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arXiv 2023年

作者： Yeh, Sing-Yuan Chang, Fu-Chieh Yueh, Chang-Wei Wu, Pei-Yuan Bernacchia, Alberto Vakili, Sattar MediaTek Research Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simple models such as linearly modeled Q-functions. To derive statistically efficient RL policies handling large state-action spaces, with more general Q-functions, some recent works have considered nonlinear function approximation using kernel ridge regression. In this work, we derive sample complexities for kernel based Q-learning when a generative model exists. We propose a nonparametric Q-learning algorithm which finds an ǫ-optimal policy in an arbitrarily large scale discounted MDP. The sample complexity of the proposed algorithm is order optimal with respect to ǫ and the complexity of the kernel (in terms of its information gain). To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model. © 2023, CC BY.

关键词： Reinforcement learning

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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The modified Allen test and a novel objective screening algorithm for hand collateral circulation using differential photoplethysmography for preoperative assessment: a pilot study

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Journal of Medical engineering and Technology 2020年第2期44卷 82-93页

作者： Elwali, Ahmed Moussavi, Zahra Biomedical Engineering Program University of Manitoba Winnipeg Canada Electrical and Computer Engineering Department University of Manitoba Winnipeg Canada

Hand-ischemia and artery-thrombosis are serious complications of hand artery cannulation/grafting. Screening the patency of hand arteries reduces the complications. Modified Allen Test (MAT), Barbeau's test, and Doppler/Duplex-ultrasonic test screen hand arterial patency. Each of them, however, either lacks objectivity, requires subject cooperation, or is not readily available in operating rooms. In this pilot study, we aim to provide a novel, objective, reliable, and inexpensive alternative using differential photo-plethysmography (PPG) measurement, and an artery selection algorithm. For 11 participants, MAT was executed, and PPG measurements of the thumb and little fingers were recorded for left and right hands separately. For each hand, four evaluations were conducted, then fed into the artery selection algorithm. Our proposed procedure has shown congruent results with the anatomy of blood circulation. It identified two arteries in two participants causing >50% blood reduction in the hand due to their occlusion. It also showed an elevated risk of accessing an artery in another 8 participants. Once the results of this study are confirmed in larger samples, the proposed procedure/algorithm may provide many indicators of the hand blood circulation, the effect of an artery occlusion on the hand, and a recommendation for selecting the best artery to access with the lowest possible complications. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diseases

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An Efficient Formulation for Optimal Placement of Capacitor Banks in Distribution Feeders Connected to Time-Varying Loads

An Efficient Formulation for Optimal Placement of Capacitor ...

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IEEE Kansas Power and Energy Conference

作者： Meisam Mahdavi Augustine Awaafo Konrad Schmitt Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

The capacitor placement in radial feeders is an efficient way to mitigate energy losses of distribution networks, where the power converted to useless heat is much higher than transmission system. These considerable power losses increase network operating costs and also decrease quality and quantity of delivered power. Shunt capacitors by compensating part of load reactive power reduce magnitude of feeder current and subsequent distribution losses. However, electricity demand variations with time affects power distributed through feeders and therefore have important role in energy losses determination. Thus, load variability may change the optimal places of capacitors, but considering whole load variations substantially increases the burden and computational time of capacitor placement problem in large-sized distribution systems. Therefore, current study poses an effective capacitor placement formulation for energy loss reduction in the presence of time-varying loads. The numerical results analysis demonstrates the good performance of the presented framework for the optimal placement of capacitor banks in radial distribution feeders with variable loads.

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Dark Current and Noise Characterization of InGaAs/GaAsSb T2SL Photodetectors 22

Dark Current and Noise Characterization of InGaAs/GaAsSb T2S...

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Optical Components and Materials XXII 2025

作者： Gil, Armando Phillips, Jamie Ettenberg, Martin H. Kurdak, Cagliyan Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Princeton Infrared Technologies Inc. 7 Deer Park Dr. Ste. E Monmouth JunctionNJ08852 United States Department of Physics University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781510684720

We investigate the dark current and dark current noise spectra of a short-wavelength infrared based blind photodiode as a function of temperature. We designed and built a custom cryogenic system with a slow temperature relaxation time to perform measurements. The measured device uses a type-II superlattice (2.9 nm InGaAs / 2.9 nm GaAsSb) absorption layer with an effective bandgap of 580 meV that is lattice-matched to InP. At temperatures above 150 K the dark current activation energy was found to be 140 meV, which rules out diffusion- or GR-limited currents and explains that large magnitudes of dark current when compared to similar devices. When normalized to current, we find that 1/f noise grows with temperature, reaches a maximum at about 200 K, then reduces as the temperature goes to 300 K. © 2025 SPIE.

关键词： Indium arsenide

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

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Procedia computer Science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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Wheat Agricultural Residences for Bioenergy Generation in Morocco

Wheat Agricultural Residences for Bioenergy Generation in Mo...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Meisam Mahdavi Francisco Jurado Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical & Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

Morocco is one of the important producers of wheat in Africa, in which this crop is the first agricultural product in the country. In spite of obtaining a huge quantity of wheat residues in the Moroccan agriculture sector, a small portion of this great bioenergy resource is reused for energy generation in the country. This is a big challenge for Morocco because its energy sector highly depends on costly fossil fuel imports. Furthermore, the country has to decrease harmful gases emission based on the Paris and Kyoto agreements. Regarding the importance of rainfall amount and water resources available for this agricultural product that is the main feedstock of Moroccan food, the use of wheat biomass to produce energy will remove high pressure from both the national energy and agriculture sectors. Besides that, the problems related to crop residue removal are reduced by using wheat biomass. Also, the use of wheat residues for energy generation pushes farmers to prevent the open firing of wheat straws and unnecessary air pollutants release. Thus, the present paper, for the first time, calculates possible energy production from wheat straws and root biomasses in Morocco, aiming to provide useful information for Morocco's energy sector.

关键词： Productivity Firing Plants (biology) Hydrogen Crops Air pollution Fossil fuels

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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Analysis of Exposure to Electromagnetic Fields Due to Multiple Sources in a Classroom Environment

Analysis of Exposure to Electromagnetic Fields Due to Multip...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Norton Escopelli Soares Giovani Bulla Claudio E. Fernandez-Rodriguez Álvaro A. A. De Salles Department of Electrical Engineering Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Electrical Engineering Federal Institute of Rio Grande do Sul Canoas Brazil Graduate Program on Electrical Engineering Federal University of Rio Grande do Sul Porto Alegre Brazil

This paper presents an analysis of the electromagnetic fields (EMF) exposure to multiple sources in a classroom environment. The research is motivated by the increasing use of electronic devices in educational environments and the need to estimate this. The methodology used involves simulating a classroom with students using laptops, which are considered as the main sources of electromagnetic radiation. The 1g and 10 g peak spacial Specific Absorption Rate (psSAR) are calculated for different parts of the human body, including the head, back, and hands. The results indicate that the psSAR is higher in the back, followed by the head and hands. When the free distance between desks is 250 mm, two-fold at the hands to forty-five-fold at the back greater psSAR are obtained when comparing to just one student with his laptop.

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