Kinetic equation for hydrogen-induced direct phase transformations in Y2Fe17 magnetic alloy

Model for evolution of the hydrogen-induced direct phase transformation in Y2Fe17 magnetic alloy has been proposed. It is shown that evolution process of hydrogen-induced direct phase transformation in Y2Fe17 type hard magnetic alloy is controlled by diffusion process of Fe atoms in low temperatures interval of 330–750 oC. On the base of Kolmogorov and Lyubov kinetic theory has been obtained kinetic equation that well described the isothermal kinetic diagram for this type transformation in Y2Fe17 alloy.Предложена модель для развития индуцированного водородом прямого фазового превращения в магнитотвердом сплаве типа Y2Fe17. Показано что процесс развития индуцированного водородом прямого фазового превращения в магнитотвердом сплаве типа Y2Fe17 в низкотемпературном интервале 330–750 oC контролируется процессами диффузии атомов Fe. На основе кинетической теории фазовых превращений Колмогорова и Любова получено кинетическое уравнение, хорошо описывающее изотермическую кинетическую диаграмму для превращения такого типа в сплаве типа Y2Fe17

Ordinary reduction of K3 surfaces

Let X be a K3 surface over a number field K. We prove that there exists a finite algebraic field extension L/K such that X has ordinary reduction at every non-archimedean place of L outside a density zero set of places.Comment: 7 page

A New Limit on the Antiproton Lifetime

Measurements of the cosmic ray pbar/p ratio are compared to predictions from an inhomogeneous disk-diffusion model of pbar production and propagation within the Galaxy, combined with a calculation of the modulation of the interstellar cosmic ray spectra as the particles propagate through the heliosphere to the Earth. The predictions agree with the observed pbar/p spectrum. Adding a finite pbar lifetime to the model, we obtain the limit tau_pbar > 0.8 Myr (90 % C.L.).Comment: 13 pages, 3 encapsulated Postscript figures, uses AASTeX; accepted by Astrophysical Journal; minor change

Luttinger-liquid-like transport in long InSb nanowires

Long nanowires of degenerate semiconductor InSb in asbestos matrix (wire diameter is around 50 \AA, length 0.1 - 1 mm) were prepared. Electrical conduction of these nanowires is studied over a temperature range 1.5 - 350 K. It is found that a zero-field electrical conduction is a power function of the temperature $G\propto T^\alpha$ with the typical exponent $\alpha \approx 4$. Current-voltage characteristics of such nanowires are found to be nonlinear and at sufficiently low temperatures follows the power law $I\propto V^\beta$. It is shown that the electrical conduction of these nanowires cannot be accounted for in terms of ordinary single-electron theories and exhibits features expected for impure Luttinger liquid. For a simple approximation of impure LL as a pure one broken into drops by weak links, the estimated weak-link density is around $10^3-10^4$ per cm.Comment: 5 pages, 2 figure

Properties of quasi-periodic pulsations in solar flares from a single active region

We investigate the properties of a set of solar flares originating from a single active region (AR) that exhibit QPPs, and look for signs of the QPP periods relating to AR properties. The AR studied, best known as NOAA 12192, was unusually long-lived and produced 181 flares. Data from the GOES, EVE, Fermi, Vernov and NoRH observatories were used to determine if QPPs were present in the flares. For the soft X-ray GOES and EVE data, the time derivative of the signal was used. Power spectra of the time series data (without any form of detrending) were inspected, and flares with a peak above the 95% confidence level in the spectrum were labelled as having candidate QPPs. The confidence levels were determined taking account of uncertainties and the possible presence of red noise. AR properties were determined using HMI line of sight magnetograms. A total of 37 flares (20% of the sample) show good evidence of having QPPs, and some of the pulsations can be seen in data from multiple instruments and in different wavebands. The QPP periods show a weak correlation with the flare amplitude and duration, but this may be due to an observational bias. A stronger correlation was found between the QPP period and duration of the QPP signal, which can be partially but not entirely explained by observational constraints. No correlations were found with the AR area, bipole separation, or average magnetic field strength. The fact that a substantial fraction of the flare sample showed evidence of QPPs using a strict detection method with minimal processing of the data demonstrates that these QPPs are a real phenomenon, which cannot be explained by the presence of red noise or the superposition of multiple unrelated flares. The lack of correlation between the QPP periods and AR properties implies that the small-scale structure of the AR is important, and/or that different QPP mechanisms act in different cases.Comment: 23 pages, 57 figures. Accepted for publication by Astronomy & Astrophysic

Diffusion and Transport Coefficients in Synthetic Opals

Opals are structures composed of the closed packing of spheres in the size range of nano-to-micro meter. They are sintered to create small necks at the points of contact. We have solved the diffusion problem in such structures. The relation between the diffusion coefficient and the termal and electrical conductivity makes possible to estimate the transport coefficients of opal structures. We estimate this changes as function of the neck size and the mean-free path of the carriers. The theory presented is also applicable to the diffusion problem in other periodic structures.Comment: Submitted to PR

Compositional Verification for Autonomous Systems with Deep Learning Components

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verification of autonomous systems that contain Deep Neural Network components. The approach uses assume-guarantee reasoning whereby {\em contracts}, encoding the input-output behavior of individual components, allow the designer to model and incorporate the behavior of the learning-enabled components working side-by-side with the other components. We illustrate the approach on an example taken from the autonomous vehicles domain