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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Xiao Li Wenteng Liang Yifeng Li Yuxuan Zhao Zhizheng Zhang Hengguang Yang Dispatch Center State Grid Jiangsu Electric Power Co. Ltd. Nanjing China School of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Taishan Polytechnic Taian China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

The recognition of entities is the basis of knowledge graph construction. Deep neural network models is currently the most effective and efficient way of entity recognition. Among them, a pop method is to apply the long short-term memory networks to model the reliance on sequences and use Conditional Random Field to model the dependencies of sequence outputs. However, due to the dense distribution of entities in current power grid corpus, problems such as low recognition accuracy and incorrect division of solid boundaries gradually arose in previous models. To address the issues, this paper propose an approach where the power grid corpus data is preprocessed based on the idea of RoBERTa and then train the word embedding model. The experimental results show that the results of word embedding model have practical potential.

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A workflow for segmenting soil and plant X-ray CT images with deep learning in Googles Colaboratory

arXiv

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arXiv 2022年

作者： Rippner, Devin A. Raja, Pranav Earles, J. Mason Buchko, Alexander Momayyezi, Mina Duong, Fiona Parkinson, Dilworth Forrestel, Elizabeth Shackel, Ken Neyhart, Jeffrey McElrone, Andrew J. United States Department of Agriculture-Agricultural Research Service Horticultural Crops Research Unit ProsserWA United States Department of Crop and Soil Sciences Washington State University ProsserWA United States Department of Biological and Agricultural Engineering University of California-Davis DavisCA United States Department of Viticulture and Enology University of California-Davis DavisCA United States Department of Computer Science California Polytechnic and State University San Luis ObispoCA United States Department of Integrative Biology San Francisco State University San FranciscoCA United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Plant Sciences University of California-Davis DavisCA United States United States Department of Agriculture-Agricultural Research Service Genetic Improvement for Fruits & Vegetables Laboratory ChatsworthNJ United States United States Department of Agriculture-Agricultural Research Service Crops Pathology and Genetics Research Unit DavisCA United States

X-ray micro-computed tomography (X-ray µCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray µCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences. © 2022, CC BY.

关键词： Soils

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RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

arXiv

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arXiv 2021年

作者： Tang, Shiyu Gong, Ruihao Wang, Yan Liu, Aishan Wang, Jiakai Chen, Xinyun Yu, Fengwei Liu, Xianglong Song, Dawn Yuille, Alan Torr, Philip H.S. Tao, Dacheng The State Key Lab of Software Development Environment Beihang University Beijing100191 China The SenseTime Beijing100191 China The Berkeley Artificial Intelligence Research Lab University of California Berkeley BerkeleyCA94701 United States The Department of Cognitive Science and Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom The JD Explore Academy Beijing101111 China

Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the benchmark of model robustness. Existing benchmarks mainly focus on evaluating defenses, but there are no comprehensive studies of how architecture design and training techniques affect robustness. Comprehensively benchmarking their relationships is beneficial for better understanding and developing robust DNNs. Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e.g., data augmentation) towards diverse noises (adversarial, natural, and system noises). Extensive experiments substantiated several insights for the first time, e.g., (1) adversarial training is effective for the robustness against all noises types for Transformers and MLP-Mixers;(2) given comparable model sizes and aligned training settings, CNNs > Transformers > MLP-Mixers on robustness against natural and system noises;Transformers > MLP-Mixers > CNNs on adversarial robustness;(3) for some light-weight architectures, increasing model sizes or using extra data cannot improve robustness. Our benchmark http://***/presents: (1) an open-source platform for comprehensive robustness evaluation;(2) a variety of pre-trained models to facilitate robustness evaluation;and (3) a new view to better understand the mechanism towards designing robust DNNs. We will continuously develop to this ecosystem for the community. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Corrigendum to “Physical workplaces and human well-being: A mixed-methods study to quantify the effects of materials, windows, and representation on biobehavioral outcomes” [Building and Environment 224 (2022) 109516]

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Building and Environment 2024年 261卷

作者： Isabella P. Douglas Elizabeth L. Murnane Lucy Zhang Bencharit Basma Altaf Jean Marcel dos Reis Costa Jackie Yang Meg Ackerson Charu Srivastava Michael Cooper Kyle Douglas Jennifer King Pablo E. Paredes Nicholas P. Camp Matthew Louis Mauriello Nicole M. Ardoin Hazel Rose Markus James A. Landay Sarah L. Billington Department of Civil & Environmental Engineering Stanford University USA School of Engineering Dartmouth College USA Department of Psychology and Child Development California Polytechnic State University USA Department of Computer Science Stanford University USA Institute for Human-Centered Artificial Intelligence Stanford University USA Psychiatry & Behavioral Sciences Department Stanford University USA Department of Psychology Stanford University USA SPARQ Stanford University USA Department of Computer and Information Sciences University of Delaware USA School of Education Stanford University USA Woods Institute for the Environment Stanford University USA

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Adapting the Re-ID Challenge for Static Sensors

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IET computer Vision 2025年第1期19卷

作者： Sundaresan, Avirath Parham, Jason Crall, Jonathan Warungu, Rosemary Muthami, Timothy Miliko, Jackson Mwangi, Margaret Holmberg, Jason Berger-Wolf, Tanya Rubenstein, Daniel Stewart, Charles Beery, Sara Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States Kitware Clifton ParkNY United States Laikipia Zebra Project Mpala Research Centre Laikipia Kenya Wild Me Conservation X Labs PortlandOR United States Computer Science and Engineering Ohio State University ColumbusOH United States Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Rensselaer Polytechnic Institute TroyNY United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

The Grévy's zebra, an endangered species native to Kenya and southern Ethiopia, has been the target of sustained conservation efforts in recent years. Accurately monitoring Grévy's zebra populations is essential for ecologists to evaluate ongoing conservation initiatives. Recently, in both 2016 and 2018, a full census of the Grévy's zebra population was enabled by the Great Grévy's Rally (GGR), a citizen science event that combines teams of volunteers to capture data with computer vision algorithms that help experts estimate the number of individuals in the population. A complementary, scalable, cost-effective and long-term Grévy's population monitoring approach involves deploying a network of camera traps, which we have done at the Mpala Research Centre in Laikipia County, Kenya. In both scenarios, a substantial majority of the images of zebras are not usable for individual identification due to ‘in-the-wild’ imaging conditions—occlusions from vegetation or other animals, oblique views, low image quality and animals that appear in the far background and are thus too small to identify. Camera trap images, without an intelligent human photographer to select the framing and focus on the animals of interest, are of even poorer quality, with high rates of occlusion and high spatiotemporal similarity within image bursts. We employ an image filtering pipeline incorporating animal detection, species identification, viewpoint estimation, quality evaluation and temporal subsampling to compensate for these factors and obtain individual crops from camera trap and GGR images of suitable quality for re-ID. We then employ the local clusterings and their alternatives (LCA) algorithm, a hybrid computer vision and graph clustering method for animal re-ID, on the resulting high-quality crops. Our method processed images taken during GGR-16 and GGR-18 in Meru County, Kenya, into 4142 highly comparable annotations, requiring only 120 contrastive same-vs-different-individual decisions f

关键词： Photointerpretation

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Security Analysis of Wireless Train Control Systems

Security Analysis of Wireless Train Control Systems

引用

IEEE Globecom Workshops

作者： Dongfeng Fang Shengjie Xu Hamid Sharif Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In order to enhance the performance of safety, capacity and timely schedules in railroad operations, information communication technologies (ICT) are introduced into railroad systems. Different entities are connected with information exchange, such as base stations, wayside devices, and locomotive systems. Based on ICT, there are many critical messages are transmitted on the air to improve the railroad operations. In this paper, we present security analysis of a general wireless train control systems (WTC). We first present a general WTC system model. Based on the general WTC system model, we study a trust model, attack models, and security objectives. A case study for identity-based security schemes is presented over the general WTC system model for securing the communication between the back office and the locomotive system. An identity (ID) - based signcryption scheme and an ID-based signature scheme are proposed to achieve confidentiality, integrity, authentication and end-to-end security. Security analysis and performance analysis of the proposed schemes are studied to show that our proposed scheme can achieve required security services with low computational and communication overhead.

关键词： Base stations Wireless communication Authentication Communication system security Control systems Jamming

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Efficient Network Anomaly Detection for Edge Gateway Defense in 5G

Efficient Network Anomaly Detection for Edge Gateway Defense...

引用

IEEE Globecom Workshops

作者： Shengjie Xu Dongfeng Fang Hamid Sharif The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Various cyber intrusions are constantly invading the edge access networks, disturbing the assured services, and further destroying critical cyberinfrastructure. Efficient and effective edge gateway defense against malicious threats for a multitude of connected physical end devices will remain as a significant problem in the next-generations wireless communications. Edge gateway equipped with network anomaly detection will serve as a first line to protect the online systems, while the robust anomaly detection under label scarcity scenarios is still challenging. In this paper, we propose an efficient network anomaly detection scheme for edge gateway defense in 5G. We first explore the system model of 5G edge access networks. We discuss the motivations of deploying an edge network intelligence for defending against cyber intrusions, and then propose an efficient network anomaly detection scheme. The proposed scheme performs well under label scarcity scenarios, and it supports the requirement with computational efficient real-time processing. Finally, we conduct a case study, and we demonstrate that a proposed scheme achieves solid performance under several evaluation metrics, and it retains efficiency in terms of computational and communication overhead.

关键词： Image edge detection Anomaly detection Logic gates Loading 5G mobile communication Base stations Computational modeling

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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