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

Heat Stress Effect on Immune Function in Dairy Cattle

This information was presented at the 2017 Cornell Nutrition Conference for Feed Manufacturers, organized by the Department of Animal Science In the College of Agriculture and Life Sciences at Cornell University. Softcover copies of the entire conference proceedings may be purchased at http://ansci.cals.cornell.edu/extension-outreach/adult-extension/dairy-management/order-proceedings-resources.This presentation will cover recent work examining the impact of heat stress in late gestation on the cow and the calf, during the dry period and subsequent lactation, including the effects on immune function. The presentation will also address the effects of heat stress on health and performance of lactating dairy cows, and use of an immune modulator to reduce the impacts of heat stress

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

Extreme points of the set of density matrices with positive partial transpose

We present a necessary and sufficient condition for a finite dimensional density matrix to be an extreme point of the convex set of density matrices with positive partial transpose with respect to a subsystem. We also give an algorithm for finding such extreme points and illustrate this by some examples.Comment: 4 pages, 2 figure

On the concepts of radial and angular kinetic energies

We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wavefunction picture and the Weyl-Wigner phase-space picture. Thus, the radial and angular kinetic energies are different quantities in the two pictures, containing different physical information, but the relation between them is well defined. We discuss this relation and illustrate its nature by examples referring to a free particle and to a ground-state hydrogen atom.Comment: 10 pages, 2 figures, accepted by Phys. Rev.

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

An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices

Detecting and reacting to user behavior and ambient context are core elements of many emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting accurate infer-ences from raw sensor data is challenging within the noisy and complex environments where these systems are deployed. Deep Learning { is one of the most promising approaches for overcom-ing this challenge, and achieving more robust and reliable infer-ence. Techniques developed within this rapidly evolving area of machine learning are now state-of-the-art for many inference tasks (such as, audio sensing and computer vision) commonly needed by IoT and wearable applications. But currently deep learning al-gorithms are seldom used in mobile/IoT class hardware because they often impose debilitating levels of system overhead (e.g., memory, computation and energy). Efforts to address this bar-rier to deep learning adoption are slowed by our lack of a system-atic understanding of how these algorithms behave at inference time on resource constrained hardware. In this paper, we present the-rst { albeit preliminary { measurement study of common deep learning models (such as Convolutional Neural Networks and Deep Neural Networks) on representative mobile and embed-ded platforms. The aim of this investigation is to begin to build knowledge of the performance characteristics, resource require-ments and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context. The results and insights of this study, lay an empirical foundation for the development of optimization methods and execution envi-ronments that enable deep learning to be more readily integrated into next-generation IoT, smartphones and wearable systems

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

Preemptive vortex-loop proliferation in multicomponent interacting Bose--Einstein condensates

We use analytical arguments and large-scale Monte Carlo calculations to investigate the nature of the phase transitions between distinct complex superfluid phases in a two-component Bose--Einstein condensate when a non-dissipative drag between the two components is being varied. We focus on understanding the role of topological defects in various phase transitions and develop vortex-matter arguments allowing an analytical description of the phase diagram. We find the behavior of fluctuation induced vortex matter to be much more complex and substantially different from that of single-component superfluids. We propose and investigate numerically a novel drag-induced ``preemptive vortex loop proliferation'' transition. Such a transition may be a quite generic feature in many multicomponent systems where symmetry is restored by a gas of several kinds of competing vortex loops.Comment: 12 pages, 10 figures. Submitted to Physical Review