检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Chen, Wubing Yang, Shangdong Li, Wenbin Hu, Yujing Liu, Xiao Gao, Yang The Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China The State Key Lab. for Novel Software Technology Nanjing University Nanjing210023 China Netease Fuxi AI Lab Netease Zhejiang Hangzhou310025 China

Effective management of multi-intersection traffic signal control (MTSC) is vital for intelligent transportation systems. Multi-agent reinforcement learning (MARL) has shown promise in achieving MTSC. However, existing MARL-based MTSC algorithms have primarily focused on capturing the spatial relationship between multi-intersection traffic signals but have overlooking the importance of the temporally stable traffic pattern. This pattern refers to the fixed positions and relatively stable traffic flow between intersections over short periods in real-world MTSC scenarios, which indicates that the learned spatial relationships between traffic signals should co-evolve over time. To this end, we propose a novel algorithm called Coevolutionary Multi-Agent Reinforcement Learning (CoevoMARL). CoevoMARL employs a graph neural network to capture the complex spatial interaction network among traffic signals. Furthermore, we propose a relationship-driven progressive LSTM (RDP-LSTM) that dynamically evolves the learned spatial interaction network over time by leveraging insights from the temporally stable traffic pattern. To accelerate convergence, we also propose the mutual information reward optimization (MIRO) technique, which strengthens the correlation between policy learning and high-performance samples by using a mutual information-based intrinsic reward. Experimental results on both synthetic and real-world datasets demonstrate the superiority of CoevoMARL over existing MTSC algorithms, providing valuable insights into incorporating the temporally stable traffic pattern. © 2023, CC BY.

关键词： Multi agent systems

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Randomness below complete theories of arithmetic

arXiv

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arXiv 2022年

作者： Barmpalias, George Wang, Wei State Key Lab of Computer Science Inst. of Software Chinese Acad. of Sciences Beijing China Inst. of Logic & Cognition Dept. of Philosophy Sun Yat-Sen University Guangzhou China

We show that reals z which compute complete extensions of arithmetic have the random join property: for each random x T z there exists random y T z such that z ≡T x y. The same is true for the truth-table and the wea...

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Randomness Below Complete Theories of Arithmetic

SSRN

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SSRN 2022年

We show that degrees containing a complete extensions of arithmetic have the random join property: they are the supremum of any random real they compute, with another random real. The same is true for the truth-table ... 详细信息

关键词： Random processes

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Growth and Irreducibility in Path-Incompressible Trees

SSRN

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SSRN 2022年

We study effective randomness-preserving transformations of path-incompressible trees. There exists a path-incompressible tree with infinitely many paths, which does not compute any perfect pathwise-random tree. Sparse perfect path-incompressible trees can be effectively densified, almost surely. We characterize the density of branching that pathwiserandom trees can have. © 2022, The Authors. All rights reserved.

关键词： Random processes

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On Efficient Packet Batching and Resource Allocation for GPU based NFV Acceleration

On Efficient Packet Batching and Resource Allocation for GPU...

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International Workshop on Quality of Service

作者： Deze Zeng Andong Zhu Lin Gu Quan Chen Minyi Guo School of Computer Science China University of Geosciences Wuhan Hubei China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Network Function Virtualization (NFV) has already become an essential technology for improving the scalab.lity and flexibility of modern computer networks. The performance gap has become the main issue that impedes the development of NFV. GPUs, with massive parallel processors, are advocated to accelerate the Virtualized Network Functions (VNFs). However, the special architecture and workflow of GPUs introduce new challenges, especially on the batched processing, and resource allocation. In this paper, we propose GPU-based NFV Acceleration framework (GNFA) with an efficient packet batching and resource allocation solution. Considering the increased latency caused by the accumulation of the GPU kernel invoking overhead, we first invent a latency reduction mechanism called SM Performance Compensation (SPC). A Partition and Adjustment based Batching and Resource Allocation (PABARA) algorithm that jointly considers batch size tuning and GPU thread allocation is also proposed. We have practically implemented GNFA and extensively evaluated its performance on some well-known VNFs. The experiment results show that GNFA can effectively promote the GPU resource utilization and improve the NFV performance in terms of per-packet latency.

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DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration

DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral M...

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International Conference on computer Vision (ICCV)

作者： Yuchun Miao Lefei Zhang Liangpei Zhang Dacheng Tao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Sydney AI Centre School of Computer Science The University of Sydney

Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (DDS2M), which works by inferring the parameters of the proposed Variational Spatio-Spectral Module (VS2M) during the reverse diffusion process, solely using the degraded HSI without any extra training data. In VS2M, a variational inference-based loss function is customized to enable the untrained spatial and spectral networks to learn the posterior distribution, which serves as the transitions of the sampling chain to help reverse the diffusion process. Benefiting from its self-supervised nature and the diffusion process, DDS2M enjoys stronger generalization ability to various HSIs compared to existing diffusion-based methods and superior robustness to noise compared to existing HSI restoration methods. Extensive experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate DDS2M’s superiority over the existing task-specific state-of-the-arts. Code is availab.e at: https://***/miaoyuchun/DDS2M.

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Disentangled Hypergraph Collab.rative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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IEEE International Conference on Web Services (ICWS)

作者： Xiao Liu Shunmei Meng Qianmu Li Xiaolong Xu Lianyong Qi Wanchun Dou Jing Zhang Xuyun Zhang Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China School of Computer and Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum (East China) China School of Cyber Science and Engineering Southeast University China School of Computing Macquarie University Australia

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collab.rative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

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Active Learning of One-Clock Timed Automata using Constraint Solving

arXiv

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arXiv 2022年

作者： Xu, Runqing An, Jie Zhan, Bohua State Key Lab. of Computer Science Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China Max Planck Institute for Software Systems Kaiserslautern Germany

Active automata learning in the framework of Angluin's L∗ algorithm has been applied to learning many kinds of automata models. In applications to timed models such as timed automata, the main challenge is to determine guards on the clock value in transitions as well as which transitions reset the clock. In this paper, we introduce a new algorithm for active learning of deterministic one-clock timed automata and timed Mealy machines. The algorithm uses observation tables that do not commit to specific choices of reset, but inst.ad rely on constraint solving to determine reset choices that satisfy readiness conditions. We evaluate our algorithm on randomly-generated examples as well as practical case studies, showing that it is applicable to larger models, and competitive with existing work for learning other forms of timed models. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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On Completeness of SDP-Based Barrier Certificate Synthesis over Unbounded Domains

arXiv

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arXiv 2023年

作者： Wu, Hao Feng, Shenghua Gan, Ting Wang, Jie Xia, Bican Zhan, Naijun State Key Lab. of Computer Science Institute of Software University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University China Academy of Mathematics and Systems Science CAS Beijing China School of Mathematical Sciences Peking University Beijing China School of Computer Science Peking University Beijing China

Barrier certificates, serving as differential invariants that witness system safety, play a crucial role in the verification of cyber-physical systems (CPS). Prevailing computational methods for synthesizing barrier certificates are based on semidefinite programming (SDP) by exploiting Putinar Positivstellensatz. Consequently, these approaches are limited by the Archimedean condition, which requires all variables to be bounded, i.e., systems are defined over bounded domains. For systems over unbounded domains, unfortunately, existing methods become incomplete and may fail to identify potential barrier certificates. In this paper, we address this limitation for the unbounded cases. We first give a complete characterization of polynomial barrier certificates by using homogenization, a recent technique in the optimization community to reduce an unbounded optimization problem to a bounded one. Furthermore, motivated by this formulation, we introduce the definition of homogenized systems and propose a complete characterization of a family of non-polynomial barrier certificates with more expressive power. Experimental results demonstrate that our two approaches are more effective while maintaining a comparable level of efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Homogenization method

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For inst.nce, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have availab.e resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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