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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yu Li Tao Wang Bingyi Kang Sheng Tang Chunfeng Wang Jintao Li Jiashi Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We find existing detection methods are unable to model few-shot classes when the dataset is extremely skewed, which can result in classifier imbalance in terms of parameter magnitude. Directly adapting long-tail classification models to detection frameworks can not solve this problem due to the intrinsic difference between detection and classification. In this work, we propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. It implicitly modulates the training process for the head and tail classes and ensures they are both sufficiently trained, without requiring any extra sampling for the instances from the tail classes. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors with various backbones and frameworks on both object detection and instance segmentation. It beats all state-of-the-art methods transferred from long-tail image classification and establishes new state-of-the-art. Code is available at https://***/FishYuLi/BalancedGroupSoftmax.

关键词： Training Object detection Proposals Adaptation models Feature extraction Computational modeling Detectors

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Observation of the out-of-plane polarized spin current from CVD grown WTe2

arXiv

引用

arXiv 2021年

作者： Shi, Shuyuan Li, Jie Hsu, Chuang-Han Lee, Kyusup Wang, Yi Yang, Li Wang, Junyong Wang, Qisheng Wu, Hao Zhang, Wenfeng Eda, Goki Liang, Gengchiau Chang, Haixin Yang, Hyunsoo Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Quantum-Nano Matter and Device Lab State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Physics Academia Sinica Taipei11529 Taiwan Key Laboratory of Materials Modification by Laser Ion and Electron Beams Ministry of Education Dalian University of Technology Dalian116024 China Centre for Advanced 2D Materials National University of Singapore 6 Science Drive 2 117546 Singapore Department of Physics National University of Singapore 117542 Singapore

Weyl semimetal Td-phase WTe2 possesses the spin-resolved band structure with strong spin-orbit coupling, holding promises as a useful spin source material. The noncentrosymmetric crystalline structure of Td-WTe2 endows the generation of the out-of-plane polarized spin, which is of great interest in magnetic memory applications. Previously, WTe2 was explored in spin devices based on mechanically exfoliated single crystal flakes with a size of micrometers. For practical spintronics applications, it is highly desirable to implement wafer-scale thin films. In this work, we utilize centimeter-scale chemical vapor deposition (CVD)-grown Td-WTe2 thin films and study the spin current generation by the spin torque ferromagnetic resonance technique. We find the in-plane and out-of-plane spin conductivities of 7.36 × 103 (ħ/2e) (Ωm)–1 and 1.76 × 103 (ħ/2e) (Ωm)–1, respectively, in CVD-growth 5 nm-WTe2. We further demonstrate the current induced magnetization switching in WTe2/NiFe at room temperature in the domain wall motion regime, which may invigorate potential spintronic device innovations based on Weyl semimetals. Copyright © 2021, The Authors. All rights reserved.

关键词： Thin films

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Sudden polarization angle jumps of the repeating fast radio burst FRB 20201124A

arXiv

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arXiv 2024年

作者： Niu, Jiarui R. Wang, W.Y. Jiang, J.C. Qu, Y. Zhou, D.J. Zhu, W.W. Lee, K.J. Han, J.L. Zhang, B. Li, D. Cao, S. Fang, Z.Y. Feng, Y. Fu, Q.Y. Jiang, P. Jing, W.C. Li, J. Li, Y. Luo, R. Meng, L.Q. Miao, C.C. Miao, X.L. Niu, C.H. Pan, Y.C. Wang, B.J. Wang, F.Y. Wang, H.Z. Wang, P. Wu, Q. Wu, Z.W. Xu, H. Xu, J.W. Xu, L. Xue, M.Y. Yang, Y.P. Yuan, M. Yue, Y.L. Zhao, D. Zhang, C.F. Zhang, D.D. Zhang, J.S. Zhang, S.B. Zhang, Y.K. Zhu, Y.H. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China The Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Astronomy Peking University Beijing100871 China Yunnan Observatories Chinese Academy of Sciences P.O.Box110 Yunnan Kunming650011 China Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District Zhejiang Province Hangzhou China School of Computer Science and Engineering Beihang University Beijing100191 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Department of Astronomy School of Physics and Materials Science Guangzhou University Guangzhou510006 China Institute of Astrophysics Central China Normal University Wuhan430079 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education China Lanzhou University Lanzhou730000 China Faculty of Electrical engineering and Computer Science Ningbo University Ningbo315211 China Pengcheng Laboratory Shenzhen518055 China South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550025 China

We report the first detection of polarization angle (PA) orthogonal jumps, a phenomenon previously only observed from radio pulsars, from a fast radio burst (FRB) source FRB 20201124A. We find three cases of orthogonal jumps in over two thousand bursts, all resembling those observed in pulsar single pulses. We propose that the jumps are due to the superposition of two orthogonal emission modes that could only be produced in a highly magnetized plasma, and they are caused by the line of sight sweeping across a rotating magnetosphere. The shortest jump timescale is of the order of one-millisecond, which hints that the emission modes come from regions smaller than the light cylinder of most pulsars or magnetars. This discovery provides convincing evidence that FRB emission originates from the complex magnetosphere of a magnetar, suggesting an FRB emission mechanism that is analogous to radio pulsars despite a huge luminosity difference between two types of objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Polarization

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Opacity of Nondeterministic Transition Systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

引用

作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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二维材料最新研究进展

引用

物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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THREE DIMENSIONAL BLIND IMAGE DECONVOLUTION FOR FLUORESCENCE MICROSCOPY USING GENERATIVE ADVERSARIAL NETWORKS

arXiv

引用

arXiv 2019年

作者： Lee, Soonam Han, Shuo Salama, Paul Dunn, Kenneth W. Delp, Edward J. Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN United States Department of Electrical and Computer Engineering Indiana University Indianapolis IN United States Division of Nephrology School of Medicine Indiana University

Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3-way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

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Machine learning in the air

arXiv

引用

arXiv 2019年

作者： Gundüz, Deniz De Kerret, Paul Sidiropoulos, Nicholas D. Gesbert, David Murthy, Chandra Van Der Schaar, Mihaela Information Processing and Communications Laboratory Department of Electrical and Electronics Engineering Imperial College London London United Kingdom Communication Systems Department EURECOM Sophia Antipolis France Department of Electrical and Computer Engineering University of Virginia Charlottesville VA United States Department of Electrical and Computer Engineering Indian Institute of Science Bangalore India

Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications. Copyright © 2019, The Authors. All rights reserved.

关键词： Intelligent buildings

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Information Transmission Through Solids Using Ultrasound Invited Paper

Information Transmission Through Solids Using Ultrasound Inv...

引用

2018 IEEE International Ultrasonics Symposium, IUS 2018

作者： Saniie, Jafar Wang, Boyang Huang, Xin Department of Electrical and Computer Engineering Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory ChicagoIL United States

ISBN: (纸本)9781538634257

Ultrasonic signals can be utilized as a viable communication method to transmit information through gas, liquid, and solid channels or a mixed media consisting of a solid interfaced with liquid and/or gas. Underwater communication channels using sound waves cause many challenges, often unforeseen, due to absorption, scattering, refractions, reverberations, multipaths, doppler shifts, temperature, salinity, and acoustic scintillation. Ultrasonic communication through the air is less prone to the environmental challenges that are often encountered in the underwater communications. Ultrasonic communication in solid channels is also adversely affected by absorption, scattering, refractions, reverberations, beam skewing, dispersion, mode conversation, multipaths, and above all these challenges are compounded by the geometrical structure of solids and type of ultrasonic waves. With solid structures many different ultrasonic wave types can be generated including Longitudinal (Compressional), Shear (Transverse), Surface-Rayleigh, Plate-Lamb (Symmetrical or Extensional Mode and Asymmetric or Flexural Mode), Plate-Love, and Stoneley (Leaky Rayleigh). The type of waves is governed by the frequency and angle of the incident waves on a solid structure where the quality of the signal for communication is limited by the composition and geometrical shape of the solids. For this study we have developed a testbed platform using the ZYNQ SoC (System-on-Chip) FPGA by Xilinx which offers reconfigurability and high-performance computational capability, high speed signal converters, power amplifiers for excitation of transducers, low noise receiving amplifiers and high frequency ultrasonic transducers for transmitting and receiving information. Theoretically, ASK (amplitude shift keying) or any form of digital modulation can be realized with the system using the concept of the software-defined ultrasonic communications (SDUC). In practice, the received signal is very complex, primarily ca

关键词： Underwater acoustics

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Target localization with jammer removal using frequency diverse array

arXiv

引用

arXiv 2019年

作者： Liu, Qi Xu, Jingwei Ding, Zhi So, Hing Cheung Department of Electrical and Computer Engineering National University of Singapore Singapore National Laboratory of Radar Signal Processing Xidian University Xi'an Shaanxi710071 China Department of Electrical and Computer Engineering University of California at Davis DavisCA95616 United States Department of Electrical Engineering City University of Hong Kong Hong Kong

A foremost task in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is to efficiently obtain the target signal in the presence of interferences. In this paper, we employ a novel"low-rank + low-rank + sparse" decomposition model to extract the low-rank desired signal and suppress the jamming signals from both barrage and burst jammers. In the literature, the barrage jamming signals, which are intentionally interfered by enemy jammer radar, are usually assumed Gaussian distributed. However, such assumption is oversimplified to hold in practice as the interferences often exhibit non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive noise or burst jamming, are involuntarily deviated from friendly radar or other working radio equipment including amplifier saturation and sensor failures, thunderstorms and man-made noise. The estimation performance of the existing estimators, relied crucially on the Gaussian noise assumption, may degrade substantially since the probability density function (PDF) of burst jamming has heavier tails that exceed a few standard deviations than the Gaussian distribution. To capture a more general signal model with burst jamming in practice, both barrage jamming and burst jamming are included and a two-step"Go Decomposition" (GoDec) method via alternating minimization is devised for such mixed jamming signal model, where the a priori rank information is exploited to suppress two kinds of jammers and estimate the desired target. Simulation results verify the robust performance of the devised scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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