Video question answering (VideoQA), a critical task in vision-language understanding and reasoning, encounters significant challenges in integrating visual concepts for compositional spatio-temporal reasoning, due to the inherent complexity of video data. Existing methods struggle to effectively capture both static and dynamic visual concepts and establish practical symbolic reasoning, which are essential for answering compositional questions. To address these challenges, we propose a neural-symbolic framework called Progressive Symbolic Reasoning based on Static-Dynamic Scene Parser Network (PSR-SDSP) for VideoQA tasks. PSR-SDSP features two key innovations: (1) the Scene Parser Network, which converts unstructured video data into static-dynamic symbolic representations, extracting structured representations of persons, objects, relationships, and action chronologies. (2) the Symbolic Reasoning Machine (SRM), which conducts top-down question decomposition and bottom-up compositional reasoning. Specifically, the SRM applies a program execution algorithm that integrates static-dynamic visual concepts with predicted programs to deduce the answers. Our results on AGQA Decomp show that PSR-SDSP outperforms existing models in compositional spatio-temporal reasoning. Experiments on the Novel Compositions and More Compositional Steps tasks highlight its potential generalization. Evaluations on datasets with varying degrees of domain differences, including STAR, ActivityNet-QA, and TVQA+, confirm PSR-SDSP's scalability.

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://github.com/DMiC-Lab-HFUT/SMDE-Transfer.

This paper investigates the bigFET gate bias voltage in power-rail electrostatic discharge (ESD) clamp circuit for enhanced transient noise immunity. Based on a timed shutoff circuit, a resistor pair is employed to bias the bigFET gate voltage to different ratios of VDD. Through detailed investigations, an optimized bigFET gate bias scheme is proposed. With the optimized scheme, enhanced transient noise immunity of the investigated circuit is achieved. Besides, the optimized scheme can induce the channel and parasitic bipolar junction transistor parallel shunt paths in ESD events, which result in a lower on-resistance. Moreover, the optimized scheme is also validated in the high-threshold-voltage bigFET samples.