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Journal articles on the topic 'Similarity of structure'

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Author: Grafiati

Published: 10 March 2023

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1

Peng Ding, Peng Ding, Dan Liu Peng Ding, Zhiyuan Zhang Dan Liu, Jie Hu Zhiyuan Zhang, and Ning Liu Jie Hu. "A Novel Discrimination Structure for Assessing Text Semantic Similarity." 網際網路技術學刊 23, no. 4 (July 2022): 709–17. http://dx.doi.org/10.53106/160792642022072304006.

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<p>Discrimination of semantic textual similarity refers to comparing the similarity between two or more entities (including words, short texts and documents) through certain strategies to obtain a specific quantitative similarity value. Traditional research put more experience into the similarity calculation of the original text content, using the matching degree or distance of characters or words as the yardstick to judge whether the text pairs are similar. However, there are still some problems to be solved in the following aspects: the key points of sentence meaning and word semantics, which play important role in the semantic expression of natural language, are not well integrated into the similarity discrimination, and the interactive features between texts are not fully utilized. To solve the above problems, this paper proposes a novel discrimination structure based on the Siamese Network model and the idea of text matching. In this structure, we introduce sentence meaning key information and word semantic information to realize the extraction of word interaction feature information, and then we realize the text vector representation by using Siamese BiLSTM. The experimental results showed that the accuracy of the proposed model is higher than that of the basic models.</p> <p> </p>

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2

Fukukawa, Tomoya, Kosuke Sekiyama, and Yasuhisa Hasegawa. "Vanishing point detection focusing on similarity structure in vineyard environments." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 278–79. http://dx.doi.org/10.1299/jsmeicam.2015.6.278.

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3

Kang, Zhao, Xiao Lu, Yiwei Lu, Chong Peng, Wenyu Chen, and Zenglin Xu. "Structure learning with similarity preserving." Neural Networks 129 (September 2020): 138–48. http://dx.doi.org/10.1016/j.neunet.2020.05.030.

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4

Rypdal, Kristoffer, Jens Juul Rasmussen, and Knud Thomsen. "Similarity structure of wave-collapse." Physica D: Nonlinear Phenomena 16, no. 3 (July 1985): 339–57. http://dx.doi.org/10.1016/0167-2789(85)90013-2.

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5

Eva, Benjamin, Reuben Stern, and Stephan Hartmann. "The Similarity of Causal Structure." Philosophy of Science 86, no. 5 (December 2019): 821–35. http://dx.doi.org/10.1086/705566.

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6

Fletcher, Samuel C. "Similarity Structure and Emergent Properties." Philosophy of Science 87, no. 2 (April 1, 2020): 281–301. http://dx.doi.org/10.1086/707563.

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7

Chaudhari, N. S., and Xiangrui Wang. "Language Structure Using Fuzzy Similarity." IEEE Transactions on Fuzzy Systems 17, no. 5 (October 2009): 1011–24. http://dx.doi.org/10.1109/tfuzz.2009.2020155.

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8

Wang, Yong Fu, Qi Dou Zhou, Zhi Yong Xie, and Xiao Jun Lv. "An Investigation of Acoustic Similarity on an Underwater Structure." Applied Mechanics and Materials 105-107 (September 2011): 84–91. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.84.

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An acoustic similarity due to two geometrically similar structures which are vibrating in heavy flow, such as in water, is investigated. The acoustic similarity states that for two geometrically similar structures, if a group of dimensionless similarity numbers are constant, the dimensionless acoustic pressure coefficient keep constant at the corresponding acoustic field points for the two flow–loaded vibrating structure systems. Numerical simulations and experiment results are presented to validate the acoustic similarity. This acoustic similarity may be useful when a small structure is employed to investigate the acoustic performance of large scale structure.

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9

Bartlett, James C., and W. Jay Dowling. "Scale Structure and Similarity of Melodies." Music Perception 5, no. 3 (1988): 285–314. http://dx.doi.org/10.2307/40285401.

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Four experiments explored an asymmetry in the perceived similarity of melodies: If a first-presented melody is "scalar" (conforms to a diatonic major scale), and is followed by a second melody slightly altered to be " nonscalar" (deviating from a diatonic major scale), subjects judge similarity to be lower than if the nonscalar melody comes first. Experiment 1 produced evidence that asymmetric similarity is not due simply to more strongly scalar melodies having greater memorability. Experiment 2 ruled out the hypothesis that asymmetric similarity depends on a taskspecific strategy reflecting demand characteristics. Experiments 3 and 4 replicated asymmetric similarity while controlling the number of onesemitone intervals in scalar versus nonscalar melodies. The data are consistent with Garner's principles that stimuli are perceived with reference to sets of alternatives and that good stimuli have few alternatives. Specifically, when melodies are presented in scalar—nonscalar order, but not when presented in nonscalar-scalar order, the first melody evokes a small set of alternatives which the second melody often violates.

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10

MINAMI, Shintaro, and George CHIKENJI. "Non-sequential Structure Similarity in Proteins." Seibutsu Butsuri 56, no. 1 (2016): 027–29. http://dx.doi.org/10.2142/biophys.56.027.

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11

Rychlewski, L., and A. Godzik. "Secondary structure prediction using segment similarity." Protein Engineering Design and Selection 10, no. 10 (October 1, 1997): 1143–53. http://dx.doi.org/10.1093/protein/10.10.1143.

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12

Gentner, Dedre, and Arthur B. Markman. "Structure mapping in analogy and similarity." American Psychologist 52, no. 1 (1997): 45–56. http://dx.doi.org/10.1037/0003-066x.52.1.45.

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13

Dzyabchenko, A. V. "Method of crystal-structure similarity searching." Acta Crystallographica Section B Structural Science 50, no. 4 (August 1, 1994): 414–25. http://dx.doi.org/10.1107/s0108768193013552.

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14

Xia, Kelin. "Persistent similarity for biomolecular structure comparison." Communications in Information and Systems 18, no. 4 (2018): 269–98. http://dx.doi.org/10.4310/cis.2018.v18.n4.a4.

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15

Kenyon, Richard. "Inflationary tilings with a similarity structure." Commentarii Mathematici Helvetici 69, no. 1 (December 1994): 169–98. http://dx.doi.org/10.1007/bf02564481.

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16

Collins, Anna, Richard I. Cooper, and David J. Watkin. "Structure matching: measures of similarity and pseudosymmetry." Journal of Applied Crystallography 39, no. 6 (November 10, 2006): 842–49. http://dx.doi.org/10.1107/s0021889806038489.

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A sizeable proportion of structures withZ′ = 2 are thought to exhibit pseudosymmetry, but establishing the extent of the deviation from true symmetry is problematic. By considering both the conformational similarity between the independent molecules and the way in which they are related in space, assessment of the pseudosymmetry of a structure becomes possible. A method of matching two groups of atoms where both these factors are quantified usingCRYSTALS[Betteridge, Carruthers, Cooper, Prout & Watkin (2003).J. Appl. Cryst.36, 1487] is described.

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17

Gentner, Dedre. "Exhuming similarity." Behavioral and Brain Sciences 24, no. 4 (August 2001): 669. http://dx.doi.org/10.1017/s0140525x01350082.

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Tenenbaum and Griffiths' paper attempts to subsume theories of similarity – including spatial models, featural models, and structure-mapping models – into a framework based on Bayesian generalization. But in so doing it misses significant phenomena of comparison. It would be more fruitful to examine how comparison processes suggest hypotheses than to try to derive similarity from Bayesian reasoning. [Shepard; Tenenbaum & Griffiths]

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18

Rifai, Muhamad Aldi, and Indra Gita Anugrah. "Semantic Search for Scientific Articles by Language Using Cosine Similarity Algorithm and Weighted Tree Similarity." Journal of Development Research 5, no. 2 (November 29, 2021): 106–14. http://dx.doi.org/10.28926/jdr.v5i2.150.

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The activity of writing scientific articles by academics at universities is one of the activities that is often carried out, but when writing scientific articles problems arise regarding the difficulty of finding ideas, literature studies, and reference sources that you want to use as references when writing. Sometimes when searching on a search engine, we have trouble finding the right document, because usually, the keywords we are looking for are not in the title section but another part of the structure. Since most search engines only match titles, other structures are usually excluded from matching. So that the search results that we do sometimes don't match what we want. In addition, usually, each scientific article has many language differences in its structure as found in the abstract section. To detect similarities through the structure of scientific articles, an algorithm is used, namely weighted tree similarity, and to detect language using the N-gram algorithm, then the cosine similarity algorithm can be used to check the level of similarity in keyword text with text in scientific articles.

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19

TERASHIMA, Chieko, Yoshiaki TANIDA, Toshio MANABE, and Hiroyuki SATO. "The Correlation between Similarity of Amino Acid Interaction Potentials and Structure Similarity." Journal of Computer Chemistry, Japan 20, no. 4 (2021): 144–46. http://dx.doi.org/10.2477/jccj.2022-0003.

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20

Chisholm, James Alexander, and Sam Motherwell. "COMPACK: a program for identifying crystal structure similarity using distances." Journal of Applied Crystallography 38, no. 1 (January 19, 2005): 228–31. http://dx.doi.org/10.1107/s0021889804027074.

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A method is presented for comparing crystal structures to identify similarity in molecular packing environments. The relative position and orientation of molecules is captured using interatomic distances, which provide a representation of structure that avoids the use of space-group and cell information. The method can be used to determine whether two crystal structures are the same to within specified tolerances and can also provide a measure of similarity for structures that do not match exactly, but have structural features in common. Example applications are presented that include the identification of an experimentally observed crystal structure from a list of predicted structures and the process of clustering a list of predicted structures to remove duplicates. Examples are also presented to demonstrate partial matching. Such searches were performed to analyse the results of the third blind test for crystal structure prediction, to identify the frequency of occurrence of a characteristic layer and a characteristic hydrogen-bonded chain.

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21

Clavero, Maria, Pedro Folgueras, Pilar Diaz-Carrasco, Miguel Ortega-Sanchez, and Miguel A. Losada. "A SIMILARITY PARAMETER FOR BREAKWATERS: THE MODIFIED IRIBARREN NUMBER." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 28. http://dx.doi.org/10.9753/icce.v36.structures.28.

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In the 14th ICCE, Battjes (1974) showed that a single similarity parameter only, embodying both the effects of slope angle and incident wave steepness, was important for many aspects of waves breaking on impermeable slopes, and suggested to call it the "Iribarren number", denoted by "Ir". Ahrens and McCartney (1975) verified the usefulness of Ir to describe run-up and stability on rough permeable slopes. Since then, many researchers applied Ir to characterize and to develop formulae for the design of breakwaters and to verify their stability. On the other hand, depending on their typology, breakwaters reflect, dissipate, transmit, and radiate incident wave energy. Partial standing wave patterns are likely to occur at all types of breakwater, thus playing an important role in defining the wave regime in front of, near (seaward and leeward), and inside the breakwater. The characteristics of the porous medium, relative grain size D/L and relative width, Aeq/L2, are relevant magnitudes in that wave pattern (Vilchez et al. 2016), being D the grain diameter, L the wave length and Aeq the porous area per unit section under the mean water level. Aeq/L2 is a scattering parameter controlling the averaged transformation of the wave inside the porous section of the structure. For a vertical porous breakwater (Type A), Aeq is simply B Â· h, and for a constant depth, the scattering parameter is reduced to B/L, which is the relative breakwater width.

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22

Malod-Dognin, Noël, Rumen Andonov, and Nicola Yanev. "Solving Maximum Clique Problem for Protein Structure Similarity." Serdica Journal of Computing 4, no. 1 (March 31, 2010): 93–100. http://dx.doi.org/10.55630/sjc.2010.4.93-100.

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Computing the similarity between two protein structures is a crucial task in molecular biology, and has been extensively investigated. Many protein structure comparison methods can be modeled as maximum weighted clique problems in specific k-partite graphs, referred here as alignment graphs. In this paper we present both a new integer programming formulation for solving such clique problems and a dedicated branch and bound algorithm for solving the maximum cardinality clique problem. Both approaches have been integrated in VAST, a software for aligning protein 3D structures largely used in the National Center for Biotechnology Information, an original clique solver which uses the well known Bron and Kerbosch algorithm (BK). Our computational results on real protein alignment instances show that our branch and bound algorithm is up to 116 times faster than BK.

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23

Taipale, Joona. "Similarity and asymmetry." Phänomenologische Forschungen 2014, no. 1 (2014): 141–54. http://dx.doi.org/10.28937/1000107780.

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This article suggests that the asymmetrical structure of the self-other relationship can be traced back to the relation between empathy and transcendental intersubjectivity. Drawing on Husserl in particular, I will first recapitulate the argument that empathy is necessarily preceded by, and built upon, structural implications to potential others, and I will then argue that the empathically encountered actual other is bound to arrive as the fulfilment or concretization of this anonymous, emptily appresented “anybody”. Because of this foundedness, empathy is necessarily built on expectations concerning the other’s similarity, and because of this initial and tacit “similarity thesis” it is bound to have an asymmetrical structure. Towards the end of my paper, I will underline particular ethical implications of this account. Most importantly,I will be claiming that genuine intersubjectivity, and ethical relationship with others, is essentially built on disappointments of our initial subjective expectations.

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24

Ekaney, Lena Y. E., Donatus B. Eni, and Fidele Ntie-Kang. "Chemical similarity methods for analyzing secondary metabolite structures." Physical Sciences Reviews 6, no. 7 (June 19, 2021): 247–64. http://dx.doi.org/10.1515/psr-2018-0129.

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Abstract The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

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25

Lamont, Alexandra, and Nicola Dibben. "Motivic Structure and the Perception of Similarity." Music Perception 18, no. 3 (2001): 245–74. http://dx.doi.org/10.1525/mp.2001.18.3.245.

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This paper presents a theoretical and empirical investigation into the ways in which different listeners perceive similarity relationships in different kinds of music. We first extend the current understanding of similarity relations in music by drawing together theory and evidence from general cognitive psychology, cognitive psychology of music, and music theory. In the empirical study, trained musicians and nonmusicians rated the similarity of pairs of extracts from piano pieces by Beethoven (Sonata op. 10, no. 1, first movement) and Schoenberg (Klavierstüück op. 33a) and provided adjective ratings for each extract. Similarity judgments were found to be context-specific and roughly equivalent for both types of listener, and were primarily based on more "surface" features such as dynamics, articulation, texture, and contour rather than on "deeper" features such as motivic or harmonic relationships. The implications for music-theoretic views of similarity are discussed.

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26

Lowet, Adam S., Chaz Firestone, and Brian J. Scholl. "Seeing structure: Shape skeletons modulate perceived similarity." Attention, Perception, & Psychophysics 80, no. 5 (March 15, 2018): 1278–89. http://dx.doi.org/10.3758/s13414-017-1457-8.

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27

Good, Andrew C., Sung Sau So, and W. Graham Richards. "Structure-activity relationships from molecular similarity matrices." Journal of Medicinal Chemistry 36, no. 4 (February 1993): 433–38. http://dx.doi.org/10.1021/jm00056a002.

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28

Ganesan, Prasanna, Hector Garcia-Molina, and Jennifer Widom. "Exploiting hierarchical domain structure to compute similarity." ACM Transactions on Information Systems 21, no. 1 (January 2003): 64–93. http://dx.doi.org/10.1145/635484.635487.

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29

Schill, Hayden M., and Timothy F. Brady. "Global scene similarity structure predicts memory performance." Journal of Vision 20, no. 11 (October 20, 2020): 614. http://dx.doi.org/10.1167/jov.20.11.614.

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30

Vacher, René, Thierry Woignier, Jacques Pelous, and Eric Courtens. "Structure and self-similarity of silica aerogels." Physical Review B 37, no. 11 (April 15, 1988): 6500–6503. http://dx.doi.org/10.1103/physrevb.37.6500.

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31

Akbal-Delibas, Bahar, Marc Pomplun, and Nurit Haspel. "Accurate Prediction of Docked Protein Structure Similarity." Journal of Computational Biology 22, no. 9 (September 2015): 892–904. http://dx.doi.org/10.1089/cmb.2015.0114.

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32

Al-Saleh, Mohammad Fraiwan. "On the Similarity Structure of Order Statistics." Communications in Statistics - Theory and Methods 36, no. 7 (May 21, 2007): 1433–39. http://dx.doi.org/10.1080/03610920601077204.

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33

Qingfeng Chen and Y.-P. P. Chen. "Function Annotation for Pseudoknot Using Structure Similarity." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (November 2011): 1535–44. http://dx.doi.org/10.1109/tcbb.2011.50.

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34

Galgonek, Jakub, David Hoksza, and Tomáš Skopal. "SProt: sphere-based protein structure similarity algorithm." Proteome Science 9, Suppl 1 (2011): S20. http://dx.doi.org/10.1186/1477-5956-9-s1-s20.

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35

Taylor, William R. "Protein structure modelling from remote sequence similarity." Journal of Biotechnology 35, no. 2-3 (June 1994): 281–91. http://dx.doi.org/10.1016/0168-1656(94)90042-6.

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36

Ren *, Bin, and Guoxing Ji. "Certain structure of Similarity-preserving linear maps." Linear and Multilinear Algebra 52, no. 1 (January 2004): 61–68. http://dx.doi.org/10.1080/0308108031000134990.

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37

Hark Gan, Hin, Rebecca A. Perlow, Sharmili Roy, Joy Ko, Min Wu, Jing Huang, Shixiang Yan, et al. "Analysis of Protein Sequence/Structure Similarity Relationships." Biophysical Journal 83, no. 5 (November 2002): 2781–91. http://dx.doi.org/10.1016/s0006-3495(02)75287-9.

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38

Snitz, Kobi, Adi Yablonka, Tali Weiss, Idan Frumin, Rehan M. Khan, and Noam Sobel. "Predicting Odor Perceptual Similarity from Odor Structure." PLoS Computational Biology 9, no. 9 (September 12, 2013): e1003184. http://dx.doi.org/10.1371/journal.pcbi.1003184.

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39

Giegé, Richard, Frank Jühling, Joern Pütz, Peter Stadler, Claude Sauter, and Catherine Florentz. "Structure of transfer RNAs: similarity and variability." Wiley Interdisciplinary Reviews: RNA 3, no. 1 (September 28, 2011): 37–61. http://dx.doi.org/10.1002/wrna.103.

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40

He, Xing-Gang, and Zhi-Ying Wen. "The self-similarity structure on infinite intervals." Journal of Mathematical Analysis and Applications 329, no. 2 (May 2007): 1094–101. http://dx.doi.org/10.1016/j.jmaa.2006.07.053.

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41

Charette, Bradley D., Richard G. MacDonald, Stefan Wetzel, David B. Berkowitz, and Herbert Waldmann. "Protein Structure Similarity Clustering: Dynamic Treatment of PDB Structures Facilitates Clustering." Angewandte Chemie 118, no. 46 (November 27, 2006): 7930–34. http://dx.doi.org/10.1002/ange.200602125.

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42

Charette, Bradley D., Richard G. MacDonald, Stefan Wetzel, David B. Berkowitz, and Herbert Waldmann. "Protein Structure Similarity Clustering: Dynamic Treatment of PDB Structures Facilitates Clustering." Angewandte Chemie International Edition 45, no. 46 (November 27, 2006): 7766–70. http://dx.doi.org/10.1002/anie.200602125.

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43

Zhang, Shanzhen, Zhiqiang Chang, Zhenqi Li, Huizi DuanMu, Zihui Li, Kening Li, Yufeng Liu, Fujun Qiu, and Yan Xu. "Calculating phenotypic similarity between genes using hierarchical structure data based on semantic similarity." Gene 497, no. 1 (April 2012): 58–65. http://dx.doi.org/10.1016/j.gene.2012.01.014.

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44

Gao, Nansha, Jiu Hui Wu, and Lie Yu. "Large band gaps in two-dimensional phononic crystals with self-similarity structure." International Journal of Modern Physics B 29, no. 04 (February 10, 2015): 1550017. http://dx.doi.org/10.1142/s0217979215500174.

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In this paper, we study the band gaps (BGs) of two-dimensional (2D) phononic crystals (PCs) composed of self-similarity shape inclusions embedded in the homogenous matrix. The dispersion relations, transmission spectra, and displacement fields of eigenmodes of the proposed structures are calculated by use of finite element method. Due to the simultaneous mechanisms of the Bragg scattering, the structure can exhibit low-frequency BGs, which can be effectively shifted by changing the geometries and degree of the self-similarity structure. The BGs are significantly dependent upon the geometrical parameters and degree of the self-similarity structure. It is concluded that, the PCs with self-similarity structure, can modulate the location and width of BGs. But it must be pointed out, the shape of self-similarity inclusion exercises a great influence on the BGs. The study in this paper is relevant to the design of tuning BGs and isolators in the low-frequency range.

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45

Mukherjee, Partha, Youakim Badr, Srushti Karvekar, and Shanmugapriya Viswanathan. "Coronavirus Genome Sequence Similarity and Protein Sequence Classification." Journal of Digital Science 3, no. 2 (December 28, 2021): 3–18. http://dx.doi.org/10.33847/2686-8296.3.2_1.

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The world currently is going through a serious pandemic due to the coronavirus disease (COVID-19). In this study, we investigate the gene structure similarity of coronavirus genomes isolated from COVID-19 patients, Severe Acute Respiratory Syndrome (SARS) patients and bats genes. We also explore the extent of similarity between their genome structures to find if the new coronavirus is similar to either of the other genome structures. Our experimental results show that there is 82.42% similarity between the CoV-2 genome structure and the bat genome structure. Moreover, we have used a bidirectional Gated Recurrent Unit (GRU) model as the deep learning technique and an improved variant of Recurrent Neural networks (i.e., Bidirectional Long Short Term Memory model) to classify the protein families of these genomes to isolate the prominent protein family accession. The accuracy of Gated Recurrent Unit (GRU) is 98% for labeled protein sequences against the protein families. By comparing the performance of the Gated Recurrent Unit (GRU) model with the Bidirectional Long Short Term Memory (Bi-LSTM) model results, we found that the GRU model is 1.6% more accurate than the Bi-LSTM model for our multiclass protein classification problem. Our experimental results would be further support medical research purposes in targeting the protein family similarity to better understand the coronavirus genomic structure.

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46

Yang, Lina, Yang Liu, Xiaochun Hu, Patrick Wang, Xichun Li, and Jun Wu. "Graph-Based Analysis of RNA Secondary Structure Similarity Comparison." Complexity 2021 (March 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/8841822.

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In organisms, ribonucleic acid (RNA) plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform (DWT) and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

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47

Nozaki, Tadasu, Tomomi Tani, Sachiko Tamura, Takeharu Nagai, and Kazuhiro Maeshima. "2P121 Local structural similarity between interphase chromatin and mitotic chromosomes in living mammalian cells(05A. Nucleic acid: Structure & Property,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S179. http://dx.doi.org/10.2142/biophys.53.s179_1.

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48

Li, Xiao, and Qingsheng Li. "Calculation of Sentence Semantic Similarity Based on Syntactic Structure." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/203475.

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Combined with the problem of single direction of the solution of the existing sentence similarity algorithms, an algorithm for sentence semantic similarity based on syntactic structure was proposed. Firstly, analyze the sentence constituent, then through analysis convert sentence similarity into words similarity on the basis of syntactic structure, then convert words similarity into concept similarity through words disambiguation, and, finally, realize the semantic similarity comparison. It also gives the comparison rules in more detail for the modifier words in the sentence which also have certain contributions to the sentence. Under the same test condition, the experiments show that the proposed algorithm is more intuitive understanding of people and has higher accuracy.

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49

An, Xiao Ning. "Research on Structure Design Parameters’ Similarity for Loading Machine." Applied Mechanics and Materials 184-185 (June 2012): 440–44. http://dx.doi.org/10.4028/www.scientific.net/amm.184-185.440.

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Calculation results are showing: There are basically specific relationships between loading machine’s weight and loading machines’ main parameters or other parameters’ combinations. So these specific relationships have certain similarity. And machine’s similarity criteria has invariance. The similarity equations derived from similarity theory basically coincide with quantitative analysis results from practical samples data. So, it is proved that there is a kind of similarity between loading machine’s practical design and development and similarity system’s design of structure size. So, the theory of simulation design is completely applied to practical design process.

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Chen, Mei, Zhichong Yang, Xiaofang Wen, Mingwei Leng, Mei Zhang, and Ming Li. "Effectively Detecting Communities by Adjusting Initial Structure via Cores." Complexity 2019 (November 3, 2019): 1–20. http://dx.doi.org/10.1155/2019/9764341.

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Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. ACC defines a novel similarity which is different from most common similarity definitions by considering not only common neighbors of two adjacent nodes but also their mutual exclusive degree. According to this similarity, ACC groups nodes together to obtain the initial community structure in the first step. In the second step, ACC adjusts the initial community structure according to cores discovered through a new local density which is defined as the influence of a node on its neighbors. The third step expands communities to yield the final community structure. To comprehensively demonstrate the performance of ACC, we compare it with seven representative state-of-the-art community detection algorithms, on small size networks with ground-truth community structures and relatively big-size networks without ground-truth community structures. Experimental results show that ACC outperforms the seven compared algorithms in most cases.

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