Blood groups and protein polymorphism in reproduction of farm animals

International audienc

Radar Target Classification Using Neural Network and Median Filter

The paper deals with Radar Target Classification based on the use of a neural network. A radar signal was acquired from the output of a J frequency band noncoherent radar. We applied the three layer feed forward neural network using the backpropagation learning algorithm. We defined classes of radar targets and designated each of them by its number. Our classification process resulted in the number of a radar target class, which the radar target belongs to

Scalable k-Means Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k-means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k-means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithm outperforms existing data summarization strategies in practice.Comment: To appear in the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD

Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron

The paper deals with classification of ground radar targets. A received radar signal backscattered from a ground radar target was digitized and in the form of radar signal matrix utilized for a feature extraction based on Singular Value Decomposition. Furthermore, singular values of a backscattered radar signal matrix, as a target feature, were utilized for Radar Target Classification by multilayer perceptron. In the learning phase of a multilayer perceptron we used the learning target set and in the testing phase the testing target set was used. The learning and testing target sets were created on the basis of real ground radar targets

Effect of particle properties of powders on the generation and transmission of raman scattering

Transmission Raman measurements of a 1 mm thick sulfur-containing disk were made at different positions as it was moved through 4 mm of aspirin (150-212 mu m) or microcrystalline cellulose (Avicel) of different size ranges (<38, 53-106, and 150-212 mu m). The transmission Raman intensity of the sulfur interlayer at 218 cm(-1) was lower when the disk was placed at the top or bottom of the powder bed, compared to positions within the bed and the difference between the sulfur intensity at the outer and inner positions increased with Avicel particle size. Also, the positional intensity difference was smaller for needle-shaped aspirin than for granular Avicel of the same size. The attenuation coefficients for the propagation of the exciting laser and transmitted Raman photons through the individual powders were the same but decreased as the particle size of Avicel increased; also, the attenuation coefficients for propagation through 150-212 mu m aspirin were almost half of those through similar sized Avicel particles. The study has demonstrated that particulate size and type affect transmitted Raman intensities and, consequently, such factors need to be considered in the analysis of powders, especially if particle properties vary between the samples

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Algorithms for Colourful Simplicial Depth and Medians in the Plane

The colourful simplicial depth of a point x in the plane relative to a configuration of n points in k colour classes is exactly the number of closed simplices (triangles) with vertices from 3 different colour classes that contain x in their convex hull. We consider the problems of efficiently computing the colourful simplicial depth of a point x, and of finding a point, called a median, that maximizes colourful simplicial depth. For computing the colourful simplicial depth of x, our algorithm runs in time O(n log(n) + k n) in general, and O(kn) if the points are sorted around x. For finding the colourful median, we get a time of O(n^4). For comparison, the running times of the best known algorithm for the monochrome version of these problems are O(n log(n)) in general, improving to O(n) if the points are sorted around x for monochrome depth, and O(n^4) for finding a monochrome median.Comment: 17 pages, 8 figure

A new technique for elucidating β\beta-decay schemes which involve daughter nuclei with very low energy excited states

A new technique of elucidating $\beta$-decay schemes of isotopes with large density of states at low excitation energies has been developed, in which a Broad Energy Germanium (BEGe) detector is used in conjunction with coaxial hyper-pure germanium detectors. The power of this technique has been demonstrated on the example of 183Hg decay. Mass-separated samples of 183Hg were produced by a deposition of the low-energy radioactive-ion beam delivered by the ISOLDE facility at CERN. The excellent energy resolution of the BEGe detector allowed $\gamma$ rays energies to be determined with a precision of a few tens of electronvolts, which was sufficient for the analysis of the Rydberg-Ritz combinations in the level scheme. The timestamped structure of the data was used for unambiguous separation of $\gamma$ rays arising from the decay of 183Hg from those due to the daughter decays