On the large-scale angular distribution of short-Gamma ray bursts

We investigate the large-scale angular distribution of the short-Gamma ray bursts (SGRBs) from BATSE experiment, using a new coordinates-free method. The analyses performed take into account the angular correlations induced by the non-uniform sky exposure during the experiment, and the uncertainty in the measured angular coordinates. Comparising the large-scale angular correlations from the data with those expected from simulations using the exposure function we find similar features. Additionally, confronting the large-angle correlations computed from the data with those obtained from simulated maps produced under the assumption of statistical isotropy we found that they are incompatible at 95% confidence level. However, such differences are restricted to the angular scales 36o - 45o, which are likely to be due to the non-uniform sky exposure. This result strongly suggests that the set of SGRBs from BATSE are intrinsically isotropic. Moreover, we also investigated a possible large-angle correlation of these data with the supergalactic plane. No evidence for such large-scale anisotropy was found.Comment: Accepted for publication in The Astrophysical Journal, 6 pages, 3 figure

A boundary integral equation method in the frequency domain for cracks under transient loading

Acknowledgments The financial support of the German Academic Exchange Service (DAAD), Engineering and Physical Sciences Research Council (EPSRC) and Advanced Research Collaboration (ARC) Programme (funded by the British Council and DAAD) is gratefully acknowledged.Peer reviewedPublisher PD

A Bayesian estimate of the CMB-large-scale structure cross-correlation

Evidences for late-time acceleration of the Universe are provided by multiple probes, such as Type Ia supernovae, the cosmic microwave background (CMB) and large-scale structure (LSS). In this work, we focus on the integrated Sachs--Wolfe (ISW) effect, i.e., secondary CMB fluctuations generated by evolving gravitational potentials due to the transition between, e.g., the matter and dark energy (DE) dominated phases. Therefore, assuming a flat universe, DE properties can be inferred from ISW detections. We present a Bayesian approach to compute the CMB--LSS cross-correlation signal. The method is based on the estimate of the likelihood for measuring a combined set consisting of a CMB temperature and a galaxy contrast maps, provided that we have some information on the statistical properties of the fluctuations affecting these maps. The likelihood is estimated by a sampling algorithm, therefore avoiding the computationally demanding techniques of direct evaluation in either pixel or harmonic space. As local tracers of the matter distribution at large scales, we used the Two Micron All Sky Survey (2MASS) galaxy catalog and, for the CMB temperature fluctuations, the ninth-year data release of the Wilkinson Microwave Anisotropy Probe (WMAP9). The results show a dominance of cosmic variance over the weak recovered signal, due mainly to the shallowness of the catalog used, with systematics associated with the sampling algorithm playing a secondary role as sources of uncertainty. When combined with other complementary probes, the method presented in this paper is expected to be a useful tool to late-time acceleration studies in cosmology.Comment: 21 pages, 15 figures, 4 tables. We extended the previous analyses including WMAP9 Q, V and W channels, besides the ILC map. Updated to match accepted ApJ versio

A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios

Research in autonomous driving for unstructured environments suffers from a lack of semantically labeled datasets compared to its urban counterpart. Urban and unstructured outdoor environments are challenging due to the varying lighting and weather conditions during a day and across seasons. In this paper, we introduce TAS500, a novel semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively. We evaluate the performance of modern semantic segmentation models with an additional focus on their efficiency. Our experiments demonstrate the advantages of fine-grained semantic classes to improve the overall prediction accuracy, especially along the class boundaries. The dataset and pretrained model are available at mucar3.de/icpr2020-tas500.Comment: Accepted at International Conference on Pattern Recognition 2020 (ICPR). For the associated project page, see https://www.mucar3.de/icpr2020-tas500/index.htm

Games as Systems for Rehabilitation: A Design Strategy for Game-based Exercise Rehabilitation for Parkinson\u27s Disease

Games are of interest for health interventions including but not limited to physical activity and rehabilitation, behavior change, motor-cognitive training, and mood elevation. Despite increased interest in using games to produce positive health outcomes, the development or selection process of games, or their suitability for a target demographic in a context of health and rehabilitation, remains ad-hoc. As a result, game-based interventions lacking application specificity produce variable outcomes that obscure the true treatment effect of game-based therapies. To address this issue, we present a design strategy for game-based rehabilitation that uses a player-centric approach to develop/select games for specific contexts such as for improving functional deficits in patients with Parkinson’s disease. This strategy establishes a relationship between the exercise rehabilitation regimen and gameplay by incorporating the rehabilitation requirements, patient condition, and player affordances, into the game world. In addition, we present guiding questions to support the application of the design strategy for improving the effectiveness of game-based rehabilitations