15,348 research outputs found
Effective Superpotentials for SO/Sp with Flavor from Matrix Models
We study matrix models related to gauge theories with flavors. We
give the effective superpotentials for gauge theories with arbitrary tree level
superpotential up to first instanton level. For quartic tree level
superpotential we obtained exact one-cut solution. We also derive
Seiberg-Witten curve for these gauge theories from matrix model argument.Comment: 17pp,2 figures, v2;refs added and to appear in MPL
Origin of Ferromagnetism and its pressure and doping dependence in TlMnO
Using NMTO-{\it downfolding} technique, we explore and establish the origin
of ferromagnetism in the pyrochlore system, TlMnO. It is
found to be driven by hybridization induced spin-polarization of the
delocalized charge carriers derived from Tl- and O- states. The
mean-field estimate of the ferromagnetic transition temperature, T,
estimated using computed exchange integrals are found to be in good agreement
with the measurements. We find an enhancement of T for moderate doping
with nonmagnetic Sb and a suppression of T upon application of pressure,
both in agreement with experimental findings.Comment: Accepted for publication in PR
Phase Diagram of a Classical Fluid in a Quenched Random Potential
We consider the phase diagram of a classical fluid in the presence of a
random pinning potential of arbitrary strength. Introducing replicas for
averaging over the quenched disorder, we use the hypernetted chain
approximation to calculate the correlations in the replicated liquid. The
freezing transition of the liquid into a nearly crystalline state is studied
using a density functional approach, and the liquid-to-glass transition is
studied using a phenomenological replica symmetry breaking approach introduced
by Mezard and Parisi. The first-order liquid-to-crystal transition is found to
change to a continuous liquid-to-glass transition as the strength of the
disorder is increased above a threshold value.Comment: 7 pages, 4 figures, to appear in EuroPhysics Letter
NiS - An unusual self-doped, nearly compensated antiferromagnetic metal
NiS, exhibiting a text-book example of a first-order transition with many
unusual properties at low temperatures, has been variously described in terms
of conflicting descriptions of its ground state during the past several
decades. We calculate these physical properties within first-principle
approaches based on the density functional theory and conclusively establish
that all experimental data can be understood in terms of a rather unusual
ground state of NiS that is best described as a self-doped, nearly compensated,
antiferromagnetic metal, resolving the age-old controversy. We trace the origin
of this novel ground state to the specific details of the crystal structure,
band dispersions and a sizable Coulomb interaction strength that is still
sub-critical to drive the system in to an insulating state. We also show how
the specific antiferromagnetic structure is a consequence of the less-discussed
90 degree and less than 90 degree superexchange interactions built in to such
crystal structures
Mass Deformations of Super Yang-Mills Theories in D= 2+1, and Super-Membranes: A Note
Mass deformations of supersymmetric Yang-Mills theories in three spacetime
dimensions are considered. The gluons of the theories are made massive by the
inclusion of a non-local gauge and Poincare invariant mass term due to
Alexanian and Nair, while the matter fields are given standard Gaussian
mass-terms. It is shown that the dimensional reduction of such mass deformed
gauge theories defined on or produces matrix quantum
mechanics with massive spectra. In particular, all known massive matrix quantum
mechanical models obtained by the deformations of dimensional reductions of
minimal super Yang-Mills theories in diverse dimensions are shown also to arise
from the dimensional reductions of appropriate massive Yang-Mills theories in
three spacetime dimensions. Explicit formulae for the gauge theory actions are
provided.Comment: 20 Page
Certifying non-existence of undesired locally stable equilibria in formation shape control problems
A fundamental control problem for autonomous vehicle formations is formation
shape control, in which the agents must maintain a prescribed formation shape
using only information measured or communicated from neighboring agents. While
a large and growing literature has recently emerged on distance-based formation
shape control, global stability properties remain a significant open problem.
Even in four-agent formations, the basic question of whether or not there can
exist locally stable incorrect equilibrium shapes remains open. This paper
shows how this question can be answered for any size formation in principle
using semidefinite programming techniques for semialgebraic problems, involving
solutions sets of polynomial equations, inequations, and inequalities.Comment: 6 pages; to appear in the 2013 IEEE Multiconference on Systems and
Contro
Amorphization of Vortex Matter and Reentrant Peak Effect in YBaCuO
The peak effect (PE) has been observed in a twinned crystal of
YBaCuO for Hc in the low field range, close to
the zero field superconducting transition temperature (T(0)) . A sharp
depinning transition succeeds the peak temperature T of the PE. The PE
phenomenon broadens and its internal structure smoothens out as the field is
increased or decreased beyond the interval between 250 Oe and 1000 Oe.
Moreover, the PE could not be observed above 10 kOe and below 20 Oe. The locus
of the T(H) values shows a reentrant characteristic with a nose like
feature located at T(H)/T(0)0.99 and H100 Oe (where
the FLL constant apenetration depth ). The upper part of
the PE curve (0.5 kOeH10 kOe) can be fitted to a melting scenario with
the Lindemann number c0.25. The vortex phase diagram near T(0)
determined from the characteristic features of the PE in
YBaCuO(Hc) bears close resemblance to that in
the 2H-NbSe system, in which a reentrant PE had been observed earlier.Comment: 15 pages and 7 figure
Evaluating Compositionality in Sentence Embeddings
An important challenge for human-like AI is compositional semantics. Recent
research has attempted to address this by using deep neural networks to learn
vector space embeddings of sentences, which then serve as input to other tasks.
We present a new dataset for one such task, `natural language inference' (NLI),
that cannot be solved using only word-level knowledge and requires some
compositionality. We find that the performance of state of the art sentence
embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We
analyze the decision rules learned by InferSent and find that they are
consistent with simple heuristics that are ecologically valid in its training
dataset. Further, we find that augmenting training with our dataset improves
test performance on our dataset without loss of performance on the original
training dataset. This highlights the importance of structured datasets in
better understanding and improving AI systems
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