Anomalous attenuation of extraordinary waves in the ionosphere heating experiments

Multiple scattering of radio waves by artificial random irregularities HF-induced in the ionosphere F region may cause significant attenuation of both ordinary and extraordinary waves together with common anomalous absorption of ordinary waves due to their non-linear conversion into plasma waves. To demonstrate existence and strength of this effect, direct measurements of attenuation of both powerful pump wave and weak probing waves of extraordinary polarization have been carried out during an experimental campaign on September 6, 7 and 9, 1999 at the Sura heating facility. The attenuation magnitude of extraordinary waves reaches of 1-10 dB over a background attenuation caused by natural irregularities. It is interpreted in the paper on the base of the theory of multiple scattering from the artificial random irregularities with characteristic scale lengths of 0.1-1 km. Simple procedure for determining of irregularity spectrum parameters from the measured attenuation of extraordinary waves has been implemented and some conclusions about the artificial irregularity formation have been obtained.Comment: 17 pages, 9 figure

Ultrasonic attenuation in magnetic fields for superconducting states with line nodes in Sr2RuO4

We calculate the ultrasonic attenuation in magnetic fields for superconducting states with line nodes vertical or horizontal relative to the RuO_2 planes. This theory, which is valid for fields near Hc2 and not too low temperatures, takes into account the effects of supercurrent flow and Andreev scattering by the Abrikosov vortex lattice. For rotating in-plane field H(theta) the attenuation alpha(theta)exhibits variations of fourfold symmetry in the rotation angle theta. In the case of vertical nodes, the transverse T100 sound mode yields the weakest(linear)H and T dependence of alpha, while the longitudinal L100 mode yields a stronger (quadratic) H and T dependence. This is in strong contrast to the case of horizontal line nodes where alpha is the same for the T100 and L100 modes (apart from a shift of pi/4 in field direction) and is roughly a quadratic function of H and T. Thus we conclude that measurements of alpha in in-plane magnetic fields for different in-plane sound modes may be an important tool for probing the nodal structure of the gap in Sr_2RuO_4.Comment: 5 pages, 6 figures, replaced in non-preprint form, to appear in Phys. Rev.

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A Generative Model of Natural Texture Surrogates

Natural images can be viewed as patchworks of different textures, where the local image statistics is roughly stationary within a small neighborhood but otherwise varies from region to region. In order to model this variability, we first applied the parametric texture algorithm of Portilla and Simoncelli to image patches of 64X64 pixels in a large database of natural images such that each image patch is then described by 655 texture parameters which specify certain statistics, such as variances and covariances of wavelet coefficients or coefficient magnitudes within that patch. To model the statistics of these texture parameters, we then developed suitable nonlinear transformations of the parameters that allowed us to fit their joint statistics with a multivariate Gaussian distribution. We find that the first 200 principal components contain more than 99% of the variance and are sufficient to generate textures that are perceptually extremely close to those generated with all 655 components. We demonstrate the usefulness of the model in several ways: (1) We sample ensembles of texture patches that can be directly compared to samples of patches from the natural image database and can to a high degree reproduce their perceptual appearance. (2) We further developed an image compression algorithm which generates surprisingly accurate images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how our approach can be used for an efficient and objective evaluation of samples generated with probabilistic models of natural images.Comment: 34 pages, 9 figure

Ionospheric effects of the solar flares of September 23, 1998 and July 29, 1999 as deduced from global GPS network data

This paper presents data from first GPS measurements of global response of the ionosphere to solar flares of September 23, 1998 and July 29, 1999. The analysis used novel technology of a global detection of ionospheric effects from solar flares (GLOBDET) as developed by one of the authors (Afraimovich E. L.). The essence of the method is that use is made of appropriate filtering and a coherent processing of variations in total electron content (TEC) in the ionosphere which is determined from GPS data, simultaneously for the entire set of visible (over a given time interval) GPS satellites at all stations used in the analysis. It was found that fluctuations of TEC, obtained by removing the linear trend of TEC with a time window of about 5 min, are coherent for all stations and beams to the GPS satellites on the dayside of the Earth. The time profile of TEC responses is similar to the time behavior of hard X-ray emission variations during flares in the energy range 25-35 keV if the relaxation time of electron density disturbances in the ionosphere of order 50-100 s is introduced. No such effect on the nightside of the Earth has been detected yet.Comment: EmTeX-386, 13 pages, 5 figure

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category