Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

Observations of the post shock break-out emission of SN 2011dh with XMM-Newton

After the occurrence of the type cIIb SN 2011dh in the nearby spiral galaxy M 51 numerous observations were performed with different telescopes in various bands ranging from radio to gamma-rays. We analysed the XMM-Newton and Swift observations taken 3 to 30 days after the SN explosion to study the X-ray spectrum of SN 2011dh. We extracted spectra from the XMM-Newton observations, which took place ~7 and 11 days after the SN. In addition, we created integrated Swift/XRT spectra of 3 to 10 days and 11 to 30 days. The spectra are well fitted with a power-law spectrum absorbed with Galactic foreground absorption. In addition, we find a harder spectral component in the first XMM-Newton spectrum taken at t ~ 7 d. This component is also detected in the first Swift spectrum of t = 3 - 10 d. While the persistent power-law component can be explained as inverse Compton emission from radio synchrotron emitting electrons, the harder component is most likely bremsstrahlung emission from the shocked stellar wind. Therefore, the harder X-ray emission that fades away after t ~ 10 d can be interpreted as emission from the shocked circumstellar wind of SN 2011dh.Comment: Accepted for publication as a Research Note in Astronomy and Astrophysic

Jamming Model for the Extremal Optimization Heuristic

Extremal Optimization, a recently introduced meta-heuristic for hard optimization problems, is analyzed on a simple model of jamming. The model is motivated first by the problem of finding lowest energy configurations for a disordered spin system on a fixed-valence graph. The numerical results for the spin system exhibit the same phenomena found in all earlier studies of extremal optimization, and our analytical results for the model reproduce many of these features.Comment: 9 pages, RevTex4, 7 ps-figures included, as to appear in J. Phys. A, related papers available at http://www.physics.emory.edu/faculty/boettcher

Extremal Optimization for Graph Partitioning

Extremal optimization is a new general-purpose method for approximating solutions to hard optimization problems. We study the method in detail by way of the NP-hard graph partitioning problem. We discuss the scaling behavior of extremal optimization, focusing on the convergence of the average run as a function of runtime and system size. The method has a single free parameter, which we determine numerically and justify using a simple argument. Our numerical results demonstrate that on random graphs, extremal optimization maintains consistent accuracy for increasing system sizes, with an approximation error decreasing over runtime roughly as a power law t^(-0.4). On geometrically structured graphs, the scaling of results from the average run suggests that these are far from optimal, with large fluctuations between individual trials. But when only the best runs are considered, results consistent with theoretical arguments are recovered.Comment: 34 pages, RevTex4, 1 table and 20 ps-figures included, related papers available at http://www.physics.emory.edu/faculty/boettcher

Riemannian Sparse Coding for Positive Definite Matrices

International audienceInspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches

Tuberous sclerosis with pulmonary lymphangioleiomyomatosis and renal angiomyolipomas. Computed tomographic findings: a case report

The authors describe a case of a 31-year-old female with tuberous sclerosis, a genetic, rare, variably expressed disease. Clinical symptoms were chest pain, and progressive dyspnea. Computed tomography scan of the chest showed bilateral, diffuse, small thin-walled cysts scattered throughout the lungs characteristic for pulmonary lymphangioleiomyomatosis. Computed tomography scan of the abdomen revealed enlarged, heterogeneous kidneys, with low density tumors corresponding to angiomyolipomas. Pulmonary lymphangioleiomyomatosis and bilateral renal angiomyolipomas are some presentations of tuberous sclerosis and the coexistence of both conditions may cause devastating morbidity and mortality