[en] Pattern recognition techniques were employed in order to investigate the possibility to find features of the elements of the periodic system that may be relevant for the description of their behaviour with respect to superconductivity. Learning machines were constructed using those elements of the periodic system whose superconducting properties have been well studied. Relevant features appear to be the electronic work function and the number of valence electrons as given by Miedema, the specific heat, the heat of melting, the heat of sublimation, the melting point and the atomic radius. The learning machines have a predicting capability of the order of 90%. The predictive power of these machines concerning the superconducting behaviour of the alkali and alkaline-earth metals belonging to a given test set, however, appears to be less convincing

[en] In 1972, V. Keilis-Borok and I. Gelfand introduced the phenomenological approach based on the morphostructural zoning and pattern recognition for identification of earthquake-prone areas. This methodology identifies seismogenic nodes capable of generating strong earthquakes on the basis of geological, morphological, and geophysical data, which do not contain information on past seismicity. In the period 1972–2018, totally, 26 worldwide seismic regions have been studied and maps showing the recognized earthquake-prone areas in each region have been published. After that, 11 of these regions were hit by earthquakes of the relevant sizes. The goal of this work is to analyze the correlation of the post-publication events with seismogenic nodes defined in these 11 regions. The test was performed using the NEIC earthquake catalog because it uniformly defines the location and magnitudes of earthquakes over the globe. The ArcMap facilities were exploited to plot the post-publication events on the maps showing the recognized seismogenic nodes. We found that about 86% of such events fall in the recognized seismogenic nodes. The performed test proved the sufficient validity of the methodology for identifying areas capable of strong earthquakes and confirms the idea on nucleating strong earthquakes at the nodes.

[en] The importance of statistical evaluation of multielemental data is illustrated using the data collected in a macro- and micro-PIXE analysis of human brain tumours. By employing a multivariate statistical classification methodology (SIMCA) it was shown that the total information collected from each specimen separates three types of tissue: High malignant, less malignant and normal brain tissue. This makes a classification of a given specimen possible based on the elemental concentrations. Partial least squares regression (PLS), a multivariate regression method, made it possible to study the relative importance of the examined nine trace elements, the dry/wet weight ratio and the age of the patient in predicting the survival time after operation for patients with the high malignant form, astrocytomas grade III-IV. The elemental maps from a microprobe analysis were also subjected to multivariate analysis. This showed that the six elements sorted into maps could be presented in three maps containing all the relevant information. The intensity in these maps is proportional to the value (score) of the actual pixel along the calculated principal components. (orig.)

[en] A micro processor M6800 is used for the automation of scanning and premeasuring BESSY tables. The tasks achieved by the micro processor are: 1. control of spooling of the four asynchronous film winding devices and switching on and off the 4 projections lamps, 2. pre-processing of the data coming from a bi-polar coordinates measuring device, 3. bi-directional interchange of informations between the operator, the BESSY table and the DEC PDP 11/34 mini computer controling the scanning operations, 4. control of the magnification on the table by swapping the projection lenses of appropriate focal lengths and the associated light boxes (under development). In connection with point 4, study is being made for the use of BESSY tables for accurate measurements (+/-5 microns), by encoding the displacements of the projections lenses. (orig.)

[en] This work presents an automation system for detecting the presence of bottom-simulating reflectors (BSRs) and other reflectors parallel to the sea bottom in real seismograms. The system uses automatic gain control to aid the thresholding method for edge detection and segmentation to calculate the sea bottom curve. After preprocessing the seismic reflection images, 2D multiresolution analysis methodology was used to develop the automated pattern recognition system which detects BSRs and other parallel reflectors in a robust manner. Synthetic and real seismic images were used to evaluate the proposed methodology, which showed excellent results in both cases. (paper)

[en] Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance

[en] Pattern recognition concepts and several sample problems are presented in flaw classification

[en] The presentation of Fig. 4 was incorrect.

[en] The last advances in complex systems research point to some essential features underlying the dynamical aspects of complex systems behavior. We present here a comprehensive description of such advances including chaos, pattern formation, solitons, dissipative structures and turbulence. (Author)

[en] A test for the statistical significance of observed differences between two measured ROC curves has been designed and evaluated. The set of observer response data for each ROC curve is assumed to be independent and to arise from an ROC curve having a form which, in the absence of statistical fluctuations in the response data, graphs as a straight line on double normal-deviate axes. Such a 'binormal' ROC is defined by two parameters, which represent the slope and one axis intercept of the normal-deviate graph