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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1901 1
1907 1
1912 1
1917 1
1921 1
1925 1
1926 1
1927 2
1931 3
1932 3
1933 2
1935 1
1936 2
1938 2
1939 1
1944 2
1945 4
1946 2
1947 14
1948 6
1949 15
1950 9
1951 17
1952 26
1953 27
1954 36
1955 30
1956 32
1957 26
1958 48
1959 36
1960 44
1961 49
1962 44
1963 50
1964 60
1965 66
1966 54
1967 56
1968 89
1969 82
1970 99
1971 105
1972 100
1973 106
1974 133
1975 397
1976 437
1977 377
1978 448
1979 505
1980 600
1981 642
1982 732
1983 903
1984 988
1985 1087
1986 1264
1987 1378
1988 1484
1989 1899
1990 2076
1991 2449
1992 2861
1993 3457
1994 4075
1995 4815
1996 5781
1997 6362
1998 6567
1999 6014
2000 6875
2001 7532
2002 8246
2003 9221
2004 11252
2005 13081
2006 14853
2007 17192
2008 19297
2009 22321
2010 25151
2011 28512
2012 31612
2013 35596
2014 40629
2015 44896
2016 48781
2017 53242
2018 60685
2019 68831
2020 83065
2021 100741
2022 109282
2023 106968
2024 39107

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Search Results

954,789 results

Results by year

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Page 1
Oscillation suppression and chimera states in time-varying networks.
Majhi S, Rakshit S, Ghosh D. Majhi S, et al. Chaos. 2022 Apr;32(4):042101. doi: 10.1063/5.0087291. Chaos. 2022. PMID: 35489845 Review.
With research in network theory being developed so fast, it has become extremely necessary to move from simple network topologies to more sophisticated and realistic descriptions of the connectivity patterns. ...Rep. 949, 1-63 (2022)] demonstrates the significance o …
With research in network theory being developed so fast, it has become extremely necessary to move from simple network topolog …
Flexible memory networks.
Curto C, Degeratu A, Itskov V. Curto C, et al. Bull Math Biol. 2012 Mar;74(3):590-614. doi: 10.1007/s11538-011-9678-9. Epub 2011 Aug 9. Bull Math Biol. 2012. PMID: 21826564
Using a standard firing rate model of recurrent networks, we develop a theory of flexible memory networks. Our main results characterize networks having the maximal number of flexible memory patterns, given a constraint graph on the network's co …
Using a standard firing rate model of recurrent networks, we develop a theory of flexible memory networks. Our main results ch …
Synchronizability of two-layer correlation networks.
Wei X, Wu X, Lu JA, Wei J, Zhao J, Wang Y. Wei X, et al. Chaos. 2021 Oct;31(10):103124. doi: 10.1063/5.0056482. Chaos. 2021. PMID: 34717320
To analyze the network's stability, we consider the analytical expressions of the smallest non-zero and largest eigenvalues of the (weighted) Laplacian matrix as well as the linking strength and the network size for two linking patterns. According to the mast …
To analyze the network's stability, we consider the analytical expressions of the smallest non-zero and largest eigenvalues of …
Industrial Wireless Sensor Networks: Protocols and Applications.
Yoo SE, Kim T. Yoo SE, et al. Sensors (Basel). 2020 Oct 14;20(20):5809. doi: 10.3390/s20205809. Sensors (Basel). 2020. PMID: 33066533 Free PMC article.
The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks …
The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, an …
Neural Networks as Geometric Chaotic Maps.
Li Z, Ravela S. Li Z, et al. IEEE Trans Neural Netw Learn Syst. 2023 Jan;34(1):527-533. doi: 10.1109/TNNLS.2021.3087497. Epub 2023 Jan 5. IEEE Trans Neural Netw Learn Syst. 2023. PMID: 34191731
The use of artificial neural networks (NNs) as models of chaotic dynamics has been rapidly expanding. ...We first confirm NN's efficiency in emulating chaos by showing that a parsimonious NN trained only on few data points can reconstruct strange attractors, extrapolate ou …
The use of artificial neural networks (NNs) as models of chaotic dynamics has been rapidly expanding. ...We first confirm NN's effici …
The persistent homology of genealogical networks.
Boyd ZM, Callor N, Gledhill T, Jenkins A, Snellman R, Webb B, Wonnacott R. Boyd ZM, et al. Appl Netw Sci. 2023;8(1):15. doi: 10.1007/s41109-023-00538-7. Epub 2023 Feb 23. Appl Netw Sci. 2023. PMID: 36852178 Free PMC article.
Yet little mathematical attention has been paid to the complex network properties of genealogical networks, especially at large scales. ...Specifically, we introduce the notion of a network's persistence curve, which encodes the network's …
Yet little mathematical attention has been paid to the complex network properties of genealogical networks, especially at larg …
A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks.
Mamun AA, Ping EP, Hossen J, Tahabilder A, Jahan B. Mamun AA, et al. Sensors (Basel). 2022 Oct 10;22(19):7682. doi: 10.3390/s22197682. Sensors (Basel). 2022. PMID: 36236780 Free PMC article. Review.
This paper elaborates on the network's architecture and the loss function for improving the performance based on the categories. The network's architecture is divided into object detection, classification, and segmentation, and each is discussed, inclu …
This paper elaborates on the network's architecture and the loss function for improving the performance based on the categorie …
Resilience of Multi-Layer Communication Networks.
Kuikka V, Rantanen H. Kuikka V, et al. Sensors (Basel). 2022 Dec 22;23(1):86. doi: 10.3390/s23010086. Sensors (Basel). 2022. PMID: 36616689 Free PMC article.
Advances in the future communication technologies and capabilities of new services in heterogeneous network systems have increased the need for modelling and analysing various aspects of both the resilience of networked systems and usability from the user's point of …
Advances in the future communication technologies and capabilities of new services in heterogeneous network systems have increased th …
Building blocks of biological networks: a review on major network motif discovery algorithms.
Masoudi-Nejad A, Schreiber F, Kashani ZR. Masoudi-Nejad A, et al. IET Syst Biol. 2012 Oct;6(5):164-74. doi: 10.1049/iet-syb.2011.0011. IET Syst Biol. 2012. PMID: 23101871 Free article. Review.
In recent years, there has been a great interest in studying different aspects of complex networks in a range of fields. One important local property of networks is network motifs, recurrent and statistically significant sub-graphs or patterns, which assists …
In recent years, there has been a great interest in studying different aspects of complex networks in a range of fields. One importan …
Detecting Information Relays in Deep Neural Networks.
Hintze A, Adami C. Hintze A, et al. Entropy (Basel). 2023 Feb 22;25(3):401. doi: 10.3390/e25030401. Entropy (Basel). 2023. PMID: 36981289 Free PMC article.
Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional …
Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. He …
954,789 results
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