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arXiv 2018年

作者： Utimula, Keishu Ichibha, Tom Maezono, Ryo Hongo, Kenta School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

Lattice thermal conductivities (LTC) for a subset of polymer crystals from the Polymer Genome Library were investigated to explore high LTC polymer systems. We employed a first-principles approach to evaluating phonon lifetimes within the third-order perturbation theory combined with density functional theory, and then solved the linearized Boltzmann transport equation with single-mode relaxation time approximated by the computed lifetime. Typical high LTC polymer systems, polyethylene (PE) crystal and fiber, were benchmarked, which is reasonably consistent with previous references, validating our approach. We then applied it to not only typical polymer crystals, but also some selected ones having structural similarities to PE. Among the latter crystals, we discovered that beta phase of Poly(vinylidenesurely fluoride) (PVF-β) crystal has higher LTC than PE at low temperature. Our detailed mode analysis revealed that the phonon lifetime of PVF-β is more locally distributed around lower frequency modes and four-times larger than that of PE. It was also found from a simple data analysis that the LTC relatively correlates with curvature of energy-volume plot. The curvature would be used as a descriptor for further exploration of high LTC polymer crystals by means of a data-driven approach beyond human-based one. Copyright © 2018, The Authors. All rights reserved.

关键词： Perturbation techniques

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Method for the calculation of the Hamaker constants of organic materials by the Lifshitz macroscopic approach with DFT

arXiv

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arXiv 2019年

作者： Takagishi, Hideyuki Masuda, Takashi Shimoda, Tatsuya Maezono, Ryo Hongo, Kenta School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

The Hamaker constants, which are coefficients providing quantitative information on intermolecular forces, were calculated for a number of different materials according to the Lifshitz theory via simple DFT calculations without any experimental measurements being performed. The physical properties (polarizability, dipole moment, molecular volume, and vibrational frequency) of organic molecules were calculated using the B3LYP density functional and the aug-cc-pVDZ basis set. Values for the Hamaker constants were obtained using the approximation of the Lorentz-Lorenz equation and Onsager's equation with these properties. It was found that, in the case of 'non-associative' materials, like hydrocarbons, ethers, ketones, aldehydes, carboxylic acids, esters, nitriles, and hydrosilanes, and halides, the calculated Hamaker constants were similar in value to their experimentally determined counterparts. Moreover, with this calculation method, it is easy to create the molecular model and the CPU time can be shortened. Copyright © 2019, The Authors. All rights reserved.

关键词： Ketones

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Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

arXiv

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arXiv 2024年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Liu, Wei Cao, Xiaochun Sun, Yingfei Huang, Qingming The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China The Tencent Data Platform Shenzhen518054 China The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-Sen University Shenzhen518107 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner. © 2024, CC BY.

关键词： Maximum likelihood estimation

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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

arXiv

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arXiv 2022年

作者： Zhang, Songling Yang, Zhaohui Chen, Mingzhe Liu, Danpu Wong, Kai-Kit Poor, H. Vincent The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310007 China Zhejiang Lab. Hangzhou31121 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS. Copyright © 2022, The Authors. All rights reserved.

关键词： Multicasting

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

TechRxiv

引用

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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An optimal algorithm for approximate nearest neighbor searching 94

An optimal algorithm for approximate nearest neighbor search...

引用

Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms

作者： Sunil Arya David M. Mount Nathan S. Netanyahu Ruth Silverman Angela Wu Max-Planck-Institut für Informatik Im Stadtwald D-66123 Saarbrücken Germany Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland Space Data and Computing Division NASA Goddard Space and Flight Center Greenbelt Maryland and Center for Automation Research University of Maryland College Park Maryland Department of Computer Science University of the District of Columbia Washington DC and Center for Automation Research University of Maryland College Park Maryland Department of Computer Science and Information Systems The American University Washington DC

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Adaptive Cleaning in Manufacturing: A Decision Tree Model for Efficient Factory Sanitation

Adaptive Cleaning in Manufacturing: A Decision Tree Model fo...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： M. Arulprakash Ramakrishnan Raman Atul A Gokhale K. Saravanan Ishwarya M.V S. Sujatha Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Artificial Intelligence and Data Science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Industrial cleanliness is crucial in production settings, and this system offers a novel way to use Internet of Things (IoT) technology to strengthen this practice. The suggested solution uses a Decision Tree (DT) model and real-time data received from IoT sensors placed strategically around the facility to enable adaptive enhancements to cleaning processes. This system of sensors keeps tabs on a number of variables, including manufacturing schedules, environmental factors, and circulation. In real-time, the DT model may use this data to optimize cleaning locations, durations, and procedures, cutting down on wasteful downtime and resource usage without compromising cleanliness. When contrasted with conventional static cleaning methods, the adaptive cleaning system shows considerable gains in both sanitation efficiency and resource occupancy. Improving cleaning using IoT technology makes it more responsive and smart, which in turn makes production more efficient and less detrimental to the environment. It explains how the IoT and DT models worked together to make manufacturing processes more adaptable, under the lens of industrial hygiene.

关键词： Adaptation models Schedules Adaptive systems Cleaning Real-time systems Production facilities Environmental factors

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Navigating Grid Dynamics with V2G Technology and Naive Bayes Optimization in IoT Systems

Navigating Grid Dynamics with V2G Technology and Naive Bayes...

引用

Innovation in Technology (INOCON), IEEE International Conference for

作者： M. Arulprakash Ramakrishnan Raman Atul A Gokhale K. Saravanan Ishwarya M. V S. Sujatha Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Artificial Intelligence and Data Science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Vehicle-to-Grid (V2G) technology and Naive Bayes optimization, which might help IoT devices, navigate grid dynamics. Electric vehicles (EVs) with bidirectional charging capability may provide grid assistance using their energy storage capacity. These cars can take and return energy from the grid using V2G technology, stabilizing the system. This paper proposes a unique technique to intelligently control energy flow between the grid and EVs using V2G technology and Naive Bayes optimization algorithms. Using previous data on energy consumption, grid circumstances, and vehicle charging behavior, the Naive Bayes algorithm makes real-time predictions and decisions. In IoT-enabled smart grids, this connection improves energy management efficiency and reliability. The implementation methods and experimental findings of the suggested strategy are discussed in the study. The system grid stability, energy usage, and grid dynamics management are also assessed. The results show that V2G technology and Naive Bayes optimization in IoT systems can intelligently navigate grid dynamics, creating a more sustainable and resilient energy infrastructure. To smart grid expertise and shows how V2G-enabled IoT devices may optimize energy distribution and ensure grid stability in the face of dynamic energy needs.

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Grids, virtualization, and clouds at Fermilab

引用

Journal of Physics: Conference Series 2014年第3期513卷

作者： S Timm K Chadwick G Garzoglio S Noh Scientific Computing Division Fermi National Accelerator Laboratory USA To whom any correspondence should be addressed Global Science experimental Data hub Center Korea Institute of Science and Technology Information South Korea

Fermilab supports a scientific program that includes experiments and scientists located across the globe. To better serve this community, in 2004, the (then) computing Division undertook the strategy of placing all of the High Throughput computing (HTC) resources in a Campus Grid known as FermiGrid, supported by common shared services. In 2007, the FermiGrid Services group deployed a service infrastructure that utilized Xen virtualization, LVS network routing and MySQL circular replication to deliver highly available services that offered significant performance, reliability and serviceability improvements. This deployment was further enhanced through the deployment of a distributed redundant network core architecture and the physical distribution of the systems that host the virtual machines across multiple buildings on the Fermilab Campus. In 2010, building on the experience pioneered by FermiGrid in delivering production services in a virtual infrastructure, the computing Sector commissioned the FermiCloud, General Physics computing Facility and Virtual Services projects to serve as platforms for support of scientific computing (FermiCloud 6 GPCF) and core computing (Virtual Services). This work will present the evolution of the Fermilab Campus Grid, Virtualization and Cloud computing infrastructure together with plans for the future.

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