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Title: Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model,
Abstract: Variable clustering is important for explanatory analysis. However, only few
dedicated methods for variable clustering with the Gaussian graphical model
have been proposed. Even more severe, small insignificant partial correlations
due to noise can dramatically change the clustering result when evaluating for
example with the Bayesian Information Criteria (BIC). In this work, we try to
address this issue by proposing a Bayesian model that accounts for negligible
small, but not necessarily zero, partial correlations. Based on our model, we
propose to evaluate a variable clustering result using the marginal likelihood.
To address the intractable calculation of the marginal likelihood, we propose
two solutions: one based on a variational approximation, and another based on
MCMC. Experiments on simulated data shows that the proposed method is similarly
accurate as BIC in the no noise setting, but considerably more accurate when
there are noisy partial correlations. Furthermore, on real data the proposed
method provides clustering results that are intuitively sensible, which is not
always the case when using BIC or its extensions. | [
0,
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] |
Title: On the universality of MOG weak field approximation at galaxy cluster scale,
Abstract: In its weak field limit, Scalar-tensor-vector gravity theory introduces a
Yukawa-correction to the gravitational potential. Such a correction depends on
the two parameters, $\alpha$ which accounts for the modification of the
gravitational constant, and $\mu^{*-1}$ which represents the scale length on
which the scalar field propagates. These parameters were found to be universal
when the modified gravitational potential was used to fit the galaxy rotation
curves and the mass profiles of galaxy clusters, both without Dark Matter. We
test the universality of these parameters using the the temperature
anisotropies due to the thermal Sunyaev-Zeldovich effect. In our model the
intra-cluster gas is in hydrostatic equilibrium within the modified
gravitational potential well and it is described by a polytropic equation of
state. We predict the thermal Sunyaev-Zeldovich temperature anisotropies
produced by Coma cluster, and we compare them with those obtained using the
Planck 2013 Nominal maps. In our analysis, we find $\alpha$ and the scale
length, respectively, to be consistent and to depart from their universal
values. Our analysis points out that the assumption of the universality of the
Yukawa-correction to the gravitational potential is ruled out at more than
$3.5\sigma$ at galaxy clusters scale, while demonstrating that such a theory of
gravity is capable to fit the cluster profile if the scale dependence of the
gravitational potential is restored. | [
0,
1,
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] |
Title: Much Faster Algorithms for Matrix Scaling,
Abstract: We develop several efficient algorithms for the classical \emph{Matrix
Scaling} problem, which is used in many diverse areas, from preconditioning
linear systems to approximation of the permanent. On an input $n\times n$
matrix $A$, this problem asks to find diagonal (scaling) matrices $X$ and $Y$
(if they exist), so that $X A Y$ $\varepsilon$-approximates a doubly
stochastic, or more generally a matrix with prescribed row and column sums.
We address the general scaling problem as well as some important special
cases. In particular, if $A$ has $m$ nonzero entries, and if there exist $X$
and $Y$ with polynomially large entries such that $X A Y$ is doubly stochastic,
then we can solve the problem in total complexity $\tilde{O}(m + n^{4/3})$.
This greatly improves on the best known previous results, which were either
$\tilde{O}(n^4)$ or $O(m n^{1/2}/\varepsilon)$.
Our algorithms are based on tailor-made first and second order techniques,
combined with other recent advances in continuous optimization, which may be of
independent interest for solving similar problems. | [
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0,
1,
0,
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0
] |
Title: Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families,
Abstract: In the protein sequence space, natural proteins form clusters of families
which are characterized by their unique native folds whereas the great majority
of random polypeptides are neither clustered nor foldable to unique structures.
Since a given polypeptide can be either foldable or unfoldable, a kind of
"folding transition" is expected at the boundary of a protein family in the
sequence space. By Monte Carlo simulations of a statistical mechanical model of
protein sequence alignment that coherently incorporates both short-range and
long-range interactions as well as variable-length insertions to reproduce the
statistics of the multiple sequence alignment of a given protein family, we
demonstrate the existence of such transition between natural-like sequences and
random sequences in the sequence subspaces for 15 domain families of various
folds. The transition was found to be highly cooperative and two-state-like.
Furthermore, enforcing or suppressing consensus residues on a few of the
well-conserved sites enhanced or diminished, respectively, the natural-like
pattern formation over the entire sequence. In most families, the key sites
included ligand binding sites. These results suggest some selective pressure on
the key residues, such as ligand binding activity, may cooperatively facilitate
the emergence of a protein family during evolution. From a more practical
aspect, the present results highlight an essential role of long-range effects
in precisely defining protein families, which are absent in conventional
sequence models. | [
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1,
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0,
0,
0
] |
Title: Thermal Molecular Focusing: Tunable Cross Effect of Phoresis and Advection,
Abstract: The control of solute fluxes through either microscopic phoresis or
hydrodynamic advection is a fundamental way to transport molecules, which are
ubiquitously present in nature and technology. We study the transport of large
solute such as DNA driven by a time-dependent thermal field in a polymer
solution. Heat propagation of a single heat spot moving back and forth gives
rise to the molecular focusing of DNA with frequency-tunable control. We
developed a theoretical model, where heat conduction, viscoelastic expansion of
walls, and the viscosity gradient of a smaller solute are coupled, and that can
explain the underlying hydrodynamic focusing and its interplay with phoretic
transports. This cross effect may allow one to design a unique miniaturized
pump in microfluidics. | [
0,
1,
0,
0,
0,
0
] |
Title: Shallow water modeling of rolling pad instability in liquid metal batteries,
Abstract: Magnetohydrodynamically induced interface instability in liquid metal
batteries is analyzed. The batteries are represented by a simplified system in
the form of a rectangular cell, in which strong vertical electric current flows
through three horizontal layers: the layer of a heavy metal at the bottom, the
layer of a light metal at the top, and the layer of electrolyte in the middle.
A new two-dimensional nonlinear model based on the conservative shallow water
approximation is derived and utilized in a numerical study. It is found that in
the case of small density difference between the electrolyte and one of the
metals, the instability closely resembles the rolling pad instability observed
earlier in the aluminum reduction cells. When the two electrolyte-metal density
differences are comparable, the dynamics of unstable systems is more complex
and characterized by interaction between two nearly symmetric or antisymmetric
interfacial waves. | [
0,
1,
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0,
0
] |
Title: Near-Optimal Clustering in the $k$-machine model,
Abstract: The clustering problem, in its many variants, has numerous applications in
operations research and computer science (e.g., in applications in
bioinformatics, image processing, social network analysis, etc.). As sizes of
data sets have grown rapidly, researchers have focused on designing algorithms
for clustering problems in models of computation suited for large-scale
computation such as MapReduce, Pregel, and streaming models. The $k$-machine
model (Klauck et al., SODA 2015) is a simple, message-passing model for
large-scale distributed graph processing. This paper considers three of the
most prominent examples of clustering problems: the uncapacitated facility
location problem, the $p$-median problem, and the $p$-center problem and
presents $O(1)$-factor approximation algorithms for these problems running in
$\tilde{O}(n/k)$ rounds in the $k$-machine model. These algorithms are optimal
up to polylogarithmic factors because this paper also shows
$\tilde{\Omega}(n/k)$ lower bounds for obtaining polynomial-factor
approximation algorithms for these problems. These are the first results for
clustering problems in the $k$-machine model.
We assume that the metric provided as input for these clustering problems in
only implicitly provided, as an edge-weighted graph and in a nutshell, our main
technical contribution is to show that constant-factor approximation algorithms
for all three clustering problems can be obtained by learning only a small
portion of the input metric. | [
1,
0,
0,
0,
0,
0
] |
Title: Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval,
Abstract: We develop procedures, based on minimization of the composition $f(x) =
h(c(x))$ of a convex function $h$ and smooth function $c$, for solving random
collections of quadratic equalities, applying our methodology to phase
retrieval problems. We show that the prox-linear algorithm we develop can solve
phase retrieval problems---even with adversarially faulty measurements---with
high probability as soon as the number of measurements $m$ is a constant factor
larger than the dimension $n$ of the signal to be recovered. The algorithm
requires essentially no tuning---it consists of solving a sequence of convex
problems---and it is implementable without any particular assumptions on the
measurements taken. We provide substantial experiments investigating our
methods, indicating the practical effectiveness of the procedures and showing
that they succeed with high probability as soon as $m / n \ge 2$ when the
signal is real-valued. | [
0,
0,
1,
1,
0,
0
] |
Title: Edge-Based Recognition of Novel Objects for Robotic Grasping,
Abstract: In this paper, we investigate the problem of grasping novel objects in
unstructured environments. To address this problem, consideration of the object
geometry, reachability and force closure analysis are required. We propose a
framework for grasping unknown objects by localizing contact regions on the
contours formed by a set of depth edges in a single view 2D depth image.
According to the edge geometric features obtained from analyzing the data of
the depth map, the contact regions are determined. Finally,We validate the
performance of the approach by applying it to the scenes with both single and
multiple objects, using Baxter manipulator. | [
1,
0,
0,
0,
0,
0
] |
Title: Surface Normals in the Wild,
Abstract: We study the problem of single-image depth estimation for images in the wild.
We collect human annotated surface normals and use them to train a neural
network that directly predicts pixel-wise depth. We propose two novel loss
functions for training with surface normal annotations. Experiments on NYU
Depth and our own dataset demonstrate that our approach can significantly
improve the quality of depth estimation in the wild. | [
1,
0,
0,
0,
0,
0
] |
Title: Piecewise excluding geodesic languages,
Abstract: The complexity of a geodesic language has connections to algebraic properties
of the group. Gilman, Hermiller, Holt, and Rees show that a finitely generated
group is virtually free if and only if its geodesic language is locally
excluding for some finite inverse-closed generating set. The existence of such
a correspondence and the result of Hermiller, Holt, and Rees that finitely
generated abelian groups have piecewise excluding geodesic language for all
finite inverse-closed generating sets motivated our work. We show that a
finitely generated group with piecewise excluding geodesic language need not be
abelian and give a class of infinite non-abelian groups which have piecewise
excluding geodesic languages for certain generating sets. The quaternion group
is shown to be the only non-abelian 2-generator group with piecewise excluding
geodesic language for all finite inverse-closed generating sets. We also show
that there are virtually abelian groups with geodesic languages which are not
piecewise excluding for any finite inverse-closed generating set. | [
0,
0,
1,
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0,
0
] |
Title: Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR,
Abstract: Purpose: The radial k-space trajectory is a well-established sampling
trajectory used in conjunction with magnetic resonance imaging. However, the
radial k-space trajectory requires a large number of radial lines for
high-resolution reconstruction. Increasing the number of radial lines causes
longer acquisition time, making it more difficult for routine clinical use. On
the other hand, if we reduce the number of radial lines, streaking artifact
patterns are unavoidable. To solve this problem, we propose a novel deep
learning approach with domain adaptation to restore high-resolution MR images
from under-sampled k-space data.
Methods: The proposed deep network removes the streaking artifacts from the
artifact corrupted images. To address the situation given the limited available
data, we propose a domain adaptation scheme that employs a pre-trained network
using a large number of x-ray computed tomography (CT) or synthesized radial MR
datasets, which is then fine-tuned with only a few radial MR datasets.
Results: The proposed method outperforms existing compressed sensing
algorithms, such as the total variation and PR-FOCUSS methods. In addition, the
calculation time is several orders of magnitude faster than the total variation
and PR-FOCUSS methods.Moreover, we found that pre-training using CT or MR data
from similar organ data is more important than pre-training using data from the
same modality for different organ.
Conclusion: We demonstrate the possibility of a domain-adaptation when only a
limited amount of MR data is available. The proposed method surpasses the
existing compressed sensing algorithms in terms of the image quality and
computation time. | [
1,
0,
0,
0,
0,
0
] |
Title: Automation in Human-Machine Networks: How Increasing Machine Agency Affects Human Agency,
Abstract: Efficient human-machine networks require productive interaction between human
and machine actors. In this study, we address how a strengthening of machine
agency, for example through increasing levels of automation, affect the human
actors of the networks. Findings from case studies within air traffic
management, crisis management, and crowd evacuation are presented, exemplifying
how automation may strengthen the agency of human actors in the network through
responsibility sharing and task allocation, and serve as a needed prerequisite
of innovation and change. | [
1,
0,
0,
0,
0,
0
] |
Title: Comment on "On the nature of magnetic stripes in cuprate superconductors," by H. Jacobsen et al., arXiv:1704.08528v2,
Abstract: Dynamics reduces the orthorhombicity of magnetic stripes in La_2CuO_4+y. The
measured stripe incommensuration can be used to determine the oxygen content of
the sample. | [
0,
1,
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0,
0,
0
] |
Title: Logically Isolated, Actually Unpredictable? Measuring Hypervisor Performance in Multi-Tenant SDNs,
Abstract: Ideally, by enabling multi-tenancy, network virtualization allows to improve
resource utilization, while providing performance isolation: although the
underlying resources are shared, the virtual network appears as a dedicated
network to the tenant. However, providing such an illusion is challenging in
practice, and over the last years, many expedient approaches have been proposed
to provide performance isolation in virtual networks, by enforcing bandwidth
reservations. We in this paper study another source for overheads and
unpredictable performance in virtual networks: the hypervisor.
The hypervisor is a critical component in multi-tenant environments, but its
overhead and influence on performance are hardly understood today. In
particular, we focus on OpenFlow-based virtualized Software Defined Networks
(vSDNs). Network virtualization is considered a killer application for SDNs: a
vSDN allows each tenant to flexibly manage its network from a logically
centralized perspective, via a simple API. For the purpose of our study, we
developed a new benchmarking tool for OpenFlow control and data planes,
enabling high and consistent OpenFlow message rates. Using our tool, we
identify and measure controllable and uncontrollable effects on performance and
overhead, including the hypervisor technology, the number of tenants as well as
the tenant type, as well as the type of OpenFlow messages. | [
1,
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0,
0
] |
Title: Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models,
Abstract: The Bayesian estimation of the unknown parameters of state-space (dynamical)
systems has received considerable attention over the past decade, with a
handful of powerful algorithms being introduced. In this paper we tackle the
theoretical analysis of the recently proposed {\it nonlinear} population Monte
Carlo (NPMC). This is an iterative importance sampling scheme whose key
features, compared to conventional importance samplers, are (i) the approximate
computation of the importance weights (IWs) assigned to the Monte Carlo samples
and (ii) the nonlinear transformation of these IWs in order to prevent the
degeneracy problem that flaws the performance of conventional importance
samplers. The contribution of the present paper is a rigorous proof of
convergence of the nonlinear IS (NIS) scheme as the number of Monte Carlo
samples, $M$, increases. Our analysis reveals that the NIS approximation errors
converge to 0 almost surely and with the optimal Monte Carlo rate of
$M^{-\frac{1}{2}}$. Moreover, we prove that this is achieved even when the mean
estimation error of the IWs remains constant, a property that has been termed
{\it exact approximation} in the Markov chain Monte Carlo literature. We
illustrate these theoretical results by means of a computer simulation example
involving the estimation of the parameters of a state-space model typically
used for target tracking. | [
0,
0,
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0,
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] |
Title: On Axiomatizability of the Multiplicative Theory of Numbers,
Abstract: The multiplicative theory of a set of numbers (which could be natural,
integer, rational, real or complex numbers) is the first-order theory of the
structure of that set with (solely) the multiplication operation (that set is
taken to be multiplicative, i.e., closed under multiplication). In this paper
we study the multiplicative theories of the complex, real and (positive)
rational numbers. These theories (and also the multiplicative theories of
natural and integer numbers) are known to be decidable (i.e., there exists an
algorithm that decides whether a given sentence is derivable form the theory);
here we present explicit axiomatizations for them and show that they are not
finitely axiomatizable. For each of these sets (of complex, real and [positive]
rational numbers) a language, including the multiplication operation, is
introduced in a way that it allows quantifier elimination (for the theory of
that set). | [
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] |
Title: On Bivariate Discrete Weibull Distribution,
Abstract: Recently, Lee and Cha (2015, `On two generalized classes of discrete
bivariate distributions', {\it American Statistician}, 221 - 230) proposed two
general classes of discrete bivariate distributions. They have discussed some
general properties and some specific cases of their proposed distributions. In
this paper we have considered one model, namely bivariate discrete Weibull
distribution, which has not been considered in the literature yet. The proposed
bivariate discrete Weibull distribution is a discrete analogue of the
Marshall-Olkin bivariate Weibull distribution. We study various properties of
the proposed distribution and discuss its interesting physical interpretations.
The proposed model has four parameters, and because of that it is a very
flexible distribution. The maximum likelihood estimators of the parameters
cannot be obtained in closed forms, and we have proposed a very efficient
nested EM algorithm which works quite well for discrete data. We have also
proposed augmented Gibbs sampling procedure to compute Bayes estimates of the
unknown parameters based on a very flexible set of priors. Two data sets have
been analyzed to show how the proposed model and the method work in practice.
We will see that the performances are quite satisfactory. Finally, we conclude
the paper. | [
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] |
Title: Parabolic induction in characteristic p,
Abstract: Let G be the group of rational points of a reductive connected group over a
finite field (resp. nonarchimedean local field of characteristic p) and R a
commutative ring. The unipotent (resp. pro-p Iwahori) invariant functor takes a
smooth representation of G to a module over the unipotent (resp. pro-p Iwahori)
Hecke R-algebra H of G. We prove that these functors for G and for a Levi
subgroup of G commute with the parabolic induction functors, as well as with
the right adjoints of the parabolic induction functors. However, they do not
commute with the left adjoints of the parabolic induction functors in general;
they do if p is invertible in R.
When R is an algebraically closed field of characteristic p, we show in the
local case that an irreducible admissible R-representation V of G is
supercuspidal (or equivalently supersingular) if and only if the H-module V^I
of its invariants by the pro-p Iwahori I admits a supersingular subquotient, if
and only if V^I is supersingular. | [
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] |
Title: Linear polygraphs applied to categorification,
Abstract: We introduce two applications of polygraphs to categorification problems. We
compute first, from a coherent presentation of an $n$-category, a coherent
presentation of its Karoubi envelope. For this, we extend the construction of
Karoubi envelope to $n$-polygraphs and linear $(n,n-1)$-polygraphs. The second
problem treated in this paper is the construction of Grothendieck
decategorifications for $(n,n-1)$-polygraphs. This construction yields a
rewriting system presenting for example algebras categorified by a linear
monoidal category. We finally link quasi-convergence of such rewriting systems
to the uniqueness of direct sum decompositions for linear
$(n-1,n-1)$-categories. | [
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0,
1,
0,
0,
0
] |
Title: Viable tensor-to-scalar ratio in a symmetric matter bounce,
Abstract: Matter bounces refer to scenarios wherein the universe contracts at early
times as in a matter dominated epoch until the scale factor reaches a minimum,
after which it starts expanding. While such scenarios are known to lead to
scale invariant spectra of primordial perturbations after the bounce, the
challenge has been to construct completely symmetric bounces that lead to a
tensor-to-scalar ratio which is small enough to be consistent with the recent
cosmological data. In this work, we construct a model involving two scalar
fields (a canonical field and a non-canonical ghost field) to drive the
symmetric matter bounce and study the evolution of the scalar perturbations in
the model. If we consider the scale associated with the bounce to be of the
order of the Planck scale, the model is completely described in terms of only
one parameter, viz the value of the scale factor at the bounce. We evolve the
scalar perturbations numerically across the bounce and evaluate the scalar
power spectra after the bounce. We show that, while the scalar and tensor
perturbation spectra are scale invariant over scales of cosmological interest,
the tensor-to-scalar ratio proves to be much smaller than the current upper
bound from the observations of the cosmic microwave background anisotropies by
the Planck mission. We also support our numerical analysis with analytical
arguments. | [
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1,
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0,
0,
0
] |
Title: Approximate Ripple Carry and Carry Lookahead Adders - A Comparative Analysis,
Abstract: Approximate ripple carry adders (RCAs) and carry lookahead adders (CLAs) are
presented which are compared with accurate RCAs and CLAs for performing a
32-bit addition. The accurate and approximate RCAs and CLAs are implemented
using a 32/28nm CMOS process. Approximations ranging from 4- to 20-bits are
considered for the less significant adder bit positions. The simulation results
show that approximate RCAs report reductions in the power-delay product (PDP)
ranging from 19.5% to 82% than the accurate RCA for approximation sizes varying
from 4- to 20-bits. Also, approximate CLAs report reductions in PDP ranging
from 16.7% to 74.2% than the accurate CLA for approximation sizes varying from
4- to 20-bits. On average, for the approximation sizes considered, it is
observed that approximate CLAs achieve a 46.5% reduction in PDP compared to the
approximate RCAs. Hence, approximate CLAs are preferable over approximate RCAs
for the low power implementation of approximate computer arithmetic. | [
1,
0,
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0,
0,
0
] |
Title: On the Robustness and Asymptotic Properties for Maximum Likelihood Estimators of Parameters in Exponential Power and its Scale Mixture Form Distributions,
Abstract: The normality assumption on data set is very restrictive approach for
modelling. The generalized form of normal distribution, named as an exponential
power (EP) distribution, and its scale mixture form have been considered
extensively to overcome the problem for modelling non-normal data set since
last decades. However, examining the robustness properties of maximum
likelihood (ML) estimators of parameters in these distributions, such as the in
uence function, gross-error sensitivity, breakdown point and
information-standardized sensitivity, has not been considered together. The
well-known asymptotic properties of ML estimators of location, scale and added
skewness parameters in EP and its scale mixture form distributions are studied
and also these ML estimators for location, scale and scale variant (skewness)
parameters can be represented as an iterative reweighting algorithm to compute
the estimates of these parameters simultaneously. | [
0,
0,
1,
1,
0,
0
] |
Title: Solving internal covariate shift in deep learning with linked neurons,
Abstract: This work proposes a novel solution to the problem of internal covariate
shift and dying neurons using the concept of linked neurons. We define the
neuron linkage in terms of two constraints: first, all neuron activations in
the linkage must have the same operating point. That is to say, all of them
share input weights. Secondly, a set of neurons is linked if and only if there
is at least one member of the linkage that has a non-zero gradient in regard to
the input of the activation function. This means that for any input in the
activation function, there is at least one member of the linkage that operates
in a non-flat and non-zero area. This simple change has profound implications
in the network learning dynamics. In this article we explore the consequences
of this proposal and show that by using this kind of units, internal covariate
shift is implicitly solved. As a result of this, the use of linked neurons
allows to train arbitrarily large networks without any architectural or
algorithmic trick, effectively removing the need of using re-normalization
schemes such as Batch Normalization, which leads to halving the required
training time. It also solves the problem of the need for standarized input
data. Results show that the units using the linkage not only do effectively
solve the aforementioned problems, but are also a competitive alternative with
respect to state-of-the-art with very promising results. | [
1,
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] |
Title: Correlating the nanostructure of Al-oxide with deposition conditions and dielectric contributions of two-level systems in perspective of superconducting quantum circuits,
Abstract: This work is concerned with Al/Al-oxide(AlO$_{x}$)/Al-layer systems which are
important for Josephson-junction-based superconducting devices such as quantum
bits. The device performance is limited by noise, which has been to a large
degree assigned to the presence and properties of two-level tunneling systems
in the amorphous AlO$_{x}$ tunnel barrier. The study is focused on the
correlation of the fabrication conditions, nanostructural and nanochemical
properties and the occurrence of two-level tunneling systems with particular
emphasis on the AlO$_{x}$-layer. Electron-beam evaporation with two different
processes and sputter deposition were used for structure fabrication, and the
effect of illumination by ultraviolet light during Al-oxide formation is
elucidated. Characterization was performed by analytical transmission electron
microscopy and low-temperature dielectric measurements. We show that the
fabrication conditions have a strong impact on the nanostructural and
nanochemical properties of the layer systems and the properties of two-level
tunneling systems. Based on the understanding of the observed structural
characteristics, routes are derived towards the fabrication of
Al/AlO$_{x}$/Al-layers systems with improved properties. | [
0,
1,
0,
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] |
Title: Senior Project Management System: Requirements, Specification, and Design Issues,
Abstract: Senior project is a typical essential course in computing educational
programs. The course involves the selection of a project problem, the
submission of various documents, and intensive communication among the project
team members and between them and the course instructors. To facilitate all
these tasks, we introduce the senior project management system (SPMS) that
organizes and manages previous, current, and proposed senior projects in all of
their stages along with proper ways of communication between the students and
course instructors. This paper explains the system requirements and
specifications and discusses related design issues. The paper shows the
importance of well documenting the specifications and requirements of software
systems and paying considerable attention to system design, which has a
positive impact on implementing high quality systems. | [
1,
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0
] |
Title: Simulation of Matrix Product State on a Quantum Computer,
Abstract: The study of tensor network theory is an important field and promises a wide
range of experimental and quantum information theoretical applications. Matrix
product state is the most well-known example of tensor network states, which
provides an effective and efficient representation of one-dimensional quantum
systems. Indeed, it lies at the heart of density matrix renormalization group
(DMRG), a most common method for simulation of one-dimensional strongly
correlated quantum systems. It has got attention from several areas varying
from solid-state systems to quantum computing and quantum simulators. We have
considered maximally entangled matrix product states (GHZ and W). Here, we
designed the quantum circuits for implementing the matrix product states. In
this paper, we simulated the matrix product states in customized IBM (2-qubit,
3-qubit and 4-qubit) quantum systems and determined the probability
distribution among the quantum states. | [
1,
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0
] |
Title: Deep Generative Models with Learnable Knowledge Constraints,
Abstract: The broad set of deep generative models (DGMs) has achieved remarkable
advances. However, it is often difficult to incorporate rich structured domain
knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a
principled framework to impose structured constraints on probabilistic models,
but has limited applicability to the diverse DGMs that can lack a Bayesian
formulation or even explicit density evaluation. PR also requires constraints
to be fully specified a priori, which is impractical or suboptimal for complex
knowledge with learnable uncertain parts. In this paper, we establish
mathematical correspondence between PR and reinforcement learning (RL), and,
based on the connection, expand PR to learn constraints as the extrinsic reward
in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is
flexible to adapt arbitrary constraints with the model jointly. Experiments on
human image generation and templated sentence generation show models with
learned knowledge constraints by our algorithm greatly improve over base
generative models. | [
0,
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] |
Title: A Proximity-Aware Hierarchical Clustering of Faces,
Abstract: In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images. | [
1,
0,
0,
0,
0,
0
] |
Title: Acoustic emission source localization in thin metallic plates: a single-sensor approach based on multimodal edge reflections,
Abstract: This paper presents a new acoustic emission (AE) source localization for
isotropic plates with reflecting boundaries. This approach that has no blind
spot leverages multimodal edge reflections to identify AE sources with only a
single sensor. The implementation of the proposed approach involves three main
steps. First, the continuous wavelet transform (CWT) and the dispersion curves
of the fundamental Lamb wave modes are utilized to estimate the distance
between an AE source and a sensor. This step uses a modal acoustic emission
approach. Then, an analytical model is proposed that uses the estimated
distances to simulate the edge-reflected waves. Finally, the correlation
between the experimental and the simulated waveforms is used to estimate the
location of AE sources. Hsu-Nielson pencil lead break (PLB) tests were
performed on an aluminum plate to validate this algorithm and promising results
were achieved. Based on these results, the paper reports the statistics of the
localization errors. | [
0,
1,
0,
0,
0,
0
] |
Title: Asymptotically Optimal Multi-Paving,
Abstract: Anderson's paving conjecture, now known to hold due to the resolution of the
Kadison-Singer problem asserts that every zero diagonal Hermitian matrix admits
non-trivial pavings with dimension independent bounds. In this paper, we
develop a technique extending the arguments of Marcus, Spielman and Srivastava
in their solution of the Kadison-Singer problem to show the existence of
non-trivial pavings for collections of matrices. We show that given zero
diagonal Hermitian contractions $A^{(1)}, \cdots, A^{(k)} \in M_n(\mathbb{C})$
and $\epsilon > 0$, one may find a paving $X_1 \amalg \cdots \amalg X_r = [n]$
where $r \leq 18k\epsilon^{-2}$ such that, \[\lambda_{max} (P_{X_i} A^{(j)}
P_{X_i}) < \epsilon, \quad i \in [r], \, j \in [k].\] As a consequence, we get
the correct asymptotic estimates for paving general zero diagonal matrices;
zero diagonal contractions can be $(O(\epsilon^{-2}),\epsilon)$ paved. As an
application, we give a simplified proof wth slightly better estimates of a
theorem of Johnson, Ozawa and Schechtman concerning commutator representations
of zero trace matrices. | [
0,
0,
1,
0,
0,
0
] |
Title: Meeting the Challenges of Modeling Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX Astrophysics Suite,
Abstract: We describe the AMReX suite of astrophysics codes and their application to
modeling problems in stellar astrophysics. Maestro is tuned to efficiently
model subsonic convective flows while Castro models the highly compressible
flows associated with stellar explosions. Both are built on the
block-structured adaptive mesh refinement library AMReX. Together, these codes
enable a thorough investigation of stellar phenomena, including Type Ia
supernovae and X-ray bursts. We describe these science applications and the
approach we are taking to make these codes performant on current and future
many-core and GPU-based architectures. | [
0,
1,
0,
0,
0,
0
] |
Title: Eliminating the unit constant in the Lambek calculus with brackets,
Abstract: We present a translation of the Lambek calculus with brackets and the unit
constant, $\mathbf{Lb}^{\boldsymbol{*}}_{\mathbf{1}}$, into the Lambek calculus
with brackets allowing empty antecedents, but without the unit constant,
$\mathbf{Lb}^{\boldsymbol{*}}$. Using this translation, we extend previously
known results for $\mathbf{Lb}^{\boldsymbol{*}}$ to
$\mathbf{Lb}^{\boldsymbol{*}}_{\mathbf{1}}$: (1) languages generated by
categorial grammars based on the Lambek calculus with brackets are context-free
(Kanazawa 2017); (2) the polynomial-time algorithm for deciding derivability of
bounded depth sequents (Kanovich et al. 2017). | [
1,
0,
1,
0,
0,
0
] |
Title: Robust Distributed Planar Formation Control for Higher-Order Holonomic and Nonholonomic Agents,
Abstract: We present a distributed formation control strategy for agents with a variety
of dynamics to achieve a desired planar formation. The proposed strategy is
fully distributed, does not require inter-agent communication or a common sense
of orientation, and can be implemented using relative position measurements
acquired by agents in their local coordinate frames. We show how the control
designed for agents with the simplest dynamical model, i.e., the
single-integrator dynamics, can be extended to holonomic agents with
higher-order dynamics such as quadrotors, and nonholonomic agents such as
unicycles and cars. We prove that the proposed strategy is robust to
saturations in the input, unmodeled dynamics, and switches in the sensing
topology. We further show that the control is relaxed in the sense that agents
can move along a rotated and scaled control direction without affecting the
convergence to the desired formation. This observation is used to design a
distributed collision avoidance strategy. We demonstrate the proposed approach
in simulations and further present a distributed robotic platform to test the
strategy experimentally. Our experimental platform consists of off-the-shelf
equipment that can be used to test and validate other multi-agent algorithms.
The code and implementation instructions for this platform are available online
and free. | [
1,
0,
0,
0,
0,
0
] |
Title: Dual Based DSP Bidding Strategy and its Application,
Abstract: In recent years, RTB(Real Time Bidding) becomes a popular online
advertisement trading method. During the auction, each DSP(Demand Side
Platform) is supposed to evaluate current opportunity and respond with an ad
and corresponding bid price. It's essential for DSP to find an optimal ad
selection and bid price determination strategy which maximizes revenue or
performance under budget and ROI(Return On Investment) constraints in P4P(Pay
For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1)
formalizing the DSP problem as a constrained optimization problem, 2) proposing
the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with
general solution, 3) and demonstrating the DSP problem is a special case of the
augmented MMKP and deriving specialized strategy. Our strategy is verified
through simulation and outperforms state-of-the-art strategies in real
application. To the best of our knowledge, our solution is the first dual based
DSP bidding framework that is derived from strict second price auction
assumption and generally applicable to the multiple ads scenario with various
objectives and constraints. | [
1,
0,
0,
1,
0,
0
] |
Title: Residual Gated Graph ConvNets,
Abstract: Graph-structured data such as social networks, functional brain networks,
gene regulatory networks, communications networks have brought the interest in
generalizing deep learning techniques to graph domains. In this paper, we are
interested to design neural networks for graphs with variable length in order
to solve learning problems such as vertex classification, graph classification,
graph regression, and graph generative tasks. Most existing works have focused
on recurrent neural networks (RNNs) to learn meaningful representations of
graphs, and more recently new convolutional neural networks (ConvNets) have
been introduced. In this work, we want to compare rigorously these two
fundamental families of architectures to solve graph learning tasks. We review
existing graph RNN and ConvNet architectures, and propose natural extension of
LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of
analytically controlled experiments on two basic graph problems, i.e. subgraph
matching and graph clustering, to test the different architectures. Numerical
results show that the proposed graph ConvNets are 3-17% more accurate and
1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than
variational (non-learning) techniques. Finally, the most effective graph
ConvNet architecture uses gated edges and residuality. Residuality plays an
essential role to learn multi-layer architectures as they provide a 10% gain of
performance. | [
1,
0,
0,
1,
0,
0
] |
Title: User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons,
Abstract: This paper describes a novel storyboarding scheme that uses a model trained
on pairwise image comparisons to identify images likely to be of interest to a
mobile robot user. Traditional storyboarding schemes typically attempt to
summarise robot observations using predefined novelty or image quality
objectives, but we propose a user training stage that allows the incorporation
of user interest when storyboarding. Our approach dramatically reduces the
number of image comparisons required to infer image interest by applying a
Gaussian process smoothing algorithm on image features extracted using a
pre-trained convolutional neural network. As a particularly valuable
by-product, the proposed approach allows the generation of user-specific
saliency or attention maps. | [
1,
0,
0,
0,
0,
0
] |
Title: Habitability of Exoplanetary Systems,
Abstract: The aim of my dissertation is to investigate habitability in extra-Solar
Systems. Most of the time, only planets are considered as possible places where
extraterrestrial life can emerge and evolve, however, their moons could be
inhabited, too. I present a comprehensive study, which considers habitability
not only on planets, but on satellites, as well. My research focuses on three
closely related topics. The first one is the circumstellar habitable zone,
which is usually used as a first proxy for determining the habitability of a
planet around the host star. The word habitability is used in the sense that
liquid water, which is essential for life as we know it, may be present on the
planetary surface. Whether the planet is habitable or not, its moon might have
a suitable surface temperature for holding water reservoirs, providing that
tidal heating is in action. Tidal heating is generated inside the satellite and
its source is the strong gravitational force of the nearby planet. The second
topic of my research explores tidal heating and the habitability of extra-solar
moons with and without stellar radiation and other related energy sources. Life
is possible to form even on icy planetary bodies, inside tidally heated
subsurface oceans. The third topic probes the possibility of identifying an
ice-covered satellite from photometric observations. A strong indication of
surface ice is the high reflectance of the body, which may be measured when the
moon disappears behind the host star, so its reflected light is blocked out by
the star. | [
0,
1,
0,
0,
0,
0
] |
Title: The HST Large Program on Omega Centauri. I. Multiple stellar populations at the bottom of the main sequence probed in NIR-Optical,
Abstract: As part of a large investigation with Hubble Space Telescope to study the
faintest stars within the globular cluster Omega Centauri, in this work we
present early results on the multiplicity of its main sequence (MS) stars,
based on deep optical and near-infrared observations. By using appropriate
color-magnitude diagrams we have identified, for the first time, the two main
stellar populations I, and II along the entire MS, from the turn-off towards
the hydrogen-burning limit. We have compared the observations with suitable
synthetic spectra of MS stars and conclude that the two MSs are consistent with
stellar populations with different metallicity, helium, and light-element
abundance. Specifically, MS-I corresponds to a metal-poor stellar population
([Fe/H]~-1.7) with Y~ 0.25 and [O/Fe]~0.30. The MS-II hosts helium-rich
(Y~0.37-0.40) stars with metallicity ranging from [Fe/H]~-1.7 to -1.4. Below
the MS knee (mF160W~19.5, our photometry reveals that each of the two main MSs
hosts stellar subpopulations with different oxygen abundances, with very O-poor
stars ([O/Fe]~-0.5) populating the MS-II. Such a complexity has never been
observed in previous studies of M-dwarfs in globular clusters. A few months
before the lunch of the James Webb Space Telescope, these results demonstrate
the power of optical and near-infrared photometry in the study of multiple
stellar populations in globular clusters. | [
0,
1,
0,
0,
0,
0
] |
Title: On Natural Language Generation of Formal Argumentation,
Abstract: In this paper we provide a first analysis of the research questions that
arise when dealing with the problem of communicating pieces of formal
argumentation through natural language interfaces. It is a generally held
opinion that formal models of argumentation naturally capture human argument,
and some preliminary studies have focused on justifying this view.
Unfortunately, the results are not only inconclusive, but seem to suggest that
explaining formal argumentation to humans is a rather articulated task.
Graphical models for expressing argumentation-based reasoning are appealing,
but often humans require significant training to use these tools effectively.
We claim that natural language interfaces to formal argumentation systems offer
a real alternative, and may be the way forward for systems that capture human
argument. | [
1,
0,
0,
0,
0,
0
] |
Title: Supervised Learning Based Algorithm Selection for Deep Neural Networks,
Abstract: Many recent deep learning platforms rely on third-party libraries (such as
cuBLAS) to utilize the computing power of modern hardware accelerators (such as
GPUs). However, we observe that they may achieve suboptimal performance because
the library functions are not used appropriately. In this paper, we target at
optimizing the operations of multiplying a matrix with the transpose of another
matrix (referred to as NT operation hereafter), which contribute about half of
the training time of fully connected deep neural networks. Rather than directly
calling the library function, we propose a supervised learning based algorithm
selection approach named MTNN, which uses a gradient boosted decision tree to
select one from two alternative NT implementations intelligently: (1) calling
the cuBLAS library function; (2) calling our proposed algorithm TNN that uses
an efficient out-of-place matrix transpose. We evaluate the performance of MTNN
on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve
96\% of prediction accuracy with very low computational overhead, which results
in an average of 54\% performance improvement on a range of NT operations. To
further evaluate the impact of MTNN on the training process of deep neural
networks, we have integrated MTNN into a popular deep learning platform Caffe.
Our experimental results show that the revised Caffe can outperform the
original one by an average of 28\%. Both MTNN and the revised Caffe are
open-source. | [
1,
0,
0,
0,
0,
0
] |
Title: Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach,
Abstract: A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies. | [
1,
0,
0,
0,
0,
0
] |
Title: The unpolarized Shafarevich Conjecture for K3 Surfaces,
Abstract: We prove the unpolarized Shafarevich conjecture for K3 surfaces: the set of
isomorphism classes of K3 surfaces over a fixed number field with good
reduction away from a fixed and finite set of places is finite. Our proof is
based on the theorems of Faltings and André, as well as the Kuga-Satake
construction. | [
0,
0,
1,
0,
0,
0
] |
Title: Unbounded product-form Petri nets,
Abstract: Computing steady-state distributions in infinite-state stochastic systems is
in general a very dificult task. Product-form Petri nets are those Petri nets
for which the steady-state distribution can be described as a natural product
corresponding, up to a normalising constant, to an exponentiation of the
markings. However, even though some classes of nets are known to have a
product-form distribution, computing the normalising constant can be hard. The
class of (closed) {\Pi}3-nets has been proposed in an earlier work, for which
it is shown that one can compute the steady-state distribution efficiently.
However these nets are bounded. In this paper, we generalise queuing Markovian
networks and closed {\Pi}3-nets to obtain the class of open {\Pi}3-nets, that
generate infinite-state systems. We show interesting properties of these nets:
(1) we prove that liveness can be decided in polynomial time, and that
reachability in live {\Pi}3-nets can be decided in polynomial time; (2) we show
that we can decide ergodicity of such nets in polynomial time as well; (3) we
provide a pseudo-polynomial time algorithm to compute the normalising constant. | [
1,
0,
0,
0,
0,
0
] |
Title: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds,
Abstract: This paper presents a discrete-time option pricing model that is rooted in
Reinforcement Learning (RL), and more specifically in the famous Q-Learning
method of RL. We construct a risk-adjusted Markov Decision Process for a
discrete-time version of the classical Black-Scholes-Merton (BSM) model, where
the option price is an optimal Q-function. Pricing is done by learning to
dynamically optimize risk-adjusted returns for an option replicating portfolio,
as in the Markowitz portfolio theory. Using Q-Learning and related methods,
once created in a parametric setting, the model is able to go model-free and
learn to price and hedge an option directly from data generated from a dynamic
replicating portfolio which is rebalanced at discrete times. If the world is
according to BSM, our risk-averse Q-Learner converges, given enough training
data, to the true BSM price and hedge ratio of the option in the continuous
time limit, even if hedges applied at the stage of data generation are
completely random (i.e. it can learn the BSM model itself, too!), because
Q-Learning is an off-policy algorithm. If the world is different from a BSM
world, the Q-Learner will find it out as well, because Q-Learning is a
model-free algorithm. For finite time steps, the Q-Learner is able to
efficiently calculate both the optimal hedge and optimal price for the option
directly from trading data, and without an explicit model of the world. This
suggests that RL may provide efficient data-driven and model-free methods for
optimal pricing and hedging of options, once we depart from the academic
continuous-time limit, and vice versa, option pricing methods developed in
Mathematical Finance may be viewed as special cases of model-based
Reinforcement Learning. Our model only needs basic linear algebra (plus Monte
Carlo simulation, if we work with synthetic data). | [
1,
0,
0,
0,
0,
0
] |
Title: End-to-end Networks for Supervised Single-channel Speech Separation,
Abstract: The performance of single channel source separation algorithms has improved
greatly in recent times with the development and deployment of neural networks.
However, many such networks continue to operate on the magnitude spectrogram of
a mixture, and produce an estimate of source magnitude spectrograms, to perform
source separation. In this paper, we interpret these steps as additional neural
network layers and propose an end-to-end source separation network that allows
us to estimate the separated speech waveform by operating directly on the raw
waveform of the mixture. Furthermore, we also propose the use of masking based
end-to-end separation networks that jointly optimize the mask and the latent
representations of the mixture waveforms. These networks show a significant
improvement in separation performance compared to existing architectures in our
experiments. To train these end-to-end models, we investigate the use of
composite cost functions that are derived from objective evaluation metrics as
measured on waveforms. We present subjective listening test results that
demonstrate the improvement attained by using masking based end-to-end networks
and also reveal insights into the performance of these cost functions for
end-to-end source separation. | [
1,
0,
0,
0,
0,
0
] |
Title: A note on searching sorted unbalanced three-dimensional arrays,
Abstract: We examine the problem of searching sequentially for a desired real value (a
key) within a sorted unbalanced three-dimensional finite real array. This
classic problem can be viewed as determining the correct dimensional threshold
function from a finite class of such functions within the array, based on
sequential queries that take the form of point samples. This note addresses the
challenge of constructing algorithms that require the minimum number of queries
necessary in the worst case, to search for a given key in arrays that have
three dimensions with sizes that are not necessarily equal. | [
1,
0,
0,
0,
0,
0
] |
Title: Isomorphism classes of four dimensional nilpotent associative algebras over a field,
Abstract: In this paper we classify the isomorphism classes of four dimensional
nilpotent associative algebras over a field F, studying regular subgroups of
the affine group AGL_4(F). In particular we provide explicit representatives
for such classes when F is a finite field, the real field R or an algebraically
closed field. | [
0,
0,
1,
0,
0,
0
] |
Title: Contemporary facets of business successes among leading companies, operating in Bulgaria,
Abstract: The current article unveils and analyzes some important factors, influencing
diversity in strategic decision-making approaches in local companies.
Researcher's attention is oriented to survey important characteristics of the
strategic moves, undertaken by leading companies in Bulgaria. | [
0,
0,
0,
0,
0,
1
] |
Title: Ensemble of Part Detectors for Simultaneous Classification and Localization,
Abstract: Part-based representation has been proven to be effective for a variety of
visual applications. However, automatic discovery of discriminative parts
without object/part-level annotations is challenging. This paper proposes a
discriminative mid-level representation paradigm based on the responses of a
collection of part detectors, which only requires the image-level labels.
Towards this goal, we first develop a detector-based spectral clustering method
to mine the representative and discriminative mid-level patterns for detector
initialization. The advantage of the proposed pattern mining technology is that
the distance metric based on detectors only focuses on discriminative details,
and a set of such grouped detectors offer an effective way for consistent
pattern mining. Relying on the discovered patterns, we further formulate the
detector learning process as a confidence-loss sparse Multiple Instance
Learning (cls-MIL) task, which considers the diversity of the positive samples,
while avoid drifting away the well localized ones by assigning a confidence
value to each positive sample. The responses of the learned detectors can form
an effective mid-level image representation for both image classification and
object localization. Experiments conducted on benchmark datasets demonstrate
the superiority of our method over existing approaches. | [
1,
0,
0,
0,
0,
0
] |
Title: Graph Model Selection via Random Walks,
Abstract: In this paper, we present a novel approach based on the random walk process
for finding meaningful representations of a graph model. Our approach leverages
the transient behavior of many short random walks with novel initialization
mechanisms to generate model discriminative features. These features are able
to capture a more comprehensive structural signature of the underlying graph
model. The resulting representation is invariant to both node permutation and
the size of the graph, allowing direct comparison between large classes of
graphs. We test our approach on two challenging model selection problems: the
discrimination in the sparse regime of an Erdös-Renyi model from a
stochastic block model and the planted clique problem. Our representation
approach achieves performance that closely matches known theoretical limits in
addition to being computationally simple and scalable to large graphs. | [
1,
0,
0,
0,
0,
0
] |
Title: On irrationality measure of Thue-Morse constant,
Abstract: We provide a non-trivial measure of irrationality for a class of Mahler
numbers defined with infinite products which cover the Thue-Morse constant. | [
0,
0,
1,
0,
0,
0
] |
Title: Adiabatic approach for natural gas pipeline computations,
Abstract: We consider slowly evolving, i.e. ADIABATIC, operational regime within a
transmission level (continental scale) natural gas pipeline system. This allows
us to introduce a set of nodal equations of reduced complexity describing gas
transients in injection/consumption UNBALANCED (so-called line-pack) cases. We
discuss, in details, construction of the UNBALANCED ADIABATIC (UA)
approximation on the basic example of a single pipe. The UA approximation is
expected to play a significant "model reduction" role in solving control,
optimization and planning problems relevant for flawless functioning of modern
natural gas networks. | [
1,
1,
0,
0,
0,
0
] |
Title: Clustering and Labelling Auction Fraud Data,
Abstract: Although shill bidding is a common auction fraud, it is however very tough to
detect. Due to the unavailability and lack of training data, in this study, we
build a high-quality labeled shill bidding dataset based on recently collected
auctions from eBay. Labeling shill biding instances with multidimensional
features is a critical phase for the fraud classification task. For this
purpose, we introduce a new approach to systematically label the fraud data
with the help of the hierarchical clustering CURE that returns remarkable
results as illustrated in the experiments. | [
0,
0,
0,
1,
0,
0
] |
Title: Investigation of channel model for weakly coupled multicore fiber,
Abstract: We investigate the evolution of decorrelation bandwidth of inter core
crosstalk (IC-XT) in homogeneous weakly coupled multicore fibers (WC-MCFs). The
modified mode-coupled equations (MCEs) are numerically solved by combining the
fourth order Runge-Kutta method and compound Simpson integral method. It can be
theoretically and numerically observed that the decorrelation bandwidth of
IC-XT decreases with transmission distance by fractional linear function. The
evolution rule of IC-XT's decorrelation bandwidth is further confirmed by
experiments, which can be used as an evaluation criterion for channel model.
Finally, we propose a new channel model with the coupling matrix of IC-XT
generated automatically by phase transfer function (PTF), which is in good
agreement with the above evaluation criterion. We believe the proposed channel
model can provide a good simulation platform for homogeneous WC-MCF based
communication systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Palindromic Subsequences in Finite Words,
Abstract: In 1999 Lyngs{\o} and Pedersen proposed a conjecture stating that every
binary circular word of length $n$ with equal number of zeros and ones has an
antipalindromic linear subsequence of length at least $\frac{2}{3}n$. No
progress over a trivial $\frac{1}{2}n$ bound has been achieved since then. We
suggest a palindromic counterpart to this conjecture and provide a non-trivial
infinite series of circular words which prove the upper bound of $\frac{2}{3}n$
for both conjectures at the same time. The construction also works for words
over an alphabet of size $k$ and gives rise to a generalization of the
conjecture by Lyngs{\o} and Pedersen. Moreover, we discuss some possible
strengthenings and weakenings of the named conjectures. We also propose two
similar conjectures for linear words and provide some evidences for them. | [
1,
0,
0,
0,
0,
0
] |
Title: Risk quantification for the thresholding rule for multiple testing using Gaussian scale mixtures,
Abstract: In this paper we study the asymptotic properties of Bayesian multiple testing
procedures for a large class of Gaussian scale mixture pri- ors. We study two
types of multiple testing risks: a Bayesian risk proposed in Bogdan et al.
(2011) where the data are assume to come from a mixture of normal, and a
frequentist risk similar to the one proposed by Arias-Castro and Chen (2017).
Following the work of van der Pas et al. (2016), we give general conditions on
the prior such that both risks can be bounded. For the Bayesian risk, the bound
is almost sharp. This result show that under these conditions, the considered
class of continuous prior can be competitive with the usual two-group model
(e.g. spike and slab priors). We also show that if the non-zeros component of
the parameter are large enough, the minimax risk can be made asymptotically
null. The separation rates obtained are consistent with the one that could be
guessed from the existing literature (see van der Pas et al., 2017b). For both
problems, we then give conditions under which an adaptive version of the result
can be obtained. | [
0,
0,
1,
1,
0,
0
] |
Title: Some recent results on the Dirichlet problem for (p,q)-Laplace equations,
Abstract: A short account of recent existence and multiplicity theorems on the
Dirichlet problem for an elliptic equation with $(p,q)$-Laplacian in a bounded
domain is performed. Both eigenvalue problems and different types of
perturbation terms are briefly discussed. Special attention is paid to possibly
coercive, resonant, subcritical, critical, or asymmetric right-hand sides. | [
0,
0,
1,
0,
0,
0
] |
Title: A convex penalty for switching control of partial differential equations,
Abstract: A convex penalty for promoting switching controls for partial differential
equations is introduced; such controls consist of an arbitrary number of
components of which at most one should be simultaneously active. Using a
Moreau-Yosida approximation, a family of approximating problems is obtained
that is amenable to solution by a semismooth Newton method. The efficiency of
this approach and the structure of the obtained controls are demonstrated by
numerical examples. | [
0,
0,
1,
0,
0,
0
] |
Title: Workload Analysis of Blue Waters,
Abstract: Blue Waters is a Petascale-level supercomputer whose mission is to enable the
national scientific and research community to solve "grand challenge" problems
that are orders of magnitude more complex than can be carried out on other high
performance computing systems. Given the important and unique role that Blue
Waters plays in the U.S. research portfolio, it is important to have a detailed
understanding of its workload in order to guide performance optimization both
at the software and system configuration level as well as inform architectural
balance tradeoffs. Furthermore, understanding the computing requirements of the
Blue Water's workload (memory access, IO, communication, etc.), which is
comprised of some of the most computationally demanding scientific problems,
will help drive changes in future computing architectures, especially at the
leading edge. With this objective in mind, the project team carried out a
detailed workload analysis of Blue Waters. | [
1,
0,
0,
0,
0,
0
] |
Title: Model-Independent Analytic Nonlinear Blind Source Separation,
Abstract: Consider a time series of measurements of the state of an evolving system,
x(t), where x has two or more components. This paper shows how to perform
nonlinear blind source separation; i.e., how to determine if these signals are
equal to linear or nonlinear mixtures of the state variables of two or more
statistically independent subsystems. First, the local distributions of
measurement velocities are processed in order to derive vectors at each point
in x-space. If the data are separable, each of these vectors must be directed
along a subspace of x-space that is traversed by varying the state variable of
one subsystem, while all other subsystems are kept constant. Because of this
property, these vectors can be used to construct a small set of mappings, which
must contain the unmixing function, if it exists. Therefore, nonlinear blind
source separation can be performed by examining the separability of the data
after it has been transformed by each of these mappings. The method is
analytic, constructive, and model-independent. It is illustrated by blindly
recovering the separate utterances of two speakers from nonlinear combinations
of their audio waveforms. | [
0,
0,
0,
1,
0,
0
] |
Title: Seven Lessons from Manyfield Inflation in Random Potentials,
Abstract: We study inflation in models with many interacting fields subject to randomly
generated scalar potentials. We use methods from non-equilibrium random matrix
theory to construct the potentials and an adaption of the 'transport method' to
evolve the two-point correlators during inflation. This construction allows,
for the first time, for an explicit study of models with up to 100 interacting
fields supporting a period of 'approximately saddle-point' inflation. We
determine the statistical predictions for observables by generating over 30,000
models with 2-100 fields supporting at least 60 efolds of inflation. These
studies lead us to seven lessons: i) Manyfield inflation is not single-field
inflation, ii) The larger the number of fields, the simpler and sharper the
predictions, iii) Planck compatibility is not rare, but future experiments may
rule out this class of models, iv) The smoother the potentials, the sharper the
predictions, v) Hyperparameters can transition from stiff to sloppy, vi)
Despite tachyons, isocurvature can decay, vii) Eigenvalue repulsion drives the
predictions. We conclude that many of the 'generic predictions' of single-field
inflation can be emergent features of complex inflation models. | [
0,
1,
0,
0,
0,
0
] |
Title: Steering Social Activity: A Stochastic Optimal Control Point Of View,
Abstract: User engagement in online social networking depends critically on the level
of social activity in the corresponding platform--the number of online actions,
such as posts, shares or replies, taken by their users. Can we design
data-driven algorithms to increase social activity? At a user level, such
algorithms may increase activity by helping users decide when to take an action
to be more likely to be noticed by their peers. At a network level, they may
increase activity by incentivizing a few influential users to take more
actions, which in turn will trigger additional actions by other users. In this
paper, we model social activity using the framework of marked temporal point
processes, derive an alternate representation of these processes using
stochastic differential equations (SDEs) with jumps and, exploiting this
alternate representation, develop two efficient online algorithms with provable
guarantees to steer social activity both at a user and at a network level. In
doing so, we establish a previously unexplored connection between optimal
control of jump SDEs and doubly stochastic marked temporal point processes,
which is of independent interest. Finally, we experiment both with synthetic
and real data gathered from Twitter and show that our algorithms consistently
steer social activity more effectively than the state of the art. | [
1,
0,
0,
1,
0,
0
] |
Title: Adaptive Clustering Using Kernel Density Estimators,
Abstract: We investigate statistical properties of a clustering algorithm that receives
level set estimates from a kernel density estimator and then estimates the
first split in the density level cluster tree if such a split is present or
detects the absence of such a split. Key aspects of our analysis include finite
sample guarantees, consistency, rates of convergence, and an adaptive
data-driven strategy for chosing the kernel bandwidth. For the rates and the
adaptivity we do not need continuity assumptions on the density such as
Hölder continuity, but only require intuitive geometric assumptions of
non-parametric nature. | [
0,
0,
0,
1,
0,
0
] |
Title: Realization of functions on the symmetrized bidisc,
Abstract: We prove a realization formula and a model formula for analytic functions
with modulus bounded by $1$ on the symmetrized bidisc \[ G\stackrel{\rm def}{=}
\{(z+w,zw): |z|<1, \, |w| < 1\}. \] As an application we prove a Pick-type
theorem giving a criterion for the existence of such a function satisfying a
finite set of interpolation conditions. | [
0,
0,
1,
0,
0,
0
] |
Title: Ground state properties of 3d metals from self-consistent GW approach,
Abstract: Self consistent GW approach (scGW) has been applied to calculate the ground
state properties (equilibrium Wigner-Seitz radius $S_{WZ}$ and bulk modulus
$B$) of 3d transition metals Sc, Ti, V, Fe, Co, Ni, and Cu. The approach
systematically underestimates $S_{WZ}$ with average relative deviation from the
experimental data about 1% and it overestimates the calculated bulk modulus
with relative error about 25%. It is shown that scGW is superior in accuracy as
compared to the local density approximation (LDA) but it is less accurate than
the generalized gradient approach (GGA) for the materials studied. If compared
to the random phase approximation (RPA), scGW is slightly less accurate, but
its error for the 3d metals looks more systematic. The systematic nature of the
deviation from the experimental data suggests that the next order of the
perturbation theory should allow one to reduce the error. | [
0,
1,
0,
0,
0,
0
] |
Title: Survival Trees for Interval-Censored Survival data,
Abstract: Interval-censored data, in which the event time is only known to lie in some
time interval, arise commonly in practice; for example, in a medical study in
which patients visit clinics or hospitals at pre-scheduled times, and the
events of interest occur between visits. Such data are appropriately analyzed
using methods that account for this uncertainty in event time measurement. In
this paper we propose a survival tree method for interval-censored data based
on the conditional inference framework. Using Monte Carlo simulations we find
that the tree is effective in uncovering underlying tree structure, performs
similarly to an interval-censored Cox proportional hazards model fit when the
true relationship is linear, and performs at least as well as (and in the
presence of right-censoring outperforms) the Cox model when the true
relationship is not linear. Further, the interval-censored tree outperforms
survival trees based on imputing the event time as an endpoint or the midpoint
of the censoring interval. We illustrate the application of the method on tooth
emergence data. | [
0,
0,
0,
1,
0,
0
] |
Title: Bayesian LSTMs in medicine,
Abstract: The medical field stands to see significant benefits from the recent advances
in deep learning. Knowing the uncertainty in the decision made by any machine
learning algorithm is of utmost importance for medical practitioners. This
study demonstrates the utility of using Bayesian LSTMs for classification of
medical time series. Four medical time series datasets are used to show the
accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we
show cherry-picked examples of confident and uncertain classifications of the
medical time series. With simple modifications of the common practice for deep
learning, significant improvements can be made for the medical practitioner and
patient. | [
1,
0,
0,
1,
0,
0
] |
Title: Motion Planning for a UAV with a Straight or Kinked Tether,
Abstract: This paper develops and compares two motion planning algorithms for a
tethered UAV with and without the possibility of the tether contacting the
confined and cluttered environment. Tethered aerial vehicles have been studied
due to their advantages such as power duration, stability, and safety. However,
the disadvantages brought in by the extra tether have not been well
investigated by the robotic locomotion community, especially when the tethered
agent is locomoting in a non-free space occupied with obstacles. In this work,
we propose two motion planning frameworks that (1) reduce the reachable
configuration space by taking into account the tether and (2) deliberately plan
(and relax) the contact point(s) of the tether with the environment and enable
an equivalent reachable configuration space as the non-tethered counterpart
would have. Both methods are tested on a physical robot, Fotokite Pro. With our
approaches, tethered aerial vehicles could find their applications in confined
and cluttered environments with obstacles as opposed to ideal free space, while
still maintaining the advantages from the usage of a tether. The motion
planning strategies are particularly suitable for marsupial heterogeneous
robotic teams, such as visual servoing/assisting for another mobile,
tele-operated primary robot. | [
1,
0,
0,
0,
0,
0
] |
Title: Star formation driven galactic winds in UGC 10043,
Abstract: We study the galactic wind in the edge-on spiral galaxy UGC 10043 with the
combination of the CALIFA integral field spectroscopy data, scanning
Fabry-Perot interferometry (FPI), and multiband photometry. We detect ionized
gas in the extraplanar regions reaching a relatively high distance, up to ~ 4
kpc above the galactic disk. The ionized gas line ratios ([N ii]/Ha, [S ii]/Ha
and [O i]/Ha) present an enhancement along the semi minor axis, in contrast
with the values found at the disk, where they are compatible with ionization
due to H ii-regions. These differences, together with the biconic symmetry of
the extra-planar ionized structure, makes UGC 10043 a clear candidate for a
galaxy with gas outflows ionizated by shocks. From the comparison of shock
models with the observed line ratios, and the kinematics observed from the FPI
data, we constrain the physical properties of the observed outflow. The data
are compatible with a velocity increase of the gas along the extraplanar
distances up to < 400 km/s and the preshock density decreasing in the same
direction. We also observe a discrepancy in the SFR estimated based on Ha (0.36
Msun/yr ) and the estimated with the CIGALE code, being the latter 5 times
larger. Nevertheless, this SFR is still not enough to drive the observed
galactic wind if we do not take into account the filling factor. We stress that
the combination of the three techniques of observation with models is a
powerful tool to explore galactic winds in the Local Universe. | [
0,
1,
0,
0,
0,
0
] |
Title: Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve,
Abstract: Objective: To establish the performance of several drive and measurement
patterns in EIT imaging of neural activity in peripheral nerve, which involves
large impedance change in the nerve's anisotropic length axis. Approach: Eight
drive and measurement electrode patterns are compared using a finite element
(FE) four cylindrical shell model of a peripheral nerve and a 32 channel
dual-ring nerve cuff. The central layer of the FE model contains impedance
changes representative of neural activity of -0.3 in the length axis and -8.8 x
10-4 in the radial axis. Four of the electrode patterns generate longitudinal
drive current, which runs perpendicular to the anisotropic axis. Main results:
Transverse current patterns produce higher resolution than longitudinal
patterns but are also more susceptible to noise and errors, and exhibit poorer
sensitivity to impedance changes in central sample locations. Three of the four
longitudinal current patterns considered can reconstruct fascicle level
impedance changes with up to 0.2 mV noise and error, which corresponds to
between -5.5 and +0.18 dB of the normalised signal standard deviation. Reducing
the spacing between the two electrode rings in all longitudinal current
patterns reduced the signal to error ratio across all depth locations of the
sample. Significance: Electrode patterns which target the large impedance
change in the anisotropic length axis can provide improved robustness against
noise and errors, which is a critical step towards real time EIT imaging of
neural activity in peripheral nerve. | [
0,
0,
0,
0,
1,
0
] |
Title: Remarks on Liouville Type Theorems for Steady-State Navier-Stokes Equations,
Abstract: Liouville type theorems for the stationary Navier-Stokes equations are proven
under certain assumptions. These assumptions are motivated by conditions that
appear in Liouvile type theorems for the heat equations with a given divergence
free drift. | [
0,
0,
1,
0,
0,
0
] |
Title: The Homogeneous Broadcast Problem in Narrow and Wide Strips,
Abstract: Let $P$ be a set of nodes in a wireless network, where each node is modeled
as a point in the plane, and let $s\in P$ be a given source node. Each node $p$
can transmit information to all other nodes within unit distance, provided $p$
is activated. The (homogeneous) broadcast problem is to activate a minimum
number of nodes such that in the resulting directed communication graph, the
source $s$ can reach any other node. We study the complexity of the regular and
the hop-bounded version of the problem (in the latter, $s$ must be able to
reach every node within a specified number of hops), with the restriction that
all points lie inside a strip of width $w$. We almost completely characterize
the complexity of both the regular and the hop-bounded versions as a function
of the strip width $w$. | [
1,
0,
0,
0,
0,
0
] |
Title: Extreme CO Isotopic Abundances in the ULIRG IRAS 13120-5453: An Extremely Young Starburst or Top-Heavy Initial Mass Function,
Abstract: We present ALMA $^{12}$CO (J=1-0, 3-2 and 6-5), $^{13}$CO (J=1-0) and
C$^{18}$O (J=1-0) observations of the local Ultra Luminous Infrared Galaxy,
IRAS 13120-5453 (dubbed "The Yo-yo"). The morphologies of the three isotopic
species differ, where $^{13}$CO shows a hole in emission towards the center. We
measure integrated brightness temperature line ratios of $^{12}$CO/$^{13}$CO
$\geq$ 60 (exceeding 200) and $^{13}$CO/C$^{18}$O $\leq$ 1 in the central
region. Assuming optical thin emission, C$^{18}$O is more abundant than
$^{13}$CO in several regions. The abundances within the central 500 pc are
consistent with enrichment of the ISM via a young starburst ($<$7Myr), a
top-heavy initial mass function or a combination of both. | [
0,
1,
0,
0,
0,
0
] |
Title: Computing Nearby Non-trivial Smith Forms,
Abstract: We consider the problem of computing the nearest matrix polynomial with a
non-trivial Smith Normal Form. We show that computing the Smith form of a
matrix polynomial is amenable to numeric computation as an optimization
problem. Furthermore, we describe an effective optimization technique to find a
nearby matrix polynomial with a non-trivial Smith form. The results are then
generalized to include the computation of a matrix polynomial having a maximum
specified number of ones in the Smith Form (i.e., with a maximum specified
McCoy rank).
We discuss the geometry and existence of solutions and how our results can
used for an error analysis. We develop an optimization-based approach and
demonstrate an iterative numerical method for computing a nearby matrix
polynomial with the desired spectral properties. We also describe an
implementation of our algorithms and demonstrate the robustness with examples
in Maple. | [
1,
0,
0,
0,
0,
0
] |
Title: Quasinonexpansive Iterations on the Affine Hull of Orbits: From Mann's Mean Value Algorithm to Inertial Methods,
Abstract: Fixed point iterations play a central role in the design and the analysis of
a large number of optimization algorithms. We study a new iterative scheme in
which the update is obtained by applying a composition of quasinonexpansive
operators to a point in the affine hull of the orbit generated up to the
current iterate. This investigation unifies several algorithmic constructs,
including Mann's mean value method, inertial methods, and multi-layer
memoryless methods. It also provides a framework for the development of new
algorithms, such as those we propose for solving monotone inclusion and
minimization problems. | [
0,
0,
1,
0,
0,
0
] |
Title: Decentralized Optimal Control for Connected Automated Vehicles at Intersections Including Left and Right Turns,
Abstract: In prior work, we addressed the problem of optimally controlling on line
connected and automated vehicles crossing two adjacent intersections in an
urban area to minimize fuel consumption while achieving maximal throughput
without any explicit traffic signaling and without considering left and right
turns. In this paper, we extend the solution of this problem to account for
left and right turns under hard safety constraints. Furthermore, we formulate
and solve another optimization problem to minimize a measure of passenger
discomfort while the vehicle turns at the intersection and we investigate the
associated tradeoff between minimizing fuel consumption and passenger
discomfort. | [
0,
0,
1,
0,
0,
0
] |
Title: The Rational Distance Problem for Equilateral Triangles,
Abstract: Let (P) denote the problem of existence of a point in the plane of a given
triangle T, that is at rational distance from all the vertices of T. In this
article, we provide a complete solution to (P) for all equilateral triangles. | [
0,
0,
1,
0,
0,
0
] |
Title: Compact Hausdorff MV-algebras: Structure, Duality and Projectivity,
Abstract: It is proved that the category $\mathbb{EM}$ of extended multisets is dually
equivalent to the category $\mathbb{CHMV}$ of compact Hausdorff MV-algebras
with continuous homomorphisms, which is in turn equivalent to the category of
complete and completely distributive MV-algebras with homomorphisms that
reflect principal maximal ideals. Urysohn-Strauss's Lemma, Gleason's Theorem,
and projective objects are also investigated for topological MV-algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: Nonvanishing theorems for twisted L-functions on unitary groups,
Abstract: We prove the nonvanishing of the twisted central critical values of a class
of automorphic L-functions for twists by all but finitely many unitary
characters in particular infinite families. The methods build on a
non-archimedean approach introduced by Greenberg in the context of the Birch
and Swinnerton-Dyer Conjecture. While this paper focuses on L-functions
associated to certain automorphic representations of unitary groups, it
illustrates how decades-old methods from Iwasawa theory can be combined with
the output of new machinery to achieve broader nonvanishing results. | [
0,
0,
1,
0,
0,
0
] |
Title: Force-induced elastic matrix-mediated interactions in the presence of a rigid wall,
Abstract: We consider an elastic composite material containing particulate inclusions
in a soft elastic matrix that is bounded by a rigid wall, e.g., the substrate.
If such a composite serves as a soft actuator, forces are imposed on or induced
between the embedded particles. We investigate how the presence of the rigid
wall affects the interactions between the inclusions in the elastic matrix. For
no-slip boundary conditions, we transfer Blake's derivation of a corresponding
Green's function from low-Reynolds-number hydrodynamics to the linearly elastic
case. Results for no-slip and free-slip surface conditions are compared to each
other and to the bulk behavior. Our results suggest that walls with free-slip
surface conditions are preferred when they serve as substrates for soft
actuators made from elastic composite materials. As we further demonstrate, the
presence of a rigid wall can qualitatively change the interactions between the
inclusions. In effect, it can switch attractive interactions into repulsive
ones (and vice versa). It should be straightforward to observe the effects in
future experiments and to combine our results, e.g., with the modeling of
biological cells and tissue on rigid surfaces. | [
0,
1,
0,
0,
0,
0
] |
Title: One-way quantum computing in superconducting circuits,
Abstract: We propose a method for the implementation of one-way quantum computing in
superconducting circuits. Measurement-based quantum computing is a universal
quantum computation paradigm in which an initial cluster-state provides the
quantum resource, while the iteration of sequential measurements and local
rotations encodes the quantum algorithm. Up to now, technical constraints have
limited a scalable approach to this quantum computing alternative. The initial
cluster state can be generated with available controlled-phase gates, while the
quantum algorithm makes use of high-fidelity readout and coherent feedforward.
With current technology, we estimate that quantum algorithms with above 20
qubits may be implemented in the path towards quantum supremacy. Moreover, we
propose an alternative initial state with properties of maximal persistence and
maximal connectedness, reducing the required resources of one-way quantum
computing protocols. | [
0,
1,
0,
0,
0,
0
] |
Title: Smooth solution to higher dimensional complex Plateau problem,
Abstract: Let $X$ be a compact connected strongly pseudoconvex $CR$ manifold of real
dimension $2n-1$ in $\mathbb{C}^{N}$. For $n\ge 3$, Yau solved the complex
Plateau problem of hypersurface type by checking a bunch of Kohn-Rossi
cohomology groups in 1981. In this paper, we generalize Yau's conjecture on
some numerical invariant of every isolated surface singularity defined by Yau
and the author to any dimension and prove that the conjecture is true for local
complete intersection singularities of dimension $n\ge 3$. As a direct
application, we solved complex Plateau problem of hypersurface type for any
dimension $n\ge 3$ by checking only one numerical invariant. | [
0,
0,
1,
0,
0,
0
] |
Title: Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices,
Abstract: We study the Steiner Tree problem, in which a set of terminal vertices needs
to be connected in the cheapest possible way in an edge-weighted graph. This
problem has been extensively studied from the viewpoint of approximation and
also parametrization. In particular, on one hand Steiner Tree is known to be
APX-hard, and W[2]-hard on the other, if parameterized by the number of
non-terminals (Steiner vertices) in the optimum solution. In contrast to this
we give an efficient parameterized approximation scheme (EPAS), which
circumvents both hardness results. Moreover, our methods imply the existence of
a polynomial size approximate kernelization scheme (PSAKS) for the considered
parameter.
We further study the parameterized approximability of other variants of
Steiner Tree, such as Directed Steiner Tree and Steiner Forest. For neither of
these an EPAS is likely to exist for the studied parameter: for Steiner Forest
an easy observation shows that the problem is APX-hard, even if the input graph
contains no Steiner vertices. For Directed Steiner Tree we prove that
approximating within any function of the studied parameter is W[1]-hard.
Nevertheless, we show that an EPAS exists for Unweighted Directed Steiner Tree,
but a PSAKS does not. We also prove that there is an EPAS and a PSAKS for
Steiner Forest if in addition to the number of Steiner vertices, the number of
connected components of an optimal solution is considered to be a parameter. | [
1,
0,
0,
0,
0,
0
] |
Title: Nerve impulse propagation and wavelet theory,
Abstract: A luminous stimulus which penetrates in a retina is converted to a nerve
message. Ganglion cells give a response that may be approximated by a wavelet.
We determine a function PSI which is associated with the propagation of nerve
impulses along an axon. Each kind of channel (inward and outward) may be open
or closed, depending on the transmembrane potential. The transition between
these states is a random event. Using quantum relations, we estimate the number
of channels susceptible to switch between the closed and open states. Our
quantum approach was first to calculate the energy level distribution in a
channel. We obtain, for each kind of channel, the empty level density and the
filled level density of the open and closed conformations. The joint density of
levels provides the transition number between the closed and open
conformations. The algebraic sum of inward and outward open channels is a
function PSI of the normalized energy E. The function PSI verifies the major
properties of a wavelet. We calculate the functional dependence of the axon
membrane conductance with the transmembrane energy. | [
0,
0,
0,
0,
1,
0
] |
Title: Generation of unipolar half-cycle pulse via unusual reflection of a single-cycle pulse from an optically thin metallic or dielectric layer,
Abstract: We present a significantly different reflection process from an optically
thin flat metallic or dielectric layer and propose a strikingly simple method
to form approximately unipolar half-cycle optical pulses via reflection of a
single-cycle optical pulse. Unipolar pulses in reflection arise due to
specifics of effectively one-dimensional pulse propagation. Namely, we show
that in considered system the field emitted by a flat medium layer is
proportional to the velocity of oscillating medium charges instead of their
acceleration as it is usually the case. When the single-cycle pulse interacts
with linear optical medium, the oscillation velocity of medium charges can be
then forced to keep constant sign throughout the pulse duration. Our results
essentially differ from the direct mirror reflection and suggest a possibility
of unusual transformations of the few-cycle light pulses in linear optical
systems. | [
0,
1,
0,
0,
0,
0
] |
Title: The Implicit Bias of Gradient Descent on Separable Data,
Abstract: We examine gradient descent on unregularized logistic regression problems,
with homogeneous linear predictors on linearly separable datasets. We show the
predictor converges to the direction of the max-margin (hard margin SVM)
solution. The result also generalizes to other monotone decreasing loss
functions with an infimum at infinity, to multi-class problems, and to training
a weight layer in a deep network in a certain restricted setting. Furthermore,
we show this convergence is very slow, and only logarithmic in the convergence
of the loss itself. This can help explain the benefit of continuing to optimize
the logistic or cross-entropy loss even after the training error is zero and
the training loss is extremely small, and, as we show, even if the validation
loss increases. Our methodology can also aid in understanding implicit
regularization n more complex models and with other optimization methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Floquet prethermalization in the resonantly driven Hubbard model,
Abstract: We demonstrate the existence of long-lived prethermalized states in the Mott
insulating Hubbard model driven by periodic electric fields. These states,
which also exist in the resonantly driven case with a large density of
photo-induced doublons and holons, are characterized by a nonzero current and
an effective temperature of the doublons and holons which depends sensitively
on the driving condition. Focusing on the specific case of resonantly driven
models whose effective time-independent Hamiltonian in the high-frequency
driving limit corresponds to noninteracting fermions, we show that the time
evolution of the double occupation can be reproduced by the effective
Hamiltonian, and that the prethermalization plateaus at finite driving
frequency are controlled by the next-to-leading order correction in the
high-frequency expansion of the effective Hamiltonian. We propose a numerical
procedure to determine an effective Hubbard interaction that mimics the
correlation effects induced by these higher order terms. | [
0,
1,
0,
0,
0,
0
] |
Title: Continuous vibronic symmetries in Jahn-Teller models,
Abstract: We develop a systematic study of Jahn-Teller (JT) models with continuous
symmetries by explor- ing their algebraic properties. The compact symmetric
spaces corresponding to JT models carrying a Lie group symmetry are identified,
and their invariants used to reduce their adiabatic potential energy surfaces
into orbit spaces. Each orbit consists of a set of JT distorted molecular
structures with equal adiabatic electronic spectrum. Molecular motion may be
decomposed into pseudorota- tional and radial. The former preserves the orbit,
while the latter maps an orbit into another. The dimensionality and topology of
the internal space of each orbit depends on the number of degener- ate states
in its adiabatic electronic spectra. Furthermore, qualitatively different
pseudorotational modes occur in orbits of different types. We also provide a
simple proof that the electronic spectrum for the space of JT minimum-energy
structures (trough) displays a universality predicted by the epikernel
principle. This result is in turn used to prove the topological equivalence
between bosonic (fermionic) JT troughs and real (quaternionic) projective
spaces, a conclusion which has outstanding physical consequences, as explained
in our work. The relevance of our study for the more common case of JT systems
with only discrete point group symmetry, and for generic asymmetric molecular
systems with conical intersections involving more than two states is likewise
discussed. In particular, we show that JT models with continuous symmetries
present the simplest models of conical intersections among an arbitrary number
of electronic state crossings. | [
0,
1,
0,
0,
0,
0
] |
Title: Augmented Lagrangian Functions for Cone Constrained Optimization: the Existence of Global Saddle Points and Exact Penalty Property,
Abstract: In the article we present a general theory of augmented Lagrangian functions
for cone constrained optimization problems that allows one to study almost all
known augmented Lagrangians for cone constrained programs within a unified
framework. We develop a new general method for proving the existence of global
saddle points of augmented Lagrangian functions, called the localization
principle. The localization principle unifies, generalizes and sharpens most of
the known results on existence of global saddle points, and, in essence,
reduces the problem of the existence of saddle points to a local analysis of
optimality conditions. With the use of the localization principle we obtain
first necessary and sufficient conditions for the existence of a global saddle
point of an augmented Lagrangian for cone constrained minimax problems via both
second and first order optimality conditions. In the second part of the paper,
we present a general approach to the construction of globally exact augmented
Lagrangian functions. The general approach developed in this paper allowed us
not only to sharpen most of the existing results on globally exact augmented
Lagrangians, but also to construct first globally exact augmented Lagrangian
functions for equality constrained optimization problems, for nonlinear second
order cone programs and for nonlinear semidefinite programs. These globally
exact augmented Lagrangians can be utilized in order to design new
superlinearly (or even quadratically) convergent optimization methods for cone
constrained optimization problems. | [
0,
0,
1,
0,
0,
0
] |
Title: Spin $q$-Whittaker polynomials,
Abstract: We introduce and study a one-parameter generalization of the q-Whittaker
symmetric functions. This is a family of multivariate symmetric polynomials,
whose construction may be viewed as an application of the procedure of fusion
from integrable lattice models to a vertex model interpretation of a
one-parameter generalization of Hall-Littlewood polynomials from [Bor17, BP16a,
BP16b].
We prove branching and Pieri rules, standard and dual (skew) Cauchy summation
identities, and an integral representation for the new polynomials. | [
0,
1,
1,
0,
0,
0
] |
Title: Lagrangians of hypergraphs: The Frankl-Füredi conjecture holds almost everywhere,
Abstract: Frankl and Füredi conjectured in 1989 that the maximum Lagrangian of all
$r$-uniform hypergraphs of fixed size $m$ is realised by the initial segment of
the colexicographic order. In particular, in the principal case
$m=\binom{t}{r}$ their conjecture states that every $H\subseteq
\mathbb{N}^{(r)}$ of size $\binom{t}{r}$ satisfies \begin{align*} \max
\{\sum_{A \in H}\prod_{i\in A} y_i \ \colon \ y_1,y_2,\ldots \geq 0; \sum_{i\in
\mathbb{N}} y_i=1 \}&\leq \frac{1}{t^r}\binom{t}{r}. \end{align*}
We prove the above statement for all $r\geq 4$ and large values of $t$ (the
case $r=3$ was settled by Talbot in 2002). More generally, we show for any
$r\geq 4$ that the Frankl-Füredi conjecture holds whenever $\binom{t-1}{r}
\leq m \leq \binom{t}{r}- \gamma_r t^{r-2}$ for a constant $\gamma_r>0$,
thereby verifying it for `most' $m\in \mathbb{N}$.
Furthermore, for $r=3$ we make an improvement on the results of
Talbot~\cite{Tb} and Tang, Peng, Zhang and Zhao~\cite{TPZZ}. | [
0,
0,
1,
0,
0,
0
] |
Title: Adversarial Attacks and Defences Competition,
Abstract: To accelerate research on adversarial examples and robustness of machine
learning classifiers, Google Brain organized a NIPS 2017 competition that
encouraged researchers to develop new methods to generate adversarial examples
as well as to develop new ways to defend against them. In this chapter, we
describe the structure and organization of the competition and the solutions
developed by several of the top-placing teams. | [
0,
0,
0,
1,
0,
0
] |
Title: Community structure of copper supply networks in the prehistoric Balkans: An independent evaluation of the archaeological record from the 7th to the 4th millennium BC,
Abstract: Complex networks analyses of many physical, biological and social phenomena
show remarkable structural regularities, yet, their application in studying
human past interaction remains underdeveloped. Here, we present an innovative
method for identifying community structures in the archaeological record that
allow for independent evaluation of the copper using societies in the Balkans,
from c. 6200 to c. 3200 BC. We achieve this by exploring modularity of
networked systems of these societies across an estimated 3000 years. We employ
chemical data of copper-based objects from 79 archaeological sites as the
independent variable for detecting most densely interconnected sets of nodes
with a modularity maximization method. Our results reveal three dominant
modular structures across the entire period, which exhibit strong spatial and
temporal significance. We interpret patterns of copper supply among prehistoric
societies as reflective of social relations, which emerge as equally important
as physical proximity. Although designed on a variable isolated from any
archaeological and spatiotemporal information, our method provides
archaeologically and spatiotemporally meaningful results. It produces models of
human interaction and cooperation that can be evaluated independently of
established archaeological systematics, and can find wide application on any
quantitative data from archaeological and historical record. | [
1,
1,
0,
0,
0,
0
] |
Title: Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm,
Abstract: This paper describes a method for clustering data that are spread out over
large regions and which dimensions are on different scales of measurement. Such
an algorithm was developed to implement a robotics application consisting in
sorting and storing objects in an unsupervised way. The toy dataset used to
validate such application consists of Lego bricks of different shapes and
colors. The uncontrolled lighting conditions together with the use of RGB color
features, respectively involve data with a large spread and different levels of
measurement between data dimensions. To overcome the combination of these two
characteristics in the data, we have developed a new weighted K-means
algorithm, called gap-ratio K-means, which consists in weighting each dimension
of the feature space before running the K-means algorithm. The weight
associated with a feature is proportional to the ratio of the biggest gap
between two consecutive data points, and the average of all the other gaps.
This method is compared with two other variants of K-means on the Lego bricks
clustering problem as well as two other common classification datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Eigenvalue Dynamics of a PT-symmetric Sturm-Liouville Operator. Criteria of the Similarity to a Self-adjoint or Normal Operator,
Abstract: The goal of the paper is to investigate the dynamics of the eigenvalues of
the Sturm-Liouville operator with summable PT-symmetric potential on the finite
interval. It turns out that the case of a complex Airy operator presents an
exactly solvable model which allows us to trace the dynamics of the movement of
the eigenvalues in all details and to find explicitly the critical parameter
values, in particular, to specify precisely the number $\varepsilon_1$ such
that for $0<\varepsilon<\varepsilon_1$ the operator has a real spectrum and is
similar to a self-adjoint operator. | [
0,
0,
1,
0,
0,
0
] |
Title: Automaton Semigroups and Groups: on the Undecidability of Problems Related to Freeness and Finiteness,
Abstract: In this paper, we study algorithmic problems for automaton semigroups and
automaton groups related to freeness and finiteness. In the course of this
study, we also exhibit some connections between the algebraic structure of
automaton (semi)groups and their dynamics on the boundary.
First, we show that it is undecidable to check whether the group generated by
a given invertible automaton has a positive relation, i. e. a relation p = 1
such that p only contains positive generators. Besides its obvious relation to
the freeness of the group, the absence of positive relations has previously
been studied and is connected to the triviality of some stabilizers of the
boundary. We show that the emptiness of the set of positive relations is
equivalent to the dynamical property that all (directed positive) orbital
graphs centered at non-singular points are acyclic. Our approach also works to
show undecidability of the freeness problem for automaton semigroups; in fact,
it shows undecidability of a strengthened version where the input automaton is
complete and invertible.
Gillibert showed that the finiteness problem for automaton semigroups is
undecidable. In the second part of the paper, we show that this undecidability
result also holds if the input is restricted to be bi-reversible and invertible
(but, in general, not complete). As an immediate consequence, we obtain that
the finiteness problem for automaton subsemigroups of semigroups generated by
invertible, yet partial automata, so called automaton-inverse semigroups, is
also undecidable. | [
1,
0,
1,
0,
0,
0
] |
Title: Terrestrial effects of moderately nearby supernovae,
Abstract: Recent data indicate one or more moderately nearby supernovae in the early
Pleistocene, with additional events likely in the Miocene. This has motivated
more detailed computations, using new information about the nature of
supernovae and the distances of these events to describe in more detail the
sorts of effects that are indicated at the Earth. This short
communication/review is designed to describe some of these effects so that they
may possibly be related to changes in the biota around these times. | [
0,
1,
0,
0,
0,
0
] |
Title: A Variational Inequality Perspective on Generative Adversarial Networks,
Abstract: Generative adversarial networks (GANs) form a generative modeling approach
known for producing appealing samples, but they are notably difficult to train.
One common way to tackle this issue has been to propose new formulations of the
GAN objective. Yet, surprisingly few studies have looked at optimization
methods designed for this adversarial training. In this work, we cast GAN
optimization problems in the general variational inequality framework. Tapping
into the mathematical programming literature, we counter some common
misconceptions about the difficulties of saddle point optimization and propose
to extend techniques designed for variational inequalities to the training of
GANs. We apply averaging, extrapolation and a novel computationally cheaper
variant that we call extrapolation from the past to the stochastic gradient
method (SGD) and Adam. | [
0,
0,
0,
1,
0,
0
] |
Title: Integrating electricity markets: Impacts of increasing trade on prices and emissions in the western United States,
Abstract: This paper analyzes the market impacts of expanding California's centralized
electricity market across the western United States and provides the first
statistical assessment of this issue. Using market data from 2015-2018, I
estimate the short-term effects of increasing regional electricity trade
between California and neighboring states on prices, emissions, and generation.
Consistent with economic theory, I find negative price impacts from regional
trade, with each 1 gigawatt-hour (GWh) increase in California electricity
imports associated with an average 0.15 dollar decrease in CAISO price. The
price effect yields significant consumer savings well in excess of
implementation costs required to set up a regional market. I find a short-term
decrease in California carbon dioxide emissions associated with trading that is
partially offset by increased emissions in neighboring regions. Specifically,
each 1 GWh increase in regional trade is associated with a net 70-ton average
decrease in CO2 emissions across the western U.S. A small amount of increased
SO2 and NOx emissions are also observed in neighboring states associated with
increased exports to California. This implies a small portion (less than 10
percent) of electricity exports to California are supplied by coal generation.
This study identifies substantial short-term monetary benefits from market
regionalization for California consumers. It also shows that California's cap
and trade program is relatively effective in limiting the carbon content of
imported electricity, even absent a regional cap on CO2. The conclusions
suggest efforts to reduce trade barriers should move forward in parallel with
strong greenhouse gas policies that cap emissions levels across the market
region. | [
0,
0,
0,
0,
0,
1
] |
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