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Journal of Chemical Information and Modeling 2023年第20期63卷 6183-6191页

作者： Case, David A. Aktulga, Hasan Metin Belfon, Kellon Cerutti, David S. Cisneros, G. Andrés Cruzeiro, Vinícius Wilian D. Forouzesh, Negin Giese, Timothy J. Götz, Andreas W. Gohlke, Holger Izadi, Saeed Kasavajhala, Koushik Kaymak, Mehmet C. King, Edward Kurtzman, Tom Lee, Tai-Sung Li, Pengfei Liu, Jian Luchko, Tyler Luo, Ray Manathunga, Madushanka Machado, Matias R. Nguyen, Hai Minh O’Hearn, Kurt A. Onufriev, Alexey V. Pan, Feng Pantano, Sergio Qi, Ruxi Rahnamoun, Ali Risheh, Ali Schott-Verdugo, Stephan Shajan, Akhil Swails, Jason Wang, Junmei Wei, Haixin Wu, Xiongwu Wu, Yongxian Zhang, Shi Zhao, Shiji Zhu, Qiang Cheatham, Thomas E. Roe, Daniel R. Roitberg, Adrian Simmerling, Carlos York, Darrin M. Nagan, Maria C. Merz, Kenneth M. Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824-1322 United States FOG Pharmaceuticals Inc. CambridgeMA02140 United States Psivant 451 D Street Suite 205 BostonMA02210 United States Department of Physics Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75801 United States Department of Chemistry and The PULSE Institute Stanford University StanfordCA94305 United States Department of Computer Science California State University Los AngelesCA90032 United States Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States San Diego Supercomputer Center University of California San Diego La Jolla CA92093-0505 United States Institute for Pharmaceutical and Medicinal Chemistry Heinrich Heine University Düsseldorf Düsseldorf40225 Germany Forschungszentrum Jülich GmbH Jülich52425 Germany Pharmaceutical Development Genentech Inc. South San FranciscoCA94080 United States Laufer Center for Physical and Quantitative Biology Department of Chemistry Stony Brook University Stony BrookNY11794 United States Departments of Molecular Biology and Biochemistry Chemical and Biomolecular Engineering Materials Science and Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States Ph.D. Programs in Chemistry Biochemistry and Biology The Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Chemistry Lehman College 250 Bedford Park Blvd West BronxNY10468 United States Department of Chemistry and Biochemistry Loyola University Chicago ChicagoIL60660 United States Beijing National Laboratory for Molecular Science

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note. ... 详细信息

关键词： Free energy Ligands Molecular mechanics Molecular modeling QM/MM

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A multiobjective genetic approach with TOPSIS to learn fuzzy rule-based classification systems 8

A multiobjective genetic approach with TOPSIS to learn fuzzy...

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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： De Oliveira Araujo, Antonio Eloy Krohling, Renato Antonio Graduate Program in Computer Science PPGI Federal University of Espirito Santo - UFES Vitoria ES Brazil Production Engineering Department LABCin and Graduate Program in Computer Science PPGI Federal University of Espirito Santo - UFES Vitoria ES Brazil

ISBN: (纸本)9781728142531

The design of a Fuzzy Rule-Based Classification System (FRBCS) taking into account accuracy and interpretability (number of rules) of the FRBCS is an important issue. In this paper, we extend our previous work by considering the average number of fuzzy labels per input variable as a new objective to be treated with TOPSIS. In addition, we employ information entropy to determine the weights of the evaluation criteria. We compare our proposal with a method that deals with the accuracy-interpretability tradeoff by means of a multiobjective evolutionary process based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results show the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Multiobjective optimization

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Reduction of interface defects in gate-recessed GaN HEMTs by neutral beam etching

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Materials Today Advances 2024年 23卷

作者： Yu, Chia Hao Chiang, Wei Hsiang Chen, Yi-Ho Samukawa, Seiji Wuu, Dong Sing Chung, Chin-Han Hsiao, Ching-Lien Horng, Ray Hua Institute of Electronics National Yang Ming Chiao Tung University Department of Materials Science and Engineering National Chung Hsing University Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Institute of Comunications Engineering National Yang Ming Chiao Tung University Department of Applied Materials and Optoelectronic Engineering National Chi Nan University International College of Semiconductor Technology National Yang Ming Chiao Tung University Thin Film Physics Division Department of Physics Chemistry and Biology (IFM) Linköping University

This study investigates and compares the impact of different etching techniques on the fabrication of GaN high electron mobility transistors (HEMTs) between the inductively coupled plasma reactive ion etching (ICP-RIE) and the neutral beam etching (NBE) for the gate recess. By conducting direct current analysis, it was found that devices manufactured using the NBE exhibited superior electrical performance as compared with those produced using the ICP-RIE. These enhanced electrical characteristics include a transconductance of up to 100.4 mS/mm, a threshold voltage (V_th) of −2.3 V, an on/off current ratio of 1.1 × 10⁹, a subthreshold swing (S.S.) of 99.63 mV/dec, and a remarkably low gate leakage current. Additionally, we noted varying degrees of hysteresis in the I–V characteristics were related to process disparities possibly leading to interface defects. Multi-frequency capacitance-voltage (C–V) measurements were used to identify the interface defects at the oxide/AlGaN interface of the gate. The results revealed that devices fabricated using the NBE exhibited a lower interface defect density as compared with those fabricated using the ICP-RIE, thereby elucidating the reduced hysteresis observed in the I–V characteristics. These findings indicated the significant advantages of the NBE process in the fabrication of GaN HEMTs. © 2024 The Authors

关键词： GaN HEMTs Gate recess Interface defects Multi-frequency C–V Neutral beam etching

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Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

arXiv

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arXiv 2022年

作者： Jin, Peng Huang, Jinfa Liu, Fenglin Wu, Xian Ge, Shen Song, Guoli Clifton, David A. Chen, Jie School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Department of Engineering Science University of Oxford United Kingdom Tencent JARVIS Lab China Oxford-Suzhou Centre for Advanced Research Suzhou China

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP [53], to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupling under Extreme Spatial Confinement

Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupl...

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International Conference on Infrared and Millimeter Waves

作者： Dasom Kim Sunghwan Kim Dukhyung Lee Shuang Liang Fuyang Tay Michael J. Manfra Dai-Sik Kim Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory and Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Department of Physics and Astronomy Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Materials Engineering Purdue University West Lafayette IN USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We report ultrastrong coupling of the cyclotron resonance of a two-dimensional electron gas in a GaAs quantum well to terahertz cavity photons under extreme confinement to the nanometer scale. This was realized by an array of nano-slots fabricated close to the quantum well layer on the GaAs substrate. Our terahertz magnetospectroscopic measurements revealed two polariton branches as a function of applied magnetic field. While the photon-like part of the two polariton branches was clearly observed, the matter-like part was generally elusive. Our electromagnetic simulations quantitatively reproduce our observation for the upper-polariton branch. However, for the lower-polariton branch, our simulations predict a pronounced resonance, which was absent in experimental spectra. This discrepancy may be a consequence of the breakdown of the dipole approximation in this extreme confinement situation.

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Optimal targeted advertising strategy for secure wireless edge metaverse

arXiv

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arXiv 2021年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Kim, Dong In Miao, Chunyan The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seamlessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Metaverse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users’ privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of "covert", targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user’s experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising. © 2021, CC BY.

关键词： Bandwidth

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Recurrence plots and convolutional neural networks applied to nonintrusive load monitoring

Recurrence plots and convolutional neural networks applied t...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Cavalca, Diego L. Fernandes, Ricardo A. S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil Federal University of São Carlos Electrical Engineering Department São Carlos Brazil

ISBN: (纸本)9781728155081

The need to increasingly promote the efficient use of electricity for residential consumers motivated some researches to obtain solutions that allow the identification of loads for use in demand response programs and in the energy consumption disaggregation. Recently, the use of Convolutional Neural Networks for load identification has been common. However, it is necessary to transform the temporal signal into an image. In this sense, the present paper proposes a novel feature engineering stage, based on Recurrence Plots, to obtain the images for Nonintrusive Load Monitoring. In order to prove its effectiveness, the Recurrence Plots were compared to the Gramian Angular Difference Fields, which had already been applied in this context. For this purpose, it was used the REDD dataset, in which only the low frequency data measured in the main distribution panel were considered. Individual load measurements were only used for the labelling of the 90-seconds windows. The Convolutional Neural Network was trained and validated, considering two different strategies (multi-label and binary models), obtaining an F1-score of 66.05% and 72.65%, respectively. © 2020 IEEE.

关键词： Machine learning

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Novel diamond shuttle to deliver flexible neural probe with reduced tissue compression

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Microsystems & Nanoengineering 2020年第1期6卷 903-915页

作者： Kyounghwan Na Zachariah J.Sperry Jiaao Lu Mihaly Voroslakos Saman S.Parizi Tim M.Bruns Euisik Yoon John P.Seymour Electrical Engineering&Computer Science Department University of MichiganAnn ArborMI 48105USA Biomedical Engineering Department University of MichiganAnn ArborMI 48105USA Biointerfaces Institute University of MichiganAnn ArborMI 48105USA The Neuroscience Institute New York UniversityNew YorkNY 10016USA Center for Nanomedicine Institute for Basic Science(IBS)Seoul 03722Korea Graduate Program of Nano Biomedical Engineering(Nano BME) Yonsei-IBS InstituteYonsei UniversitySeoul 03722Korea Vivian L Smith Department of Neurosurgery University of Texas Health Science CenterHoustonTX 77030USA Department of Electrical and Computer Engineering Rice UniversityHoustonTX 77005USA

The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural *** devices implanted through dura mater and thick epineurium would ideally create minimal compression and acute damage as they reach the neurons of *** demonstrate that a three-dimensional diamond shuttle can be easily made with a vertical support to deliver ultra-compliant polymer microelectrodes(4.5-µm thick)through dura mater and thick *** diamond shuttle has 54%less cross-sectional area than an equivalently stiff silicon shuttle,which we simulated will result in a 37%reduction in blood vessel *** also discovered that higher frequency oscillation of the shuttle(200Hz)significantly reduced tissue compression regardless of the insertion speed,while slow speeds also independently reduced tissue *** and recording performance are demonstrated in rat and feline models,but the large design space of these tools are suitable for research in a variety of animal models and nervous system targets.

关键词： sectional neural thick

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Beyond Bounds on Light Scattering with Complex Frequency Excitations

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Physical Review Letters 2022年第20期129卷 203601-203601页

作者： Seunghwi Kim Sergey Lepeshov Alex Krasnok Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads 343 Kgs. Lyngby DK-2800 Denmark Department of Electrical and Computer Engineering Florida International University Miami Florida 33174 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality, and energy conservation imply strict bounds on the degree of control over scattering from small particles, with implications on the performance of many optical devices. Here, we demonstrate that these bounds can be surpassed by considering excitations at complex frequencies, yielding extreme scattering responses as tailored nanoparticles reach a quasi-steady-state regime. These mechanisms can be used to engineer light scattering of nanostructures beyond conventional limits for noninvasive sensing, imaging, and nanoscale light manipulation.

关键词： Metamaterials Mie scattering

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