Changes in social information processing that occur during adolescence are thought to rely on the functional and structural maturation of a network of interconnected brain regions referred to as “the social brain.” The morphology of these brain regions, individually, is thought to be associated with functional specialization and/or ability, but little is known about the relationship between the morphology of the network and its functional specialization. Studies suggest that repeatedly executed psychological processes are not only reflected in functional networks, but may also be related to coordinated morphological changes in the brain across multiple regions that are functionally and structurally connected. The present study sought to explore changes in neuromorphological covariation that occur in the social brain network between adolescence and adulthood (Aim 1), using magnetic resonance imaging and graph theory, and link the properties of this covariance to self-reported and behavioral aspects of social information processing, specifically resistance to peer influence (Aim 2.1), rejection sensitivity (Aim 2.2), and the control of automatic reactions to socially relevant stimuli (Aim 2.3). The specificity of these results to social stimuli was assessed by also analyzing covariance properties in relation to a non-social measure of cognitive functioning (Aim 2.4). Subjects were 217 healthy right-handed individuals between the ages of 13 and 25 – 77 adolescents (ages 13-17), 73 young adults (ages 18-21), and 67 adults (ages 22-25). Analyses involved extracting cortical thickness values for the social brain network for each subject, and conducting group-level graph theoretic analyses. Results suggest that older subjects, subjects who are less sensitive to social stimuli and those who perform better on a behavioral inhibition task, all share one characteristic: the density of covariance in the structural social brain network is low compared to individuals who are younger, more sensitive to social stimuli, and who perform worse on a behavioral inhibition task. Furthermore, this pattern was not observed in a non-social measure of cognitive functioning, suggesting a level of specificity to social information processing in the reported findings. By suggesting that selective structural covariance in the social brain may be characteristic of maturity but also more adaptive in social contexts, the findings from the present study contribute to the idea that adolescence is a time of great opportunity for shaping the brain's structural architecture. / Psychology

Forests ecosystems cover about 30% of the Earth’s land surface, corresponding to an area of roughly 42 million km2 globally. Forests play an important role in the global carbon cycle by exchanging carbon dioxide (CO2) with the atmosphere. Annually, forests act to effectively sequester large amounts of anthropogenically-emitted CO2 from the atmosphere through photosynthetic processes. Through the unparalleled increase of CO2 emissions over the past century and the subsequent climatic inconsistencies due to global climate change, the carbon sink-capacity of the world’s forests remains uncertain. Furthermore, since increasing temperatures have been shown to extend the vegetative growing season in forests, phenological responses to this change are of particular interest. In an effort to effectively assess the future carbon sequestration potential of forests, a better understanding of the climatic controls on phenology, and its influence on carbon processes, is needed. The eddy covariance (EC) technique is a stand-level, in-situ, method used widely to assess the net CO2 exchange across the canopy-atmosphere interface. Together with meteorological data, the sequestration of CO2 and the subsequent ecosystem productivity can be quantified over various time scales (half-hours to decades). This dissertation reports results from field observations of EC measured fluxes that were used to study the climatic impacts on forest phenology and the resulting carbon dynamics in southern Ontario, Canada. The study sites, part of the Turkey Point Observatory, consisted of two similarly-aged, temperate, North American forests growing under similar climatic and edaphic conditions: the 80-year old (in 2019) white pine plantation (coniferous evergreen) and 90+ year-old, naturally-regenerated, white oak (deciduous broadleaf) forest. These forests were studied from 2012 to 2017, using the EC technique, digital phenological cameras, and remote-sensing measurements. At the deciduous broadleaf forest, mid-summer (July and August) meteorological conditions were the key period in determining the annual carbon sink-strength of the site, acting to regulate the interannual variability in carbon uptake. The forest experienced higher net ecosystem productivity (+NEP; carbon sink) when soil temperatures ranged from 15 to 20°C and vapor pressure deficit was 0.7 and 1.2 kPa. From 2012 to 2016, the forest remained a net annual sink, with mean NEP of 206 ± 92 g C m-2 yr-1, similar to that of other North American deciduous forests. Spring and autumn phenological transition dates were calculated for each year (2012 to 2017) from measured EC data and digital camera greenness indices. The timing of spring and autumn transition dates were impacted by seasonal changes in air temperature and other meteorological variables. Contrary to past studies, an earlier growing season start did not equate to increased annual carbon uptake. In autumn, a later end to the deciduous forest growing season negatively impacted the net carbon uptake of the forest, as ecosystem respiration (RE) outweighed the gains of photosynthesis. The digital camera indices failed to capture the peak dates of photosynthesis, but accurately measured the spring and autumn transition dates, which may be useful in future remote sensing applications. A comparison of the two forests from 2012 to 2017 found the coniferous forest to have higher but more variable annual NEP (218 ± 109 g C m-2 yr-1) compared to that of the deciduous broadleaf forest (200 ± 83 g C m-2 yr-1). Similarly, the mean annual evapotranspiration (ET) was higher (442 ± 33 mm yr-1) at the coniferous forest compared to that of the broadleaf forest (388 ± 34 mm yr-1). The greatest difference between years resulted from the response to heat and drought. During drought years, deciduous carbon and water fluxes were less sensitive to changes in temperature or water availability compared to the evergreen forest. Carotenoid sensitive vegetative indices and the red-edge chlorophyll index were shown to effectively capture seasonal changes in photosynthesis phenology within both forests via proximal remote sensing measurements during the 2016 growing season. Satellite vegetative indices were highly correlated to EC photosynthesis, but significant interannual variability resulted from either meteorological inputs or the heterogeneous landscapes of the agriculturally-dominated study area. This dissertation improved our understanding of the dynamics of carbon exchange within the northeastern North American deciduous forest ecozone, through the examination of climatic variability and its impact on carbon and phenology. This dissertation also contributed to efforts being made to better evaluate the impact of species composition on carbon dynamics in geographically similar forests. Moreover, the use of the digital phenological camera observations and remote sensing techniques to complement and better understand the fluxes observed with the EC method was innovative and may help other researchers in future studies. / Dissertation / Doctor of Philosophy (PhD)

No / The purpose of this paper is to highlight the importance of constraints in the theory of relativity and, in particular, what philosophical work they do for Einstein's views on the laws of physics. Einstein presents a view of local ``structure laws'' which he characterizes as the most appropriate form of physical laws. Einstein was committed to a view of science, which presents a synthesis between rational and empirical elements as its hallmark. If scientific constructs are free inventions of the human mind, as Einstein, held, the question arises how such rational constructs, including the symbolic formulation of the laws of physics, can represent physical reality. Representation in turn raises the question of realism. Einstein uses a number of constraints in the theory of relativity to show that by imposing constraints on the rational elements a certain ``fit'' between theory and reality can be achieved. Fit is to be understood as satisfaction of constraint. His emphasis on reference frames in the STR and more general coordinate systems in the GTR, as well as his emphasis on the symmetries of the theory of relativity suggests that Einstein's realism is akin to a certain form of structural realism. His version of structural realism follows from the theory of relativity and is independent of any current philosophical debates about structural realism.

Yes / The essay revisits the puzzle of the ‘passage’ of time in relation to EPR-type measurements and asks what philosophical consequences can be drawn from them. Some argue that the lack of invariance of temporal order in the measurement of a space-like related EPR pair, under relativistic motion, casts serious doubts on the ‘reality’ of the lapse of time. Others argue that certain features of quantum mechanics establish a tensed theory of time – understood here as Possibilism or the growing block universe. The paper analyzes the employment of frame-invariant entropic clocks in a relativistic setting and argues that tenselessness does not imply timelessness. But this conclusion does not support a tensed theory of time, which requires a preferred foliation. It is argued that the only reliable inference from the EPR example and the use of entropic clocks is an inference not just to a Leibnizian order of the succession of events but a frame-invariant order according to some selected clocks.

Educational researchers are often interested in phenomena that unfold over time within a person and at the same time, relationships between their characteristics that are stable over time. Since variables in a longitudinal study reflect both within- and between-person effects, researchers need to disaggregate them to understand the phenomenon of interest correctly. Although the person-mean centering technique has been believed as the gold standard of the disaggregation method, recent studies found that the centering did not work when there was a trend in the predictor. Hence, they proposed some detrending techniques to remove the systematic change; however, they were only applicable to multilevel models. Therefore, this dissertation develops novel detrending methods based on structural equation modeling (SEM). It also establishes the links between centering and detrending by reviewing a broad range of literature. The proposed SEM-based detrending methods are compared to the existing centering and detrending methods through a series of Monte Carlo simulations. The results indicate that (a) model misspecification for the time-varying predictors or outcomes leads to large bias of and standard error, (b) statistical properties of estimates of the within- and between-person effects are mostly determined by the type of between-person predictors (i.e., observed or latent), and (c) for unbiased estimation of the effects, models with latent between-person predictors require nonzero growth factor variances, while those with observed predictors at the between level need either nonzero or zero variance, depending on the parameter. As concluding remarks, some practical recommendations are provided based on the findings of the present study. / Doctor of Philosophy / Educational researchers are often interested in longitudinal phenomena within a person and relations between the person's characteristics. Since repeatedly measured variables reflect their within- and between-person aspects, researchers need to disaggregate them statistically to understand the phenomenon of interest. Recent studies found that the traditional centering method, where the individual's average of a predictor was subtracted from the original predictor value, could not correctly disentangle the within- and between-person effects when the predictor showed a systematic change over time (i.e., trend). They proposed some techniques to remove the trend; however, the detrending methods were only applicable to multilevel models. Therefore, the present study develops novel detrending methods using structural equation modeling. The proposed models are compared to the existing methods through a series of Monte Carlo simulations, where we can manipulate a data-generating model and its parameter values. The results indicate that (a) model misspecification for the time-varying predictor or outcome leads to systematic deviation of the estimates from their true values, (b) statistical properties of estimates of the effects are mostly determined by the type of between-person predictors (i.e., observed or latent), and (c) the latent predictor models require nonzero growth factor variances for unbiased estimation, while the observed predictor models need either nonzero or zero variance, depending on the parameter. As concluding remarks, some recommendations for the practitioners are provided.

Air quality management strategies in the US are developed largely from estimates of emissions, some highly uncertain, rather than actual measurements. Improved knowledge based on measurements of real-world emissions is needed to increase the effectiveness of these strategies. Consequently, the objectives of this research were to (1) quantify relationships among urban emissions sources, land use, and demographics, (2) determine the spatial and temporal variability of emissions, and (3) evaluate the accuracy of official emissions estimates. These objectives guided three field campaigns that employed a unique mobile laboratory equipped to measure pollutant fluxes by eddy covariance. The first campaign, conducted in Norfolk, Virginia, represented the first time fluxes of nitrogen oxides (NO_x) were measured by eddy covariance in an urban environment. Fluxes agreed to within 10% of estimates in the National Emissions Inventory (NEI), but were three times higher than those of an inventory used for air quality modeling and planning. Additionally, measured fluxes were correlated with road density and increased development. The second campaign took place in the Tijuana-San Diego border region. Distinct spatial differences in fluxes of carbon dioxide (CO₂), NO_x, and particles were revealed across four sampling locations with the lowest fluxes occurring in a residential neighborhood and the highest ones at a port of entry characterized by heavy motor vehicle traffic. Additionally, observed emissions of NO_x and carbon monoxide were significantly higher than those in emissions inventories, suggesting the need for further refinement of the inventories. The third campaign focused on emissions at a regional airport in Roanoke, Virginia. NOx and particle number emissions indices (EIs) were calculated for aircraft, in terms of grams of pollutant emitted per kilogram of fuel burned. Observed NO_x EIs were ~20% lower than those in an international databank. NO_x EIs from takeoffs were significantly higher than those from taxiing, but relative differences for particle EIs were mixed. Observed NO_x fluxes at the airport agreed to within 25% of estimates derived from the NEI. The results of this research will provide greater knowledge of urban impacts to air quality and will improve associated management strategies through increased accuracy of official emissions estimates. / Ph. D.

No / An experimental study of coupling compensation for AOA estimation using compact low profile antenna arrays with element separations of a quarter wavelength has been conducted. Two circular arrays of low profile miniaturised logarithmic spiral antennas deployed on a circular metal plate were used for data acquisition. Using the MUSIC direction-finding algorithm, the AOA estimation errors in receiving mode were observed before and after compensation: the errors were significantly decreased by coupling compensation.

Yes / This paper presents a novel closed-form covariance model using covariance matrix decomposition for both continuous-time and discrete-time stochastic systems which are subjected to Gaussian noises. Different from the existing covariance models, it has been shown that the order of the presented model can be reduced to the order of original systems and the parameters of the model can be obtained by Kronecker product and Hadamard product which imply a uniform expression. Furthermore, the associated controller design can be simplified due to the use of the reduced-order structure of the model. Based on this model, the state and output covariance assignment algorithms have been developed with parametric state and output feedback, where the computational complexity is reduced and the extended free parameters of parametric feedback supply flexibility to the optimization. As an extension, the reduced-order closed-form covariance model for stochastic systems with parameter uncertainties is also presented in this paper. A simulated example is included to show the effectiveness of the proposed control algorithm, where encouraging results have been obtained. / National Natural Science Foundation of China [grant number 61573022], [grant number 61290323] and [grant number 61333007]

Les nuages de points en trois dimensions sont un format de données très commun en robotique mobile. Ils sont souvent produits par des capteurs spécialisés de type lidar. Les nuages de points générés par ces capteurs sont utilisés dans des tâches impliquant de l’estimation d’état, telles que la cartographie ou la localisation. Les algorithmes de recalage de nuages de points, notamment l’algorithme ICP (Iterative Closest Point), nous permettent de prendre des mesures d’égo-motion nécessaires à ces tâches. La fusion des recalages dans des chaînes existantes d’estimation d’état dépend d’une évaluation précise de leur incertitude. Cependant, les méthodes existantes d’estimation de l’incertitude se prêtent mal aux données en trois dimensions. Ce mémoire vise à estimer l’incertitude de recalages 3D issus d’Iterative Closest Point (ICP). Premièrement, il pose des fondations théoriques desquelles nous pouvons articuler une estimation de la covariance. Notamment, il révise l’algorithme ICP, avec une attention spéciale sur les parties qui sont importantes pour l’estimation de la covariance. Ensuite, un article scientifique inséré présente CELLO-3D, notre algorithme d’estimation de la covariance d’ICP. L’article inséré contient une validation expérimentale complète du nouvel algorithme. Il montre que notre algorithme performe mieux que les méthodes existantes dans une grande variété d’environnements. Finalement, ce mémoire est conclu par des expérimentations supplémentaires, qui sont complémentaires à l’article. / Three-dimensional point clouds are an ubiquitous data format in robotics. They are produced by specialized sensors such as lidars or depth cameras. The point clouds generated by those sensors are used for state estimation tasks like mapping and localization. Point cloud registration algorithms, such as Iterative Closest Point (ICP), allow us to make ego-motion measurements necessary to those tasks. The fusion of ICP registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. Unfortunately, existing covariance estimation methods often scale poorly to the 3D case. This thesis aims to estimate the uncertainty of ICP registrations for 3D point clouds. First, it poses theoretical foundations from which we can articulate a covariance estimation method. It reviews the ICP algorithm, with a special focus on the parts of it that are pertinent to covariance estimation. Then, an inserted article introduces CELLO-3D, our data-driven covariance estimation method for ICP. The article contains a thorough experimental validation of the new algorithm. The latter is shown to perform better than existing covariance estimation techniques in a wide variety of environments. Finally, this thesis comprises supplementary experiments, which complement the article.

High-accuracy localization is vital for many applications and is a fundamental prerequisite for enabling autonomous missions. Modern navigation systems often rely heavily on Global Navigation Satellite Systems (GNSS) for achieving high localization accuracy over extended periods of time, which has necessitated alternative localization methods that can be used in GNSS-disturbed environments. One popular alternative that has emerged is Collaborative Localization (CL), which is a method where agents of a swarm combine knowledge of their own state with relative measurements of other agents to achieve a localization accuracy that is better than what a single agent can achieve on its own. Performing this in a decentralized manner introduces the challenge of how to account for unknown inter-agent correlations, which typically leads to the need for using conservative fusion methods such as Covariance Intersection (CI) to preserve consistency. Many existing CL algorithms that utilize CI assume agents to have perception systems capable of identifying the relative position of other swarm members. These algorithms do therefore not work in systems where, e.g., agents are only capable of measuring range to each other. Other CI algorithms that support more generic measurement models can require large amounts of data to be exchanged when agents communicate, which could lead to issues in bandwidth-limited systems. This thesis develops a consistent decentralized collaborative localization algorithm based on CI that supports range-only measurements between agents and requires a communication effort that is constant in the number of agents in the swarm. The algorithm, referred to as the PSCI algorithm, was found to maintain satisfactory performance in various scenarios but exhibits slightly increased sensitivity to the measurement geometry compared to an already existing, more communication-heavy, CI-based algorithm. Moreover, the thesis highlights the impact of linearization errors in range-only CL systems and shows that performing CI-fusion before the range-observation measurement update, with a clever choice of CI cost function, can reduce linearization errors for the PSCI algorithm. A comparison between the PSCI algorithm and an already existing algorithm, referred to as the Cross-Covariance Approximation (CCA) algorithm, has further been conducted through a sensitivity analysis with respect to communication rate and the number of GNSS agents. The simulation results indicate that the PSCI algorithm exhibits diminishing improvement in Root Mean Square Error (RMSE) with increased communication rates, while the RMSE of the CCA algorithm reaches a local minimum, subsequently showing overconfidence with higher rates. Lastly, evaluation under a varying number of GNSS agents indicates that cooperative benefits for the PSCI filter are marginal when uncertainty levels are uniform across agents. However, the PSCI algorithm demonstrates superior performance improvements with an increased number of GNSS agents compared to the CCA algorithm, attributed to the overconfidence of the latter.