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npj Ocean Sustainability 2024年第1期3卷 1-8页

作者： Laura Kaikkonen Rebecca J. Shellock Samiya Ahmed Selim Renis Auma Ojwala Beatriz S. Dias Sarah Mahadeo Shenghui Li Charles I. Addey Ignacio Gianelli Katherine M. Maltby Sara Garcia-Morales Juliano Palacios-Abrantes Ayodele Oloko Shan Jiang Marta Albo-Puigserver Virginia A. García Alonso Chelsey A. Baker Colleen B. Bove Stephanie Brodie Lol Iana Dahlet Jewel Das Sebastian C. A. Ferse Aislinn Dunne Ellen Johannesen Julia Jung Eugenia Merayo Garcia Denis B. Karcher Lucia Millan Kasali Oladepo Lawal Kelly Ortega-Cisneros Stephanie Otoabasi-Akpan Durlave Roy Samina Sharmin Rouf Szymon Smoliński Natasa Vaidianu Chris Whidden Mia Strand Finnish Environment Institute Helsinki Finland University of Helsinki Helsinki Finland Centre for Sustainable Development Reform Faculty of Law and Justice University of New South Wales Sydney NSW Australia Center for Sustainable Development University of Liberal Arts Bangladesh Dhaka Bangladesh World Maritime University-Sasakawa Global Ocean Institute Malmö Sweden Cooperative Institute for Climate Ocean and Ecosystem Studies University of Washington Seattle WA USA Guangdong Academy of Coastal Economic Belt Development Guangdong Ocean University Zhanjiang China Department of Oceanography University of Hawai’i at Manoa Honolulu HI USA EqualSea Lab-CRETUS. Department of Applied Economics Universidade de Santiago de Compostela Spain South American Institute for Resilience and Sustainability Studies (SARAS) Maldonado Uruguay Gulf of Maine Research Institute Portland ME USA Marine Environmental and Sciences Centre (MARE-ARNET) University of Lisbon Lisbon Portugal Ecology and Biodiversity Institute (IEB) Santiago Chile Institute for the Oceans and Fisheries The University of British Columbia Vancouver BC Canada State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai China Centro Oceanográfico de Baleares Instituto Español de Oceanografía (IEO-CSIC) Ecosystem Oceanography Group Palma Spain Departamento de Biodiversidad y Biología Experimental (DBBE) Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires AR Argentina National Oceanography Centre Marine Systems Modelling Southampton UK Ursinus College Biology Department Collegeville PA USA Environment Commonwealth Scientific and Industrial Research Organisation Brisbane QLD Australia Leibniz Centre for Tropical Marine Research (ZMT) Bremen Germany Instituto de Estudos Costeiros Universidade Federal do Pará (UFPA) Bragança Aldeia Brazil Institute of Marine Sciences University of Chittagong Chattogram Bangladesh Faculty for Fisheries and Marine Scien

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Optimal community structure for social contagions

arXiv

引用

arXiv 2018年

作者： Su, Zhen Wang, Wei Li, Lixiang Stanley, H. Eugene Braunstein, Lidia A. College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing MII Key Lab. of Computer Networks & Communications Chongqing400065 China Cybersecurity Research Institute Sichuan University Chengdu610065 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Center for Polymer Studies and Department of Physics Boston University BostonMA02215 United States Departamento de Física Facultad de Ciencias Exactas y Naturales Universidad Nacional de Mar del Plata-CONICET Funes 3350 Mar del Plata7600 Argentina

Community structure is an important factor in the behavior of real-world networks because it strongly affects the stability and thus the phase transition order of the spreading dynamics. We here propose a reversible social contagion model of community networks that includes the factor of social reinforcement. In our model an individual adopts a social contagion when the number of received units of information exceeds its adoption threshold. We use meanfield approximation to describe our proposed model, and the results agree with numerical simulations. The numerical simulations and theoretical analyses both indicate that there is a first-order phase transition in the spreading dynamics, and that a hysteresis loop emerges in the system when there is a variety of initially-adopted seeds. We find an optimal community structure that maximizes spreading dynamics. We also find a rich phase diagram with a triple point that separates the no-diffusion phase from the two diffusion phases. Copyright © 2018, The Authors. All rights reserved.

关键词： Structural optimization

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Deep ranking: Triplet MatchNet for music metric learning

Deep ranking: Triplet MatchNet for music metric learning

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Lu Kailun Wu Zhiyao Duan Changshui Zhang Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing China Department of Electrical and Computer Engineering University of Rochester NY USA

ISBN: (纸本)9781509041183

Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper, we propose a deep neural network named Triplet MatchNet to learn metrics directly from raw audio signals of triplets of music excerpts with human-annotated relative similarity in a supervised fashion. It has the advantage of learning highly nonlinear feature representations and metrics in this end-to-end architecture. Experiments on a widely used music similarity measure dataset show that our method significantly outperforms three state-of-the-art music metric learning methods. Experiments also show that the learned features better preserve the partial orders of the relative similarity than handcrafted features.

关键词： Metric learning music similarity deep learning convolutional neural networks

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Rating-boosted latent topics: Understanding users and items with ratings and reviews 25

Rating-boosted latent topics: Understanding users and items ...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Tan, Yunzhi Zhang, Min Liu, Yiqun Ma, Shaoping State Key Laboratory of Intelligent Technology and Systems Tsinghua National TNLIST Lab Department of Computer Science Tsinghua University Beijing100084 China

The performance of a recommendation system relies heavily on the feedback of users. Most of the traditional recommendation algorithms based only on historical ratings will encounter several difficulties given the problem of data sparsity. Users' feedback usually contains rich textual reviews in addition to numerical ratings. In this paper, we exploit textual review information, as well as ratings, to model user preferences and item features in a shared topic space and subsequently introduce them into a matrix factorization model for recommendation. To this end, the data sparsity problem is alleviated and good interpretability of the recommendation results is gained. Another contribution of this work is that we model the item feature distributions with rating-boosted reviews which combine textual reviews with user sentiments. Experimental results on 26 real-world datasets from Amazon demonstrate that our approach significantly improves the rating prediction accuracy compared with various state-of-the-art models, such as LFM, HFT, CTR and RMR models. And much higher improvement is achieved for users who have few ratings, which verifies the effectiveness of the proposed approach for sparse data. Moreover, our method also benefits much from reviews on top-N recommendation tasks.

关键词： Artificial intelligence

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Correction to: Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups

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European radiology 2019年第7期29卷 3957页

作者： En-Hong Zhuo Wei-Jing Zhang Hao-Jiang Li Guo-Yi Zhang Bing-Zhong Jing Jian Zhou Chun-Yan Cui Ming-Yuan Chen Ying Sun Li-Zhi Liu Hong-Min Cai School of Computer Science and Engineering South China University of Technology Guangzhou 510006 Guangdong Province People's Republic of China. State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center No. 651 Dongfeng East Road Yuexiu District Guangzhou Guangdong Province People's Republic of China. Department of Radiation Oncology Cancer Center First People's Hospital of Foshan Foshan 528000 Guangdong Province People's Republic of China. State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center No. 651 Dongfeng East Road Yuexiu District Guangzhou Guangdong Province People's Republic of China. liulizh@***. School of Computer Science and Engineering South China University of Technology Guangzhou 510006 Guangdong Province People's Republic of China. hmcai@***. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou People's Republic of China. hmcai@***.

The original version of this article, published on 14 March 2019, unfortunately contained a mistake.

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26 PFLOPS Stencil Computations for Atmospheric Modeling on Sunway TaihuLight

26 PFLOPS Stencil Computations for Atmospheric Modeling on S...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Yulong Ao Chao Yang Xinliang Wang Wei Xue Haohuan Fu Fangfang Liu Lin Gan Ping Xu Wenjing Ma University of Chinese Academy of Sciences China Chinese Academy of Sciences Institute of Software and State Key Laboratory of Computer Science China MOE Key Lab for Earth System Modeling. and Center for Earth System Science Tsinghua University China National Supercomputing Center in Wuxi Institute of Software Chinese Academy of Sciences Beijing CN

Stencil computation arises from a broad set of scientific and engineering applications and often plays a critical role in the performance of extreme-scale simulations. Due to the memory bound nature, it is a challenging task to optimize stencil computation kernels on modern supercomputers with relatively high computing throughput whilst relatively low data-moving capability. This work serves as a demonstration on the details of the algorithms, implementations and optimizations of a real-world stencil computation in 3D nonhydrostatic atmospheric modeling on the newly announced Sunway TaihuLight supercomputer. At the algorithm level, we present a computation-communication overlapping technique to reduce the inter-process communication overhead, a locality aware blocking method to fully exploit on-chip parallelism with enhanced data locality, and a collaborative data accessing scheme for sharing data among different threads. In addition, a variety of effective hardware specific implementation and optimization strategies on both the process and thread-level, from the fine-grained data management to the data layout transformation, are developed to further improve the performance. Our experiments demonstrate that a single-process many-core speedup of as high as 170x can be achieved by using the proposed algorithm and optimization strategies. The code scales well to millions of cores in terms of strong scalability. And for the weak-scaling tests, the code can scale in a nearly ideal way to the full system scale of more than 10 million cores, sustaining 25.96 PFLOPS in double precision, which is 20% of the peak performance.

关键词： Atmospheric modeling Instruction sets Supercomputers Optimization Three-dimensional displays computer architecture Mathematical model

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Few-example object detection with model communication

arXiv

引用

arXiv 2017年

作者： Dong, Xuanyi Zheng, Liang Ma, Fan Yang, Yi Meng, Deyu Centre for Artificial Intelligence University of Technology SydneyNSW Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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7.3: Compare Variation of User's EEG Owing to Parallax Cue

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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An intra-body communication research platform based on virtual instrument

An intra-body communication research platform based on virtu...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Lin Shi Gao Yueming Zhang Hengfei Lu?ev Vasi? ?eljka Vai Mang-I Du Min Cifrek Mario Pun Sio-Hang College of Physics and Information Engineering Fuzhou University Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province Faculty of Electrical Engineering and Computing University of Zagreb State Key Laboratory of Analog and Mixed Signal VLSI University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Key Lab of Eco-industrial Green Technology of Fujian Province

An intra-body communication(IBC) research platform based on virtual instrument was established for the hardware design of IBC transceiver. Firstly, the feasibility of constructing IBC research platform using optic-coupling circuit was verified via comparison of three kinds of measurement methods based on virtual instrument. Then, ASK, FSK and BPSK modulation-demodulation were realized via lab VIEW programming, and user interface of the platform was also designed. The platform could achieve signal transmission or recover under different coupling type, different channel distance and different modulation-demodulation method, etc., by means of dynamic adjusting of platform parameters. Experiment results of capacitive-coupling under the platform indicated the authenticity and feasibility of signal transmission. Finally, a full-floating IBC transceiver was designed aided by the parameters provided by the research platform under the scene of capacitive-coupling, FSK modulation and 180 cm channel distance. Moreover, Bit Error Rate(BER) of the transceiver was less than 1 e-5, which meant that it is effective and convenient for the design of IBC transceiver aided by virtual instrument platform.

关键词： Intra-body Communication(IBC) virtual instrument transceiver modulation-demodulation

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