We introduce and analyse a minimal network model of semantic memory in the human brain. The model is a global associative memory structured as a collection of N local modules, each coding a feature, which can take S possible values, with a global sparseness a (the average fraction of features describing a concept). We show that, under optimal conditions, the number cM of modules connected on av...

Recently, a new approach to multiple removal has been introduced: estimation of primaries by sparse inversion (EPSI). Although based on the same relationship between primaries and multiples as surface-related multiple elimination (SRME), it involves quite a different process: instead of prediction and subtraction of multiples, in EPSI the unknown primaries are the parameters of a large-scale in...

We propose a method for separating speech signals with little distortion when the signals outnumber the sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks that extract a signal at time points where the number of active sources is estimated to be only one. Howev...

A new robust blind microphone array method to enhance speech signals generated by multiple moving sources in a noisy environment is presented. This approach is based on a two-stage scheme. A subband clustering time-delay estimation algorithm is first used to localize the dominant speech sources. The speech enhancement is performed in a second stage, based on the acquired spatial information, by...

The existing patch sparsity based image inpainting algorithms have some problems in maintaining structure coherence and neighborhood consistence. To address the above problems, a robust and adaptive image inpainting algorithm based on a novel structure sparsity is proposed. The main improvement includes the following three aspects. Firstly, a novel structure sparsity function is defined accordi...

This paper presents research resulting in a neural network model relating product design specifications and performance testing results using data from a sanitary ware manufacturer. The main constraint of the work was the limited availability of actual data for neural network training and testing, a situation often found in real situations where a priori product knowledge is limited during the ...

Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time s...

A novel approach to single frame multichannel blind image deconvolution is formulated recently as non-negative matrix factorization (NMF) problem with sparseness constraint imposed on the unknown mixing vector. Unlike most of the blind image deconvolution algorithms, the NMF approach requires no a priori knowledge about the blurring kernel and original image. The experimental performance evalua...

We examined the visual perception of joint actions, in which two individuals coordinate their body movements in space and time to achieve a joint goal. Animations of interacting action pairs (partners in human interactions) and non-interacting action pairs (individual actors sampled from different interaction sequences) were shown in the experiment. Participants were asked to rate how likely th...