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arXiv 2024年

作者： Li, Changhao Li, Haoling Xue, Mengqi Fang, Gongfan Zhou, Sheng Feng, Zunlei Wang, Huiqiong Song, Mingli Song, Jie School of Software Technology Zhejiang University Hangzhou China School of Computer and Computing Science Hangzhou City University Hangzhou China College of Design and Engineering National University of Singapore Singapore Ningbo Innovation Center Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China

Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics, leaving the progress in this area not fully comprehended. To fill this gap, we present the first comprehensive benchmark, termed PruningBench, for structural pruning. PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques;2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e.g., CNNs and ViTs) and tasks (e.g., classification and detection);3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards. We provide an online pruning platform http://*** for customizing pruning tasks and reproducing all results in this paper. Leaderboard results can be available on https://***/HollyLee2000/PruningBench. © 2024, CC BY.

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Low-Range-Sidelobe Waveform Design for MIMO-OFDM ISAC Systems

Low-Range-Sidelobe Waveform Design for MIMO-OFDM ISAC System...

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IEEE International Conference on Communications (ICC)

作者： Peishi Li Zichao Xiao Ming Li Rang Liu Qian Liu School of Information and Communication Engineering Dalian University of Technology Dalian Liaoning China Center for Pervasive Communications and Computing University of California Irvine CA USA School of Computer Science and Technology Dalian University of Technology Dalian Liaoning China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization. In this paper, we consider a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system and propose a novel waveform design to provide better radar ranging performance by taking range sidelobe suppression into consideration. In specific, we aim to design the MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level (ISL) while satisfying the quality of service (QoS) requirements of multi-user communications and the transmit power constraint. To achieve a lower ISL, the symbol-level precoding (SLP) technique is employed to fully exploit the degrees of freedom (DoFs) of the waveform design in both temporal and spatial domains. An efficient algorithm utilizing majorization-minimization (MM) framework is developed to solve the non-convex waveform design problem. Simulation results reveal radar ranging performance improvement and demonstrate the benefits of the proposed SLP-based low-range-sidelobe waveform design in ISAC systems.

关键词： Wireless communication Simulation Precoding OFDM Estimation Quality of service Radar

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

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SWAMamba: A Sliding Window Attention Mamba Framework for Predicting Translation Elongation Rates 39

SWAMamba: A Sliding Window Attention Mamba Framework for Pre...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zeng, Xi Ni, Fei Jiao, Shaoqing Lu, Dazhi Hao, Jianye Peng, Jiajie AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University China College of Intelligence and Computing Tianjin University China School of Computer Science Research and Development Institute Northwestern Polytechnical University Shenzhen China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology China

ISBN: (纸本)157735897X

Translation elongation is essential for cellular proteostasis and is implicated in cancer and neurodegeneration. Accurately predicting the rate of ribosome elongation in each codon (also called ribosomal A site) on mRNA is important for understanding and modulating protein synthesis. However, predicting elongation rates is challenging due to the trade-off between capturing distal codon interactions and focusing on proximal codon effects at the A site. Approaches capturing distal codon interactions in the coding sequences (CDS) of mRNA fail to effectively differentiate critical regions (codons near the A site) due to insufficient effective mechanisms for focusing on these regions. Conversely, due to the limitations of models when handling long mRNA sequences, some methods simplify inputs by conditioning solely on proximal codons surrounding the A site, leading to the loss of important information from distal codons. To address this issue, we leverage Mamba’s success in capturing long-range dependencies to enable the consideration of distant codons’ impact on the A site. Additionally, we introduce a sliding window attention mechanism to emphasize the proximal codons around the A site during ribosome elongation. Building on these advancements, we present Sliding Window Attention Mamba (SWAMamba), a novel framework that simultaneously leverages both proximal and distal codon effects on the A site. We conduct comprehensive evaluations on ribosome data across four species and find that SWAMamba significantly outperformed current state-of-the-art methods in predicting translation elongation rates. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Hao Wu Junxiao Deng Hao Fan Shadi Ibrahim Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Inria Univ. Rennes CNRS IRISA France

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.

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Low Range-Doppler Sidelobe ISAC Waveform Design: A Low-Complexity Approach

arXiv

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arXiv 2025年

作者： Li, Peishi Li, Ming Liu, Rang Liu, Qian Swindlehurst, A. Lee The School of Information and Communication Engineering Dalian University of Technology Dalian116024 China The Center for Pervasive Communications and Computing University of California IrvineCA92697 United States The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Integrated sensing and communication (ISAC) is a pivotal enabler for next-generation wireless networks. A key challenge in ISAC systems lies in designing dual-functional waveforms that can achieve satisfactory radar sensing accuracy by effectively suppressing range-Doppler sidelobes. However, existing solutions are often computationally intensive, limiting their practicality in multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC deployments. This paper presents a novel low-complexity algorithm leveraging the augmented Lagrangian method (ALM) and Riemannian conjugate gradient (RCG) optimization techniques to address these challenges. The proposed algorithm achieves superior sidelobe suppression compared to state-of-the-art methods while dramatically reducing computational complexity, making it highly suitable for real-world MIMO-OFDM ISAC systems. Simulation results demonstrate that the proposed approach not only outperforms existing benchmarks in sidelobe reduction but also accelerates convergence, ensuring efficient performance across communication and sensing tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation

arXiv

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arXiv 2024年

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

arXiv

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arXiv 2023年

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

Language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application needs. To overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of LLMs. For such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts. Comprehensive experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Clustering-Based Color Reordering Method for Reversible Data Hiding in Palette Images

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IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Embeddings

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Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

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IEEE Transactions on Services computing 2025年

作者： Wu, Xiaohu Yu, Han Casale, Giuliano Gao, Guanyu Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing China Nanyang Technological University WeBank-NTU Joint Research Centre on Fintech Singapore Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Imperial College London Department of Computing London United Kingdom Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Premier cloud service providers (CSPs) offer on-demand and spot instances (i.e., virtual machines) with time-varying features in availability and price. While interacting with a CSP, what concerns users is the process of cost-effectively utilizing these instances, possibly in addition to self-owned instances. A job in data-intensive applications is represented by a directed acyclic graph (DAG) whose nodes represent tasks and the DAG can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we make some mild assumptions on the usage of various instances and give an analytical understanding of the expected behaviors while utilizing instances. Based on this, we propose informed heuristic policies to determine the allocation of deadlines and instances. The policies are parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, intuitive greedy policies are used as baselines to validate the effectiveness of the proposed analytical solutions. The cost improvement is up to 23.58% when spot and on-demand instances are considered and up to 50.08% when self-owned instances are also considered. © 2008-2012 IEEE.

关键词： Cloud platforms

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