Molecular Orbital Theory of the Electronic Structure of Organic Compounds I. Dioxodiazacycloalkanes

The stability of some heterocyclic compounds derived from the condensation of succinic and glutaric anhydrides with aliphatic diamines is compared using calculated values for the total energies. Further, some results derived from the electronic population analysis and the available experimental data are used to give information about the corresponding electronic structure. The CNDO/2 and INDO methods have been used for the calculations.Publicado on line en 2014.Facultad de Ciencias Exacta

Shape Matching and Object Recognition

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45 % correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces

Efficient Recognition of Partially Visible Objects Using a Logarithmic Complexity Matching Technique

An important task in computer vision is the recognition of partially visible two-dimensional objects in a gray scale image. Recent works addressing this problem have attempted to match spatially local features from the image to features generated by models of the objects. However, many algo rithms are considerably less efficient than they might be, typ ically being O(IN) or worse, where I is the number offeatures in the image and N is the number of features in the model set. This is invariably due to the feature-matching portion of the algorithm. In this paper we discuss an algorithm that significantly improves the efficiency offeature matching. In addition, we show experimentally that our recognition algo rithm is accurate and robust. Our algorithm uses the local shape of contour segments near critical points, represented in slope angle-arclength space (θ-s space), as fundamental fea ture vectors. These feature vectors are further processed by projecting them onto a subspace in θ-s space that is obtained by applying the Karhunen-Loève expansion to all such fea tures in the set of models, yielding the final feature vectors. This allows the data needed to store the features to be re duced, while retaining nearly all information important for recognition. The heart of the algorithm is a technique for performing matching between the observed image features and the precomputed model features, which reduces the runtime complexity from O(IN) to O(I log I + I log N), where I and N are as above. The matching is performed using a tree data structure, called a kD tree, which enables multidi mensional searches to be performed in O(log) time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66975/2/10.1177_027836498900800608.pd

From Images to Shape Models for Object Detection

This research was supported by the EADS foundation, INRIA, CNRS, and SNSF. V. Ferrari was funded by a fellowship of the EADS foundation and by SNSF.International audienceWe present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes)

The association of adverse life events and parental mental health with emotional and behavioral outcomes in young adults with autism spectrum disorder

People with autism spectrum disorder (ASD) are at increased risk of developing co-occurring mental health difficulties across the lifespan. Exposure to adverse life events and parental mental health difficulties are known risk factors for developing a range of mental health difficulties. This study investigates the association of adverse life events, parental stress and mental health with emotional and behavioral problems in young adults with ASD. One hundred and fifteen young adults with ASD derived from a population-based longitudinal study were assessed at three time-points (12-, 16-, and 23-year) on questionnaire measures of emotional and behavioral problems. Parent-reported exposure to adverse life events and parental stress/mental health were measured at age 23. We used structural equation modeling to investigate the stability of emotional and behavioral problems over time, and the association between adverse life events and parental stress and mental health and emotional and behavioral outcomes at 23-year. Our results indicate that exposure to adverse life events was significantly associated with increased emotional and behavioral problems in young adults with ASD, while controlling for symptoms in childhood and adolescence. Higher reported parental stress and mental health difficulties were associated with a higher frequency of behavioral, but not emotional problems, and did not mediate the impact of adverse life events. These results suggest that child and adolescent emotional and behavioral problems, exposure to life events and parent stress and mental health are independently associated, to differing degrees, with emotional or behavioral outcomes in early adulthood. Lay Summary: People with autism experience high rates of mental health difficulties throughout childhood and into adult life. Adverse life events and parental stress and mental health may contribute to poor mental health in adulthood. We used data at three time points (12-, 16-, and 23-year) to understand how these factors relate to symptoms at 23-year. We found that emotional and behavioral problems in childhood, adverse life events and parent mental health were all associated with increased emotional and behavioral problems in adulthood