Steffen B. Petersen (113674), Maria Teresa Neves-Petersen (113668), Svend B. Henriksen (148313) et al. · Figshare · 2016
<p>(A) Number of pairs in the 8D matrix (non-randomized, cells with rank ≥50). The insert shows in detail the distribution of the pairs not clearly seen in the main panel (from pair VAL-CYS onwards). (B) Ratio plot between the “number of pairs in the 8D matrix” and the “number of pairs in the randomized 8D matrix” (for the cells with rank ≥50).</p>
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Wentaoyu211 · Zenodo (CERN European Organization for Nuclear Research) · 2023
<strong>Full Changelog</strong>: https://github.com/wentaoyu211/Pairwise-tree-tree-interactions/commits/v1.0.0
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Amon Masache (20416863), Precious Mdlongwa (20416866), Daniel Maposa (20416869) et al. · Figshare · 2024
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Joanna L. Harris (11014158), Guy M. W. Stevens (5756087) · Figshare · 2021
<p>Higher values indicate a stronger interaction effect; near zero indicates negligible interactions. All interactions were significant (<i>p</i><0.01). Subset model of Hanifaru Bay data without (Subset) and with (Subset (S)) hourly detection count of the sentinel tag.</p> <p>(DOCX)</p>
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Kayson Fakhar (12890364), Claus C. Hilgetag (7446701) · Figshare · 2022
<p>An interaction matrix resulting from the PCIA procedure in which warmer colors show greater synergy and cooler colors indicate functional overlap (left). We excluded the values within a range of ±2 SD above and beyond the mean and applied the “Sprague effect” condition to the thresholded matrix (middle). On the right-hand side, we plotted the interaction network in which the nodes represent connections in the actual network, and the edges are interactions among them. Arrows show paradoxical-lesion effects (i → j).</p>
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Julian R Hughes (90468), Ana M Meireles (90469), Katherine H Fisher (90470) et al. · Figshare · 2015
<div><p>(A) A total of 66 of the 270 MAPS show direct pairwise interactions with another MAP, gathered from GRID and Flybase; displayed here as an interaction network. Proteins are indicated by circular nodes and interactions by lines, or edges.</p>\n <p>(B) The distribution of interactions within randomly selected sets of proteins. The average number of interactions is 42.71 (s.d. ± 13.09; 1,000 repeats). The MAP set shows 92 interactions, 3.77 standard deviations away from the mean of the distribution.</p>\n <p>(C) A bar chart representing the size and compositions of complexes in (A). Any complex containing either characterised mitotic/cell cycle proteins, or uncharacterised proteins showing an RNAi phenotype are represented here. The largest putative complex (indicated by an asterisk [*]) contains both classes of proteins.</p></div>
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Masanao Aoki · Cambridge University Press eBooks · 1996
So far, we have concentrated on modeling a large collection of microeconomic agents subject to field-effect externality. We have shown that we can specify the transition rates for microeconomic units in such a way that they satisfy the detailed balance conditions, and that we establish Gibbs distributions as equilibrium distributions for the state of finite-state Markov chains, and we have investigated the implications of multiple equilibria for the resulting macroeconomic dynamics.
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Xiaokan Guo (3118179), James Q. Boedicker (2498185) · Figshare · 2016
<p>(A) We measured metabolic rates at seven different ratios from 1/8 to 7/8 for the pair <i>Ec</i> and <i>Av</i> and the pair <i>Ec</i> and <i>So</i>. Interaction coefficients were extracted from the data and used to plot the best-fit model. (B) All pairwise combinations of the four strains were assayed. Predictions using solved interaction coefficients were compared to experimental measurements. The normalized strengths of interaction for all 6 pairwise combinations are shown in (C) for species ratio 1:1. Red numbers show confidence intervals at confidence level 95%. Error bars show standard errors of at least three measurements.</p>
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Guzmán Gil‐Ramírez, Marcos Chas, Pablo Ballester · Journal of the American Chemical Society · 2010
Trimethylamine N-oxide and trimethylphosphine oxide guests are pairwise encapsulated in a dimeric capsule self-assembled from a tetraurea aryl extended calix[4]pyrrole. The capsule possesses polar functional groups in its interior capable of controlling the orientation of the included guests. A modest selectivity is observed during the encapsulation process that is beyond the controls of size or shape exclusion.
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Oliver M. Cliff, Annie G. Bryant, Joseph T. Lizier et al. · Nature Computational Science · 2023
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Guzmán Gil-Ramírez (1336569), Marcos Chas (2061427), Pablo Ballester (836089) · Figshare · 2016
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Y. A. Pykh · arXiv (Cornell University) · 2015
In this paper we combine the three universalisms: pairwise interactions concept, dynamical systems theory and relative entropy analysis to develop a theory of entropy issues. We introduce two hypotheses concerning the structure and types properties of the system's entities interactions and derive generalized replicator dynamic equations. Then we construct energy-like and entropy-like Lyapunov-Meyer functions (LMF) for these equations. We show that energy-like LMF contains no information about the equilibrium of the system and is a substantial generalization of the Fisher's fundamental theorem of natural selection. If there are exists nontrivial equilibrium point for generalized replicator system then we construct entropy-like LMF for this system and prove that it is a relative entropy function or the function of information divergence. We prove that negative relative entropy is a convex function for a probability space and receive new distance measure between two probability distributions. Also we use Legendre-Donkin-Fenchel transformation for dual coordinates. As result we establish the set of next important links: nonlinear pairwise interaction - generalized Fisher (replicator) equations - Lyapunov-Meyer functions - relative entropy - distance measure - LDF-transformation - duality. In particular it follows from these links that nonlinear pairwise interaction is the origin of all known entropy functions.
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Jay R. Walton, Luis A. Rivera−Rivera · Elsevier eBooks · 2025
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Dávid Iclănzan, D. Dumitrescu, Béat Hirsbrunner · Adaptation, learning, and optimization · 2010
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Булгакова Мария Александровна, Bulgakova Mariia · Research Repository Saint Petersburg State University (Saint Petersburg State University) · 2018
Работа посвящена динамическим сетевым кооперативным играм с попарным взаимодействием. Описаны возможности игроков при попарном взаимодействии, построена характеристическая функция. Исследованы ее свойства, в частности, доказана выпуклость характеристической функции. Рассмотрены решения такого класса игр на примере вектора Шепли и С-ядра. Выявлены условия для сильной динамической устойчивости С-ядра.
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Benjamin J. Walker, Kenta Ishimoto, Eamonn A. Gaffney · Physical Review Fluids · 2019
Simulations of synchronized spermatozoa reveal stable pairwise swimming, with swimmers speeding up as they approach each other from above. However, swimming side-by-side proves unstable, highlighting anisotropy in planar-beating flagellates that is not captured by simple singularity representations.
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Linjiang Yu, Linjiang Yu · 2002
This paper investigates how structures of pairwise pixel interactions are changing at subsequent levels of Gaussian or Laplacian pyramids representing spatially homogeneous image textures. Structural differences between the parent and child levels of the pyramid are analysed using the modelbased interaction maps (MBIM) describing relative interaction energies in a generic Gibbs random field model of the texture. It is shown that characteristic interaction structures at most levels in the pyramid remain almost the same in spite of the reduced image scales. The periodic structures for regular mosaics are closely related between the neighboring pyramid levels. The like dependencies hold for the close-range characteristic interactions in stochastic textures.
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Javad Salimi Sartakhti (3605675), Mohammad Hossein Manshaei (3605669), Soroosh Bateni (3605672) et al. · Figshare · 2016
<p>A comparison of our model and the pairwise game of Dingli et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168856#pone.0168856.ref013" target="_blank">13</a>] with <i>b</i>>1 (row A), <i>b</i><1, <i>b+d</i><1 (row B) or <i>b</i><1, <i>b+d</i>>1 (row C). The arrows show the direction of the dynamics, and the colors show its speed (the euclidean distance between the frequencies at time <i>t</i> and <i>t</i>+1).</p>
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Fitzgerald, Jeffrey D., Sharpee, Tatyana O. · arXiv (Cornell University) · 2009
Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input signals and network states that allow the network to convey the maximal information about input signals drawn from a given distribution. This mapping also produces a set of linear equations for calculating the optimal Ising model coupling constants, as well as geometric properties that indicate the applicability of the pairwise Ising model. We show that the optimal pairwise interactions are on average zero for Gaussian and uniformly distributed inputs, whereas they are non-zero for inputs approximating those in natural environments. These non-zero network interactions are predicted to increase in strength as the noise in the response functions of each network node increases. This approach also suggests ways for how interactions with unmeasured parts of the network can be inferred from the parameters of response functions for the measured network nodes.
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Jeffrey D. Fitzgerald, Tatyana O. Sharpee · Physical Review E · 2009
Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input signals and network states that allow the network to convey the maximal information about input signals drawn from a given distribution. This mapping also produces a set of linear equations for calculating the optimal Ising-model coupling constants, as well as geometric properties that indicate the applicability of the pairwise Ising model. We show that the optimal pairwise interactions are on average zero for Gaussian and uniformly distributed inputs, whereas they are nonzero for inputs approximating those in natural environments. These nonzero network interactions are predicted to increase in strength as the noise in the response functions of each network node increases. This approach also suggests ways for how interactions with unmeasured parts of the network can be inferred from the parameters of response functions for the measured network nodes.
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