检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Schrom, Edward Kinzig, Ann P. Forrest, Stephanie Graham, Andrea L. Levin, Simon A. Bergstrom, Carl T. Castillo-Chavez, Carlos Collins, James P. de Boer, Rob J. Doupé, Adam Ensafi, Roya Feldman, Stuart Grenfell, Bryan T. Halderman, J. Alex Huijben, Silvie Maley, Carlo Moses, Melanie Perelson, Alan S. Perrings, Charles Plotkin, Joshua Rexford, Jennifer Tiwari, Mohit Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ08544 United States School of Life Sciences Arizona State University TempeAZ85287 United States Biodesign Center for Biocomputation Security and Society Arizona State University TempeAZ85287 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85287 United States Santa Fe Institute Santa FeNM87501 United States Department of Biology University of Washington SeattleWA98195 United States School of Human Evolution and Social Change Arizona State University TempeAZ85287 United States Theoretical Biology and Bioinformatics Utrecht University Utrecht3584 CH Netherlands Center for Cybersecurity and Trusted Foundations Global Security Initiative Arizona State University TempeAZ85287 United States Department of Electrical Engineering and Computer Science Computer Science and Engineering Division University of Michigan Ann ArborMI48109 United States Schmidt Futures New YorkNY10011 United States Princeton School of Public and International Affairs Princeton University PrincetonNJ08544 United States Center for Computer Security and Society University of Michigan Ann ArborMI48109 United States Arizona Cancer Evolution Center Arizona State University TempeAZ85287 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States Department of Biology University of New Mexico AlbuquerqueNM87131 United States Theoretical Biology and Biophysics Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer Science Princeton University PrincetonNJ08540 United States Department of Electrical and Computer Engineering University of Texas AustinTX78712 United States

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the ‘self’, are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms. © 2023, The Authors. All rights reserved.

关键词： Cybersecurity

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How does the primate brain combine generative and discriminative computations in vision?

arXiv

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arXiv 2024年

作者： Peters, Benjamin DiCarlo, James J. Gureckis, Todd Haefner, Ralf Isik, Leyla Tenenbaum, Joshua Konkle, Talia Naselaris, Thomas Stachenfeld, Kimberly Tavares, Zenna Tsao, Doris Yildirim, Ilker Kriegeskorte, Nikolaus Zuckerman Mind Brain Behavior Institute Columbia University United States School of Psychology & Neuroscience University of Glasgow United Kingdom Department of Brain and Cognitive Sciences MIT United States McGovern Institute for Brain Research MIT United States NSF Center for Brains Minds and Machines MIT United States Quest for Intelligence Schwarzman College of Computing MIT United States Department of Psychology New York University United States Brain and Cognitive Sciences University of Rochester United States Center for Visual Science University of Rochester United States Department of Cognitive Science Johns Hopkins University United States Computer Science and Artificial Intelligence Laboratory MIT United States Department of Psychology Harvard University United States Center for Brain Science Harvard University United States Kempner Institute for Natural and Artificial Intelligence Harvard University United States Department of Neuroscience University of Minnesota United States DeepMind United Kingdom Data Science Institute Columbia University United States Dept of Molecular & Cell Biology University of California Berkeley United States Howard Hughes Medical Institute United States Department of Psychology Yale University United States Department of Statistics and Data Science Yale University United States Department of Psychology Columbia University United States Department of Neuroscience Columbia University United States Department of Electrical Engineering Columbia University United States

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision. © 2024, CC BY.

关键词： Feedforward neural networks

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The shadow of a laser beam

arXiv

引用

arXiv 2024年

作者： Abrahao, Raphael A. Morin, Henri P.N. Pagé, Jordan T.R. Safari, Akbar Boyd, Robert W. Lundeen, Jeff S. Department of Physics University of Ottawa 25 Templeton Street OttawaONK1N 6N5 Canada Brookhaven National Laboratory UptonNY11973 United States Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Institute of Optics University of Rochester RochesterNY14627 United States Joint Centre for Extreme Photonics University of Ottawa National Research Council of Canada 100 Sussex Drive OttawaONK1A 0R6 Canada

Light, being massless, casts no shadow;under ordinary circumstances, photons pass right through each other unimpeded. Here, we demonstrate a laser beam acting like an object – the beam casts a shadow upon a surface when the beam is illuminated by another light source. We observe a regular shadow in the sense it can be seen by the naked eye, it follows the contours of the surface it falls on, and it follows the position and shape of the object (the laser beam). Specifically, we use a nonlinear optical process involving four atomic levels of ruby. We are able to control the intensity of a transmitted laser beam by applying another perpendicular laser beam. We experimentally measure the dependence of the contrast of the shadow on the power of the laser beam, finding a maximum of approximately of approximately 22%, similar to that of a shadow of a tree on a sunny day. We provide a theoretical model that predicts the contrast of the shadow. This work opens new possibilities for fabrication, imaging, and illumination. Copyright © 2024, The Authors. All rights reserved.

关键词： Laser beams

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Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

arXiv

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arXiv 2024年

作者： Li, Yuanzhao Bai, Yunhe Feng, Yang Luan, Jianli Gao, Zongwei Chen, Yang Tong, Yitian Liu, Ruixuan Chong, Su Kong Wang, Kang L. Zhou, Xiaodong Shen, Jian Zhang, Jinsong Wang, Yayu Chen, Chui-Zhen Xie, XinCheng Feng, Xiao He, Ke Xue, Qi-Kun Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200438 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China School of Physical Science and Technology Soochow University Suzhou215006 China Institute for Advanced Study Soochow University Suzhou215006 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China Southern University of Science and Technology Shenzhen518055 China Frontier Science Center for Quantum Information Beijing100084 China Hefei National Laboratory Hefei230088 China

In the realm of topological quantum physics, it is widely accepted that a global bandgap is necessary to achieve quantized Hall conductance, irrespective of its origin-magnetic field, exchange coupling or other mechanisms. A material exhibiting the quantum anomalous Hall (QAH) effect had been considered equivalent to a Chern insulator, a band insulator with non-zero Chern number. However, a Chern Anderson insulator (CAI), which arises in a conducting state with exchange-induced Berry curvature splitting and disorder-induced Anderson localization, can in principle exhibit the QAH effect. In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy (MBE). We observe a reentrant QAH effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the CAI state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization. © 2024, CC BY-NC-ND.

关键词： Molecular beam epitaxy

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A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes

arXiv

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arXiv 2020年

作者： Khairy, Sami Balaprakash, Prasanna Cai, Lin X. Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States Mathematics and Computer Science Division and Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States

The canonical solution methodology for finite constrained Markov decision processes (CMDPs), where the objective is to maximize the expected infinite-horizon discounted rewards subject to the expected infinite-horizon discounted costs constraints, is based on convex linear programming. In this brief, we first prove that the optimization objective in the dual linear program of a finite CMDP is a piece-wise linear convex function (PWLC) with respect to the Lagrange penalty multipliers. Next, we propose a novel two-level Gradient-Aware Search (GAS) algorithm which exploits the PWLC structure to find the optimal state-value function and Lagrange penalty multipliers of a finite CMDP. The proposed algorithm is applied in two stochastic control problems with constraints: robot navigation in a grid world and solar-powered unmanned aerial vehicle (UAV)-based wireless network management. We empirically compare the convergence performance of the proposed GAS algorithm with binary search (BS), Lagrangian primal-dual optimization (PDO), and Linear Programming (LP). Compared with benchmark algorithms, it is shown that the proposed GAS algorithm converges to the optimal solution faster, does not require hyper-parameter tuning, and is not sensitive to initialization of the Lagrange penalty multiplier. Copyright © 2020, The Authors. All rights reserved.

关键词： Wireless networks

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

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Characterisation of state-preparation uncertainty in quantum key distribution

arXiv

引用

arXiv 2022年

作者： Huang, Anqi Mizutani, Akihiro Lo, Hoi-Kwong Makarov, Vadim Tamaki, Kiyoshi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Graduate School of Engineering Science Osaka University Toyonaka Osaka560-8531 Japan Department of Electrical & Computer Engineering Department of Physics University of Toronto TorontoONM5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street TorontoONM5G 1L5 Canada Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterising the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100 km of fiber. The measured intensity fluctuation intervals are however so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterisation methods pave the way for a future certification standard. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum cryptography

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Handling ECG vital signs in personalized ubiquitous telemedicine services 9th

Handling ECG vital signs in personalized ubiquitous telemedi...

引用

9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018

作者： Papaioannou, Maria Mandellos, George Panagiotakopoulos, Theodor Lymperopoulos, Dimitrios Wired Communication Laboratory Department of Electrical and Computer Engineering University of Patras Patras Greece Mobile and Pervasive Computing Quality and Ambient Intelligence Laboratory School of Science and Technology Hellenic Open University Patras26335 Greece

ISBN: (纸本)9783030051945

Nowadays, telemedicine services are based on real time acquisition and processing of several types of in vitro patient data, especially vital signs. In this context, the storage, transmission, and management of digital ECG signals are major topics of debate and research nowadays as ECG is one of the most commonly performed examinations all over the world. Hence, many efforts have been already spent in constructing low power and small size ECG biosensors as well as in developing the adequate protocols for organizing and assessing the collected data. Despite SCP-ECG is the common accepted protocol, an excessive amount of ECG formats has been proposed and implemented by a plethora of researchers. This paper presents the SCP-ECG protocol and surveys the current state of medical frameworks and systems for collecting and organizing ECG data and other biosignals’ data that are commonly used for the provision of personalized and ubiquitous telemedicine services. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Electrocardiograms

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UseGeo - A UAV-based multi-sensor dataset for geospatial research

引用

ISPRS Open Journal of Photogrammetry and Remote Sensing 2024年 13卷

作者： Nex, F. Stathopoulou, E.K. Remondino, F. Yang, M.Y. Madhuanand, L. Yogender, Y. Alsadik, B. Weinmann, M. Jutzi, B. Qin, R. ITC Department of Earth Observation Science Faculty ITC University of Twente Enschede Netherlands 3D Optical Metrology (3DOM) Unit Bruno Kessler Foundation (FBK) Trento Italy Visual Computing Group University of Bath Bath United Kingdom Department of Physical Geography Faculty of Geosciences University of Utrecht Utrecht Netherlands Inter-university Department of Regional & Urban Studies and Planning (DIST) Politecnico di Torino Italy Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities;although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain;even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://***/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction. © 2024 The Authors

关键词： 3D reconstruction Deep learning LiDAR Monocular depth estimation Multi-view stereo Stereo matching UAV

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Faithful Transfer of 3D Propagation Characteristics of Deterministic and Random Optical Fields to Coded Aperture Imaging Systems Using Lucy-Richardson-Rosen Algorithm

Faithful Transfer of 3D Propagation Characteristics of Deter...

引用

Next Generation Electronics (NEleX), International Conference on

作者： Agnes Pristy Ignatius Xavier Francis Gracy Arockiaraj Shivasubramanian Gopinath Aravind Simon John Francis Rajeswary Andra Naresh Kumar Reddy Rashid A. Ganeev M. Scott Arockia Singh S.D. Milling Tania Vijayakumar Anand Institute of Physics University of Tartu Estonia School of Electrical and Computer Engineering Ben Gurion University of the Negev Israel Laboratory of Nonlinear Optics University of Latvia Riga Latvia Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Physics and Astronomy Chirchik State Pedagogical University Chirchik Uzbekistan Dr. Jeyasekharan Hospital Nagercoil Tamil Nadu India Dr. Scott's Laser and Piles Fistula Center Nagercoil Tamil Nadu India Department of Orthodontics Rajas Dental College and Hospital Tirunelveli Tamil Nadu India Darshan Dental and Orthodontic Clinic Kanyakumari Tamil Nadu India Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM) School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

engineering the complex amplitude and polarization of light is essential for various applications. In this direction, many deterministic and random optical beams such as Airy Bessel, and self-rotating beams were developed. While the above beams satisfied the requirements for the targeted applications, they are not suitable for imaging applications in spite of the valuable axial characteristics they possess, as they are not effective object-image mapping elements. Consequently, when exotic beams were implemented for direct imaging, only a distorted image was obtained. However, the scenario is different in coded aperture imaging (CAI) methods, where the imaging mode is indirect, consisting of optical recording and computational image recovery. Therefore, the point spread function (PSF) in CAI is not the recorded intensity distribution but the reconstructed intensity distribution. By employing a suitable computational reconstruction method, it is possible to convert the recorded intensity distribution into a Delta-like function. In this study, Lucy-Richardson-Rosen algorithm has been implemented as a generalized image recovery method for a wide range of optical beams, and the performance is validated in both simulation and optical experiments.

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