Incremental Learning for Robot Perception through HRI

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on image datasets and real-world robotics scenarios. We present a novel paradigm for incrementally improving a robot's visual perception through active human interaction. In this paradigm, the user introduces novel objects to the robot by means of pointing and voice commands. Given this information, the robot visually explores the object and adds images from it to re-train the perception module. Our base perception module is based on recent development in object detection and recognition using deep learning. Our method leverages state of the art CNNs from off-line batch learning, human guidance, robot exploration and incremental on-line learning

Phenomenology of SUSY with scalar sequestering

The defining feature of scalar sequestering is that the MSSM squark and slepton masses as well as all entries of the scalar Higgs mass matrix vanish at some high scale. This ultraviolet boundary condition - scalar masses vanish while gaugino and Higgsino masses are unsuppressed - is independent of the supersymmetry breaking mediation mechanism. It is the result of renormalization group scaling from approximately conformal strong dynamics in the hidden sector. We review the mechanism of scalar sequestering and prove that the same dynamics which suppresses scalar soft masses and the B_mu term also drives the Higgs soft masses to -|mu|^2. Thus the supersymmetric contribution to the Higgs mass matrix from the mu-term is exactly canceled by the soft masses. Scalar sequestering has two tell-tale predictions for the superpartner spectrum in addition to the usual gaugino mediation predictions: Higgsinos are much heavier (mu > TeV) than scalar Higgses (m_A ~ few hundred GeV), and third generation scalar masses are enhanced because of new positive contributions from Higgs loops.Comment: 16 pages and 3 figure

Institutional diversity in the euro area: Any evidence of convergence?

In recent years differences in the institutional structure across euro area countries are becoming a cause of concern both for some individual Member States and for the functioning of the Economic and Monetary Union (EMU). From a global competitiveness perspective, we deal with the diversity in the institutional environment in the EMU. In particular, we assess whether the changes in the state of institutions provide convergence across euro area countries between 2006 and 2015. In addition, among the institutional indicators considered, we compute which institutional aspect contributes more to overall inequality in the state of institutions, as well as the contribution of each country to inequality considering as benchmark the country with the highest institutional quality. According to these country contributions, we highlight distinct patterns of convergence between ‘core’ and ‘periphery’ euro area countries and raise potential links between the institutional changes across euro area countries and both the differences in the intensity of the financial and economic crisis, and the policy responses in terms of fiscal consolidation applied by the respective national governments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Correction system for polyphonic piano recordings

M.I. Martin-Erdozain, I. Barbancho, A. Rosa-Pujazon, A.M. Barbancho, "Correction system for polyphonic piano recordings", XXVIII Simposium Nacional de la Unión Científica Internacional de Radio, Santiago de Compostela, España, 2013n this paper, a support tool for piano rehearsal is presented. The system analyses a given piano polyphonic recording to find the times, pitch and duration of the notes and figures played, taking into account the possibility of playing more than one note simultaneously as well as covering the whole piano frequency range. In order to do so, the system uses an onset detection algorithm to segment the input signal into partitions which are then analysed in the time and frequency domains. Then, the system correlates the data extracted from the partitions with the score of the original piece, identifying the positions and type of the mistakes performed by the user, and providing her/him with the corresponding feedback. The experiments conducted showed that the application is capable of analysing a given recording and indicate the musician the mistakes made.This work has been funded by the Ministerio de Economia y Competitividad of the Spanish Government under Project No. TIN2010-21089-C03-02 and by the Ministerio de Industria, Turismo y Comercio under Project No. TSI-090100-2011-25