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Wavelet-based Technique for Mobile Device with Low Resolution Depth Image-based Rendering

Journal of computers (Taiwan) 2018年第6期29卷 168-180页

作者： Lu, Shengrong Hsia, Chih-Hsien Cheng, Wei-Han Li, Hsin-Ting Chiang, Jen-Shiun Zhang, Ling Tseng, Hsien-Wei Gong, Cihun-Siyong Alex School of Information Engineering Longyan University Longyan city Fujian364012 China Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan Department of Electrical and Computer Engineering Tamkang University New Taiepi Taiwan Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Portable Energy System Group of Green Technology Research Center Chang Gung University Taoyuan Taiwan Department of Ophthalmology Chang Gung Memorial Hospital Taoyuan Taiwan Master Program of Arts and Technology Chinese Culture University Taiwan

Depth Image-Based Rendering (DIBR) is an approach to generate a 3-D image by the original 2-D color image with the corresponding 2-D depth map. Although DIBR is a quite convenient to convert 2D images to 3D ones, there is a disadvantage in DIBR system that it cannot reach real-time processing due to the computing time. Against this, this paper proposes a method which is based on discrete wavelet transform and adaptive edge-oriented smoothing process, to speed up the computing of the system. The proposed method still preserves the original texture and thus the proposed method not only preserves the vertical texture but also reduces at least 60% of the computing time of the DIBR system. © 2018 computer Society of the Republic of China. All rights reserved.

关键词： Discrete wavelet transforms

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Room-temperature lasing action in GaN quantum wells in the infrared 1.5 µm region

arXiv

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arXiv 2018年

作者： Ho, V.X. Al tahtamouni, T.M. Jiang, H.X. Lin, J.Y. Zavada, J.M. Vinh, N.Q. Department of Physics Center for Soft Matter and Biological Physics Virginia Tech BlacksburgVA24061 United States Materials Science & Technology Program College of Arts & Sciences Qatar University Doha2713 Qatar Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Large-scale optoelectronics integration is strongly limited by the lack of efficient light sources, which could be integrated with the silicon complementary metal-oxide-semiconductor (CMOS) technology. Persistent efforts continue to achieve efficient light emission from silicon in the extending the silicon technology into fully integrated optoelectronic circuits. Here, we report the realization of room-temperature stimulated emission in the technologically crucial 1.5 µm wavelength range from Er-doped GaN multiple-quantum wells on silicon and sapphire. Employing the well-acknowledged variable stripe technique, we have demonstrated an optical gain up to 170 cm-1 in the multiple-quantum well structures. The observation of the stimulated emission is accompanied by the characteristic threshold behavior of emission intensity as a function of pump fluence, spectral linewidth narrowing and excitation length. The demonstration of room-temperature lasing at the minimum loss window of optical fibers and in the eye-safe wavelength region of 1.5 µm are highly sought-after for use in many applications including defense, industrial processing, communication, medicine, spectroscopy and imaging. As the synthesis of Er-doped GaN epitaxial layers on silicon and sapphire has been successfully demonstrated, the results laid the foundation for achieving hybrid GaN-Si lasers providing a new pathway towards full photonic integration for silicon optoelectronics. Driven by the strong need for cheap and integrable Si-based optoelectronic devices for a wide range of applications, continuing endeavors have been made to develop structures for light emission, modulation, and detection in this material system. Recent breakthroughs including the demonstration of a high-speed optical modulator in Si,1-2 photodetectors3, and waveguides4 have brought the concept of transition from electrical to optical interconnects closer to realization. However, the base for silicon photonics, namely, a group IV la

关键词： Semiconductor quantum wells

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Predictable and precise template-free CRISPR editing of pathogenic variants (vol 563, pg 646, 2018)

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NATURE 2019年第7746期567卷 E1-E2页

作者： Shen, Max W. Arbab, Mandana Hsu, Jonathan Y. Worstell, Daniel Culbertson, Sannie J. Krabbe, Olga Cassa, Christopher A. Liu, David R. Gifford, David K. Sherwood, Richard I. Computational and Systems Biology Program Massachusetts Institute of Technology Cambridge USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA Merkin Institute of Transformative Technologies in Healthcare Broad Institute of Harvard and MIT Cambridge USA Department of Chemistry and Chemical Biology Harvard University Cambridge USA Howard Hughes Medical Institute Harvard University Cambridge USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge USA Molecular Pathology Unit Center for Cancer Research and Center for Computational and Integrative Biology Massachusetts General Hospital Charlestown USA Division of Genetics Department of Medicine Brigham and Women’s Hospital and Harvard Medical School Boston USA Hubrecht Institute for Developmental Biology and Stem Cell Research Royal Netherlands Academy of Arts and Sciences (KNAW) Utrecht The Netherlands Broad Institute of MIT and Harvard Cambridge USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

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Soft Electromagnetic Actuators: Miniature Soft Electromagnetic Actuators for Robotic Applications (Adv. Funct. Mater. 18/2018)

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Advanced Functional Materials 2018年第18期28卷

作者： Thanh Nho Do Hung Phan Thuc‐Quyen Nguyen Yon Visell Department of Electrical Computer Engineering Media Arts and Technology Program California NanoSystems Institute University of California Santa Barbara CA 93106 USA Department of Chemistry and Biochemistry University of California Santa Barbara CA 93106 USA

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

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The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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BHEL Smart Wall Blowing System: New Product Development in Manufacturing Industry

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Vikalpa 2021年第4期45卷 240 - 249页

作者： Abhishek Kumar R. Dhanuskodi R. Kaliappan K. Nandakumar Abhishek Kumar teaches design philosophies at Anant National University Ahmedabad. He earned his Ph.D in Management from Pondicherry University. He is an Economics graduate from Calcutta University and MBA from BIM Trichy. He has published more than 20 articles in reputed international journals has authored two books written articles and columns for newspapers and is quoted on issues related to leadership and marketing by various media platforms. His research work comprises construction of brand personality scale for media aesthetics and phenomenological design. His recent publications are on philosophy of a photograph hermeneutic reality of product and on philosophy of intimate spaces. R. Dhanuskodi has nearly 40 years of R&D experience at BHEL India in technical areas applicable for thermal power plants. He is a life member of The Institution of Engineers (India) and The Combustion Institute. He has won two BHEL’s Excel awards under the best author category for technical papers. He has visited France Netherlands and Germany under Indo-Europe Clean Coal Development Program. He has guided 42 UG PG and PhD project works. He holds 11 patents 40 copyrights and 2 design registrations. He has presented papers in 20 conferences and published in 10 national and international journals. R. Kaliappan completed his bachelors in electrical and electronics engineering and Masters in Computer Science. He has 36 years of research experience in different fields of power generation and power plant subsystems such as heat transfer studies on boiler circulation efficiency improvements of boiler subsystems product improvements/ enhancements and setting up test facilities for research studies. He has published a number of technical papers on MHD power generation and heat transfer studies in various national and international journals. He has more than 25 patents and copyrights on products development related to power boilers. He has won BHEL’S gold medal for product development for Smart Wall Blowing system. K. Nandakumar

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Abstracts

引用

JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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DNA steganalysis using deep recurrent neural networks

arXiv

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arXiv 2017年

作者： Bae, Ho Lee, Byunghan Kwon, Sunyoung Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Electrical and Computer Engineering Seoul National University Seoul08826 Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 ASRI INMC Seoul National University Seoul08826

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are vari-ous methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods de-pend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution varia-tions by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools. Copyright © 2017, The Authors. All rights reserved.

关键词： Steganography

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Procedural content generation via machine learning (PCGML)

arXiv

引用

arXiv 2017年

作者： Summerville, Adam Snodgrass, Sam Guzdial, Matthew Holmgård, Christoffer Hoover, Amy K. Isaksen, Aaron Nealen, Andy Togelius, Julian Department of Computational Media University of California Santa CruzCA95064 United States College of Computing and Informatics Drexel University PhiladelpiaPA19104 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Duck and Cover Games ApS Copenhagen K1311 Denmark College of Arts Media and Design Northeastern University BostonMA02115 United States Department of Computer Science and Engineering New York University BrooklynNY11201 United States

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement;this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks;Markov models: n-grams and multi-dimensional Markov chains;clustering;and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge representation

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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