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Found 11 papers, 2 papers with code
Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks.
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
K-Level Reasoning with Large Language Models
While Large Language Models (LLMs) have demonstrated their proficiency in complex reasoning tasks, their performance in dynamic, interactive, and competitive scenarios - such as business strategy and stock market analysis - remains underexplored.
Power grid operational risk assessment using graph neural network surrogates
The excellent accuracy of GNN-based reliability and risk assessment further suggests that GNN surrogate has the potential to be applied in real-time and hours-ahead risk quantification.
Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
MC simulation necessitates solving a large number of DC OPF problems corresponding to the samples of stochastic grid variables (power demand and renewable generation), which is computationally prohibitive.
Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction
The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction.
Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting
The battery capacity in different cycles can be measured with the temporal patterns extracted from the streaming sensor data based on the attention mechanism.
High thermoelectric power factor of poly(3-hexylthiophene) through in-plane alignment and doping with a molybdenum dithiolene complex
Here we report a record thermoelectric power factor of up to 160 $\mu$ W m-1 K-2 for the conjugated polymer poly(3-hexylthiophene) (P3HT).
Applied Physics Materials Science
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models.
Triad State Space Construction for Chaotic Signal Classification with Deep Learning
Inspired by the well-known permutation entropy (PE), an effective image encoding scheme for chaotic time series, Triad State Space Construction (TSSC), is proposed.
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification
Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed.
