1,843 research outputs found
Nilpotent Symmetries in Jackiw-Pi Model: Augmented Superfield Approach
We derive the complete set of off-shell nilpotent (s^2_{(a)b} = 0) and
absolutely anticommuting (s_b s_{ab} + s_{ab} s_b = 0)
Becchi-Rouet-Stora-Tyutin (BRST) (s_b) as well as anti-BRST symmetry
transformations (s_{ab}) corresponding to the combined Yang-Mills and
non-Yang-Mills symmetries of the (2 + 1)-dimensional Jackiw-Pi model within the
framework of augmented superfield formalism. The absolute anticommutativity of
the (anti-)BRST symmetries is ensured by the existence of two sets of
Curci-Ferrari (CF) type of conditions which emerge naturally in this formalism.
The presence of CF conditions enables us to derive the coupled but equivalent
Lagrangian densities. We also capture the (anti-)BRST invariance of the coupled
Lagrangian densities in the superfield formalism. The derivation of the
(anti-)BRST transformations of the auxiliary field \rho is one of the key
findings which can neither be generated by the nilpotent (anti-)BRST charges
nor by the requirements of the nilpotency and/or absolute anticommutativity of
the (anti-)BRST transformations. Finally, we provide a bird's-eye view on the
role of auxiliary field for various massive models and point out few striking
similarities and some glaring differences among them.Comment: LaTex file: 24 pages, no figures, minor modifications in the title
and text, references expanded, version to appear in IJT
Augmented Superfield Approach to Non-Yang-Mills Symmetries of Jackiw-Pi Model: Novel Observations
We derive the off-shell nilpotent and absolutely anticommuting
Becchi-Rouet-Stora-Tyutin (BRST) as well as anti-BRST symmetry transformations
corresponding to the non-Yang-Mills symmetry transformations of (2 + 1)-
dimensional Jackiw-Pi (JP) model within the framework of "augmented" superfield
formalism. The Curci-Ferrari restriction, which is a hallmark of non-Abelian
1-form gauge theories, does not appear in this case. One of the novel features
of our present investigation is the derivation of proper (anti-)BRST symmetry
transformations corresponding to the auxiliary field \rho that can not be
derived by any conventional means.Comment: LaTeX file, 17 pages, journal version, typos fixed, references
modifie
Canonical brackets from continuous symmetries: Abelian 2-form gauge theory
We derive the canonical (anti-)commutation relations amongst the creation and
annihilation operators of the various basic fields, present in the four (3 +
1)-dimensional (4D) free Abelian 2-from gauge theory, with the help of
continuous symmetry transformations within the framework of
Becchi-Rouet-Stora-Tyutin (BRST) formalism. We show that all the six continuous
symmetries of the theory lead to the exactly the same non-vanishing
(anti-)commutator amongst the creation and annihilation operators of the normal
mode expansion of the basic fields of the theory.Comment: LaTeX file, 16 pages, No figure
On the Support Recovery of Jointly Sparse Gaussian Sources using Sparse Bayesian Learning
In this work, we provide non-asymptotic, probabilistic guarantees for
successful recovery of the common nonzero support of jointly sparse Gaussian
sources in the multiple measurement vector (MMV) problem. The support recovery
problem is formulated as the Type-II maximum likelihood (ML) estimation of the
variance hyperparameters of a joint sparsity inducing Gaussian prior on the
source signals. We derive conditions under which the resulting nonconvex
constrained optimization perfectly recovers the nonzero support of a
joint-sparse Gaussian source ensemble with arbitrarily high probability. The
support error probability decays exponentially with the number of MMVs at a
rate that depends on the smallest restricted singular value and the nonnegative
null space property of the self Khatri-Rao product of the sensing matrix. Our
support consistency guarantee for the constrained Type-II ML solution extends
to any global solution of the multiple sparse Bayesian learning (M-SBL)
optimization whose nonzero coefficients lie inside a bounded interval. Our
analysis confirms that nonzero supports of size as high as O() are
recoverable from measurements per sparse vector. For the case of noiseless
measurements, we show that a single MMV is sufficient for perfect recovery of
the -sparse support by M-SBL, provided all subsets of columns of the
sensing matrix are linearly independent
A Hubbard model for ultracold bosonic atoms interacting via zero-point-energy induced three-body interactions
We show that for ultra-cold neutral bosonic atoms held in a three-dimensional
periodic potential or optical lattice, a Hubbard model with dominant,
attractive three-body interactions can be generated. In fact, we derive that
the effect of pair-wise interactions can be made small or zero starting from
the realization that collisions occur at the zero-point energy of an optical
lattice site and the strength of the interactions is energy dependent from
effective-range contributions. We determine the strength of the two- and
three-body interactions for scattering from van-der-Waals potentials and near
Fano-Feshbach resonances. For van-der-Waals potentials, which for example
describe scattering of alkaline-earth atoms, we find that the pair-wise
interaction can only be turned off for species with a small negative scattering
length, leaving the Sr isotope a possible candidate. Interestingly, for
collisional magnetic Feshbach resonances this restriction does not apply and
there often exist magnetic fields where the two-body interaction is small. We
illustrate this result for several known narrow resonances between alkali-metal
atoms as well as chromium atoms. Finally, we compare the size of the three-body
interaction with hopping rates and describe limits due to three-body
recombination
The Parameterized Complexity of Packing Arc-Disjoint Cycles in Tournaments
Given a directed graph on vertices and a positive integer , the
Arc-Disjoint Cycle Packing problem is to determine whether has
arc-disjoint cycles. This problem is known to be W[1]-hard in general directed
graphs. In this paper, we initiate a systematic study on the parameterized
complexity of the problem restricted to tournaments. We show that the problem
is fixed-parameter tractable and admits a polynomial kernel when parameterized
by the solution size . In particular, we show that it can be solved in
time and has a kernel with
vertices. The primary ingredient in both these results is a
min-max theorem that states that every tournament either contains
arc-disjoint triangles or has a feedback arc set of size at most . Our
belief is that this combinatorial result is of independent interest and could
be useful in other problems related to cycles in tournaments
Increasing Superconducting Tc's by a Factor of 1000 with StripeLike Hopping Anisotropies
We have studied the enhancement of the superconducting transition
temperature, Tc, in a t-J-U model of electrons moving on a square lattice in
which anisotropic electronic hopping is introduced. The inclusion of such
hopping mimics, in a approximate fashion, a potentially important
characteristic of materials possessing stripelike charge and spin correlations.
For this model we have calculated Tc for singlet pairing using the non
self-consistent Thouless criterion, and find a dramatic enhancement of Tc
induced by hopping anisotropies. Further, the maximum increase in Tc is
obtained when the system is pushed towards the extreme anisotropy limit, that
is, when the hopping of electrons is confined to occur in 1+0^+ dimensions. We
demonstrate that in this limit the increase in Tc, with respect to the
isotropic system, can be of the order of 1000. We have also determined that in
the extreme anisotropy limit the superconducting gap is an equal mixture of s
and d pairing symmetries (two choices of such a combination being s + d and s +
id) owing to the reduced (square to rectangular) symmetry of the system in the
presence of hopping anisotropies. Thus, the presence of d-wave superconducting
features in materials whose symmetry is very different from that of a
two-dimensional square lattice, with the anisotropy produced by the appearance
of stripes, is not unexpected.Comment: 8 pages (Revtex), 4 eps figure
Investigation of the Thermoelectric Properties of ZnVO Compound in High Temperature Region
In the present work, we report the experimental thermopower () data
for ZnVO compound in the high temperature range 300-600 K. The
value of is found to be 184 and 126 V/K at 300
and 600 K, respectively. The temperature dependent behavior of
is almost linear in the measured temperature range. To understand the large and
positive value observed in this compound, we have also investigated
the electronic and thermoelectric properties by combining the
\textit{ab-initio} electronic structures calculations with Boltzmann transport
theory. Within the local spin density approximation plus Hubbard U, the
anti-ferromagnetic ground state calculation gives an energy gap 0.33 eV
for U=3.7 eV, which is in accordance with the experimental results. The
effective mass for holes in the valance band is found nearly four times that of
electrons in conduction band. The large effective mass of holes are mainly
responsible for the observed positive and large value in this
compound. There is reasonably good matching between calculated and experimental
data in the temperature range 300-410 K. The power factor calculation
shows that thermoelectric properties in high temperature region can be enhanced
by tuning the sample synthesis conditions and suitable doping. The estimated
value of \textit{figure-of-merit}, ZT, at different absolute temperature
suggest that ZnVO compound can be a good thermoelectric material in
high temperature range.Comment: 10 pages, 8 figures, 1 table (to appear in J. Phys. D: Appl. Phys.
Detecting Adversarial Samples from Artifacts
Deep neural networks (DNNs) are powerful nonlinear architectures that are
known to be robust to random perturbations of the input. However, these models
are vulnerable to adversarial perturbations--small input changes crafted
explicitly to fool the model. In this paper, we ask whether a DNN can
distinguish adversarial samples from their normal and noisy counterparts. We
investigate model confidence on adversarial samples by looking at Bayesian
uncertainty estimates, available in dropout neural networks, and by performing
density estimation in the subspace of deep features learned by the model. The
result is a method for implicit adversarial detection that is oblivious to the
attack algorithm. We evaluate this method on a variety of standard datasets
including MNIST and CIFAR-10 and show that it generalizes well across different
architectures and attacks. Our findings report that 85-93% ROC-AUC can be
achieved on a number of standard classification tasks with a negative class
that consists of both normal and noisy samples.Comment: Submitted to ICML 201
Automatic Phone Slip Detection System
Mobile phones are becoming increasingly advanced and the latest ones are
equipped with many diverse and powerful sensors. These sensors can be used to
study different position and orientation of the phone which can help smartphone
manufacture to track about their customers handling from the recorded log. The
inbuilt sensors such as the accelerometer and gyroscope present in our phones
are used to obtain data for acceleration and orientation of the phone in the
three axes for different phone vulnerable position. From the data obtained
appropriate features are extracted using various feature extraction techniques.
The extracted features are then given to classifier such as neural network to
classify them and decide whether the phone is in a vulnerable position to fall
or it is in a safe position .In this paper we mainly concentrated on various
case of handling the smartphone and classified by training the neural network.Comment: Accepted for publication in Springer LNE
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