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arXiv 2018年

作者： Hu, Zhiwen Bai, Zixuan Bian, Kaigui Wang, Tao Song, Lingyang National Engineering Laboratory for Big Data Analysis and Applications School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China

—Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock data Challenge

arXiv

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arXiv 2022年

作者： Schäfer, Marlin Benedikt Zelenka, Ondřej Nitz, Alexander H. Wang, He Wu, Shichao Guo, Zong-Kuan Cao, Zhoujian Ren, Zhixiang Nousi, Paraskevi Stergioulas, Nikolaos Iosif, Panagiotis Koloniari, Alexandra E. Tefas, Anastasios Passalis, Nikolaos Salemi, Francesco Vedovato, Gabriele Klimenko, Sergey Mishra, Tanmaya Brügmann, Bernd Cuoco, Elena Huerta, E.A. Messenger, Chris Ohme, Frank Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut HannoverD-30167 Germany Leibniz Universität Hannover HannoverD-30167 Germany Friedrich-Schiller-Universität Jena JenaD-07743 Germany Michael Stifel Center Jena JenaD-07743 Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Department of Astronomy Beijing Normal University Beijing100875 China Peng Cheng Laboratory Shenzhen518055 China Department of Informatics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Department of Physics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 Darmstadt64291 Germany Università di Trento Dipartimento di Fisica Povo TrentoI-38123 Italy INFN Trento Institute for Fundamental Physics and Applications Povo TrentoI-38123 Italy INFN Sezione di Padova PadovaI-35131 Italy Department of Physics University of Florida PO Box 118440 GainesvilleFL32611-8440 United States Cascina PisaI-56021 Italy Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 PisaI-56127 Italy Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States SUPA School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥ 200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Partial, composite fractional operators, and their properties and applications 3

Partial, composite fractional operators, and their propertie...

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3rd International Conference on Mechanical, Electronic and Information Technology Engineering, ICMITE 2017

作者： Deng, Kaiying Deng, Jingwei Li, Suduo College of Mathematics and Computer Science Northwest Minzu University Lanzhou730124 China Key Lab of Streaming Data Computing Technologies and Applications Lanzhou730030 China

The paper discusses the properties of the partial fractional integrals, the partial fractional derivatives, and the composite fractional integrals and derivatives. Some basic formulas are derived;and the relations between the Riemann-Liouville and Caputo ones are also presented. Finally, as the specific application, a two dimensional Abel integral equation is solved. © The Authors, published by EDP sciences, 2017.

关键词： Integral equations

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Developments and applications of data Deidentification Technology under Big data

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Journal of Electronic science and Technology 2017年第3期15卷 231-239页

作者： Hung-Li Chen Yao-Tung Tsou Bo-Chen Tai Szu-Chuang Li Yen-Nun Huang Chia-Mu Yu Yu-Shian Chiu Department of Communications Engineering Feng Chia University Taichung 400 Research Center for Information Technology Innovation Academia Sinica Taipei 100 Department of Computer Science and Engineering National Chung Hsing University Data Analytics Technology &Applications Institute for Information Industry Taipei 100

In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive *** instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate *** such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big *** a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent *** this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection ***,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.

关键词： Index Terms--Deidentification differential privacy.

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Perspectives for self-driving labs in synthetic biology

arXiv

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

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A new verifier-based anonymous password-authenticated key exchange protocol

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Journal of Information Hiding and Multimedia Signal Processing 2018年第6期9卷 1595-1602页

作者： Chen, Chien-Ming Wang, Guangjie Fang, Weicheng Wu, Tsu-Yang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou China

In the information age, privacy have aroused wide concern. User identity can be tracked in a public network if it is not stored or transmitted in a secure way. To enhance the security, many anonymous password authenticated key exchange protocols have been proposed to anonymize user’s identity from the server. However, few of them focus on the stolen verifier attacks which assumes a powerful adversary who is accessible to the server’s database or even secret keys. In this paper, we propose a new verifier-based anonymous password-authenticated key exchange protocols. It employs an existed algebraic MAC as verifier to resist such attacks. We also show that the protocol is secure and efficient through analysis. © 2018 Ubiquitous International. All rights reserved.

关键词： Authentication

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Hierarchical routing strategy for wireless sensor network

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Journal of Information Hiding and Multimedia Signal Processing 2018年第1期9卷 256-264页

作者： Pan, Jeng-Shyang Kong, Ling Ping Sung, Tien-Wen Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Chaoyang University of Technology Taichung Taiwan School of Computer Science Engineering and Mathematics Flinders University Australia

Wireless sensor networks (WSNs) are designed for a large scale monitoring applications such as military surveillance, medical treatment, environmental monitoring and industry management. In this network, usually hundreds or thousands of low-cost sensor nodes are deployed. These sensing nodes detect the events in the environment and pass an upstream message towards a Sink node. The Sink node is responsible for processing data those are collected by sensor nodes. However, bottleneck exists for WSN: these inner nodes whose positions are close to the Sink node run out of power much earlier than those outer nodes. Because those inner nodes not only perform sensing operation, but also replay data originated from other nodes. In this paper, GAHL routing scheme based on genetic algorithm and Harel method is proposed to solve the problem. The scheme finds the small number of center heads in the network. Those center heads play a role in balancing the workload of inner nodes. With the involvement of center head, a three-tier network is built. From the simulation experiment, the scheme shows a good performance compared to other well-known algorithms. We observe a decrease in the number of center heads, as well as an improvement in the stability of the scheme. © 2018, Ubiquitous International. All rights reserved.

关键词： Genetic algorithms

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Special issue on 2016 International Conference on Intelligence science and Big data Engineering

Special issue on 2016 International Conference on Intelligen...

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作者： Zheng, Wei-Shi Gao, Xinbo Li, Yuanqing Lai, Jianhuang School of Data and Computer Science Sun Yat-sen University China State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China School of Automation Science and Engineering South China University of Technology China Guangzhou Key Laboratory of Brain Computer Interface and Applications Guangzhou 510640 China School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China

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FPGA demonstration for WLAN-OFDM baseband receiver

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Journal of Information Hiding and Multimedia Signal Processing 2018年第5期9卷 1281-1292页

作者： Wu, Chih-Feng Shiue, Muh-Tian Sung, Tien-Wen Pan, Jeng-Shyang School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Central University Taoyuan32001 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

The FPGA demonstration for WLAN-OFDM baseband receiver is presented in this paper. In this OFDM baseband receiver, the CORDIC computing is employed to realize various arithmetics of baseband signal process and then to reduce the computational complexity of baseband receiver. For a large open office channel with 100 ns RMS delay spread, the measured EVMs of WLAN-OFDM baseband receiver are −13.3, −16.6, −25.4 and −31.0 dB for BPSK, QPSK, 16-QAM and 64-QAM, respectively. Finally, the physical design of WLAN-OFDM baseband receiver with 0.18 µm 1P6M CMOS technology indicates that the core area and power consumption are 1.2 mm2 (1094 µm × 1092 µm) and 33.2 mW, respectively, with supply voltage of 1.8 V and operating clock of 40 MHz. © 2018, Ubiquitous International. All rights reserved.

关键词： Signal to noise ratio

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Mean-field theory for the Nagel-Schreckenberg model with overtaking strategy

arXiv

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arXiv 2018年

作者： Su, Zhu Deng, Weibing Han, Jihui Li, Wei Cai, Xu National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan Complexity Science Center Institute of Particle Physics Central China Normal University Wuhan School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, a modified model considered overtaking strategy (NSOS) has been proposed [1, 2]. In this paper, we focus on the theoretical analysis of traffic ow for NSOS model by using mean-eld method. In the special case of vmax = 1 where vehicles can not overtake preceding ones, the features of stationary state can be obtained exactly. However, in the case of vmax >1 where overtaking happens, some approximative methods have to be took into account. The main results are that we find the reason why trac ow is increased in the regime where densities exceed the maximum ow density, and the inuence of traffic ow on the transition density is dominated by the braking probability p. Copyright © 2018, The Authors. All rights reserved.

关键词： Mean field theory

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