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Comments on Yu et al’s Shared data Integrity Verification Protocol 4th

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Comments on Yu et al’s Shared Data Integrity Verification P...

4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China School of Computer Science and Technology Harbin Institute of Technology - Shenzhen Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9783319685267

Recently, Yu et al. proposed a secure shared data integrity verification protocol called SDVIP 2 to ensure the integrity of outsourced file in the cloud. Unfortunately, we exploit the vulnerability of their protocol in this paper. We demonstrate an active adversary can modify the outsourced file such that the auditor is unable to detect in the auditing process. At the end of this paper, we also provide two suggestions to fix the proposed attack. © 2018, Springer International Publishing AG.

关键词： Cloud storage

Accurate Recovery of Internet Traffic data: A Sequential Tensor Completion Approach

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Accurate Recovery of Internet Traffic Data: A Sequential Ten...

作者： Xie, Kun Wang, Lele Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Guangxing Cao, Jiannong Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University Changsha410006 China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Network Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high. © 1993-2012 IEEE.

关键词： Tensors

Deep learning with quantized neural networks for gravitational wave forecasting of eccentric compact binary coalescence

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arXiv 2020年

作者： Wei, Wei Huerta, E.A. Yun, Mengshen Loutrel, Nicholas Shaikh, Md Arif Kumar, Prayush Haas, Roland Kindratenko, Volodymyr National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States University of Chicago ChicagoIL60637 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present the first application of deep learning forecasting for binary neutron stars, neutron star-black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced LIGO noise available at the Gravitational Wave Open science Center to: 1) quantify how fast neural networks identify these signals before the binary components merge;2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified;and 3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Gravity waves

Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

GazeGAN: A robust generative adversarial saliency model based on invariance analysis of human gaze during scene free viewing

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arXiv 2019年

作者： Che, Zhaohui Borji, Ali Zhai, Guangtao Min, Xiongkuo Guo, Guodong Le Callet, Patrick Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China MarkableAI United States Institute of Deep Learning Baidu Research Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States Equipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

—data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time-consuming and expensive. Most of current studies on human attention and saliency modeling have used high-quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label-preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label-preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified U-Net is proposed as the generator of the GazeGAN, which combines classic "skip connections" with a novel "center-surround connection (CSC)", in order to leverage multi-level features. We also propose a histogram loss based on Alternative Chi-Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. We also provide a comprehensive quantitative comparison of 22 state-of-the-art saliency models on distorted images, which contributes a robustness benchmark for saliency community. Our code and data are available at: https://***/CZHQuality/

关键词： Luminance

UNRAVELING MRD DYNAMICS TO ENHANCE OUTCOME PREDICTION IN MANTLE CELL LYMPHOMA—INSIGHTS FROM THE FONDAZIONE ITALIANA LINFOMI MCL0208 CLINICAL TRIAL

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Hematological Oncology 2025年第S3期43卷

作者： F. Cordero S. Ferrero S. Pernice E. Genuardi D. Volpatto R. Sirovich A. M. Civita A. Evangelista S. Ragaini A. Di Rocco A. Re V. Stefanoni F. Cavallo C. Boccomini M. Balzarotti V. R. Zilioli M. Gomes da Silva L. Arcaini P. Tosi Z. Gian Maria D. Tortarolo E. Hoster C. Pott E. Macintyre M. Dreyling M. Ladetto Computer Science University of Turin Torino Italy Department of Molecular Biotechnology and Health Sciences University of Turin Torino Italy Mathematics University of Turin Turin Italy Unit of Cancer Epidemiology AOU Citta' della Salute e della Scienza di Torino Turin Italy Medicina traslazionale e di precisione Universita' di Roma Sapienza Roma Italy Hematology Ospedali Civili Brescia Italy University of Bologna Institute of Hematology “L. e A. Seràgnoli” Bologna Italy Hematology Division 2 Città della Salute e della Scienza di Torino Turin Italy Istituto Clinico Humanitas Milano Italy Hematology ASST Grande Ospedale Metropolitano Niguarda Milano Italy Departamento de Hematologia Instituto Português de Oncologia Lisboa Portugal Department of Hematology Oncology Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Matteo Pavia Italy UO Ematologia Ospedale Degli Infermi Rimini Italy Electrical and Information Engineering Department Polytechnic University of Bari Bari Italy Ludwig-Maximilians-Universität Munich Institute for Medical Information Processing Biometry and Epidemiology München Germany Department of Medicine Hematology and Oncology University Hospital Schleswig-Holstein Kiel Germany APHP-Centre-Université Paris Cité Paris France Department of Medicine III Ludwig-Maximilians-Universität München Munich Germany University of Eastern Piedmont Alessandria Italy

TDE hosts are green and centrally concentrated: Signatures of a post-merger system

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arXiv 2020年

作者： Hammerstein, Erica Gezari, Suvi van Velzen, Sjoert Cenko, S. Bradley Roth, Nathaniel Ward, Charlotte Frederick, Sara Hung, Tiara Graham, Matthew Foley, Ryan J. Bellm, Eric C. Cannella, Christopher Drake, Andrew J. Kupfer, Thomas Laher, Russ R. Mahabal, Ashish A. Masci, Frank J. Riddle, Reed Rojas-Bravo, César Smith, Roger Department of Astronomy University of Maryland College ParkMD20742 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Center for Cosmology and Particle Physics New York University NY10003 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Texas Tech University Department of Physics & Astronomy Box 41051 79409 LubbockTX United States IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA United States

We study the properties of the galaxies hosting the first 19 tidal disruption events (TDEs) detected with the Zwicky Transient Facility (ZTF) within the context of a carefully constructed, representative host galaxy sample. We find that the ZTF sample of TDE hosts is dominated by compact "green valley" galaxies. After we restrict the comparison sample to galaxies with a similar concentration, as measured by Sersic index, we find this green valley over representation is even larger. That is, concentrated red sequence galaxies are not producing TDEs at elevated levels. We present host galaxy spectra which show that E+A galaxies are overrepresented in the ZTF sample by a factor of ≈22, which is lower than previous TDE host galaxy studies have found. We find that this overrepresentation can be fully accounted for when taking into account the masses, colors, and Sérsic indices of the ZTF TDE hosts. The combination of both green colors and high Sérsic index of the typical TDE host galaxy could be explained if the TDE rate is temporarily enhanced following a merger that leads to a higher central concentration of stars. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

Fundamental Physics Opportunities with the Next-Generation Event Horizon Telescope

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arXiv 2023年

作者： Ayzenberg, Dimitry Blackburn, Lindy Brito, Richard Britzen, Silke Broderick, Avery E. Carballo-Rubio, Raúl Cardoso, Vitor Chael, Andrew Chatterjee, Koushik Chen, Yifan Cunha, Pedro V.P. Davoudiasl, Hooman Denton, Peter B. Doeleman, Sheperd S. Eichhorn, Astrid Eubanks, Marshall Fang, Yun Foschi, Arianna Fromm, Christian M. Galison, Peter Ghosh, Sushant G. Gold, Roman Gurvits, Leonid I. Hadar, Shahar Held, Aaron Houston, Janice Hu, Yichao Johnson, Michael D. Kocherlakota, Prashant Natarajan, Priyamvada Olivares, Héctor Palumbo, Daniel Pesce, Dominic W. Rajendran, Surjeet Roy, Rittick Saurabh Shao, Lijing Tahura, Shammi Tamar, Aditya Tiede, Paul Vincent, Frédéric H. Visinelli, Luca Wang, Zhiren Wielgus, Maciek Xue, Xiao Yakut, Kadri Yang, Huan Younsi, Ziri Theoretical Astrophysics Eberhard-Karls Universität Tübingen TübingenD-72076 Germany Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Harvard University 20 Garden Street CambridgeMA02138 United States CENTRA Departamento de Física Instituto Superior Técnico – IST Universidade de Lisboa – UL Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Waterloo Centre for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada CP3-Origins University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Florida Space Institute University of Central Florida 12354 Research Parkway Partnership 1 OrlandoFL32826 United States Niels Bohr International Academy Niels Bohr Institute Blegdamsvej 17 Copenhagen2100 Denmark Princeton Gravity Initiative Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Physics University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States Campus de Santiago Aveiro3810-183 Portugal High Energy Theory Group Physics Department Brookhaven National Laboratory UptonNY11973 United States Asteroid Initiatives LLC United States Perimeter Institute for Theoretical Physics WaterlooONN2L2Y5 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Strasse 31 Würzburg97074 Germany Institut für Theoretische Physik Goethe Universität Frankfurt Max-von

The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable. Copyright © 2023, The Authors. All rights reserved.

关键词： Telescopes

A joint learning and communications framework for federated learning over wireless networks

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arXiv 2019年

作者： Chen, Mingzhe Yang, Zhaohui Saad, Walid Yin, Changchuan Vincent Poor, H. Cui, Shuguang Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Centre for Telecommunications Research Department of Informatics King's College London WC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms