Bacteriostatic conformal coating for electronic components

Coating for electronic components used in space applications has bacteriostatic qualities capable of hindering bacterial reproduction, both vegetative and sporulative viable microorganisms. It exhibits high electrical resistivity, a low outgassing rate, and is capable of restraining electronic components when subjected to mechanical vibrations

Bacteriostatic conformal coating and methods of application Patent

Development of bacteriostatic conformal coating and methods of applicatio

Asymptotic Exponential Arbitrage and Utility-based Asymptotic Arbitrage in Markovian Models of Financial Markets

Consider a discrete-time infinite horizon financial market model in which the logarithm of the stock price is a time discretization of a stochastic differential equation. Under conditions different from those given in a previous paper of ours, we prove the existence of investment opportunities producing an exponentially growing profit with probability tending to $1$ geometrically fast. This is achieved using ergodic results on Markov chains and tools of large deviations theory. Furthermore, we discuss asymptotic arbitrage in the expected utility sense and its relationship to the first part of the paper.Comment: Forthcoming in Acta Applicandae Mathematica

Etude des tumeurs équines à partir des cas autopsiés à l'Institut de Pathologie du Cheval (Dozule, France) de 1986 a 1998

L'auteur a réalisé une étude rétrospective qui a permis de déterminer la prévalence et la distribution des tumeurs équines observées à l'autopsie à l'institut de Pathologie du Cheval, en Basse-Normandie, sur une période de 13 ans. La prévalence tumorale est de 4,69% ou de 8,84% si l'on ne considère que les chevaux autopsiés de plus d'un an. 120 chevaux âgés de 14,7 ans en moyenne présentaient une ou plusieurs tumeurs. Parmi eux, 14 étaient porteurs de 2 types tumoraux différents. Au total, 134 tumeurs ont donc été analysées. Elles se répartissaient en 18 types tumoraux différents dont les caractéristiques sont détaillées par l'auteur et complétées par des données bibliographiques. Les tumeurs les plus fréquentes étaient asymptomatiques. Il s'agissait d'adénomes thyroïdiens (34 cas), de lipomes (30 cas) et de cholestéatomes (29 cas). Les autres tumeurs observées étaient par ordre de fréquence décroissant des lymphosarcomes, des mélanomes, des adénomes hypophysaires, des hémangiosarcomes, des mésothéliomes, des tumeurs de la granulosa, des adénocarcinomes rénaux, des carcinomes épidermoïdes, un papillome cutané, un hématome de l'ethmoïde, un léiomyosarcome gastrique, un méningiome, un séminome, un liposarcome et un hémangiome. Les tumeurs bénignes étaient nettement prédominantes (80%)

P ORTOLAN: a Model-Driven Cartography Framework

Processing large amounts of data to extract useful information is an essential task within companies. To help in this task, visualization techniques have been commonly used due to their capacity to present data in synthesized views, easier to understand and manage. However, achieving the right visualization display for a data set is a complex cartography process that involves several transformation steps to adapt the (domain) data to the (visualization) data format expected by visualization tools. To maximize the benefits of visualization we propose Portolan, a generic model-driven cartography framework that facilitates the discovery of the data to visualize, the specification of view definitions for that data and the transformations to bridge the gap with the visualization tools. Our approach has been implemented on top of the Eclipse EMF modeling framework and validated on three different use cases

First Detection of Cosmic Microwave Background Lensing and Lyman-{\alpha} Forest Bispectrum

We present the first detection of a correlation between the Lyman-$\alpha$ forest and cosmic microwave background (CMB) lensing. For each Lyman-$\alpha$ forest in SDSS-III/BOSS DR12, we correlate the one-dimensional power spectrum with the CMB lensing convergence on the same line of sight from Planck. This measurement constitutes a position-dependent power spectrum, or a squeezed bispectrum, and quantifies the non-linear response of the Lyman-$\alpha$ forest power spectrum to a large-scale overdensity. The signal is measured at 5~$\sigma$ and is consistent with the $\Lambda$CDM expectation. We measure the linear bias of the Lyman-$\alpha$ forest with respect to the dark matter distribution, and constrain a combination of non-linear terms including the non-linear bias. This new observable provides a consistency check for the Lyman-$\alpha$ forest as a large-scale structure probe and tests our understanding of the relation between intergalactic gas and dark matter. In the future, it could be used to test hydrodynamical simulations and calibrate the relation between the Lyman-$\alpha$ forest and dark matter.Comment: 8 pages, 7 figures; accepted for publication in Phys. Rev.

A Convolutional Neural Network Approach to Supernova Time-Series Classification

One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically unfeasible. Thus, the ability to identify SNe by type using time-series image data alone is crucial, especially in light of the increasing breadth and depth of upcoming telescopes. We present a convolutional neural network method for fast supernova time-series classification, with observed brightness data smoothed in both the wavelength and time directions with Gaussian process regression. We apply this method to full duration and truncated SN time-series, to simulate retrospective as well as real-time classification performance. Retrospective classification is used to differentiate cosmologically useful Type Ia SNe from other SN types, and this method achieves >99% accuracy on this task. We are also able to differentiate between 6 SN types with 60% accuracy given only two nights of data and 98% accuracy retrospectively.Comment: Accepted at the ICML 2022 Workshop on Machine Learning for Astrophysic