Canadian Niagara Power Glass Slide - Large Group in Tunnel

A large group of men in hard hats in the tunnel. The names of the men are unknown.

Canadian Niagara Power Company - Large group photo in tunnel

Large group of men standing in the tunnel wearing boots and hard hats.

STRUCTURAL ANALYSIS, LAYER THICKNESS MEASUREMENTS AND MINERALOGICAL INVESTIGATION OF THE LARGE INTERIOR LAYERED DEPOSIT WITHIN GANGES CHASMA, VALLES MARINERIS, MARS

The interior layered deposit (ILD) in Ganges Chasma, Valles Marineris, is a 4.25 km high mound that extends approximately 110 km from west to east. The deposition, deformation, and erosion history of the Ganges ILD records aids in identifying the processes that formed and shaped the Chasma. To interpret structural and geomorphic processes acting on the ILD, multiple layer attitudes and layer thickness transects were conducted on the Ganges ILD. Mineralogical data was analyzed to determine correlations between materials and landforms. Layer thickness measurements indicate that the majority of layers are between 0.5 m and 4 m throughout the ILD. Three major benches dominate the Ganges ILD. Layer thicknesses increase at the ILD benches, suggesting that the benches are formed from the gradual thickening of layers. This indicates that the benches are depositional features draping over basement topography. Layer attitudes indicate overall shallow dips generally confined to a North-South direction that locally appear to follow bench topography. Layering is disrupted on a scale of 40 m to 150 m in 12 separate locations throughout the ILD. In all locations, underlying layering is disturbed by overlying folded layers in a trough-like geometry. These features are interpreted to have formed as submarine channels in a lacustrine setting, subsequently infilled by sediments. Subsequently, the channels were eroded to the present topography, resulting in the thin, curved layering observed. Data cannot conclusively support one ILD formation hypothesis, but does indicate that the Ganges ILD postdates Chasma formation. The presence of water altered minerals, consistently thin layering, and layer orientations provide strong evidence that the ILD formed in a lacustrine setting.

Photograph - Woman Standing by a Large Tree Trunk

A woman wearing a hat and holding a small purse. She is standing next to a large tree trunk and there are benches behind her.

Safe Handling of Large Animals

The article discusses improving welfare by reducing fear by studying: Animal Sensory Perception, Animal Behavior Patterns, Animal Habituation and Temperament, Effects of Previous Handling, Training Animals, Training Time and Temperament, Genetic Effects on Handling, Handling of escaped Animals, Facilities, Aggression in Grazing Animals, Inherent Danger of Large Animals, Cattle and Car Accidents.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Factor Analysis of a Large DSGE Model

We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allow us to shed some light on the practical benefits and limitations of using factor analysis techniques on economic data. We explain in what sense the artificial data can be thought of having a factor structure, study the theoretical and finite sample properties of the principal components estimates of the factor space, investigate the substantive reason(s) for the good performance of di¤usion index forecasts, and assess the quality of the factor analysis of highly dissagregated data. In all our exercises, we explain the precise relationship between the factors and the basic macroeconomic shocks postulated by the model.