An iterative Multiscale Finite-Volume algorithm converging to the exact solution

The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

Methods for testing association between uncertain genotypes and quantitative traits.

Interpretability and power of genome-wide association studies can be increased by imputing unobserved genotypes, using a reference panel of individuals genotyped at higher marker density. For many markers, genotypes cannot be imputed with complete certainty, and the uncertainty needs to be taken into account when testing for association with a given phenotype. In this paper, we compare currently available methods for testing association between uncertain genotypes and quantitative traits. We show that some previously described methods offer poor control of the false-positive rate (FPR), and that satisfactory performance of these methods is obtained only by using ad hoc filtering rules or by using a harsh transformation of the trait under study. We propose new methods that are based on exact maximum likelihood estimation and use a mixture model to accommodate nonnormal trait distributions when necessary. The new methods adequately control the FPR and also have equal or better power compared to all previously described methods. We provide a fast software implementation of all the methods studied here; our new method requires computation time of less than one computer-day for a typical genome-wide scan, with 2.5 M single nucleotide polymorphisms and 5000 individuals.

Omnidirectional Depth Computation from a Single Image

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception. A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications

Partial disjoint path for multi-layer protection in GMPLS networks

In this paper, different recovery methods applied at different network layers and time scales are used in order to enhance the network reliability. Each layer deploys its own fault management methods. However, current recovery methods are applied to only a specific layer. New protection schemes, based on the proposed partial disjoint path algorithm, are defined in order to avoid protection duplications in a multi-layer scenario. The new protection schemes also encompass shared segment backup computation and shared risk link group identification. A complete set of experiments proves the efficiency of the proposed methods in relation with previous ones, in terms of resources used to protect the network, the failure recovery time and the request rejection ratio

Prospective universal application of mycobacterial interspersed repetitive-unit-variable-number tandem-repeat genotyping to characterize Mycobacterium tuberculosis isolates for fast identification of clustered and orphan cases.

The use of molecular tools for genotyping Mycobacterium tuberculosis isolates in epidemiological surveys in order to identify clustered and orphan strains requires faster response times than those offered by the reference method, IS6110 restriction fragment length polymorphism (RFLP) genotyping. A method based on PCR, the mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) genotyping technique, is an option for fast fingerprinting of M. tuberculosis, although precise evaluations of correlation between MIRU-VNTR and RFLP findings in population-based studies in different contexts are required before the methods are switched. In this study, we evaluated MIRU-VNTR genotyping (with a set of 15 loci [MIRU-15]) in parallel to RFLP genotyping in a 39-month universal population-based study in a challenging setting with a high proportion of immigrants. For 81.9% (281/343) of the M. tuberculosis isolates, both RFLP and MIRU-VNTR types were obtained. The percentages of clustered cases were 39.9% (112/281) and 43.1% (121/281) for RFLP and MIRU-15 analyses, and the numbers of clusters identified were 42 and 45, respectively. For 85.4% of the cases, the RFLP and MIRU-15 results were concordant, identifying the same cases as clustered and orphan (kappa, 0.7). However, for the remaining 14.6% of the cases, discrepancies were observed: 16 of the cases clustered by RFLP analysis were identified as orphan by MIRU-15 analysis, and 25 cases identified as orphan by RFLP analysis were clustered by MIRU-15 analysis. When discrepant cases showing subtle genotypic differences were tolerated, the discrepancies fell from 14.6% to 8.6%. Epidemiological links were found for 83.8% of the cases clustered by both RFLP and MIRU-15 analyses, whereas for the cases clustered by RFLP or MIRU-VNTR analysis alone, links were identified for only 30.8% or 38.9% of the cases, respectively. The latter group of cases mainly comprised isolates that could also have been clustered, if subtle genotypic differences had been tolerated. MIRU-15 genotyping seems to be a good alternative to RFLP genotyping for real-time interventional schemes. The correlation between MIRU-15 and IS6110 RFLP findings was reasonable, although some uncertainties as to the assignation of clusters by MIRU-15 analysis were identified.

Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Ultra-fast recovery from right neglect after 'awake surgery' for slow-growing tumor invading the left parietal area

It is now possible to perform resections of slow-growing tumors in awake patients. Using direct electrical stimulation, real-time functional mapping of the brain can be used to prevent the resection of essential areas near the tumor. Simple clinical observations of patients with a resection of slow-growing tumors have demonstrated substantial recovery within a few days of such 'awake surgery'. The aim of this study was to investigate the kinetics of recovery following the resection of slow-growing tumors invading the left parietal area and to focus mainly on its rapidity. Two patients were assessed by standard line bisection tests and compared with eight healthy individuals. Independently of the pure nature of the symptoms, we report that the patients rapidly and substantially recovered from pronounced right neglect. They were tested 48 hours after the surgery and the recovery was significant for both patients after less than 4 hours. Strikingly, for one patient, recovery was ultra fast and substantial in the first practice session within less than 7 minutes: it occurred without verbal feedback and was substantially retained during the following testing session. Its rapidity suggests a process of unmasking redundant networks. With the slow growth of the lesion, the contralesional hemisphere is probably progressively prepared for rapid unmasking of homologue networks. These results have major clinical implications. For patients with an invading left-side tumor, it is now clear that line bisections are required before, during, and after awake surgery to: plan the surgery, control the quality of the resection, and also optimize the rehabilitation of the patient

Three-dimensional high-resolution fast spin-echo coronary magnetic resonance angiography.

Due to SNR constraints, current "bright-blood" 3D coronary MRA approaches still suffer from limited spatial resolution when compared to conventional x-ray coronary angiography. Recent 2D fast spin-echo black-blood techniques maximize signal for coronary MRA at no loss in image spatial resolution. This suggests that the extension of black-blood coronary MRA with a 3D imaging technique would allow for a further signal increase, which may be traded for an improved spatial resolution. Therefore, a dual-inversion 3D fast spin-echo imaging sequence and real-time navigator technology were combined for high-resolution free-breathing black-blood coronary MRA. In-plane image resolution below 400 microm was obtained. Magn Reson Med 45:206-211, 2001.

Constraint algorithm for k-presymplectic Hamiltonian systems. Application to singular field theories

The k-symplectic formulation of field theories is especially simple, since only tangent and cotangent bundles are needed in its description. Its defining elements show a close relationship with those in the symplectic formulation of mechanics. It will be shown that this relationship also stands in the presymplectic case. In a natural way,one can mimick the presymplectic constraint algorithm to obtain a constraint algorithmthat can be applied to k-presymplectic field theory, and more particularly to the Lagrangian and Hamiltonian formulations offield theories defined by a singular Lagrangian, as well as to the unified Lagrangian-Hamiltonian formalism (Skinner--Rusk formalism) for k-presymplectic field theory. Two examples of application of the algorithm are also analyzed.