Electron-phonon interactions and high-temperature thermodynamics of vanadium and its alloys

Inelastic neutron scattering was used to measure the phonon densities of states (DOSs) for pure V and solid solutions of V with 6 to 7at% of Co, Nb, and Pt, at temperatures from 10 K to 1323 K. Ancillary measurements of heat capacity and thermal expansion are reported on V and V-7at%Co and used to help identify the different sources of entropy. Pure V exhibits an anomalous anharmonic stiffening of phonons with increasing temperature. This anharmonicity is suppressed by Co and Pt, but not by isoelectronic Nb solutes. The changes in phonon frequency with alloying and with temperature both correlate to the decrease in electron density of states (DOS) at the Fermi level as calculated using density functional theory. The effects of both temperature and alloying can be understood in terms of an adiabatic electron-phonon interaction (EPI), which broadens sharp features in the electron DOS. These results show that the adiabatic EPI can influence the phonon thermodynamics at temperatures exceeding 1000 K, and that thermal trends of phonons may help assess the strength of the EPI

Collection of Meteorites from Grove Mountains, East Antarctica.

第3回極域科学シンポジウム/第35回南極隕石シンポジウム 11月29日（木） 国立国語研究所 2階講

COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation

The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances

Optimization of double drive pulse pumping in Ne-like Ge x-ray lasers

Pumping of the Ne-like Ge x-ray laser with two 100 ps duration pulses (a prepulse and main pulse) is investigated using a fluid and atomic physics code coupled to a 3D ray tracing postprocessor code. The modeling predicts the optimum ratio of the irradiance of the two pulses for the maximum x-ray laser output resulting from the balance between the relative lower electron density gradients and wider gain region which is produced with a larger prepulse and the higher peak gain coefficients produced with a small prepulse. With a longer pulse interval between prepulse and main pulse, a relatively lower optimum pulse ratio is found. The threshold irradiance of the main driving pulse with a prepulse required to make an order of magnitude enhancement of laser output compared to irradiation without a prepulse is also found at 3-4x10(13) W/cm(2) for Ne-like Ge. (C) 1998 American Institute of Physics

Design and operation of the wide angular-range chopper spectrometer ARCS at the Spallation Neutron Source

The wide angular-range chopper spectrometer ARCS at the Spallation Neutron Source (SNS) is optimized to provide a high neutron flux at the sample position with a large solid angle of detector coverage. The instrument incorporates modern neutron instrumentation, such as an elliptically focused neutron guide, high speed magnetic bearing choppers, and a massive array of ^3He linear position sensitive detectors. Novel features of the spectrometer include the use of a large gate valve between the sample and detector vacuum chambers and the placement of the detectors within the vacuum, both of which provide a window-free final flight path to minimize background scattering while allowing rapid changing of the sample and sample environment equipment. ARCS views the SNS decoupled ambient temperature water moderator, using neutrons with incident energy typically in the range from 15 to 1500 meV. This range, coupled with the large detector coverage, allows a wide variety of studies of excitations in condensed matter, such as lattice dynamics and magnetism, in both powder and single-crystal samples. Comparisons of early results to both analytical and Monte Carlo simulation of the instrument performance demonstrate that the instrument is operating as expected and its neutronic performance is understood. ARCS is currently in the SNS user program and continues to improve its scientific productivity by incorporating new instrumentation to increase the range of science covered and improve its effectiveness in data collection