The role of demonstrator familiarity and language cues on infant imitation from television

An imitation procedure was used to investigate the impact of demonstrator familiarity and language cues on infant learning from television. Eighteen-month-old infants watched two pre-recorded videos showing an adult demonstrating a sequence of actions with two sets of stimuli. Infants' familiarity with the demonstrator and the language used during the demonstration varied as a function of experimental condition. Immediately after watching each video, infants' ability to reproduce the target actions was assessed. A highly familiar demonstrator did not enhance infants' performance. However, the addition of a narrative, developed from mothers' naturalistic description of the event, facilitated learning from an unfamiliar demonstrator. We propose that the differential effect of demonstrator familiarity and language cues may reflect the infants' ability to distinguish between important and less important aspects in a learning situation. (C) 2010 Elsevier Inc. All rights reserved

The MAJORANA DEMONSTRATOR: A Search for Neutrinoless Double-beta Decay of Germanium-76

The observation of neutrinoless double-beta decay would determine whether the neutrino is a Majorana particle and provide information on the absolute scale of neutrino mass. The MAJORANA Collaboration is constructing the DEMONSTRATOR, an array of germanium detectors, to search for neutrinoless double-beta decay of 76-Ge. The DEMONSTRATOR will contain 40 kg of germanium; up to 30 kg will be enriched to 86% in 76-Ge. The DEMONSTRATOR will be deployed deep underground in an ultra-low-background shielded environment. Operation of the DEMONSTRATOR aims to determine whether a future tonne-scale germanium experiment can achieve a background goal of one count per tonne-year in a 4-keV region of interest around the 76-Ge neutrinoless double-beta decay Q-value of 2039 keV.Comment: Submitted to AIP Conference Proceedings, 19th Particles & Nuclei International Conference (PANIC 2011), Massachusetts Institute of Technology, Cambridge, MA, USA, July 24-29, 2011; 3 pages, 1 figur

A demonstrator for the Micro-Vertex-Detector of the CBM experiment

CMOS sensors are the most promising candidates for the Micro-Vertex-Detector (MVD) of the CBM experiment at GSI, as they provide an unprecedented compromise between spatial resolution, low material budget, adequate radiation tolerance and readout speed. To study the integration of these sensors into a detector module, a so-called MVD-demonstrator has been developed. The demonstrator and its in-beam performance will be presented and discussed in this work

Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of 76Ge to Excited States of 76Se

The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76Ge can decay into three E.S.s of 76Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 2.1) ⋅ 1024 y. In particular, for the 2v transition to the first 0+ E.S. of 76Se, a lower half-life limit of 0.68 ⋅ 1024 at 90% CL was achieved

Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis

This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient to estimate an accurate reward function. Observing that each demonstrator has an inherent reward for each state and the task-specific behaviors mainly depend on a small number of key states, we propose a meta IRL algorithm that first models the reward function for each task as a distribution conditioned on a baseline reward function shared by all tasks and dependent only on the demonstrator, and then finds the most likely reward function in the distribution that explains the task-specific behaviors. We test the method in a simulated environment on path planning tasks with limited demonstrations, and show that the accuracy of the learned reward function is significantly improved. We also apply the method to analyze the motion of a patient under rehabilitation.Comment: arXiv admin note: text overlap with arXiv:1707.0939