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Defending against advanced persistent threat: A risk management perspective 1st

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Defending against advanced persistent threat: A risk managem...

1st International Conference on Science of Cyber Security, SciSec 2018

作者： Zhong, Xiang Yang, Lu-Xing Yang, Xiaofan Xiong, Qingyu Wen, Junhao Tang, Yuan Yan School of Big Data and Software Engineering Chongqing University Chongqing400044 China School of Information Technology Deakin University MelbourneVIC3125 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China Department of Computer and Information Science The University of Macau China

ISBN: (纸本)9783030030254

advanced persistent threat (APT) as a new form of cyber attack has posed a severe threat to modern organizations. When an APT has been detected, the target organization has to develop a response resource allocation strategy to mitigate her potential loss. This paper suggests a risk management approach to solving this APT response problem. First, we present three state evolution models. Thereby we assess the organization’s potential loss. On this basis, we propose two kinds of game-theoretic models of the APT response problem. This work initiates the study of the APT response problem. © Springer Nature Switzerland AG 2018.

关键词： Game theory

Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities

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Deep Learning Based Automated Extraction of Intra-Retinal La...

International Conference on e-health Networking, applications and Services (HealthCom)

作者： Taimur Hassan Anam Usman M. Usman Akram M. Furqan Masood Ubaidullah Yasin Department of Computer & Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering Islamabad Pakistan Department of Ophthalmology Armed Forces Institute of Ophthalmology Rawalpindi Pakistan

ISBN: (纸本)9781538642955

Extraction of retinal layers from optical coherence tomography (OCT) scans is critical for analyzing retinal anomalies and manual segmentation of these retinal layers is a very cumbersome task. Recently, deep learning has gained much popularity in medical image analysis due to its underlying precision and robustness. Many researchers have utilized deep learning for extracting retinal layers from OCT images. However, to the best of our knowledge, there is no literature available that presents a robust segmentation framework that is able to extract retinal layers from OCT scans having different retinal pathological syndromes. Therefore, this paper presents a deep convolutional neural network and structure tensor-based segmentation framework (CNN-STSF) for the fully automated segmentation of up to eight retinal layers from normal as well as diseased OCT scans. First of all, the proposed framework computes coherent tensor from the candidate scan through which retinal layers are extracted. Afterwards, the pixels representing the layers are further classified using cloud based deep convolutional neural network (CNN) model trained on 1,200 retinal layers patches. CNN model in the proposed framework computes the probability of each layer pixels and assign it to be part of that layer for which it has the highest probability. The proposed framework was tested and validated on more than 39,000 retinal OCT scans from different publicly available datasets and from local Armed Forces Institute of Ophthalmology (AFIO) dataset where it outperformed all the existing solutions by achieving the overall layer segmentation accuracy of 0.9375.

关键词： Retina Tensile stress Convolution Image segmentation Pathology Computational modeling Blindness

The medical segmentation decathlon

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

2022 Review of Data-Driven Plasma Science

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Discovery of dual "little red dots" indicates excess clustering on kilo-parsec scales

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arXiv 2024年

作者： Tanaka, Takumi S. Silverman, John D. Shimasaku, Kazuhiro Arita, Junya Akins, Hollis B. Inayoshi, Kohei Ding, Xuheng Onoue, Masafusa Liu, Zhaoxuan Casey, Caitlin M. Lambrides, Erini Kokorev, Vasily Jin, Shuowen Faisst, Andreas L. Drakos, Nicole Shen, Yue Li, Junyao Zhuang, Mingyang Fei, Qinyue Ito, Kei Ren, Wenke Matsui, Suin Ando, Makoto Hatano, Shun Fujii, Michiko S. Kartaltepe, Jeyhan S. Koekemoer, Anton M. Liu, Daizhong McCracken, Henry Joy Rhodes, Jason Robertson, Brant E. Franco, Maximilien Andika, Irham T. Cloonan, Aidan P. Fan, Xiaohui Gozaliasl, Ghassem Harish, Santosh Hayward, Christopher C. Huertas-Company, Marc Kakkad, Darshan Kinugawa, Tomoya Roy, Namrata Shuntov, Marko Talia, Margherita Toft, Sune Vijayan, Aswin P. Zhang, Yiyang The University of Tokyo Institutes for Advanced Study The University of Tokyo Kashiwa Chiba277-8583 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan UTIAS The University of Tokyo Kashiwa Chiba277-8583 Japan Center for Astrophysical Sciences Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Research Center for the Early Universe Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Astronomy The University of Texas at Austin 2515 Speedway Boulevard Stop C1400 AustinTX78712 United States Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China School of Physics and Technology Wuhan University Wuhan430072 China Denmark NASA-Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands DTU Space Technical University of Denmark Elektrovej Building 328 Kgs. Lyngby2800 Denmark Caltech/IPAC 1200 E. California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Hawaii Hilo 200 W Kawili St. HiloHI96720 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy School of Physics Peking University Beijing100871 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Osawa 2-21-1 Mitaka Tokyo181-8588 Japan Laboratory for Multiwave

"Little Red Dots" (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST). They are characterized by a red color in the rest-frame optical band, compact morphology, and broad Balmer emission lines (FWHM >∼ 1000 km s−1) that suggest an AGN nature. Using a method of pixel-by-pixel color selection and relaxing the compactness criteria, we identify three of the first dual LRD candidates in the COSMOS-Web survey with projected separations of 0.′′2-0.′′4 (1-2 pkpc at their photometric redshifts). A comparison between existing LRD samples and mock data reveals that the projected separations of these dual LRD candidates are unlikely to result from chance projections of objects at different redshifts. In one case (CW-B5-15958), the dual LRD includes two bright sources (mF444W = 24.3 and 24.8) with characteristic V-shape spectral energy distribution (SEDs) and photometric redshifts consistent with each other. We find that CW-B5-15958 has a faint off-centered component and a companion galaxy. In the other two dual systems, the brighter LRD exhibits a V-shape SED, while the fainter LRD (mF444W>∼ 26) is undetected in both F115W and F150W. These discoveries suggest that the angular auto-correlation function (ACF) of LRDs exhibits a significant excess (∼ 3 × 102 times) on sub-arcsec (kilo-parsec) separations compared to the extrapolation of a power-law ACF of JWST-found AGNs measured over 10′′-100′′. Follow-up spectroscopic confirmation of their redshifts and the construction of a larger sample are essential to advance our understanding of the evolution of supermassive black holes and the importance of mergers in the early universe. © 2024, CC BY.

关键词： Pixels

Comparing recent PTA results on the nanohertz stochastic gravitational wave background

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arXiv 2023年

作者： Agazie, G. Antoniadis, J. Anumarlapudi, A. Archibald, A.M. Arumugam, P. Arumugam, S. Arzoumanian, Z. Askew, J. Babak, S. Bagchi, M. Bailes, M. Bak Nielsen, A.-S. Baker, P.T. Bassa, C.G. Bathula, A. Bécsy, B. Berthereau, A. Bhat, N.D.R. Blecha, L. Bonetti, M. Bortolas, E. Brazier, A. Brook, P.R. Burgay, M. Burke-Spolaor, S. Burnette, R. Caballero, R.N. Cameron, A. Case, R. Chalumeau, A. Champion, D.J. Chanlaridis, S. Charisi, M. Chatterjee, S. Chatziioannou, K. Cheeseboro, B.D. Chen, S. Chen, Z.-C. Cognard, I. Cohen, T. Coles, W.A. Cordes, J.M. Cornish, N.J. Crawford, F. Cromartie, H.T. Crowter, K. Curylo, M. Cutler, C.J. Dai, S. Dandapat, S. Deb, D. DeCesar, M.E. DeGan, D. Demorest, P.B. Deng, H. Desai, S. Desvignes, G. Dey, L. Dhanda-Batra, N. Di Marco, V. Dolch, T. Drachler, B. Dwivedi, C. Ellis, J.A. Falxa, M. Feng, Y. Ferdman, R.D. Ferrara, E.C. Fiore, W. Fonseca, E. Franchini, A. Freedman, G.E. Gair, J.R. Garver-Daniels, N. Gentile, P.A. Gersbach, K.A. Glaser, J. Good, D.C. Goncharov, B. Gopakumar, A. Graikou, E. Grießmeier, J.-M. Guillemot, L. Gültekin, K. Guo, Y.J. Gupta, Y. Grunthal, K. Hazboun, J.S. Hisano, S. Hobbs, G.B. Hourihane, S. Hu, H. Iraci, F. Islo, K. Izquierdo-Villalba, D. Jang, J. Jawor, J. Janssen, G.H. Jennings, R.J. Jessner, A. Johnson, A.D. Jones, M.L. Joshi, B.C. Kaiser, A.R. Kaplan, D.L. Kapur, A. Kareem, F. Karuppusamy, R. Keane, E.F. Keith, M.J. Kelley, L.Z. Kerr, M. Key, J.S. Kharbanda, D. Kikunaga, T. Klein, T.C. Kolhe, N. Kramer, M. Krishnakumar, M.A. Kulkarni, A. Laal, N. Lackeos, K. Lam, M.T. Lamb, W.G. Larsen, B.B. Lazio, T.J.W. Lee, K.J. Levin, Y. Lewandowska, N. Littenberg, T.B. Liu, K. Liu, T. Liu, Y. Lommen, A. Lorimer, D.R. Lower, M.E. Luo, J. Luo, R. Lynch, R.S. Lyne, A.G. Ma, C.-P. Maan, Y. Madison, D.R. Main, R.A. Manchester, R.N. Mandow, R. Mattson, M.A. McEwen, A. McKee, J.W. McLaughlin, M.A. McMann, N. Meyers, B.W. Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Institute of Astrophysics FORTH N. Plastira 100 Heraklion70013 Greece Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Newcastle University NE1 7RU United Kingdom Department of Physics Indian Institute of Technology Roorkee Roorkee247667 India Department of Electrical Engineering IIT Hyderabad Telangana Kandi502284 India X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Australia Université Paris Cité CNRS Astroparticule et Cosmologie Paris75013 France The Institute of Mathematical Sciences C.I.T. Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Fakultät für Physik Universität Bielefeld Postfach 100131 Bielefeld33501 Germany Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali140306 India Department of Physics Oregon State University CorvallisOR97331 United States Laboratoire de Physique et Chimie de l'Environnement et de l'Espace Université d'Orléans CNRS Orléans45071 Cedex 02 France Observatoire Radioastronomique de Nançay Observatoire de Paris Université PSL Université d'Orléans CNRS Nançay18330 France International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Physics Department University of Florida GainesvilleFL32611 United States Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca

The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different choices in modeling their data, we perform a comparison of the GWB and individual pulsar noise parameters across the results reported from the PTAs that constitute the International Pulsar Timing Array (IPTA). We show that despite making different modeling choices, there is no significant difference in the GWB parameters that are measured by the different PTAs, agreeing within 1σ. The pulsar noise parameters are also consistent between different PTAs for the majority of the pulsars included in these analyses. We bridge the differences in modeling choices by adopting a standardized noise model for all pulsars and PTAs, finding that under this model there is a reduction in the tension in the pulsar noise parameters. As part of this reanalysis, we"extended" each PTA's data set by adding extra pulsars that were not timed by that PTA. Under these extensions, we find better constraints on the GWB amplitude and a higher signal-to-noise ratio for the Hellings and Downs correlations. These extensions serve as a prelude to the benefits offered by a full combination of data across all pulsars in the IPTA, i.e., the IPTA's Data Release 3, which will involve not just adding in additional pulsars, but also including data from all three PTAs where any given pulsar is timed by more than as single PTA. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

Robot Soccer Using Deep Q Network

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Robot Soccer Using Deep Q Network

International Conference on Platform technology and Service (PlatCon)

作者： Jinwon Kim Bongsu Kim Jinwoo Yoon Marley Lee Sunah Jung Jae young Choi Center for Advanced Image and Information Technology Chon Buk National University Jeonju Chon Buk Korea Department of Software Engineering Chon Buk National University Jeonju Chon Buk Korea Department of Computer Engineering Sungkyunkwan University Korea

Reinforcement Learning is one of brilliant way to develop intelligent agents in the field of Artificial Intelligence. This paper proposes a RL algorithm called Deep Q Network and presents applications of this algorithm to the decision-making problems challenged in the RoboCup. Four scenarios were defined to develop decision-making for a SSL in various situations using the proposed algorithm. Furthermore, a Convolutional Neural Network model was used as a function approximator in each application. The experimental results showed that the proposed Reinforcement Learning algorithm had effectively trained the Reinforcement Learning agent to acquire good decision making. The Reinforcement Learning agent showed good performance under specified experimental conditions.

关键词： Learning (artificial intelligence) Mobile robots Feature extraction Decision making

Quantum Simulation for High Energy Physics

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arXiv 2022年

作者： Bauer, Christian W. Davoudi, Zohreh Balantekin, A. Baha Bhattacharya, Tanmoy Carena, Marcela de Jong, Wibe A. Draper, Patrick El-Khadra, Aida Gemelke, Nate Hanada, Masanori Kharzeev, Dmitri Lamm, Henry Li, Ying-Ying Liu, Junyu Lukin, Mikhail Meurice, Yannick Monroe, Christopher Nachman, Benjamin Pagano, Guido Preskill, John Rinaldi, Enrico Roggero, Alessandro Santiago, David I. Savage, Martin J. Siddiqi, Irfan Siopsis, George Van Zanten, David Wiebe, Nathan Yamauchi, Yukari Yeter-Aydeniz, Kübra Zorzetti, Silvia Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin MadisonWI53706 United States T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois UrbanaIL61801 United States QuEra Computing Inc BostonMA02135 United States Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University NY11794-3800 United States Department of Physics Brookhaven National Laboratory UptonNY11973 United States Pritzker School of Molecular Engineering Chicago Quantum Exchange Kadanoff Center for Theoretical Physics University of Chicago IL60637 United States QBraid Co. Harper Court 5235 ChicagoIL60615 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Physics and Astronomy Department Rice University HoustonTX77005 United States Institute for Quantum Information and Matter Californi

It is for the first time that quantum simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information Sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond. © 2022, CC BY.

关键词： High energy physics