Results 141 to 150 of about 17,292 (295)
User interface for natural deduction [PDF]
, 2016 Tema ovog diplomskog rada je izrada programskog sučelja za prirodnu dedukciju. A svrha samog diplomskog rada je omogućiti korisniku korištenje progamskog sučelja kako bi mogao kreirati dokaze za prirodnu dedukciju kroz aplikaciju.
Škrobo, Ivica
core
Solving MaxSAT with Natural Deduction
, 2018 We rethink the way Natural Deduction systems are usually used in logic, and adapt them in order to solve the MaxSAT problem. By adapting some existing rules and defining some new ones, the Natural Deduction method can be used to provide an elegant and ...
Manyà, Felip +2 more
core +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
Natural Deduction Rules for Tomova’s Natural Implications
, 2018 Natural Deduction Rules for Tomova’s Natural ...
Petrukhin, Yaroslav, Shangin, Vasilyi
core
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks
Advanced Intelligent Discovery, EarlyView.Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç +2 more
wiley +1 more source
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
Advanced Intelligent Discovery, EarlyView.This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
FirePassive Fourier transform infrared (FTIR) spectral imaging technology is easily affected by complex background radiation for leakage monitoring at natural gas stations, leading to low gas identification sensitivity, poor detection accuracy and a high ...
Jinrui Deng +8 more
doaj +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source