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Relevant bibliographies by topics / Interval Sequences

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Journal articles

Academic literature on the topic 'Interval Sequences'

Author: Grafiati

Published: 10 June 2023

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Journal articles on the topic "Interval Sequences"

1

Bar-Noy, Amotz, Keerti Choudhary, David Peleg, and Dror Rawitz. "Efficiently Realizing Interval Sequences." SIAM Journal on Discrete Mathematics 34, no. 4 (2020): 2318–37. http://dx.doi.org/10.1137/20m1326489.

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2

Dewsnap and Fischer. "INTERVAL MAPS AND KOENIGS' SEQUENCES." Real Analysis Exchange 25, no. 1 (1999): 205. http://dx.doi.org/10.2307/44153071.

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3

Lychak, M. M. "Interval characteristics of chaotic sequences." Cybernetics and Systems Analysis 40, no. 5 (2004): 678–88. http://dx.doi.org/10.1007/s10559-005-0005-z.

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4

Cysarz, D., H. Bettermann, and P. van Leeuwen. "Entropies of short binary sequences in heart period dynamics." American Journal of Physiology-Heart and Circulatory Physiology 278, no. 6 (2000): H2163—H2172. http://dx.doi.org/10.1152/ajpheart.2000.278.6.h2163.

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Dynamic aspects of R-R intervals have often been analyzed by means of linear and nonlinear measures. The goal of this study was to analyze binary sequences, in which only the dynamic information is retained, by means of two different aspects of regularity. R-R interval sequences derived from 24-h electrocardiogram (ECG) recordings of 118 healthy subjects were converted to symbolic binary sequences that coded the beat-to-beat increase or decrease in the R-R interval. Shannon entropy was used to quantify the occurrence of short binary patterns (length N = 5) in binary sequences derived from 10-min intervals. The regularity of the short binary patterns was analyzed on the basis of approximate entropy (ApEn). ApEn had a linear dependence on mean R-R interval length, with increasing irregularity occurring at longer R-R interval length. Shannon entropy of the same sequences showed that the increase in irregularity is accompanied by a decrease in occurrence of some patterns. Taken together, these data indicate that irregular binary patterns are more probable when the mean R-R interval increases. The use of surrogate data confirmed a nonlinear component in the binary sequence. Analysis of two consecutive 24-h ECG recordings for each subject demonstrated good intraindividual reproducibility of the results. In conclusion, quantification of binary sequences derived from ECG recordings reveals properties that cannot be found using the full information of R-R interval sequences.

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5

Belfer, Alexander, and Martin C. Golumbic. "Counting endpoint sequences for interval orders and interval graphs." Discrete Mathematics 114, no. 1-3 (1993): 23–39. http://dx.doi.org/10.1016/0012-365x(93)90353-u.

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6

Repp, Bruno H., Justin London, and Peter E. Keller. "Phase Correction in Sensorimotor Synchronization with Nonisochronous Sequences." Music Perception 26, no. 2 (2008): 171–75. http://dx.doi.org/10.1525/mp.2008.26.2.171.

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PHASE CORRECTION, WHICH IS NECESSARY for synchronization of movements with a rhythm, has been studied primarily with isochronous sequences.We used a phase perturbation method to examine phase correction in synchronization with nonisochronous sequences (3:2 interval ratios), using musically trained participants. In isochronous control sequences, the phase correction response (PCR) of the tap following a small phase shift was larger when the intervals were long (600 ms) than when they were short (400 ms). In nonisochronous cyclic two-interval patterns, we found a similar dependence of the PCR on the duration of the interval following a phase shift. In three-interval patterns, however, there was no clear dependence on interval duration. The metrical interpretation of the sequences (downbeat location) had no effect on phase correction. In general, phase correction was as effective with nonisochronous as with isochronous sequences.

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7

Agafonov, A. Y., A. D. Fomicheva, G. A. Starostin, and A. P. Kryukova. "Implicit Learning of the Time Interval Sequence." Experimental Psychology (Russia) 14, no. 1 (2021): 108–21. http://dx.doi.org/10.17759/exppsy.2021140104.

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The article considers the studies performed in the «Sequence Learning» paradigm. A special case of this experimental approach is the method of temporal sequences memorization. The elements of such sequences are time intervals instead of stimulus or their spatial localization. The item of the conducted and described study was implicit learning of the time interval sequence. The goal of the experiment was to check the possibility of unconscious acquisition of the temporal sequences, not related to the sequences of another type of organization. To process the obtained results, mixed linear models were used. It was found that the learning of time interval sequences can occur regardless of the presence of regularity in the reaction order (motor sequence) and without rules in stimuli organization (structural sequence) or in the order of their localization (spatial sequence).

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8

Bentbib, A. H. "Acceleration of convergence of interval sequences." Journal of Computational and Applied Mathematics 51, no. 3 (1994): 395–409. http://dx.doi.org/10.1016/0377-0427(92)00120-x.

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9

Roh, Jong-Won, and Byoung-Kee Yi. "Efficient indexing of interval time sequences." Information Processing Letters 109, no. 1 (2008): 1–12. http://dx.doi.org/10.1016/j.ipl.2008.08.003.

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10

Debnath, Shyamal, and Subrata Saha. "Matrix transformation on statistically convergent sequence spaces of interval number sequences." Proyecciones (Antofagasta) 35, no. 2 (2016): 187–95. http://dx.doi.org/10.4067/s0716-09172016000200004.

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