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Single-shot Wide-field Optical Section Imaging

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arXiv 2021年

作者： Hu, Yuyao Liang, Dong Wang, Jing Xuan, Yaping Zhao, Fu Liu, Jun Li, Ruxin State Key Laboratory of High Field Laser Physics CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China University Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Physics Science and Engineering Tongji University Shanghai200093 China Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Optical sectioning technology has been widely used in various fluorescence microscopes owing to its background removing capability. Here, a virtual HiLo based on edge detection (V-HiLo-ED) is proposed to achieve wide-field optical sectioning, which requires only single wide-field image. Compared with conventional optical sectioning technologies, its imaging speed can be increased by at least twice, meanwhile maintaining nice optical sectioning performance, low cost, and excellent artifact suppression capabilities. Furthermore, the new V-HiLo-ED can also be extended to other non-fluorescence imaging fields. This simple, cost-effective and easy-to-extend method will benefit many research and application fields that needs to remove out-of-focus blurred images. © 2021, CC BY.

关键词： Edge detection

Discovery of Charge Order and Corresponding Edge State in Kagome Magnet FeGe

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Physical Review Letters 2022年第16期129卷 166401-166401页

作者： Jia-Xin Yin Yu-Xiao Jiang Xiaokun Teng Md. Shafayat Hossain Sougata Mardanya Tay-Rong Chang Zijin Ye Gang Xu M. Michael Denner Titus Neupert Benjamin Lienhard Han-Bin Deng Chandan Setty Qimiao Si Guoqing Chang Zurab Guguchia Bin Gao Nana Shumiya Qi Zhang Tyler A. Cochran Daniel Multer Ming Yi Pengcheng Dai M. Zahid Hasan Laboratory for Topological Quantum Matter and Advanced Spectroscopy (B7) Department of Physics Princeton University Princeton New Jersey 08544 USA Department of Physics and Astronomy Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Department of Physics National Cheng Kung University Tainan 70101 Taiwan Wuhan National High Magnetic Field Center & School of Physics Huazhong University of Science and Technology Wuhan 430074 China Department of Physics University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Laboratory for Quantum Emergence department of physics Southern University of Science and Technology Shenzhen Guangdong 518055 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 639798 Singapore Laboratory for Muon Spin Spectroscopy Paul Scherrer Institute CH-5232 Villigen PSI Switzerland Lawrence Berkeley National Laboratory Berkeley California 94720 USA Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA Quantum Science Center Oak Ridge Tennessee 37830 USA

Kagome materials often host exotic quantum phases, including spin liquids, Chern gap, charge density wave, and superconductivity. Existing scanning microscopy studies of the kagome charge order have been limited to nonkagome surface layers. Here, we tunnel into the kagome lattice of FeGe to uncover features of the charge order. Our spectroscopic imaging identifies a 2×2 charge order in the magnetic kagome lattice, resembling that discovered in kagome superconductors. Spin mapping across steps of unit cell height demonstrates the existence of spin-polarized electrons with an antiferromagnetic stacking order. We further uncover the correlation between antiferromagnetism and charge order anisotropy, highlighting the unusual magnetic coupling of the charge order. Finally, we detect a pronounced edge state within the charge order energy gap, which is robust against the irregular shape fluctuations of the kagome lattice edges. We discuss our results with the theoretically considered topological features of the kagome charge order including unconventional magnetism and bulk-boundary correspondence.

关键词： Charge density waves Density of states Edge states Electronic structure Topological materials

Dynamic resolution network

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arXiv 2021年

作者： Zhu, Mingjian Han, Kai Wu, Enhua Zhang, Qiulin Nie, Ying Lan, Zhenzhong Wang, Yunhe Zhejiang University China Huawei Noah’s Ark Lab State Key Lab of Computer Science ISCAS & University of Chinese Academy of Sciences China School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study University of Macau China BUPT.

Deep convolutional neural networks (CNNs) are often of sophisticated design with numerous learnable parameters for the accuracy reason. To alleviate the expensive costs of deploying them on mobile devices, recent works have made huge efforts for excavating redundancy in pre-defined architectures. Nevertheless, the redundancy on the input resolution of modern CNNs has not been fully investigated, i.e., the resolution of input image is fixed. In this paper, we observe that the smallest resolution for accurately predicting the given image is different using the same neural network. To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample. Wherein, a resolution predictor with negligible computational costs is explored and optimized jointly with the desired network. Specifically, the predictor learns the smallest resolution that can retain and even exceed the original recognition accuracy for each image. During the inference, each input image will be resized to its predicted resolution for minimizing the overall computation burden. We then conduct extensive experiments on several benchmark networks and datasets. The results show that our DRNet can be embedded in any off-the-shelf network architecture to obtain a considerable reduction in computational complexity. For instance, DR-ResNet-50 achieves similar performance with an about 34% computation reduction, while gaining 1.4% accuracy increase with 10% computation reduction compared to the original ResNet-50 on ImageNet. Code will be available at https://***/mindspore/models/tree/master/research/cv/DRNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

Online Carpooling using Expander Decompositions

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arXiv 2020年

作者： Gupta, Anupam Krishnaswamy, Ravishankar Kumar, Amit Singla, Sahil Computer Science Department Carnegie Mellon University United States Microsoft Research United States Department of Computer Science and Engineering Indian Institute of Technology Delhi India Department of Computer Science Princeton University School of Mathematics Institute for Advanced Study United States

We consider the online carpooling problem: given n vertices, a sequence of edges arrive over time. When an edge et = (ut , vt ) arrives at time step t, the algorithm must orient the edge either as vt → ut or ut → vt , with the objective of minimizing the maximum discrepancy of any vertex, i.e., the absolute difference between its in-degree and out-degree. Edges correspond to pairs of persons wanting to ride together, and orienting denotes designating the driver. The discrepancy objective then corresponds to every person driving close to their fair share of rides they participate in. In this paper, we design efficient algorithms which can maintain polylog(n, T) maximum discrepancy (w.h.p) over any sequence of T arrivals, when the arriving edges are sampled independently and uniformly from any given graph G. This provides the first polylogarithmic bounds for the online (stochastic) carpooling problem. Prior to this work, the best known bounds were O (Formula Presented)discrepancy for any adversarial sequence of arrivals, or O (loglog n)-discrepancy bounds for the stochastic arrivals when G is the complete graph. The technical crux of our paper is in showing that the simple greedy algorithm, which has provably good discrepancy bounds when the arriving edges are drawn uniformly at random from the complete graph, also has polylog discrepancy when G is an expander graph. We then combine this with known expander-decomposition results to design our overall algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

Ant based routing algorithm for balanced the load and optimized the AMNET lifetime

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AIP Conference Proceedings 2023年第1期2523卷

作者： H. Azath A. K. Velmurugan K. Padmanaban A. M. Senthil Kumar Murugan Subbiah 1School of Computing Science and Engineering VIT Bhopal University India. 2Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh-522502 India. 3Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh 522502 India 4Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 5Embedded System Vee Eee Technologies Chennai Tamil Nadu India

Mobile ad hoc networks (MANETs) are perfect for positions here lacking preset structure. Nowadays, may experience from partitioning network. This restriction builds MANETs incompatible for applications for example, crisis management and battleground communications. To deal with this weakness, an Autonomous Mobile Mesh Network (AMMNET) is used. AMMNET introduce a disseminated tracking the client clarification to handle with the active nature of client movement, and this approach for dynamic topology alteration in accordance with the movement sample of the clients. However, the significant problems in AMMNET are to utilize an energy-efficient routing approach to extend the network lifetime. This approach introduced Ant Based Routing Algorithm for balanced the load and Optimized the AMNET lifetime (ARAB). It utilizes a well-organized heuristic update technique to optimize the path concern. This algorithm using pheromone trail revises to balance the utilization of energy. As a result, minimize the energy utilization induced with the control overhead. This approach balances the energy between clusters and evades preliminary network partitioning. Our simulation outcomes specify that ARAB is healthy against partitioning and accomplished of offering high forwarder throughput for the mobile clients.

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Machine learning IoT based framework for analysing heart disease prediction

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AIP Conference Proceedings 2023年第1期2523卷

作者： A. K. Velmurugan K. Padmanaban A. M. Senthil Kumar H. Azath Murugan Subbiah 1Computer Science and Engineering KoneruLakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh-522502 India 2Computer Science and Engineering Vel Tech RangarajanDrSagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 3School of Computing Science and Engineering VIT Bhopal University India 4Vee Eee Technologies Chennai Tamil Nadu India

Coronary illness forecast is exceptionally fundamental in the present climate. Different explores have, as of now, been done to anticipate coronary illness from the huge dataset. IoT environment essentially produces information from various sensors and predicts the illness plausibility accordingly. Different synthetic dataset content diverse body boundaries extricated by explicit sensor esteem, the significant pretended by machine learning (ML) algorithm. This paper proposes coronary illness prediction with the mix of IoT and hybrid ML approach, which integrates the ANN with RNN. This examination has a sufficient degree for progress due to most boundaries, for example, increment record, blood vessel firmness, increment pressure, and so on. The proposed RNN gives better arrangement and infection expectation precision over the other ML calculations. This framework considered synthetic data, which is essentially used to anticipate coronary illness probability and prediction. The trial investigation shows the viability of the proposed methodology over other existing methods.

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Dialogue State Induction Using Neural Latent Variable Models

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arXiv 2020年

作者： Min, Qingkai Qin, Libo Teng, Zhiyang Liu, Xiao Zhang, Yue School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Technology Beijing Institute of Technology China

Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful dialogue states. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech processing

Network controllability of structural connectomes in the neonatal brain

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Nature Communications 2023年第1期14卷 5820页

作者： Sun, Huili Jiang, Rongtao Dai, Wei Dufford, Alexander J. Noble, Stephanie Spann, Marisa N. Gu, Shi Scheinost, Dustin Department of Biomedical Engineering Yale University New Haven 06520 CT United States Department of Radiology & Biomedical Imaging Yale School of Medicine New Haven 06510 CT United States Department of Biostatistics Yale School of Public Health New Haven 06510 CT United States Department of Psychiatry and Center for Mental Health Innovation Oregon Health & Science University Portland 97239 OR United States Department of Psychology Northeastern University Boston 02115 MA United States Department of Bioengineering Northeastern University Boston 02115 MA United States Center for Cognitive and Brain Health Northeastern University Boston United States Department of Psychiatry Vagelos College of Physicians and Surgeons Columbia University New York 10032 NY United States New York State Psychiatric Institute New York 10032 NY United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China Department of Statistics & Data Science Yale University New Haven 06520 CT United States Child Study Center Yale School of Medicine New Haven 06510 CT United States Wu Tsai Institute Yale University 100 College Street New Haven 06510 CT United States

White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant’s brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth. © 2023, Springer Nature Limited.

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Deep Learning Techniques for In-Crop Weed Identification: A Review

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arXiv 2021年

作者： Hu, Kun Wang, Zhiyong Coleman, Guy Bender, Asher Yao, Tingting Zeng, Shan Song, Dezhen Schumann, Arnold Walsh, Michael School of Computer Science The University of Sydney NSW2006 Australia School of Life and Environmental Sciences The University of Sydney NSW2006 Australia Australian Centre for Field Robotics The University of Sydney NSW2008 Australia College of Information Science and Technology Dalian Maritime University Dalian116000 China College of Mathematics and Computer Science Wuhan Polytechnic University Hubei430023 China Department of Computer Science and Engineering Texas A&M University College StationTX77843-3127 United States Citrus Research and Education Center University of Florida FL33850-2299 United States

Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem. © 2021, CC BY.

关键词： Weed control