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First-order logic automated theorem proving is an important research branch in the field of artificial intelligence, and the clause selection strategy plays an important role in improving the capability of theorem proving. Multi-clause contradiction separation deduction is recognized as the first proposed multi-clause deduction method, which has many good deduction characteristics. In order to better guide the clause selection in multi-clause deduction, multi-clause deduction method based on dynamic combination optimization of strategies is proposed. This method dynamically combines and iteratively optimizes the number of words and function term depth of clause, fully exploring the existing multi-clause deduction heuristic strategies while improving the adaptability of different problems to strategies, achieving efficient clause selection of multi-clause deduction. Based on this method, we demonstrate corresponding algorithm implementations, which can dynamically adjusts the clause selection strategy according to the deduction process of different problems, thereby improving the theorem proving capability of multi-clause deduction. We applied the algorithm to the international advanced Eprover 2.6, to form an improved SDCO_E prover. The performance of SDCO_E is evaluated by two sets of experiments: Experiment 1 took the international first-order logic automated theorem prover competition problems (1 500 in total) in recent three years as test object, SDCO_E solved 35 problems more than the original Epover 2.6, and the total number of theorem proofs has increased by 3.06%; Experiment 2 took problems with a rating of 1 in the TPTP benchmark as test object, SDCO_E solved 6 problems in the field of Number Theory and Set Theory, which has not been solved by all other provers. The experimental results show that the proposed multi-clause deduction algorithm can be effectively applied to first-order logic automated theorem proving.(一阶逻辑自动定理证明是人工智能领域重要的研究分支，其中子句选择策略对于提升定理证明能力具有重要作用。多元矛盾体分离演绎具有许多良好的演绎特性。为更好地指导多元演绎中的子句选择，提出了一种策略动态组合优化多元演绎方法，通过将子句文字数大小和函数项深度进行动态组合并迭代优化，在充分发掘现有多元演绎启发式策略的同时，提升策略应对不同问题的自适应性，实现多元演绎中的子句高效选择。基于该方法给出了相应的算法实现，根据不同问题的演绎过程动态调整子句的选择策略，提升了多元演绎的定理证明能力。同时，将该算法应用于国际先进的证明器Eprover 2.6，形成了改进的证明器SDCO_E，进一步通过2组实验评估SDCO_E的性能。实验1用2021—2023年国际一阶逻辑自动定理证明器竞赛组的共1 500个问题进行测试，结果表明，SDCO_E比Eprover 2.6多证明了35个问题，证明定理总数增加了3.06%；实验2用TPTP库中难度系数为1的问题进行测试，结果表明，SDCO_E能证明其他证明器无法证明的6个数论与集合论领域的问题。实验结果表明，策略动态组合优化多元演绎算法能有效应用于一阶逻辑自动定理证明。)

As the proliferation of video content continues, and many video archives lack suitable metadata, therefore, video retrieval, particularly through example‐based search, has become increasingly crucial. Existing metadata often fails to meet the needs of specific types of searches, especially when videos contain elements from different modalities, such as visual and audio. Consequently, developing video retrieval methods that can handle multi‐modal content is essential. An innovative Multi‐modal Video Search by Examples (MVSE) framework is introduced, employing state‐of‐the‐art techniques in its various components. In designing MVSE, the authors focused on accuracy, efficiency, interactivity, and extensibility, with key components including advanced data processing and a user‐friendly interface aimed at enhancing search effectiveness and user experience. Furthermore, the framework was comprehensively evaluated, assessing individual components, data quality issues, and overall retrieval performance using high‐quality and low‐quality BBC archive videos. The evaluation reveals that: (1) multi‐modal search yields better results than single‐modal search; (2) the quality of video, both visual and audio, has an impact on the query precision. Compared with image query results, audio quality has a greater impact on the query precision (3) a two‐stage search process (i.e. searching by Hamming distance based on hashing, followed by searching by Cosine similarity based on embedding); is effective but increases time overhead; (4) large‐scale video retrieval is not only feasible but also expected to emerge shortly. [ABSTRACT FROM AUTHOR]

This study utilizes both experimental and computational approaches to investigate the performance of Lu 2 Ti 2 O 7 (LTO) and Lu 1.5 Ce 0.5 Ti 2 O 7+ x (LCTO) pyrochlores under high pressure. The structural changes of LTO and LCTO pyrochlores were characterized using in-situ synchrotron x-ray diffraction (SXRD) and in-situ Raman spectroscopy at pressures up to 44.6 GPa. The kinks in P - a and P - V curves at around 5 GPa are mainly attributed to the interaction between the pressure medium and the isostructural changes. The onset pressures for transitioning from the cubic pyrochlore phase ( Fd-3 m ) to the monoclinic phase ( P2 1 ) are observed at 32.5 GPa and 38.1 GPa, respectively. It is important to note that at the highest measured pressures, the phase transition remains incomplete. This partial transition is likely the result of oriented disorder among cations and anions under high pressure. In addition, introducing Ce as a dopant significantly enhances structural stability. This can be explained by the larger ionic radius of Ce, which hinders the disordering process., (© 2024 IOP Publishing Ltd.)