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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering

Robust Geometry-Preserving Depth Estimation Using Differenti...

引用

IEEE/CVF International conference on computer Vision (ICCV)

作者： Zhang, Chi Yin, Wei Yu, Gang Wang, Zhibin Chen, Tao Fu, Bin Zhou, Joey Tianyi Shen, Chunhua Tencent Shenzhen Peoples R China DJI Technol Shenzhen Peoples R China Fudan Univ Shanghai Peoples R China Zhejiang Univ Hangzhou Peoples R China ASTAR Frontier Res Ctr Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350307184

In this study, we address the challenge of 3D scene structure recovery from monocular depth estimation. While traditional depth estimation methods leverage labeled datasets to directly predict absolute depth, recent advancements advocate for mix-dataset training, enhancing generalization across diverse scenes. However, such mixed dataset training yields depth predictions only up to an unknown scale and shift, hindering accurate 3D reconstructions. Existing solutions necessitate extra 3D datasets or geometry-complete depth annotations, constraints that limit their versatility. In this paper, we propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. To produce realistic 3D structures, we render novel views of the reconstructed scenes and design loss functions to promote depth estimation consistency across different views. Comprehensive experiments underscore our framework's superior generalization capabilities, surpassing existing state-of-the-art methods on several benchmark datasets without leveraging extra training information. Moreover, our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients using solely unlabeled images.

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A Transfer Learning Framework for high-Accurate Cross-Workload Design Space Exploration of CPU 42

A Transfer Learning Framework for High-Accurate Cross-Worklo...

引用

42nd IEEE/ACM International conference on computer-Aided Design (ICCAD)

作者： Wang, Duo Yan, Mingyu Teng, Yihan Han, Dengke Dang, Haoran Ye, Xiaochun Fan, Dongrui Chinese Acad Sci SKLP Inst Comp Technol Beijing Peoples R China UNiv Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350322255

To perform cross-workload design space exploration of CPU, previous works implicitly transfer knowledge from several existing source workloads and try to make predictions on the target one. However, they do not fully explore the transferability across workloads and their single basic prediction models limit the prediction accuracy. In this paper, an open-source Transfer learning Ensemble Design Space Exploration framework (TrEnDSE) is proposed to perform cross-workload performance predictions. The black-box transferability between workloads is quantitatively dissected and explicitly utilized as sample weights for training. Moreover, an ensemble bagging learning model and an uncertainty-driven iterative optimization method are proposed to perform accurate and robust prediction, with these sample weights leveraged. Experiments on SPEC CPU 2017 demonstrate that TrEnDSE can reduce cycle per instruction prediction error by 54% and power prediction error by 34% compared with the state-of-the-art work.

关键词： Design Space Exploration Cross-workload Prediction Model Transfer Learning CPU Microarchitecture

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PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting

PRESS: Defending Privacy in Retrieval-Augmented Generation v...

引用

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

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security Oxford Brookes College Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350368741

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system. © 2025 IEEE.

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Spatio-Temporal Fusion Network for Traffic Flow Prediction 20

Spatio-Temporal Fusion Network for Traffic Flow Prediction

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Quanyou, Lei Lijun, Wu Jianping, Li Yu, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Intelligent transportation systems (ITSs) rely heavily on traffic flow forecasting to facilitate efficient traffic management and alleviate congestion. This paper proposes an innovative hybrid network-spatio-temporal fusion network (STFN), designed to forecast traffic flow with accuracy and efficacy. To capture spatial dependencies and temporal dynamics in traffic data effectively, the proposed model incorporates two key components: residual graph attention networks (RGAT) to model complex spatial correlations between adjacent traffic sensors and bidirectional LSTM (Bi-LSTM) to extract latent temporal features and sequential patterns. An attentional feature fusion (AFF) mechanism is introduced to adaptively integrate complementary features from heterogeneous sources. Experimental results indicate the superior performance of STFN compared to state-of-the-art methods regarding accuracy and efficiency. © 2023 IEEE.

关键词： Long short-term memory

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Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

Extensions to the SENSEI In situ Framework for Heterogeneous...

引用

2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Loring, Burlen Bethel, E. Wes Weber, Gunther Mahoney, Michael W. Lawrence Berkeley Lab BerkeleyCA United States San Francisco State University Lbl San FranciscoCA United States Lawrence Berkeley Lab Icsi Uc Berkeley BerkeleyCA United States

ISBN: (纸本)9798400707858

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance. © 2023 Owner/Author.

关键词： program processors

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Research on road traffic sign recognition method based on HOG features and SVM

Research on road traffic sign recognition method based on HO...

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2024 International conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Chen, Jingshun Zhou, Fangxiao School of Mathematics and Computer Science PanZhiHua University SiChuan PanZhiHua 617000 China

ISBN: (纸本)9781510689275

Achieving traffic sign recognition is an important part of improving the safety of traffic system. Aiming at the poor generalisation ability of template matching methods and the complex model and high computational resource requirements of deep learning methods, this paper proposes a traffic sign recognition method based on HOG features and SVM. First, the image is transformed into HSV colour space, and specific colours are filtered by specific colour thresholds. After the operation of filling and opening operation, the contour is extracted, relying on the shape features to achieve the extraction of traffic signs, and then the pre-trained SVM model is used to complete the classification and recognition based on the HOG features of traffic signs. The effect of image size on SVM classification performance is analysed through experiments, and the recognition method is tested experimentally using the GTSRB dataset. The results show that the method has high accuracy and good real-time performance in recognising traffic signs, and can effectively complete the traffic sign recognition task. © 2025 SPIE.

关键词： HOG feature HSV color space Shape detection SVM Traffic signs

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Comprehensive Analysis of Computational and I/O Resource Patterns Across Queues 21

Comprehensive Analysis of Computational and I/O Resource Pat...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Bin, Huang Gang, Xian University Of Electronic Science And Technology Of China Chengdu China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798331519254

In the current computing landscape, with the explosive growth of computing tasks, effective job resource utilization is of utmost importance for enhancing system performance. This study conducts a comprehensive analysis of job resource usage, focusing on the running time of job queues, core and memory resource utilization, and the characteristic distribution of job read-write I/O. Using a dataset containing 51,987 jobs from the Gaia Cluster over 3 months, we explore various aspects. The running time analysis reveals that different queues have distinct job runtime distributions. For instance, the interactive queue has a significant proportion of short-runtime jobs (30%-40%) for rapid data operations and long-runtime jobs (30%-40%) for complex tasks. The normal queue is dominated by medium-runtime jobs (50%-60%) to balance resource utilization. Core resource usage shows a trend where medium-core jobs are predominant (60%-80%) across queues due to factors like task complexity and cost-effectiveness. Memory usage is closely related to runtime, with medium-memory jobs corresponding to medium runtime and large-memory jobs having longer runtimes. In terms of I/O, the interactive queue has a high proportion of read-intensive and write-intensive jobs, while the normal queue is mainly write-intensive. These findings provide a detailed understanding of job resource usage patterns and offer practical guidance for optimizing computing systems. © 2024 IEEE.

关键词： Job analysis

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Distributed and Adaptive Message Dissemination for Vehicle Platooning in Hybrid Traffic

Distributed and Adaptive Message Dissemination for Vehicle P...

引用

2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xiang Zhang, Fuxin College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665410205

In vehicular networks, vehicle in the platooning relies on dissemination of beacons to perceive the status of neighbor vehicles and then take control low to maintain a constant inter-vehicle distance. Vehicle platooning communication has stringent high-reliability and low-latency requirements. In this paper, we focus on resource allocation strategies for vehicle platoons in hybrid scenarios. First, we propose a two-dimensional Markov model to describe the channel contention (back-off) processes for platoons and individual vehicles. Based on the model, we then can derive the transmission probabilities of beacons and event messages for platoons and individual vehicles. Finally, we design a distributed and adaptive beacon control scheme to determine the optimal beacon rates for vehicles in the platoons. The simulation results demonstrate that our efforts substantially enhance platooning communication reliability and ensure optimal performance for vehicle safety applications. © 2024 IEEE.

关键词： Markov processes

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LPGL: A Model Applicable to Data Augmentation Algorithms for Depression Detection 3

LPGL: A Model Applicable to Data Augmentation Algorithms for...

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3rd International conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Wang, Shaoqiang Wang, Shudong Wang, Yifan China University of Petroleum East China Qingdao China Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9798350331578

The clinical detection of depression generally judges depression and the degree of depression by scoring according to the depression scale. For doctors, it is time-consuming to perform the same scale screening for each patient, and the program based on computer-Aided diagnosis has practical significance. At present, large-scale neural networks often fall into the dilemma of insufficient training data labeling in practical applications. We propose an algorithm based on label propagation that combines semi-supervised learning with graph structure learning (LPGL). The LPGL algorithm can simultaneously utilize three types of data: labeled data, unlabeled data, and data enhancement, so that the trained classifier is better than the training data and outperforms than other few-shot learning methods when labels are insufficient. This article uses the AVEC 2013 dataset to verify the superiority of the LPGL algorithm in terms of accuracy. Early diagnosis of depression is also conducive to early medical intervention, which will be of great help in alleviating depression. © 2023 IEEE.

关键词： computer aided diagnosis

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CrCo- Mlgcn: A Cross-scale Co-learning Based Multi-Level Graph Convolutional Network for Brain-computer Interface 4

CrCo- Mlgcn: A Cross-scale Co-learning Based Multi-Level Gra...

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4th International conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Yang, Wenchao Ma, Yulan Li, Yang Beihang University Department of Automation Science and Electrical Engineering Beijing China Beihang University Department of Automation Science and Electrical-Engineering Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

ISBN: (纸本)9798350380323

Functional near-infrared spectroscopy (fNIRS) decoding is a crucial foundation for Brain-computer Interface (BCI) technology. However, existing methods commonly concentrate on time-frequency features and overlook positional information, thereby failing to fully utilize the high spatial resolution advantages of fNIRS. Furthermore, some methods employ multi-branch structures, but they fail to consider the cooperative interaction between the branches, leading to suboptimal performance. To address these limitations, we propose a cross-scale co-learning based multilevel graph convolutional network (CrCo-MLGCN). Specifically, a multi-level graph convolution module is crafted with three branches to extract features from different levels, including local, regional, and global levels. Then, a cross-scale co-learning module is employed to harmonize the model's multiple branches, ensuring efficient parameter utilization and preventing redundancy. Experimental results on two public datasets demonstrate that the proposed CrCo-MLGCN outperforms current state-of-the-art approaches, confirming the effectiveness of our proposed method. © 2024 IEEE.

关键词： Near infrared spectroscopy

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