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arXiv 2023年

作者： Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

An accurate understanding of a self-driving vehicle’s surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird’s-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. 1 © 2023, CC BY.

关键词： Birds

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun 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 Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Estimation of Structural Vibration Modal Properties Using a Spike-Based Computing Paradigm 1

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40th IMAC, A Conference and Exposition on Structural Dynamics, 2022

作者： Allen, Jabari Chu, Raymond Sims, Troy Cattaneo, Alessandro Taylor, Gregory Sornborger, Andrew Mascareñas, David Department of Electrical and Computer Engineering FAMU-FSU College of Engineering Florida A&M University TallahasseeFL United States Department of Structural Engineering Jacobs School of Engineering University of California San Diego La JollaCA United States Department of Electrical Engineering New Mexico Institute of Mining & Technology SocorroNM United States Los Alamos National Laboratory Los AlamosNM United States Department of Mechanical and Aerospace Engineering New Mexico State University Las CrucesNM United States

ISBN: (数字)9783031041228

ISBN: (纸本)9783031041211

Spiking neural networks are an emerging concept that draws inspiration from the computational neuroscience research community. Spiking neural networks combine spike-based computing and machine-learning-based neural networks that emulate the operation of the human brain. Spiking neural networks have the ability to be easily integrated into neuromorphic hardware, such as Intel’s Loihi chip. The advantages of neuromorphic hardware are its high-speed computation and low-power consumption in comparison to traditional electronics. These factors have an important role for the future of smart systems and the reliability of structural health monitoring. Currently, coupling spike-based computing to continuous-valued signals, which are typically measured in structural dynamics, is rare. This paper aims to explore spiking neural networks and their possible application in structural dynamics and modal analysis using Nengo, a large-scale neural network simulation package. In this work, we implement output-only modal identification techniques that rely on solving the blind source separation problem using spike neural networks to extract the natural frequencies, mode shapes, and damping ratios of a simulated structural system being exposed to dynamic loading. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Modal analysis

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Train faster, perform better: modular adaptive training in over-parameterized models 23

Train faster, perform better: modular adaptive training in o...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yubin Shi Yixuan Chen Mingzhi Dong Xiaochen Yang Dongsheng Li Yujiang Wang Robert Dick Qin Lv Yingying Zhao Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University School of Mathematics Statistics University of Glasgow Microsoft Research Asia Shanghai China Department of Engineering Science University of Oxford Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained, modular-level learning dynamics of over-parameterized models to attain a more efficient and fruitful training strategy. Empirical evidence reveals that when scaling down into network modules, such as heads in self-attention models, we can observe varying learning patterns implicitly associated with each module's trainability. To describe such modular-level learning capabilities, we introduce a novel concept dubbed modular neural tangent kernel (mNTK), and we demonstrate that the quality of a module's learning is tightly associated with its mNTK's principal eigenvalue λmax. A large λmax indicates that the module learns features with better convergence, while those miniature ones may impact generalization negatively. Inspired by the discovery, we propose a novel training strategy termed Modular Adaptive Training (MAT) to update those modules with their λmax exceeding a dynamic threshold selectively, concentrating the model on learning common features and ignoring those superfluous ones. Unlike most existing training schemes with a complete BP cycle across all network modules, MAT can significantly save computations by its partially-updating strategy and can further improve performance. Experiments show that MAT nearly halves the computational cost of model training and outperforms the accuracy of baselines.

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PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

PeRP: Personalized Residual Policies For Congestion Mitigati...

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International Conference on Intelligent Transportation

作者： Aamir Hasan Neeloy Chakraborty Haonan Chen Jung-Hoon Cho Cathy Wu Katherine Driggs-Campbell Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Institute for Data Systems & Society (IDSS) at the Massachusetts Institute of Technology

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver's intrinsic traits on how they follow instructions in an unsupervised manner with a variational autoencoder. Then, a policy conditioned on the inferred trait adapts the action of the PC policy to provide the driver with a personalized recommendation. Our system is trained in simulation with novel driver modeling of instruction adherence. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines. 1

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Decision-Making lifecycle of the Bid Process Information System

Decision-Making lifecycle of the Bid Process Information Sys...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Trabelsi, Salma Zahaf, Sahbi Zekri, Manel Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Tunisia University of Tunis El-Manar Institut Superieur d'Informatique Tunis Tunisia MIRACL Laboratory Higher Institute of Computer and Multimedia Sakiet Ezzit Technopole Sfax242-3021 Tunisia Department of Computer Science Faculty of Sciences Tunis Tunisia

"Three fit" problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and cooperative dimensions are four dimensions that have been identified to deal with such failures. Therefore, in this work, we are particularly interested in handling this failure by proposing an approach that focuses on the decision dimension of BPIS. This improves at the end of the various stages of the data warehouse lifecycle and takes into consideration the user's knowledge by focusing on their individual needs too. © 2022 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Anonymous Authentication Technology Review in Vehicle Networking Environment 11th

Anonymous Authentication Technology Review in Vehicle Networ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Zhang, Haoyan Liang, Guangjun He, Jiacheng Ji, Mingtao Department of Computer Information and Cyber Security Jiangsu Police Institute NanjingJiangsu210031 China Jiangsu Electronic Data Forensics Analysis Engineering Research Center NanjingJiangsu210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department NanjingJiangsu210031 China

ISBN: (纸本)9789811665530

In the era of big data, the development of the Internet of things is in the ascendant, and the technology of car networking is becoming more and more mature. This paper will first introduce the anonymous authentication technology widely used in the Internet of things, and then take the Internet of Vehicles as an example to analyze the symbolic achievements of anonymous authentication technology in the development process, and point out the existing technical problems. Finally, the future application prospect and research direction of anonymous authentication technology are prospected. It is hoped that this paper can provide scholars with new ideas and certain reference value, and make people have a systematic understanding of anonymous authentication technology, improving people’s security awareness of protecting their own privacy information. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Resilient Collaborative Caching for Multi-Edge Systems with Robust Federated Deep Learning

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IEEE/ACM Transactions on Networking 2024年

作者： Chen, Zheyi Liang, Jie Yu, Zhengxin Cheng, Hongju Min, Geyong Li, Jie Fuzhou University College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies. © 1993-2012 IEEE.

关键词： cache space partitioning content popularity prediction Multi-edge collaborative caching proactive cache replacement robust federated deep learning

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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