University of York

About: University of York is a education organization based out in York, York, United Kingdom. It is known for research contribution in the topics: Population & Health care. The organization has 22089 authors who have published 56925 publications receiving 2458285 citations. The organization is also known as: York University & Ebor..

Short-Term Music Training Enhances Verbal Intelligence and Executive Function

Abstract: Researchers have designed training methods that can be used to improve mental health and to test the efficacy of education programs. However, few studies have demonstrated broad transfer from such training to performance on untrained cognitive activities. Here we report the effects of two interactive computerized training programs developed for preschool children: one for music and one for visual art. After only 20 days of training, only children in the music group exhibited enhanced performance on a measure of verbal intelligence, with 90% of the sample showing this improvement. These improvements in verbal intelligence were positively correlated with changes in functional brain plasticity during an executive-function task. Our findings demonstrate that transfer of a high-level cognitive skill is possible in early childhood.

Better late than never: information retrieval from black holes.

Abstract: We show that, in order to preserve the equivalence principle until late times in unitarily evaporating black holes, the thermodynamic entropy of a black hole must be primarily entropy of entanglement across the event horizon. For such black holes, we show that the information entering a black hole becomes encoded in correlations within a tripartite quantum state, the quantum analogue of a one-time pad, and is only decoded into the outgoing radiation very late in the evaporation. This behavior generically describes the unitary evaporation of highly entangled black holes and requires no specially designed evolution. Our work suggests the existence of a matter-field sum rule for any fundamental theory.

Local scale control for edge detection and blur estimation

Abstract: We show that knowledge of sensor properties and operator norms can be exploited to define a unique, locally computable minimum reliable scale for local estimation at each point in the image. This method for local scale control is applied to the problem of detecting and localizing edges in images with shallow depth of field and shadows. We show that edges spanning a broad range of blur scales and contrasts can be recovered accurately by a single system with no input parameters other than the second moment of the sensor noise. A natural dividend of this approach is a measure of the thickness of contours which can be used to estimate focal and penumbral blur. Local scale control is shown to be important for the estimation of blur in complex images, where the potential for interference between nearby edges of very different blur scale requires that estimates be made at the minimum reliable scale.