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arXiv 2024年

作者： Xie, Qiujie Feng, Qiming Zhang, Tianqi Li, Qingqiu Yang, Linyi Zhang, Yuejie Feng, Rui He, Liang Gao, Shang Zhang, Yue School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Tongji University China University College London United Kingdom Huawei Noah’s Ark Lab Canada Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention China Deakin University Australia Westlake University China Westlake Institute for Advanced Study China

Large Language Models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted the attention of the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs and complexity. In this paper, we introduce a benchmark for LLMs personification, including a strategy for constructing virtual characters’ life stories from the ground up, a Multi-Agent Cognitive Mechanism capable of simulating human cognitive processes, and a psychology-guided evaluation method to assess human simulations from both self and observational perspectives. Experimental results demonstrate that our constructed simulacra can produce personified responses that align with their target characters. We hope this work will serve as a benchmark in the field of human simulation, paving the way for future research. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

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EventHallusion: Diagnosing Event Hallucinations in Video LLMs

arXiv

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arXiv 2024年

作者： Zhang, Jiacheng Jiao, Yang Chen, Shaoxiang Zhao, Na Tan, Zhiyu Li, Hao Chen, Jingjing Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore Shanghai Academy of Artificial Intelligence for Science China Meituan China

Recently, Multimodal Large Language Models (MLLMs) have made significant progress in the video comprehension field. Despite remarkable content reasoning and instruction following capabilities they demonstrated, the hallucination problem of these VideoLLMs is less explored compared with its counterpart in the image domain. To mitigate this gap, we propose EventHallusion, a novel benchmark that focuses on assessing the VideoLLMs’ hallucination toward event, the crux of video analysis. From a hallucination attribution perspective, our EventHallusion benchmark is curated to assess a VideoLLM’s susceptibility toward language priors and vision-language biases. On the other hand, we also propose a simple yet effective method, called Temporal Contrastive Decoding (TCD), to tackle the hallucination problems of VideoLLMs. The proposed TCD method rectifies the model’s bias toward its priors during the decoding stage by comparing the original video with a modified version, in which temporal cues are ambiguited. Through comprehensive evaluation of eight open-source and three closed-source VideoLLMs on the proposed EventHallusion benchmark, we observe that the open-source models suffer significantly from hallucination problems, whereas the closed-source ones perform markedly better. By further equipping open-source VideoLLMs with the proposed TCD approach, evident performance improvements are achieved across most metrics in the EventHallusion benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Distortion-Disentangled Contrastive Learning

Distortion-Disentangled Contrastive Learning

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jinfeng Wang Sifan Song Jionglong Su S. Kevin Zhou School of AIAC Xi’an Jiaotong-Liverpool University Suzhou China School of BME & Suzhou Institute for Advanced Research Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) University of Science and Technology of China Suzhou China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Self-supervised learning is well known for its remarkable performance in representation learning and various downstream computer vision tasks. Recently, Positive-pair-Only Contrastive Learning (POCL) has achieved reliable performance without the need to construct positive-negative training sets. It reduces memory requirements by lessening the dependency on the batch size. The POCL method typically uses a single objective function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions. This objective function implicitly enables the model to filter out or ignore the distortion variant representation (DVR) affected by different distortions. However, some recent studies have shown that proper use of DVR in contrastive can optimize the performance of models in some downstream domain-specific tasks. In addition, these POCL methods have been observed to be sensitive to augmentation strategies. To address these limitations, we propose a novel POCL framework named Distortion-Disentangled Contrastive Learning (DDCL) and a Distortion-Disentangled Loss (DDL). Our approach is the first to explicitly and adaptively disentangle and exploit the DVR inside the model and feature stream to improve the representation utilization efficiency, robustness and representation ability. Experiments demonstrate our framework’s superiority to Barlow Twins and Simsiam in terms of convergence, representation quality (including transferability and generalization), and robustness on several datasets.

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A Semantic Link Network Model for Supporting Traceability of Logistics on Blockchain

arXiv

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arXiv 2025年

作者： Sun, Xiaoping Zhuge, Sirui Zhuge, Hai Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China King’s College London United Kingdom Publicis Sapient London United Kingdom Great Bay University Dongguan China Great Bay Institute for Advanced Study Dongguan China

The ability of tracing states of logistic transportations requires an efficient storage and retrieval of the state of logistic transportations and locations of logistic objects. However, the restriction of sharing states and locations of logistic objects across organizations from different countries makes it hard to deploy a centralized database for implementing the traceability in a cross-border logistic system. This paper proposes a semantic data model on Blockchain to represent a logistic process based on the Semantic Link Network model where each semantic link represents a logistic transportation of a logistic object between two parties. A state representation model is designed to represent the states of a logistic transportation with semantic links. It enables the locations of logistic objects to be derived from the link states. A mapping from the semantic links to the blockchain transactions is designed to enable schema of semantic links and states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistic object to reach the target in logarithmic steps on the blockchain platform. A reward-penalty policy is designed to allow participants to confirm the state of links on blockchain. Analysis and simulation demonstrate the flexibility, effectiveness and the efficiency of Semantic Link Network on immutable blockchain for implementing logistic traceability. © 2025, CC BY.

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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Location IoU: A New Evaluation and Loss for Bounding Box Regression in Object Detection

Location IoU: A New Evaluation and Loss for Bounding Box Reg...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lu Yang Kai Zhang Jiaqi Liu Chongke Bi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Autobrain (Tianjin) Technology LTD Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the field of object detection bounding box regression, IoU (Intersection over Union) is a commonly used evaluation metric for measuring the overlap between predicted and ground truth bounding boxes. One limitation of IoU, however, is that the gradient goes to zero when there is no intersection between bounding boxes. Recent evaluation metrics have been developed to overcome this problem by incorporating penalty terms and other techniques for improving IoU. Despite these advances, existing methods still suffer to some degree from the problems of small gradients and slow rate of convergence. In this paper, a new evaluation method named LIoU (Location IoU) is proposed to address these issues. It can maximize the shared area of the intersection when used as the loss function and incorporate a variable gradient parameter to fit to datasets of different sizes. The experimental results indicate that we achieve 0.27% and 3.48% accuracy improvements respectively in SSD and YOLOv5 network on the VOC dataset, and 2.23% in the YOLOv5 network on the COCO dataset. The results of this study highlight the effectiveness of LIoU in improving the performance of object detection models.

关键词： YOLO Measurement Location awareness Accuracy Shape Neural networks Benchmark testing

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Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding

arXiv

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arXiv 2023年

作者： Wei, Yuecen Yuan, Haonan Fu, Xingcheng Sun, Qingyun Peng, Hao Li, Xianxian Hu, Chunming Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Software Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China

Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a new inductive bias into the Graph Neural Networks (GNNs) in various tasks, it implies latent topological relations for attackers to improve their inference attack performance, leading to serious privacy leakage issues. In addition, existing privacy-preserving frameworks suffer from reduced protection ability in hierarchical propagation due to the deficiency of adaptive upper-bound estimation of the hierarchical perturbation boundary. It is of great urgency to effectively leverage the hierarchical property of data while satisfying privacy guarantees. To solve the problem, we propose the Poincaré Differential Privacy framework, named PoinDP, to protect the hierarchy-aware graph embedding based on hyperbolic geometry. Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincaré model in hyperbolic space. Then, the Personalized Hierarchy-aware Sensitivity is designed to measure the sensitivity of the hierarchical structure and adaptively allocate the privacy protection strength. Besides, the Hyperbolic Gaussian Mechanism (HGM) is proposed to extend the Gaussian mechanism in Euclidean space to hyperbolic space to realize random perturbations that satisfy differential privacy under the hyperbolic space metric. Extensive experiment results on five real-world datasets demonstrate the proposed PoinDP's advantages of effective privacy protection while maintaining good performance on the node classification task. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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Lifting the Answer: Reranking Candidates on Data Augmented Text-to-SQL

Lifting the Answer: Reranking Candidates on Data Augmented T...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Guang-Yu Cheng Shuo Yu Wen-Bin Jiang Shuai Ma Xiang Ao Qing He Henan Institutes of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Baidu inc Beijing China

The text-to-SQL task is a realistic and challenging job, which aims to translate natural language questions into corresponding SQL queries. When attempting to generate SQL queries in the text-to-SQL task, prevailing semantic parsing models employ beam search to generate several candidates. Based on our pilot study, we observe that the gold-standard SQL answer may exist in the n-best candidate list produced by the decoder, rather than the first. Hence, we aim to lift the correct answer given several candidates generated by semantic parsing models for text-to-SQL. To this end, we propose a reranking module that reorders the n-best list of candidate SQL queries by pair-wise hinge loss. Meanwhile, a data augmentation module is leveraged to enrich the inadequate training instances for providing candidates with better qualities. These two modules can be easily grafted onto text-to-SQL backbone networks, and extensive experiments on the cross-domain text-to-SQL benchmark Spider demonstrate that our method achieves 73.8% in accuracy on the Spider dataset, surpassing the base model by up to 3.5%.

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Emerging theory and phenomena in thermal conduction: A selective review

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Science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 2-17页

作者： Jie Chen Jia He Dongkai Pan Xiaotian Wang Nuo Yang Jiaojiao Zhu Shengyuan A.Yang Gang Zhang Center for Phononics and Thermal Energy Science China-EU Joint Lab for NanophononicsMOE Key Laboratory of Advanced Microstructured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China School of Energy and Power Engineering Huazhong University of Science and Technology(HUST)Wuhan 430074China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recently, emerging phonon phenomena have been discovered and rapidly developed, which have become an active hot research topic. In this review article, we present state-of-the-art advances in several fascinating phonon transport phenomena. First, we summarize the recent progress on the wave nature of phonons, including phonon coherence and its effects on thermal conductivity and the topological properties of phonons. Then, we discuss the particle nature of phonons, including the weak coupling of phonons and the high-order phonon anharmonicity. Finally, we present the summary and a brief outlook. This review presents the advanced understanding of some emerging phonon phenomena in solid materials, which provides new opportunities for further advancement in a wide variety of applications.

关键词： phonon transport coherence topological phonon high-order phonon interaction

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Deep Learning Super-Resolution-Based Channel Completion for Massive MISO Systems

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IEEE Signal Processing Letters 2025年 32卷 2254-2258页

作者： Zu, Keke He, Yuhan Chen, Hongyang Zheng, Yu Haardt, Martin Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Zhejiang China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Research Center for Graph Computing Zhejiang Lab Hangzhou China JD Intelligent Cities Research Beijing China Communications Research Lab Ilmenau University of Technology Ilmenau Germany

With the deployment of large-scale antenna arrays, the already limited time-frequency resources are becoming increasingly scarce. In this study, we propose a novel Laplacian Pyramid Channel Completion Network (LPCCNet) designed for channel completion, thereby reducing the demand for time-frequency resources in massive MIMO systems. Compared with existing network models, the proposed LPCCNet, by employing a progressive upsampling architecture, effectively mitigates aliasing effects, suppresses error propagation, and achieves a substantial reduction in computational complexity. The simulation results show that LPCCNet achieves a superior channel completion quality compared to existing methods, particularly in rapidly time-varying scenarios. © 1994-2012 IEEE.

关键词： Channel completion deep learning super-resolution

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