Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Evolutionary Approaches to Optimization Problems in Chimera Topologies

Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over $1000$ hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.Comment: 8 pages, 5 figures, 3 table

Nonflammable, antistatic, and heat-sealable film

Antistatic, heat-sealable, nonflammable films prepared from polyvinylidene fluoride and polyvinylidene chloride resin

Hydrogen atom in phase space. The Kirkwood-Rihaczek representation

We present a phase-space representation of the hydrogen atom using the Kirkwood-Rikaczek distribution function. This distribution allows us to obtain analytical results, which is quite unique because an exact analytical form of the Wigner functions corresponding to the atom states is not known. We show how the Kirkwood-Rihaczek distribution reflects properties of the hydrogen atom wave functions in position and momentum representations.Comment: 5 pages (and 5 figures

Mapping the Wigner distribution function of the Morse oscillator into a semi-classical distribution function

The mapping of the Wigner distribution function (WDF) for a given bound-state onto a semiclassical distribution function (SDF) satisfying the Liouville equation introduced previously by us is applied to the ground state of the Morse oscillator. Here we give results showing that the SDF gets closer to the corresponding WDF as the number of levels of the Morse oscillator increases. We find that for a Morse oscillator with one level only, the agreement between the WDF and the mapped SDF is very poor but for a Morse oscillator of ten levels it becomes satisfactory.Comment: Revtex, 27 pages including 13 eps figure

Suspension platform interferometer for the AEI 10\,m prototype: concept, design and optical layout

At present a 10\,m prototype interferometer facility is being set up at the AEI Hannover. One unique feature of the prototype will be the suspension platform interferometer (SPI). The purpose of the SPI is to monitor and stabilise the relative motion between three seismically isolated optical tables. The in-vacuum tables are suspended in an L-shaped configuration with an arm length of 11.65\,m. The design goal of the SPI is to stabilise longitudinal differential displacements to a level of 100\,pm/$\sqrt{\mathrm{Hz}}$ between 10\,mHz and 100\,Hz and relative angular noise of 10\,nrad/$\sqrt{\mathrm{Hz}}$ in the same frequency band. This paper covers the design aspects of the SPI, e.g. cross-coupling between the different degrees of freedom and fibre pointing noise are investigated. A simulation is presented which shows that with the chosen optical design of the SPI all degrees of table motion can be sensed in a fully decoupled way. Furthermore, a proof of principle test of the SPI sensing scheme is shown.Comment: 12 pages, 6 figure

Shape of retracting foils that model morphing bodies controls shed energy and wake structure

The flow mechanisms of shape-changing moving bodies are investigated through the simple model of a foil that is rapidly retracted over a spanwise distance as it is towed at constant angle of attack. It is shown experimentally and through simulation that by altering the shape of the tip of the retracting foil, different shape-changing conditions may be reproduced, corresponding to: (i) a vanishing body, (ii) a deflating body and (iii) a melting body. A sharp-edge, ‘vanishing-like’ foil manifests strong energy release to the fluid; however, it is accompanied by an additional release of energy, resulting in the formation of a strong ring vortex at the sharp tip edges of the foil during the retracting motion. This additional energy release introduces complex and quickly evolving vortex structures. By contrast, a streamlined, ‘shrinking-like’ foil avoids generating the ring vortex, leaving a structurally simpler wake. The ‘shrinking’ foil also recovers a large part of the initial energy from the fluid, resulting in much weaker wake structures. Finally, a sharp edged but hollow, ‘melting-like’ foil provides an energetic wake while avoiding the generation of a vortex ring. As a result, a melting-like body forms a simple and highly energetic and stable wake, that entrains all of the original added mass fluid energy. The three conditions studied correspond to different modes of flow control employed by aquatic animals and birds, and encountered in disappearing bodies, such as rising bubbles undergoing phase change to fluid

Competition between Diffusion and Fragmentation: An Important Evolutionary Process of Nature

We investigate systems of nature where the common physical processes diffusion and fragmentation compete. We derive a rate equation for the size distribution of fragments. The equation leads to a third order differential equation which we solve exactly in terms of Bessel functions. The stationary state is a universal Bessel distribution described by one parameter, which fits perfectly experimental data from two very different system of nature, namely, the distribution of ice crystal sizes from the Greenland ice sheet and the length distribution of alpha-helices in proteins.Comment: 4 pages, 3 figures, (minor changes

HITRAP: A facility at GSI for highly charged ions

An overview and status report of the new trapping facility for highly charged ions at the Gesellschaft fuer Schwerionenforschung is presented. The construction of this facility started in 2005 and is expected to be completed in 2008. Once operational, highly charged ions will be loaded from the experimental storage ring ESR into the HITRAP facility, where they are decelerated and cooled. The kinetic energy of the initially fast ions is reduced by more than fourteen orders of magnitude and their thermal energy is cooled to cryogenic temperatures. The cold ions are then delivered to a broad range of atomic physics experiments.Comment: 8 pages, 11 figure

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks