检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

arXiv

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arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. © 2023, CC BY-NC-ND.

关键词： Diseases

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Graphical representation and hierarchical decomposition mechanism for vertex-cover solution space

arXiv

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arXiv 2019年

作者： Wei, Wei Feng, Xiangnan Wang, Jiannan Liu, Xue Zheng, Zhiming School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Research Institute of Frontier Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Center for Humans and Machines Max Planck Institute for Human Development Berlin14195 Germany

In this paper, solution space organization of minimum vertex-cover problem is deeply investigated using the König-Egérvary (KE) graph and theorem, in which a hierarchical decomposition mechanism named KE-layer structure of general graphs is proposed to reveal the complexity of vertex-cover. An algorithm to verify the KE graph is given by the solution space expression of vertex-cover, and the relation between multi-layer KE graphs and maximal matching is illustrated and proved. Furthermore, a framework to calculate the KE-layer number and approximate the minimal vertex-cover is provided, with different strategies of switching nodes and counting energy. The phase transition phenomenon between different KE-layers are studied with the transition points located, and searching of vertex-cover got by this strategy presents comparable advantage against several other methods. The graphical representation and hierarchical decomposition provide a new perspective to illustrate the intrinsic complexity for large-scale graphs/systems recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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A persistent excess of galaxy-galaxy strong lensing observed in galaxy clusters

arXiv

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arXiv 2023年

作者： Meneghetti, Massimo Cui, Weiguang Rasia, Elena Yepes, Gustavo Acebron, Ana Angora, Giuseppe Bergamini, Pietro Borgani, Stefano Calura, Francesco Despali, Giulia Giocoli, Carlo Granata, Giovanni Grillo, Claudio Knebe, Alexander Macciò, Andrea V. Mercurio, Amata Moscardini, Lauro Natarajan, Priyamvada Ragagnin, Antonio Rosati, Piero Vanzella, Eros INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 BolognaI-40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 BolognaI-40127 Italy Departamento de Física Teórica Facultad de Ciencias Universidad Autónoma de Madrid Módulo 8 MadridE-28049 Spain Facultad de Ciencias Universidad Autónoma de Madrid MadridE-28049 Spain Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom INAF – Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Istituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy Dipartimento di Fisica Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy Dipartimento di Fisica e Scienze della Terra Università degli Studi di Ferrara Via Saragat 1 FerraraI-44122 Italy INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Gobetti 93/2 BolognaI-40129 Italy INAF - IASF Milano via A. Corti 12 MilanoI-20133 Italy International Centre for Radio Astronomy Research University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia New York University Abu Dhabi PO Box Abu Dhabi129188 United Arab Emirates New York University Abu Dhabi United Arab Emirates Max-Planck-Institut für Astronomie Königstuhl 17 HeidelbergD-69117 Germany FiscianoI–84084 Italy Department of Astronomy Yale University New HavenCT06511 United States Department of Physics Yale University New HavenCT06520 United States ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy

Context. Previous studies have revealed that the estimated probability of galaxy-galaxy strong lensing in observed galaxy clusters exceeds the expectations from the Λ Cold Dark Matter cosmological model by one order of magnitude. Aims. We aim to understand the origin of this excess by analyzing a larger set of simulated galaxy clusters, and investigating how the theoretical expectations vary under different adopted prescriptions and numerical implementations of star formation and feedback in simulations. Methods. We performed a ray-tracing analysis of 324 galaxy clusters from the Three Hundred project, comparing the Gadget-X and Gizmo-Simba runs. These simulations, which start from the same initial conditions, were performed with different implementations of hydrodynamics and galaxy formation models tailored to match different observational properties of the intracluster medium and cluster galaxies. Results. We find that galaxies in the Gizmo-Simba simulations develop denser stellar cores than their Gadget-X counterparts. Consequently, their probability for galaxy-galaxy strong lensing is higher by a factor of ∼ 3. This increment is still insufficient to fill the gap with observations as a discrepancy by a factor ∼ 4 still persists. In addition, we find that several simulated galaxies have Einstein radii that are too large compared to observations. Conclusions. We conclude that a persistent excess of galaxy-galaxy strong lensing exists in observed galaxy clusters. The origin of this discrepancy with theoretical predictions is still unexplained in the framework of the cosmological hydrodynamical simulations. This might signal a hitherto unknown issue with either the simulation methods or our assumptions regarding the standard cosmological model. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism

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Academic radiology 2025年

作者： Sohaib Asif Yuqi Yan Bojian Feng Meiling Wang Yuxin Zheng Tian Jiang Ruyi Fu Jincao Yao Lujiao Lv Mei Song Lin Sui Zheng Yin Vicky Yang Wang Dong Xu Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. D.X.) Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. V.Y.W. D.X.) Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. V.Y.W. D.X.) Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou Zhejiang 310022 China (S.A. Y.Y. B.F. T.J. J.Y. L.L. M.S. M.S. L.S. V.Y.W. D.X.). Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou Zhejiang 310022 China (S.A. Y.Y. B.F. T.J. J.Y. L.L. M.S. M.S. L.S. V.Y.W. D.X.) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.). Second Clinical College Zhejiang University of Traditional Chinese Medicine Hangzhou China (M.W. Y.Z. Z.Y.). Hangzhou Fuyang Hospital of Orthopedics of Traditional Chinese Medicine Special Inspection Department Zhejiang China (R.F.). Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.) Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province Hangzhou 310022 China (J.Y.). Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.). Electronic address: xudong@***.

RATIONALE AND OBJECTIVES:Early detection of malignant lesions in ultrasound images is crucial for effective cancer diagnosis and treatment. While traditional methods rely on radiologists, deep learning models can improve accuracy, reduce errors, and enhance efficiency. This study explores the application of a deep learning model for classifying benign and malignant lesions, focusing on its performance and interpretability. MATERIALS AND METHODS:In this study, we proposed a feature fusion-based deep learning model for classifying benign and malignant lesions in ultrasound images. The model leverages advanced architectures such as MobileNetV2 and DenseNet121, enhanced with feature fusion and attention mechanisms to boost classification accuracy. The clinical dataset comprises 2171 images collected from 1758 patients between December 2020 and May 2024. Additionally, we utilized the publicly available BUSI dataset, consisting of 780 images from female patients aged 25 to 75, collected in 2018. To enhance interpretability, we applied Grad-CAM, Saliency Maps, and shapley additive explanations (SHAP) techniques to explain the model's decision-making. A comparative analysis with radiologists of varying expertise levels is also conducted. RESULTS:The proposed model exhibited the highest performance, achieving an AUC of 0.9320 on our private dataset and an area under the curve (AUC) of 0.9834 on the public dataset, significantly outperforming traditional deep convolutional neural network models. It also exceeded the diagnostic performance of radiologists, showcasing its potential as a reliable tool for medical image classification. The model's success can be attributed to its incorporation of advanced architectures, feature fusion, and attention mechanisms. The model's decision-making process was further clarified using interpretability techniques like Grad-CAM, Saliency Maps, and SHAP, offering insights into its ability to focus on relevant image features for accurate classi

关键词： Attention mechanisms Deep learning Feature fusion Medical diagnostics Ultrasound imaging

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A fine-grained perspective on the robustness of global cargo ship transportation networks

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Journal of Geographical Sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

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Insight-HXMT and GECAM-C observations of the brightest-of-all-time GRB 221009A

arXiv

引用

arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Brute-force attack mitigation on remote access services via software-defined perimeter

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Scientific reports 2025年第1期15卷 18599页

作者： Francis A Ruambo Elijah E Masanga Beston Lufyagila Abdelhamied A Ateya Ahmed A Abd El-Latif May Almousa Bassem Abd-El-Atty School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China. College of Information Communication and Technology Mbeya University of Science and Technology (MUST) P.O Box 131 Mbeya Tanzania. Research and Development Department Aifrruis Laboratories Mbeya Tanzania. EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University 11586 Riyadh Saudi Arabia. Department of Electronics and Communications Engineering Zagazig University Zagazig 44519 Sharqia Egypt. EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University 11586 Riyadh Saudi Arabia. aabdellatif@psu.edu.sa. Center of Excellence in Quantum and Intelligent Computing Prince Sultan University 11586 Riyadh Saudi Arabia. aabdellatif@psu.edu.sa. Department of Mathematics and Computer Science Faculty of Science Menoufia University Shibin Al Kawm 32511 Egypt. aabdellatif@psu.edu.sa. Information Technology Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science Faculty of Computers and Information Luxor University Luxor 85957 Egypt. bassem.abdelatty@fci.luxor.edu.eg.

Remote Access Services (RAS)-including protocols such as Remote Desktop Protocol (RDP), Secure Shell (SSH), Virtual Network computing (VNC), Telnet, File Transfer Protocol (FTP), and Secure File Transfer Protocol (SFTP)-are essential to modern network infrastructures, particularly with the rise of remote work and cloud adoption. However, their exposure significantly increases the risk of brute-force attacks (BFA), where adversaries systematically guess credentials to gain unauthorized access. Traditional defenses like IP blocklisting and multifactor authentication (MFA) often struggle with scalability and adaptability to distributed attacks. This study introduces a zero-trust-aligned Software-Defined Perimeter (SDP) architecture that integrates Single Packet Authorization (SPA) for service cloaking and Connection Tracking (ConnTrack) for real-time session analysis. A Docker-based prototype was developed and tested, demonstrating no successful BFA attempts observed, latency reduction by above 10% across all evaluated RAS protocols, and the system CPU utilization reduction by 48.7% under attack conditions without impacting normal throughput. It also proved effective against connection-oriented attacks, including port scanning and distributed denial of service (DDoS) attacks. The proposed architecture offers a scalable and efficient security framework by embedding proactive defense at the authentication layer. This work advances zero-trust implementations and delivers practical, low-overhead protection for securing RAS against evolving cyber threats.

关键词： Brute-force attacks Remote access services (RAS) Single packet authorization (SPA) Software-defined perimeter (SDP) Zero-trust architecture

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The GAPS program at TNG XLVII: The unusual formation history of V1298 Tau

arXiv

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arXiv 2023年

作者： Turrini, D. Marzari, F. Polychroni, D. Claudi, R. Desidera, S. Mesa, D. Pinamonti, M. Sozzetti, A. Mascareño, A. Suárez Damasso, M. Benatti, S. Malavolta, L. Micela, G. Zinzi, A. Béjar, V.J.S. Biazzo, K. bignamini, A. Bonavita, M. Borsa, F. Del Burgo, C. Chauvin, G. Delorme, P. Hernández, J.I. González Gratton, R. Hagelberg, J. Janson, M. Langlois, M. Lanza, A.F. Lazzoni, C. Lodieu, N. Maggio, A. Mancini, L. Molinari, E. Molinaro, M. Murgas, F. Nardiello, D. Inaf - Osservatorio Astrofisico di Torino via Osservatorio 20 Pino TorineseI-10025 Italy Icsc National Research Centre for High Performance Computing Big Data and Quantum Computing Via Magnanelli 2 Casalecchio di RenoI-40033 Italy Inaf - Istituto di Astrofisica e Planetologia Spaziali via Fosso del Cavaliere 100 RomaI-00133 Italy Dipartimento di Fisica e Astronomia Università di Padova Via Marzolo 8 PadovaI-35121 Italy Inaf - Osservatorio Astronomico di Padova Vicolo dell'Osservatorio 5 PadovaI-35122 Italy Inaf - Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy Inaf - Osservatorio Astronomico di Palermo Piazza del Parlamento 1 Palermo90134 Italy Space Science Data Center - Asi Via del Politecnico snc Roma00133 Italy Instituto de Astrofísica de Canarias Tenerife La LagunaE-38200 Spain Universidad de la Laguna Dept. Astrofísica Tenerife La LagunaE-38206 Spain Inaf - Osservatorio Astronomico di Roma Via Frascati 33 Monte Porzio CatoneI-00040 Italy School of Physical Sciences The Open University Walton Hall Milton KeynesMK7 6AA United Kingdom Supa Institute for Astronomy University of Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Inaf - Osservatorio Astronomico di Brera Via E. Bianchi 46 MerateI-23087 Italy Instituto Nacional de Astrofísica Óptica y Electrónica Luis Enrique Erro 1 Sta. Ma. Tonantzintla Puebla Mexico Universitè Côte d'Azur Observatoire de la Côte d'Azur Cnrs Laboratoire Lagrange Nice France Universitè Grenoble Alpes Cnrs Ipag Grenoble38000 France Geneva Observatory University of Geneva Chemin Pegasi 51 Versoix1290 Switzerland Department of Astronomy Stockholm University AlbaNova University Center Stockholm10691 Sweden Cral Umr 5574 Cnrs Université de Lyon Ecole Normale Supérieure de Lyon 46 allée d'Italie Lyon69364 Cedex 07 France Inaf - Osservatorio Astrofisico di Catania Via Santa Sofia 78 Catania95123 Italy Department of Physics and Astronomy University of Ex

Context. Observational data from space and ground-based campaigns reveal that the 10-30 Ma old V1298 Tau star hosts a compact and massive system of four planets. Mass estimates are available for the two outer giant planets and point to unexpectedly high densities for their young ages. Aims. We investigate the formation of these two outermost giant planets, V1298 Tau b and e, and the present dynamical state of V1298 Tau's global architecture to shed light on the history of this young and peculiar extrasolar system. Methods. We perform detailed N-body simulations to explore the link between the densities of V1298 Tau b and e and their migration and accretion of planetesimals within the native circumstellar disk. We combine N-body simulations and the normalized angular momentum deficit (NAMD) analysis of the architecture to characterize V1298 Tau's dynamical state and connect it to the formation history of the system. We search for outer planetary companions to constrain V1298 Tau's planetary architecture and the extension of its primordial circumstellar disk. Results. The high densities of V1298 Tau b and e suggest they formed quite distant from their host star, likely beyond the CO2snowline. The higher nominal density of V1298 Tau e suggests it formed farther out than V1298 Tau b. The current architecture of V1298 Tau is not characterized by resonant chains. Planet-planet scattering with an outer giant planet is the most likely cause for the instability, but our search for outer companions using SPHERE and GAIA observations can currently exclude only the presence of planets more massive than 2 MJ. Conclusions. The most plausible scenario for V1298 Tau's formation is that the system is formed by convergent migration and resonant trapping of planets born in a compact and plausibly massive disk. The migration of V1298 Tau b and e leaves in its wake a dynamically excited protoplanetary disk and naturally creates the conditions for the later breaking of the resonant chain

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Needs analysis for the development of interactive virtual reality-based educational media on combustion engine mechanical technology

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IOP Conference Series: Materials Science and Engineering 2020年第1期979卷

作者： Sutiman A Yudantoko A Yudianto M Solikin I W Adiyasa N M Suaib F Sholichin D A Irawati Automotive Engineering Education Department Universitas Negeri Yogyakarta Colombo Street 1 Yogyakarta Indonesia. School of Computing Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia.

Needs analysis is the first and necessary step for the researchers to design a newly developed learning media to find out a set of specific requirements to conduct the research activities. The study aims (1) to investigate the user's learning preference, (2) to select the suitable learning materials and to choose the appropriate type of engines, and (3) to identify the needs for the virtual reality development. A focus group discussion (FGD) comprising some representative users and experts in learning media has been involved in the activities with the use of the FGD guidance sheet as the supplementary tools. Additionally, the literature study has also been conducted to collect the necessary additional data to perform a 3D design of the engine. The data were analyzed qualitatively by examining the comment from the users, the experts, and the outcome of the literature review study. It was then concluded that (1) the users require the learning process, which does not need to alternate the learning objects. (2) The two-stroke single-cylinder engine was decided to choose by considering the complexity of the engine mechanical construction. Finally, (3) the needs for engine assembly drawing and virtual reality development have also been described.

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