Bayesian Inference for State-Space Models With Student-t Mixture Distributions.

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model.

Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI3 (R2 = 0.656) shows the highest correlation, and it is followed by indexes ORSDI1 (R2 = 0.642), ORSDI4 (R2 = 0.628), and ORSDI2 (R2 = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.

A spectral three-dimensional color space model of tree crown health.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89-0.90; median Kappa 0.85-0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

Extracting Chinese events with a joint label space model.

The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninformative and independent one-hot vectors, ignoring the potential loss of useful label information, thereby making it difficult for these models to incorporate interactive features on the label level. In this paper, we propose a joint label space framework to improve Chinese event extraction. Specifically, the model converts labels of all subtasks into a dense matrix, giving each Chinese character a shared label distribution via an incrementally refined attention mechanism. Then the learned label embeddings are also used as the weight of the output layer for each subtask, hence adjusted along with model training. In addition, we incorporate the word lexicon into the character representation in a soft probabilistic manner, hence alleviating the impact of word segmentation errors. Extensive experiments on Chinese and English benchmarks demonstrate that our model outperforms state-of-the-art methods.

Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics.

The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.

Microbial Protocols for Spacecraft: 1. Effects of Surface Texture, Low Pressure, and UV Irradiation on Recovery of Microorganisms from Surfaces.

Modeling risks for the forward contamination of planetary surfaces from endemic bioburdens on landed spacecraft requires precise data on the biocidal effects of space factors on microbial survival. Numerous studies have been published over the preceding 60 years on the survival of diverse microorganisms exposed to solar heating, solar ultraviolet (UV) irradiation, vacuum, ionizing radiation, desiccation, and many other planetary surface conditions. These data were generated with diverse protocols that can impair the interpretations of the results due to dynamic experimental errors inherent in all lab protocols. The current study (1) presents data on how metal surfaces can affect spore adhesion, (2) proposes doping and extraction protocols that can achieve very high recovery rates (close to 100%) from aluminum coupons with four Bacillus spp., (3) establishes a timeline in which dried spores on aluminum coupons should be used to minimize aging effects of spore monolayers, (4) confirms that vacuum alone does not dislodge spores dried on aluminum coupons, and (5) establishes that multiple UV irradiation sources yield similar results if properly cross-calibrated. The protocols are given to advance discussions in the planetary protection community on how to standardize lab protocols to align results from diverse labs into a coherent interpretation of how space conditions will degrade microbial survival over time.

A phase space model of a Versa HD linear accelerator for application to Monte Carlo dose calculation in a real-time adaptive workflow.

PURPOSE: This study aims to develop and validate a simple geometric model of the accelerator head, from which a particle phase space can be calculated for application to fast Monte Carlo dose calculation in real-time adaptive photon radiotherapy. With this objective in view, the study investigates whether the phase space model can facilitate dose calculations which are compatible with those of a commercial treatment planning system, for convenient interoperability. MATERIALS AND METHODS: A dual-source model of the head of a Versa HD accelerator (Elekta AB, Stockholm, Sweden) was created. The model used parameters chosen to be compatible with those of 6-MV flattened and 6-MV flattening filter-free photon beams in the RayStation treatment planning system (RaySearch Laboratories, Stockholm, Sweden). The phase space model was used to calculate a photon phase space for several treatment plans, and the resulting phase space was applied to the Dose Planning Method (DPM) Monte Carlo dose calculation algorithm. Simple fields and intensity-modulated radiation therapy (IMRT) treatment plans for prostate and lung were calculated for benchmarking purposes and compared with the convolution-superposition dose calculation within RayStation. RESULTS: For simple square fields in a water phantom, the calculated dose distribution agrees to within ±2% with that from the commercial treatment planning system, except in the buildup region, where the DPM code does not model the electron contamination. For IMRT plans of prostate and lung, agreements of ±2% and ±6%, respectively, are found, with slightly larger differences in the high dose gradients. CONCLUSIONS: The phase space model presented allows convenient calculation of a phase space for application to Monte Carlo dose calculation, with straightforward translation of beam parameters from the RayStation beam model. This provides a basis on which to develop dose calculation in a real-time adaptive setting.

Polymeric membranes for environmental remediation: A product space model perspective.

The development of polymeric membranes from polymers such as polystyrene (PS), polyvinylchloride (PVC), and their associated family has brought great momentum to the environmental remediation universe, mainly due to their surprisingly diverse and multi-purpose nature. Their usage has surged 20 times in the last half-century and is likely to double again in the coming 20 years. As a result, the polymeric materials economy and commercialization of research become increasingly important as a possible option for a country to boost prosperity while decreasing its reliance on limited raw resources and mitigating negative externalities. This transformation demands a systematic strategy, which involves progress beyond improving the existing models and building new avenues for collaboration. In this work, a sophisticated system, i.e., product space model (PSM), has been presented, explicitly appraising the opportunity space for United Kingdom, Italy, Poland, India, Canada, Indonesia, Brazil, Saudi Arabia, Russia and Colombia for their potential future industrialization and commercialization of polymeric membranes for environmental remediation. The results revealed that UK, Italy, Poland and India are at advantageous positions owing to their close proximity of (distance<2) and their placement in Parsimonious policy, which is the most desired quadrant of Policy Map of PSM, Canada and Indonesia have medium level opportunities, while Russia and Saudi Arabia have opportunities with more challenges to fully exploit the unexploited polymers products in terms of membranes for environmental remediation and prove favorable for export diversification, sustainable economic growth, and commercialization.

Using state space models to monitor and estimate the effects of interventions on treatment risk and milk yield in dairy farms.

Fast, flexible, and internally valid analytical tools are needed to evaluate the effects of management interventions made on dairy farms to support decisions about which interventions to continue or discontinue. The objective of this observational study was to demonstrate the use of state space models (SSM) to monitor and estimate the effect of interventions on 2 specific outcomes: a dynamic linear model (DLM) evaluating herd-level milk yield and a dynamic generalized linear model evaluating treatment risk in a pragmatic pretest/posttest design under field conditions. This demonstration study is part of a Danish common learning project that ran from March 2020 to May 2021 within the framework of veterinary herd health consultancy in relation to reducing antimicrobial use and improving herd health. Specific interventions for 2 commercial herds were suggested by 4 visiting farmers and were implemented during the project period. The intervention for herd 1 was the application of teat sealers, implemented in August 2020. For herd 2, the intervention was an adjustment of cubicles for cows of parity 2 and above, implemented from November 2020. A shift to an automatic milking system in October 2020 was also modeled as an intervention for herd 1 because the 2 interventions coincided. Data available from the Danish Cattle Database on obligatory registrations for individual cow movements and treatments, as well as test day information on milk yield, were used for model building and testing. Data from a 3-yr period before the project were used to calibrate the SSM to herd conditions, and data from the study period (March 2020 to May 2021) were used for monitoring and intervention testing based on application of the SSM. Herd bulk tank milk recordings were added to the data set during the study period to increase the precision of the estimates in the DLM. The developed SSM monitored herd-level milk yield and the overall probability of treatment throughout the study period in both herds. Furthermore, at the time of intervention, the SSM estimated the effect on herd-level milk yield and treatment risk associated with the implemented intervention in each herd. The SSM were used because they can be calibrated to herd conditions and they take into account herd dynamics and autocorrelation and provide standard deviations of estimates. For herd 1, the intervention effect of applying teat sealers was inconclusive with the current SSM application. For herd 2, no statistically significant changes in cow treatment risk or milk production were identified following the adjustment of cubicles. The use of SSM on observational data under field conditions shows that in this case, the interventions had a nonspecific onset of effect, were implemented during unstable times, and had varying coherence with the measured outcomes, making fully automated SSM analysis difficult. However, similar or expanded SSM with both monitoring and effect estimation functions could, if applied under the right conditions, serve as improved data-based decision support tools for farmers (and veterinarians) to minimize the risk of misinterpreting data due to confounding bias related to dynamics in dairy herds.

Research on Building Space Model Method Based on Big Data Map Visual Design.

In order to explore the architectural space model of design, a visualization method based on big data map is proposed. Referring to the tile pyramid model, a multidimensional aggregation pyramid model (MAP) is proposed, which extends the 2D spatial hierarchical aggregation of tile pyramid to the multidimensional of time/space/attribute and supports the multidimensional hierarchical aggregation of time, space, and attribute. Then, taking spark cluster as the parallel preprocessing tool and HBase distributed database as the persistent storage of map model data, an open-source distributed visualization framework (MAP-Vis) is realized. Then, the BIM model is reconstructed, and a component instance hierarchical splitting strategy based on IFC structure tree is proposed to separate the digital and analog of the original IFC file. The reconstructed IFC model file is transformed into glTF format file, and the dual relationship mapping of geometric space and semantic attributes is completed in the transformation process. Finally, the visibility detection algorithm of BS-AB scene components based on the hierarchical bounding volume (BVH) structure is proposed to eliminate the visibility of building components. The experimental results show that BIMviews is slow to load the IFC file of the experimental object and obtain the model data, with an average of about 40 s, and the Caton is obvious. However, it only takes about 7 s to load glTF file into big data map visualization design by Three.js. It is verified again that glTF format is more suitable for BIM model data than IFC format. The visualization design, display, and interaction based on big data map are based on glTF format. It proves the effectiveness of big data map visualization.

A simple neural vector space model for medical concept normalization using concept embeddings.

OBJECTIVE: Medical concept normalization (MCN), the task of linking textual mentions to concepts in an ontology, provides a solution to unify different ways of referring to the same concept. In this paper, we present a simple neural MCN model that takes mentions as input and directly predicts concepts. MATERIALS AND METHODS: We evaluate our proposed model on clinical datasets from ShARe/CLEF eHealth 2013 shared task and 2019 n2c2/OHNLP shared task track 3. Our neural MCN model consists of an encoder, and a normalized temperature-scaled softmax (NT-softmax) layer that maximizes the cosine similarity score of matching the mention to the correct concept. We adopt SAPBERT as the encoder and initialize the weights in the NT-softmax layer with pre-computed concept embeddings from SAPBERT. RESULTS: Our proposed neural model achieves competitive performance on ShARe/CLEF 2013 and establishes a new state-of-the-art on 2019-n2c2-MCN. Yet this model is simpler than most prior work: it requires no complex pipelines, no hand-crafted rules, and no preprocessing, making it simpler to apply in new settings. DISCUSSION: Analyses of our proposed model show that the NT-softmax is better than the conventional softmax on the MCN task, and both the CUI-less threshold parameter and the initialization of the weight vectors in the NT-softmax layer contribute to the improvements. CONCLUSION: We propose a simple neural model for clinical MCN, an one-step approach with simpler inference and more effective performance than prior work. Our analyses demonstrate future work on MCN may require more effort on unseen concepts.

Time-Space Simulation, Health Risk Warning and Policy Recommendations of Environmental Capacity for Heavy Metals in the Pearl River Basin, China.

In China, the environmental capacity problem of heavy metals has long been hidden in the Pearl River Basin creating a contradiction between the economic development and environmental health. Thus, this research calculated the environmental capacity of heavy metals in the agricultural land of the urban agglomeration in the Pearl River Basin, evaluated the health risk warning capacity using a comprehensive index. The results showed that the static capacity order of heavy metals in the study area was As > Pb > Zn > Cr > Hg > Cu > Ni > Cd. The dynamic capacity showed an upward trend, and it fluctuated in some cities. The remaining capacity of Cr and Ni was relatively poor, and the comprehensive soil quality index of the Pearl River Basin was 0.64. The pollution level was of grade IV, which belongs to the medium capacity, but the soil pollution risk still existed, which threaten the health of local resident. In this regard, this study also put forward some countermeasures for pollution control. Thus, studying the soil heavy metal environmental capacity can provide a reference for heavy metal pollution control and health risk early warning in the Pearl River Basin.

Upper Limb Motor Function Quantification in Post-Stroke Rehabilitation Using Muscle Synergy Space Model.

The muscle synergy hypothesis assumes that the nervous system controls muscles in groups to simplify behavioral tasks, which makes it possible for modularizing motor function assessment. This paper presents a new assessment method based on muscle synergy space (MSS) model to evaluate motor functions after stroke. It consists of spatiotemporal feature module, muscle activation module and synergy activation module, and focuses on the spatial and temporal characteristics of muscle synergies via synergy vectors and activation coefficients. We further applied this method to reveal spatial and temporal characteristics difference of muscle synergy between healthy controls and stroke patients. The effectiveness and accuracy of MSS model were proved by significant positive correlations between Fugl-Meyer score and the total number of optimal synergies of three modules. This measurement methodology could serve as a quantitative indicator for motor function and provide more scientific rehabilitation guidance.

Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables.

In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts' assessment. Specifically, we improved and validated its scores' performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.

Late-life disability trajectories in Yoruba Nigerians and the Spanish population: a state space model in continuous time.

OBJECTIVES: We compared the trajectory of activities of daily living (ADL) in a nationally representative sample of older Nigerians with their Spanish peers and identified factors to explain country-specific growth models. METHODS: Data from two household multistage probability samples were used, comprising older adults from Spain (n = 2,011) and Nigeria (n = 1,704). All participants underwent assessment for ADL. Risk factors including sex, household income, urbanicity, years of education, depression, alcohol consumption and smoking were assessed using validated methods. State-space model in continuous time (SSM-CT) methods were used for trajectory comparison. RESULTS: Compared with Nigerians (µADL80=0.44, SE = 0.015, p < 0.001), Spanish older adults had higher disability scores (µADL80=1.23, SE = 0.021, p < 0.001). In SSM-CT models, the rate of increase in disability was faster in Nigerians (Nigeria: ß = 0.061, p<.01; Spain: ß = 0.028, p < 0.010). An increasing course of disability in the Spanish sample was predicted by female sex, lower education and depression diagnosis. CONCLUSION: The rate of increase in disability was faster in older Nigerians living in an economically disadvantaged context.

State-Space Model of an Electro-Mechanical-Acoustic Contactless Energy Transfer System Based on Multiphysics Networks.

Contactless energy transfer systems are mainly divided into acoustic, inductive, capacitive, and optical, in which main applications are related to biomedical, wireless chargers, and sensors in metal enclosures. When solids are used as transfer media, ultrasound transducers based on piezoelectricity can be used for through-wall power transfer, which can be named as an electro-mechanical-acoustic contactless energy transfer system. This work presents a state-space model derived from a multiphysics network that includes all the multiphysical power conversion without separating the stages and including the real physical elements, like as the piezoelectric parameters and the geometry of the transfer media. The model is compared to the experimental response of the system and evaluated for different scenarios regarding transducers types and solid transfer media. An error analysis has shown that the maximum quadratic error between theoretical and experimental responses is 3.7%.

Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models.

State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled by SSMs depend on the accuracy of the inferred/learned model. Most of the existing inference techniques for SSMs are capable of dealing with very small systems, unable to be applied to most of the large-scale practical problems. Toward this, this article introduces a two-stage Bayesian optimization (BO) framework for scalable and efficient inference in SSMs. The proposed framework maps the original large parameter space to a reduced space, containing a small linear combination of the original space. This reduced space, which captures the most variability in the inference function (e.g., log likelihood or log a posteriori), is obtained by eigenvalue decomposition of the covariance of gradients of the inference function approximated by a particle filtering scheme. Then, an exponential reduction in the search space of parameters during the inference process is achieved through the proposed two-stage BO policy, where the solution of the first-stage BO policy in the reduced space specifies the search space of the second-stage BO in the original space. The proposed framework's accuracy and speed are demonstrated through several experiments, including real metagenomics data from a gut microbial community.

Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed using deep neural networks (DNNs) to represent the state transition and observation generation, both of which constitute a stochastic nonlinear state-space model. A new bidirectional recurrent neural network (RNN), creating a connection of the hidden layer between a forward RNN and a backward RNN, is proposed to generate the filtering estimation and the smoothing estimation of process states which further generate observations with DNN-based process models. The smoothing estimator and the process model are first learned offline with all collected samples. Then the filtering estimator is fine-tuned by the learned smoother and process models to achieve real-time monitoring since the filter state is estimated based on the past and the current observations. Two indices are designed based on the learned model for monitoring the process anomaly. The proposed process monitoring model can deal with complex nonlinearities, process dynamics, and process uncertainties, all of which can be very challenging for the existing methods, such as kernel mapping and stacked auto-encoder. Two case studies validate that the effectiveness of the proposed method outperforms the other comparative methods by at least 10% when using the averaged fault detection rate in the industrial experimental data.

The systematic evaluation of an embodied control interface for virtual reality.

Embodied interfaces are promising for virtual reality (VR) because they can improve immersion and reduce simulator sickness compared to more traditional handheld interfaces (e.g., gamepads). We present a novel embodied interface called the Limbic Chair. The chair is composed of two separate shells that allow the user's legs to move independently while sitting. We demonstrate the suitability of the Limbic Chair in two VR scenarios: city navigation and flight simulation. We compare the Limbic Chair to a gamepad using performance measures (i.e., time and accuracy), head movements, body sway, and standard questionnaires for measuring presence, usability, workload, and simulator sickness. In the city navigation scenario, the gamepad was associated with better presence, usability, and workload scores. In the flight simulation scenario, the chair was associated with less body sway (i.e., less simulator sickness) and fewer head movements but also slower performance and higher workload. In all other comparisons, the Limbic Chair and gamepad were similar, showing the promise of the Chair for replacing some control functions traditionally executed using handheld devices.

On the interpretation of linear Riemannian tangent space model parameters in M/EEG.

Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signal. In simulations and two real MEG and EEG datasets, we demonstrate the validity of the proposed approach and investigate its behavior when the model assumptions are violated. Our results confirm that Riemannian tangent space methods are robust to differences in the source patterns across observations. We found that this robustness property also transfers to the associated patterns.