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Endotaxial Stabilization of 2D Charge Density Waves with Long-Range Order

Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Sung, Suk Hyun Agarwal, Nishkarsh Baggari, Ismail El Kezer, Patrick Goh, Yin Min Schnitzer, Noah Shen, Jeremy M Chiang, Tony Liu, Yu Lu, Wenjian Sun, Yuping Kourkoutis, Lena F Heron, John T Sun, Kai Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI USA Rowland Institute at Harvard University Cambridge MA USA Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI USA John A. Paulson School of Engineering and Applied Science Harvard University Cambridge MA USA Department of Materials Science and Engineering Cornell University Ithaca NY USA Kavli Institute at Cornell for Nanoscale Science Ithaca NY USA Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei PR China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing PR China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei PR China School of Applied and Engineering Physics Cornell University Ithaca NY USA Applied Physics Program University of Michigan Ann Arbor MI USA Department of Physics University of Michigan Ann Arbor MI USA

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Quantitative assessment of the role of undocumented infection in the 2019 novel coronavirus (COVID-19) pandemic

arXiv

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arXiv 2020年

作者： Long, Yong-Shang Zhai, Zheng-Meng Han, Li-Lei Kang, Jie Li, Yi-Lin Lin, Zhao-Hua Zeng, Lang Wu, Da-Yu Hao, Chang-Qing Tang, Ming Liu, Zonghua Lai, Ying-Cheng State Key Laboratory of Precision Spectroscopy School of Physics and Electronic Science East China Normal University Shanghai200241 China School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

An urgent problem in controlling COVID-19 spreading is to understand the role of undocumented infection. We develop a five-state model for COVID-19, taking into account the unique features of the novel coronavirus, with key parameters determined by the government reports and mathematical optimization. Tests using data from China, South Korea, Italy, and Iran indicate that the model is capable of generating accurate prediction of the daily accumulated number of confirmed cases and is entirely suitable for real-time prediction. The drastically disparate testing and diagnostic standards/policies among different countries lead to large variations in the estimated parameter values such as the duration of the outbreak, but such uncertainties have little effect on the occurrence time of the inflection point as predicted by the model, indicating its reliability and robustness. Model prediction for Italy suggests that insufficient government action leading to a large fraction of undocumented infection plays an important role in the abnormally high mortality in that country. With the data currently available from United Kingdom, our model predicts catastrophic epidemic scenarios in the country if the government did not impose strict travel and social distancing restrictions. A key finding is that, if the percentage of undocumented infection exceeds a threshold, a non-negligible hidden population can exist even after the the epidemic has been deemed over, implying the likelihood of future outbreaks should the currently imposed strict government actions be relaxed. This could make COVID-19 evolving into a long-term epidemic or a community disease a real possibility, suggesting the necessity to conduct universal testing and monitoring to identify the hidden individuals. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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On mean absolute error for deep neural network based vector-to-vector regression

arXiv

引用

arXiv 2020年

作者： Qi, Jun Du, Jun Siniscalchi, Sabato Marco Ma, Xiaoli Lee, Chin-Hui School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei230027 China Faculty of Architecture and Engineering University of Enna "Kore" Enna94100 Italy Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector-to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

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Entangled sensor-networks for dark-matter searches

arXiv

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arXiv 2022年

作者： Brady, Anthony J. Gao, Christina Harnik, Roni Liu, Zhen Zhang, Zheshen Zhuang, Quntao Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Theoretical Physics Division Fermi National Accelerator Laboratory P.O. Box 500 BataviaIL60510 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Illinois Center for Advanced Studies of the Universe University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States J. C. Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States

The hypothetical axion particle (of unknown mass) is a leading candidate for dark matter (DM). Many experiments search for axions with microwave cavities, where an axion may convert into a cavity photon, leading to a feeble excess in the output power of the cavity. Recent work [Nature 590, 238 (2021)] has demonstrated that injecting squeezed vacuum into the cavity can substantially accelerate the axion search. Here, we go beyond and provide a theoretical framework to leverage the benefits of quantum squeezing in a network setting consisting of many sensor-cavities. By forming a local sensor network, the signals among the cavities can be combined coherently to boost the axion search. Furthermore, injecting multipartite entanglement across the cavities-generated by splitting a squeezed vacuum-enables a global noise reduction. We explore the performance advantage of such a local, entangled sensor-network, which enjoys both coherence between the axion signals and entanglement between the sensors. Our analyses are pertinent to next-generation DM-axion searches aiming to leverage a network of sensors and quantum resources in an optimal way. Finally, we assess the possibility of using a more exotic quantum state, the Gottesman-Kitaev-Preskill (GKP) state. Despite a constant-factor improvement in the scan-time relative to a single-mode squeezed-state in the ideal case, the advantage of employing a GKP state disappears when a practical measurement scheme is considered. Copyright © 2022, The Authors. All rights reserved.

关键词： Sensor networks

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)

arXiv

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arXiv 2023年

作者： Waldmannstetter, Diana Ezhov, Ivan Wiestler, Benedikt Campi, Francesco Kukuljan, Ivan Ehrlich, Stefan Vinayahalingam, Shankeeth Baheti, Bhakti Chakrabarty, Satrajit Baid, Ujjwal Bakas, Spyridon Schwarting, Julian Metz, Marie Kirschke, Jan S. Rueckert, Daniel Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Neuherberg Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Healthcare Technologies SETLabs Research Munich Germany Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre Nijmegen Netherlands Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St.Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden

Accurate image registration is pivotal in bio-medical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize sub-resolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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A telemedicine application for remote diagnosis and assessment of mood disorders 9th

A telemedicine application for remote diagnosis and assessme...

引用

9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018

作者： Konstantopoulou, Georgia Panagiotakopoulos, Theodor Mandellos, George J. Asimakopoulos, Konstantinos Lymberopoulos, Dimitrios K. Special Office for Health Consulting Services University of Patras Patras Greece Mobile and Pervasive Computing Quality and Ambient Intelligence Laboratory School of Science and Technology Hellenic Open University Patras26335 Greece Wire Communication Laboratory Electrical and Computer Engineering Department University of Patras Rion26504 Greece Department of Psychiatry School of Medicine University of Patras Patras Greece

ISBN: (纸本)9783030051945

Depression in its various forms is a widespread phenomenon in modern societies. Its high prevalence, associated costs, the chronic nature it develops and the challenges in diagnosing it put a lot of pressure on public healthcare systems. In response to these challenges, ICT-based approaches are increasingly implemented to support effective patient management and discovery. This paper presents a web application named "feeldistress", which is based on a novel distress evaluation framework to enable remote diagnosis of anxiety and depression, facilitate continuous evaluation of patients and assist prevention of suicide. The developed application was used and evaluated from a qualitative perspective by 117 students (47% women) who had visited the Special Office for Health Consulting Services of the University of Patras between 2014 and 2017. The majority of the users were very satisfied by the functionality, usability and appearance of the application showing it can be extremely useful tool for someone before hitting the door of a mental health specialist. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Telemedicine

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Fuzzy Domain Adaptation via Variational Inference for Evolving Concept Drift

引用

IEEE Transactions on Fuzzy Systems 2025年

作者： Wang, Chuang Wang, Zidong Sheng, Weiguo Liu, Qingqiang Dong, Hongli Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China Northeast Petroleum University Sanya Offshore Oil & Gas Research Institute Sanya572025 China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China Northeast Petroleum University School of Electrical & Information Engineering Daqing163318 China

The concept of fuzzy domain adaptation (FDA) isfocused on transferring a model trained in a source domainto a target domain, where intrinsic distribution discrepanciesexist in non-stationary and non-deterministic environments. Inthis paper, a novel drift decoupling-based variational adaptationnetwork (DD-VAN) is proposed for FDA, allowing for the learningof intra-domain evolutionary patterns and inter-domain uncer-tainties. The DD-VAN algorithm is implemented in three mainsteps: (1) an intra-domain evolutionary trend modeling module isfirst employed to capture unknown temporal variations throughan autoencoder architecture with variational inference;(2) aprototype-assisted fuzzy clustering module is used to estimate themembership degree of the target data, characterizing the inherentuncertainty and imprecision present in real-world distributions;and (3) a membership-aware domain fuzzy matching module isutilized to learn the gradual transitions between category-relateddata pairs in the source and target domains by introducing un-certainties. Furthermore, it is theoretically demonstrated that theinferred posterior distributions of latent codes can be optimizedto align with the corresponding prior distributions by minimizingthe Kullback-Leibler divergence. Extensive experiments are con-ducted on cross-domain tasks involving both synthetic and real-world datasets, and the experimental results suggest that the DD-VAN algorithm outperforms existing state-of-the-art methods. © 1993-2012 IEEE.

关键词： Variational techniques

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Statistical approach to detection of attacks for stochastic cyber-physical systems

arXiv

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arXiv 2020年

作者： Marelli, Damián Sui, Tianju Fu, Minyue Lu, Renquan School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Control Science and Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guandong University of Technology Guangzhou China

We study the problem of detecting an attack on a stochastic cyber-physical system. We aim to treat the problem in its most general form. We start by introducing the notion of asymptotically detectable attacks, as those attacks introducing changes to the system’s output statistics which persist asymptotically. We then provide a necessary and sufficient condition for asymptotic detectability. This condition preserves generality as it holds under no restrictive assumption on the system and attacking scheme. To show the importance of this condition, we apply it to detect certain attacking schemes which are undetectable using simple statistics. Our necessary and sufficient condition naturally leads to an algorithm which gives a confidence level for attack detection. We present simulation results to illustrate the performance of this algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Analyzing upper bounds on mean absolute errors for deep neural network based vector-to-vector regression

arXiv

引用

arXiv 2020年

作者： Qi, Jun Du, Jun Marco Siniscalchi, Sabato Ma, Xiaoli Lee, Chin-Hui School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei230027 China Faculty of Architecture and Engineering University of Enna Kore Enna94100 Italy Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image denoising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the "over-parametrization" technique. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

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