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共 4313 条记录，以下是111-120 订阅

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Frequency-Domain Guided Multiple parallel Kernels network for Low-Light Remote Sensing Image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： Visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers Image restoration Kernel Remote sensing Image enhancement

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Application of Various Novel Computer Image processing (CIP) Algorithms in Digital Media Art Images

Application of Various Novel Computer Image Processing (CIP)...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training Image coding Art Image analysis Translation Image edge detection Digital images Semantics Media Feature extraction

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30th International conference on parallel and distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

30th International Conference on Parallel and Distributed Pr...

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30th International conference on parallel and distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856372

The proceedings contain 24 papers. The special focus in this conference is on parallel and distributed processing Techniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication Algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional Neural networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird based on Ultra Long Term network;vision Transformer-based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted

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Proceedings 10th euromicro Workshop on parallel, distributed and network-based processing

Proceedings 10th Euromicro Workshop on Parallel, Distributed...

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euromicro conference on parallel, distributed and network-based processing

ISBN: (纸本)0769514448

The following topics are dealt with: parallel, distributed and network-based processing; performance analysis; Web computing; failure handling; Java and Jini; parallel and distributed programming tools for grids; unorthodox computing architectures; systems and applications; message passing; scheduling; algorithms; and mobile ad hoc networks.

关键词： Computer networks Concurrent computing distributed computing Grid computing Performance analysis Java parallel programming Computer architecture Service oriented architecture Message passing

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Fast and scalable in-network lock management using lock fission 24

Fast and scalable in-network lock management using lock fiss...

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Proceedings of the 18th USENIX conference on Operating Systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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A Transformer-Block-Wise Collaborative Training Mechanism with Hybrid parallelism Over Heterogeneous networks

A Transformer-Block-Wise Collaborative Training Mechanism wi...

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IEEE conference on Wireless Communications and networking

作者： Jiewei Chen Jingrong Wang Shaoyong Guo Jiakai Hao Xuesong Qiu Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications State Grid Beijing Information & Communication Company Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of AI-Generated Content (AIGC) services in wireless networks, efficient and high-quality distributed training of Large Language Models (LLMs) has become essential for enabling the large-scale application of next generation AI technologies. However, the extensive parameters of LLMs impose significant demands on memory, computing power and communication resources in heterogeneous networks. To efficiently utilize the dispersed network resources, this paper presents a First-Pipeline- Then-Federated Learning (FPTFL) approach with a hybrid parallel scheduling strategy to facilitate the training of Transformer-based LLMs. We propose a block-wise splitting mechanism to partition the Transformer's encoder into distinct segments, which are deployed cross individual devices. The encoder parameters and intermediate smashed data are uploaded to the edge server, where the whole model is updated through federated aggregation. Particularly, we develop a fine-grained computation-efficient method based on pipeline parallelism, enabling the segments to cooperatively train the entire encoder. An optimization problem is formulated to determine the LLM segments and the number of micro-batches under network resource constraints, with the goal of minimizing the total latency of LLM training services. Simulation results demonstrate that our approach enables Transformer-based model training on resource-constrained devices, preserves model performance, and reduces waiting time.

关键词： Training Performance evaluation Large language models Wireless networks Pipelines parallel processing Transformers Heterogeneous networks Classification algorithms Optimization

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Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird based on Ultra Long Term network 30th

Jump Like a Frog: Optimization of Renewable Energy Predict...

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作者： Du, Xingbang Zhang, Enzhi Hokkaido University Hokkaido Sapporo060-0808 Japan

ISBN: (纸本)9783031856372

Renewable energy generation forecasting plays crucial roles in advanced smart grid and sustainable practices. Although many RNN related methods have been utilized to predict power generation time series data, they often struggle to capture very long-term correlations efficiently due to the vanishing gradient issue. To address this challenge, we have introduced the Ultra long term network model that incorporated LSTM, SKIP LSTM and Dense components. This model effectively captures long-term patterns while mitigating the vanishing gradient problem associated with capturing very long term patterns. Our application of this model to renewable power prediction has yielded better performance when compared through metrics like MSE and MAE than previous models such as LSTM, GRU and Simple RNN models in time series analysis within smart grids. The integration of this model holds promise for enhancing the intelligence of renewable energy grids. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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Simulation of Protection Mechanisms based on "Nervous network System" against Infrastructure Attacks

Simulation of Protection Mechanisms Based on "Nervous Networ...

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21st euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Kotenko, Igor Shorov, Andrey Novikova, Evgenia Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat Lab Comp Secur Problems St Petersburg Russia

ISBN: (纸本)9780769549392;9781467353212

The paper is devoted to the analysis of the network protection mechanism "nervous network system" based on the bio-inspired metaphor. Packet-level simulation is proposed in order to investigate the "nervous network system" protection mechanism. We describe the architecture of the protection system based on the given mechanism, its operation algorithms and present results of the experiments. Using obtained results the efficiency of the protection mechanism "nervous network system" against infrastructure attacks is analyzed.

关键词： computer network protection simulation bio-inspired approaches infrastructure attacks DDoS-attacks network worms

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The PPPSpace: Innovative Concepts for Permanent Capturing, Persistent Storing, and parallel processing and Distributing Events

The PPPSpace: Innovative Concepts for Permanent Capturing, P...

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18th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Gross, Tom Beckmann, Christoph Schirmer, Maximilian Bauhaus Univ Weimar Fac Media D-99423 Weimar Germany

ISBN: (纸本)9780769539393

Media spaces provide users with flexible support for easy interaction with technology and with each other, both at the same place and over distance. From a technological perspective the development of these environments is often inefficient, since most environments are developed specifically, without any synergies or reuse of previous concepts and implementations. In this paper we present the cooperative media space PPPSpace that is based on powerful technical parallel and distributed software engineering concepts and at the same time easy to use for end-users.

关键词： distributed System Event Notification Infrastructure Media Space End-User Configuration

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Efficient parallel Execution of Streaming Applications on Multi-Core Processors

Efficient Parallel Execution of Streaming Applications on Mu...

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19th International euromicro conference on parallel, distributed and network-based processing (PDP)

作者： Schuele, Tobias Siemens AG Syst Architecture & Platforms Corp Technol Munich Germany

ISBN: (纸本)9780769543284

We propose a method for the parallel execution of applications that process continuous streams of data. Unlike pipeline-based approaches, which are frequently employed to parallelize software for multi-core processors, our method supports nonlinear structures that may contain conditionals. Nonlinear structures reduce the latency for processing an element from a stream, which is particularly important for embedded systems that are subject to real-time constraints.

关键词： Multi-core processors parallel execution Time constraints Embedded systems Continuous flow Streaming Latency NONLINEAR STRUCTURES processing Element streams computer software

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