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arXiv 2023年

作者： Li, Zhihao Zou, Boyi Wang, Haiqin Su, Jian Wang, Dong Xu, Xinpeng Department of Civil Engineering Shantou University 243 Daxue Road Guangdong Shantou515063 China Department of Physics MATEC Key Lab Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Technion – Israel Institute of Technology Haifa32000 Israel

A deep learning-based computational method has been proposed for soft matter dynamics – the deep Onsager-Machlup method (DOMM), which combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle – Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method constructed using variational formulations of physics models to solve static problems in physics as discussed in our former work [Wang et al, Soft Matter, 2022, 18, 6015-6031]. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics. © 2023, CC BY-NC-ND.

关键词： Computational methods

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Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

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A Fuzzy ESO-based Joint Angle Control Design of Snake Robots

A Fuzzy ESO-based Joint Angle Control Design of Snake Robots

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IEEE International Conference on Robotics and Biomimetics

作者： Lili Wu Yang Liu Junfang Zhou Zhigang Wang Yushuang Wang Yongchen Tang Beijing Electro-Mechanical Engineering Institute Beijing China 91039 Army School of Electrical and Information Engineering Tianjin University Tianjin China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan China

ISBN: (纸本)9781665405362

The joint angle control of snake robot is an important component for the path tracking control. However, existing controllers can not realize the accurate control of the joint angle of snake robots since they rely on model information and can not adapt to the uncertain environment. Here, aiming at the above problems, a fuzzy extended state observer (FESO)-based joint angle controller for snake robots is designed. The total system disturbance including model uncertainty and external disturbance is estimated by FESO and then rejected by adding a corresponding part to the control law. Moreover, the peaking phenomenon caused by initial estimation errors can be suppressed in the proposed FESO. Finally, the effectiveness of the designed joint angle controller is verified by simulations.

关键词： Adaptation models Estimation error Uncertainty Control design Conferences Biomimetics Biological system modeling

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Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

arXiv

引用

arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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Intersection-free Robot Manipulation with Soft-Rigid Coupled Incremental Potential Contact

arXiv

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arXiv 2023年

作者： Du, Wenxin Yao, Siqiong Wang, Xinlei Xu, Yuhang Xu, Wenqiang Lu, Cewu The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center of Biostatistics and Data Science National Center for Translational Medicine Shanghai Jiao Tong University China Qing Yuan Research Institute MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China ZenusTech Inc Philippines

This paper presents a novel simulation platform, ZeMa, designed for robotic manipulation tasks concerning soft objects. Such simulation ideally requires three properties: two-way soft-rigid coupling, intersection-free guarantees, and frictional contact modeling, with acceptable runtime suitable for deep and reinforcement learning tasks. Current simulators often satisfy only a subset of these needs, primarily focusing on distinct rigid-rigid or soft-soft interactions. The proposed ZeMa prioritizes physical accuracy and integrates the incremental potential contact method, offering unified dynamics simulation for both soft and rigid objects. It efficiently manages soft-rigid contact, operating 75x faster than baseline tools with similar methodologies like IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp generation, penetrated grasp repair, and reinforcement learning for grasping, successfully transferring the trained RL policy to real-world scenarios. More experiments and videos can be found in the supplementary materials and on the website: https: //***/view/zema-ipc. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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In-memory join algorithms on GPUs for large-data 21

In-memory join algorithms on GPUs for large-data

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guo, Chengxin Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9781728120584

In traditional databases, join is one of the most computationally expensive operations in query processing. During the past years, GPU has been adopted to improve the performance of join processing because of the features of massive parallelism and high memory bandwidth. Limited by the capacity of GPU memory and the absence of virtual memory management, however, handling the relations that exceed the capacity of the GPU memory is a challenge for GPU-based join algorithms. Because of the high computing throughput provided by GPUs and the low bandwidth of data communication between the CPUs and the GPUs, data have to be partitioned to fit the features of GPUs and to reduce the cost of data transmission. Furthermore, a series of novel techniques have been developed on the GPUs, which can benefit the join algorithms. In this work, we focus on the optimizing of processing join operator on large relations and propose the designs of in-memory hash join and sort-merge join on GPUs. We present the data partition method on the GPUs implemented with a pipeline mechanism. Furthermore, the shuffle instructions and the CUDA streams are applied in our algorithms to best utilize the GPUs. Experimental results indicate that our hash join algorithm delivers up to 1.51X and 1.24X speedup over the state-of-the-art hash join algorithm on CPUs on NVIDIA GTX1080ti-Pascal GPU and TitanV-Volta GPU respectively. For sort-merge join, our algorithm achieves up to 3.52X and 2.21X improvements on the same GPUs respectively compared to the baselines on CPUs. © 2019 IEEE.

关键词： Graphics processing unit

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Parallel hybrid join algorithm on GPU 21

Parallel hybrid join algorithm on GPU

引用

作者： Guo, Chengxin Chen, Hong Zhang, Feng Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9781728120584

In data analytics applications, join is a general and time consuming operation. Optimizing join algorithms can benefit the query processing significantly. The emerging of GPUs provides a massive parallelism solution for improving the performance of the join operation. The hash join (HJ) and sort merge join (SMJ), which are two widely used join algorithms, have been proved effective for efficient join processing on the GPUs. Both algorithms have their own advantages and drawbacks, offering the chance of combining the advantages of HJ and SMJ on GPUs. In processing join operation on GPUs, data need to be transmitted between the CPU and the GPU due to the discrete GPU memory design, which causes performance degradation because of the high PCIe data transfer overhead. As GPUs are becoming more powerful than before, the performance gap between data transmission and GPU execution increases, which makes it even harder to implement an efficient join on GPUs. In this paper, we focus on the optimization of join algorithms on GPUs. We propose the Parallel Hybrid Join algorithm on GPUs(PHYJ) to combine the advantages of HJ and SMJ, and overlap the data communication and GPU execution with a pipeline mechanism. In our evaluation, the PHYJ shows up to 1.72X and 1.55X speedup over the up-to-date HJ and SMJ algorithms respectively on a NVIDIA GTX 1080ti-Pascal GPU. On the TitanV-Volta GPU, up to 1.54X and 1.42X improvements can be achieved over the baseline HJ and SMJ algorithms respectively. © 2019 IEEE.

关键词： Graphics processing unit

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Evaluating the Long-Term Effects of Near-Natural Restoration on Post-Fire Forest Dynamics in a Wildland-Urban Interface Landscape

SSRN

引用

SSRN 2023年

作者： Xiao, Jiangtao Lin, Yang Zhou, Wangming Qiao, Zeyu Chang, Yu Yu, Xinran Li, Yuanyuan Ren, Ping Fang, Lei Key Lab of Land Resources Evaluation and Monitoring in Southwest China Ministry of Education Sichuan Normal University Chengdu610066 China School of Geography and Resources Sciences Sichuan Normal University Chengdu610066 China CAS Key Laboratory of Forest and Ecology Management Institute of Applied Ecology Chinese Academy of Sciences Shenyang110016 China School of Land and Resources Surveying and Mapping Engineering Shandong Agriculture and Engineering University Jinan250100 China Big Data Research Center for Ecology and Environment Shandong University Qingdao266237 China Environment Research Institute Shandong University Qingdao266237 China

Wildland fire is an important disturbance agent that can drastically alter forest structures and erode forest ecosystem services. Forests in the wildland-urban interface (WUI) are of high value but vulnerable to fires due to abundant fire ignitions and flammable forest fuels. It is critically important to quickly restore the post-fire WUI forest landscape to maintain ecosystem service provision following near-natural restoration strategies. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that artificial plantations can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored, but biomass under both scenarios tended to equal out at the end of the simulation. At the species level, the biomass of pioneer tree species such as Chinese pine and Black locust decreased under the near-natural restoration scenario, while the biomass of other species started to increase, not only in terms of biomass, but the results also showed that post-fire planting accelerated the restoration of forest biodiversity and increased biodiversity. By 2070, the Shannon‒Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the evolution time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest restoration to inform post-fire forest management practi

关键词： Biomass

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Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research

arXiv

引用

arXiv 2024年

作者： Cao, Qinglong Chen, Yuntian Lu, Lu Sun, Hao Zeng, Zhenzhong Yang, Xiaokang Zhang, Dongxiao MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China Department of Statistics and Data Science Yale University New HavenCT06511 United States School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to industry, yet hindering VLM research in academia. To address this challenge and foster sustainable and equitable VLM research, we present the Generalized Domain Prompt Learning (GDPL) framework. GDPL facilitates the transfer of VLMs’ robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources. By leveraging small-scale domain-specific foundation models and minimal prompt samples, GDPL empowers the language branch with domain knowledge through quaternion networks, uncovering cross-modal relationships between domain-specific vision features and natural vision-based contextual embeddings. Simultaneously, GDPL guides the vision branch into specific domains through hierarchical propagation of generated vision prompt features, grounded in well-matched vision-language relations. Furthermore, to fully harness the domain adaptation potential of VLMs, we introduce a novel low-rank adaptation approach. Extensive experiments across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, validate the efficacy of GDPL, demonstrating its ability to achieve state-of-the-art domain recognition performance in a prompt learning paradigm. Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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