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arXiv 2024年

作者： Elena, Alin Marin Kamath, Prathami Divakar Inizan, Théo Jaffrelot Rosen, Andrew S. Zanca, Federica Persson, Kristin A. Scientific Computing Department Science and Technology Facilities Council Keckwick Lane Daresbury Cheshire WA4 1PT United Kingdom Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States

Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics. © 2024, CC BY.

关键词： Carbon capture

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Media and responsible AI governance: a game-theoretic and LLM analysis

arXiv

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arXiv 2025年

作者： Balabanova, Nataliya Bashir, Adeela Bova, Paolo Buscemi, Alessio Cimpeanu, Theodor da Fonseca, Henrique Correia Di Stefano, Alessandro Duong, Manh Hong Domingos, Elias Fernández Fernandes, Antonio Han, The Anh Krellner, Marcus Ogbo, Ndidi Bianca Powers, Simon T. Proverbio, Daniele Santos, Fernando P. Shamszaman, Zia Ush Song, Zhao School of Mathematics University of Birmingham United Kingdom School Computing Engineering and Digital Technologies Teesside University United Kingdom Luxembourg Institute of Science and Technology Luxembourg School of Mathematics and Statistics University of St Andrews United Kingdom INESC-ID and Instituto Superior Técnico Universidade de Lisboa Portugal Machine Learning Group Université libre de Bruxelles Belgium AI Lab Vrije Universiteit Brussel Belgium Division of Computing Science and Mathematics University of Stirling United Kingdom Department of Industrial Engineering University of Trento Italy University of Amsterdam Netherlands

This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes. The research explores two key mechanisms for achieving responsible governance, safe AI development and adoption of safe AI: incentivising effective regulation through media reporting, and conditioning user trust on commentariats’ recommendation. The findings highlight the crucial role of the media in providing information to users, potentially acting as a form of"soft" regulation by investigating developers or regulators, as a substitute to institutional AI regulation (which is still absent in many regions). Both game-theoretic analysis and LLM-based simulations reveal conditions under which effective regulation and trustworthy AI development emerge, emphasising the importance of considering the influence of different regulatory regimes from an evolutionary game-theoretic perspective. The study concludes that effective governance requires managing incentives and costs for high quality commentaries. © 2025, CC BY.

关键词： digital elevation model

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Which one is Kaphrao? Identify Thai Herbs with Similar Leaf Structure using Transfer Learning of Deep Convolutional Neural Networks

Which one is Kaphrao? Identify Thai Herbs with Similar Leaf ...

引用

IEEE Region 10 International Conference TENCON

作者： Sila Temsiririrkkul Prarinya Siritanawan Rungravi Temsiririrkkul Division of Computing Science and Digital Technology Faculty of Science and Technology Huachiew Chalermprakiet University Samutprakarn Thailand School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Faculty of Pharmacy Thammasat University Pathumthani Thailand

ISBN: (纸本)9781665495332

The recent Deep Learning algorithms have demonstrated incredible performance in many fields of research, especially an object detection task where algorithm advances are at a level that can be applied to real-world problems. Classification of plants by leaf image is one of the problems that has been of interest among computer vision researchers for a long time because of its challenges due to a large number of species and their complex features. There are many approaches presented and show promising performance for the task. However, in the tropical area where the diversity of plants is rich, examples of the physical similarity of the leaves in different species can be found easily. Moreover, it is reported that many accidents of misidentifying plants and their usages can be life-threatening. Thus, specimens of plants with similar leaf structures are interested in this research. We present a method to identify plants that are similar in morphological characters of leaf. Using image processing and deep learning techniques and transfer learning of several deep convolutional neural network architectures: VGG-16, ResNet-50, and InceptionV3, the proposed method can identify seven Lamiaceae plants yielded high accuracy prediction of 98.71%, 91.32%, and 98.17%, respectively.

关键词： Deep learning Image processing Conferences Transfer learning Computer architecture Object detection Classification algorithms

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Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different computing Capacity

arXiv

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arXiv 2023年

作者： Nguyen, Loc X. Tun, Ye Lin Tun, Yan Kyaw Nguyen, Minh N.H. Zhang, Chaoning Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Digital Science and Technology Institute The University of Danang Vietnam-Korea University of Information and Communication Technology Da Nang550000 Viet Nam The Department of Artificial Intelligence School of Computing Kyung Hee University Yongin-si17104 Korea Republic of The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Measuring fairness preferences is important for artificial intelligence in health care

引用

The Lancet digital Health 2024年第5期6卷 e302-e304页

作者： Näher, Anatol-Fiete Krumpal, Ivar Antão, Esther-Maria Ong, Erika Rojo, Marina Kaggwa, Fred Balzer, Felix Celi, Leo Anthony Braune, Katarina Wieler, Lothar H Agha-Mir-Salim, Louis Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam14482 Germany Institute of Medical Informatics Charité–Universitätsmedizin Berlin Berlin Germany Faculty of Social Science and Philosophy University of Leipzig Leipzig Germany College of Medicine University of the Philippines Manila Philippines Department of Public Health School of Medicine University of Buenos Aires Buenos Aires Argentina Department of Computer Science Faculty of Computing and Informatics Mbarara University of Science and Technology Mbarara Uganda Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T H Chan School of Public Health BostonMA United States

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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SGUQ: Staged Graph Convolution Neural Network for Alzheimer’s Disease Diagnosis using Multi-Omics Data

arXiv

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arXiv 2024年

作者： Tao, Liang Xie, Yixin Deng, Jeffrey D. Shen, Hui Deng, Hong-Wen Zhou, Weihua Zhao, Chen Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Information Technology Kennesaw State University MariettaGA30060 United States Geisel School of Medicine Dartmouth College Hamover NH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Applied Computing Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ). SGUQ begins with mRNA and progressively incorporates DNA methylation and miRNA data only when necessary, reducing overall costs and exposure to harmful tests. Experimental results indicate that 46.23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16.04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved an accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods significantly. The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. Our implementation is publicly available at https://***/chenzhao2023/multiomicsuncertainty. © 2024, CC BY.

关键词： Convolutional neural networks

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Sguq: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis Using Multi-Omics Data

SSRN

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SSRN 2024年

作者： Tao, Liang Xie, Yixin Deng, Jeffrey D. Shen, Hui Deng, Hong-Wen Zhou, Weihua Zhao, Chen Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Information Technology Kennesaw State University MariettaGA30060 United States Geisel School of Medicine Dartmouth College HanoverNH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Applied Computing Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Background and Objective: Alzheimer’s disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. We aim to propose a cost-effective method for AD ***: To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ) model. SGUQ begins with mRNA and progressively incorporates miRNA and DNA methylation data only when necessary, reducing overall costs and exposure to harmful ***: Experimental results indicate that 0.94% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 33.96% of the samples can achieve reliable predictions when combining two omics data types (mRNA + miRNA). In addition, the proposed staged SGUQ achieved an accuracy of 0.878 on ROSMAP dataset, which outperformed existing methods ***: The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Evaluation of carbon based interconnects for digital signalling in printed flexible electronics on sustainable substrates

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年

作者： Wang, Zixin Saleh, Mahdi Alwattar, Aula Alfredsson, Maria Horne, Robert J. Ang, Chee S. Condell, Joan Flowers, Emily Matcham, Faith Robinson, Tony Rooksby, John Saha, Sian Rose, Louise Batchelor, John C. Casson, Alexander J. Henry Royce Institute for Advanced Materials Manchester United Kingdom The University of Manchester School of Engineering Department of Electrical and Electronic Engineering Manchester United Kingdom The University of Manchester School of Natural Sciences Department of Chemistry Manchester United Kingdom University of Kent School of Chemistry and Forensic Science Canterbury United Kingdom University of Kent School of Engineering Canterbury United Kingdom University of Kent School of Computing Canterbury United Kingdom Ulster University School of Computing Engineering and Intelligent Systems Derry United Kingdom King’s College London Division of Digital Health and Applied Technology Assessment London United Kingdom University of Sussex School of Psychology Falmer United Kingdom Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

Printed electronics using flexible substrates are an emerging area, allowing next-generation electronics to conform and flex with different surfaces, from human skin to clothing. In the hybrid integration, or sea-of-rigids, approach, conventional microchips are mounted onto (generally) plastic substrates such as polyethylene naphthalate (PEN), with (typically) printed silver tracks for interconnections between components. An ongoing research direction is to replace plastic substrates with biodegradable substrates and to replace silver tracks with non-heavy metal-based tracks. While the substrates and tracks form only part of an overall system, replacing them is a step towards increased sustainability and helps to meet net-zero goals for printed electronic systems. Previously, several papers have investigated printed carbon tracks for low-frequency analog sensing applications. This paper explores the feasibility of using printed carbon tracks on biodegradable substrates for high-frequency applications such as digital signaling over a Serial-Peripheral Interface (SPI). We investigate the printability, thermal stability, and electrical conductivity of carbon ink screen-printed onto six commercially available sustainable and flexible substrates. Our results demonstrate that multi-layer screen printing substantially reduced the electrical resistance of carbon tracks, enabling SPI communication at frequencies up to 16 MHz with three layers of carbon ink. A Natureflex™ substrate provided the best balance of printability, thermal stability, and electrical performance. Substrates such as greaseproof paper and ClearFilm PU showed potential for flexible electronics, but require further optimization. This study provides valuable insights into selecting and optimizing biodegradable substrates for high-frequency digital systems, supporting the move towards more sustainable printed electronics. © 2022 IEEE.

关键词： Variable frequency oscillators

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Validity,Reliability,and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

引用

International Journal of Mental Health Promotion 2024年第4期26卷 293-302页

作者： Kamolthip Ruckwongpatr Chirawat Paratthakonkun Usanut Sangtongdee Iqbal Pramukti Ira Nurmala Kanokwan Angkasith Weena Thanachaisakul Jatuphum Ketchatturat Mark DGriffiths Yi-Kai Kao Chung-Ying Lin Institute of Allied Health Sciences College of MedicineNational Cheng Kung UniversityTainan701401Taiwan College of Sports Science and Technology Mahidol UniversityNakhon Pathom73170Thailand Department of Forensic Science Royal Police Cadet AcademyNakhon Pathom73110Thailand School of Computing Engineeringand Digital TechnologyTeesside UniversityMiddlesbroughUK Faculty of Nursing Universitas PadjadjaranWest Java45363Indonesia Division of Health Promotion and Behavioral Sciences Faculty of Public HealthUniversitas AirlanggaSurabaya60115Indonesia Faculty of Education Chiang Mai UniversityChiang Mai50200Thailand Faculty of Education Khon Kaen UniversityKhon Kaen40002Thailand International Gaming Research Unit Psychology DepartmentNottingham Trent UniversityNottinghamNG14FQUK Division of Colorectal Surgery E-DA HospitalKaohsiung82445Taiwan College of Medicine National Cheng Kung UniversityTainan701401Taiwan Biostatistics Consulting Center National Cheng Kung University HospitalTainan701401Taiwan

Background:In recent years,there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these ***,there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in *** present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale(SABAS)and Bergen Social Media Addiction Scale(BSMAS).Method:A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic information,SABAS,BSMAS,and the Internet Gaming Disorder Scale-Short Form(IGDS9-SF).Results:Confirmatory Factor Analyses(CFAs)found that both the SABAS and BSMAS had a one-factor *** demonstrated adequate psychometric properties of both instruments and also supported measurement invariance across ***,scores on the SABAS and BSMAS were correlated with scores on the ***:The results indicated that the SABAS and BSMAS are useful psychometric instruments for assessing the risk of smartphone addiction and social media addiction among Thai young adults.

关键词： Factor analysis smartphone addiction social media addiction smartphone application-based addiction scale bergen social media addiction scale psychometric validation

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