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Physiological Information Preserving Video Compression for rPPG

IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3563-3575页

作者： Wang, Jieying Shan, Caifeng Liu, Zhaoyang Zhou, Shuwang Shu, Minglei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China Nanjing University State Key Laboratory for Novel Software Technology and School of Intelligence Science and Technology Nanjing210023 China Shandong Artificial Intelligence Institute Jinan250014 China

Remote photoplethysmography (rPPG) has recently attracted much attention due to its non-contact measurement convenience and great potential in health care and computer vision applications. Early rPPG studies were mostly developed on self-collected uncompressed video data, which limited their application in scenarios that require long-distance real-time video transmission, and also hindered the generation of large-scale publicly available benchmark datasets. In recent years, with the popularization of high-definition video and the rise of telemedicine, the pressure of storage and real-time video transmission under limited bandwidth have made the compression of rPPG video inevitable. However, video compression can adversely affect rPPG measurements. This is due to the fact that conventional video compression algorithms are not specifically proposed to preserve physiological signals. Based on this, we propose a video compression scheme specifically designed for rPPG application. The proposed approach consists of three main strategies: 1) facial ROI-based computational resource reallocation;2) rPPG signal preserving bit resource reallocation;and 3) temporal domain up- and down-sampling coding. UBFC-rPPG, ECG-Fitness, and a self-collected dataset are used to evaluate the performance of the proposed method. The results demonstrate that the proposed method can preserve almost all physiological information after compressing the original video to 1/60 of its original size. The proposed method is expected to promote the development of telemedicine and deep learning techniques relying on large-scale datasets in the field of rPPG measurement. © 2013 IEEE.

关键词： Image compression

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Improving Adaptive Runoff Forecasts in Data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Quantum Pseudorandom Scramblers 22nd

Quantum Pseudorandom Scramblers

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22nd Theory of Cryptography Conference, TCC 2024

作者： Lu, Chuhan Qin, Minglong Song, Fang Yao, Penghui Zhao, Mingnan Computer Science Department Portland State University Portland United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Hefei National Laboratory Hefei230088 China

ISBN: (纸本)9783031780165

Quantum pseudorandom state generators (PRSGs) have stimulated exciting developments in recent years. A PRSG, on a fixed initial (e.g., all-zero) state, produces an output state that is computationally indistinguishable from a Haar random state. However, pseudorandomness of the output state is not guaranteed on other initial states. In fact, known PRSG constructions provably fail on some initial states. In this work, we propose and construct quantum Pseudorandom State Scramblers (PRSSs), which can produce a pseudorandom state on an arbitrary initial state. In the information-theoretical setting, we obtain a scrambler which maps an arbitrary initial state to a distribution of quantum states that is close to Haar random in total variation distance. As a result, our scrambler exhibits a dispersing property. Loosely, it can span an ϵ-net of the state space. This significantly strengthens what standard PRSGs can induce, as they may only concentrate on a small region of the state space provided that the average output state approximates a Haar random state. Our PRSS construction develops a parallel extension of the famous Kac’s walk, and we show that it mixes exponentially faster than the standard Kac’s walk. This constitutes the core of our proof. We also describe a few applications of PRSSs. While our PRSS construction assumes a post-quantum one-way function, PRSSs are potentially a weaker primitive and can be separated from one-way functions in a relativized world similar to standard PRSGs. © International Association for Cryptologic Research 2025.

关键词： Distribution functions

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Complementary Learning System Theory-based Active Learning for Audio Classification

Complementary Learning System Theory-based Active Learning f...

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

作者： Geng, Hui Gao, Zijian Wan, Tianjiao Feng, Dawei Wang, Changjian Xu, Kele College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (纸本)9798350368741

Deep learning has significantly advanced the audio classification, achieving remarkable results. However, these successes often rely on extensive manual annotation of audio, a labor-intensive and costly process. Active Learning (AL) presents a promising solution by minimizing the required amount of annotation through the iterative selection of the most informative audio samples. Current AL methods for audio classification typically depend solely on the latest model checkpoint, overlooking the dynamics of the entire training process. The Complementary Learning Systems (CLS) theory posits that the interplay between short-term and long-term memory systems can effectively measure sample uncertainty, offering a means to capture training dynamics. In this work, we introduce a novel AL framework for audio classification, termed CLS-AL, which addresses the limitations of existing methods by simultaneously maintaining both short-term and long-term memory models. This dual-memory approach allows for a more comprehensive consideration of training dynamics. The divergence in predictions between these memory models provides a new metric for evaluating the uncertainty of unlabeled samples, enhancing the effectiveness of the AL sample selection process. We demonstrate the effectiveness and generalizability of CLS-AL through extensive experiments on a diverse set of audio datasets, showing that CLS-AL obviously outperforms existing state-of-the-art methods. © 2025 IEEE.

关键词： Active Learning Audio Classification Complementary Learning System Uncertainty Estimation

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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Pattern Shifting or Knowledge Losing? A Forgetting Perspective for Understanding the Effect of Instruction Fine-Tuning 23rd

Pattern Shifting or Knowledge Losing? A Forgetting Perspect...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Chunkang Cao, Boxi Lu, Yaojie Lin, Hongyu Cao, Liu Zeng, Ke Wan, Guanglu Cai, Xunliang Han, Xianpei Sun, Le Chinese Information Processing Laboratory Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Meituan Beijing China

ISBN: (纸本)9789819783663

Instruction Fine-Tuning (IFT) emerges as an essential step of training large language models to robustly carry out tasks of interest. However, there lacks a systematic investigation about the underlying mechanisms of instruction fine-tuning, particularly on the forgetting phenomenon after IFT, known as alignment tax. Therefore, to understand the mechanism of IFT from the forgetting perspective, we investigate the alternation of the text pattern and knowledge within models throughout the entire IFT process. Specifically, we restore fine-tuned models to their base version by training them on the data sharing a similar distribution with the pre-training corpus and compare their results Our experiment indicates that there is a stage transition of forgetting during IFT process: (1) Pseudo Forgetting: in this stage, models mainly shift their familiar text pattern away from pre-training data format while the world knowledge is preserved. Consequently, models will recover to their original performance when they are restored to the base version. (2) Actual Forgetting: in this stage, models forget the acquired knowledge as well. Therefore, they fail to reach the original performance even if they are restored to the base version. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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Going Beyond Passages: Readability Assessment for Book-Level Long Texts 23rd

Going Beyond Passages: Readability Assessment for Book-Leve...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Li, Wenbiao Sun, Rui Zhang, Tianyi Wu, Yunfang MOE Key Laboratory of Computational Linguistics Peking University Beijing China School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China

ISBN: (纸本)9789819783663

Readability assessment for book-level long text is widely needed in real educational applications. However, most of the current researches focus on passage-level readability assessment and little work has been done to process ultra-long texts. In order to process the long sequence of book texts better and to enhance pretrained models with difficulty knowledge, we propose a novel model DSDR, difficulty-aware segment pre-training and difficulty multi-view representation. Specifically, we split all books into multiple fixed-length segments and employ unsupervised clustering to obtain difficulty-aware segments, which are used to re-train the pretrained model to learn difficulty knowledge. Accordingly, a long text is represented by averaging multiple vectors of segments with varying difficulty levels. We construct a new dataset of Graded Children’s Books to evaluate model performance. Our proposed model achieves promising results, outperforming both the traditional SVM classifier and several popular pretrained models. In addition, our work establishes a new prototype for book-level readability assessment, which provides an important benchmark for related research in future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Difficulty knowledge Readability Assessment Ultra-long texts processing

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Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions 10th

Enhancing Multi-modal Regular Expression Synthesis via Larg...

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10th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2024

作者： Tang, Zipan Yan, Yixuan Li, Rongchen Dong, Hanze Chen, Haiming Gao, Hongyu Beijing University of Technology Beijing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Real-world regular expressions (regexes) are widely used in practice. Since regexes are difficult to comprehend and write, automatically synthesizing regexes has been an important research problem. However, current techniques face challenges like weak capability in generalization and severely limited support of extended features of regexes. In this paper, we address these challenges via Large Language Models (LLMs) together with semantic manipulations of sub-expressions, and propose PowerSyn, a framework for regex synthesis based on both natural language descriptions and examples and supports extended features. Specifically, we design the prompt suitable for synthesizing regexes with LLMs and present novel algorithms to manipulate sub-expressions, which consider the matching relation, and are guided by examples. The evaluation results demonstrate the significant efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segmentation Network Based on Local Attention and Mamba 27th

Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segment...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Feifei Shi, Fei Ren, Dayong Jia, Zhenhong Wang, Jianyi School of Computer Science and Technology Xinjiang University Urumqi Xinjiang830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783031781032

Brain tissue segmentation is critical for diagnosing and treating brain diseases. While Mamba-based models excel in the medical field, they face performance bottlenecks with high-resolution MRI images, often losing local feature information in complex texture structures. To address these challenges and enable deployment in resource-limited settings, we propose Dual-MambaNet, a lightweight segmentation model based on Mamba. In Dual-MambaNet, we introduce the Outlook attention module to capture local complex textures and structures in brain MRI images. Subsequently, we combined it with the Mamba block to construct a feature extractor (FE) encoder layer to couple local and global features. Additionally, we integrate dual decoder branches and a multi-level pixel contrastive loss function(MPCL) to better integrate local and global features. This method optimizes global feature representation by refining local complex textures and structural details, effectively capturing multi-level features in MRI images. Experimental results on public brain MRI datasets OASIS1 and MRBrainS13 demonstrate that Dual-MambaNet achieves high segmentation accuracy with minimal parameters and computational complexity, making it suitable for deployment in resource-limited medical environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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