8104	98	19	neighbourhood	neighbourhood	NOUN
app01-8104	98	20	radius	radius	NOUN
app01-8104	98	21	hs	hs	PROPN
app01-8104	98	22	,	,	PUNCT
app01-8104	98	23	and	and	CCONJ
app01-8104	98	24	the	the	DET
app01-8104	98	25	type	type	NOUN
app01-8104	98	26	of	of	ADP
app01-8104	98	27	pooling	pool	VERB
app01-8104	98	28	layer	layer	NOUN
app01-8104	98	29	used	use	VERB
app01-8104	98	30	(	(	PUNCT
app01-8104	98	31	max	max	NOUN
app01-8104	98	32	or	or	CCONJ
app01-8104	98	33	average	average	ADJ
app01-8104	98	34	)	)	PUNCT
app01-8104	98	35	.	.	PUNCT
app01-8104	99	1	for	for	ADP
app01-8104	99	2	comparison	comparison	NOUN
app01-8104	99	3	,	,	PUNCT
app01-8104	99	4	the	the	DET
app01-8104	99	5	value	value	NOUN
app01-8104	99	6	of	of	ADP
app01-8104	99	7	εd	εd	NOUN
app01-8104	99	8	for	for	ADP
app01-8104	99	9	the	the	DET
app01-8104	99	10	reference	reference	NOUN
app01-8104	99	11	image	image	NOUN
app01-8104	99	12	is	be	AUX
app01-8104	99	13	0.097	0.097	NUM
app01-8104	99	14	.	.	PUNCT
app01-8104	100	1	best	good	ADJ
app01-8104	100	2	according	accord	VERB
app01-8104	100	3	to	to	ADP
app01-8104	100	4	εs2	εs2	PROPN
app01-8104	100	5	.	.	PUNCT
app01-8104	101	1	we	we	PRON
app01-8104	101	2	conclude	conclude	VERB
app01-8104	101	3	that	that	SCONJ
app01-8104	101	4	other	other	ADJ
app01-8104	101	5	spatial	spatial	ADJ
app01-8104	101	6	statistics	statistic	NOUN
app01-8104	101	7	such	such	ADJ
app01-8104	101	8	as	as	ADP
app01-8104	101	9	two	two	NUM
app01-8104	101	10	-	-	PUNCT
app01-8104	101	11	point	point	NOUN
app01-8104	101	12	cluster	cluster	NOUN
app01-8104	101	13	function	function	NOUN
app01-8104	101	14	or	or	CCONJ
app01-8104	101	15	lineal	lineal	ADJ
app01-8104	101	16	path	path	NOUN
app01-8104	101	17	should	should	AUX
app01-8104	101	18	be	be	AUX
app01-8104	101	19	added	add	VERB
app01-8104	101	20	to	to	ADP
app01-8104	101	21	the	the	DET
app01-8104	101	22	suite	suite	NOUN
app01-8104	101	23	of	of	ADP
app01-8104	101	24	error	error	NOUN
app01-8104	101	25	measures	measure	NOUN
app01-8104	101	26	as	as	ADV
app01-8104	101	27	well	well	ADV
app01-8104	101	28	to	to	PART
app01-8104	101	29	capture	capture	VERB
app01-8104	101	30	both	both	CCONJ
app01-8104	101	31	the	the	DET
app01-8104	101	32	global	global	ADJ
app01-8104	101	33	distribution	distribution	NOUN
app01-8104	101	34	of	of	ADP
app01-8104	101	35	a	a	DET
app01-8104	101	36	microstructure	microstructure	NOUN
app01-8104	101	37	and	and	CCONJ
app01-8104	101	38	its	its	PRON
app01-8104	101	39	local	local	ADJ
app01-8104	101	40	characteristics	characteristic	NOUN
app01-8104	101	41	.	.	PUNCT
app01-8104	102	1	on	on	ADP
app01-8104	102	2	the	the	DET
app01-8104	102	3	other	other	ADJ
app01-8104	102	4	hand	hand	NOUN
app01-8104	102	5	,	,	PUNCT
app01-8104	102	6	the	the	DET
app01-8104	102	7	assessment	assessment	NOUN
app01-8104	102	8	of	of	ADP
app01-8104	102	9	the	the	DET
app01-8104	102	10	smoothing	smooth	VERB
app01-8104	102	11	model	model	NOUN
app01-8104	102	12	by	by	ADP
app01-8104	102	13	εd	εd	ADP
app01-8104	102	14	values	value	NOUN
app01-8104	102	15	was	be	AUX
app01-8104	102	16	generally	generally	ADV
app01-8104	102	17	in	in	ADP
app01-8104	102	18	accordance	accordance	NOUN
app01-8104	102	19	with	with	ADP
app01-8104	102	20	the	the	DET
app01-8104	102	21	quality	quality	NOUN
app01-8104	102	22	of	of	ADP
app01-8104	102	23	the	the	DET
app01-8104	102	24	visual	visual	ADJ
app01-8104	102	25	appearance	appearance	NOUN
app01-8104	102	26	of	of	ADP
app01-8104	102	27	the	the	DET
app01-8104	102	28	reconstructed	reconstruct	VERB
app01-8104	102	29	images	image	NOUN
app01-8104	102	30	.	.	PUNCT
app01-8104	103	1	our	our	PRON
app01-8104	103	2	results	result	NOUN
app01-8104	103	3	show	show	VERB
app01-8104	103	4	that	that	SCONJ
app01-8104	103	5	taking	take	VERB
app01-8104	103	6	more	more	ADJ
app01-8104	103	7	layers	layer	NOUN
app01-8104	103	8	with	with	ADP
app01-8104	103	9	less	less	ADJ
app01-8104	103	10	neurons	neuron	NOUN
app01-8104	103	11	in	in	ADP
app01-8104	103	12	each	each	PRON
app01-8104	103	13	was	be	AUX
app01-8104	103	14	favourable	favourable	ADJ
app01-8104	103	15	to	to	ADP
app01-8104	103	16	the	the	DET
app01-8104	103	17	approach	approach	NOUN
app01-8104	103	18	with	with	ADP
app01-8104	103	19	less	less	ADJ
app01-8104	103	20	but	but	CCONJ
app01-8104	103	21	more	more	ADV
app01-8104	103	22	populated	populated	ADJ
app01-8104	103	23	layers	layer	NOUN
app01-8104	103	24	.	.	PUNCT
app01-8104	104	1	perhaps	perhaps	ADV
app01-8104	104	2	as	as	SCONJ
app01-8104	104	3	expected	expect	VERB
app01-8104	104	4	,	,	PUNCT
app01-8104	104	5	the	the	DET
app01-8104	104	6	larger	large	ADJ
app01-8104	104	7	neighbourhood	neighbourhood	NOUN
app01-8104	104	8	radius	radius	NOUN
app01-8104	104	9	was	be	AUX
app01-8104	104	10	considered	consider	VERB
app01-8104	104	11	during	during	ADP
app01-8104	104	12	training	training	NOUN
app01-8104	104	13	of	of	ADP
app01-8104	104	14	the	the	DET
app01-8104	104	15	generative	generative	ADJ
app01-8104	104	16	model	model	NOUN
app01-8104	104	17	,	,	PUNCT
app01-8104	104	18	the	the	PRON
app01-8104	104	19	better	well	ADJ
app01-8104	104	20	the	the	DET
app01-8104	104	21	results	result	NOUN
app01-8104	104	22	were	be	AUX
app01-8104	104	23	.	.	PUNCT
app01-8104	105	1	surprisingly	surprisingly	ADV
app01-8104	105	2	,	,	PUNCT
app01-8104	105	3	nrp	nrp	NOUN
app01-8104	105	4	=	=	SYM
app01-8104	105	5	2	2	NUM
app01-8104	105	6	pooling	pooling	NOUN
app01-8104	105	7	size	size	NOUN
app01-8104	105	8	was	be	AUX
app01-8104	105	9	better	well	ADJ
app01-8104	105	10	for	for	ADP
app01-8104	105	11	the	the	DET
app01-8104	105	12	largest	large	ADJ
app01-8104	105	13	neighbourhood	neighbourhood	NOUN
app01-8104	105	14	radius	radius	NOUN
app01-8104	105	15	,	,	PUNCT
app01-8104	105	16	while	while	SCONJ
app01-8104	105	17	larger	large	ADJ
app01-8104	105	18	pooling	pooling	NOUN
app01-8104	105	19	was	be	AUX
app01-8104	105	20	preferential	preferential	ADJ
app01-8104	105	21	in	in	ADP
app01-8104	105	22	all	all	DET
app01-8104	105	23	other	other	ADJ
app01-8104	105	24	cases	case	NOUN
app01-8104	105	25	.	.	PUNCT
app01-8104	106	1	in	in	ADP
app01-8104	106	2	the	the	DET
app01-8104	106	3	case	case	NOUN
app01-8104	106	4	of	of	ADP
app01-8104	106	5	the	the	DET
app01-8104	106	6	smoothing	smooth	VERB
app01-8104	106	7	model	model	NOUN
app01-8104	106	8	,	,	PUNCT
app01-8104	106	9	considering	consider	VERB
app01-8104	106	10	larger	large	ADJ
app01-8104	106	11	neighbourhood	neighbourhood	NOUN
app01-8104	106	12	radius	radius	NOUN
app01-8104	106	13	hs	hs	INTJ
app01-8104	106	14	beyond	beyond	ADP
app01-8104	106	15	certain	certain	ADJ
app01-8104	106	16	threshold	threshold	NOUN
app01-8104	106	17	(	(	PUNCT
app01-8104	106	18	in	in	ADP
app01-8104	106	19	our	our	PRON
app01-8104	106	20	case	case	NOUN
app01-8104	106	21	hs	hs	X
app01-8104	106	22	=	=	SYM
app01-8104	106	23	7	7	X
app01-8104	106	24	)	)	PUNCT
app01-8104	106	25	did	do	AUX
app01-8104	106	26	not	not	PART
app01-8104	106	27	improve	improve	VERB
app01-8104	106	28	its	its	PRON
app01-8104	106	29	performance	performance	NOUN
app01-8104	106	30	.	.	PUNCT
app01-8104	107	1	this	this	PRON
app01-8104	107	2	can	can	AUX
app01-8104	107	3	be	be	AUX
app01-8104	107	4	attributed	attribute	VERB
app01-8104	107	5	to	to	ADP
app01-8104	107	6	the	the	DET
app01-8104	107	7	different	different	ADJ
app01-8104	107	8	purpose	purpose	NOUN
app01-8104	107	9	of	of	ADP
app01-8104	107	10	both	both	DET
app01-8104	107	11	models	model	NOUN
app01-8104	107	12	;	;	PUNCT
app01-8104	107	13	while	while	SCONJ
app01-8104	107	14	the	the	DET
app01-8104	107	15	generative	generative	ADJ
app01-8104	107	16	model	model	NOUN
app01-8104	107	17	needs	need	VERB
app01-8104	107	18	information	information	NOUN
app01-8104	107	19	from	from	ADP
app01-8104	107	20	distant	distant	ADJ
app01-8104	107	21	points	point	NOUN
app01-8104	107	22	to	to	PART
app01-8104	107	23	properly	properly	ADV
app01-8104	107	24	distribute	distribute	VERB
app01-8104	107	25	the	the	DET
app01-8104	107	26	material	material	NOUN
app01-8104	107	27	with	with	ADP
app01-8104	107	28	the	the	DET
app01-8104	107	29	sample	sample	NOUN
app01-8104	107	30	,	,	PUNCT
app01-8104	107	31	the	the	DET
app01-8104	107	32	smoothing	smooth	VERB
app01-8104	107	33	model	model	NOUN
app01-8104	107	34	is	be	AUX
app01-8104	107	35	by	by	ADP
app01-8104	107	36	its	its	PRON
app01-8104	107	37	nature	nature	NOUN
app01-8104	107	38	local	local	ADJ
app01-8104	107	39	.	.	PUNCT
app01-8104	108	1	on	on	ADP
app01-8104	108	2	the	the	DET
app01-8104	108	3	other	other	ADJ
app01-8104	108	4	hand	hand	NOUN
app01-8104	108	5	,	,	PUNCT
app01-8104	108	6	larger	large	ADJ
app01-8104	108	7	pooling	pooling	NOUN
app01-8104	108	8	layer	layer	NOUN
app01-8104	108	9	was	be	AUX
app01-8104	108	10	consistently	consistently	ADV
app01-8104	108	11	outperforming	outperform	VERB
app01-8104	108	12	the	the	DET
app01-8104	108	13	smaller	small	ADJ
app01-8104	108	14	one	one	NOUN
app01-8104	108	15	in	in	ADP
app01-8104	108	16	all	all	DET
app01-8104	108	17	tests	test	NOUN
app01-8104	108	18	.	.	PUNCT
app01-8104	109	1	most	most	ADV
app01-8104	109	2	importantly	importantly	ADV
app01-8104	109	3	,	,	PUNCT
app01-8104	109	4	we	we	PRON
app01-8104	109	5	noticed	notice	VERB
app01-8104	109	6	that	that	SCONJ
app01-8104	109	7	the	the	DET
app01-8104	109	8	values	value	NOUN
app01-8104	109	9	of	of	ADP
app01-8104	109	10	εd	εd	NOUN
app01-8104	109	11	were	be	AUX
app01-8104	109	12	significantly	significantly	ADV
app01-8104	109	13	lower	low	ADJ
app01-8104	109	14	for	for	ADP
app01-8104	109	15	the	the	DET
app01-8104	109	16	smoothing	smooth	VERB
app01-8104	109	17	models	model	NOUN
app01-8104	109	18	using	use	VERB
app01-8104	109	19	an	an	DET
app01-8104	109	20	average	average	ADJ
app01-8104	109	21	pooling	pooling	NOUN
app01-8104	109	22	layer	layer	NOUN
app01-8104	109	23	instead	instead	ADV
app01-8104	109	24	of	of	ADP
app01-8104	109	25	a	a	DET
app01-8104	109	26	maximum	maximum	ADJ
app01-8104	109	27	pooling	pool	VERB
app01-8104	109	28	layer	layer	NOUN
app01-8104	109	29	.	.	PUNCT
app01-8104	110	1	this	this	PRON
app01-8104	110	2	is	be	AUX
app01-8104	110	3	probably	probably	ADV
app01-8104	110	4	due	due	ADJ
app01-8104	110	5	to	to	ADP
app01-8104	110	6	the	the	DET
app01-8104	110	7	fact	fact	NOUN
app01-8104	110	8	that	that	SCONJ
app01-8104	110	9	the	the	DET
app01-8104	110	10	average	average	ADJ
app01-8104	110	11	pooling	pooling	NOUN
app01-8104	110	12	layer	layer	NOUN
app01-8104	110	13	ignores	ignore	VERB
app01-8104	110	14	sharp	sharp	ADJ
app01-8104	110	15	features	feature	NOUN
app01-8104	110	16	in	in	ADP
app01-8104	110	17	the	the	DET
app01-8104	110	18	image	image	NOUN
app01-8104	110	19	(	(	PUNCT
app01-8104	110	20	e.	e.	PROPN
app01-8104	110	21	g.	g.	PROPN
app01-8104	110	22	individual	individual	ADJ
app01-8104	110	23	pixels	pixel	NOUN
app01-8104	110	24	whose	whose	DET
app01-8104	110	25	phase	phase	NOUN
app01-8104	110	26	was	be	AUX
app01-8104	110	27	not	not	PART
app01-8104	110	28	correctly	correctly	ADV
app01-8104	110	29	identified	identify	VERB
app01-8104	110	30	in	in	ADP
app01-8104	110	31	the	the	DET
app01-8104	110	32	part	part	NOUN
app01-8104	110	33	1	1	NUM
app01-8104	110	34	of	of	ADP
app01-8104	110	35	the	the	DET
app01-8104	110	36	reconstruction	reconstruction	NOUN
app01-8104	110	37	)	)	PUNCT
app01-8104	110	38	,	,	PUNCT
app01-8104	110	39	which	which	PRON
app01-8104	110	40	allowed	allow	VERB
app01-8104	110	41	us	we	PRON
app01-8104	110	42	to	to	PART
app01-8104	110	43	smooth	smooth	VERB
app01-8104	110	44	out	out	ADP
app01-8104	110	45	the	the	DET
app01-8104	110	46	edges	edge	NOUN
app01-8104	110	47	of	of	ADP
app01-8104	110	48	the	the	DET
app01-8104	110	49	reconstructed	reconstructed	ADJ
app01-8104	110	50	pattern	pattern	NOUN
app01-8104	110	51	in	in	ADP
app01-8104	110	52	the	the	DET
app01-8104	110	53	image	image	NOUN
app01-8104	110	54	.	.	PUNCT
app01-8104	111	1	however	however	ADV
app01-8104	111	2	,	,	PUNCT
app01-8104	111	3	it	it	PRON
app01-8104	111	4	is	be	AUX
app01-8104	111	5	important	important	ADJ
app01-8104	111	6	to	to	PART
app01-8104	111	7	emphasize	emphasize	VERB
app01-8104	111	8	that	that	SCONJ
app01-8104	111	9	these	these	DET
app01-8104	111	10	observations	observation	NOUN
app01-8104	111	11	are	be	AUX
app01-8104	111	12	specific	specific	ADJ
app01-8104	111	13	for	for	ADP
app01-8104	111	14	the	the	DET
app01-8104	111	15	considered	consider	VERB
app01-8104	111	16	microstructure	microstructure	NOUN
app01-8104	111	17	.	.	PUNCT
app01-8104	112	1	6	6	X
app01-8104	112	2	.	.	X
app01-8104	112	3	conclusions	conclusion	NOUN
app01-8104	112	4	despite	despite	SCONJ
app01-8104	112	5	the	the	DET
app01-8104	112	6	simplicity	simplicity	NOUN
app01-8104	112	7	of	of	ADP
app01-8104	112	8	the	the	DET
app01-8104	112	9	proposed	propose	VERB
app01-8104	112	10	ann	ann	PROPN
app01-8104	112	11	-	-	PUNCT
app01-8104	112	12	based	base	VERB
app01-8104	112	13	model	model	NOUN
app01-8104	112	14	,	,	PUNCT
app01-8104	112	15	accompanied	accompany	VERB
app01-8104	112	16	by	by	ADP
app01-8104	112	17	the	the	DET
app01-8104	112	18	ease	ease	NOUN
app01-8104	112	19	of	of	ADP
app01-8104	112	20	implementation	implementation	NOUN
app01-8104	112	21	facilitated	facilitate	VERB
app01-8104	112	22	by	by	ADP
app01-8104	112	23	the	the	DET
app01-8104	112	24	tensorflow	tensorflow	NOUN
app01-8104	112	25	framework	framework	NOUN
app01-8104	112	26	and	and	CCONJ
app01-8104	112	27	the	the	DET
app01-8104	112	28	keras	keras	PROPN
app01-8104	112	29	sequential	sequential	ADJ
app01-8104	112	30	api	api	NOUN
app01-8104	112	31	,	,	PUNCT
app01-8104	112	32	the	the	DET
app01-8104	112	33	model	model	NOUN
app01-8104	112	34	generates	generate	VERB
app01-8104	112	35	meaningful	meaningful	ADJ
app01-8104	112	36	microstructural	microstructural	ADJ
app01-8104	112	37	geometries	geometry	NOUN
app01-8104	112	38	.	.	PUNCT
app01-8104	113	1	the	the	DET
app01-8104	113	2	combination	combination	NOUN
app01-8104	113	3	of	of	ADP
app01-8104	113	4	a	a	DET
app01-8104	113	5	causal	causal	ADJ
app01-8104	113	6	model	model	NOUN
app01-8104	113	7	used	use	VERB
app01-8104	113	8	for	for	ADP
app01-8104	113	9	reconstruction	reconstruction	NOUN
app01-8104	113	10	and	and	CCONJ
app01-8104	113	11	a	a	DET
app01-8104	113	12	noncausal	noncausal	ADJ
app01-8104	113	13	smoothing	smoothing	NOUN
app01-8104	113	14	model	model	NOUN
app01-8104	113	15	in	in	ADP
app01-8104	113	16	particular	particular	ADJ
app01-8104	113	17	yielded	yield	VERB
app01-8104	113	18	satisfying	satisfying	NOUN
app01-8104	113	19	results	result	NOUN
app01-8104	113	20	,	,	PUNCT
app01-8104	113	21	cf	cf	INTJ
app01-8104	113	22	.	.	PUNCT
app01-8104	113	23	figure	figure	NOUN
app01-8104	113	24	4	4	NUM
app01-8104	113	25	,	,	PUNCT
app01-8104	113	26	considering	consider	VERB
app01-8104	113	27	the	the	DET
app01-8104	113	28	fact	fact	NOUN
app01-8104	113	29	that	that	SCONJ
app01-8104	113	30	the	the	DET
app01-8104	113	31	models	model	NOUN
app01-8104	113	32	knew	know	VERB
app01-8104	113	33	only	only	ADV
app01-8104	113	34	a	a	DET
app01-8104	113	35	limited	limited	ADJ
app01-8104	113	36	local	local	ADJ
app01-8104	113	37	information	information	NOUN
app01-8104	113	38	.	.	PUNCT
app01-8104	114	1	we	we	PRON
app01-8104	114	2	believe	believe	VERB
app01-8104	114	3	that	that	SCONJ
app01-8104	114	4	even	even	ADV
app01-8104	114	5	better	well	ADJ
app01-8104	114	6	results	result	NOUN
app01-8104	114	7	can	can	AUX
app01-8104	114	8	be	be	AUX
app01-8104	114	9	obtained	obtain	VERB
app01-8104	114	10	by	by	ADP
app01-8104	114	11	,	,	PUNCT
app01-8104	114	12	e.g.	e.g.	ADV
app01-8104	114	13	,	,	PUNCT
app01-8104	114	14	incorporating	incorporate	VERB
app01-8104	114	15	the	the	DET
app01-8104	114	16	considered	consider	VERB
app01-8104	114	17	errors	error	NOUN
app01-8104	114	18	directly	directly	ADV
app01-8104	114	19	in	in	ADP
app01-8104	114	20	the	the	DET
app01-8104	114	21	loss	loss	NOUN
app01-8104	114	22	function	function	NOUN
app01-8104	114	23	during	during	ADP
app01-8104	114	24	the	the	DET
app01-8104	114	25	training	training	NOUN
app01-8104	114	26	process	process	NOUN
app01-8104	114	27	of	of	ADP
app01-8104	114	28	individual	individual	ADJ
app01-8104	114	29	networks	network	NOUN
app01-8104	114	30	.	.	PUNCT
app01-8104	115	1	yet	yet	ADV
app01-8104	115	2	,	,	PUNCT
app01-8104	115	3	this	this	PRON
app01-8104	115	4	remains	remain	VERB
app01-8104	115	5	to	to	PART
app01-8104	115	6	be	be	AUX
app01-8104	115	7	done	do	VERB
app01-8104	115	8	in	in	ADP
app01-8104	115	9	our	our	PRON
app01-8104	115	10	future	future	ADJ
app01-8104	115	11	work	work	NOUN
app01-8104	115	12	.	.	PUNCT
app01-8104	116	1	the	the	DET
app01-8104	116	2	need	need	NOUN
app01-8104	116	3	for	for	ADP
app01-8104	116	4	the	the	DET
app01-8104	116	5	margin	margin	NOUN
app01-8104	116	6	of	of	ADP
app01-8104	116	7	initial	initial	ADJ
app01-8104	116	8	values	value	NOUN
app01-8104	116	9	during	during	ADP
app01-8104	116	10	reconstruction	reconstruction	NOUN
app01-8104	116	11	might	might	AUX
app01-8104	116	12	be	be	AUX
app01-8104	116	13	seen	see	VERB
app01-8104	116	14	as	as	ADP
app01-8104	116	15	a	a	DET
app01-8104	116	16	limitation	limitation	NOUN
app01-8104	116	17	restricting	restrict	VERB
app01-8104	116	18	the	the	DET
app01-8104	116	19	model	model	NOUN
app01-8104	116	20	to	to	ADP
app01-8104	116	21	generating	generate	VERB
app01-8104	116	22	microstructural	microstructural	ADJ
app01-8104	116	23	samples	sample	NOUN
app01-8104	116	24	only	only	ADV
app01-8104	116	25	as	as	ADV
app01-8104	116	26	large	large	ADJ
app01-8104	116	27	as	as	ADP
app01-8104	116	28	the	the	DET
app01-8104	116	29	reference	reference	NOUN
app01-8104	116	30	one	one	NUM
app01-8104	116	31	,	,	PUNCT
app01-8104	116	32	from	from	ADP
app01-8104	116	33	which	which	PRON
app01-8104	116	34	the	the	DET
app01-8104	116	35	margin	margin	NOUN
app01-8104	116	36	can	can	AUX
app01-8104	116	37	be	be	AUX
app01-8104	116	38	easily	easily	ADV
app01-8104	116	39	copied	copy	VERB
app01-8104	116	40	.	.	PUNCT
app01-8104	117	1	however	however	ADV
app01-8104	117	2	,	,	PUNCT
app01-8104	117	3	a	a	DET
app01-8104	117	4	possible	possible	ADJ
app01-8104	117	5	solution	solution	NOUN
app01-8104	117	6	is	be	AUX
app01-8104	117	7	to	to	PART
app01-8104	117	8	take	take	VERB
app01-8104	117	9	the	the	DET
app01-8104	117	10	reference	reference	NOUN
app01-8104	117	11	sample	sample	NOUN
app01-8104	117	12	,	,	PUNCT
app01-8104	117	13	dismember	dismember	VERB
app01-8104	117	14	it	it	PRON
app01-8104	117	15	into	into	ADP
app01-8104	117	16	pieces	piece	NOUN
app01-8104	117	17	and	and	CCONJ
app01-8104	117	18	reorder	reorder	VERB
app01-8104	117	19	the	the	DET
app01-8104	117	20	pieces	piece	NOUN
app01-8104	117	21	so	so	SCONJ
app01-8104	117	22	that	that	SCONJ
app01-8104	117	23	they	they	PRON
app01-8104	117	24	form	form	VERB
app01-8104	117	25	the	the	DET
app01-8104	117	26	margin	margin	NOUN
app01-8104	117	27	of	of	ADP
app01-8104	117	28	desired	desire	VERB
app01-8104	117	29	size	size	NOUN
app01-8104	117	30	.	.	PUNCT
app01-8104	118	1	alternatively	alternatively	ADV
app01-8104	118	2	,	,	PUNCT
app01-8104	118	3	starting	start	VERB
app01-8104	118	4	from	from	ADP
app01-8104	118	5	different	different	ADJ
app01-8104	118	6	parts	part	NOUN
app01-8104	118	7	of	of	ADP
app01-8104	118	8	the	the	DET
app01-8104	118	9	reference	reference	NOUN
app01-8104	118	10	microstructure	microstructure	NOUN
app01-8104	118	11	,	,	PUNCT
app01-8104	118	12	a	a	DET
app01-8104	118	13	set	set	NOUN
app01-8104	118	14	of	of	ADP
app01-8104	118	15	smaller	small	ADJ
app01-8104	118	16	samples	sample	NOUN
app01-8104	118	17	can	can	AUX
app01-8104	118	18	be	be	AUX
app01-8104	118	19	generated	generate	VERB
app01-8104	118	20	;	;	PUNCT
app01-8104	118	21	these	these	DET
app01-8104	118	22	samples	sample	NOUN
app01-8104	118	23	can	can	AUX
app01-8104	118	24	be	be	AUX
app01-8104	118	25	then	then	ADV
app01-8104	118	26	assembled	assemble	VERB
app01-8104	118	27	together	together	ADV
app01-8104	118	28	while	while	SCONJ
app01-8104	118	29	blending	blend	VERB
app01-8104	118	30	the	the	DET
app01-8104	118	31	microstructure	microstructure	NOUN
app01-8104	118	32	in	in	ADP
app01-8104	118	33	their	their	PRON
app01-8104	118	34	overlaps	overlap	NOUN
app01-8104	118	35	using	use	VERB
app01-8104	118	36	,	,	PUNCT
app01-8104	118	37	e.g.	e.g.	ADV
app01-8104	118	38	,	,	PUNCT
app01-8104	118	39	image	image	NOUN
app01-8104	118	40	quilting	quilting	NOUN
app01-8104	118	41	[	[	X
app01-8104	118	42	6	6	NUM
app01-8104	118	43	]	]	PUNCT
app01-8104	118	44	.	.	PUNCT
app01-8104	119	1	acknowledgements	acknowledgement	NOUN
app01-8104	119	2	m.	m.	NOUN
app01-8104	119	3	doškář	doškář	PROPN
app01-8104	119	4	and	and	CCONJ
app01-8104	119	5	j.	j.	PROPN
app01-8104	119	6	zeman	zeman	PROPN
app01-8104	119	7	gratefully	gratefully	ADV
app01-8104	119	8	acknowledge	acknowledge	VERB
app01-8104	119	9	support	support	NOUN
app01-8104	119	10	by	by	ADP
app01-8104	119	11	the	the	DET
app01-8104	119	12	czech	czech	PROPN
app01-8104	119	13	science	science	PROPN
app01-8104	119	14	foundation	foundation	PROPN
app01-8104	119	15	,	,	PUNCT
app01-8104	119	16	project	project	VERB
app01-8104	119	17	no	no	NOUN
app01-8104	119	18	.	.	NOUN
app01-8104	119	19	19	19	NUM
app01-8104	119	20	-	-	PUNCT
app01-8104	119	21	26143x	26143x	NUM
app01-8104	119	22	.	.	PUNCT
app01-8104	120	1	references	reference	NOUN
app01-8104	120	2	[	[	X
app01-8104	120	3	1	1	NUM
app01-8104	120	4	]	]	PUNCT
app01-8104	120	5	m.	m.	NOUN
app01-8104	120	6	g.	g.	PROPN
app01-8104	120	7	d.	d.	PROPN
app01-8104	120	8	geers	geers	PROPN
app01-8104	120	9	,	,	PUNCT
app01-8104	120	10	v.	v.	PROPN
app01-8104	120	11	g.	g.	PROPN
app01-8104	120	12	kouznetsova	kouznetsova	PROPN
app01-8104	120	13	,	,	PUNCT
app01-8104	120	14	k.	k.	PROPN
app01-8104	120	15	matouš	matouš	PROPN
app01-8104	120	16	,	,	PUNCT
app01-8104	120	17	j.	j.	PROPN
app01-8104	120	18	yvonnet	yvonnet	PROPN
app01-8104	120	19	.	.	PUNCT
app01-8104	121	1	homogenization	homogenization	NOUN
app01-8104	121	2	methods	method	NOUN
app01-8104	121	3	and	and	CCONJ
app01-8104	121	4	multiscale	multiscale	NOUN
app01-8104	121	5	modeling	modeling	NOUN
app01-8104	121	6	:	:	PUNCT
app01-8104	121	7	nonlinear	nonlinear	ADJ
app01-8104	121	8	problems	problem	NOUN
app01-8104	121	9	.	.	PUNCT
app01-8104	122	1	in	in	ADP
app01-8104	122	2	e.	e.	PROPN
app01-8104	122	3	stein	stein	PROPN
app01-8104	122	4	,	,	PUNCT
app01-8104	122	5	r.	r.	PROPN
app01-8104	122	6	de	de	X
app01-8104	122	7	borst	borst	PROPN
app01-8104	122	8	,	,	PUNCT
app01-8104	122	9	t.	t.	PROPN
app01-8104	122	10	j.	j.	PROPN
app01-8104	122	11	r.	r.	PROPN
app01-8104	122	12	hughes	hughes	PROPN
app01-8104	122	13	(	(	PUNCT
app01-8104	122	14	eds	eds	PROPN
app01-8104	122	15	.	.	PUNCT
app01-8104	122	16	)	)	PUNCT
app01-8104	122	17	,	,	PUNCT
app01-8104	122	18	encyclopedia	encyclopedia	NOUN
app01-8104	122	19	of	of	ADP
app01-8104	122	20	computational	computational	ADJ
app01-8104	122	21	mechanics	mechanic	NOUN
app01-8104	122	22	second	second	NOUN
app01-8104	122	23	edition	edition	NOUN
app01-8104	122	24	,	,	PUNCT
app01-8104	122	25	pp	pp	PROPN
app01-8104	122	26	.	.	PUNCT
app01-8104	122	27	1–34	1–34	NOUN
app01-8104	122	28	.	.	PUNCT
app01-8104	123	1	john	john	PROPN
app01-8104	123	2	wiley	wiley	PROPN
app01-8104	123	3	&	&	CCONJ
app01-8104	123	4	sons	sons	PROPN
app01-8104	123	5	,	,	PUNCT
app01-8104	123	6	ltd	ltd	PROPN
app01-8104	123	7	,	,	PUNCT
app01-8104	123	8	chichester	chichester	PROPN
app01-8104	123	9	,	,	PUNCT
app01-8104	123	10	uk	uk	PROPN
app01-8104	123	11	,	,	PUNCT
app01-8104	123	12	2017	2017	NUM
app01-8104	123	13	.	.	PUNCT
app01-8104	124	1	https://doi.org/10.1002/9781119176817.ecm107	https://doi.org/10.1002/9781119176817.ecm107	PROPN
app01-8104	124	2	.	.	PUNCT
app01-8104	125	1	[	[	X
app01-8104	125	2	2	2	X
app01-8104	125	3	]	]	PUNCT
app01-8104	125	4	s.	s.	PROPN
app01-8104	125	5	torquato	torquato	PROPN
app01-8104	125	6	.	.	PUNCT
app01-8104	126	1	random	random	ADJ
app01-8104	126	2	heterogeneous	heterogeneous	ADJ
app01-8104	126	3	materials	material	NOUN
app01-8104	126	4	:	:	PUNCT
app01-8104	126	5	microstructure	microstructure	ADJ
app01-8104	126	6	and	and	CCONJ
app01-8104	126	7	macroscopic	macroscopic	ADJ
app01-8104	126	8	properties	property	NOUN
app01-8104	126	9	.	.	PUNCT
app01-8104	127	1	no	no	INTJ
app01-8104	127	2	.	.	NOUN
app01-8104	128	1	16	16	NUM
app01-8104	128	2	in	in	ADP
app01-8104	128	3	interdisciplinary	interdisciplinary	ADJ
app01-8104	128	4	applied	apply	VERB
app01-8104	128	5	mathematics	mathematic	NOUN
app01-8104	128	6	.	.	PUNCT
app01-8104	129	1	springer	springer	NOUN
app01-8104	129	2	,	,	PUNCT
app01-8104	129	3	new	new	PROPN
app01-8104	129	4	york	york	PROPN
app01-8104	129	5	,	,	PUNCT
app01-8104	129	6	2002	2002	NUM
app01-8104	129	7	.	.	PUNCT
app01-8104	130	1	[	[	X
app01-8104	130	2	3	3	X
app01-8104	130	3	]	]	PUNCT
app01-8104	130	4	j.	j.	PROPN
app01-8104	130	5	zeman	zeman	PROPN
app01-8104	130	6	,	,	PUNCT
app01-8104	130	7	m.	m.	PROPN
app01-8104	130	8	šejnoha	šejnoha	PROPN
app01-8104	130	9	.	.	PUNCT
app01-8104	131	1	from	from	ADP
app01-8104	131	2	random	random	ADJ
app01-8104	131	3	microstructures	microstructure	NOUN
app01-8104	131	4	to	to	ADP
app01-8104	131	5	representative	representative	ADJ
app01-8104	131	6	volume	volume	NOUN
app01-8104	131	7	elements	element	NOUN
app01-8104	131	8	.	.	PUNCT
app01-8104	132	1	modelling	modelling	NOUN
app01-8104	132	2	and	and	CCONJ
app01-8104	132	3	simulation	simulation	NOUN
app01-8104	132	4	in	in	ADP
app01-8104	132	5	materials	material	NOUN
app01-8104	132	6	science	science	NOUN
app01-8104	132	7	and	and	CCONJ
app01-8104	132	8	engineering	engineering	NOUN
app01-8104	132	9	15(4):s325	15(4):s325	NUM
app01-8104	132	10	–	–	PUNCT
app01-8104	132	11	s335	s335	PROPN
app01-8104	132	12	,	,	PUNCT
app01-8104	132	13	2007	2007	NUM
app01-8104	132	14	.	.	PUNCT
app01-8104	133	1	https://doi.org/10.1088/0965-0393/15/4/s01	https://doi.org/10.1088/0965-0393/15/4/s01	PROPN
app01-8104	133	2	.	.	PUNCT
app01-8104	134	1	[	[	X
app01-8104	134	2	4	4	NUM
app01-8104	134	3	]	]	PUNCT
app01-8104	134	4	l.-y	l.-y	NOUN
app01-8104	134	5	.	.	PUNCT
app01-8104	135	1	wei	wei	PROPN
app01-8104	135	2	,	,	PUNCT
app01-8104	135	3	m.	m.	NOUN
app01-8104	135	4	levoy	levoy	PROPN
app01-8104	135	5	.	.	PUNCT
app01-8104	136	1	fast	fast	ADJ
app01-8104	136	2	texture	texture	ADJ
app01-8104	136	3	synthesis	synthesis	NOUN
app01-8104	136	4	using	use	VERB
app01-8104	136	5	tree	tree	NOUN
app01-8104	136	6	-	-	PUNCT
app01-8104	136	7	structured	structure	VERB
app01-8104	136	8	vector	vector	NOUN
app01-8104	136	9	quantization	quantization	NOUN
app01-8104	136	10	.	.	PUNCT
app01-8104	137	1	in	in	ADP
app01-8104	137	2	proceedings	proceeding	NOUN
app01-8104	137	3	of	of	ADP
app01-8104	137	4	the	the	DET
app01-8104	137	5	27th	27th	ADJ
app01-8104	137	6	annual	annual	ADJ
app01-8104	137	7	conference	conference	NOUN
app01-8104	137	8	on	on	ADP
app01-8104	137	9	computer	computer	NOUN
app01-8104	137	10	graphics	graphic	NOUN
app01-8104	137	11	and	and	CCONJ
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app01-8104	157	14	.	.	PUNCT
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app01-8104	160	23	3	3	NUM
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app01-8104	160	28	5	5	NUM
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app01-8104	160	30	6	6	NUM
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