Mixed materials, such as concrete, are composed of different components that are scattered randomly. Determining where each part ends up, whether in concrete or underground rock, can help scientists ...

Abstract: Hidden Markov Models (HMMs) have long been a powerful tool in the realm of text classification, offering an effective approach for modeling sequence data. With the surge in digital text data ...

A new Apple-backed AI model trained on Apple Watch behavioral data can now predict a wide range of health conditions more accurately than traditional sensor-based approaches, according to a recently ...

Abstract: We use Markov categories to generalize the basic theory of Markov chains and hidden Markov models to an abstract setting. This comprises characterizations of hidden Markov models in terms of ...

This package implements computational models for analyzing choice behavior using mixture-of-agents frameworks. The core innovation is decomposing complex decision-making into interpretable cognitive ...

School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, United States Understanding the nature of climatic change impacts on spatial and temporal ...

As AI adoption accelerates, the AI model supply chain is becoming a critical but often underexamined source of risk. From third-party training data to prebuilt models and infrastructure dependencies, ...

It is a well-known fact that different model families can use different tokenizers. However, there has been limited analysis on how the process of “tokenization” itself varies across these tokenizers.