Article
Computer Science, Artificial Intelligence
Qin Tao, Yajing Si, Fali Li, Peiyang Li, Yuqin Li, Shu Zhang, Feng Wan, Dezhong Yao, Peng Xu
Summary: This study revealed the dynamic process of decision response and feedback in gambling, showing that different decisions and feedback lead to distinct brain network activities.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Chemistry, Analytical
Lina Gao, Changyuan Wang, Gongpu Wu
Summary: The loss of situational awareness among pilots is a significant human factor that affects aviation safety. Without a proper system to detect pilot perception errors, studies have shown that pilot perception errors are one of the main reasons for the lack of situational awareness. This study examines the changes in pilots' eye movements during various flight tasks from the perspective of visual awareness. The proposed algorithm, based on a hidden semi-Markov model (HSMM), shows an accuracy of 93.55% in detecting the pilot's visuoperceptual state, outperforming the hidden Markov model (HMM) in flexibility.
Article
Computer Science, Artificial Intelligence
Kai Li, Chaochao Qiu, Xinzhao Zhou, Mingsong Chen, Yongcheng Lin, Xianshi Jia, Bin Li
Summary: This paper proposes an improved HSMM model for labeling spindle vibration signals in milling processes, achieving automatic labeling and improving labeling efficiency. Experimental results demonstrate the effectiveness and robustness of the proposed model.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Kai Li, Chaochao Qiu, Xinzhao Zhou, Mingsong Chen, Yongcheng Lin, Xianshi Jia, Bin Li
Summary: This paper proposes an improved HSMM for modeling and tagging of spindle vibration signals in the milling process. By reducing the dimension of observation sequences and explicitly modeling the state duration, the automatic tagging objective is achieved. Experimental results confirm the effectiveness and robustness of the proposed model.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Tong Xia, Yong Li, Yue Yu, Fengli Xu, Qingmin Liao, Depeng Jin
Summary: With the increasing urbanization process, modeling people's activities in urban space is a challenging task. The State-sharing Hidden Markov Model (SSHMM) is a novel time-series modeling method that uncovers urban dynamics and learns semantics-rich dynamics highly correlated with region functions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Junji Jiang, Likang Wu, Hongke Zhao, Hengshu Zhu, Wei Zhang
Summary: Stock movement forecasting is often treated as a sequence prediction task using time series data. While deep learning models have been increasingly employed for fitting dynamic stock time series, few of them have focused on understanding the internal dynamics of the market system. To address this, the proposed HMM-ALSTM framework integrates the Hidden Markov Model (HMM) into the deep learning process, allowing for the discovery of hidden states and patterns that contribute to the stock time series data.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Neurosciences
Keyu Chen, Chaofan Li, Wei Sun, Yunyun Tao, Ruidi Wang, Wen Hou, Dong-Qiang Liu
Summary: The study utilized a hidden Markov model to model the dynamic activity of brain networks, finding that older adults exhibit less antagonistic instances between the DMN and attention systems, as well as a prolongation of inactive periods for all networks.
Article
Automation & Control Systems
Renhai Chen, Shimin Yuan, Chenlin Ma, Huihui Zhao, Zhiyong Feng
Summary: This article discusses the advantages and disadvantages of GPS and cellular-based positioning. It highlights the challenges of cellular-based positioning and proposes a novel algorithm called THMM to improve its accuracy. The algorithm is optimized based on the characteristics of cellular-based data and the experimental results demonstrate its effectiveness.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Ecology
Brett T. McClintock
Summary: Mixed HMMs did not significantly improve state assignment compared to standard HMMs, and reliable estimation of random effects required larger sample sizes. Random effects accounting for unexplained individual variation can improve estimation of state transition probabilities and measurable covariate effects.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Computer Science, Artificial Intelligence
Tao Li, Zhaojie Wang, Guoyu Yang, Yang Cui, Yuling Chen, Xiaomei Yu
Summary: Selfish mining attacks aim to obtain higher revenues compared with honest parties by exploiting vulnerabilities in the consensus mechanism, but are impractical due to high forking rates leading honest parties to exit the system. An improved selfish mining approach based on hidden Markov decision processes (SMHMDP) is proposed, which can balance revenues and forking rates by allowing semi-selfish miners to mine on the public chain with a small probability rho. Simulation results show that SMHMDP can benefit selfish miners within an acceptable forking rate without becoming an armchair strategist.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Abdessatar Souissi, El Gheteb Soueidi
Summary: This paper aims to expand on previous research on quantum hidden Markov processes by introducing the concept of entangled hidden Markov processes. These are hidden Markov processes in which the hidden processes themselves are entangled Markov processes. The paper provides an explicit expression for the joint expectation of these processes and demonstrates that the approach also applies to the classical case.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Biochemical Research Methods
Chengze Shen, Minhyuk Park, Tandy Warnow
Summary: Accurate multiple sequence alignment is challenging, especially for data sets with sequence length heterogeneity. Existing methods have made progress in addressing the first two challenges, but sequence length heterogeneity remains a significant issue. This study introduces a new method, WITCH, which improves alignment accuracy by weighting and ranking HMMs, using multiple HMMs, and using a consensus algorithm that considers the weights.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2022)
Article
Neurosciences
Takahiro Ezaki, Yu Himeno, Takamitsu Watanabe, Naoki Masuda
Summary: Recent studies have suggested that summarizing brain activity into hidden states dynamics is a promising tool for understanding brain function. Hidden Markov models (HMMs) are commonly used to infer neural dynamics, but the impact of assuming Markovian structure in neural time series data needs further examination. Comparisons between GMM and HMM show that HMM generally provides better accuracy in estimating hidden states, but GMM can be a viable alternative under certain conditions like low sampling frequency or short data length.
EUROPEAN JOURNAL OF NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Luo, Yuting Jiang, Anqi Wang, Juan Wang, Zhancheng Zhang, Xiao-Jun Wu
Summary: In this paper, a novel multi-state contextual hidden Markov model (MCHMM) in the non-subsampled Shearlet transform (NSST) domain is proposed for image fusion. The proposed method improves upon the traditional two-state hidden Markov model by developing a multi-state model and a soft context variable, resulting in improved fusion results. The experimental results on several datasets demonstrate that the proposed method outperforms other fusion methods in both subjective and objective evaluations.
PATTERN RECOGNITION
(2023)
Article
Thermodynamics
Jinchun Zhang, Feiyu Xv, Jinxiu Hou
Summary: This paper focuses on the recognition of degradation of firebricks in a gasifier combustor and analysis of their residual life, which is important for safe operation. The results show that degradation is faster in the jet area compared to the arch crown and pipe area. The transformation of degradation state takes longer in the early and middle stages of firebrick life. The residual life of firebricks in the jet area is shorter than in the arch crown and pipe area. The HSMM method provides new solutions for firebrick maintenance strategies.
Article
Mathematical & Computational Biology
Pavel Sountsov, Paul Miller
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2015)
Review
Neurosciences
Paul Miller
CURRENT OPINION IN NEUROBIOLOGY
(2016)
Article
Neurosciences
Jonathan Cannon, Paul Miller
JOURNAL OF NEUROPHYSIOLOGY
(2016)
Article
Neurosciences
Brian F. Sadacca, Narendra Mukherjee, Tony Vladusich, Jennifer X. Li, Donald B. Katz, Paul Miller
JOURNAL OF NEUROSCIENCE
(2016)
Article
Neurosciences
Ian K. Christie, Paul Miller, Stephen D. Van Hooser
JOURNAL OF NEUROPHYSIOLOGY
(2017)
Article
Mathematical & Computational Biology
Jonathan Cannon, Paul Miller
JOURNAL OF MATHEMATICAL NEUROSCIENCE
(2017)
Article
Computer Science, Cybernetics
Paul Miller, Jonathan Cannon
BIOLOGICAL CYBERNETICS
(2019)
Article
Neurosciences
Stephen D. Van Hooser, Gina M. Escobar, Arianna Maffei, Paul Miller
JOURNAL OF NEUROPHYSIOLOGY
(2014)
Article
Mathematical & Computational Biology
Paul Miller
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2013)
Article
Mathematical & Computational Biology
Benjamin Ballintyn, Benjamin Shlaer, Paul Miller
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2019)
Article
Mathematical & Computational Biology
Bolun Chen, Paul Miller
JOURNAL OF MATHEMATICAL NEUROSCIENCE
(2020)
Article
Biochemical Research Methods
John Ksander, Donald B. Katz, Paul Miller
Summary: The study investigated decision models on whether to continue sampling a stimulus or switch, finding that highly hedonic stimuli can reduce time spent on following stimuli and neural activity patterns could predict choice to leave a stimulus. The models offer testable predictions and propose a neural circuit-based framework for explaining foraging choices.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Neurosciences
Abuzar Mahmood, Jessica Steindler, Hannah Germaine, Paul Miller, Donald B. Katz
Summary: This study demonstrates the coupled dynamics of basolateral amygdala (BLA) and gustatory cortical (GC) during taste processing in rats. BLA and GC responses are correlated in terms of response magnitude across trials and within single responses, and changes in the coherence of local field potentials between BLA and GC are epoch-specific. The simultaneous transitions in BLA and GC, despite trial-to-trial variability in transition latencies, suggest collective processing in a distributed neural network.
JOURNAL OF NEUROSCIENCE
(2023)
Article
Behavioral Sciences
Benjamin Ballintyn, John Ksander, Donald B. B. Katz, Paul Miller
Summary: Food or taste preference tests are similar to naturalistic decisions, where animals select stimuli to sample and determine how long to sample them. The data from these tests indicate the preference for each stimulus based on the relative amounts sampled and consumed. Analysis of the ongoing sampling dynamics reveals hidden aspects of the decision-making process and its underlying neural circuit mechanisms. In this study, the authors performed a dynamic analysis of two factors contributing to preferences in a two-alternative task: the duration distribution of sampling bouts for each stimulus and the likelihood of returning to the same stimulus or switching to the alternative. The results support a specific computational model of decision making, where the duration of sampling bouts follows an exponential distribution correlated with the palatability of the stimulus and its alternative, with the influence of the alternative stimulus on bout durations decaying over time.
BEHAVIORAL NEUROSCIENCE
(2023)
Article
Psychology, Experimental
Katheryn A. Q. Cousins, Hayim Dar, Arthur Wingfield, Paul Miller
MEMORY & COGNITION
(2014)