40,035 research outputs found
Optimal Global Test for Functional Regression
This paper studies the optimal testing for the nullity of the slope function
in the functional linear model using smoothing splines. We propose a
generalized likelihood ratio test based on an easily implementable data-driven
estimate. The quality of the test is measured by the minimal distance between
the null and the alternative set that still allows a possible test. The lower
bound of the minimax decay rate of this distance is derived, and test with a
distance that decays faster than the lower bound would be impossible. We show
that the minimax optimal rate is jointly determined by the smoothing spline
kernel and the covariance kernel. It is shown that our test attains this
optimal rate. Simulations are carried out to confirm the finite-sample
performance of our test as well as to illustrate the theoretical results.
Finally, we apply our test to study the effect of the trajectories of oxides of
nitrogen () on the level of ozone ()
A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine
The Precision Medicine Initiative states that treatments for a patient should
take into account not only the patient's disease, but his/her specific genetic
variation as well. The vast biomedical literature holds the potential for
physicians to identify effective treatment options for a cancer patient.
However, the complexity and ambiguity of medical terms can result in vocabulary
mismatch between the physician's query and the literature. The physician's
search intent (finding treatments instead of other types of studies) is
difficult to explicitly formulate in a query. Therefore, simple ad hot
retrieval approach will suffer from low recall and precision. In this paper, we
propose a new retrieval system that helps physicians identify effective
treatments in precision medicine. Given a cancer patient with a specific
disease, genetic variation, and demographic information, the system aims to
identify biomedical publications that report effective treatments. We approach
this goal from two directions. First, we expand the original disease and gene
terms using biomedical knowledge bases to improve recall of the initial
retrieval. We then improve precision by promoting treatment-related
publications to the top using a machine learning reranker trained on 2017 Text
Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation
results on 2018 PM track corpus show that the proposed approach effectively
improves both recall and precision, achieving performance comparable to the top
entries on the leaderboard of 2018 PM track.Comment: 32 page
On The Critical Number of Finite Groups (II)
Let G be a finite group and S a subset of G\{0}. We call S an additive basis
of G if every element of G can be expressed as a sum over a nonempty subset in
some order. Let cr(G) be the smallest integer t such that every subset of G\{0}
of cardinality t is an additive basis of G. In this paper, we determine cr(G)
for the following cases: (i) G is a finite nilpotent group; (ii) G is a group
of even order which possesses a subgroup of index 2
Multiply Warped Products with a Quarter-symmetric Connection
In this paper, we study the Einstein warped products and multiply warped
products with a quarter-symmetric connection. We also study warped products and
multiply warped products with a quarter-symmetric connection with constant
scalar curvature. Then apply our results to generalized Robertson-Walker
spacetimes with a quarter-symmetric connection and generalized Kasner
space-times with a quarter-symmetric connection.Comment: 41 pages. arXiv admin note: text overlap with arXiv:1207.509
Simultaneous Sparse Dictionary Learning and Pruning
Dictionary learning is a cutting-edge area in imaging processing, that has
recently led to state-of-the-art results in many signal processing tasks. The
idea is to conduct a linear decomposition of a signal using a few atoms of a
learned and usually over-completed dictionary instead of a pre-defined basis.
Determining a proper size of the to-be-learned dictionary is crucial for both
precision and efficiency of the process, while most of the existing dictionary
learning algorithms choose the size quite arbitrarily. In this paper, a novel
regularization method called the Grouped Smoothly Clipped Absolute Deviation
(GSCAD) is employed for learning the dictionary. The proposed method can
simultaneously learn a sparse dictionary and select the appropriate dictionary
size. Efficient algorithm is designed based on the alternative direction method
of multipliers (ADMM) which decomposes the joint non-convex problem with the
non-convex penalty into two convex optimization problems. Several examples are
presented for image denoising and the experimental results are compared with
other state-of-the-art approaches
Global classical solutions to partially dissipative hyperbolic systems violating the Kawashima condition
This paper considers the Cauchy problem for the quasilinear hyperbolic system
of balance laws in , . The system is partially
dissipative in the sense that there is an eigen-family violating the Kawashima
condition. By imposing certain supplementary degeneracy conditions with respect
to the non-dissipative eigen-family, global unique smooth solutions near
constant equilibria are constructed. The proof is based on the introduction of
the partially normalized coordinates, a delicate structural analysis, a family
of scaled energy estimates with minimum fractional derivative counts and a
refined decay estimates of the dissipative components of the solution.Comment: 44pp. It is revised so that the results can be applied to Euler
equation
Optimal Estimation for the Functional Cox Model
Functional covariates are common in many medical, biodemographic, and
neuroimaging studies. The aim of this paper is to study functional Cox models
with right-censored data in the presence of both functional and scalar
covariates. We study the asymptotic properties of the maximum partial
likelihood estimator and establish the asymptotic normality and efficiency of
the estimator of the finite-dimensional estimator. Under the framework of
reproducing kernel Hilbert space, the estimator of the coefficient function for
a functional covariate achieves the minimax optimal rate of convergence under a
weighted -risk. This optimal rate is determined jointly by the censoring
scheme, the reproducing kernel and the covariance kernel of the functional
covariates. Implementation of the estimation approach and the selection of the
smoothing parameter are discussed in detail. The finite sample performance is
illustrated by simulated examples and a real application
Davenport constant of the multiplicative semigroup of the quotient ring \frac{\F_p[x]}{\langle f(x)\rangle}
Let be a finite commutative semigroup. The Davenport constant
of , denoted , is defined to be the least positive
integer such that every sequence of elements in of length
at least contains a subsequence with the sum of all terms from
equaling the sum of all terms from . Let \F_p[x] be a polynomial ring in
one variable over the prime field \F_p, and let f(x)\in \F_p[x]. In this
paper, we made a study of the Davenport constant of the multiplicative
semigroup of the quotient ring \frac{\F_p[x]}{\langle f(x)\rangle}. Among
other results, we mainly prove that, for any prime and any polynomial
f(x)\in \F_p[x] which can be factorized into several pairwise non-associted
irreducible polynomials in \F_p[x], then
where
denotes the multiplicative semigroup of the quotient
ring \frac{\F_p[x]}{\langle f(x)\rangle} and
denotes the group of units of the semigroup .Comment: 9 page
A problem of Wang on Davenport constant for the multiplicative semigroup of the quotient ring of \F_2[x]
Let \F_q[x] be the ring of polynomials over the finite field \F_q, and
let be a polynomial of \F_q[x]. Let R=\frac{\F_q[x]}{(f)} be a quotient
ring of \F_q[x] with 0\neq R\neq \F_q[x]. Let be the
multiplicative semigroup of the ring , and let be
the group of units of . The Davenport constant of the multiplicative semigroup is the least
positive integer such that for any polynomials
g_1,g_2,\ldots,g_{\ell}\in \F_q[x], there exists a subset with In this manuscript, we proved that for the case of , where \begin{displaymath}
\delta_f=\left\{\begin{array}{ll} 0 & \textrm{if $\gcd(x*(x+1_{\mathbb{F}_2}),\
f)=1_{\F_{2}}}\\ 1 & \textrm{if \gcd(x*(x+1_{\mathbb{F}_2}),\ f)\in \{x, \
x+1_{\mathbb{F}_2}\}}\\ 2 & \textrm{if
gcd(x*(x+1_{\mathbb{F}_2}),f)=x*(x+1_{\mathbb{F}_2}) }\\ \end{array} \right.
\end{displaymath} which partially answered an open problem of Wang on Davenport
constant for the multiplicative semigroup of \frac{\F_q[x]}{(f)}$ (G.Q. Wang,
\emph{Davenport constant for semigroups II,} Journal of Number Theory, 155
(2015) 124--134).Comment: 12 page
New Constructions of Permutation Polynomials of the Form over
Permutation polynomials over finite fields have been studied extensively
recently due to their wide applications in cryptography, coding theory,
communication theory, among others. Recently, several authors have studied
permutation trinomials of the form over
, where , and are
integers. Their methods are essentially usage of a multiplicative version of
AGW Criterion because they all transformed the problem of proving permutation
polynomials over into that of showing the corresponding
fractional polynomials permute a smaller set , where
. Motivated by these results,
we characterize the permutation polynomials of the form
over such that
is arbitrary and is also an arbitrary prime power.
Using AGW Criterion twice, one is multiplicative and the other is additive, we
reduce the problem of proving permutation polynomials over
into that of showing permutations over a small subset of a proper subfield
, which is significantly different from previously known
methods. In particular, we demonstrate our method by constructing many new
explicit classes of permutation polynomials of the form
over . Moreover, we can explain
most of the known permutation trinomials, which are in [6, 13, 14, 16, 20, 29],
over finite field with even characteristic.Comment: 29 pages. An early version of this paper was presented at Fq13 in
Naples, Ital
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