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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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A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Yucen Ma, Li Yu, Zhemeng Zhang, Songjian Gao, Hui Fang, Jun Gao, Xiaofeng Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems College of Computer Science and Engineering Shanghai200240 China Didi Global Inc. Beijing100190 China

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called Multi-Candidate Carpooling Route Planning (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets. Our code is available at https://***/GaoYucen/Lite-GD © 2002-2012 IEEE.

关键词： Network coding

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Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

arXiv

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arXiv 2025年

作者： Xu, Jinda Song, Yuhao Wang, Daming Zhao, Weiwei Chen, Minghua Chen, Kangliang Li, Qinya Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China HAOMO.AI Technology Co. Ltd. China

In an era overwhelmed by vast amounts of data, the effective curation of web-crawl datasets is essential for optimizing model performance. This paper tackles the challenges associated with the unstructured and heterogeneous nature of such datasets. Traditional heuristic curation methods often inadequately capture complex features, resulting in biases and the exclusion of relevant data. We introduce an advanced, learning-driven approach, Ensemble Curation Of DAta ThroUgh Multimodal Operators (EcoDatum), incorporating a novel quality-guided deduplication method to ensure balanced feature distributions. EcoDatum strategically integrates various unimodal and multimodal data curation operators within a weak supervision ensemble framework, utilizing automated optimization to score each data point effectively. EcoDatum, which significantly improves the data curation quality and efficiency, outperforms existing state-of-the-art (SOTA) techniques, ranked 1st on the DataComp leaderboard, with an average performance score of 0.182 across 38 diverse evaluation datasets. This represents a 28% improvement over the DataComp baseline method, demonstrating its effectiveness in improving dataset curation and model training efficiency. © 2025, CC BY-NC-SA.

关键词： Data quality

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Embedding differential privacy in decision tree algorithm with different depths

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Science China(Information Sciences) 2017年第8期60卷 136-150页

作者： Xuanyu BAI Jianguo YAO Mingxuan YUAN Ke DENG Xike XIE Haibing GUAN Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University

Differential privacy(DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining(DM)logic is considered in the DP implementation. However, although one-step DM computation for decision tree(DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo(MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation(previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large(e.g., ? = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity.

关键词： differential privacy decision tree exponential mechanism exhaustive search MCMC

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Scale-Shift Attention in Polarization Domain for Fine-Grained Classification of Satellite ISAR Images

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IEEE Transactions on Multimedia 2025年

作者： Xin, Zewei Li, Qinya Sheng, Bowen Wu, Fan Chen, Guihai Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai200240 China Pinghu Space Awareness Laboratory Technology Co. Ltd. Pinghu314200 China Pinghu Laboratory Pinghu314200 China

Traditional fine-grained classification focuses on visible light domains, such as animals and cars. However, these methods often perform poorly when applied to radar images and images of satellites because of challenges such as distinguishing between noise and objects and the significant scale differences among object components. To address these unique scenarios, we propose the scale-shift attention in polarization domain (SAPD) method for fine-grained classification in satellite ISAR images. Specifically, radar emits different types of waves, each with distinct imaging effects. We utilize multipolarization inputs and introduce a polarization domain query module to integrate complementary features from various radar wave types captured from the same viewpoint. This multipolarization learning helps distinguish noise and leverages complementary features from different inputs. Moreover, to handle the substantial scale differences between centimeter-level payloads and the overall meter-level structure of satellites, we propose a scale-shift attention mechanism based on shift kernels. This mechanism extends attention in the direction specified by the shift kernel by incorporating adjacent pixels, allowing for the diffusion of attention. This is beneficial for capturing features of satellite components with varying scales and shapes. Extensive experiments on a novel satellite ISAR image dataset validate the effectiveness and superiority of the SAPD. © 1999-2012 IEEE.

关键词： Inverse synthetic aperture radar

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Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory

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Journal of Computer Science & Technology 2019年第1期34卷 94-112页

作者： Yang Hong Yang Zheng Fan Yang Bin-Yu Zang Hai-Bing Guan Hai-Bo Chen Shanghai Key Laboratory for Scalable Computing Systems Shanghai Jiao Tong UniversityShanghai 200240China

The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many *** the scalability is limited within a single node;therefore programmers still have to redesign applications to scale out over multiple *** paper revisits the design and implementation of distributed shared memory (DSM)as a way to scale out applications optimized for non-uniform memory access (NUMA)architecture over a well-connected *** paper presents MAGI,an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network *** is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory *** also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA)to reduce the number of page faults and the cost of the coherence *** has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized *** deployed MAGI over an 8-node RDMA-enabled *** evaluation shows that MAGI achieves up to 9.25:4 speedup compared with an unoptimized implementation,leading to a sealable performance for large-scale data-intensive applications.

关键词： distributed shared memory (DSM) scalability multieore evolution non-uniform memory access (NUMA) remote direct memory access (RDMA)

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Optimizations for High Performance Network Virtualization

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Journal of Computer Science & Technology 2016年第1期31卷 107-116页

作者： Fan-Fu Zhou Ru-Hui Ma Jian Li Li-Xia Chen Wei-Dong Qiu Hai-Bing Guan Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China

The increasing requirements of intensive interoperaterbility among the distributed nodes desiderate the high performance network connections, owing to the substantial growth of cloud computing and datacenters. Network I/O virtualization aggregates the network resource and separates it into manageable parts for particular servers or devices, which provides effective consolidation and elastic management with high agility, flexibility and scalability as well as reduced cost and cabling. However, both network I/O virtualization aggregation and the increasing network speed incur higher traffic density, which generates a heavy system stress for I/O data moving and I/O event processing. Consequently, many researchers have dedicated to enhancing the system performance and alleviating the system overhead for high performance networking virtualizatiou. This paper first elaborates the mainstreaming I/O virtualization methodologies, including device emulation, split-driver model and hardware assisted model. Then, the paper discusses and compares their specific advantages in addition to performance bottlenecks in practical utilities. This paper mainly focuses on the comprehensive survey of state- of-the-art approaches for performance optimizations and improvements as well as the portability management for network I/O virtualization. The approaches include various novel data delivery schemes, overhead mitigations for interrupt processing and adequate resource allocations for dynamic network states. Finally, we highlight the diversity of I/O virtualization besides the performance improvements in network virtualization infrastructure.

关键词： network virtualization single root I/O virtualization （SR-IOV） virtual machine monitor （VMM）

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Bipartite-Oriented Distributed Graph Partitioning for Big Learning

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Journal of Computer Science & Technology 2015年第1期30卷 20-29页

作者：陈榕施佳鑫陈海波臧斌宇 Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Shanghai 200240 China

Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive repli- cation of vertices as well as significant pressure on network communication. This article identifies the challenges and oppor- tunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition- ing algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X （from 1.16X） for four typical MLDM algorithnls, due to reducing up to 80% vertex replication, and up to 96% network traffic.

关键词： bipartite graph graph partitioning graph-parallel system

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Partitioning the Conventional DBT System for Multiprocessors

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Journal of Computer Science & Technology 2011年第3期26卷 474-490页

作者：马汝辉管海兵朱二周杨洪波杨吟冬梁阿磊 Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

Noticeable performance improvement via ever-increasing transistors is gradually trapped into a predicament since software cannot logically and efficiently utilize hardware resource, such as multi-core resource. This is an inevitable problem in dynamic binary translation （DBT） system as well. Though special purpose hardware as aide tool, through some interfaces, provided by DBT enables the system to achieve higher performance, the limitation of it is significant, that is, it is impossible to be used widely by another one. To overcome this drawback, we focus on building compatible software architecture to acquire higher performance without platform dependence. In this paper, we propose a novel multithreaded architecture for DBT system through partitioning distinct function module, which is to adequately utilize multiprocessors resource. This new architecture devides couples the common DBT system （DBTs） working routine into dynamic translation, optimization, and translated code execution phases, and then ramifies them into different threads to enable them concurrently executed. In this new architecture, several efficient novel methods are presented to cope with intractable work that puzzles most researchers, such as communication mechanism, cache layout, and mutual exclusion between threads. Experimental results using SPECint 2000 indicate that this new architecture for DBT system can achieve higher performance -- speed up the traditional DBT system by about average 10.75%, with better CPU utilization.

关键词： DBT optimization microprocessor multi-core SPECint 2000

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