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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]

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]

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]

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]

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]

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]

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]

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.)

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.)

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]

Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In this research, we confirm the possibilities of classifying students' learning behavior using data obtained from multimodal distribution. We employed computer algorithms to classify students' learning behavior in animated programming classrooms and used information from this classification to predict learning outcomes. Specifically, our study indicates the presence of three clusters of students in the domain of "stay active", "stay passive", and "to-passive". We also found a relationship between these profiles and learning outcomes. We discussed our findings in accordance with the engagement and instructional quality models and believed that our statistical approach will support the ongoing refinement of the models in the context of behavioral profiling and classroom interaction. We recommend that further studies should identify different epistemological frames in diverse classroom settings to provide sufficient explanations of students' learning processes. [ABSTRACT FROM AUTHOR]

Many of the modern human populations inherited a fraction of their genomes from archaic humans such as Neanderthals and Denisovans following ancient introgressions. Non-African populations have around 1–3 of Neanderthal ancestry, while some Oceanians and south-east Asians have in total up to of Neanderthal and Denisovan ancestry. Accurately inferring genome tracts introgressed from these archaic population is important for reconstructing the history of human past, studying natural selection of these archaic components which in turn might be important to understanding functional role of their genes. Here we present a new Hidden Markov Model based approach to accurately detect these archaic tracts in modern humans by simultaneously using information from an unadmixed reference outgroup and archaic genomes. Using simulations we show that it outperforms a state of the art method both by precision and recall while being robust to the misspecification of population scenario parameters. [ABSTRACT FROM AUTHOR]

Search operations performed by adaptive autonomous maritime vehicles have been a topic of considerable interest for many years. Such operations require carefully scheduled coordination of multiple vehicles performing search tasks across the region of interest. Due to the inherent uncertainty of the maritime environment, however, an initially planned search schedule may not be maintained if the vehicles have significant capability to adapt their tasks to match the environment they detect in real time. We propose a multi-vehicle adaptive algorithm for dynamic evaluation and elastic re-planning of variable-length tasks commonly found in the maritime environments. In adaptive evaluation and re-planning problems, a set of tasks are initially planned for execution by adaptive, autonomous search vehicles. Tasks are allocated to search vehicles under a pre-defined schedule based on prior knowledge and desired outcome. Because of the vehicles’ autonomy and reactivity to in situ conditions such as environment or target pose, the precise duration and actions required by each task are unknown a priori. We develop a hidden Markov model (HMM) for propagating task estimates, coupled with a quadratic-programming-based elastic re-scheduler. The result is an integrated estimate-and-schedule adaptation scheme that quickly and efficiently re-plans the vehicles’ schedules based on in situ observations. The numerical simulation results show that this novel HMM approach decreases avoidable schedule variation by over a factor of two compared to existing methods. [ABSTRACT FROM AUTHOR]

In recent years, speaker verification has been extensively explored and has significantly improved its effectiveness. It analyzes the voiceprint characteristics of speakers and finds out the differences in voiceprint characteristics between speakers for verification. In this paper, we propose a text-dependent speaker verification system and its hardware implementation of the feature extraction. The proposed speaker verification system includes two phases: enrollment and verification. In the enrollment phase, the speaker has to provide appropriate speech, such as continuous number strings, sentences, or phrases for building the speakers' models in the system. In the verification phase, the verified speech is substituted into the enrolled speaker models, and the similarity between the speech and the models is used to discriminate. We further design the whole system in a system-on-a-chip (SoC). We focus on the Mel-frequency cepstral coefficients (MFCCs) pre-processing module on FPGA and implement the lightweight the post-processing models such as Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) in software. A piece of speech data can be processed in 53.6ms to meet the real-time way. The proposed speaker verification system has a 93.3% accuracy rate. The overall architecture consumes only 4.26W on Xilinx ZCU104. Moreover, the proposed MFCC chip was implemented in TSMC 90nm, and the gate count is 276k at 1 volt while power consumption is 41.15 mW with a 200 MHz operating frequency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Abstract 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.

The conversion funnel is a model describing the stages consumers go through in their journey toward a purchase. This journey often lasts several days to weeks and can include multiple visits to a seller’s website. A large body of literature has focused on using observable search patterns to identify consumers’ hidden purchasing stages and to estimate their likelihood of conversion. We propose a novel set of measures to better reveal the consumer’s hidden stage in the funnel. These measures are based on the diversity of the searches that a customer engages in while browsing an e-commerce website, and they include not only the number of different products that are searched for, but also measures that rely on unobserved similarities among products, captured in a product network (in which products are assumed to be “similar” if they are frequently co-searched). We operationalize and evaluate our proposed measures using a large-scale dataset from a medium-sized tourism website used for comparing and booking flights. We estimate a hidden Markov model to show that our proposed diversity measures are associated with progress in the funnel and consumers’ conversion likelihood. Specifically, we show that consumers go through different distinguishable stages (states) in their journey, characterized by different values of our proposed diversity measures. To demonstrate the managerial and business implications of our theory, we show that incorporating search-diversity measures into a baseline prediction model significantly improves the model’s performance in predicting purchase likelihood and churn. [ABSTRACT FROM AUTHOR]

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging resting-state dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models recover connectivity from intensity-based hidden states, or from connectivity summed across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.