Vision is essential for human navigation. The World Health Organization (WHO) estimates that 43.3 million people were blind in 2020, and this number is projected to reach 61 million by 2050. Modern scene understanding...
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Knowledge graphs (KGs) are widely acknowledged as incomplete, and new entities are constantly emerging in the real world. Inductive KG reasoning aims to predict missing facts for these new entities. Among existing mod...
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In the foundry industries,process design has traditionally relied on manuals and complex theoretical *** the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casti...
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In the foundry industries,process design has traditionally relied on manuals and complex theoretical *** the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design *** technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design ***,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design *** process may be significantly slowed down due to repetitive modifications and extended design *** a result,there are increasingly urgent demands for a real-time verification mechanism to address this ***,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting ***,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process *** is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners,making it a crucial aspect of cooperative multi-agent reinforcement learning(MARL).Achieving satisfying performan...
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Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners,making it a crucial aspect of cooperative multi-agent reinforcement learning(MARL).Achieving satisfying performance of AI agents poses a long-standing ***,ah-hoc teamwork and zero-shot coordination have shown promising advancements in open-world settings,requiring agents to coordinate efficiently with a range of unseen human ***,these methods usually assume an overly idealistic scenario by assuming homogeneity between the agent and the partner,which deviates from real-world *** facilitate the practical deployment and application of human-AI coordination in open and real-world environments,we propose the first benchmark for open and real-world human-AI coordination(ORC)called *** includes widely used human-AI coordination ***,within the context of real-world scenarios,ORCBench considers heterogeneity between AI agents and partners,encompassing variations in capabilities and observations,which aligns more closely with real-world ***,we introduce a framework known as Heterogeneous training with Communication(HeteC)for *** builds upon a heterogeneous training framework and enhances partner population diversity by using mixed partner training and frozen historical ***,HeteC incorporates a communication module that enables human partners to communicate with AI agents,mitigating the adverse effects of partially observable *** a series of experiments,we demonstrate the effectiveness of HeteC in improving coordination *** contribution serves as an initial but important step towards addressing the challenges of ORC.
The intermittent and fluctuating solar irradiance makes photovoltaic (PV) power generation unstable, which brings great challenges to the power grid system. Existing deep learning-based PV power generation prediction ...
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To ensure high-performance processing capabilities across diverse application scenarios, Big Data frameworks such as Spark and Flink usually provide a number of performance-related parameters to configure. Considering...
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To ensure high-performance processing capabilities across diverse application scenarios, Big Data frameworks such as Spark and Flink usually provide a number of performance-related parameters to configure. Considering the computation scale and the characteristic of repeated executions of typical recurring Big Data processing jobs, how to automatically tune parameters for performance optimization has emerged as a hot research topic in both academic and industry. With the advantages in interpretability and generalization ability, causal inference-based methods recently prove their advancement over conventional search-based and machine learning-based methods. However, the complexity of Big Data frameworks, the time-varying input dataset size of a recurring job and the limitation of a single causal structure learning algorithm together prevent these methods from practical application. Therefore, in this paper, we design and implement CausalConf, a datasize-aware configuration auto-tuning approach for recurring Big Data processing jobs via adaptive causal structure learning. Specifically, the offline training phase is responsible for training multiple datasize-aware causal structure models with different causal structure learning algorithms, while the online tuning phase is responsible for recommending the next promising configuration in an iterative manner via the Multi-Armed Bandit-based optimal intervention set selection as well as the novel datasize-aware causal Bayesian optimization. To evaluate the performance of CausalConf, a series of experiments are conducted on our local Spark cluster with 9 different previously unknown target applications from HiBench. Experimental results show that the performance speed ratio achieved by CausalConf compared to the four recent and representative baselines can respectively reach 1.45×, 1.31×, 1.26× and 1.54× on average and up to 2.53×, 1.55×, 1.57×, 2.18×. Besides, the average total online tuning cost of CausalConf is reduced b
Graph Convolutional Networks (GCNs) are widely used in various domains. However, training distributed full-batch GCNs on large-scale graphs poses challenges due to inefficient memory access patterns and high communica...
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ISBN:
(纸本)9798400714436
Graph Convolutional Networks (GCNs) are widely used in various domains. However, training distributed full-batch GCNs on large-scale graphs poses challenges due to inefficient memory access patterns and high communication overhead. This paper presents a general and efficient GCN training framework on CPU supercomputers. It comprises a general aggregation kernel designed to optimize irregular memory access and a quantization method with label propagation to reduce communication overhead. Experimental results show that our method achieves a speedup of up to 4.1× compared with the SoTA implementations.
The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, whi...
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In the product conceptual design, designers utilize multiple design representations to ideate, externalize, and refine concepts iteratively. Mixed representations, defined as the simultaneous presentation of multiple ...
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