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SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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Joint Timing and Frequency Synchronization for Coherent Optical SEFDM Systems

Joint Timing and Frequency Synchronization for Coherent Opti...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Jinze Shi Xinwei Du Shuai Liu Qian Wang Changyuan Yu Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China Department of Electronic and Information Engineering Photonics Research Institute The Hong Kong Polytechnic University Hung Hom Hong Kong

In this paper, we propose a joint timing and frequency synchronization algorithm for spectrally efficient frequency-division multiplexing (SEFDM) systems by utilizing a training symbol composed of a conjugate symmetric sequence. The timing metric is designed to be the multiplication of two timing metrics which aims to reduce the amplitude of sidelobes, thus ensuring the timing synchronization performance. In addition, according to the phase relationship between the four halves in the designed training symbol, we then derive a closed form expression of the the carrier frequency offset (CFO) estimate. Simulation results verify the superior timing and frequency synchronization accuracy of the proposed method compared with the existing synchronization algorithms.

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Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk Prediction

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SSRN 2024年

作者： Alhaek, Fares Li, Tianrui Rajeh, Taha M. Javed, Muhammad Hafeez Liang, Weichao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

The proliferation of vehicles and the intricate layout of road systems have contributed to a significant rise in traffic accidents, posing a pressing concern globally. Despite the advancements facilitated by deep learning, several challenges persist in the domain of traffic accident prediction. Firstly, the sparsity of accident data in certain city regions presents a significant obstacle, particularly when attempting fine-grained predictions at a local level. Secondly, the intricate spatial-temporal relations inherent in traffic accident data pose a challenge for existing prediction models. In response to these formidable challenges, this paper presents GLST-TARP, a novel model for predicting traffic accident risk in urban environments by leveraging both global semantic and localized geographic spatial-temporal relations. Specifically, we construct multi-graphs to encode global static and dynamic spatial relations, incorporating attention mechanisms to adaptively focus on relevant information and temporal relations. Additionally, we employ channel-wise convolutional neural network blocks to extract localized geographic features and enhance predictive accuracy. The proposed model is trained using a Huber loss function tailored for regression tasks, relieving the impact of zero values during optimization. Experimental results demonstrate that GLST-TARP outperforms state-of-the-art methods in predicting traffic accident risks, showcasing its potential for enhancing urban safety and transportation management. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-Aware POI Recommendation 3

A Fuzzy Clustering Based Collaborative Filtering Algorithm f...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Yin, Minghao Liu, Yanheng Zhou, Xu Sun, Geng College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-Aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy clustering

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Vector Magnetic Field Sensor Based on Etched Seven-Core Fiber Helical Long-Period Grating with Magnetic Fluid

Vector Magnetic Field Sensor Based on Etched Seven-Core Fibe...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Siyu Chen Chen Jiang Yuehe Zhao Yuehui Ma Chengbo Mou Yunqi Liu The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Advanced Photonic Technology Lab College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and Telecommunications Nanjing China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai China

We demonstrated a vector magnetic field sensor utilizing an etched seven-core fiber helical long-period grating, showcasing a high-sensitivity response of up to 10.8 nm/mT within magnetic field intensity measurement r... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fibers Optical fiber sensors Magnetic field measurement Measurement by laser beam Magnetic liquids Lasers and electrooptics Fiber gratings Vectors Magnetic fields Laser applications

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Efficient Pre-trained Semantics Refinement for Video Temporal Grounding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ao Liu, Huijun Zhu, Yiqing Ge, Yongxin Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Chongqing University College of Computer Science Chongqing401331 China Chongqing University National Elite Institute of Engineering Chongqing401331 China

Video Temporal Grounding (VTG) is a fine-grained video understanding task that aims to ground the relevant video moments corresponding to given language queries. Most existing approaches utilize powerful Vision-Language Models (VLMs), augmented with additional network architectures or specialized modules to supplement temporal reasoning capabilities. Despite achieving impressive performance, these approaches tend to overlook a critical issue that pre-trained visual and textual representations are not specifically optimized for VTG. Particularly, such representations often suffer from inter-modal semantic misalignment referring to the inconsistency between different modalities, and intra-modal semantic confusion, caused by insufficient discriminability within visual modality. To address these limitations, we propose an efficient semantics refinement framework built upon pre-trained models, featuring two core components. First, the Modal knowledge Bidirectional Propagation (MKBP) component promotes inter-modal semantic alignment via bidirectional enrichment of textual and visual semantics, exploiting their complementary strengths without introducing additional parameters. Second, the Content Context Contrast Learning (C3L) component alleviates intra-modal semantic confusion within visual modality by bringing query-specific visual features closer while separating irrelevant ones. Comprehensive experiments on six benchmark datasets demonstrate the superior performance of our proposed methods. © 1991-2012 IEEE.

关键词： C (programming language)

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jicun Li Xingjian Li Tianyang Wang Shi Wang Yanan Cao Chengzhong Xu Dejing Dou Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Big Data Lab Baidu Research State Key Lab of IOTSC University of Macau University of Alabama at Birmingham Institute of Information Engineering Chinese Academy of Sciences

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method.

关键词： Cross layer design Bit error rate Signal processing Stability analysis Robustness Acoustics Behavioral sciences

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A Power-System Economic Dispatching Based on Enhanced Group Search Optimizer 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Nguyen, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Economic dispatch (ED) is one of the critical tasks in a power system’s optimization issues, but its characteristics with non-convex, high-dimensional, non-linear, and non-differentiable, so that caused solutions are more challenging in reality. This paper suggests an enhanced group search optimizer (EGSO) for solving the ED model with the valve-point effect and the different fuels. The EGSO adopts the original group search optimizer (GSO) by changing polar to Cartesian coordinate’s conversion and adding cyclic translation operators to the search behavior of the finder and walker to avoid falling into local optimal and slow convergence of the GSO. In the experiment section, the testing power systems of the ED model are used to validate the EGSO performance. The experimental results reveal that EGSO outperformed the other algorithms regarding convergence speed, the time required to complete calculations, and impacts with lower fuel cost. Additionally, with excellent outcomes in optimization issues involving multiple local optimal values show the EGSO is practical, quick, and efficient in addressing the challenging ED model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Transmitter IQ Mismatch Estimation by Number-Theoretic Net-Based Gaussian Particle Filter for Coherent Optical Communications

Transmitter IQ Mismatch Estimation by Number-Theoretic Net-B...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuai Liu Yangfan Xu Xinwei Du Qian Wang Changyuan Yu Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China Department of Electronic and Information Engineering Photonics Research Institute The Hong Kong Polytechnic University Hung Hom Hong Kong

In this paper, we propose a novel approach for estimating in-phase/quadrature-phase (IQ) mismatch at the transmitter using a number-theoretic net (NT-net)-based Gaussian particle filter (NT-GPF). The proposed method generates particles on an ellipse of the given bivariate Gaussian distribution, which helps locate the correct region of the actual state parameters more efficiently. Simulation results demonstrate that the NT-GPF-based approach offers higher estimation accuracy, with the mean squared error (MSE) consistently lower than that of traditional GPF across different particle numbers. Moreover, the proposed NT-GPF-based algorithm significantly improves estimation efficiency and noise tolerance, requiring only 21 particles under low signal-to-noise ratio (SNR).

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Once read is enough: domain-specific pretraining-free language models with cluster-guided sparse experts for long-tail domain knowledge 24

Once read is enough: domain-specific pretraining-free langua...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Dong Mengyi Chen Jixian Zhou Yubin Shi Yixuan Chen Mingzhi Dong Yujiang Wang Dongsheng Li Xiaochen Yang Rui Zhu Robert Dick Qin Lv Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow Bayes Business School City St George's University of London Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

ISBN: (纸本)9798331314385

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

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