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K-closedness of weighted Hardy spaces on the two-dimensional torus | It is proved that, under certain restrictions on weights, a pair of weighted
Hardy spaces on the two-dimensional torus is K-closed in the pair of the
corresponding weighted Lebesgue spaces. By now, K-closedness of Hardy spaces on
the two-dimensional torus was considered either in the case of no weights or in
the case of weights that split into a product of two functions of one variable
(the so-called "split weights"). Here the case of certain nonsplit weights is
studied.
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Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis | Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.
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PS-DBSCAN: An Efficient Parallel DBSCAN Algorithm Based on Platform Of AI (PAI) | We present PS-DBSCAN, a communication efficient parallel DBSCAN algorithm
that combines the disjoint-set data structure and Parameter Server framework in
Platform of AI (PAI). Since data points within the same cluster may be
distributed over different workers which result in several disjoint-sets,
merging them incurs large communication costs. In our algorithm, we employ a
fast global union approach to union the disjoint-sets to alleviate the
communication burden. Experiments over the datasets of different scales
demonstrate that PS-DBSCAN outperforms the PDSDBSCAN with 2-10 times speedup on
communication efficiency.
We have released our PS-DBSCAN in an algorithm platform called Platform of AI
(PAI - this https URL) in Alibaba Cloud. We have also
demonstrated how to use the method in PAI.
| 1 | 0 | 0 | 0 | 0 | 0 |
Non-singular spacetimes with a negative cosmological constant: IV. Stationary black hole solutions with matter fields | We use an elliptic system of equations with complex coefficients for a set of
complex-valued tensor fields as a tool to construct infinite-dimensional
families of non-singular stationary black holes, real-valued Lorentzian
solutions of the Einstein-Maxwell-dilaton-scalar
fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations with a negative
cosmological constant. The families include an infinite-dimensional family of
solutions with the usual AdS conformal structure at conformal infinity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Non-Negative Matrix Factorization Test Cases | Non-negative matrix factorization (NMF) is a prob- lem with many
applications, ranging from facial recognition to document clustering. However,
due to the variety of algorithms that solve NMF, the randomness involved in
these algorithms, and the somewhat subjective nature of the problem, there is
no clear "correct answer" to any particular NMF problem, and as a result, it
can be hard to test new algorithms. This paper suggests some test cases for NMF
algorithms derived from matrices with enumerable exact non-negative
factorizations and perturbations of these matrices. Three algorithms using
widely divergent approaches to NMF all give similar solutions over these test
cases, suggesting that these test cases could be used as test cases for
implementations of these existing NMF algorithms as well as potentially new NMF
algorithms. This paper also describes how the proposed test cases could be used
in practice.
| 1 | 0 | 1 | 0 | 0 | 0 |
Inequalities for the fundamental Robin eigenvalue of the Laplacian for box-shaped domains | This document consists of two papers, both submitted, and supplementary
material. The submitted papers are here given as Parts I and II.
Part I establishes results, used in Part II, 'on functions and inverses, both
positive, decreasing and convex'.
Part II uses results from Part I to extablish 'inequalities for the
fundamental Robin eigenvalue for the Laplacian on N-dimensional boxes'
| 0 | 0 | 1 | 0 | 0 | 0 |
Non-Hamiltonian isotopic Lagrangians on the one-point blow-up of CP^2 | We show that two Hamiltonian isotopic Lagrangians in
(CP^2,\omega_\textup{FS}) induce two Lagrangian submanifolds in the one-point
blow-up (\widetilde{CP}^2,\widetilde{\omega}_\rho) that are not Hamiltonian
isotopic. Furthermore, we show that for any integer k>1 there are k Hamiltonian
isotopic Lagrangians in (CP^2,\omega_\textup{FS}) that induce k Lagrangian
submanifolds in the one-point blow-up such that no two of them are Hamiltonian
isotopic.
| 0 | 0 | 1 | 0 | 0 | 0 |
Berezinskii-Kosterlitz-Thouless Type Scenario in Molecular Spin Liquid $A$Cr$_2$O$_4$ | The spin relaxation in chromium spinel oxides $A$Cr$_{2}$O$_{4}$ ($A=$ Mg,
Zn, Cd) is investigated in the paramagnetic regime by electron spin resonance
(ESR). The temperature dependence of the ESR linewidth indicates an
unconventional spin-relaxation behavior, similar to spin-spin relaxation in the
two-dimensional (2D) chromium-oxide triangular lattice antiferromagnets. The
data can be described in terms of a generalized Berezinskii-Kosterlitz-Thouless
(BKT) type scenario for 2D systems with additional internal symmetries. Based
on the characteristic exponents obtained from the evaluation of the ESR
linewidth, short-range order with a hidden internal symmetry is suggested.
| 0 | 1 | 0 | 0 | 0 | 0 |
Continuum percolation theory of epimorphic regeneration | A biophysical model of epimorphic regeneration based on a continuum
percolation process of fully penetrable disks in two dimensions is proposed.
All cells within a randomly chosen disk of the regenerating organism are
assumed to receive a signal in the form of a circular wave as a result of the
action/reconfiguration of neoblasts and neoblast-derived mesenchymal cells in
the blastema. These signals trigger the growth of the organism, whose cells
read, on a faster time scale, the electric polarization state responsible for
their differentiation and the resulting morphology. In the long time limit, the
process leads to a morphological attractor that depends on experimentally
accessible control parameters governing the blockage of cellular gap junctions
and, therefore, the connectivity of the multicellular ensemble. When this
connectivity is weakened, positional information is degraded leading to more
symmetrical structures. This general theory is applied to the specifics of
planaria regeneration. Computations and asymptotic analyses made with the model
show that it correctly describes a significant subset of the most prominent
experimental observations, notably anterior-posterior polarization (and its
loss) or the formation of four-headed planaria.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multiplicity of solutions for a nonhomogeneous quasilinear elliptic problem with critical growth | It is established some existence and multiplicity of solution results for a
quasilinear elliptic problem driven by $\Phi$-Laplacian operator. One of these
solutions is built as a ground state solution. In order to prove our main
results we apply the Nehari method combined with the concentration compactness
theorem in an Orlicz-Sobolev framework. One of the difficulties in dealing with
this kind of operator is the lost of homogeneity properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mahonian STAT on rearrangement class of words | In 2000, Babson and Steingrímsson generalized the notion of permutation
patterns to the so-called vincular patterns, and they showed that many Mahonian
statistics can be expressed as sums of vincular pattern occurrence statistics.
STAT is one of such Mahonian statistics discoverd by them. In 2016, Kitaev and
the third author introduced a words analogue of STAT and proved a joint
equidistribution result involving two sextuple statistics on the whole set of
words with fixed length and alphabet. Moreover, their computer experiments
hinted at a finer involution on $R(w)$, the rearrangement class of a given word
$w$. We construct such an involution in this paper, which yields a comparable
joint equidistribution between two sextuple statistics over $R(w)$. Our
involution builds on Burstein's involution and Foata-Schützenberger's
involution that utilizes the celebrated RSK algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks | Critical analysis of the state of the art is a necessary task when
identifying new research lines worthwhile to pursue. To such an end, all the
available work related to the field of interest must be taken into account. The
key point is how to organize, analyze, and make sense of the huge amount of
scientific literature available today on any topic. To tackle this problem, we
present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus,
a modify SMS protocol is used relying on the scientific references metadata to
extract, process and interpret the wealth of information contained in nowadays
research literature. As a test case, the procedure is applied to determine the
current state and perspectives of community detection in complex networks. Our
results show that community detection is a still active, far from exhausted, in
development, field. In addition, we find that, by far, the most exploited
methods are those related to determining hierarchical community structures. On
the other hand, the results show that fuzzy clustering techniques, despite
their interest, are underdeveloped as well as the adaptation of existing
algorithms to parallel or, more specifically, distributed, computational
systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
SPEW: Synthetic Populations and Ecosystems of the World | Agent-based models (ABMs) simulate interactions between autonomous agents in
constrained environments over time. ABMs are often used for modeling the spread
of infectious diseases. In order to simulate disease outbreaks or other
phenomena, ABMs rely on "synthetic ecosystems," or information about agents and
their environments that is representative of the real world. Previous
approaches for generating synthetic ecosystems have some limitations: they are
not open-source, cannot be adapted to new or updated input data sources, and do
not allow for alternative methods for sampling agent characteristics and
locations. We introduce a general framework for generating Synthetic
Populations and Ecosystems of the World (SPEW), implemented as an open-source R
package. SPEW allows researchers to choose from a variety of sampling methods
for agent characteristics and locations when generating synthetic ecosystems
for any geographic region. SPEW can produce synthetic ecosystems for any agent
(e.g. humans, mosquitoes, etc), provided that appropriate data is available. We
analyze the accuracy and computational efficiency of SPEW given different
sampling methods for agent characteristics and locations and provide a suite of
diagnostics to screen our synthetic ecosystems. SPEW has generated over five
billion human agents across approximately 100,000 geographic regions in about
70 countries, available online.
| 0 | 1 | 0 | 1 | 0 | 0 |
Bayesian hierarchical weighting adjustment and survey inference | We combine Bayesian prediction and weighted inference as a unified approach
to survey inference. The general principles of Bayesian analysis imply that
models for survey outcomes should be conditional on all variables that affect
the probability of inclusion. We incorporate the weighting variables under the
framework of multilevel regression and poststratification, as a byproduct
generating model-based weights after smoothing. We investigate deep
interactions and introduce structured prior distributions for smoothing and
stability of estimates. The computation is done via Stan and implemented in the
open source R package "rstanarm" ready for public use. Simulation studies
illustrate that model-based prediction and weighting inference outperform
classical weighting. We apply the proposal to the New York Longitudinal Study
of Wellbeing. The new approach generates robust weights and increases
efficiency for finite population inference, especially for subsets of the
population.
| 0 | 0 | 0 | 1 | 0 | 0 |
Comparison of the h-index for Different Fields of Research Using Bootstrap Methodology | An important disadvantage of the h-index is that typically it cannot take
into account the specific field of research of a researcher. Usually sample
point estimates of the average and median h-index values for the various fields
are reported that are highly variable and dependent of the specific samples and
it would be useful to provide confidence intervals of prediction accuracy. In
this paper we apply the non-parametric bootstrap technique for constructing
confidence intervals for the h-index for different fields of research. In this
way no specific assumptions about the distribution of the empirical hindex are
required as well as no large samples since that the methodology is based on
resampling from the initial sample. The results of the analysis showed
important differences between the various fields. The performance of the
bootstrap intervals for the mean and median h-index for most fields seems to be
rather satisfactory as revealed by the performed simulation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Impact of Intervals on the Emotional Effect in Western Music | Every art form ultimately aims to invoke an emotional response over the
audience, and music is no different. While the precise perception of music is a
highly subjective topic, there is an agreement in the "feeling" of a piece of
music in broad terms. Based on this observation, in this study, we aimed to
determine the emotional feeling associated with short passages of music;
specifically by analyzing the melodic aspects. We have used the dataset put
together by Eerola et. al. which is comprised of labeled short passages of film
music. Our initial survey of the dataset indicated that other than "happy" and
"sad" labels do not possess a melodic structure. We transcribed the main melody
of the happy and sad tracks and used the intervals between the notes to
classify them. Our experiments have shown that treating a melody as a
bag-of-intervals do not possess any predictive power whatsoever, whereas
counting intervals with respect to the key of the melody yielded a classifier
with 85% accuracy.
| 1 | 0 | 0 | 0 | 1 | 0 |
Nonlocal Pertubations of Fractional Choquard Equation | We study the equation \begin{equation} (-\Delta)^{s}u+V(x)u=
(I_{\alpha}*|u|^{p})|u|^{p-2}u+\lambda(I_{\beta}*|u|^{q})|u|^{q-2}u \quad\mbox{
in } \R^{N}, \end{equation} where $I_\gamma(x)=|x|^{-\gamma}$ for any
$\gamma\in (0,N)$, $p, q >0$, $\alpha,\beta\in (0,N)$, $N\geq 3$ and $ \lambda
\in R$. First, the existence of a groundstate solutions using minimization
method on the associated Nehari manifold is obtained. Next, the existence of
least energy sign-changing solutions is investigated by considering the Nehari
nodal set.
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Clustering Patients with Tensor Decomposition | In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as clustering patient records, to more commonly used
distance-based methods. We run the algorithm on two datasets of healthcare
records, obtaining clinically meaningful results.
| 1 | 0 | 0 | 1 | 0 | 0 |
Posterior distribution existence and error control in Banach spaces in the Bayesian approach to UQ in inverse problems | We generalize the results of \cite{Capistran2016} on expected Bayes factors
(BF) to control the numerical error in the posterior distribution to an
infinite dimensional setting when considering Banach functional spaces and now
in a prior setting. The main result is a bound on the absolute global error to
be tolerated by the Forward Map numerical solver, to keep the BF of the
numerical vs. the theoretical model near to 1, now in this more general
setting, possibly including a truncated, finite dimensional approximate prior
measure. In so doing we found a far more general setting to define and prove
existence of the infinite dimensional posterior distribution than that depicted
in, for example, \cite{Stuart2010}. Discretization consistency and rates of
convergence are also investigated in this general setting for the Bayesian
inverse problem.
| 0 | 0 | 1 | 1 | 0 | 0 |
High-$T_c$ mechanism through analysis of diverging effective mass for YaBa$_2$Cu$_3$O$_{6+x}$ and pairing symmetry in cuprate superconductors | In order to clarify the high-$T_c$ mechanism in inhomogeneous cuprate layer
superconductors, we deduce and find the correlation strength not revealed
before, contributing to the formation of the Cooper pair and the 2-D density of
state, and demonstrate the pairing symmetry in the superconductors still
controversial. To the open questions, the fitting and analysis of the diverging
effective mass with decreasing doping, extracted from the acquired
quantum-oscillation data in underdoped YBCOO$_{6+x}$ superconductors, can
provide solutions. Here, the results of the fitting using the extended
Brinkman-Rice(BR) picture reveal the nodal constant Fermi energy with the
maximum carrier density, a constant Coulomb correlation strength
$k_{BR}$=$U/U_c$>0.90, and a growing Fermi arc from the nodal Fermi point to
the isotropic Fermi surface with an increasing $x$. The growing of the Fermi
arc indicates that a superconducting gap develops with $x$ from the node to the
anti-node. The large $k_{BR}$ results from the $d$-wave MIT for the pseudogap
phase in lightly doped superconductors, which can be direct evidence for
high-$T_c$ superconductivity. The quantum critical point is regarded as the
nodal Fermi point satisfied with the BR picture. The experimentally-measured
mass diverging behavior is an average effect and the true effective mass is
constant. As an application of the nodal constant carrier density, to find a
superconducting node gap, the ARPES data and tunneling data are analyzed. The
superconducting node gap is a precursor of $s$-wave symmetry in underdoped
cuprates. The half-flux quantum, induced by the circulation of $d$-wave
supercurrent and observed by the phase sensitive Josephson-pi junction
experiments, is not shown due to anisotropic or asymmetric effect appearing in
superconductors with trapped flux. The absence of $d$-wave superconducting
pairing symmetry is also revealed.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Long Term Fréchet distribution: Estimation, Properties and its Application | In this paper a new long-term survival distribution is proposed. The so
called long term Fréchet distribution allows us to fit data where a part of
the population is not susceptible to the event of interest. This model may be
used, for example, in clinical studies where a portion of the population can be
cured during a treatment. It is shown an account of mathematical properties of
the new distribution such as its moments and survival properties. As well is
presented the maximum likelihood estimators (MLEs) for the parameters. A
numerical simulation is carried out in order to verify the performance of the
MLEs. Finally, an important application related to the leukemia free-survival
times for transplant patients are discussed to illustrates our proposed
distribution
| 0 | 0 | 1 | 1 | 0 | 0 |
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space | Many engineering problems require identifying feasible domains under implicit
constraints. One example is finding acceptable car body styling designs based
on constraints like aesthetics and functionality. Current active-learning based
methods learn feasible domains for bounded input spaces. However, we usually
lack prior knowledge about how to set those input variable bounds. Bounds that
are too small will fail to cover all feasible domains; while bounds that are
too large will waste query budget. To avoid this problem, we introduce Active
Expansion Sampling (AES), a method that identifies (possibly disconnected)
feasible domains over an unbounded input space. AES progressively expands our
knowledge of the input space, and uses successive exploitation and exploration
stages to switch between learning the decision boundary and searching for new
feasible domains. We show that AES has a misclassification loss guarantee
within the explored region, independent of the number of iterations or labeled
samples. Thus it can be used for real-time prediction of samples' feasibility
within the explored region. We evaluate AES on three test examples and compare
AES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm
and the straddle heuristic -- that operate over fixed input variable bounds.
| 1 | 0 | 0 | 1 | 0 | 0 |
On diagrams of simplified trisections and mapping class groups | A simplified trisection is a trisection map on a 4-manifold such that, in its
critical value set, there is no double point and cusps only appear in triples
on innermost fold circles. We give a necessary and sufficient condition for a
3-tuple of systems of simple closed curves in a surface to be a diagram of a
simplified trisection in terms of mapping class groups. As an application of
this criterion, we show that trisections of spun 4-manifolds due to Meier are
diffeomorphic (as trisections) to simplified ones. Baykur and Saeki recently
gave an algorithmic construction of a simplified trisection from a directed
broken Lefschetz fibration. We also give an algorithm to obtain a diagram of a
simplified trisection derived from their construction.
| 0 | 0 | 1 | 0 | 0 | 0 |
Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment | Many localization algorithms use a spatiotemporal window of sensory
information in order to recognize spatial locations, and the length of this
window is often a sensitive parameter that must be tuned to the specifics of
the application. This letter presents a general method for environment-driven
variation of the length of the spatiotemporal window based on searching for the
most significant localization hypothesis, to use as much context as is
appropriate but not more. We evaluate this approach on benchmark datasets using
visual and Wi-Fi sensor modalities and a variety of sensory comparison
front-ends under in-order and out-of-order traversals of the environment. Our
results show that the system greatly reduces the maximum distance traveled
without localization compared to a fixed-length approach while achieving
competitive localization accuracy, and our proposed method achieves this
performance without deployment-time tuning.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Gaia-ESO Survey: Dynamical models of flattened, rotating globular clusters | We present a family of self-consistent axisymmetric rotating globular cluster
models which are fitted to spectroscopic data for NGC 362, NGC 1851, NGC 2808,
NGC 4372, NGC 5927 and NGC 6752 to provide constraints on their physical and
kinematic properties, including their rotation signals. They are constructed by
flattening Modified Plummer profiles, which have the same asymptotic behaviour
as classical Plummer models, but can provide better fits to young clusters due
to a slower turnover in the density profile. The models are in dynamical
equilibrium as they depend solely on the action variables. We employ a fully
Bayesian scheme to investigate the uncertainty in our model parameters
(including mass-to-light ratios and inclination angles) and evaluate the
Bayesian evidence ratio for rotating to non-rotating models. We find convincing
levels of rotation only in NGC 2808. In the other clusters, there is just a
hint of rotation (in particular, NGC 4372 and NGC 5927), as the data quality
does not allow us to draw strong conclusions. Where rotation is present, we
find that it is confined to the central regions, within radii of $R \leq 2
r_h$. As part of this work, we have developed a novel q-Gaussian basis
expansion of the line-of-sight velocity distributions, from which general
models can be constructed via interpolation on the basis coefficients.
| 0 | 1 | 0 | 0 | 0 | 0 |
A two-dimensional data-driven model for traffic flow on highways | Based on experimental traffic data obtained from German and US highways, we
propose a novel two-dimensional first-order macroscopic traffic flow model. The
goal is to reproduce a detailed description of traffic dynamics for the real
road geometry. In our approach both the dynamic along the road and across the
lanes is continuous. The closure relations, being necessary to complete the
hydrodynamic equation, are obtained by regression on fundamental diagram data.
Comparison with prediction of one-dimensional models shows the improvement in
performance of the novel model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cut Finite Element Methods for Elliptic Problems on Multipatch Parametric Surfaces | We develop a finite element method for the Laplace--Beltrami operator on a
surface described by a set of patchwise parametrizations. The patches provide a
partition of the surface and each patch is the image by a diffeomorphism of a
subdomain of the unit square which is bounded by a number of smooth trim
curves. A patchwise tensor product mesh is constructed by using a structured
mesh in the reference domain. Since the patches are trimmed we obtain cut
elements in the vicinity of the interfaces. We discretize the Laplace--Beltrami
operator using a cut finite element method that utilizes Nitsche's method to
enforce continuity at the interfaces and a consistent stabilization term to
handle the cut elements. Several quantities in the method are conveniently
computed in the reference domain where the mappings impose a Riemannian metric.
We derive a priori estimates in the energy and $L^2$ norm and also present
several numerical examples confirming our theoretical results.
| 0 | 0 | 1 | 0 | 0 | 0 |
The rationality problem for forms of $\overline{M_{0, n}}$ | Let $X$ be a del Pezzo surface of degree $5$ defined over a field $F$. A
theorem of Yu. I. Manin and P. Swinnerton-Dyer asserts that every Del Pezzo
surface of degree $5$ is rational. In this paper we generalize this result as
follows. Recall that del Pezzo surfaces of degree $5$ over a field $F$ are
precisely the twisted $F$-forms of the moduli space $\overline{M_{0, 5}}$ of
stable curves of genus $0$ with $5$ marked points. Suppose $n \geq 5$ is an
integer, and $F$ is an infinite field of characteristic $\neq 2$. It is easy to
see that every twisted $F$-form of $\overline{M_{0, n}}$ is unirational over
$F$. We show that
(a) if $n$ is odd, then every twisted $F$-form of $\overline{M_{0, n}}$ is
rational over $F$.
(b) If $n$ is even, there exists a field extension $F/k$ and a twisted
$F$-form $X$ of $\overline{M_{0, n}}$ such that $X$ is not retract rational
over $F$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The p-adic Kummer-Leopoldt constant - Normalized p-adic regulator | The p-adic Kummer--Leopoldt constant kappa\_K of a number field K is
(assuming the Leopoldt conjecture) the least integer c such that for all n
\textgreater{}\textgreater{} 0, any global unit of K, which is locally a
p^(n+c)th power at the p-places, is necessarily the p^nth power of a global
unit of K. This constant has been computed by Assim \& Nguyen Quang Do using
Iwasawa's techniques,after intricate studies and calculations by many authors.
We give an elementary p-adic proof and an improvement of these results, then a
class field theory interpretation of kappa\_K. We give some applications
(including generalizations of Kummer's lemma on regular pth cyclotomic fields)
and a natural definition of the normalized p-adic regulator for any K and any
p$\ge$2.This is done without analytical computations, using only class field
theoryand especially the properties of the so-called p-torsion group T\_K of
Abelian p-ramification theory over K.
| 0 | 0 | 1 | 0 | 0 | 0 |
Regularization Learning Networks: Deep Learning for Tabular Datasets | Despite their impressive performance, Deep Neural Networks (DNNs) typically
underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning
tasks. We propose that applying a different regularization coefficient to each
weight might boost the performance of DNNs by allowing them to make more use of
the more relevant inputs. However, this will lead to an intractable number of
hyperparameters. Here, we introduce Regularization Learning Networks (RLNs),
which overcome this challenge by introducing an efficient hyperparameter tuning
scheme which minimizes a new Counterfactual Loss. Our results show that RLNs
significantly improve DNNs on tabular datasets, and achieve comparable results
to GBTs, with the best performance achieved with an ensemble that combines GBTs
and RLNs. RLNs produce extremely sparse networks, eliminating up to 99.8% of
the network edges and 82% of the input features, thus providing more
interpretable models and reveal the importance that the network assigns to
different inputs. RLNs could efficiently learn a single network in datasets
that comprise both tabular and unstructured data, such as in the setting of
medical imaging accompanied by electronic health records. An open source
implementation of RLN can be found at
this https URL.
| 0 | 0 | 0 | 1 | 0 | 0 |
Numerical dimension and locally ample curves | In the paper \cite{Lau16}, it was shown that the restriction of a
pseudoeffective divisor $D$ to a subvariety $Y$ with nef normal bundle is
pseudoeffective. Assuming the normal bundle is ample and that $D|_Y$ is not
big, we prove that the numerical dimension of $D$ is bounded above by that of
its restriction, i.e. $\kappa_{\sigma}(D)\leq \kappa_{\sigma}(D|_Y)$. The main
motivation is to study the cycle classes of "positive" curves: we show that the
cycle class of a curve with ample normal bundle lies in the interior of the
cone of curves, and the cycle class of an ample curve lies in the interior of
the cone of movable curves. We do not impose any condition on the singularities
on the curve or the ambient variety. For locally complete intersection curves
in a smooth projective variety, this is the main result of Ottem \cite{Ott16}.
The main tool in this paper is the theory of $q$-ample divisors.
| 0 | 0 | 1 | 0 | 0 | 0 |
Local-global principles in circle packings | We generalize work of Bourgain-Kontorovich and Zhang, proving an almost
local-to-global property for the curvatures of certain circle packings, to a
large class of Kleinian groups. Specifically, we associate in a natural way an
infinite family of integral packings of circles to any Kleinian group $\mathcal
A\leq\textrm{PSL}_2(K)$ satisfying certain conditions, where $K$ is an
imaginary quadratic field, and show that the curvatures of the circles in any
such packing satisfy an almost local-to-global principle. A key ingredient in
the proof of this is that $\mathcal A$ possesses a spectral gap property, which
we prove for any infinite-covolume, geometrically finite, Zariski dense
Kleinian group in $\textrm{PSL}_2(\mathcal{O}_K)$ containing a Zariski dense
subgroup of $\textrm{PSL}_2(\mathbb{Z})$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Underscreening in concentrated electrolytes | Screening of a surface charge by electrolyte and the resulting interaction
energy between charged objects is of fundamental importance in scenarios from
bio-molecular interactions to energy storage. The conventional wisdom is that
the interaction energy decays exponentially with object separation and the
decay length is a decreasing function of ion concentration; the interaction is
thus negligible in a concentrated electrolyte. Contrary to this conventional
wisdom, we have shown by surface force measurements that the decay length is an
increasing function of ion concentration and Bjerrum length for concentrated
electrolytes. In this paper we report surface force measurements to test
directly the scaling of the screening length with Bjerrum length. Furthermore,
we identify a relationship between the concentration dependence of this
screening length and empirical measurements of activity coefficient and
differential capacitance. The dependence of the screening length on the ion
concentration and the Bjerrum length can be explained by a simple scaling
conjecture based on the physical intuition that solvent molecules, rather than
ions, are charge carriers in a concentrated electrolyte.
| 0 | 1 | 0 | 0 | 0 | 0 |
Asymptotics for Small Nonlinear Price Impact: a PDE Approach to the Multidimensional Case | We provide an asymptotic expansion of the value function of a
multidimensional utility maximization problem from consumption with small
non-linear price impact. In our model cross-impacts between assets are allowed.
In the limit for small price impact, we determine the asymptotic expansion of
the value function around its frictionless version. The leading order
correction is characterized by a nonlinear second order PDE related to an
ergodic control problem and a linear parabolic PDE. We illustrate our result on
a multivariate geometric Brownian motion price model.
| 0 | 0 | 0 | 0 | 0 | 1 |
Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation | Probabilistic atlases provide essential spatial contextual information for
image interpretation, Bayesian modeling, and algorithmic processing. Such
atlases are typically constructed by grouping subjects with similar demographic
information. Importantly, use of the same scanner minimizes inter-group
variability. However, generalizability and spatial specificity of such
approaches is more limited than one might like. Inspired by Commowick
"Frankenstein's creature paradigm" which builds a personal specific anatomical
atlas, we propose a data-driven framework to build a personal specific
probabilistic atlas under the large-scale data scheme. The data-driven
framework clusters regions with similar features using a point distribution
model to learn different anatomical phenotypes. Regional structural atlases and
corresponding regional probabilistic atlases are used as indices and targets in
the dictionary. By indexing the dictionary, the whole brain probabilistic
atlases adapt to each new subject quickly and can be used as spatial priors for
visualization and processing. The novelties of this approach are (1) it
provides a new perspective of generating personal specific whole brain
probabilistic atlases (132 regions) under data-driven scheme across sites. (2)
The framework employs the large amount of heterogeneous data (2349 images). (3)
The proposed framework achieves low computational cost since only one affine
registration and Pearson correlation operation are required for a new subject.
Our method matches individual regions better with higher Dice similarity value
when testing the probabilistic atlases. Importantly, the advantage the
large-scale scheme is demonstrated by the better performance of using
large-scale training data (1888 images) than smaller training set (720 images).
| 0 | 0 | 0 | 1 | 1 | 0 |
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems | We present a machine learning framework for multi-agent systems to learn both
the optimal policy for maximizing the rewards and the encoding of the high
dimensional visual observation. The encoding is useful for sharing local visual
observations with other agents under communication resource constraints. The
actor-encoder encodes the raw images and chooses an action based on local
observations and messages sent by the other agents. The machine learning agent
generates not only an actuator command to the physical device, but also a
communication message to the other agents. We formulate a reinforcement
learning problem, which extends the action space to consider the communication
action as well. The feasibility of the reinforcement learning framework is
demonstrated using a 3D simulation environment with two collaborating agents.
The environment provides realistic visual observations to be used and shared
between the two agents.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimal investment-consumption problem post-retirement with a minimum guarantee | We study the optimal investment-consumption problem for a member of defined
contribution plan during the decumulation phase. For a fixed annuitization
time, to achieve higher final annuity, we consider a variable consumption rate.
Moreover, to eliminate the ruin possibilities and having a minimum guarantee
for the final annuity, we consider a safety level for the wealth process which
consequently yields a Hamilton-Jacobi-Bellman (HJB) equation on a bounded
domain. We apply the policy iteration method to find approximations of solution
of the HJB equation. Finally, we give the simulation results for the optimal
investment-consumption strategies, optimal wealth process and the final annuity
for different ranges of admissible consumptions. Furthermore, by calculating
the present market value of the future cash flows before and after the
annuitization, we compare the results for different consumption policies.
| 0 | 0 | 0 | 0 | 0 | 1 |
Parametrices for the light ray transform on Minkowski spacetime | We consider restricted light ray transforms arising from an inverse problem
of finding cosmic strings. We construct a relative left parametrix for the
transform on two tensors, which recovers the space-like and some light-like
singularities of the two tensor.
| 0 | 0 | 1 | 0 | 0 | 0 |
Derivative Principal Component Analysis for Representing the Time Dynamics of Longitudinal and Functional Data | We propose a nonparametric method to explicitly model and represent the
derivatives of smooth underlying trajectories for longitudinal data. This
representation is based on a direct Karhunen--Loève expansion of the
unobserved derivatives and leads to the notion of derivative principal
component analysis, which complements functional principal component analysis,
one of the most popular tools of functional data analysis. The proposed
derivative principal component scores can be obtained for irregularly spaced
and sparsely observed longitudinal data, as typically encountered in biomedical
studies, as well as for functional data which are densely measured. Novel
consistency results and asymptotic convergence rates for the proposed estimates
of the derivative principal component scores and other components of the model
are derived under a unified scheme for sparse or dense observations and mild
conditions. We compare the proposed representations for derivatives with
alternative approaches in simulation settings and also in a wallaby growth
curve application. It emerges that representations using the proposed
derivative principal component analysis recover the underlying derivatives more
accurately compared to principal component analysis-based approaches especially
in settings where the functional data are represented with only a very small
number of components or are densely sampled. In a second wheat spectra
classification example, derivative principal component scores were found to be
more predictive for the protein content of wheat than the conventional
functional principal component scores.
| 0 | 0 | 1 | 1 | 0 | 0 |
Stokes phenomenon and confluence in non-autonomous Hamiltonian systems | This article studies a confluence of a pair of regular singular points to an
irregular one in a generic family of time-dependent Hamiltonian systems in
dimension 2. This is a general setting for the understanding of the
degeneration of the sixth Painleve equation to the fifth one. The main result
is a theorem of sectoral normalization of the family to an integrable formal
normal form, through which is explained the relation between the local
monodromy operators at the two regular singularities and the non-linear Stokes
phenomenon at the irregular singularity of the limit system. The problem of
analytic classification is also addressed.
Key words: Non-autonomous Hamiltonian systems; irregular singularity;
non-linear Stokes phenomenon; wild monodromy; confluence; local analytic
classification; Painleve equations.
| 0 | 0 | 1 | 0 | 0 | 0 |
A General Sequential Delay-Doppler Estimation Scheme for Sub-Nyquist Pulse-Doppler Radar | Sequential estimation of the delay and Doppler parameters for sub-Nyquist
radars by analog-to-information conversion (AIC) systems has received wide
attention recently. However, the estimation methods reported are AIC-dependent
and have poor performance for off-grid targets. This paper develops a general
estimation scheme in the sense that it is applicable to all AICs regardless
whether the targets are on or off the grids. The proposed scheme estimates the
delay and Doppler parameters sequentially, in which the delay estimation is
formulated into a beamspace direction-of- arrival problem and the Doppler
estimation is translated into a line spectrum estimation problem. Then the
well-known spatial and temporal spectrum estimation techniques are used to
provide efficient and high-resolution estimates of the delay and Doppler
parameters. In addition, sufficient conditions on the AIC to guarantee the
successful estimation of off-grid targets are provided, while the existing
conditions are mostly related to the on-grid targets. Theoretical analyses and
numerical experiments show the effectiveness and the correctness of the
proposed scheme.
| 1 | 0 | 1 | 0 | 0 | 0 |
The independence number of the Birkhoff polytope graph, and applications to maximally recoverable codes | Maximally recoverable codes are codes designed for distributed storage which
combine quick recovery from single node failure and optimal recovery from
catastrophic failure. Gopalan et al [SODA 2017] studied the alphabet size
needed for such codes in grid topologies and gave a combinatorial
characterization for it.
Consider a labeling of the edges of the complete bipartite graph $K_{n,n}$
with labels coming from $F_2^d$ , that satisfies the following condition: for
any simple cycle, the sum of the labels over its edges is nonzero. The minimal
d where this is possible controls the alphabet size needed for maximally
recoverable codes in n x n grid topologies.
Prior to the current work, it was known that d is between $(\log n)^2$ and
$n\log n$. We improve both bounds and show that d is linear in n. The upper
bound is a recursive construction which beats the random construction. The
lower bound follows by first relating the problem to the independence number of
the Birkhoff polytope graph, and then providing tight bounds for it using the
representation theory of the symmetric group.
| 1 | 0 | 1 | 0 | 0 | 0 |
Spin Angular Momentum of Proton Spin Puzzle in Complex Octonion Spaces | The paper focuses on considering some special precessional motions as the
spin motions, separating the octonion angular momentum of a proton into six
components, elucidating the proton angular momentum in the proton spin puzzle,
especially the proton spin, decomposition, quarks and gluons, and polarization
and so forth. J. C. Maxwell was the first to use the quaternions to study the
electromagnetic fields. Subsequently the complex octonions are utilized to
depict the electromagnetic field, gravitational field, and quantum mechanics
and so forth. In the complex octonion space, the precessional equilibrium
equation infers the angular velocity of precession. The external
electromagnetic strength may induce a new precessional motion, generating a new
term of angular momentum, even if the orbital angular momentum is zero. This
new term of angular momentum can be regarded as the spin angular momentum, and
its angular velocity of precession is different from the angular velocity of
revolution. The study reveals that the angular momentum of the proton must be
separated into more components than ever before. In the proton spin puzzle, the
orbital angular momentum and magnetic dipole moment are independent of each
other, and they should be measured and calculated respectively.
| 0 | 1 | 0 | 0 | 0 | 0 |
Physics-Informed Regularization of Deep Neural Networks | This paper presents a novel physics-informed regularization method for
training of deep neural networks (DNNs). In particular, we focus on the DNN
representation for the response of a physical or biological system, for which a
set of governing laws are known. These laws often appear in the form of
differential equations, derived from first principles, empirically-validated
laws, and/or domain expertise. We propose a DNN training approach that utilizes
these known differential equations in addition to the measurement data, by
introducing a penalty term to the training loss function to penalize divergence
form the governing laws. Through three numerical examples, we will show that
the proposed regularization produces surrogates that are physically
interpretable with smaller generalization errors, when compared to other common
regularization methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gradient-based Filter Design for the Dual-tree Wavelet Transform | The wavelet transform has seen success when incorporated into neural network
architectures, such as in wavelet scattering networks. More recently, it has
been shown that the dual-tree complex wavelet transform can provide better
representations than the standard transform. With this in mind, we extend our
previous method for learning filters for the 1D and 2D wavelet transforms into
the dual-tree domain. We show that with few modifications to our original
model, we can learn directional filters that leverage the properties of the
dual-tree wavelet transform.
| 0 | 0 | 0 | 1 | 0 | 0 |
Assimilated LVEF: A Bayesian technique combining human intuition with machine measurement for sharper estimates of left ventricular ejection fraction and stronger association with outcomes | The cardiologist's main tool for measuring systolic heart failure is left
ventricular ejection fraction (LVEF). Trained cardiologist's report both a
visual and machine-guided measurement of LVEF, but only use this machine-guided
measurement in analysis. We use a Bayesian technique to combine visual and
machine-guided estimates from the PARTNER-IIA Trial, a cohort of patients with
aortic stenosis at moderate risk treated with bioprosthetic aortic valves, and
find our combined estimate reduces measurement errors and improves the
association between LVEF and a 1-year composite endpoint.
| 0 | 0 | 0 | 1 | 0 | 0 |
DBSCAN: Optimal Rates For Density Based Clustering | We study the problem of optimal estimation of the density cluster tree under
various assumptions on the underlying density. Building up from the seminal
work of Chaudhuri et al. [2014], we formulate a new notion of clustering
consistency which is better suited to smooth densities, and derive minimax
rates of consistency for cluster tree estimation for Holder smooth densities of
arbitrary degree \alpha. We present a computationally efficient, rate optimal
cluster tree estimator based on a straightforward extension of the popular
density-based clustering algorithm DBSCAN by Ester et al. [1996]. The procedure
relies on a kernel density estimator with an appropriate choice of the kernel
and bandwidth to produce a sequence of nested random geometric graphs whose
connected components form a hierarchy of clusters. The resulting optimal rates
for cluster tree estimation depend on the degree of smoothness of the
underlying density and, interestingly, match minimax rates for density
estimation under the supremum norm. Our results complement and extend the
analysis of the DBSCAN algorithm in Sriperumbudur and Steinwart [2012].
Finally, we consider level set estimation and cluster consistency for densities
with jump discontinuities, where the sizes of the jumps and the distance among
clusters are allowed to vanish as the sample size increases. We demonstrate
that our DBSCAN-based algorithm remains minimax rate optimal in this setting as
well.
| 0 | 0 | 1 | 1 | 0 | 0 |
Steganographic Generative Adversarial Networks | Steganography is collection of methods to hide secret information ("payload")
within non-secret information ("container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Minimal Sum Labeling of Graphs | A graph $G$ is called a sum graph if there is a so-called sum labeling of
$G$, i.e. an injective function $\ell: V(G) \rightarrow \mathbb{N}$ such that
for every $u,v\in V(G)$ it holds that $uv\in E(G)$ if and only if there exists
a vertex $w\in V(G)$ such that $\ell(u)+\ell(v) = \ell(w)$. We say that sum
labeling $\ell$ is minimal if there is a vertex $u\in V(G)$ such that
$\ell(u)=1$. In this paper, we show that if we relax the conditions (either
allow non-injective labelings or consider graphs with loops) then there are sum
graphs without a minimal labeling, which partially answers the question posed
by Miller, Ryan and Smyth in 1998.
| 1 | 0 | 0 | 0 | 0 | 0 |
Provably efficient RL with Rich Observations via Latent State Decoding | We study the exploration problem in episodic MDPs with rich observations
generated from a small number of latent states. Under certain identifiability
assumptions, we demonstrate how to estimate a mapping from the observations to
latent states inductively through a sequence of regression and clustering
steps---where previously decoded latent states provide labels for later
regression problems---and use it to construct good exploration policies. We
provide finite-sample guarantees on the quality of the learned state decoding
function and exploration policies, and complement our theory with an empirical
evaluation on a class of hard exploration problems. Our method exponentially
improves over $Q$-learning with naïve exploration, even when $Q$-learning has
cheating access to latent states.
| 1 | 0 | 0 | 1 | 0 | 0 |
Markov Chain Lifting and Distributed ADMM | The time to converge to the steady state of a finite Markov chain can be
greatly reduced by a lifting operation, which creates a new Markov chain on an
expanded state space. For a class of quadratic objectives, we show an analogous
behavior where a distributed ADMM algorithm can be seen as a lifting of
Gradient Descent algorithm. This provides a deep insight for its faster
convergence rate under optimal parameter tuning. We conjecture that this gain
is always present, as opposed to the lifting of a Markov chain which sometimes
only provides a marginal speedup.
| 1 | 0 | 1 | 1 | 0 | 0 |
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis | Generating versatile and appropriate synthetic speech requires control over
the output expression separate from the spoken text. Important non-textual
speech variation is seldom annotated, in which case output control must be
learned in an unsupervised fashion. In this paper, we perform an in-depth study
of methods for unsupervised learning of control in statistical speech
synthesis. For example, we show that popular unsupervised training heuristics
can be interpreted as variational inference in certain autoencoder models. We
additionally connect these models to VQ-VAEs, another, recently-proposed class
of deep variational autoencoders, which we show can be derived from a very
similar mathematical argument. The implications of these new probabilistic
interpretations are discussed. We illustrate the utility of the various
approaches with an application to acoustic modelling for emotional speech
synthesis, where the unsupervised methods for learning expression control
(without access to emotional labels) are found to give results that in many
aspects match or surpass the previous best supervised approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
Fast failover of multicast sessions in software-defined networks | With the rapid growth of services that stream to groups of users comes an
increased importance of and demand for reliable multicast. In this paper, we
turn to software-defined networking and develop a novel general-purpose
multi-failure protection algorithm to provide quick failure recovery, via Fast
Failover (FF) groups, for dynamic multicast groups. This extends previous
research, which either could not realize fast failover, worked only for single
link failures, or was only applicable to static multicast groups. However,
while FF is know to be fast, it requires pre-installing back-up rules. These
additional memory requirements, which in a multicast setting are even more
pronounced than for unicast, are often mentioned as a big disadvantage of using
FF.
We develop an OpenFlow application for resilient multicast, with which we
study FF resource usage, in an attempt to better understand the trade-off
between recovery time and resource usage. Our tests on a realistic network
suggest that using FF groups can reduce the recovery time of the network
significantly compared to other methods, especially when the latency between
the controller and the switches is relatively large.
| 1 | 0 | 0 | 0 | 0 | 0 |
AI Safety Gridworlds | We present a suite of reinforcement learning environments illustrating
various safety properties of intelligent agents. These problems include safe
interruptibility, avoiding side effects, absent supervisor, reward gaming, safe
exploration, as well as robustness to self-modification, distributional shift,
and adversaries. To measure compliance with the intended safe behavior, we
equip each environment with a performance function that is hidden from the
agent. This allows us to categorize AI safety problems into robustness and
specification problems, depending on whether the performance function
corresponds to the observed reward function. We evaluate A2C and Rainbow, two
recent deep reinforcement learning agents, on our environments and show that
they are not able to solve them satisfactorily.
| 1 | 0 | 0 | 0 | 0 | 0 |
Maximality of Galois actions for abelian varieties | Let $\{\rho_\ell\}_\ell$ be the system of $\ell$-adic representations arising
from the $i$th $\ell$-adic cohomology of a complete smooth variety $X$ defined
over a number field $K$. Denote the image of $\rho_\ell$ by $\Gamma_\ell$ and
its Zariski closure, which is a linear algebraic group over $\mathbb{Q}_\ell$,
by $\mathbf{G}_\ell$. We prove that $\mathbf{G}_\ell^{red}$, the quotient of
$\mathbf{G}_\ell^\circ$ by its unipotent radical, is unramified over a totally
ramified extension of $\mathbb{Q}_\ell$ for all sufficiently large $\ell$. We
give a sufficient condition on $\{\rho_\ell\}_\ell$ such that for all
sufficiently large $\ell$, $\Gamma_\ell$ is in some sense maximal compact in
$\mathbf{G}_\ell(\mathbb{Q}_\ell)$. Since the condition is satisfied when $X$
is an abelian variety by the Tate conjecture, we obtain maximality of Galois
actions for abelian varieties.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Application of Multi-band Forced Photometry to One Square Degree of SERVS: Accurate Photometric Redshifts and Implications for Future Science | We apply The Tractor image modeling code to improve upon existing multi-band
photometry for the Spitzer Extragalactic Representative Volume Survey (SERVS).
SERVS consists of post-cryogenic Spitzer observations at 3.6 and 4.5 micron
over five well-studied deep fields spanning 18 square degrees. In concert with
data from ground-based near-infrared (NIR) and optical surveys, SERVS aims to
provide a census of the properties of massive galaxies out to z ~ 5. To
accomplish this, we are using The Tractor to perform "forced photometry." This
technique employs prior measurements of source positions and surface brightness
profiles from a high-resolution fiducial band from the VISTA Deep Extragalactic
Observations (VIDEO) survey to model and fit the fluxes at lower-resolution
bands. We discuss our implementation of The Tractor over a square degree test
region within the XMM-LSS field with deep imaging in 12 NIR/optical bands. Our
new multi-band source catalogs offer a number of advantages over traditional
position-matched catalogs, including 1) consistent source cross-identification
between bands, 2) de-blending of sources that are clearly resolved in the
fiducial band but blended in the lower-resolution SERVS data, 3) a higher
source detection fraction in each band, 4) a larger number of candidate
galaxies in the redshift range 5 < z < 6, and 5) a statistically significant
improvement in the photometric redshift accuracy as evidenced by the
significant decrease in the fraction of outliers compared to spectroscopic
redshifts. Thus, forced photometry using The Tractor offers a means of
improving the accuracy of multi-band extragalactic surveys designed for galaxy
evolution studies. We will extend our application of this technique to the full
SERVS footprint in the future.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stacked Structure Learning for Lifted Relational Neural Networks | Lifted Relational Neural Networks (LRNNs) describe relational domains using
weighted first-order rules which act as templates for constructing feed-forward
neural networks. While previous work has shown that using LRNNs can lead to
state-of-the-art results in various ILP tasks, these results depended on
hand-crafted rules. In this paper, we extend the framework of LRNNs with
structure learning, thus enabling a fully automated learning process. Similarly
to many ILP methods, our structure learning algorithm proceeds in an iterative
fashion by top-down searching through the hypothesis space of all possible Horn
clauses, considering the predicates that occur in the training examples as well
as invented soft concepts entailed by the best weighted rules found so far. In
the experiments, we demonstrate the ability to automatically induce useful
hierarchical soft concepts leading to deep LRNNs with a competitive predictive
power.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs | Organisations store huge amounts of data from multiple heterogeneous sources
in the form of Knowledge Graphs (KGs). One of the ways to query these KGs is to
use SPARQL queries over a database engine. Since SPARQL follows exact match
semantics, the queries may return too few or no results. Recent works have
proposed query relaxation where the query engine judiciously replaces a query
predicate with similar predicates using weighted relaxation rules mined from
the KG. The space of possible relaxations is potentially too large to fully
explore and users are typically interested in only top-k results, so such query
engines use top-k algorithms for query processing. However, they may still
process all the relaxations, many of whose answers do not contribute towards
top-k answers. This leads to computation overheads and delayed response times.
We propose Spec-QP, a query planning framework that speculatively determines
which relaxations will have their results in the top-k answers. Only these
relaxations are processed using the top-k operators. We, therefore, reduce the
computation overheads and achieve faster response times without adversely
affecting the quality of results. We tested Spec-QP over two datasets - XKG and
Twitter, to demonstrate the efficiency of our planning framework at reducing
runtimes with reasonable accuracy for query engines supporting relaxations.
| 1 | 0 | 0 | 0 | 0 | 0 |
SurfClipse: Context-Aware Meta Search in the IDE | Despite various debugging supports of the existing IDEs for programming
errors and exceptions, software developers often look at web for working
solutions or any up-to-date information. Traditional web search does not
consider the context of the problems that they search solutions for, and thus
it often does not help much in problem solving. In this paper, we propose a
context-aware meta search tool, SurfClipse, that analyzes an encountered
exception and its context in the IDE, and recommends not only suitable search
queries but also relevant web pages for the exception (and its context). The
tool collects results from three popular search engines and a programming Q & A
site against the exception in the IDE, refines the results for relevance
against the context of the exception, and then ranks them before
recommendation. It provides two working modes--interactive and proactive to
meet the versatile needs of the developers, and one can browse the result pages
using a customized embedded browser provided by the tool.
Tool page: www.usask.ca/~masud.rahman/surfclipse
| 1 | 0 | 0 | 0 | 0 | 0 |
Chomp on numerical semigroups | We consider the two-player game chomp on posets associated to numerical
semigroups and show that the analysis of strategies for chomp is strongly
related to classical properties of semigroups. We characterize, which player
has a winning-strategy for symmetric semigroups, semigroups of maximal
embedding dimension and several families of numerical semigroups generated by
arithmetic sequences. Furthermore, we show that which player wins on a given
numerical semigroup is a decidable question. Finally, we extend several of our
results to the more general setting of subsemigroups of $\mathbb{N} \times T$,
where $T$ is a finite abelian group.
| 1 | 0 | 1 | 0 | 0 | 0 |
Single Index Latent Variable Models for Network Topology Inference | A semi-parametric, non-linear regression model in the presence of latent
variables is applied towards learning network graph structure. These latent
variables can correspond to unmodeled phenomena or unmeasured agents in a
complex system of interacting entities. This formulation jointly estimates
non-linearities in the underlying data generation, the direct interactions
between measured entities, and the indirect effects of unmeasured processes on
the observed data. The learning is posed as regularized empirical risk
minimization. Details of the algorithm for learning the model are outlined.
Experiments demonstrate the performance of the learned model on real data.
| 0 | 0 | 0 | 1 | 0 | 0 |
Quadratically-Regularized Optimal Transport on Graphs | Optimal transportation provides a means of lifting distances between points
on a geometric domain to distances between signals over the domain, expressed
as probability distributions. On a graph, transportation problems can be used
to express challenging tasks involving matching supply to demand with minimal
shipment expense; in discrete language, these become minimum-cost network flow
problems. Regularization typically is needed to ensure uniqueness for the
linear ground distance case and to improve optimization convergence;
state-of-the-art techniques employ entropic regularization on the
transportation matrix. In this paper, we explore a quadratic alternative to
entropic regularization for transport over a graph. We theoretically analyze
the behavior of quadratically-regularized graph transport, characterizing how
regularization affects the structure of flows in the regime of small but
nonzero regularization. We further exploit elegant second-order structure in
the dual of this problem to derive an easily-implemented Newton-type
optimization algorithm.
| 1 | 0 | 1 | 0 | 0 | 0 |
Discussion on "Random-projection ensemble classification" by T. Cannings and R. Samworth | Discussion on "Random-projection ensemble classification" by T. Cannings and
R. Samworth. We believe that the proposed approach can find many applications
in economics such as credit scoring (e.g. Altman (1968)) and can be extended to
more general type of classifiers. In this discussion we would like to draw
authors attention to the copula-based discriminant analysis (Han et al. (2013)
and He et al. (2016)).
| 0 | 0 | 1 | 1 | 0 | 0 |
Deep Asymmetric Multi-task Feature Learning | We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can
learn deep representations shared across multiple tasks while effectively
preventing negative transfer that may happen in the feature sharing process.
Specifically, we introduce an asymmetric autoencoder term that allows reliable
predictors for the easy tasks to have high contribution to the feature learning
while suppressing the influences of unreliable predictors for more difficult
tasks. This allows the learning of less noisy representations, and enables
unreliable predictors to exploit knowledge from the reliable predictors via the
shared latent features. Such asymmetric knowledge transfer through shared
features is also more scalable and efficient than inter-task asymmetric
transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for
multitask learning and image classification, on which it significantly
outperforms existing symmetric and asymmetric multitask learning models, by
effectively preventing negative transfer in deep feature learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sub-nanometre resolution of atomic motion during electronic excitation in phase-change materials | Phase-change materials based on Ge-Sb-Te alloys are widely used in industrial
applications such as nonvolatile memories, but reaction pathways for
crystalline-to-amorphous phase-change on picosecond timescales remain unknown.
Femtosecond laser excitation and an ultrashort x-ray probe is used to show the
temporal separation of electronic and thermal effects in a long-lived ($>$100
ps) transient metastable state of Ge$_{2}$Sb$_{2}$Te$_{5}$ with muted
interatomic interaction induced by a weakening of resonant bonding. Due to a
specific electronic state, the lattice undergoes a reversible nondestructive
modification over a nanoscale region, remaining cold for 4 ps. An independent
time-resolved x-ray absorption fine structure experiment confirms the existence
of an intermediate state with disordered bonds. This newly unveiled effect
allows the utilization of non-thermal ultra-fast pathways enabling artificial
manipulation of the switching process, ultimately leading to a redefined speed
limit, and improved energy efficiency and reliability of phase-change memory
technologies.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Density Result for Real Hyperelliptic Curves | Let $\{\infty^+, \infty^-\}$ be the two points above $\infty$ on the real
hyperelliptic curve $H: y^2 = (x^2 - 1) \prod_{i=1}^{2g} (x - a_i)$. We show
that the divisor $([\infty^+] - [\infty^-])$ is torsion in $\operatorname{Jac}
J$ for a dense set of $(a_1, a_2, \ldots, a_{2g}) \in (-1, 1)^{2g}$. In fact,
we prove by degeneration to a nodal $\mathbb{P}^1$ that an associated period
map has derivative generically of full rank.
| 0 | 0 | 1 | 0 | 0 | 0 |
On a class of integrable systems of Monge-Ampère type | We investigate a class of multi-dimensional two-component systems of
Monge-Ampère type that can be viewed as generalisations of heavenly-type
equations appearing in self-dual Ricci-flat geometry. Based on the
Jordan-Kronecker theory of skew-symmetric matrix pencils, a classification of
normal forms of such systems is obtained. All two-component systems of
Monge-Ampère type turn out to be integrable, and can be represented as the
commutativity conditions of parameter-dependent vector fields. Geometrically,
systems of Monge-Ampère type are associated with linear sections of the
Grassmannians. This leads to an invariant differential-geometric
characterisation of the Monge-Ampère property.
| 0 | 1 | 1 | 0 | 0 | 0 |
When is the mode functional the Bayes classifier? | In classification problems, the mode of the conditional probability
distribution, i.e., the most probable category, is the Bayes classifier under
zero-one or misclassification loss. Under any other cost structure, the mode
fails to persist.
| 0 | 0 | 1 | 1 | 0 | 0 |
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data | One of the main benefits of a wrist-worn computer is its ability to collect a
variety of physiological data in a minimally intrusive manner. Among these
data, electrodermal activity (EDA) is readily collected and provides a window
into a person's emotional and sympathetic responses. EDA data collected using a
wearable wristband are easily influenced by motion artifacts (MAs) that may
significantly distort the data and degrade the quality of analyses performed on
the data if not identified and removed. Prior work has demonstrated that MAs
can be successfully detected using supervised machine learning algorithms on a
small data set collected in a lab setting. In this paper, we demonstrate that
unsupervised learning algorithms perform competitively with supervised
algorithms for detecting MAs on EDA data collected in both a lab-based setting
and a real-world setting comprising about 23 hours of data. We also find,
somewhat surprisingly, that incorporating accelerometer data as well as EDA
improves detection accuracy only slightly for supervised algorithms and
significantly degrades the accuracy of unsupervised algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
CredSaT: Credibility Ranking of Users in Big Social Data incorporating Semantic Analysis and Temporal Factor | The widespread use of big social data has pointed the research community in
several significant directions. In particular, the notion of social trust has
attracted a great deal of attention from information processors | computer
scientists and information consumers | formal organizations. This is evident in
various applications such as recommendation systems, viral marketing and
expertise retrieval. Hence, it is essential to have frameworks that can
temporally measure users credibility in all domains categorised under big
social data. This paper presents CredSaT (Credibility incorporating Semantic
analysis and Temporal factor): a fine-grained users credibility analysis
framework for big social data. A novel metric that includes both new and
current features, as well as the temporal factor, is harnessed to establish the
credibility ranking of users. Experiments on real-world dataset demonstrate the
effectiveness and applicability of our model to indicate highly domain-based
trustworthy users. Further, CredSaT shows the capacity in capturing spammers
and other anomalous users.
| 1 | 0 | 0 | 0 | 0 | 0 |
On singular Finsler foliation | In this paper we introduce the concept of singular Finsler foliation, which
generalizes the concepts of Finsler actions, Finsler submersions and (regular)
Finsler foliations. We show that if $\mathcal{F}$ is a singular Finsler
foliation on a Randers manifold $(M,Z)$ with Zermelo data $(\mathtt{h},W),$
then $\mathcal{F}$ is a singular Riemannian foliation on the Riemannian
manifold $(M,\mathtt{h} )$. As a direct consequence we infer that the regular
leaves are equifocal submanifolds (a generalization of isoparametric
submanifolds) when the wind $W$ is an infinitesimal homothety of $\mathtt{h}$
(e.,g when $W$ is killing vector field or $M$ has constant Finsler curvature).
We also present a slice theorem that relates local singular Finsler
foliations on Finsler manifolds with singular Finsler foliations on Minkowski
spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Early Experiences with Crowdsourcing Airway Annotations in Chest CT | Measuring airways in chest computed tomography (CT) images is important for
characterizing diseases such as cystic fibrosis, yet very time-consuming to
perform manually. Machine learning algorithms offer an alternative, but need
large sets of annotated data to perform well. We investigate whether
crowdsourcing can be used to gather airway annotations which can serve directly
for measuring the airways, or as training data for the algorithms. We generate
image slices at known locations of airways and request untrained crowd workers
to outline the airway lumen and airway wall. Our results show that the workers
are able to interpret the images, but that the instructions are too complex,
leading to many unusable annotations. After excluding unusable annotations,
quantitative results show medium to high correlations with expert measurements
of the airways. Based on this positive experience, we describe a number of
further research directions and provide insight into the challenges of
crowdsourcing in medical images from the perspective of first-time users.
| 1 | 0 | 0 | 0 | 0 | 0 |
Convergent Iteration in Sobolev Space for Time Dependent Closed Quantum Systems | Time dependent quantum systems have become indispensable in science and its
applications, particularly at the atomic and molecular levels. Here, we discuss
the approximation of closed time dependent quantum systems on bounded domains,
via iterative methods in Sobolev space based upon evolution operators.
Recently, existence and uniqueness of weak solutions were demonstrated by a
contractive fixed point mapping defined by the evolution operators. Convergent
successive approximation is then guaranteed. This article uses the same mapping
to define quadratically convergent Newton and approximate Newton methods.
Estimates for the constants used in the convergence estimates are provided. The
evolution operators are ideally suited to serve as the framework for this
operator approximation theory, since the Hamiltonian is time dependent. In
addition, the hypotheses required to guarantee quadratic convergence of the
Newton iteration build naturally upon the hypotheses used for the
existence/uniqueness theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Prototyping and Experimentation of a Closed-Loop Wireless Power Transmission with Channel Acquisition and Waveform Optimization | A systematic design of adaptive waveform for Wireless Power Transfer (WPT)
has recently been proposed and shown through simulations to lead to significant
performance benefits compared to traditional non-adaptive and heuristic
waveforms. In this study, we design the first prototype of a closed-loop
wireless power transfer system with adaptive waveform optimization based on
Channel State Information acquisition. The prototype consists of three
important blocks, namely the channel estimator, the waveform optimizer, and the
energy harvester. Software Defined Radio (SDR) prototyping tools are used to
implement a wireless power transmitter and a channel estimator, and a voltage
doubler rectenna is designed to work as an energy harvester. A channel adaptive
waveform with 8 sinewaves is shown through experiments to improve the average
harvested DC power at the rectenna output by 9.8% to 36.8% over a non-adaptive
design with the same number of sinewaves.
| 1 | 0 | 0 | 0 | 0 | 0 |
Image-based immersed boundary model of the aortic root | Each year, approximately 300,000 heart valve repair or replacement procedures
are performed worldwide, including approximately 70,000 aortic valve
replacement surgeries in the United States alone. This paper describes progress
in constructing anatomically and physiologically realistic immersed boundary
(IB) models of the dynamics of the aortic root and ascending aorta. This work
builds on earlier IB models of fluid-structure interaction (FSI) in the aortic
root, which previously achieved realistic hemodynamics over multiple cardiac
cycles, but which also were limited to simplified aortic geometries and
idealized descriptions of the biomechanics of the aortic valve cusps. By
contrast, the model described herein uses an anatomical geometry reconstructed
from patient-specific computed tomography angiography (CTA) data, and employs a
description of the elasticity of the aortic valve leaflets based on a
fiber-reinforced constitutive model fit to experimental tensile test data.
Numerical tests show that the model is able to resolve the leaflet biomechanics
in diastole and early systole at practical grid spacings. The model is also
used to examine differences in the mechanics and fluid dynamics yielded by
fresh valve leaflets and glutaraldehyde-fixed leaflets similar to those used in
bioprosthetic heart valves. Although there are large differences in the leaflet
deformations during diastole, the differences in the open configurations of the
valve models are relatively small, and nearly identical hemodynamics are
obtained in all cases considered.
| 1 | 1 | 0 | 0 | 0 | 0 |
Large-Scale Cox Process Inference using Variational Fourier Features | Gaussian process modulated Poisson processes provide a flexible framework for
modelling spatiotemporal point patterns. So far this had been restricted to one
dimension, binning to a pre-determined grid, or small data sets of up to a few
thousand data points. Here we introduce Cox process inference based on Fourier
features. This sparse representation induces global rather than local
constraints on the function space and is computationally efficient. This allows
us to formulate a grid-free approximation that scales well with the number of
data points and the size of the domain. We demonstrate that this allows MCMC
approximations to the non-Gaussian posterior. We also find that, in practice,
Fourier features have more consistent optimization behavior than previous
approaches. Our approximate Bayesian method can fit over 100,000 events with
complex spatiotemporal patterns in three dimensions on a single GPU.
| 0 | 0 | 0 | 1 | 0 | 0 |
Buildings-to-Grid Integration Framework | This paper puts forth a mathematical framework for Buildings-to-Grid (BtG)
integration in smart cities. The framework explicitly couples power grid and
building's control actions and operational decisions, and can be utilized by
buildings and power grids operators to simultaneously optimize their
performance. Simplified dynamics of building clusters and building-integrated
power networks with algebraic equations are presented---both operating at
different time-scales. A model predictive control (MPC)-based algorithm that
formulates the BtG integration and accounts for the time-scale discrepancy is
developed. The formulation captures dynamic and algebraic power flow
constraints of power networks and is shown to be numerically advantageous. The
paper analytically establishes that the BtG integration yields a reduced total
system cost in comparison with decoupled designs where grid and building
operators determine their controls separately. The developed framework is
tested on standard power networks that include thousands of buildings modeled
using industrial data. Case studies demonstrate building energy savings and
significant frequency regulation, while these findings carry over in network
simulations with nonlinear power flows and mismatch in building model
parameters. Finally, simulations indicate that the performance does not
significantly worsen when there is uncertainty in the forecasted weather and
base load conditions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Oxygen Partial Pressure during Pulsed Laser Deposition: Deterministic Role on Thermodynamic Stability of Atomic Termination Sequence at SrRuO3/BaTiO3 Interface | With recent trends on miniaturizing oxide-based devices, the need for
atomic-scale control of surface/interface structures by pulsed laser deposition
(PLD) has increased. In particular, realizing uniform atomic termination at the
surface/interface is highly desirable. However, a lack of understanding on the
surface formation mechanism in PLD has limited a deliberate control of
surface/interface atomic stacking sequences. Here, taking the prototypical
SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) heterostructure as a model system, we
investigated the formation of different interfacial termination sequences
(BaO-RuO2 or TiO2-SrO) with oxygen partial pressure (PO2) during PLD. We found
that a uniform SrO-TiO2 termination sequence at the SRO/BTO interface can be
achieved by lowering the PO2 to 5 mTorr, regardless of the total background gas
pressure (Ptotal), growth mode, or growth rate. Our results indicate that the
thermodynamic stability of the BTO surface at the low-energy kinetics stage of
PLD can play an important role in surface/interface termination formation. This
work paves the way for realizing termination engineering in functional oxide
heterostructures.
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A Data-Driven Analysis of the Influence of Care Coordination on Trauma Outcome | OBJECTIVE: To test the hypothesis that variation in care coordination is
related to LOS. DESIGN We applied a spectral co-clustering methodology to
simultaneously infer groups of patients and care coordination patterns, in the
form of interaction networks of health care professionals, from electronic
medical record (EMR) utilization data. The care coordination pattern for each
patient group was represented by standard social network characteristics and
its relationship with hospital LOS was assessed via a negative binomial
regression with a 95% confidence interval. SETTING AND PATIENTS This study
focuses on 5,588 adult patients hospitalized for trauma at the Vanderbilt
University Medical Center. The EMRs were accessed by healthcare professionals
from 179 operational areas during 158,467 operational actions. MAIN OUTCOME
MEASURES: Hospital LOS for trauma inpatients, as an indicator of care
coordination efficiency. RESULTS: Three general types of care coordination
patterns were discovered, each of which was affiliated with a specific patient
group. The first patient group exhibited the shortest hospital LOS and was
managed by a care coordination pattern that involved the smallest number of
operational areas (102 areas, as opposed to 125 and 138 for the other patient
groups), but exhibited the largest number of collaborations between operational
areas (e.g., an average of 27.1 connections per operational area compared to
22.5 and 23.3 for the other two groups). The hospital LOS for the second and
third patient groups was 14 hours (P = 0.024) and 10 hours (P = 0.042) longer
than the first patient group, respectively.
| 1 | 0 | 0 | 0 | 0 | 0 |
Construction of Non-asymptotic Confidence Sets in 2-Wasserstein Space | In this paper, we consider a probabilistic setting where the probability
measures are considered to be random objects. We propose a procedure of
construction non-asymptotic confidence sets for empirical barycenters in
2-Wasserstein space and develop the idea further to construction of a
non-parametric two-sample test that is then applied to the detection of
structural breaks in data with complex geometry. Both procedures mainly rely on
the idea of multiplier bootstrap (Spokoiny and Zhilova (2015), Chernozhukov et
al. (2014)). The main focus lies on probability measures that have commuting
covariance matrices and belong to the same scatter-location family: we proof
the validity of a bootstrap procedure that allows to compute confidence sets
and critical values for a Wasserstein-based two-sample test.
| 0 | 0 | 1 | 1 | 0 | 0 |
Limits on statistical anisotropy from BOSS DR12 galaxies using bipolar spherical harmonics | We measure statistically anisotropic signatures imprinted in
three-dimensional galaxy clustering using bipolar spherical harmonics (BipoSHs)
in both Fourier space and configuration space. We then constrain a well-known
quadrupolar anisotropy parameter $g_{2M}$ in the primordial power spectrum,
parametrized by $P(\vec{k}) = \bar{P}(k) [ 1 + \sum_{M} g_{2M} Y_{2M}(\hat{k})
]$, with $M$ determining the direction of the anisotropy. Such an anisotropic
signal is easily contaminated by artificial asymmetries due to specific survey
geometry. We precisely estimate the contaminated signal and finally subtract it
from the data. Using the galaxy samples obtained by the Baryon Oscillation
Spectroscopic Survey Data Release 12, we find no evidence for violation of
statistical isotropy, $g_{2M}$ for all $M$ to be of zero within the $2\sigma$
level. The $g_{2M}$-type anisotropy can originate from the primordial curvature
power spectrum involving a directional-dependent modulation $g_* (\hat{k} \cdot
\hat{p})^2$. The bound on $g_{2M}$ is translated into $g_*$ as $-0.09 < g_* <
0.08$ with a $95\%$ confidence level when $\hat{p}$ is marginalized over.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Central Limit Theorem for Wasserstein type distances between two different laws | This article is dedicated to the estimation of Wasserstein distances and
Wasserstein costs between two distinct continuous distributions $F$ and $G$ on
$\mathbb R$. The estimator is based on the order statistics of (possibly
dependent) samples of $F$ resp. $G$. We prove the consistency and the
asymptotic normality of our estimators. \begin{it}Keywords:\end{it} Central
Limit Theorems- Generelized Wasserstein distances- Empirical processes- Strong
approximation- Dependent samples.
| 0 | 0 | 1 | 1 | 0 | 0 |
Confidence Intervals for Stochastic Arithmetic | Quantifying errors and losses due to the use of Floating-Point (FP)
calculations in industrial scientific computing codes is an important part of
the Verification, Validation and Uncertainty Quantification (VVUQ) process.
Stochastic Arithmetic is one way to model and estimate FP losses of accuracy,
which scales well to large, industrial codes. It exists in different flavors,
such as CESTAC or MCA, implemented in various tools such as CADNA, Verificarlo
or Verrou. These methodologies and tools are based on the idea that FP losses
of accuracy can be modeled via randomness. Therefore, they share the same need
to perform a statistical analysis of programs results in order to estimate the
significance of the results. In this paper, we propose a framework to perform a
solid statistical analysis of Stochastic Arithmetic. This framework unifies all
existing definitions of the number of significant digits (CESTAC and MCA), and
also proposes a new quantity of interest: the number of digits contributing to
the accuracy of the results. Sound confidence intervals are provided for all
estimators, both in the case of normally distributed results, and in the
general case. The use of this framework is demonstrated by two case studies of
large, industrial codes: Europlexus and code\_aster.
| 1 | 0 | 0 | 1 | 0 | 0 |
Contaminated speech training methods for robust DNN-HMM distant speech recognition | Despite the significant progress made in the last years, state-of-the-art
speech recognition technologies provide a satisfactory performance only in the
close-talking condition. Robustness of distant speech recognition in adverse
acoustic conditions, on the other hand, remains a crucial open issue for future
applications of human-machine interaction. To this end, several advances in
speech enhancement, acoustic scene analysis as well as acoustic modeling, have
recently contributed to improve the state-of-the-art in the field. One of the
most effective approaches to derive a robust acoustic modeling is based on
using contaminated speech, which proved helpful in reducing the acoustic
mismatch between training and testing conditions.
In this paper, we revise this classical approach in the context of modern
DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric
context windowing, close-talk based supervision, and close-talk based
pre-training. The experimental results, obtained using both real and simulated
data, show a significant advantage in using these three methods, overall
providing a 15% error rate reduction compared to the baseline systems. The same
trend in performance is confirmed either using a high-quality training set of
small size, and a large one.
| 1 | 0 | 0 | 0 | 0 | 0 |
Co-segmentation for Space-Time Co-located Collections | We present a co-segmentation technique for space-time co-located image
collections. These prevalent collections capture various dynamic events,
usually by multiple photographers, and may contain multiple co-occurring
objects which are not necessarily part of the intended foreground object,
resulting in ambiguities for traditional co-segmentation techniques. Thus, to
disambiguate what the common foreground object is, we introduce a
weakly-supervised technique, where we assume only a small seed, given in the
form of a single segmented image. We take a distributed approach, where local
belief models are propagated and reinforced with similar images. Our technique
progressively expands the foreground and background belief models across the
entire collection. The technique exploits the power of the entire set of image
without building a global model, and thus successfully overcomes large
variability in appearance of the common foreground object. We demonstrate that
our method outperforms previous co-segmentation techniques on challenging
space-time co-located collections, including dense benchmark datasets which
were adapted for our novel problem setting.
| 1 | 0 | 0 | 0 | 0 | 0 |
Speaker Diarization with LSTM | For many years, i-vector based audio embedding techniques were the dominant
approach for speaker verification and speaker diarization applications.
However, mirroring the rise of deep learning in various domains, neural network
based audio embeddings, also known as d-vectors, have consistently demonstrated
superior speaker verification performance. In this paper, we build on the
success of d-vector based speaker verification systems to develop a new
d-vector based approach to speaker diarization. Specifically, we combine
LSTM-based d-vector audio embeddings with recent work in non-parametric
clustering to obtain a state-of-the-art speaker diarization system. Our system
is evaluated on three standard public datasets, suggesting that d-vector based
diarization systems offer significant advantages over traditional i-vector
based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000
CALLHOME, while our model is trained with out-of-domain data from voice search
logs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Almost h-conformal semi-invariant submersions from almost quaternionic Hermitian manifolds | As a generalization of Riemannian submersions, horizontally conformal
submersions, semi-invariant submersions, h-semi-invariant submersions, almost
h-semi-invariant submersions, conformal semi-invariant submersions, we
introduce h-conformal semi-invariant submersions and almost h-conformal
semi-invariant submersions from almost quaternionic Hermitian manifolds onto
Riemannian manifolds.
We study their properties: the geometry of foliations, the conditions for
total manifolds to be locally product manifolds, the conditions for such maps
to be totally geodesic, etc. Finally, we give some examples of such maps.
| 0 | 0 | 1 | 0 | 0 | 0 |
Use of Source Code Similarity Metrics in Software Defect Prediction | In recent years, defect prediction has received a great deal of attention in
the empirical software engineering world. Predicting software defects before
the maintenance phase is very important not only to decrease the maintenance
costs but also increase the overall quality of a software product. There are
different types of product, process, and developer based software metrics
proposed so far to measure the defectiveness of a software system. This paper
suggests to use a novel set of software metrics which are based on the
similarities detected among the source code files in a software project. To
find source code similarities among different files of a software system,
plagiarism and clone detection techniques are used. Two simple similarity
metrics are calculated for each file, considering its overall similarity to the
defective and non defective files in the project. Using these similarity
metrics, we predict whether a specific file is defective or not. Our
experiments on 10 open source data sets show that depending on the amount of
detected similarity, proposed metrics could achieve significantly better
performance compared to the existing static code metrics in terms of the area
under the curve (AUC).
| 1 | 0 | 0 | 0 | 0 | 0 |
Connecting pairwise spheres by depth: DCOPS | We extend the classical notion of the spherical depth in \mathbb{R}^k, to the
important setup of data on a Riemannian manifold. We show that this notion of
depth satisfies a set of desirable properties. For the empirical version of
this depth function both uniform consistency and the asymptotic distribution
are studied. Consistency is also shown for functional data. The behaviour of
the depth is illustrated through several examples for Riemannian manifold data.
| 0 | 0 | 1 | 1 | 0 | 0 |
Estimating the spectral gap of a trace-class Markov operator | The utility of a Markov chain Monte Carlo algorithm is, in large part,
determined by the size of the spectral gap of the corresponding Markov
operator. However, calculating (and even approximating) the spectral gaps of
practical Monte Carlo Markov chains in statistics has proven to be an extremely
difficult and often insurmountable task, especially when these chains move on
continuous state spaces. In this paper, a method for accurate estimation of the
spectral gap is developed for general state space Markov chains whose operators
are non-negative and trace-class. The method is based on the fact that the
second largest eigenvalue (and hence the spectral gap) of such operators can be
bounded above and below by simple functions of the power sums of the
eigenvalues. These power sums often have nice integral representations. A
classical Monte Carlo method is proposed to estimate these integrals, and a
simple sufficient condition for finite variance is provided. This leads to
asymptotically valid confidence intervals for the second largest eigenvalue
(and the spectral gap) of the Markov operator. The efficiency of the method is
studied. For illustration, the method is applied to Albert and Chib's (1993)
data augmentation (DA) algorithm for Bayesian probit regression, and also to a
DA algorithm for Bayesian linear regression with non-Gaussian errors (Liu,
1996).
| 0 | 0 | 1 | 1 | 0 | 0 |
Tidal synchronization of an anelastic multi-layered body: Titan's synchronous rotation | This paper presents one analytical tidal theory for a viscoelastic
multi-layered body with an arbitrary number of homogeneous layers. Starting
with the static equilibrium figure, modified to include tide and differential
rotation, and using the Newtonian creep approach, we find the dynamical
equilibrium figure of the deformed body, which allows us to calculate the tidal
potential and the forces acting on the tide generating body, as well as the
rotation and orbital elements variations. In the particular case of the
two-layer model, we study the tidal synchronization when the gravitational
coupling and the friction in the interface between the layers is added. For
high relaxation factors (low viscosity), the stationary solution of each layer
is synchronous with the orbital mean motion (n) when the orbit is circular, but
the spin rates increase if the orbital eccentricity increases. For low
relaxation factors (high viscosity), as in planetary satellites, if friction
remains low, each layer can be trapped in different spin-orbit resonances with
frequencies n/2,n,3n/2,... . We apply the theory to Titan. The main results
are: i) the rotational constraint does not allow us confirm or reject the
existence of a subsurface ocean in Titan; and ii) the crust-atmosphere exchange
of angular momentum can be neglected. Using the rotation estimate based on
Cassini's observation, we limit the possible value of the shell relaxation
factor, when a subsurface ocean is assumed, to 10^-9 Hz, which correspond to a
shell's viscosity 10^18 Pa s, depending on the ocean's thickness and viscosity
values. In the case in which the ocean does not exist, the maximum shell
relaxation factor is one order of magnitude smaller and the corresponding
minimum shell's viscosity is one order higher.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Lie algebras responsible for zero-curvature representations of multicomponent (1+1)-dimensional evolution PDEs | Zero-curvature representations (ZCRs) are one of the main tools in the theory
of integrable $(1+1)$-dimensional PDEs. According to the preprint
arXiv:1212.2199, for any given $(1+1)$-dimensional evolution PDE one can define
a sequence of Lie algebras $F^p$, $p=0,1,2,3,\dots$, such that representations
of these algebras classify all ZCRs of the PDE up to local gauge equivalence.
ZCRs depending on derivatives of arbitrary finite order are allowed.
Furthermore, these algebras provide necessary conditions for existence of
Backlund transformations between two given PDEs. The algebras $F^p$ are defined
in arXiv:1212.2199 in terms of generators and relations.
In the present paper, we describe some methods to study the structure of the
algebras $F^p$ for multicomponent $(1+1)$-dimensional evolution PDEs. Using
these methods, we compute the explicit structure (up to non-essential nilpotent
ideals) of the Lie algebras $F^p$ for the Landau-Lifshitz, nonlinear
Schrodinger equations, and for the $n$-component Landau-Lifshitz system of
Golubchik and Sokolov for any $n>3$. In particular, this means that for the
$n$-component Landau-Lifshitz system we classify all ZCRs (depending on
derivatives of arbitrary finite order), up to local gauge equivalence and up to
killing nilpotent ideals in the corresponding Lie algebras.
The presented methods to classify ZCRs can be applied also to other
$(1+1)$-dimensional evolution PDEs. Furthermore, the obtained results can be
used for proving non-existence of Backlund transformations between some PDEs,
which will be described in forthcoming publications.
| 0 | 1 | 1 | 0 | 0 | 0 |
TRAMP: Tracking by a Real-time AMbisonic-based Particle filter | This article presents a multiple sound source localization and tracking
system, fed by the Eigenmike array. The First Order Ambisonics (FOA) format is
used to build a pseudointensity-based spherical histogram, from which the
source position estimates are deduced. These instantaneous estimates are
processed by a wellknown tracking system relying on a set of particle filters.
While the novelty within localization and tracking is incremental, the
fully-functional, complete and real-time running system based on these
algorithms is proposed for the first time. As such, it could serve as an
additional baseline method of the LOCATA challenge.
| 1 | 0 | 0 | 0 | 0 | 0 |
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity | In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme jumps of point density (in our experiments three orders of magnitude).
The backbone of our approach is a combination of octree data partitioning,
local Delaunay tetrahedralization and graph cut optimization. Graph cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process.
| 1 | 0 | 0 | 0 | 0 | 0 |
General Bounds for Incremental Maximization | We propose a theoretical framework to capture incremental solutions to
cardinality constrained maximization problems. The defining characteristic of
our framework is that the cardinality/support of the solution is bounded by a
value $k\in\mathbb{N}$ that grows over time, and we allow the solution to be
extended one element at a time. We investigate the best-possible competitive
ratio of such an incremental solution, i.e., the worst ratio over all $k$
between the incremental solution after $k$ steps and an optimum solution of
cardinality $k$. We define a large class of problems that contains many
important cardinality constrained maximization problems like maximum matching,
knapsack, and packing/covering problems. We provide a general
$2.618$-competitive incremental algorithm for this class of problems, and show
that no algorithm can have competitive ratio below $2.18$ in general.
In the second part of the paper, we focus on the inherently incremental
greedy algorithm that increases the objective value as much as possible in each
step. This algorithm is known to be $1.58$-competitive for submodular objective
functions, but it has unbounded competitive ratio for the class of incremental
problems mentioned above. We define a relaxed submodularity condition for the
objective function, capturing problems like maximum (weighted) ($b$-)matching
and a variant of the maximum flow problem. We show that the greedy algorithm
has competitive ratio (exactly) $2.313$ for the class of problems that satisfy
this relaxed submodularity condition.
Note that our upper bounds on the competitive ratios translate to
approximation ratios for the underlying cardinality constrained problems.
| 1 | 0 | 1 | 0 | 0 | 0 |
Arrangements of pseudocircles on surfaces | A pseudocircle is a simple closed curve on some surface. Arrangements of
pseudocircles were introduced by Grünbaum, who defined them as collections of
pseudocircles that pairwise intersect in exactly two points, at which they
cross. There are several variations on this notion in the literature, one of
which requires that no three pseudocircles have a point in common. Working
under this definition, Ortner proved that an arrangement of pseudocircles is
embeddable into the sphere if and only if all of its subarrangements of size at
most $4$ are embeddable into the sphere. Ortner asked if an analogous result
held for embeddability into a compact orientable surface $\Sigma_g$ of genus
$g>0$. In this paper we answer this question, under an even more general
definition of an arrangement, in which the pseudocircles in the collection are
not required to intersect each other, or that the intersections are crossings:
it suffices to have one pseudocircle that intersects all other pseudocircles in
the collection. We show that under this more general notion, an arrangement of
pseudocircles is embeddable into $\Sigma_g$ if and only if all of its
subarrangements of size at most $4g+5$ are embeddable into $\Sigma_g$, and that
this can be improved to $4g+4$ under the concept of an arrangement used by
Ortner. Our framework also allows us to generalize this result to arrangements
of other objects, such as arcs.
| 1 | 0 | 1 | 0 | 0 | 0 |
Scalable methods for Bayesian selective inference | Modeled along the truncated approach in Panigrahi (2016), selection-adjusted
inference in a Bayesian regime is based on a selective posterior. Such a
posterior is determined together by a generative model imposed on data and the
selection event that enforces a truncation on the assumed law. The effective
difference between the selective posterior and the usual Bayesian framework is
reflected in the use of a truncated likelihood. The normalizer of the truncated
law in the adjusted framework is the probability of the selection event; this
is typically intractable and it leads to the computational bottleneck in
sampling from such a posterior. The current work lays out a primal-dual
approach of solving an approximating optimization problem to provide valid
post-selective Bayesian inference. The selection procedures are posed as
data-queries that solve a randomized version of a convex learning program which
have the advantage of preserving more left-over information for inference. We
propose a randomization scheme under which the optimization has separable
constraints that result in a partially separable objective in lower dimensions
for many commonly used selective queries to approximate the otherwise
intractable selective posterior. We show that the approximating optimization
under a Gaussian randomization gives a valid exponential rate of decay for the
selection probability on a large deviation scale. We offer a primal-dual method
to solve the optimization problem leading to an approximate posterior; this
allows us to exploit the usual merits of a Bayesian machinery in both low and
high dimensional regimes where the underlying signal is effectively sparse. We
show that the adjusted estimates empirically demonstrate better frequentist
properties in comparison to the unadjusted estimates based on the usual
posterior, when applied to a wide range of constrained, convex data queries.
| 0 | 0 | 0 | 1 | 0 | 0 |
Modified Recursive Cholesky (Rchol) Algorithm: An Explicit Estimation and Pseudo-inverse of Correlation Matrices | The Cholesky decomposition plays an important role in finding the inverse of
the correlation matrices. As it is a fast and numerically stable for linear
system solving, inversion, and factorization compared to singular valued
decomposition (SVD), QR factorization and LU decomposition. As different
methods exist to find the Cholesky decomposition of a given matrix. This paper
presents the comparative study of a proposed RChol algorithm with the
conventional methods. The RChol algorithm is an explicit way to estimate the
modified Cholesky factors of a dynamic correlation matrix.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Utility-Driven Multi-Queue Admission Control Solution for Network Slicing | The combination of recent emerging technologies such as network function
virtualization (NFV) and network programmability (SDN) gave birth to the
Network Slicing revolution. 5G networks consist of multi-tenant infrastructures
capable of offering leased network "slices" to new customers (e.g., vertical
industries) enabling a new telecom business model: Slice-as-aService (SlaaS).
In this paper, we aim i ) to study the slicing admission control problem by
means of a multi-queuing system for heterogeneous tenant requests, ii ) to
derive its statistical behavior model, and iii ) to provide a utility-based
admission control optimization. Our results analyze the capability of the
proposed SlaaS system to be approximately Markovian and evaluate its
performance as compared to legacy solutions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Statistical inference for network samples using subgraph counts | We consider that a network is an observation, and a collection of observed
networks forms a sample. In this setting, we provide methods to test whether
all observations in a network sample are drawn from a specified model. We
achieve this by deriving, under the null of the graphon model, the joint
asymptotic properties of average subgraph counts as the number of observed
networks increases but the number of nodes in each network remains finite. In
doing so, we do not require that each observed network contains the same number
of nodes, or is drawn from the same distribution. Our results yield joint
confidence regions for subgraph counts, and therefore methods for testing
whether the observations in a network sample are drawn from: a specified
distribution, a specified model, or from the same model as another network
sample. We present simulation experiments and an illustrative example on a
sample of brain networks where we find that highly creative individuals' brains
present significantly more short cycles.
| 1 | 0 | 0 | 1 | 0 | 0 |
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