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Found 13 papers, 2 papers with code
Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints
Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts.
Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop
How and to what extent the source bias affects the neural recommendation models within feedback loop remains unknown.
Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration
The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems from solely human-written to a coexistence with LLM-generated content.
Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience.
Equitable Networked Microgrid Topology Reconfiguration for Wildfire Risk Mitigation
The increasing number of wildfires in recent years consistently challenges the safe and reliable operations of power systems.
A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems
Accurate and quick identification of high-impedance faults is critical for the reliable operation of distribution systems.
Neural Retrievers are Biased Towards LLM-Generated Content
Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher.
Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature
Results: From the 198 unique concepts in CIHLex, 62. 1% could be matched to at least one term in the UMLS.
Mitigating the Impact of Uncertain Wildfire Risk on Power Grids through Topology Control
Wildfires pose a significant threat to the safe and reliable operations of the electric grid.
Appliance Level Short-term Load Forecasting via Recurrent Neural Network
Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems.
Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations
Effective and timely responses to unexpected contingencies are crucial for enhancing the resilience of power grids.
Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature
MATERIALS AND METHODS: We created SemRepDS (an extension of SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28, 884 DS terms not found in the UMLS.
Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review
Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model
