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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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HOW DOES THE BRAIN COMPUTE WITH PROBABILITIES?

arXiv

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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment

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Digital Communications and Networks 2025年

作者： Sowmya Madhavan M.G. Aruna G.P. Ramesh Abdul Lateef Haroon Phulara Shaik Dhulipalla Ramya Krishna Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Yelahanka Bangalore India Department of Artificial Intelligence and Machine Learning Dayanand Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering St Peter's Institute of Higher Education and Research Chennai India Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

Sixth-generation (6G) communication system promises unprecedented data density and transformative applications over different industries. However, managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient machine learning (ML) training and aggregation, often leading to increased energy consumption and reduced model generalization. To solve this problem, this research proposes a Weighted Proximal Policy-based Federated learning approach integrated with ResNet50 and Scaled Exponential Linear Unit activation function (WPPFL-RS). The proposed method optimizes resource allocation such as CPU and memory, through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds. The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption, solving complex challenges in 6G-enabled edge computing. This make sures that efficient resource utilization and enhanced performance in heterogeneous edge networks. The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks, a significant improvement over the baseline Deep Reinforcement learning (DRL), which recorded 11.39 s. This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.

关键词： Cybertwin Federated learning ResNet50 Resource allocation Scaled exponential linear Unit Weighted proximal policy

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Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for ∆-machine learning

arXiv

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arXiv 2024年

作者： Krug, Simon León Khan, Danish von Lilienfeld, O. Anatole Machine Learning Group Technische Universität Berlin Berlin Charlottenburg 10587 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George campus TorontoONM5S 3H6 Canada Berlin Institute for the Foundations of Learning and Data Charlottenburg Berlin10587 Germany Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Department of Materials Science and Engineering University of Toronto St. George campus TorontoONM5S 3E4 Canada Department of Physics University of Toronto St. George campus TorontoONM5S 1A7 Canada

We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this Ansatz for the electronic binding potential, E(d) = (Eu − Es)(formula presented), where Eu, Ec, Es correspond to the energies of united atom, calibration at d0, and sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.7 to 2.5 Åand differences in nuclear charge from which only one single point (with elements of nuclear charge Z1, Z2 and distance d0) is drawn to calibrate Ec. Using reference data from pbe0/cc-pVDZ, we present numerical evidence for the electronic ground-state of all neutral diatomics with 8, 10, 12, 14 electrons. We assess the validity of our model by comparison to legacy interatomic potentials (Harmonic oscillator, Lennard-Jones, and Morse) within the most relevant range of binding (0.7-2.5 Å), and find comparable accuracy if restricted to single diatomics, and significantly better predictive power when extrapolating to the entire iso-electronic series. We also investigated ∆-learning of the electronic absolute energy using our model as baseline. This baseline model results in a systematic improvement, effectively reducing training data needs for reaching chemical accuracy by up to an order of magnitude from ∼1000 to ∼100. By contrast, using AHA+Morse as a baseline hardly leads to any improvement, and sometimes even deteriorates the predictive power. Inferring the energy of unseen CO converges to a prediction error of ∼0.1 Ha in direct learning, and ∼0.04 Ha with our baseline. © 2024, CC BY.

关键词： Harmonic analysis

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MMA regularization: decorrelating weights of neural networks by maximizing the minimal angles 20

MMA regularization: decorrelating weights of neural networks...

引用

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Zhennan Wang Canqun Xiang Wenbin Zou Chen Xu Shenzhen Key Laboratory of Advanced Machine Learning and Applications Guangdong Key Laboratory of Intelligent Information Processing Institute of Artificial Intelligence and Advanced Communication College of Electronics and Information Engineering Shenzhen University Institute of Artificial Intelligence and Advanced Communication College of Mathematics and Statistics Shenzhen University

ISBN: (纸本)9781713829546

The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a novel diversity regularization method to address this issue, which makes the normalized weight vectors of neurons or filters distributed on a hypersphere as uniformly as possible, through maximizing the minimal pairwise angles (MMA). This method can easily exert its effect by plugging the MMA regularization term into the loss function with negligible computational overhead. The MMA regularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method in neural network training. Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets. In addition, experiments on face verification show that MMA regularization is also effective for feature learning.

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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From quantum alchemy to Hammett's equation: Covalent bonding from atomic energy partitioning

arXiv

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arXiv 2022年

作者： Sahre, Michael J. von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Währinger Str. 42 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We present an intuitive and general analytical approximation estimating the energy of covalent single and double bonds between participating atoms in terms of their respective nuclear charges with just three parameters, [EAB ≈ a − bZAZB + c(ZA7/3 + ZB7/3)]. The functional form of our expression models an alchemical atomic energy decomposition between participating atoms A and B. After calibration, reasonably accurate bond energy estimates are obtained for hydrogen-saturated diatomics composed of p-block elements coming from the same row 2 ≤ n ≤ 4 in the periodic table. Corresponding changes in bond energies due to substitution of atom B by C can be obtained via simple formulas. While being of different functional form and origin, our model is as simple and accurate as Pauling’s well-known electronegativity model. Analysis indicates that the model’s response in covalent bonding to variation in nuclear charge is near-linear—which is consistent with Hammett’s equation. © 2022, CC BY.

关键词： Atoms

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Ethical and legal challenges with IoT in home digital twins

引用

MethodsX 2025年 14卷

作者： Dhinakaran, D. Edwin Raja, S. Ramathilagam, A. Vennila, G. Alagulakshmi, A. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Information Technology Ramco Institute of Technology Rajapalayam India

Home Digital Twins represent a transformative application of IoT in the home environment, turning conventional living spaces into intelligent ecosystems. This paper explores the ethical and legal challenges associated with these technologies, focusing on critical issues such as privacy, data security, and accountability. The study integrates real-world case studies of privacy controversies and cybersecurity breaches to illustrate potential vulnerabilities in IoT-enabled systems. Furthermore, it examines the complexities of regulatory compliance, including cross-border data flows and liability concerns in the event of system failures, with a focus on frameworks such as GDPR, CCPA, and India's Digital Personal Data Protection Bill. The methodology includes an in-depth analysis of existing legal frameworks, industry best practices, and technical mitigation strategies to propose actionable guidelines for addressing these challenges. Key findings emphasize the necessity of robust legal frameworks, user-centered design principles, and transparent data practices to foster trust and security in IoT systems. By advocating for a balance between technological innovation and ethical accountability, this paper highlights opportunities for sustainable and responsible IoT development that upholds user rights and societal values. © 2025 The Author(s)

关键词： Cybersecurity Data security Home digital twins Internet of Things Privacy Trust

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Encrypted machine learning of molecular quantum properties

arXiv

引用

arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

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