Stabilized lowest order finite element approximation for linear three-field poroelasticity

A stabilized conforming mixed finite element method for the three-field (displacement, fluid flux and pressure) poroelasticity problem is developed and analyzed. We use the lowest possible approximation order, namely piecewise constant approximation for the pressure and piecewise linear continuous elements for the displacements and fluid flux. By applying a local pressure jump stabilization term to the mass conservation equation we ensure stability and avoid pressure oscillations. Importantly, the discretization leads to a symmetric linear system. For the fully discretized problem we prove existence and uniqueness, an energy estimate and an optimal a-priori error estimate, including an error estimate for the divergence of the fluid flux. Numerical experiments in 2D and 3D illustrate the convergence of the method, show the effectiveness of the method to overcome spurious pressure oscillations, and evaluate the added mass effect of the stabilization term.Comment: 25 page

Phylogeny-Aware Placement and Alignment Methods for Short Reads

In recent years bioinformatics has entered a new phase: New sequencing methods, generally referred to as Next Generation Sequencing (NGS) have become widely available. This thesis introduces algorithms for phylogeny aware analysis of short sequence reads, as generated by NGS methods in the context of metagenomic studies. A considerable part of this work focuses on the technical (w.r.t. performance) challenges of these new algorithms, which have been developed specifically to exploit parallelism

The GICHD Regional Support Centre: An Approach to Regional Information Management

Ask most people in mine action what is meant by regional information management and they will talk to you about the consolidation of country-specific mine action information at centralized regional locations. They may talk about the need for data aggregation, the reluctance of programmes to provide data and the generally slow pace of the work. In almost all cases, they will mention data analysis and comparisons between the work completed in different programmes as key elements in regional systems. Most of the examples given will focus on efforts that fell short of expectations and failed to deliver on the promise of increased efficiency and improved resource allocation so often touted as reasons for regional or global data management. In short, for many people, regional information management is a concept that has seldom managed to maintain the constant flow of unconsolidated and standardized data required to achieve its real potential over the long term

A poroelastic model coupled to a fluid network with applications in lung modelling

Here we develop a lung ventilation model, based a continuum poroelastic representation of lung parenchyma and a 0D airway tree flow model. For the poroelastic approximation we design and implement a lowest order stabilised finite element method. This component is strongly coupled to the 0D airway tree model. The framework is applied to a realistic lung anatomical model derived from computed tomography data and an artificially generated airway tree to model the conducting airway region. Numerical simulations produce physiologically realistic solutions, and demonstrate the effect of airway constriction and reduced tissue elasticity on ventilation, tissue stress and alveolar pressure distribution. The key advantage of the model is the ability to provide insight into the mutual dependence between ventilation and deformation. This is essential when studying lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be used to form a better understanding of integrated lung mechanics in both the healthy and diseased states

DSeg: Direct Line Segments Detection

This paper presents a model-driven approach to detect image line segments. The approach incrementally detects segments on the gradient image using a linear Kalman filter that estimates the supporting line parameters and their associated variances. The algorithm is fast and robust with respect to image noise and illumination variations, it allows the detection of longer line segments than data-driven approaches, and does not require any tedious parameters tuning. An extension of the algorithm that exploits a pyramidal approach to enhance the quality of results is proposed. Results with varying scene illumination and comparisons to classic existing approaches are presented

Measurement of a Vacuum-Induced Geometric Phase

Berry's geometric phase naturally appears when a quantum system is driven by an external field whose parameters are slowly and cyclically changed. A variation in the coupling between the system and the external field can also give rise to a geometric phase, even when the field is in the vacuum state or any other Fock state. Here we demonstrate the appearance of a vacuum-induced Berry phase in an artificial atom, a superconducting transmon, interacting with a single mode of a microwave cavity. As we vary the phase of the interaction, the artificial atom acquires a geometric phase determined by the path traced out in the combined Hilbert space of the atom and the quantum field. Our ability to control this phase opens new possibilities for the geometric manipulation of atom-cavity systems also in the context of quantum information processing.Comment: 5 + 6 page

Proton charge and magnetic rms radii from the elastic epep scattering data

The elastic electron-proton scattering data are analysed in order to determine proton charge and magnetic rms radii, r_E and r_M. Along with the usual statistical error, we try to estimate a systematic error in the radii, caused by the inadequacy of particular form factor parameterization employed. The range of data to use in the analysis is chosen so as to minimize the total (statistical + systematic) error. We obtain r_E = 0.912 +- 0.009 (stat) +- 0.007 (syst) fm, and r_M = 0.876 +- 0.010 (stat) +- 0.016 (syst) fm. The cross-section data were corrected for two-photon exchange. We found that without such corrections obtained r_E and r_M are somewhat smaller while the quality of fit is worse.Comment: 6 pages, 4 figures. Numbers slightly changed due to discovered error in minimization program. Sec.III revised, discussion of G_E behaviour added

Camera Relocalization with Ellipsoidal Abstraction of Objects

International audienceWe are interested in AR applications which take place in man-made GPS-denied environments, as industrial or indoor scenes. In such environments, relocalization may fail due to repeated patterns and large changes in appearance which occur even for small changes in viewpoint. We investigate in this paper a new method for relocalization which operates at the level of objects and takes advantage of the impressive progress realized in object detection. Recent works have opened the way towards object oriented reconstruction from elliptic approximation of objects detected in images. We go one step further and propose a new method for pose computation based on ellipse/ellipsoid correspondences. We consider in this paper the practical common case where an initial guess of the rotation matrix of the pose is known, for instance with an inertial sensor or from the estimation of orthogonal vanishing points. Our contributions are twofold: we prove that a closed-form estimate of the translation can be computed from one ellipse-ellipsoid correspondence. The accuracy of the method is assessed on the LINEMOD database using only one correspondence. Second, we prove the effectiveness of the method on real scenes from a set of object detections generated by YOLO. A robust framework that is able to choose the best set of hypotheses is proposed and is based on an appropriate estimation of the reprojection error of ellipsoids. Globally, considering pose at the level of object allows us to avoid common failures due to repeated structures. In addition, due to the small combinatory induced by object correspondences, our method is well suited to fast rough localization even in large environments