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Immigration and emigration are key demographic processes of animal population dynamics. However, we have limited knowledge on how fine-scale movement varies over space and time. We developed a Bayesian integrated population model using individual mark-recapture and count data to characterize fine-scale movement of stream fish at 20-m resolution in a 740-m study area every two months for 28 months. Our study targeted small-bodied fish, for which imperfect capture was accounted for (bluehead chub Nocomis leptocephalus, creek chub Semotilus atromaculatus and mottled sculpin Cottus bairdii). Based on data from 2021 individuals across all species, we found that proportions of immigrants in 20-m sections averaged 30–42% among the study species, but they varied over space and time. Creek chub immigrants increased during warmer intervals when individuals grew more and transitioned between body size classes, suggesting that immigration was due to ontogenetic habitat shifts. There was a weak pattern across the species that individuals were more likely to leave 20-m sections when flow was higher. Water-column species (bluehead chub and creek chub) were more likely to immigrate into and stay in deeper sections with more pool area. Across all species and occasions, number of immigrants to stream sections did not decrease with number of individuals that survived and stayed in the same sections. Thus, the habitat did not appear saturated, and our data provided no evidence that intra-specific interactions affected fine-scale movement at our fish densities. In conclusion, high turnover rates characterized fish movement among stream sections and their variation was associated with temporal and spatial shifts in abiotic conditions. [ABSTRACT FROM AUTHOR]

With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explored. One popular approach is incorporating transformers, though their quadratic computational complexity poses challenges. Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this paper, we explore the respective strengths and weaknesses of nmODEs and SSMs and propose a novel architecture, the nmSSM decoder, which combines the advantages of both approaches. This architecture possesses powerful nonlinear representation capabilities while retaining the ability to preserve input and process global information. We construct nmSSM-UNet using the nmSSM decoder and conduct comprehensive experiments on the PH2, ISIC2018, and BU-COCO datasets to validate its effectiveness in medical image segmentation. The results demonstrate the promising application value of nmSSM-UNet. Additionally, we conducted ablation experiments to verify the effectiveness of our proposed improvements on SSMs and nmODEs. [ABSTRACT FROM AUTHOR]

The International Passenger Survey (IPS) is undertaken by the Office for National Statistics to measure tourism flows and tourist expenditure, and international migration. It is interviewer-administered, and the questionnaire instrument was changed in 2017 to 2018 from a paper questionnaire (completed by the interviewer) to an electronic questionnaire administered with a tablet. For operational reasons no parallel run was possible, but the new questionnaire was rolled out progressively to sampling locations. This phased introduction supported the estimation of the effects of the new questionnaire on the main outputs from the survey. We describe initial simulations designed to estimate the power of the phased introduction approach to detect important difference in the IPS outputs, and analyses of the survey estimates at different stages up to the end of 2019 using state-space models, to estimate the discontinuities in the survey outputs. We make an assessment of the effectiveness of the overall approach. [ABSTRACT FROM AUTHOR]

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods. [ABSTRACT FROM AUTHOR]

This paper makes a twofold contribution. First, it develops the dynamic factor model of by allowing for fractional integration instead of imposing the classical dichotomy between I(0) stationary and I(1) non-stationary series. This more general setup provides valuable information on the degree of persistence and mean-reverting properties of the series. Second, the proposed framework is used to analyse five annual US Real Economic Activity series (Employees, Energy, Industrial Production, Manufacturing, Personal Income) over the period from 1967 to 2019 in order to shed light on their degree of persistence and cyclical behaviour. The results indicate that economic activity in the US is highly persistent and is also characterised by cycles with a periodicity of 6 years and 8 months. [ABSTRACT FROM AUTHOR]

We summarize popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing, and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, abile. [ABSTRACT FROM AUTHOR]

A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are customarily based on mechanistic models derived from physical laws of particle movement that are deterministic and do not account for measurement errors inherent in data collection. The resulting analysis compromises on reliably learning about mechanistic factors such as ventilation rates, aerosol generation rates, and filtration efficiencies from field measurements. This manuscript develops a Bayesian state-space modeling framework that synthesizes information from the mechanistic system as well as the field data. We derive a stochastic model from finite difference approximations of differential equations explaining particle concentrations. Our inferential framework trains the mechanistic system using the field measurements from the chamber experiments and delivers reliable estimates of the underlying physical process with fully model-based uncertainty quantification. Our application falls within the realm of the Bayesian "melding" of mechanistic and statistical models and is of significant relevance to environmental hygienists and public health researchers working on assessing the performance of aerosol removal rates for rail car fleets. [ABSTRACT FROM AUTHOR]

Automated radio telemetry systems (ARTS) have the potential to revolutionise our understanding of animal movement by providing a near‐continuous record of individual locations in the wild. However, localisation errors in ARTS data can be very high, especially in natural landscapes with complex vegetation structure and topography. This curtails the research questions that may be addressed with this technology. We set up an ARTS grid in a valley with heterogeneous vegetation cover in the Colombian high Andes and applied an analytical pipeline to test the effectiveness of localisation methods. We performed calibration trials to simulate animal movement in high‐ or low‐flight, or walking on the ground, and compared workflows with varying decisions related to signal cleaning, selection, smoothing, and interpretation, along with four multilateration approaches. We also quantified the influence of spatial features on the system's accuracy. Results showed large variation in localisation error, ranging between 0.4–43.4 m and 474–1929 m, depending on the localisation method used. We found that the selection of higher radio signal strengths and data smoothing based on the temporal autocorrelation are useful tools to improve accuracy. Moreover, terrain ruggedness, height of movement, vegetation type, and the location of animals inside or outside the grid area influence localisation error. In the case of our study system, thousands of location points were successfully estimated for two high‐altitude hummingbird species that previously lacked movement data. Our case study on hummingbirds suggests ARTS grids can be used to estimate small animals' home ranges, associations with vegetation types, and seasonality in occurrence. We present a comparative localisation pipeline, highlighting the variety of possible decisions while processing radio signal data. Overall, this study provides guidance to improve the resolution of location estimates, broadening the application of this tracking technology in the study of the spatial ecology of wild populations. [ABSTRACT FROM AUTHOR]

The mortality of Atlantic salmon is one of the main challenges to achieving its sustainable production. This sector benefits from generating many data, some of which are collated in a standardized way, on a monthly basis at site level, and are accessible to the public. This continuously updated resource might provide opportunities to monitor mortality and prompt producers and inspectors to further investigate when mortality is higher than expected. This study aimed to use the available open-source data to develop production cycle level dynamic linear models (DLMs) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. To achieve this, several production cycle level DLMs were created: one univariate DLM that includes just mortality; and various multivariate DLMs that include mortality and different combinations of environmental variables. While environmental information is not collated in a standardized way across all sites, open-source remote-sensed satellite resources provide continuous, standardized estimates. By combining environmental and mortality data, we seek to investigate whether adding environmental variables enhanced the estimates of mortality, and if so, which variables were most informative in this respect. The multivariate model performed better than the univariate DLM (P = .004), with salinity as the only significant contributor out of 12 environmental variables. Both models exhibited uncertainty related to the mortality estimates. Warnings were generated when any observation fell above the 95% credible interval. Approximately 30% of production cycles and more than 50% of sites experienced at least one warning between 2015 and 2020. Occurrences of these warnings were non-uniformly distributed across space and time, with the majority happening in the summer and autumn months. Recommendations for model improvement include employing shorter time periods for data aggregation, such as weekly instead of on a monthly basis. Furthermore, developing a model that takes hierarchical relationships into account could offer a promising approach. [ABSTRACT FROM AUTHOR]

Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model's ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods. [ABSTRACT FROM AUTHOR]

Fish in all the world’s oceans exhibit variable body size and growth over time, with some populations exhibiting long-term declines in size. These patterns can be caused by a range of biotic, abiotic, and anthropogenic factors and impact the productivity of harvested populations. Within a given species, individuals often exhibit a range of life history strategies that may cause some groups to be buffered against change. One of the most studied declines in size-at-age has been in populations of salmon; Chinook salmon in the Northeast Pacific Ocean are the largest-bodied salmon species and have experienced long-term declines in size. Using long-term monitoring data, we develop novel size and growth models to link observed changes in Chinook size to life history traits and environmental variability. Our results identify three distinct trends in size across the 48 stocks in our study. Differences among populations are correlated with ocean distribution, migration timing, and freshwater residence. We provide evidence that trends are driven by interannual variation in certain oceanographic processes and competition with pink salmon.

Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images. [ABSTRACT FROM AUTHOR]

Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in psychology and related sciences imply working with several variables that are intercorrelated as they unfold over time, such as cognitive and cortical development. Therefore, bivariate developmental models are required. This study aimed to assess the effectiveness of continuous-time bivariate Latent Change Score (CT-BLCS) models for recovering the trajectories of two interdependent developmental processes using data from diverse ALDs. Through a Monte Carlo simulation study, the efficacy of different sampling designs and sample sizes was examined. The study fills a gap in the literature by examining the performance of ALDs in bivariate systems, providing specific recommendations for future application of ALDs for studying interrelated developmental variables. [ABSTRACT FROM AUTHOR]

Investigating the effects of climatic variability on biological diversity, productivity, and stability is key to understanding possible futures for ecosystems under accelerating climate change. A critical question for estuarine ecosystems is, how does climatic variability influence juvenile recruitment of different fish species and life histories that use estuaries as nurseries? Here we examined spatiotemporal abundance trends and environmental responses of 18 fish species that frequently spend the juvenile stage rearing in the San Francisco Estuary, CA, USA. First, we constructed multivariate autoregressive state-space models using age-0 fish abundance, freshwater flow (flow), and sea surface temperature data (SST) collected over four decades. Next, we calculated coefficients of variation (CV) to assess portfolio effects (1) within and among species, life histories (anadromous, marine opportunist, or estuarine dependent), and the whole community; and (2) within and among regions of the estuary. We found that species abundances varied over space and time (increasing, decreasing, or dynamically stable); and in 83% of cases, in response to environmental conditions (wet/dry, cool/warm periods). Anadromous species responded strongly to flow in the upper estuary, marine opportunist species responded to flow and/or SST in the lower estuary, and estuarine dependent species had diverse responses across the estuary. Overall, the whole community when considered across the entire estuary had the lowest CV, and life histories and species provided strong biological insurance to the portfolio (2.4- to 3.5-fold increases in stability, respectively). Spatial insurance also increased stability, although to a lesser extent (up to 1.6-fold increases). Our study advances the notion that fish recruitment stability in estuaries is controlled by biocomplexity-life history diversity and spatiotemporal variation in the environment. However, intensified drought and marine heatwaves may increase the risk of multiple consecutive recruitment failures by synchronizing species dynamics and trajectories via Moran effects, potentially diminishing estuarine nursery function.

To monitor the dynamic behavior of degrading systems over time, a flexible hierarchical discrete-time state-space model (SSM) is introduced that can mathematically characterize the stochastic evolution of the latent states (discrete, continuous, or hybrid) of degrading systems, dynamic measurements collected from condition monitoring sources (e.g., sensors with mixed-type outputs), and the failure process. This flexible SSM is inspired by Bayesian hierarchical modeling and recurrent neural networks without imposing prior knowledge regarding the stochastic structure of the system dynamics and its variables. The temporal behavior of degrading systems and the relationship between variables of the corresponding system dynamics are fully characterized by stochastic neural networks without having to define parametric relationships/distributions between deterministic and stochastic variables. A Bayesian filtering-based learning method is introduced to train the structure of the proposed framework with historical data. Also, the steps to utilize the proposed framework for inference and prediction of the latent states and sensor outputs are discussed. Numerical experiments are provided to demonstrate the application of the proposed framework for degradation system modeling and monitoring. [ABSTRACT FROM AUTHOR]

The state of biodiversity for most of the world is largely enigmatic due to a lack of long‐term population monitoring data. Citizen science programs could substantially contribute to resolving this data crisis, but there are noted concerns on whether methods can overcome the biases and imprecision inherent to aggregated opportunistic observations. We explicitly test this question by examining the temporal correlation of population time‐series estimated from opportunistic citizen science data and a rigorous fishery‐independent survey that concurrently sampled populations of coral‐reef fishes (n = 87) in Key Largo, Florida, USA, over 25 years. The majority of species exhibited positive temporal correlations between population time‐series, but survey congruence varied considerably amongst taxonomic and trait‐based groups. Overall, these results suggest that citizen scientists can be effective sentinels of ecological change, and that there may be substantial value in leveraging their observations to monitor otherwise data‐limited marine species. [ABSTRACT FROM AUTHOR]

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics. [ABSTRACT FROM AUTHOR]

We propose a new method to specify linear models for vectors of time series with some convenient properties. First, it provides a unified modeling approach for single and multiple time series, as the same decisions are required in both cases. Second, it is scalable, meaning that it provides a quick preliminary model, which can be refined in subsequent modeling phases if required. Third, it is optionally automatic, because the specification depends on a few key parameters which can be determined either automatically or by human decision. And last, it is parsimonious, as it allows one to choose and impose a canonical structure by a novel estimation procedure. Several examples with simulated and real data illustrate its application in practice. [ABSTRACT FROM AUTHOR]

In the post-financial-crisis era, advanced economies have increasingly adopted unconventional monetary policies such as zero interest rate policy, negative interest rate policy, forward guidance communication, and international coordination policies. Consequently, the traditional Taylor rule has lost some of its explanatory power. This analysis extends the Taylor rule from a single-country to a multicountry analysis using cross-country panel data, incorporating nonmacro factors and stationary correlation in the diffusion matrix for a dynamic factor analysis, specifically covering the Group of Seven countries with datasets compiled by Bloomberg L.P. for the period 1999–2022. This approach comprehensively models these unconventional monetary policies, demonstrating greater statistical validity than existing models. Notably, the model extracts the impact of zero interest rate policy and negative interest rate policy as nonmacro factors and presents the high correlation of residuals as indicative of international coordination among central banks. Additionally, by interpreting the discrepancy between the Taylor rule and actual rate as unintended interest rate fluctuations by central banks, the study posits that interest rates will eventually return to the central bank's intended fair value. The model's estimation errors could be treated akin to bond value factors in global risk premia. [ABSTRACT FROM AUTHOR]

Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced, low-dimensional state-space model for a given dynamical system from a univariate measurement time series. This article studies the potential of HAVOK for application to mechanical oscillators by investigating which information of the underlying system can be extracted from the state-space model generated by HAVOK. Extensive parameter studies are performed to point out the strengths and pitfalls of the algorithm and ultimately yield recommendations for choosing tuning parameters. The application of the algorithm to real-world friction brake system measurements concludes this study. [ABSTRACT FROM AUTHOR]

This work deals with doubly fed induction generator (DFIG) modeling and stability when connected to weak AC grids. A detailed state-space model that includes the phase-locked loop (PLL) is developed. This work aims to determine the influence of the network's strength on DFIG stability through the short-circuit ratio (SCR). The critical values of the proportional control parameters of the grid-side and rotor-side converters (RSC and GSC), as well as PLL, which make the system unstable, are calculated for different SCR values. Finally, PSCAD/EMTDC dynamic simulations are used to validate the critical control parameters obtained by studying the eigenvalues of the DFIG state-space model regarding system stability. [ABSTRACT FROM AUTHOR]

In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA ('Just Another Red List Assessment') tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments.We coupled the state‐of‐the‐art VAST ('Vector Autoregressive Spatio‐Temporal') modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments.The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently.Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks. [ABSTRACT FROM AUTHOR]

We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the literature suffer from either long-term numerical instability due to particle-path degeneracy or, in the case that degeneracy is remedied by particle approximation of the so-called backward kernel, high computational demands. In order to balance optimally computational speed against numerical stability, we propose to furnish a (fast) naive particle smoother, propagating recursively a sample of particles and associated smoothing statistics, with an adaptive backward-sampling-based updating rule which allows the number of (costly) backward samples to be kept at a minimum. This yields a new, function-specific additive smoothing algorithm, AdaSmooth, which is computationally fast, numerically stable and easy to implement. The algorithm is provided with rigorous theoretical results guaranteeing its consistency, asymptotic normality and long-term stability as well as numerical results demonstrating empirically the clear superiority of AdaSmooth to existing algorithms. for this article are available online. [ABSTRACT FROM AUTHOR]

The labor force surveys (LFS) of all EU countries underwent a substantial redesign in January 2021. To ensure coherent labor market time series for the main indicators in the Norwegian LFS, we model the impact of the redesign. We use a state-space model that takes explicit account of the rotating pattern of the LFS. We also include auxiliary variables related to employment and unemployment that are highly correlated with the LFS variables we consider. The results of a parallel run are also included in the model. The purpose of the article is to quantify the structural breaks due to the redesign. This article makes two contributions to the literature on the effects of redesign in surveys with a rotating panel, such as the LFS. First, we suggest a symmetric specification of the process of the wave-specific effects. Second, we account for substantial fluctuations in the labor force estimates due to the COVID-19 pandemic in the time around the LFS redesign by applying time-varying hyperparameters for both the LFS variables and the auxiliary variables. The specification with time-varying hyperparameters shows a better fit compared to the specification with time-invariant hyperparameters. [ABSTRACT FROM AUTHOR]

While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers. [ABSTRACT FROM AUTHOR]

Image/feature decomposition plays a crucial role in image fusion tasks. However, in deep learning-based fusion methods, existing decomposition operations tend to be either overly simplistic or excessively intricate to learn effectively. In this paper, a novel Fusion network (MVF) based on the Morphing convolutional (MConv) structure and the light-weight Visual state space block (LWVB) is proposed, which is used to extract richer deep features and enhance the quality of the fused images. The proposed structure incorporates convolutional kernels of different shapes to extract detail features from various directions (horizontal, vertical, and diagonal). These kernels consist of fixed shapes with learnable values, ensuring a simplified learning process while retaining the advantages of non-deep-learning filters. Moreover, to efficiently capture global spatial information without incurring significant storage overhead, we modify the Visual State Space (VSS) Block of VMamba and propose the light-weight VSS block which can compress and extract global information at both high and low frequencies, thereby enhancing the spatial awareness capabilities of proposed module. Comparison experiments show that the proposed fusion framework, utilizing the morphing convolutional structure and LWVB, achieves superior fusion performance compared to the state-of-the-art fusion methods.

There are expectations that increasing temperatures will-lead to significant changes in structure and function of montane meadows, including greater water stress on vegetation and lowered vegetation production and productivity. We evaluated spatio-temporal dynamics in production and productivity in meadows within the Sierra Nevada mountain range of North America by: (1) compiling Landsat satellite data for the Normalized Difference Vegetation Index (NDVI) across a 37-year period (1985-2021) for 8,095 meadows >2,500m elevation; then, (2) used state-space models, changepoint analysis, geographically-weighted regression (GWR), and distance-decay analysis (DDA) to: (a) identify meadows with decreasing, increasing or no trends for NDVI; (b) detect meadows with abrupt changes (changepoints) in NDVI; and (c) evaluate variation along gradients of latitude, longitude, and elevation for eight indices of temporal dynamics in annual production (mean growing season NDVI; MGS) and productivity (rate of spring greenup; RSP). Meadows with no long-term change or evidence of increasing NDVI were 2.6x more frequent as those with decreasing NDVI (72% vs. 28%). Abrupt changes in NDVI were detected in 48% of the meadows; they occurred in every year of the study and with no indication that their frequency had changed over time. The intermixing of meadows with different temporal dynamics was a consistent pattern for monthly NDVI and, especially, the eight annual indices of MGS and RSP. The DDA showed temporal dynamics in pairs of meadow within a few 100m of each other were often as different as those hundreds of kilometers apart. Our findings point strongly toward a great diversity of temporal dynamics in meadow production and productivity in the SNV. The heterogeneity in spatial patterns indicated that production and productivity of meadow vegetation is being driven by interplay among climatic, physiographic and biotic factors at basin and meadow scales. Thus, when evaluating spatio-temporal dynamics in condition for many high elevation meadow systems, what might often be considered "noise" may provide greater insight than a "signal" embedded within a large amount of variability. [ABSTRACT FROM AUTHOR]

This paper presents a model-based approach for stochastic primary balance and public debt simulations to assess fiscal sustainability in selected OECD countries. Fiscal behaviour is modelled by means of a fiscal reaction function with time-varying coefficients, which is then, together with a time-varying coefficient vector autoregression, embedded in a stochastic debt sustainability analysis framework. In a pseudo-out-of-sample forecasting exercise using vintage datasets, the model is evaluated against its frequently used fixed coefficient pendant and the European Commission's Economic Forecasts at different horizons. The results indicate that stochastic debt sustainability analyses based on time-varying fiscal reaction functions and vector autoregressions perform competitively in terms of mean squared error and forecast bias at different horizons, especially with respect to public debt as well as short-term primary balance forecasts. Thus, models of this sort should be considered for complementary use at policy institutions, using them jointly with more 'discretionary' approaches to fiscal sustainability analysis. [ABSTRACT FROM AUTHOR]

Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead. However, much of the research up to now has been restricted to cases in which a model of state transition dynamics can be formulated in advance and the simulation budget is unrestricted. These methods fail to address the problem of state inference when simulations are computationally expensive and the Markovian state transition dynamics are undefined. The approach proposed in this manuscript enables LFI of states with a limited number of simulations by estimating the transition dynamics and using state predictions as proposals for simulations. In the experiments with non-stationary user models, the proposed method demonstrates significant improvement in accuracy for both state inference and prediction, where a multi-output Gaussian process is used for LFI of states and a Bayesian neural network as a surrogate model of transition dynamics. [ABSTRACT FROM AUTHOR]

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio‐temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi‐scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios. [ABSTRACT FROM AUTHOR]

Recently, various state-space implementations of surplus production models (SPMs) have been developed for data-limited stocks. Often, catches and fishing effort are underestimated and discards are ignored. This results in biased estimates of stock status and reference points. Therefore, we conduct a sensitivity analysis for different under-reporting scenarios (due to non-declared landings, by-catch, and discards) on model estimates and thus advice for the black hake species in northwest Africa. Two modelling frameworks were used, namely a stochastic SPM in continuous time (SPiCT) and Just Another Bayesian Biomass Assessment (JABBA). A common set of diagnostics was developed to allow comparison across modelling frameworks. Scenarios correspond to hypotheses about misreporting and assumptions and priors that were kept consistent. The ratio of current fishing mortality over the fishing pressure that gives the maximum sustainable yield, F/FMSY , is most affected by under-reporting. Results are sensitive to the prior assumed for the initial depletion level, B0/K , and research is needed. If the misreporting is changing over time, relative quantities (e.g. F/FMSY) and trends are biased, while if misreporting (or at least a part of misreporting) is constant, relative quantities are unbiased. Therefore, the nature of any trend in misreporting should be investigated. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed‐effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed‐effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed‐effects state‐space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew‐t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG‐315) clinical trial data set. [ABSTRACT FROM AUTHOR]

Abstract Background Seasonal long-distance movements are a common feature in many taxa allowing animals to deal with seasonal habitats and life-history demands. Many species use different strategies to prioritize time- or energy-minimization, sometimes employing stop-over behaviours to offset the physiological burden of the directed movement associated with migratory behaviour. Migratory strategies are often limited by life-history and environmental constraints, but can also be modulated by the predictability of resources en route. While theory on population-wide strategies (e.g. energy-minimization) are well studied, there are increasing evidence for individual-level variation in movement patterns indicative of finer scale differences in migration strategies. Methods We aimed to explore sources of individual variation in migration strategies for long-distance migrators using satellite telemetry location data from 41 narwhal spanning a 21-year period. Specifically, we aimed to determine and define the long-distance movement strategies adopted and how environmental variables may modulate these movements. Fine-scale movement behaviours were characterized using move-persistence models, where changes in move-persistence, highlighting autocorrelation in a movement trajectory, were evaluated against potential modulating environmental covariates. Areas of low move-persistence, indicative of area-restricted search-type behaviours, were deemed to indicate evidence of stop-overs along the migratory route. Results Here, we demonstrate two divergent migratory tactics to maintain a similar overall energy-minimization strategy within a single population of narwhal. Narwhal migrating offshore exhibited more tortuous movement trajectories overall with no evidence of spatially-consistent stop-over locations across individuals. Nearshore migrating narwhal undertook more directed routes, contrasted by spatially-explicit stop-over behaviour in highly-productive fjord and canyon systems along the coast of Baffin Island for periods of several days to several weeks. Conclusions Within a single population, divergent migratory tactics can achieve a similar overall energy-minimizing strategy within a species as a response to differing trade-offs between predictable and unpredictable resources. Our methodological approach, which revealed the modulators of fine-scale migratory movements and predicted regional stop-over sites, is widely applicable to a variety of other aquatic and terrestrial species. Quantifying marine migration strategies will be key for adaptive conservation in the face of climate change and ever increasing human pressures.

In this study, different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed, and the panel data models (PDMs) are highlighted for longitudinal data sets. The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation. The models could simultaneously capture the heterogeneity and update forecast through inspections. PDMs are applied to tackle the cross-section heterogeneity of longitudinal data, and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data. To illustrate the methodology, three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China. Estimation results obtained by ordinary least square (OLS) estimator and system generalized method of moments (SGMM) are compared for two dynamic instances. The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time. There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time. In addition, SGMM does not obtain higher prediction accuracy than OLS in this case. Finally, it is recommended to specify the inspection intervals as several constants with integer multiples.

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions.

Summary: State‐space models require an accurate knowledge of the process error (Q$$ \mathbf{Q} $$) and measurement error (R$$ \mathbf{R} $$) covariance matrices for exact state estimation. Even though the matrix R$$ \mathbf{R} $$ can be, in many situations, considered to be known from the measuring instrument specifications, it is still a challenge to infer the Q$$ \mathbf{Q} $$ matrix online while providing reliable estimates along with a low computational cost. In this article, we propose an analytically tractable online Bayesian inference method for inferring the Q$$ \mathbf{Q} $$ matrix in state‐space models. We refer to this method as approximate Gaussian variance inference (AGVI) using which we are able to treat the error variance and covariance terms in the full Q$$ \mathbf{Q} $$ matrix as Gaussian hidden states and infer them simultaneously with the other hidden states in a closed‐form manner. The two case studies show that the method is able to provide statistically consistent estimates for the mean and uncertainties of the error variance terms for univariate and multivariate cases. The method also exceeds the performance of the existing adaptive Kalman filter methods both in terms of accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Objective: State‐space models are a flexible modeling approach and are often fit to ecological time series data exhibiting temporal autocorrelation. Traditionally, angler effort data collected using on‐site creel surveys are analyzed using design‐based methods. With some exceptions, state‐space models are rarely used to model creel survey data that are also generally a time series of temporally autocorrelated counts. Methods: In this study, we demonstrated how to fit state‐space models to a time series of angler counts in 11 sections of three trout fisheries in Idaho. The basic model was extended to make inference about processes that may regulate angling dynamics, such as "population growth rate" of angling effort and recreational carrying capacity. Result: Estimated angling effort varied from 21,179 h in the lowermost section of the Henrys Fork Snake River to 199,457 h in the uppermost section of the South Fork Snake River. The finite population growth rate of angling effort was 1.82 when transitioning from a weekday to a weekend, suggesting that angling effort was 1.82 times greater, on average, on Saturdays than on Fridays, and the population growth rate was 0.38 (i.e., 0.38 times smaller) when transitioning from a Sunday to a Monday. Estimated carrying capacity among fishery sections varied from 129 daily hours of angling effort on the Big Lost River to 693 h on the middle section of the South Fork Snake River. Carrying capacity was 1.88 times higher on fishery sections that had ≥0.5 access points/km than on sections with <0.5 access points/km. Conclusion: The state‐space models used in this study can be modified or extended to fit a variety of data types or can be used to evaluate additional hypotheses regarding the angling process. Impact statementState‐space models are a flexible modeling approach that are rarely applied to angling effort data. Here we demonstrated how to fit various types of state‐space models to angling effort data and evaluated hypotheses regarding angling effort. Catch rates had little effect on recreational carrying capacity, but carrying capacity was 1.88 times higher in fishery sections with high access compared to low access. [ABSTRACT FROM AUTHOR]

While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused on the forecasting performance of the model parameters and not on the applicability of the models in a trading environment. Drawing on the analogy of volatility smile like a term structure in the context of interest rates in fixed‐income markets, we evaluate the performance of the Dynamic Nelson–Siegel (DNS) approach to modeling the dynamics of volatility smile in a trading environment against competing alternatives. Using model‐based mispricing as our sorting criterion, and deploying a trading strategy of going long the options in the upper deciles and going short the options in the lower deciles, we show that dynamic models consistently outperform their static counterparts, with the worst dynamic model outperforming the best static model in terms of the percentage of mean returns from the trading portfolios and the Sharpe ratio. Specifically, we find that the DNS model consistently outperforms all other competing specifications on most of our selected criteria. [ABSTRACT FROM AUTHOR]

State-space models (SSMs) are commonly used to model time series data where the observations depend on an unobserved latent process. However, inference on the model parameters of an SSM can be challenging, especially when the likelihood of the data given the parameters is not available in closed-form. One approach is to jointly sample the latent states and model parameters via Markov chain Monte Carlo (MCMC) and/or sequential Monte Carlo approximation. These methods can be inefficient, mixing poorly when there are many highly correlated latent states or parameters, or when there is a high rate of sample impoverishment in the sequential Monte Carlo approximations. We propose a novel block proposal distribution for Metropolis-within-Gibbs sampling on the joint latent state and parameter space. The proposal distribution is informed by a deterministic hidden Markov model (HMM), defined such that the usual theoretical guarantees of MCMC algorithms apply. We discuss how the HMMs are constructed, the generality of the approach arising from the tuning parameters, and how these tuning parameters can be chosen efficiently in practice. We demonstrate that the proposed algorithm using HMM approximations provides an efficient alternative method for fitting state-space models, even for those that exhibit near-chaotic behavior. [ABSTRACT FROM AUTHOR]