Articles
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01/15/2021--
01/15/2021
Geometric Properties of Generalized Bessel Function associated with the Exponential Function
Sufficient conditions are determined on the parameters such that the
generalized and normalized Bessel function of the first kind and other related
functions belong to subclasses of starlike and convex functions defined in the
unit disk associated with the exponential mapping. Several differential
subordination implications are derived for analytic functions involving Bessel
function and the operator introduced by Baricz \emph{et al.} [Differential
subordinations involving generalized Bessel functions, Bull. Malays. Math. Sci.
Soc. {\bf 38} (2015), no.~3, 1255--1280]. These results are obtained by
constructing suitable class of admissible functions. Examples involving
trigonometric and hyperbolic functions are provided to illustrate the obtained
results.
Adiba Naz
Sumit Nagpal
V. Ravichandran
11/10/2013--
07/14/2012
Starlikeness, convexity and close-to-convexity of harmonic mappings
In 1984, Clunie and Sheil-Small proved that a sense-preserving harmonic
function whose analytic part is convex, is univalent and close-to-convex. In
this paper, certain cases are discussed under which the conclusion of this
result can be strengthened and extended to fully starlike and fully convex
harmonic mappings. In addition, we investgate the properties of functions in
the class $\mathcal{M}(\alpha)$ $(|\alpha|\leq 1)$ consisting of harmonic
functions $f=h+\overline{g}$ with $g'(z)=\alpha zh'(z)$, $\RE
(1+{zh''(z)}/{h'(z)})>-{1}/{2} $ $ \mbox{for} |z|<1 $. The coefficient
estimates, growth results, area theorem and bounds for the radius of
starlikeness and convexity of the class $\mathcal{M}(\alpha)$ are determined.
In particular, the bound for the radius of convexity is sharp for the class
$\mathcal{M}(1)$.
Sumit Nagpal
V. Ravichandran
10/25/2013--
01/13/2013
A comprehensive class of harmonic functions defined by convolution and its connection with integral transforms and hypergeometric functions
For given two harmonic functions $\Phi$ and $\Psi$ with real coefficients in
the open unit disk $\mathbb{D}$, we study a class of harmonic functions
$f(z)=z-\sum_{n=2}^{\infty}A_nz^{n}+\sum_{n=1}^{\infty}B_n\bar{z}^n$ $(A_n, B_n
\geq 0)$ satisfying \[\RE \frac{(f*\Phi)(z)}{(f*\Psi)(z)}>\alpha \quad (0\leq
\alpha <1, z \in \mathbb{D});\] * being the harmonic convolution. Coefficient
inequalities, growth and covering theorems, as well as closure theorems are
determined. The results obtained extend several known results as special cases.
In addition, we study the class of harmonic functions $f$ that satisfy $\RE
f(z)/z>\alpha$ $(0\leq \alpha <1, z \in \mathbb{D})$. As an application, their
connection with certain integral transforms and hypergeometric functions is
established.
Sumit Nagpal
V. Ravichandran
02/23/2013--
02/23/2013
Univalence and convexity in one direction of the convolution of harmonic mappings
Let $\mathcal{H}$ denote the class of all complex-valued harmonic functions
$f$ in the open unit disk normalized by $f(0)=0=f_{z}(0)-1=f_{\bar{z}}(0)$, and
let $\mathcal{A}$ be the subclass of $\mathcal{H}$ consisting of normalized
analytic functions. For $\phi \in \mathcal{A}$, let
$\mathcal{W}_{H}^{-}(\phi):=\{f=h+\bar{g} \in \mathcal{H}:h-g=\phi\}$ and
$\mathcal{W}_{H}^{+}(\phi):=\{f=h+\bar{g} \in \mathcal{H}:h+g=\phi\}$ be
subfamilies of $\mathcal{H}$. In this paper, we shall determine the conditions
under which the harmonic convolution $f_1*f_2$ is univalent and convex in one
direction if $f_1 \in \mathcal{W}_{H}^{-}(z)$ and $f_2 \in
\mathcal{W}_{H}^{-}(\phi)$. A similar analysis is carried out if $f_1 \in
\mathcal{W}_{H}^{-}(z)$ and $f_2 \in \mathcal{W}_{H}^{+}(\phi)$. Examples of
univalent harmonic mappings constructed by way of convolution are also
presented.
Sumit Nagpal
V. Ravichandran
10/10/2018--
10/10/2018
VI modules in non-describing characteristic, Part II
We classify all irreducible generic $\mathrm{VI}$-modules in non-describing
characteristic. Our result degenerates to yield a classification of irreducible
generic $\mathrm{FI}$-modules in arbitrary characteristic. Our result can also
be viewed as a classification theorem for a natural class of representations of
$\mathbf{GL}_{\infty}(\mathbf{F}_q)$.
Rohit Nagpal
06/13/2021--
11/18/2020
Symmetric subvarieties of infinite affine space
We classify the subvarieties of infinite dimensional affine space that are
stable under the infinite symmetric group. We determine the defining equations
and point sets of these varieties as well as the containments between them.
Rohit Nagpal
Andrew Snowden
01/05/2007--
01/04/2007
A nearly optimal and deterministic summary structure for update data streams
The paper has been withdrawn due to an error in Lemma 1.
Sumit Ganguly
04/09/2012--
04/09/2012
R.F. Pollution Reduction in Cellular Communication
Erroneous submission in violation of copyright removed by arXiv admin.
Sumit Katiyar
R. K. Jain
N. K. Agrawal
07/31/2014--
07/31/2014
Twitter User Classification using Ambient Metadata
Microblogging websites, especially Twitter have become an important means of
communication, in today's time. Often these services have been found to be
faster than conventional news services. With millions of users, a need was felt
to classify users based on ambient metadata associated with their user
accounts. We particularly look at the effectiveness of the profile description
field in order to carry out the task of user classification. Our results show
that such metadata can be an effective feature for any classification task.
Chirag Nagpal
Khushboo Singhal
05/14/2019--
05/14/2019
Nonlinear Semi-Parametric Models for Survival Analysis
Semi-parametric survival analysis methods like the Cox Proportional Hazards
(CPH) regression (Cox, 1972) are a popular approach for survival analysis.
These methods involve fitting of the log-proportional hazard as a function of
the covariates and are convenient as they do not require estimation of the
baseline hazard rate. Recent approaches have involved learning non-linear
representations of the input covariates and demonstrate improved performance.
In this paper we argue against such deep parameterizations for survival
analysis and experimentally demonstrate that more interpretable semi-parametric
models inspired from mixtures of experts perform equally well or in some cases
better than such overly parameterized deep models.
Chirag Nagpal
Rohan Sangave
Amit Chahar
Parth Shah
Artur Dubrawski
Bhiksha Raj
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