id	sid	eid	entity	type
app01-10442	1	1	acta polytechnica ctu proceedings https://doi.org/10.14311/app.2024.51.0075	PERSON
app01-10442	1	2	acta polytechnica ctu	PERSON
app01-10442	1	3	51:75–80	CARDINAL
app01-10442	1	4	2024	CARDINAL
app01-10442	1	5	2024	DATE
app01-10442	2	1	4.0	CARDINAL
app01-10442	2	2	the czech technical university	ORG
app01-10442	2	3	prague	GPE
app01-10442	2	4	jan rychlík∗	GPE
app01-10442	2	5	roman mouček university	ORG
app01-10442	2	6	west bohemia	GPE
app01-10442	2	7	department of computer science	ORG
app01-10442	2	8	8	CARDINAL
app01-10442	2	9	301 00	CARDINAL
app01-10442	2	10	czech	NORP
app01-10442	2	11	∗	CARDINAL
app01-10442	3	1	approximately one-third	CARDINAL
app01-10442	6	1	montreal	GPE
app01-10442	7	1	67%	PERCENT
app01-10442	9	1	1	CARDINAL
app01-10442	9	2	approximately one-third	CARDINAL
app01-10442	11	1	rem	ORG
app01-10442	11	2	non-rem	PERSON
app01-10442	16	1	cnn	ORG
app01-10442	25	1	2	CARDINAL
app01-10442	26	1	1	CARDINAL
app01-10442	26	2	four	CARDINAL
app01-10442	26	3	one	CARDINAL
app01-10442	27	1	bonn university	ORG
app01-10442	29	1	nearly 79%	PERCENT
app01-10442	29	2	71%	PERCENT
app01-10442	29	3	nn neural	ORG
app01-10442	29	4	61%	PERCENT
app01-10442	29	5	70%	PERCENT
app01-10442	29	6	about 53%	PERCENT
app01-10442	30	1	1	CARDINAL
app01-10442	31	1	germany	GPE
app01-10442	31	2	aachen university of applied sciences	ORG
app01-10442	31	3	2	CARDINAL
app01-10442	32	1	18	CARDINAL
app01-10442	32	2	52 days	DATE
app01-10442	33	1	75	CARDINAL
app01-10442	33	2	https://www.cvut.cz/en	DATE
app01-10442	33	3	jan rychlík	GPE
app01-10442	33	4	roman mouček	GPE
app01-10442	33	5	acta polytechnica ctu proceedings	ORG
app01-10442	34	1	eight	CARDINAL
app01-10442	34	2	two	CARDINAL
app01-10442	34	3	rem	PERSON
app01-10442	34	4	nrem	ORG
app01-10442	34	5	over 98%	PERCENT
app01-10442	34	6	more than 94%	PERCENT
app01-10442	34	7	close to 92%	PERCENT
app01-10442	35	1	58%	PERCENT
app01-10442	36	1	rem	PERSON
app01-10442	36	2	nrem	PERSON
app01-10442	36	3	2	CARDINAL
app01-10442	37	1	1d	CARDINAL
app01-10442	37	2	cnn	ORG
app01-10442	37	3	rem	PERSON
app01-10442	37	4	non-rem	PERSON
app01-10442	37	5	93.47%	PERCENT
app01-10442	37	6	3	CARDINAL
app01-10442	38	1	197	CARDINAL
app01-10442	39	1	seven	CARDINAL
app01-10442	39	2	four 1d	DATE
app01-10442	39	3	cnn	ORG
app01-10442	39	4	three	CARDINAL
app01-10442	40	1	eog	ORG
app01-10442	40	2	five	CARDINAL
app01-10442	41	1	94.15%	PERCENT
app01-10442	42	1	3	CARDINAL
app01-10442	43	1	1	CARDINAL
app01-10442	43	2	non-rem	PERSON
app01-10442	44	1	0.5 to 2 seconds	QUANTITY
app01-10442	44	2	11 to 16hz	DATE
app01-10442	46	1	1	CARDINAL
app01-10442	48	1	5	CARDINAL
app01-10442	49	1	canadian	NORP
app01-10442	49	2	seven	CARDINAL
app01-10442	49	3	only two	CARDINAL
app01-10442	50	1	1	CARDINAL
app01-10442	50	2	4	CARDINAL
app01-10442	51	1	montreal	GPE
app01-10442	51	2	6	CARDINAL
app01-10442	52	1	5	CARDINAL
app01-10442	52	2	6	CARDINAL
app01-10442	53	1	maas	PERSON
app01-10442	53	2	6	CARDINAL
app01-10442	54	1	3	CARDINAL
app01-10442	54	2	ss2	PERSON
app01-10442	54	3	1	CARDINAL
app01-10442	55	1	about two percent	PERCENT
app01-10442	57	1	10−4	CARDINAL
app01-10442	60	1	about ninety-eight percent	CARDINAL
app01-10442	61	1	4	CARDINAL
app01-10442	63	1	4.1	CARDINAL
app01-10442	64	1	2	CARDINAL
app01-10442	64	2	one	CARDINAL
app01-10442	65	1	76	CARDINAL
app01-10442	66	1	51/2024	CARDINAL
app01-10442	66	2	2	CARDINAL
app01-10442	66	3	ss2	NORP
app01-10442	66	4	7.26	CARDINAL
app01-10442	66	5	19	CARDINAL
app01-10442	66	6	11204	CARDINAL
app01-10442	66	7	217	CARDINAL
app01-10442	66	8	151	CARDINAL
app01-10442	66	9	569	CARDINAL
app01-10442	66	10	86	CARDINAL
app01-10442	66	11	2.218605	CARDINAL
app01-10442	66	12	0.335915	CARDINAL
app01-10442	66	13	7	CARDINAL
app01-10442	66	14	59min	CARDINAL
app01-10442	66	15	1	CARDINAL
app01-10442	66	16	ss2	NORP
app01-10442	66	17	2	CARDINAL
app01-10442	67	1	one	CARDINAL
app01-10442	67	2	two	CARDINAL
app01-10442	67	3	one	CARDINAL
app01-10442	70	1	4.2	CARDINAL
app01-10442	71	1	cnn	ORG
app01-10442	71	2	cnn	ORG
app01-10442	71	3	3	CARDINAL
app01-10442	77	1	3	CARDINAL
app01-10442	78	1	cnn	ORG
app01-10442	78	2	8	CARDINAL
app01-10442	79	1	4.3	CARDINAL
app01-10442	79	2	rnn	ORG
app01-10442	81	1	lstms	PERSON
app01-10442	83	1	4	CARDINAL
app01-10442	84	1	5	CARDINAL
app01-10442	84	2	three	CARDINAL
app01-10442	84	3	cnn	ORG
app01-10442	85	1	ad77	PRODUCT
app01-10442	85	2	jan rychlík	GPE
app01-10442	85	3	roman mouček	GPE
app01-10442	85	4	acta polytechnica ctu	ORG
app01-10442	85	5	4	CARDINAL
app01-10442	86	1	lstm neural network	ORG
app01-10442	86	2	9	CARDINAL
app01-10442	91	1	cnn	ORG
app01-10442	94	1	cnn	ORG
app01-10442	94	2	cnn	ORG
app01-10442	94	3	cnn	ORG
app01-10442	94	4	cnn	ORG
app01-10442	94	5	cnn	ORG
app01-10442	95	1	cnn	ORG
app01-10442	95	2	5	CARDINAL
app01-10442	96	1	seven	CARDINAL
app01-10442	96	2	four	CARDINAL
app01-10442	96	3	32	CARDINAL
app01-10442	97	1	384	CARDINAL
app01-10442	98	1	64	CARDINAL
app01-10442	99	1	two	CARDINAL
app01-10442	100	1	5.1	CARDINAL
app01-10442	100	2	nearly 200,000	CARDINAL
app01-10442	100	3	51.2%	PERCENT
app01-10442	101	1	montreal	GPE
app01-10442	102	1	two	CARDINAL
app01-10442	109	1	zero	CARDINAL
app01-10442	110	1	64	CARDINAL
app01-10442	115	1	6	CARDINAL
app01-10442	115	2	2	CARDINAL
app01-10442	116	1	6	CARDINAL
app01-10442	117	1	64.63%	PERCENT
app01-10442	117	2	52.81%	PERCENT
app01-10442	118	1	cnn	ORG
app01-10442	118	2	60.87%	PERCENT
app01-10442	118	3	cnn	ORG
app01-10442	118	4	52.81%	PERCENT
app01-10442	118	5	cnn	ORG
app01-10442	118	6	67.15%	PERCENT
app01-10442	118	7	64.12%	PERCENT
app01-10442	118	8	2	CARDINAL
app01-10442	119	1	7	CARDINAL
app01-10442	119	2	8	CARDINAL
app01-10442	122	1	51/2024	CARDINAL
app01-10442	122	2	5	CARDINAL
app01-10442	123	1	cnn	ORG
app01-10442	124	1	cnn	ORG
app01-10442	125	1	6	CARDINAL
app01-10442	127	1	7	CARDINAL
app01-10442	130	1	lstm-cnn	ORG
app01-10442	130	2	52.81%	PERCENT
app01-10442	131	1	cnn	ORG
app01-10442	131	2	60–65%	PERCENT
app01-10442	132	1	cnn	ORG
app01-10442	132	2	67%	PERCENT
app01-10442	133	1	7	CARDINAL
app01-10442	135	1	70–75%	PERCENT
app01-10442	136	1	64.12%	PERCENT
app01-10442	139	1	79	CARDINAL
app01-10442	139	2	jan rychlík	GPE
app01-10442	139	3	roman mouček	GPE
app01-10442	139	4	acta polytechnica ctu	ORG
app01-10442	139	5	8	CARDINAL
app01-10442	141	1	sgs-2022-016	DATE
app01-10442	141	2	sgs-2022-016	DATE
app01-10442	142	1	1	CARDINAL
app01-10442	142	2	s. t. george	PERSON
app01-10442	142	3	s. radha	PERSON
app01-10442	144	1	7:9981–10003	CARDINAL
app01-10442	144	2	2020	DATE
app01-10442	146	1	2	CARDINAL
app01-10442	146	2	n. grieger	PERSON
app01-10442	146	3	j. t. c.	PERSON
app01-10442	146	4	s. wendel	PERSON
app01-10442	147	1	11:12245	CARDINAL
app01-10442	147	2	2021	CARDINAL
app01-10442	148	1	3	CARDINAL
app01-10442	148	2	d. zhao	PERSON
app01-10442	148	3	r. jiang	PERSON
app01-10442	148	4	m. feng	PERSON
app01-10442	149	1	1d	CARDINAL
app01-10442	149	2	cnn	ORG
app01-10442	150	1	30(2):323–336	CARDINAL
app01-10442	150	2	2022	DATE
app01-10442	151	1	4	CARDINAL
app01-10442	151	2	i. b. iotchev	PERSON
app01-10442	151	3	e. kubinyi	PERSON
app01-10442	152	1	96(3):1021–1034	CARDINAL
app01-10442	152	2	2021	DATE
app01-10442	154	1	5	CARDINAL
app01-10442	154	2	k. lacourse	PERSON
app01-10442	154	3	b. yetton	PERSON
app01-10442	154	4	s. mednick	PERSON
app01-10442	154	5	s. c. warby	PERSON
app01-10442	156	1	7:190, 2020	DATE
app01-10442	158	1	6	CARDINAL
app01-10442	159	1	montreal	GPE
app01-10442	159	2	2015	DATE
app01-10442	160	1	2022	CARDINAL
app01-10442	163	1	2019	DATE
app01-10442	164	1	2022	CARDINAL
app01-10442	165	1	https://towardsdatascience.com/everything-youneed-to-know-about-neural-networks-andbackpropagation-machine-learning-made-easy/e5285bc2be3a	CARDINAL
app01-10442	166	1	8	CARDINAL
app01-10442	166	2	ibm	ORG
app01-10442	167	1	convolutional neural networks	ORG
app01-10442	167	2	2020	DATE
app01-10442	168	1	2022	CARDINAL
app01-10442	169	1	9	CARDINAL
app01-10442	170	1	lstm recurrent neural networks	ORG
app01-10442	170	2	2021	CARDINAL
app01-10442	171	1	2022	CARDINAL
app01-10442	172	1	80	CARDINAL
app01-10442	172	2	https://towardsdatascience.com/lstm-recurrent-neural-networks-how-to-teach-a-network-to-remember-the-past-55e54c2ff22e	GPE
app01-10442	172	3	https://towardsdatascience.com/lstm-recurrent-neural-networks-how-to-teach-a-network-to-remember-the-past-55e54c2ff22e	GPE
app01-10442	172	4	https://towardsdatascience.com/lstm-recurrent-neural-networks-how-to-teach-a-network-to-remember-the-past-55e54c2ff22e acta	GPE
app01-10442	172	5	polytechnica ctu	ORG
app01-10442	172	6	51:75–80	CARDINAL
app01-10442	172	7	2024 1	DATE
app01-10442	172	8	2	CARDINAL
app01-10442	172	9	3	CARDINAL
app01-10442	172	10	4	CARDINAL
app01-10442	172	11	4.1	CARDINAL
app01-10442	172	12	4.2	CARDINAL
app01-10442	172	13	cnn	ORG
app01-10442	173	1	4.3	CARDINAL
app01-10442	173	2	5	CARDINAL
app01-10442	173	3	5.1	CARDINAL
app01-10442	173	4	6	CARDINAL
app01-10442	173	5	7	CARDINAL