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arXiv 2025年

作者： Larsen, Alexander Swaby, Tera N. Kobulnicky, Henry A. Cañas, Caleb I. Kanodia, Shubham Libby-Roberts, Jessica Monson, Andrew Gupta, Arvind Cochran, William Mahadevan, Suvrath Bender, Chad Diddams, Scott A. Halverson, Samuel Lin, Andrea S.J. Moe, Maxwell Ninan, Joe Robertson, Paul Roy, Arpita Schwab, Christian Stefansson, Gudmundur Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Department of Astronomy & Astrophysics The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 1111 Engineering Dr. BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Department of Astronomy California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Brown dwarfs bridge the gap between stars and planets, providing valuable insight into both planetary and stellar formation mechanisms. Yet the census of transiting brown dwarf companions, in particular around M dwarf stars, remains incomplete. We report the discovery of two transiting brown dwarfs around low-mass hosts using a combination of space- and ground-based photometry along with near-infrared radial velocities. We characterize TOI-5389Ab (68.0+2−2.22 MJ) and TOI-5610b (40.4+1−1.00 MJ), two moderately massive brown dwarfs orbiting early M dwarf hosts (Teff = 3569±59 K and 3618±59 K, respectively). For TOI-5389Ab, the best fitting parameters are period P = 10.40046 ± 0.00002 days, radius RBD = 0.824+0−0.033031 RJ, and low eccentricity e = 0.0962+0−0.00270046. In particular, this constitutes one of the most extreme substellar-stellar companion-to-host mass ratios of q = 0.150. For TOI-5610b, the best fitting parameters are period P = 7.95346 ± 0.00002 days, radius RBD = 0.887+0−0.031031 RJ, and moderate eccentricity e = 0.354+0−0.011012. Both targets are expected to have shallow but potentially observable secondary transits: 500 ppm in Johnson K band for both. A statistical analysis of M-dwarf/BD systems reveals for the first time that those at short orbital periods (P J BD J companions (q © 2025, CC0.

关键词： Stars

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Controlling a networked SIS model via a single input over undirected graphs

arXiv

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arXiv 2020年

作者： Wang, Dan Liu, Ji Paré, Philip E. Chen, Wei Qiu, Li Beck, Carolyn L. Başar, Tamer Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electrical and Computer Engineering Stony Brook University NY11794 United States Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing China Coordinated Science Laboratory University of Illinois at Urbana-Champaign IL61801 United States

This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition on the values of curing and infection rates for the healthy state to be exponentially stable is obtained via the analysis of signed Laplacians when the control input is the curing budget of a single agent. In the case when the healthy state is stabilizable, an explicit expression for the minimum curing budget is provided. The utility of the algorithm is demonstrated using a simulation over a network of cities in the northeastern United States. Copyright © 2020, The Authors. All rights reserved.

关键词： Budget control

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Stochastic and mixed flower graphs

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Physical Review E 2020年第5期101卷 052315-052315页

作者： C. Tyler Diggans Erik M. Bollt Daniel ben-Avraham Clarkson Center for Complex Systems Science (C3S2) Clarkson University Potsdam New York 13699 USA Department of Physics Clarkson University Potsdam New York 13699 USA Air Force Research Laboratory: Information Directorate Rome New York 13441 USA Department of Electrical and Computer Engineering Clarkson University Potsdam New York 13699 USA

Stochasticity is introduced to a well studied class of recursively grown graphs: (u,v)-flower nets, which have power-law degree distributions as well as small-world properties (when u=1). The stochastic variant interpolates between different (deterministic) flower graphs thus adding flexibility to the model. The random multiplicative growth process involved, however, leads to a spread ensemble of networks with finite variance for the number of links, nodes, and loops. Nevertheless, the degree exponent and loopiness exponent attain unique values in the thermodynamic limit of infinitely large graphs. We also study a class of mixed flower networks, closely related to the stochastic flowers, but which are grown recursively in a deterministic way. The deterministic growth of mixed flower-nets eliminates ensemble spreads, and their recursive growth allows for exact analysis of their (uniquely defined) mixed properties.

关键词： Growth processes Networks & random structures Stochastic analysis methods

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Why botnets work: Distributed brute-force attacks need no synchronization

Why botnets work: Distributed brute-force attacks need no sy...

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作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Medard, Muriel Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv6997801 Israel EA Digital Platform-Data and AI Electronic Arts Redwood CityCA94065 United States Department of Electrical Engineering Ben-Gurion University of the Negev Beersheba8410501 Israel

In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols. While some solutions exist to prevent online brute-force attacks that arise from one single IP address, attacks performed by botnets are more challenging. In this paper, we analyze these distributed attacks by using a simplified model. Our aim is to understand the impact of distribution and asynchronization on the overall computational effort necessary to breach a system. Our result is based on guesswork, a measure of the number of queries (guesses) before a correct sequence, such as a password, is found in an optimal attack. Guesswork is a direct surrogate for time and computational effort of guessing a sequence from a set of sequences with the associated likelihoods. We model the lack of synchronization by a worst-case optimization in which the queries made by multiple adversarial agents are received in the worst possible order for the adversary, resulting in a min-max formulation. We show that, even without synchronization, and for sequences of growing length, the asymptotic optimal performance is achievable by using randomized guesses drawn from an appropriate distribution. Therefore, randomization is key for the distributed asynchronous attacks. In other words, asynchronous guessers can asymptotically perform brute-force attacks as efficiently as synchronized guessers. © 2019 IEEE.

关键词： Botnet

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Correction: Sub-nL thin-film differential scanning calorimetry chip for rapid thermal analysis of liquid samples

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Lab on a chip 2024年第2期24卷 383页

作者： Sheng Ni Hanliang Zhu Pavel Neuzil Levent Yobas Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR China. eelyobas@ust.hk. The Ministry of Education Key Laboratory of Micro/Nano Systems for Aerospace Department of Microsystem Engineering School of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. The Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 616 00 Brno-Královo Pole Czech Republic. Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR China.

Correction for 'Sub-nL thin-film differential scanning calorimetry chip for rapid thermal analysis of liquid samples' by Sheng Ni , , 2023, , 1926-1934, https://***/10.1039/D2LC01094A.

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GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification

arXiv

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arXiv 2019年

作者： Yin, Xuwang Kolouri, Soheil Rohde, Gustavo K. Department of Electrical and Computer Engineering University of Virginia United States Information and Systems Sciences Laboratory HRL Laboratories LLC.

The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the attacker is oblivious to the detection mechanism. In this paper we propose a principled adversarial example detection method that can withstand norm-constrained white-box attacks. Inspired by one-versus-the-rest classification, in a K class classification problem, we train K binary classifiers where the i-th binary classifier is used to distinguish between clean data of class i and adversarially perturbed samples of other classes. At test time, we first use a trained classifier to get the predicted label (say k) of the input, and then use the k-th binary classifier to determine whether the input is a clean sample (of class k) or an adversarially perturbed example (of other classes). We further devise a generative approach to detecting/classifying adversarial examples by interpreting each binary classifier as an unnormalized density model of the class-conditional data. We provide comprehensive evaluation of the above adversarial example detection/classification methods, and demonstrate their competitive performances and compelling properties. Code is available at https://***/xuwangyin/GAT-Generative-Adversarial-Training 1 Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Content-aware robust semantic transmission of images over wireless channels with GANs

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Digital Communications and Networks 2024年

作者： Xuyang Chen Daquan Feng Qi He Yao Sun Gaojie Chen Xiang-Gen Xia College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu 611731 China James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UK 5GIC & 6GIC Institute for Communication Systems University of Surrey Guildford GU2 7XH UK Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA

Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.

关键词： Semantic communication GANs Image transmission ROI

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Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network

arXiv

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arXiv 2020年

作者： Wu, Xin Li, Wei Hong, Danfeng Tian, Jiaojiao Tao, Ran Du, Qian School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China Wessling82234 Germany Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States

This is a pre-print of a paper accepted by ISPRS Journal Photogrammetry and Remote Sensing. Please note that compared to the published version, we corrected several F1-Scores in Tables (marked in red) due to miscalculation. Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site. Copyright © 2020, The Authors. All rights reserved.

关键词： Remote sensing

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Photovoltaics-driven power production can support human exploration on Mars

arXiv

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arXiv 2021年

作者： Abel, Anthony J. Berliner, Aaron J. Mirkovic, Mia Collins, William D. Arkin, Adam P. Clark, Douglas S. BerkeleyCA94720 United States Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States Department of Bioengineering University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Climate and Ecosystems Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Earth and Planetary Sciences University of California BerkeleyCA94720 United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

A central question surrounding possible human exploration of Mars is whether crewed missions can be supported by available technologies using in situ resources. Here, we show that photovoltaics-based power systems would be adequate and practical to sustain a crewed outpost for an extended period over a large fraction of the planet’s surface. Climate data were integrated into a radiative transfer model to predict spectrally-resolved solar flux across the Martian surface. This informed detailed balance calculations for solar cell devices that identified optimal bandgap combinations for maximizing production capacity over a Martian year. We then quantified power systems, manufacturing, and agricultural demands for a six-person mission, which revealed that photovoltaics-based power generation would require © 2021, CC BY-NC-SA.

关键词： Climate models

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Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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