FENet: Focusing Enhanced Network for Lane Detection

Inspired by human driving focus, this research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss - targeted innovations addressing obstacles to precise lane detection for autonomous driving. Experiments demonstrate our Focusing Sampling strategy, emphasizing vital distant details unlike uniform approaches, significantly boosts both benchmark and practical curved/distant lane recognition accuracy essential for safety. While FENetV1 achieves state-of-the-art conventional metric performance via enhancements isolating perspective-aware contexts mimicking driver vision, FENetV2 proves most reliable on the proposed Partial Field analysis. Hence we specifically recommend V2 for practical lane navigation despite fractional degradation on standard entire-image measures. Future directions include collecting on-road data and integrating complementary dual frameworks to further breakthroughs guided by human perception principles. The Code is available at https://github.com/HanyangZhong/FENet.Comment: This article has been accepted by ICME2024. The Code is available at https://github.com/HanyangZhong/FENe

LLM-SAP: Large Language Models Situational Awareness Based Planning

This study explores integrating large language models (LLMs) with situational awareness-based planning (SAP) to enhance the decision-making capabilities of AI agents in dynamic and uncertain environments. We employ a multi-agent reasoning framework to develop a methodology that anticipates and actively mitigates potential risks through iterative feedback and evaluation processes. Our approach diverges from traditional automata theory by incorporating the complexity of human-centric interactions into the planning process, thereby expanding the planning scope of LLMs beyond structured and predictable scenarios. The results demonstrate significant improvements in the model's ability to provide comparative safe actions within hazard interactions, offering a perspective on proactive and reactive planning strategies. This research highlights the potential of LLMs to perform human-like action planning, thereby paving the way for more sophisticated, reliable, and safe AI systems in unpredictable real-world applications.Comment: This article has been accepted by ICME2024 Workshop MML4SG. Website:https://github.com/HanyangZhong/Situational_Planning_dataset

Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions

This paper examines the role of cognitive biases in the decision-making processes of large language models (LLMs), challenging the conventional goal of eliminating all biases. When properly balanced, we show that certain cognitive biases can enhance decision-making efficiency through rational deviations and heuristic shortcuts. By introducing heuristic moderation and an abstention option, which allows LLMs to withhold responses when uncertain, we reduce error rates, improve decision accuracy, and optimize decision rates. Using the Balance Rigor and Utility (BRU) dataset, developed through expert collaboration, our findings demonstrate that targeted inspection of cognitive biases aligns LLM decisions more closely with human reasoning, enhancing reliability and suggesting strategies for future improvements. This approach offers a novel way to leverage cognitive biases to improve the practical utility of LLMs across various applications

Type-Specific inositol 1,4,5-Trisphosphate Receptor Localization in the Vomeronasal Organ and its interaction with a Transient Receptor Potential Channel, TRPC2

The vomeronasal organ (VNO) is the receptor portion of the accessory olfactory system and transduces chemical cues that identify social hierarchy, reproductive status, conspecifics and prey. Signal transduction in VNO neurons is apparently accomplished via an inositol 1,4,5-trisphosphate (IP3)-activated calcium conductance that includes a different set of G proteins than those identified in vertebrate olfactory sensory neurons. We used immunohistochemical (IHC) and SDS-PAGE/western analysis to localize three IP3 receptors (IP3R) in the rat VNO epithelium. Type-I IP3R expression was weak or absent. Antisera for type-II and -III IP3R recognized appropriate molecular weight proteins by SDS-PAGE, and labeled protein could be abolished by pre-adsorption of the respective antibody with antigenic peptide. In tissue sections, type-II IP3R immunoreactivity was present in the supporting cell zone but not in the sensory cell zone. Type-III IP3R immunoreactivity was present throughout the sensory zone and overlapped that of transient receptor potential channel 2 (TRPC2) in the microvillar layer of sensory epithelium. Co-immunoprecipitation of type-III IP3R and TRPC2 from VNO lysates confirmed the overlapping immunoreactivity patterns. The protein-protein interaction complex between type-III IP3R and TRPC2 could initiate calcium signaling leading to electrical signal production in VNO neurons

Function of hyperekplexia-causing alpha(1)R271Q/L glycine receptors is restored by shifting the affected residue out of the allosteric signaling pathway

BACKGROUND AND PURPOSE Glycine receptor a1 subunit R271Q and R271L (a1R271Q/L) mutations cause the neuromotor disorder, hereditary hyperekplexia. Studies suggest that the 271 residue is located within the allosteric signalling pathway linking the agonist binding site to the channel gate. The present study aimed to investigate a possible mechanism for restoring the function of the a1R271Q/L glycine receptor

The Application of Interdisciplinary In Airborne Electromechanical System and Its Enlightenment to the Cultivation of Graduate Students’ Innovative Ability

With the progress of science and technology, intelligent monitoring and diagnosis system has developed rapidly. Intelligent diagnosis technology, as an engineering application of artificial intelligence, has developed rapidly both at home and abroad in recent years. Research shows that intelligent diagnosis technology is a comprehensive industry integrating multiple technologies and interdisciplinary disciplines, and it is also a partial epitome of contemporary scientific and technological progress. Combined with the development of intelligent diagnosis technology of airborne electromechanical system and the important task of colleges and universities as the undertaker of high-end talent training, this paper puts forward that the current talent training mode needs to be adjusted according to the needs of science and technology, and teaching practice reform should be carried out from professional fields, discipline categories, practical training platforms and other aspects, so as to provide reserve talents for China's scientific and technological progress