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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10372 条记录，以下是661-670 订阅

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Image Interpolation Utilizing Neighboring Pixels as References in Conjunction with Multilayer Perceptron Neural Networks 2nd

Image Interpolation Utilizing Neighboring Pixels as Referenc...

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Lu, Tzu Chuen Vo, Thanh Nhan Jana, Biswapati Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science Vidyasagar University Midnapore721102 India

ISBN: (纸本)9789819777853

This study seeks to investigate the utilization of neural networks for the generation of high-quality image interpolation. This introduction endeavors to deliver a comprehensive exploration of the foundational principles underpinning interpolation, coupled with an investigation into the substantial potential presented by neural networks in this field. We introduce an image interpolation approach and substantiate its exceptional performance through empirical evidence. Additionally, we develop methodologies for evaluating experimental interpolation outcomes and explore potential applications in other domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interpolation

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Mitigating Hughes Phenomenon: Improving Hyperspectral Imaging Classification Through Active Learning for Generalization Enhancement 20

Mitigating Hughes Phenomenon: Improving Hyperspectral Imagin...

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20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Butt, Muhammad Hassaan Farooq Li, Jian Ping Butt, Muhammad Adnan Farooq Ahmad, Muhammad Tunio, Muhammad Hanif Ahmed, Awais School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China National University of Computer and Emerging Sciences Islamabad Department of Computer Science Chiniot35400 Pakistan

ISBN: (纸本)9798350318982

In the domain of hyperspectral imaging classification (HIC), the challenge of limited labeled training samples, commonly referred to as the Hughes phenomenon, poses a significant obstacle. As hyperspectral datasets capture extensive spectral information, the Hughes phenomenon manifests when labeled samples are insufficient compared to the numerous spectral bands, resulting in reduced classification accuracy. This paper addresses the ill-posed conditions introduced by the Hughes phenomenon and explores how it adversely impacts classification algorithms in hyperspectral imaging (HI). Active learning (AL) emerges as a strategic solution to counteract the limitations posed by ill-posed conditions. By iteratively selecting and annotating the most important samples, AL allows for the augmentation of the training set without the need for an extensive collection of labeled data. This proactive approach mitigates the detrimental effects of limited labeled samples, enhancing the generalization performance of classification algorithms in HI. © 2023 IEEE.

关键词： Hyperspectral imaging

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Towards Real-Time high-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-Decoder Architecture 16th

Towards Real-Time High-Definition Image Snow Removal: Effici...

引用

16th Asian conference on computer Vision (ACCV)

作者： Ye, Tian Chen, Sixiang Liu, Yun Ye, Yi Bai, JinBin Chen, Erkang Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9783031263125;9783031263132

In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation varies from image to image. Recent methods adopt deep neural networks to recover clean scenes from snowy images directly. However, due to the paradox caused by the variation of complex snowy degradation, achieving reliable high-Definition image desnowing performance in real time is a considerable challenge. We develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder architecture for realtime HD image desnowing. The general idea of our proposed network is to utilize the multi-scale feature flow fully and implicitly to mine clean cues from features. Compared with previous state-of-the-art desnowing methods, our approach achieves a better complexity-performance trade-off and effectively handles the processing difficulties of HD and Ultra-HD images. The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a largemargin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB on the SRRS test dataset. The source code is available at https://***/Owen718/Towards-Real time-highDefinition-Image-Snow-Removal-Efficient-Pyramid-Network.

关键词： Desnowing Real-time Asymmetrical encoder-decoder architecture

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A Non-blocking Network Design for Terabit Capacity Optical Interconnects 23

A Non-blocking Network Design for Terabit Capacity Optical I...

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IEEE 23rd International conference on high performance Switching and Routing (IEEE HPSR)

作者： Yang, Xiaoxue Hu, Bing Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Lab Intelligent Network Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9781665406079

With the rapid development of internet applications such as cloud computing and streaming media, people put higher demands on data center (DC) switching capabilities. Technology limitations of the backplane cause bottlenecks in its performance, which limits the growth rate of the communication ability of DC. The technology of waveguides based on polymer materials and waveguide connectors on the backplane has become an essential issue in the last few years. Architectures of optical interconnection remain to be explored. We propose a strictly non-blocking Clos-based optical architecture, which uses densely integrated optical waveguides to connect optical chips and achieve on-board networking. In addition, we design a Ring-Clos architecture to reduce the packet loss rate. The simulation results indicate that our design has good performance on throughput, packet loss rate, and latency.

关键词： Integrated optics Optical interconnections Optical switches Backplanes Packet loss computer architecture Optical fiber networks

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COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval

COTS: Collaborative Two-Stream Vision-Language Pre-Training ...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Lu, Haoyu Fei, Nanyi Huo, Yuqi Gao, Yizhao Lu, Zhiwu Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recently, two-stream methods like CUP and ALIGN with high inference efficiency have also shown promising performance, however;they only consider instance-level alignment between the two streams (thus there is still room for improvement). To overcome these limitations, we propose a novel Collaborative Two-Stream vision-language pre-training model termed COTS for image-text retrieval by enhancing cross-modal interaction. In addition to instance-level alignment via momentum contrastive learning, we leverage two extra levels of cross-modal interactions in our COTS: (1) Token-level interaction - a masked vision-language modeling (MVLM) learning objective is devised without using a cross-stream network module, where variational autoencoder is imposed on the visual encoder to generate visual tokens for each image. (2) Task-level interaction - a KL-alignment learning objective is devised between text-to-image and image-to-text retrieval tasks, where the probability distribution per task is computed with the negative queues in momentum contrastive learning. Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10,800x faster in inference) ***. the latest single-stream methods. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-of-the-art on the widely-used MSR-VTT dataset.

关键词： Visualization computer vision Collaboration Streaming media Probability distribution Pattern recognition Task analysis

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KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning 30

KEHRL: Learning Knowledge-Enhanced Language Representations ...

引用

Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Dongyang Zhang, Taolin Huang, Longtao Wang, Chengyu He, Xiaofeng Xue, Hui School of Computer Science and Technology East China Normal University China Alibaba Group China

ISBN: (纸本)9782493814104

Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning. Previous works treat knowledge enhancement as two independent operations, i.e., knowledge injection and knowledge integration. In this paper, we propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL), which jointly addresses the problems of detecting positions for knowledge injection and integrating external knowledge into the model in order to avoid injecting inaccurate or irrelevant knowledge. Specifically, a high-level reinforcement learning (RL) agent utilizes both internal and prior knowledge to iteratively detect essential positions in texts for knowledge injection, which filters out less meaningful entities to avoid diverting the knowledge learning direction. Once the entity positions are selected, a relevant triple filtration module is triggered to perform low-level RL to dynamically refine the triples associated with polysemic entities through binary-valued actions. Experiments validate KEHRL's effectiveness in probing factual knowledge and enhancing the model's performance on various natural language understanding tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yu, Xinlei Elazab, Ahmed Ge, Ruiquan Jin, Hui Jiang, Xinchen Jia, Gangyong Wu, Qing Shi, Qinglei Wang, Changmiao Hangzhou Dianzi University School of Computer Science Hangzhou China Shenzhen University School of Biomedical Engineering Shenzhen China Chinese University of Hong Kong Shenzhen School of Management and Economics Shenzhen China Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350386226

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics. © 2024 IEEE.

关键词： Diagnosis

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A Time-Sensitive Networking Traffic Scheduling Method Based on Q-Learning Routing Optimization 18

A Time-Sensitive Networking Traffic Scheduling Method Based ...

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18th International conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Li, Jin Wei, Min Huo, Chengjie Kim, Keecheon Chongqing University of Posts and Telecommunications China-Korea Belt and Road Joint Laboratory on Industrial Internet of Things Chongqing China Konkuk University Dept. of Computer Information & Communications Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

With the rapid development of industrial automation, higher requirements are put forward for reliable and deterministic communication in industrial networks. And time-sensitive networking (TSN) is a promising technology that can satisfy such deterministic transmission requirements. Currently, TSN typically uses the shortest path routing (SPR) algorithm to determine the transmission path of traffic. However, the SPR algorithm may cause a high load on a single path, which makes it difficult to improve the schedulability and determinism of time-triggered (TT) traffic. In this paper, a TSN traffic scheduling method based on Q-learning routing optimization for TT traffic is proposed, and the transmission performance of the proposed method is tested. The results show that the delay and jitter of TT traffic are reduced after using this method. © 2024 IEEE.

关键词： Reinforcement learning

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Design of Propulsion Motor for EV to Mitigate Shaft Voltage Considering Electromagnetic performance 26

Design of Propulsion Motor for EV to Mitigate Shaft Voltage ...

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26th International conference on Electrical Machines and Systems, ICEMS 2023

作者： Yoon, Han-Joon Bae, Su-Bin Wang, Chang Hyeon Kim, Yong-Jae Jung, Sang-Yong Sunkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Chosun University Department of Electrical Engineering Gwanju Korea Republic of

ISBN: (纸本)9798350317589

This research proposes a design method to reduce shaft voltage of the propulsion motors based on the motor configuration considering electromagnetic performances. First, design variables that affect parasitic capacitance are investigated by FEM. Many articles have been submitted, in which they used the insulated rotor, ceramic bearing, and ground ring. However, since the methods require manufacturing costs, it is not effective to mitigate the shaft voltage. In addition, electro-magnetic performances according to the variables are analyzed by FEM. As a result, Lcr is the most significant variable to decrease the shaft voltage. optimization for reduction of the shaft voltage considering the electromagnetic charateristisc is performed. As a result, the shaft voltage of the optimization model has 22.52 % lesser shaft voltage maintaining the electromagnetic characteristics. © 2023 IEEE.

关键词： Capacitance

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HOTGP- higher-Order Typed Genetic programming 23

HOTGP- Higher-Order Typed Genetic Programming

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Genetic and Evolutionary Computation conference (GECCO)

作者： Fernandes, Matheus Campos de Franca, Fabricio Olivetti Francesquini, Emilio Fed Univ ABC UFABC Santo Andre SP Brazil

ISBN: (纸本)9798400701191

program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute force is usually not viable and search heuristics, such as genetic programming, also have difficulty navigating it without any guidance. In this paper we present HOTGP, a new genetic programming algorithm that synthesizes pure, typed, and functional programs. HOTGP leverages the knowledge provided by the rich data-types associated with the specification and the built-in grammar to constrain the search space and improve the performance of the synthesis. The grammar is based on Haskell's standard base library (the synthesized code can be directly compiled using any standard Haskell compiler) and includes support for higher-order functions,..-functions, and parametric polymorphism. Experimental results show that, when compared to 6 stateof-the-art algorithms using a standard set of benchmarks, HOTGP is competitive and capable of synthesizing the correct programs more frequently than any other of the evaluated algorithms.

关键词： Inductive program Synthesis Genetic programming Functional programming

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