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Title: Natural Extension of Hartree-Fock through extremal $1$-fermion information: Overview and application to the lithium atom,
Abstract: Fermionic natural occupation numbers do not only obey Pauli's exclusion
principle but are even stronger restricted by so-called generalized Pauli
constraints. Whenever given natural occupation numbers lie on the boundary of
the allowed region the corresponding $N$-fermion quantum state has a
significantly simpler structure. We recall the recently proposed natural
extension of the Hartree-Fock ansatz based on this structural simplification.
This variational ansatz is tested for the lithium atom. Intriguingly, the
underlying mathematical structure yields universal geometrical bounds on the
correlation energy reconstructed by this ansatz. | [
0,
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0,
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] |
Title: Homotopy Parametric Simplex Method for Sparse Learning,
Abstract: High dimensional sparse learning has imposed a great computational challenge
to large scale data analysis. In this paper, we are interested in a broad class
of sparse learning approaches formulated as linear programs parametrized by a
{\em regularization factor}, and solve them by the parametric simplex method
(PSM). Our parametric simplex method offers significant advantages over other
competing methods: (1) PSM naturally obtains the complete solution path for all
values of the regularization parameter; (2) PSM provides a high precision dual
certificate stopping criterion; (3) PSM yields sparse solutions through very
few iterations, and the solution sparsity significantly reduces the
computational cost per iteration. Particularly, we demonstrate the superiority
of PSM over various sparse learning approaches, including Dantzig selector for
sparse linear regression, LAD-Lasso for sparse robust linear regression, CLIME
for sparse precision matrix estimation, sparse differential network estimation,
and sparse Linear Programming Discriminant (LPD) analysis. We then provide
sufficient conditions under which PSM always outputs sparse solutions such that
its computational performance can be significantly boosted. Thorough numerical
experiments are provided to demonstrate the outstanding performance of the PSM
method. | [
1,
0,
1,
1,
0,
0
] |
Title: Fractional Operators with Inhomogeneous Boundary Conditions: Analysis, Control, and Discretization,
Abstract: In this paper we introduce new characterizations of spectral fractional
Laplacian to incorporate nonhomogeneous Dirichlet and Neumann boundary
conditions. The classical cases with homogeneous boundary conditions arise as a
special case. We apply our definition to fractional elliptic equations of order
$s \in (0,1)$ with nonzero Dirichlet and Neumann boundary condition. Here the
domain $\Omega$ is assumed to be a bounded, quasi-convex Lipschitz domain. To
impose the nonzero boundary conditions, we construct fractional harmonic
extensions of the boundary data. It is shown that solving for the fractional
harmonic extension is equivalent to solving for the standard harmonic extension
in the very-weak form. The latter result is of independent interest as well.
The remaining fractional elliptic problem (with homogeneous boundary data) can
be realized using the existing techniques. We introduce finite element
discretizations and derive discretization error estimates in natural norms,
which are confirmed by numerical experiments. We also apply our
characterizations to Dirichlet and Neumann boundary optimal control problems
with fractional elliptic equation as constraints. | [
0,
0,
1,
0,
0,
0
] |
Title: An alternative definition of cobordism map of ECH,
Abstract: In this article, we reformulate the cobordism map of embedded contact
homology, which is induced by exact symplectic cobordism and defined as direct
limit of homomorphisms called filtered ECH cobordism map. The filtered ECH
cobordism map is defined by counting embedded holomorphic curves with zero ECH
index and we prove that it is independent on almost complex structure by
Seiberg Witten theory. Moreover, our definition in fact is equivalent to the
existing definition. | [
0,
0,
1,
0,
0,
0
] |
Title: Healthy imperfect dark matter from effective theory of mimetic cosmological perturbations,
Abstract: We study the stability of a recently proposed model of scalar-field matter
called mimetic dark matter or imperfect dark matter. It has been known that
mimetic matter with higher derivative terms suffers from gradient instabilities
in scalar perturbations. To seek for an instability-free extension of imperfect
dark matter, we develop an effective theory of cosmological perturbations
subject to the constraint on the scalar field's kinetic term. This is done by
using the unifying framework of general scalar-tensor theories based on the ADM
formalism. We demonstrate that it is indeed possible to construct a model of
imperfect dark matter which is free from ghost and gradient instabilities. As a
side remark, we also show that mimetic $F({\cal R})$ theory is plagued with the
Ostrogradsky instability. | [
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1,
0,
0,
0,
0
] |
Title: Disorder-protected topological entropy after a quantum quench,
Abstract: Topological phases of matter are considered the bedrock of novel quantum
materials as well as ideal candidates for quantum computers that possess
robustness at the physical level. The robustness of the topological phase at
finite temperature or away from equilibrium is therefore a very desirable
feature. Disorder can improve the lifetime of the encoded topological qubits.
Here we tackle the problem of the survival of the topological phase as detected
by topological entropy, after a sudden quantum quench. We introduce a method to
study analytically the time evolution of the system after a quantum quench and
show that disorder in the couplings of the Hamiltonian of the toric code and
the resulting Anderson localization can make the topological entropy resilient. | [
0,
1,
0,
0,
0,
0
] |
Title: The evolution of gravitons in accelerating cosmologies: the case of extended gravity,
Abstract: We discuss the production and evolution of cosmological gravitons showing how
the cosmological background affects their dynamics. Besides, the detection of
cosmological gravitons could constitute an extremely important signature to
discriminate among different cosmological models. Here we consider the cases of
scalar-tensor gravity and $f(R)$ gravity where it is demonstrated the
amplification of graviton amplitude changes if compared with General
Relativity. Possible observational constraints are discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: CO2 packing polymorphism under confinement in cylindrical nanopores,
Abstract: We investigate the effect of cylindrical nano-confinement on the phase
behaviour of a rigid model of carbon dioxide using both molecular dynamics and
well tempered metadynamics. To this aim we study a simplified pore model across
a parameter space comprising pore diameter, CO2-pore wall potential and CO2
density. In order to systematically identify ordering events within the pore
model we devise a generally applicable approach based on the analysis of the
distribution of intermolecular orientations. Our simulations suggest that,
while confinement in nano-pores inhibits the formation of known crystal
structures, it induces a remarkable variety of ordered packings unrelated to
their bulk counterparts, and favours the establishment of short range order in
the fluid phase. We summarise our findings by proposing a qualitative phase
diagram for this model. | [
0,
1,
0,
0,
0,
0
] |
Title: CLIC: Curriculum Learning and Imitation for feature Control in non-rewarding environments,
Abstract: In this paper, we propose an unsupervised reinforcement learning agent called
CLIC for Curriculum Learning and Imitation for Control. This agent learns to
control features in its environment without external rewards, and observes the
actions of a third party agent, Bob, who does not necessarily provide explicit
guidance. CLIC selects which feature to train on and what to imitate from Bob's
behavior by maximizing its learning progress. We show that CLIC can effectively
identify helpful behaviors in Bob's actions, and imitate them to control the
environment faster. CLIC can also follow Bob when he acts as a mentor and
provides ordered demonstrations. Finally, when Bob controls features than the
agent cannot, or in presence of a hierarchy between aspects of the environment,
we show that CLIC ignores non-reproducible and already mastered behaviors,
resulting in a greater benefit from imitation. | [
1,
0,
0,
1,
0,
0
] |
Title: On the structure of continua with finite length and Golab's semicontinuity theorem,
Abstract: The main results in this note concern the characterization of the length of
continua 1 (Theorems 2.5) and the parametrization of continua with finite
length (Theorem 4.4). Using these results we give two independent and
relatively elementary proofs of Golab's semicontinuity theorem. | [
0,
0,
1,
0,
0,
0
] |
Title: Identifying combinatorially symmetric Hidden Markov Models,
Abstract: We provide a sufficient criterion for the unique parameter identification of
combinatorially symmetric Hidden Markov Models based on the structure of their
transition matrix. If the observed states of the chain form a zero forcing set
of the graph of the Markov model then it is uniquely identifiable and an
explicit reconstruction method is given. | [
0,
0,
1,
1,
0,
0
] |
Title: Systems of small linear forms and Diophantine approximation on manifolds,
Abstract: We develop the theory of Diophantine approximation for systems of
simultaneously small linear forms, which coefficients are drawn from any given
analytic non-degenerate manifolds. This setup originates from a problem of
Sprindžuk from the 1970s on approximations to several real numbers by
conjugate algebraic numbers. Our main result is a Khintchine type theorem,
which convergence case is established without usual monotonicity constrains and
the divergence case is proved for Hausdorff measures. The result encompasses
several previous findings and, within the setup considered, gives the best
possible improvement of a recent theorem of Aka, Breuillard, Rosenzweig and
Saxcé on extremality. | [
0,
0,
1,
0,
0,
0
] |
Title: Generalized Zero-Shot Learning via Synthesized Examples,
Abstract: We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/unseen classes, given their respective class attributes.
These exemplars can subsequently be used to train any off-the-shelf
classification model. One of the key aspects of our encoder-decoder
architecture is a feedback-driven mechanism in which a discriminator (a
multivariate regressor) learns to map the generated exemplars to the
corresponding class attribute vectors, leading to an improved generator. Our
model's ability to generate and leverage examples from unseen classes to train
the classification model naturally helps to mitigate the bias towards
predicting seen classes in generalized zero-shot learning settings. Through a
comprehensive set of experiments, we show that our model outperforms several
state-of-the-art methods, on several benchmark datasets, for both standard as
well as generalized zero-shot learning. | [
1,
0,
0,
1,
0,
0
] |
Title: Dependence and dependence structures: estimation and visualization using distance multivariance,
Abstract: Distance multivariance is a multivariate dependence measure, which can detect
dependencies between an arbitrary number of random vectors each of which can
have a distinct dimension. Here we discuss several new aspects and present a
concise overview. We relax the required moment conditions considerably and show
that distance multivariance unifies (and extends) distance covariance and the
Hilbert-Schmidt independence criterion HSIC, moreover also the classical linear
dependence measures: covariance, Pearson's correlation and the RV coefficient
appear as limiting cases. For measures based on distance multivariance the
corresponding resampling tests are introduced, and several related measures are
defined: a new multicorrelation which satisfies a natural set of multivariate
dependence measure axioms and $m$-multivariance which is a new dependence
measure yielding tests for pairwise independence and independence of higher
order. These tests are computationally feasible and under very mild moment
conditions they are consistent against all alternatives. Moreover, a general
visualization scheme for higher order dependencies is proposed.
Many illustrative examples are included. All functions for the use of
distance multivariance in applications are published in the R-package
'multivariance'. | [
0,
0,
1,
1,
0,
0
] |
Title: Hybrid simulation scheme for volatility modulated moving average fields,
Abstract: We develop a simulation scheme for a class of spatial stochastic processes
called volatility modulated moving averages. A characteristic feature of this
model is that the behaviour of the moving average kernel at zero governs the
roughness of realisations, whereas its behaviour away from zero determines the
global properties of the process, such as long range dependence. Our simulation
scheme takes this into account and approximates the moving average kernel by a
power function around zero and by a step function elsewhere. For this type of
approach the authors of [8], who considered an analogous model in one
dimension, coined the expression hybrid simulation scheme. We derive the
asymptotic mean square error of the simulation scheme and compare it in a
simulation study with several other simulation techniques and exemplify its
favourable performance in a simulation study. | [
0,
0,
0,
1,
0,
0
] |
Title: Thermodynamic dislocation theory for non-uniform plastic deformations,
Abstract: The present paper extends the thermodynamic dislocation theory developed by
Langer, Bouchbinder, and Lookmann to non-uniform plastic deformations. The free
energy density as well as the positive definite dissipation function are
proposed. The governing equations are derived from the variational equation. As
illustration, the problem of plane strain constrained shear of single crystal
deforming in single slip is solved within the proposed theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Integrated Fabry-Perot cavities as a mechanism for enhancing micro-ring resonator performance,
Abstract: We propose and experimentally demonstrate the enhancement in the filtering
quality (Q) factor of an integrated micro-ring resonator (MRR) by embedding it
in an integrated Fabry-Perot (FP) cavity formed by cascaded Sagnac loop
reflectors (SLRs). By utilizing coherent interference within the FP cavity to
reshape the transmission spectrum of the MRR, both the Q factor and the
extinction ratio (ER) can be significantly improved. The device is
theoretically analyzed, and practically fabricated on a silicon-on-insulator
(SOI) wafer. Experimental results show that up to 11-times improvement in Q
factor, together with an 8-dB increase in ER, can be achieved via our proposed
method. The impact of varying structural parameters on the device performance
is also investigated and verified by the measured spectra of the fabricated
devices with different structural parameters. | [
0,
1,
0,
0,
0,
0
] |
Title: Dark matter spin determination with directional direct detection experiments,
Abstract: If the dark matter particle has spin 0, only two types of WIMP-nucleon
interaction can arise from the non-relativistic reduction of renormalisable
single-mediator models for dark matter-quark interactions. Based on this
crucial observation, we show that about 100 signal events at next generation
directional detection experiments can be enough to enable a $2\sigma$ rejection
of the spin 0 dark matter hypothesis in favour of alternative hypotheses where
the dark matter particle has spin 1/2 or 1. In this context directional
sensitivity is crucial, since anisotropy patterns in the sphere of nuclear
recoil directions depend on the spin of the dark matter particle. For
comparison, about 100 signal events are expected in a CF$_4$ detector operating
at a pressure of 30 torr with an exposure of approximately 26,000
cubic-meter-detector days for WIMPs of 100 GeV mass and a WIMP-Fluorine
scattering cross-section of 0.25 pb. Comparable exposures are within reach of
an array of cubic meter time projection chamber detectors. | [
0,
1,
0,
0,
0,
0
] |
Title: Layers and Matroids for the Traveling Salesman's Paths,
Abstract: Gottschalk and Vygen proved that every solution of the subtour elimination
linear program for traveling salesman paths is a convex combination of more and
more restrictive "generalized Gao-trees". We give a short proof of this fact,
as a layered convex combination of bases of a sequence of increasingly
restrictive matroids. A strongly polynomial, combinatorial algorithm follows
for finding this convex combination, which is a new tool offering polyhedral
insight, already instrumental in recent results for the $s-t$ path TSP. | [
1,
0,
0,
0,
0,
0
] |
Title: Understanding and Comparing Deep Neural Networks for Age and Gender Classification,
Abstract: Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition. | [
1,
0,
0,
1,
0,
0
] |
Title: Variational discretization of a control-constrained parabolic bang-bang optimal control problem,
Abstract: We consider a control-constrained parabolic optimal control problem without
Tikhonov term in the tracking functional. For the numerical treatment, we use
variational discretization of its Tikhonov regularization: For the state and
the adjoint equation, we apply Petrov-Galerkin schemes from [Daniels et al
2015] in time and usual conforming finite elements in space. We prove a-priori
estimates for the error between the discretized regularized problem and the
limit problem. Since these estimates are not robust if the regularization
parameter tends to zero, we establish robust estimates, which --- depending on
the problem's regularity --- enhance the previous ones. In the special case of
bang-bang solutions, these estimates are further improved. A numerical example
confirms our analytical findings. | [
0,
0,
1,
0,
0,
0
] |
Title: Uncertainty quantification in graph-based classification of high dimensional data,
Abstract: Classification of high dimensional data finds wide-ranging applications. In
many of these applications equipping the resulting classification with a
measure of uncertainty may be as important as the classification itself. In
this paper we introduce, develop algorithms for, and investigate the properties
of, a variety of Bayesian models for the task of binary classification; via the
posterior distribution on the classification labels, these methods
automatically give measures of uncertainty. The methods are all based around
the graph formulation of semi-supervised learning.
We provide a unified framework which brings together a variety of methods
which have been introduced in different communities within the mathematical
sciences. We study probit classification in the graph-based setting, generalize
the level-set method for Bayesian inverse problems to the classification
setting, and generalize the Ginzburg-Landau optimization-based classifier to a
Bayesian setting; we also show that the probit and level set approaches are
natural relaxations of the harmonic function approach introduced in [Zhu et al
2003].
We introduce efficient numerical methods, suited to large data-sets, for both
MCMC-based sampling as well as gradient-based MAP estimation. Through numerical
experiments we study classification accuracy and uncertainty quantification for
our models; these experiments showcase a suite of datasets commonly used to
evaluate graph-based semi-supervised learning algorithms. | [
1,
0,
0,
1,
0,
0
] |
Title: Defect-induced large spin-orbit splitting in the monolayer of PtSe$_2$,
Abstract: The effect of spin-orbit coupling (SOC) on the electronic properties of
monolayer (ML) PtSe$_2$ is dictated by the presence of the crystal inversion
symmetry to exhibit spin polarized band without characteristic of spin
splitting. Through fully-relativistic density-functional theory calculations,
we show that large spin-orbit splitting can be induced by introducing point
defects. We calculate stability of native point defects such as a Se vacancy
(V$_{\texttt{Se}}$), a Se interstitial (Se$_{i}$), a Pt vacancy
(V$_{\texttt{Pt}}$), and a Pt interstitial (Pt$_{i}$), and find that both the
V$_{\texttt{Se}}$ and Se$_{i}$ have the lowest formation energy. We also find
that in contrast to the Se$_{i}$ case exhibiting spin degeneracy in the defect
states, the large spin-orbit splitting up to 152 meV is observed in the defect
states of the V$_{\texttt{Se}}$. Our analyses of orbital contributions to the
defect states show that the large spin splitting is originated from the strong
hybridization between Pt-$d_{x{^2}+y{^2}}+d_{xy}$ and Se-$p_{x}+p_{y}$
orbitals. Our study clarifies that the defects play an important role in the
spin splitting properties of the PtSe$_2$ ML, which is important for designing
future spintronic devices. | [
0,
1,
0,
0,
0,
0
] |
Title: Clusters of Integers with Equal Total Stopping Times in the 3x + 1 Problem,
Abstract: The clustering of integers with equal total stopping times has long been
observed in the 3x + 1 Problem, and a number of elementary results about it
have been used repeatedly in the literature. In this paper we introduce a
simple recursively defined function C(n), and we use it to give a necessary and
sufficient condition for pairs of consecutive even and odd integers to have
trajectories which coincide after a specific pair-dependent number of steps.
Then we derive a number of standard total stopping time equalities, including
the ones in Garner (1985), as well as several novel results. | [
0,
0,
1,
0,
0,
0
] |
Title: Modelling the Milky Way's globular cluster system,
Abstract: We construct a model for the Galactic globular cluster system based on a
realistic gravitational potential and a distribution function (DF) analytic in
the action integrals. The DF comprises disc and halo components whose
functional forms resemble those recently used to describe the stellar discs and
stellar halo. We determine the posterior distribution of our model parameters
using a Bayesian approach. This gives us an understanding of how well the
globular cluster data constrain our model. The favoured parameter values of the
disc and halo DFs are similar to values previously obtained from fits to the
stellar disc and halo, although the cluster halo system shows clearer rotation
than does the stellar halo. Our model reproduces the generic features of the
globular cluster system, namely the density profile, the mean rotation
velocity. The fraction of disc clusters coincides with the observed fraction of
metal-rich clusters. However, the data indicate either incompatibility between
catalogued cluster distances and current estimates of distance to the Galactic
Centre, or failure to identify clusters behind the bulge. As the data for our
Galaxy's components increase in volume and precision over the next few years,
it will be rewarding to revisit the present analysis. | [
0,
1,
0,
0,
0,
0
] |
Title: Diversity-aware Multi-Video Summarization,
Abstract: Most video summarization approaches have focused on extracting a summary from
a single video; we propose an unsupervised framework for summarizing a
collection of videos. We observe that each video in the collection may contain
some information that other videos do not have, and thus exploring the
underlying complementarity could be beneficial in creating a diverse
informative summary. We develop a novel diversity-aware sparse optimization
method for multi-video summarization by exploring the complementarity within
the videos. Our approach extracts a multi-video summary which is both
interesting and representative in describing the whole video collection. To
efficiently solve our optimization problem, we develop an alternating
minimization algorithm that minimizes the overall objective function with
respect to one video at a time while fixing the other videos. Moreover, we
introduce a new benchmark dataset, Tour20, that contains 140 videos with
multiple human created summaries, which were acquired in a controlled
experiment. Finally, by extensive experiments on the new Tour20 dataset and
several other multi-view datasets, we show that the proposed approach clearly
outperforms the state-of-the-art methods on the two problems-topic-oriented
video summarization and multi-view video summarization in a camera network. | [
1,
0,
0,
0,
0,
0
] |
Title: Derivation relations and duality for the sum of multiple zeta values,
Abstract: We show that the duality relation for the sum of multiple zeta values with
fixed weight, depth and $k_1$ is deduced from the derivation relations, which
was first conjectured by N. Kawasaki and T. Tanaka. | [
0,
0,
1,
0,
0,
0
] |
Title: Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field,
Abstract: Statistical pattern recognition methods have provided competitive solutions
for variable star classification at a relatively low computational cost. In
order to perform supervised classification, a set of features is proposed and
used to train an automatic classification system. Quantities related to the
magnitude density of the light curves and their Fourier coefficients have been
chosen as features in previous studies. However, some of these features are not
robust to the presence of outliers and the calculation of Fourier coefficients
is computationally expensive for large data sets. We propose and evaluate the
performance of a new robust set of features using supervised classifiers in
order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic
pole field. We calculated the proposed set of features on six types of variable
stars and on a set of Be star candidates reported in the literature. We
evaluated the performance of these features using classification trees and
random forests along with K-nearest neighbours, support vector machines, and
gradient boosted trees methods. We tuned the classifiers with a 10-fold
cross-validation and grid search. We validated the performance of the best
classifier on a set of OGLE-IV light curves and applied this to find new Be
star candidates. The random forest classifier outperformed the others. By using
the random forest classifier and colour criteria we found 50 Be star candidates
in the direction of the Gaia south ecliptic pole field, four of which have
infrared colours consistent with Herbig Ae/Be stars. Supervised methods are
very useful in order to obtain preliminary samples of variable stars extracted
from large databases. As usual, the stars classified as Be stars candidates
must be checked for the colours and spectroscopic characteristics expected for
them. | [
0,
1,
0,
0,
0,
0
] |
Title: Infinite-Dimensionality in Quantum Foundations: W*-algebras as Presheaves over Matrix Algebras,
Abstract: In this paper, W*-algebras are presented as canonical colimits of diagrams of
matrix algebras and completely positive maps. In other words, matrix algebras
are dense in W*-algebras. | [
1,
0,
1,
0,
0,
0
] |
Title: MSE estimates for multitaper spectral estimation and off-grid compressive sensing,
Abstract: We obtain estimates for the Mean Squared Error (MSE) for the multitaper
spectral estimator and certain compressive acquisition methods for multi-band
signals. We confirm a fact discovered by Thomson [Spectrum estimation and
harmonic analysis, Proc. IEEE, 1982]: assuming bandwidth $W$ and $N$ time
domain observations, the average of the square of the first $K=2NW$ Slepian
functions approaches, as $K$ grows, an ideal band-pass kernel for the interval
$[-W,W]$. We provide an analytic proof of this fact and measure the
corresponding rate of convergence in the $L^{1}$ norm. This validates a
heuristic approximation used to control the MSE of the multitaper estimator.
The estimates have also consequences for the method of compressive acquisition
of multi-band signals introduced by Davenport and Wakin, giving MSE
approximation bounds for the dictionary formed by modulation of the critical
number of prolates. | [
1,
0,
1,
1,
0,
0
] |
Title: Communication-Free Parallel Supervised Topic Models,
Abstract: Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC)
methods are commonly used in learning graphical models. However, MCMC cannot be
directly applied in learning topic models because of the quasi-ergodicity
problem caused by multimodal distribution of topics. In this paper, we develop
an embarrassingly parallel MCMC algorithm for sLDA. Our algorithm works by
switching the order of sampled topics combination and labeling variable
prediction in sLDA, in which it overcomes the quasi-ergodicity problem because
high-dimension topics that follow a multimodal distribution are projected into
one-dimension document labels that follow a unimodal distribution. Our
empirical experiments confirm that the out-of-sample prediction performance
using our embarrassingly parallel algorithm is comparable to non-parallel sLDA
while the computation time is significantly reduced. | [
1,
0,
0,
1,
0,
0
] |
Title: On the Performance of Millimeter Wave-based RF-FSO Multi-hop and Mesh Networks,
Abstract: This paper studies the performance of multi-hop and mesh networks composed of
millimeter wave (MMW)-based radio frequency (RF) and free-space optical (FSO)
links. The results are obtained in cases with and without hybrid automatic
repeat request (HARQ). Taking the MMW characteristics of the RF links into
account, we derive closed-form expressions for the networks' outage probability
and ergodic achievable rates. We also evaluate the effect of various parameters
such as power amplifiers efficiency, number of antennas as well as different
coherence times of the RF and the FSO links on the system performance. Finally,
we determine the minimum number of the transmit antennas in the RF link such
that the same rate is supported in the RF- and the FSO-based hops. The results
show the efficiency of the RF-FSO setups in different conditions. Moreover,
HARQ can effectively improve the outage probability/energy efficiency, and
compensate for the effect of hardware impairments in RF-FSO networks. For
common parameter settings of the RF-FSO dual-hop networks, outage probability
of 10^{-4} and code rate of 3 nats-per-channel-use, the implementation of HARQ
with a maximum of 2 and 3 retransmissions reduces the required power, compared
to cases with open-loop communication, by 13 and 17 dB, respectively. | [
1,
0,
0,
0,
0,
0
] |
Title: Pi Visits Manhattan,
Abstract: Is it possible to draw a circle in Manhattan, using only its discrete network
of streets and boulevards? In this study, we will explore the construction and
properties of circular paths on an integer lattice, a discrete space where the
distance between two points is not governed by the familiar Euclidean metric,
but the Manhattan or taxicab distance, a metric linear in its coordinates. In
order to achieve consistency with the continuous ideal, we need to abandon
Euclid's very original definition of the circle in favour of a parametric
construction. Somewhat unexpectedly, we find that the Euclidean circle's
defining constant $\pi$ can be recovered in such a discrete setting. | [
0,
0,
1,
0,
0,
0
] |
Title: A Bernstein Inequality For Exponentially Growing Graphs,
Abstract: In this article we present a Bernstein inequality for sums of random
variables which are defined on a graphical network whose nodes grow at an
exponential rate. The inequality can be used to derive concentration
inequalities in highly-connected networks. It can be useful to obtain
consistency properties for nonparametric estimators of conditional expectation
functions which are derived from such networks. | [
0,
0,
1,
1,
0,
0
] |
Title: Monolithic InGaAs nanowire array lasers on silicon-on-insulator operating at room temperature,
Abstract: Chip-scale integrated light sources are a crucial component in a broad range
of photonics applications. III-V semiconductor nanowire emitters have gained
attention as a fascinating approach due to their superior material properties,
extremely compact size, and the capability to grow directly on
lattice-mismatched silicon substrates. Although there have been remarkable
advances in nanowire-based emitters, their practical applications are still in
the early stages due to the difficulties in integrating nanowire emitters with
photonic integrated circuits (PICs). Here, we demonstrate for the first time
optically pumped III-V nanowire array lasers monolithically integrated on
silicon-on-insulator (SOI) platform. Selective-area growth of purely
single-crystalline InGaAs/InGaP core/shell nanowires on an SOI substrate
enables the nanowire array to form a photonic crystal nanobeam cavity with
superior optical and structural properties, resulting in the laser to operate
at room temperature. We also show that the nanowire array lasers are
effectively coupled with SOI waveguides by employing nanoepitaxy on a
pre-patterned SOI platform. These results represent a new platform for
ultra-compact and energy-efficient optical links, and unambiguously point the
way toward practical and functional nanowire lasers. | [
0,
1,
0,
0,
0,
0
] |
Title: DMFT study on the electron-hole asymmetry of the electron correlation strength in the high Tc cuprates,
Abstract: Recent experiments revealed a striking asymmetry in the phase diagram of the
high temperature cuprate superconductors. The correlation effect seems strong
in the hole-doped systems and weak in the electron-doped systems. On the other
hand, a recent theoretical study shows that the interaction strengths (the
Hubbard U) are comparable in these systems. Therefore, it is difficult to
explain this asymmetry by their interaction strengths. Given this background,
we analyze the one-particle spectrum of a single band model of a cuprate
superconductor near the Fermi level using the dynamical mean field theory. We
find the difference in the "visibility" of the strong correlation effect
between the hole- and electron-doped systems. This can explain the
electron-hole asymmetry of the correlation strength without introducing the
difference in the interaction strength. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines,
Abstract: By establishing a connection between bi-directional Helmholtz machines and
information theory, we propose a generalized Helmholtz machine. Theoretical and
experimental results show that given \textit{shallow} architectures, the
generalized model outperforms the previous ones substantially. | [
0,
0,
0,
1,
0,
0
] |
Title: KMS states on the C*-algebras of Fell bundles over groupoids,
Abstract: We consider fiberwise singly generated Fell-bundles over etale groupoids.
Given a continuous real-valued 1-cocycle on the groupoid, there is a natural
dynamics on the cross-sectional algebra of the Fell bundle. We study the
Kubo-Martin-Schwinger equilibrium states for this dynamics. Following work of
Neshveyev on equilibrium states on groupoid C*-algebras, we describe the
equilibrium states of the cross-sectional algebra in terms of measurable fields
of traces on the C*-algebras of the restrictions of the Fell bundle to the
isotropy subgroups of the groupoid. As a special case, we obtain a description
of the trace space of the cross-sectional algebra. We apply our result to
generalise Neshveyev's main theorem to twisted groupoid C*-algebras, and then
apply this to twisted C*-algebras of strongly connected finite k-graphs. | [
0,
0,
1,
0,
0,
0
] |
Title: Breaking the 3/2 barrier for unit distances in three dimensions,
Abstract: We prove that every set of $n$ points in $\mathbb{R}^3$ spans
$O(n^{295/197+\epsilon})$ unit distances. This is an improvement over the
previous bound of $O(n^{3/2})$. A key ingredient in the proof is a new result
for cutting circles in $\mathbb{R}^3$ into pseudo-segments. | [
1,
0,
1,
0,
0,
0
] |
Title: Periodic orbits of planets in binary systems,
Abstract: Periodic solutions of the three body problem are very important for
understanding its dynamics either in a theoretical framework or in various
applications in celestial mechanics. In this paper we discuss the computation
and continuation of periodic orbits for planetary systems. The study is
restricted to coplanar motion. Staring from known results of two-planet systems
around single stars, we perform continuation of solutions with respect to the
mass and approach periodic orbits of single planets in two-star systems. Also,
families of periodic solutions can be computed for fixed masses of the
primaries. When they are linearly stable, we can conclude about the existence
of phase space domains of long-term orbital stability. | [
0,
1,
0,
0,
0,
0
] |
Title: Review of image quality measures for solar imaging,
Abstract: The observations of solar photosphere from the ground encounter significant
problems due to the presence of Earth's turbulent atmosphere. Prior to applying
image reconstruction techniques, the frames obtained in most favorable
atmospheric conditions (so-called lucky frames) have to be carefully selected.
However, the estimation of the quality of images containing complex
photospheric structures is not a trivial task and the standard routines applied
in night-time Lucky Imaging observations are not applicable. In this paper we
evaluate 36 methods dedicated for the assessment of image quality which were
presented in the rich literature over last 40 years. We compare their
effectiveness on simulated solar observations of both active regions and
granulation patches, using reference data obtained by the Solar Optical
Telescope on the Hindoe satellite. To create the images affected by a known
degree of atmospheric degradation, we employ the Random Wave Vector method
which faithfully models all the seeing characteristics. The results provide
useful information about the methods performance depending on the average
seeing conditions expressed by the ratio of the telescope's aperture to the
Fried parameter, $D/r_0$. The comparison identifies three methods for
consideration by observers: Helmli and Scherer's Mean, Median Filter Gradient
Similarity, and Discrete Cosine Transform Energy Ratio. While the first one
requires less computational effort and can be used effectively virtually in any
atmospherics conditions, the second one shows its superiority at good seeing
($D/r_0<4$). The last one should be considered mainly for the post-processing
of strongly blurred images. | [
0,
1,
0,
0,
0,
0
] |
Title: Joint Maximum Likelihood Estimation for High-dimensional Exploratory Item Response Analysis,
Abstract: Multidimensional item response theory is widely used in education and
psychology for measuring multiple latent traits. However, exploratory analysis
of large-scale item response data with many items, respondents, and latent
traits is still a challenge. In this paper, we consider a high-dimensional
setting that both the number of items and the number of respondents grow to
infinity. A constrained joint maximum likelihood estimator is proposed for
estimating both item and person parameters, which yields good theoretical
properties and computational advantage. Specifically, we derive error bounds
for parameter estimation and develop an efficient algorithm that can scale to
very large datasets. The proposed method is applied to a large scale
personality assessment data set from the Synthetic Aperture Personality
Assessment (SAPA) project. Simulation studies are conducted to evaluate the
proposed method. | [
0,
0,
0,
1,
0,
0
] |
Title: Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox,
Abstract: Statistical learning using imprecise probabilities is gaining more attention
because it presents an alternative strategy for reducing irreplicable findings
by freeing the user from the task of making up unwarranted high-resolution
assumptions. However, model updating as a mathematical operation is inherently
exact, hence updating imprecise models requires the user's judgment in choosing
among competing updating rules. These rules often lead to incompatible
inferences, and can exhibit unsettling phenomena like dilation, contraction and
sure loss, which cannot occur with the Bayes rule and precise probabilities. We
revisit a number of famous "paradoxes", including the three prisoners/Monty
Hall problem, revealing that a logical fallacy arises from a set of marginally
plausible yet jointly incommensurable assumptions when updating the underlying
imprecise model. We establish an equivalence between Simpson's paradox and an
implicit adoption of a pair of aggregation rules that induce sure loss. We also
explore behavioral discrepancies between the generalized Bayes rule, Dempster's
rule and the Geometric rule as alternative posterior updating rules for Choquet
capacities of order 2. We show that both the generalized Bayes rule and
Geometric rule are incapable of updating without prior information regardless
of how strong the information in our data is, and that Dempster's rule and the
Geometric rule can mathematically contradict each other with respect to
dilation and contraction. Our findings show that unsettling updates reflect a
collision between the rules' assumptions and the inexactness allowed by the
model itself, highlighting the invaluable role of judicious judgment in
handling low-resolution information, and the care we must take when applying
learning rules to update imprecise probabilities. | [
0,
0,
1,
1,
0,
0
] |
Title: Planar segment processes with reference mark distributions, modeling and estimation,
Abstract: The paper deals with planar segment processes given by a density with respect
to the Poisson process. Parametric models involve reference distributions of
directions and/or lengths of segments. These distributions generally do not
coincide with the corresponding observed distributions. Statistical methods are
presented which first estimate scalar parameters by known approaches and then
the reference distribution is estimated non-parametrically. Besides a general
theory we offer two models, first a Gibbs type segment process with reference
directional distribution and secondly an inhomogeneous process with reference
length distribution. The estimation is demonstrated in simulation studies where
the variability of estimators is presented graphically. | [
0,
0,
1,
1,
0,
0
] |
Title: A Note on Bayesian Model Selection for Discrete Data Using Proper Scoring Rules,
Abstract: We consider the problem of choosing between parametric models for a discrete
observable, taking a Bayesian approach in which the within-model prior
distributions are allowed to be improper. In order to avoid the ambiguity in
the marginal likelihood function in such a case, we apply a homogeneous scoring
rule. For the particular case of distinguishing between Poisson and Negative
Binomial models, we conduct simulations that indicate that, applied
prequentially, the method will consistently select the true model. | [
0,
0,
1,
1,
0,
0
] |
Title: Quantum criticality in many-body parafermion chains,
Abstract: We construct local generalizations of 3-state Potts models with exotic
critical points. We analytically show that these are described by non-diagonal
modular invariant partition functions of products of $Z_3$ parafermion or
$u(1)_6$ conformal field theories (CFTs). These correspond either to
non-trivial permutation invariants or block diagonal invariants, that one can
understand in terms of anyon condensation. In terms of lattice parafermion
operators, the constructed models correspond to parafermion chains with
many-body terms. Our construction is based on how the partition function of a
CFT depends on symmetry sectors and boundary conditions. This enables to write
the partition function corresponding to one modular invariant as a linear
combination of another over different sectors and boundary conditions, which
translates to a general recipe how to write down a microscopic model, tuned to
criticality. We show that the scheme can also be extended to construct critical
generalizations of $k$-state Potts models. | [
0,
1,
0,
0,
0,
0
] |
Title: Simulation-based reachability analysis for nonlinear systems using componentwise contraction properties,
Abstract: A shortcoming of existing reachability approaches for nonlinear systems is
the poor scalability with the number of continuous state variables. To mitigate
this problem we present a simulation-based approach where we first sample a
number of trajectories of the system and next establish bounds on the
convergence or divergence between the samples and neighboring trajectories. We
compute these bounds using contraction theory and reduce the conservatism by
partitioning the state vector into several components and analyzing contraction
properties separately in each direction. Among other benefits this allows us to
analyze the effect of constant but uncertain parameters by treating them as
state variables and partitioning them into a separate direction. We next
present a numerical procedure to search for weighted norms that yield a
prescribed contraction rate, which can be incorporated in the reachability
algorithm to adjust the weights to minimize the growth of the reachable set. | [
1,
0,
0,
0,
0,
0
] |
Title: User Donations in a Crowdsourced Video System,
Abstract: Crowdsourced video systems like YouTube and Twitch.tv have been a major
internet phenomenon and are nowadays entertaining over a billion users. In
addition to video sharing and viewing, over the years they have developed new
features to boost the community engagement and some managed to attract users to
donate, to the community as well as to other users. User donation directly
reflects and influences user engagement in the community, and has a great
impact on the success of such systems. Nevertheless, user donations in
crowdsourced video systems remain trade secrets for most companies and to date
are still unexplored. In this work, we attempt to fill this gap, and we obtain
and provide a publicly available dataset on user donations in one crowdsourced
video system named BiliBili. Based on information on nearly 40 thousand
donators, we examine the dynamics of user donations and their social
relationships, we quantitively reveal the factors that potentially impact user
donation, and we adopt machine-learned classifiers and network representation
learning models to timely and accurately predict the destinations of the
majority and the individual donations. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynein catch bond as a mediator of codependent bidirectional cellular transport,
Abstract: Intracellular bidirectional transport of cargo on Microtubule filaments is
achieved by the collective action of oppositely directed dynein and kinesin
motors. Experimental investigations probing the nature of bidirectional
transport have found that in certain cases, inhibiting the activity of one type
of motor results in an overall decline in the motility of the cellular cargo in
both directions. This somewhat counter-intuitive observation, referred to as
paradox of codependence is inconsistent with the existing paradigm of a
mechanistic tug-of-war between oppositely directed motors. Existing theoretical
models do not take into account a key difference in the functionality of
kinesin and dynein. Unlike kinesin, dynein motors exhibit catchbonding, wherein
the unbinding rates of these motors from the filaments are seen to decrease
with increasing force on them. Incorporating this catchbonding behavior of
dynein in a theoretical model and using experimentally relevant measures
characterizing cargo transport, we show that the functional divergence of the
two motors species manifests itself as an internal regulatory mechanism for
bidirectional transport and resolves the paradox of codependence. Our model
reproduces the key experimental features in appropriate parameter regimes and
provides an unifying framework for bidirectional cargo transport. | [
0,
0,
0,
0,
1,
0
] |
Title: Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked Eigenvalue of Sample Covariance Matrices,
Abstract: We study the asymptotic distributions of the spiked eigenvalues and the
largest nonspiked eigenvalue of the sample covariance matrix under a general
covariance matrix model with divergent spiked eigenvalues, while the other
eigenvalues are bounded but otherwise arbitrary. The limiting normal
distribution for the spiked sample eigenvalues is established. It has distinct
features that the asymptotic mean relies on not only the population spikes but
also the nonspikes and that the asymptotic variance in general depends on the
population eigenvectors. In addition, the limiting Tracy-Widom law for the
largest nonspiked sample eigenvalue is obtained.
Estimation of the number of spikes and the convergence of the leading
eigenvectors are also considered. The results hold even when the number of the
spikes diverges. As a key technical tool, we develop a Central Limit Theorem
for a type of random quadratic forms where the random vectors and random
matrices involved are dependent. This result can be of independent interest. | [
0,
0,
1,
1,
0,
0
] |
Title: Nonparametric Poisson regression from independent and weakly dependent observations by model selection,
Abstract: We consider the non-parametric Poisson regression problem where the integer
valued response $Y$ is the realization of a Poisson random variable with
parameter $\lambda(X)$. The aim is to estimate the functional parameter
$\lambda$ from independent or weakly dependent observations
$(X_1,Y_1),\ldots,(X_n,Y_n)$ in a random design framework.
First we determine upper risk bounds for projection estimators on finite
dimensional subspaces under mild conditions. In the case of Sobolev ellipsoids
the obtained rates of convergence turn out to be optimal.
The main part of the paper is devoted to the construction of adaptive
projection estimators of $\lambda$ via model selection. We proceed in two
steps: first, we assume that an upper bound for $\Vert \lambda \Vert_\infty$ is
known. Under this assumption, we construct an adaptive estimator whose
dimension parameter is defined as the minimizer of a penalized contrast
criterion. Second, we replace the known upper bound on $\Vert \lambda
\Vert_\infty$ by an appropriate plug-in estimator of $\Vert \lambda
\Vert_\infty$. The resulting adaptive estimator is shown to attain the minimax
optimal rate up to an additional logarithmic factor both in the independent and
the weakly dependent setup. Appropriate concentration inequalities for Poisson
point processes turn out to be an important ingredient of the proofs.
We illustrate our theoretical findings by a short simulation study and
conclude by indicating directions of future research. | [
0,
0,
1,
1,
0,
0
] |
Title: Ontology-Aware Token Embeddings for Prepositional Phrase Attachment,
Abstract: Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distribution over relevant semantic concepts. We use the new,
context-sensitive embeddings in a model for predicting prepositional phrase(PP)
attachments and jointly learn the concept embeddings and model parameters. We
show that using context-sensitive embeddings improves the accuracy of the PP
attachment model by 5.4% absolute points, which amounts to a 34.4% relative
reduction in errors. | [
1,
0,
0,
0,
0,
0
] |
Title: Bayesian Detection of Abnormal ADS in Mutant Caenorhabditis elegans Embryos,
Abstract: Cell division timing is critical for cell fate specification and
morphogenesis during embryogenesis. How division timings are regulated among
cells during development is poorly understood. Here we focus on the comparison
of asynchrony of division between sister cells (ADS) between wild-type and
mutant individuals of Caenorhabditis elegans. Since the replicate number of
mutant individuals of each mutated gene, usually one, is far smaller than that
of wild-type, direct comparison of two distributions of ADS between wild-type
and mutant type, such as Kolmogorov- Smirnov test, is not feasible. On the
other hand, we find that sometimes ADS is correlated with the life span of
corresponding mother cell in wild-type. Hence, we apply a semiparametric
Bayesian quantile regression method to estimate the 95% confidence interval
curve of ADS with respect to life span of mother cell of wild-type individuals.
Then, mutant-type ADSs outside the corresponding confidence interval are
selected out as abnormal one with a significance level of 0.05. Simulation
study demonstrates the accuracy of our method and Gene Enrichment Analysis
validates the results of real data sets. | [
0,
0,
0,
1,
1,
0
] |
Title: Towards a constraint solver for proving confluence with invariant and equivalence of realistic CHR programs,
Abstract: Confluence of a nondeterministic program ensures a functional input-output
relation, freeing the programmer from considering the actual scheduling
strategy, and allowing optimized and perhaps parallel implementations. The more
general property of confluence modulo equivalence ensures that equivalent
inputs are related to equivalent outputs, that need not be identical.
Confluence under invariants is also considered. Constraint Handling Rules (CHR)
is an important example of a rewrite based logic programming language, and we
aim at a mechanizable method for proving confluence modulo equivalence of
terminating programs. While earlier approaches to confluence for CHR programs
concern an idealized logic subset, we refer to a semantics compatible with
standard Prolog-based implementations. We specify a meta-level constraint
language in which invariants and equivalences can be expressed and manipulated,
extending our previous theoretical results towards a practical implementation. | [
1,
0,
0,
0,
0,
0
] |
Title: cGANs with Projection Discriminator,
Abstract: We propose a novel, projection based way to incorporate the conditional
information into the discriminator of GANs that respects the role of the
conditional information in the underlining probabilistic model. This approach
is in contrast with most frameworks of conditional GANs used in application
today, which use the conditional information by concatenating the (embedded)
conditional vector to the feature vectors. With this modification, we were able
to significantly improve the quality of the class conditional image generation
on ILSVRC2012 (ImageNet) 1000-class image dataset from the current
state-of-the-art result, and we achieved this with a single pair of a
discriminator and a generator. We were also able to extend the application to
super-resolution and succeeded in producing highly discriminative
super-resolution images. This new structure also enabled high quality category
transformation based on parametric functional transformation of conditional
batch normalization layers in the generator. | [
0,
0,
0,
1,
0,
0
] |
Title: The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios,
Abstract: The QLBS model is a discrete-time option hedging and pricing model that is
based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines
the famous Q-Learning method for RL with the Black-Scholes (-Merton) model's
idea of reducing the problem of option pricing and hedging to the problem of
optimal rebalancing of a dynamic replicating portfolio for the option, which is
made of a stock and cash. Here we expand on several NuQLear (Numerical
Q-Learning) topics with the QLBS model. First, we investigate the performance
of Fitted Q Iteration for a RL (data-driven) solution to the model, and
benchmark it versus a DP (model-based) solution, as well as versus the BSM
model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for
the model, where we only observe prices and actions (re-hedges) taken by a
trader, but not rewards. Third, we outline how the QLBS model can be used for
pricing portfolios of options, rather than a single option in isolation, thus
providing its own, data-driven and model independent solution to the (in)famous
volatility smile problem of the Black-Scholes model. | [
0,
0,
0,
0,
0,
1
] |
Title: InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity,
Abstract: We demonstrate an approach to face attribute detection that retains or
improves attribute detection accuracy across gender and race subgroups by
learning demographic information prior to learning the attribute detection
task. The system, which we call InclusiveFaceNet, detects face attributes by
transferring race and gender representations learned from a held-out dataset of
public race and gender identities. Leveraging learned demographic
representations while withholding demographic inference from the downstream
face attribute detection task preserves potential users' demographic privacy
while resulting in some of the best reported numbers to date on attribute
detection in the Faces of the World and CelebA datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Reputation is required for cooperation to emerge in dynamic networks,
Abstract: Melamed, Harrell, and Simpson have recently reported on an experiment which
appears to show that cooperation can arise in a dynamic network without
reputational knowledge, i.e., purely via dynamics [1]. We believe that their
experimental design is actually not testing this, in so far as players do know
the last action of their current partners before making a choice on their own
next action and subsequently deciding which link to cut. Had the authors given
no information at all, the result would be a decline in cooperation as shown in
[2]. | [
1,
0,
0,
0,
0,
0
] |
Title: Active Anomaly Detection via Ensembles,
Abstract: In critical applications of anomaly detection including computer security and
fraud prevention, the anomaly detector must be configurable by the analyst to
minimize the effort on false positives. One important way to configure the
anomaly detector is by providing true labels for a few instances. We study the
problem of label-efficient active learning to automatically tune anomaly
detection ensembles and make four main contributions. First, we present an
important insight into how anomaly detector ensembles are naturally suited for
active learning. This insight allows us to relate the greedy querying strategy
to uncertainty sampling, with implications for label-efficiency. Second, we
present a novel formalism called compact description to describe the discovered
anomalies and show that it can also be employed to improve the diversity of the
instances presented to the analyst without loss in the anomaly discovery rate.
Third, we present a novel data drift detection algorithm that not only detects
the drift robustly, but also allows us to take corrective actions to adapt the
detector in a principled manner. Fourth, we present extensive experiments to
evaluate our insights and algorithms in both batch and streaming settings. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup. | [
0,
0,
0,
1,
0,
0
] |
Title: Effect of Decreasing Cobalt Content on the Electrochemical Properties and Structural Stability of Li_(1-x)Ni_(y)Co_(z)Al_(0.05)O_(2) Type Cathode Materials,
Abstract: In Lithium ion batteries (LIBs), proper design of cathode materials
influences its intercalation behavior, overall cost, structural stability, and
its impact on environment. At present, the most common type of cathode
materials, NCA , has very high cobalt concentration. Since cobalt is toxic and
expensive, the existing design of cathode materials is not cost-effective, and
environmentally benign. However, these immensely important issues have not yet
been properly addressed. Therefore, we have performed density functional theory
(DFT) calculations to investigate three types of NCA cathode materials
NCA_(Co=0.15), NCA_(Co=0.10), NCA_(Co=0.05). Our results show that even if the
cobalt concentration is significantly decreased from NCA_(Co=0.15) to
NCA_(Co=0.05), variation in intercalation potential and specific capacity is
negligible. For example, in case of 50% Li concentration, voltage drop is
~0.12V while change in specific capacity is negligible. Moreover, decrease in
cobalt concentration doesn't influence the structural stability. We have also
explored the influence of sodium doping on the electrochemical and structural
properties of these three structures. Our results provide insight into the
design of cathode materials with reduced cobalt concentration, environmentally
benign, low-cost cathode materials. | [
0,
1,
0,
0,
0,
0
] |
Title: Dispersive estimates for massive Dirac operators in dimension two,
Abstract: We study the massive two dimensional Dirac operator with an electric
potential. In particular, we show that the $t^{-1}$ decay rate holds in the
$L^1\to L^\infty$ setting if the threshold energies are regular. We also show
these bounds hold in the presence of s-wave resonances at the threshold. We
further show that, if the threshold energies are regular that a faster decay
rate of $t^{-1}(\log t)^{-2}$ is attained for large $t$, at the cost of
logarithmic spatial weights. The free Dirac equation does not satisfy this
bound due to the s-wave resonances at the threshold energies. | [
0,
0,
1,
0,
0,
0
] |
Title: A novel agent-based simulation framework for sensing in complex adaptive environments,
Abstract: In this paper we present a novel Formal Agent-Based Simulation framework
(FABS). FABS uses formal specification as a means of clear description of
wireless sensor networks (WSN) sensing a Complex Adaptive Environment. This
specification model is then used to develop an agent-based model of both the
wireless sensor network as well as the environment. As proof of concept, we
demonstrate the application of FABS to a boids model of self-organized flocking
of animals monitored by a random deployment of proximity sensors. | [
1,
1,
0,
0,
0,
0
] |
Title: Transversal fluctuations of the ASEP, stochastic six vertex model, and Hall-Littlewood Gibbsian line ensembles,
Abstract: We consider the ASEP and the stochastic six vertex model started with step
initial data. After a long time, $T$, it is known that the one-point height
function fluctuations for these systems are of order $T^{1/3}$. We prove the
KPZ prediction of $T^{2/3}$ scaling in space. Namely, we prove tightness (and
Brownian absolute continuity of all subsequential limits) as $T$ goes to
infinity of the height function with spatial coordinate scaled by $T^{2/3}$ and
fluctuations scaled by $T^{1/3}$. The starting point for proving these results
is a connection discovered recently by Borodin-Bufetov-Wheeler between the
stochastic six vertex height function and the Hall-Littlewood process (a
certain measure on plane partitions). Interpreting this process as a line
ensemble with a Gibbsian resampling invariance, we show that the one-point
tightness of the top curve can be propagated to the tightness of the entire
curve. | [
0,
0,
1,
0,
0,
0
] |
Title: Instantons in self-organizing logic gates,
Abstract: Self-organizing logic is a recently-suggested framework that allows the
solution of Boolean truth tables "in reverse," i.e., it is able to satisfy the
logical proposition of gates regardless to which terminal(s) the truth value is
assigned ("terminal-agnostic logic"). It can be realized if time non-locality
(memory) is present. A practical realization of self-organizing logic gates
(SOLGs) can be done by combining circuit elements with and without memory. By
employing one such realization, we show, numerically, that SOLGs exploit
elementary instantons to reach equilibrium points. Instantons are classical
trajectories of the non-linear equations of motion describing SOLGs, and
connect topologically distinct critical points in the phase space. By linear
analysis at those points, we show that these instantons connect the initial
critical point of the dynamics, with at least one unstable direction, directly
to the final fixed point. We also show that the memory content of these gates
only affects the relaxation time to reach the logically consistent solution.
Finally, we demonstrate, by solving the corresponding stochastic differential
equations, that since instantons connect critical points, noise and
perturbations may change the instanton trajectory in the phase space, but not
the initial and final critical points. Therefore, even for extremely large
noise levels, the gates self-organize to the correct solution. Our work
provides a physical understanding of, and can serve as an inspiration for, new
models of bi-directional logic gates that are emerging as important tools in
physics-inspired, unconventional computing. | [
1,
0,
0,
0,
0,
0
] |
Title: Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments,
Abstract: A robot that can carry out a natural-language instruction has been a dream
since before the Jetsons cartoon series imagined a life of leisure mediated by
a fleet of attentive robot helpers. It is a dream that remains stubbornly
distant. However, recent advances in vision and language methods have made
incredible progress in closely related areas. This is significant because a
robot interpreting a natural-language navigation instruction on the basis of
what it sees is carrying out a vision and language process that is similar to
Visual Question Answering. Both tasks can be interpreted as visually grounded
sequence-to-sequence translation problems, and many of the same methods are
applicable. To enable and encourage the application of vision and language
methods to the problem of interpreting visually-grounded navigation
instructions, we present the Matterport3D Simulator -- a large-scale
reinforcement learning environment based on real imagery. Using this simulator,
which can in future support a range of embodied vision and language tasks, we
provide the first benchmark dataset for visually-grounded natural language
navigation in real buildings -- the Room-to-Room (R2R) dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Dissecting spin-phonon equilibration in ferrimagnetic insulators by ultrafast lattice excitation,
Abstract: To gain control over magnetic order on ultrafast time scales, a fundamental
understanding of the way electron spins interact with the surrounding crystal
lattice is required. However, measurement and analysis even of basic collective
processes such as spin-phonon equilibration have remained challenging. Here, we
directly probe the flow of energy and angular momentum in the model insulating
ferrimagnet yttrium iron garnet. Following ultrafast resonant lattice
excitation, we observe that magnetic order reduces on distinct time scales of 1
ps and 100 ns. Temperature-dependent measurements, a spin-coupling analysis and
simulations show that the two dynamics directly reflect two stages of
spin-lattice equilibration. On the 1-ps scale, spins and phonons reach
quasi-equilibrium in terms of energy through phonon-induced modulation of the
exchange interaction. This mechanism leads to identical demagnetization of the
ferrimagnet's two spin-sublattices and a novel ferrimagnetic state of increased
temperature yet unchanged total magnetization. Finally, on the much slower,
100-ns scale, the excess of spin angular momentum is released to the crystal
lattice, resulting in full equilibrium. Our findings are relevant for all
insulating ferrimagnets and indicate that spin manipulation by phonons,
including the spin Seebeck effect, can be extended to antiferromagnets and into
the terahertz frequency range. | [
0,
1,
0,
0,
0,
0
] |
Title: Inferring health conditions from fMRI-graph data,
Abstract: Automated classification methods for disease diagnosis are currently in the
limelight, especially for imaging data. Classification does not fully meet a
clinician's needs, however: in order to combine the results of multiple tests
and decide on a course of treatment, a clinician needs the likelihood of a
given health condition rather than binary classification yielded by such
methods. We illustrate how likelihoods can be derived step by step from first
principles and approximations, and how they can be assessed and selected,
illustrating our approach using fMRI data from a publicly available data set
containing schizophrenic and healthy control subjects. We start from the basic
assumption of partial exchangeability, and then the notion of sufficient
statistics and the "method of translation" (Edgeworth, 1898) combined with
conjugate priors. This method can be used to construct a likelihood that can be
used to compare different data-reduction algorithms. Despite the
simplifications and possibly unrealistic assumptions used to illustrate the
method, we obtain classification results comparable to previous, more realistic
studies about schizophrenia, whilst yielding likelihoods that can naturally be
combined with the results of other diagnostic tests. | [
0,
0,
0,
1,
1,
0
] |
Title: Rich-clubness test: how to determine whether a complex network has or doesn't have a rich-club?,
Abstract: The rich-club concept has been introduced in order to characterize the
presence of a cohort of nodes with a large number of links (rich nodes) that
tend to be well connected between each other, creating a tight group (club).
Rich-clubness defines the extent to which a network displays a topological
organization characterized by the presence of a node rich-club. It is crucial
for the investigation of internal organization and function of networks arising
in systems of disparate fields such as transportation, social, communication
and neuroscience. Different methods have been proposed for assessing the
rich-clubness and various null-models have been adopted for performing
statistical tests. However, a procedure that assigns a unique value of
rich-clubness significance to a given network is still missing. Our solution to
this problem grows on the basis of three new pillars. We introduce: i) a
null-model characterized by a lower rich-club coefficient; ii) a fair strategy
to normalize the level of rich-clubness of a network in respect to the
null-model; iii) a statistical test that, exploiting the maximum deviation of
the normalized rich-club coefficient attributes a unique p-value of
rich-clubness to a given network. In conclusion, this study proposes the first
attempt to quantify, using a unique measure, whether a network presents a
significant rich-club topological organization. The general impact of our study
on engineering and science is that simulations investigating how the functional
performance of a network is changing in relation to rich-clubness might be more
easily tuned controlling one unique value: the proposed rich-clubness measure. | [
1,
1,
0,
0,
0,
0
] |
Title: Structured Variational Inference for Coupled Gaussian Processes,
Abstract: Sparse variational approximations allow for principled and scalable inference
in Gaussian Process (GP) models. In settings where several GPs are part of the
generative model, theses GPs are a posteriori coupled. For many applications
such as regression where predictive accuracy is the quantity of interest, this
coupling is not crucial. Howewer if one is interested in posterior uncertainty,
it cannot be ignored. A key element of variational inference schemes is the
choice of the approximate posterior parameterization. When the number of latent
variables is large, mean field (MF) methods provide fast and accurate posterior
means while more structured posterior lead to inference algorithm of greater
computational complexity. Here, we extend previous sparse GP approximations and
propose a novel parameterization of variational posteriors in the multi-GP
setting allowing for fast and scalable inference capturing posterior
dependencies. | [
1,
0,
0,
1,
0,
0
] |
Title: Imaging anyons with scanning tunneling microscopy,
Abstract: Anyons are exotic quasi-particles with fractional charge that can emerge as
fundamental excitations of strongly interacting topological quantum phases of
matter. Unlike ordinary fermions and bosons, they may obey non-abelian
statistics--a property that would help realize fault tolerant quantum
computation. Non-abelian anyons have long been predicted to occur in the
fractional quantum Hall (FQH) phases that form in two-dimensional electron
gases (2DEG) in the presence of a large magnetic field, su ch as the
$\nu=\tfrac{5}{2}$ FQH state. However, direct experimental evidence of anyons
and tests that can distinguish between abelian and non-abelian quantum ground
states with such excitations have remained elusive. Here we propose a new
experimental approach to directly visualize the structure of interacting
electronic states of FQH states with the scanning tunneling microscope (STM).
Our theoretical calculations show how spectroscopy mapping with the STM near
individual impurity defects can be used to image fractional statistics in FQH
states, identifying unique signatures in such measurements that can distinguish
different proposed ground states. The presence of locally trapped anyons should
leave distinct signatures in STM spectroscopic maps, and enables a new approach
to directly detect - and perhaps ultimately manipulate - these exotic
quasi-particles. | [
0,
1,
0,
0,
0,
0
] |
Title: Proof of Correspondence between Keys and Encoding Maps in an Authentication Code,
Abstract: In a former paper the authors introduced two new systematic authentication
codes based on the Gray map over a Galois ring. In this paper, it is proved the
one-to-one onto correspondence between keys and encoding maps for the second
introduced authentication code. | [
1,
0,
1,
0,
0,
0
] |
Title: Following the density perturbations through a bounce with AdS/CFT Correspondence,
Abstract: A bounce universe model, known as the coupled-scalar-tachyon bounce (CSTB)
universe, has been shown to solve the Horizon, Flatness and Homogeneity
problems as well as the Big Bang Singularity problem. Furthermore a scale
invariant spectrum of primordial density perturbations generated from the phase
of pre-bounce contraction is shown to be stable against time evolution. In this
work we study the detailed dynamics of the bounce and its imprints on the scale
invariance of the spectrum. The dynamics of the gravitational interactions near
the bounce point may be strongly coupled as the spatial curvature becomes big.
There is no a prior reason to expect the spectral index of the primordial
perturbations of matter density can be preserved. By encoding the bounce
dynamics holographically onto the dynamics of dual Yang-Mills system while the
latter is weakly coupled, via the AdS/CFT correspondence, we can safely evolve
the spectrum of the cosmic perturbations with full control. In this way we can
compare the post-bounce spectrum with the pre-bounce one: in the CSTB model we
explicitly show that the spectrum of primordial density perturbations generated
in the contraction phase preserves its stability as well as scale invariance
throughout the bounce process. | [
0,
1,
0,
0,
0,
0
] |
Title: Privacy in Information-Rich Intelligent Infrastructure,
Abstract: Intelligent infrastructure will critically rely on the dense instrumentation
of sensors and actuators that constantly transmit streaming data to cloud-based
analytics for real-time monitoring. For example, driverless cars communicate
real-time location and other data to companies like Google, which aggregate
regional data in order to provide real-time traffic maps. Such traffic maps can
be extremely useful to the driver (for optimal travel routing), as well as to
city transportation administrators for real-time accident response that can
have an impact on traffic capacity. Intelligent infrastructure monitoring
compromises the privacy of drivers who continuously share their location to
cloud aggregators, with unpredictable consequences.
Without a framework for protecting the privacy of the driver's data, drivers
may be very conservative about sharing their data with cloud-based analytics
that will be responsible for adding the intelligence to intelligent
infrastructure. In the energy sector, the Smart Grid revolution relies
critically on real-time metering of energy supply and demand with very high
granularity. This is turn enables real-time demand response and creates a new
energy market that can incorporate unpredictable renewable energy sources while
ensuring grid stability and reliability. However, real-time streaming data
captured by smart meters contain a lot of private information, such as our home
activities or lack of, which can be easily inferred by anyone that has access
to the smart meter data, resulting not only in loss of privacy but potentially
also putting us at risk. | [
1,
0,
0,
0,
0,
0
] |
Title: Towards equation of state for a market: A thermodynamical paradigm of economics,
Abstract: Foundations of equilibrium thermodynamics are the equation of state (EoS) and
four postulated laws of thermodynamics. We use equilibrium thermodynamics
paradigms in constructing the EoS for microeconomics system that is a market.
This speculation is hoped to be first step towards whole pictures of
thermodynamical paradigm of economics. | [
0,
0,
0,
0,
0,
1
] |
Title: On the sub-Gaussianity of the Beta and Dirichlet distributions,
Abstract: We obtain the optimal proxy variance for the sub-Gaussianity of Beta
distribution, thus proving upper bounds recently conjectured by Elder (2016).
We provide different proof techniques for the symmetrical (around its mean)
case and the non-symmetrical case. The technique in the latter case relies on
studying the ordinary differential equation satisfied by the Beta
moment-generating function known as the confluent hypergeometric function. As a
consequence, we derive the optimal proxy variance for the Dirichlet
distribution, which is apparently a novel result. We also provide a new proof
of the optimal proxy variance for the Bernoulli distribution, and discuss in
this context the proxy variance relation to log-Sobolev inequalities and
transport inequalities. | [
0,
0,
1,
1,
0,
0
] |
Title: Chaos in three coupled rotators: From Anosov dynamics to hyperbolic attractors,
Abstract: Starting from Anosov chaotic dynamics of geodesic flow on a surface of
negative curvature, we develop and consider a number of self-oscillatory
systems including those with hinged mechanical coupling of three rotators and a
system of rotators interacting through a potential function. These results are
used to design an electronic circuit for generation of rough (structurally
stable) chaos. Results of numerical integration of the model equations of
different degree of accuracy are presented and discussed. Also, circuit
simulation of the electronic generator is provided using the NI Multisim
environment. Portraits of attractors, waveforms of generated oscillations,
Lyapunov exponents, and spectra are considered and found to be in good
correspondence for the dynamics on the attractive sets of the self-oscillatory
systems and for the original Anosov geodesic flow. The hyperbolic nature of the
dynamics is tested numerically using a criterion based on statistics of angles
of intersection of stable and unstable subspaces of the perturbation vectors at
a reference phase trajectory on the attractor. | [
0,
1,
0,
0,
0,
0
] |
Title: A novel analytical method for analysis of electromagnetic scattering from inhomogeneous spherical structures using duality principles,
Abstract: In this article, a novel analytical approach is presented for the analysis of
electromagnetic (EM) scattering from radially inhomogeneous spherical
structures (RISSs) based on the duality principle. According to the spherical
symmetry, similar angular dependencies in all the regions are considered using
spherical harmonics. To extract the radial dependency, the system of
differential equations of wave propagation toward the inhomogeneity direction
is equated with the dual planar ones. A general duality between electromagnetic
fields and parameters and scattering parameters of the two structures is
introduced. The validity of the proposed approach is verified through a
comprehensive example. The presented approach substitutes a complicated problem
in spherical coordinate to an easy, well posed, and a previously solved problem
in planar geometry. This approach is valid for all continuously varying
inhomogeneity profiles. One of the major advantages of the proposed method is
the capability of studying two general and applicable types of RISSs. As an
interesting application, a new class of lens antenna based on the physical
concept of the gradient refractive index material is introduced. The approach
is used to analyze the EM scattering from the structure and validate strong
performance of the lens. | [
0,
1,
0,
0,
0,
0
] |
Title: Causal Inference with Two Versions of Treatment,
Abstract: Causal effects are commonly defined as comparisons of the potential outcomes
under treatment and control, but this definition is threatened by the
possibility that the treatment or control condition is not well-defined,
existing instead in more than one version. A simple, widely applicable analysis
is proposed to address the possibility that the treatment or control condition
exists in two versions with two different treatment effects. This analysis
loses no power in the main comparison of treatment and control, provides
additional information about version effects, and controls the family-wise
error rate in several comparisons. The method is motivated and illustrated
using an on-going study of the possibility that repeated head trauma in high
school football causes an increase in risk of early on-set dementia. | [
0,
0,
0,
1,
0,
0
] |
Title: Characterization of Multi-scale Invariant Random Fields,
Abstract: Applying certain flexible geometric sampling of a multi-scale invariant (MSI)
field we provide a multi-dimensional multi-selfsimilar field which has a one to
one correspondence with such sampled MSI field. This sampling enables us to
characterize harmonic-like representation and spectral density function of the
sampled MSI field. Imposing Markov property for the MSI field, we find that the
covariance function and spectral density matrix of such sampled Markov MSI
field are characterized by the covariance functions of samples of the first
scale rectangle. We present an example of MSI field as two-dimensional simple
fractional Brownian motion. We consider a real data example of the
precipitation in some area of Brisbane in Australia for some special period. We
show that precipitation on this area has MSI property and estimate time
dependent scale and Hurst parameters of this MSI field in three dimension as
latitude, longitude and time. Our method enables one to predict precipitation
in time and place. | [
0,
0,
0,
1,
0,
0
] |
Title: Interpretable 3D Human Action Analysis with Temporal Convolutional Networks,
Abstract: The discriminative power of modern deep learning models for 3D human action
recognition is growing ever so potent. In conjunction with the recent
resurgence of 3D human action representation with 3D skeletons, the quality and
the pace of recent progress have been significant. However, the inner workings
of state-of-the-art learning based methods in 3D human action recognition still
remain mostly black-box. In this work, we propose to use a new class of models
known as Temporal Convolutional Neural Networks (TCN) for 3D human action
recognition. Compared to popular LSTM-based Recurrent Neural Network models,
given interpretable input such as 3D skeletons, TCN provides us a way to
explicitly learn readily interpretable spatio-temporal representations for 3D
human action recognition. We provide our strategy in re-designing the TCN with
interpretability in mind and how such characteristics of the model is leveraged
to construct a powerful 3D activity recognition method. Through this work, we
wish to take a step towards a spatio-temporal model that is easier to
understand, explain and interpret. The resulting model, Res-TCN, achieves
state-of-the-art results on the largest 3D human action recognition dataset,
NTU-RGBD. | [
1,
0,
0,
0,
0,
0
] |
Title: Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding,
Abstract: t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for
dimensionality reduction and visualization that has become widely popular in
recent years. Efficient implementations of t-SNE are available, but they scale
poorly to datasets with hundreds of thousands to millions of high dimensional
data-points. We present Fast Fourier Transform-accelerated Interpolation-based
t-SNE (FIt-SNE), which dramatically accelerates the computation of t-SNE. The
most time-consuming step of t-SNE is a convolution that we accelerate by
interpolating onto an equispaced grid and subsequently using the fast Fourier
transform to perform the convolution. We also optimize the computation of input
similarities in high dimensions using multi-threaded approximate nearest
neighbors. We further present a modification to t-SNE called "late
exaggeration," which allows for easier identification of clusters in t-SNE
embeddings. Finally, for datasets that cannot be loaded into the memory, we
present out-of-core randomized principal component analysis (oocPCA), so that
the top principal components of a dataset can be computed without ever fully
loading the matrix, hence allowing for t-SNE of large datasets to be computed
on resource-limited machines. | [
1,
0,
0,
1,
0,
0
] |
Title: An arithmetic site of Connes-Consani type for imaginary quadratic fields with class number 1,
Abstract: We construct, for imaginary quadratic number fields with class number 1, an
arithmetic site of Connes-Consani type. The main difficulty here is that the
constructions of Connes and Consani and part of their results strongly rely on
the natural order existing on real numbers which is compatible with basic
arithmetic operations. Of course nothing of this sort exists in the case of
imaginary quadratic number fields with class number 1. We first define what we
call arithmetic site for such number fields, we then calculate the points of
those arithmetic sites and we express them in terms of the adèles class space
considered by Connes to give a spectral interpretation of zeroes of Hecke L
functions of number fields. We get therefore that for a fixed imaginary
quadratic number field with class number 1, that the points of our arithmetic
site are related to the zeroes of the Dedekind zeta function of the number
field considered and to the zeroes of some Hecke L functions. We then study the
relation between the spectrum of the ring of integers of the number field and
the arithmetic site. Finally we construct the square of the arithmetic site. | [
0,
0,
1,
0,
0,
0
] |
Title: Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution,
Abstract: With the increased application of model-based whole-body control in legged
robots, there has been a resurgence of research interest into methods for
accurate system identification. An important class of methods focuses on the
inertial parameters of rigid-body systems. These parameters consist of the
mass, first mass moment (related to center of mass location), and rotational
inertia matrix of each link. The main contribution of this paper is to
formulate physical-consistency constraints on these parameters as Linear Matrix
Inequalities (LMIs). The use of these constraints in identification can
accelerate convergence and increase robustness to noisy data. It is critically
observed that the proposed LMIs are expressed in terms of the covariance of the
mass distribution, rather than its rotational moments of inertia. With this
perspective, connections to the classical problem of moments in mathematics are
shown to yield new bounding-volume constraints on the mass distribution of each
link. While previous work ensured physical plausibility or used convex
optimization in identification, the LMIs here uniquely enable both advantages.
Constraints are applied to identification of a leg for the MIT Cheetah 3 robot.
Detailed properties of transmission components are identified alongside link
inertias, with parameter optimization carried out to global optimality through
semidefinite programming. | [
1,
0,
0,
0,
0,
0
] |
Title: Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning,
Abstract: In this paper, we consider the block-sparse signals recovery problem in the
context of multiple measurement vectors (MMV) with common row sparsity
patterns. We develop a new method for recovery of common row sparsity MMV
signals, where a pattern-coupled hierarchical Gaussian prior model is
introduced to characterize both the block-sparsity of the coefficients and the
statistical dependency between neighboring coefficients of the common row
sparsity MMV signals. Unlike many other methods, the proposed method is able to
automatically capture the block sparse structure of the unknown signal. Our
method is developed using an expectation-maximization (EM) framework.
Simulation results show that our proposed method offers competitive performance
in recovering block-sparse common row sparsity pattern MMV signals. | [
1,
0,
0,
1,
0,
0
] |
Title: A temperate rocky super-Earth transiting a nearby cool star,
Abstract: M dwarf stars, which have masses less than 60 per cent that of the Sun, make
up 75 per cent of the population of the stars in the Galaxy [1]. The
atmospheres of orbiting Earth-sized planets are observationally accessible via
transmission spectroscopy when the planets pass in front of these stars [2,3].
Statistical results suggest that the nearest transiting Earth-sized planet in
the liquid-water, habitable zone of an M dwarf star is probably around 10.5
parsecs away [4]. A temperate planet has been discovered orbiting Proxima
Centauri, the closest M dwarf [5], but it probably does not transit and its
true mass is unknown. Seven Earth-sized planets transit the very low-mass star
TRAPPIST-1, which is 12 parsecs away [6,7], but their masses and, particularly,
their densities are poorly constrained. Here we report observations of LHS
1140b, a planet with a radius of 1.4 Earth radii transiting a small, cool star
(LHS 1140) 12 parsecs away. We measure the mass of the planet to be 6.6 times
that of Earth, consistent with a rocky bulk composition. LHS 1140b receives an
insolation of 0.46 times that of Earth, placing it within the liquid-water,
habitable zone [8]. With 90 per cent confidence, we place an upper limit on the
orbital eccentricity of 0.29. The circular orbit is unlikely to be the result
of tides and therefore was probably present at formation. Given its large
surface gravity and cool insolation, the planet may have retained its
atmosphere despite the greater luminosity (compared to the present-day) of its
host star in its youth [9,10]. Because LHS 1140 is nearby, telescopes currently
under construction might be able to search for specific atmospheric gases in
the future [2,3]. | [
0,
1,
0,
0,
0,
0
] |
Title: Boundary driven Brownian gas,
Abstract: We consider a gas of independent Brownian particles on a bounded interval in
contact with two particle reservoirs at the endpoints. Due to the Brownian
nature of the particles, infinitely many particles enter and leave the system
in each time interval. Nonetheless, the dynamics can be constructed as a Markov
process with continuous paths on a suitable space. If $\lambda_0$ and
$\lambda_1$ are the chemical potentials of the boundary reservoirs, the
stationary distribution (reversible if and only if $\lambda_0=\lambda_1$) is a
Poisson point process with intensity given by the linear interpolation between
$\lambda_0$ and $\lambda_1$. We then analyze the empirical flow that it is
defined by counting, in a time interval $[0,t]$, the net number of particles
crossing a given point $x$. In the stationary regime we identify its statistics
and show that it is given, apart an $x$ dependent correction that is bounded
for large $t$, by the difference of two independent Poisson processes with
parameters $\lambda_0$ and $\lambda_1$. | [
0,
0,
1,
0,
0,
0
] |
Title: Fully Resolved Numerical Simulations of Fused Deposition Modeling. Part II-Solidification, Residual Stresses, and Modeling of the Nozzle,
Abstract: Purpose - This paper continues the development of a comprehensive methodology
for fully resolved numerical simulations of fusion deposition modeling.
Design/methodology/approach - A front-tracking/finite volume method introduced
in Part I to simulate the heat transfer and fluid dynamics of the deposition of
a polymer filament on a fixed bed is extended by adding an improved model for
the injection nozzle, including the shrinkage of the polymer as it cools down,
and accounting for stresses in the solid. Findings - The accuracy and
convergence properties of the new method are tested by grid refinement and the
method is shown to produce convergent solutions for the shape of the filament,
the temperature distribution, the shrinkage and the solid stresses. Research
limitations/implications - The method presented in the paper focuses on
modeling the fluid flow, the cooling and solidification, as well as volume
changes and residual stresses, using a relatively simple viscoelastic
constitutive model. More complex material models, depending, for example, on
the evolution of the configuration tensor, are not included. Practical
implications - The ability to carry out fully resolved numerical simulations of
the fusion deposition process is expected to be critical for the validation of
mathematical models for the material behavior, to help explore new deposition
strategies, and to provide the "ground truth" for the development of reduced
order models. Originality/value - The paper completes the development of the
first numerical method for fully resolved simulation of fusion filament
modeling. | [
0,
1,
0,
0,
0,
0
] |
Title: High Dimensional Cluster Analysis Using Path Lengths,
Abstract: A hierarchical scheme for clustering data is presented which applies to
spaces with a high number of dimension ($N_{_{D}}>3$). The data set is first
reduced to a smaller set of partitions (multi-dimensional bins). Multiple
clustering techniques are used, including spectral clustering, however, new
techniques are also introduced based on the path length between partitions that
are connected to one another. A Line-Of-Sight algorithm is also developed for
clustering. A test bank of 12 data sets with varying properties is used to
expose the strengths and weaknesses of each technique. Finally, a robust
clustering technique is discussed based on reaching a consensus among the
multiple approaches, overcoming the weaknesses found individually. | [
1,
0,
0,
0,
0,
0
] |
Title: Forecasting elections using compartmental models of infections,
Abstract: To forecast political elections, popular pollsters gather polls and combine
information from them with fundamental data such as historical trends, the
national economy, and incumbency. This process is complicated, and it includes
many subjective choices (e.g., when identifying likely voters, estimating
turnout, and quantifying other sources of uncertainty), leading to forecasts
that differ between sources even when they use the same underlying polling
data. With the goal of shedding light on election forecasts (using the United
States as an example), we develop a framework for forecasting elections from
the perspective of dynamical systems. Through a simple approach that borrows
ideas from epidemiology, we show how to combine a compartmental model of
disease spreading with public polling data to forecast gubernatorial,
senatorial, and presidential elections at the state level. Our results for the
2012 and 2016 U.S. races are largely in agreement with those of popular
pollsters, and we use our new model to explore how subjective choices about
uncertainty impact results. Our goals are to open up new avenues for improving
how elections are forecast, to increase understanding of the results that are
reported by popular news sources, and to illustrate a fascinating example of
data-driven forecasting using dynamical systems. We conclude by forecasting the
senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6
November. | [
1,
0,
0,
0,
0,
0
] |
Title: Online Learning Without Prior Information,
Abstract: The vast majority of optimization and online learning algorithms today
require some prior information about the data (often in the form of bounds on
gradients or on the optimal parameter value). When this information is not
available, these algorithms require laborious manual tuning of various
hyperparameters, motivating the search for algorithms that can adapt to the
data with no prior information. We describe a frontier of new lower bounds on
the performance of such algorithms, reflecting a tradeoff between a term that
depends on the optimal parameter value and a term that depends on the
gradients' rate of growth. Further, we construct a family of algorithms whose
performance matches any desired point on this frontier, which no previous
algorithm reaches. | [
1,
0,
0,
1,
0,
0
] |
Title: Sparse Identification for Nonlinear Optical Communication Systems: SINO Method,
Abstract: We introduce low complexity machine learning based approach for mitigating
nonlinear impairments in optical fiber communications systems. The immense
intricacy of the problem calls for the development of "smart" methodology,
simplifying the analysis without losing the key features that are important for
recovery of transmitted data. The proposed sparse identification method for
optical systems (SINO) allows to determine the minimal (optimal) number of
degrees of freedom required for adaptive mitigation of detrimental nonlinear
effects. We demonstrate successful application of the SINO method both for
standard fiber communication links and for few-mode
spatial-division-multiplexing systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Serial Correlations in Single-Subject fMRI with Sub-Second TR,
Abstract: When performing statistical analysis of single-subject fMRI data, serial
correlations need to be taken into account to allow for valid inference.
Otherwise, the variability in the parameter estimates might be under-estimated
resulting in increased false-positive rates. Serial correlations in fMRI data
are commonly characterized in terms of a first-order autoregressive (AR)
process and then removed via pre-whitening. The required noise model for the
pre-whitening depends on a number of parameters, particularly the repetition
time (TR). Here we investigate how the sub-second temporal resolution provided
by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
time series. We fit a higher-order AR model and then estimate the optimal AR
model order for a sequence with a TR of less than 600 ms providing whole brain
coverage. We show that physiological noise modelling successfully reduces the
required AR model order, but remaining serial correlations necessitate an
advanced noise model. We conclude that commonly used noise models, such as the
AR(1) model, are inadequate for modelling serial correlations in fMRI using
sub-second TRs. Rather, physiological noise modelling in combination with
advanced pre-whitening schemes enable valid inference in single-subject
analysis using fast fMRI sequences. | [
0,
0,
0,
1,
0,
0
] |
Title: Randomization-based Inference for Bernoulli-Trial Experiments and Implications for Observational Studies,
Abstract: We present a randomization-based inferential framework for experiments
characterized by a strongly ignorable assignment mechanism where units have
independent probabilities of receiving treatment. Previous works on
randomization tests often assume these probabilities are equal within blocks of
units. We consider the general case where they differ across units and show how
to perform randomization tests and obtain point estimates and confidence
intervals. Furthermore, we develop a rejection-sampling algorithm to conduct
randomization-based inference conditional on ancillary statistics, covariate
balance, or other statistics of interest. Through simulation we demonstrate how
our algorithm can yield powerful randomization tests and thus precise
inference. Our work also has implications for observational studies, which
commonly assume a strongly ignorable assignment mechanism. Most methodologies
for observational studies make additional modeling or asymptotic assumptions,
while our framework only assumes the strongly ignorable assignment mechanism,
and thus can be considered a minimal-assumption approach. | [
0,
0,
0,
1,
0,
0
] |
Title: Cloaking using complementary media for electromagnetic waves,
Abstract: Negative index materials are artificial structures whose refractive index has
negative value over some frequency range. The study of these materials has
attracted a lot of attention in the scientific community not only because of
their many potential interesting applications but also because of challenges in
understanding their intriguing properties due to the sign-changing coefficients
in equations describing their properties. In this paper, we establish cloaking
using complementary media for electromagnetic waves. This confirms and extends
the suggestions in two dimensions of Lai et al. for the full Maxwell equations.
The analysis is based on the reflecting and removing localized singularity
techniques, three-sphere inequalities, and the fact that the Maxwell equations
can be reduced to a weakly coupled second order elliptic equations. | [
0,
0,
1,
0,
0,
0
] |
Title: Deterministic parallel analysis: An improved method for selecting the number of factors and principal components,
Abstract: Factor analysis and principal component analysis (PCA) are used in many
application areas. The first step, choosing the number of components, remains a
serious challenge. Our work proposes improved methods for this important
problem. One of the most popular state-of-the-art methods is Parallel Analysis
(PA), which compares the observed factor strengths to simulated ones under a
noise-only model. This paper proposes improvements to PA. We first de-randomize
it, proposing Deterministic Parallel Analysis (DPA), which is faster and more
reproducible than PA. Both PA and DPA are prone to a shadowing phenomenon in
which a strong factor makes it hard to detect smaller but more interesting
factors. We propose deflation to counter shadowing. We also propose to raise
the decision threshold to improve estimation accuracy. We prove several
consistency results for our methods, and test them in simulations. We also
illustrate our methods on data from the Human Genome Diversity Project, where
they significantly improve the accuracy. | [
0,
0,
0,
1,
0,
0
] |
Title: Laplacian Spectrum of non-commuting graphs of finite groups,
Abstract: In this paper, we compute the Laplacian spectrum of non-commuting graphs of
some classes of finite non-abelian groups. Our computations reveal that the
non-commuting graphs of all the groups considered in this paper are L-integral.
We also obtain some conditions on a group $G$ so that its non-commuting graph
is L-integral. | [
0,
0,
1,
0,
0,
0
] |
Title: Application of Coulomb energy density functional for atomic nuclei: Case studies of local density approximation and generalized gradient approximation,
Abstract: We test the Coulomb exchange and correlation energy density functionals of
electron systems for atomic nuclei in the local density approximation (LDA) and
the generalized gradient approximation (GGA). For the exchange Coulomb
energies, it is found that the deviation between the LDA and GGA ranges from
around $ 11 \, \% $ in $ {}^{4} \mathrm{He} $ to around $ 2.2 \, \% $ in $
{}^{208} \mathrm{Pb} $, by taking the Perdew-Burke-Ernzerhof (PBE) functional
as an example of the GGA\@. For the correlation Coulomb energies, it is shown
that those functionals of electron systems are not suitable for atomic nuclei. | [
0,
1,
0,
0,
0,
0
] |
Title: Transient frequency control with regional cooperation for power networks,
Abstract: This paper proposes a centralized and a distributed sub-optimal control
strategy to maintain in safe regions the real-time transient frequencies of a
given collection of buses, and simultaneously preserve asymptotic stability of
the entire network. In a receding horizon fashion, the centralized control
input is obtained by iteratively solving an open-loop optimization aiming to
minimize the aggregate control effort over controllers regulated on individual
buses with transient frequency and stability constraints. Due to the
non-convexity of the optimization, we propose a convexification technique by
identifying a reference control input trajectory. We then extend the
centralized control to a distributed scheme, where each subcontroller can only
access the state information within a local region. Simulations on a IEEE-39
network illustrate our results. | [
1,
0,
0,
0,
0,
0
] |
Title: Unsupervised prototype learning in an associative-memory network,
Abstract: Unsupervised learning in a generalized Hopfield associative-memory network is
investigated in this work. First, we prove that the (generalized) Hopfield
model is equivalent to a semi-restricted Boltzmann machine with a layer of
visible neurons and another layer of hidden binary neurons, so it could serve
as the building block for a multilayered deep-learning system. We then
demonstrate that the Hopfield network can learn to form a faithful internal
representation of the observed samples, with the learned memory patterns being
prototypes of the input data. Furthermore, we propose a spectral method to
extract a small set of concepts (idealized prototypes) as the most concise
summary or abstraction of the empirical data. | [
1,
1,
0,
0,
0,
0
] |
Title: One-Step Fabrication of pH-Responsive Membranes and Microcapsules through Interfacial H-Bond Polymer Complexation,
Abstract: Biocompatible microencapsulation is of widespread interest for the targeted
delivery of active species in fields such as pharmaceuticals, cosmetics and
agro-chemistry. Capsules obtained by the self-assembly of polymers at
interfaces enable the combination of responsiveness to stimuli,
biocompatibility and scaled up production. Here, we present a one-step method
to produce in situ membranes at oil-water interfaces, based on the hydrogen
bond complexation of polymers between H-bond acceptor and donor in the oil and
aqueous phases, respectively. This robust process is realized through different
methods, to obtain capsules of various sizes, from the micrometer scale using
microfluidics or rotor-stator emulsification up to the centimeter scale using
drop dripping. The polymer layer exhibits unique self-healing and pH-responsive
properties. The membrane is viscoelastic at pH = 3, softens as pH is
progressively raised, and eventually dissolves above pH = 6 to release the oil
phase. This one-step method of preparation paves the way to the production of
large quantities of functional capsules. | [
0,
1,
0,
0,
0,
0
] |
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