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AQC: Achieving Precise Bandwidth Allocation with Augmented Queues for Credit-Based Proactive Congestion Control 24th

AQC: Achieving Precise Bandwidth Allocation with Augmented ...

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24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Zihan Gong, Yani Hu, Dinghuang Li, Cunlu Yuan, Guoyuan Dong, Dezun College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615414

As data center networks expand, the increase in buffer queues and decrease in their depth have led to the need for efficient Congestion Control (CC) protocols, particularly those that balance high bandwidth and low latency. Existing protocols are either reactive or proactive, with proactive protocols gaining traction but facing challenges in coexisting with reactive ones. The Augmented Queue (AQ) has been proposed to address this, but it struggles with precise bandwidth control for proactive protocols, leading to unfair allocation. This paper introduces AQC, a precise bandwidth control mechanism for credit-based CC strategies. AQC integrates a Proactive-CC-Plane within AQ, ensuring fair bandwidth control across multiple credit-based protocols and resolving competition between proactive and reactive protocols. Deployed on Top of Rack (ToR) switches, AQC improves performance by accurately controlling bandwidth in both single-pair and multi-pair host scenarios. Experimental results show that AQC ensures bandwidth fairness, reduces flow completion time by up to 5.7x, and significantly decreases switch buffer occupation by 8.23x and 6.99x for Explicit Congestion Notification (ECN)-based and Round-Trip Time (RTT)-based protocols, respectively, while reducing flow completion time by 3.7x and 2.6x. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Optimizing Multiple Consumer-specific Objectives in End-to-End Ensemble Machine Learning Serving 17

Optimizing Multiple Consumer-specific Objectives in End-to-E...

引用

17th IEEE/ACM International conference on Utility and Cloud Computing, UCC 2024

作者： Nguyen, Minh-Tri Truong, Hong-Linh Arcaini, Paolo Ishikawa, Fuyuki Department of Computer Science Aalto University Espoo Finland National Institute of Informatics Tokyo Japan

ISBN: (纸本)9798350367201

Optimizing the quality of machine learning (ML) services for individual consumers with specific objectives is crucial for improving consumer satisfaction. In this context, end-to-end ensemble ML serving (EEMLS) faces many challenges in selecting and deploying ensembles of ML models on diverse resources across the edge-cloud continuum. This paper provides a method for evaluating the runtime performance of inference services via consumer-defined metrics. We enable ML consumers to define high-level metrics and consider consumer satisfaction in estimating service costs. Moreover, we introduce a time-efficient ensemble selection algorithm to optimize the EEMLS with intricate trade-offs between service quality and costs. Our intensive experiments demonstrate that the algorithm can be executed periodically despite the extensive search space, enabling dedicated optimization for individual consumers in dynamic contexts. © 2024 IEEE.

关键词： End-to-End ML Ensemble ML Ensemble Selection ML Serving performance Evaluation

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MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation 39

MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3...

引用

39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Yang, Zhifei Lu, Keyang Zhang, Chao Qi, Jiaxing Jiang, Hanqi Ma, Ruifei Yin, Shenglin Xu, Yifan Xing, Mingzhe Xiao, Zhen Long, Jieyi Liu, Xiangde Zhai, Guangyao School of Computer Science Peking University China School of Artificial Intelligence Beihang University China Beijing Digital Native Digital City Research Center China School of Computer Science and Engineering Beihang University China Theta Labs Inc. United States Technical University of Munich Germany

ISBN: (纸本)157735897X

Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable data representation that facilitates these applications. However, current graph-based methods for scene generation are constrained to text-based inputs and exhibit insufficient adaptability to flexible user inputs, hindering the ability to precisely control object geometry. To address this issue, we propose MMGDreamer, a dual-branch diffusion model for scene generation that incorporates a novel Mixed-Modality Graph, visual enhancement module, and relation predictor. The mixed-modality graph allows object nodes to integrate textual and visual modalities, with optional relationships between nodes. It enhances adaptability to flexible user inputs and enables meticulous control over the geometry of objects in the generated scenes. The visual enhancement module enriches the visual fidelity of text-only nodes by constructing visual representations using text embeddings. Furthermore, our relation predictor leverages node representations to infer absent relationships between nodes, resulting in more coherent scene layouts. Extensive experimental results demonstrate that MMGDreamer exhibits superior control of object geometry, achieving state-of-the-art scene generation performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

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Sparse Non-Contact Multiple People Localization and Vital Signs Monitoring Via FMCW Radar 48

Sparse Non-Contact Multiple People Localization and Vital Si...

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48th IEEE International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Eder, Yonathan Liu, Zhuoyang Eldar, Yonina C. Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Non-contact vital signs monitoring (NCVSM) of multiple people is becoming a necessity in healthcare due to increasing morbidity and manpower shortage. In meeting these requirements, frequency modulated continuous wave (FMCW) radars have shown great potential. However, current techniques present difficulties in locating and monitoring humans in noisy environments containing multiple objects. In this work, we first develop a model for NCVSM of multiple people via FMCW radar, based on a single-input-multiple-output setup. By considering the sparse nature of the modeled signals along with human-typical cardiopulmonary characteristics, we provide a joint-sparse recovery mechanism to accurately localize targets in a clutter-rich scenario where existing techniques struggle. Then, we present a robust method for NCVSM of the found individuals, with improved performance results when compared to current NCVSM techniques using several statistical metrics. Our approach offers excellent performance in a medical application where high accuracy is required. © 2023 IEEE.

关键词： Medical applications

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FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos

FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expres...

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

作者： Wang, Yan Sun, Yixuan Huang, Yiwen Liu, Zhongying Gao, Shuyong Zhang, Wei Ge, Weifeng Zhang, Wenqiang Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Happy" expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frame-works were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on https://***/wangyanckxx/FERV39k.

关键词： computer vision Image recognition Face recognition Manuals computer architecture Benchmark testing Robustness

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Multivariate Time Series Prediction based on Improved Transformer Model in Computing System 2

Multivariate Time Series Prediction based on Improved Transf...

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2nd International conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2023

作者： Gong, Menghao Sun, Jianpeng Xie, Xiguo Zheng, Yu Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Co. Ltd Product Enterprise Department Beijing China

ISBN: (纸本)9798350331448

In IT operations and maintenance applications within computing systems, monitored data is often modeled as Multivariate Time Series (MTS). Predicting MTS has been extensively studied, and various models, including statistical algorithms and deep learning networks, have been proposed to capture multidimensional and nonlinear features. However, current models suffer from several issues. Firstly, for data with multiple dimensions in computer systems, existing models do not sufficiently and explicitly explore and exploit cross-dimensional dependencies. Secondly, there is a demand for real-time processing of large volumes of data in IT operations and maintenance applications within computing systems, and current deep learning network models incur high computational costs. Finally, the temporal characteristics of time series data are not effectively utilized. To address these challenges, we propose a Transformer-based time series prediction model. Building upon the current model, we introduce a 2D Transformer model to extract cross-dimensional features, utilize a sparse attention mechanism to reduce computational costs, and introduce a novel positional encoding to replace traditional positional encoding. We evaluate the performance on real-world datasets from actual applications within computing systems. Significant correlations between prediction results and actual outcomes are observed. The current model is implemented and applied within the computing system at Zhengzhou University. We compare our model with several state-of-the-art baseline methods, and empirical results demonstrate that our model achieves superior performance. © 2023 IEEE.

关键词： Forecasting

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An Efficient Feature Selection Method for high Dimensional Data Based on Improved BOA in AIoT 9th

An Efficient Feature Selection Method for High Dimensional D...

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19th International conference on Advanced Data Mining and Applications, ADMA 2023

作者： Sun, Weifeng Xu, Hao Liu, Bo Zhang, Bowei School of Software Dalian University of Technology Dalian116620 China

ISBN: (纸本)9783031466762

Feature selection can eliminate irrelevant redundant information in high-dimensional data, reduce dimensions, improve classification performance, and relieve the computational burden of machine learning. In this paper, we propose a feature selection method based on improved binary butterfly op-timization algorithm (BCSBOA). BCSBOA uses Tent chaotic mapping to improve the population initialization method, which makes the distribution of population individuals more uniform and improves population diversity. The mutation perturbation strategy is adopted to improve the ability of the algorithm to escape from local optimum. Through the adaptive weight strategy, the optimization ability of the global and local search stages is further balanced. In addition, BCSBOA maps the solution to the binary space through the Sigmoid function, proposes a new fitness function, jointly optimizes the classification accuracy and the number of features, and finally improves the feature selection effect. Simulation results of the algorithm performance over 10 UCI data sets show that, the classification accuracy and data-feature subset size have been improved. These findings also support the feasibility of our algorithm in improving machine learning performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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Subsystem Discovery in high-Dimensional Time-Series Using Masked Autoencoders 27

Subsystem Discovery in High-Dimensional Time-Series Using Ma...

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27th European conference on Artificial Intelligence, ECAI 2024

作者： Sarapisto, Teemu Wei, Haoyu Heljanko, Keijo Klami, Arto Ruotsalainen, Laura Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781643685489

Deep neural networks are increasingly used for time series tasks, yet they often struggle to interpretably model high-dimensional data. In this context, we consider the task of learning easy to understand connections between time-series variables, and organizing them into subsystems, directly from observed data. Our approach reconstructs multivariate time-series with a masked autoencoder, where all information between individual variables is mediated by a learned adjacency matrix. This intuitive pairwise relationship enables grouping of variables without prior knowledge of cluster quantity or size, and is particularly useful for analyzing complex sensor systems with unknown structural interdependencies. Our method simultaneously learns a useful signal representation and aids in understanding the underlying processes. We show that we can learn the correct subsystems from simulated data, and demonstrate identification of plausible subsystem structure from high-dimensional real-world data. In addition, we show that the model retains high predictive performance. © 2024 The Authors.

关键词： Deep neural networks

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Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Disentangled Feature Distillation for Light Field Super-Reso...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhou, Linjie Gao, Wei Li, Ge Yuan, Hui Zhao, Tiesong Yue, Guanghui School of Electronic and Computer Engineering Peking University Shenzhen China School of Control Science and Engineering Shandong University Jinan China Fuzhou University Department of Communication Engineering Fuzhou China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350313154

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications. © 2023 IEEE.

关键词： Distillation

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