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arXiv 2025年

作者： Marcelyn, Steeve Cuthbert Gao, Yucen Zhang, Yuzhe Gao, Xiaofeng Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The proliferation of GPS-enabled devices has led to the accumulation of a substantial corpus of historical trajectory data. By leveraging these data for training machine learning models, researchers have devised novel data-driven methodologies that address the personalized route recommendation (PRR) problem. In contrast to conventional algorithms such as Dijkstra’s shortest path algorithm, these novel algorithms possess the capacity to discern and learn patterns within the data, thereby facilitating the generation of more personalized paths. However, once these models have been trained, their application is constrained to the generation of routes that align with their training patterns. This limitation renders them less adaptable to novel scenarios and the deployment of multiple machine learning models might be necessary to address new possible scenarios, which can be costly as each model must be trained separately. Inspired by recent advances in the field of Large Language Models (LLMs), we leveraged their natural language understanding capabilities to develop a unified model to solve the PRR problem while being seamlessly adaptable to new scenarios without additional training. To accomplish this, we combined the extensive knowledge LLMs acquired during training with further access to external hand-crafted context information, similar to RAG (Retrieved Augmented Generation) systems, to enhance their ability to generate paths according to user-defined requirements. Extensive experiments on different datasets show a considerable uplift in LLM performance on the PRR problem. © 2025, CC BY.

关键词： Natural language processing systems

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PAM: an efficient power-aware multilevel cache policy to reduce energy consumption of storage systems

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Frontiers of computer science 2019年第4期13卷 850-863页

作者： Xiaodong MENG Chentao WU Minyi GUO Long ZHENG Jingyu ZHANG Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science & Engineering Shanghai Jiao Tong University Shanghai 200240 China

Energy consumption is one of the most significant aspects of large-scale storage systems where multilevel caches are widely used. In a typical hierarchical storage structure, upper-level storage serves as a cache for the lower level, forming a distributed multilevel cache system. In the past two decades, several classic LRU-based multilevel cache policies have been proposed to improve the overall I/O performance of storage systems. However, few power-aware multilevel cache policies focus on the storage devices in the bottom level, which consume more than 27% of the energy of the whole system [1]. To address this problem, we propose a novel power-aware multilevel cache (PAM) policy that can reduce the energy consumption of high-performance and I/O bandwidth storage devices. In our PAM policy, an appropriate number of cold dirty blocks in the upper level cache are identified and selected to flush directly to the storage devices, providing high probability extension of the lifetime of disks in standby mode. To demonstrate the effectiveness of our proposed policy, we conduct several simulations with real-world traces. Compared to existing popular cache schemes such as PA? LRU, PB-LRU, and Demote, PAM reduces power consumption by up to 15% under different I/O workloads, and improves energy efficiency by up to 50.5%.

关键词： storage system multilevel cache energy consumption I/O performance hint

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Thermo-mechanical energy storage technologies:Innovations,challenges and future directions

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能源前沿（英文） 2025年第2期19卷 115-116页

作者： Yao Zhao Mingjia Li Kai Wang Adriano Sciacovelli Chris Qin Steven Lecompte André D.Thess College of Smart Energy Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Non-carbon Energy Conversion and Utilization Institute Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhou 310027China Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province Zhejiang UniversityHangzhou 310027China Department of Civil and Mechanical Engineering Technical University of DenmarkKongens Lyngby 2800Denmark School of Engineering and Computer Science Washington State UniversityVancouverWA 98686USA Department of Electromechanical Systems and Metal EngineeringGhent UniversityGhent 9000Belgium Institute of Engineering Thermodynamics German Aerospace Center(DLR)Stuttgart 70569Germany University of Stuttgart Stuttgart 70569Germany

Thermo-mechanical energy storage(TMES)technologies are increasingly recognized as essential solutions for enhancing the efficiency and stability of energy systems,particularly in the integration of renewable energy *** technologies leverage the conversion between thermal,electrical and mechanical energy to store and release energy,providing an effective tool to address the intermittency and variability of renewable sources such as wind and *** editorial explores the fundamental principles,innovations,challenges,and future research directions of TMES technologies,with a focus on developments in compressed-air energy storage(CAES),liquid-air energy storage(LAES),Carnot battery/pumped-thermal energy storage(PTES),and carbon dioxide energy storage(CDES).

关键词： technologies future storage energy challenges directions innovations mechanical thermo

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Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective

arXiv

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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Remote State Estimation for Complex Networks Under Probabilistic Bit Flips: A Transmission Power Allocation Scheme

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IEEE Transactions on Automatic Control 2025年

作者： Song, Jiahao Wang, Zidong Liu, Qinyuan He, Xiao Tsinghua University Department of Automation Beijing100084 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Tongji University Key Laboratory of Embedded System and Service Computing Shanghai200092 China Shanghai Artificial Intelligence Laboratory Shanghai China

In this paper, the problem of remote state estimation is investigated for a class of complex networks with noisy wireless communication channels. The employment of the binary encoding scheme allows for the description of the influence of channel noises as probabilistic bit flips, where the probability of these bit flips is influenced by several factors with the signal-to-noise ratio (SNR) being a crucial one. In engineering practice, the method of adjusting the transmission power for each node in a complex network is commonly applied to modify the SNR so as to reduce the data distortion caused by bit flips. Furthermore, due to restricted communication resources, the total available transmission power is often limited particularly in large-scale systems such as complex networks. Consequently, the allocation of transmission power for all nodes under a total transmission power constraint becomes a concern. Utilizing the ultimately bounded filtering method, we devise a transmission-power-dependent state estimator, by which the combined effect of probabilistic bit flips and transmission power allocation on estimation performance is analyzed. Moreover, the task of co-designing the transmission power allocation scheme and estimator gains is modeled as an optimization problem, which is then addressed through a two-step optimization strategy. The existence of a unique optimal transmission power allocation scheme is also proven. Finally, numerical simulation examples are provided to demonstrate the effectiveness of the proposed co-design approach. © 1963-2012 IEEE.

关键词： Large scale systems

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Zeroth-order methods for constrained nonconvex nonsmooth stochastic optimization 24

Zeroth-order methods for constrained nonconvex nonsmooth sto...

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Proceedings of the 41st International Conference on Machine Learning

作者： Zhuanghua Liu Cheng Chen Luo Luo Bryan Kian Hsiang Low Department of Computer Science National University of Singapore Singapore Singapore and CNRS@CREATE LTD Singapore Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Data Science Fudan University Shanghai China and Shanghai Key Laboratory for Contemporary Applied Mathematics Department of Computer Science National University of Singapore Singapore Singapore

This paper studies the problem of solving nonconvex nonsmooth optimization over a closed convex set. Most previous works tackle such problems by transforming the constrained problem into an unconstrained problem. However, they only provide asymptotic convergence analysis for their methods. In this work, we provide the nonasymptotic convergence analysis for solving constrained nonconvex nonsmooth optimization. We first generalize classical gradient mapping and the Frank-Wolfe gap in the nonsmooth setting. Then we introduce novel notions of approximate stationarity concerning such generalized quantities. We also propose several stochastic zeroth-order algorithms for the problem, along with their nonasymptotic convergence guarantees of obtaining the proposed approximate stationarity. Finally, we conduct numerical experiments that demonstrate the effectiveness of our algorithms.

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Learning Topic Emotion and Logical Semantic for Video Paragraph Captioning

SSRN

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SSRN 2024年

作者： Li, Qinyu Wang, Hanli Yi, Xiaokai Department of Computer Science & Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai200092 China

Video paragraph captioning aims to generate multiple descriptive sentences for videos, which strive to replicate human writing in accuracy, logicality, and richness. However, current research focuses on the accuracy and temporal order of events, ignoring emotion and other critical logical relations embedded in human language, such as causal and adversative relations. The ignorance impairs the reasonable transition across generated event descriptions and restricts the vividness of expression, resulting in a gap from the standard of human language. To resolve the problem, a framework that integrates logic and emotion representation learning is proposed to narrow the gap. Concretely, a large-scale inter-event relation corpus is constructed based on the EMVPC dataset. This corpus is named EMVPC-EvtRel (standing for "EMVPC-Event Relations") and contains six widely-used logical relations in human writing, 127 explicit inter-sentence connectives, and over 20,000 pairs of event segments with newly annotated logical relations. A logical semantic representation learning method is developed for recognizing the dependencies between visual events, thereby enhancing the characteristics of video contents and boosting the logicality of generated paragraphs. Moreover, a fine-grained emotion recognition module is designed to uncover emotion features embedded in videos. Finally, experimental results on the EMVPC dataset demonstrate the superiority of the proposed method compared to existing state-of-the-art approaches. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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A Proof System for Cyber-Physical systems with Shared-Variable Concurrency 1

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23rd International Conference on Formal Engineering Methods, ICFEM 2022

作者： Li, Ran Zhu, Huibiao Banach, Richard Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Department of Computer Science University of Manchester Oxford Road ManchesterM13 9PL United Kingdom

ISBN: (数字)9783031172441

ISBN: (纸本)9783031172434

Cyber-physical system (CPS) is about the interplay of discrete behaviors and continuous behaviors. The combination of the physical and the cyber may cause hardship for the modeling and verification of CPS. Hence, a language based on shared variables was proposed to realize the interaction in CPS. In this paper, we formulate a proof system for this language. To handle the parallel composition with shared variables, we extend classical Hoare triples and bring the trace model into our proof system. The introduction of the trace may complicate our specification slightly, but it can realize a compositional proof when the program is executing. Meanwhile, this introduction can set up a bridge between our proof system and denotational semantics. Throughout this paper, we also present some examples to illustrate the usage of our proof system intuitively. © 2022, Springer Nature Switzerland AG.

关键词： Cyber Physical System

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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