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arXiv 2022年

作者： Bathina, Krishna C. ten Thij, Marijn Bollen, Johan School of Informatics Computing and Engineering Indiana University United States Delft Institute of Applied Mathematics Delft University of Technology Netherlands Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Natural disasters can have devastating and long-lasting effects on a community’s emotional well-being. These effects may be distributed unequally, affecting some communities more profoundly and possibly over longer time periods than others. Here, we analyze the effects of four major US hurricanes, namely, Irma, Harvey, Florence, and Dorian on the emotional well-being of the affected communities and regions. We show that a community’s emotional response to a hurricane event can be measured from the content of social media that its population posted before, during, and after the hurricane. For each hurricane making landfall in the US, we observe a significant decrease in sentiment in the affected areas before and during the hurricane followed by a rapid return to pre-hurricane baseline, often within 1-2 weeks. However, some communities exhibit markedly different rates of decline and return to previous equilibrium levels. This points towards the possibility of measuring the emotional resilience of communities from the dynamics of their online emotional response. © 2022, CC BY.

关键词： Hurricanes

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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A modified FCN-based method for Left Ventricle endocardium and epicardium segmentation with new block modules

A modified FCN-based method for Left Ventricle endocardium a...

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National Foundation for science and Technology Development Conference on Information and Computer science (NICS)

作者： Do-Hai-Ninh Nham Minh-Nhat Trinh Tien-Thanh Tran Van-Truong Pham Thi-Thao Tran School of Applied Mathematics and Informatics Hanoi University of Science and Technology Smart Health Center Vingroup Big Data Institute

ISBN: (纸本)9781665410021

Cardiac segmentation of medical magnetic resonance images has been crucial nowadays owing to its necessity for cardiac problems diagnosis. In the increasing demand of advanced procedures for cardiac disease diagnosis and inspired by the structure of receptive field block; in this paper, we propose a new block module then further assembling into a deep fully convolutional neural network to deal with automated left ventricle segmentation. With only one learning stage, our proposed model is trained end-to-end, pixels-to-pixels and validated on two popular cardiac MRI benchmarks, ACDC and SunnyBrook datasets. Several experiments have proved that our new model architecture has a better performance than previous segmentation methods with enhanced feature discriminability and robustness, despite having much less training parameters.

关键词： Training Deep learning Computer science Image segmentation Magnetic resonance imaging Magnetic resonance Robustness

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Game-Theoretic Analysis on CBDC Adoption 1st

Game-Theoretic Analysis on CBDC Adoption

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1st BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020

作者： Mou, Chenqi Tsai, Wei-Tek Jiang, Xiaofang Yang, Dong LMIB – School of Mathematics and Systems Science Beihang University Beijing100191 China SKLSDE – School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

ISBN: (纸本)9789811611599

As an important blockchain application, CBDC (Central Bank Digital Currency) has received significant worldwide attention as it can restructure financial market, affect national currency policies, and introduce new regulation policies and mechanisms. It is widely predicted that CBDC will introduce numerous digital currency competitions in various aspects of the global financial market, and winners will lead the next wave of digital currency market. This paper applies the game theory to study the competitions between different countries, in particular to analyze whether they should adopt the CBDC program. We propose two game-theoretic models for CBDC adoption, both analyzing whether to adopt the CBDC program via the Nash equilibrium. Both game-theoretic models draw the same conclusion that each country should adopt the CBDC program regardless of the choices of other counties. In other words, current currency leaders should adopt CBDC because it may lose the premier status, and other countries should adopt CBDC otherwise they risk of getting even further behind in the digital economy. According to our game-theoretic models, the current market leader who has 90% of market shares may lose about 19.2% shares if it is not the first mover. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Routing of data Between the Nodes in Mobile Adhoc Networks using Machine Learning Modelling

Routing of Data Between the Nodes in Mobile Adhoc Networks u...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ramesh Babu, P Ranjan Nayak, Nihar Meenakshi, K Das, Saswata Selvakanmani, S. Ananda Kumar, K S Wollega University College of Engineering Technology Department of Computer Science Nekemte Ethiopia Department of Mca Andhra Pradesh Chittoor India Department of Mathematics Bengaluru India Cognizant Technology Solutions India Pvt. Ltd. West Bengal Kolkata India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Dilla University School of Computing and Informatics College of Engineering and Technology Snnpr Dilla Ethiopia

ISBN: (纸本)9781665484251

The routing of data between the packets in mobile adhoc networks has severe limitations that involves communication and computational overhead, which has to be reduced for effective transmission of data packets. In this paper, we develop a machine learning assisted routing mechanism that has three different modules to serve the purpose of reduced overhead during routing. The study uses genetic algorithm to resolve the routing constraints between the source and destination nodes. The modelling is specifically designed to overcome the limitations associated with the generation of overheads. The simulation is conducted to test the efficacy of the models against various existing mechanisms. The results of simulation shows that the proposed method achieves higher rate of packet delivery rate, throughput and reduced delay. © 2022 IEEE.

关键词： Machine learning

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Fast sequence to graph alignment using the graph wavefront algorithm

arXiv

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arXiv 2022年

作者： Zhang, Haowen Wu, Shiqi Aluru, Srinivas Li, Heng School of Computational Science and Engineering Georgia Institute of Technology United States Department of Mathematics Nanjing University China Institute for Data Engineering and Science Georgia Institute of Technology United States Department of Data Sciences Dana-Farber Cancer Institute United States Department of Biomedical Informatics Harvard Medical School United States

Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them. It is a powerful data structure for studying multiple similar genomes. Sequence-to-graph alignment is an essential step for the construction and the analysis of pan-genome graphs. However, existing algorithms incur runtime proportional to the product of sequence length and graph size, making them inefficient for aligning long sequences against large graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a new method for aligning a sequence to a sequence graph. Although the worst-case time complexity of Gwfa is the same as the existing algorithms, it is designed to run faster for closely matching sequences, and its runtime in practice often increases only moderately with the edit distance of the optimal alignment. On four real datasets, Gwfa is up to four orders of magnitude faster than other exact sequence-to-graph alignment algorithms. We also propose a graph pruning heuristic on top of Gwfa, which can achieve an additional ∼10-fold speedup on large graphs. © 2022, CC BY.

关键词： Wavefronts

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Adjusting for Participation Bias in Case-Control Genetic Association Studies for Rare Diseases

arXiv

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arXiv 2024年

作者： Wang, Le Li, Zhengbang Fitzpatrick, Ben Weinberg, Clarice Chen, Jinbo Department of Mathematics Statistics and Data Science Loyola Marymount University Los AngelesCA90045 United States School of Mathematics and Statistics Central China Normal University Hubei Wuhan430079 China Biostatistics and Computational Biology Branch National Institute of Environmental Health Sciences DurhamNC27709 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States

Collection of genotype data in case-control genetic association studies may often be incomplete for reasons related to genes themselves. This non-ignorable missingness structure, if not appropriately accounted for, can result in participation bias in association analyses. To deal with this issue, Chen et al. [7] proposed to collect additional genetic information from family members of individuals whose genotype data were not available, and developed a maximum likelihood method for bias correction. In this study, we develop an estimating equation approach to analyzing data collected from this design that allows adjustment of covariates. It jointly estimates odds ratio parameters for genetic association and missingness, where a logistic regression model is used to relate missingness to genotype and other covariates. Our method allows correlation between genotype and covariates while using genetic information from family members to provide information on the missing genotype data. In the estimating equation for genetic association parameters, we weight the contribution of each genotyped subject to the empirical likelihood score function by the inverse probability that the genotype data are available. We evaluate large and finite sample performance of our method via simulation studies and apply it to a family-based case-control study of breast cancer. Copyright © 2024, The Authors. All rights reserved.

关键词： Disease control

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GTB-PPI:Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

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Genomics, Proteomics & Bioinformatics 2020年第5期18卷 582-592页

作者： Bin Yu Cheng Chen Hongyan Zhou Bingqiang Liu Qin Ma School of Life Sciences University of Science and Technology of ChinaHefei 230027China College of Mathematics and Physics Qingdao University of Science and TechnologyQingdao 266061China Artificial Intelligence and Biomedical Big Data Research Center Qingdao University of Science and TechnologyQingdao 266061China School of Mathematics Shandong UniversityJinan 250100China Department of Biomedical Informatics College of MedicineThe Ohio State UniversityColumbusOH 43210USA

Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug *** the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in *** this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature ***,GTB-PPI model is ***-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,*** addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling *** results show that GTB-PPI can significantly improve accuracy of PPI *** code and datasets of GTB-PPI can be downloaded from https://***/QUST-AIBBDRC/GTB-PPI/.

关键词： Protein-protein interaction Feature fusion L1-regularized logistic regression Gradient tree boosting Machine learning

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Quantum algorithm for Gaussian process regression

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Physical Review A 2022年第1期106卷 012406-012406页

作者： Meng-Han Chen Chao-Hua Yu Jian-Liang Gao Kai Yu Song Lin Gong-De Guo Jing Li College of Mathematics and Informatics Fujian Normal University Fuzhou 350117 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330032 China Research Computing & Data Science Graduate School & Business School Imperial College London United Kingdom State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

The Gaussian process is a widely used model for regression problems in supervised machine learning. However, predicting new inputs via a Gaussian process model becomes computationally inefficient when training a large data set. This paper proposes a fast quantum algorithm for prediction based on the Gaussian process regression. The proposed quantum algorithm consists of two subalgorithms: the first one aims to efficiently prepare the squared exponential covariance matrices and covariance functions vector with annihilation and creation operators; the other is to obtain predictive mean values and covariance values for new inputs. Evidence is also shown that the proposed quantum Gaussian process regression algorithm can achieve quadratic speedup over the classical counterpart.

关键词： Quantum algorithms

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