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11th Asia Joint Conference on Information Security, AsiaJCIS 2016

作者： Kuo, Wen-Chung Li, Jyun-Jia Wang, Chun-Cheng Wuu, Lih-Chyau Huang, Yu-Chih Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Taiwan Graduate School of Engineering Science and Technology Doctoral Program National Yunlin University of Science and Technology Taiwan Department of Information Management Tainan University of Technology Taiwan

ISBN: (纸本)9781509022854

Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enhance the embedding rate and good embedding capacity and the other is an optimization problem used to solve the overflow/ underflow problem. In fact, they propose the similar brute force method to solve overflow/underflow problem in their proposed scheme. To overcome the overflow/underflow problem exactly, we will propose the close form to solve this problem in this paper. Then, an improvement data hiding scheme based on FFEMD (Formula Fully Exploiting Modification Directions) and pixel value differencing method is proposed. According to our analysis, it is not only to keep the advantages of Shen-Huang scheme but also to use the close form to solve the overflow/ underflow problem. © 2016 IEEE.

关键词： Pixels

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Coherently Enhanced Wireless Power Transfer

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Physical Review Letters 2018年第14期120卷 143901-143901页

作者： Alex Krasnok Denis G. Baranov Andrey Generalov Sergey Li Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Physics Chalmers University of Technology 412 96 Gothenburg Sweden Moscow Institute of Physics and Technology Dolgoprudny 141700 Russia Department of Electronics and Nanoengineering Aalto University 02150 Espoo Finland ITMO University St. Petersburg 197101 Russia Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Extraction of electromagnetic energy by an antenna from impinging external radiation is at the basis of wireless communications and wireless power transfer (WPT). The maximum of transferred energy is ensured when the antenna is conjugately matched, i.e., when it is resonant and it has an equal coupling with free space and its load. This condition, however, can be easily affected by changes in the environment, preventing optimal operation of a WPT system. Here, we introduce the concept of coherently enhanced WPT that allows us to bypass this difficulty and achieve dynamic control of power transfer. The approach relies on coherent excitation of the waveguide connected to the antenna load with a backward propagating signal of specific amplitude and phase. This signal creates a suitable interference pattern at the load resulting in a modification of the local wave impedance, which in turn enables conjugate matching and a largely increased amount of extracted energy. We develop a simple theoretical model describing this concept, demonstrate it with full-wave numerical simulations for the canonical example of a dipole antenna, and verify experimentally in both near-field and far-field regimes.

关键词： Electromagnetism engineering Light propagation, transmission & absorption Photonics

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Streaming k-means clustering with fast queries

arXiv

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arXiv 2017年

作者： Zhang, Yu Tangwongsan, Kanat Tirthapura, Srikanta Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States Compute Science Program Mahidol University International College Thailand Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States

We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide substantial improvement in the query time for cluster centers while retaining the desirable properties of provably small approximation error and low space usage. Our algorithms rely on a novel idea of "coreset caching" that systematically reuses coresets (summaries of data) computed for recent queries in answering the current clustering query. We present both theoretical analysis and detailed experiments demonstrating their correctness and efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Correction to: Analysis of the Regularization Parameters of Primal–Dual Interior Method for Convex Objectives Applied to 1H Low Field Nuclear Magnetic Resonance Data Processing

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Applied Magnetic Resonance 2018年第1期50卷 521-521页

作者： Campisi-Pinto, Salvatore Levi, Ofer Benson, Diamanta Cohen, Meir Resende, Maysa Teixeira Saunders, Michael Linder, Charles Wiesman, Zeev Phyto-Lipid Biotechnology Laboratory Department of Biotechnology Engineering Faculty of Engineering Sciences Ben-Gurion University of the Negev Beer-Sheva Israel Department of Mathematics and Computer Science Program of Industrial Engineering and Management The Open University Ra’anana Israel Department of Management Science and Engineering (MS&E) Stanford University Stanford USA

In the original publication, the acknowledgment section was not published and it is given below now.

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Shape-independent hardness estimation using deep learning and a GelSight tactile sensor

arXiv

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arXiv 2017年

作者： Yuan, Wenzhen Zhu, Chenzhuo Owens, Andrew Srinivasan, Mandayam A. Adelson, Edward H. Department of Mechanical Engineering Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States CSAIL MIT CambridgeMA United States Department of Electronical Engineering Tsinghua University Beijing100084 China Electrical Engineering and Computer Science Department U.C. Berkeley CambridgeMA02139 United States Research Laboratory of Electronics Department of Mechanical Engineering MIT CambridgeMA02139 United States UCL TouchLab Computer Science Department UCL London United Kingdom Department of Brain and Cognitive Sciences CSAIL MIT CambridgeMA02139 United States

Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale. Copyright © 2017, The Authors. All rights reserved.

关键词： Hardness

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Author Correction: RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

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Nature communications 2021年第1期12卷 3128页

作者： Alessandro Bonetti Federico Agostini Ana Maria Suzuki Kosuke Hashimoto Giovanni Pascarella Juliette Gimenez Leonie Roos Alex J Nash Marco Ghilotti Christopher J F Cameron Matthew Valentine Yulia A Medvedeva Shuhei Noguchi Eneritz Agirre Kaori Kashi Joachim Luginbühl Riccardo Cazzoli Saumya Agrawal Nicholas M Luscombe Mathieu Blanchette Takeya Kasukawa Michiel de Hoon Erik Arner Boris Lenhard Charles Plessy Gonçalo Castelo-Branco Valerio Orlando Piero Carninci RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. alessandro.bonetti@riken.jp. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. alessandro.bonetti@riken.jp. The Francis Crick Institute 1 Midland Road London NW1 1AT UK. RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. Department of Medicine (H7) Karolinska Institutet Stockholm 141 86 Sweden. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. Faculty of Medicine Imperial College London Institute of Clinical Sciences London W12 0NN UK. MRC London Institute of Medical Sciences London W12 0NN UK. School of Computer Science McGill University Montréal QC Canada. Department of Biochemistry and Goodman Cancer Research Centre McGill University Montréal QC Canada. Institute of Bioengineering Research Centre of Biotechnology Russian Academy of Science 117312 Moscow Russia. Department of Computational Biology Vavilov Institute of General Genetics Russian Academy of Science 119991 Moscow Russia. Department of Biological and Medical Physics Moscow Institute of Physics and Technology 141701 Dolgoprudny Moscow Region Russia. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy. UCL Genetics Institute University College London London WC1E 6BT UK. Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha Onna-son Kunigami-gun Okinawa 904-0495 Japan. Sars International Centre for Marine Molecular Biology University of Bergen 5008 Bergen Norway. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. valerio.orlando@kaust.edu.sa. KAUST Environmental Epigenetics Program King Abdullah University of Science and Technology (KAUS

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MATLAB-based analysis of labor dispatch and work distribution: An example involving caseworkers in central Taiwan

MATLAB-based analysis of labor dispatch and work distributio...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Oh, Chin-Yu Hsu, Meng-Chueh Tsai, Chang-Ming Jhang, Jyun-Yao Chen, Yu-Ren Chen, Bo-Xiang Chen, Yi-Chung Teng, Chun-Wen Chen, Shu-Hui Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Ph. D. Program of Business Feng Chia University Taichung40724 Taiwan Graduate Institute of Public Policy and social innovation Feng Chia University Taichung40724 Taiwan General Education Center Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9781509048977

The dispatching of labor and distribution of tasks among workers are important aspects of corporate operations. Labor dispatch refers to the dispatching of employees to meet with clients;however, dispatches commonly vary with time. This means that any analysis pertaining to labor dispatch and work distribution should also vary with time. In this study, MATLAB was used to analyze data from anonymous caseworkers in central Taiwan in order to identify relationships between the location of their offices and the homes of their clients. Our aim was to determine the rationality behind labor dispatch and work distribution. This led us to develop a novel method by which to dispatch labor in order to achieve a more equitable distribution of work. Simulations demonstrate the efficacy of the proposed scheme in improving labor dispatch and work distribution. © 2017 IEEE.

关键词： MATLAB

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A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

arXiv

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arXiv 2017年

作者： Harirchi, Farshad Kim, Doohyun Khalil, Omar Liu, Sijia Elvati, Paolo Hero, Alfred Violi, Angela Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2125 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109-2125 United States Biophysics Program Departments of Chemical Engineering Biomedical Engineering Macromolecular Science and Engineering University of Michigan Ann ArborMI48109-2125 United States

Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures the interactions between reacting species and products. Determining the minimal chemical interactions underlying the dynamic behavior of systems is a major task. In this paper, we introduce a novel approach for the identification of the inuential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of inuential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2andC3H8in a constant-volume homogeneous reactor. The inuential reactions identified by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the inuential reactions identified at different times and conditions and that for both H2andC3H8this reduced mechanism performs closely to the parent mechanism as a function of ignition delay over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for mechanism analysis and mechanism reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning systems

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Visualization of 3-Dimensional Vectors in a Dynamic Embryonic System—WormGUIDES

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Journal of computer and Communications 2017年第12期5卷 70-79页

作者： Eric Wang Anthony Santella Zi Wang Dali Wang Zhirong Bao Department of Electric Engineering and Computer Science University of Tennessee Knoxville TN USA Coding Club Farragut High School Knoxville TN USA Developmental Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Environmental Science Division Oak Ridge National Laboratory Oak Ridge TN USA

WormGUIDES is an open-source dynamic embryonic system designed to facilitate global understanding of cellular decisions in the developing nervous system of the nematode C. elegans. WormGUIDES was designed to allow investigation and exploration of the observational results of the C. elegans life cycle from laboratory experiments. In the process of a mechanistic C. elegans model development, some functionalities of WormGUIDES needed to be enhanced to support model validation and verification. In this study, a new way to visualize 3-dimentional vectors within WormGUIDES was investigated and presented. Then, the practical values of this method were demonstrated by visualizing two biologically significant directions (i.e., division orientation and cell polarity) of individual embryonic cells in C. elegans. Lastly, a mathematic approach was designed to illustrate the differences between these two sets of vectors and provide easy indications of the location of these individual cells that have large data discrepancies within the C. elegans embryonic system.

关键词： Embryonic Data Visualization WormGUIDES Workflow Software Architecture Division Orientation Cell Polarity C. elegans

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Publisher Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2023年第1期55卷 166页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Paul Jongjoon Choi Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

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