45,623 research outputs found
Alternating-Direction Line-Relaxation Methods on Multicomputers
We study the multicom.puter performance of a three-dimensional Navier–Stokes solver based on alternating-direction line-relaxation methods. We compare several multicomputer implementations, each of which combines a particular line-relaxation method and a particular distributed block-tridiagonal solver. In our experiments, the problem size was determined by resolution requirements of the application. As a result, the granularity of the computations of our study is finer than is customary in the performance analysis of concurrent block-tridiagonal solvers. Our best results were obtained with a modified half-Gauss–Seidel line-relaxation method implemented by means of a new iterative block-tridiagonal solver that is developed here. Most computations were performed on the Intel Touchstone Delta, but we also used the Intel Paragon XP/S, the Parsytec SC-256, and the Fujitsu S-600 for comparison
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis
While estimation of the marginal (total) causal effect of a point exposure on
an outcome is arguably the most common objective of experimental and
observational studies in the health and social sciences, in recent years,
investigators have also become increasingly interested in mediation analysis.
Specifically, upon evaluating the total effect of the exposure, investigators
routinely wish to make inferences about the direct or indirect pathways of the
effect of the exposure, through a mediator variable or not, that occurs
subsequently to the exposure and prior to the outcome. Although powerful
semiparametric methodologies have been developed to analyze observational
studies that produce double robust and highly efficient estimates of the
marginal total causal effect, similar methods for mediation analysis are
currently lacking. Thus, this paper develops a general semiparametric framework
for obtaining inferences about so-called marginal natural direct and indirect
causal effects, while appropriately accounting for a large number of
pre-exposure confounding factors for the exposure and the mediator variables.
Our analytic framework is particularly appealing, because it gives new insights
on issues of efficiency and robustness in the context of mediation analysis. In
particular, we propose new multiply robust locally efficient estimators of the
marginal natural indirect and direct causal effects, and develop a novel double
robust sensitivity analysis framework for the assumption of ignorability of the
mediator variable.Comment: Published in at http://dx.doi.org/10.1214/12-AOS990 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
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