USING OF AIRBORNE LIDAR ALTIMETRY AND SEMI-AUTOMATED GIS TOOLS FOR IDENTIFICATION AND MAPPING OF FLUVIAL TERRACES IN THE AUGŠDAUGAVA SPILLWAY VALLEY

River terraces are one of the typical and widespread Quaternary fluvial landforms in Latvia. Until recently, distinguishing and mapping of these features have required extensive field surveys. However, the availability of high resolution digital elevation models (DEM) derived from airborne laser altimetry and application of modern geographic information systems (GIS) provides sufficient background to resolve these tasks. In this study, we apply an integrated methodology based on using of remote sensing, i.e. laser scanning or Light Detection and Ranging (LiDAR) data and combining of semi-automated TerEx tools for the detection of fluvial terraces in DEM. The empirical tests performed using the tools in the study area reveal that the application of TerEx gives the reliable results. However, presence of minor landforms which increase the topographical roughness of the surface directly influence the quality of extracted data, thus leading to the necessity of additional manual editing. The obtained data indicate that the terrace sequence in the Augšdaugava spillway valley consists of eight different terrace levels – T1 to T8. Only terraces T1 and T2 are easily recognizable in the study area, whereas the upper terraces do not have explicit edges

Minimum Distance Estimation of Milky Way Model Parameters and Related Inference

We propose a method to estimate the location of the Sun in the disk of the Milky Way using a method based on the Hellinger distance and construct confidence sets on our estimate of the unknown location using a bootstrap based method. Assuming the Galactic disk to be two-dimensional, the sought solar location then reduces to the radial distance separating the Sun from the Galactic center and the angular separation of the Galactic center to Sun line, from a pre-fixed line on the disk. On astronomical scales, the unknown solar location is equivalent to the location of us earthlings who observe the velocities of a sample of stars in the neighborhood of the Sun. This unknown location is estimated by undertaking pairwise comparisons of the estimated density of the observed set of velocities of the sampled stars, with densities estimated using synthetic stellar velocity data sets generated at chosen locations in the Milky Way disk according to four base astrophysical models. The "match" between the pair of estimated densities is parameterized by the affinity measure based on the familiar Hellinger distance. We perform a novel cross-validation procedure to establish a desirable "consistency" property of the proposed method.Comment: 25 pages, 10 Figures. This version incorporates the suggestions made by the referees. To appear in SIAM/ASA Journal on Uncertainty Quantificatio

Task-based Augmented Contour Trees with Fibonacci Heaps

This paper presents a new algorithm for the fast, shared memory, multi-core computation of augmented contour trees on triangulations. In contrast to most existing parallel algorithms our technique computes augmented trees, enabling the full extent of contour tree based applications including data segmentation. Our approach completely revisits the traditional, sequential contour tree algorithm to re-formulate all the steps of the computation as a set of independent local tasks. This includes a new computation procedure based on Fibonacci heaps for the join and split trees, two intermediate data structures used to compute the contour tree, whose constructions are efficiently carried out concurrently thanks to the dynamic scheduling of task parallelism. We also introduce a new parallel algorithm for the combination of these two trees into the output global contour tree. Overall, this results in superior time performance in practice, both in sequential and in parallel thanks to the OpenMP task runtime. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the run-time efficiency of our approach and its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Learning to Generate 3D Training Data

Human-level visual 3D perception ability has long been pursued by researchers in computer vision, computer graphics, and robotics. Recent years have seen an emerging line of works using synthetic images to train deep networks for single image 3D perception. Synthetic images rendered by graphics engines are a promising source for training deep neural networks because it comes with perfect 3D ground truth for free. However, the 3D shapes and scenes to be rendered are largely made manual. Besides, it is challenging to ensure that synthetic images collected this way can help train a deep network to perform well on real images. This is because graphics generation pipelines require numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this dissertation, we propose automatic generation pipelines of synthetic data that aim to improve the task performance of a trained network. We explore both supervised and unsupervised directions for automatic optimization of 3D decisions. For supervised learning, we demonstrate how to optimize 3D parameters such that a trained network can generalize well to real images. We first show that we can construct a pure synthetic 3D shape to achieve state-of-the-art performance on a shape-from-shading benchmark. We further parameterize the decisions as a vector and propose a hybrid gradient approach to efficiently optimize the vector towards usefulness. Our hybrid gradient is able to outperform classic black-box approaches on a wide selection of 3D perception tasks. For unsupervised learning, we propose a novelty metric for 3D parameter evolution based on deep autoregressive models. We show that without any extrinsic motivation, the novelty computed from autoregressive models alone is helpful. Our novelty metric can consistently encourage a random synthetic generator to produce more useful training data for downstream 3D perception tasks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163240/1/ydawei_1.pd

Energetics of lipid bilayers with applications to deformations induced by inclusions

A new energy for the description of large deformations of lipid bilayers is formulated with mathematical rigor. This energy is derived by considering the smectic A liquid crystalline nature of lipid bilayers and the coupling between the deformations of the layers and their constituent lipid molecules. Analogies between smectic A liquid crystals, with an infinite number of layers, and lipid bilayers, with a finite number of layers, are further discussed. The novelty of the energy density is demonstrated by studying the large deformations of planar lipid bilayers induced by cylindrical inclusions. The results of this study are directly compared with the results obtained using May's theoretical framework [May, Eur. Biophys. J., 2000, 29, 17–28] in which small deformations are assumed. As expected, the proposed energy density predicts larger distortions of the lipid molecules and deformations of the lipid bilayers close to an inclusion