In this paper, we present a new graph model of sparse matrix decomposition for parallel sparse matrix–vector multiplication. Our model differs from previous graph-based approaches in two main respects. Firstly, our model is based on edge colouring rather than vertex partitioning. Secondly, our model is able to correctly quantify and minimise the total communication volume of the parallel sparse...

putative activity of hydroalcoholic and aqueous infusion extracts of echium amoenum l. was investigated in mice using the rotarod model of motor coordination and the elevated plus maze model of anxiety. the extracts were administered intraperitonealy (i.p.) once, one hour before performing the tests. preliminary phytochemical study of the plant, with standard procedures, showed that it contains...

In this study, the problem of sparse enrollment data for in-set versus out-of-set speaker recognition is addressed. The challenge here is that both the training speaker data (5 s) and test material (2~6 s) is of limited test duration. The limited enrollment data result in a sparse acoustic model space for the desired speaker model. The focus of this study is on filling these acoustic holes by h...

This paper presents an algorithm for extracting planar patches by integrating both intensity and range data provided by a stereo system. For dealing with noisy and sparse range data, the initial segmentation is based on intensity information, and then the resulted regions are thresholded using depth data. This new algorithm, different from the existing ones that use only range data in the segme...

In this work, we investigate how to automatically reassign the manually annotated labels at the image-level to those contextually derived semantic regions. First, we propose a bi-layer sparse coding formulation for uncovering how an image or semantic region can be robustly reconstructed from the over-segmented image patches of an image set. We then harness it for the automatic label to region a...

In this article, we explore two forms of selectivity in sensory neurons. The first we call classic selectivity, referring to the stimulus that optimally stimulates a neuron. If a neuron is linear, then this stimulus can be determined by measuring the response to an orthonormal basis set (the receptive field). The second type of selectivity we call hyperselectivity; it is either implicitly or ex...

Truncated singular value decomposition (TSVD) is a popular method for solving linear discrete ill-posed problems with a small to moderately sized matrix A. Regularization is achieved by replacing the matrix A by its best rank-k approximant, which we denote by Ak. The rank may be determined in a variety of ways, e.g., by the discrepancy principle or the L-curve criterion. This paper describes a ...

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...

We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while being as similar as possible to the en...

We give a construction that produces 6-sparse Steiner triple systems of order v for all sufficiently large v of the form 3p, p prime and p ≡ 3 (mod 4). We also give a complete list of all 429 6-sparse systems with v < 10000 produced by this construction.