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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jiufang Chen Ye Yuan Chunlin He School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chinese Academy of Sciences Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing China School of Computer Science China West Normal University Nanchong Sichuan China

ISBN: (纸本)9781665426220

A latent factor analysis (LFA) model can efficiently address a high-dimensional and sparse (HiDS) matrix with a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's performance depends heavily on its hyper-parameters. The popular method is based on grid-search, which fails because of expensive computation and time-consuming. aiming at implementing a hyper-parameter-free LFA model, this study proposes an adaptive moment estimation-incorporated particle swarm optimization (Adam-PSO) algorithm that efficiently addresses the premature issues in a PSO algorithm. With achieved the hyper-parameter adaptation in an SGD-based LFA model, an Adam-PSO-based LFA (APL) model possessing hyper-parameter-free training is further implemented. Empirical studies on four HiDS matrices indicate that compared with state-of-the-art models with hyper-parameter adaptation settings, an APL model achieves the most efficient hyper-parameter-free training and highly competitive prediction accuracy for missing data of an HiDS matrix. Hence, it fits the need of real applications with high scalability and efficiency.

关键词： Training Adaptation models Computational modeling Stochastic processes Predictive models Prediction algorithms data models

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Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

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Nano-Micro Letters 2020年第3期12卷 199-217页

作者： Linsheng Huang Xiaowen Zhou Rui Xue Pengfei Xu Siliang Wang Chao Xu Wei Zeng Yi Xiong Hongqian Sang Dong Liang National Engineering Research Center for Agro‑Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601People’s Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco‑Dyeing and FinishingWuhan Textile UniversityWuhan 430073People’s Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro‑and Nano‑Structures and Center for Electron MicroscopyWuhan UniversityWuhan 430072People’s Republic of China Department of Physics King’s College LondonThe StrandLondon WC2R 2LSUK

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar *** on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 *** fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 *** the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in *** champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

关键词： In situ fabrication Multi-dimensional heterojunction Oxygen vacancy scramble effect Electron transport layer Perovskite solar cells

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Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches

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Informatics in Medicine Unlocked 2023年 37卷 101176页

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr NW Atlanta 30332 GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University 55 Park Pl NE Atlanta 30303 GA United States Department of Computer Science Georgia State University 25 Park PlaceSuite 700 Atlanta 30303 GA United States

The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of transparency are (1) confidence estimation and (2) explainability. Confidence estimation approaches indicate confidence in individual predictions. Explainability methods give insight into the importance of features to model predictions. In this study, we integrate confidence estimation and explainability approaches for the first time. We demonstrate their viability for schizophrenia diagnosis using resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data. We compare two confidence estimation approaches: Monte Carlo dropout (MCD) and MC batch normalization (MCBN). We combine them with two gradient-based explainability approaches, saliency and layer-wise relevance propagation (LRP), and examine their effects upon explanations. We find that MCD often adversely affects model gradients, making it ill-suited for integration with gradient-based explainability methods. In contrast, MCBN does not affect model gradients. Additionally, we find many participant-level differences between regular explanations and the distributions of explanations for combined explainability and confidence estimation approaches. This suggests that a similar confidence estimation approach used in a clinical context with explanations only output for the regular model would likely not yield adequate explanations. We hope that our findings will provide a starting point for the integration of the two fields, provide useful guidance for future studies, and accelerate the development of transparent neuroimaging clinical decision support systems. © 2023 The Authors

关键词： Clinical decision support systems Explainable artificial intelligence Gradient-based explainability methods Monte Carlo batch normalization Monte Carlo dropout Neuroimaging

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Roadmap for phase change materials in photonics and beyond (vol 26, 107946, 2023)

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Iscience 2023年第12期26卷 107946页

作者： Prabhathan, Patinharekandy Sreekanth, Kandammathe Valiyaveedu Teng, Jinghua Ko, Joo Hwan Yoo, Young Jin Jeong, Hyeon-Ho Lee, Yubin Zhang, Shoujun Cao, Tun Popescu, Cosmin-Constantin Mills, Brian Gu, Tian Fang, Zhuoran Chen, Rui Tong, Hao Wang, Yi He, Qiang Lu, Yitao Liu, Zhiyuan Yu, Han Mandal, Avik Cui, Yihao Ansari, Abbas Sheikh Bhingardive, Viraj Kang, Myungkoo Lai, Choon Kong Merklein, Moritz Mueller, Maximilian J. Song, Young Min Tian, Zhen Hu, Juejun Losurdo, Maria Majumdar, Arka Miao, Xiangshui Chen, Xiao Gholipour, Behrad Richardson, Kathleen A. Eggleton, Benjamin J. Sharda, Kanudha Wuttig, Matthias Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Singapore Centre for Disruptive Photonic Technologies The Photonic Institute 50 Nanyang Avenue Singapore 639798 Singapore Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A∗STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea School of Materials Science and Engineering Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea DELL Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering Key Laboratory of Optoelectronic Information Technology (Ministry of Education of China) Tianjin University Tianjin 300072 China DELL School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian 116024 China Department of Materials Science &amp Engineering Massachusetts Institute of Technology Cambridge MA USA Materials Research Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Electrical &amp Computer Engineering University of Washington Washington Seattle USA Wuhan National Research Center for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Institute of Advanced Materials Beijing Normal University Beijing 100875 China Nanoscale Optics Lab ECE Department University of Alberta Edmonton Canada CREOL College of Optics and Photonics University of Central Florida Orlando FL USA Institute of Photonics and Optical Science (IPOS) School of Physics The University of Sydney New South Wales NSW 2006 Australia The University of Sydney Nano Institute (Sydney Nano) The University of Sydney New South Wales NSW 2006 Australia Insti

Phase Change Materials (PCMs) have demonstrated tremendous potential as a platform for achieving diverse functionalities in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum, ranging from terahertz to visible frequencies. This comprehensive roadmap reviews the material and device aspects of PCMs, and their diverse applications in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum. It discusses various device configurations and optimization techniques, including deep learning-based metasurface design. The integration of PCMs with Photonic Integrated Circuits and advanced electric-driven PCMs are explored. PCMs hold great promise for multifunctional device development, including applications in non-volatile memory, optical data storage, photonics, energy harvesting, biomedical technology, neuromorphic computing, thermal management, and flexible electronics.

关键词： Physics Applied physics Materials application

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (ai) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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CT Scan Synthesis for Promoting computer-aided Diagnosis Capacity of COVID-19 16th

CT Scan Synthesis for Promoting Computer-Aided Diagnosis Cap...

引用

16th International Conference on Intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on ai-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct ai-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Prediction of concrete compressive strength employing machine learning techniques

引用

Materials Today: Proceedings 2023年

作者： Keshav Malhotra Dhaneshwar Mishra Charanjeet Singh Tumrate Department of Computer Science (AI and ML) Manipal University Jaipur India Department of Mechanical Engineering Manipal University Jaipur India Multiscale Simulation Research Center (MSRC) Manipal University Jaipur India Department of Civil Engineering Manipal University Jaipur India

Machine learning (ML) methods have proven to be reliable techniques in estimating, classifying, and predicting material strength based on varying material properties. It is observed that the nature of the problem and the data at hand governs the selection of an appropriate ML method. Therefore, to determine the accuracy of ML models to predict concrete compressive strength , fifteen different ML algorithms were applied to a given concrete compressive strength dataset. Among all the ML algorithms used, the prediction accuracy of the Support Vector Regressor (SVR) was the highest (88.18%) due to its effective performance when dealing with continuous target variables and nonlinear relationships among the features and the target.

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Resource estimations for the Hamiltonian simulation in correlated electron materials

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Physical Review A 2022年第1期106卷 012612-012612页

作者： Shu Kanno Suguru Endo Takeru Utsumi Tomofumi Tada Department of Materials Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8552 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama Kanagawa 226-8501 Japan Kyushu University Platform of Inter/Transdisciplinary Energy Research (Q-PIT) Kyushu University Fukuoka 819-0395 Japan

Correlated electron materials, such as superconductors and magnetic materials, are regarded as fascinating targets in quantum computing. However, the quantitative resources, specifically the number of quantum gates and qubits, required to perform a quantum algorithm to simulate correlated electron materials remain unclear. In this study, we estimate the resources required for the Hamiltonian simulation algorithm for correlated electron materials, specifically for organic superconductors, iron-based superconductors, binary transition-metal oxides, and perovskite oxides, using the fermionic swap network. The effective Hamiltonian derived using the ab initio downfolding method is adopted for the Hamiltonian simulation, and a procedure for the resource estimation by using the fermionic swap network for the effective Hamiltonians including the exchange interactions is proposed. For example, in the system for the 102 unit cells, the estimated numbers of gates per Trotter step and qubits are approximately 107 and 103, respectively, on average for the correlated electron materials. Furthermore, our results show that the number of interaction terms in the effective Hamiltonian, especially for the Coulomb interaction terms, is dominant in the gate resources when the number of unit cells constituting the whole system is up to 102, whereas the number of fermionic swap operations is dominant when the number of unit cells is more than 103.

关键词： Quantum algorithms Strongly correlated systems Ab initio calculations Density functional theory

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Determination of photonuclear reaction cross-sections on stable p-shell nuclei by using deep neural networks

arXiv

引用

arXiv 2020年

作者： Akkoyun, Serkan Kaya, Hüseyin Seker, Abdulkadir Yesilyurt, Saliha Department of Physics Faculty of Science Sivas Cumhuriyet University Sivas Turkey Department of Computer Engineering Sivas Cumhuriyet University Faculty of Engineering Sivas Turkey Artificial Intelligence Systems and Data Science Application and Research Center Sivas Cumhuriyet University Sivas58140 Turkey

The photonuclear reactions which is induced by high-energetic photon are one of the important type of reactions in the nuclear structure studies. In this reaction, a target material is bombarded by photons with the energies in the range of gamma-ray energy scale and the photons can statistically be absorbed by a nucleus in the target material. In order to get rid of the excess energies of the excited target nuclei, it can first emit protons, neutrons, alphas and light particles according to the separation energy thresholds. After this emitting process, generally an unstable nucleus can be formed. By the investigation of this products forming after photonuclear reactions, nuclear structure information can be obtained. In the present work, (γ, n) photonuclear reaction cross-sections on stable p-shell nuclei have been estimated by using neural network method. The main purpose of this study is to find neural network structures that give the best estimations on the cross-sections and to compare them with each other and available literature data. According to the results, the method is convenient for this task. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

引用

arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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