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Title: Impact of Continuous Integration on Code Reviews,
Abstract: Peer code review and continuous integration often interleave with each other
in the modern software quality management. Although several studies investigate
how non-technical factors (e.g., reviewer workload), developer participation
and even patch size affect the code review process, the impact of continuous
integration on code reviews is not yet properly understood. In this paper, we
report an exploratory study using 578K automated build entries where we
investigate the impact of automated builds on the code reviews. Our
investigation suggests that successfully passed builds are more likely to
encourage new code review participation in a pull request. Frequently built
projects are found to be maintaining a steady level of reviewing activities
over the years, which was quite missing from the rarely built projects.
Experiments with 26,516 automated build entries reported that our proposed
model can identify 64% of the builds that triggered new code reviews later. | [
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] |
Title: New Algorithms for Unordered Tree Inclusion,
Abstract: The tree inclusion problem is, given two node-labeled trees $P$ and $T$ (the
"pattern tree" and the "text tree"), to locate every minimal subtree in $T$ (if
any) that can be obtained by applying a sequence of node insertion operations
to $P$. The ordered tree inclusion problem is known to be solvable in
polynomial time while the unordered tree inclusion problem is NP-hard. The
currently fastest algorithm for the latter is from 1995 and runs in
$O(poly(m,n) \cdot 2^{2d}) = O^{\ast}(4^{d})$ time, where $m$ and $n$ are the
sizes of the pattern and text trees, respectively, and $d$ is the degree of the
pattern tree. Here, we develop a new algorithm that improves the exponent $2d$
to $d$ by considering a particular type of ancestor-descendant relationships
and applying dynamic programming, thus reducing the time complexity to
$O^{\ast}(2^{d})$. We then study restricted variants of the unordered tree
inclusion problem where the number of occurrences of different node labels
and/or the input trees' heights are bounded and show that although the problem
remains NP-hard in many such cases, if the leaves of $P$ are distinctly labeled
and each label occurs at most $c$ times in $T$ then it can be solved in
polynomial time for $c = 2$ and in $O^{\ast}(1.8^d)$ time for $c = 3$. | [
1,
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] |
Title: Learning to Price with Reference Effects,
Abstract: As a firm varies the price of a product, consumers exhibit reference effects,
making purchase decisions based not only on the prevailing price but also the
product's price history. We consider the problem of learning such behavioral
patterns as a monopolist releases, markets, and prices products. This context
calls for pricing decisions that intelligently trade off between maximizing
revenue generated by a current product and probing to gain information for
future benefit. Due to dependence on price history, realized demand can reflect
delayed consequences of earlier pricing decisions. As such, inference entails
attribution of outcomes to prior decisions and effective exploration requires
planning price sequences that yield informative future outcomes. Despite the
considerable complexity of this problem, we offer a tractable systematic
approach. In particular, we frame the problem as one of reinforcement learning
and leverage Thompson sampling. We also establish a regret bound that provides
graceful guarantees on how performance improves as data is gathered and how
this depends on the complexity of the demand model. We illustrate merits of the
approach through simulations. | [
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] |
Title: Inverse Reinforce Learning with Nonparametric Behavior Clustering,
Abstract: Inverse Reinforcement Learning (IRL) is the task of learning a single reward
function given a Markov Decision Process (MDP) without defining the reward
function, and a set of demonstrations generated by humans/experts. However, in
practice, it may be unreasonable to assume that human behaviors can be
explained by one reward function since they may be inherently inconsistent.
Also, demonstrations may be collected from various users and aggregated to
infer and predict user's behaviors. In this paper, we introduce the
Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster
demonstrations and learn multiple reward functions from demonstrations that may
be generated from more than one behaviors. Our method is iterative: It
alternates between clustering demonstrations into different behavior clusters
and inverse learning the reward functions until convergence. It is built upon
the Expectation-Maximization formulation and non-parametric clustering in the
IRL setting. Further, to improve the computation efficiency, we remove the need
of completely solving multiple IRL problems for multiple clusters during the
iteration steps and introduce a resampling technique to avoid generating too
many unlikely clusters. We demonstrate the convergence and efficiency of the
proposed method through learning multiple driver behaviors from demonstrations
generated from a grid-world environment and continuous trajectories collected
from autonomous robot cars using the Gazebo robot simulator. | [
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] |
Title: $z^\circ$-ideals in intermediate rings of ordered field valued continuous functions,
Abstract: A proper ideal $I$ in a commutative ring with unity is called a
$z^\circ$-ideal if for each $a$ in $I$, the intersection of all minimal prime
ideals in $R$ which contain $a$ is contained in $I$. For any totally ordered
field $F$ and a completely $F$-regular topological space $X$, let $C(X,F)$ be
the ring of all $F$-valued continuous functions on $X$ and $B(X,F)$ the
aggregate of all those functions which are bounded over $X$. An explicit
formula for all the $z^\circ$-ideals in $A(X,F)$ in terms of ideals of closed
sets in $X$ is given. It turns out that an intermediate ring $A(X,F)\neq
C(X,F)$ is never regular in the sense of Von-Neumann. This property further
characterizes $C(X,F)$ amongst the intermediate rings within the class of
$P_F$-spaces $X$. It is also realized that $X$ is an almost $P_F$-space if and
only if each maximal ideal in $C(X,F)$ is $z^\circ$-ideal. Incidentally this
property also characterizes $C(X,F)$ amongst the intermediate rings within the
family of almost $P_F$-spaces. | [
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] |
Title: Mosquito Detection with Neural Networks: The Buzz of Deep Learning,
Abstract: Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts. | [
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] |
Title: The inseparability of sampling and time and its influence on attempts to unify the molecular and fossil records,
Abstract: The two major approaches to studying macroevolution in deep time are the
fossil record and reconstructed relationships among extant taxa from molecular
data. Results based on one approach sometimes conflict with those based on the
other, with inconsistencies often attributed to inherent flaws of one (or the
other) data source. What is unquestionable is that both the molecular and
fossil records are limited reflections of the same evolutionary history, and
any contradiction between them represents a failure of our existing models to
explain the patterns we observe. Fortunately, the different limitations of each
record provide an opportunity to test or calibrate the other, and new
methodological developments leverage both records simultaneously. However, we
must reckon with the distinct relationships between sampling and time in the
fossil record and molecular phylogenies. These differences impact our
recognition of baselines, and the analytical incorporation of age estimate
uncertainty. These differences in perspective also influence how different
practitioners view the past and evolutionary time itself, bearing important
implications for the generality of methodological advancements, and differences
in the philosophical approach to macroevolutionary theory across fields. | [
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1,
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] |
Title: The imprints of bars on the vertical stellar population gradients of galactic bulges,
Abstract: This is the second paper of a series aimed to study the stellar kinematics
and population properties of bulges in highly-inclined barred galaxies. In this
work, we carry out a detailed analysis of the stellar age, metallicity and
[Mg/Fe] of 28 highly-inclined ($i > 65^{o}$) disc galaxies, from S0 to S(B)c,
observed with the SAURON integral-field spectrograph. The sample is divided
into two clean samples of barred and unbarred galaxies, on the basis of the
correlation between the stellar velocity and h$_3$ profiles, as well as the
level of cylindrical rotation within the bulge region. We find that while the
mean stellar age, metallicity and [Mg/Fe] in the bulges of barred and unbarred
galaxies are not statistically distinct, the [Mg/Fe] gradients along the minor
axis (away from the disc) of barred galaxies are significantly different than
those without bars. For barred galaxies, stars that are vertically further away
from the midplane are in general more [Mg/Fe]--enhanced and thus the vertical
gradients in [Mg/Fe] for barred galaxies are mostly positive, while for
unbarred bulges the [Mg/Fe] profiles are typically negative or flat. This
result, together with the old populations observed in the barred sample,
indicates that bars are long-lasting structures, and therefore are not easily
destroyed. The marked [Mg/Fe] differences with the bulges of unbarred galaxies
indicate that different formation/evolution scenarios are required to explain
their build-up, and emphasizes the role of bars in redistributing stellar
material in the bulge dominated regions. | [
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] |
Title: A structure-preserving split finite element discretization of the split 1D wave equations,
Abstract: We introduce a new finite element (FE) discretization framework applicable
for covariant split equations. The introduction of additional differential
forms (DF) that form pairs with the original ones permits the splitting of the
equations into topological momentum and continuity equations and
metric-dependent closure equations that apply the Hodge-star operator. Our
discretization framework conserves this geometrical structure and provides for
all DFs proper FE spaces such that the differential operators hold in strong
form. We introduce lowest possible order discretizations of the split 1D wave
equations, in which the discrete momentum and continuity equations follow by
trivial projections onto piecewise constant FE spaces, omitting partial
integrations. Approximating the Hodge-star by nontrivial Galerkin projections
(GP), the two discrete metric equations follow by projections onto either the
piecewise constant (GP0) or piecewise linear (GP1) space.
Our framework gives us three schemes with significantly different behavior.
The split scheme using twice GP1 is unstable and shares the dispersion relation
with the P1-P1 FE scheme that approximates both variables by piecewise linear
spaces (P1). The split schemes that apply a mixture of GP1 and GP0 share the
dispersion relation with the stable P1-P0 FE scheme that applies piecewise
linear and piecewise constant (P0) spaces. However, the split schemes exhibit
second order convergence for both quantities of interest. For the split scheme
applying twice GP0, we are not aware of a corresponding standard formulation to
compare with. Though it does not provide a satisfactory approximation of the
dispersion relation as short waves are propagated much too fast, the discovery
of the new scheme illustrates the potential of our discretization framework as
a toolbox to study and find FE schemes by new combinations of FE spaces. | [
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] |
Title: Quantum quench dynamics,
Abstract: Quench dynamics is an active area of study encompassing condensed matter
physics and quantum information, with applications to cold-atomic gases and
pump-probe spectroscopy of materials. Recent theoretical progress in studying
quantum quenches is reviewed. Quenches in interacting one dimensional systems
as well as systems in higher spatial dimensions are covered. The appearance of
non-trivial steady states following a quench in exactly solvable models is
discussed, and the stability of these states to perturbations is described.
Proper conserving approximations needed to capture the onset of thermalization
at long times are outlined. The appearance of universal scaling for quenches
near critical points, and the role of the renormalization group in capturing
the transient regime, are reviewed. Finally the effect of quenches near
critical points on the dynamics of entanglement entropy and entanglement
statistics is discussed. The extraction of critical exponents from the
entanglement statistics is outlined. | [
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] |
Title: R&D On Beam Injection and Bunching Schemes In The Fermilab Booster,
Abstract: Fermilab is committed to upgrade its accelerator complex to support HEP
experiments at the intensity frontier. The ongoing Proton Improvement Plan
(PIP) enables us to reach 700 kW beam power on the NuMI neutrino targets. By
the end of the next decade, the current 400 MeV normal conducting LINAC will be
replaced by an 800 MeV superconducting LINAC (PIP-II) with an increased beam
power >50% of the PIP design goal. Both in PIP and PIP-II era, the existing
Booster is going to play a very significant role, at least for next two
decades. In the meanwhile, we have recently developed an innovative beam
injection and bunching scheme for the Booster called "early injection scheme"
that continues to use the existing 400 MeV LINAC and implemented into
operation. This scheme has the potential to increase the Booster beam intensity
by >40% from the PIP design goal. Some benefits from the scheme have already
been seen. In this paper, I will describe the basic principle of the scheme,
results from recent beam experiments, our experience with the new scheme in
operation, current status, issues and future plans. This scheme fits well with
the current and future intensity upgrade programs at Fermilab. | [
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] |
Title: Deep Structured Generative Models,
Abstract: Deep generative models have shown promising results in generating realistic
images, but it is still non-trivial to generate images with complicated
structures. The main reason is that most of the current generative models fail
to explore the structures in the images including spatial layout and semantic
relations between objects. To address this issue, we propose a novel deep
structured generative model which boosts generative adversarial networks (GANs)
with the aid of structure information. In particular, the layout or structure
of the scene is encoded by a stochastic and-or graph (sAOG), in which the
terminal nodes represent single objects and edges represent relations between
objects. With the sAOG appropriately harnessed, our model can successfully
capture the intrinsic structure in the scenes and generate images of
complicated scenes accordingly. Furthermore, a detection network is introduced
to infer scene structures from a image. Experimental results demonstrate the
effectiveness of our proposed method on both modeling the intrinsic structures,
and generating realistic images. | [
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] |
Title: Machine Learning Techniques for Stellar Light Curve Classification,
Abstract: We apply machine learning techniques in an attempt to predict and classify
stellar properties from noisy and sparse time series data. We preprocessed over
94 GB of Kepler light curves from MAST to classify according to ten distinct
physical properties using both representation learning and feature engineering
approaches. Studies using machine learning in the field have been primarily
done on simulated data, making our study one of the first to use real light
curve data for machine learning approaches. We tuned our data using previous
work with simulated data as a template and achieved mixed results between the
two approaches. Representation learning using a Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN) produced no successful predictions, but our work
with feature engineering was successful for both classification and regression.
In particular, we were able to achieve values for stellar density, stellar
radius, and effective temperature with low error (~ 2 - 4%) and good accuracy
(~ 75%) for classifying the number of transits for a given star. The results
show promise for improvement for both approaches upon using larger datasets
with a larger minority class. This work has the potential to provide a
foundation for future tools and techniques to aid in the analysis of
astrophysical data. | [
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] |
Title: Frequency measurement of the clock transition of an indium ion sympathetically-cooled in a linear trap,
Abstract: We report frequency measurement of the clock transition in an 115In+ ion
sympathetically-cooled with Ca+ ions in a linear rf trap. The Ca+ ions are used
as a probe of the external electromagnetic field and as the coolant for
preparing the cold In+. The frequency is determined to be 1 267 402 452 901
049.9 (6.9) Hz by averaging 36 measurements using an optical frequency comb
referenced to the frequency standards located in the same site. | [
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] |
Title: Sentence-level dialects identification in the greater China region,
Abstract: Identifying the different varieties of the same language is more challenging
than unrelated languages identification. In this paper, we propose an approach
to discriminate language varieties or dialects of Mandarin Chinese for the
Mainland China, Hong Kong, Taiwan, Macao, Malaysia and Singapore, a.k.a., the
Greater China Region (GCR). When applied to the dialects identification of the
GCR, we find that the commonly used character-level or word-level uni-gram
feature is not very efficient since there exist several specific problems such
as the ambiguity and context-dependent characteristic of words in the dialects
of the GCR. To overcome these challenges, we use not only the general features
like character-level n-gram, but also many new word-level features, including
PMI-based and word alignment-based features. A series of evaluation results on
both the news and open-domain dataset from Wikipedia show the effectiveness of
the proposed approach. | [
1,
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] |
Title: Measuring abstract reasoning in neural networks,
Abstract: Whether neural networks can learn abstract reasoning or whether they merely
rely on superficial statistics is a topic of recent debate. Here, we propose a
dataset and challenge designed to probe abstract reasoning, inspired by a
well-known human IQ test. To succeed at this challenge, models must cope with
various generalisation `regimes' in which the training and test data differ in
clearly-defined ways. We show that popular models such as ResNets perform
poorly, even when the training and test sets differ only minimally, and we
present a novel architecture, with a structure designed to encourage reasoning,
that does significantly better. When we vary the way in which the test
questions and training data differ, we find that our model is notably
proficient at certain forms of generalisation, but notably weak at others. We
further show that the model's ability to generalise improves markedly if it is
trained to predict symbolic explanations for its answers. Altogether, we
introduce and explore ways to both measure and induce stronger abstract
reasoning in neural networks. Our freely-available dataset should motivate
further progress in this direction. | [
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] |
Title: On fractional powers of Bessel operators,
Abstract: This paper was published in the special issue of the Journal of Inequalities
and Special Functions dedicated to Professor Ivan Dimovski's contributions to
different fields of mathematics: transmutation theory, special functions,
integral transforms, function theory etc.
In this paper we study fractional powers of the Bessel differential operator.
The fractional powers are defined explicitly in the integral form without use
of integral transforms in its definitions. Some general properties of the
fractional powers of the Bessel differential operator are proved and some are
listed. Among them are different variations of definitions, relations with the
Mellin and Hankel transforms, group property, generalized Taylor formula with
Bessel operators, evaluation of resolvent integral operator in terms of the
Wright or generalized Mittag--Leffler functions. At the end, some topics are
indicated for further study and possible generalizations. Also the aim of the
paper is to attract attention and give references to not widely known results
on fractional powers of the Bessel differential operator. | [
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] |
Title: Small Telescope Exoplanet Transit Surveys: XO,
Abstract: The XO project aims at detecting transiting exoplanets around bright stars
from the ground using small telescopes. The original configuration of XO
(McCullough et al. 2005) has been changed and extended as described here. The
instrumental setup consists of three identical units located at different
sites, each composed of two lenses equipped with CCD cameras mounted on the
same mount. We observed two strips of the sky covering an area of 520 deg$^2$
for twice nine months. We build lightcurves for ~20,000 stars up to magnitude
R~12.5 using a custom-made photometric data reduction pipeline. The photometric
precision is around 1-2% for most stars, and the large quantity of data allows
us to reach a millimagnitude precision when folding the lightcurves on
timescales that are relevant to exoplanetary transits. We search for periodic
signals and identify several hundreds of variable stars and a few tens of
transiting planet candidates. Follow-up observations are underway to confirm or
reject these candidates. We found two close-in gas giant planets so far, in
line with the expected yield. | [
0,
1,
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0
] |
Title: Divide-and-Conquer Reinforcement Learning,
Abstract: Standard model-free deep reinforcement learning (RL) algorithms sample a new
initial state for each trial, allowing them to optimize policies that can
perform well even in highly stochastic environments. However, problems that
exhibit considerable initial state variation typically produce high-variance
gradient estimates for model-free RL, making direct policy or value function
optimization challenging. In this paper, we develop a novel algorithm that
instead partitions the initial state space into "slices", and optimizes an
ensemble of policies, each on a different slice. The ensemble is gradually
unified into a single policy that can succeed on the whole state space. This
approach, which we term divide-and-conquer RL, is able to solve complex tasks
where conventional deep RL methods are ineffective. Our results show that
divide-and-conquer RL greatly outperforms conventional policy gradient methods
on challenging grasping, manipulation, and locomotion tasks, and exceeds the
performance of a variety of prior methods. Videos of policies learned by our
algorithm can be viewed at this http URL | [
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] |
Title: Whipping of electrified visco-capillary jets in airflows,
Abstract: An electrified visco-capillary jet shows different dynamic behavior, such as
cone forming, breakage into droplets, whipping and coiling, depending on the
considered parameter regime. The whipping instability that is of fundamental
importance for electrospinning has been approached by means of stability
analysis in previous papers. In this work we alternatively propose a model
framework in which the instability can be computed straightforwardly as the
stable stationary solution of an asymptotic Cosserat rod description. For this
purpose, we adopt a procedure by Ribe (Proc. Roy. Soc. Lond. A, 2004)
describing the jet dynamics with respect to a frame rotating with the a priori
unknown whipping frequency that itself becomes part of the solution. The rod
model allows for stretching, bending and torsion, taking into account inertia,
viscosity, surface tension, electric field and air drag. For the resulting
parametric boundary value problem of ordinary differential equations we present
a continuation-collocation method. On top of an implicit Runge-Kutta scheme of
fifth order, our developed continuation procedure makes the efficient and
robust simulation and navigation through a high-dimensional parameter space
possible. Despite the simplicity of the employed electric force model the
numerical results are convincing, the whipping effect is qualitatively well
characterized. | [
0,
1,
1,
0,
0,
0
] |
Title: On Optimizing Feedback Interval for Temporally Correlated MIMO Channels With Transmit Beamforming And Finite-Rate Feedback,
Abstract: A receiver with perfect channel state information (CSI) in a point-to-point
multiple-input multiple-output (MIMO) channel can compute the transmit
beamforming vector that maximizes the transmission rate. For frequency-division
duplex, a transmitter is not able to estimate CSI directly and has to obtain a
quantized transmit beamforming vector from the receiver via a rate-limited
feedback channel. We assume that time evolution of MIMO channels is modeled as
a Gauss-Markov process parameterized by a temporal-correlation coefficient.
Since feedback rate is usually low, we assume rank-one transmit beamforming or
transmission with single data stream. For given feedback rate, we analyze the
optimal feedback interval that maximizes the average received power of the
systems with two transmit or two receive antennas. For other system sizes, the
optimal feedback interval is approximated by maximizing the rate difference in
a large system limit. Numerical results show that the large system
approximation can predict the optimal interval for finite-size system quite
accurately. Numerical results also show that quantizing transmit beamforming
with the optimal feedback interval gives larger rate than the existing
Kalman-filter scheme does by as much as 10% and than feeding back for every
block does by 44% when the number of feedback bits is small. | [
1,
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] |
Title: RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans,
Abstract: We describe a deep learning approach for automated brain hemorrhage detection
from computed tomography (CT) scans. Our model emulates the procedure followed
by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
the model sifts through 2D cross-sectional slices while paying close attention
to potential hemorrhagic regions. Further, the model utilizes 3D context from
neighboring slices to improve predictions at each slice and subsequently,
aggregates the slice-level predictions to provide diagnosis at CT level. We
refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it
employs original DenseNet architecture along with adding the components of
attention for slice level predictions and recurrent neural network layer for
incorporating 3D context. The real-world performance of RADnet has been
benchmarked against independent analysis performed by three senior radiologists
for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at
CT level that is comparable to radiologists. Further, RADnet achieves higher
recall than two of the three radiologists, which is remarkable. | [
0,
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] |
Title: Median statistics estimates of Hubble and Newton's Constant,
Abstract: Robustness of any statistics depends upon the number of assumptions it makes
about the measured data. We point out the advantages of median statistics using
toy numerical experiments and demonstrate its robustness, when the number of
assumptions we can make about the data are limited. We then apply the median
statistics technique to obtain estimates of two constants of nature, Hubble
Constant ($H_0$) and Newton's Gravitational Constant($G$), both of which show
significant differences between different measurements. For $H_0$, we update
the analysis done by Chen and Ratra (2011) and Gott et al. (2001) using $576$
measurements. We find after grouping the different results according to their
primary type of measurement, the median estimates are given by
$H_0=72.5^{+2.5}_{-8}$ km/sec/Mpc with errors corresponding to 95% c.l.
(2$\sigma$) and $G=6.674702^{+0.0014}_{-0.0009} \times 10^{-11} \mathrm{N
m^{2}kg^{-2}}$ corresponding to 68% c.l. (1$\sigma$). | [
0,
1,
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] |
Title: Re-parameterizing and reducing families of normal operators,
Abstract: We present a new proof of results of Kurdyka & Paunescu, and of Rainer, about
real-analytic multi-parameters generalizations of classical results by Rellich
and Kato about the reduction in families of univariate deformations of normal
operators over real or complex vector spaces of finite dimensions.
Given a real analytic family of normal operators over a finite dimensional
real or complex vector space, there exists a locally finite composition of
blowings-up with smooth centers re-parameterizing the given family such that at
each point of the source space of the re-parameterizing mapping, there exists a
neighbourhood of any given point over which exists a real analytic orthonormal
frame in which the pull back of the operator is in reduced form at every point
of the neighbourhood.
A free by-product of our proof is the local real analyticity of the
eigen-values, which in all prior works was a prerequisite step to get local
regular reducing bases. | [
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] |
Title: Urban Scene Segmentation with Laser-Constrained CRFs,
Abstract: Robots typically possess sensors of different modalities, such as colour
cameras, inertial measurement units, and 3D laser scanners. Often, solving a
particular problem becomes easier when more than one modality is used. However,
while there are undeniable benefits to combine sensors of different modalities
the process tends to be complicated. Segmenting scenes observed by the robot
into a discrete set of classes is a central requirement for autonomy as
understanding the scene is the first step to reason about future situations.
Scene segmentation is commonly performed using either image data or 3D point
cloud data. In computer vision many successful methods for scene segmentation
are based on conditional random fields (CRF) where the maximum a posteriori
(MAP) solution to the segmentation can be obtained by inference. In this paper
we devise a new CRF inference method for scene segmentation that incorporates
global constraints, enforcing the sets of nodes are assigned the same class
label. To do this efficiently, the CRF is formulated as a relaxed quadratic
program whose MAP solution is found using a gradient-based optimisation
approach. The proposed method is evaluated on images and 3D point cloud data
gathered in urban environments where image data provides the appearance
features needed by the CRF, while the 3D point cloud data provides global
spatial constraints over sets of nodes. Comparisons with belief propagation,
conventional quadratic programming relaxation, and higher order potential CRF
show the benefits of the proposed method. | [
1,
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] |
Title: The GAPS Programme with HARPS-N at TNG. XIII. The orbital obliquity of three close-in massive planets hosted by dwarf K-type stars: WASP-43, HAT-P-20 and Qatar-2,
Abstract: In the framework of the GAPS project, we are conducting an observational
programme aimed at the determination of the orbital obliquity of known
transiting exoplanets. The targets are selected to probe the obliquity against
a wide range of stellar and planetary physical parameters. We exploit
high-precision radial velocity (RV) measurements, delivered by the HARPS-N
spectrograph at the 3.6m Telescopio Nazionale Galileo, to measure the
Rossiter-McLaughlin (RM) effect in RV time-series bracketing planet transits,
and to refine the orbital parameters determinations with out-of-transit RV
data. We also analyse new transit light curves obtained with several 1-2m class
telescopes to better constrain the physical fundamental parameters of the
planets and parent stars. We report here on new transit spectroscopic
observations for three very massive close-in giant planets: WASP43b, HATP20b
and Qatar2b orbiting dwarf K-type stars with effective temperature well below
5000K. We find lambda = 3.5pm6.8 deg for WASP43b and lambda = -8.0pm6.9 deg for
HATP20b, while for Qatar2, our faintest target, the RM effect is only
marginally detected, though our best-fit value lambda = 15pm20 deg is in
agreement with a previous determination. In combination with stellar rotational
periods derived photometrically, we estimate the true spin-orbit angle, finding
that WASP43b is aligned while the orbit of HATP20b presents a small but
significant obliquity (Psi=36 _{-12}^{+10} deg). By analyzing the CaII H&K
chromospheric emission lines for HATP20 and WASP43, we find evidence for an
enhanced level of stellar activity which is possibly induced by star-planet
interactions. | [
0,
1,
0,
0,
0,
0
] |
Title: Probabilistic Program Equivalence for NetKAT,
Abstract: We tackle the problem of deciding whether two probabilistic programs are
equivalent in Probabilistic NetKAT, a formal language for specifying and
reasoning about the behavior of packet-switched networks. We show that the
problem is decidable for the history-free fragment of the language by
developing an effective decision procedure based on stochastic matrices. The
main challenge lies in reasoning about iteration, which we address by designing
an encoding of the program semantics as a finite-state absorbing Markov chain,
whose limiting distribution can be computed exactly. In an extended case study
on a real-world data center network, we automatically verify various
quantitative properties of interest, including resilience in the presence of
failures, by analyzing the Markov chain semantics. | [
1,
0,
0,
0,
0,
0
] |
Title: Spatially Adaptive Colocalization Analysis in Dual-Color Fluorescence Microscopy,
Abstract: Colocalization analysis aims to study complex spatial associations between
bio-molecules via optical imaging techniques. However, existing colocalization
analysis workflows only assess an average degree of colocalization within a
certain region of interest and ignore the unique and valuable spatial
information offered by microscopy. In the current work, we introduce a new
framework for colocalization analysis that allows us to quantify colocalization
levels at each individual location and automatically identify pixels or regions
where colocalization occurs. The framework, referred to as spatially adaptive
colocalization analysis (SACA), integrates a pixel-wise local kernel model for
colocalization quantification and a multi-scale adaptive propagation-separation
strategy for utilizing spatial information to detect colocalization in a
spatially adaptive fashion. Applications to simulated and real biological
datasets demonstrate the practical merits of SACA in what we hope to be an
easily applicable and robust colocalization analysis method. In addition,
theoretical properties of SACA are investigated to provide rigorous statistical
justification. | [
0,
0,
0,
1,
0,
0
] |
Title: On consistent vertex nomination schemes,
Abstract: Given a vertex of interest in a network $G_1$, the vertex nomination problem
seeks to find the corresponding vertex of interest (if it exists) in a second
network $G_2$. A vertex nomination scheme produces a list of the vertices in
$G_2$, ranked according to how likely they are judged to be the corresponding
vertex of interest in $G_2$. The vertex nomination problem and related
information retrieval tasks have attracted much attention in the machine
learning literature, with numerous applications to social and biological
networks. However, the current framework has often been confined to a
comparatively small class of network models, and the concept of statistically
consistent vertex nomination schemes has been only shallowly explored. In this
paper, we extend the vertex nomination problem to a very general statistical
model of graphs. Further, drawing inspiration from the long-established
classification framework in the pattern recognition literature, we provide
definitions for the key notions of Bayes optimality and consistency in our
extended vertex nomination framework, including a derivation of the Bayes
optimal vertex nomination scheme. In addition, we prove that no universally
consistent vertex nomination schemes exist. Illustrative examples are provided
throughout. | [
0,
0,
0,
1,
0,
0
] |
Title: Converse passivity theorems,
Abstract: Passivity is an imperative concept and a widely utilized tool in the analysis
and control of interconnected systems. It naturally arises in the modelling of
physical systems involving passive elements and dynamics. While many theorems
on passivity are known in the theory of robust control, very few converse
passivity results exist. This paper establishes various versions of converse
passivity theorems for nonlinear feedback systems. In particular, open-loop
passivity is shown to be necessary to ensure closed-loop passivity from an
input-output perspective. Moreover, the stability of the feedback
interconnection of a specific system with an arbitrary passive system is shown
to imply passivity of the system itself. | [
0,
0,
1,
0,
0,
0
] |
Title: Neural Networks retrieving Boolean patterns in a sea of Gaussian ones,
Abstract: Restricted Boltzmann Machines are key tools in Machine Learning and are
described by the energy function of bipartite spin-glasses. From a statistical
mechanical perspective, they share the same Gibbs measure of Hopfield networks
for associative memory. In this equivalence, weights in the former play as
patterns in the latter. As Boltzmann machines usually require real weights to
be trained with gradient descent like methods, while Hopfield networks
typically store binary patterns to be able to retrieve, the investigation of a
mixed Hebbian network, equipped with both real (e.g., Gaussian) and discrete
(e.g., Boolean) patterns naturally arises. We prove that, in the challenging
regime of a high storage of real patterns, where retrieval is forbidden, an
extra load of Boolean patterns can still be retrieved, as long as the ratio
among the overall load and the network size does not exceed a critical
threshold, that turns out to be the same of the standard
Amit-Gutfreund-Sompolinsky theory. Assuming replica symmetry, we study the case
of a low load of Boolean patterns combining the stochastic stability and
Hamilton-Jacobi interpolating techniques. The result can be extended to the
high load by a non rigorous but standard replica computation argument. | [
0,
1,
0,
0,
0,
0
] |
Title: Link colorings and the Goeritz matrix,
Abstract: We discuss the connection between colorings of a link diagram and the Goeritz
matrix. | [
0,
0,
1,
0,
0,
0
] |
Title: Left-invariant Grauert tubes on SU(2),
Abstract: Let M be a real analytic Riemannian manifold. An adapted complex structure on
TM is a complex structure on a neighborhood of the zero section such that the
leaves of the Riemann foliation are complex submanifolds. This structure is
called entire if it may be extended to the whole of TM. We call such manifolds
Grauert tubes, or simply tubes. We consider here the case of M = G a compact
connected Lie group with a left-invariant metric, and try to determine for
which such metrics the associated tube is entire. It is well-known that the
Grauert tube of a bi-invariant metric on a Lie group is entire. The case of the
smallest group SU(2) is treated completely, thanks to the complete
integrability of the geodesic flow for such a metric, a standard result in
classical mechanics. Along the way we find a new obstruction to tubes being
entire which is made visible by the complete integrability. (New reference and
exposition shortened, 11/17/2017.) | [
0,
0,
1,
0,
0,
0
] |
Title: Ricci flow on cone surfaces and a three-dimensional expanding soliton,
Abstract: The main objective of this thesis is the study of the evolution under the
Ricci flow of surfaces with singularities of cone type. A second objective,
emerged from the techniques we use, is the study of families of Ricci flow
solitons in dimension 2 and 3. The Ricci flow is an evolution equation for
Riemannian manifolds, introduced by R. Hamilton in 1982. It is from the
achievements made by G. Perelman with this technique in 2002 when the Ricci
flow has been established in a discipline itself, generating a great interest
in the community. This thesis contains four original results. First result is a
complete classification of solitons in smooth and cone surfaces. This
classification completes the preceding results found by Hamilton, Chow and Wu
and others, and we obtain explicit descriptions of all solitons in dimension 2.
Second result is a Geometrization of cone surfaces by Ricci flow. This result,
which uses the aforementioned first result, extends the theory of Hamilton to
the singular case. This is the most comprehensive result in the thesis, for
which we use and develop analysis and PDE techniques, as well as comparison
geometry techniques. Third result is the existence of a Ricci flow that removes
cone singularities. This clearly exposes the non-uniqueness of solutions to the
flow , in analogy to the Ricci flow with cusps of P. Topping. The fourth result
is the construction of a new expanding gradient Ricci soliton in dimension 3.
Just as we do with solitons on cone surfaces, we give an explicit construction
using techniques of phase portraits. We also prove that this is the only
soliton with its topology and its lower bound of the curvature, and besides
this is a critical case amongst all expanding solitons in dimension 3 with
curvature bounded below. | [
0,
0,
1,
0,
0,
0
] |
Title: Do metric fluctuations affect the Higgs dynamics during inflation?,
Abstract: We show that the dynamics of the Higgs field during inflation is not affected
by metric fluctuations if the Higgs is an energetically subdominant light
spectator. For Standard Model parameters we find that couplings between Higgs
and metric fluctuations are suppressed by $\mathcal{O}(10^{-7})$. They are
negligible compared to both pure Higgs terms in the effective potential and the
unavoidable non-minimal Higgs coupling to background scalar curvature. The
question of the electroweak vacuum instability during high energy scale
inflation can therefore be studied consistently using the Jordan frame action
in a Friedmann--Lemaître--Robertson--Walker metric, where the Higgs-curvature
coupling enters as an effective mass contribution. Similar results apply for
other light spectator scalar fields during inflation. | [
0,
1,
0,
0,
0,
0
] |
Title: Quantum Origami: Transversal Gates for Quantum Computation and Measurement of Topological Order,
Abstract: In topology, a torus remains invariant under certain non-trivial
transformations known as modular transformations. In the context of
topologically ordered quantum states of matter, these transformations encode
the braiding statistics and fusion rules of emergent anyonic excitations and
thus serve as a diagnostic of topological order. Moreover, modular
transformations of higher genus surfaces, e.g. a torus with multiple handles,
can enhance the computational power of a topological state, in many cases
providing a universal fault-tolerant set of gates for quantum computation.
However, due to the intrusive nature of modular transformations, which
abstractly involve global operations and manifold surgery, physical
implementations of them in local systems have remained elusive. Here, we show
that by folding manifolds, modular transformations can be applied in a single
shot by independent local unitaries, providing a novel class of transversal
logic gates for fault-tolerant quantum computation. Specifically, we
demonstrate that multi-layer topological states with appropriate boundary
conditions and twist defects allow modular transformations to be effectively
implemented by a finite sequence of local SWAP gates between the layers. We
further provide methods to directly measure the modular matrices, and thus the
fractional statistics of anyonic excitations, providing a novel way to directly
measure topological order. | [
0,
1,
0,
0,
0,
0
] |
Title: A Decentralized Framework for Real-Time Energy Trading in Distribution Networks with Load and Generation Uncertainty,
Abstract: The proliferation of small-scale renewable generators and price-responsive
loads makes it a challenge for distribution network operators (DNOs) to
schedule the controllable loads of the load aggregators and the generation of
the generators in real-time. Additionally, the high computational burden and
violation of the entities' (i.e., load aggregators' and generators') privacy
make a centralized framework impractical. In this paper, we propose a
decentralized energy trading algorithm that can be executed by the entities in
a real-time fashion. To address the privacy issues, the DNO provides the
entities with proper control signals using the Lagrange relaxation technique to
motivate them towards an operating point with maximum profit for entities. To
deal with uncertainty issues, we propose a probabilistic load model and robust
framework for renewable generation. The performance of the proposed algorithm
is evaluated on an IEEE 123-node test feeder. When compared with a benchmark of
not performing load management for the aggregators, the proposed algorithm
benefits both the load aggregators and generators by increasing their profit by
17.8%and 10.3%, respectively. When compared with a centralized approach, our
algorithm converges to the solution of the DNO's centralized problem with a
significantly lower running time in 50 iterations per time slot. | [
1,
0,
0,
0,
0,
0
] |
Title: White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions,
Abstract: Electrical brain stimulation is currently being investigated as a therapy for
neurological disease. However, opportunities to optimize such therapies are
challenged by the fact that the beneficial impact of focal stimulation on both
neighboring and distant regions is not well understood. Here, we use network
control theory to build a model of brain network function that makes
predictions about how stimulation spreads through the brain's white matter
network and influences large-scale dynamics. We test these predictions using
combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data
who volunteered to participate in an extensive stimulation regimen. We posit a
specific model-based manner in which white matter tracts constrain stimulation,
defining its capacity to drive the brain to new states, including states
associated with successful memory encoding. In a first validation of our model,
we find that the true pattern of white matter tracts can be used to more
accurately predict the state transitions induced by direct electrical
stimulation than the artificial patterns of null models. We then use a targeted
optimal control framework to solve for the optimal energy required to drive the
brain to a given state. We show that, intuitively, our model predicts larger
energy requirements when starting from states that are farther away from a
target memory state. We then suggest testable hypotheses about which structural
properties will lead to efficient stimulation for improving memory based on
energy requirements. Our work demonstrates that individual white matter
architecture plays a vital role in guiding the dynamics of direct electrical
stimulation, more generally offering empirical support for the utility of
network control theoretic models of brain response to stimulation. | [
0,
0,
0,
0,
1,
0
] |
Title: Community detection in networks via nonlinear modularity eigenvectors,
Abstract: Revealing a community structure in a network or dataset is a central problem
arising in many scientific areas. The modularity function $Q$ is an established
measure quantifying the quality of a community, being identified as a set of
nodes having high modularity. In our terminology, a set of nodes with positive
modularity is called a \textit{module} and a set that maximizes $Q$ is thus
called \textit{leading module}. Finding a leading module in a network is an
important task, however the dimension of real-world problems makes the
maximization of $Q$ unfeasible. This poses the need of approximation techniques
which are typically based on a linear relaxation of $Q$, induced by the
spectrum of the modularity matrix $M$. In this work we propose a nonlinear
relaxation which is instead based on the spectrum of a nonlinear modularity
operator $\mathcal M$. We show that extremal eigenvalues of $\mathcal M$
provide an exact relaxation of the modularity measure $Q$, however at the price
of being more challenging to be computed than those of $M$. Thus we extend the
work made on nonlinear Laplacians, by proposing a computational scheme, named
\textit{generalized RatioDCA}, to address such extremal eigenvalues. We show
monotonic ascent and convergence of the method. We finally apply the new method
to several synthetic and real-world data sets, showing both effectiveness of
the model and performance of the method. | [
1,
0,
0,
1,
0,
0
] |
Title: Scale-invariant unconstrained online learning,
Abstract: We consider a variant of online convex optimization in which both the
instances (input vectors) and the comparator (weight vector) are unconstrained.
We exploit a natural scale invariance symmetry in our unconstrained setting:
the predictions of the optimal comparator are invariant under any linear
transformation of the instances. Our goal is to design online algorithms which
also enjoy this property, i.e. are scale-invariant. We start with the case of
coordinate-wise invariance, in which the individual coordinates (features) can
be arbitrarily rescaled. We give an algorithm, which achieves essentially
optimal regret bound in this setup, expressed by means of a coordinate-wise
scale-invariant norm of the comparator. We then study general invariance with
respect to arbitrary linear transformations. We first give a negative result,
showing that no algorithm can achieve a meaningful bound in terms of
scale-invariant norm of the comparator in the worst case. Next, we compliment
this result with a positive one, providing an algorithm which "almost" achieves
the desired bound, incurring only a logarithmic overhead in terms of the norm
of the instances. | [
1,
0,
0,
1,
0,
0
] |
Title: Tuning Pairing Amplitude and Spin-Triplet Texture by Curving Superconducting Nanostructures,
Abstract: We investigate the nature of the superconducting state in curved
nanostructures with Rashba spin-orbit coupling (RSOC). In bent nanostructures
with inhomogeneous curvature we find a local enhancement or suppression of the
superconducting order parameter, with the effect that can be tailored by tuning
either the RSOC strength or the carrier density. Apart from the local
superconducting spin-singlet amplitude control, the geometric curvature
generates non-trivial textures of the spin-triplet pairs through a spatial
variation of the d-vector. By employing the representative case of an
elliptically deformed quantum ring, we demonstrate that the amplitude of the
d-vector strongly depends on the strength of the local curvature and it
generally exhibits a three-dimensional profile whose winding is tied to that of
the single electron spin in the normal state. Our findings unveil novel paths
to manipulate the quantum structure of the superconducting state in RSOC
nanostructures through their geometry. | [
0,
1,
0,
0,
0,
0
] |
Title: Thermal conductivity changes across a structural phase transition: the case of high-pressure silica,
Abstract: By means of first-principles calculations, we investigate the thermal
properties of silica as it evolves, under hydrostatic compression, from a
stishovite phase into a CaCl$_2$-type structure. We compute the thermal
conductivity tensor by solving the linearized Boltzmann transport equation
iteratively in a wide temperature range, using for this the pressure-dependent
harmonic and anharmonic interatomic couplings obtained from first principles.
Most remarkably, we find that, at low temperatures, SiO$_2$ displays a large
peak in the in-plane thermal conductivity and a highly anisotropic behavior
close to the structural transformation. We trace back the origin of these
features by analyzing the phonon contributions to the conductivity. We discuss
the implications of our results in the general context of continuous structural
transformations in solids, as well as the potential geological interest of our
results for silica. | [
0,
1,
0,
0,
0,
0
] |
Title: Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution,
Abstract: A core technique used by popular proxy-based circumvention systems like Tor,
Psiphon, and Lantern is to secretly share the IP addresses of circumvention
proxies with the censored clients for them to be able to use such systems. For
instance, such secretly shared proxies are known as bridges in Tor. However, a
key challenge to this mechanism is the insider attack problem: censoring agents
can impersonate as benign censored clients in order to obtain (and then block)
such secretly shared circumvention proxies.
In this paper, we perform a fundamental study on the problem of insider
attack on proxy-based circumvention systems. We model the proxy distribution
problem using game theory, based on which we derive the optimal strategies of
the parties involved, i.e., the censors and circumvention system operators.
That is, we derive the optimal proxy distribution mechanism of a
circumvention system like Tor, against the censorship adversary who also takes
his optimal censorship strategies.
This is unlike previous works that design ad hoc mechanisms for proxy
distribution, against non-optimal censors.
We perform extensive simulations to evaluate our optimal proxy assignment
algorithm under various adversarial and network settings. Comparing with the
state-of-the-art prior work, we show that our optimal proxy assignment
algorithm has superior performance, i.e., better resistance to censorship even
against the strongest censorship adversary who takes her optimal actions. We
conclude with lessons and recommendation for the design of proxy-based
circumvention systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Mod-$p$ isogeny classes on Shimura varieties with parahoric level structure,
Abstract: We study the special fiber of the integral models for Shimura varieties of
Hodge type with parahoric level structure constructed by Kisin and Pappas in
[KP]. We show that when the group is residually split, the points in the mod
$p$ isogeny classes have the form predicted by the Langlands Rapoport
conjecture in [LR].
We also verify most of the He-Rapoport axioms for these integral models
without the residually split assumption. This allows us to prove that all
Newton strata are non-empty for these models. | [
0,
0,
1,
0,
0,
0
] |
Title: Bayesian Optimization with Gradients,
Abstract: Bayesian optimization has been successful at global optimization of
expensive-to-evaluate multimodal objective functions. However, unlike most
optimization methods, Bayesian optimization typically does not use derivative
information. In this paper we show how Bayesian optimization can exploit
derivative information to decrease the number of objective function evaluations
required for good performance. In particular, we develop a novel Bayesian
optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for
which we show one-step Bayes-optimality, asymptotic consistency, and greater
one-step value of information than is possible in the derivative-free setting.
Our procedure accommodates noisy and incomplete derivative information, comes
in both sequential and batch forms, and can optionally reduce the computational
cost of inference through automatically selected retention of a single
directional derivative. We also compute the d-KG acquisition function and its
gradient using a novel fast discretization-free technique. We show d-KG
provides state-of-the-art performance compared to a wide range of optimization
procedures with and without gradients, on benchmarks including logistic
regression, deep learning, kernel learning, and k-nearest neighbors. | [
1,
0,
1,
1,
0,
0
] |
Title: How to cut a cake with a gram matrix,
Abstract: In this article we study the problem of fair division. In particular we study
a notion introduced by J. Barbanel that generalizes super envy-free fair
division. We give a new proof of his result. Our approach allows us to give an
explicit bound for this kind of fair division. Furthermore, we also give a
theoretical answer to an open problem posed by Barbanel in 1996. Roughly
speaking, this question is: how can we decide if there exists a fair division
satisfying some inequalities constraints? Furthermore, when all the measures
are given with piecewise constant density functions then we show how to
construct effectively such a fair division. | [
1,
0,
0,
0,
0,
0
] |
Title: Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer,
Abstract: In magnetic resonant coupling (MRC) enabled multiple-input multiple-output
(MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each
with one single coil are used to enhance the efficiency of simultaneous power
transfer to multiple single-coil receivers (RXs) by constructively combining
their induced magnetic fields at the RXs, a technique termed "magnetic
beamforming". In this paper, we study the optimal magnetic beamforming design
in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region
that constitutes all the achievable power tuples for all RXs, subject to the
given total power constraint over all TXs as well as their individual peak
voltage and current constraints. We characterize each boundary point of the
power region by maximizing the sum-power deliverable to all RXs subject to
their minimum harvested power constraints. For the special case without the TX
peak voltage and current constraints, we derive the optimal TX current
allocation for the single-RX setup in closed-form as well as that for the
multi-RX setup. In general, the problem is a non-convex quadratically
constrained quadratic programming (QCQP), which is difficult to solve. For the
case of one single RX, we show that the semidefinite relaxation (SDR) of the
problem is tight. For the general case with multiple RXs, based on SDR we
obtain two approximate solutions by applying time-sharing and randomization,
respectively. Moreover, for practical implementation of magnetic beamforming,
we propose a novel signal processing method to estimate the magnetic MIMO
channel due to the mutual inductances between TXs and RXs. Numerical results
show that our proposed magnetic channel estimation and adaptive beamforming
schemes are practically effective, and can significantly improve the power
transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT
systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Bouncy Hybrid Sampler as a Unifying Device,
Abstract: This work introduces a class of rejection-free Markov chain Monte Carlo
(MCMC) samplers, named the Bouncy Hybrid Sampler, which unifies several
existing methods from the literature. Examples include the Bouncy Particle
Sampler of Peters and de With (2012), Bouchard-Cote et al. (2015) and the
Hamiltonian MCMC. Following the introduced general framework, we derive a new
sampler called the Quadratic Bouncy Hybrid Sampler. We apply this novel sampler
to the problem of sampling from a truncated Gaussian distribution. | [
0,
0,
0,
1,
0,
0
] |
Title: Lifshitz interaction can promote ice growth at water-silica interfaces,
Abstract: At air-water interfaces, the Lifshitz interaction by itself does not promote
ice growth. On the contrary, we find that the Lifshitz force promotes the
growth of an ice film, up to 1-8 nm thickness, near silica-water interfaces at
the triple point of water. This is achieved in a system where the combined
effect of the retardation and the zero frequency mode influences the
short-range interactions at low temperatures, contrary to common understanding.
Cancellation between the positive and negative contributions in the Lifshitz
spectral function is reversed in silica with high porosity. Our results provide
a model for how water freezes on glass and other surfaces. | [
0,
1,
0,
0,
0,
0
] |
Title: Reverse iterative volume sampling for linear regression,
Abstract: We study the following basic machine learning task: Given a fixed set of
$d$-dimensional input points for a linear regression problem, we wish to
predict a hidden response value for each of the points. We can only afford to
attain the responses for a small subset of the points that are then used to
construct linear predictions for all points in the dataset. The performance of
the predictions is evaluated by the total square loss on all responses (the
attained as well as the hidden ones). We show that a good approximate solution
to this least squares problem can be obtained from just dimension $d$ many
responses by using a joint sampling technique called volume sampling. Moreover,
the least squares solution obtained for the volume sampled subproblem is an
unbiased estimator of optimal solution based on all n responses. This
unbiasedness is a desirable property that is not shared by other common subset
selection techniques.
Motivated by these basic properties, we develop a theoretical framework for
studying volume sampling, resulting in a number of new matrix expectation
equalities and statistical guarantees which are of importance not only to least
squares regression but also to numerical linear algebra in general. Our methods
also lead to a regularized variant of volume sampling, and we propose the first
efficient algorithms for volume sampling which make this technique a practical
tool in the machine learning toolbox. Finally, we provide experimental evidence
which confirms our theoretical findings. | [
0,
0,
0,
1,
0,
0
] |
Title: Learning from Complementary Labels,
Abstract: Collecting labeled data is costly and thus a critical bottleneck in
real-world classification tasks. To mitigate this problem, we propose a novel
setting, namely learning from complementary labels for multi-class
classification. A complementary label specifies a class that a pattern does not
belong to. Collecting complementary labels would be less laborious than
collecting ordinary labels, since users do not have to carefully choose the
correct class from a long list of candidate classes. However, complementary
labels are less informative than ordinary labels and thus a suitable approach
is needed to better learn from them. In this paper, we show that an unbiased
estimator to the classification risk can be obtained only from complementarily
labeled data, if a loss function satisfies a particular symmetric condition. We
derive estimation error bounds for the proposed method and prove that the
optimal parametric convergence rate is achieved. We further show that learning
from complementary labels can be easily combined with learning from ordinary
labels (i.e., ordinary supervised learning), providing a highly practical
implementation of the proposed method. Finally, we experimentally demonstrate
the usefulness of the proposed methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Dusty winds in active galactic nuclei: reconciling observations with models,
Abstract: This letter presents a revised radiative transfer model for the infrared (IR)
emission of active galactic nuclei (AGN). While current models assume that the
IR is emitted from a dusty torus in the equatorial plane of the AGN, spatially
resolved observations indicate that the majority of the IR emission from 100 pc
in many AGN originates from the polar region, contradicting classical torus
models. The new model CAT3D-WIND builds upon the suggestion that the dusty gas
around the AGN consists of an inflowing disk and an outflowing wind. Here, it
is demonstrated that (1) such disk+wind models cover overall a similar
parameter range of observed spectral features in the IR as classical clumpy
torus models, e.g. the silicate feature strengths and mid-IR spectral slopes,
(2) they reproduce the 3-5{\mu}m bump observed in many type 1 AGN unlike torus
models, and (3) they are able to explain polar emission features seen in IR
interferometry, even for type 1 AGN at relatively low inclination, as
demonstrated for NGC3783. These characteristics make it possible to reconcile
radiative transfer models with observations and provide further evidence of a
two-component parsec-scaled dusty medium around AGN: the disk gives rise to the
3-5{\mu}m near-IR component, while the wind produces the mid-IR emission. The
model SEDs will be made available for download. | [
0,
1,
0,
0,
0,
0
] |
Title: Continuity of Utility Maximization under Weak Convergence,
Abstract: In this paper we find sufficient conditions for the continuity of the value
of the utility maximization problem from terminal wealth with respect to the
convergence in distribution of the underlying processes. We provide several
examples which illustrate that without these conditions, we cannot generally
expect continuity to hold. Finally, we apply our results to the computation of
the minimum shortfall in the Heston model by building an appropriate lattice
approximation. | [
0,
0,
0,
0,
0,
1
] |
Title: Introducing the anatomy of disciplinary discernment: an example from astronomy,
Abstract: Education is increasingly being framed by a competence mindset; the value of
knowledge lies much more in competence performativity and innovation than in
simply knowing. Reaching such competency in areas such as astronomy and physics
has long been known to be challenging. The movement from everyday conceptions
of the world around us to a disciplinary interpretation is fraught with
pitfalls and problems. Thus, what underpins the characteristics of the
disciplinary trajectory to competence becomes an important educational
consideration. In this article we report on a study involving what students and
lecturers discern from the same disciplinary semiotic resource. We use this to
propose an Anatomy of Disciplinary Discernment (ADD), a hierarchy of what is
focused on and how it is interpreted in an appropriate, disciplinary manner, as
an overarching fundamental aspect of disciplinary learning. Students and
lecturers in astronomy and physics were asked to describe what they could
discern from a video simulation of travel through our Galaxy and beyond. 137
people from nine countries participated. The descriptions were analysed using a
hermeneutic interpretive study approach. The analysis resulted in the
formulation of five qualitatively different categories of discernment; the ADD,
reflecting a view of participants' competence levels. The ADD reveals four
increasing levels of disciplinary discernment: Identification, Explanation,
Appreciation, and Evaluation. This facilitates the identification of a clear
relationship between educational level and the level of disciplinary
discernment. The analytical outcomes of the study suggest how teachers of
science, after using the ADD to assess the students disciplinary knowledge, may
attain new insights into how to create more effective learning environments by
explicitly crafting their teaching to support the crossing of boundaries in the
ADD model. | [
0,
1,
0,
0,
0,
0
] |
Title: Well-posedness and dispersive decay of small data solutions for the Benjamin-Ono equation,
Abstract: This article represents a first step toward understanding the long time
dynamics of solutions for the Benjamin-Ono equation. While this problem is
known to be both completely integrable and globally well-posed in $L^2$, much
less seems to be known concerning its long time dynamics. Here, we prove that
for small localized data the solutions have (nearly) dispersive dynamics almost
globally in time. An additional objective is to revisit the $L^2$ theory for
the Benjamin-Ono equation and provide a simpler, self-contained approach. | [
0,
0,
1,
0,
0,
0
] |
Title: A vehicle-to-infrastructure communication based algorithm for urban traffic control,
Abstract: We present in this paper a new algorithm for urban traffic light control with
mixed traffic (communicating and non communicating vehicles) and mixed
infrastructure (equipped and unequipped junctions). We call equipped junction
here a junction with a traffic light signal (TLS) controlled by a road side
unit (RSU). On such a junction, the RSU manifests its connectedness to equipped
vehicles by broadcasting its communication address and geographical
coordinates. The RSU builds a map of connected vehicles approaching and leaving
the junction. The algorithm allows the RSU to select a traffic phase, based on
the built map. The selected traffic phase is applied by the TLS; and both
equipped and unequipped vehicles must respect it. The traffic management is in
feedback on the traffic demand of communicating vehicles. We simulated the
vehicular traffic as well as the communications. The two simulations are
combined in a closed loop with visualization and monitoring interfaces. Several
indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE
802.11p communication performances (end-to-end delay, throughput) are derived
and illustrated in three dimension maps. We then extended the traffic control
to a urban road network where we also varied the number of equipped junctions.
Other indicators are shown for road traffic performances in the road network
case, where high gains are experienced in the simulation results. | [
1,
0,
1,
0,
0,
0
] |
Title: Some Theorems on Optimality of a Single Observation Confidence Interval for the Mean of a Normal Distribution,
Abstract: We consider the problem of finding a proper confidence interval for the mean
based on a single observation from a normal distribution with both mean and
variance unknown. Portnoy (2017) characterizes the scale-sign invariant rules
and shows that the Hunt-Stein construction provides a randomized invariant rule
that improves on any given randomized rule in the sense that it has greater
minimal coverage among all procedures with a fixed expected length.
Mathematical results here provide a specific mixture of two non-randomized
invariant rules that achieve the minimax optimality. A multivariate confidence
set based on a single observation vector is also developed. | [
0,
0,
1,
1,
0,
0
] |
Title: Reliability and applicability of magnetic force linear response theory: Numerical parameters, predictability, and orbital resolution,
Abstract: We investigated the reliability and applicability of so-called magnetic force
linear response method to calculate spin-spin interaction strengths from
first-principles. We examined the dependence on the numerical parameters
including the number of basis orbitals and their cutoff radii within
non-orthogonal LCPAO (linear combination of pseudo-atomic orbitals) formalism.
It is shown that the parameter dependence and the ambiguity caused by these
choices are small enough in comparison to the other computation approach and
experiments. Further, we tried to pursue the possible extension of this
technique to a wider range of applications. We showed that magnetic force
theorem can provide the reasonable estimation especially for the case of
strongly localized moments even when the ground state configuration is unknown
or the total energy value is not accessible. The formalism is extended to carry
the orbital resolution from which the matrix form of the magnetic coupling
constant is calculated. From the applications to Fe-based superconductors
including LaFeAsO, NaFeAs, BaFe$_2$As$_2$ and FeTe, the distinctive
characteristics of orbital-resolved interactions are clearly noticed in between
single-stripe pnictides and double-stripe chalcogenides. | [
0,
1,
0,
0,
0,
0
] |
Title: Verifying Probabilistic Timed Automata Against Omega-Regular Dense-Time Properties,
Abstract: Probabilistic timed automata (PTAs) are timed automata (TAs) extended with
discrete probability distributions.They serve as a mathematical model for a
wide range of applications that involve both stochastic and timed behaviours.
In this work, we consider the problem of model-checking linear
\emph{dense-time} properties over {PTAs}. In particular, we study linear
dense-time properties that can be encoded by TAs with infinite acceptance
criterion.First, we show that the problem of model-checking PTAs against
deterministic-TA specifications can be solved through a product construction.
Based on the product construction, we prove that the computational complexity
of the problem with deterministic-TA specifications is EXPTIME-complete. Then
we show that when relaxed to general (nondeterministic) TAs, the model-checking
problem becomes undecidable.Our results substantially extend state of the art
with both the dense-time feature and the nondeterminism in TAs. | [
1,
0,
0,
0,
0,
0
] |
Title: On the number of integer polynomials with multiplicatively dependent roots,
Abstract: In this paper, we give some counting results on integer polynomials of fixed
degree and bounded height whose distinct non-zero roots are multiplicatively
dependent. These include sharp lower bounds, upper bounds and asymptotic
formulas for various cases, although in general there is a logarithmic gap
between lower and upper bounds. | [
0,
0,
1,
0,
0,
0
] |
Title: New irreducible tensor product modules for the Virasoro algebra,
Abstract: In this paper, we obtain a class of Virasoro modules by taking tensor
products of the irreducible Virasoro modules $\Omega(\lambda,\alpha,h)$ defined
in \cite{CG}, with irreducible highest weight modules $V(\theta,h)$ or with
irreducible Virasoro modules Ind$_{\theta}(N)$ defined in \cite{MZ2}. We obtain
the necessary and sufficient conditions for such tensor product modules to be
irreducible, and determine the necessary and sufficient conditions for two of
them to be isomorphic. These modules are not isomorphic to any other known
irreducible Virasoro modules. | [
0,
0,
1,
0,
0,
0
] |
Title: Polarization dynamics in a photon BEC,
Abstract: It has previously been shown that a dye-filled microcavity can produce a
Bose-Einstein condensate of photons. Thermalization of photons is possible via
repeated absorption and re-emission by the dye molecules. In this paper, we
theoretically explore the behavior of the polarization of light in this system.
We find that in contrast to the near complete thermalization between different
spatial modes of light, thermalization of polarization states is expected to
generally be incomplete. We show that the polarization degree changes
significantly from below to above threshold, and explain the dependence of
polarization on all relevant material parameters. | [
0,
1,
0,
0,
0,
0
] |
Title: Combining symmetry breaking and restoration with configuration interaction: extension to z-signature symmetry in the case of the Lipkin Model,
Abstract: Background: Ab initio many-body methods whose numerical cost scales
polynomially with the number of particles have been developed over the past
fifteen years to tackle closed-shell mid-mass nuclei. Open-shell nuclei have
been further addressed by implementing variants based on the concept of
spontaneous symmetry breaking (and restoration).
Purpose: In order to access the spectroscopy of open-shell nuclei more
systematically while controlling the numerical cost, we design a novel
many-body method that combines the merit of breaking and restoring symmetries
with those brought about by low-rank individual excitations.
Methods: The recently proposed truncated configuration-interaction method
based on optimized symmetry-broken and -restored states is extended to the
z-signature symmetry associated with a discrete subgroup of SU(2). The
highly-truncated N-body Hilbert subspace within which the Hamiltonian is
diagonalized is spanned by a z-signature broken and restored Slater determinant
vacuum and associated low-rank excitations.
Results: The proposed method provides an excellent reproduction of the
ground-state energy and of low-lying excitation energies of various
z-signatures and total angular momenta. In doing so, the successive benefits of
(i) breaking the symmetry, (ii) restoring the symmetry, (iii) including
low-rank particle-hole excitations and (iv) optimizing the amount by which the
underlying vacuum breaks the symmetry are illustrated.
Conclusions: The numerical cost of the newly designed variational method is
polynomial with respect to the system size. The present study confirms the
results obtained previously for the attractive pairing Hamiltonian in
connection with the breaking and restoration of U(1) global gauge symmetry.
These two studies constitute a strong motivation to apply this method to
realistic nuclear Hamiltonians. | [
0,
1,
0,
0,
0,
0
] |
Title: Nonlocal Cauchy problems for wave equations and applications,
Abstract: In this paper, the existence, the uniqueness and estimates of solution to the
integral Cauchy problem for linear and nonlinear abstract wave equations are
proved. The equation includes a linear operator A defined in a Banach space E,
in which by choosing E and A we can obtain numerous classis of nonlocal initial
value problems for wave equations which occur in a wide variety of physical
systems. | [
0,
0,
1,
0,
0,
0
] |
Title: A two-phase gradient method for quadratic programming problems with a single linear constraint and bounds on the variables,
Abstract: We propose a gradient-based method for quadratic programming problems with a
single linear constraint and bounds on the variables. Inspired by the GPCG
algorithm for bound-constrained convex quadratic programming [J.J. Moré and
G. Toraldo, SIAM J. Optim. 1, 1991], our approach alternates between two phases
until convergence: an identification phase, which performs gradient projection
iterations until either a candidate active set is identified or no reasonable
progress is made, and an unconstrained minimization phase, which reduces the
objective function in a suitable space defined by the identification phase, by
applying either the conjugate gradient method or a recently proposed spectral
gradient method. However, the algorithm differs from GPCG not only because it
deals with a more general class of problems, but mainly for the way it stops
the minimization phase. This is based on a comparison between a measure of
optimality in the reduced space and a measure of bindingness of the variables
that are on the bounds, defined by extending the concept of proportioning,
which was proposed by some authors for box-constrained problems. If the
objective function is bounded, the algorithm converges to a stationary point
thanks to a suitable application of the gradient projection method in the
identification phase. For strictly convex problems, the algorithm converges to
the optimal solution in a finite number of steps even in case of degeneracy.
Extensive numerical experiments show the effectiveness of the proposed
approach. | [
0,
0,
1,
0,
0,
0
] |
Title: Euler characteristic and Akashi series for Selmer groups over global function fields,
Abstract: Let $A$ be an abelian variety defined over a global function field $F$ of
positive characteristic $p$ and let $K/F$ be a $p$-adic Lie extension with
Galois group $G$. We provide a formula for the Euler characteristic
$\chi(G,Sel_A(K)_p)$ of the $p$-part of the Selmer group of $A$ over $K$. In
the special case $G=\mathbb{Z}_p^d$ and $A$ a constant ordinary variety, using
Akashi series, we show how the Euler characteristic of the dual of $Sel_A(K)_p$
is related to special values of a $p$-adic $\mathcal{L}$-function. | [
0,
0,
1,
0,
0,
0
] |
Title: The effect of boundary conditions on mixing of 2D Potts models at discontinuous phase transitions,
Abstract: We study Swendsen--Wang dynamics for the critical $q$-state Potts model on
the square lattice. For $q=2,3,4$, where the phase transition is continuous,
the mixing time $t_{\textrm{mix}}$ is expected to obey a universal power-law
independent of the boundary conditions. On the other hand, for large $q$, where
the phase transition is discontinuous, the authors recently showed that
$t_{\textrm{mix}}$ is highly sensitive to boundary conditions:
$t_{\textrm{mix}} \geq \exp(cn)$ on an $n\times n$ box with periodic boundary,
yet under free or monochromatic boundary conditions, $t_{\textrm{mix}}
\leq\exp(n^{o(1)})$.
In this work we classify this effect under boundary conditions that
interpolate between these two (torus vs. free/monochromatic). Specifically, if
one of the $q$ colors is red, mixed boundary conditions such as
red-free-red-free on the 4 sides of the box induce $t_{\textrm{mix}} \geq
\exp(cn)$, yet Dobrushin boundary conditions such as red-red-free-free, as well
as red-periodic-red-periodic, induce sub-exponential mixing. | [
0,
0,
1,
0,
0,
0
] |
Title: Number of thermodynamic states in the three-dimensional Edwards-Anderson spin glass,
Abstract: The question of the number of thermodynamic states present in the
low-temperature phase of the three-dimensional Edwards-Anderson Ising spin
glass is addressed by studying spin and link overlap distributions using
population annealing Monte Carlo simulations. We consider overlaps between
systems with the same boundary condition-which are the usual quantities
measured-and also overlaps between systems with different boundary conditions,
both for the full systems and also within a smaller window within the system.
Our results appear to be fully compatible with a single pair of pure states
such as in the droplet/scaling picture. However, our results for whether or not
domain walls induced by changing boundary conditions are space filling or not
are also compatible with scenarios having many thermodynamic states, such as
the chaotic pairs picture and the replica symmetry breaking picture. The
differing results for spin overlaps in same and different boundary conditions
suggest that finite-size effects are very large for the system sizes currently
accessible in low-temperature simulations. | [
0,
1,
0,
0,
0,
0
] |
Title: Mechanics of disordered auxetic metamaterials,
Abstract: Auxetic materials are of great engineering interest not only because of their
fascinating negative Poisson's ratio, but also due to their increased toughness
and indentation resistance. These materials are typically synthesized polyester
foams with a very heterogeneous structure, but the role of disorder in auxetic
behavior is not fully understood. Here, we provide a systematic theoretical and
experimental investigation in to the effect of disorder on the mechanical
properties of a paradigmatic auxetic lattice with a re-entrant hexagonal
geometry. We show that disorder has a marginal effect on the Poisson's ratio
unless the lattice topology is altered, and in all cases examined the disorder
preserves the auxetic characteristics. Depending on the direction of loading
applied to these disordered auxetic lattices, either brittle or ductile failure
is observed. It is found that brittle failure is associated with a
disorder-dependent tensile strength, whereas in ductile failure disorder does
not affect strength. Our work thus provides general guidelines to optimize
elasticity and strength of disordered auxetic metamaterials. | [
0,
1,
0,
0,
0,
0
] |
Title: Coherent Oscillations of Driven rf SQUID Metamaterials,
Abstract: Through experiments and numerical simulations we explore the behavior of rf
SQUID (radio frequency superconducting quantum interference device)
metamaterials, which show extreme tunability and nonlinearity. The emergent
electromagnetic properties of this metamaterial are sensitive to the degree of
coherent response of the driven interacting SQUIDs. Coherence suffers in the
presence of disorder, which is experimentally found to be mainly due to a dc
flux gradient. We demonstrate methods to recover the coherence, specifically by
varying the coupling between the SQUID meta-atoms and increasing the
temperature or the amplitude of the applied rf flux. | [
0,
1,
0,
0,
0,
0
] |
Title: Localizing virtual structure sheaves by cosections,
Abstract: We construct a cosection localized virtual structure sheaf when a
Deligne-Mumford stack is equipped with a perfect obstruction theory and a
cosection of the obstruction sheaf. | [
0,
0,
1,
0,
0,
0
] |
Title: Regularized Greedy Column Subset Selection,
Abstract: The Column Subset Selection Problem provides a natural framework for
unsupervised feature selection. Despite being a hard combinatorial optimization
problem, there exist efficient algorithms that provide good approximations. The
drawback of the problem formulation is that it incorporates no form of
regularization, and is therefore very sensitive to noise when presented with
scarce data. In this paper we propose a regularized formulation of this
problem, and derive a correct greedy algorithm that is similar in efficiency to
existing greedy methods for the unregularized problem. We study its adequacy
for feature selection and propose suitable formulations. Additionally, we
derive a lower bound for the error of the proposed problems. Through various
numerical experiments on real and synthetic data, we demonstrate the
significantly increased robustness and stability of our method, as well as the
improved conditioning of its output, all while remaining efficient for
practical use. | [
0,
0,
0,
1,
0,
0
] |
Title: The Cartan Algorithm in Five Dimensions,
Abstract: In this paper we introduce an algorithm to determine the equivalence of five
dimensional spacetimes, which generalizes the Karlhede algorithm for four
dimensional general relativity. As an alternative to the Petrov type
classification, we employ the alignment classification to algebraically
classify the Weyl tensor. To illustrate the algorithm we discuss three
examples: the singly rotating Myers-Perry solution, the Kerr (anti) de Sitter
solution, and the rotating black ring solution. We briefly discuss some
applications of the Cartan algorithm in five dimensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Uniform convergence for the incompressible limit of a tumor growth model,
Abstract: We study a model introduced by Perthame and Vauchelet that describes the
growth of a tumor governed by Brinkman's Law, which takes into account friction
between the tumor cells. We adopt the viscosity solution approach to establish
an optimal uniform convergence result of the tumor density as well as the
pressure in the incompressible limit. The system lacks standard maximum
principle, and thus modification of the usual approach is necessary. | [
0,
0,
1,
0,
0,
0
] |
Title: Clustering in Hilbert space of a quantum optimization problem,
Abstract: The solution space of many classical optimization problems breaks up into
clusters which are extensively distant from one another in the Hamming metric.
Here, we show that an analogous quantum clustering phenomenon takes place in
the ground state subspace of a certain quantum optimization problem. This
involves extending the notion of clustering to Hilbert space, where the
classical Hamming distance is not immediately useful. Quantum clusters
correspond to macroscopically distinct subspaces of the full quantum ground
state space which grow with the system size. We explicitly demonstrate that
such clusters arise in the solution space of random quantum satisfiability
(3-QSAT) at its satisfiability transition. We estimate both the number of these
clusters and their internal entropy. The former are given by the number of
hardcore dimer coverings of the core of the interaction graph, while the latter
is related to the underconstrained degrees of freedom not touched by the
dimers. We additionally provide new numerical evidence suggesting that the
3-QSAT satisfiability transition may coincide with the product satisfiability
transition, which would imply the absence of an intermediate entangled
satisfiable phase. | [
1,
1,
0,
0,
0,
0
] |
Title: Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship,
Abstract: Echo chambers, i.e., situations where one is exposed only to opinions that
agree with their own, are an increasing concern for the political discourse in
many democratic countries. This paper studies the phenomenon of political echo
chambers on social media. We identify the two components in the phenomenon: the
opinion that is shared ('echo'), and the place that allows its exposure
('chamber' --- the social network), and examine closely at how these two
components interact. We define a production and consumption measure for
social-media users, which captures the political leaning of the content shared
and received by them. By comparing the two, we find that Twitter users are, to
a large degree, exposed to political opinions that agree with their own. We
also find that users who try to bridge the echo chambers, by sharing content
with diverse leaning, have to pay a 'price of bipartisanship' in terms of their
network centrality and content appreciation. In addition, we study the role of
'gatekeepers', users who consume content with diverse leaning but produce
partisan content (with a single-sided leaning), in the formation of echo
chambers. Finally, we apply these findings to the task of predicting partisans
and gatekeepers from social and content features. While partisan users turn out
relatively easy to identify, gatekeepers prove to be more challenging. | [
1,
0,
0,
0,
0,
0
] |
Title: Resonance enhancement of two photon absorption by magnetically trapped atoms in strong rf-fields,
Abstract: Applying a many mode Floquet formalism for magnetically trapped atoms
interacting with a polychromatic rf-field, we predict a large two photon
transition probability in the atomic system of cold $^{87}Rb$ atoms. The
physical origin of this enormous increase in the two photon transition
probability is due to the formation of avoided crossings between eigen-energy
levels originating from different Floquet sub-manifolds and redistribution of
population in the resonant intermediate levels to give rise to the resonance
enhancement effect. Other exquisite features of the studied atom-field
composite system include the splitting of the generated avoided crossings at
the strong field strength limit and a periodic variation of the single and two
photon transition probabilities with the mode separation frequency of the
polychromatic rf-field. This work can find applications to characterize
properties of cold atom clouds in the magnetic traps using rf-spectroscopy
techniques. | [
0,
1,
0,
0,
0,
0
] |
Title: Unveiling the AGN in IC 883: discovery of a parsec-scale radio jet,
Abstract: IC883 is a luminous infrared galaxy (LIRG) classified as a starburst-active
galactic nucleus (AGN) composite. In a previous study we detected a
low-luminosity AGN (LLAGN) radio candidate. Here we report on our radio
follow-up at three frequencies which provides direct and unequivocal evidence
of the AGN activity in IC883. Our analysis of archival X-ray data, together
with the detection of a transient radio source with luminosity typical of
bright supernovae, give further evidence of the ongoing star formation
activity, which dominates the energetics of the system. At sub-parsec scales,
the radio nucleus has a core-jet morphology with the jet being a newly ejected
component showing a subluminal proper motion of 0.6c-1c. The AGN contributes
less than two per cent of the total IR luminosity of the system. The
corresponding Eddington factor is ~1E-3, suggesting this is a low-accretion
rate engine, as often found in LLAGNs. However, its high bolometric luminosity
(~10E44erg/s) agrees better with a normal AGN. This apparent discrepancy may
just be an indication of the transition nature of the nucleus from a system
dominated by star-formation, to an AGN-dominated system. The nucleus has a
strongly inverted spectrum and a turnover at ~4.4GHz, thus qualifying as a
candidate for the least luminous (L_5.0GHz ~ 6.3E28erg/s/Hz) and one of the
youngest (~3000yr) gigahertz-peaked spectrum (GPS) sources. If the GPS origin
for the IC883 nucleus is confirmed, then advanced mergers in the LIRG category
are potentially key environments to unveil the evolution of GPS sources into
more powerful radio galaxies. | [
0,
1,
0,
0,
0,
0
] |
Title: Counting triangles, tunable clustering and the small-world property in random key graphs (Extended version),
Abstract: Random key graphs were introduced to study various properties of the
Eschenauer-Gligor key predistribution scheme for wireless sensor networks
(WSNs). Recently this class of random graphs has received much attention in
contexts as diverse as recommender systems, social network modeling, and
clustering and classification analysis. This paper is devoted to analyzing
various properties of random key graphs. In particular, we establish a zero-one
law for the the existence of triangles in random key graphs, and identify the
corresponding critical scaling. This zero-one law exhibits significant
differences with the corresponding result in Erdos-Renyi (ER) graphs. We also
compute the clustering coefficient of random key graphs, and compare it to that
of ER graphs in the many node regime when their expected average degrees are
asymptotically equivalent. For the parameter range of practical relevance in
both wireless sensor network and social network applications, random key graphs
are shown to be much more clustered than the corresponding ER graphs. We also
explore the suitability of random key graphs as small world models in the sense
of Watts and Strogatz. | [
1,
0,
1,
0,
0,
0
] |
Title: Convergence Rates of Variational Posterior Distributions,
Abstract: We study convergence rates of variational posterior distributions for
nonparametric and high-dimensional inference. We formulate general conditions
on prior, likelihood, and variational class that characterize the convergence
rates. Under similar "prior mass and testing" conditions considered in the
literature, the rate is found to be the sum of two terms. The first term stands
for the convergence rate of the true posterior distribution, and the second
term is contributed by the variational approximation error. For a class of
priors that admit the structure of a mixture of product measures, we propose a
novel prior mass condition, under which the variational approximation error of
the generalized mean-field class is dominated by convergence rate of the true
posterior. We demonstrate the applicability of our general results for various
models, prior distributions and variational classes by deriving convergence
rates of the corresponding variational posteriors. | [
0,
0,
1,
1,
0,
0
] |
Title: V773 Cas, QS Aql, and BR Ind: Eclipsing Binaries as Parts of Multiple Systems,
Abstract: Eclipsing binaries remain crucial objects for our understanding of the
universe. In particular, those that are components of multiple systems can help
us solve the problem of the formation of these systems. Analysis of the radial
velocities together with the light curve produced for the first time precise
physical parameters of the components of the multiple systems V773 Cas, QS Aql,
and BR Ind. Their visual orbits were also analyzed, which resulted in slightly
improved orbital elements. What is typical for all these systems is that their
most dominant source is the third distant component. The system V773 Cas
consists of two similar G1-2V stars revolving in a circular orbit and a more
distant component of the A3V type. Additionally, the improved value of parallax
was calculated to be 17.6 mas. Analysis of QS Aql resulted in the following:
the inner eclipsing pair is composed of B6V and F1V stars, and the third
component is of about the B6 spectral type. The outer orbit has high
eccentricity of about 0.95, and observations near its upcoming periastron
passage between the years 2038 and 2040 are of high importance. Also, the
parallax of the system was derived to be about 2.89 mas, moving the star much
closer to the Sun than originally assumed. The system BR Ind was found to be a
quadruple star consisting of two eclipsing K dwarfs orbiting each other with a
period of 1.786 days; the distant component is a single-lined spectroscopic
binary with an orbital period of about 6 days. Both pairs are moving around
each other on their 148 year orbit. | [
0,
1,
0,
0,
0,
0
] |
Title: Variance bounding of delayed-acceptance kernels,
Abstract: A delayed-acceptance version of a Metropolis--Hastings algorithm can be
useful for Bayesian inference when it is computationally expensive to calculate
the true posterior, but a computationally cheap approximation is available; the
delayed-acceptance kernel targets the same posterior as its parent
Metropolis-Hastings kernel. Although the asymptotic variance of any functional
of the chain cannot be less than that obtained using its parent, the average
computational time per iteration can be much smaller and so for a given
computational budget the delayed-acceptance kernel can be more efficient.
When the asymptotic variance of all $L^2$ functionals of the chain is finite,
the kernel is said to be variance bounding. It has recently been noted that a
delayed-acceptance kernel need not be variance bounding even when its parent
is. We provide sufficient conditions for inheritance: for global algorithms,
such as the independence sampler, the error in the approximation should be
bounded; for local algorithms, two alternative sets of conditions are provided.
As a by-product of our initial, general result we also supply sufficient
conditions on any pair of proposals such that, for any shared target
distribution, if a Metropolis-Hastings kernel using one of the proposals is
variance bounding then so is the Metropolis-Hastings kernel using the other
proposal. | [
0,
0,
1,
1,
0,
0
] |
Title: Positive Herz-Schur multipliers and approximation properties of crossed products,
Abstract: For a $C^*$-algebra $A$ and a set $X$ we give a Stinespring-type
characterisation of the completely positive Schur $A$-multipliers on
$K(\ell^2(X))\otimes A$. We then relate them to completely positive Herz-Schur
multipliers on $C^*$-algebraic crossed products of the form
$A\rtimes_{\alpha,r} G$, with $G$ a discrete group, whose various versions were
considered earlier by Anantharaman-Delaroche, Bédos and Conti, and Dong and
Ruan. The latter maps are shown to implement approximation properties, such as
nuclearity or the Haagerup property, for $A\rtimes_{\alpha,r} G$. | [
0,
0,
1,
0,
0,
0
] |
Title: Bias-Variance Tradeoff of Graph Laplacian Regularizer,
Abstract: This paper presents a bias-variance tradeoff of graph Laplacian regularizer,
which is widely used in graph signal processing and semi-supervised learning
tasks. The scaling law of the optimal regularization parameter is specified in
terms of the spectral graph properties and a novel signal-to-noise ratio
parameter, which suggests selecting a mediocre regularization parameter is
often suboptimal. The analysis is applied to three applications, including
random, band-limited, and multiple-sampled graph signals. Experiments on
synthetic and real-world graphs demonstrate near-optimal performance of the
established analysis. | [
1,
0,
0,
1,
0,
0
] |
Title: Dual Discriminator Generative Adversarial Nets,
Abstract: We propose in this paper a novel approach to tackle the problem of mode
collapse encountered in generative adversarial network (GAN). Our idea is
intuitive but proven to be very effective, especially in addressing some key
limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and
reverse KL divergences into a unified objective function, thus it exploits the
complementary statistical properties from these divergences to effectively
diversify the estimated density in capturing multi-modes. We term our method
dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has
two discriminators; and together with a generator, it also has the analogy of a
minimax game, wherein a discriminator rewards high scores for samples from data
distribution whilst another discriminator, conversely, favoring data from the
generator, and the generator produces data to fool both two discriminators. We
develop theoretical analysis to show that, given the maximal discriminators,
optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL
divergences between data distribution and the distribution induced from the
data generated by the generator, hence effectively avoiding the mode collapsing
problem. We conduct extensive experiments on synthetic and real-world
large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made
our best effort to compare our D2GAN with the latest state-of-the-art GAN's
variants in comprehensive qualitative and quantitative evaluations. The
experimental results demonstrate the competitive and superior performance of
our approach in generating good quality and diverse samples over baselines, and
the capability of our method to scale up to ImageNet database. | [
1,
0,
0,
1,
0,
0
] |
Title: A Compressive Sensing Approach to Community Detection with Applications,
Abstract: The community detection problem for graphs asks one to partition the n
vertices V of a graph G into k communities, or clusters, such that there are
many intracluster edges and few intercluster edges. Of course this is
equivalent to finding a permutation matrix P such that, if A denotes the
adjacency matrix of G, then PAP^T is approximately block diagonal. As there are
k^n possible partitions of n vertices into k subsets, directly determining the
optimal clustering is clearly infeasible. Instead one seeks to solve a more
tractable approximation to the clustering problem. In this paper we reformulate
the community detection problem via sparse solution of a linear system
associated with the Laplacian of a graph G and then develop a two-stage
approach based on a thresholding technique and a compressive sensing algorithm
to find a sparse solution which corresponds to the community containing a
vertex of interest in G. Crucially, our approach results in an algorithm which
is able to find a single cluster of size n_0 in O(nlog(n)n_0) operations and
all k clusters in fewer than O(n^2ln(n)) operations. This is a marked
improvement over the classic spectral clustering algorithm, which is unable to
find a single cluster at a time and takes approximately O(n^3) operations to
find all k clusters. Moreover, we are able to provide robust guarantees of
success for the case where G is drawn at random from the Stochastic Block
Model, a popular model for graphs with clusters. Extensive numerical results
are also provided, showing the efficacy of our algorithm on both synthetic and
real-world data sets. | [
1,
0,
0,
1,
0,
0
] |
Title: Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods,
Abstract: Uncertainty quantification is a critical missing component in radio
interferometric imaging that will only become increasingly important as the
big-data era of radio interferometry emerges. Since radio interferometric
imaging requires solving a high-dimensional, ill-posed inverse problem,
uncertainty quantification is difficult but also critical to the accurate
scientific interpretation of radio observations. Statistical sampling
approaches to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC)
sampling, can in principle recover the full posterior distribution of the
image, from which uncertainties can then be quantified. However, traditional
high-dimensional sampling methods are generally limited to smooth (e.g.
Gaussian) priors and cannot be used with sparsity-promoting priors. Sparse
priors, motivated by the theory of compressive sensing, have been shown to be
highly effective for radio interferometric imaging. In this article proximal
MCMC methods are developed for radio interferometric imaging, leveraging
proximal calculus to support non-differential priors, such as sparse priors, in
a Bayesian framework. Furthermore, three strategies to quantify uncertainties
using the recovered posterior distribution are developed: (i) local
(pixel-wise) credible intervals to provide error bars for each individual
pixel; (ii) highest posterior density credible regions; and (iii) hypothesis
testing of image structure. These forms of uncertainty quantification provide
rich information for analysing radio interferometric observations in a
statistically robust manner. | [
0,
1,
0,
1,
0,
0
] |
Title: Fairer and more accurate, but for whom?,
Abstract: Complex statistical machine learning models are increasingly being used or
considered for use in high-stakes decision-making pipelines in domains such as
financial services, health care, criminal justice and human services. These
models are often investigated as possible improvements over more classical
tools such as regression models or human judgement. While the modeling approach
may be new, the practice of using some form of risk assessment to inform
decisions is not. When determining whether a new model should be adopted, it is
therefore essential to be able to compare the proposed model to the existing
approach across a range of task-relevant accuracy and fairness metrics. Looking
at overall performance metrics, however, may be misleading. Even when two
models have comparable overall performance, they may nevertheless disagree in
their classifications on a considerable fraction of cases. In this paper we
introduce a model comparison framework for automatically identifying subgroups
in which the differences between models are most pronounced. Our primary focus
is on identifying subgroups where the models differ in terms of
fairness-related quantities such as racial or gender disparities. We present
experimental results from a recidivism prediction task and a hypothetical
lending example. | [
1,
0,
0,
1,
0,
0
] |
Title: Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks,
Abstract: This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup. | [
1,
0,
0,
1,
0,
0
] |
Title: Fast-slow asymptotic for semi-analytical ignition criteria in FitzHugh-Nagumo system,
Abstract: We study the problem of initiation of excitation waves in the FitzHugh-Nagumo
model. Our approach follows earlier works and is based on the idea of
approximating the boundary between basins of attraction of propagating waves
and of the resting state as the stable manifold of a critical solution. Here,
we obtain analytical expressions for the essential ingredients of the theory by
singular perturbation using two small parameters, the separation of time scales
of the activator and inhibitor, and the threshold in the activator's kinetics.
This results in a closed analytical expression for the strength-duration curve. | [
0,
1,
0,
0,
0,
0
] |
Title: What drives galactic magnetism?,
Abstract: We aim to use statistical analysis of a large number of various galaxies to
probe, model, and understand relations between different galaxy properties and
magnetic fields. We have compiled a sample of 55 galaxies including low-mass
dwarf and Magellanic-types, normal spirals and several massive starbursts, and
applied principal component analysis (PCA) and regression methods to assess the
impact of various galaxy properties on the observed magnetic fields. According
to PCA the global galaxy parameters (like HI, H2, and dynamical mass, star
formation rate (SFR), near-infrared luminosity, size, and rotational velocity)
are all mutually correlated and can be reduced to a single principal component.
Further PCA performed for global and intensive (not size related) properties of
galaxies (such as gas density, and surface density of the star formation rate,
SSFR), indicates that magnetic field strength B is connected mainly to the
intensive parameters, while the global parameters have only weak relationships
with B. We find that the tightest relationship of B is with SSFR, which is
described by a power-law with an index of 0.33+-0.03. The observed weaker
associations of B with galaxy dynamical mass and the rotational velocity we
interpret as indirect ones, resulting from the observed connection of the
global SFR with the available total H2 mass in galaxies. Using our sample we
constructed a diagram of B across the Hubble sequence which reveals that high
values of B are not restricted by the Hubble type. However, weaker fields
appear exclusively in later Hubble types and B as low as about 5muG is not seen
among typical spirals. The processes of generation of magnetic field in the
dwarf and Magellanic-type galaxies are similar to those in the massive spirals
and starbursts and are mainly coupled to local star-formation activity
involving the small-scale dynamo mechanism. | [
0,
1,
0,
0,
0,
0
] |
Title: Regular Separability of One Counter Automata,
Abstract: The regular separability problem asks, for two given languages, if there
exists a regular language including one of them but disjoint from the other.
Our main result is decidability, and PSpace-completeness, of the regular
separability problem for languages of one counter automata without zero tests
(also known as one counter nets). This contrasts with undecidability of the
regularity problem for one counter nets, and with undecidability of the regular
separability problem for one counter automata, which is our second result. | [
1,
0,
0,
0,
0,
0
] |
Title: Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel,
Abstract: Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in each iteration. We propose an incremental learning algorithm for SVDD that
uses the Gaussian kernel. This algorithm builds on the observation that all
support vectors on the boundary have the same distance to the center of sphere
in a higher-dimensional feature space as mapped by the Gaussian kernel
function. Each iteration involves only the existing support vectors and the new
data point. Moreover, the algorithm is based solely on matrix manipulations;
the support vectors and their corresponding Lagrange multiplier $\alpha_i$'s
are automatically selected and determined in each iteration. It can be seen
that the complexity of our algorithm in each iteration is only $O(k^2)$, where
$k$ is the number of support vectors. Experimental results on some real data
sets indicate that FISVDD demonstrates significant gains in efficiency with
almost no loss in either outlier detection accuracy or objective function
value. | [
0,
0,
0,
1,
0,
0
] |
Title: Comparing distributions by multiple testing across quantiles or CDF values,
Abstract: When comparing two distributions, it is often helpful to learn at which
quantiles or values there is a statistically significant difference. This
provides more information than the binary "reject" or "do not reject" decision
of a global goodness-of-fit test. Framing our question as multiple testing
across the continuum of quantiles $\tau\in(0,1)$ or values $r\in\mathbb{R}$, we
show that the Kolmogorov--Smirnov test (interpreted as a multiple testing
procedure) achieves strong control of the familywise error rate. However, its
well-known flaw of low sensitivity in the tails remains. We provide an
alternative method that retains such strong control of familywise error rate
while also having even sensitivity, i.e., equal pointwise type I error rates at
each of $n\to\infty$ order statistics across the distribution. Our one-sample
method computes instantly, using our new formula that also instantly computes
goodness-of-fit $p$-values and uniform confidence bands. To improve power, we
also propose stepdown and pre-test procedures that maintain control of the
asymptotic familywise error rate. One-sample and two-sample cases are
considered, as well as extensions to regression discontinuity designs and
conditional distributions. Simulations, empirical examples, and code are
provided. | [
0,
0,
1,
1,
0,
0
] |
Title: Magnetic droplet nucleation with homochiral Neel domain wall,
Abstract: We investigate the effect of the Dzyaloshinskii Moriya interaction (DMI) on
magnetic domain nucleation in a ferromagnetic thin film with perpendicular
magnetic anisotropy. We propose an extended droplet model to determine the
nucleation field as a function of the in-plane field. The model can explain the
experimentally observed nucleation in a CoNi microstrip with the interfacial
DMI. The results are also reproduced by micromagnetic simulation based on the
string model. The electrical measurement method proposed in this study can be
widely used to quantitatively determine the DMI energy density. | [
0,
1,
0,
0,
0,
0
] |
Title: Cost-complexity pruning of random forests,
Abstract: Random forests perform bootstrap-aggregation by sampling the training samples
with replacement. This enables the evaluation of out-of-bag error which serves
as a internal cross-validation mechanism. Our motivation lies in using the
unsampled training samples to improve each decision tree in the ensemble. We
study the effect of using the out-of-bag samples to improve the generalization
error first of the decision trees and second the random forest by post-pruning.
A preliminary empirical study on four UCI repository datasets show consistent
decrease in the size of the forests without considerable loss in accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: Chemical abundances of fast-rotating massive stars. I. Description of the methods and individual results,
Abstract: Aims: Recent observations have challenged our understanding of rotational
mixing in massive stars by revealing a population of fast-rotating objects with
apparently normal surface nitrogen abundances. However, several questions have
arisen because of a number of issues, which have rendered a reinvestigation
necessary; these issues include the presence of numerous upper limits for the
nitrogen abundance, unknown multiplicity status, and a mix of stars with
different physical properties, such as their mass and evolutionary state, which
are known to control the amount of rotational mixing. Methods: We have
carefully selected a large sample of bright, fast-rotating early-type stars of
our Galaxy (40 objects with spectral types between B0.5 and O4). Their
high-quality, high-resolution optical spectra were then analysed with the
stellar atmosphere modelling codes DETAIL/SURFACE or CMFGEN, depending on the
temperature of the target. Several internal and external checks were performed
to validate our methods; notably, we compared our results with literature data
for some well-known objects, studied the effect of gravity darkening, or
confronted the results provided by the two codes for stars amenable to both
analyses. Furthermore, we studied the radial velocities of the stars to assess
their binarity. Results: This first part of our study presents our methods and
provides the derived stellar parameters, He, CNO abundances, and the
multiplicity status of every star of the sample. It is the first time that He
and CNO abundances of such a large number of Galactic massive fast rotators are
determined in a homogeneous way. | [
0,
1,
0,
0,
0,
0
] |
Title: Morphology and Motility of Cells on Soft Substrates,
Abstract: Recent experiments suggest that the interplay between cells and the mechanics
of their substrate gives rise to a diversity of morphological and migrational
behaviors. Here, we develop a Cellular Potts Model of polarizing cells on a
visco-elastic substrate. We compare our model with experiments on endothelial
cells plated on polyacrylamide hydrogels to constrain model parameters and test
predictions. Our analysis reveals that morphology and migratory behavior are
determined by an intricate interplay between cellular polarization and
substrate strain gradients generated by traction forces exerted by cells
(self-haptotaxis). | [
0,
0,
0,
0,
1,
0
] |
Title: A domain-specific language and matrix-free stencil code for investigating electronic properties of Dirac and topological materials,
Abstract: We introduce PVSC-DTM (Parallel Vectorized Stencil Code for Dirac and
Topological Materials), a library and code generator based on a domain-specific
language tailored to implement the specific stencil-like algorithms that can
describe Dirac and topological materials such as graphene and topological
insulators in a matrix-free way. The generated hybrid-parallel (MPI+OpenMP)
code is fully vectorized using Single Instruction Multiple Data (SIMD)
extensions. It is significantly faster than matrix-based approaches on the node
level and performs in accordance with the roofline model. We demonstrate the
chip-level performance and distributed-memory scalability of basic building
blocks such as sparse matrix-(multiple-) vector multiplication on modern
multicore CPUs. As an application example, we use the PVSC-DTM scheme to (i)
explore the scattering of a Dirac wave on an array of gate-defined quantum
dots, to (ii) calculate a bunch of interior eigenvalues for strong topological
insulators, and to (iii) discuss the photoemission spectra of a disordered Weyl
semimetal. | [
1,
1,
0,
0,
0,
0
] |
Title: A cross-correlation-based estimate of the galaxy luminosity function,
Abstract: We extend existing methods for using cross-correlations to derive redshift
distributions for photometric galaxies, without using photometric redshifts.
The model presented in this paper simultaneously yields highly accurate and
unbiased redshift distributions and, for the first time, redshift-dependent
luminosity functions, using only clustering information and the apparent
magnitudes of the galaxies as input. In contrast to many existing techniques
for recovering unbiased redshift distributions, the output of our method is not
degenerate with the galaxy bias b(z), which is achieved by modelling the shape
of the luminosity bias. We successfully apply our method to a mock galaxy
survey and discuss improvements to be made before applying our model to real
data. | [
0,
1,
0,
0,
0,
0
] |
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