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

作者： Peng Wang Zexi Li Ningyu Zhang Ziwen Xu Yunzhi Yao Yong Jiang Pengjun Xie Fei Huang Huajun Chen Zhejiang University Alibaba Group Zhejiang University and Zhejiang Key Laboratory of Big Data Intelligent Computing

ISBN: (纸本)9798331314385

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (non-parametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle—reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e.g., GPT, LLaMA, and Mistral. Code is available at https://***/zjunlp/EasyEdit.

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Numerical study of microorganisms swimming near a convex wall in a Giesekus fluid

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Physical Review E 2025年第1期111卷 015103-015103页

作者： Chenlin Zhu Fangyuan Peng Dingyi Pan Zhaosheng Yu Zhaowu Lin Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China

The motion of microorganisms in complex fluids stands out as a prominent subject within fluid mechanics. In our study, we utilize the fictitious domain method to investigate the locomotion of squirmers along a convex wall in Giesekus viscoelastic fluids. This study examines the influence of fluid elasticity and wall curvature on squirmer particles, analyzing their movement patterns in detail. Near the convex wall, three distinct behavioral characteristics emerge: scattering, orbiting forward, and orbiting backward. The findings reveal that, compared with Newtonian fluids, squirmers exhibit a stronger tendency to be attracted toward the wall in viscoelastic fluids. This behavior is attributed to the elastic stress of the fluid, which generates a reverse torque on microbial particles, altering their movement direction and hindering their escape from the wall. Notably, as the wall curvature decreases, the likelihood of particles escaping diminishes.

关键词： Forward scattering

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on big data computing and Communications, bigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Complex integrity constraint discovery: measuring trust in modern intelligent railroad systems

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Journal of zhejiang University-Science A(Applied Physics & Engineering) 2022年第10期23卷 832-837页

作者： Wen-tao HU Da-wei JIANG Sai WU Ke CHEN Gang CHEN Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang UniversityHangzhou 310027China

1Introduction data are at the heart of intelligent rail systems in the high-speed transportation sector(Zhou et al.,2020;Ho et al.,2021;Hu et al.,2021;Chen et al.,2022).The core of modern intelligent railroad systems typically includes rail transportation and equipment monitoring models learned from large datasets,which are often optimized for specific data and workloads(Zhu et al.,2019;Tan et al.,2020).While these intelligent railroad systems have been widely adopted and successful,their reliability and proper function will change as the data used *** the data used(on which the system operates)deviates from the fundamental constraints of the initial data(on which the system is trained)then,in that case,the system performance degrades,and the results inferred by the system model become unreliable,so the system model must be retrained and redeployed to re-store reliable inference results(Sharma and Chandel,2013).The mechanism for assessing the trustworthiness of intelligent rail system inferences is of paramount importance,especially for rail systems performing safety-critical or high-impact operations.

关键词： constraint intelligent operations

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

arXiv

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arXiv 2025年

作者： Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments.1 Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

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China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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Diabetic retinopathy identification based on multi-sourcefree domain adaptation

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International Journal of Ophthalmology(English edition) 2024年第7期17卷 1193-1204页

作者： Guang-Hua Zhang Guang-Ping Zhuo Zhao-Xia Zhang Bin Sun Wei-Hua Yang Shao-Chong Zhang School of Big Data Intelligent Diagnosis&Treatment Industry Taiyuan UniversityTaiyuan 030032Shanxi ProvinceChina Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal UniversityTaiyuan 030619Shanxi ProvinceChina Shanxi Eye Hospital Taiyuan 030002Shanxi ProvinceChina College of Computer Science and Technology Taiyuan Normal UniversityTaiyuan 030619Shanxi ProvinceChina Shenzhen Eye Institute Shenzhen Eye HospitalJinan UniversityShenzhen 518040Guangdong ProvinceChina

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled ***:A multi-SFDA method was proposed for DR *** method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target ***,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class *** is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification *** demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target ***:The multi-SFDA method provides an effective approach to overcome the challenges in DR *** method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

关键词： diabetic retinopathy multisource-free domain adaptation pseudo-label generation softmaxconsistence minimization

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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