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Introduction to the Special Issue on Formal Methods and Models for System Design

ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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Unsupervised Multimodal Graph Contrastive Semantic Anchor Space Dynamic Knowledge Distillation Network for Cross-Media Hash Retrieval

Unsupervised Multimodal Graph Contrastive Semantic Anchor Sp...

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International Conference on Data engineering

作者： Yang Yu Meiyu Liang Mengran Yin Kangkang Lu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Cross-media hash retrieval are efficient and effective techniques for retrieval on multi-media database. The success of the Multimodal Large Models (MLM) provides a valuable direction to enhance the accuracy of multimodal hash retrieval, which achieves decent retrieval accuracy with finetuning the pretrained multimodal large models, but their massive model parameters significantly reduce retrieval efficiency. Knowledge Distillation (KD) methods enable small models to learn from the knowledge of larger models, achieving a reduction in model parameter count while ensuring a certain level of accuracy. However, current KD methods face challenges when applied in the multimodal domain, as it requires preserving the multimodal semantic information while minimizing accuracy degradation. To address these challenges, we propose a novel unsupervised multimodal graph contrastive semantic anchor space dynamic knowledge distillation network for cross-media hash retrieval (GASKN). Firstly, to obtain a multimodal semantic anchor space, we construct a large multimodal fusion teacher model using the BEiT-3 model as the backbone. This teacher model is capable of encoding data from different modalities, such as images and text, using the same multimodal encoder to acquire multimodal hash codes that contain rich information from both modalities simultaneously. Secondly, to ensure efficient retrieval capabilities for the student model, we utilize the ALBERT text encoding model and the BiFormer image encoding model as the compact student model's backbones. This allows us to build a lightweight student model with only a twentieth of the parameter count of the teacher model. We propose a dynamic knowledge distillation technique to transfer the multimodal semantic anchor space knowledge embedded in the multimodal large teacher model to the lightweight student model as much as possible. Thirdly, to further distill the structural knowledge of the semantic anchor space from the teacher model to

关键词： Knowledge engineering Degradation Accuracy Image coding Databases Heuristic algorithms Semantics

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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Multi-agent policy transfer via task relationship modeling

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Science China(Information Sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on software engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： computer languages Codes Supervised learning Natural languages Semantics Task analysis software engineering

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On the properties of kullback-leibler divergence between multivariate Gaussian distributions 23

On the properties of kullback-leibler divergence between mul...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yufeng Zhang Jialu Pan Kenli Li Wanwei Liu Zhenbang Chen Xinwang Liu Ji Wang College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer State Key Laboratory for High Performance Computing Key laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Kullback-Leibler (KL) divergence is one of the most important measures to calculate the difference between probability distributions. In this paper, we theoretically study several properties of KL divergence between multivariate Gaussian distributions. Firstly, for any two n-dimensional Gaussian distributions Ɲ1 and Ɲ2, we prove that when KL(Ɲ2‖Ɲ1) ≤ ε (ε > 0) the supremum of KL (Ɲ1‖Ɲ2) is (1/2) ((-W0 (-e-(1+2ε)))-1 + log(-W0 (-e-(1+2ε))) - 1), where W0 is the principal branch of Lambert W function. For small ε, the supremum is ε + 2ε1.5 + O (ε2). This quantifies the approximate symmetry of small KL divergence between Gaussian distributions. We further derive the infimum of KL(Ɲ1‖Ɲ2) when KL(Ɲ2‖Ɲ1) ≥ M (M > 0). We give the conditions when the supremum and infimum can be attained. Secondly, for any three n-dimensional Gaussian distributions Ɲ1, Ɲ2, and Ɲ3, we theoretically show that an upper bound of KL (Ɲ1‖Ɲ3) is 3ε1 + 3ε2 + 2√ε1ε2 + o(ε1)+ o(ε2) when KL (Ɲ1‖Ɲ2) ≤ ε1 and KL(Ɲ2‖Ɲ3) ≤ ε2 (ε1, ε2 ≥ 0). This reveals that KL divergence between Gaussian distributions follows a relaxed triangle inequality. Note that, all these bounds in the theorems presented in this work are independent of the dimension n. Finally, we discuss several applications of our theories in deep learning, reinforcement learning, and sample complexity research.

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Identifying key Tag Distribution in Large-Scale RFID Systems

Identifying Key Tag Distribution in Large-Scale RFID Systems

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International Workshop on Quality of Service

作者： Yanyan Wang Jia Liu Shen-Huan Lyu Zhihao Qu Bin Tang Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

With the proliferation of RFID-enabled applications, multiple readers are required for the complete coverage of numerous tags in a large-scale RFID system. In this scenario, we sometimes pay more attention to a subset of tags instead of all, which are referred to as key tags. In this paper, we study an under-investigated problem key tag distribution identification, which aims to identify which key tags are beneath which readers. This is crucial for efficiently managing specific items of interest, which can quickly pinpoint key tags and help RFID readers covering these tags collaborate to improve the tag inventory efficiency. Since key tags typically make up a small part of all tags, it is time consuming to deal with all tags in the traditional way. We propose a protocol called Kadept that identifies the key tag distribution by using a sophisticatedly designed filter that teases out key tags as well as assigns each of them a singleton slot for response. With this design, a great number of trivial (non-key) tags will keep silent and free up bandwidth resources for key tags, and each key tag is sorted in a collision-free way and can be identified with only 1-bit response, which significantly improves the time efficiency. We theoretically analyze how can we optimize protocol parameters of Kadept and conduct extensive simulations under different tag distribution scenarios. Compared with the state-of-the-art, Kadept can improve the time efficiency by a factor of 3.7×, when the ratio of key tags to all tags is 0.1.

关键词： Analytical models Protocols Quality of service Bandwidth Radiofrequency identification

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Towards Better Dynamic Graph Learning: New Architecture and Unified Library

arXiv

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arXiv 2023年

作者： Yu, Le Sun, Leilei Du, Bowen Lv, Weifeng State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes’ historical first-hop interactions by: (i) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences;(ii) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes’ correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://***/yule-BUAA/DyGLib. Copyright © 2023, The Authors. All rights reserved.

关键词： Memory architecture

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Self-powered tactile sensor for real-time recognition of Morse code based on machine learning

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Nano Research 2025年第2期18卷 760-768页

作者： Shenxing Tan Yang Jiang Xujiang Chao Fei Liang Ripeng Li Tao Jiang Hai-Dong Yu Zhong Lin Wang Guangzhou Institute of Blue Energy Guangzhou 510555China Frontiers Science Center for Flexible Electronics Xi'an Institute of Flexible Electronics(IFE)and Xi'an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Beijing Key Laboratory of Micro-Nano Energy and Sensor Center for High-Entropy Energy and SystemsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Developing lightweight,green,and flexible wearable electronics with high sensitivity and multifunctional sensing capabilities is of important significance in the field of outdoor sports,such as mountaineering,animal tracking and *** work proposes a silk fibroin fibers-based triboelectric nanogenerator(SF TENG)to harvest tiny energy from human fingertip tapping and act as a self-powered tactile *** SF-TENG adopts a green,efficient,and low-cost fabrication strategy,in which a breathable and electropositive silk fibroin fiber membrane and a silver conductive layer are prepared by electrostatic spinning and magnetron sputtering,and combined with a conductive cloth and a breathable tape to form a flexible sensor that can be attached to a human *** thin and soft portable TENG device,having a thickness of only 0.3 mm and a mass of 354 mg at the dimension of 4.5 cm×4.5 cm,can generate a maximum power density of 1.0 mW·m^(–2).Furthermore,the SF-TENG has excellent sensitivity of 1.767 mV·Pa^(–1) with good cyclic *** superior sensing characteristics provide new avenues for Morse code applications toward outdoor wearable autonomous *** proposed SF-TENG offers promising solutions in multi-scenario outdoor sport,human-machine interface interaction,and security systems.

关键词： flexible electronics triboelectric nanogenerator silk fibroin Morse code translation

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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