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17,201 | Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification | Deep neural networks (DNNs) have transformed several artificial intelligence
research areas including computer vision, speech recognition, and natural
language processing. However, recent studies demonstrated that DNNs are
vulnerable to adversarial manipulations at testing time. Specifically, suppose
we have a testing example, whose label can be correctly predicted by a DNN
classifier. An attacker can add a small carefully crafted noise to the testing
example such that the DNN classifier predicts an incorrect label, where the
crafted testing example is called adversarial example. Such attacks are called
evasion attacks. Evasion attacks are one of the biggest challenges for
deploying DNNs in safety and security critical applications such as
self-driving cars. In this work, we develop new methods to defend against
evasion attacks. Our key observation is that adversarial examples are close to
the classification boundary. Therefore, we propose region-based classification
to be robust to adversarial examples. For a benign/adversarial testing example,
we ensemble information in a hypercube centered at the example to predict its
label. In contrast, traditional classifiers are point-based classification,
i.e., given a testing example, the classifier predicts its label based on the
testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets
demonstrate that our region-based classification can significantly mitigate
evasion attacks without sacrificing classification accuracy on benign examples.
Specifically, our region-based classification achieves the same classification
accuracy on testing benign examples as point-based classification, but our
region-based classification is significantly more robust than point-based
classification to various evasion attacks.
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17,202 | Partition-free families of sets | Let $m(n)$ denote the maximum size of a family of subsets which does not
contain two disjoint sets along with their union. In 1968 Kleitman proved that
$m(n) = {n\choose m+1}+\ldots +{n\choose 2m+1}$ if $n=3m+1$. Confirming the
conjecture of Kleitman, we establish the same equality for the cases $n=3m$ and
$n=3m+2$, and also determine all extremal families. Unlike the case $n=3m+1$,
the extremal families are not unique. This is a plausible reason behind the
relative difficulty of our proofs. We completely settle the case of several
families as well.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,203 | Measuring the Galactic Cosmic Ray Flux with the LISA Pathfinder Radiation Monitor | Test mass charging caused by cosmic rays will be a significant source of
acceleration noise for space-based gravitational wave detectors like LISA.
Operating between December 2015 and July 2017, the technology demonstration
mission LISA Pathfinder included a bespoke monitor to help characterise the
relationship between test mass charging and the local radiation environment.
The radiation monitor made in situ measurements of the cosmic ray flux while
also providing information about its energy spectrum. We describe the monitor
and present measurements which show a gradual 40% increase in count rate
coinciding with the declining phase of the solar cycle. Modulations of up to
10% were also observed with periods of 13 and 26 days that are associated with
co-rotating interaction regions and heliospheric current sheet crossings. These
variations in the flux above the monitor detection threshold (approximately 70
MeV) are shown to be coherent with measurements made by the IREM monitor
on-board the Earth orbiting INTEGRAL spacecraft. Finally we use the measured
deposited energy spectra, in combination with a GEANT4 model, to estimate the
galactic cosmic ray differential energy spectrum over the course of the
mission.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,204 | Thread-Modular Static Analysis for Relaxed Memory Models | We propose a memory-model-aware static program analysis method for accurately
analyzing the behavior of concurrent software running on processors with weak
consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
our method is a unified framework for deciding the feasibility of inter-thread
interferences to avoid propagating spurious data flows during static analysis
and thus boost the performance of the static analyzer. We formulate the
checking of interference feasibility as a set of Datalog rules which are both
efficiently solvable and general enough to capture a range of hardware-level
memory models. Compared to existing techniques, our method can significantly
reduce the number of bogus alarms as well as unsound proofs. We implemented the
method and evaluated it on a large set of multithreaded C programs. Our
experiments showthe method significantly outperforms state-of-the-art
techniques in terms of accuracy with only moderate run-time overhead.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,205 | Bidirectional Evaluation with Direct Manipulation | We present an evaluation update (or simply, update) algorithm for a
full-featured functional programming language, which synthesizes program
changes based on output changes. Intuitively, the update algorithm retraces the
steps of the original evaluation, rewriting the program as needed to reconcile
differences between the original and updated output values. Our approach,
furthermore, allows expert users to define custom lenses that augment the
update algorithm with more advanced or domain-specific program updates.
To demonstrate the utility of evaluation update, we implement the algorithm
in Sketch-n-Sketch, a novel direct manipulation programming system for
generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style
functional program to generate HTML output. When the user directly manipulates
the output using a graphical user interface, the update algorithm reconciles
the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by
authoring ten examples comprising approximately 1400 lines of code in total.
These examples demonstrate how a variety of HTML documents and applications can
be developed and edited interactively in Sketch-n-Sketch, mitigating the
tedious edit-run-view cycle in traditional programming environments.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,206 | Extreme Event Statistics in a Drifting Markov Chain | We analyse extreme event statistics of experimentally realized Markov chains
with various drifts. Our Markov chains are individual trajectories of a single
atom diffusing in a one dimensional periodic potential. Based on more than 500
individual atomic traces we verify the applicability of the Sparre Andersen
theorem to our system despite the presence of a drift. We present detailed
analysis of four different rare event statistics for our system: the
distributions of extreme values, of record values, of extreme value occurrence
in the chain, and of the number of records in the chain. We observe that for
our data the shape of the extreme event distributions is dominated by the
underlying exponential distance distribution extracted from the atomic traces.
Furthermore, we find that even small drifts influence the statistics of extreme
events and record values, which is supported by numerical simulations, and we
identify cases in which the drift can be determined without information about
the underlying random variable distributions. Our results facilitate the use of
extreme event statistics as a signal for small drifts in correlated
trajectories.
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17,207 | On a Possibility of Self Acceleration of Electrons in a Plasma | The self-consistent nonlinear interaction of a monoenergetic bunch with cold
plasma is considered. It is shown that under certain conditions a
self-acceleration of the bunch tail electrons up to high energies is possible.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,208 | An Adaptive Strategy for Active Learning with Smooth Decision Boundary | We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,209 | Towards Adversarial Retinal Image Synthesis | Synthesizing images of the eye fundus is a challenging task that has been
previously approached by formulating complex models of the anatomy of the eye.
New images can then be generated by sampling a suitable parameter space. In
this work, we propose a method that learns to synthesize eye fundus images
directly from data. For that, we pair true eye fundus images with their
respective vessel trees, by means of a vessel segmentation technique. These
pairs are then used to learn a mapping from a binary vessel tree to a new
retinal image. For this purpose, we use a recent image-to-image translation
technique, based on the idea of adversarial learning. Experimental results show
that the original and the generated images are visually different in terms of
their global appearance, in spite of sharing the same vessel tree.
Additionally, a quantitative quality analysis of the synthetic retinal images
confirms that the produced images retain a high proportion of the true image
set quality.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,210 | Tailoring the SiC surface - a morphology study on the epitaxial growth of graphene and its buffer layer | We investigate the growth of the graphene buffer layer and the involved step
bunching behavior of the silicon carbide substrate surface using atomic force
microscopy. The formation of local buffer layer domains are identified to be
the origin of undesirably high step edges in excellent agreement with the
predictions of a general model of step dynamics. The applied polymer-assisted
sublimation growth method demonstrates that the key principle to suppress this
behavior is the uniform nucleation of the buffer layer. In this way, the
silicon carbide surface is stabilized such that ultra-flat surfaces can be
conserved during graphene growth on a large variety of silicon carbide
substrate surfaces. The analysis of the experimental results describes
different growth modes which extend the current understanding of epitaxial
graphene growth by emphasizing the importance of buffer layer nucleation and
critical mass transport processes.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,211 | Polarization of the Vaccination Debate on Facebook | Vaccine hesitancy has been recognized as a major global health threat. Having
access to any type of information in social media has been suggested as a
potential powerful influence factor to hesitancy. Recent studies in other
fields than vaccination show that access to a wide amount of content through
the Internet without intermediaries resolved into major segregation of the
users in polarized groups. Users select the information adhering to theirs
system of beliefs and tend to ignore dissenting information. In this paper we
assess whether there is polarization in Social Media use in the field of
vaccination. We perform a thorough quantitative analysis on Facebook analyzing
2.6M users interacting with 298.018 posts over a time span of seven years and 5
months. We used community detection algorithms to automatically detect the
emergent communities from the users activity and to quantify the cohesiveness
over time of the communities. Our findings show that content consumption about
vaccines is dominated by the echo-chamber effect and that polarization
increased over years. Communities emerge from the users consumption habits,
i.e. the majority of users only consumes information in favor or against
vaccines, not both. The existence of echo-chambers may explain why social-media
campaigns providing accurate information may have limited reach, may be
effective only in sub-groups and might even foment further polarization of
opinions. The introduction of dissenting information into a sub-group is
disregarded and can have a backfire effect, further reinforcing the existing
opinions within the sub-group.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,212 | Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors | Personal electronic devices including smartphones give access to behavioural
signals that can be used to learn about the characteristics and preferences of
individuals. In this study, we explore the connection between demographic and
psychological attributes and the digital behavioural records, for a cohort of
7,633 people, closely representative of the US population with respect to
gender, age, geographical distribution, education, and income. Along with the
demographic data, we collected self-reported assessments on validated
psychometric questionnaires for moral traits and basic human values and
combined this information with passively collected multi-modal digital data
from web browsing behaviour and smartphone usage. A machine learning framework
was then designed to infer both the demographic and psychological attributes
from the behavioural data. In a cross-validated setting, our models predicted
demographic attributes with good accuracy as measured by the weighted AUROC
score (Area Under the Receiver Operating Characteristic), but were less
performant for the moral traits and human values. These results call for
further investigation since they are still far from unveiling individuals'
psychological fabric. This connection, along with the most predictive features
that we provide for each attribute, might prove useful for designing
personalised services, communication strategies, and interventions, and can be
used to sketch a portrait of people with a similar worldview.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,213 | Isotonic regression in general dimensions | We study the least squares regression function estimator over the class of
real-valued functions on $[0,1]^d$ that are increasing in each coordinate. For
uniformly bounded signals and with a fixed, cubic lattice design, we establish
that the estimator achieves the minimax rate of order
$n^{-\min\{2/(d+2),1/d\}}$ in the empirical $L_2$ loss, up to poly-logarithmic
factors. Further, we prove a sharp oracle inequality, which reveals in
particular that when the true regression function is piecewise constant on $k$
hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of
convergence of $(k/n)^{\min(1,2/d)}$, again up to poly-logarithmic factors.
Previous results are confined to the case $d \leq 2$. Finally, we establish
corresponding bounds (which are new even in the case $d=2$) in the more
challenging random design setting. There are two surprising features of these
results: first, they demonstrate that it is possible for a global empirical
risk minimisation procedure to be rate optimal up to poly-logarithmic factors
even when the corresponding entropy integral for the function class diverges
rapidly; second, they indicate that the adaptation rate for shape-constrained
estimators can be strictly worse than the parametric rate.
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17,214 | Extraction of Schottky barrier height insensitive to temperature via forward currentvoltage- temperature measurements | The thermal stability of most electronic and photo-electronic devices
strongly depends on the relationship between Schottky Barrier Height (SBH) and
temperature. In this paper, the possible of thermionic current depicted via
correct and reliability relationship between forward current and voltage is
consequently discussed, the intrinsic SBH insensitive to temperature can be
calculated by modification on Richardson- Dushman`s formula suggested in this
paper. The results of application on four hetero-junctions prove that the
method proposed is credible in this paper, this suggests that the I/V/T method
is a feasible alternative to characterize these heterojunctions.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,215 | Emergent Open-Endedness from Contagion of the Fittest | In this paper, we study emergent irreducible information in populations of
randomly generated computable systems that are networked and follow a
"Susceptible-Infected-Susceptible" contagion model of imitation of the fittest
neighbor. We show that there is a lower bound for the stationary prevalence (or
average density of "infected" nodes) that triggers an unlimited increase of the
expected local emergent algorithmic complexity (or information) of a node as
the population size grows. We call this phenomenon expected (local) emergent
open-endedness. In addition, we show that static networks with a power-law
degree distribution following the Barabási-Albert model satisfy this lower
bound and, thus, display expected (local) emergent open-endedness.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,216 | Incompressible Limit of isentropic Navier-Stokes equations with Navier-slip boundary | This paper concerns the low Mach number limit of weak solutions to the
compressible Navier-Stokes equations for isentropic fluids in a bounded domain
with a Navier-slip boundary condition. In \cite{DGLM99}, it has been proved
that if the velocity is imposed the homogeneous Dirichlet boundary condition,
as the Mach number goes to 0, the velocity of the compressible flow converges
strongly in $L^2$ under the geometrical assumption (H) on the domain. We
justify the same strong convergence when the slip length in the Navier
condition is the reciprocal of the square root of the Mach number.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,217 | On Some Exponential Sums Related to the Coulter's Polynomial | In this paper, the formulas of some exponential sums over finite field,
related to the Coulter's polynomial, are settled based on the Coulter's
theorems on Weil sums, which may have potential application in the construction
of linear codes with few weights.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,218 | Distribution-Preserving k-Anonymity | Preserving the privacy of individuals by protecting their sensitive
attributes is an important consideration during microdata release. However, it
is equally important to preserve the quality or utility of the data for at
least some targeted workloads. We propose a novel framework for privacy
preservation based on the k-anonymity model that is ideally suited for
workloads that require preserving the probability distribution of the
quasi-identifier variables in the data. Our framework combines the principles
of distribution-preserving quantization and k-member clustering, and we
specialize it to two variants that respectively use intra-cluster and Gaussian
dithering of cluster centers to achieve distribution preservation. We perform
theoretical analysis of the proposed schemes in terms of distribution
preservation, and describe their utility in workloads such as covariate shift
and transfer learning where such a property is necessary. Using extensive
experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
the merits of our algorithms over standard k-anonymization for a hallmark
health care application where an insurance company wishes to understand the
risk in entering a new market. Furthermore, by empirically quantifying the
reidentification risk, we also show that the proposed approaches indeed
maintain k-anonymity.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,219 | Using controlled disorder to probe the interplay between charge order and superconductivity in NbSe2 | The interplay between superconductivity and charge density waves (CDW) in
$H$-NbSe2 is not fully understood despite decades of study. Artificially
introduced disorder can tip the delicate balance between two competing forms of
long-range order, and reveal the underlying interactions that give rise to
them. Here we introduce disorders by electron irradiation and measure in-plane
resistivity, Hall resistivity, X-ray scattering, and London penetration depth.
With increasing disorder, $T_{\textrm{c}}$ varies nonmonotonically, whereas
$T_{\textrm{CDW}}$ monotonically decreases and becomes unresolvable above a
critical irradiation dose where $T_{\textrm{c}}$ drops sharply. Our results
imply that CDW order initially competes with superconductivity, but eventually
assists it. We argue that at the transition where the long-range CDW order
disappears, the cooperation with superconductivity is dramatically suppressed.
X-ray scattering and Hall resistivity measurements reveal that the short-range
CDW survives above the transition. Superconductivity persists to much higher
dose levels, consistent with fully gapped superconductivity and moderate
interband pairing.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,220 | A training process for improving the quality of software projects developed by a practitioner | Background: The quality of a software product depends on the quality of the
software process followed in developing the product. Therefore, many higher
education institutions (HEI) and software organizations have implemented
software process improvement (SPI) training courses to improve the software
quality. Objective: Because the duration of a course is a concern for HEI and
software organizations, we investigate whether the quality of software projects
will be improved by reorganizing the activities of the ten assignments of the
original personal software process (PSP) course into a modified PSP having
fewer assignments (i.e., seven assignments). Method: The assignments were
developed by following a modified PSP with fewer assignments but including the
phases, forms, standards, and logs suggested in the original PSP. The
measurement of the quality of the software assignments was based on defect
density. Results: When the activities in the original PSP were reordered into
fewer assignments, as practitioners progress through the PSP training, the
defect density improved with statistical significance. Conclusions: Our
modified PSP could be applied in academy and industrial environments which are
concerned in the sense of reducing the PSP training time
| 1 | 0 | 0 | 0 | 0 | 0 |
17,221 | Gaia Data Release 1. Cross-match with external catalogues - Algorithm and results | Although the Gaia catalogue on its own will be a very powerful tool, it is
the combination of this highly accurate archive with other archives that will
truly open up amazing possibilities for astronomical research. The advanced
interoperation of archives is based on cross-matching, leaving the user with
the feeling of working with one single data archive. The data retrieval should
work not only across data archives, but also across wavelength domains. The
first step for seamless data access is the computation of the cross-match
between Gaia and external surveys. The matching of astronomical catalogues is a
complex and challenging problem both scientifically and technologically
(especially when matching large surveys like Gaia). We describe the cross-match
algorithm used to pre-compute the match of Gaia Data Release 1 (DR1) with a
selected list of large publicly available optical and IR surveys. The overall
principles of the adopted cross-match algorithm are outlined. Details are given
on the developed algorithm, including the methods used to account for position
errors, proper motions, and environment; to define the neighbours; and to
define the figure of merit used to select the most probable counterpart.
Statistics on the results are also given. The results of the cross-match are
part of the official Gaia DR1 catalogue.
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17,222 | Masses of Kepler-46b, c from Transit Timing Variations | We use 16 quarters of the \textit{Kepler} mission data to analyze the transit
timing variations (TTVs) of the extrasolar planet Kepler-46b (KOI-872). Our
dynamical fits confirm that the TTVs of this planet (period
$P=33.648^{+0.004}_{-0.005}$ days) are produced by a non-transiting planet
Kepler-46c ($P=57.325^{+0.116}_{-0.098}$ days). The Bayesian inference tool
\texttt{MultiNest} is used to infer the dynamical parameters of Kepler-46b and
Kepler-46c. We find that the two planets have nearly coplanar and circular
orbits, with eccentricities $\simeq 0.03$ somewhat higher than previously
estimated. The masses of the two planets are found to be
$M_{b}=0.885^{+0.374}_{-0.343}$ and $M_{c}=0.362^{+0.016}_{-0.016}$ Jupiter
masses, with $M_{b}$ being determined here from TTVs for the first time. Due to
the precession of its orbital plane, Kepler-46c should start transiting its
host star in a few decades from now.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,223 | Recovering water wave elevation from pressure measurements | The reconstruction of water wave elevation from bottom pressure measurements
is an important issue for coastal applications, but corresponds to a difficult
mathematical problem. In this paper we present the derivation of a method which
allows the elevation reconstruction of water waves in intermediate and shallow
waters. From comparisons with numerical Euler solutions and wave-tank
experiments we show that our nonlinear method provides much better results of
the surface elevation reconstruction compared to the linear transfer function
approach commonly used in coastal applications. More specifically, our
methodaccurately reproduces the peaked and skewed shape of nonlinear wave
fields. Therefore, it is particularly relevant for applications on extreme
waves and wave-induced sediment transport.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,224 | Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization | The Schatten quasi-norm was introduced to bridge the gap between the trace
norm and rank function. However, existing algorithms are too slow or even
impractical for large-scale problems. Motivated by the equivalence relation
between the trace norm and its bilinear spectral penalty, we define two
tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove
that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms,
respectively. By applying the two defined Schatten quasi-norms to various rank
minimization problems such as MC and RPCA, we only need to solve much smaller
factor matrices. We design two efficient linearized alternating minimization
algorithms to solve our problems and establish that each bounded sequence
generated by our algorithms converges to a critical point. We also provide the
restricted strong convexity (RSC) based and MC error bounds for our algorithms.
Our experimental results verified both the efficiency and effectiveness of our
algorithms compared with the state-of-the-art methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,225 | Spectral Radii of Truncated Circular Unitary Matrices | Consider a truncated circular unitary matrix which is a $p_n$ by $p_n$
submatrix of an $n$ by $n$ circular unitary matrix by deleting the last $n-p_n$
columns and rows. Jiang and Qi (2017) proved that the maximum absolute value of
the eigenvalues (known as spectral radius) of the truncated matrix, after
properly normalized, converges in distribution to the Gumbel distribution if
$p_n/n$ is bounded away from $0$ and $1$. In this paper we investigate the
limiting distribution of the spectral radius under one of the following four
conditions: (1). $p_n\to\infty$ and $p_n/n\to 0$ as $n\to\infty$; (2).
$(n-p_n)/n\to 0$ and $(n-p_n)/(\log n)^3\to\infty$ as $n\to\infty$; (3).
$n-p_n\to\infty$ and $(n-p_n)/\log n\to 0$ as $n\to\infty$ and (4). $n-p_n=k\ge
1$ is a fixed integer. We prove that the spectral radius converges in
distribution to the Gumbel distribution under the first three conditions and to
a reversed Weibull distribution under the fourth condition.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,226 | Information Assisted Dictionary Learning for fMRI data analysis | In this paper, the task-related fMRI problem is treated in its matrix
factorization formulation. The focus of the reported work is on the dictionary
learning (DL) matrix factorization approach. A major novelty of the paper lies
in the incorporation of well-established assumptions associated with the GLM
technique, which is currently in use by the neuroscientists. These assumptions
are embedded as constraints in the DL formulation. In this way, our approach
provides a framework of combining well-established and understood techniques
with a more ``modern'' and powerful tool. Furthermore, this paper offers a way
to relax a major drawback associated with DL techniques; that is, the proper
tuning of the DL regularization parameter. This parameter plays a critical role
in DL-based fMRI analysis since it essentially determines the shape and
structures of the estimated functional brain networks. However, in actual fMRI
data analysis, the lack of ground truth renders the a priori choice of the
regularization parameter a truly challenging task. Indeed, the values of the DL
regularization parameter, associated with the $\ell_1$ sparsity promoting norm,
do not convey any tangible physical meaning. So it is practically difficult to
guess its proper value. In this paper, the DL problem is reformulated around a
sparsity-promoting constraint that can directly be related to the minimum
amount of voxels that the spatial maps of the functional brain networks occupy.
Such information is documented and it is readily available to neuroscientists
and experts in the field.
The proposed method is tested against a number of other popular techniques
and the obtained performance gains are reported using a number of synthetic
fMRI data. Results with real data have also been obtained in the context of a
number of experiments and will be soon reported in a different publication.
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17,227 | Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models | Motivated by the task of clustering either $d$ variables or $d$ points into
$K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W)
$K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been
shown in the literature to have good statistical properties in a variety of
settings, but remains intractable to solve in practice. To this end we propose
FORCE, a new algorithm to solve this SDP relaxation. Compared to the naive
interior point method, our method reduces the computational complexity of
solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to
$\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an
$\epsilon$-optimal solution. Our method combines a primal first-order method
with a dual optimality certificate search, which when successful, allows for
early termination of the primal method. We show for certain variable clustering
problems that, with high probability, FORCE is guaranteed to find the optimal
solution to the SDP relaxation and provide a certificate of exact optimality.
As verified by our numerical experiments, this allows FORCE to solve the P-W
SDP with dimensions in the hundreds in only tens of seconds. For a variation of
the P-W SDP where $K$ is not known a priori a slight modification of FORCE
reduces the computational complexity of solving this problem as well: from
$\tilde{O}(d^7\log\epsilon^{-1})$ using a standard SDP solver to
$\tilde{O}(d^{4}\epsilon^{-1})$.
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17,228 | Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data | We present wide-field (167 deg$^2$) weak lensing mass maps from the Hyper
Supreme-Cam Subaru Strategic Program (HSC-SSP). We compare these weak lensing
based dark matter maps with maps of the distribution of the stellar mass
associated with luminous red galaxies. We find a strong correlation between
these two maps with a correlation coefficient of $\rho=0.54\pm0.03$ (for a
smoothing size of $8'$). This correlation is detected even with a smaller
smoothing scale of $2'$ ($\rho=0.34\pm 0.01$). This detection is made uniquely
possible because of the high source density of the HSC-SSP weak lensing survey
($\bar{n}\sim 25$ arcmin$^{-2}$). We also present a variety of tests to
demonstrate that our maps are not significantly affected by systematic effects.
By using the photometric redshift information associated with source galaxies,
we reconstruct a three-dimensional mass map. This three-dimensional mass map is
also found to correlate with the three-dimensional galaxy mass map.
Cross-correlation tests presented in this paper demonstrate that the HSC-SSP
weak lensing mass maps are ready for further science analyses.
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17,229 | A Time-spectral Approach to Numerical Weather Prediction | Finite difference methods are traditionally used for modelling the time
domain in numerical weather prediction (NWP). Time-spectral solution is an
attractive alternative for reasons of accuracy and efficiency and because time
step limitations associated with causal, CFL-like critera are avoided. In this
work, the Lorenz 1984 chaotic equations are solved using the time-spectral
algorithm GWRM. Comparisons of accuracy and efficiency are carried out for both
explicit and implicit time-stepping algorithms. It is found that the efficiency
of the GWRM compares well with these methods, in particular at high accuracy.
For perturbative scenarios, the GWRM was found to be as much as four times
faster than the finite difference methods. A primary reason is that the GWRM
time intervals typically are two orders of magnitude larger than those of the
finite difference methods. The GWRM has the additional advantage to produce
analytical solutions in the form of Chebyshev series expansions. The results
are encouraging for pursuing further studies, including spatial dependence, of
the relevance of time-spectral methods for NWP modelling.
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17,230 | Trivial Constraints on Orbital-free Kinetic Energy Density Functionals | Kinetic energy density functionals (KEDFs) are central to orbital-free
density functional theory. Limitations on the spatial derivative dependencies
of KEDFs have been claimed from differential virial theorems. We point out a
central defect in the argument: the relationships are not true for an arbitrary
density but hold only for the minimizing density and corresponding chemical
potential. Contrary to the claims therefore, the relationships are not
constraints and provide no independent information about the spatial derivative
dependencies of approximate KEDFs. A simple argument also shows that validity
for arbitrary $v$-representable densities is not restored by appeal to the
density-potential bijection.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,231 | The Multi-layer Information Bottleneck Problem | The muti-layer information bottleneck (IB) problem, where information is
propagated (or successively refined) from layer to layer, is considered. Based
on information forwarded by the preceding layer, each stage of the network is
required to preserve a certain level of relevance with regards to a specific
hidden variable, quantified by the mutual information. The hidden variables and
the source can be arbitrarily correlated. The optimal trade-off between rates
of relevance and compression (or complexity) is obtained through a
single-letter characterization, referred to as the rate-relevance region.
Conditions of successive refinabilty are given. Binary source with BSC hidden
variables and binary source with BSC/BEC mixed hidden variables are both proved
to be successively refinable. We further extend our result to Guassian models.
A counterexample of successive refinability is also provided.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,232 | A geometric approach to non-linear correlations with intrinsic scatter | We propose a new mathematical model for $n-k$-dimensional non-linear
correlations with intrinsic scatter in $n$-dimensional data. The model is based
on Riemannian geometry, and is naturally symmetric with respect to the measured
variables and invariant under coordinate transformations. We combine the model
with a Bayesian approach for estimating the parameters of the correlation
relation and the intrinsic scatter. A side benefit of the approach is that
censored and truncated datasets and independent, arbitrary measurement errors
can be incorporated. We also derive analytic likelihoods for the typical
astrophysical use case of linear relations in $n$-dimensional Euclidean space.
We pay particular attention to the case of linear regression in two dimensions,
and compare our results to existing methods. Finally, we apply our methodology
to the well-known $M_\text{BH}$-$\sigma$ correlation between the mass of a
supermassive black hole in the centre of a galactic bulge and the corresponding
bulge velocity dispersion. The main result of our analysis is that the most
likely slope of this correlation is $\sim 6$ for the datasets used, rather than
the values in the range $\sim 4$-$5$ typically quoted in the literature for
these data.
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17,233 | Computing simplicial representatives of homotopy group elements | A central problem of algebraic topology is to understand the homotopy groups
$\pi_d(X)$ of a topological space $X$. For the computational version of the
problem, it is well known that there is no algorithm to decide whether the
fundamental group $\pi_1(X)$ of a given finite simplicial complex $X$ is
trivial. On the other hand, there are several algorithms that, given a finite
simplicial complex $X$ that is simply connected (i.e., with $\pi_1(X)$
trivial), compute the higher homotopy group $\pi_d(X)$ for any given $d\geq 2$.
%The first such algorithm was given by Brown, and more recently, Čadek et
al.
However, these algorithms come with a caveat: They compute the isomorphism
type of $\pi_d(X)$, $d\geq 2$ as an \emph{abstract} finitely generated abelian
group given by generators and relations, but they work with very implicit
representations of the elements of $\pi_d(X)$. Converting elements of this
abstract group into explicit geometric maps from the $d$-dimensional sphere
$S^d$ to $X$ has been one of the main unsolved problems in the emerging field
of computational homotopy theory.
Here we present an algorithm that, given a~simply connected space $X$,
computes $\pi_d(X)$ and represents its elements as simplicial maps from a
suitable triangulation of the $d$-sphere $S^d$ to $X$. For fixed $d$, the
algorithm runs in time exponential in $size(X)$, the number of simplices of
$X$. Moreover, we prove that this is optimal: For every fixed $d\geq 2$, we
construct a family of simply connected spaces $X$ such that for any simplicial
map representing a generator of $\pi_d(X)$, the size of the triangulation of
$S^d$ on which the map is defined, is exponential in $size(X)$.
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17,234 | Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling | Optimization is becoming a crucial element in industrial applications
involving sustainable alternative energy systems. During the design of such
systems, the engineer/decision maker would often encounter noise factors (e.g.
solar insolation and ambient temperature fluctuations) when their system
interacts with the environment. In this chapter, the sizing and design
optimization of the solar powered irrigation system was considered. This
problem is multivariate, noisy, nonlinear and multiobjective. This design
problem was tackled by first using the Fuzzy Type II approach to model the
noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
context of a weighted sum framework) was employed to solve this multiobjective
fuzzy design problem. This method was then used to construct the approximate
Pareto frontier as well as to identify the best solution option in a fuzzy
setting. Comprehensive analyses and discussions were performed on the generated
numerical results with respect to the implemented solution methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,235 | Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture | In this paper, we study convergence properties of the gradient
Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian
Mixture Models for general number of clusters and mixing coefficients. We
derive the convergence rate depending on the mixing coefficients, minimum and
maximum pairwise distances between the true centers and dimensionality and
number of components; and obtain a near-optimal local contraction radius. While
there have been some recent notable works that derive local convergence rates
for EM in the two equal mixture symmetric GMM, in the more general case, the
derivations need structurally different and non-trivial arguments. We use
recent tools from learning theory and empirical processes to achieve our
theoretical results.
| 1 | 0 | 1 | 1 | 0 | 0 |
17,236 | The Gravitational-Wave Physics | The direct detection of gravitational wave by Laser Interferometer
Gravitational-Wave Observatory indicates the coming of the era of
gravitational-wave astronomy and gravitational-wave cosmology. It is expected
that more and more gravitational-wave events will be detected by currently
existing and planned gravitational-wave detectors. The gravitational waves open
a new window to explore the Universe and various mysteries will be disclosed
through the gravitational-wave detection, combined with other cosmological
probes. The gravitational-wave physics is not only related to gravitation
theory, but also is closely tied to fundamental physics, cosmology and
astrophysics. In this review article, three kinds of sources of gravitational
waves and relevant physics will be discussed, namely gravitational waves
produced during the inflation and preheating phases of the Universe, the
gravitational waves produced during the first-order phase transition as the
Universe cools down and the gravitational waves from the three phases:
inspiral, merger and ringdown of a compact binary system, respectively. We will
also discuss the gravitational waves as a standard siren to explore the
evolution of the Universe.
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17,237 | Multivariant Assertion-based Guidance in Abstract Interpretation | Approximations during program analysis are a necessary evil, as they ensure
essential properties, such as soundness and termination of the analysis, but
they also imply not always producing useful results. Automatic techniques have
been studied to prevent precision loss, typically at the expense of larger
resource consumption. In both cases (i.e., when analysis produces inaccurate
results and when resource consumption is too high), it is necessary to have
some means for users to provide information to guide analysis and thus improve
precision and/or performance. We present techniques for supporting within an
abstract interpretation framework a rich set of assertions that can deal with
multivariance/context-sensitivity, and can handle different run-time semantics
for those assertions that cannot be discharged at compile time. We show how the
proposed approach can be applied to both improving precision and accelerating
analysis. We also provide some formal results on the effects of such assertions
on the analysis results.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,238 | An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems | Through the development of efficient algorithms, data structures and
preprocessing techniques, real-world shortest path problems in street networks
are now very fast to solve. But in reality, the exact travel times along each
arc in the network may not be known. This lead to the development of robust
shortest path problems, where all possible arc travel times are contained in a
so-called uncertainty set of possible outcomes.
Research in robust shortest path problems typically assumes this set to be
given, and provides complexity results as well as algorithms depending on its
shape. However, what can actually be observed in real-world problems are only
discrete raw data points. The shape of the uncertainty is already a modelling
assumption. In this paper we test several of the most widely used assumptions
on the uncertainty set using real-world traffic measurements provided by the
City of Chicago. We calculate the resulting different robust solutions, and
evaluate which uncertainty approach is actually reasonable for our data. This
anchors theoretical research in a real-world application and allows us to point
out which robust models should be the future focus of algorithmic development.
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17,239 | An experimental comparison of velocities underneath focussed breaking waves | Nonlinear wave interactions affect the evolution of steep wave groups, their
breaking and the associated kinematic field. Laboratory experiments are
performed to investigate the effect of the underlying focussing mechanism on
the shape of the breaking wave and its velocity field. In this regard, it is
found that the shape of the wave spectrum plays a substantial role. Broader
underlying wave spectra leads to energetic plungers at a relatively low
amplitude. For narrower spectra waves break at a higher amplitudes but with a
less energetic spiller. Comparison with standard engineering methods commonly
used to predict the velocity underneath extreme waves shows that, under certain
conditions, the measured velocity profile strongly deviates from engineering
predictions.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,240 | Full Momentum and Energy Resolved Spectral Function of a 2D Electronic System | The single-particle spectral function measures the density of electronic
states (DOS) in a material as a function of both momentum and energy, providing
central insights into phenomena such as superconductivity and Mott insulators.
While scanning tunneling microscopy (STM) and other tunneling methods have
provided partial spectral information, until now only angle-resolved
photoemission spectroscopy (ARPES) has permitted a comprehensive determination
of the spectral function of materials in both momentum and energy. However,
ARPES operates only on electronic systems at the material surface and cannot
work in the presence of applied magnetic fields. Here, we demonstrate a new
method for determining the full momentum and energy resolved electronic
spectral function of a two-dimensional (2D) electronic system embedded in a
semiconductor. In contrast with ARPES, the technique remains operational in the
presence of large externally applied magnetic fields and functions for
electronic systems with zero electrical conductivity or with zero electron
density. It provides a direct high-resolution and high-fidelity probe of the
dispersion and dynamics of the interacting 2D electron system. By ensuring the
system of interest remains under equilibrium conditions, we uncover delicate
signatures of many-body effects involving electron-phonon interactions,
plasmons, polarons, and a novel phonon analog of the vacuum Rabi splitting in
atomic systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,241 | Complete parallel mean curvature surfaces in two-dimensional complex space-forms | The purpose of this article is to determine explicitly the complete surfaces
with parallel mean curvature vector, both in the complex projective plane and
the complex hyperbolic plane. The main results are as follows: When the
curvature of the ambient space is positive, there exists a unique such surface
up to rigid motions of the target space. On the other hand, when the curvature
of the ambient space is negative, there are `non-trivial' complete parallel
mean curvature surfaces generated by Jacobi elliptic functions and they exhaust
such surfaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,242 | Parallelized Linear Classification with Volumetric Chemical Perceptrons | In this work, we introduce a new type of linear classifier that is
implemented in a chemical form. We propose a novel encoding technique which
simultaneously represents multiple datasets in an array of microliter-scale
chemical mixtures. Parallel computations on these datasets are performed as
robotic liquid handling sequences, whose outputs are analyzed by
high-performance liquid chromatography. As a proof of concept, we chemically
encode several MNIST images of handwritten digits and demonstrate successful
chemical-domain classification of the digits using volumetric perceptrons. We
additionally quantify the performance of our method with a larger dataset of
binary vectors and compare the experimental measurements against predicted
results. Paired with appropriate chemical analysis tools, our approach can work
on increasingly parallel datasets. We anticipate that related approaches will
be scalable to multilayer neural networks and other more complex algorithms.
Much like recent demonstrations of archival data storage in DNA, this work
blurs the line between chemical and electrical information systems, and offers
early insight into the computational efficiency and massive parallelism which
may come with computing in chemical domains.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,243 | Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification | Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because spatial annotations of the labels are generally not provided.
In this paper, we propose a unified deep neural network that exploits both
semantic and spatial relations between labels with only image-level
supervisions. Given a multi-label image, our proposed Spatial Regularization
Network (SRN) generates attention maps for all labels and captures the
underlying relations between them via learnable convolutions. By aggregating
the regularized classification results with original results by a ResNet-101
network, the classification performance can be consistently improved. The whole
deep neural network is trained end-to-end with only image-level annotations,
thus requires no additional efforts on image annotations. Extensive evaluations
on 3 public datasets with different types of labels show that our approach
significantly outperforms state-of-the-arts and has strong generalization
capability. Analysis of the learned SRN model demonstrates that it can
effectively capture both semantic and spatial relations of labels for improving
classification performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,244 | The critical binary star separation for a planetary system origin of white dwarf pollution | The atmospheres of between one quarter and one half of observed single white
dwarfs in the Milky Way contain heavy element pollution from planetary debris.
The pollution observed in white dwarfs in binary star systems is, however, less
clear, because companion star winds can generate a stream of matter which is
accreted by the white dwarf. Here we (i) discuss the necessity or lack thereof
of a major planet in order to pollute a white dwarf with orbiting minor planets
in both single and binary systems, and (ii) determine the critical binary
separation beyond which the accretion source is from a planetary system. We
hence obtain user-friendly functions relating this distance to the masses and
radii of both stars, the companion wind, and the accretion rate onto the white
dwarf, for a wide variety of published accretion prescriptions. We find that
for the majority of white dwarfs in known binaries, if pollution is detected,
then that pollution should originate from planetary material.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,245 | A quantum Mirković-Vybornov isomorphism | We present a quantization of an isomorphism of Mirković and Vybornov which
relates the intersection of a Slodowy slice and a nilpotent orbit closure in
$\mathfrak{gl}_N$ , to a slice between spherical Schubert varieties in the
affine Grassmannian of $PGL_n$ (with weights encoded by the Jordan types of the
nilpotent orbits). A quantization of the former variety is provided by a
parabolic W-algebra and of the latter by a truncated shifted Yangian. Building
on earlier work of Brundan and Kleshchev, we define an explicit isomorphism
between these non-commutative algebras, and show that its classical limit is a
variation of the original isomorphism of Mirković and Vybornov. As a
corollary, we deduce that the W-algebra is free as a left (or right) module
over its Gelfand-Tsetlin subalgebra, as conjectured by Futorny, Molev, and
Ovsienko.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,246 | Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach | This paper is concerned with a multi-asset mean-variance portfolio selection
problem under model uncertainty. We develop a continuous time framework for
taking into account ambiguity aversion about both expected return rates and
correlation matrix of the assets, and for studying the effects on portfolio
diversification. We prove a separation principle for the associated robust
control problem, which allows to reduce the determination of the optimal
dynamic strategy to the parametric computation of the minimal risk premium
function. Our results provide a justification for under-diversification, as
documented in empirical studies. We explicitly quantify the degree of
under-diversification in terms of correlation and Sharpe ratio ambiguity. In
particular, we show that an investor with a poor confidence in the expected
return estimation does not hold any risky asset, and on the other hand, trades
only one risky asset when the level of ambiguity on correlation matrix is
large. This extends to the continuous-time setting the results obtained by
Garlappi, Uppal and Wang [13], and Liu and Zeng [24] in a one-period model. JEL
Classification: G11, C61 MSC Classification: 91G10, 91G80, 60H30
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17,247 | TensorLayer: A Versatile Library for Efficient Deep Learning Development | Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,248 | Effects of excess carriers on native defects in wide bandgap semiconductors: illumination as a method to enhance p-type doping | Undesired unintentional doping and doping limits in semiconductors are
typically caused by compensating defects with low formation energies. Since the
formation energy of a charged defect depends linearly on the Fermi level,
doping limits can be especially pronounced in wide bandgap semiconductors where
the Fermi level can vary substantially. Introduction of non-equilibrium carrier
concentrations during growth or processing alters the chemical potentials of
band carriers and thus provides the possibility of modifying populations of
charged defects in ways impossible at thermal equilibrium. Herein we
demonstrate that, for an ergodic system with excess carriers, the rates of
carrier capture and emission involving a defect charge transition level
rigorously determine the admixture of electron and hole quasi-Fermi levels
determining the formation energy of non-zero charge states of that defect type.
To catalog the range of possible responses to excess carriers, we investigate
the behavior of a single donor-like defect as functions of extrinsic doping and
energy of the charge transition level. The technologically most important
finding is that excess carriers will increase the formation energy of
compensating defects for most values of the charge transition level in the
bandgap. Thus, it may be possible to overcome limitations on doping imposed by
native defects. Cases also exist in wide bandgap semiconductors in which the
concentration of defects with the same charge polarity as the majority dopant
is either left unchanged or actually increases. The causes of these various
behaviors are rationalized in terms of the capture and emission rates and
guidelines for carrying out experimental tests of this model are given.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,249 | LATTE: Application Oriented Social Network Embedding | In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,250 | Giant paramagnetism induced valley polarization of electrons in charge-tunable monolayer MoSe2 | For applications exploiting the valley pseudospin degree of freedom in
transition metal dichalcogenide monolayers, efficient preparation of electrons
or holes in a single valley is essential. Here, we show that a magnetic field
of 7 Tesla leads to a near-complete valley polarization of electrons in MoSe2
monolayer with a density 1.6x10^{12} cm^{-2}; in the absence of exchange
interactions favoring single-valley occupancy, a similar degree of valley
polarization would have required a pseudospin g-factor exceeding 40. To
investigate the magnetic response, we use polarization resolved
photoluminescence as well as resonant reflection measurements. In the latter,
we observe gate voltage dependent transfer of oscillator strength from the
exciton to the attractive-Fermi-polaron: stark differences in the spectrum of
the two light helicities provide a confirmation of valley polarization. Our
findings suggest an interaction induced giant paramagnetic response of MoSe2,
which paves the way for valleytronics applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,251 | Highrisk Prediction from Electronic Medical Records via Deep Attention Networks | Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the high risky vascular disease from symbolic medical histories
sequence of hypertension patients such as ICD-10 and pharmacy codes only,
Medical History-based Prediction using Attention Network (MeHPAN). We
demonstrate two types of attention models based on 1) bidirectional gated
recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
with respect to precision, recall, f1-measure and area under the curve (AUC).
Experimental results show that our MeHPAN methods outperform standard
classification models. Comparing two MeHPANs, R-MeHPAN provides more better
discriminative capability with respect to all metrics while C-MeHPAN presents
much shorter training time with competitive accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,252 | Agent based simulation of the evolution of society as an alternate maximization problem | Understanding the evolution of human society, as a complex adaptive system,
is a task that has been looked upon from various angles. In this paper, we
simulate an agent-based model with a high enough population tractably. To do
this, we characterize an entity called \textit{society}, which helps us reduce
the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We
propose a very realistic setting, where we design a joint alternate
maximization step algorithm to maximize a certain \textit{fitness} function,
which we believe simulates the way societies develop. Our key contributions
include (i) proposing a novel protocol for simulating the evolution of a
society with cheap, non-optimal joint alternate maximization steps (ii)
providing a framework for carrying out experiments that adhere to this
joint-optimization simulation framework (iii) carrying out experiments to show
that it makes sense empirically (iv) providing an alternate justification for
the use of \textit{society} in the simulations.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,253 | Can a heart rate variability biomarker identify the presence of autism spectrum disorder in eight year old children? | Autonomic nervous system (ANS) activity is altered in autism spectrum
disorder (ASD). Heart rate variability (HRV) derived from electrocardiogram
(ECG) has been a powerful tool to identify alterations in ANS due to a plethora
of pathophysiological conditions, including psychological ones such as
depression. ECG-derived HRV thus carries a yet to be explored potential to be
used as a diagnostic and follow-up biomarker of ASD. However, few studies have
explored this potential. In a cohort of boys (ages 8 - 11 years) with (n=18)
and without ASD (n=18), we tested a set of linear and nonlinear HRV measures,
including phase rectified signal averaging (PRSA), applied to a segment of ECG
collected under resting conditions for their predictive properties of ASD. We
identified HRV measures derived from time, frequency and geometric
signal-analytical domains which are changed in ASD children relative to peers
without ASD and correlate to psychometric scores (p<0.05 for each). Receiver
operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite
being a small cohort lacking external validation, these promising preliminary
results warrant larger prospective validation studies.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,254 | Semantic Entity Retrieval Toolkit | Unsupervised learning of low-dimensional, semantic representations of words
and entities has recently gained attention. In this paper we describe the
Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our
previously published entity representation models. The toolkit provides a
unified interface to different representation learning algorithms, fine-grained
parsing configuration and can be used transparently with GPUs. In addition,
users can easily modify existing models or implement their own models in the
framework. After model training, SERT can be used to rank entities according to
a textual query and extract the learned entity/word representation for use in
downstream algorithms, such as clustering or recommendation.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,255 | Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection | In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each
associated with an unknown reward distribution. We are required to identify the
$k$ arms with the largest means by taking as few samples as possible. In this
paper, we make progress towards a complete characterization of the
instance-wise sample complexity bounds for the Best-$k$-Arm problem. On the
lower bound side, we obtain a novel complexity term to measure the sample
complexity that every Best-$k$-Arm instance requires. This is derived by an
interesting and nontrivial reduction from the Best-$1$-Arm problem. We also
provide an elimination-based algorithm that matches the instance-wise lower
bound within doubly-logarithmic factors. The sample complexity of our algorithm
strictly dominates the state-of-the-art for Best-$k$-Arm (module constant
factors).
| 1 | 0 | 0 | 1 | 0 | 0 |
17,256 | Dimensions of equilibrium measures on a class of planar self-affine sets | We study equilibrium measures (Käenmäki measures) supported on
self-affine sets generated by a finite collection of diagonal and anti-diagonal
matrices acting on the plane and satisfying the strong separation property. Our
main result is that such measures are exact dimensional and the dimension
satisfies the Ledrappier-Young formula, which gives an explicit expression for
the dimension in terms of the entropy and Lyapunov exponents as well as the
dimension of the important coordinate projection of the measure. In particular,
we do this by showing that the Käenmäki measure is equal to the sum of (the
pushforwards) of two Gibbs measures on an associated subshift of finite type.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,257 | Hubble PanCET: An isothermal day-side atmosphere for the bloated gas-giant HAT-P-32Ab | We present a thermal emission spectrum of the bloated hot Jupiter HAT-P-32Ab
from a single eclipse observation made in spatial scan mode with the Wide Field
Camera 3 (WFC3) aboard the Hubble Space Telescope (HST). The spectrum covers
the wavelength regime from 1.123 to 1.644 microns which is binned into 14
eclipse depths measured to an averaged precision of 104 parts-per million. The
spectrum is unaffected by a dilution from the close M-dwarf companion
HAT-P-32B, which was fully resolved. We complemented our spectrum with
literature results and performed a comparative forward and retrieval analysis
with the 1D radiative-convective ATMO model. Assuming solar abundance of the
planet atmosphere, we find that the measured spectrum can best be explained by
the spectrum of a blackbody isothermal atmosphere with Tp = 1995 +/- 17K, but
can equally-well be described by a spectrum with modest thermal inversion. The
retrieved spectrum suggests emission from VO at the WFC3 wavelengths and no
evidence of the 1.4 micron water feature. The emission models with temperature
profiles decreasing with height are rejected at a high confidence. An
isothermal or inverted spectrum can imply a clear atmosphere with an absorber,
a dusty cloud deck or a combination of both. We find that the planet can have
continuum of values for the albedo and recirculation, ranging from high albedo
and poor recirculation to low albedo and efficient recirculation. Optical
spectroscopy of the planet's day-side or thermal emission phase curves can
potentially resolve the current albedo with recirculation degeneracy.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,258 | One Model To Learn Them All | Deep learning yields great results across many fields, from speech
recognition, image classification, to translation. But for each problem,
getting a deep model to work well involves research into the architecture and a
long period of tuning. We present a single model that yields good results on a
number of problems spanning multiple domains. In particular, this single model
is trained concurrently on ImageNet, multiple translation tasks, image
captioning (COCO dataset), a speech recognition corpus, and an English parsing
task. Our model architecture incorporates building blocks from multiple
domains. It contains convolutional layers, an attention mechanism, and
sparsely-gated layers. Each of these computational blocks is crucial for a
subset of the tasks we train on. Interestingly, even if a block is not crucial
for a task, we observe that adding it never hurts performance and in most cases
improves it on all tasks. We also show that tasks with less data benefit
largely from joint training with other tasks, while performance on large tasks
degrades only slightly if at all.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,259 | Porcupine Neural Networks: (Almost) All Local Optima are Global | Neural networks have been used prominently in several machine learning and
statistics applications. In general, the underlying optimization of neural
networks is non-convex which makes their performance analysis challenging. In
this paper, we take a novel approach to this problem by asking whether one can
constrain neural network weights to make its optimization landscape have good
theoretical properties while at the same time, be a good approximation for the
unconstrained one. For two-layer neural networks, we provide affirmative
answers to these questions by introducing Porcupine Neural Networks (PNNs)
whose weight vectors are constrained to lie over a finite set of lines. We show
that most local optima of PNN optimizations are global while we have a
characterization of regions where bad local optimizers may exist. Moreover, our
theoretical and empirical results suggest that an unconstrained neural network
can be approximated using a polynomially-large PNN.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,260 | Configuration Path Integral Monte Carlo Approach to the Static Density Response of the Warm Dense Electron Gas | Precise knowledge of the static density response function (SDRF) of the
uniform electron gas (UEG) serves as key input for numerous applications, most
importantly for density functional theory beyond generalized gradient
approximations. Here we extend the configuration path integral Monte Carlo
(CPIMC) formalism that was previously applied to the spatially uniform electron
gas to the case of an inhomogeneous electron gas by adding a spatially periodic
external potential. This procedure has recently been successfully used in
permutation blocking path integral Monte Carlo simulations (PB-PIMC) of the
warm dense electron gas [Dornheim \textit{et al.}, Phys. Rev. E in press,
arXiv:1706.00315], but this method is restricted to low and moderate densities.
Implementing this procedure into CPIMC allows us to obtain exact finite
temperature results for the SDRF of the electron gas at \textit{high to
moderate densities} closing the gap left open by the PB-PIMC data. In this
paper we demonstrate how the CPIMC formalism can be efficiently extended to the
spatially inhomogeneous electron gas and present the first data points.
Finally, we discuss finite size errors involved in the quantum Monte Carlo
results for the SDRF in detail and present a solution how to remove them that
is based on a generalization of ground state techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,261 | Superzone gap formation and low lying crystal electric field levels in PrPd$_2$Ge$_2$ single crystal | The magnetocrystalline anisotropy exhibited in PrPd$_2$Ge$_2$ single crystal
has been investigated by measuring the magnetization, magnetic susceptibility,
electrical resistivity and heat capacity. PrPd$_2$Ge$_2$ crystallizes in the
well known ThCr$_2$Si$_2$\--type tetragonal structure. The antiferromagnetic
ordering is confirmed as 5.1~K with the [001]-axis as the easy axis of
magnetization. A superzone gap formation is observed from the electrical
resistivity measurement when the current is passed along the [001] direction.
The crystal electric field (CEF) analysis on the magnetic susceptibility,
magnetization and the heat capacity measurements confirms a doublet ground
state with a relatively low over all CEF level splitting. The CEF level
spacings and the Zeeman splitting at high fields become comparable and lead to
metamagnetic transition at 34~T due to the CEF level crossing.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,262 | Adaptive Real-Time Software Defined MIMO Visible Light Communications using Spatial Multiplexing and Spatial Diversity | In this paper, we experimentally demonstrate a real-time software defined
multiple input multiple output (MIMO) visible light communication (VLC) system
employing link adaptation of spatial multiplexing and spatial diversity.
Real-time MIMO signal processing is implemented by using the Field Programmable
Gate Array (FPGA) based Universal Software Radio Peripheral (USRP) devices.
Software defined implantation of MIMO VLC can assist in enabling an adaptive
and reconfigurable communication system without hardware changes. We measured
the error vector magnitude (EVM), bit error rate (BER) and spectral efficiency
performance for single carrier M-QAM MIMO VLC using spatial diversity and
spatial multiplexing. Results show that spatial diversity MIMO VLC improves
error performance at the cost of spectral efficiency that spatial multiplexing
should enhance. We propose the adaptive MIMO solution that both modulation
schema and MIMO schema are dynamically adapted to the changing channel
conditions for enhancing the error performance and spectral efficiency. The
average error-free spectral efficiency of adaptive 2x2 MIMO VLC achieved 12
b/s/Hz over 2 meters indoor dynamic transmission.
| 1 | 0 | 1 | 0 | 0 | 0 |
17,263 | Maximum Principle Based Algorithms for Deep Learning | The continuous dynamical system approach to deep learning is explored in
order to devise alternative frameworks for training algorithms. Training is
recast as a control problem and this allows us to formulate necessary
optimality conditions in continuous time using the Pontryagin's maximum
principle (PMP). A modification of the method of successive approximations is
then used to solve the PMP, giving rise to an alternative training algorithm
for deep learning. This approach has the advantage that rigorous error
estimates and convergence results can be established. We also show that it may
avoid some pitfalls of gradient-based methods, such as slow convergence on flat
landscapes near saddle points. Furthermore, we demonstrate that it obtains
favorable initial convergence rate per-iteration, provided Hamiltonian
maximization can be efficiently carried out - a step which is still in need of
improvement. Overall, the approach opens up new avenues to attack problems
associated with deep learning, such as trapping in slow manifolds and
inapplicability of gradient-based methods for discrete trainable variables.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,264 | Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations | With the popularity of Linked Open Data (LOD) and the associated rise in
freely accessible knowledge that can be accessed via LOD, exploiting LOD for
recommender systems has been widely studied based on various approaches such as
graph-based or using different machine learning models with LOD-enabled
features. Many of the previous approaches require construction of an additional
graph to run graph-based algorithms or to extract path-based features by
combining user- item interactions (e.g., likes, dislikes) and background
knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
based on particularly lightweight LOD-enabled features which can be directly
obtained via a public SPARQL Endpoint without any additional effort to
construct a graph. Firstly, we aim to study whether using FM with these
lightweight LOD-enabled features can provide competitive performance compared
to a learning-to-rank approach leveraging LOD as well as other well-established
approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
out to what extent each set of LOD-enabled features contributes to the
recommendation performance. Experimental evaluation on a standard dataset shows
that our proposed approach using FM with lightweight LOD-enabled features
provides the best performance compared to other approaches in terms of five
evaluation metrics. In addition, the study of the recommendation performance
based on different sets of LOD-enabled features indicate that property-object
lists and PageRank scores of items are useful for improving the performance,
and can provide the best performance through using them together for FM. We
observe that subject-property lists of items does not contribute to the
recommendation performance but rather decreases the performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,265 | Wave propagation and homogenization in 2D and 3D lattices: a semi-analytical approach | Wave motion in two- and three-dimensional periodic lattices of beam members
supporting longitudinal and flexural waves is considered. An analytic method
for solving the Bloch wave spectrum is developed, characterized by a
generalized eigenvalue equation obtained by enforcing the Floquet condition.
The dynamic stiffness matrix is shown to be explicitly Hermitian and to admit
positive eigenvalues. Lattices with hexagonal, rectangular, tetrahedral and
cubic unit cells are analyzed. The semi-analytical method can be asymptotically
expanded for low frequency yielding explicit forms for the Christoffel matrix
describing wave motion in the quasistatic limit.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,266 | Waring's problem for unipotent algebraic groups | In this paper, we formulate an analogue of Waring's problem for an algebraic
group $G$. At the field level we consider a morphism of varieties $f\colon
\mathbb{A}^1\to G$ and ask whether every element of $G(K)$ is the product of a
bounded number of elements $f(\mathbb{A}^1(K)) = f(K)$. We give an affirmative
answer when $G$ is unipotent and $K$ is a characteristic zero field which is
not formally real.
The idea is the same at the integral level, except one must work with
schemes, and the question is whether every element in a finite index subgroup
of $G(\mathcal{O})$ can be written as a product of a bounded number of elements
of $f(\mathcal{O})$. We prove this is the case when $G$ is unipotent and
$\mathcal{O}$ is the ring of integers of a totally imaginary number field.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,267 | Spreading of localized attacks in spatial multiplex networks | Many real-world multilayer systems such as critical infrastructure are
interdependent and embedded in space with links of a characteristic length.
They are also vulnerable to localized attacks or failures, such as terrorist
attacks or natural catastrophes, which affect all nodes within a given radius.
Here we study the effects of localized attacks on spatial multiplex networks of
two layers. We find a metastable region where a localized attack larger than a
critical size induces a nucleation transition as a cascade of failures spreads
throughout the system, leading to its collapse. We develop a theory to predict
the critical attack size and find that it exhibits novel scaling behavior. We
further find that localized attacks in these multiplex systems can induce a
previously unobserved combination of random and spatial cascades. Our results
demonstrate important vulnerabilities in real-world interdependent networks and
show new theoretical features of spatial networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
17,268 | Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree | The reconstruction of sparse signals requires the solution of an
$\ell_0$-norm minimization problem in Compressed Sensing. Previous research has
focused on the investigation of a single candidate to identify the support
(index of nonzero elements) of a sparse signal. To ensure that the optimal
candidate can be obtained in each iteration, we propose here an iterative
greedy reconstruction algorithm (GSRA). First, the intersection of the support
sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit
(SP) is set as the initial support set. Then, a hope-tree is built to expand
the set. Finally, a developed decreasing subspace pursuit method is used to
rectify the candidate set. Detailed simulation results demonstrate that GSRA is
more accurate than other typical methods in recovering Gaussian signals, 0--1
sparse signals, and synthetic signals.
| 1 | 0 | 1 | 0 | 0 | 0 |
17,269 | Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems | In this paper, we propose a fault detection and isolation based attack-aware
multi-sensor integration algorithm for the detection of cyberattacks in
autonomous vehicle navigation systems. The proposed algorithm uses an extended
Kalman filter to construct robust residuals in the presence of noise, and then
uses a parametric statistical tool to identify cyberattacks. The parametric
statistical tool is based on the residuals constructed by the measurement
history rather than one measurement at a time in the properties of
discrete-time signals and dynamic systems. This approach allows the proposed
multi-sensor integration algorithm to provide quick detection and low false
alarm rates for applications in dynamic systems. An example of INS/GNSS
integration of autonomous navigation systems is presented to validate the
proposed algorithm by using a software-in-the-loop simulation.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,270 | Turbulence, cascade and singularity in a generalization of the Constantin-Lax-Majda equation | We study numerically a Constantin-Lax-Majda-De Gregorio model generalized by
Okamoto, Sakajo and Wunsch, which is a model of fluid turbulence in one
dimension with an inviscid conservation law. In the presence of the viscosity
and two types of the large-scale forcings, we show that turbulent cascade of
the inviscid invariant, which is not limited to quadratic quantity, occurs and
that properties of this model's turbulent state are related to singularity of
the inviscid case by adopting standard tools of analyzing fluid turbulence.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,271 | Fitting phase--type scale mixtures to heavy--tailed data and distributions | We consider the fitting of heavy tailed data and distribution with a special
attention to distributions with a non--standard shape in the "body" of the
distribution. To this end we consider a dense class of heavy tailed
distributions introduced recently, employing an EM algorithm for the the
maximum likelihood estimates of its parameters. We present methods for fitting
to observed data, histograms, censored data, as well as to theoretical
distributions. Numerical examples are provided with simulated data and a
benchmark reinsurance dataset. We empirically demonstrate that our model can
provide excellent fits to heavy--tailed data/distributions with minimal
assumptions
| 0 | 0 | 1 | 1 | 0 | 0 |
17,272 | Deep Incremental Boosting | This paper introduces Deep Incremental Boosting, a new technique derived from
AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
the required training time and improves generalisation. We draw inspiration
from Transfer of Learning approaches to reduce the start-up time to training
each incremental Ensemble member. We show a set of experiments that outlines
some preliminary results on some common Deep Learning datasets and discuss the
potential improvements Deep Incremental Boosting brings to traditional Ensemble
methods in Deep Learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,273 | Empirical Likelihood for Linear Structural Equation Models with Dependent Errors | We consider linear structural equation models that are associated with mixed
graphs. The structural equations in these models only involve observed
variables, but their idiosyncratic error terms are allowed to be correlated and
non-Gaussian. We propose empirical likelihood (EL) procedures for inference,
and suggest several modifications, including a profile likelihood, in order to
improve tractability and performance of the resulting methods. Through
simulations, we show that when the error distributions are non-Gaussian, the
use of EL and the proposed modifications may increase statistical efficiency
and improve assessment of significance.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,274 | Grassmannian flows and applications to nonlinear partial differential equations | We show how solutions to a large class of partial differential equations with
nonlocal Riccati-type nonlinearities can be generated from the corresponding
linearized equations, from arbitrary initial data. It is well known that
evolutionary matrix Riccati equations can be generated by projecting linear
evolutionary flows on a Stiefel manifold onto a coordinate chart of the
underlying Grassmann manifold. Our method relies on extending this idea to the
infinite dimensional case. The key is an integral equation analogous to the
Marchenko equation in integrable systems, that represents the coodinate chart
map. We show explicitly how to generate such solutions to scalar partial
differential equations of arbitrary order with nonlocal quadratic
nonlinearities using our approach. We provide numerical simulations that
demonstrate the generation of solutions to
Fisher--Kolmogorov--Petrovskii--Piskunov equations with nonlocal
nonlinearities. We also indicate how the method might extend to more general
classes of nonlinear partial differential systems.
| 0 | 1 | 1 | 0 | 0 | 0 |
17,275 | The Reinhardt Conjecture as an Optimal Control Problem | In 1934, Reinhardt conjectured that the shape of the centrally symmetric
convex body in the plane whose densest lattice packing has the smallest density
is a smoothed octagon. This conjecture is still open. We formulate the
Reinhardt Conjecture as a problem in optimal control theory. The smoothed
octagon is a Pontryagin extremal trajectory with bang-bang control. More
generally, the smoothed regular $6k+2$-gon is a Pontryagin extremal with
bang-bang control. The smoothed octagon is a strict (micro) local minimum to
the optimal control problem. The optimal solution to the Reinhardt problem is a
trajectory without singular arcs. The extremal trajectories that do not meet
the singular locus have bang-bang controls with finitely many switching times.
Finally, we reduce the Reinhardt problem to an optimization problem on a
five-dimensional manifold. (Each point on the manifold is an initial condition
for a potential Pontryagin extremal lifted trajectory.) We suggest that the
Reinhardt conjecture might eventually be fully resolved through optimal control
theory. Some proofs are computer-assisted using a computer algebra system.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,276 | Deep submillimeter and radio observations in the SSA22 field. I. Powering sources and Lyα escape fraction of Lyα blobs | We study the heating mechanisms and Ly{\alpha} escape fractions of 35
Ly{\alpha} blobs (LABs) at z = 3.1 in the SSA22 field. Dust continuum sources
have been identified in 11 of the 35 LABs, all with star formation rates (SFRs)
above 100 Msun/yr. Likely radio counterparts are detected in 9 out of 29
investigated LABs. The detection of submm dust emission is more linked to the
physical size of the Ly{\alpha} emission than to the Ly{\alpha} luminosities of
the LABs. A radio excess in the submm/radio detected LABs is common, hinting at
the presence of active galactic nuclei. Most radio sources without X-ray
counterparts are located at the centers of the LABs. However, all X-ray
counterparts avoid the central regions. This may be explained by absorption due
to exceptionally large column densities along the line-of-sight or by LAB
morphologies, which are highly orientation dependent. The median Ly{\alpha}
escape fraction is about 3\% among the submm-detected LABs, which is lower than
a lower limit of 11\% for the submm-undetected LABs. We suspect that the large
difference is due to the high dust attenuation supported by the large SFRs, the
dense large-scale environment as well as large uncertainties in the extinction
corrections required to apply when interpreting optical data.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,277 | Modeling temporal constraints for a system of interactive scores | In this chapter we explain briefly the fundamentals of the interactive scores
formalism. Then we develop a solution for implementing the ECO machine by
mixing petri nets and constraints propagation. We also present another solution
for implementing the ECO machine using concurrent constraint programming.
Finally, we present an extension of interactive score with conditional
branching.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,278 | Electronic structure of ThRu2Si2 studied by angle-resolved photoelectron spectroscopy: Elucidating the contribution of U 5f states in URu2Si2 | The electronic structure of ThRu2Si2 was studied by angle-resolved
photoelectron spectroscopy (ARPES) with incident photon energies of hn=655-745
eV. Detailed band structure and the three-dimensional shapes of Fermi surfaces
were derived experimentally, and their characteristic features were mostly
explained by means of band structure calculations based on the density
functional theory. Comparison of the experimental ARPES spectra of ThRu2Si2
with those of URu2Si2 shows that they have considerably different spectral
profiles particularly in the energy range of 1 eV from the Fermi level,
suggesting that U 5f states are substantially hybridized in these bands. The
relationship between the ARPES spectra of URu2Si2 and ThRu2Si2 is very
different from the one between the ARPES spectra of CeRu2Si2 and LaRu2Si2,
where the intrinsic difference in their spectra is limited only in the very
vicinity of the Fermi energy. The present result suggests that the U 5f
electrons in URu2Si2 have strong hybridization with ligand states and have an
essentially itinerant character.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,279 | Non-zero constant curvature factorable surfaces in pseudo-Galilean space | Factorable surfaces, i.e. graphs associated with the product of two functions
of one variable, constitute a wide class of surfaces. Such surfaces in the
pseudo-Galilean space with zero Gaussian and mean curvature were obtained in
[1]. In this study, we provide new classification results relating to the
factorable surfaces with non-zero Gaussian and mean curvature.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,280 | Darboux and Binary Darboux Transformations for Discrete Integrable Systems. II. Discrete Potential mKdV Equation | The paper presents two results. First it is shown how the discrete potential
modified KdV equation and its Lax pairs in matrix form arise from the
Hirota-Miwa equation by a 2-periodic reduction. Then Darboux transformations
and binary Darboux transformations are derived for the discrete potential
modified KdV equation and it is shown how these may be used to construct exact
solutions.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,281 | Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates | Sequential change-point detection when the distribution parameters are
unknown is a fundamental problem in statistics and machine learning. When the
post-change parameters are unknown, we consider a set of detection procedures
based on sequential likelihood ratios with non-anticipating estimators
constructed using online convex optimization algorithms such as online mirror
descent, which provides a more versatile approach to tackle complex situations
where recursive maximum likelihood estimators cannot be found. When the
underlying distributions belong to a exponential family and the estimators
satisfy the logarithm regret property, we show that this approach is nearly
second-order asymptotically optimal. This means that the upper bound for the
false alarm rate of the algorithm (measured by the average-run-length) meets
the lower bound asymptotically up to a log-log factor when the threshold tends
to infinity. Our proof is achieved by making a connection between sequential
change-point and online convex optimization and leveraging the logarithmic
regret bound property of online mirror descent algorithm. Numerical and real
data examples validate our theory.
| 1 | 0 | 1 | 1 | 0 | 0 |
17,282 | Algorithms in the classical Néron Desingularization | We give algorithms to construct the Néron Desingularization and the easy
case from \cite{KK} of the General Néron Desingularization.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,283 | Recent Advances in Neural Program Synthesis | In recent years, deep learning has made tremendous progress in a number of
fields that were previously out of reach for artificial intelligence. The
successes in these problems has led researchers to consider the possibilities
for intelligent systems to tackle a problem that humans have only recently
themselves considered: program synthesis. This challenge is unlike others such
as object recognition and speech translation, since its abstract nature and
demand for rigor make it difficult even for human minds to attempt. While it is
still far from being solved or even competitive with most existing methods,
neural program synthesis is a rapidly growing discipline which holds great
promise if completely realized. In this paper, we start with exploring the
problem statement and challenges of program synthesis. Then, we examine the
fascinating evolution of program induction models, along with how they have
succeeded, failed and been reimagined since. Finally, we conclude with a
contrastive look at program synthesis and future research recommendations for
the field.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,284 | Generator Reversal | We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we propose instead to use more flexible code distributions. These
distributions are estimated non-parametrically by reversing the generator map
during training. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,285 | Finite model reasoning over existential rules | Ontology-based query answering (OBQA) asks whether a Boolean conjunctive
query is satisfied by all models of a logical theory consisting of a relational
database paired with an ontology. The introduction of existential rules (i.e.,
Datalog rules extended with existential quantifiers in rule-heads) as a means
to specify the ontology gave birth to Datalog+/-, a framework that has received
increasing attention in the last decade, with focus also on decidability and
finite controllability to support effective reasoning. Five basic decidable
fragments have been singled out: linear, weakly-acyclic, guarded, sticky, and
shy. Moreover, for all these fragments, except shy, the important property of
finite controllability has been proved, ensuring that a query is satisfied by
all models of the theory iff it is satisfied by all its finite models. In this
paper we complete the picture by demonstrating that finite controllability of
OBQA holds also for shy ontologies, and it therefore applies to all basic
decidable Datalog+/- classes. To make the demonstration, we devise a general
technique to facilitate the process of (dis)proving finite controllability of
an arbitrary ontological fragment. This paper is under consideration for
acceptance in TPLP.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,286 | On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization | Despite their popularity, the practical performance of asynchronous
stochastic gradient descent methods (ASGD) for solving large scale machine
learning problems are not as good as theoretical results indicate. We adopt and
analyze a synchronous K-step averaging stochastic gradient descent algorithm
which we call K-AVG. We establish the convergence results of K-AVG for
nonconvex objectives and explain why the K-step delay is necessary and leads to
better performance than traditional parallel stochastic gradient descent which
is a special case of K-AVG with $K=1$. We also show that K-AVG scales better
than ASGD. Another advantage of K-AVG over ASGD is that it allows larger
stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD
implementations and achieves better accuracies and faster convergence for
\cifar dataset.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,287 | Adversarial Neural Machine Translation | In this paper, we study a new learning paradigm for Neural Machine
Translation (NMT). Instead of maximizing the likelihood of the human
translation as in previous works, we minimize the distinction between human
translation and the translation given by an NMT model. To achieve this goal,
inspired by the recent success of generative adversarial networks (GANs), we
employ an adversarial training architecture and name it as Adversarial-NMT. In
Adversarial-NMT, the training of the NMT model is assisted by an adversary,
which is an elaborately designed Convolutional Neural Network (CNN). The goal
of the adversary is to differentiate the translation result generated by the
NMT model from that by human. The goal of the NMT model is to produce high
quality translations so as to cheat the adversary. A policy gradient method is
leveraged to co-train the NMT model and the adversary. Experimental results on
English$\rightarrow$French and German$\rightarrow$English translation tasks
show that Adversarial-NMT can achieve significantly better translation quality
than several strong baselines.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,288 | Surface group amalgams that (don't) act on 3-manifolds | We determine which amalgamated products of surface groups identified over
multiples of simple closed curves are not fundamental groups of 3-manifolds. We
prove each surface amalgam considered is virtually the fundamental group of a
3-manifold. We prove that each such surface group amalgam is abstractly
commensurable to a right-angled Coxeter group from a related family. In an
appendix, we determine the quasi-isometry classes among these surface amalgams
and their related right-angled Coxeter groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,289 | Shading Annotations in the Wild | Understanding shading effects in images is critical for a variety of vision
and graphics problems, including intrinsic image decomposition, shadow removal,
image relighting, and inverse rendering. As is the case with other vision
tasks, machine learning is a promising approach to understanding shading - but
there is little ground truth shading data available for real-world images. We
introduce Shading Annotations in the Wild (SAW), a new large-scale, public
dataset of shading annotations in indoor scenes, comprised of multiple forms of
shading judgments obtained via crowdsourcing, along with shading annotations
automatically generated from RGB-D imagery. We use this data to train a
convolutional neural network to predict per-pixel shading information in an
image. We demonstrate the value of our data and network in an application to
intrinsic images, where we can reduce decomposition artifacts produced by
existing algorithms. Our database is available at
this http URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,290 | Koszul cycles and Golod rings | Let $S$ be the power series ring or the polynomial ring over a field $K$ in
the variables $x_1,\ldots,x_n$, and let $R=S/I$, where $I$ is proper ideal
which we assume to be graded if $S$ is the polynomial ring. We give an explicit
description of the cycles of the Koszul complex whose homology classes generate
the Koszul homology of $R=S/I$ with respect to $x_1,\ldots,x_n$. The
description is given in terms of the data of the free $S$-resolution of $R$.
The result is used to determine classes of Golod ideals, among them proper
ordinary powers and proper symbolic powers of monomial ideals. Our theory is
also applied to stretched local rings.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,291 | PacGAN: The power of two samples in generative adversarial networks | Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little diversity, even when trained on diverse datasets. This phenomenon,
known as mode collapse, has been the main focus of several recent advances in
GANs. Yet there is little understanding of why mode collapse happens and why
existing approaches are able to mitigate mode collapse. We propose a principled
approach to handling mode collapse, which we call packing. The main idea is to
modify the discriminator to make decisions based on multiple samples from the
same class, either real or artificially generated. We borrow analysis tools
from binary hypothesis testing---in particular the seminal result of Blackwell
[Bla53]---to prove a fundamental connection between packing and mode collapse.
We show that packing naturally penalizes generators with mode collapse, thereby
favoring generator distributions with less mode collapse during the training
process. Numerical experiments on benchmark datasets suggests that packing
provides significant improvements in practice as well.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,292 | Stein-like Estimators for Causal Mediation Analysis in Randomized Trials | Causal mediation analysis aims to estimate the natural direct and indirect
effects under clearly specified assumptions. Traditional mediation analysis
based on Ordinary Least Squares (OLS) relies on the absence of unmeasured
causes of the putative mediator and outcome. When this assumption cannot be
justified, Instrumental Variables (IV) estimators can be used in order to
produce an asymptotically unbiased estimator of the mediator-outcome link.
However, provided that valid instruments exist, bias removal comes at the cost
of variance inflation for standard IV procedures such as Two-Stage Least
Squares (TSLS). A Semi-Parametric Stein-Like (SPSL) estimator has been proposed
in the literature that strikes a natural trade-off between the unbiasedness of
the TSLS procedure and the relatively small variance of the OLS estimator.
Moreover, the SPSL has the advantage that its shrinkage parameter can be
directly estimated from the data. In this paper, we demonstrate how this
Stein-like estimator can be implemented in the context of the estimation of
natural direct and natural indirect effects of treatments in randomized
controlled trials. The performance of the competing methods is studied in a
simulation study, in which both the strength of hidden confounding and the
strength of the instruments are independently varied. These considerations are
motivated by a trial in mental health evaluating the impact of a primary
care-based intervention to reduce depression in the elderly.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,293 | Structure-Based Subspace Method for Multi-Channel Blind System Identification | In this work, a novel subspace-based method for blind identification of
multichannel finite impulse response (FIR) systems is presented. Here, we
exploit directly the impeded Toeplitz channel structure in the signal linear
model to build a quadratic form whose minimization leads to the desired channel
estimation up to a scalar factor. This method can be extended to estimate any
predefined linear structure, e.g. Hankel, that is usually encountered in linear
systems. Simulation findings are provided to highlight the appealing advantages
of the new structure-based subspace (SSS) method over the standard subspace
(SS) method in certain adverse identification scenarii.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,294 | On Certain Analytical Representations of Cellular Automata | We extend a previously introduced semi-analytical representation of a
decomposition of CA dynamics in arbitrary dimensions and neighborhood schemes
via the use of certain universal maps in which CA rule vectors are derivable
from the equivalent of superpotentials. The results justify the search for
alternative analog models of computation and their possible physical
connections.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,295 | Strong consistency and optimality for generalized estimating equations with stochastic covariates | In this article we study the existence and strong consistency of GEE
estimators, when the generalized estimating functions are martingales with
random coefficients. Furthermore, we characterize estimating functions which
are asymptotically optimal.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,296 | Synthesis and electronic properties of Ruddlesden-Popper strontium iridate epitaxial thin films stabilized by control of growth kinetics | We report on the selective fabrication of high-quality Sr$_2$IrO$_4$ and
SrIrO$_3$ epitaxial thin films from a single polycrystalline Sr$_2$IrO$_4$
target by pulsed laser deposition. Using a combination of X-ray diffraction and
photoemission spectroscopy characterizations, we discover that within a
relatively narrow range of substrate temperature, the oxygen partial pressure
plays a critical role in the cation stoichiometric ratio of the films, and
triggers the stabilization of different Ruddlesden-Popper (RP) phases. Resonant
X-ray absorption spectroscopy measurements taken at the Ir $L$-edge and the O
$K$-edge demonstrate the presence of strong spin-orbit coupling, and reveal the
electronic and orbital structures of both compounds. These results suggest that
in addition to the conventional thermodynamics consideration, higher members of
the Sr$_{n+1}$Ir$_n$O$_{3n+1}$ series can possibly be achieved by kinetic
control away from the thermodynamic limit. These findings offer a new approach
to the synthesis of ultra-thin films of the RP series of iridates and can be
extended to other complex oxides with layered structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,297 | A proof on energy gap for Yang-Mills connection | In this note, we prove an ${L^{\frac{n}{2}}}$-energy gap result for
Yang-Mills connections on a principal $G$-bundle over a compact manifold
without using Lojasiewicz-Simon gradient inequality (arXiv:1502.00668).
| 0 | 0 | 1 | 0 | 0 | 0 |
17,298 | Realisability of Pomsets via Communicating Automata | Pomsets are a model of concurrent computations introduced by Pratt. They can
provide a syntax-oblivious description of semantics of coordination models
based on asynchronous message-passing, such as Message Sequence Charts (MSCs).
In this paper, we study conditions that ensure a specification expressed as a
set of pomsets can be faithfully realised via communicating automata. Our main
contributions are (i) the definition of a realisability condition accounting
for termination soundness, (ii) conditions for global specifications with
"multi-threaded" participants, and (iii) the definition of realisability
conditions that can be decided directly over pomsets. A positive by-product of
our approach is the efficiency gain in the verification of the realisability
conditions obtained when restricting to specific classes of choreographies
characterisable in term of behavioural types.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,299 | Complex pattern formation driven by the interaction of stable fronts in a competition-diffusion system | The ecological invasion problem in which a weaker exotic species invades an
ecosystem inhabited by two strongly competing native species is modelled by a
three-species competition-diffusion system. It is known that for a certain
range of parameter values competitor-mediated coexistence occurs and complex
spatio-temporal patterns are observed in two spatial dimensions. In this paper
we uncover the mechanism which generates such patterns. Under some assumptions
on the parameters the three-species competition-diffusion system admits two
planarly stable travelling waves. Their interaction in one spatial dimension
may result in either reflection or merging into a single homoclinic wave,
depending on the strength of the invading species. This transition can be
understood by studying the bifurcation structure of the homoclinic wave. In
particular, a time-periodic homoclinic wave (breathing wave) is born from a
Hopf bifurcation and its unstable branch acts as a separator between the
reflection and merging regimes. The same transition occurs in two spatial
dimensions: the stable regular spiral associated to the homoclinic wave
destabilizes, giving rise first to an oscillating breathing spiral and then
breaking up producing a dynamic pattern characterized by many spiral cores. We
find that these complex patterns are generated by the interaction of two
planarly stable travelling waves, in contrast with many other well known cases
of pattern formation where planar instability plays a central role.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,300 | Solitons with rings and vortex rings on solitons in nonlocal nonlinear media | Nonlocality is a key feature of many physical systems since it prevents a
catastrophic collapse and a symmetry-breaking azimuthal instability of intense
wave beams in a bulk self-focusing nonlinear media. This opens up an intriguing
perspective for stabilization of complex topological structures such as
higher-order solitons, vortex rings and vortex ring-on-line complexes. Using
direct numerical simulations, we find a class of cylindrically-symmetric $n$-th
order spatial solitons having the intensity distribution with a central bright
spot surrounded by $n$ bright rings of varying size. We investigate dynamical
properties of these higher-order solitons in a media with thermal nonlocal
nonlinear response. We show theoretically that a vortex complex of vortex ring
and vortex line, carrying two independent winding numbers, can be created by
perturbation of the stable optical vortex soliton in nonlocal nonlinear media.
| 0 | 1 | 0 | 0 | 0 | 0 |
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