检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Cooperation and distributed optimization for the unreliable wireless game with indirect reciprocity

引用

Science China(Information Sciences) 2017年第11期60卷 129-145页

作者： Changbing TANG Xiang LI Zhen WANG Jianmin HAN College of Mathematics Physics and Information Engineering Zhejiang Normal University Adaptive Networks and Control Lab Department of Electronic EngineeringFudan University Research Center of Smart Networks and Systems Fudan University School of Cyberspace Hangzhou Dianzi University

Cooperation in packet forwarding among users and operators of a distributed wireless network has been widely studied. However, because of the limited computational resources, users in wireless communication do not prefer to cooperate with others unless cooperation may improve their own performance. Therefore, the key problem in cooperation enforcement must be solved first to enable a wireless network to be efficient. Yet,most of the existing game-theoretic cooperation stimulation approaches assume that the interactions between any pair of players(users) are long-lasting. In this paper, we apply game theory to optimize the communication efficiency of a distributed wireless network with finite number of interactions between any pair of players. Based on the mechanism of indirect reciprocity, we theoretically analyze the optimal action rule with the method of dynamic programming, and derive the approximate threshold of benefit-to-cost ratio to achieve the optimal action rule. Furthermore, we adopt the replicator dynamics to assess the evolutionary stability of the optimal action rule against the perturbation effect. Numerical illustrations verify the performance of the proposed method on wireless cooperation.

关键词： wireless cooperation distributed optimization game theory indirect reciprocity optimal action evolutionary stability

来源：评论

学校读者我要写书评

暂无评论

Toward the universal quantum cloner limit for designing compact photonic CNOT gate

arXiv

引用

arXiv 2019年

作者： Gueddana, Amor Lakshminarayanan, Vasudevan Green & Smart Communication Systems Lab Gres'Com Engineering School of Communication of Tunis Sup'Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We suggest a near deterministic compact model of a photonic CNOT gate based on a quantum dot trapped in a double sided optical microcavity and a universal cloner. Our design surpasses the cloner optimal limit of 5/6 and we show that it provides fidelity around 91 % in the weak coupling regime. Copyright © 2019, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

来源：评论

学校读者我要写书评

暂无评论

Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM systems

arXiv

引用

arXiv 2019年

作者： Wang, Bolei Jian, Mengnan Gao, Feifei Ye Li, Geoffrey Jin, Shi Lin, Hai State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States National Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Information Systems Graduate School of Engineering Osaka Prefecture University SakaiOsaka Japan

With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole array will become large and comparable to the time-domain sample period, which is known as the spatial-wideband effect. In this case, different subcarriers in an orthogonal frequency division multiplexing (OFDM) system will "see" distinct angles of arrival (AoAs) for the same path. This effect is known as beam squint, resulting from the spatial-wideband effect, and makes the approaches based on the conventional multiple-input multiple-output (MIMO) model, such as channel estimation and precoding, inapplicable. After discussing the relationship between beam squint and the spatial-wideband effect, we propose a channel estimation scheme for frequency-division duplex (FDD) mmWave massive MIMO-OFDM systems with hybrid analog/digital precoding, which takes the beam squint effect into consideration. A super-resolution compressed sensing approach is developed to extract the frequency-insensitive parameters of each uplink channel path, i.e., the AoA and the time delay, and the frequency-sensitive parameter, i.e., the complex channel gain. With the help of the reciprocity of these frequency-insensitive parameters in FDD systems, the downlink channel estimation can be greatly simplified, where only limited pilots are needed to obtain downlink complex gains and reconstruct downlink channels. Furthermore, the uplink and downlink Copyright © 2019, The Authors. All rights reserved.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Broadband enhancement of on-chip single photon extraction via tilted hyperbolic metamaterials

arXiv

引用

arXiv 2020年

作者： Shen, Lian Lin, Xiao Shalaginov, Mikhail Low, Tony Zhang, Xianmin Zhang, Baile Chen, Hongsheng Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Electromagnetics Academy at Zhejiang University Zhejiang University Hangzhou310027 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore DepartmentofMaterialsScienceandEngineering MassachusettsInstituteofTechnology 77 MassachusettsAvenue CambridgeMA02139 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore

A fundamental building block for on-chip quantum photonics is a single-photon source with high repetition rates, which can enable many applications such as high-speed quantum communication and quantum information processing. Ideally, such single photon sources would then require a large on-chip photon extraction decay rate, namely the rate of excited photons coupled into nanofibers or waveguides, over a broad spectral range. However, this goal has remained elusive till date. Here we propose a feasible scheme to enhance the on-chip photon extraction decay rate of quantum emitters, through the tilting of the optical axis of hyperbolic metamaterials with respect to the end-facet of nanofibers. Importantly, the revealed scheme is applicable to arbitrarily orientated quantum emitters over a broad spectral range, e.g., up to ~80 nm for visible light. The underlying physics relies on the emerging unique feature of hyperbolic metamaterials if their optical axis is judiciously tilted. That is, their supported high-k (i.e., wavevector) hyperbolic eigenmodes, which are intrinsically confined inside them if their optical axis is un-tilted, can now become momentum-matched with the guided modes of nanofibers, and more importantly, they can safely couple into nanofibers almost without reflection. Copyright © 2020, The Authors. All rights reserved.

关键词： Nanofibers

来源：评论

学校读者我要写书评

暂无评论

Towards computational models and applications of insect visual systems for motion perception: A review

arXiv

引用

arXiv 2019年

作者： Fu, Qinbing Wang, Hongxin Hu, Cheng Yue, Shigang Yue, Shigang School of Mechanical and Electrical Engineering Guangzhou University China Machine Life and Intelligence Research Centre Guangzhou University China Computational Intelligence Lab School of Computer Science University of Lincoln United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit ies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonies and hover ies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Spin-orbit torque based physical unclonable function

arXiv

引用

arXiv 2019年

作者： Finocchio, G. Moriyama, T. de Rose, R. Siracusano, G. Lanuzza, M. Puliafito, V. Chiappini, S. Crupi, F. Zeng, Z. Ono, T. Carpentieri, M. Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Institute for Chemical Research Kyoto University Uji Kyoto611-0011 Japan Department of Computer Engineering Modeling Electronics and Systems Engineering University of Calabria RendeI-87036 Italy Department of Electric Electronic and Computer Engineering University of Catania CataniaI-95125 Italy Istituto Nazionale di Geofisica e Vulcanologia Via di Vigna Murata 605 RomaI-00143 Italy Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS Suzhou Jiangsu215123 China Department of Electrical and Information Engineering Polytechnic of Bari Bari70125 Italy

This paper introduces the concept of spin-orbit-torque-MRAM (SOT-MRAM) based physical unclonable function (PUF). The secret of the PUF is stored into a random state of a matrix of perpendicular SOT-MRAMs. Here, we show experimentally and with micromagnetic simulations that this random state is driven by the intrinsic nonlinear dynamics of the free layer of the memory excited by the SOT. In detail, a large enough current drives the magnetization along an in-plane direction. Once the current is removed, the in-plane magnetic state becomes unstable evolving towards one of the two perpendicular stable configurations randomly. In addition, an hybrid CMOS/spintronics model is used to evaluate the electrical characteristics of a PUF realized with an array of 16×16 SOT-MRAM cells. Beyond robustness against voltage and temperature variations, hardware authentication based on this PUF scheme has additional advantages over other PUF technologies such as non-volatility (no power consumption in standby mode), reconfigurability (the secret can be rewritten), and scalability. We believe that this work is a step forward the design of spintronic devices for application in security. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic recording

来源：评论

学校读者我要写书评

暂无评论

Failure Dependency-based Protection for Multi-Segment Fiber-Wireless（FiWi） Access Network

Failure Dependency-based Protection for Multi-Segment Fiber-...

引用

2018中国信息通信大会(CICC2018)

作者： Yejun Liu Lei Guo Yinpeng Yu School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Smart Systems Lab School of Computer Science and Engineering Northeastern University SINOCHEM COMMERCE CO. LTD.

Survivability is one of the key issues for next generation optical access network,since network component failure may interrupt many high-rate traffic *** a promising optical access technology,Fiber-Wireless（FiWi）access network has been widely acknowledged as a cost-efficient solution for the survivable *** FiWi,the wireless front-end can protect the optical back-end by wirelessly ***,in the multi-segment FiWi,where different segments are usually far away from each other,it may be impossible to protect FiWi against segment failure（caused by feeder fiber cut）by wirelessly rerouting because the wireless path will be unavailable between different *** approaches were proposed to protect passive optical network by deploying backup fibers,which have been extended to multi-segment ***,most of the previous works ignore the failure dependency of different fiber links,e.g.,Shared-Risk Link Group（SRLG）,which may lead to the simultaneous failures of multiple *** this paper,we focus on the survivability of multi-segment FiWi against single SRLG failure,which is a typical and notable failure scenario in multi-segment FiWi network,and propose a new protection scheme called Backup Resource Configuration based on Failure Dependency（BRCFD）.A failure probability model is proposed to estimate the failure probability of each single segment and the failure dependency of each segment *** BRCFD,we deal with the full protection of each segment whose failure probability exceeds the specified threshold,aiming to minimize the consumption of backup capacity and the deployment cost of backup *** the constraint of failure dependency,both Integer Linear Programming（ILP）approach and heuristic approach are proposed for the optimization of allocating backup capacity and deploying backup fibers in *** results demonstrate that our BRCFD scheme can effectively reduce the consumption of backup *** is also scalable

关键词： Backup capacity backup fiber failure dependency Fiber-Wireless（FiWi） survivability

来源：评论

学校读者我要写书评

暂无评论

Deep learning for image and point cloud fusion in autonomous driving: A review

arXiv

引用

arXiv 2020年

作者： Cui, Yaodong Chen, Ren Chu, Wenbo Chen, Long Tian, Daxin Li, Ying Cao, Dongpu The Waterloo CogDrive Lab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada Research Institute Co. Ltd. Beijing100176 China School of Data and Computer Science Sun Yat-sen University Zhuhai519082 China Waytous Inc. Qingdao266109 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

引用

Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：