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arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

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GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning

arXiv

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arXiv 2023年

作者： Yao, Jiacheng Yang, Zhaohui Xu, Wei Chen, Mingzhe Niyato, Dusit Southeast University Nanjing210096 China The Zhejiang Lab Hangzhou311121 China The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Computer Science and Engineering Nanyang Technological University 308232 Singapore

Due to the dynamics of wireless environment and limited bandwidth, wireless federated learning (FL) is challenged by frequent transmission errors and incomplete aggregation from devices. In order to overcome these challenges, we propose a Global MOdel REuse strategy (GoMORE) that reuses the outdated global model to replace the local model parameters once a transmission error occurs. We analytically prove that the proposed GoMORE is strictly superior over the existing strategy, especially at low signal-to-noise ratios (SNRs). In addition, based on the derived expression of weight divergence, we further optimize the number of participating devices in the model aggregation to maximize the FL performance with limited communication resources. Numerical results verify that the proposed GoMORE successfully approaches the performance upper bound by an ideal transmission. It also mitigates the negative impact of non-independent and non-identically distributed (non-IID) data while achieving over 5 dB reduction in energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Mode-independent resonances in cascaded-mode resonators

Mode-independent resonances in cascaded-mode resonators

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Ginis, Vincent Benea-Chelmus, Ileana-Cristina Lu, Jinsheng Piccardo, Marco Capasso, Federico John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Data Lab/Applied Physics Vrije Universiteit Brussel Brussel1050 Belgium Hybrid Photonics Laboratory École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland Center for Nano Science and Technology Istituto Italiano di Tecnologia Milan20133 Italy

ISBN: (纸本)9798350345995

Optical resonators are optical components with numerous applications in modern physics and technology [1]. Indeed, they can provide spectral selectivity of optical signals and increase the intensity of light in a small volume of space. For these reasons, among others, they are important components in devices such as lasers. Resonators can also be used for frequency doubling, sum and difference frequency generation, optical parametric amplification and optical isolation. They have a wide range of applications, including chemical, biological and thermal spectroscopy and optical communication networks. In addition, resonators provide an ideal platform for studying and controlling quantum mechanical interactions. © 2023 IEEE.

关键词： Frequency doublers

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-Zhonglab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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引用

Design Automation Conference

作者： Jin Zhao Yu Zhang Jian Cheng Yiyang Wu Chuyue Ye Hui Yu Zhiying Huang Hai Jin Xiaofei Liao Lin Gu Haikun Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab Hangzhou China

Temporal graph processing is used to handle the snapshots of the temporal graph, which concerns changes in graph over time. Although several software/hardware solutions have been designed for efficient temporal graph processing, they still suffer from serious irregular data access due to the uncoordinated graph traversal. To overcome these limitations, this paper proposes SaGraph, a domain-specific hardware accelerator to support the efficient processing of temporal graph. Specifically, temporal graph processing shows strong data access similarity, i.e., most graph accesses of the processing of different snapshots are the same and usually refer to a small fraction of vertices. SaGraph can dynamically coordinate the graph traversals and adaptively cache the vertex states to fully exploit the data access similarity for smaller data access overhead. We implemented and evaluated SaGraph on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software and hardware solutions, SaGraph achieves 8.5×-157.3×, 4.2×-16.1× speedups and 34.7×-423.6×, 5.3×-14.7× energy savings, respectively.

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Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial

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BMC Medicine 2025年第1期23卷 1-14页

作者： Wang, Minmin Zhou, Hetong Zhang, Xiaomei Chen, Qianfeng Tong, Qiqi Han, Qiai Zhao, Xudong Wang, Dandan Lai, Jianbo He, Hongjian Zhang, Shaomin Hu, Shaohua Department of Psychiatry The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Westlake Institute for Optoelectronics Westlake University Hangzhou China Nanhu Brain-Computer Interface Institute Hangzhou China Zhejiang Key Laboratory of Precision Psychiatry Hangzhou China Department of Psychiatry Huzhou Third Municipal Hospital Huzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China School of Physics Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Brain Research Institute of Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou China

Background: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes. Methods: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3 weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3). Results: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed. Conclusions: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients. Trial registration: *** identifier: NCT05964777. © The Author(s) 2025.

关键词： Diffusion tensor imaging Electric field Individualization Repetitive transcranial magnetic stimulation Transcranial alternating current stimulation

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Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting

arXiv

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arXiv 2023年

作者： Yang, Zhaohui Chen, Mingzhe Zhang, Zhaoyang Huang, Chongwen Zhejiang Lab Zhejiang Hangzhou311121 China The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopted, while the private small-sized semantic information is transmitted through private message and the common knowledge is transmitted through common message. Due to limited wireless resource, both computation energy and transmission energy are considered. This joint computation and communication problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under computation, latency, and transmit power constraints. To solve this problem, an alternating algorithm is proposed where the closed-form solutions for semantic information extraction ratio and computation frequency are obtained at each step. Numerical results verify the effectiveness of the proposed algorithm. © 2023, CC BY-SA.

关键词： Wireless networks

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SG-NeRF: Neural Surface Reconstruction with Scene Graph Optimization

arXiv

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arXiv 2024年

作者： Chen, Yiyang Dong, Siyan Wang, Xulong Cai, Lulu Zheng, Youyi Yang, Yanchao State Key Lab of CAD&CG Zhejiang University China Chohotech Co. Ltd. Institute of Data Science The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

3D surface reconstruction from images is essential for numerous applications. Recently, Neural Radiance Fields (NeRFs) have emerged as a promising framework for 3D modeling. However, NeRFs require accurate camera poses as input, and existing methods struggle to handle significantly noisy pose estimates (i.e., outliers), which are commonly encountered in real-world scenarios. To tackle this challenge, we present a novel approach that optimizes radiance fields with scene graphs to mitigate the influence of outlier poses. Our method incorporates an adaptive inlier-outlier confidence estimation scheme based on scene graphs, emphasizing images of high compatibility with the neighborhood and consistency in the rendering quality. We also introduce an effective intersection-over-union (IoU) loss to optimize the camera pose and surface geometry, together with a coarse-to-fine strategy to facilitate the training. Furthermore, we propose a new dataset containing typical outlier poses for a detailed evaluation. Experimental results on various datasets consistently demonstrate the effectiveness and superiority of our method over existing approaches, showcasing its robustness in handling outliers and producing high-quality 3D reconstructions. Our code and data are available at: https://***/Iris-cyy/SG-NeRF. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Mapping Trees Outside Forests Using Semantic Segmentation

Mapping Trees Outside Forests Using Semantic Segmentation

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Moritz Lucas Viacheslav Barkov Ralf Pecenka Martin Atzmueller Björn Waske Joint Lab Artificial Intelligence & Data Science Osnabrück University Germany Leibniz Institute for Agricultural Engineering and Bioeconomy Germany German Research Center for Artificial Intelligence (DFKI) Remote Sensing Osnabrück (RSO) Osnabrück University Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Trees Outside Forests (TOF) are an important part of landscapes and agroforestry systems, since they provide a variety of ecosystem services. They contribute to biomass and carbon stocks, support biodiversity conservation and enrich soil. This study investigates the potential of semantic segmentation to distinguish between Forest and the TOF classes Patch, Linear and Tree in aerial images. Information about these classes is crucial in order to understand their distribution and influence on the environment. An automated labeling approach with manual refinement is implemented for generating training and validation data. To map TOFs an adapted Pyramid Attention Network is trained. As this classification task is dependent on contextual information a spatial context postprocessing method is applied. The approach shows reliable results on independent validation data.

关键词： Training Forests Semantic segmentation Geoscience and remote sensing Manuals Soil labeling

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