检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function

arXiv

引用

arXiv 2025年

作者： Song, Qiuchen Jing, Shusen Zhang, Shuai Zhang, Songyang Huang, Chuan Shenzhen Future Network of Intelligence Institute School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Department of Radiation Oncology University of California San FranciscoCA94118 United States Department of Data Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China

In a distributed mixture-of-experts (MoE) system, a server collaborates with multiple specialized expert clients to perform inference. The server extracts features from input data and dynamically selects experts based on their areas of specialization to produce the final output. Although MoE models are widely valued for their flexibility and performance benefits, adapting distributed MoEs to operate effectively in wireless networks has remained unexplored. In this work, we introduce a novel channel-aware gating function for wireless distributed MoE, which incorporates channel conditions into the MoE gating mechanism. To train the channel-aware gating, we simulate various signal-to-noise ratios (SNRs) for each expert’s communication channel and add noise to the features distributed to the experts based on these SNRs. The gating function then utilizes both features and SNRs to optimize expert selection. Unlike conventional MoE models which solely consider the alignment of features with the specializations of experts, our approach additionally considers the impact of channel conditions on expert performance. Experimental results demonstrate that the proposed channel-aware gating scheme outperforms traditional MoE models. Copyright © 2025, The Authors. All rights reserved.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

On Optimal Finite-length Binary Codes of Four Codewords for Binary Symmetric Channels

On Optimal Finite-length Binary Codes of Four Codewords for ...

引用

International Symposium on Information Theory and Its Applications, ISITA

作者： Yanyan Dong Shenghao Yang School of Science and Engineering and the Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9784885523304

ISBN: (纸本)9781728128559

Finite-length binary codes of four codewords are studied for memoryless binary symmetric channels (BSCs) with the maximum likelihood decoding. For any block-length, best linear codes of four codewords have been explicitly characterized, but whether linear codes are better than nonlinear codes or not is unknown in general. In this paper, we show that for any block-length, there exists an optimal code of four codewords that is either linear or in a subset of nonlinear codes, called Class-I codes. Based on the analysis of Class-I codes, we derive sufficient conditions such that linear codes are optimal. For block-length less than or equal to 8, our analytical results show that linear codes are optimal. For block-length up to 300, numerical evaluations show that linear codes are optimal.

关键词： Sufficient conditions Binary codes Linear codes Maximum likelihood decoding Information theory

来源：评论

学校读者我要写书评

暂无评论

DAMIA: Leveraging domain adaptation as a defense against membership inference attacks

arXiv

引用

arXiv 2020年

作者： Huang, Hongwei Luo, Weiqi Zeng, Guoqiang Weng, Jian Zhang, Yue Yang, Anjia College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

Deep Learning (DL) techniques allow one to train a model from a dataset, and the model can be adopted to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. Being a core engine of DL, models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the dataset the model trained on. Efforts have been made to hinder the attack but unfortunately, they may be subject to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense to counter membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both the original dataset and the introduced dataset. Seeing that the model is obfuscated, membership inference fails, while the extracted common features provide supports for usability. Extensive experiments have been conducted, which validates our intuition. The model counters the membership inference attacks and has a negligible footprint to the usability. Our experiment also excludes a few factors that may hinder the performance of DAMIA, providing a potential guideline that instructs the vendors and researchers to benefit from our solution in a timely manner. Copyright © 2020, The Authors. All rights reserved.

关键词： network security

来源：评论

学校读者我要写书评

暂无评论

Modeling the Correlations of Relations for Knowledge Graph Embedding

引用

Journal of Computer Science & technology 2018年第2期33卷 323-334页

作者： Ji-Zhao Zhu Yan-Tao Jia Jun Xu Jian-Zhong Qiao Xue-Qi Cheng College of Computer Science and Engineering Northeastern University Shenyang 110169 China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 110190 China

Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples （Steve Jobs, PlaceOfBrith, California） and （Apple Inc., Location, California） share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe （Translation-Based Method via Modeling the Correlations of Relations）, learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.

关键词： knowledge graph embedding low-rank matrix decomposition

来源：评论

学校读者我要写书评

暂无评论

Memory-efficient GPU-based exact and parallel triangle counting in large graphs 21

Memory-efficient GPU-based exact and parallel triangle count...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ouyang, Zhiyou Wu, Shanni Zhao, Tongtong Yue, Dong Zhang, Tengfei Institute of Advanced Technology School of Automation Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Institute of Advanced Technology School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

Triangle counting is one of the basic research topics in many practical problems, such as clustering coefficients, transitivity ratio and traffic network complexity, etc., all of which can be converted into triangle counting issues. In this paper, a memory efficient GPU-based parallel triangle counting algorithm is proposed for large undirected graphs, which based on Polak's parallel forward triangle counting algorithm and improved the algorithm flow, edges sorting and triangle counting kernel function to increase the memory usage efficiency and run time performance. Algorithm comparison with the description of proposed algorithm steps and the implementation details is also introduced in the paper. Performance evaluations show that the proposed algorithm achieves 5 to 10 times speedup over the existing parallel forward algorithm, moreover, the GPU memory usage is reduced to a maximum of 50% compared to the existing algorithm. © 2019 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

BAR: Blockwise adaptive recoding for batched network coding

arXiv

引用

arXiv 2021年

作者： Yin, Hoover H.F. Yang, Shenghao Zhou, Qiaoqiao Yung, Lily M.L. Ng, Ka Hei The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Physics The Chinese University of Hong Kong Hong Kong Hong Kong

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time. Copyright © 2021, The Authors. All rights reserved.

关键词： network coding

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-based Fast Authentication and Collaborative Video data Forwarding Scheme for Vehicular networks

A Blockchain-based Fast Authentication and Collaborative Vid...

引用

IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Weihui Qiu Xin Yang Ming Wei Wei Ren Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Wuhan Institute of Marine Electric Propulsion CSSC P.R. China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Institute of Information Engineering Beijing P.R. China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang P.R. China

ISBN: (纸本)9781665400374

Internet of Things (IoT) current present two trends with respect to ubiquity and mobility. The number of devices increases remarkably and most forthcoming devices are mobile, e.g., Internet of Vehicles (IoV). In large scale mobile IoT, the management of device identification, fast authentication, and certification of public keys experiences upcoming difficulties. Centralized security architecture may not be suitable and in contrast cross-domain security architecture will tackle the ubiquity and mobility better. We note that blockchain is a new decentralized architecture equipped with basic cryptographic settings, which provides new tools such as token and guarantees many security properties such as block integrity. In this paper we design a blockchain-based fast authentication video data forwarding scheme for IoV. Compared with existing protocols, it has the advantages of decentralization, high authentication efficiency, strong trust, and resistance to common attacks. We evaluate our scheme with real experiments over Hyperledger Fabric and the authentication delay is manageable (e.g, 4 seconds in IoV).

关键词： Resistance Protocols Authentication Public key Computer architecture Near field communication Ubiquitous computing

来源：评论

学校读者我要写书评

暂无评论

A unified adaptive recoding framework for Batched network coding

arXiv

引用

arXiv 2021年

作者： Yin, Hoover H.F. Tang, Bin Ng, Ka Hei Yang, Shenghao Wang, Xishi Zhou, Qiaoqiao The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The School of Computer and Information Hohai University Nanjing China The Department of Physics The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong

Batched network coding is a variation of random linear network coding which has low computational and storage costs. In order to adapt to random fluctuations in the number of erasures in individual batches, it is not optimal to recode and transmit the same number of packets for all batches. Different distributed optimization models, which are called adaptive recoding schemes, were formulated for this purpose. The key component of these optimization problems is the expected value of the rank distribution of a batch at the next network node, which is also known as the expected rank. In this paper, we put forth a unified adaptive recoding framework with an arbitrary recoding field size. We show that the expected rank functions are concave when the packet loss pattern is a stationary stochastic process, which covers but not limited to independent packet loss and Gilbert-Elliott packet loss model. Under this concavity assumption, we show that there always exists a solution which not only can minimize the randomness on the number of recoded packets but also can tolerate rank distribution errors due to inaccurate measurements or limited precision of the machine. We provide an algorithm to obtain such an optimal optimal solution, and propose tuning schemes that can turn any feasible solution into a desired optimal solution. © 2021, CC BY-NC-ND.

关键词： Packet loss

来源：评论

学校读者我要写书评

暂无评论

Unveiling the corrosion evolution and pitting mechanism of X70 pipeline steel in typical near-neutral/bentonite soil environment

引用

Journal of Materials Science & technology 2025年

作者： Guangming Yang Lulu Zeng Hua Shen Huaiyun Cui Tianqi Chen Zengyao Chen Chengwei Xu Chao Liu Feng Huang Zhiyong Liu Zhichao Che Cuiwei Du Xiaogang Li Key Laboratory for Corrosion and Protection (MOE) Institute of Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 China National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing Beijing 100083 China Jilin Oilfield Company China National Petroleum Corporation Songyuan 138000 China Corrosion and control Research Center Branch of Science and Technology Research Institute of National Oil and Gas Pipeline Network Group Co Tianjin 300457 China The State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and Technology Wuhan 430081 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing 100083 China

This work investigated the effect of dissolved oxygen (DO) on the corrosion behavior and pitting mechanism of X70 pipeline steel in near-neutral soil-bentonite simulated environment. The localized corrosion evolution to uniform corrosion was quantitatively analyzed using statistical methods. In situ characterization reveals that the localized corrosion mechanism is induced by core-shell inclusions. The microcracks occur between MnS and core oxides, the galvanic effect exists between MnS and matrix, and the high distortion region of the matrix promotes the localized corrosion initiation, while the galvanic effect between ferrite and pearlite aids propagation. Interestingly, the pitting growth rates are higher at low DO concentrations. Furthermore, the lower DO and longer immersion time enhance rust layer protection and reduce the corrosion rate of X70 pipeline steel.

关键词：

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：