We present the HelioCubed, a high-order magnetohydrodynamic (MHD) code designed for modeling the inner heliosphere. The code is designed to achieve 4th order accuracy both in space and in time. In addition, HelioCubed...
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In this paper, the personalization of the video-based frame-level facial expression recognition is studied for multi-user systems if a small amount of short videos are available for each user. At first, embeddings of ...
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In this paper we investigate how to stack products on a storage yard for efficient retrieval. The objective is to minimise both the transport distance and the number of stack shuffles. Previous research on yard storag...
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Graphical models became a popular tool in machine learning and data analysis. Graphical Model Selection Problem is a problem to recover a specific graph structure (graphical model) from a set of data. In many cases th...
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In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propos...
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Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting ...
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Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and the corresponding optimal policy. Consequently, representing sequential decision-making problems as probabilistic inference can have considerable value, as, in principle, the inference offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of policy optimization. In this study, we propose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, to tackle these interpretability challenges. Our approach uses formal methods to achieve the interpretability for convergence guarantee, training transparency, and intrinsic decision-interpretation. To demonstrate its practicality, we showcase guaranteed interpretability with a global convergence rate Θ(1/√T) in simulation and in practical quadrotor tasks. In comparison with state-of-the-art benchmarks, including TRPO-IPO, PCPO, and CRPO, we empirically verify that AWaVO offers a reasonable trade-off between high performance and sufficient interpretability. Copyright 2024 by the author(s)
We present a novel algorithm BADF(Bounding Volume Hierarchy Based Adaptive Distance Fields)for accelerating the construction of ADFs(adaptive distance fields)of rigid and deformable models on graphics processing *** a...
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We present a novel algorithm BADF(Bounding Volume Hierarchy Based Adaptive Distance Fields)for accelerating the construction of ADFs(adaptive distance fields)of rigid and deformable models on graphics processing *** approach is based on constructing a bounding volume hierarchy(BVH)and we use that hierarchy to generate an octree-based *** exploit the coherence between successive frames and sort the grid points of the octree to accelerate the *** approach is applicable to rigid and deformable *** GPU-based(graphics processing unit based)algorithm is about 20x--50x faster than current mainstream central processing unit based *** BADF algorithm can construct the distance fields for deformable models with 60k triangles at interactive rates on an NVIDIA GTX GeForce ***,we observe 3x speedup over prior GPU-based ADF algorithms.
Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of introducing and extending monitoring solutions to improve prod...
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BSML is a high-level language for programming parallel algorithms. Built upon the OCaml language, it provides a safe setting for the implementation of BSP algorithms and for avoiding concurrency related problems (dead...
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An earlier study defined a KKT-proximity measure to test the convergence property of an evolutionary algorithm for solving single-objective optimization problems. In this paper, we extend this measure for testing conv...
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