检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,281 篇 会议
47 篇 期刊文献
3 册 图书

馆藏范围

4,331 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,432 篇 工学
- 2,168 篇 计算机科学与技术...
- 1,080 篇 软件工程
- 607 篇 电气工程
- 586 篇 信息与通信工程
- 185 篇 仪器科学与技术
- 138 篇 控制科学与工程
- 120 篇 电子科学与技术（可...
- 64 篇 生物工程
- 59 篇 机械工程
- 43 篇 动力工程及工程热...
- 38 篇 交通运输工程
- 31 篇 生物医学工程（可授...
- 27 篇 安全科学与工程
- 26 篇 网络空间安全
- 24 篇 光学工程
- 23 篇 土木工程
- 22 篇 材料科学与工程（可...
- 20 篇 建筑学
- 19 篇 航空宇航科学与技...
418 篇 理学
- 253 篇 数学
- 86 篇 物理学
- 72 篇 生物学
- 51 篇 系统科学
- 42 篇 统计学（可授理学、...
261 篇 管理学
- 179 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 65 篇 工商管理
35 篇 医学
- 30 篇 临床医学
33 篇 法学
- 19 篇 社会学
18 篇 经济学
- 18 篇 应用经济学
11 篇 农学
7 篇 教育学
7 篇 文学
2 篇 军事学
1 篇 艺术学

主题

543 篇 parallel process...
259 篇 computer archite...
238 篇 computational mo...
207 篇 distributed comp...
186 篇 concurrent compu...
179 篇 computer network...
164 篇 network topology
157 篇 hardware
157 篇 graphics process...
148 篇 routing
130 篇 bandwidth
123 篇 application soft...
120 篇 neural networks
120 篇 protocols
107 篇 scalability
104 篇 multicore proces...
103 篇 throughput
102 篇 algorithm design...
102 篇 kernel
101 篇 computer science

机构

15 篇 institute of inf...
14 篇 institute of com...
14 篇 science and tech...
13 篇 college of compu...
13 篇 school of cyber ...
13 篇 shandong fundame...
12 篇 school of comput...
10 篇 science and tech...
9 篇 univ chinese aca...
9 篇 university of ch...
9 篇 natl univ def te...
9 篇 natl univ def te...
8 篇 shenyang aerospa...
8 篇 beijing univ pos...
8 篇 nanjing univ aer...
8 篇 beijing universi...
7 篇 shanghai jiao to...
7 篇 college of compu...
7 篇 chinese acad sci...
7 篇 national key lab...

作者

17 篇 bagherzadeh nade...
16 篇 kotenko igor
14 篇 j. duato
12 篇 wang yijie
11 篇 daneshtalab maso...
10 篇 ebrahimi masoume...
10 篇 yijie wang
10 篇 nader bagherzade...
10 篇 igor kotenko
10 篇 lexi xu
9 篇 li dongsheng
9 篇 marco aldinucci
9 篇 matsutani hiroki
8 篇 hiroki matsutani
8 篇 masoumeh ebrahim...
8 篇 plosila juha
8 篇 marco danelutto
8 篇 wang huaimin
7 篇 tenhunen hannu
7 篇 xinzhou cheng

语言

4,273 篇 英文
49 篇 其他
11 篇 中文
1 篇 乌克兰文

检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4331 条记录，以下是591-600 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Enhancing the Performance of distributed Generalized Neural network

Enhancing the Performance of Distributed Generalized Neura...

引用

Future of Information and Communication conference, FICC 2025

作者： Kandel, Narayan Xu, Lili Dev, Roger Clemson University ClemsonSC29630 United States LexisNexis Risk Solutions 1000 Alderman Dr AlpharettaGA30005 United States

ISBN: (纸本)9783031844591

The Generalized Neural network (GNN) bundle combines the parallel processing capabilities of the High-Performance Computing Cluster (HPCC Systems) with the Keras and TensorFlow frameworks. It facilitates efficient development of neural network applications by leveraging the Enterprise Control Language (ECL), a robust Big Data processing language designed for distributed computing. In this work, we address performance challenges in distributed neural networks, suggesting that training on a single high-performance node (such as those with GPUs) or a limited number of nodes can yield better results in some cases. To improve performance, we optimized neural network training by capping the number of nodes, which minimized communication overhead and enhanced efficiency. Our research contributes to the ongoing development of the GNN bundle by demonstrating effective neural network training in distributed environments. We explore the nuances of parallelization and highlight the importance of selecting the right training approach based on the specific requirements of the neural network and the available computing resources. The findings offer valuable guidance for implementing efficient neural network training strategies in distributed computing contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Priority-based Task Scheduling for Computation Offloading in Fog Computing 1

引用

21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Yin, Jiaying Fu, Jing Wu, Jingjin Zheng, Shiming BNU HKBU United Int Coll Div Sci & Technol Zhuhai Guangdong Peoples R China RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (数字)9783030953843

ISBN: (纸本)9783030953843;9783030953836

Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than 78.6% of all simulation runs.

关键词： Priority-based policies Energy efficiency Fog computing Computational offloading Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Memristor based Federated Learning for network Security on the Edge using processing in Memory (PIM) Computing

Memristor Based Federated Learning for Network Security on t...

引用

IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Alam, Shahanur Yakopcic, Chris Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Artificial Intelligence (AI) is moving towards the edge. Training an AI model for edge computing on a centralized server increases latency, and the privacy of edge users is jeopardized due to private data transfer through a less secure communication channels. Additionally, existing high-power computing systems are battling with memory and data transfer bottlenecks between the processor and memory. Federated Learning (FL) is a collaborative AI learning paradigm for distributed local devices that operates without transferring local data. Local participant devices share the updated network parameters with the central server instead of sending the original data. The central server updates the global AI model and deploys the model to the local clients. As the local data resides only on the edge, these devices need to be protected from cyberattacks. The Federated Intrusion Detection System (FIDS) could be a viable system to protect edge devices as opposed to a centralized protection system. However, on-device training of the model in resource constrained devices may suffer from excessive power drain, in addition to memory and area overhead. In this work we present a memristor based system for AI training on edge devices. Memristor devices are ideal candidates for processing in memory, as their dynamic resistance properties allow them to perform multiply-add operations in parallel in the analog domain with extreme efficiency. Alternatively, existing CMOS-based PIM systems are typically developed for edge inference based on pretrained weights, and are not equipped for on-chip training. We show the effectiveness of the system, where successful learning and recognition is achieved completely within edge devices. The classification accuracy of the memristor system shows negligible loss when compared a software implementation. To the best of our knowledge, this first demonstration of a memristor based federated learning system. We demonstrate the effectiveness of this system as

关键词： PIM memristor Federated Learning low power Intrusion Detection

来源：评论

学校读者我要写书评

暂无评论

A Scalable Inference Pipeline for 3D Axon Tracing Algorithms

A Scalable Inference Pipeline for 3D Axon Tracing Algorithms

引用

IEEE High Performance Extreme Computing Virtual conference (HPEC)

作者： Fenelon, Benjamin Gjesteby, Lars A. Guan, Webster Park, Juhyuk Chung, Kwanghun Brattain, Laura J. MIT Lincoln Lab Human Hlth & Performance Syst Lexington MA USA MIT Inst Med Engn & Sci Cambridge MA USA

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

High inference times of machine learning-based axon tracing algorithms pose a significant challenge to the practical analysis and interpretation of large-scale brain imagery. This paper explores a distributed data pipeline that employs a SLURM-based job array to run multiple machine learning algorithm predictions simultaneously. Image volumes were split into N (1-16) equal chunks that are each handled by a unique compute node and stitched back together into a single 3D prediction. Preliminary results comparing the inference speed of 1 versus 16 node job arrays demonstrated a 90.95% decrease in compute time for 32 GB input volume and 88.41% for 4 GB input volume. The general pipeline may serve as a baseline for future improved implementations on larger input volumes which can be tuned to various application domains.

关键词： Brain mapping machine learning axon tracing high performance computing parallel processing

来源：评论

学校读者我要写书评

暂无评论

Multi-task Allocation based on Edge Interaction Assistance in Mobile Crowdsensing 21st

Multi-task Allocation Based on Edge Interaction Assistance i...

引用

21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Li, Wenjuan Feng, Guangsheng Huang, Yun Liu, Yuzheng Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China China Aerosp Sci & Ind Corp Res Inst 2 Beijing Peoples R China

ISBN: (纸本)9783030953911;9783030953904

This paper studies the problem of multi-task allocation in hotspots. Due to the frequent task requests in hotspots, the traditional mobile crowdsensing (MCS) cannot meet its requirements, so we adopt a distributed MCS architecture to complete this process. This paper proposes an MCS multi-task allocation framework based on edge interaction assistance, which uses edge nodes to distribute the computing load of MCS platform. In the case of short time and heavy tasks, what this paper needs to do is to minimize delay and cost while ensuring data quality, so as to express the multi-task assignment problem as a multi-objective optimization problem. Through the analysis of the process, we propose a two-phase allocation scheme (TPAS) to determine task allocation. The first phase is the task selection based on cosine similarity. The number of tasks can be reduced by considering the similarity of tasks. The second phase is task allocation based on the improved NSGA-II algorithm. The simulation results show that under the premise of satisfying the task data quality, TPAS can obtain lower task cost and delay, and significantly improve the task completion rate.

关键词： Mobile crowdsensing Multi-task allocation Similarity of tasks NSGA-II algorithm

来源：评论

学校读者我要写书评

暂无评论

NAP: network Adaptive Proxy for Dynamic Traffic Management in Edge Computing

NAP: Network Adaptive Proxy for Dynamic Traffic Management i...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Zhongjun Mao Xin Li Yanling Bu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In dynamic edge environments, many nodes are constantly changing their geographical locations, leading to unstable network links and even disconnections among nodes relying on wireless communication. Existing research mainly considers optimizing resource allocation and task processing delays, ignoring the impact of transmission delays in fluctuating network environments. However, the transmission time of tasks or requests may be longer than the processing time, which can significantly affect the quality of service for delay-sensitive services. In such scenarios, the static strategies of EdgeMesh always perform terribly under unstable network. This makes the transmission time of task data fluctuate frequently, resulting in an increase in response time. Therefore, we proposed an adaptive strategy based on network-aware to improve the transmission delay. We implemented such a strategy by developing a proxy named NAP to assist EdgeMesh in dynamic traffic management. In order to adapt to the complex and volatile conditions of edge networks, NAP monitors every node in real-time. It determines the feasible path with the shortest expected transmission time based on the size of different task data, network and resources status among nodes, then proxies the traffic and forwards it in the kernel state. Numerous experimental results demonstrate that NAP can reduce task transmission time by $50 \%$ and significantly improve the end-to-end request latency in edge computing environments compared to native strategies of EdgeMesh.

关键词： Wireless communication Performance evaluation Adaptive systems Source coding Quality of service Real-time systems Delays Time factors Resource management Edge computing

来源：评论

学校读者我要写书评

暂无评论

BFR-RetinaNet: An Improved RetinaNet Model for Vehicle Detection in Aerial Images 1

引用

21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Zhang, J. I. N. Luo, Meng Sun, Cheng Qu, Peiqi Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China Hunan Normal Univ Coll Math & Stat Changsha Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China

ISBN: (数字)9783030953843

ISBN: (纸本)9783030953843;9783030953836

Vehicle detection in aerial images has been applied in many fields and attracted more and more scholars' attention. In the task, the objects are multidirectional and arranged densely, the background information is complex, and the scale of the object is different. To achieve better detection performance, an improved detection model BFR-RetinaNet is proposed, which is based on the single-stage object detection model RetinaNet. BFR-RetinaNet optimizes vehicle positioning by adding a directional anchor box regression branch. Simultaneously, the model introduces a balanced feature pyramid structure to enhance the extraction of features and reduce the interference of complex backgrounds. The experimental results on the aerial dataset show that the precision, recall, and average precision of the proposed model have been improved to varying degrees. It achieves 86.2% Precision, 98.4% Recall, and 90.8% Average Precision (AP), which is 7.96, 0.45, and 0.58 points higher than R-RetinaNet.

关键词： Vehicle detection Aerial imagery Oriented bounding box RetinaNet Feature pyramid network

来源：评论

学校读者我要写书评

暂无评论

FleCSI 2.0: The Flexible Computational Science Infrastructure Project 27th

FleCSI 2.0: The Flexible Computational Science Infrastructur...

引用

27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Bergen, Ben Demeshko, Irina Ferenbaugh, Charles Herring, Davis Lo, Li-Ta Loiseau, Julien Ray, Navamita Reisner, Andrew Los Alamos Natl Lab Appl Comp Sci Grp CCS 7 Los Alamos NM 87545 USA Los Alamos Natl Lab Lagrangian Codes Grp XCP 1 Los Alamos NM 87545 USA

ISBN: (纸本)9783031061561;9783031061554

The FleCSI 2.0 programming system supports multiphysics application development through a runtime abstraction layer, and by providing core topology types that can be customized for specific numerical methods. The abstraction layer provides a single-source programming interface for distributed and shared-memory data parallelism through task and kernel execution, and has been demonstrated to introduce virtually no runtime overhead. FleCSI's core topology types represent a rich set of basic data structures that can be specialized to create applicationfacing interfaces for a variety of different physics packages. Using the FleCSI control and data models, it is straightforward to compose multiple packages to create full multiphysics applications. When used with a task-based backend, FleCSI offers extended runtime analysis that can increase task concurrency, facilitate load balancing, and allow for portability across heterogeneous computing architectures.

关键词： Multiphysics Computational science Applied mathematics Task-based runtimes Heterogeneity Performance portability Accelerators

来源：评论

学校读者我要写书评

暂无评论

Accelerating Adversarial Attack using Process-in-Memory Architecture 18

Accelerating Adversarial Attack using Process-in-Memory Arch...

引用

18th IEEE International conference on Mobility, Sensing and networking (MSN)

作者： Liu, Shiyi Bavikadi, Sathwika Sen, Tanmoy Shen, Haiying Sutradhar, Purab Ranjan Ganguly, Amlan Dinakarrao, Sai Manoj Pudukotai Smith, Brian L. Univ Virginia Dept Engn Syst & Environm Charlottesville VA 22903 USA George Mason Univ Dept Elect & Comp Engn Fairfax VA USA Univ Virginia Dept Comp Sci Charlottesville VA USA Rochester Inst Technol Rochester NY USA

ISBN: (纸本)9781665464574

Recent research has demonstrated that machine learning algorithms are vulnerable to adversarial attacks, in which small but carefully crafted input perturbations can lead to algorithm failure. It has been demonstrated that certain adversarial attack algorithms are capable of producing these types of perturbations. These attack methods are inapplicable when the attack must be generated in near real time. The use of a hardware accelerator, such as a Process-in-Memory (PIM) architecture, is a potential method for addressing this issue. The PIM architecture is regarded as a superior option for data-intensive applications such as solving optimization problems and Deep Neural networks (DNN) due to its capacity for ultra-low-latency parallel processing. However, implementing an adversarial attack algorithm directly on the PIM platform is inefficient due to the PIM architecture's complexity and overhead costs. To address this issue, we utilize a novel adversarial attack scheme based on the PIM that leverages Look-up-Table (LUT)-based processing. The proposed LUT-based PIM architecture is capable of being dynamically programmed to execute the operations necessary for an adversarial attack algorithm. Our simulations reveal that the proposed method is capable of achieving an ultra-low operating delay and energy-efficiency performance.

关键词： Black-box adversarial attack Deep neural network processing in memory (PIM)

来源：评论

学校读者我要写书评

暂无评论

High performance binding affinity prediction with a Transformer-based surrogate model

High performance binding affinity prediction with a Transfor...

引用

1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Vasan, Archit Gokdemir, Ozan Brace, Alexander Ramanathan, Arvind Brettin, Thomas Stevens, Rick Vishwanath, Venkatram Argonne Natl Lab Lemont IL 60439 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350364613;9798350364606

In the current paradigm of drug discovery pipelines, identification of compounds that bind to a target with high affinity constitutes the first step. This is typically performed using resource-intensive experimental methods to screen vast chemical search spaces - a key bottleneck in the drug-discovery pipeline. To streamline this process, highly-scalable computational screening methods with acceptable fidelity are needed to screen larger portions of the chemical search space and identify promising candidates to he validated using experiments. Machine learning methods, namely, surrogate models have recently evolved into favorable alternatives to perform this computational screening. In this work, we present Simple SMILES Transformer (SST), an accurate and highly-scalable binding affinity prediction method that approximates the computationally-intensive molecular docking process using an encoder-only Transformer architecture. We benchmark our model against two baselines that feature fundamentally different approaches to docking surrogates: RegGO, a MORDRED fingerprint based multi-layer perceptron model, and Chemprop, a directed message-passing graph neural network. Unlike Chemprop and RegGO, our method operates solely on the SMILES representation of molecules without needing additional featurization, which leads to reduced preprocessing overhead, higher inference throughput and thus better scalability. We train SST in a distributed fashion on the Polaris supercomputer at the Argonne Leadership Computing Facility (ALCF). We then deploy it at an unprecedented scale for inference across 256 compute nodes of ALCF's Aurora supercomputer to screen 22 billion compounds in 40 minutes in search of hits with high binding affinity to oncoprotein RtcB ligase. SST predictions emphasize several molecular motifs that have previously been confirmed to interact with residues in their target binding pockets.

关键词： drug discovery virtual screening docking surrogates high performance computing transformers

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共434页 << < 56 57 58 59 60 61 62 63 64 65 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：