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10th International Conference on Learning Representations, ICLR 2022

作者： Wang, Yuanfei Zhong, Fangwei Xu, Jing Wang, Yizhou Center for Data Science Peking University China School of Artificial Intelligence Peking University China Center on Frontiers of Computing Studies School of Computer Science Peking University China Adv. Inst. of Info. Tech Peking University China China

Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide "when" and with "whom" to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions to accomplish the sub-goals. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage. The experiments show that the proposed model not only outperforms the state-of-the-art methods on reward and communication efficiency, but also shows good generalization across different scales of the environment. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Intelligent agents

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Largest Angle Path Distance for Multi-Manifold Clustering

Largest Angle Path Distance for Multi-Manifold Clustering

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Chen, Haoyu Little, Anna Narayan, Akil University of Utah Department of Mathematics Salt Lake CityUT84112 United States University of Utah Utah Center for Data Science Department of Mathematics Salt Lake CityUT84112 United States University of Utah Scientific Computing & Imaging Inst. Department of Mathematics Salt Lake CityUT84112 United States

ISBN: (纸本)9798350328851

We propose a novel, angle-based path metric for the multi-manifold clustering problem. This metric, which we call the largest-angle path distance (LAPD), is computed as a bottleneck path distance in a graph constructed on d-simplices of data points. When data is sampled from a collection of d-dimensional manifolds which may intersect, the method can cluster the manifolds with high accuracy and automatically detect how many manifolds are present. By leveraging fast approximation schemes for bottleneck distance, this method exhibits quasi-linear computational complexity in the number of data points. In addition to being highly scalable, the method outperforms existing algorithms in numerous numerical experiments on intersecting manifolds, and exhibits robustness with respect to noise and curvature in the data. © 2023 IEEE.

关键词： Numerical methods

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Acceleration of BAM I/O on distributed file systems

Acceleration of BAM I/O on distributed file systems

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Satoshi Ito Satoru Miyano Kenji Ono Dept. of Integrated Analysis M&D Data Science Center Tokyo Medical and Dental University Tokyo Japan Section of Applied Data Science Res. Inst. for Information Technology Kyushu University Fukuoka Japan

Rapid advances in high-throughput sequencers have made it possible to obtain large amounts of whole genome data quickly and inexpensively. As the amount of data increases, the increase in computation time has become a serious problem. One of the main causes of this problem is file I/O performance. Most pipelines do not implement file I/O suitable for distributed file systems, which are common storage systems in parallel computers used for large-scale analysis. In this study, we developed an I/O system adapted to distributed file systems. In the proposed system, storage access frequency was suppressed by I/O buffers, and thread parallelization by OpenMP was employed. We tested the developed system and a high parallel speedup was achieved. Availability: The developed system is freely available from https://***/SatoshiITO/GAPS

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Autoregressive Sequence Modeling for 3D Medical Image Representation 39

Autoregressive Sequence Modeling for 3D Medical Image Repres...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Siwen Wang, Churan Gao, Fei Su, Lixian Zhang, Fandong Wang, Yizhou Yu, Yizhou School of Computing and Data Science The University of Hong Kong Hong Kong Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University China Deepwise AI Lab China State Key Lab of General Artificial Intelligence Inst. for Artificial Intelligence Peking University China

ISBN: (纸本)157735897X

Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image as a whole thereby overlooking the extensive, complex relationships among local regions from one or multiple images. In this work, we introduce a pioneering method for learning 3D medical image representations through an autoregressive pre-training framework. Our approach sequences various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence. By employing an autoregressive sequence modeling task, we predict the next visual token in the sequence, which allows our model to deeply understand and integrate the contextual information inherent in 3D medical images. Additionally, we implement a random startup strategy to avoid overestimating token relationships and to enhance the robustness of learning. The effectiveness of our approach is demonstrated by the superior performance over others on nine downstream tasks in public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

arXiv

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arXiv 2024年

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Dept. of Electronics and Communications Engineering Pyramids Higher Inst. for Engineering & Technology Egypt College of Computing and Data Science Nanyang Technological University Singapore

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8’s versatility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22, 424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making PYOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8’s architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024, CC BY.

关键词： Convolutional neural networks

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p-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

p-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Mohamed R. Elshamy Heba M. Emara Mohamed R. Shoaib Abdel-Hameed A. Badawy Klipsch School of ECE New Mexico State University Las Cruces NM United States Dept. of Electronics and Communications Engineering Pyramids Higher Inst. for Engineering & Technology Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving.

关键词： Training Performance evaluation Adaptation models Accuracy Computational modeling Tiny machine learning Computer architecture Real-time systems Computational efficiency Image classification

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SPENT+: A Category- And Region-aware Successive POI Recommendation Model

SPENT+: A Category- And Region-aware Successive POI Recommen...

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22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021

作者： Lai, Hsu-Chao Lu, Yi-Shu Wang, Mu-Fan Chen, Yi-Cheng Shih, Wen-Yueh Huang, Jiun-Long Inst. of Computer Science and Engineering National Yang Ming Chiao Tung University Taiwan National Central University Department of Information Management Taiwan Inst. of Data Science and Engineering National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9784885523328

To facilitate successive Point-of-Interests (POI) recommendation, the categories of POIs and the regions where POIs are located are seldom considered in existing models. In view of this, we extend a state-of-the-art model SPENT, named SPENT+, by taking the category and the region into considerations. In SPENT+, we formulate category- and region-aware check-in sequences, design the similarity trees to aggregate similar features, and finally establish the category latent vectors and region latent vectors, respectively. The above two latent vectors are aggregated as the category-region-aware latent vectors. Therefore, the category-region-latent vectors are sent to an LSTM together with conventional check-in sequences to improve successive POI recommendation. We conduct two real datasets, Gowalla and Foursquare, and compare with state-of-the-art methods in experiments. Results show that SPENT+ outperforms the baselines in terms of precision and recall. © 2021 IEICE.

关键词： Vectors

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Causal discovery via conditional independence testing with proxy variables 24

Causal discovery via conditional independence testing with p...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mingzhou Liu Xinwei Sun Yu Qiao Yizhou Wang School of Computer Science and Center on Frontiers of Computing Studies (CFCS) Peking University Sch. of Data Science Fudan University Dep. of Automation Shanghai Jiao Tong University Center on Frontiers of Computing Studies (CFCS) and School of Computer Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology and Nat'l Key Lab of General Artificial Intelligence Peking University

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery.

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Largest Angle Path Distance for Multi-Manifold Clustering

Largest Angle Path Distance for Multi-Manifold Clustering

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International Conference on Sampling Theory and Applications (SampTA)

作者： Haoyu Chen Anna Little Akil Narayan Department of Mathematics University of Utah Salt Lake City Utah Department of Mathematics Utah Center for Data Science University of Utah Salt Lake City Utah Department of Mathematics Scientific Computing & Imaging Inst. University of Utah Salt Lake City Utah

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Real-time Holistic Robot Pose Estimation with Unknown States

arXiv

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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