The amino acid sequence of the transmembrane protein and its corresponding positions on the cell membrane are transformed into a hidden Markov process. After evaluating the parameters, the Viterbi ...

Sparse early-stage data limits accurate geological risk assessment, increasing the chance of undetected hazards ahead of the TBM. By integrating borehole-derived information through an observation ...

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 ...

Hidden Markov Models (HMMs) have emerged as a powerful tool for analyzing time series of neural activity. Gaussian HMMs and their time-resolved extension, Time-Delay Embedded HMMs (TDE-HMMs), have ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I closely examine an innovative way of ...

Abstract: This paper presents the use of a hierarchical hidden Markov model (H2M2) for decoding brain signals from a tetraplegic patient. The H2M2 is a dynamic classifier used in this study to decode ...

Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks, such as machine translation and question-answering. However, a ...

Hebrew University Researchers addressed the challenge of understanding how information flows through different layers of decoder-based large language models (LLMs). Specifically, it investigates ...