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Статті в журналах з теми "Network sequences"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Byrnes, Sean, Anthony N. Burkitt, David B. Grayden, and Hamish Meffin. "Learning a Sparse Code for Temporal Sequences Using STDP and Sequence Compression." Neural Computation 23, no. 10 (2011): 2567–98. http://dx.doi.org/10.1162/neco_a_00184.

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A spiking neural network that learns temporal sequences is described. A sparse code in which individual neurons represent sequences and subsequences enables multiple sequences to be stored without interference. The network is founded on a model of sequence compression in the hippocampus that is robust to variation in sequence element duration and well suited to learn sequences through spike-timing dependent plasticity (STDP). Three additions to the sequence compression model underlie the sparse representation: synapses connecting the neurons of the network that are subject to STDP, a competiti

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2

Wei, Fang-ping, Sheng Li, and Hong-ru Ma. "Network of tRNA gene sequences." Journal of Shanghai Jiaotong University (Science) 13, no. 5 (2008): 611–16. http://dx.doi.org/10.1007/s12204-008-0611-9.

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3

Nishitani, Yoshi, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Hajime Sawai, and Shinichi Tamura. "Detection of M-Sequences from Spike Sequence in Neuronal Networks." Computational Intelligence and Neuroscience 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/862579.

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In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembli

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4

Rocco, Claudio M., Kash Barker, Jose Moronta, and Jose E. Ramirez-Marquez. "Community detection and resilience in multi-source, multi-terminal networks." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (2018): 616–26. http://dx.doi.org/10.1177/1748006x17751516.

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Many networks, particularly infrastructure networks, have multiple source nodes and multiple terminal nodes. And many such networks exhibit community structures, wherein the network is partitioned into groups of densely connected nodes with sparse connections between groups, based on topology or spatial characteristics, among others. This article proposes an approach for evaluating the effects of disruptive events, or the disconnection of network components due to failures or attacks, to the community structures and to the total network. The approach enables the assessment of resilience, evalu

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5

Allaby, Robin G., and Terence A. Brown. "Network Analysis Provides Insights Into Evolution of 5S rDNA Arrays in Triticum and Aegilops." Genetics 157, no. 3 (2001): 1331–41. http://dx.doi.org/10.1093/genetics/157.3.1331.

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Abstract We have used network analysis to study gene sequences of the Triticum and Aegilops 5S rDNA arrays, as well as the spacers of the 5S-DNA-A1 and 5S-DNA-2 loci. Network analysis describes relationships between 5S rDNA sequences in a more realistic fashion than conventional tree building because it makes fewer assumptions about the direction of evolution, the extent of sexual isolation, and the pattern of ancestry and descent. The networks show that the 5S rDNA sequences of Triticum and Aegilops species are related in a reticulate manner around principal nodal sequences. The spacer networ

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6

Ren, Zhuo-Ming, Xiao Pan, and Yi-Cheng Zhang. "Significance of the Nested Structure in Multiplex World Trade Networks." Complexity 2020 (December 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/8827840.

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The hierarchically nested structure is widely observed in a broad range of real systems, encompassing ecological networks, economic and trade networks, communication networks, among many others. However, there remain statistical challenges of the prevalence of nestedness. In response to this problem, we focus on the effect of incomplete information and the inputted matrix size, the role of network density and degree sequences, and the relevance of degree-degree correlation to conduct systematic research on the significance of the nested structure according to multiplex world trade networks. Fi

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7

Mathur, Rinku, and Neeru Adlakha. "Binary sequences-based approach for construction of evolutionary network." International Journal of Biomathematics 07, no. 02 (2014): 1450012. http://dx.doi.org/10.1142/s1793524514500120.

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Evolutionary studies have been of prime importance to life scientists since ancient times. The advancements in technology has made it possible to make available the massive amounts of genomic data. The abundance of genomic data poses new challenges for biologists, computer scientists and mathematicians to develop approaches for discovery of new relationships in data and evolutionary networks. In this work, nucleotide sequences are converted into binary sequences to explore the network among different species. A new approach based on binary sequences has been proposed to reconstruct the accurat

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8

Azha Javed and Muhammad Javed Iqbal. "Classification of Biological Data using Deep Learning Technique." NUML International Journal of Engineering and Computing 1, no. 1 (2022): 13–26. http://dx.doi.org/10.52015/nijec.v1i1.10.

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A huge amount of newly sequenced proteins is being discovered on daily basis. The mainconcern is how to extract the useful characteristics of sequences as the input features for thenetwork. These sequences are increasing exponentially over the decades. However, it is veryexpensive to characterize functions for biological experiments and also, it is really necessaryto find the association between the information of datasets to create and improve medicaltools. Recently machine learning algorithms got huge attention and are widely used. Thesealgorithms are based on deep learning architecture and

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9

MORITA, Kenta, Haruhiko TAKASE, Hiroharu KAWANAKA, and Naoki MORITA. "Extraction of Frequent Sub-Sequences from Long Sequence Using Neural Network." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 31, no. 1 (2019): 592–96. http://dx.doi.org/10.3156/jsoft.31.1_592.

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10

Weiss, Michael, Henrike Hultsch, Iris Adam, Constance Scharff, and Silke Kipper. "The use of network analysis to study complex animal communication systems: a study on nightingale song." Proceedings of the Royal Society B: Biological Sciences 281, no. 1785 (2014): 20140460. http://dx.doi.org/10.1098/rspb.2014.0460.

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The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales ( Luscinia megarhynchos ) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measure

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11

Liu, Zhiguo, Weijie Li, Jianxin Feng, and Jiaojiao Zhang. "Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network." Sensors 22, no. 22 (2022): 8678. http://dx.doi.org/10.3390/s22228678.

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The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we propose a satellite network traffic forecasting method with an improved gate recurrent unit (GRU). This method combines the attention mechanism with GRU neural network, fully mines the characteristics of self-similarity and long correlation among traffic data sequences, pays attention to the importance of traffic data and hidden state, learns the

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12

Charles, Adam S., Han Lun Yap, and Christopher J. Rozell. "Short-Term Memory Capacity in Networks via the Restricted Isometry Property." Neural Computation 26, no. 6 (2014): 1198–235. http://dx.doi.org/10.1162/neco_a_00590.

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Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the

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13

C, Ms Devadharshini. "MUSIC GENERATION USING NEURAL NETWORK." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33813.

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This study investigates the utilization of recurrent neural networks (RNNs) for generating music through MIDI files. By encoding musical data into sequences, RNN models are trained to learn patterns and structures inherent in compositions. Through the analysis of MIDI data and the evaluation of generated sequences, the effectiveness of RNNs in autonomously creating cohesive musical pieces is explored, advancing the frontier of AI-driven musical composition. Keywords—Music Generation, Long Short-Term Memory, Recurrent Neural Network, MIDI data .

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14

Bántay, László, and János Abonyi. "Network-based visualisation of frequent sequences." PLOS ONE 19, no. 5 (2024): e0301262. http://dx.doi.org/10.1371/journal.pone.0301262.

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Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understan

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15

Xue, Kun, Xiaoxia Han, Jinde Wu, Yadi Shen, Xinying Xu, and Gang Xie. "Community detection based on competitive walking network embedding method." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 9 (2022): 093402. http://dx.doi.org/10.1088/1742-5468/ac8807.

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Abstract Currently, much of the information of the real world is network-structured, and extracting hidden information from network-structured data helps to understand the corresponding systems, but can also be a challenging problem. In recent years, network embedding has been an effective way to extract network information, which represents nodes in complex networks as low-dimensional space vectors, while preserving the properties of the network. Community attributes are an important property of networks, and in most network embedding algorithms, the community structure is usually ignored or

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16

Chen, Chun-Chi, Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon. "TOPAS: network-based structural alignment of RNA sequences." Bioinformatics 35, no. 17 (2019): 2941–48. http://dx.doi.org/10.1093/bioinformatics/btz001.

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Abstract Motivation For many RNA families, the secondary structure is known to be better conserved among the member RNAs compared to the primary sequence. For this reason, it is important to consider the underlying folding structures when aligning RNA sequences, especially for those with relatively low sequence identity. Given a set of RNAs with unknown structures, simultaneous RNA alignment and folding algorithms aim to accurately align the RNAs by jointly predicting their consensus secondary structure and the optimal sequence alignment. Despite the improved accuracy of the resulting alignmen

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17

Mazzotti, Giorgia, Luca Bianco, Enrico Lavezzo, Martina Bado, Stefano Toppo, and Paolo Fontana. "Viral Network Analyzer (VirNA): A Novel Minimum Spanning Networks Algorithm for Investigating Viral Evolution." International Journal of Molecular Sciences 26, no. 5 (2025): 2008. https://doi.org/10.3390/ijms26052008.

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Next Generation Sequencing technologies are essential in public health surveillance for tracking pathogen evolution, spread, and the emergence of new variants. However, the extensive sequencing of viral genomes during recent pandemics has highlighted the limitations of traditional molecular phylogenetic algorithms in capturing fine-grained evolutionary details when analyzed sequences are highly similar and datasets are large-scale. VirNA (Viral Network Analyzer) addresses this challenge by reconstructing detailed mutation patterns and tracing pathogen evolutionary routes in specific geographic

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18

Zhao, Xiaoyong, Chengjin Huang, and Lei Wang. "MTC-NET: A Multi-Channel Independent Anomaly Detection Method for Network Traffic." Biomimetics 9, no. 10 (2024): 615. http://dx.doi.org/10.3390/biomimetics9100615.

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In recent years, deep learning-based approaches, particularly those leveraging the Transformer architecture, have garnered widespread attention for network traffic anomaly detection. However, when dealing with noisy data sets, directly inputting network traffic sequences into Transformer networks often significantly degrades detection performance due to interference and noise across dimensions. In this paper, we propose a novel multi-channel network traffic anomaly detection model, MTC-Net, which reduces computational complexity and enhances the model’s ability to capture long-distance depende

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19

Terán, Aneliz Ninahuanca, Emma Torres Tola, Daniela Andrea Arteaga Voigt, and Ruddy Luna Barrón. "Spike(s) protein gene microevolution of SARS CoV-2 virus in Bolivian Population." Journal of Health & Biological Sciences 12, no. 1 (2024): 1–8. https://doi.org/10.12662/2317-3076jhbs.v12i1.5426.p1-8.2024.

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Objective: analyze the population gene structure and phylogeny of the S gene of the SARS CoV-2 virus of COVID-19 (+) patients from the Plurinational State of Bolivia and then correlate its phylogeny with the different waves of contagion. Methods: three SARS-CoV-2 samples obtained by nasopharyngeal swabs from positive COVID-19 patients were sequenced by Sanger sequencing. 488 sequences of Bolivian SARS-CoV-2 were downloaded from GISAID until September 25, 2023. The genetic structure and phylogeny were analyzed to correlate the presence of the different variants with the waves of contagion. Resu

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20

Thiam, Patrick, Hans A. Kestler, and Friedhelm Schwenker. "Two-Stream Attention Network for Pain Recognition from Video Sequences." Sensors 20, no. 3 (2020): 839. http://dx.doi.org/10.3390/s20030839.

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Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressi

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21

Bondarev, V. "Training a digital model of a deep spiking neural network using backpropagation." E3S Web of Conferences 224 (2020): 01026. http://dx.doi.org/10.1051/e3sconf/202022401026.

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Deep spiking neural networks are one of the promising eventbased sensor signal processing concepts. However, the practical application of such networks is difficult with standard deep neural network training packages. In this paper, we propose a vector-matrix description of a spike neural network that allows us to adapt the traditional backpropagation algorithm for signals represented as spike time sequences. We represent spike sequences as binary vectors. This enables us to derive expressions for the forward propagation of spikes and the corresponding spike training algorithm based on the bac

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22

Cross, Frederick R., Nicolas E. Buchler, and Jan M. Skotheim. "Evolution of networks and sequences in eukaryotic cell cycle control." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1584 (2011): 3532–44. http://dx.doi.org/10.1098/rstb.2011.0078.

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The molecular networks regulating the G1–S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phyl

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23

Guerreiro, Lucas, Filipi Nascimento Silva, and Diego Raphael Amancio. "Identifying the perceived local properties of networks reconstructed from biased random walks." PLOS ONE 19, no. 1 (2024): e0296088. http://dx.doi.org/10.1371/journal.pone.0296088.

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Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when analyzing texts, the underlying network structure generating a particular sequence of words is not available. In this paper, we analyze whether one can recover the underlying local properties of networks generating sequences of symbols for d

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24

Lesyk, V. O., and A. Yu Doroshenko. "Neural network application to pseudorandom sequence generation simulation." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 280–87. https://doi.org/10.15407/pp2024.02-03.280.

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The article discusses the methodology of using neural networks to simulate pseudorandom sequences, which allows finding the hidden structure and sequence algorithms to reduce the observed processes to deterministic ones. To improve the quality of simulation of sequences of generated numbers, models of recurrent neural networks are used, taking into account their ability to adapt to the generated continuous sequences. The paper proposes a method for using and tuning recurrent neural networks and the influence of selected hyperparameters that determine the internal structure, size of the input s

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25

Sarode, M. V., and P. R. Deshmukh. "Image Sequence Denoising with Motion Estimation in Color Image Sequences." Engineering, Technology & Applied Science Research 1, no. 6 (2011): 139–43. http://dx.doi.org/10.48084/etasr.54.

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In this paper, we investigate the denoising of image sequences i.e. video, corrupted with Gaussian noise and Impulse noise. In relation to single image denoising techniques, denoising of sequences aims to utilize the temporal dimension. This approach gives faster algorithms and better output quality. This paper focuses on the removal of different types of noise introduced in image sequences while transferring through network systems and video acquisition. The approach introduced consists of motion estimation, motion compensation, and filtering of image sequences. Most of the estimation approac

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26

Farooq, Mamoona, Asif Abd ul Rehman, M. Khalid Mahmood, and Daud Ahmad. "Upper Bound Sequences of Rotationally Symmetric Triangular Prism Constructed as Halin Graph Using Local Fractional Metric Dimension." VFAST Transactions on Mathematics 9, no. 1 (2021): 13–27. http://dx.doi.org/10.21015/vtm.v9i1.1020.

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In this paper, we consider rotationally symmetric traingular planar network with possible planar symmetries. We ﬁnd local fractional metric dimension of planar symmetries. The objective is to search sequences of local fractional metric dimension of triangular prism planar networks by joining diﬀerent copies. We propose and prove generalized formulas of all sequences for local fractinal metricdimension over triangular prism.

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27

DAŞ, Bihter, and Suat TORAMAN. "Classifying Protein Sequences Using Convolutional Neural Network." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 9, no. 4 (2020): 1663–71. http://dx.doi.org/10.17798/bitlisfen.662816.

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28

Yan, Sheng, Yu Chen, Yan Song, and Minjie Zhu. "Frequent Attack Sequences-based Network log Mining." Journal of Physics: Conference Series 1176 (March 2019): 032052. http://dx.doi.org/10.1088/1742-6596/1176/3/032052.

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29

Moussa, Mostafa, and Hoda ElMaraghy. "Master assembly network for alternative assembly sequences." Journal of Manufacturing Systems 51 (April 2019): 17–28. http://dx.doi.org/10.1016/j.jmsy.2019.02.001.

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30

Detlefsen, Nina K., and Allan Leck Jensen. "Modelling optimal crop sequences using network flows." Agricultural Systems 94, no. 2 (2007): 566–72. http://dx.doi.org/10.1016/j.agsy.2007.02.002.

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31

Földiák, Peter. "Learning Invariance from Transformation Sequences." Neural Computation 3, no. 2 (1991): 194–200. http://dx.doi.org/10.1162/neco.1991.3.2.194.

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The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of comp

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32

Li, Guanlin, Yao Hu, Hao Wang, Qun Hao, and Yu Zhang. "Shearography-Based Near-Surface Defect Detection in Composite Materials: A Spatiotemporal Object Detection Neural Network Trained Only with Simulated Data." Nanomaterials 15, no. 7 (2025): 523. https://doi.org/10.3390/nano15070523.

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Shearography is a non-destructive defect detection technique that, when combined with neural networks, can efficiently and accurately detect near-surface defects in composite materials. However, the high cost of the dataset significantly limits the application of neural networks in shearography. Current simulation data generation techniques fail to eliminate the discrepancies between simulated and experimental data, resulting in suboptimal performance when training neural networks with only simulated data. To address this issue, this paper utilizes phase map sequences measured by shearography

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33

Chueh, Hao-En, Shun-Chuan Ho, Shih-Peng Chang, and Ping-Yu Hsu. "Online Intrusion Behaviors: Sequences and Time Intervals." Social Behavior and Personality: an international journal 38, no. 10 (2010): 1307–12. http://dx.doi.org/10.2224/sbp.2010.38.10.1307.

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In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder b

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34

Wang, Xiaohang, and Hongmin Deng. "A Multi-Feature Representation of Skeleton Sequences for Human Interaction Recognition." Electronics 9, no. 1 (2020): 187. http://dx.doi.org/10.3390/electronics9010187.

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Inspired from the promising performances achieved by recurrent neural networks (RNN) and convolutional neural networks (CNN) in action recognition based on skeleton, this paper presents a deep network structure which combines both CNN for classification and RNN to achieve attention mechanism for human interaction recognition. Specifically, the attention module in this structure is utilized to give various levels of attention to various frames by different weights, and the CNN is employed to extract the high-level spatial and temporal information of skeleton data. These two modules seamlessly f

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35

Zhu, Xiao-Min, Weijun Liu, and Xu Yang. "Spectral Conditions, Degree Sequences, and Graphical Properties." Mathematics 11, no. 20 (2023): 4264. http://dx.doi.org/10.3390/math11204264.

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Integrity, tenacity, binding number, and toughness are significant parameters with which to evaluate network vulnerability and stability. However, we hardly use the definitions of these parameters to evaluate directly. According to the methods, concerning the spectral radius, we show sufficient conditions for a graph to be k-integral, k-tenacious, k-binding, and k-tough, respectively. In this way, the vulnerability and stability of networks can be easier to characterize in the future.

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36

Xu, Xuguang, Cunqian Feng, and Lixun Han. "Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network." Remote Sensing 14, no. 22 (2022): 5863. http://dx.doi.org/10.3390/rs14225863.

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Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second,

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37

HU, YANJING, QINGQI PEI, and LIAOJUN PANG. "Instruction Clustering Analysis for Unknown Network Protocol's Abnormal Behavior." Journal of Interconnection Networks 15, no. 03n04 (2015): 1540002. http://dx.doi.org/10.1142/s0219265915400022.

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Protocol's abnormal behavior analysis is an important task in protocol reverse analysis. Traditional protocol reverse analysis focus on the protocol message format, but protocol behavior especially the abnormal behavior is rare studied. In this paper, protocol behavior is represented by the labeled behavior instruction sequences. Similar behavior instruction sequences mean the similar protocol behavior. Using our developed virtual analysis platform HiddenDisc, we can capture a variety of known or unknown protocols' behavior instruction sequences. All kinds of executed or unexecuted instruction

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He, Yan, Ying Tang, Qun Hua, et al. "Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study." JMIR Public Health and Surveillance 10 (May 29, 2024): e56593. http://dx.doi.org/10.2196/56593.

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Background The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies’ emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources. Objective This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dy

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WANG, YANCHUN, WEIGANG SUN, JINGYUAN ZHANG, and SEN QIN. "ON THE CONDITIONAL MATCHING OF FRACTAL NETWORKS." Fractals 24, no. 04 (2016): 1650054. http://dx.doi.org/10.1142/s0218348x16500547.

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In this paper, we propose a new matching (called a conditional matching), where the condition refers to the matching of the new constructed network which includes all the nodes in the original network. We then enumerate the conditional matchings of the new network and prove that the number of conditional matchings is just the product of degree sequences of the original network. We choose two families of fractal networks to show our obtained results, including the pseudofractal network and Cayley tree. Finally, we calculate the entropy of the conditional matchings on the considered networks and

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40

HERTZ, JOHN, and ADAM PRÜGEL-BENNETT. "LEARNING SYNFIRE CHAINS: TURNING NOISE INTO SIGNAL." International Journal of Neural Systems 07, no. 04 (1996): 445–50. http://dx.doi.org/10.1142/s0129065796000427.

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We develop a model of cortical coding of stimuli by the sequences of activation patterns that they ignite in an initially random network. Hebbian learning then stabilizes these sequences, making them attractors of the dynamics. There is a competition between the capacity of the network and the stability of the sequences; for small stability parameter ∊ (the strength of the mean stabilizing PSP in the neurons in a learned sequence) the capacity is proportional to 1/∊2. For ∊ of the order of or less than the PSPs of the untrained network, the capacity exceeds that for sequences learned from tabu

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Portillo-Portillo, Jose, Gabriel Sanchez-Perez, Linda K. Toscano-Medina, et al. "FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences." Entropy 24, no. 7 (2022): 942. http://dx.doi.org/10.3390/e24070942.

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Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame

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Wang, Changyuan, Ting Yan, and Hongbo Jia. "Spatial-Temporal Feature Representation Learning for Facial Fatigue Detection." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (2018): 1856018. http://dx.doi.org/10.1142/s0218001418560189.

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In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and t

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Heider, Dominik, Jessica Appelmann, Tuygun Bayro, et al. "A Computational Approach for the Identification of Small GTPases Based on Preprocessed Amino Acid Sequences." Technology in Cancer Research & Treatment 8, no. 5 (2009): 333–41. http://dx.doi.org/10.1177/153303460900800503.

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The prediction of essential biological features based on a given protein sequence is a challenging task in computational biology. To limit the amount of in vitro verification, the prediction of essential biological activities gives the opportunity to detect so far unknown sequences with similar properties. Besides the application within the identification of proteins being involved in tumorigenesis, other functional classes of proteins can be predicted. The prediction accuracy depends on the selected machine learning approach and even more on the composition of the descriptor set used. A compu

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44

Zhao, Zhengqiao, Stephen Woloszynek, Felix Agbavor, Joshua Chang Mell, Bahrad A. Sokhansanj, and Gail L. Rosen. "Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network." PLOS Computational Biology 17, no. 9 (2021): e1009345. http://dx.doi.org/10.1371/journal.pcbi.1009345.

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Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach

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Boroojeni, Asma Azizi, Jeremy Dewar, Tong Wu, and James M. Hyman. "Generating bipartite networks with a prescribed joint degree distribution." Journal of Complex Networks 5, no. 6 (2017): 839–57. http://dx.doi.org/10.1093/comnet/cnx014.

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Abstract We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling sys

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Forseth, Kiefer, Xaq Pitkow, Simon Fischer-Baum, and Nitin Tandon. "149 Dynamical Network State Sequences for Human Language Production." Neurosurgery 70, Supplement_1 (2024): 33–34. http://dx.doi.org/10.1227/neu.0000000000002809_149.

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INTRODUCTION: Speech requires the selection of a conceptual representation, the construction of a word form, and the execution of a complex articulatory plan. Investigating this global system requires high-resolution recordings with an analytic approach to resolve discrete cognitive states. We integrated autoregressive hidden Markov models to resolve trial-by-trial state transition sequences in distributed networks derived from a large-scale electrocorticographic dataset with complete coverage of language-dominant cortex. METHODS: Intracranial electrodes (n = 25810, 134 patients), including bo

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Yuan, Dan, Xia Zhong, Yiping Li, et al. "Molecular Transmission Network of Newly Reported HIV Infections in Pengzhou, Sichuan Province: A Study Based on Genomics and Spatial Epidemiology." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 2523. http://dx.doi.org/10.3390/ijerph20032523.

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Objective: The objective of this study was to understand the molecular transmission characteristics of newly reported HIV infections in the city of Pengzhou, Sichuan Province, to analyze the risk factors of transmission network and spatial clustering and the transmission characteristics, and to provide a scientific basis for precision prevention and intervention. Methods: Anticoagulated whole blood was collected from newly reported HIV infections in Pengzhou from March 2019 to August 2021. After the plasma was isolated, the HIV-1 pol gene was amplified and sequenced by reverse transcriptase po

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Gillett, Maxwell, Ulises Pereira, and Nicolas Brunel. "Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning." Proceedings of the National Academy of Sciences 117, no. 47 (2020): 29948–58. http://dx.doi.org/10.1073/pnas.1918674117.

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Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence o

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Gupta, Anand, Hardeo Kumar Thakur, Anmoll Kumar Jain, and Prakhar Rustagi. "Analysis of Regular Patterns in Un-Weighted Directed Graphs." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijirr.289571.

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Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining

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Gao, Tao, and Zhenjing Yao. "Sensors Network for Ultrasonic Ranging System." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 3 (2013): 47–59. http://dx.doi.org/10.4018/ijapuc.2013070105.

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The spectrum matching and correlation characteristic are both important in the multiple-user ultrasonic ranging system. As people know, an ultrasonic ranging system, which has a bell-shaped magnitude spectrum, acts like a band-pass filter. If the spectrum of the excitation signal does not match that of the ultrasonic ranging system, some of energy cannot be transmitted by the ultrasonic system. In other words, it does not make full use of the bandwidth of the ultrasonic ranging system. The good correlation characteristics can eliminate cross-talk among multichannel ultrasonic sensors firing si

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