Distributed Snapshot algorithm for multi-active object-based applications

International audienceThis paper exposes an adaptation of the classic algorithm for consistent snapshot in distributed systems with asynchronous processes due to Chandy&Lamport. A snapshot in this context is described as the consistent set of states of all involved communicating processes that allows recovering the whole system after a crash. The reconstructed system state is consistent, even if messages injected into the system from the outside while the snapshot was ongoing may have been lost (if such messages can not be replayed). We expose how to adapt this algorithm to a particular distributed programming model, the Active Object model (in its multi-active version). We applied it successfully to a non trivial distributed application programmed using Active Objects serving as a publish/subscribe and storage of events middleware, dubbed the EventCloud

LOFAR Sparse Image Reconstruction

Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKAComment: Published in A&A, 19 pages, 9 figure

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

A kinematic study of the Taurus-Auriga T association

Aims: This is the first paper in a series dedicated to investigating the kinematic properties of nearby associations of young stellar objects. Here we study the Taurus-Auriga association, with the primary objective of deriving kinematic parallaxes for individual members of this low-mass star-forming region. Methods: We took advantage of a recently published catalog of proper motions for pre-main sequence stars, which we supplemented with radial velocities from various sources found in the CDS databases. We searched for stars of the Taurus-Auriga region that share the same space velocity, using a modified convergent point method that we tested with extensive Monte Carlo simulations. Results: Among the sample of 217 Taurus-Auriga stars with known proper motions, we identify 94 pre-main sequence stars that are probable members of the same moving group and several additional candidates whose pre-main sequence evolutionary status needs to be confirmed. We derive individual parallaxes for the 67 moving group members with known radial velocities and give tentative parallaxes for other members based on the average spatial velocity of the group. The Hertzsprung-Russell diagram for the moving group members and a discussion of their masses and ages are presented in a companion paper.Comment: accepted for publication by A&