Inconsistency Measurement based on Variables in Minimal Unsatisfiable Subsets

International audienceMeasuring inconsistency degrees of knowledge bases (KBs) provides important context information for facilitating inconsistency handling. Several semantic and syntax based measures have been proposed separately. In this paper, we propose a new way to define inconsistency measurements by combining semantic and syntax based approaches. It is based on counting the variables of minimal unsatisfiable subsets (MUSes) and minimal correction subsets (MCSes), which leads to two equivalent inconsistency degrees, named IDMUS and IDMCS. We give the theoretical and experimental comparisons between them and two purely semantic-based inconsistency degrees: 4-valued and the Quasi Classical semantics based inconsistency degrees. More- over, the computational complexities related to our new inconsistency measurements are studied. As it turns out that computing the exact inconsistency degrees is intractable in general, we then propose and evaluate an anytime algorithm to make IDMUS and IDMCS usable in knowledge management applications. In particular, as most of syntax based measures tend to be difficult to compute in reality due to the exponential number of MUSes, our new inconsistency measures are practical because the numbers of variables in MUSes are often limited or easily to be approximated. We evaluate our approach on the DC benchmark. Our encourag- ing experimental results show that these new inconsistency measure- ments or their approximations are efficient to handle large knowledge bases and to better distinguish inconsistent knowledge bases

Evaluation and Research on the Classification Training Mode of Animation Talents Based on Teaching Dynamics from the Perspective of New Liberal Arts

The creation of new artistic forms is an important aspect of the classification of artistic construction. The training of animation talents should be based on the market demand for professionals in the animation industry. While emphasizing the training of professional characteristics, it is important to consider the cross-complementation of related disciplines. The animation major at the Zhongnan University of Economics and Law has taken the initiative to investigate the market. They started by addressing the challenges faced in the development of the animation major and focused on the classification training of animation talents within the newly established new art department. As a result, they have developed a new professional curriculum system, a system for cross-integration of different disciplines, and a practical training system. These initiatives have facilitated the classification of platforms, the integration of multiple disciplines, and the development and training of practical skills within the animation major. We are actively exploring and practicing a new training method for animation talents in the art department. Our goal is to train high-quality applied animation talents who can contribute to local economic and social development. By doing so, we aim to promote the coordinated development of the art department and the field of animation. In order to quantify the teaching effect, we introduced a dynamic method to model the teaching system. This model was mathematically established as a dynamic system. Through modeling and analysis, the data reflected the positive impact of educational innovation and reform on teaching, thereby demonstrating the effectiveness of our innovation and reform

Li-Yau Inequality and Liouville Property to a Semilinear Heat Equation on Riemannian Manifolds

This work deals with the Entire solutions of a nonlinear equation. The first part of this paper is devoted to investigation of the Liouville property on compact manifolds, which extends a result by Castorina-Mantegazza [4] for positive f. Secondly, we will turn to non-compact manifolds and prove a Liouville theorem under the assumptions of boundedness of the Ricci curvature from below, diffeomorphism of M with R^N and sub-criticality of p defined below. Finally, we also present simplified proofs of Yau's theorem for harmonic function and Gidas-Spruck's theorem for elliptic semilinear equation. Our proofs are based on Li-Yau type estimation for nonlinear equations

Effects of rotational velocity on microstructures and mechanical properties of surface compensation friction stir welded 6005A-T6 aluminum alloy

Surface compensation friction stir welding (SCFSW) is successfully applied to weld 6005A-T6 aluminum alloy in order to eliminate disadvantages caused by flash and arc corrugation. The effects of rotational velocity on the microstructures and mechanical properties of SCFSW joints are investigated. The joints with equal thickness with respect to the workpiece to be welded are obtained using 4 mm thick plates with a convex platform in this study. The results show that welding process parameters exert a significant influence on the microstructures of nugget zone (NZ). Tensile strength and elongation of joints are both firstly increased and then decreased with an increase in the rotational velocity from 800 rpm to 1500 rpm under a constant welding speed of 200 mm/min. When the rotational velocity is 1300 rpm, the tensile strength and elongation reach the maximum values of 226 MPa and 6.5%, which are 75% and 67% of base metal (BM), respectively. The fracture surface morphology represents the typical ductile fracture. The hardness of NZ is lower than that of BM and the lowest hardness of joint is located at thermo-mechanically affected zone (TMAZ) on the advancing side (AS)

Offline Prioritized Experience Replay

Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constraints between the learned policy and the behavior policy. However, these constraints are applied equally to well-performing and inferior actions through uniform sampling, which might negatively affect the learned policy. To alleviate this issue, we propose Offline Prioritized Experience Replay (OPER), featuring a class of priority functions designed to prioritize highly-rewarding transitions, making them more frequently visited during training. Through theoretical analysis, we show that this class of priority functions induce an improved behavior policy, and when constrained to this improved policy, a policy-constrained offline RL algorithm is likely to yield a better solution. We develop two practical strategies to obtain priority weights by estimating advantages based on a fitted value network (OPER-A) or utilizing trajectory returns (OPER-R) for quick computation. OPER is a plug-and-play component for offline RL algorithms. As case studies, we evaluate OPER on five different algorithms, including BC, TD3+BC, Onestep RL, CQL, and IQL. Extensive experiments demonstrate that both OPER-A and OPER-R significantly improve the performance for all baseline methods. Codes and priority weights are availiable at https://github.com/sail-sg/OPER.Comment: preprin