Entropy based comparison of neural networks for classification

In recent years, multilayer feedforward neural networks (NN) have been shown to be very effective tools in many different applications. A natural and essential step in continuing the diffusion of these tools in day by day use is their hardware implementation which is by far the most cost effective solution for large scale use. When the hardware implementation is contemplated, the issue of the size of the NN becomes crucial because the size is directly proportional with the cost of the implementation. In this light, any theoretical results which establish bounds on the size of a NN for a given problem is extremely important. In the same context, a particularly interesting case is that of the neural networks using limited integer weights. These networks are particularly suitable for hardware implementation because they need less space for storing the weights and the fixed point, limited precision arithmetic has much cheaper implementations in comparison with its floating point counterpart. This paper presents an entropy based analysis which completes, unifies and correlates results partially presented in [Beiu, 1996, 1997a] and [Draghici, 1997]. Tight bounds for real and integer weight neural networks are calculated

An efficient hardware architecture for a neural network activation function generator

This paper proposes an efficient hardware architecture for a function generator suitable for an artificial neural network (ANN). A spline-based approximation function is designed that provides a good trade-off between accuracy and silicon area, whilst also being inherently scalable and adaptable for numerous activation functions. This has been achieved by using a minimax polynomial and through optimal placement of the approximating polynomials based on the results of a genetic algorithm. The approximation error of the proposed method compares favourably to all related research in this field. Efficient hardware multiplication circuitry is used in the implementation, which reduces the area overhead and increases the throughput

Antioxidant activity in selected tomato genotypes cultivated in conventional and organic culture systems

The present study is a compilation of results obtained at the Vegetable Research and Development Station Bacau regarding the influence of the culture system on the quantitative and qualitative yield of tomatoes. The present study provides comparative information regarding yield achievements of tomato genotypes cultivated in two different culture systems (conventional and organic), in order to highlight the suitability of the cultivation system. The best yield results were obtained at a density of 30,000 plants per hectare in both culture systems. All studied genotypes resulted in quantitatively superior yield in the conventional system as opposed to the organic system. Another purpose of this study was to determine the difference in antioxidant activity of tomato genotypes cultivated in the ecological and the conventional culture systems. The results indicate the suitability of the tomato to organic cultivation, highlighting the potential of the tomato species to be utilized as a significant source of natural antioxidants, and also the influence of cultivation systems on the accumulation of antioxidant compounds.Keywords: Lycopersicon esculentum, polyphenols, flavones, yieldAfrican Journal of Biotechnology Vol. 12(20), pp. 2884-289

Open Educational Resources for Learning Waste Management in Rural Communities

Environmental protection is close related with environmental education. Environmental education is as necessary in the educational system as outside it. The present study, starting from the perception of the representatives of the local rural public administration and the citizens of the rural communities on environmental protection issues: (i) highlights the need for information and education in the field of environmental protection, especially with regard to environmental legislation and waste management; (ii) demonstrates the usefulness of environmental educational projects and the use of open educational resources, as resources that can be easily adapted to the real needs of education and information

A splitting theorem for Kahler manifolds whose Ricci tensors have constant eigenvalues

It is proved that a compact Kahler manifold whose Ricci tensor has two distinct, constant, non-negative eigenvalues is locally the product of two Kahler-Einstein manifolds. A stronger result is established for the case of Kahler surfaces. Irreducible Kahler manifolds with two distinct, constant eigenvalues of the Ricci tensor are shown to exist in various situations: there are homogeneous examples of any complex dimension n > 1, if one eigenvalue is negative and the other positive or zero, and of any complex dimension n > 2, if the both eigenvalues are negative; there are non-homogeneous examples of complex dimension 2, if one of the eigenvalues is zero. The problem of existence of Kahler metrics whose Ricci tensor has two distinct, constant eigenvalues is related to the celebrated (still open) Goldberg conjecture. Consequently, the irreducible homogeneous examples with negative eigenvalues give rise to complete, Einstein, strictly almost Kahler metrics of any even real dimension greater than 4.Comment: 18 pages; final version; accepted for publication in International Journal of Mathematic

Two-dimensional enrichment analysis for mining high-level imaging genetic associations

Enrichment analysis has been widely applied in the genome-wide association studies (GWAS), where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases

Journalism Studies

This entry traces the history of journalism studies and asks whether journalism studies are a discipline or field or research method. Different interests involved in journalism studies – journalists, journalism educators and journalism scholars - make it difficult to find a single vision of what it entails. As a new field it requires its own methodologies even though these may be borrowed from other disciplines. It also requires its own body of literature. The origins of journalism studies are somewhat imprecise but we can identify five phases of evolution: normative, empirical turn, sociological turn, global-comparative turn, and digital turn. Journalism studies also encompass the education of journalists. Many journalism scholars now reside in journalism departments side by side with their practitioner colleagues. Historically the study and practice of journalism was entwined over the debate of whether the occupation of journalism should be regarded as a craft or a profession and indeed its place in the academy

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Discrepancies among transcriptome studies are frequently reported, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks normalizes data against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using an in-house printed chip and GeneChip. The framework is based on a common statistical characteristic of microarray data, and each data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other methods

Interpreting microarray experiments via co-expressed gene groups analysis

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information. We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co- expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO)1. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments