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arXiv 2017年

作者： Wang, DeLiang Chen, Jitong Department of Computer Science and Engineering Center for Cognitive and Brain Sciences Ohio State University ColumbusOH43210 United States Center of Intelligent Acoustics and Immersive Communications Northwestern Polytechnical University Xi’an China Department of Computer Science and Engineering Ohio State University ColumbusOH43210 United States Silicon Valley AI Lab at Baidu Research 1195 Bordeaux Drive SunnyvaleCA94089 United States

Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This article provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement (speech-nonspeech separation), speaker separation (multi-talker separation), and speech dereverberation, as well as multi-microphone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source. Copyright © 2017, The Authors. All rights reserved.

关键词： Separation

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Erratum to: Past review, current progress, and challenges ahead on the cocktail party problem

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Frontiers of Information Technology & Electronic engineering 2018年第4期19卷 582页

作者： Yan-min QIAN Chao WENG Xuan-kai CHANG Shuai WANG Dong YU Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) Tencent Tencent AI Lab Bellevue USA (GRID:grid.16821.3c)

In the original version of this article, the affiliations are incorrect. The correct affiliations are given *** corresponding author’s E-mail address should be yanminqian@***.

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Data analysis with fuzzy measure on intuitionistic fuzzy sets

Data analysis with fuzzy measure on intuitionistic fuzzy set...

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International Multiconference of Engineers and computer Scientists 2016, IMECS 2016

作者： Lee, Sanghyuk Man, Ka Lok Lim, Eng Gee Leach, Mark Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University 111 Ren'ai Road Suzhou Jiangsu215123 China Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University 111 Ren'ai Road Suzhou Jiangsu215123 China Biomedical Engineering Center Chiang Mai University Thailand

ISBN: (纸本)9789881404763

Study of fuzzy entropy on intuitionistic fuzzy sets (IFSs) were proposed, and analyzed. Built in uncertainty in IFSs, it is named as hesitance. It is contained in fuzzy membership function in itself by definition. Hence, designing fuzzy entropy is not easy because there is no general entropy definition about IFSs. By considering existing fuzzy entropy definitions, fuzzy entropy on IFSs is designed and proved its usefulness. Similarity measure was also extended with the information of entropy, and the similarity measure is verified its usefulness by the proof.

关键词： Entropy

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Effects of edge on graphene plasmons as revealed by infrared nanoimaging

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Light(science & Applications) 2016年第1期5卷 266-274页

作者： Qingyang Xu Teng Ma Mohammad Danesh Bannur Nanjunda Shivananju Sheng Gan Jingchao Song Cheng-Wei Qiu Hui-Ming Cheng Wencai Ren Qiaoliang Bao Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow University199 Ren-ai RoadSuzhou 215123JiangsuChina Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of Sciences72 Wenhua RoadShenyang 110016China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Department of Materials Science and Engineering Monash UniversityClaytonVictoria 3800Australia

We used scattering-type scanning near-field optical microscopy(s-SNOM)to investigate the plasmonic properties of edges in well-defined graphene nanostructures,including sharp tapers,nanoribbons and nanogaps,which were all fabricated via the growth-etching chemical vapor deposition(GECVD)*** obtained near-field images revealed the localized plasmon modes along the graphene nanoribbon;these modes strongly depended on the size of the graphene pattern,the angle of the tapered graphene and the infrared excitation *** interesting plasmon modes were verified by numerical simulations and explained by the reflection,and interference of electromagnetic waves at the graphene–SiO_(2) *** constructive interference at the graphene nanogap caused by charge accumulation was demonstrated for the first *** the infrared nanoimaging technique,greater plasmon broadening was observed in the zigzag edge than in the armchair *** study suggests that graphene edges should be separated by an effective working distance to avoid the overlapping of localized plasmon modes,which is very important for the design of graphene-based plasmonic circuits and devices.

关键词： edge chirality graphene graphene plasmon nanoribbon nanogap near-field microscopy s-SNOM

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ml) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ml algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Evaluating race and sex diversity in the world's largest companies using deep neural networks

arXiv

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arXiv 2017年

作者： Chekanov, Konstantin Mamoshina, Polina Yampolskiy, Roman V. Timofte, Radu Scheibye-Knudsen, Morten Zhavoronkov, Alex Youth Laboratories Ltd Diversity Ai Group Skolkovo Innovation Center Nobel Street 5 Moscow143026 Insilico Medicine Emerging Technology Centers Jhu 1101 33rd Street BaltimoreMD21218 United States Computer Science Department University of Oxford Oxford United Kingdom Computer Engineering and Computer Science Duthie Center for Engineering University of Louisville LouisvilleKY40292 United States Computer Vision Lab Department of Information Technology and Electrical Engineering Eth Zürich Switzerland Center for Healthy Aging Department of Cellular and Molecular Medicine University of Copenhagen Biogerontology Research Foundation 2354 Chynoweth House Trevissome Park TruroTR4 8UN United Kingdom Moscow Institute of Physics and Technology Institutskiy per. 9 Dolgoprudny141701 Russia

Diversity is one of the fundamental properties for survival of species, populations and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Data mining based on the emergency medical database the association between the reason of calling ambulance service and the medical history of patients

Data mining based on the emergency medical database the asso...

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2016 International Conference on Applied System Innovation, IEEE ICASI 2016

作者： Lin, Jhe-Nan Sun, Wei-Zen Ma, Huei-Ming Yang, Meng-Han Department of Computer Science and Information Engineering National Kaohsiung University of Applied Sciences No. 415 Chien Kung Rd. Kaohsiung80778 Taiwan College of Medicine National Taiwan University No. 1 Jen Ai Rord Sec. 1 Taipei10051 Taiwan

ISBN: (纸本)9781467398886

Many diverse application areas are experiencing an explosive increase in the availability of data to analysis, including the medical and biological sciences. Based on the emergency medical database, this research has tried to mine the association between the reason of calling ambulance service and the medical history of patients. The acute conditions, which make people call the ambulance service, may be derived from the chronic diseases. The mined associations of this research are potentially beneficial for developing appropriate managements of the chronic illnesses. © 2016 IEEE.

关键词： Data mining

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Analyzing seasonal incidence patterns of epileptic seizure using various statistical methods

Analyzing seasonal incidence patterns of epileptic seizure u...

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2016 International Conference on Machine Learning and Cybernetics, ICmlC 2016

ISBN: (纸本)9781509003891

An epileptic seizure is a brief episode of symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is a chronic disorder that affects people of all ages, and one of the most common neurological diseases globally. Epilepsy can have both genetic and acquired causes, nevertheless, the exact mechanism of epilepsy is still unknown. It merits further investigation under which circumstances the brain shifts into the epileptic condition. On the other hand, the influence of the environment on the human organism is one central problem for scientific investigation. It has been suggested previously that incidence of epileptic seizure varies significantly with the weather. Thus, based on the emergency medical service database, this study analyzed the seasonal incidence patterns of epileptic seizure in Taiwan using statistical testing methods. Moreover, various types of meteorological data have been evaluated respectively for their associations with epileptic seizure using the idea of partial correlation. © 2016 IEEE.

关键词： Brain

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Measuring the difference of packet latencies in data center

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ICIC Express Letters 2016年第12期10卷 2881-2889页

作者： Wang, Jiehua Zhu, Xiaohui Chen, Surong School of Computer Science and Technology Nantong University No. 9 Seyuan Road Nantong226019 China Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University No. 111 Ren’ai Road Suzhou Industrial Park Suzhou215123 China

More and more applications are being moved to data center. However, little research for the characteristic of the difference of packet latencies in data center was carried out. Packet latency is a key issue of latency-sensitive applications. In this paper, we explore the nature of packet latency in a 3-level tree topology data center and make a detailed study of the difference of packet latencies by using NS2 to analyze the packetlevel traces with different data transmission protocols. We find that though the workload and over-subscription in data center have a significant effect on the difference of packet latencies for most of the data transmission protocols, pFabric has a better performance than other protocols and the difference of packet latencies is much smaller than others. We also use ELR (equal-length routing) to equalize the pathway length for all packets and find that the difference of pathway length has little impaequal-length routingct to the difference of packet latencies on regular TCP protocols, but has a significant effect on pFabric. We also verify that pFabric with ELR has a higher performance than other data transmission protocols for the difference of packet latencies and can meet the requirements of most real-time applications. © 2016 ICIC International.

关键词： Trees (mathematics)

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