10,581 research outputs found
Vibrational dynamics of solid poly(ethylene oxide)
Molecular dynamics (MD) simulations of crystalline poly(ethylene oxide) (PEO)
have been carried out in order to study its vibrational properties. The
vibrational density of states has been calculated using a normal mode analysis
(NMA) and also through the velocity autocorrelation function of the atoms.
Results agree well with experimental spectroscopic data. System size effects in
the crystalline state, studied through a comparison between results for 16 unit
cells and that for one unit cell has shown important differences in the
features below 100 cm^-1. Effects of interchain interactions are examined by a
comparison of the spectra in the condensed state to that obtained for an
isolated oligomer of ethylene oxide. Calculations of the local character of the
modes indicate the presence of collective excitations for frequencies lower
than 100 cm^-1, in which around 8 to 12 successive atoms of the polymer
backbone participate. The backbone twisting of helical chains about their long
axes is dominant in these low frequency modes.Comment: 19 pages, 7 figures (Phys.Rev.B submitted on 28.11.2002) Revised
versio
What we don't know about time
String theory has transformed our understanding of geometry, topology and
spacetime. Thus, for this special issue of Foundations of Physics commemorating
"Forty Years of String Theory", it seems appropriate to step back and ask what
we do not understand. As I will discuss, time remains the least understood
concept in physical theory. While we have made significant progress in
understanding space, our understanding of time has not progressed much beyond
the level of a century ago when Einstein introduced the idea of space-time as a
combined entity. Thus, I will raise a series of open questions about time, and
will review some of the progress that has been made as a roadmap for the
future.Comment: 15 pages; Essay for a special issue of Foundations of Physics
commemorating "Forty years of string theory
How a well-adapting immune system remembers
An adaptive agent predicting the future state of an environment must weigh
trust in new observations against prior experiences. In this light, we propose
a view of the adaptive immune system as a dynamic Bayesian machinery that
updates its memory repertoire by balancing evidence from new pathogen
encounters against past experience of infection to predict and prepare for
future threats. This framework links the observed initial rapid increase of the
memory pool early in life followed by a mid-life plateau to the ease of
learning salient features of sparse environments. We also derive a modulated
memory pool update rule in agreement with current vaccine response experiments.
Our results suggest that pathogenic environments are sparse and that memory
repertoires significantly decrease infection costs even with moderate sampling.
The predicted optimal update scheme maps onto commonly considered competitive
dynamics for antigen receptors
Arithmetic properties of blocks of consecutive integers
This paper provides a survey of results on the greatest prime factor, the
number of distinct prime factors, the greatest squarefree factor and the
greatest m-th powerfree part of a block of consecutive integers, both without
any assumption and under assumption of the abc-conjecture. Finally we prove
that the explicit abc-conjecture implies the Erd\H{o}s-Woods conjecture for
each k>2.Comment: A slightly corrected and extended version of a paper which will
appear in January 2017 in the book From Arithmetic to Zeta-functions
published by Springe
Drag of two-dimensional small-amplitude symmetric and asymmetric wavy walls in turbulent boundary layers
Included are results of an experimental investigation of low-speed turbulent flow over multiple two-dimensional transverse rigid wavy surfaces having a wavelength on the order of the boundary-layer thickness. Data include surface pressure and total drag measurements on symmetric and asymmetric wall waves under a low-speed turbulent boundary-layer flow. Several asymmetric wave configurations exhibited drag levels below the equivalent symmetric (sine) wave. The experimental results compare favorably with numerical predictions from a Reynolds-averaged Navier-Stokes spectral code. The reported results are of particular interest for the estimation of drag, the minimization of fabrication waviness effects, and the study of wind-wave interactions
D3-branes dynamics and black holes
Using the D3-brane as the fundamental tool, we adress two aspects of D-branes
physics. The first regards the interaction between two electromagnetic dual
D-branes in 10 dimensions. In particular, we give a meaning to {\it both} even
and odd spin structure contributions, the latter being non vanishing for non
zero relative velocity (and encoding the Lorentz-like contribution). The
second aspect regards the D-brane/black holes correspondence. We show how the 4
dimensional configuration corresponding to a {\it single} D3-brane wrapped on
the orbifold T^6/Z_3 represents a regular Reissner-Nordstrom solution of d=4
N=2 supergravityComment: 8 pages, latex, 1 eps figure. Talk presented by M. Bertolini at the
conference "Quantum aspects of gauge theories, supergravity and unification"
in Corfu`; to appear in the proceeding
Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
Can we improve detection in the thermal domain by borrowing features from
rich domains like visual RGB? In this paper, we propose a pseudo-multimodal
object detector trained on natural image domain data to help improve the
performance of object detection in thermal images. We assume access to a
large-scale dataset in the visual RGB domain and relatively smaller dataset (in
terms of instances) in the thermal domain, as is common today. We propose the
use of well-known image-to-image translation frameworks to generate pseudo-RGB
equivalents of a given thermal image and then use a multi-modal architecture
for object detection in the thermal image. We show that our framework
outperforms existing benchmarks without the explicit need for paired training
examples from the two domains. We also show that our framework has the ability
to learn with less data from thermal domain when using our approach. Our code
and pre-trained models are made available at
https://github.com/tdchaitanya/MMTODComment: Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 201
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