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arXiv 2023年

作者： Wei, Jun Hu, Yiwen Cui, Shuguang Zhou, S. Kevin Li, Zhen FNii CUHK-Shenzhen Shenzhen China SSE CUHK-Shenzhen Shenzhen China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China South China Hospital Shenzhen University Shenzhen China

Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations are much cheaper and more accessible. Thus, to reduce labeling cost, we propose to learn a weakly supervised polyp segmentation model (i.e.,WeakPolyp) completely based on bounding box annotations. However, coarse bounding boxes contain too much noise. To avoid interference, we introduce the mask-to-box (M2B) transformation. By supervising the outer box mask of the prediction instead of the prediction itself, M2B greatly mitigates the mismatch between the coarse label and the precise prediction. But, M2B only provides sparse supervision, leading to non-unique predictions. Therefore, we further propose a scale consistency (SC) loss for dense supervision. By explicitly aligning predictions across the same image at different scales, the SC loss largely reduces the variation of predictions. Note that our WeakPolyp is a plug-and-play model, which can be easily ported to other appealing backbones. Besides, the proposed modules are only used during training, bringing no computation cost to inference. Extensive experiments demonstrate the effectiveness of our proposed WeakPolyp, which surprisingly achieves a comparable performance with a fully supervised model, requiring no mask annotations at all. Codes are available at https://***/weijun88/WeakPolyp. © 2023, CC BY.

关键词： Forecasting

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Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

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Nano research 2021年第7期14卷 2133-2138页

作者： Hanchen Wang Jilei Chen Tao Yu Chuanpu Liu Chenyang Guo Song Liu Ka Shen Hao Jia Tao Liu Jianyu Zhang Marco A.Cabero Z Qiuming Song Sa Tu Mingzhong Wu Xiufeng Han Ke Xia Dapeng Yu Gerrit E.W.Bauer Haiming Yu Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Max Planck Institute for the Structure and Dynamics of Matter 22761 HamburgGermany Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100190China Shenzhen Institute for Quantum Science and Engineering(SIQSE) and Department of PhysicsSouthern University of Science and Technology(SUSTech)Shenzhen 518055China Department of Physics Beijing Normal UniversityBeijing 100875China Department of Physics Colorado State UniversityFort CollinsColorado 80523USA Kavli Institute of Nanoscience Delft University of Technology2628 CJ DelftThe Netherlands Institute for Materials Research WPI-AIMR and CSNRTohoku UniversitySendai 980-8577Japan Zernike Institute for Advanced Materials University of GroningenGroningenNijenborgh 49747 AG GroningenThe Netherlands

Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two distant nanomagnets via spin waves remains a major challenge for *** we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm *** in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave *** coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian *** results are attractive for analog neuromorphic computing that requires unidirectional information transmission.

关键词： spintronics nanomagnets spin waves coherent coupling nonreciprocity

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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Recent Trends in Computer Science and Technology (ICRTCST), International Conference on

作者： S. RadhaMahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Computer and Information Research Scientist Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. The interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. The atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize The model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. The results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins.

关键词： Proteins Drugs Computer science Accuracy Convolution Machine learning Predictive models

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Single-shot mid-infrared incoherent holography using Lucy-Richardson-Rosen algorithm

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Opto-Electronic Science 2022年第3期1卷 1-8,I0001页

作者： Vijayakumar Anand Molong Han Jovan Maksimovic Soon Hock Ng Tomas Katkus Annaleise Klein Keith Bambery Mark J.Tobin Jitraporn Vongsvivut Saulius Juodkazis Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials(SEAM) School of ScienceComputing and Engineering TechnologiesOptical Sciences CenterSwinburne University of TechnologyHawthornMelbourneVictoria 3122Australia Institute of Physics University of Tartu50411 TartuEstonia Infrared Microspectroscopy(IRM)Beamline ANSTO-Australian SynchrotronClaytonVictoria 3168Australia Tokyo Tech World Research Hub Initiative(WRHI) School of Materials and Chemical TechnologyTokyo Institute of Technology2-12-1OokayamaMeguro-kuTokyo 152-8550Japan

In recent years,there has been a significant transformation in the field of incoherent imaging with new possibilities of compressing three-dimensional(3D)information into a two-dimensional intensity distribution without two-beam interference(TBI).Most of the incoherent 3D imagers without TBI are based on scattering by a random phase mask exhibiting sharp autocorrelation and low cross-correlation along the ***,during reconstruction,high lateral and axial resolutions are *** based on scattering requires an astronomical photon budget and is therefore precluded in many power-sensitive *** this study,a proof-of-concept 3D imaging method without TBI using deterministic fields has been demonstrated.A new reconstruction method called the Lucy-Richardson-Rosen algorithm has been developed for this imaging *** believe that the proposed approach will cause a paradigm-shift in the current state-of-the-art incoherent imaging,fluorescence microscopy,mid-infrared fingerprinting,astronomical imaging,and fast object recognition applications.

关键词： imaging holography mid-infrared spectroscopy incoherent optics computational optics mid-infrared imaging

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A New Global Solar-induced Chlorophyll Fluorescence(SIF)Data Product from TanSat Measurements

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Advances in Atmospheric Sciences 2021年第3期38卷 341-345页

作者： Lu YAO Dongxu YANG Yi LIU Jing WANG Liangyun LIU Shanshan DU Zhaonan CAI Naimeng LU Daren LYU Maohua WANG Zengshan YIN Yuquan ZHENG Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China Changchun Institute of Optics Fine Mechanics and PhysicsChangchun 130033China Shanghai Engineering Center for Microsatellites Shanghai 201210China

The Chinese Carbon Dioxide Observation Satellite Mission(TanSat)is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence(SIF)signals with its advanced technical *** on the institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing(IAPCAS)platform,we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based *** paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps.A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD(singular value decomposition)SIF product is also performed for follow-up algorithm *** comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all *** future SIF data product applications and requirements for SIF space observation are discussed.

关键词： TanSat solar-induced chlorophyll fluorescence retrieval algorithm remote sensing

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How to use open-pFind in deep proteomics data analysis?—A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data

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Biophysics Reports 2021年第3期7卷 207-226页

作者： Guangcan Shao Yong Cao Zhenlin Chen Chao Liu Shangtong Li Hao Chi Meng-Qiu Dong School of Life Sciences Peking University National Institute of Biological Sciences Beijing Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CASInstitute of Computing TechnologyCAS University of Chinese Academy of Sciences Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang University

High-throughput proteomics based on mass spectrometry（MS） analysis has permeated biomedical science and propelled numerous research projects. p Find 3 is a database search engine for high-speed and in-depth proteomics data analysis. p Find 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using p Find 3 in conjunction with p Parse for data pre-processing and if needed, p Quant for quantitation. This streamlined p Parse-p Findp Quant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on15N-labeling, SILAC-labeling, or TMT/i TRAQ labeling.

关键词： Mass spectrometry Protein identification Search engine Open-pFind Quantitation

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Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

Communication Resources Constrained Hierarchical Federated L...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wei-Bin Kou Shuai Wang Guangxu Zhu Bin Luo Yingxian Chen Derrick Wing Kwan Ng Yik-Chung Wu Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Damo Institute Alibaba China School of Electrical Engineering and Telecommunications the University of New South Wales Australia

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

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Generation of Schrödinger Cat States with Wigner Negativity Using a Low-Loss Continuous-Wave Optical Parametric Amplifier

Generation of Schrödinger Cat States with Wigner Negativity...

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Kawasaki, Akito Takase, Kan Jeong, Byung Kyu Kashiwazaki, Takahiro Kazama, Takushi Enbutsu, Koji Watanabe, Kei Umeki, Takeshi Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Asavanant, Warit Endo, Mamoru Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

We observed Wigner negativity of Schrödinger cat states generated with a low-loss continuous-wave optical parametric amplifier, paving the way toward fault-tolerant and universal quantum computation with terahert... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical parametric amplifiers

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Generation of Schrödinger Cat States with Wigner Negativity Using a Low-Loss Continuous-Wave Optical Parametric Amplifier

Generation of Schrödinger Cat States with Wigner Negativity...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Akito Kawasaki Kan Takase Byung Kyu Jeong Takahiro Kashiwazaki Takushi Kazama Koji Enbutsu Kei Watanabe Takeshi Umeki Shigehito Miki Hirotaka Terai Masahiro Yabuno Fumihiro China Warit Asavanant Mamoru Endo Jun-ichi Yoshikawa Akira Furusawa Department of Applied Physics School of Engineering The University of Tokyo Tokyo Japan NTT Device Technology Labs NTT Corporation Kanagawa Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Hyogo Japan Graduate School of Engineering Kobe University Hyogo Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing Saitama Japan

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Fault tolerance Quantum computing Stimulated emission Fault tolerant systems Lasers and electrooptics Optical amplifiers Nonlinear optics

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