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arXiv 2017年

作者： Zhang, Jiawei Cui, Limeng Yu, Philip S. Lv, Yuanhua IFM Lab Department of Computer Science Florida State University FL United States School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China University of Illinois at Chicago ChicagoIL United States Shanghai Institute for Advanced Communication and Data Science Fudan University Shanghai China Microsoft SunnyvaleCA United States

Employees in companies can be divided into *** communities, and those who frequently socialize with each other will be treated as close friends and are grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) online company internal sources and (2) online enterprise social networks (ESNs). Each of the information sources also contain multiple categories of employees' socialization activities at the same time. In this paper, we propose to detect the social communities of the employees in companies based on the broad learning *** with both these online and online information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection"(BL-Ecd) problem. To address the problem, a novel broad learning based community detection framework named "HeterogeneoUs Multi-sOurce ClusteRing"(Humor) is introduced in this paper. Based on the various enterprise social intimacy measures introduced in this paper, Humor detects a set of micro community structures of the employees based on each of the socialization activities respectively. To obtain the (globally) consistent community structure of employees in the company, Humor further fuses these micro community structures via two broad learning phases: (1) intra-fusion of micro community structures to obtain the online and online (locally) consistent communities respectively, and (2) inter-fusion of the online and online communities to achieve the (globally) consistent community structure of employees. Extensive experiments conducted on real-world enterprise datasets demonstrate our method can perform very well in addressing the BL-Ecd problem. Copyright © 2017, The Authors. All rights reserved.

关键词： Personnel

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Memristors: A Spin–Orbit-Torque Memristive Device (Adv. Electron. Mater. 4/2019)

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advanced Electronic Materials 2019年第4期5卷

作者： Shuai Zhang Shijiang Luo Nuo Xu Qiming Zou Min Song Jijun Yun Qiang Luo Zhe Guo Ruofan Li Weicheng Tian Xin Li Hengan Zhou Huiming Chen Yue Zhang Xiaofei Yang Wanjun Jiang Ka Shen Jeongmin Hong Zhe Yuan Li Xi Ke Xia Sayeef Salahuddin Bernard Dieny Long You School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 USA Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln NE 68588-0511 USA Faculty of Physics and Electronic Science Hubei University Wuhan 430062 China Key Laboratory for Magnetism and Magnetic Materials of Ministry of Education Lanzhou University Lanzhou 730000 China Department of Physics Tsinghua University Beijing 100084 China The Center for Advanced Quantum Studies and Department of Physics Beijing Normal University 100875 Beijing China Université Grenoble Alpes CEA CNRS Grenoble INP INAC-Spintec 38000 Grenoble France Wuhan National Lab for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China

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Electrocatalysis: Topotactic engineering of Ultrathin 2D Nonlayered Nickel Selenides for Full Water Electrolysis (Adv. Energy Mater. 14/2018)

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advanced Energy Materials 2018年第14期8卷

作者： Hao Wu Xin Lu Gengfeng Zheng Ghim Wei Ho Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Lab of Advanced Materials Department of Chemistry Fudan University Shanghai 200433 China Engineering Science Programme National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore Institute of Materials Research and Engineering A*STAR (Agency for Science Technology and Research) 3 Research Link Singapore 117602 Singapore

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BL-MNE: Emerging heterogeneous social network embedding through broad learning with aligned autoencoder

arXiv

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arXiv 2017年

作者： Zhang, Jiawei Xia, Congying Zhang, Chenwei Cui, L. Fu, Yanjie Yu, Philip S. Ifm Lab Department of Computer Science Florida State University FL United States University of Illinois at Chicago ChicagoIL United States School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Missouri University of Science and Technology MO United States Shanghai Institute for Advanced Communication and Data Science Fudan University Shanghai China

Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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Active self-paced learning for cost-effective and progressive face identification

arXiv

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arXiv 2017年

作者： Lin, Liang Wang, Keze Meng, Deyu Zuo, Wangmeng Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China Engineering Research Center Advanced Computing Engineering Software of Ministry of Education China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University China School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://***/projects/aspl/ Copyright © 201

关键词： Cost effectiveness

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Cicero: Middleware for developing persuasive mobile applications 11th

Cicero: Middleware for developing persuasive mobile applicat...

引用

11th International Conference on Persuasive Technology, PERSUASIVE 2016

作者： D’Aloia, Antonello Lelli, Matteo Lee, Duckki Helal, Sumi Bellavista, Paolo Department of Computer Science and Engineering University of Bologna Bologna Italy Creative Innovation Center Advanced Convergence R and D Lab LG Electronics Seoul Korea Republic of Mobile and Pervasive Computing Lab University of Florida Gainesville United States

ISBN: (纸本)9783319315096

We present Cicero – a middleware solution to support developers design and implement persuasive mobile apps. Based on the Action-Behavior Model (ABM), Cicero provides developers with powerful class libraries and collaboration methodology to streamline the development of mobile persuasive apps without requiring a steep knowledge of behavior science theory or venturing into domain-specific knowledge and artifacts. Cicero guides the developers in following the ABM steps, provides APIs for cyber sense and cyber influence, and embodies the necessary model computations including measuring end-user compliance and response to influence and persuasion. Cicero also facilitates the engagement of domain experts in a clearly defined collaborative role. Here we also originally detail the design and implementation of an Android version of the Cicero middleware and we present a use case to practically exemplify how Cicero can facilitate the application developers’ work. © Springer International Publishing Switzerland 2016.

关键词： Middleware

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A simplified, lossless re-analysis of PAPER-64

arXiv

引用

arXiv 2019年

作者： Kolopanis, Matthew Jacobs, Daniel C. Cheng, Carina Parsons, Aaron R. Kohn, Saul A. Pober, Jonathan C. Aguirre, James E. Ali, Zaki S. Bernardi, Gianni Bradley, Richard F. Carilli, Chris L. DeBoer, David R. Dexter, Matthew R. Dillon, Joshua S. Kerrigan, Joshua Klima, Pat Liu, Adrian MacMahon, David H.E. Moore, David F. Thyagarajan, Nithyanandan Nunhokee, Chuneeta D. Walbrugh, William P. Walker, Andre School of Earth and Space Exploration Arizona State U. TempeAZ United States Astronomy Dept. U. California BerkeleyCA United States Radio Astronomy Lab. U. California BerkeleyCA United States Dept. of Physics and Astronomy U. Penn. PhiladelphiaPA United States Dept. of Physics Brown University Providence RI United States INAF-Istituto di Radioastronomia via Gobetti 101 Bologna40129 Italy Department of Physics and Electronics Rhodes University PO Box 94 Grahamstown6140 South Africa South African Radio Astronomy Observatory Black River Park 2 Fir Street Observatory Cape Town7925 South Africa Dept. of Electrical and Computer Engineering U. Virginia CharlottesvilleVA United States National Radio Astronomy Obs. CharlottesvilleVA United States Dept. of Astronomy U. Virginia CharlottesvilleVA United States National Radio Astronomy Observatory SocorroNM United States Cavendish Lab. Cambridge United Kingdom Department of Physics and McGill Space Institute McGill University MontrealQC Canada CIFAR Azrieli Global Scholar Gravity & the Extreme Universe Program Canadian Institute for Advanced Research 661 University Ave. Suite 505 TorontoONM5G 1M1 Canada

We present limits on the 21cm power spectrum from the Epoch of Reionization (EoR) using data from the 64 antenna configuration of the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER) analyzed through a power spectrum pipeline independent from previous PAPER analyses. Previously reported results from PAPER have been found to contain significant signal loss (Cheng et al. 2018). Several lossy steps from previous PAPER pipelines have not been included in this analysis, namely: delay-based foreground filtering, optimal fringe-rate filtering, and empirical covariance-based estimators. Steps which remain in common with previous analyses include redundant calibration and local sidereal time (LST) binning. The power spectra reported here are effectively the result of applying a linear Fourier transform analysis to the calibrated, LST binned data. This analysis also uses more data than previous publications, including the complete available redshift range of z ∼ 7.5 to 11. In previous PAPER analyses, many power spectrum measurements were found to be detections of noncosmological power at levels of significance ranging from two to hundreds of times the theoretical noise. Here, excess power is examined using redundancy between baselines and power spectrum jackknives. The upper limits we find on the 21cm power spectrum from reionization are (1500 mK)2, (1900 mK)2, (280 mK)2, (200 mK)2, (380 mK)2, (300 mK)2 at redshifts z = 10.87, 9.93, 8.68, 8.37, 8.13, and 7.48, respectively. For reasons described in (Cheng et al. 2018), these limits supersede all previous PAPER results (Ali et al. 2018). Copyright © 2019, The Authors. All rights reserved.

关键词： Power spectrum

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An expectation-maximization eigenvector clustering approach to direction of arrival estimation of multiple speech sources 41

An expectation-maximization eigenvector clustering approach ...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

作者： Xiao, Xiong Zhao, Shengkui Nguyen, Thi Ngoc Tho Jones, Douglas L. Chng, Eng Siong Li, Haizhou Temasek Lab at NTU Nanyang Technological University Singapore Advanced Digital Sciences Center Singapore School of Computer Engineering Nanyang Technological University Singapore Department of Human Language Technology Institute for Infocomm Research Singapore

ISBN: (纸本)9781479999880

This paper presents an eigenvector clustering approach for estimating the direction of arrival (DOA) of multiple speech signals using a microphone array. Existing clustering approaches usually only use low frequencies to avoid spatial aliasing. In this study, we propose a probabilistic eigenvector clustering approach to use all frequencies. In our work, time-frequency (TF) bins dominated by only one source are first detected using a combination of noise-floor tracking, onset detection and coherence test. For each selected TF bin, the largest eigenvector of its spatial covariance matrix is extracted for clustering. A mixture density model is introduced to model the distribution of the eigenvectors, where each component distribution corresponds to one source and is parameterized by the source DOA. To use eigenvectors of all frequencies, the steering vectors of all frequencies of the sources are used in the distribution function. The DOAs of the sources can be estimated by maximizing the likelihood of the eigenvectors using an expectation-maximization (EM) algorithm. Simulation and experimental results show that the proposed approach significantly improves the root-mean-square error (RMSE) for DOA estimation of multiple speech sources compared to the MUSIC algorithm implemented on the single-source dominated TF bins and our previous clustering approach. © 2016 IEEE.

关键词： direction of arrival eigenvector clustering expectation-maximization microphone arrays spatial covariance

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A Decision Support Framework for the Choice of Languages and Methods for the Design of Real Time Embedded Systems

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Journal of Software engineering and Applications 2016年第7期9卷 353-397页

作者： Valéry M. Monthe Laurent Nana Georges E. Kouamou Claude Tangha Department of Computer Science University of Yaounde 1 Yaounde Cameroon Lab-STICC (UMR CNRS 6285) Department of Computer Science University of Brest Brest France Department of Computer Engineering National Advanced School of Engineering Yaounde Cameroon

The choice of methods or design languages is a crucial phase in the development of systems and software, also for real time and embedded systems. An open question that remains in the design of these types of systems is to build a method, or to choose one among those existing, capable to cover the life cycle of a project, and particularly the development phases. This article contributes to answer the question, by proposing an approach based on a multi-criteria comparative study, of few languages and methods dedicated to the design of real time and embedded systems. The underlying objective of this work is to present to designers a wide range of approaches, and elements that can guide their choices. In order to reach this goal, we propose different comparison criteria. Each criterion is divided into sub-criteria, so that the designers can refine their choices according to the qualities they prefer and wish to have in the method or language. We also define a rating scale which is used to assess the retained languages and methods. The scores obtained from this assessment are presented in tables, one table per criterion, followed by a summary table giving the overall scores. Graphics built from these tables are provided and intend to facilitate the judgement and thus the choice of the designers.

关键词： Design Method Modelling Language Real-Time System Embedded System Comparison Criteria

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

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arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi labella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States 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 Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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