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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services data models Robustness Bioinformatics

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Retraction notice to “Automation industry in cyber physical systems based on 4G IoT networks for operation controlling and monitoring management” [Optik 272 (2023) 170308]

引用

Optik 2025年 333卷

作者： K.V. Mahesan Anupong Wongchai C. Tamizhselvan S. Manoj Kumar P. Vijayakumar Bharat Singh Dr. Ambedkar Institute of Technology Bangalore India Department of Agricultural Economy and Development Faculty of Agriculture Chiang Mai University Thailand Department of Data Science and Business System College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore 641407 India School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Mechanical Engineering GLA University Mathura UP India

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

引用

arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles

arXiv

引用

arXiv 2023年

作者： Lin, Jerry Yu, Sungduk Peng, Liran Beucler, Tom Wong-Toi, Eliot Hu, Zeyuan Gentine, Pierre Geleta, Margarita Pritchard, Mike Department of Earth System Sciences University of California at Irvine IrvineCA United States Multimodal Cognitive AI Intel Labs Santa ClaraCA95054 United States Faculty of Geosciences and Environment University of Lausanne Lausanne Switzerland Expertise Center for Climate Extremes University of Lausanne Lausanne Switzerland Department of Statistics University of California at Irvine IrvineCA United States Department of Earth and Planetary Sciences Harvard University United States NVIDIA Research United States LEAP Science and Technology Center School of Engineering and Applied Sciences Climate School Columbia University United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA United States Department of Biomedical Data Science Stanford University School of Medicine Palo AltoCA United States

Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large-scale general circulation model (GCM)) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi-automated, end-to-end pipeline for O(100) size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error (MSE) to a Mean Absolute Error (MAE) loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of O(100) may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise. © 2023, CC BY.

关键词： Parameterization

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Causally-informed Deep Learning to Improve Climate Models and Projections

arXiv

引用

arXiv 2023年

作者： Iglesias-Suarez, Fernando Gentine, Pierre Solino-Fernandez, Breixo Beucler, Tom Pritchard, Michael Runge, Jakob Eyring, Veronika Institute of Atmospheric Physics Oberpfaffenhofen Germany Columbia University New York United States Science and Technology Center Columbia University New York United States University of Lausanne Institute of Earth Surface Dynamics Lausanne Switzerland University of California Department of Earth System Science Irvine United States NVIDIA Corporation Santa Clara United States Institute of Data Science Jena Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany Bremen Germany

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious nonphysical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally-informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally-informed neural network parameterizations retain many convection-related properties and accurately generate the climate of the original high-resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non-causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way towards more physically-based causal deep learning approaches also in other scientific disciplines. © 2023, CC BY-NC-ND.

关键词： Climate models

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Clairvoyance: Intelligent route planning for electric buses based on urban big data

arXiv

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arXiv 2021年

作者： Lu, Xiangyong Ota, Kaoru Dong, Mianxiong Yu, Chen Jin, Hai Graduate School of Information Sciences Tohoku University Sendai Japan Serviecs Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Information and Electronic Engineering Muroran Institute of Technology Hokkaido Muroran Japan

Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce the local carbon emissions. In order to cut the carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people’s trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people’s trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on the real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case 4

Modeling and performances assessments of PV/T bifluid hybrid...

引用

4th International Conference on Electrical and Information Technologies, ICEIT 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Mohamed First University Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Oujda Morocco Picardie Jules Verne University Modelization Information and Systems Laboratory Amiens France University of Catania DIEEI: Department of Electric Electronic and Computer Engineering Catania Italy

ISBN: (数字)9781728143415

ISBN: (纸本)9781728143415

The objective of the present work is the performance assessments of a bi-fluid photovoltaic/thermal solar collector (PV/T). Based on energy-balance equations, a mathematical model of PV/T hybrid collector has been developed and simulated using Matlab Software. This model takes into account the effect of the three modes of fluid operation under the same PV/T system, namely: the air mode, the water mode, and the simultaneous mode of water and air. The results show that the lowest PV module temperature is observed in the simultaneous mode (air water) compared to others modes. It's maximum value achieved is around 62°C in the bi-fluid mode against 80°C and 73°C respectively in the air mode and the water mode. The maximum efficiencies of 66.97 % (thermal efficiency) and 10.1% (electrical efficiency) are also observed, when both fluids are operated simultaneously compared to others modes. Finally, the working in simultaneous mode is beneficial to transmit more heat, produce more thermal energy and consequently improve the electrical and thermal performances of the PV/T hybrid collector. Moreover, the theoretical model developed is validated, evidencing a good fit between simulation results and the experimental data available in literature. The PV/T collector designed in this study has a variety of applications, as solar drying in agriculture, air conditioning, space heating and hot water supply for domestic applications. © 2020 IEEE.

关键词： Air

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Strategy evolution on dynamic networks

arXiv

引用

arXiv 2023年

作者： Su, Qi McAvoy, Alex Plotkin, Joshua B. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Center for Mathematical Biology University of Pennsylvania PhiladelphiaPA19104 United States

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures. © 2023, CC BY.

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High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes

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Nano Research 2021年第12期14卷 4610-4615页

作者： Zhong-Hai Ji Lili Zhang Dai-Ming Tang Chien-Ming Chen Torbjörn EMNordling Zheng-De Zhang Cui-Lan Ren Bo Da Xin Li Shu-Yu Guo Chang Liu Hui-Ming Cheng Shenyang National Laboratory for Materials Science Institute of Metal Research(IMR)Chinese Academy of SciencesShenyang110016China School of Materials Science and Engineering University of Science and Technology of ChinaHefei230026China International Center for Materials Nanoarchitectonics(MANA) National Institute for Materials Science(NIMS)1-1 NamikiTsukubaIbaraki305-0044Japan Department of Mechanical Engineering National Cheng Kung UniversityNo.1University RoadTainan City701Taiwan Department of Applied Physics and Electronics UmeåUniversity90187UmeåSweden Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai201800China Research and Services Division of Materials Data and Integrated System National Institute for Materials Science(NIMS)Ibaraki305-0047Japan Tsinghua-Berkeley Shenzhen Institute(TBSI) Tsinghua UniversityShenzhen518055China

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes(SWCNTs).Here,a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality *** cobalt(Co)nanoparticles were deposited on a numerically marked silicon wafer as catalysts,and parameters of temperature,reduction time and carbon precursor were *** crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity(IG/ID)was extracted automatically and mapped to the growth parameters to build a database.1,280 data were collected to train machine learning *** forest regression(RFR)showed high precision in predicting the growth conditions for high-quality SWCNTs,as validated by further chemical vapor deposition(CVD)*** method shows great potential in structure-controlled growth of SWCNTs.

关键词： single-wall carbon nanotube high throughput machine learning optimization chemical vapor deposition

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