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arXiv 2024年

作者： He, Zhijuan Jiang, Kai Tan, Liwei Wang, Xin Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China

Self-consistent field theory (SCFT) is one of the most widely-used framework in studying the equilibrium phase behaviors of inhomogenous polymers. For liquid crystalline polymeric systems, the main numerical challenges of solving SCFT encompass efficiently solving plenty of six dimensional partial differential equations (PDEs), precisely determining the subtle energy difference among self-assembled structures, and developing effective iterative methods for nonlinear SCF iteration. To address these challenges, this work introduces a suite of high-order and efficient numerical methods tailored for SCFT of liquid-crystalline polymers. These methods include various advaced PDE solvers, an improved Anderson iteration algorithm to accelerate SCFT calculations, and an optimization technique of adjusting the computational domain during the SCF iterations. Extensive numerical tests demonstrate the efficiency of the proposed methods. Based on these algorithms, we further explore the self-assembly behavior of liquid crystalline polymers through simulations in four, five, and six dimensions, uncovering intricate three-dimensional spatial structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Crystalline materials

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Human-like conceptual representations emerge from language prediction

arXiv

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arXiv 2025年

作者： Xu, Ningyu Zhang, Qi Du, Chao Luo, Qiang Qiu, Xipeng Huang, Xuanjing Zhang, Menghan School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China Ministry of Education Key Laboratory of Contemporary Anthropology Fudan University Shanghai China

People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Non-autoregressive Machine Translation with Probabilistic Context-free Grammar

arXiv

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arXiv 2023年

作者： Gui, Shangtong Shao, Chenze Ma, Zhengrui Zhang, Xishan Chen, Yunji Feng, Yang State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Cambricon Technologies China

Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and performance degradation compared to autoregressive (AT) models due to the assumption of conditional independence among target tokens. To address these limitations, we propose a novel approach called PCFG-NAT, which leverages a specially designed Probabilistic Context-Free Grammar (PCFG) to enhance the ability of NAT models to capture complex dependencies among output tokens. Experimental results on major machine translation benchmarks demonstrate that PCFG-NAT further narrows the gap in translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a deeper understanding of the generated sentences, addressing the lack of satisfactory explainability in neural machine translation.2 Copyright © 2023, The Authors. All rights reserved.

关键词： Computer aided language translation

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS.

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Blockchain-Assisted Computation Offloading Collaboration: A Hierarchical Game to Fortify IoT Security and Resilience

SSRN

引用

SSRN 2023年

作者： Zhou, Zitao Ye, Fang Li, Yingsong Geng, Xiaoyu Zhang, Sitong Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Underwater Acoustic Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education College of Electronic and Information Engineering Anhui University Hefei China

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has become an important solution to address the data processing challenges faced by resource-constrained devices. However, security and privacy issues have become a major concern in the computation offloading collaboration process. This paper proposes a blockchain-assisted computation offloading collaboration scheme, Blockchain-Assisted Computation Offloading Collaboration (BACOC). The scheme uses blockchain technology to assist computation offloading collaboration under IoT conditions to fortify security and resilience during the computation offloading process. At the same time, a hierarchical game model based on Stackelberg game theory and contract theory is adopted to determine the optimal service pricing strategy. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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An Unsupervised Malicious Web Request Detection based on Transformer and Contrastive Learning

引用

IEEE Transactions on Network and Service Management 2025年

作者： He, Shiming Zhang, Ying Liang, Diqing Sharma, Pradip Kumar Changsha University of Science and Technology School of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

The World Wide Web (Web) is a crucial part of the Internet. Web attacks are becoming more and more serious and complex. Malicious Web request detection aims to rapidly and accurately identify abnormal attacks on the network. Deep learning is being applied to malicious Web request detection, resulting in high detection performance. However, most deep learning-based methods are supervised and ignore special characters, which are hard to detect unknown malicious Web requests. The labels of Web request are fewer and Web request data is insufficient. Therefore, we propose an unsupervised malicious Web request detection based on transformer and contrastive learning (UTCDetector). UTCDetector exploits preprocessing and 2-gram word segmentationto preserve special characters, extracts semantic feature by Transformer, and leverages hypersphere loss function and contrastive learning to handle insufficient Web data without abnormal label. Since the public Web request datasets (CSIC 2010, CSIC TORPEDA 2012, and ECML/PKDD 2007) were created before 2012, we collected Web requests from a university Web application server in 2023 to build a private dataset named School 2023. This dataset contains more modern and complex attacks. The experimental results on the four datasets demonstrate that our method achieves a higher F1-score than other existing methods and ablation variants. © 2004-2012 IEEE.

关键词： Contrastive Learning

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OPTIMAL L∞(L2) AND L1(L2) A POSTERIORI ERROR ESTIMATES FOR THE FULLY DISCRETE APPROXIMATIONS OF TIME FRACTIONAL PARABOLIC DIFFERENTIAL EQUATIONS

arXiv

引用

arXiv 2023年

作者： Cao, Jiliang Wang, Wansheng Xiao, Aiguo Department of Mathematics Shanghai Normal University Shanghai200234 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

We derive optimal order a posteriori error estimates in the L∞(L2) and L1(L2)-norms for the fully discrete approximations of time fractional parabolic differential equations. For the discretization in time, we use the L1 methods, while for the spatial discretization, we use standard conforming finite element methods. The linear and quadratic space-time reconstructions are introduced, which are generalizations of the elliptic space reconstruction. Then the related a posteriori error estimates for the linear and quadratic space-time reconstructions play key roles in deriving global and pointwise final error estimates. Numerical experiments verify and complement our theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Finite element method

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Few-Shot Object Detection Based on Generalized Features

Few-Shot Object Detection Based on Generalized Features

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Artificial Intelligence and intelligent Information processing (AIIIP), International Conference on

作者： Qiuqin Chen Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou Fuzhou China

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods.

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

引用

Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Xu, Saijuan Yang, Liliang Liu, Genggeng Department of Information Engineering Fujian Business University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q-learning, the proposed global router adopts the Double Deep Q-Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router. © 2023 Saijuan Xu et al.

关键词： Reinforcement learning

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