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A Rate-Distortion Approach to Caching

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A Rate-Distortion Approach to Caching

作者： Timo, Roy Bidokhti, Shirin Saeedi Wigger, Michele Geiger, Bernhard C. Ericsson ResearchTorshamnsgatan Stockholm23164 80 Sweden Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Communications and Electronics Department Telecom ParisTech Paris75634 France Institute for Communications Engineering Technical University of Munich Munich80333 Germany Signal Processing and Speech Communication Laboratory Graz University of Technology Graz8010 Austria

In this paper, we consider a lossy single-user caching problem with correlated sources. We first describe the fundamental interplay between the source correlations, the capacity of the user's cache, the user's reconstruction distortion requirements, and the final delivery-phase (compression) rate. We then illustrate this interplay using a multivariate Gaussian source example and a binary symmetric source example. To fully explore the effect of the user's distortion requirements, we formulate the caching problem using f-separable distortion functions recently introduce by Shkel and Verdú. The class of f-separable distortion functions includes separable distortion functions as a special case, and our analysis covers both the expected-and excess-distortion settings in detail. We also determine what 'common information' should be placed in the cache, and what information should be transmitted during the delivery phase. To this end, two new common-information measures are introduced for caching, and their relationship to the common-information measures of Wyner, Gács, and Körner is discussed in detail. © 1963-2012 IEEE.

关键词： signal distortion

Screening and evaluation of potential clinically significant HIV drug combinations against SARS-CoV-2 virus

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arXiv 2020年

作者： Tomić, Draško Skala, Karolj Szasz, Attila Marcel Rezeli, Melinda Vrca, Vesna Bačić Pirkić, Boris Petrik, Jozsef Janđel, Vladimir Periša, Marija Milković Rogina, Branka Medved Mesarić, Josip Davidović, Davor Lipić, Tomislav Center for Informatics and Computing Ruđer Bošković Institute Zagreb Croatia Department of Bioinformatics and Cancer Center Semmelweis University Budapest Hungary Department of Biomedical Engineering Lund University Lund Sweden Central Hospital Pharmacy Clinical Hospital Center Dubrava Zagreb Croatia Surgery Orthopedics and Ophthalmology Clinic Faculty of Veterinary Medicine University of Zagreb Zagreb Croatia Department of Medical Biochemistry and Hematology Faculty of Pharmacy and Biochemistry University of Zagreb Zagreb Croatia Deprtment of Obstetricy Clinical Hospital Centre Zagreb Croatia Institute of Pathology School of Medicine University of Zagreb Zagreb Croatia Laboratory for Information and Signal Processing Division of Electronics Ruđer Bosković Institute Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

In this study, we investigated the inhibition of SARS-CoV-2 spike glycoprotein with HIV drugs and their combinations. This glycoprotein is essential for the reproduction of the SARS-COV-2 virus, so its inhibition opens new avenues for the treatment of patients with COVID-19 disease. In doing so, we used the VINI in silico model of cancer, whose high accuracy in finding effective drugs and their combinations was confirmed in vitro by comparison with existing results from NCI-60 bases, and in vivo by comparison with existing clinical trial results. In the first step, the VINI model calculated the inhibition efficiency of SARS-CoV-2 spike glycoprotein with 44 FDA-approved antiviral drugs. Of these drugs, HIV drugs have been shown to be effective, while others mainly have shown weak or no efficiency. Subsequently, the VINI model calculated the inhibition efficiency of all possible double and triple HIV drug combinations, and among them identified ten with the highest inhibition efficiency. These ten combinations were analyzed by Medscape drug-drug interaction software and LexiComp Drug Interactions. All combinations except the combination of cobicistat_abacavir_rilpivirine appear to have serious interactions (risk rating category D) when dosage adjustments/reductions are required for possible toxicity. Finally, the VINI model compared the inhibition efficiency of cobicistat_abacivir_rilpivirine combination with cocktails and individual drugs already used or planned to be tested against SARS-CoV-2. Combination cobicistat_abacivir_rilpivirine demonstrated the highest inhibition of SARS-CoV-2 spike glycoprotein over others. Thus, this combination seems to be a promising candidate for the further in vitro testing and clinical trials. Copyright © 2020, The Authors. All rights reserved.

关键词： SARS

Mitigating Intentional Electromagnetic Interferences over the GSM-R System with Adaptive Filters

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Mitigating Intentional Electromagnetic Interferences over th...

International Conference on Applied Electromagnetics and Communications (ICECom)

作者： Artur N. de São José Virginie Deniau Úrsula do C. Resende Ricardo Adriano Graduate Program in Electrical Engineering Federal University of Minas Gerais French Institute of Science and Technology for Transport France Laboratory on Electronics Waves and Signal Processing for Transport French Institute of Science and Technology for Transport Development and Networks Villeneuve d’Ascq France Eletrical Engineering Department Federal Center for Technological Education of Minas Gerais Belo Horizonte Brazil Eletrical Engineering Department Federal University of Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9781728155142

ISBN: (纸本)9781728155159

Intentional electromagnetic interference is a potential issue for railway networks based on ground-to-train communications. Reported mitigation techniques usually rely on electromagnetic compatibility classical procedures, which are not always the best option for such an unpredictable event. In this work, offline adaptive filtering is used to recover global system for mobile communications - railway damaged data.

关键词： Jamming Rail transportation Base stations Time-frequency analysis Downlink Noise measurement Convergence

Observation of Hermitian and Non-Hermitian Diabolic Points and Exceptional Rings in Parity-Time symmetric ZRC and RLC Dimers

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arXiv 2020年

作者： Tabeu, Stéphane Boris Fotsa-Ngaffo, Fernande Kenfack-Jiotsa, Aurélien Laboratory of Electronics and Signal Processing Department of Electrical and Telecommunication Engineering National Advanced School of Engineering University of Yaoundé I P.O. Box 8390 Yaoundé Cameroon Laboratory of Mechanics Materials and Structures Department of Physics University of Yaoundé I P.O. Box 812 Yaoundé Cameroon Nonlinear Physics and Complex Systems Group Department of Physics Higher Teacher’s Training College University of Yaoundé I P.O. Box 47 Yaoundé Cameroon Institute of Wood Technologies University of Yaoundé I P.O. Box 306 Mbalmayo Cameroon Department of Physics Faculty of Science University of Buea P.O. Box 63 Buea Cameroon Nonlinear Physics and Complex Systems Group Department of Physics Higher Teacher’s Training College University of Yaoundé I P.O. Box 47 Yaoundé Cameroon P.O. Box 105 Yaoundé Cameroon

We present the observation of diabolic points in Hermitian and non-Hermitian electronics dimers. The condition of unbreakable Parity-time symmetry is established for both PT-symmetric ZRC and RLC dimers. We show how appears non-Hermitian degeneracy points in the spectrum and how they are protected against a Hermitian perturbation. When a non-Hermitian perturbation is added in the setup, the non-Hermitian diabolic point (NHDP) turns into a ring of exceptional points as in some Dirac and Weyl semimetals. Some experimental simulations of oscillations around these particular points in LTspice are in perfect accordance with the one predicted analytically and numerically. This work opens a gold road for investigations on topological electrical circuits for robust transport of information at room temperature. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

Publisher Correction: Brain imaging with portable low-field MRI

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Nature Reviews Bioengineering

Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

Common Limitations of image processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

NTIRE 2021 Challenge on image Deblurring

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： image enhancement

A method to read numbers in Thai nutrition facts label by using SVM

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A method to read numbers in Thai nutrition facts label by us...

International Workshop on Advanced image Technology (IWAIT)

作者： Supawadee Ekkasan Theekapun Charoenpong Kosin Chamnongthai Supiya Charoensiriwath Department of Biomedical Engineering Srinakharinwijot University Nakhonnayok Thailand Department of Electrical and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Thailand Biomedical Signal Processing Laboratory National Electronics and Computer Technology Center Pathumthani Thailand

ISBN: (纸本)9781538626160

Due to a problem of current research occurring when reading the nutrition facts label with Thai font. In this paper, we proposed a method to read the amount of the nutrition numbers on nutrition facts label with Thai font by using integrated image processing technique. This method consists of three steps: label extraction, number segmentation, and number classification. Nutrition facts label image is used as input of algorithm. First, the label is extracted by using a local adaptive threshold. Four sides of a label box are detected from histogram in horizontal and vertical axis. Second, numbers are segmented based on blob region analysis. Blob of number can be defined according to blob sequence in a line. A number in blob of number is then separated. Third, a number is divided into 5×7 regions. A number vector defined from 35 regions is used to classify the number by the support vector machine. To test the performance of the proposed method, number of the amount of the nutrition is extracted from twenty Thai labels. In such labels, there are 180 numbers. A number is classified into number 0-9. By using support vector machine for classification, the accuracy is 81.11%. The experimental result shows the satisfactory results. This is the first method for reading number in Thai nutrition facts label.

关键词： Erbium Data acquisition Data mining Graphics Tools

Deep learning enabled fast optical characterization of two-dimensional materials

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arXiv 2019年

作者： Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Leong, Wei Sun Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Palacios, Tomás Yin, Jihao Kong, Jing Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry Boston University BostonMA02215 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Instituto de Ciencia Molecular Universidad de Valencia c/Catedrático José Beltrán 2 Paterna46980 Spain Division of Materials Science and Engineering Boston University BostonMA02215 United States

Characterization of nanomaterial morphologies with advanced microscopy and/or spectroscopy tools plays an indispensable role in nanoscience and nanotechnology research 1 2, 3, 4, 5, as rich information about the chemical compositions, crystallography, other physical and chemical properties, as well as the growth mechanism can be extracted from morphology analysis. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features are often unused because of difficulties in processing the data and finding the correlations. Such difficulties can be well addressed by deep learning 6 7, 8, 9. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural network based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can be used to predict physical properties of the materials. Finally, a transfer learning technique is applied to adapt the pretrained network to more optical characterization applications such as identifying layer numbers of chemically synthesized graphene domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Data handling