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Energetic electron precipitation driven by electromagnetic ion cyclotron waves from ELFIN’s low altitude perspective

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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same mlT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

Creating and manipulating a Laughlin-type ν = 1/3 fractional quantum Hall state on a quantum computer with linear depth circuits

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arXiv 2020年

作者： Rahmani, Armin Sung, Kevin J. Putterman, Harald Roushan, Pedram Ghaemi, Pouyan Jiang, Zhang Department of Physics and Astronomy and Advanced Materials Science and Engineering Center Western Washington University BellinghamWA98225 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Google AI Quantum Santa BarbaraCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Physics Department City College of the City University of New York New YorkNY10031 United States Graduate Center of the City University of New York New YorkNY10016 United States

Here we present an efficient quantum algorithm to generate an equivalent many-body state to Laughlin’s ν = 1/3 fractional quantum Hall state on a digitized quantum computer. Our algorithm only uses quantum gates acting on neighboring qubits in a quasi one-dimensional setting, and its circuit depth is linear in the number of qubits, i.e., the number of Landau orbitals in the second quantized picture. We identify correlation functions that serve as signatures of the Laughlin state and discuss how to obtain them on a quantum computer. We also discuss a generalization of the algorithm for creating quasiparticles in the Laughlin state. This paves the way for several important studies, including quantum simulation of non-equilibrium dynamics and braiding of quasiparticles in quantum Hall states. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

Vertebrae segmentation from X-ray images using convolutional neural network 2018

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Vertebrae segmentation from X-ray images using convolutional...

1st International Conference on Information Hiding and Image Processing, IHIP 2018

作者： Fu, Min-Jun Lin, Chii-Jen Sun, Yung-Nien Kuok, Chan-Pang Horng, Ming-Huwi Institute of Medical Informatics National Cheng Kung University Tainan Taiwan Department of Orthopedics National Cheng Kung University Hospital Tainan Taiwan MOST AI Biomedical Research Center Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan

ISBN: (纸本)9781450365468

The X-ray examination can effectively help for the diagnosis and analysis of spinal diseases because it possesses the properties of fast, non-invasive, low radiation dose and low cost. In order to obtain the valuable quantitative information of the spine, the automated computer aided tools are developed. The segmentation of vertebrae is an important step to analyze the disease severity from the spinal X-ray images. Although there were numerous studies for automatic vertebrae segmentation in the literature, the segmentation of vertebrae from the frontal X-ray images is still a challenging topic. In this paper, a hybrid method is proposed to segment the thoracic and lumbar vertebrae from an anteroposterior (AP) full spine X-ray image. We apply image processing techniques to detect the vertebral regions and then use the convolutional neural network (CNN) to segment the vertebrae. The segmentation performance using the proposed method is remarkably high with DSC value of 0.941. © 2018 Association for Computing Machinery.

关键词： Diagnosis

Dynamic class learning approach for smart CBIR 6th

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Dynamic class learning approach for smart CBIR

6th National Conference on computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017

作者： Pahariya, Girraj Ravindran, Balaraman Das, Sukhendu Department of Computer Science and Engineering IIT Madras Chennai India Robert Bosch Centre for Data Science and AI IIT Madras Chennai India Visualization and Perception Lab IIT Madras Chennai India

ISBN: (纸本)9789811300196

Smart Content Based Image Retrieval (CBIR) helps to simultaneously localize and recognize all object(s) present in a scene, for image retrieval task. The major drawbacks in such kind of system are: (a) overhead for addition of new class is high - addition of new class requires manual annotation of large number of samples and retraining of an entire object model;and (b) use of handcrafted features for recognition and localization task, which limits its performance. In this era of data proliferation where it is easy to discover new object categories and hard to label all of them i.e. less amount of labeled samples for training which raises the above mentioned drawbacks. In this work, we propose an approach which cuts down the overhead of labelling the data and re-training on an entire module to learn new classes. The major components in proposed framework are: (a) selection of an appropriate pre-trained deep model for learning a new class;and (b) learning new class by utilizing selected deep model with less supervision (i.e. with the least amount of labeled data) using a concept of triplet learning. To show the effectiveness of the proposed technique of new class learning, we have performed an evaluation on CIFAR-10, PASCAL VOC2007 and Imagenet datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Content based retrieval

Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification

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Attribute-Driven Feature Disentangling and Temporal Aggregat...

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Yiru Zhao Xu Shen Zhongming Jin Hongtao Lu Xian-sheng Hua Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Alibaba Damo Academy Alibaba Group

ISBN: (纸本)9781728132945

Video-based person re-identification plays an important role in surveillance video analysis, expanding image-based methods by learning features of multiple frames. Most existing methods fuse features by temporal average-pooling, without exploring the different frame weights caused by various viewpoints, poses, and occlusions. In this paper, we propose an attribute-driven method for feature disentangling and frame re-weighting. The features of single frames are disentangled into groups of sub-features, each corresponds to specific semantic attributes. The sub-features are re-weighted by the confidence of attribute recognition and then aggregated at the temporal dimension as the final representation. By means of this strategy, the most informative regions of each frame are enhanced and contributes to a more discriminative sequence representation. Extensive ablation studies verify the effectiveness of feature disentangling as well as temporal re-weighting. The experimental results on the iLIDS-VID, PRID-2011 and MARS datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches.

关键词： Frame FRAMES Persons Videotapes Occlusion confidence Single frame

A review on generative adversarial networks: Algorithms, theory, and applications

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arXiv 2020年

作者： Gui, Jie Sun, Zhenan Wen, Yonggang Tao, Dacheng Ye, Jieping Department of Computational Medicine and Bioinformatics University of Michigan United States Center for Research on Intelligent Perception and Computing Chinese Academy of Sciences Beijing100190 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore UBTECH Sydney Artificial Intelligence Center School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney Australia DiDi AI Labs China University of Michigan Ann Arbor United States

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

Asymmetric Cross-Guided Attention Network for Actor and Action Video Segmentation From Natural Language Query

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Asymmetric Cross-Guided Attention Network for Actor and Acti...

International Conference on computer Vision (ICCV)

作者： Hao Wang Cheng Deng Junchi Yan Dacheng Tao School of Electronic Engineering Xidian University Xi’an China Tencent AI Lab Shenzhen China Department of CSE and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Australia

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Actor and action video segmentation from natural language query aims to selectively segment the actor and its action in a video based on an input textual description. Previous works mostly focus on learning simple correlation between two heterogeneous features of vision and language via dynamic convolution or fully convolutional classification. However, they ignore the linguistic variation of natural language query and have difficulty in modeling global visual context, which leads to unsatisfactory segmentation performance. To address these issues, we propose an asymmetric cross-guided attention network for actor and action video segmentation from natural language query. Specifically, we frame an asymmetric cross-guided attention network, which consists of vision guided language attention to reduce the linguistic variation of input query and language guided vision attention to incorporate query-focused global visual context simultaneously. Moreover, we adopt multi-resolution fusion scheme and weighted loss for foreground and background pixels to obtain further performance improvement. Extensive experiments on Actor-Action Dataset Sentences and J-HMDB Sentences show that our proposed approach notably outperforms state-of-the-art methods.

关键词： Feature extraction Visualization Linguistics Convolution Dogs Task analysis

Modeling named entity embedding distribution into hypersphere

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arXiv 2019年

作者： Zhang, Zhuosheng Tang, Bingjie Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Computer Science Department Brown University RI United States

This work models named entity distribution from a way of visualizing topological structure of embedding space, so that we make an assumption that most, if not all, named entities (NEs) for a language tend to aggregate together to be accommodated by a specific hypersphere in embedding space. Thus we present a novel open definition for NE which alleviates the obvious drawback in previous closed NE definition with a limited NE dictionary. Then, we show two applications with introducing the proposed named entity hypersphere model. First, using a generative adversarial neural network to learn a transformation matrix of two embedding spaces, which results in a convenient determination of named entity distribution in the target language, indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces. Second, the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-theart results. Only assuming that embeddings are available, we show a prior knowledge free approach on effective named entity distribution depiction. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Subword ELMo

arXiv 2019年

作者： Li, Jiangtong Zhao, Hai Li, Zuchao Bi, Wei Liu, Xiaojiang Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Tencent AI Lab Shenzhen China

Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models. However, the character is an insufficient and unnatural linguistic unit for word representation. Thus we introduce Embedding from Subword-aware Language Models (ESuLMo) which learns word representation from subwords using unsupervised segmentation over words. We show that ESuLMo can enhance four benchmark NLP tasks more effectively than ELMo, including syntactic dependency parsing, semantic role labeling, implicit discourse relation recognition and textual entailment, which brings a meaningful improvement over ELMo. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings