Stochastic Development Regression on Non-Linear Manifolds

We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded in the connection of the manifold. We propose an estimation procedure which applies the Laplace approximation of the likelihood function. A simulation study of the performance of the model is performed and the model is applied to a real dataset of Corpus Callosum shapes

COVID-19 Detection on Chest x-ray Images by Combining Histogram-oriented Gradient and Convolutional Neural Network Features

The COVID-19 coronavirus epidemic has spread rapidly worldwide after a person became infected with a severe health problem. The World Health Organization has declared the coronavirus a global threat (WHO). Early detection of COVID 19, particularly in cases with no apparent symptoms, may reduce the patients mortality rate. COVID 19 detection using machine learning techniques will aid healthcare systems around the world in recovering patients more rapidly. This disease is diagnosed using x-ray images of the chest; therefore, this study proposed a machine vision method for detecting COVID-19 in x-ray images of the chest. The histogram-oriented gradient (HOG) and convolutional neural network (CNN) features extracted from x-ray images were fused and classified using support vector machine (SVM) and softmax. The proposed feature fusion technique (99.36 percent) outperformed individual feature extraction methods such as HOG (87.34 percent) and CNN (93.64 percent)

Hernie obturatrice étranglée: à propos de deux cas

La hernie obturatrice (HO) est rare. Elle est à l'origine de 0,2 à 1,6% des occlusions mécaniques de l'intestin grêle avec un taux de mortalité etmorbidité après chirurgie est respectivement de 35 et 18%. Nous rapportons le cas de deux patientes chez qui le diagnostic de HO étranglée est établie dans le cadre du bilan d'une occlusion. La HO est une entité dont le diagnostic préopératoire est difficile en raison de la faible spécificité clinique. L'examen tomodensitométrique semble être une aide majeure au diagnostic  étiologique. Mais une fois le diagnostic d'occlusion posé, une intervention en urgence permettra d'en préciser l'étiologie et d'en réaliser le traitement. Tout retard thérapeutique majore la mortalité et la morbidité

Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically "learn" models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? In this work, we investigate existing algorithms for learning probabilistic models for model checking, propose an evolution-based approach for better controlling the degree of generalization and conduct an empirical study in order to answer the questions. One of our findings is that the effectiveness of learning may sometimes be limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP

Superdeformation in 198^{198}Po

The $^{174}$Yb($^{29}$Si,5n) reaction at 148 MeV with thin targets was used to populate high-angular momentum states in $^{198}$Po. Resulting $\gamma$ rays were observed with Gammasphere. A weakly-populated superdeformed band of 10 $\gamma$-ray transitions was found and has been assigned to $^{198}$Po. This is the first observation of a SD band in the $A \approx 190$ region in a nucleus with $Z > 83$. The ${\cal J}^{(2)}$ of the new band is very similar to those of the yrast SD bands in $^{194}$Hg and $^{196}$Pb. The intensity profile suggests that this band is populated through states close to where the SD band crosses the yrast line and the angular momentum at which the fission process dominates.Comment: 10 pages, revtex, 2 figs. available on request, submitted to Phys. Rev. C. (Rapid Communications

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements

Magnetar-like X-Ray Bursts from a Rotation-powered Pulsar, PSR J1119-6127

Two energetic hard X-ray bursts have recently triggered the Fermi and Swift space observatories from the rotation powered pulsar, PSR J1119-6127. We have performed in depth spectral and temporal analyses of these two events. Our extensive searches in both observatory data for lower luminosity bursts uncovered 10 additional events from the source. We report here on the timing and energetics of the 12 bursts from PSR J1119-6127 during its burst active phase of 2016 July 26 and 28. We also found a spectral softer X-ray flux enhancement in a post burst episode, which shows evidence of cooling. We discuss here the implications of these results on the nature of this unusual high-field radio pulsar, which firmly place it within the typical magnetar population.Comment: Revised version, accepted for publication in ApJL. An expanded version of Table 1, as well as the light curves of all Fermi/GBM detected bursts can be found at http://magnetars.sabanciuniv.edu/psrj1119.ph

Invariant higher-order variational problems II

Motivated by applications in computational anatomy, we consider a second-order problem in the calculus of variations on object manifolds that are acted upon by Lie groups of smooth invertible transformations. This problem leads to solution curves known as Riemannian cubics on object manifolds that are endowed with normal metrics. The prime examples of such object manifolds are the symmetric spaces. We characterize the class of cubics on object manifolds that can be lifted horizontally to cubics on the group of transformations. Conversely, we show that certain types of non-horizontal geodesics on the group of transformations project to cubics. Finally, we apply second-order Lagrange--Poincar\'e reduction to the problem of Riemannian cubics on the group of transformations. This leads to a reduced form of the equations that reveals the obstruction for the projection of a cubic on a transformation group to again be a cubic on its object manifold.Comment: 40 pages, 1 figure. First version -- comments welcome

Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping

The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of template images. In this paper, we consider a set of images randomly warped from a mean template which has to be recovered. For this, we define an appropriate statistical parametric model to generate random diffeomorphic deformations in two-dimensions. Then, we focus on the problem of estimating the mean pattern when the images are observed with noise. This problem is challenging both from a theoretical and a practical point of view. M-estimation theory enables us to build an estimator defined as a minimizer of a well-tailored empirical criterion. We prove the convergence of this estimator and propose a gradient descent algorithm to compute this M-estimator in practice. Simulations of template extraction and an application to image clustering and classification are also provided