A novel CMB polarization likelihood package for large angular scales built from combined WMAP and Planck LFI legacy maps

We present a CMB large-scale polarization dataset obtained by combining WMAP Ka, Q and V with Planck 70 GHz maps. We employ the legacy frequency maps released by the WMAP and Planck collaborations and perform our own Galactic foreground mitigation technique, which relies on Planck 353 GHz for polarized dust and on Planck 30 GHz and WMAP K for polarized synchrotron. We derive a single, optimally-noise-weighted, low-residual-foreground map and the accompanying noise covariance matrix. These are shown, through $\chi^2$ analysis, to be robust over an ample collection of Galactic masks. We use this dataset, along with the Planck legacy Commander temperature solution, to build a pixel-based low-resolution CMB likelihood package, whose robustness we test extensively with the aid of simulations, finding excellent consistency. Using this likelihood package alone, we constrain the optical depth to reionazation $\tau=0.069^{+0.011}_{-0.012}$ at $68\%$ C.L., on 54\% of the sky. Adding the Planck high-$\ell$ temperature and polarization legacy likelihood, the Planck lensing likelihood and BAO observations we find $\tau=0.0714_{-0.0096}^{+0.0087}$ in a full $\Lambda$CDM exploration. The latter bounds are slightly less constraining than those obtained employing \Planck\ HFI CMB data for large angle polarization, that only include EE correlations. Our bounds are based on a largely independent dataset that does include also TE correlations. They are generally well compatible with Planck HFI preferring slightly higher values of $\tau$. We make the low-resolution Planck and WMAP joint dataset publicly available along with the accompanying likelihood code.Comment: The WMAP+LFI likelihood module is available on \http://www.fe.infn.it/u/pagano/low_ell_datasets/wmap_lfi_legacy

A representation of robotic behaviors using component port arbitration

Presented at DSLRob 2014 (arXiv:cs/1411.7148)Developing applications considering reactiveness, scalability and re-usability has always been at the center of attention of robotic researchers. Behavior-based architectures have been proposed as a programming paradigm to develop robust and complex behaviors as integration of simpler modules whose activities are directly modulated by sensory feedback or input from other models. The design of behavior based systems, however, becomes increasingly difficult as the complexity of the application grows. This article proposes an approach for modeling and coordinating behaviors in distributed architectures based on port arbitration which clearly separates representation of the behaviors from the composition of the software components. Therefore, based on different behavioral descriptions, the same software components can be reused to implement different applications

Design and Implementation of a YARP Device Driver Interface: The Depth-Sensor Case

This work illustrates the design phases leading to the development of a new YARP device interface along with its client/server implementation. In order to obtain a smoother integration and a more reliable software usability, while avoiding common errors during the design phases, a new interface is created in the YARP network when a new family of devices is introduced

Evidence for pseudogap and phase-coherence gap separation by Andreev reflection experiments in Au/La_{2-x}Sr_{x}CuO_4 point-contact junctions

We present new Au/La_{2-x}Sr_{x}CuO_{4} (LSCO) point-contact conductance measures as a function of voltage and temperature in samples with 0.08 <= x <= 0.2. Andreev reflection features disappear at about the bulk Tc, giving no evidence of gap for T > Tc. The fit of the normalized conductance at any T < Tc supports a (s + d)-wave symmetry of the gap, whose dominant low-T s component follows the Tc(x) curve in contrast with recent angle-resolved photoemission spectroscopy and quasiparticle tunneling data. These results prove the separation between pseudogap and phase-coherence superconducting gap in LSCO at x <= 0.2.Comment: 4 pages, 4 eps figures, 1 table (RevTeX). Labels added to Fig. 1; Fig. 3 resized; references added; short discussion about ballistic contact regime adde

Speeding-up Object Detection Training for Robotics with FALKON

Latest deep learning methods for object detection provide remarkable performance, but have limits when used in robotic applications. One of the most relevant issues is the long training time, which is due to the large size and imbalance of the associated training sets, characterized by few positive and a large number of negative examples (i.e. background). Proposed approaches are based on end-to-end learning by back-propagation [22] or kernel methods trained with Hard Negatives Mining on top of deep features [8]. These solutions are effective, but prohibitively slow for on-line applications.In this paper we propose a novel pipeline for object detection that overcomes this problem and provides comparable performance, with a 60x training speedup. Our pipeline combines (i) the Region Proposal Network and the deep feature extractor from [22] to efficiently select candidate RoIs and encode them into powerful representations, with (ii) the FALKON [23] algorithm, a novel kernel-based method that allows fast training on large scale problems (millions of points). We address the size and imbalance of training data by exploiting the stochastic subsampling intrinsic into the method and a novel, fast, bootstrapping approach.We assess the effectiveness of the approach on a standard Computer Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a real robotic scenario with the iCubWorld Transformations [18] dataset