Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks

Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate the efficacy of the proposed progressive label distillation approach, we experimented with learning a deep limited vocabulary speech recognition network based on generated 500ms input utterances distilled progressively from 1000ms source training data, and demonstrated a significant increase in test accuracy of almost 78% compared to direct learning.Comment: 9 page

Effective hadronic Lagrangian for charm mesons

An effective hadronic Lagrangian including the charm mesons is introduced to study their interactions in hadronic matter. Using coupling constants that are determined either empirically or by the SU(4) symmetry, we have evaluated the absorption cross sections of $J/\psi$ and the scattering cross sections of $D$ and $D^*$ by $\pi$ and $\rho$ mesons.Comment: 5 pages, 4 eps figures, presented at Strangeness 2000, Berkeley. Uses iopart.cl

The Impacts of Knowledge Interaction with Manufacturing Clients on KIBS Firms Innovation Behaviour

knowledge-intensive business services (KIBS), knowledge interaction, innovations systems

A modelling study of beta-amyloid induced change in hippocampal theta rhythm

Many dementia cases, such as Alzheimer’s disease (AD), are characterized by an increase in low frequency field potential oscillations. However, a definitive understanding of the effects of the beta-Amyloid peptide, which is a main marker of AD, on the low frequency theta rhythm (4-7Hz) is still unavailable. In this work, we investigate the neural mechanisms associated with beta-Amyloid toxicity using a conductance-based neuronal network model of the hippocampus CA1 region. We simulate the effects of beta-Amyloid on the A-type fast inactivating K+ channel by modulating the maximum conductance of the current in pyramidal cells, denoted by gA. Our simulation results demonstrate that as gA decreases (through A[beta]
blockage), the theta band power first increases then decreases. Thus there exists a value of gA that maximizes the theta band power. The neuronal and network mechanism underlying the change in theta rhythm is systematically analyzed. We show that the increase in theta power is due to the improved synchronization of pyramidal neurons, and the theta decrease is induced by the faster depolarisation of pyramidal neurons