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arXiv 2024年

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd China

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services None of modern ANNS systems can address these issues simultaneously. We present FusionANNS, a high-throughput, low-latency cost-efficient, and high-accuracy ANNS system for billion scale datasets using SSDs and only one entry-level GPU The key idea of FusionANNS lies in CPU/GPU collabo rative filtering and re-ranking mechanisms, which signifi cantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We imple ment FusionANNS and compare it with the state-of-the-art SSD-based ANNS system–SPANN and GPU-accelerated in memory ANNS system–RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation

arXiv

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arXiv 2024年

作者： Luo, Jialin Wang, Yuanzhi Gu, Ziqi Qiu, Yide Yao, Shuaizhen Wang, Fuyun Xu, Chunyan Zhang, Wenhua Wang, Dan Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, MultiGSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS. © 2024, CC BY-NC-SA.

关键词： Visual languages

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Study on key technologies of GNSS-based train state perception for traincentric railway signaling

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High-Speed Railway 2023年第1期1卷 47-55页

作者： Baigen Cai Jingnan Liu Xurong Dong Jiang Liu School of Computer and Information Technology Bejing Jiaotong UniversityBeijing 100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing 100044China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China GNSS Research Center Wuhan UniversityWuhan 430079China Space Engineering University Beijing 101416China

The application of Global Navigation Satellite systems(GNSSs)in the intelligent railway systems is rapidly developing all over the *** the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side *** the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical *** this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance *** to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are *** demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite system(BDS)and related *** reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

关键词： Railway signaling Train control Global Navigation Satellite system Sensor data fusion Virtual Balise Train integrity Performance evaluation

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

Multi-Scale Dynamic and Hierarchical Relationship Modeling f...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Universiry of Leicester Monash University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hi-erarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatiotemporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and mag-nitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

关键词： Gold Adaptation models computer vision Graphical models Codes Adaptive systems Computational modeling

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Interactive-Wear: An intelligent watch application to aid memory for intentions and everyday functioning in older adults with cognitive impairments 1

Interactive-Wear: An intelligent watch application to aid me...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Weakley, Alyssa Pimento, Sasha Neil Agarwal, Hritvik Cook, Diane Schmitter-Edgecombe, Maureen Farias, Sarah Tomaszewski Lorabi, Rahma Minor, Bryan Karia, Saumya Dept of Computer Science University Of California Davis Davis United States Dept of Psychology Washington State University Pullman United States Dept of Neurology University Of California Davis Davis United States School of EECS Washington State University Pullman United States Dept of System Engineering University of Texas Dallas Dallas United States

ISBN: (纸本)9798350371987

In this technical paper, we review an innovative speech-to-text wearable watch application (app) called Interactive-Wear (I-Wear) designed to improve memory for intended actions of individuals with Alzheimer’s disease (AD). I-Wear transcribes speech spoken into the wearable app to text displayed on a web-based platform, Interactive-Care, designed to improve everyday functioning of individuals with AD and their caregivers. Regarding performance metrics, we achieved a low word error rate of 2% (98% accuracy) and a fast execution time (M=1.5 seconds). The user interface was evaluated by AD experts who rated it as highly accessible and intuitive. They also reported high ease of use and potential benefit for both individuals with cognitive impairment and their caregivers. Next, we will deploy the I-Wear to AD participants and will utilize multi-modal sensing (I-Wear + unobtrusive sensors) and artificial intelligence models to improve our understanding of cognitive and functional trajectories, detection of cognitive and health changes, and just-in-time interventions to enhance independence. ©2024 IEEE.

关键词： Assisted living

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China 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 Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Phase apodized silicon Bragg grating filter with varying sidewall ridge width

Phase apodized silicon Bragg grating filter with varying sid...

引用

OptoElectronics and Communications Conference, OECC

作者： Xinyu Zhang Wei Jiang Shuo Yuan Haipeng Liu Wenbo Ren Xincheng Xia Jijun Feng School of Mathematics and Physics China University of Geosciences Wuhan P.R. China Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System (Ministry of Education) School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

In this work, phase apodized silicon Bragg grating filters with varying sidewall ridge width and location were investigated, while the resonance wavelength, extinction ratio and rejection bandwidth can be tuned flexibly. The grating filters with a waveguide width of 500 nm and grating period of 400 nm were fabricated and characterized for a proof of concept. The presented sidewall ridge modulated apodized grating can be expected to have great application prospects for optical communications and semiconductor lasers.

关键词： Bragg gratings Bandwidth Waveguide lasers Extinction ratio Silicon photonics Resonance Optical fiber communication Semiconductor waveguides Gratings Optical waveguides

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Scalable Optimal Margin Distribution Machine

arXiv

引用

arXiv 2023年

作者： Wang, Yilin Cao, Nan Zhang, Teng Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the latest margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. However, it suffers from the ubiquitous scalability problem regarding both computation time and memory storage as other kernel methods. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. For nonlinear kernels, we put forward a novel distribution-aware partition method to make the local ODM trained on each partition be close and converge fast to the global one. When linear kernel is applied, we extend a communication efficient SVRG method to accelerate the training further. Extensive empirical studies validate that our proposed method is highly computational efficient and almost never worsen the generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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