Covariance Measurements Research Articles

Exploring combined effects of DNA methylation and copy number on gene expression with a two-stage approach.

DNA methylation and copy number may be associated with each other to some extent, in positive or negative ways. Whether differential methylation and copy number variation have combined effects on gene expression is largely unknown. We use a multivariate linear model to formulate the relationship among the three genomic measurements, gene expression, copy number, and methylation levels. We propose a method that combines a distance covariance measure and the group LASSO to analyze multiple types of genomic data collectively for the purpose of insightfully revealing how gene expression is potentially affected by both copy number variation and differential methylation levels in cellular process. Our approach is of two stages, the first is a variable screening process in which a variable selection method is utilized by employing the concept of joint distance covariance (JdCov) of random vectors, and the second is to implement a penalized regression approach, a group LASSO, on the screened data of much lower dimension. The two-stage approach is tested in extensive simulation studies and shown to be effective. The approach is then applied to the TCGA Melanoma data, which consists of gene expression, methylation and copy number measurements of more than 300 patients and relationship of some genes with methylation and copy number measurements were revealed for the involved subjects.

Face spoofing detection using Heterogeneous Auto-Similarities of Characteristics

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art.

Dynamics of evapotranspiration from concurrent above- and below-canopy flux measurements in a montane Sierra Nevada forest

Evapotranspiration (ET) from the land surface to the atmosphere is a major component of Earth's water cycle, and comprises both transpiration (T) of xylem water from plants and evaporation (E) of water from soils and vegetation surfaces. These two component fluxes respond differently to changes in temperature, water availability and atmospheric CO2 concentrations. Concurrent eddy covariance (EC) measurements above and below forest canopies provide a promising approach to partition ET into E and T. However, below-canopy EC measurements are rare, and questions remain regarding their spatial variability, canopy coupling, and temporal dynamics. To address these challenges, we measured and partitioned ET over more than three years, using concurrent above- and below-canopy EC towers in a montane forest at Sagehen Creek in California's Sierra Nevada mountains. This is the establishing study for the AmeriFlux site US-SHC. The main environmental control for ET was available energy; other important controls were canopy & soil temperature, soil moisture, vapor pressure deficit, and wind speed. Below-canopy measurements at two locations within the above-canopy footprint were similar to one another, suggesting low spatial heterogeneity in understory ET near the creek at our Sagehen site. We observed a total forest ET of 606 ± 50 mm yr−1 with 275 ± 17 mm yr−1 measured in the understory (all mean ± SD) during the water years 2018–2020. Interannual variability in ET and T was small despite large variability in precipitation totals; thus the P–ET water balance was mainly driven by variations in water supply. Partitioning the components of total forest ET at Sagehen with concurrent EC measurements showed that on average, 67–74% of ET originated from T (47% from trees and 20–27% from understory vegetation), while 26–33% were from E (mostly from the understory). Our results demonstrate the potential of concurrent above- and below-canopy EC measurements for ET partitioning.

Estimation for single-index spatial autoregressive model with covariate measurement errors

This paper explores the estimators of parameters for a spatial data single-index model which has measurement errors of covariates in the nonparametric part. The related estimations are considered to combine a local-linear smoother based simulation-extrapolation (SIMEX) algorithm, the estimation equation and the estimation method for profile maximum likelihood. Under regular conditions, asymptotic properties of the link function and uncertain estimators are derived. As verified in simulations, the performance of the estimators is satisfactory. Finally, an application to a real dataset is illustrated.

Spatial modelling of infectious diseases with covariate measurement error

Abstract In spatial infectious disease models, it is typical to assume that only the distance between susceptible and infectious individuals is important for modelling, but not the actual spatial locations of the individuals. Recently introduced geographically-dependent individual level models (GD-ILMs) can be used to also consider the effect of spatial locations of individuals and the distance between susceptible and infectious individuals for determining the risk of infection. In these models, it is assumed that the covariates used to predict the occurrence of disease are measured accurately. However, there are many applications in which covariates are prone to measurement error. For instance, to study risk factors for influenza, people with low socio-economic status (SES) are known to be more at risk compared to the rest of the population. However, SES is prone to measurement error. In this paper, we propose a GD-ILM which accounts for measurement error in both individual-level and area-level covariates. A Monte Carlo expectation conditional maximisation algorithm is used for inference. We use models fitted to data to predict areas with high average infectivity rates. We evaluate the performance of the proposed approach through simulation studies and by a real-data application on influenza data in Manitoba, Canada.

Robust depth-verified RGB-D visual odometry with structural regularities for indoor environments

This paper proposes an RGB-D visual odometry method that leverages point, line, plane features and Manhattan structures to achieve robust frame tracking and precise pose estimation, especially in textureless scenes. A validation method is introduced that ensures accurate frame-to-frame rotation estimation by comparing rotation angles computed from multiple Manhattan structures. Depth verification methods involving parameter fitting and outlier removal for point, line, and plane features are implemented by investigating the covariance of sensor depth measurements. We also employ local bundle adjustment in the local mapping thread to refine keyframe poses and landmarks. Comprehensive ablation studies confirm the effectiveness of our contributions. Experimental results on public datasets demonstrate that our method achieves obvious advantages in accuracy and robustness while maintaining real-time performance.

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved.

Ecosystem carbon exchange across China's coastal wetlands: Spatial patterns, mechanisms, and magnitudes

Coastal wetlands are of great importance for global carbon cycle and climate mitigation because of their strong carbon uptake capacity. However, the national-scale ecosystem carbon exchange dynamics in coastal wetlands, including magnitudes, spatial patterns, and controlling mechanisms, remains poorly understood. In this study, we utilized eddy covariance measurements from China's coastal wetlands to quantify carbon fluxes, assess their spatial patterns, and explore the controlling mechanisms. Integrating climate, vegetation, and soil factors, we constructed a cascaded relationship network to reveal the potential controlling mechanisms of spatial variations in carbon fluxes. The results revealed that gross primary production (GPP) and ecosystem respiration (ER) were 1405 ± 656 (mean ± sd) gC m−2 yr−1and 893 ± 465, respectively. Net ecosystem production (NEP) in coastal wetlands (567 ± 348 gC m−2 yr−1) exceeded China's terrestrial and marine ecosystems by 2–8 times, highlighting their significant carbon sink capacity. The carbon sink capacity in mangroves was significantly higher than salt marsh, exhibiting a twofold difference in NEP. Spatially, carbon fluxes displayed negative correlations with latitude, indicating the influence of climate features. Although tropical climate zone exhibited significantly higher carbon fluxes than temperate zone, no differences were observed between subtropics zone with others due to the distribution of mixed plants and largest area. The structural equation model (SEM) showed that the climate factors, including temperature, precipitation, and net radiation indirectly promoted GPP and ER through regulating the physiological process of mangrove and salt marsh, as well as soil carbon production and consumption. The cascaded relationship of climate-vegetation-soil showed in SEM explained 71–85 % of the spatial variations in GPP and ER, and ultimately accounted for 85 % of NEP. The carbon consumption efficiency (ER/GPP) in coastal vegetations was of 0.6, lower than that of global and China's terrestrial ecosystems, suggesting their strong efficiency in carbon fixation in coastal wetlands. Lastly, the scenario simulation results implied the importance of coastal vegetation restoration on enhancing blue carbon benefits.

Methane Gas Ebullition Dynamics From Different Subtropical Wetland Vegetation Communities in Big Cypress National Preserve, Florida Are Revealed Using a Multi‐Method, Multi‐Scale Approach

AbstractMethane (CH4) emissions from peat soils are highly variable in space and time and are influenced by changes in biogeochemical controls and other environmental factors. Areas or times with disproportionally high CH4 emissions in wetlands may develop where conditions are especially conducive for microbial processes like methanogenesis. Currently, eddy covariance methods are employed to quantify CH4 exchanges over several extensive subtropical forested wetland communities in the Big Cypress National Preserve, Florida. In this work, we investigate the importance of multi‐scale measurements to characterize CH4 ebullition dynamics from subtropical wetlands. Our approach uses a combination of gas traps, time‐lapse photography, and capacitance probes to characterize ebullition dynamics from two different wetland vegetation communities for comparison to eddy covariance CH4 measurements at the site. Ground‐penetrating radar surveys and soil sampling are used to assess differences in subsurface properties between sites that influence ebullition. Our results show that the mean measurement bias between fluxes measured in this study and the eddy covariance measurements over the same period was 10–14 times larger during the wet season when ebullition rates were greatest, than the dry season, when ebullition rates were smallest. This suggests that eddy covariance measurements may underestimate the CH4 contribution of ebullition across heterogeneous wetland vegetation communities and that the comparability of CH4 fluxes from methods varying in spatio‐temporal scale changes in response to subtropical Florida seasonality. Our work suggests that these methods can be used to complement eddy covariance measurements and improve the characterization of ebullition dynamics in subtropical wetlands.

The relationship between obstructive sleep apnea and haemoglobin A1c and the moderating role of glycaemic status in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

This study investigated the relationship between obstructive sleep apnea and haemoglobin A1c (HbA1c) among Hispanics/Latinos in the United States and assessed whether this relationship was moderated by glycaemic status. This was a cross-sectional analysis of the Hispanic Community Health Study/Study of Latinos cohort. The sample consisted of 13,394 participants with valid measures of obstructive sleep apnea, HbA1c, and study covariates. Obstructive sleep apnea was assessed with the apnea-hypopnea index and categorised as obstructive sleep apnea if the apnea-hypopnea index was ≥5 events/h. HbA1c measures were obtained through fasting blood samples. Fasting plasma glucose (FPG), 2-h post-load plasma glucose (2h-PG) and use of antihyperglycaemic medications were used to define glycaemic status (i.e., normoglycaemia [FPG < 5.6 mmol/L (< 100 mg/dL) and 2h-PG < 7.8 mmol/L (140 mg/dL)], prediabetes [FPG 5.6-6.9 mmol/L (100-125 mg/dL), and/or 2h-PG 7.8-11.0 mmol/L (140-199 mg/dL)], diabetes without treatment [FPG > 7.0 mmol/L (≥ 126 mg/dL) and/or 2h-PG ≥ 11.1 mmol/L (≥ 200 mg/dL)], and diabetes with treatment. Multivariable linear regression was used to calculate adjusted least square means. Overall, 25.9% of the sample had obstructive sleep apnea and 49.2% had normal glycaemic levels, 36.1% had prediabetes, 6.5% diabetes without receiving treatment, and 8.3% diabetes and undergoing treatment for it. Participants with obstructive sleep apnea had significantly higher adjusted mean HbA1c (adjusted mean [standard error] 5.85 [0.03)]) than those without (5.70 [0.02)]; p < 0.001). Models stratified by diabetes status showed that the association between obstructive sleep apnea (versus not) and higher HbA1c was only for participants with normal glycaemic status (adjusted mean [standard error] 5.27 [0.01] versus 5.30 [0.01]; p = 0.013) and prediabetes (5.59 [0.01] versus 5.66 [0.01]; p < 0.001). In conclusion, obstructive sleep apnea was associated with higher HbA1c in a diverse sample of Hispanic/Latino adults in the United States. This association was present only for participants with normal glycaemic status or with prediabetes. Studies are needed to further understand the clinical implications of the association between obstructive sleep apnea and HbA1c according to glycaemic status.

Prevalence and socio-behavioral determinants of early childhood caries in children 1-5- year- old in Iran.

According to the World Health Organization (WHO), early childhood caries (ECC) is still considered a major global health problem despite the general improvement in oral health practice. This study aimed to assess ECC's prevalence, severity, and key social and behavioral determinants in Iranian children 1-5- years of age. This cross-sectional study recruited Iranian 1-5-year-olds with a three-stage stratified clustered sampling method. After providing consent, parents were interviewed using a validated questionnaire, including questions on child characteristics and behavioral factors. Each child underwent a dental examination by one of four trained and calibrated dentists. Associations between key covariates of interest and primary outcome measures were assessed by multivariable logistic regression modeling and multivariate generalized negative binomial regression modeling after weightings were applied. Statistical analysis was performed using SPSS V25 and Stata V14.2 software packages. A p-value ≤ 0.05 was considered significant. The mean age of 909 participants was 41.1±1.2 months, 48.6% were male, and 38.1% lived in rural areas. The overall prevalence of ECC was 53.2% and mean dmft score was 2.7± 0.3. Key determinants associated with ECC included living rurally (p = 0.009, OR = 2.13), consuming sweet drinks, sugary snacks, or both [p-value = 0.02 (OR = 2.53), and p-value<0.001 (OR = 4.96), respectively], and visible plaque (p<0.001, OR = 3.41). Covariates associated with dmft scores included residing in rural regions (p = 0.02, IRR = 1.31), having both sugary snacks and sweet drinks (p = 0.02, IRR = 1.85) compared to those had none, and visible dental plaque (p<0.001, IRR = 2.06). The prevalence of dental caries in children is high in Iran. The increase of ECC prevalence and severity from toddlers to preschoolers emphasizes on the critical importance of early interventions in toddlers. Improving access to care for rural children is essential along with the need to change dietary and self-care behaviors through multilevel efforts.

Practical Considerations for Sandwich Variance Estimation in 2-Stage Regression Settings.

In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993-2005) and the Hispanic Community Health Study/Study of Latinos (2008-2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error.

Prediction of carcass traits in fattening Chios and Serres lambs using real-time ultrasonography and live body weight measurements pre-slaughter

The objective of this study was to assess the capability of predicting carcass traits and meat cuts weights, in fattening lambs of indigenous Greek dairy sheep breeds, using ultrasound measurements and live body weight measurements pre-slaughter. A total of 187 lambs of Chios and Serres breeds were involved in the study. Body condition score, live body weight (LBW), and ultrasound measurements of Longissimus lumborum muscle depth (LMD) and subcutaneous fat thickness (SFT) at the lumbar region were recorded pre-slaughter. After slaughter, the carcasses were classified using five-degree grading systems for muscle development and fat deposition, while hot (HCW) and cold carcass (CCW) and meat cuts weights were measured. The statistical analyses included descriptive statistics and linear regression models to estimate the fixed effects of sex and the covariances of LBW, BCS, and ultrasound measurements on the studied traits. High R2 values (0.60 ≤ R2 ≤ 0.92) were observed in the models predicting HCW, CCW, forequarter, leg chump on shank off, the short loin, the eye of the short loin, and foreshank weights. Among the models estimated LMD, SFT, and LBW as significant predictors, the ones predicting hot and cold carcass weights, the short loin, the eye of the short loin, and the eye of the rack weights were successfully validated. Other models including BCS, LBW, sex, and either one or none of the ultrasonography measurements as predictors were also validated and presented.

Adaptive multi-object tracking based on sensors fusion with confidence updating

Multi-object tracking (MOT) systems typically rely on object detection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to unstable tracking. Regarding this problem, we proposed an adaptive MOT method based on detection confidence. At first, we use a simple data fusion method to combine the detection results of LiDAR and camera to reduce the large number of false detections. And then we used a factor based on confidence to adjust the estimating covariance matrix and measurement covariance matrix adaptively. The algorithm can judge which is more reliable between prediction and detection, and choose which is more important in the update step. Meanwhile, we set a factor based on confidence to control the search range in the data association module. Our method reduces the impact of detector error while ensuring accuracy and speed, and improves the robustness of the MOT algorithm. Through experiments conducted on the KITTI multi-object tracking dataset, our method has demonstrated significant advantages over state-of-the-art (SOTA) methods in terms of both accuracy and processing speed. The results of MOTA for 90.02% and FPS for 262.

Quality evaluation for measurements of wind field and turbulent fluxes from a UAV-based eddy covariance system

Abstract. Instrumentation packages for eddy covariance (EC) measurements have been developed for unoccupied aerial vehicles (UAVs) to measure the turbulent fluxes of latent heat (LE), sensible heat (H), and CO2 (Fc) in the atmospheric boundary layer. This study aims to evaluate the performance of this UAV-based EC system. First, the measurement precision (1σ) of georeferenced wind was estimated to be 0.07 m s−1. Then, the effect of the calibration parameter and aerodynamic characteristics of the UAV on wind measurement was examined by conducting a set of calibration flights. The results showed that the calibration improved the quality of the measured wind field, and the influence of upwash and the leverage effect can be ignored in wind measurement by the UAV. Third, for the measurements of turbulent fluxes, the error caused by instrumental noise was estimated to be 0.03 µmolm-2s-1 for Fc, 0.02 W m−2 for H, and 0.08 W m−2 for LE. Fourth, data from the standard operational flights were used to assess the influence of resonance on the measurements and to test the sensitivity of the measurement under the variation (±30 %) in the calibration parameters around their optimum value. The results showed that the effect of resonance mainly affected the measurement of CO2 (∼5 %). The pitch offset angle (εθ) significantly affected the measurement of vertical wind (∼30 %) and turbulent fluxes (∼15 %). The heading offset angle (εψ) mainly affected the measurement of horizontal wind (∼15 %), and other calibration parameters had no significant effect on the measurements. The results lend confidence to the use of the UAV-based EC system and suggest future improvements for the optimization of the next-generation system.

DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices

A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Quantifying the impact of climate smart agricultural practices on soil carbon storage relative to conventional management

Climate smart agricultural practices have received considerable attention recently for their potential for climate change mitigation through sequestering atmospheric carbon. Despite the enthusiasm for climate smart practices, there is limited evidence they are more effective at removing carbon from the atmosphere and storing it in the soil than current practices. We hypothesized that a field with aspirational (ASP) practices (i.e., no-till corn-soybean-wheat-hay rotation with cover crops) would accumulate more soil organic carbon (SOC) versus a business-as-usual (BAU) field (i.e., conventional-tillage, corn-soybean-soybean rotation). We used deep soil cores (1 m) to assess changes in soil organic carbon (ΔSOC) between 2016 and 2022 for the two fields and compare with estimates based on eddy covariance calculation of ΔSOC. We found that the ASP field had ΔSOC that was positive, and larger than the BAU field. Both the soil sample method (ΔSOCSS) and the eddy covariance method (ΔSOCEC) agreed on this point, but the magnitude of ΔSOC was much larger when estimated with soil samples (ΔSOCSS was 1.9 ± 1.7 % yr−1 and -0.7 ± 1.3 % yr−1 at ASP and BAU, respectively) than with eddy covariance (ΔSOCEC was 0.80 ± 0.09 % yr−1 and 0.12 ± 0.06 % yr−1 at ASP and BAU, respectively). Finally, we used the continuous measurements of carbon fluxes from the eddy covariance towers to examine how conservation practices (cover crops, no-till, or expanded crop rotation) led to increased carbon storage. We found that unharvested cover crops add carbon to the soil that offsets net carbon losses that otherwise reduce soil carbon storage when the field is fallow. No-till and expanded crop rotations also affect the carbon budget of the agro-ecosystems. Results from this study illustrate the value of conservation practices in a changing climate and the value of eddy covariance measurements for assessing climate smart practices.

Biomechanical changes identified during a marathon race among high-school aged runners

BackgroundDespite the increasing popularity of endurance running competitions among adolescent runners, there is currently limited information regarding expected biomechanical changes across the duration of a long-distance running event, and the relationship between young runners’ biomechanics and running performance. Wearable technology offers an ecological means to continuously assess runners’ biomechanical data during outdoor running competitions. Research questionDo adolescent athletes adopt changes in sensor-derived biomechanics throughout a marathon race, and are there relationships between race performance and biomechanical features among young marathoners? MethodsFourteen high-school aged runners (9 M, 5 F; age: 16 ± 1 years, height: 170.8 ± 7.5 cm; mass: 63.6 ± 9.4 kg) wore lace-mounted sensors to record step-by-step biomechanics during a marathon race. Official race segment completion times were extracted across 5 race segments (5-K, 15-K, Half Marathon [21.1-K], 35-K, Marathon [42.2-K]). Within-participant repeated measures of covariance (pace) were conducted to assess changes in biomechanics across the race, with Bonferroni post-hoc comparisons. Pearson’s r correlations were performed to assess the relationship between race finish times and biomechanics. ResultsPace was significantly slower (p-range: 0.002–0.005), contact times significantly longer, and stride lengths significantly shorter in the final segment compared to middle segments (p-range: 0.003–0.004). The rate of shock accumulation was significantly higher in the final race segment compared to the first three segments (p-range: 0.001–0.002). Moderate relationships existed between finish times and pace (r = −0.63), stride length (r = −0.62), and contact time (r = 0.51). SignificanceAdolescent runners altered their gait patterns in the final marathon segment compared to earlier segments. Spatiotemporal measures were moderately correlated with race finish times, suggesting a link between faster run pace, increased stride lengths, and reduced contact time for improved running performance during an endurance race.

Extended target tracking under multitarget tracking framework for convex polytope shapes

This paper discusses the problem of extended target tracking for a single 2D extended target with a convex polytopic shape and known dynamics. Extended targets are those that produce multiple measurements for a single frame. One of the major challenges in extended target tracking is the joint uncertainty in the shape and the kinematics of the target. Another challenge is the lack of visibility due to self-occlusion in targets with a finite extent (as opposed to zero extent for point targets). To address these challenges, we develop a framework for tracking single (or widely separated) extended targets. This framework is based on the existing point multitarget tracking framework by modeling different parts of an extended target as separate targets. An algorithm is developed using the proposed framework for tracking convex polytope-shaped targets. The proposed shape function consists only of the boundary of the target since the center may not be observable. The algorithm is capable of dynamically changing the number of parameters used to describe the shape as more parts of the target become visible over time. The performance of the algorithm is evaluated for various scenarios using root mean square error (RMSE) of velocity, center, and intersection over union (IoU) metrics. It is seen that the algorithm is able to handle the self-occlusion problem and estimate the whole target shape even when different parts of the target are visible at different frames, for various shapes, and for various conditions of measurement noise covariance and number of measurements. New faces are added to the shape estimate as more parts of the target become visible. The algorithm is able to conserve the parts of the target that were visible in the previous frames but are no longer visible.

On reconstruction of states from evolution induced by quantum dynamical semigroups perturbed by covariant measures

On reconstruction of states from evolution induced by quantum dynamical semigroups perturbed by covariant measures