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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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CONSONANCE IN MUSIC - THE PYTHAGOREAN APPROACH REVISITED

arXiv

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arXiv 2025年

作者： Cichowlas, Jan Dlotko, Pawel Kus, Marek SpalińSki, Jan Faculty of Mathematics and Information Science Warsaw University of Technology 00-661 Warsaw Poland Dioscuri Centre in Topological Data Analysis Mathematical Institute Polish Academy of Sciences 00-656 Warsaw Poland Center for Theoretical Physics Polish Academy of Sciences 02-668 Warsaw Poland

The Pythagorean school attributed consonance in music to simplicity of frequency ratios between musical tones. In the last two centuries, the consonance curves developed by Helmholtz, Plompt and Levelt shifted focus to psycho-acoustic considerations in perceiving consonances. The appearance of peaks of these curves at the ratios considered by the Pythagorean school, and which were a consequence of an attempt to understand the world by nice mathematical proportions, remained a curiosity. This paper addresses this curiosity, by describing a mathematical model of musical sound, along with a mathematical definition of consonance. First, we define pure, complex and mixed tones as mathematical models of musical sound. By a sequence of numerical experiments and analytic calculations, we show that continuous cosine similarity, abbreviated as cosim, applied to these models quantifies the elusive concept of consonance as a frequency ratio which gives a local maximum of the cosim function. We prove that these maxima occur at the ratios considered as consonant in classical music theory. Moreover, we provide a simple explanation why the number of musical intervals considered as consonant by musicians is finite, but has been increasing over the centuries. Specifically, our formulas show that the number of consonant intervals changes with the depth of the tone (the number of harmonics present). Copyright © 2025, The Authors. All rights reserved.

关键词： Music

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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Sustainable North-South Africa Collaboration for Disaster and Crisis Management: A Strategic Capacity Development Framework using Open science, Artificial Intelligence and Geoinformatics

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data science Journal 2025年 24卷

作者： Ibrahim, Rania Elsayed Motshegwa, Tshiamo Benouar, Djillali Khodja, Mohamed Ead, Hamed Zhang, Lili Crawley, Francais McHunu, Nokuthula P. Hachigonta, Sepo Hodson, Simon McBride, Vanessa Yohannes, Teketel National Authority for Remote Sensing and Space Sciences Egypt National Research Foundation South Africa Department of Computer Science University of Botswana Botswana Algerian Academy for Science and Technology Algeria Faculty of Science Cairo University Egypt Egyptian Embassy in Morocco Morocco Computer Network Information Center Chinese Academy of Sciences China CODATA International Data Policy Committee Belgium University of KwaZulu Natal South Africa CODATA France International Science Council South Africa

Background: This paper explores strategies to enhance North-South collaboration to foster multi-and trans-disciplinary research in disaster risk reduction (DRR) and crisis management, emphasizing the role of open science, Artificial Intelligence (AI) and geoinformatics in developing a resilient and proactive approach to these challenges, as well as drawing on lessons learned from the COVID-19 pandemic, the paper highlights how collaboration and data sharing can enhance preparedness, response, and recovery in future crises. Methods: The study uses a mixed approach to gather and analyze information. Qualitative methods focus on gathering descriptive data through engaging with experts and a structured review. By investigating literature, a comprehensive strategic framework for minimizing disaster risk, and for crisis management, is developed to provide adequate guidance to policymakers on implementation strategies and the application of how to build the human, institutional, and technological capacity required for effective disaster and crisis management (DCM). The events were used to engage with experts to provide recommendations and opportunities to enhance crisis response, capacity development and policy. Findings: By presenting a comprehensive framework that bridges theoretical knowledge with practical application, our findings indicate that governments must address the challenges of DCM to minimize losses. The practices surrounding disaster recovery can provide opportunities for policymakers to reassess priorities and redirect resources toward building sustainable communities. Originality: There is no strategic framework that provides adequate guidance to policymakers on implementation strategies or the application of how to build the human, institutional and technological capacity required for effective DCM. This article develops a Decision Support Framework for DCM. We outline how open science promotes cross-disciplinary collaboration and the sharing of knowledg

关键词： Risk management

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CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data

arXiv

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arXiv 2025年

作者： Malinowski, Jakub Kostrzewa, Marcin Balcerek, Michal Tomczuk, Weronika Szwabiński, Janusz Hugo Steinhaus Center Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wybrzeże Stanislawa Wyspiańskiego 27 Wroclaw50-370 Poland Department of Artificial Intelligence Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Stanislawa Wyspiańskiego 27 Wroclaw50-370 Poland Dioscuri Centre in Topological Data Analysis Mathematical Institute Polish Academy of Sciences ul. Śniadeckich 8 Warsaw00-656 Poland

Change point detection has become an important part of the analysis of the single-particle tracking data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter and biological physics. In this paper, we propose CINNAMON, a hybrid approach to classifying single-particle tracking trajectories, detecting change points within them, and estimating diffusion parameters in the segments between the change points. Our method is based on a combination of neural networks, feature-based machine learning, and statistical techniques. It has been benchmarked in the second Anomalous Diffusion Challenge. The method offers a high level of interpretability due to its analytical and feature-based components. A potential use of features from topological data analysis is also discussed. Copyright © 2025, The Authors. All rights reserved.

关键词： Change detection

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The economics of global personality diversity

arXiv

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arXiv 2025年

作者： McCarthy, Paul X. Gong, Xian Coetzer, Marieth Rizoiu, Marian-Andrei Kern, Margaret L. Johnson, John A. Holden, Richard Braesemann, Fabian The Data Science Institute University of Technology SydneyNSW Australia School of Computer Science and Engineering UNSW Sydney NSW Australia Faculty of Engineering and Information Technology University of Technology Sydney Australia Oxford Internet Institute University of Oxford Oxford United Kingdom Einstein Center Digital Future Berlin Germany Melbourne Graduate School of Education The University of Melbourne ParkvilleVIC Australia Penn State University Pennsylvania United States

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index (Ψ-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per capita and provides an additional 2.8% explanatory power beyond institutional quality and immigration, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics. © 2025, CC BY.

关键词： Inflation

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Sustainable AI: Mathematical Foundations of Spiking Neural Networks

arXiv

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arXiv 2025年

作者： Fono, Adalbert Singh, Manjot Araya, Ernesto Petersen, Philipp C. Boche, Holger Kutyniok, Gitta Department of Mathematics Ludwig-Maximilians-Universität München Germany Faculty of Mathematics and Research Network Data Science University of Vienna Austria Institute of Theoretical Information Technology Technical University of Munich Germany Munich Germany Department of Physics and Technology University of Tromsø Tromsø Norway Institute of Robotics and Mechatronics DLR-German Aerospace Center Germany

Deep learning’s success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm. Copyright © 2025, The Authors. All rights reserved.

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Channel-shuffled transformers for cross-modality person re-identification in video

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Scientific Reports 2025年第1期15卷 1-13页

作者： Kasantikul, Rangwan Kusakunniran, Worapan Wu, Qiang Wang, Zhiyong Faculty of Information and Communication Technology Mahidol University 999 Phuttamonthon 4 Road Nakhon Pathom Salaya 73170 Thailand School of Electrical and Data Engineering University of Technology Sydney 15 Broadway Ultimo 2007 NSW Australia School of Computer Science The University of Sydney Camperdown 2006 NSW Australia

Effective implementation of person re-identification (Re-ID) across different modalities (such as daylight vs night-vision) is crucial for Surveillance applications. information from multiple frames is essential for effective re-identification, where visual components from individual frames become less reliable. While transformers can enhance the temporal information extraction, the large number of channels required for effective feature encoding introduces scaling challenges. This could lead to overfitting and instability during training. Therefore, we proposed a novel Channel-Shuffled Temporal Transformer (CSTT) for processing multi-frame sequences in conjunction with a ResNet backbone to form Hybrid Channel-Shuffled Transformer Net (HCSTNET). Replacing fully connected layers in standard multi-head attention with ShuffleNet-like structures is important for integration of transformer attention with a ResNet backbone. Applying ShuffleNet-like structures reduces overfitting through parameter reduction with channel-grouping, and further improves learned attention using channel-shuffling. According to our tests with the SYSU-MM01 dataset in comparison against simple averaging of multiple frames, only the temporal transformer with channel-shuffling achieved a measurable improvement over the baseline. We have also investigated the optimal partitioning of feature maps therein. © The Author(s) 2025.

关键词： Channel Shuffling RGB-IR Re-ID Surveillance Transformer Video-Based Person Re-identification

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