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The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource—access to receptive mates—produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

The spatial mismatch between jobs and housing in cities creates long daily travels that exacerbate climate change, air pollution, and traffic congestion. Yet, not enough research on occupational differences has been done. This study first applies the Hidden Markov Mixture Model (H3M) to model travel patterns for different occupation groups in Hong Kong. Then, the Variational Bayesian Hierarchical EM algorithm is used to identify common lifestyle clusters. Next, a binary logistic regression is developed to examine whether the lifestyle clusters can be explained by jobs-housing balance. This study is among the first to consider travel patterns as a Markov process and apply H3M to examine jobs-housing balance by fine-grain occupation group. The method is transferable and universally applicable; and the results provide occupation-specific insights on jobs-housing balance in an Asian context. The research findings suggest that different occupation groups have different travel patterns in Hong Kong. Two lifestyle clusters, "balanced and compact activity space" and "work-oriented and extensive travels", are unveiled. Notably, the latter is associated a lower level of jobs-housing balance. Some occupations in the quaternary industry ("information and communications", "profession, science and technology", "real estate", and "finance and insurance") are having more serious jobs-housing imbalance. The paper concludes with a discussion on improving the occupation-specific jobs-housing balance in accordance with Hong Kong's future development goals. [ABSTRACT FROM AUTHOR]

Background: In prey, patterns of individual habitat selection and movement can be a consequence of an individuals' anti-predator behavior. Adjustments of anti-predator behavior are important for prey to increase their survival. Hunters may alter the anti-predator behavior of prey. In long-lived animals, experience may cause behavioral changes during individuals' lifetime, which may result in altered habitat selection and movement. Our knowledge of which specific events related to hunting activity induce behavioral changes in solitary living species is still limited. Methods: We used offspring loss in a solitary and long-lived ungulate species, moose (Alces alces), as our model system. We investigated whether offspring loss to hunters induces behavioral changes in a species subjected to heavy human harvest but free from natural predation. To test for behavioral change in relation to two proxies for experience (calf fate and age), we combined movement data from 51 adult female moose with data on their offspring survival and female age. We tested for adjustments in females' habitat selection and movement following calf harvest using Hidden Markov Models and integrated Step Selection Analysis to obtain behavioral state specific habitat selection coefficients. Results: We found that females with a harvested calf modified habitat selection and movement during the following hunting season. Female moose selected for shorter distance to roads during the night, selected for shorter distance to forests and greater distance to human settlements following calf harvest than females who had not lost a calf. The survival of twins in a given hunting season was related to female age. Older females we more likely to have twins survive the hunting season. Conclusions: Our findings suggest that losing offspring to human harvest imposes behavioral changes in a long-lived ungulate species, leading to adjustments in females' habitat selection and movement behavior, which may lower the risk of encountering hunters. In our study, female moose that experienced calf loss selected for lower distance to forest and selected for greater distance to human settlements during periods of high hunting pressure compared to females without the experience of calf loss during the previous hunting season. We interpret this as potential learning effects. [ABSTRACT FROM AUTHOR]

AbstractIn this article we present an algorithm for faster computation of HMM likelihood gradients when the observation space is discrete. Many parameter estimation algorithms for HMM require repeated computation of gradient to optimize the likelihood function. Gradient computation is costly therefore its faster computation can improve their performance greatly. We develop an algorithm for faster computation of gradient using ideas from data compression. Our algorithm decreased computation cost of gradients by a factor of three to five. We apply our methods to speed up the Baum-Welch algorithm by similar factors. [ABSTRACT FROM AUTHOR]

The article deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial‐of‐service (DoS) attack. DoS attack is modeled as a discrete‐time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN‐based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed‐loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques. [ABSTRACT FROM AUTHOR]

During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large‐scale changes in land use, climate, and potential carry‐over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White‐fronted Geese (Anser albifrons) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre‐nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western‐ and eastern‐Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital‐income breeding continuum was influenced by longitude, revealing geographic effects of life‐history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision‐making across broad scales. [ABSTRACT FROM AUTHOR]

In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent variable represents a genealogy. Because genealogies are continuous, while HMMs are discrete, SMC requires discretizing the space of trees in a way that is awkward and creates bias. In this work, we propose a method that circumvents this requirement, enabling SMC-based inference to be performed in the natural setting of a continuous state space. We derive fast, exact procedures for frequentist and Bayesian inference using SMC. Compared to existing methods, ours requires minimal user intervention or parameter tuning, no numerical optimization or E-M, and is faster and more accurate. for this article are available online. [ABSTRACT FROM AUTHOR]

The characterisation of resting‐state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high‐density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium‐density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high‐density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG‐derived RSNs to those from MEG, including their ability to capture age‐related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject‐specific structural MRI images. [ABSTRACT FROM AUTHOR]

Summary: In this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict‐feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example. [ABSTRACT FROM AUTHOR]

Copyright of Yegah Musicology Journal / Yegah Müzikoloji Dergisi is the property of International Yegah Music Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Backgroud: Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. Results: Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function. Conclusions: This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE. [ABSTRACT FROM AUTHOR]

AbstractMultistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical data, for example. Assuming that a continuous-time Markov process is time-homogeneous, a closed-form likelihood function can be derived from the Kolmogorov forward equations – a system of differential equations with a well-known matrix-exponential solution. Unfortunately, however, the forward equations do not admit an analytical solution for continuous-time, time-inhomogeneous Markov processes, and so researchers and practitioners often make the simplifying assumption that the process is piecewise time-homogeneous. In this paper, we provide intuitions and illustrations of the potential biases for parameter estimation that may ensue in the more realistic scenario that the piecewise-homogeneous assumption is violated, and we advocate for a solution for likelihood computation in a truly time-inhomogeneous fashion. Particular focus is afforded to the context of multistate Markov models that allow for state label misclassifications, which applies more broadly to hidden Markov models (HMMs), and Bayesian computations bypass the necessity for computationally demanding numerical gradient approximations for obtaining maximum likelihood estimates (MLEs). Supplemental materials are available online. [ABSTRACT FROM AUTHOR]

Sorting signals are crucial for the anchoring of proteins to the cell surface in archaea and bacteria. These proteins often feature distinct motifs at their C-terminus, cleaved by sortase or sortase-like enzymes. Gram-positive bacteria exhibit the LPXTGX consensus motif, cleaved by sortases, while Gram-negative bacteria employ exosortases recognizing motifs like PEP. Archaea utilize exosortase homologs known as archaeosortases for signal anchoring. Traditionally identification of such C-terminal sorting signals was performed with profile Hidden Markov Models (pHMMs). The Cell-Wall PREDiction (CW-PRED) method introduced for the first time a custom-made class HMM for proteins in Gram-positive bacteria that contain a cell wall sorting signal which begins with an LPXTG motif, followed by a hydrophobic domain and a tail of positively charged residues. Here we present a new and updated version of CW-PRED for predicting C-terminal sorting signals in Archaea, Gram-positive, and Gram-negative bacteria. We used a large training set and several model enhancements that improve motif identification in order to achieve better discrimination between C-terminal signals and other proteins. Cross-validation demonstrates CW-PRED's superiority in sensitivity and specificity compared to other methods. Application of the method in reference proteomes reveals a large number of potential surface proteins not previously identified. The method is available for academic use at http://195.251.108.230/apps.compgen.org/CW-PRED/ and as standalone software. [ABSTRACT FROM AUTHOR]

In movement analysis, correlated random walk (CRW) models often use so‐called turning angles, which are measured relative to the previous movement direction. To segregate between different movement modes, hidden Markov models (HMMs) describe movements as piecewise stationary CRWs in which the distributions of turning angles and step sizes depend on the underlying state. This typically allows for the segregation of movement modes that show different movement speeds. We show that in some cases, it may be interesting to investigate absolute angles, that is, biased random walks (BRWs) instead of turning angles. In particular, while discrimination between states in the turning angle setting can only rely on movement speed, models with absolute angles can be used to discriminate between sections of different movement directions. A preprocessing algorithm is provided that enables the analysis of absolute angles in the existing R package moveHMM. In a data set of movements of cell organelles, models using not the turning angle but the absolute angle could capture interesting additional properties. Goodness‐of‐fit was increased for HMMs with absolute angles, and HMMs with absolute angles tended to choose a higher number of states, suggesting the existence and relevance of prominent directional changes in the present data set. These results suggest that models with absolute angles can provide important information in the analysis of movement patterns if the existence and frequency of directional changes is of biological importance. [ABSTRACT FROM AUTHOR]

Speech assistive system for people with neuro disorders is a highly challenging task till date. Any kind of neuro cognitive disability affects the speech production mechanism that leads to speech impairment. Representation learning methods have recently emerged to improve the outcome of machine learning algorithms. In case of complex recognition tasks such as disordered speech recognition, learning compact and efficient representations for disordered speech utterances is important. Recent deep learning-based architectures need sufficiently large amount of impaired speech samples which are tedious with respect to neurologically disabled people. In this work, we focus on proposing a representation learning approach that uses traditional sequential model such as Hidden Markov Model (HMM) which works moderately well with small amount of impaired speech data. We propose a novel sequence to vector-based HMM State Sequence (HMM-SS) approach which is very compact and has proved to be an efficient representation for disordered speech utterances. The efficiency of the proposed HMM-SS approach is assessed using four datasets, namely 50 words of TORGO, 100-common words dataset of the UA-SPEECH, 50 help-seeking words and 100-common words of Impaired speech corpus in Tamil corpus. The proposed approach outperforms the baseline HMM, DNN-HMM and a recent state-of-the-art approach for all four datasets. The discriminative ability and the compactness of the proposed representation proved effective for disordered speech recognition. [ABSTRACT FROM AUTHOR]

Effective seabird conservation requires understanding their marine spatial ecology. Tracking can reveal details of their foraging ecology and habitat use, as well as the suitability of marine protected areas for at-sea conservation, but results are often regionally specific. Here we characterised the foraging behaviour of tropical breeding brown boobies Sula leucogaster in the Chagos Archipelago, Western Indian Ocean, and tested habitat requirements. GPS tracking of thirteen individuals from two colonies, located 142 km apart on the same atoll (Great Chagos Bank), showed similar foraging effort and habitat preferences despite differences in season and breeding stage. Brown boobies from both tracked populations foraged close to the colony along the atoll shelf edge, avoiding deep oceanic areas and shallow waters of the Great Chagos Bank atoll, but within the Chagos Archipelago Marine Protected Area. Sea-level height anomaly and sea surface temperature were important foraging predictors at both sites, although birds experienced distinct environmental conditions between colonies. These results suggest that while brown boobies have colony-specific at-sea foraging areas, similarities in habitat drivers of distribution and foraging behaviour can inform predictions of distributions at other colonies within the archipelago, with important benefits for at-sea conservation efforts. [ABSTRACT FROM AUTHOR]

Bipolar disorder (BD) is a chronic psychiatric condition characterized by large episodic changes in mood and energy. Recently, BD has been proposed to be conceptualized as chronic cyclical mood instability, as opposed to the traditional view of alternating discrete episodes with stable periods in-between. Recognizing this mood instability may improve care and call for high-frequency measures coupled with advanced statistical models. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment data in 20 patients with BD, yielding ∼9,820 assessments in total. Ecological momentary assessment data comprised self-report questionnaires (5 × daily) measuring manic and depressive constructs. Manic and depressive symptoms were also assessed weekly using the Altman Self-Rating Mania Scale and the Quick Inventory for Depressive Symptomatology Self-Report. Alignment between HMM-uncovered momentary mood states and weekly questionnaires was assessed with a multilevel linear model. HMM uncovered four mood states: neutral, elevated, mixed, and lowered, which aligned with weekly symptom scores. On average, patients remained <25 hr in one state. In almost half of the patients, mood instability was observed. Switching between mood states, three patterns were identified: patients switching predominantly between (a) neutral and lowered states, (b) neutral and elevated states, and (c) mixed, elevated, and lowered states. In all, elevated and lowered mood states were interspersed by mixed states. The results indicate that chronic mood instability is a key feature of BD, even in "relatively" euthymic periods. This should be considered in theoretical and clinical conceptualizations of the disorder. General Scientific Summary: The article investigates bipolar disorder with advanced statistical methods and adds evidence challenging the traditional view of alternating discrete mood episodes with stable periods in-between. Instead, it suggests that chronic mood instability may be a significant aspect of bipolar disorder and calls for high-frequency assessments using advanced statistical models to better understand and improve care for individuals with the disorder. [ABSTRACT FROM AUTHOR]

Some of the collected data by wireless sensor networks (WSNs) often digress from the normal pattern of the set and are called the 'outliers' or 'anomalies'. These outlier data are usually derived from an error that can be due to the constraints of sensor nodes such as problems in the processing unit, power supply, and component failure or an event occurring at the development site of sensor nodes, including the earthquake or flood, affecting the quality of the collected data and their reliability for decision making. In this work, a method is proposed for detecting outlier data in networks based on the hidden Markov model and the copula theory. Copula was used because of its ability to deal with multivariate sets and its unlimitedness in such a way that it required no assumption about data distribution, and Markov has been applied due to its good performance in forecasting. The results of evaluations on the actual data from the Intel laboratory represent the efficiency of the proposed method. Bases on the performance evaluations of the proposed method, in comparison with a relatively new work leading to good results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many wildlife species vary habitat selection across space, time, and behavior to maximize rewards and minimize risk. Multi‐scale research approaches that identify variation in wildlife habitat selection can highlight not only habitat preferences and risk tolerance but also movement strategies that afford coexistence or cause conflict with humans. Here, we examined how anthropogenic and natural features influenced coyote (Canis latrans) habitat selection in a mixed‐use, agricultural landscape in Mendocino County, California, USA. We used resource selection functions and hidden Markov models to test whether coyote selection for anthropogenic and natural features varied by time of day or by behavioral state (resting, foraging, and traveling). We found that coyotes avoided development, but, contrary to our expectations, coyotes selected for roads, agriculture, and areas with risk of human encounter and rifle use regardless of diel period or behavioral state. While traveling, coyotes increased selection for roads and avoided ruggedness, indicating that unpaved roads may enhance connectivity for coyotes in mixed‐use landscapes. Finally, we found that coyotes selected for mountain lion habitat when resting and at night, signifying that risk from natural predators was not a factor in habitat selection at coarse scales. Coyote habitat selection for places and times associated with human activity, without variation across scales, signals a potential for conflict if coyotes are perceived by people as a nuisance. [ABSTRACT FROM AUTHOR]

Abstract Background In prey, patterns of individual habitat selection and movement can be a consequence of an individuals’ anti-predator behavior. Adjustments of anti-predator behavior are important for prey to increase their survival. Hunters may alter the anti-predator behavior of prey. In long-lived animals, experience may cause behavioral changes during individuals’ lifetime, which may result in altered habitat selection and movement. Our knowledge of which specific events related to hunting activity induce behavioral changes in solitary living species is still limited. Methods We used offspring loss in a solitary and long-lived ungulate species, moose (Alces alces), as our model system. We investigated whether offspring loss to hunters induces behavioral changes in a species subjected to heavy human harvest but free from natural predation. To test for behavioral change in relation to two proxies for experience (calf fate and age), we combined movement data from 51 adult female moose with data on their offspring survival and female age. We tested for adjustments in females’ habitat selection and movement following calf harvest using Hidden Markov Models and integrated Step Selection Analysis to obtain behavioral state specific habitat selection coefficients. Results We found that females with a harvested calf modified habitat selection and movement during the following hunting season. Female moose selected for shorter distance to roads during the night, selected for shorter distance to forests and greater distance to human settlements following calf harvest than females who had not lost a calf. The survival of twins in a given hunting season was related to female age. Older females we more likely to have twins survive the hunting season. Conclusions Our findings suggest that losing offspring to human harvest imposes behavioral changes in a long-lived ungulate species, leading to adjustments in females' habitat selection and movement behavior, which may lower the risk of encountering hunters. In our study, female moose that experienced calf loss selected for lower distance to forest and selected for greater distance to human settlements during periods of high hunting pressure compared to females without the experience of calf loss during the previous hunting season. We interpret this as potential learning effects.

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost.

Abstract Backgroud Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. Results Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p

Accurate prediction of cloud resource instances is becoming increasingly important for public cloud users and cloud service providers, because it touches on the reasonable reservation of cloud resources with minimize costs. However, current methods do not predict the instance types of cloud resources based on the application workloads from users, and less consider the characteristics of workload data changes in the real-time prediction. To solve these problems, this paper proposes an application workload-dependent cloud resource instance prediction model to predict appropriate cloud instance resource usage in a timely manner. Firstly, we adopt a trend degree (TD) to classify all requested workloads into three types of wave trend patterns. Next, a Hidden Markov model based cloud resource prediction method (HMM-CPM) tracing the requested workload trends is presented. Finally, the reasonable cloud instance types following the patterns of the requested workloads can be predicted. The simulation results show that the proposed method can predict cloud resource instance types in the scenario with certain workload fluctuation, and the prediction accuracy is higher than the existing related approaches. [ABSTRACT FROM AUTHOR]

Abstract Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

Background: Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15–30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. Objective: We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. Methods: We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. Results: The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. Conclusions: We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Objective Background Methods Results Conclusion To explore hidden Markov models (HMMs) as an approach for defining clinically meaningful headache‐frequency‐based groups in migraine.Monthly headache frequency in patients with migraine is known to vary over time. This variation has not been completely characterized and is not well accounted for in the classification of individuals as having chronic or episodic migraine, a diagnosis with potentially significant impacts on the individual. This study investigated variation in reported headache frequency in a migraine population and proposed a model for classifying individuals by frequency while accounting for natural variation.The American Registry for Migraine Research (ARMR) was a longitudinal multisite study of United States adults with migraine. Study participants completed quarterly questionnaires and daily headache diaries. A series of HMMs were fit to monthly headache frequency data calculated from the diary data of ARMR.Changes in monthly headache frequency tended to be small, with 47% of transitions resulting in a change of 0 or 1 day. A substantial portion (24%) of months reflected daily headache with individuals ever reporting daily headache likely to consistently report daily headache. An HMM with four states with mean monthly headache frequency emissions of 3.52 (95% Prediction Interval [PI] 0–8), 10.10 (95% PI 4–17), 20.29 (95% PI 12–28), and constant 28 days/month had the best fit of the models tested. Of sequential month‐to‐month headache frequency transitions, 12% were across the 15‐headache days chronic migraine cutoff. Under the HMM, 38.7% of those transitions involved a change in the HMM state, and the remaining 61.3% of the time, a change in chronic migraine classification was not accompanied by a change in the HMM state.A divide between the second and third states of this model aligns most strongly with the current episodic/chronic distinction, although there is a meaningful overlap between the states that supports the need for flexibility. An HMM has appealing properties for classifying individuals according to their headache frequency while accounting for natural variation in frequency. This empirically derived model may provide an informative classification approach that is more stable than the use of a single cutoff value. [ABSTRACT FROM AUTHOR]

The fight against bacterial antibiotic resistance must be given critical attention to avert the current and emerging crisis of treating bacterial infections due to the inefficacy of clinically relevant antibiotics. Intrinsic genetic mutations and transferrable antibiotic resistance genes (ARGs) are at the core of the development of antibiotic resistance. However, traditional alignment methods for detecting ARGs have limitations. Artificial intelligence (AI) methods and approaches can potentially augment the detection of ARGs and identify antibiotic targets and antagonistic bactericidal and bacteriostatic molecules that are or can be developed as antibiotics. This review delves into the literature regarding the various AI methods and approaches for identifying and annotating ARGs, highlighting their potential and limitations. Specifically, we discuss methods for (1) direct identification and classification of ARGs from genome DNA sequences, (2) direct identification and classification from plasmid sequences, and (3) identification of putative ARGs from feature selection. [ABSTRACT FROM AUTHOR]

Abstract.Empirical studies of natural language have demonstrated that word frequencies follow power law distributions. However, standard statistical models often fail to capture this property. The Pitman-Yor process (PYP), a Bayesian non parametric model capable of generating power law distributions, has been widely used in probabilistic topic models to handle data with an infinite number of components. However, existing PYP topic models rarely account for the relationships between topics. Hidden Markov models (HMMs) are popular models for modeling topic relationships. To address this limitation, we propose a probabilistic topic model that combines HMM with Pitman-Yor priors. The posterior inference was performed by using variational Bayes methods. We applied our method to text categorization and compared it with two related topic models: the hidden Markov topic model and hierarchical PYP topic model. [ABSTRACT FROM AUTHOR]

The human brain exhibits spatio‐temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography‐functional magnetic resonance imaging (EEG‐fMRI) eyes open (EO) and eyes closed (EC) resting‐state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG‐defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window‐based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting‐state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha‐BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in‐depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics. [ABSTRACT FROM AUTHOR]

The intelligent prediction of urban road traffic congestion is of great significance for improving traffic efficiency, reducing congestion, and optimizing resource allocation, thereby improving the overall mobility and quality of life of the city. In order to overcome the shortcomings of traditional prediction methods for urban road traffic congestion, such as poor prediction accuracy and long prediction time, an intelligent prediction method of urban road traffic congestion based on knowledge graph technology is proposed in this paper. Urban road data are collected in a static and dynamic way, and urban road traffic data are clustered by improved K-means clustering algorithm. Using stepwise regression method to repair urban road traffic data, combining knowledge graph technology to mine the influencing factors of urban road traffic congestion, and combining hidden Markov model to realize intelligent prediction of urban road traffic congestion. The experimental results show that the average absolute error of this method is 0.378, the mean root mean square error is 1.284, and the time formula of urban road traffic congestion intelligent prediction is below 0.32s. It prove that new the prediction method has the characteristics of high precision and high efficiency. [ABSTRACT FROM AUTHOR]

A continuous energy supply to the load side is required by modern power systems. This calls for a sound understanding of how to forecast load demand in the present and the future with the least degree of inaccuracy. Typically, a sequential method with two steps—forecasting and optimization—is used to derive judgments from data. For achieving this goal, optimized power flow is focused in this paper through load forecasting, mode selection, and optimization of power forecasting. Firstly, load forecasting is implemented using time series, and economic and weather-related information for the different consumer's load. Then mode selection is implemented using Hidden Markov Model that determines the requested load for grid-connected or RES mode. When composite RES is developed, the percentage of serviced load rises as more renewable energy sources are added. Following the implementation of the consumer load and mode selection, optimization is used to improve the power flow. The empirical findings show enhanced prescriptive performance when compared to answers found in single- and multi-household contexts. Also, we offer insightful information on how explaining performance is described. [ABSTRACT FROM AUTHOR]

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human‐centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. In this paper, I summarize my research in visual cognition, language processing, and explainable AI to demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. [ABSTRACT FROM AUTHOR]

Alcohol oxidase (AOX) oxidizes alcohols to produce carbonyl compounds and peroxides. Its promoter (PAOX1) is widely used in methylotrophic yeasts. A promising yeast expression system (Pichia sp. strain SO) was developed for bacterial lipase expression regulated by PAOX1 of Komagataella phaffii (previously known as Pichia pastoris). However, its unidentified AOX gene and the protein structure have deterred the search for the best inducer. This study was aimed to identify the yeast species and determine the best inducer for PAOX1 upregulation using in silico AOX protein analysis. Morphological (scanning and transmission electron microscopies) and carbon assimilation analyses confirmed isolate SO as Meyerozyma guilliermondii (previously known as Pichia guilliiermondii). Using Hidden-Markov model and degenerate PCR, the LCAO gene (2091 bp) was discovered in M. guilliermondii strain SO. The enzyme, MgFAO1 shared 14% similarity to K. phaffii AOX1 protein (KpAOX1). Molecular docking of MgFAO1 three-dimensional structure predicted using AlphaFold2 showed its preference toward long-chain 1-dodecanol as the substrate unlike KpAOX1 (short-chain methanol). While the alcohol-binding pocket in MgFAO1 was more hydrophobic compared to KpAOX1, 1-dodecanol could be a better inducer for protein expression in M. guilliermondii strain SO. Thus, in silico pipeline employed in this study can help identify homologous proteins in other expression hosts and their preferred substrates for promoter upregulation. However, the computational analyses were merely predictions and further wet-lab validation is required. Yet, this strategy allows cost-efficient screening of potential inducers for microbe-based protein production in the industries, reducing the production cost and offering cheaper options for consumers. [ABSTRACT FROM AUTHOR]

Microcredit is a method of lending small amounts of money to lowincome individuals who have no access to traditional financial institutions. Upon applying for a loan, an individual may either be able to repay it and be granted a loan again, otherwise s/he demands for a new loan. These events influence certain factors, which can be illustrated through a hidden Markov model (HMM). This study provides a hidden Markov representation of microcredit taking into consideration the borrower’s acquisition of small businesses. Model algorithms used in addressing the problems in HMM, such as the Viterbi algorithm, are discussed and implemented via numerical examples. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is a distributed collection of tiny, low-power, wireless devices which are deployed in a physical environment to monitor the various environmental conditions. The data collected by the positioned sensor nodes is transmitted through the destination nodes by using multi hop communications. WSNs offer numerous advantages over the othernetworks, including enhanced flexibility, low cost, and simplified deployment. Due to the resource- constraint nature of WSN, it faces various challenges and issues that need to be addressed in order to ensure reliable and secure data transmission. The nodes of WSN are highly vulnerable to various types of security attacks namely black hole attack, Denial of Service (DoS), and node compromise attack. Among these attacks, black hole attack causes a serious threat to the nodes in the network. This attack is carried out by malicious nodes that intentionally drop all data packets and control packets without forwarding them to the intended destination. To ensure the security of the network for black hole attack, it is necessary to design an efficient Intrusion Detection Technique for detecting malicious nodes. In this work, a novel Fuzzy Logic-based Intrusion Detection System with Hidden Markov Model (FIDS-HMM) is proposed to identify the malicious nodes and mitigate the black hole attack. Moreover, an HMM is employed in the proposed protocol to monitor the energy levels of the nodes in order to detect the malicious nodes effectively. The implementation of the proposed protocol is carried out by using NS2 simulator. Simulation results justify the proposed protocol namely FIDS-HMM provides an efficient detection mechanism for black hole attacks in the network. Moreover, the proposed protocol improves the Quality of Service (QoS) parameters, namely packet delivery ratio, delay, and throughput in the network with efficiency. [ABSTRACT FROM AUTHOR]

Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl , a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix , our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D - and f -statistics in terms of accuracy and sensitivity to detect introgressed loci. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server. [ABSTRACT FROM AUTHOR]

The aim is to demonstrate the reliability of a population at consecutive points in time, where a sample at each current point must prove that at least 100p$$ p $$% of the devices function until the next point with a probability of at least 1−ω$$ 1-\omega $$. To test the reliability of the population, we flexibilise standard lifetime models by allowing the unknown parameter(s) of the corresponding counting process to vary in time. At the same time, we assign a prior distribution that assumes the parameters to be constant within a certain interval. This flexibilisation has several advantages: it can be applied for all parametric lifetimes; its Markov property allows the efficient derivation of the number of defective devices, even for a large number of testing times; and the inference is less certain and hence more realistic and leads to less frequent acceptance of poor quality populations. On the other hand, the inference is stabilised by the informative prior. Based on the flexibilisation of the homogeneous Poisson process (HPP), we derive acceptance sampling plans to test the future reliability of a population. Applying the zero failure sampling plans on simulations of Weibull processes shows their good frequentist properties and their robustness. In the case of utility meters subject to German regulations (Mess‐ und Eichverordnung (MessEV). 2014: 2010–2073.), application of the derived sequential sampling plans when the conditions of these plans are met can lead to an extension of the verification validity period. These sampling plans protect the consumer better than those from an HPP and are still cost efficient. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Railway track bed defects affect the normal operation of trains and pose great safety risks. In order to detect such issues early, we developed a railway track bed defect detection method which uses optical fibre sensors and an improved HMM (hidden Markov model) to detect the signals collected by a DAS (distributed acoustic sensing) system. First, by analysing the physical process of train operation and determining the number of hidden states, a waveform segmentation method based on average amplitude was used to solve the problem of unequal signal lengths. Second, an adaptive power spectrum energy ratio calculation method was employed to extract track fault features, a set of which was constructed by combining various quantity features. Then, normal and abnormal models were trained according to the sensor measurement area. Finally, the probability of detecting the signal with each model was compared to determine whether the signal was abnormal. Experiments were conducted to compare the applicability of the waveform segmentation method and the feature extraction method. The results show that the HMM based on both waveform segmentation and track bed defect feature sets had the highest recognition rate, the lowest number of false detection areas, and a greater impact on the signal in the early development stage of track bed defects. The proposed method, therefore, has strong recognition ability, which makes it suitable for track bed defect detection. [ABSTRACT FROM AUTHOR]

Objectives: The presence of occult nodal metastases in patients with oral tongue squamous cell carcinomas (OTSCCs) has implications for treatment. More than 30% of patients will have occult nodal metastases, yet a considerable number of patients undergo unnecessary invasive neck dissection to confirm nodal status. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement at the lymph node level (LNL) given the location of macroscopic metastases and the tumor stage using the MRI method. Materials and methods: A total of 108 patients of OTSCCs were included in the study. A hidden Markov model (HMM) was used to compute the probabilities of transitions between states over time based on MRI. Learning of the transition probabilities was performed via Markov chain Monte Carlo sampling and was based on a dataset of OTSCC patients for whom involvement of individual LNLs was reported. Results: Our model found that the most common involvement was that of level I and level II, corresponding to a high probability of ?b1 = 0.39 ± 0.05, ?b2 = 0.53 ± 0.09; lymph node level I had metastasis, and the probability of metastasis in lymph node II was high (93.79%); lymph node level II had metastasis, and the probability of metastasis in lymph node III was small (7.88%). Lymph nodes progress faster in the early stage and slower in the late stage. Conclusion: An HMM can produce an algorithm that is able to predict nodal metastasis evolution in patients with OTSCCs by analyzing the macroscopic metastases observed in the upstream levels, and tumor category. [ABSTRACT FROM AUTHOR]

The paper investigates the problem of the time arrow in terms of artistic creation. The statistical model of artwork signal processing is established in order to identify a duality relation between psychological and intrinsic time. The intrinsic time is linked to the time operator of wavelets, and the psychological one is metaphorically related to the spatial domain of an artwork. The increase of irreducible randomness along the timeline is formulated by the second law of thermodynamics. The dual statement concerns an increase in statistical complexity, which is the definition of self-organization. In that manner, two arrows of time which are opposed and dual to each other are recognized. The authors have indicated a link to the theory of musical forms, the originality issue, and the perspective problem. Some repercussions for art theory, neuroaesthetics, and psychophysics have also been implied. [ABSTRACT FROM AUTHOR]

Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%. [ABSTRACT FROM AUTHOR]

A Hidden Markov Model (HMM) is a couple of stochastic processes: A hidden Markov process and an observed emission process. Generally, the HMMs are used to study the hidden behavior of random systems through some observed emission sequences generated by the phenomenon under study. In this frame-work, we propose to solve the likelihood and the decoding problems of HMMs whose state space is composed of a continuous part and a discrete part. We adapt forward, backward and Viterbi algorithms to the case of our proposal. Numerical examples and Monte Carlo simulations are considered to show the efficiency and the adaptation of the algorithms for the proposed model. [ABSTRACT FROM AUTHOR]

Hidden markov model (HMM) is a statistical markov model in which the system being modeled and is assumed to be a markov process with unobservable (i.e., Hidden) states. In HMM, the state is not directly visible but the output depend on the state is visible. Each state has a probability distribution over the possible output tokens. The model is referred to as a hidden markov model even if these parameters are known exactly. The viterbi is one of the estimate underlying state path in hidden markov models. In this paper, viterbi path is derived using hidden markov model. [ABSTRACT FROM AUTHOR]

Purpose: To investigate dynamic functional connectivity (dFC) within the cerebellar‐whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. Methods: Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum‐whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. Results: Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. Conclusion: Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high‐order and low‐level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA. [ABSTRACT FROM AUTHOR]

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data. • It studies a generalized HMM where observation depends on two-step states. • Two-filter smoothing formula and its generalization are established. • Auxiliary variable sampling technique is applied to draw samples. • It proposes two particle smoothing algorithms by using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]