3,747 research outputs found
Generation and development of small-amplitude disturbances in a laminar boundary layer in the presence of an acoustic field
A low-turbulence subsonic wind tunnel was used to study the influence of acoustic disturbances on the development of small sinusoidal oscillations (Tollmien-Schlichting waves) which constitute the initial phase of turbulent transition. It is found that acoustic waves propagating opposite to the flow generate vibrations of the model (plate) in the flow. Neither the plate vibrations nor the acoustic field itself have any appreciable influence on the stability of the laminar boundary layer. The influence of an acoustic field on laminar boundary layer disturbances is limited to the generation of Tollmien-Schlichting waves at the leading-edge of the plate
Tectonic asymmetry of the earth and other planets
The structures of Earth, Mars, Venus, and the Moon are examined and compared. Global tectonic characteristics are presented for each. A comparison of the tectonics reveals the structural asymetry of these planets and the moon. Tectonic asymmetry information for the group is used to interpret certain aspects of the earth's geological past
On the dichotomy of a system of linear differential equations with conditionally periodic coefficients
We show that a system of linear differential equations with conditionally periodic coefficients is exponentially dichotomous if and only if the spectrum of the monodromy operator does not meet the unit circle. © 2013 Pleiades Publishing, Ltd
Tectonics and volcanisms of Mars
Televised images of Mars transmitted from interplanetary stations are used to develop a theory of the structure and development of the planet. Crater chronology, the structure of planetary bodies in the Earth group, and a comparison of the Earth planetary bodies are among the factors included
Systematic limits on sin^2{2theta_{13}} in neutrino oscillation experiments with multi-reactors
Sensitivities to sin^2{2theta_{13}} without statistical errors (``systematic
limit'') are investigated in neutrino oscillation experiments with multiple
reactors. Using an analytical approach, we show that the systematic limit on
sin^2{2theta_{13}} is dominated by the uncorrelated systematic error sigma_u of
the detector. Even in an experiment with multi-detectors and multi-reactors, it
turns out that most of the systematic errors including the one due to the
nature of multiple sources is canceled as in the case with a single reactor
plus two detectors, if the near detectors are placed suitably. The case of the
KASKA plan (7 reactors and 3 detectors) is investigated in detail, and it is
explicitly shown that it does not suffer from the extra uncertainty due to
multiple reactors.Comment: 26 pages, 10 eps-files, revtex
Electron Transfer Across A Peptide-peptide Interface Within A Designed Metalloprotein
Mechanistic studies of biological electron-transfer (ET) reactions have involved the use of surface-derivatized proteins, protein−protein complexes, and polypeptide-bridged donor−acceptor compounds. These latter studies seek to use well-defined model systems to better define the role of the intervening protein matrix in mediating biological electron transfers. However, whereas many in vivo ET reactions occur across a noncovalent protein−protein interface, the primary role of the peptide spacers found in current model systems is to provide a covalent link between the donor and acceptor sites. As such, these systems are poorly suited to probe the mechanisms of ET reactions occurring across a peptide−peptide interface
Dynamical Susceptibility in KDP-type Crysals above and below Tc II
The path probability method (PPM) in the tetrahedron-cactus approximation is
applied to the Slater-Takagi model with dipole-dipole interaction for
KH2PO4-type hydrogen-bonded ferroelectric crystals in order to derive a small
dip structure in the real part of dynamical susceptibility observed at the
transition temperature Tc. The dip structure can be ascribed to finite
relaxation times of electric dipole moments responsible for the first order
transition with contrast to the critical slowing down in the second order
transition. The light scattering intensity which is related to the imaginary
part of dynamical susceptibility is also calculated above and below the
transition temperature and the obtained central peak structure is consistent
with polarization fluctuation modes in Raman scattering experiments.Comment: 8 pages, 11 figure
Relativistic many-body calculation of low-energy dielectronic resonances in Be-like carbon
We apply relativistic configuration-interaction method coupled with many-body
perturbation theory (CI+MBPT) to describe low-energy dielectronic
recombination. We combine the CI+MBPT approach with the complex rotation method
(CRM) and compute the dielectronic recombination spectrum for Li-like carbon
recombining into Be-like carbon. We demonstrate the utility and evaluate the
accuracy of this newly-developed CI+MBPT+CRM approach by comparing our results
with the results of the previous high-precision study of the CIII system
[Mannervik et al., Phys. Rev. Lett. 81, 313 (1998)].Comment: 6 pages, 1 figure; v2,v3: fixed reference
A container-based workflow for distributed training of deep learning algorithms in HPC clusters
Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters
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