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[Submitted on 11 Jul 2024] View a PDF of the paper titled Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment, by Muhy Eddin Za'ter and 2 other authors View PDF HTML (experimental) Abstract:This paper develops a novel machine learning-based framework using Semi-Supervised Multi-Task Learning (SS-MTL) for power system dynamic security assessment that is accurate, reliable, and aware of topological changes. The learning algorithm underlying the proposed framework integrates conditional masked encoders and employs multi-task learning for classification-aware feature representation, which improves the accuracy and scalability to larger systems. Additionally, this framework incorporates a confidence measure for its predictions,…

arXiv:2407.08876v1 Announce Type: new Abstract: De gustibus non est disputandum ("there is no accounting for others' tastes") is a common Latin maxim describing how many solutions in life are determined by people's personal preferences. Many household tasks, in particular, can only be considered fully successful when they account for personal preferences such as the visual aesthetic of the scene. For example, setting a table could be optimized by arranging utensils according to traditional rules of Western table setting decorum, without considering the color, shape, or material of each object, but this may not be a completely satisfying solution for a…

arXiv:2407.08892v1 Announce Type: new Abstract: Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over long contexts. Recently, several methods have been proposed to compress the prompt to reduce the context length. However, there has been little work on comparing the different proposed methods across different tasks through a standardized analysis. This has led to conflicting results. To address this, here we perform a comprehensive characterization and evaluation of different prompt compression methods. In particular, we analyze extractive compression, summarization-based abstractive compression,…

arXiv:2407.08868v1 Announce Type: new Abstract: Accurate estimate of long-term risk is critical for safe decision-making, but sampling from rare risk events and long-term trajectories can be prohibitively costly. Risk gradient can be used in many first-order techniques for learning and control methods, but gradient estimate is difficult to obtain using Monte Carlo (MC) methods because the infinitesimal devisor may significantly amplify sampling noise. Motivated by this gap, we propose an efficient method to evaluate long-term risk probabilities and their gradients using short-term samples without sufficient risk events. We first derive that four types of long-term risk probability are solutions of…

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