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Temporal motifs in patent opposition and collaboration networks

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arXiv 2021年

作者： Liu, Penghang Masuda, Naoki Kito, Tomomi Sarıyüce, Ahmet Erdem Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Tokyo Japan

Patents are intellectual properties that reflect innovative activities of companies and organizations. The literature is rich with the studies that analyze the citations among the patents and the collaboration relations among companies that own the patents. However, the adversarial relations between the patent owners are not as well investigated. One proxy to model such relations is the patent opposition, which is a legal activity in which a company challenges the validity of a patent. Characterizing the patent oppositions, collaborations, and the interplay between them can help better understand the companies’ business strategies. Temporality matters in this context as the order and frequency of oppositions and collaborations characterize their interplay. In this study, we construct a two-layer temporal network to model the patent oppositions and collaborations among the companies. We utilize temporal motifs to analyze the oppositions and collaborations from structural and temporal perspectives. We first characterize the frequent motifs in patent oppositions and investigate how often the companies of different sizes attack other companies. We show that large companies tend to engage in opposition with multiple companies. Then we analyze the temporal interplay between collaborations and oppositions. We find that two adversarial companies are more likely to collaborate in the future than two collaborating companies oppose each other in the future. © 2021, CC BY.

关键词： Patents and inventions

Finite-size effects on the convergence time in continuous-opinion dynamics

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arXiv 2021年

作者： Jo, Hang-Hyun Masuda, Naoki Department of Physics The Catholic University of Korea Bucheon14662 Korea Republic of Department of Mathematics State University of New York at Buffalo 14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 14260-5030 United States

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0;1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph theory

Gillespie algorithms for stochastic multiagent dynamics in populations and networks

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arXiv 2021年

作者： Masuda, Naoki Vestergaard, Christian L. Department of Mathematics State University of New York at Buffalo NY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo NY United States Decision and Bayesian Computation Department of Neuroscience Cnrs Umr 3571 Department of Computational Biology Cnrs Usr 3756 Institut Pasteur Paris France

Many multiagent dynamics, including various collective dynamics occurring on networks, can be modeled as a stochastic process in which the agents in the system change their state over time in interaction with each other. The Gillespie algorithms are popular algorithms that exactly simulate such stochastic multiagent dynamics when each state change is driven by a discrete event, the dynamics is defined in continuous time, and the stochastic law of event occurrence is governed by independent Poisson processes. In the first main part of this volume, we provide a tutorial on the Gillespie algorithms focusing on simulation of social multiagent dynamics occurring in populations and networks. We do not assume advanced knowledge of mathematics (or computer science or physics). We clarify why one should use the continuous-time models and the Gillespie algorithms in many cases, instead of easier-to-understand discrete-time models. In the remainder of this volume, we review recent extensions of the Gillespie algorithms aiming to add more reality to the model (i.e., non-Poissonian cases) or to speed up the simulations. © 2021, CC BY.

关键词： Stochastic systems

A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System data

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A Modified Clustering Using Representatives to Enhance and O...

International Conference on computational science and computational Intelligence (CSCI)

作者： Cheronika Manyfield-Donald Tor A. Kwembe Jing-Ru C. Cheng Computational Data-Enabled Science and Engineering Jackson State University Jackson MS USA Department of Mathematics and Statistical Sciences Jackson State University Jackson MS USA Information Technology Lab. Engineer Research and Development Center U.S. Army Corps of Engineers Vicksburg MS USA

ISBN: (纸本)9781665458429

In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways.

关键词： Machine learning algorithms Tracking Scientific computing Clustering methods Clustering algorithms Machine learning data collection

Biases in Inverse Ising Estimates of Near-Critical Behaviour

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arXiv 2023年

作者： Kloucek, Maximilian B. Machon, Thomas Kajimura, Shogo Royall, C. Patrick Masuda, Naoki Turci, Francesco H.H. Wills Laboratory School of Physics University of Bristol Bristol United Kingdom Bristol Centre for Functional Nanomaterials University of Bristol Bristol United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL Paris75005 France Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as Pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick (SK) model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer-to-criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging (fMRI) dataset from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Criticality (nuclear fission)

Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

A growth model for water distribution networks with loops

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arXiv 2021年

作者： Sugishita, Kashin Abdel-Mottaleb, Noha Zhang, Qiong Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs which generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modeled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency, and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modeled pipe diameters. The difference between the empirical and modeled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

Biases in inverse Ising estimates of near-critical behavior

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Physical Review E 2023年第1期108卷 014109-014109页

作者： Maximilian B. Kloucek Thomas Machon Shogo Kajimura C. Patrick Royall Naoki Masuda Francesco Turci School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Bristol Centre for Functional Nanomaterials HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto 606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL 75005 Paris France Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

关键词： Critical phenomena Cortical networks Spin glasses Functional magnetic resonance imaging Ising model

Correction to: Mitigation strategies against cascading failures within a project activity network

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Journal of computational Social science 2021年第1期5卷 1097-1098页

作者： Ellinas, Christos Nicolaides, Christos Masuda, Naoki Nodes and Links Ltd Cambridge UK Department of Business and Public Administration University of Cyprus Nicosia Cyprus Initiative on the Digital Economy Massachusetts Institute of Technology Cambridge USA Department of Mathematics State University of New York at Buffalo Buffalo USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo USA Faculty of Management and Economics Dalian University of Technology Dalian China