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International Conference on Bioinformatics and Biomedical engineering (ICBBE)

作者： Peng Xu Shaozhi Wu Shadnaz Asgari Magdalena Kasprowicz Marvin Bergsneider Xiao Hu Center of Neuroinformatics School of Life Science and Technology University of Electronic Science and Technology Chengdu China Neural Systems and Dynamics Laboratory Department of Neurosurgery the David Geffen School of Medicine University of California Los Angeles USA School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Biomedical Engineering Graduate Program Henry Samueli School of Engineering and Applied Science University of California Los Angeles USA University of California Los Angeles Los Angeles CA US

Changes of ICP waveform morphology are characterized with different patients' states like hypertension, hydrocephalus and traumatic brain injury etc. Morphological clustering and analysis of ICP pulse (MOCAIP) approach is recently developed to extract ICP morphology feature, in which hierarchical clustering is used to extract the dominated pulse. In this paper, we evaluate the feasibility of using principle component analysis (PCA) and independent component analysis (ICA) to extract dominated pulse. The comparative study among clustering, PCA and ICP based approaches shows that PCA approach may be an alternative of clustering approach to extract dominated pulse in a faster fashion when dataset is of large size.

关键词： Independent component analysis Morphology Principal component analysis Biomedical engineering Feature extraction Electrocardiography Tellurium Hypertension Data mining Laboratories

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

arXiv

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arXiv 2022年

作者： Jin, Peng Huang, Jinfa Liu, Fenglin Wu, Xian Ge, Shen Song, Guoli Clifton, David A. Chen, Jie School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Department of Engineering Science University of Oxford United Kingdom Tencent JARVIS Lab China Oxford-Suzhou Centre for Advanced Research Suzhou China

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP [53], to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Mechanically reprogrammable Pancharatnam-Berry metasurface for microwaves

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Advanced Photonics 2022年第1期4卷 69-79页

作者： Quan Xu Xiaoqiang Su Xueqian Zhang Lijuan Dong Lifeng Liu Yunlong Shi Qiu Wang Ming Kang Andrea Alù Shuang Zhang Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education of China)TianjinChina Shanxi Datong University Institute of Solid State Physics and College of Physics and Electronic ScienceShanxi Province Key Laboratory of Microstructure Electromagnetic Functional MaterialsDatongChina Wuhan University of Technology School of Information EngineeringWuhanChina Tianjin Normal University College of Physics and Materials ScienceTianjinChina City University of New York Advanced Science Research CenterPhotonics InitiativeNew YorkUnited States City University of New York Graduate CenterPhysics ProgramNew YorkUnited States University of Hong Kong Faculty of ScienceDepartment of PhysicsHong KongChina University of Hong Kong Department of Electrical and Electronic EngineeringHong KongChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Metasurfaces have enabled the realization of several optical functionalities over an ultrathin platform,fostering the exciting field of flat *** metasurfaces are achieved by arranging a layout of static meta-atoms to imprint a desired operation on the impinging wavefront,but their functionality cannot be *** and programmability of metasurfaces are the next important step to broaden their impact,adding customized on-demand functionality in which each meta-atom can be individually *** demonstrate a mechanical metasurface platform with controllable rotation at the meta-atom level,which can implement continuous Pancharatnam–Berry phase control of circularly polarized *** the proof-of-concept experiments,we demonstrate metalensing,focused vortex beam generation,and holographic imaging in the same metasurface template,exhibiting versatility and superior *** dynamic control of electromagnetic waves using a single,low-cost metasurface paves an avenue towards practical applications,driving the field of reprogrammable intelligent metasurfaces for a variety of applications.

关键词： reprogrammable metasurfaces Pancharatnam-Berry phase mechanical metasurfaces microwaves

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DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

DiffusionRet: Generative Text-Video Retrieval with Diffusion...

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International Conference on computer Vision (ICCV)

作者： Peng Jin Hao Li Zesen Cheng Kehan Li Xiangyang Ji Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China Peng Cheng Laboratory Shenzhen China

Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query). This is accomplished through a diffusion-based text-video retrieval framework (Diffusion-Ret), which models the retrieval task as a process of gradually generating joint distribution from noise. During training, DiffusionRet is optimized from both the generation and discrimination perspectives, with the generator being optimized by generation loss and the feature extractor trained with contrastive loss. In this way, DiffusionRet cleverly leverages the strengths of both generative and discriminative methods. Extensive experiments on five commonly used text-video retrieval benchmarks, including MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo, with superior performances, justify the efficacy of our method. More encouragingly, without any modification, DiffusionRet even performs well in out-domain retrieval settings. We believe this work brings fundamental insights into the related fields. Code is available at https://***/jpthu17/DiffusionRet.

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Dirichlet tessellation for high-dimensional RSS clustering in Wi-Fi environment

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ICIC Express Letters 2014年第7期8卷 2043-2048页

作者： Zhou, Mu Tian, Zengshan Xu, Kunjie Tang, Xiaomou Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Nanan District Chongqing 400065 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Graduate Telecommunication and Networking Program School of Information Science University of Pittsburgh PA 15260 United States

The received signal strength (RSS) clustering has played a significant role in site surveying and data pre-processing for the location tracking in Wi-Fi environment. To this end, this paper presents a novel clustering algorithm to cluster the raw RSS vectors with large similarities in both the RSS and physical locations together. The clusters of RSS vectors are determined by the cells which are generated by the Dirichlet tessellation in a transformed low-dimensional space. Our algorithm consists of two steps. We first use the Laplacian embedding to transform the raw measurements into a low-dimensional space, while preserving the RSS and location neighborhood relations. Second, after the seeds of cells are carefully selected, we will tessellate the low-dimensional space by the Dirichlet cells to obtain the clustering results. The experiments conducted in an actual public Wi-Fi environment prove that our algorithm can achieve the smallest cluster scattering in both the RSS and locations compared with the existing affinity propagation clustering, K-means clustering and binary support vector classifier (SVC) algorithms. © 2014 ISSN 1881-803X.

关键词： Wi-Fi

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

引用

Cell 2025年

作者： Jonathan E Pekar Spyros Lytras Mahan Ghafari Andrew F Magee Edyth Parker Yu Wang Xiang Ji Jennifer L Havens Aris Katzourakis Tetyana I Vasylyeva Marc A Suchard Alice C Hughes Joseph Hughes Andrew Rambaut David L Robertson Simon Dellicour Michael Worobey Joel O Wertheim Philippe Lemey Institute of Ecology and Evolution University of Edinburgh Edinburgh UK Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Medicine University of California San Diego La Jolla CA 92093 USA. Electronic address: jpekar@ed.ac.uk. Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow UK. Electronic address: spyros@g.ecc.u-tokyo.ac.jp. Department of Biology University of Oxford Oxford UK. Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Institute of Genomics and Global Health Redeemer's University Ede Osun State Nigeria. Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA. Department of Mathematics School of Science and Engineering Tulane University New Orleans LA USA. Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA. Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Population Health and Disease Prevention University of California Irvine Irvine CA 92617 USA. Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA 90095 USA Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. School of Biological Sciences University of Hong Kong Ho

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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Floor plan construction via anonymous Wi-Fi signal fragment matching

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ICIC Express Letters, Part B: Applications 2014年第4期5卷 981-987页

作者： Zhou, Mu Tian, Zengshan Xu, Kunjie Tang, Xiaomou Wang, Zhuoran Chongqing Key Lab of Mobile Communications Technology School of Communication and Information Engineering Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Nan'an Dist. Chongqing 400065 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Graduate Telecommunications and Networking Program University of Pittsburgh PA 15260 United States

Floor plan construction is the basis for fingerprint-based localization and people's activity learning, especially in the environments where the geometrical map is not available or does not exist. To this end, we propose a new received signal strength (RSS) fragment matching approach to identify the location points (LPs) in floor plan as the overlapped pieces between the fragments. Then, by means of the Smith-Waterman alignment, each LP can be recognized as a winning path in scoring space. Finally, we use the Allen's interval algebra to assemble the time-stamped LPs into a floor plan in target area. The experiments with the actual Wi-Fi RSS measurements recorded in HKUST campus demonstrate that our proposed approach performs better than the conventional K-means clustering and spectral clustering in floor plan construction. © 2014 ICIC International.

关键词： Algebra

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Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning

Video-Text as Game Players: Hierarchical Banzhaf Interaction...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Peng Jin Jinfa Huang Pengfei Xiong Shangxuan Tian Chang Liu Xiangyang Ji Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Shopee Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China Peng Cheng Laboratory Shenzhen China

Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering bench-marks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which have a far-reaching impact on the community. Project page is available at https://***/HBI/.

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ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

ACSeg: Adaptive Conceptualization for Unsupervised Semantic ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kehan Li Zhennan Wang Zesen Cheng Runyi Yu Yian Zhao Guoli Song Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Peng Cheng Laboratory Shenzhen China Dalian University of Technology Department of Automation and BNRist Tsinghua University Beijing China

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg.

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