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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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GA optimization of OBF TS fuzzy models with linear and non linear local models

GA optimization of OBF TS fuzzy models with linear and non l...

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9th Brazilian Symposium on Neural Networks, SBRN'06

作者： Medeiros, Anderson V. Amaral, Wagner C. Campello, Ricardo J. G. B. State University of Campinas School of Electrical and Computer Engineering Campinas SP 13083-970 Brazil Catholic University of Santos Graduate Program in Computer Science Santos SP 11070-906 Brazil

ISBN: (纸本)0769526802

OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Although encouraging application results have been obtained, no automatic procedure had yet been developed to optimize the design parameters of these models. This paper elaborates on the use of a genetic algorithm (GA) especially designed for this task, in which a fitness function based on the Akaike information criterion plays a key role by considering both model accuracy and parsimony aspects. The use of linear (actually affine) and nonlinear local models is also investigated. The proposed methodology is evaluated in the modeling of a real nonlinear magnetic levitation system. © 2006 IEEE.

关键词： Fuzzy sets

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TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLEM: A CASE STUDY IN FLORIDA, USA

TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLE...

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2024 Winter Simulation Conference, WSC 2024

作者： Monteiro, Almir Antonio Schneider, Rafael de Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil COPPEAD Graduate School of Business Federal University of Rio de Janeiro Brazil

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model’s output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability. © 2024 IEEE.

关键词： Monte Carlo methods

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AN EQUIVALENCY CONDITION OF NONSINGULARITY IN NONLINEAR SEMIDEFINITE programMING

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Journal of Systems science & Complexity 2010年第4期23卷 822-829页

作者： Chengjin LI Wenyu SUN Raimundo J. B. de SAMPAIO School of Mathematical Sciences Nanjing Normal and Computer Science Fujian Normal University Wenyu SUN University Nanjing 210046 China School of Mathematics Fuzhou 350007 China School of Mathematical Sciences Nanjing Normal University Nanjing 210046 China Pontifical Catholic University of Parana ( PUCPR) Graduate Program in Production and Systems Engineering (PPGEPS) CEP 81611-970 Curitiba Parana Brazil

In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,... 详细信息

关键词： Equivalency condition of nonsingularity nonlinear semidefinite programming semidefinite programming.

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Phylogeographic and genetic network assessment of COVID-19 mitigation protocols on SARS-CoV-2 transmission in university campus residences

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eBioMedicine 2025年 116卷 105729-105729页

作者： Wertheim, Joel O. Vasylyeva, Tetyana I. Wood, Robert J. Cantrell, Kalen Contreras, Soraya Piña Feldheim, Aryeh Goyal, Ravi Havens, Jennifer L. Knight, Rob Laurent, Louise C. Moshiri, Niema Neuhard, Robert Sathe, Shashank Satterlund, Alysson Scioscia, Angela Song, Angela Y. Aigner, Stefan Andersen, Kristian G. Baer, Nathan A. Betty, Maryann Birmingham, Amanda Castro-Martinez, Anelizze Cheung, Willi De Hoff, Peter Fisch, Kathleen M. King, Alison J. Gangavarapu, Karthik Hakim, Abbas Henson, Benjamin Jepsen, Kristen Mac, Christina H. Ngo, Toan T. Nguyen, Kelly N. Ostrander, Tyler R. Perkins, Sarah Plascencia, Ashley Rivera, Andrea Rivera, Ariana Salido, Rodolfo A. Saucedo, Kieran C. Schwab, Madison Steedman, Allison L. Veder, Anthony Weiss, Alana Yeo, Gene W. Zeller, Mark schooley, Robert T. Anderson, Cheryl M. Martin, Natasha K. Department of Medicine University of California San Diego La Jolla CA United States Department of Population Health and Disease Prevention University of California Irvine Irvine CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Pediatrics University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA United States Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA United States University of California San Diego La Jolla CA United States

Background: Congregate living provides an ideal setting for SARS-CoV-2 transmission in which many outbreaks and superspreading events occurred. To avoid large outbreaks, universities turned to remote operations during the initial COVID-19 pandemic waves in 2020 and 2021. In late-2021, the University of California San Diego (UC San Diego) facilitated the return of students to campus with comprehensive testing, vaccination, masking, wastewater surveillance, and isolation policies. Methods: We performed molecular epidemiological and phylogeographic analysis of 4418 SARS-CoV-2 genomes sampled from UC San Diego students during the Omicron waves between December 2021 and September 2022, representing 58% of students with confirmed SARS-CoV-2 infection. We overlaid these analyses across on-campus residential information to assess the spread and persistence of SARS-CoV-2 within university residences. Findings: Within campus residences, SARS-CoV-2 transmission was frequent among students residing in the same room or suite. However, a quarter of pairs of suitemates with concurrent infections had distantly related viruses, suggesting separate sources of infection during periods of high incidence in the surrounding community. Students with concurrent infections residing in the same building were not at substantial increased probability of being members of the same transmission cluster. Genetic network and phylogeographic inference indicated that only between 3.1 and 12.4% of infections among students could be associated with transmission within buildings outside of individual suites. The only super-spreading event we detected was related to a large event outside campus residences. Interpretation: We found little evidence for sustained SARS-CoV-2 transmission within individual buildings, aside from students who resided in the same suite. Even in the face of heightened community transmission during the 2021–2022 Omicron waves, congregate living did not result in a heightened risk

关键词： Mitigation Phylogeography SARS-CoV-2 Transmission cluster University

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Cooperation for Creativity in Design

Cooperation for Creativity in Design

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The 8th International Conference on computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Adriana Vivacqua Jano de Souza COPPE-Graduate School of Engineering Computer Science Program COPPE-Graduate School of Engineering Computer Science Program DCC/IM-Institute of Mathematics UFRJ-F

Creativity has become an important asset in today'sfast changing environment. It is especially important indesign, which demands innovative solutions for *** believe that creativity can and should beencouraged in work groups. In this paper, we proposea framework for supporting creativity in problemsolving. Through the use of problem, domain and usermodels and information retrieval and agent technology,we hope to support creative cooperative work.

关键词： Collaborative work computer science Mathematics Problem-solving Information retrieval Knowledge management Environmental economics Organizing Product design Writing

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Measurement of capacitance and electrical potential of plural points for estimation of permittivity distribution

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IEEJ Transactions on Fundamentals and Materials 2013年第4期133卷 173-179+5页

作者： Okada, Shota Ohchi, Masashi Furukawa, Tatsuya Itoh, Hideaki Fukumoto, Hisao Master's Program in Electrical Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino 275-0016 Japan Department of Electrical Electronics and Computer Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino 275-0016 Japan Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga 840-8502 Japan

Recently, a method for estimating permittivity distributions was reported. The method combined a numerical technique (Finite Element Method) with Genetic Algorithm (GA). However, methods for estimating the distribution of permittivity have not yet been established. The purpose of this study is to obtain experimental data in order to improve the estimation method. We measured capacitance and electrical potential of plural points when an acrylic rod is placed in various positions within a measurement system. © 2013 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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