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arXiv 2024年

作者： He, Yuting Huang, Fuxiang Jiang, Xinrui Nie, Yuxiang Wang, Minghao Wang, Jiguang Chen, Hao The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science The Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering Division of Life Science State Key Laboratory of Molecular Neuroscience Hong Kong University of Science and Technology Hong Kong SIATHKUST Joint Laboratory of Cell Evolution and Digital Health Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Guangdong Shenzhen518045 China Hong Kong Center for Neurodegenerative Diseases InnoHK Hong Kong

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial intelligence (AI) models, breaking the contradiction between limited AI models and diverse healthcare practices. Much more widespread healthcare scenarios will benefit from the development of a healthcare foundation model (HFM), improving their advanced intelligent healthcare services. Despite the impending widespread deployment of HFMs, there is currently a lack of clear understanding about how they work in the healthcare field, their current challenges, and where they are headed in the future. To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey. It first conducted a comprehensive overview of the HFM including the methods, data, and applications for a quick grasp of the current progress. Then, it made an in-depth exploration of the challenges present in data, algorithms, and computing infrastructures for constructing and widespread application of foundation models in healthcare. This survey also identifies emerging and promising directions in this field for future development. We believe that this survey will enhance the community's comprehension of the current progress of HFM and serve as a valuable source of guidance for future development in this field. The latest HFM papers and related resources are maintained on our website. © 2024, CC BY-NC-ND.

关键词： Artificial intelligence

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. CLEO 20... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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Model-Driven Inverse Lithography Via Hypergraph Convolutional Neural Networks and its Experimental Demonstration

SSRN

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SSRN 2024年

作者： Zhang, Shengen Ma, Xu Huang, Chaojun Wang, Fuli Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Inverse lithography technology (ILT) is a pivotal approach to correct the optical proximity effect in a lithography process by optimizing the mask pattern at the pixel level. However, traditional ILT algorithms are computationally intensive due to the huge data volume throughput. This paper proposes a novel ILT method based on model-driven hypergraph convolutional network (MHGCN) to improve the computational efficiency and lithography image fidelity simultaneously. In the MHGCN framework, a dense concentric circular sampling (DCCS) template is first used to extract the feature matrices of the target layout, which are then imported to a hyperedge graph convolutional network (encoder) to predict the ILT result. Then, a lithography-model-based decoder is used to calculate the wafer image of the ILT mask. A low-cost unsupervised training method of the network is developed through the collaboration between the encoder and decoder. To demonstrate the proposed method, a digital-micromirror-device (DMD) maskless lithography testbed is established, and the imaging model of the testbed is calibrated based on a set of training layout patterns. The superiority of the proposed method in both of optimization capacity and computational efficiency are proved by the simulation and experimental results. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception

Advswap: Covert Adversarial Perturbation with High Frequency...

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International Conference on Intelligent Transportation

作者： Yuanhao Huang Qinfan Zhang Jiandong Xing Mengyue Cheng Haiyang Yu Yilong Ren Xiao Xiong School of Transportation Science and Engineering Beihang University Beijing P.R.China State Key Lab of Intelligent Transportation System Beijing P.R.China Zhongguancun Laboratory Beijing P.R.China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability.

关键词： Wavelet transforms Training Neural networks Noise Visual systems Robustness Classification algorithms Data mining Information exchange Autonomous vehicles

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Collaborative Streaming and Super Resolution Adaptation for Mobile Immersive Videos

Collaborative Streaming and Super Resolution Adaptation for ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lei Zhang Haotian Guo Yanjie Dong Fangxin Wang Laizhong Cui Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China SSE and FNii The Chinese University of Hong Kong Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement, while enabling trade-off between playback smoothness and video quality.

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Hundredfold increase of stimulated Brillouin-scattering bandwidth in whispering-gallery mode resonators

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Photonics Research 2023年第6期11卷 917-924页

作者： GUOPING LIN JINGYI TIAN TANG SUN QINGHAI SONG YANNE K.CHEMBO Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System School of ScienceHarbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Shenzhen 518055China Department of Electrical and Computer Engineering&Institute for Research in Electronics and Applied Physics(IREAP) University of MarylandCollege ParkMaryland 20742USA

Backward stimulated Brillouin scattering(SBS)is widely exploited for various applications in optics and *** typically features a narrow gain bandwidth of a few tens of megahertz in fluoride *** we report a hundredfold increase of SBS bandwidth in whispering-gallery mode *** crystalline orientation results in a large variation of the acoustic phase velocity upon propagation along the periphery,from which a broad Brillouin gain is *** 2.5 GHz wide Brillouin gain profile is theoretically found and experimentally *** phenomena with Brillouin shift frequencies ranging from 11.73 to 14.47 GHz in ultrahigh Q Z-cut magnesium fluoride cavities pumped at the telecommunication wavelength are ***,the Brillouin-Kerr comb in this device is *** 400 comb lines spanning across a spectral window of 120 nm are *** finding paves a new way for tailoring and harnessing the Brillouin gain in crystals.

关键词： scattering pumped Brillouin

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An Ensemble Learning Method for Software Defect Prediction Targeting Ranking

An Ensemble Learning Method for Software Defect Prediction T...

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2025 2nd International Conference on Algorithms, Software engineering and Network Security (ASENS)

作者： Xiaowei Zhao Lu Lu Weibiao Chen Jiahao Cheng Xinyan Yi School of Computer Science and Engineering South China University of Technology Guangzhou China Digital Research Department China Southern Power Grid Guangzhou China Pengcheng Laboratory Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Software defects pose a significant threat to system stability and user experience. Traditional defect prediction methods often fall short in practical testing scenarios. In response to this, we propose SeDI (Semantics-Enhanced DT-NN Integration), an ensemble learning approach for ordinary software defect prediction. SeDI integrates static software metrics with deep semantic features extracted from abstract syntax trees, leveraging a gradient boosting ranking model and a Multi-layer Perceptron to enhance ranking accuracy. The model's performance is evaluated using FPA and PD20 metrics, demonstrating superior ranking accuracy and improved performance in the top 20% of predicted modules.

关键词： Accuracy Source coding Semantics Predictive models Feature extraction Software User experience Ensemble learning Software measurement Testing

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Microcomb based silicon photonics data link with 100Gbps/Lane PAM4 transmission

Microcomb based silicon photonics data link with 100Gbps/Lan...

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CLEO: Science and Innovations, S and I 2022

作者： Shu, Haowen Shen, Bitao Chang, Lin Tao, Yuansheng Xie, Weiqiang Jin, Ming Netherton, Andrew Zhang, Xuguang Qin, Jun Chen, Ruixuan Yu, Shaohua Wang, Xingjun Bowers, John E. State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

We achieve 100 Gbps/Lane PAM4 transmission by employing an AlGaAsOI microcomb source and silicon modulators. With 20 parallel wavelength channels utilized at C-band, an aggregate data rate of 2 Tbit/s is achieved. ... 详细信息

ISBN: (纸本)9781557528209

关键词： Gallium compounds

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Initialization with Description Information and Incorporating Structure Learning for Efficient Dynamic Knowledge Graph Embedding

Initialization with Description Information and Incorporatin...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yao Liu Ye Liu Xin Wang Beijing Key Laboratory of Digital Media Beihang University Beijing China The School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Knowledge graphs (KGs) are an efficient method for representing heterogeneous knowledge. In KGs, knowledge is represented as triples and transformed into a low-dimensional space using graph embedding techniques, benefiting downstream tasks such as recommender systems and question answering. In real-world scenarios, knowledge graphs are continuously updated with new facts. However, existing KG embedding models only support static graph embeddings, requiring the re-training of the entire graph whenever knowledge updates occur, resulting in time-consuming. There are limited models that support dynamic embedding, which utilizes already trained embeddings to generate representations of new data to improve embedding efficiency. However, these models often ignore the semantic information inherent in the new data itself and the unique structural information in the KG, resulting in low embedding quality. To address this issue, we propose an embedding method specifically designed for dynamic knowledge graphs. Initially, we obtain the semantic representation by quantifying the descriptive information of the added nodes as an initialization vector. Next, to ensure compliance with the graph structure in the KG, we leverage the triple structure of neighboring nodes to the new nodes, and iteratively train the semantic representation to satisfy the structural constraints of KG, resulting in an embedding vector that incorporates both semantic and structural information. We compare our model with existing KG embedding models on two benchmark datasets in link prediction task. The experimental results demonstrate that our model achieves higher efficiency while maintaining the same level of accuracy, thereby validating its effectiveness.

关键词： Computational modeling Semantics Knowledge graphs Predictive models Benchmark testing Vectors Data models

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YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

arXiv

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Traffic signs

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