title:
Bayesian Modeling of Motion Perception using Dynamical Stochastic
  Textures

abstract:
  A common practice to account for psychophysical biases in vision is to frame
them as consequences of a dynamic process relying on optimal inference with
respect to a generative model. The present study details the complete
formulation of such a generative model intended to probe visual motion
perception with a dynamic texture model. It is first derived in a set of
axiomatic steps constrained by biological plausibility. We extend previous
contributions by detailing three equivalent formulations of this texture model.
First, the composite dynamic textures are constructed by the random aggregation
of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these
textures are cast as solutions to a stochastic partial differential equation
(sPDE). This essential step enables real time, on-the-fly texture synthesis
using time-discretized auto-regressive processes. It also allows for the
derivation of a local motion-energy model, which corresponds to the
log-likelihood of the probability density. The log-likelihoods are essential
for the construction of a Bayesian inference framework. We use the dynamic
texture model to psychophysically probe speed perception in humans using
zoom-like changes in the spatial frequency content of the stimulus. The human
data replicates previous findings showing perceived speed to be positively
biased by spatial frequency increments. A Bayesian observer who combines a
Gaussian likelihood centered at the true speed and a spatial frequency
dependent width with a "slow speed prior" successfully accounts for the
perceptual bias. More precisely, the bias arises from a decrease in the
observer's likelihood width estimated from the experiments as the spatial
frequency increases. Such a trend is compatible with the trend of the dynamic
texture likelihood width.


title: Superfast Low-Rank Approximation and Least Squares Regression

abstract:
  Low Rank Approximation is among most fundamental subjects of numerical linear
algebra having important applications to various areas of modern computing and
%they range from machine learning theory and %neural networks to data mining
and analysis. The known algorithms compute such approximations by using more
flops than the input matrix has entries, but we prove that much fewer flops
than entries are sufficient in the case of the average input ("flop" stands for
"floating point arithmetic operation"). We prove this twice -- for the
solutions by means of two distinct algorithms, and we analyze them by applying
two different approaches. Our analysis of both algorithms is quite involved,
but we devise them mostly by simplifying, combining, and ameliorating the known
techniques, although we propose some technical novelties for further enhancing
the performance of the popular Cross-Approximation Algorithms. They are highly
efficient empirically, and we prove that they are efficient for the average
input.
  We specify some narrow classes of hard inputs for which the presented
algorithms fail with high probability even when we randomize them, but we
narrow such classes further by means of preprocessing with new sparse and
structured multipliers.
  The average complexity estimates do not cover many realistic input classes,
but our formal analysis is in good accordance with the results of our tests
applied to benchmark inputs from discretized PDEs and Integral quations and to
random inputs.
  Our work should already be of practical value but also leads to research
challenges. At the end we list some of them, propose two novel extensions of
our progress -- to the acceleration of the Fast Multipole Method and Conjugate
Gradient algorithms, and explore and slightly extend the recent techniques of
Osinsky, which enhance the output accuracy of CUR Approximation.


title:
Mirror and triplet displacement energies within nuclear DFT: numerical
  stability

abstract:
  Isospin-symmetry-violating class II and III contact terms are introduced into
the Skyrme energy density functional to account for charge dependence of the
strong nuclear interaction. The two new coupling constants are adjusted to
available experimental data on triplet and mirror displacement energies,
respectively. We present preliminary results of the fit, focusing on its
numerical stability with respect to the basis size.


title: Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

abstract:
  A novel method to obtain hierarchical and overlapping clusters from network
data -i.e., a set of nodes endowed with pairwise dissimilarities- is presented.
The introduced method is hierarchical in the sense that it outputs a nested
collection of groupings of the node set depending on the resolution or degree
of similarity desired, and it is overlapping since it allows nodes to belong to
more than one group. Our construction is rooted on the facts that a
hierarchical (non-overlapping) clustering of a network can be equivalently
represented by a finite ultrametric space and that a convex combination of
ultrametrics results in a cut metric. By applying a hierarchical
(non-overlapping) clustering method to multiple dithered versions of a given
network and then convexly combining the resulting ultrametrics, we obtain a cut
metric associated to the network of interest. We then show how to extract a
hierarchical overlapping clustering structure from the aforementioned cut
metric. Furthermore, the so-called overlapping function is presented as a tool
for gaining insights about the data by identifying meaningful resolutions of
the obtained hierarchical structure. Additionally, we explore hierarchical
overlapping quasi-clustering methods that preserve the asymmetry of the data
contained in directed networks. Finally, the presented method is illustrated
via synthetic and real-world classification problems including handwritten
digit classification and authorship attribution of famous plays.


title:
Radiative bound-state-formation cross-sections for dark matter
  interacting via a Yukawa potential

abstract:
  We calculate the cross-sections for the radiative formation of bound states
by dark matter whose interactions are described in the non-relativistic regime
by a Yukawa potential. These cross-sections are important for cosmological and
phenomenological studies of dark matter with long-range interactions, residing
in a hidden sector, as well as for TeV-scale WIMP dark matter. We provide the
leading-order contributions to the cross-sections for the dominant capture
processes occurring via emission of a vector or a scalar boson. We offer a
detailed inspection of their features, including their velocity dependence
within and outside the Coulomb regime, and their resonance structure. For pairs
of annihilating particles, we compare bound-state formation with annihilation.


title: The fate of stringy AdS vacua and the WGC

abstract:
  The authors of arXiv:1610.01533 have recently proposed a stronger version of
the weak gravity conjecture (WGC), based on which they concluded that all those
non-supersymmetric AdS vacua that can be embedded within a constistent theory
of quantum gravity necessarily develop instabilities. In this paper we further
elaborate on this proposal by arguing that the aforementioned instabilities
have a perturbative nature and arise from the crucial interplay between the
closed and the open string sectors of the theory.


title: Volume analysis of supercooled water under high pressure

abstract:
  Motivated by an experimental finding on the density of supercooled water at
high pressure [O. Mishima, J. Chem. Phys. 133, 144503 (2010)] we performed
atomistic molecular dynamics simulations study of bulk water in the
isothermal-isobaric ensemble. Cooling and heating cycles at different isobars
and isothermal compression at different temperatures are performed on the water
sample with pressures that range from 0 to 1.0 GPa. The cooling simulations are
done at temperatures that range from 40 K to 380 K using two different cooling
rates, 10 K/ns and 10 K/5 ns. For the heating simulations we used the slowest
heating rate (10 K/5 ns) by applying the same range of isobars. Our analysis of
the variation of the volume of the bulk water sample with temperature at
different pressures from both isobaric cooling/heating and isothermal
compression cycles indicates a concave-downward curvature at high pressures
that is consistent with the experiment for emulsified water. In particular, a
strong concave down curvature is observed between the temperatures 180 K and
220 K. Below the glass transition temperature, which is around 180 K at 1GPa,
the volume turns to concave upward curvature. No crystallization of the
supercooled liquid state was observed below 180 K even after running the system
for an additional microsecond.


title:
Spontaneous generation of a temperature anisotropy in a strongly coupled
  magnetized plasma

abstract:
  A magnetic field was recently shown to enhance field-parallel heat conduction
in a strongly correlated plasma whereas cross-field conduction is reduced. Here
we show that in such plasmas, the magnetic field has the additional effect of
inhibiting the isotropization process between field-parallel and cross-field
temperature components thus leading to the emergence of strong and long-lived
temperature anisotropies when the plasma is locally perturbed. An extended heat
equation is shown to describe this process accurately.


title:
$(1+3)$D topological superconductors: screening and confinement in
  presence of external fields

abstract:
  Adopting the gauge-invariant and path-dependent variables formalism, we
compute the interaction energy for a topological field theory describing
$(1+3)$D topological superconductors in presence of external fields. As a
result, in the case of a constant electric field- strength expectation value,
we show that the interaction energy describes a purely screening phase, encoded
in a Yukawa potential. On the other hand, in the case of a constant magnetic
field-strength and for a very small Josephson coupling constant, the
particle-antiparticle binding potential displays a linear term leading to the
confinement of static charge probes along with a screening contribution.


title: Towards Robust Adaptive Radiation Therapy Strategies

abstract:
  The authors propose robust adaptive strategies based on stochastic minimax
optimization for a series of simulated treatments on a one-dimensional patient
phantom. The plan applied during the first fractions should be able to handle
anticipated systematic and random errors. At scheduled fractions, the impact of
the measured errors on the delivered dose distribution is evaluated. For a
patient receiving a dose that does not satisfy clinical goals, the plan is
reoptimized based on these individually measured errors. The adapted plan is
then applied during subsequent fractions until a new scheduled adaptation
becomes necessary. In the first adaptive strategy, the measured systematic and
random error scenarios and their assigned probabilities are updated to guide
the robust reoptimization. In the second strategy, the grade of
conservativeness is adapted in response to the measured dose delivery errors.
In the third strategy, the uncertainty margins around the target are
recalculated based on the measured errors. The simulated systematic and random
errors are either similar to the anticipated errors or unpredictably larger, in
order to critically evaluate the performance of the adaptive strategies. The
simulations show that robustly optimized treatment plans provide sufficient
treatment quality for those treatment error scenarios similar to the
anticipated error scenarios. Combining robust planning with adaptation leads to
improved OAR protection. In case of unpredictably larger treatment errors, the
first strategy performs best at improving treatment quality in terms of target
coverage and OAR protection. According to the simulations, these robust
adaptive treatment strategies are able to identify candidates for an adaptive
treatment, thus giving the opportunity to provide individualized plans, and
improve their treatment quality through adaptation.


title: Learning to Rank Scientific Documents from the Crowd

abstract:
  Finding related published articles is an important task in any science, but
with the explosion of new work in the biomedical domain it has become
especially challenging. Most existing methodologies use text similarity metrics
to identify whether two articles are related or not. However biomedical
knowledge discovery is hypothesis-driven. The most related articles may not be
ones with the highest text similarities. In this study, we first develop an
innovative crowd-sourcing approach to build an expert-annotated
document-ranking corpus. Using this corpus as the gold standard, we then
evaluate the approaches of using text similarity to rank the relatedness of
articles. Finally, we develop and evaluate a new supervised model to
automatically rank related scientific articles. Our results show that authors'
ranking differ significantly from rankings by text-similarity-based models. By
training a learning-to-rank model on a subset of the annotated corpus, we found
the best supervised learning-to-rank model (SVM-Rank) significantly surpassed
state-of-the-art baseline systems.


title:
Nonequilibrium and relaxation effects in tunnel superconducting
  junctions

abstract:
  The specific property of a planar tunnel junction with thin-film diffusive
plates and long enough leads is an essential enhancement of its transmission
coefficient compared to the bare transparency of the tunnel barrier [1,2]. In
voltage-biased junctions, this creates favourable conditions for strong
nonequilibrium of quasiparticles in the junction plates and leads, produced by
multiparticle tunneling. We study theoretically the interplay between the
nonequilibrium and relaxation processes in such junctions and found that
nonequilibrium in the leads noticeably modifies the current-voltage
characteristic at $eV > 2\Delta$, especially the excess current, whereas strong
diffusive relaxation restores the result of the classical tunnel model. At $eV
\leq 2\Delta$, the diffusive relaxation decreases the peaks of the
multiparticle currents. The inelastic relaxation in the junction plates
essentially suppresses the $n$-particle currents ($n>2$) by the factor $n$ for
odd and $n/2$ for even $n$. The results may be important for the problem of
decoherence in Josephson-junction based superconducting qubits.


title:
Constraining stellar physics from red-giant stars in binaries - stellar
  rotation, mixing processes and stellar activity

abstract:
  The unparalleled photometric data obtained by NASA's Kepler Space Telescope
has led to an improved understanding of stellar structure and evolution - in
particular for solar-like oscillators in this context. Binary stars are
fascinating objects. Because they were formed together, binary systems provide
a set of two stars with very well constrained parameters. Those can be used to
study properties and physical processes, such as the stellar rotation, dynamics
and rotational mixing of elements and allows us to learn from the differences
we find between the two components. In this work, we discussed a detailed study
of the binary system KIC9163796, discovered through Kepler photometry. The
ground-based follow-up spectroscopy showed that this system is a double-lined
spectroscopic binary, with a mass ratio close to unity. However, the
fundamental parameters of the components of this system as well as their
lithium abundances differ substantially. Kepler photometry of this system
allows to perform a detailed seismic analysis as well as to derive the orbital
period and the surface rotation rate of the primary component of the system.
Indications of the seismic signature of the secondary are found. The differing
parameters are best explained with both components located in the early and the
late phase of the first dredge up at the bottom of the red-giant branch.
Observed lithium abundances in both components are in good agreement with
prediction of stellar models including rotational mixing. By combining
observations and theory, a comprehensive picture of the system can be drawn.


title:
Searching Trees with Permanently Noisy Advice: Walking and Query
  Algorithms

abstract:
  We consider a search problem on trees in which the goal is to find an
adversarially placed treasure, while relying on local, partial information.
Specifically, each node in the tree holds a pointer to one of its neighbors,
termed \emph{advice}. A node is faulty with probability $q$. The advice at a
non-faulty node points to the neighbor that is closer to the treasure, and the
advice at a faulty node points to a uniformly random neighbor. Crucially, the
advice is {\em permanent}, in the sense that querying the same node again would
yield the same answer. Let $\Delta$ denote the maximal degree. Roughly
speaking, when considering the expected number of {\em moves}, i.e., edge
traversals, we show that a phase transition occurs when the {\em noise
parameter} $q$ is about $1/\sqrt{\Delta}$. Below the threshold, there exists an
algorithm with expected move complexity $O(D\sqrt{\Delta})$, where $D$ is the
depth of the treasure, whereas above the threshold, every search algorithm has
expected number of moves which is both exponential in $D$ and polynomial in the
number of nodes~$n$. In contrast, if we require to find the treasure with
probability at least $1-\delta$, then for every fixed $\varepsilon > 0$, if
$q<1/\Delta^{\varepsilon}$ then there exists a search strategy that with
probability $1-\delta$ finds the treasure using $(\delta^{-1}D)^{O(\frac 1
\varepsilon)}$ moves. Moreover, we show that $(\delta^{-1}D)^{\Omega(\frac 1
\varepsilon)}$ moves are necessary. Besides the number of moves, we also study
the number of advice {\em queries} required to find the treasure. Roughly
speaking, for this complexity, we show similar threshold results to those
previously stated, where the parameter $D$ is replaced by $\log n$.


title:
Sub-Nyquist sampling boosts targeted light transport through opaque
  scattering media

abstract:
  Optical time-reversal techniques are being actively developed to focus light
through or inside opaque scattering media. When applied to biological tissue,
these techniques promise to revolutionize biophotonics by enabling deep-tissue
non-invasive optical imaging, optogenetics, optical tweezers and photodynamic
therapy. In all previous optical time-reversal experiments, the scattered light
field was well-sampled during wavefront measurement and wavefront
reconstruction, following the Nyquist sampling criterion. Here, we overturn
this conventional practice by demonstrating that even when the scattered field
is under-sampled, light can still be focused through or inside opaque media.
Even more surprisingly, we show both theoretically and experimentally that the
focus achieved by under-sampling is usually about one order of magnitude
brighter than that achieved by conventional well-sampling conditions. Moreover,
sub-Nyquist sampling improves the signal-to-noise ratio and the collection
efficiency of the scattered light. We anticipate that this newly explored
under-sampling scheme will transform the understanding of optical time reversal
and boost the performance of optical imaging, manipulation, and communication
through opaque scattering media.


title: A Catalog of New Spectroscopically Confirmed Massive OB Stars in Carina

abstract:
  The Carina star-forming region is one of the largest in the Galaxy, and its
massive star population is still being unveiled. The large number of stars
combined with high, and highly variable, interstellar extinction makes it
inherently difficult to find OB stars in this type of young region. We present
the results of a spectroscopic campaign to study the massive star population of
the Carina Nebula, with the primary goal to confirm or reject previously
identified Carina OB star candidates. A total of 141 known O- and B-type stars
and 94 candidates were observed, of which 73 candidates had a high enough
signal-to-noise ratio to classify. We find 23 new OB stars within the Carina
Nebula, a 32% confirmation rate. One of the new OB stars has blended spectra
and is suspected to be a double-lined spectroscopic binary (SB2). We also
reclassify the spectral types of the known OB stars and discover nine new SB2s
among this population. Finally, we discuss the spatial distribution of these
new OB stars relative to known structures in the Carina NSebula.


title:
The Loewner difference equation and convergence of loop-erased random
  walk

abstract:
  We revisit the convergence of loop-erased random walk, LERW, to SLE(2) when
the curves are parametrized by capacity. We construct a coupling of the chordal
version of LERW and chordal SLE(2) based on the Green's function for LERW as
martingale observable and using an elementary discrete-time Loewner
"difference" equation. This coupling is different than the ones previously
considered in this context. Our recent work (arXiv:1603.05203) on the
convergence of LERW parametrized by length to SLE(2) parameterized by Minkowski
content uses specific features of the coupling constructed here.


title: Downburst Prediction Applications of GOES over the Western United States

abstract:
  Over the western United States, the hazards posed to aviation operations by
convective storm-generated downbursts have been extensively documented. Other
significant hazards posed by convective downbursts over the intermountain
western U.S. include the rapid intensification and propagation of wildfires and
the sudden generation of visibility-reducing dust storms (haboobs). The
existing suite of GOES downburst prediction algorithms employs the GOES sounder
to calculate potential of occurrence based on conceptual models of favorable
environmental thermodynamic profiles for downburst generation. Previous
research has demonstrated the effectiveness of the Dry Microburst Index (DMI)
as a prediction tool for convectively generated high winds. A more
recently-developed diagnostic nowcasting product, the Microburst Windspeed
Potential Index (MWPI) is designed to diagnose attributes of a favorable
downburst environment: 1) the presence of convective available potential energy
(CAPE), and 2) the presence of a deep surface-based or elevated mixed layer
with a large temperature lapse rate. This paper presents an updated assessment
of the MWPI algorithm, case studies demonstrating effective operational use of
the MWPI product, and recent validation results. MWPI data were collected for
downburst events that occurred during the 2014 convective season and were
validated against surface observations of convective wind gusts as recorded by
wind sensors in high-quality mesonetworks. Favorable validation results include
a statistically significant correlation (r > 0.6) and low mean error (< 1 kt)
between MWPI values and confirmed downburst wind speeds measured in situ.


title: Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting

abstract:
  In this work, we introduce a highly efficient algorithm to address the
nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix
factorization (NMF) with an additional underapproximation constraint. NMU
results are interesting as, compared to traditional NMF, they present
additional sparsity and part-based behavior, explaining unique data features.
To show these features in practice, we first present an application to the
analysis of climate data. We then present an NMU-based algorithm to robustly
fit multiple parametric models to a dataset. The proposed approach delivers
state-of-the-art results for the estimation of multiple fundamental matrices
and homographies, outperforming other alternatives in the literature and
exemplifying the use of efficient NMU computations.


title: The radial dependence of dark matter distribution in M33

abstract:
  The stellar and gaseous mass distributions, as well as the extended rotation
curve in the nearby galaxy M33 are used to derive the radial distribution of
dark matter density in the halo and to test cosmological models of galaxy
formation and evolution. Two methods are examined to constrain dark mass
density profiles. The first method deals directly with fitting the rotation
curve data in the range of galactocentric distances $0.24\,\text{kpc}\leq
r\leq22.72\,\text{kpc}.$ As found in a previous paper by
\citet{Corbelli:2014lga}, and using the results of collisionless $\Lambda-$Cold
Dark Matter numerical simulations, we confirm that the Navarro-Frenkel-White
(hereafter NFW) dark matter profile provides a better fit to the rotation curve
data than the cored Burkert profile (hereafter BRK) profile. The second method
relies on the local equation of centrifugal equilibrium and on the rotation
curve slope. In the aforementioned range of distances we fit the observed
velocity profile, using a function which has a rational dependence on the
radius, and derive the slope of the rotation curve. Following
\citet{Salucci:2010qr} we then infer the effective matter densities. In the
radial range $9.53\,\text{kpc}\leq r\leq22.72\,\text{kpc}$ the uncertainties
induced by the luminous matter (stars and gas) becomes negligible, because the
dark matter density dominates, and we can determine locally the radial
distribution of dark matter. With this second method we tested the NFW and the
BRK dark matter profiles and confirm that both profiles are compatible with the
data even though in this case the cored BRK density profile provides a more
reasonable value for the baryonic-to-dark matter ratio.


title: Pitfalls of exergy analysis

abstract:
  The well-known Gouy-Stodola theorem states that a device produces maximum
useful power when working reversibly, that is with no entropy production inside
the device. This statement then leads to a method of thermodynamic optimization
based on entropy production minimization. Exergy destruction (difference
between exergy of fuel and exhausts) is also given by entropy production inside
the device. Therefore, assessing efficiency of a device by exergy analysis is
also based on the Gouy-Stodola theorem. However, assumptions that had led to
the Gouy-Stodola theorem are not satisfied in several optimization scenarios,
e.g. non-isothermal steady-state fuel cells, where both entropy production
minimization and exergy analysis should be used with caution. We demonstrate,
using non-equilibrium thermodynamics, a few cases where entropy production
minimization and exergy analysis should not be applied.


title:
A collocation method based on extended cubic B-splines for numerical
  solutions of the Klein-Gordon equation

abstract:
  A generalization of classical cubic B-spline functions with a parameter is
used as basis in the collocation method. Some initial boundary value problems
constructed on the nonlinear Klein-gordon equation are solved by the proposed
method for extension various parameters. The coupled system derived as a result
of the reduction of the time order of the equation is integrated in time by the
Crank-Nicolson method. After linearizing the nonlinear term, the collocation
procedure is implemented. Adapting the initial conditions provides a linear
iteration system for the fully integration of the equation. The validity of the
method is investigated by measuring the maximum errors between analytical and
the numerical solutions. The absolute relative changes of the conservation laws
describing the energy and the momentum are computed for both problems.


title:
Platooning of Connected Vehicles with Undirected Topologies: Robustness
  Analysis and Distributed H-infinity Controller Synthesis

abstract:
  This paper considers the robustness analysis and distributed
$\mathcal{H}_{\infty}$ (H-infinity) controller synthesis for a platoon of
connected vehicles with undirected topologies. We first formulate a unified
model to describe the collective behavior of homogeneous platoons with external
disturbances using graph theory. By exploiting the spectral decomposition of a
symmetric matrix, the collective dynamics of a platoon is equivalently
decomposed into a set of subsystems sharing the same size with one single
vehicle. Then, we provide an explicit scaling trend of robustness measure
$\gamma$-gain, and introduce a scalable multi-step procedure to synthesize a
distributed $\mathcal{H}_{\infty}$ controller for large-scale platoons. It is
shown that communication topology, especially the leader's information, exerts
great influence on both robustness performance and controller synthesis.
Further, an intuitive optimization problem is formulated to optimize an
undirected topology for a platoon system, and the upper and lower bounds of the
objective are explicitly analyzed, which hints us that coordination of multiple
mini-platoons is one reasonable architecture to control large-scale platoons.
Numerical simulations are conducted to illustrate our findings.


title:
A distinguished geometry perspective on multi-time affine quadratic
  Lagrangians

abstract:
  For a space endowed with a general quadratic multi-time Lagrangian and an
associated non-linear connection, the paper constructs the main
Riemann-Lagrange distinguished geometric objects (linear connection, torsion
and curvature).


title:
Information-Theoretic Bounds and Approximations in Neural Population
  Coding

abstract:
  While Shannon's mutual information has widespread applications in many
disciplines, for practical applications it is often difficult to calculate its
value accurately for high-dimensional variables because of the curse of
dimensionality. This paper is focused on effective approximation methods for
evaluating mutual information in the context of neural population coding. For
large but finite neural populations, we derive several information-theoretic
asymptotic bounds and approximation formulas that remain valid in
high-dimensional spaces. We prove that optimizing the population density
distribution based on these approximation formulas is a convex optimization
problem which allows efficient numerical solutions. Numerical simulation
results confirmed that our asymptotic formulas were highly accurate for
approximating mutual information for large neural populations. In special
cases, the approximation formulas are exactly equal to the true mutual
information. We also discuss techniques of variable transformation and
dimensionality reduction to facilitate computation of the approximations.


title: Explicit Implicit Function Theorem for All Fields

abstract:
  We give an explicit implicit function theorem for formal power series that is
valid for all fields, which implies in particular Lagrange inversion formula
and and Flajolet-Soria coefficient extraction formula known for fields of
characteristic 0.


title: Chromatic Zagreb indices for graphical embodiment of colour clusters

abstract:
  For a colour cluster $\mathbb{C} =(\mathcal{C}_1,\mathcal{C}_2,
\mathcal{C}_3,\ldots,\mathcal{C}_\ell)$, where $\mathcal{C}_i$ is a colour
class such that $|\mathcal{C}_i|=r_i$, a positive integer, we investigate two
types of simple connected graph structures $G^{\mathbb{C}}_1$,
$G^{\mathbb{C}}_2$ which represent graphical embodiments of the colour cluster
such that the chromatic numbers
$\chi(G^{\mathbb{C}}_1)=\chi(G^{\mathbb{C}}_2)=\ell$ and
$\min\{\varepsilon(G^{\mathbb{C}}_1)\}=\min\{\varepsilon(G^{\mathbb{C}}_2)\}
=\sum\limits_{i=1}^{\ell}r_i-1$. Therefore, the problem is the edge-minimality
inverse to finding the chromatic number of a given simple connected graph. In
this paper, we also discuss the chromatic Zagreb indices corresponding to
$G^{\mathbb{C}}_1$, $G^{\mathbb{C}}_2$.


title: A general framework for denoising phaseless diffraction measurements

abstract:
  We propose a general framework to recover underlying images from noisy
phaseless diffraction measurements based on the alternating directional method
of multipliers and the plug-and-play technique. The algorithm consists of
three-step iterations: (i) Solving a generalized least square problem with the
maximum a posteriori (MAP) estimate of the noise, (ii) Gaussian denoising and
(iii) updating the multipliers. The denoising step utilizes higher order
filters such as total generalized variation and nonlocal sparsity based filters
including nonlocal mean (NLM) and Block-matching and 3D filtering (BM3D)
filters. The multipliers are updated by a symmetric technique to increase
convergence speed. The proposed method with low computational complexity is
provided with theoretical convergence guarantee, and it enables recovering
images with sharp edges, clean background and repetitive features from noisy
phaseless measurements. Numerous numerical experiments for Fourier phase
retrieval (PR) as coded diffraction and ptychographic patterns are performed to
verify the convergence and efficiency, showing that our proposed method
outperforms the state-of-art PR algorithms without any regularization and those
with total variational regularization.


title: Predicted reentrant melting of dense hydrogen at ultra-high pressures

abstract:
  The phase diagram of hydrogen is one of the most important challenges in
high-pressure physics and astrophysics. Especially, the melting of dense
hydrogen is complicated by dimer dissociation, metallization and nuclear
quantum effect of protons, which together lead to a cold melting of dense
hydrogen when above 500 GPa. Nonetheless, the variation of the melting curve at
higher pressures is virtually uncharted. Here we report that using ab initio
molecular dynamics and path integral simulations based on density functional
theory, a new atomic phase is discovered, which gives an uplifting melting
curve of dense hydrogen when beyond 2 TPa, and results in a reentrant
solid-liquid transition before entering the Wigner crystalline phase of
protons. The findings greatly extend the phase diagram of dense hydrogen, and
put metallic hydrogen into the group of alkali metals, with its melting curve
closely resembling those of lithium and sodium.

categories: cond-mat.mtrl-sci astro-ph.EP physics.chem-ph physics.comp-ph


title:
Stratifying High Dimensional Data Based on Proximity to the Convex Hull
  Boundary

abstract:
  The convex hull of a set of points, $C$, serves to expose extremal properties
of $C$ and can help identify elements in $C$ of high interest. For many
problems, particularly in the presence of noise, the true vertex set (and
facets) may be difficult to determine. One solution is to expand the list of
high interest candidates to points lying near the boundary of the convex hull.
We propose a quadratic program for the purpose of stratifying points in a data
cloud based on proximity to the boundary of the convex hull. For each data
point, a quadratic program is solved to determine an associated weight vector.
We show that the weight vector encodes geometric information concerning the
point's relationship to the boundary of the convex hull. The computation of the
weight vectors can be carried out in parallel, and for a fixed number of points
and fixed neighborhood size, the overall computational complexity of the
algorithm grows linearly with dimension. As a consequence, meaningful
computations can be completed on reasonably large, high dimensional data sets.


title:
An integral equation-based numerical solver for Taylor states in
  toroidal geometries

abstract:
  We develop an algorithm for the numerical calculation of Taylor states in
toroidal and toroidal shell geometries using an analytical framework developed
for the solution to the time-harmonic Maxwell equations. Taylor states are a
special case of what are known as Beltrami fields, or linear force-free fields.
The scheme of this work relies on the generalized Debye source representation
of Maxwell fields and an integral representation of Beltrami fields which
immediately yields a well-conditioned second-kind integral equation. This
integral equation has a unique solution whenever the Beltrami parameter
$\lambda$ is not a member of a discrete, countable set of resonances which
physically correspond to spontaneous symmetry breaking. Several numerical
examples relevant to magnetohydrodynamic equilibria calculations are provided.
Lastly, our approach easily generalizes to arbitrary geometries, both bounded
and unbounded, and of varying genus.


title:
STDP-based spiking deep convolutional neural networks for object
  recognition

abstract:
  Previous studies have shown that spike-timing-dependent plasticity (STDP) can
be used in spiking neural networks (SNN) to extract visual features of low or
intermediate complexity in an unsupervised manner. These studies, however, used
relatively shallow architectures, and only one layer was trainable. Another
line of research has demonstrated - using rate-based neural networks trained
with back-propagation - that having many layers increases the recognition
robustness, an approach known as deep learning. We thus designed a deep SNN,
comprising several convolutional (trainable with STDP) and pooling layers. We
used a temporal coding scheme where the most strongly activated neurons fire
first, and less activated neurons fire later or not at all. The network was
exposed to natural images. Thanks to STDP, neurons progressively learned
features corresponding to prototypical patterns that were both salient and
frequent. Only a few tens of examples per category were required and no label
was needed. After learning, the complexity of the extracted features increased
along the hierarchy, from edge detectors in the first layer to object
prototypes in the last layer. Coding was very sparse, with only a few thousands
spikes per image, and in some cases the object category could be reasonably
well inferred from the activity of a single higher-order neuron. More
generally, the activity of a few hundreds of such neurons contained robust
category information, as demonstrated using a classifier on Caltech 101,
ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over
other unsupervised techniques such as random crops (HMAX) or auto-encoders.
Taken together, our results suggest that the combination of STDP with latency
coding may be a key to understanding the way that the primate visual system
learns, its remarkable processing speed and its low energy consumption.


title:
Very weak solutions of subquadratic parabolic systems with non-standard
  $p(x,t)$-growth

abstract:
  The aim of this paper is to establish a higher integrability result for very
weak solutions of certain parabolic systems whose model is the parabolic
$p(x,t)$-Laplacian system. Under assumptions on the exponent function
$p:\Omega_T=\Omega\times (0,T)\to\left(\frac{2n}{n+2},2\right]$, it is shown
that any very weak solution $u:\Omega_T\rightarrow\mathbb{R}^N$ with
$|Du|^{p(\cdot)(1-\varepsilon)}\in L^1(\Omega_T)$ belongs to the natural energy
spaces, i.e. $|Du|^{p(\cdot)}\in L^1_{\operatorname{loc}}(\Omega_T)$, provided
$\epsilon>0$ is small enough. This extends the main result of [V. B\"ogelein
and Q. Li, Nonlinear Anal., 98 (2014), pp. 190-225] to the subquadratic case.


title: Learning Continuous Semantic Representations of Symbolic Expressions

abstract:
  Combining abstract, symbolic reasoning with continuous neural reasoning is a
grand challenge of representation learning. As a step in this direction, we
propose a new architecture, called neural equivalence networks, for the problem
of learning continuous semantic representations of algebraic and logical
expressions. These networks are trained to represent semantic equivalence, even
of expressions that are syntactically very different. The challenge is that
semantic representations must be computed in a syntax-directed manner, because
semantics is compositional, but at the same time, small changes in syntax can
lead to very large changes in semantics, which can be difficult for continuous
neural architectures. We perform an exhaustive evaluation on the task of
checking equivalence on a highly diverse class of symbolic algebraic and
boolean expression types, showing that our model significantly outperforms
existing architectures.


abstract:
  We classify all possible JSJ decompositions of doubles of free groups of rank
two and we then compute the Makanin-Razborov diagram of a particular double of
a free group and deduce that in general limit groups are not freely subgroup
separable.


title: Some invariance properties of cyclic cohomology with coefficients

abstract:
  In this paper, we further explore the conceptual approach to cyclic
cohomology with coefficients. In particular we give a derived version of the
definition with better invariance properties. We show that the new definition
agrees with the old under certain conditions and we prove, for the new
definition, both its Morita invariance and its $2$-Morita invariance, under
suitable interpretations of these terms. More generally, we prove a version of
Shapiro's lemma for cyclic cohomology with coefficients.


title:
Stability of rhombohedral phases in vanadium at high-pressure and
  high-temperature: first-principles investigations

abstract:
  The pressure-induced transition of vanadium from BCC to rhombohedral
structures is unique and intriguing among transition metals. In this work, the
stability of these phases is revisited by using density functional theory. At
finite temperatures, a novel transition of rhombohedral phases back to BCC
phase induced by thermal electrons is discovered. This reentrant transition is
found not driven by phonons, instead it is the electronic entropy that
stabilizes the latter phase, which is totally out of expectation. Parallel to
this transition, we find a peculiar and strong increase of the shear modulus
C44 with increasing temperature. It is counter-intuitive in the sense that it
suggests an unusual harding mechanism of vanadium by temperature. With these
stability analyses, the high-pressure and finite-temperature phase diagram of
vanadium is proposed. Furthermore, the dependence of the stability of RH phases
on the Fermi energy and chemical environment is investigated. The results
demonstrate that the position of the Fermi level has a significant impact on
the phase stability, and follows the band-filling argument. Besides the Fermi
surface nesting, we find that the localization/delocalization of the d orbitals
also contributes to the instability of rhombohedral distortions in vanadium.


title:
Sparsely-Connected Neural Networks: Towards Efficient VLSI
  Implementation of Deep Neural Networks

abstract:
  Recently deep neural networks have received considerable attention due to
their ability to extract and represent high-level abstractions in data sets.
Deep neural networks such as fully-connected and convolutional neural networks
have shown excellent performance on a wide range of recognition and
classification tasks. However, their hardware implementations currently suffer
from large silicon area and high power consumption due to the their high degree
of complexity. The power/energy consumption of neural networks is dominated by
memory accesses, the majority of which occur in fully-connected networks. In
fact, they contain most of the deep neural network parameters. In this paper,
we propose sparsely-connected networks, by showing that the number of
connections in fully-connected networks can be reduced by up to 90% while
improving the accuracy performance on three popular datasets (MNIST, CIFAR10
and SVHN). We then propose an efficient hardware architecture based on
linear-feedback shift registers to reduce the memory requirements of the
proposed sparsely-connected networks. The proposed architecture can save up to
90% of memory compared to the conventional implementations of fully-connected
neural networks. Moreover, implementation results show up to 84% reduction in
the energy consumption of a single neuron of the proposed sparsely-connected
networks compared to a single neuron of fully-connected neural networks.


title: Almost universal codes for MIMO wiretap channels

abstract:
  Despite several works on secrecy coding for fading and MIMO wiretap channels
from an error probability perspective, the construction of
information-theoretically secure codes over such channels remains an open
problem. In this paper, we consider a fading wiretap channel model where the
transmitter has only partial statistical channel state information. Our channel
model includes static channels, i.i.d. block fading channels, and ergodic
stationary fading with fast decay of large deviations for the eavesdropper's
channel.
  We extend the flatness factor criterion from the Gaussian wiretap channel to
fading and MIMO wiretap channels, and establish a simple design criterion where
the normalized product distance / minimum determinant of the lattice and its
dual should be maximized simultaneously.
  Moreover, we propose concrete lattice codes satisfying this design criterion,
which are built from algebraic number fields with constant root discriminant in
the single-antenna case, and from division algebras centered at such number
fields in the multiple-antenna case. The proposed lattice codes achieve strong
secrecy and semantic security for all rates $R<C_b-C_e-\kappa$, where $C_b$ and
$C_e$ are Bob and Eve's channel capacities respectively, and $\kappa$ is an
explicit constant gap. Furthermore, these codes are almost universal in the
sense that a fixed code is good for secrecy for a wide range of fading models.
  Finally, we consider a compound wiretap model with a more restricted
uncertainty set, and show that rates $R<\bar{C}_b-\bar{C}_e-\kappa$ are
achievable, where $\bar{C}_b$ is a lower bound for Bob's capacity and
$\bar{C}_e$ is an upper bound for Eve's capacity for all the channels in the
set.


title:
Epistemic extensions of combined classical and intuitionistic
  propositional logic

abstract:
  Logic $L$ was introduced by Lewitzka [7] as a modal system that combines
intuitionistic and classical logic: $L$ is a conservative extension of CPC and
it contains a copy of IPC via the embedding $\varphi\mapsto\square\varphi$. In
this article, we consider $L3$, i.e. $L$ augmented with S3 modal axioms, define
basic epistemic extensions and prove completeness w.r.t. algebraic semantics.
The resulting logics combine classical knowledge and belief with intuitionistic
truth. Some epistemic laws of Intuitionistic Epistemic Logic studied by Artemov
and Protopopescu [1] are reflected by classical modal principles. In
particular, the implications "intuitionistic truth $\Rightarrow$ knowledge
$\Rightarrow$ classical truth" are represented by the theorems
$\square\varphi\rightarrow K\varphi$ and $K\varphi\rightarrow\varphi$ of our
logic $EL3$, where we are dealing with classical instead of intuitionistic
knowledge. Finally, we show that a modification of our semantics yields
algebraic models for the systems of Intuitionistic Epistemic Logic introduced
in [1].


title:
An ultracold, optically trapped mixture of {87}Rb and metastable {4}He
  atoms

abstract:
  We report on the realization of an ultracold (<25~muK) mixture of rubidium
({87}Rb) and metastable triplet helium ({4}He) in an optical dipole trap. Our
scheme involves laser cooling in a dual-species magneto-optical trap,
simultaneous MW- and RF-induced forced evaporative cooling in a quadrupole
magnetic trap, and transfer to a single-beam optical dipole trap. We observe
long trapping lifetimes for the doubly spin-stretched spin-state mixture and
measure much shorter lifetimes for other spin-state combinations. We discuss
prospects for realizing quantum degenerate mixtures of alkali-metal and
metastable helium atoms.


title: The Link Between the Local Bubble and Radioisotopic Signatures on Earth

abstract:
  Traces of 2-3 Myr old 60Fe were recently discovered in a manganese crust and
in lunar samples. We have found that this signal is extended in time and is
present in globally distributed deep-sea archives. A second 6.5-8.7 Myr old
signature was revealed in a manganese crust. The existence of the Local Bubble
hints to a recent nearby supernova-activity starting 13 Myr ago. With
analytical and numerical models generating the Local Bubble, we explain the
younger 60Fe-signature and thus link the evolution of the solar neighborhood to
terrestrial anomalies.


title: Quark mass effects in quark number susceptibilities

abstract:
  The quark degrees of freedom of the QGP with special focus on mass effects
are investigated. A next-to-leading-order perturbation theory approach with
quark mass dependence is applied and compared to lattice QCD results.


title: Online containers for hypergraphs, with applications to linear equations

abstract:
  A set of containers for a hypergraph G is a collection of vertex subsets,
such that for every independent (or, indeed, merely sparse) set in G there is
some subset in the collection which contains it. No set in the collection
should be large and the collection itself should be relatively small.
Containers with useful properties have been exhibited by Balogh, Morris and
Samotij and by the authors, along with several applications.
  Our purpose here is to give a simpler algorithm than the one we used
previously, which nevertheless yields containers with all the properties needed
for our previous theorem. Moreover this algorithm produces containers having
the so-called online property, allowing previous colouring applications to be
extended to all, not just simple, hypergraphs.
  For illustrative purposes, we include a complete proof of a slightly weaker
but simpler version of the theorem, which for many (perhaps most) applications
is plenty. We also present applications to the number of solution-free sets of
linear equations, including the number of Sidon sets, announced previously but
not proven.


title:
Longest interval between zeros of the tied-down random walk, the
  Brownian bridge and related renewal processes

abstract:
  The probability distribution of the longest interval between two zeros of a
simple random walk starting and ending at the origin, and of its continuum
limit, the Brownian bridge, was analysed in the past by Ros\'en and Wendel,
then extended by the latter to stable processes. We recover and extend these
results using simple concepts of renewal theory, which allows to revisit past
or recent works of the physics literature.


title:
A modified Belinfante/Rosenfeld Procedure for Testing the Compatibility
  of General-Covariant Continuum Physics and General Relativity Theory

abstract:
  Creating a modified Belinfante/Rosenfeld procedure, Mathisson-Papapetrou-like
equations are derived by which a comparison of General-Covariant Continuum
Physics with General Relativity Theory becomes possible.


title:
Learning Recurrent Span Representations for Extractive Question
  Answering

abstract:
  The reading comprehension task, that asks questions about a given evidence
document, is a central problem in natural language understanding. Recent
formulations of this task have typically focused on answer selection from a set
of candidates pre-defined manually or through the use of an external NLP
pipeline. However, Rajpurkar et al. (2016) recently released the SQuAD dataset
in which the answers can be arbitrary strings from the supplied text. In this
paper, we focus on this answer extraction task, presenting a novel model
architecture that efficiently builds fixed length representations of all spans
in the evidence document with a recurrent network. We show that scoring
explicit span representations significantly improves performance over other
approaches that factor the prediction into separate predictions about words or
start and end markers. Our approach improves upon the best published results of
Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'s
baseline by > 50%.


title: Kullback-Leibler Divergence for the Normal-Gamma Distribution

abstract:
  We derive the Kullback-Leibler divergence for the normal-gamma distribution
and show that it is identical to the Bayesian complexity penalty for the
univariate general linear model with conjugate priors. Based on this finding,
we provide two applications of the KL divergence, one in simulated and one in
empirical data.


title:
Relative modification of prompt psi(2S) and J/psi yields from pp to PbPb
  collisions at sqrt(s[NN]) = 5.02 TeV

abstract:
  The relative modification of the prompt psi(2S) and J/psi yields from pp to
PbPb collisions, at the center of mass energy of 5.02 TeV per nucleon pair, is
presented. The analysis is based on pp and PbPb data samples collected by the
CMS experiment at the LHC in 2015, corresponding to integrated luminosities of
28.0 inverse picobarns and 464 inverse microbarns, respectively. The double
ratio of measured yields of prompt charmonia reconstructed through their decays
into muon pairs, (N[psi(2S)]/N[J/psi])[PbPb] / (N[psi(2S)]/N[J/psi])[pp], is
determined as a function of PbPb collision centrality and charmonium transverse
momentum pt, in two kinematic intervals: abs(y) < 1.6 covering 6.5 < pt < 30
GeV/c and 1.6 < abs(y) < 2.4 covering 3 < pt < 30 GeV/c. The
centrality-integrated double ratios are 0.36 +/- 0.08 (stat) +/-0.05 (syst) in
the first interval and 0.24 +/- 0.22 (stat) +/- 0.09 (syst) in the second. The
double ratio is lower than unity in all the measured bins, suggesting that the
psi(2S) yield is more suppressed than the J/psi yield in the explored phase
space.


title: Exceedance Probabilities for the Dirichlet Distribution

abstract:
  We derive an efficient method to calculate exceedance probabilities (EP) for
the Dirichlet distribution when the number of event types is larger than two.
Also, we present an intuitive application of Dirichlet EPs and compare our
method to a sampling approach which is the current practice in neuroimaging
model selection.


title: Liquidity induced asset bubbles via flows of ELMMs

abstract:
  We consider a constructive model for asset price bubbles, where the market
price $W$ is endogenously determined by the trading activity on the market and
the fundamental price $W^F$ is exogenously given, as in the work of Jarrow,
Protter and Roch (2012). To justify $W^F$ from a fundamental point of view, we
embed this constructive approach in the martingale theory of bubbles, see
Jarrow, Protter and Shimbo (2010) and Biagini, F\"ollmer and Nedelcu (2014), by
showing the existence of a flow of equivalent martingale measures for $W$,
under which $W^F$ equals the expectation of the discounted future cash flow. As
an application, we study bubble formation and evolution in a financial network.


title: Resonant-state expansion of light propagation in non-uniform waveguides

abstract:
  A new rigorous approach for precise and efficient calculation of light
propagation along non-uniform waveguides is presented. Resonant states of a
uniform waveguide, which satisfy outgoing-wave boundary conditions, form a
natural basis for expansion of the local electromagnetic field. Using such an
expansion at fixed frequency, we convert the wave equation for light
propagation in a non-uniform waveguide into an ordinary second-order matrix
differential equation for the expansion coefficients depending on the
coordinate along the waveguide. We illustrate the method on several examples of
non-uniform planar waveguides and evaluate its efficiency compared to the
aperiodic Fourier modal method and the finite element method, showing
improvements of one to four orders of magnitude. A similar improvement can be
expected also for applications in other fields of physics showing wave
phenomena, such as acoustics and quantum mechanics.


title:
Limits on dynamically generated spin-orbit coupling: Absence of $l=1$
  Pomeranchuk instabilities in metals

abstract:
  An ordered state in the spin sector that breaks parity without breaking
time-reversal symmetry, i.e., that can be considered as dynamically generated
spin-orbit coupling, was proposed to explain puzzling observations in a range
of different systems. Here we derive severe restrictions for such a state that
follow from a Ward identity related to spin conservation. It is shown that
$l=1$ spin-Pomeranchuk instabilities are not possible in non-relativistic
systems since the response of spin-current fluctuations is entirely incoherent
and non-singular. This rules out relativistic spin-orbit coupling as an
emergent low-energy phenomenon. We illustrate the exotic physical properties of
the remaining higher angular momentum analogues of spin-orbit coupling and
derive a geometric constraint for spin-orbit vectors in lattice systems.


title: Local density approximations from finite systems

abstract:
  The local density approximation (LDA) constructed through quantum Monte Carlo
calculations of the homogeneous electron gas (HEG) is the most common
approximation to the exchange-correlation functional in density functional
theory. We introduce an alternative set of LDAs constructed from slablike
systems of one, two and three electrons that resemble the HEG within a finite
region, and illustrate the concept in one dimension. Comparing with the exact
densities and Kohn-Sham potentials for various test systems, we find that the
LDAs give a good account of the self-interaction correction, but are less
reliable when correlation is stronger or currents flow.


title: Skyrme fields, multi-instantons and the $SU(\infty)$-Toda equation

abstract:
  We construct Skyrme fields from holonomy of the spin connection of
multi-Taub-NUT instantons with the centres positioned along a line in
$\mathbb{R}^3.$ Our family of Skyrme fields includes the Taub-NUT Skyrme field
previously constructed by Dunajski. However, we demonstrate that different
gauges of the spin connection can result in Skyrme fields with different
topological degrees. As a by-product, we present a method to compute the
degrees of the Taub-NUT and Atiyah-Hitchin Skyrme fields analytically; these
degrees are well defined as a preferred gauge is fixed by the $SU(2)$ symmetry
of the two metrics.
  Regardless of the gauge, the domain of our Skyrme fields is the space of
orbits of the axial symmetry of the multi-Taub-NUT instantons. We obtain an
expression for the induced Einstein-Weyl metric on the space and its associated
solution to the $SU(\infty)$-Toda equation.


title: VLA Measurements of Faraday Rotation through Coronal Mass Ejections

abstract:
  Coronal mass ejections (CMEs) are large-scale eruptions of plasma from the
Sun that play an important role in space weather. Faraday rotation (FR) is the
rotation of the plane of polarization that results when a linearly polarized
signal passes through a magnetized plasma such as a CME. FR observations of a
source near the Sun can provide information on the plasma structure of a CME
shortly after launch.
  We report on simultaneous white-light and radio observations made of three
CMEs in August 2012. We made sensitive Very Large Array (VLA) full-polarization
observations using 1 - 2 GHz frequencies of a "constellation" of radio sources
through the solar corona at heliocentric distances that ranged from 6 - 15
solar radii. Of the nine sources observed, three were occulted by CMEs: two
sources (0842+1835 and 0900+1832) were occulted by a single CME and one source
(0843+1547) was occulted by two CMEs. In addition to our radioastronomical
observations, which represent one of the first active hunts for CME Faraday
rotation since Bird et al. (1985) and the first active hunt using the VLA, we
obtained white-light coronagraph images from the LASCO/C3 instrument to
determine the Thomson scattering brightness, BT, providing a means to
independently estimate the plasma density and determine its contribution to the
observed Faraday rotation.
  A constant density force-free flux rope embedded in the background corona was
used to model the effects of the CMEs on BT and FR. We demonstrate this model's
ability to successfully reproduce both BT and FR profiles. The plasma densities
(6 - 22 x 10$^3$ cm$^{-3}$) and axial magnetic field strengths (2 - 12 mG)
inferred from our models are consistent with the modeling work of Liu et al.
(2007) and Jensen & Russell (2008), as well as previous CME FR observations by
Bird et al. (1985).


title: Full covariance of CMB and lensing reconstruction power spectra

abstract:
  CMB and lensing reconstruction power spectra are powerful probes of
cosmology. However they are correlated, since the CMB power spectra are lensed
and the lensing reconstruction is constructed using CMB multipoles. We perform
a full analysis of the auto- and cross-covariances, including polarization
power spectra and minimum variance lensing estimators, and compare with
simulations of idealized future CMB-S4 observations. Covariances sourced by
fluctuations in the unlensed CMB and instrumental noise can largely be removed
by using a realization-dependent subtraction of lensing reconstruction noise,
leaving a relatively simple covariance model that is dominated by
lensing-induced terms and well described by a small number of principal
components. The correlations between the CMB and lensing power spectra will be
detectable at the level of $\sim 5\sigma$ for a CMB-S4 mission, and neglecting
those could underestimate some parameter error bars by several tens of percent.
However we found that the inclusion of external priors or data sets to estimate
parameter error bars can make the impact of the correlations almost negligible.


title:
An immersed-boundary method for compressible viscous flow and its
  application in gas-kinetic BGK scheme

abstract:
  An immersed-boundary (IB) method is proposed and applied in the gas-kinetic
BGK scheme to simulate incompressible/compressible viscous flow with
stationary/moving boundary. In present method the ghost-cell technique is
adopted to fulfill the boundary condition on the immersed boundary. A novel
idea "local boundary determination" is put forward to identify the ghost cells,
each of which may have several different ghost-cell constructions corresponding
to different boundary segments, thus eliminating the singularity of the ghost
cell. Furthermore, the so-called "fresh-cell" problem when implementing the IB
method in moving-boundary simulation is resolved by a simple extrapolation in
time. The method is firstly applied in the gas-kinetic BGK scheme to simulate
the Taylor-Couette flow, where the second-order spatial accuracy of the method
is validated and the "super-convergence" of the BGK scheme is observed. Then
the test cases of supersonic flow around a stationary cylinder, incompressible
flow around an oscillating cylinder and compressible flow around a moving
airfoil are conducted to verify the capability of the present method in
simulating compressible flows and handling the moving boundary.


title:
Multiple Sclerosis and Geomagnetic Disturbances: Investigating a
  Potentially Important Environmental Risk Factor

abstract:
  Multiple sclerosis (MS) is the most common disabling neurological disorders
in the young adults. In spite of many researches, the cause of the disease has
remained mainly not understood. All evidences indicate that environmental risk
factors play key roles in this disease etiology. Various hypotheses have been
posited up to now on the presumed disease risk factors, however, they were not
successful in explaining all MS features. The aim of this article is to
introduce the concept of the newly proposed "geomagnetic disturbance hypothesis
of MS" and its abilities in explaining the special features of the disease to
encourage medical geologist and other biomedical researchers to contribute to
this area of research.


title: Semi-supervised deep learning by metric embedding

abstract:
  Deep networks are successfully used as classification models yielding
state-of-the-art results when trained on a large number of labeled samples.
These models, however, are usually much less suited for semi-supervised
problems because of their tendency to overfit easily when trained on small
amounts of data. In this work we will explore a new training objective that is
targeting a semi-supervised regime with only a small subset of labeled data.
This criterion is based on a deep metric embedding over distance relations
within the set of labeled samples, together with constraints over the
embeddings of the unlabeled set. The final learned representations are
discriminative in euclidean space, and hence can be used with subsequent
nearest-neighbor classification using the labeled samples.


title:
Estimating the marginal likelihood with Integrated nested Laplace
  approximation (INLA)

abstract:
  The marginal likelihood is a well established model selection criterion in
Bayesian statistics. It also allows to efficiently calculate the marginal
posterior model probabilities that can be used for Bayesian model averaging of
quantities of interest. For many complex models, including latent modeling
approaches, marginal likelihoods are however difficult to compute. One recent
promising approach for approximating the marginal likelihood is Integrated
Nested Laplace Approximation (INLA), design for models with latent Gaussian
structures. In this study we compare the approximations obtained with INLA to
some alternative approaches on a number of examples of different complexity. In
particular we address a simple linear latent model, a Bayesian linear
regression model, logistic Bayesian regression models with probit and logit
links, and a Poisson longitudinal generalized linear mixed model.


title: Polarization of gamma-ray bursts in the dissipative photosphere model

abstract:
  The MeV spectral peak of gamma-ray bursts (GRBs) is best explained as
photospheric emission from a dissipative relativistic jet. The observed
non-blackbody spectrum shows that sub-photospheric dissipation involves both
thermal plasma heating and injection of nonthermal particles, which quickly
cool through inverse Compton scattering and emission of synchrotron radiation.
Synchrotron photons emitted around and above the photosphere are predicted to
dominate the low-energy part of the GRB spectrum, starting from roughly a
decade in energy below the MeV peak. We show that this leads to a unique
polarization signature: a rise in GRB polarization toward lower energies. We
compute the polarization degree of GRB radiation as a function of photon energy
for a generic jet model, and show the predictions for GRBs 990123, 090902B and
110721A. The expected polarization is significant in the X-ray band, in
particular for bursts similar to GRB 090902B. Radiation in the MeV peak (and at
higher energies) is unpolarized as long as the jet is approximately uniform on
angular scales $\delta\theta \gtrsim \Gamma^{-1}$ where $\Gamma$ is the bulk
Lorentz factor of the jet.


title: Observation of individual tracer atoms in an ultracold dilute gas

abstract:
  Understanding the motion of a tracer particle in a rarefied gas is of
fundamental and practical importance. We report the experimental investigation
of individual Cs atoms impinging on a dilute cloud of ultracold Rb atoms with
variable density. We study the nonequilibrium relaxation of the initial
nonthermal state and detect the effect of single collisions which has eluded
observation so far. We show that after few collisions, the measured spatial
distribution of the light tracer atoms is correctly described by a generalized
Langevin equation with a velocity-dependent friction coefficient, over a large
range of Knudsen numbers.


title:
The skyrmion switch: turning magnetic skyrmion bubbles on and off with
  an electric field

abstract:
  Nanoscale magnetic skyrmions are considered as potential information carriers
for future spintronics memory and logic devices. Such applications will require
the control of their local creation and annihilation, which involves so far
solutions that are either energy consuming or difficult to integrate. Here we
demonstrate the control of skyrmion bubbles nucleation and annihilation using
electric field gating, an easily integrable and potentially energetically
efficient solution. We present a detailed stability diagram of the skyrmion
bubbles in a Pt/Co/oxide trilayer and show that their stability can be
controlled via an applied electric field. An analytical bubble model, with the
Dzyaloshinskii-Moriya interaction imbedded in the domain wall energy, account
for the observed electrical skyrmion switching effect. This allows us to unveil
the origin of the electrical control of skyrmions stability and to show that
both magnetic dipolar interaction and the Dzyaloshinskii-Moriya interaction
play an important role in the skyrmion bubble stabilization.


title:
Fiber ring resonator with nanofiber section for chiral cavity quantum
  electrodynamics and multimode strong coupling

abstract:
  We experimentally realize an optical fiber ring resonator that includes a
tapered section with subwavelength-diameter waist. In this section, the guided
light exhibits a significant evanescent field which allows for efficient
interfacing with optical emitters. A commercial tunable fiber beam splitter
provides simple and robust coupling to the resonator. Key parameters of the
resonator such as its out-coupling rate, free spectral range, and birefringence
can be adjusted. Thanks to the low taper- and coupling-losses, the resonator
exhibits an unloaded finesse of F=75+/-1, sufficient for reaching the regime of
strong coupling for emitters placed in the evanescent field. The system is
ideally suited for trapping ensembles of laser-cooled atoms along the nanofiber
section. Based on measured parameters, we estimate that the system can serve as
a platform for optical multimode strong coupling experiments. Finally, we
discuss the possibilities of using the resonator for applications based on
chiral quantum optics.


title: Ways of Conditioning Generative Adversarial Networks

abstract:
  The GANs are generative models whose random samples realistically reflect
natural images. It also can generate samples with specific attributes by
concatenating a condition vector into the input, yet research on this field is
not well studied. We propose novel methods of conditioning generative
adversarial networks (GANs) that achieve state-of-the-art results on MNIST and
CIFAR-10. We mainly introduce two models: an information retrieving model that
extracts conditional information from the samples, and a spatial bilinear
pooling model that forms bilinear features derived from the spatial cross
product of an image and a condition vector. These methods significantly enhance
log-likelihood of test data under the conditional distributions compared to the
methods of concatenation.


title: Learning heat diffusion graphs

abstract:
  Effective information analysis generally boils down to properly identifying
the structure or geometry of the data, which is often represented by a graph.
In some applications, this structure may be partly determined by design
constraints or pre-determined sensing arrangements, like in road transportation
networks for example. In general though, the data structure is not readily
available and becomes pretty difficult to define. In particular, the global
smoothness assumptions, that most of the existing works adopt, are often too
general and unable to properly capture localized properties of data. In this
paper, we go beyond this classical data model and rather propose to represent
information as a sparse combination of localized functions that live on a data
structure represented by a graph. Based on this model, we focus on the problem
of inferring the connectivity that best explains the data samples at different
vertices of a graph that is a priori unknown. We concentrate on the case where
the observed data is actually the sum of heat diffusion processes, which is a
quite common model for data on networks or other irregular structures. We cast
a new graph learning problem and solve it with an efficient nonconvex
optimization algorithm. Experiments on both synthetic and real world data
finally illustrate the benefits of the proposed graph learning framework and
confirm that the data structure can be efficiently learned from data
observations only. We believe that our algorithm will help solving key
questions in diverse application domains such as social and biological network
analysis where it is crucial to unveil proper geometry for data understanding
and inference.


title: Multi-task learning with deep model based reinforcement learning

abstract:
  In recent years, model-free methods that use deep learning have achieved
great success in many different reinforcement learning environments. Most
successful approaches focus on solving a single task, while multi-task
reinforcement learning remains an open problem. In this paper, we present a
model based approach to deep reinforcement learning which we use to solve
different tasks simultaneously. We show that our approach not only does not
degrade but actually benefits from learning multiple tasks. For our model, we
also present a new kind of recurrent neural network inspired by residual
networks that decouples memory from computation allowing to model complex
environments that do not require lots of memory.


title:
The multi-flavor Schwinger model with chemical potential - Overcoming
  the sign problem with Matrix Product States

abstract:
  During recent years there has been an increasing interest in the application
of matrix product states, and more generally tensor networks, to lattice gauge
theories. This non-perturbative method is sign problem free and has already
been successfully used to compute mass spectra, thermal states and phase
diagrams, as well as real-time dynamics for Abelian and non-Abelian gauge
models. In previous work we showed the suitability of the method to explore the
zero-temperature phase structure of the multi-flavor Schwinger model at
non-zero chemical potential, a regime where the conventional Monte Carlo
approach suffers from the sign problem. Here we extend our numerical study by
looking at the spatially resolved chiral condensate in the massless case. We
recover spatial oscillations, similar to the theoretical predictions for the
single-flavor case, with a chemical potential dependent frequency and an
amplitude approximately given by the homogeneous zero density condensate value.


title:
Time-dependent resonant tunneling transport: Keldysh and Kadanoff-Baym
  nonequilibrium Green's functions in an analytically soluble problem

abstract:
  Here we address two nonequilibrium Green's functions approaches for a
resonant tunneling structure under a sudden switch of a bias. Our aim is to
stress that the time-dependent Keldysh formulation of Jauho, Wingreen and Meir,
and the partition-free scheme of Stefanucci and Almbladh are formally
equivalent in the ubiquitous case of wide-band limit and noninteracting
electrons, if leads and dot are in equilibrium before the time-dependent
perturbation. We develop explicit closed formulas of the lesser Green's
function and time-dependent current, reminding that the different integration
limits preclude a face-to-face comparison of two approaches. This study sheds
light on both practices, which are of great interest to the mesoscopic
transport community.


title: NNLO QCD predictions for single jet inclusive production at the LHC

abstract:
  We report the first calculation of fully differential jet production in all
partonic channels at next-to-next-to leading order (NNLO) in perturbative QCD
and compare to the available ATLAS 7 TeV data. We discuss the size and shape of
the perturbative corrections along with their associated scale variation across
a wide range in jet transverse momentum, $p_{T}$, and rapidity, $y$. We find
significant effects, especially at low $p_{T}$, and discuss the possible
implications for Parton Distribution Function fits.


title: L-Borderenergetic graphs and Normalized Laplacian Energy

abstract:
  In this paper we present new L-borderenergetic graphs, this is, graphs which
are L-noncospectral with Kn but have the same Laplacian energy. We also present
some graphs which are noncospectral to respective normalized Laplacian energy
and they have the same normalized Laplacian energy.


title:
Tying Word Vectors and Word Classifiers: A Loss Framework for Language
  Modeling

abstract:
  Recurrent neural networks have been very successful at predicting sequences
of words in tasks such as language modeling. However, all such models are based
on the conventional classification framework, where the model is trained
against one-hot targets, and each word is represented both as an input and as
an output in isolation. This causes inefficiencies in learning both in terms of
utilizing all of the information and in terms of the number of parameters
needed to train. We introduce a novel theoretical framework that facilitates
better learning in language modeling, and show that our framework leads to
tying together the input embedding and the output projection matrices, greatly
reducing the number of trainable variables. Our framework leads to state of the
art performance on the Penn Treebank with a variety of network models.


title:
International Portfolio Optimisation with Integrated Currency Overlay
  Costs and Constraints

abstract:
  Portfolio optimisation typically aims to provide an optimal allocation that
minimises risk, at a given return target, by diversifying over different
investments. However, the potential scope of such risk diversification can be
limited if investments are concentrated in only one country, or more
specifically one currency. Multi-currency portfolio is an alternative to
achieve higher returns and more diversified portfolios but it requires a
careful management of the entailed risks from changes in exchange rates.
  The deviation between asset and currency exposures in a portfolio is defined
as the "currency overlay". This paper addresses risk mitigation by allowing
currency overlay and asset allocation be optimised together. We propose a model
of the international portfolio optimisation problem in which the currency
overlay is constructed by holding foreign exchange rate forward contracts.
Crucially, the cost of carry, transaction costs, and margin requirement of
forward contracts are also taken into account in portfolio return calculation.
This novel extension of previous overlay models improves the accuracy of risk
and return calculation of portfolios; furthermore, our experimental results
show that inclusion of such costs significantly changes the optimal decisions.
Effects of constraints imposed to reduce transaction costs associated are
examined and the empirical results show that risk-return compensation of
portfolios varies significantly with different return targets.


title:
Companion forms and explicit computation of PGL2 number fields with very
  little ramification

abstract:
  In previous works, we described algorithms to compute the number field cut
out by the mod ell representation attached to a modular form of level N=1. In
this article, we explain how these algorithms can be generalised to forms of
higher level N.
  As an application, we compute the Galois representations attached to a few
forms which are supersingular or admit a companion mod ell with ell=13 (and
soon ell=41), and we obtain previously unknown number fields of degree ell+1
whose Galois closure has Galois group PGL(2,ell) and a root discriminant that
is so small that it beats records for such number fields.
  Finally, we give a formula to predict the discriminant of the fields obtained
by this method, and we use it to find other interesting examples, which are
unfortunately out of our computational reach.


title: Functorial characterization of Mittag-Leffler modules

abstract:
  We give a functorial characterization of Mittag-Leffler modules and strict
Mittag-Leffler modules.


title: Portal Connecting Dark Photons and Axions

abstract:
  The dark photon and the axion (or axion-like particle) are popular light
particles of the hidden sector. Each of them has been actively searched for
through the couplings called the vector portal and the axion portal. We
introduce a new portal connecting the dark photon and the axion
(axion--photon--dark photon, axion--dark photon--dark photon), which emerges in
the presence of the two particles. This dark axion portal is genuinely new
couplings, not just from a product of the vector portal and the axion portal,
because of the internal structure of these couplings. We present a simple model
that realizes the dark axion portal and discuss why it warrants a rich
phenomenology.


title: Topologically-enforced bifurcations in superconducting circuits

abstract:
  The relation of topological insulators and superconductors and the field of
nonlinear dynamics is widely unexplored. To address this subject, we adopt the
linear coupling geometry of the Su-Schrieffer-Heeger model, a paradigmatic
example for a topological insulator, and render it nonlinearly in the context
of superconducting circuits. As a consequence, the system exhibits
topologically-enforced bifurcations as a function of the topological control
parameter, which finally gives rise to chaotic dynamics, separating phases
which exhibit clear topological features.


title:
Evaluation of Ionospheric Densities Using Coincident OII 83.4 nm Airglow
  and the Millstone Hill Radar

abstract:
  We test the utility of the OII 83.4 nm emission feature as a measure of
ionospheric parameters. Observed with the Remote Atmospheric and Ionospheric
Detection System (RAIDS) Extreme Ultraviolet Spectrograph on the International
Space Station (ISS), limb profiles of 83.4 nm emissions are compared to
predicted dayglow emission profiles from a theoretical model incorporating
ground-based electron density profiles measured by the Millstone Hill radar and
parameterized by a best-fit Chapman-{\alpha} function. Observations and models
are compared for periods of conjunction between Millstone Hill and the RAIDS
fields-of-view. These RAIDS observations show distinct differences in topside
morphology between two days, 15 January and 10 March 2010, closely matching the
forward model morphology and demonstrating that 83.4 nm emission is sensitive
to changes in the ionospheric density profile from the 340 km altitude of the
ISS during solar minimum. We find no significant difference between 83.4 nm
emission profiles modeled assuming a constant scale height Chapman-{\alpha}
best-fit to the ISR measurements and those assuming varying scale height.


title: GUP parameter from quantum corrections to the Newtonian potential

abstract:
  We propose a technique to compute the deformation parameter of the
generalized uncertainty principle by using the leading quantum corrections to
the Newtonian potential. We just assume General Relativity as theory of
Gravitation, and the thermal nature of the GUP corrections to the Hawking
spectrum. With these minimal assumptions our calculation gives, to first order,
a specific numerical result. The physical meaning of this value is discussed,
and compared with the previously obtained bounds on the generalized uncertainty
principle deformation parameter.


title: Infinitesimal aspects of idempotents in Banach algebras

abstract:
  We investigate infinitesimal properties of sets of ordered $n$-uples of
idempotents in a symmetric Banach $*$-algebra. These sets are called flag
manifolds and carry several interesting bundles that hold an important role in
some areas of operator theory. In this direction, we introduce and study
Stiefel bundles on flag manifolds, which are extensions of the well known
Stiefel bundles on Grassmannians. The main ingredient of our investigation is
the notion of connection on an infinite-dimensional bundle, and we survey some
equivalent ocurrences of such a notion in the literature.


title:
A fair monetization model to reconcile authors and consumers of
  intellectual property

abstract:
  In this small article one compromise monetization strategy is proposed, which
hopefully may lead to a more satisfactory coexistence of IP manufacturers and
consumers. The motto is "fair exchange": you use our IP-product, we use your
product (in form of money); when you do not need our product any more, we
change back.


title: TE-TM Electromagnetic modes and states in quantum physics

abstract:
  We propose, as another pedagogical approach, a quantification of the e.m.
field better adapted in isolated systems, a quantification of e.m. fields in
finite-spacetime which does not rely explicitly on the notion of photon, nor on
the general application of gauge. Being based on the development of e.m. field
in TE-TM modes and states, it obeys the limit conditions of a finite-spacetime
which follows from the solution of an eigenvalue equation and allows to
interpret more profoundly some phenomenons, notably: the equivalence of a Pauli
principle in stationary TE-TM states, the notion of non-locality in a radiation
field, a modified form of the De Broglie analysis and the notion of
wave-paquets in finite-spacetime.


title: Quantumness of Correlations in Fermionic Systems

abstract:
  We present a new approach for the quantification of quantumness of
correlations in fermionic systems. We study the Multipartite Relative Entropy
of Quantumness in such systems, and show how the symmetries in the states can
be used to obtain analytical solutions. Numerical evidences about the
uniqueness of such solutions are also presented. Supported by these results, we
show that the minimization of the Multipartite Relative Entropy of Quantumness,
over certain choices of its modes multipartitions, reduces to the notion of
Quantumness of Indistinguishable Particles. By means of an activation protocol,
we characterize the class of states without quantumness of correlations. As an
example, we calculate the dynamics of quantumness of correlations for a purely
dissipative system, whose stationary states exhibit interesting topological
non-local correlations.


title: Counting independent sets in cubic graphs of given girth

abstract:
  We prove a tight upper bound on the independence polynomial (and total number
of independent sets) of cubic graphs of girth at least 5. The bound is achieved
by unions of the Heawood graph, the point/line incidence graph of the Fano
plane.
  We also give a tight lower bound on the total number of independent sets of
triangle-free cubic graphs. This bound is achieved by unions of the Petersen
graph.
  We conjecture that in fact all Moore graphs are extremal for the scaled
number of independent sets in regular graphs of a given minimum girth,
maximizing this quantity if their girth is even and minimizing if odd. The
Heawood and Petersen graphs are instances of this conjecture, along with
complete graphs, complete bipartite graphs, and cycles.


title: A photonic Carnot engine powered by a quantum spin-star network

abstract:
  We propose a spin-star network, where a central spin-$1/2$ is coupled with
XXZ interaction to $N$ outer spin-$1/2$ particles, as a quantum fuel. If the
network is in thermal equilibrium with a cold bath, the central spin can have
an effective temperature larger than the bath one and scaling nonlinearly with
$N$. The nonlinearity can be tuned to $N^2, N^3$ or $N^4$ with the anisotropy
parameter of the coupling. Using a stream of central-spin particles to pump a
micromaser cavity, we calculate the dynamics of the cavity field using a
coarse-grained master equation. Our study reveals that the central-spin beam
effectively acts as a hot reservoir to the cavity field and brings the field to
a thermal steady-state whose temperature benefits from the same nonlinear
enhancement with $N$, and results in a highly efficient photonic Carnot engine.
The validity of our conclusions is tested against the presence of atomic and
cavity damping using a microscopic master equation method for typical microwave
cavity-QED parameters. An alternative equivalent scheme where the spin-$1/2$ is
coupled to a macroscopic spin-$(N/2)$ particle is also discussed.


title:
Thermodynamic State Variables in Quasi-Equilibrium Ultracold Neutral
  Plasma

abstract:
  The pressure and internal energy of an ultracold plasma in a state of
quasi-equilibrium are evaluated using classical molecular dynamics simulations.
Coulomb collapse is avoided by modeling electron-ion interactions using an
attractive Coulomb potential with a repulsive core. We present a method to
separate the contribution of classical bound states, which form due to
recombination, from the contribution of free charges when evaluating these
thermodynamic state variables. It is found that the contribution from free
charges is independent of the choice of repulsive core length-scale when it is
sufficiently short-ranged. The partial pressure associated with the free
charges is found to closely follow that of the one-component plasma model,
reaching negative values at strong coupling, while the total system pressure
remains positive. This pseudo-potential model is also applied to
Debye-H\"{u}ckel theory to describe the weakly coupled regime.


title: Using Hover to Compromise the Confidentiality of User Input on Android

abstract:
  We show that the new hover (floating touch) technology, available in a number
of today's smartphone models, can be abused by any Android application running
with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input
into other applications. Leveraging this attack, a malicious application
running on the system is therefore able to profile user's behavior, capture
sensitive input such as passwords and PINs as well as record all user's social
interactions. To evaluate our attack we implemented Hoover, a proof-of-concept
malicious application that runs in the system background and records all input
to foreground applications. We evaluated Hoover with 40 users, across two
different Android devices and two input methods, stylus and finger. In the case
of touchscreen input by finger, Hoover estimated the positions of users' clicks
within an error of 100 pixels and keyboard input with an accuracy of 79%.
Hoover captured users' input by stylus even more accurately, estimating users'
clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss
ways of mitigating this attack and show that this cannot be done by simply
restricting access to permissions or imposing additional cognitive load on the
users since this would significantly constrain the intended use of the hover
technology.


title: A bijective study of Basketball walks

abstract:
  The Catalan numbers count many classes of combinatorial objects. The most
emblematic such objects are probably the Dyck walks and the binary trees, and,
whenever another class of combinatorial objects is counted by the Catalan
numbers, it is natural to search for an explicit bijection between the latter
objects and one of the former objects. In most cases, such a bijection happens
to be relatively simple but it might sometimes be more intricate.
  In this work, we focus on so-called \emph{basketball walks}, which are
integer-valued walks with step-set $\{-2,-1,+1,+2\}$. The presence of $-2$ as
an allowed step makes it impossible to use the classical {\L}ukasiewicz
encoding of trees by integer-valued walks, and thus a different strategy is
needed. We give an explicit bijection that maps, for each $n\ge 2$, $n$-step
basketball walks from $0$ to $0$ that visit $1$ and are positive except at
their extremities to $n$-leaf binary trees. Moreover, we can partition the
steps of a walk into $\pm 1$-steps, odd $+2$-steps or even $-2$-steps, and odd
$-2$-steps or even $+2$-steps, and these three types of steps are mapped
through our bijection to double leaves, left leaves, and right leaves of the
corresponding tree.
  We also prove that basketball walks from $0$ to $1$ that are positive except
at the origin are in bijection with increasing unary-binary trees with
associated permutation avoiding $213$. We furthermore give the refined
generating function of these objects with an extra variable accounting for the
unary nodes.


title: Space-Efficient Re-Pair Compression

abstract:
  Re-Pair is an effective grammar-based compression scheme achieving strong
compression rates in practice. Let $n$, $\sigma$, and $d$ be the text length,
alphabet size, and dictionary size of the final grammar, respectively. In their
original paper, the authors show how to compute the Re-Pair grammar in expected
linear time and $5n + 4\sigma^2 + 4d + \sqrt{n}$ words of working space on top
of the text. In this work, we propose two algorithms improving on the space of
their original solution. Our model assumes a memory word of $\lceil\log_2
n\rceil$ bits and a re-writable input text composed by $n$ such words. Our
first algorithm runs in expected $\mathcal O(n/\epsilon)$ time and uses
$(1+\epsilon)n +\sqrt n$ words of space on top of the text for any parameter
$0<\epsilon \leq 1$ chosen in advance. Our second algorithm runs in expected
$\mathcal O(n\log n)$ time and improves the space to $n +\sqrt n$ words.


title: Why comparing survival curves between two subgroups may be misleading

abstract:
  We analyse an issue when comparing survival curves between two subgroups. We
show that there is a direct relationship between estimates of subgroups'
survival at a time point and positive and negative predictive values in the
binary classification settings. Our findings present a case where current
methods of comparing survival curves between subgroups may be misleading. We
think that this ought to be taken into account during the validation of
prognostic diagnostic tests that predict two prognostic subgroups for a given
disease or treatment, when the validation data set consists of censored data.


title: Transport mirages in single-molecule devices

abstract:
  Molecular systems can exhibit a complex, chemically tailorable inner
structure which allows for targeting of specific mechanical, electronic and
optical properties. At the single-molecule level, two major complementary ways
to explore these properties are molecular quantum-dot structures and scanning
probes. This article outlines comprehensive principles of electron-transport
spectroscopy relevant to both these approaches and presents a new,
high-resolution experiment on a high-spin single-molecule junction exemplifying
these principles. Such spectroscopy plays a key role in further advancing our
understanding of molecular and atomic systems, in particular the relaxation of
their spin. In this joint experimental and theoretical analysis, particular
focus is put on the crossover between resonant regime [single-electron
tunneling (SET)] and the off-resonant regime [inelastic electron (co)tunneling
(IETS)]. We show that the interplay of these two processes leads to unexpected
mirages of resonances not captured by either of the two pictures alone.
Although this turns out to be important in a large fraction of the possible
regimes of level positions and bias voltages, it has been given little
attention in molecular transport studies. Combined with nonequilibrium IETS --
four-electron pump-probe excitations -- these mirages provide crucial
information on the relaxation of spin excitations. Our encompassing physical
picture is supported by a master-equation approach that goes beyond weak
coupling. The present work encourages the development of a broader connection
between the fields of molecular quantum-dot and scanning probe spectroscopy.


title: On the local pressure of the Navier-Stokes equations and related systems

abstract:
  In the study of local regularity of weak solutions to systems related to
incompressible viscous fluids local energy estimates serve as important
ingredients. However, this requires certain informations on the pressure. This
fact has been used by V. Scheffer in the notion of a suitable weak to the
Navier-Stokes equation, and in the proof of the partial regularity due to
Caffarelli. Kohn and Nirenberg. In general domains, or in case of complex
viscous fluid models a global pressure doesn't necessarily exist. To overcome
this problem, in the present paper we construct a local pressure distribution
by showing that every distribution $ \partial _t \bu +\bF $, which vanishs on
the set of smooth solenoidal vector fields can be represented by a distribution
$ \partial _t \nabla p_h +\nabla p_0 $, where $\nabla p_h \sim \bu $ and $
\nabla p_0 \sim \bF$.


title:
Refined Weak Coupling Limit: Coherence, Entanglement and
  Non-Markovianity

abstract:
  We study the properties of a refined weak coupling limit that preserves
complete positivity in order to describe non-Markovian dynamics in the
spin-boson model. With this tool, we show the system presents a rich and new
non-Markovian phenomenology. This implies a dynamical difference between
entanglement and coherence: the latter undergoes revivals whereas the former
not, despite the induced dynamics being fully incoherent. In addition, the
evolution presents "quasieternal" non-Markovianity, becoming non-divisible at
any time period where the system evolves qualitatively. Furthermore, the method
allows for an exact derivation of a master equation that accounts for a
reversible energy exchange between system and environment. Specifically, this
is obtained in the form of a time-dependent Lamb shift term.


title: UMDFaces: An Annotated Face Dataset for Training Deep Networks

abstract:
  Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer vision community needs larger and more varied datasets to
make further progress.
  In this paper we introduce a new face dataset, called UMDFaces, which has
367,888 annotated faces of 8,277 subjects. We also introduce a new face
recognition evaluation protocol which will help advance the state-of-the-art in
this area. We discuss how a large dataset can be collected and annotated using
human annotators and deep networks. We provide human curated bounding boxes for
faces. We also provide estimated pose (roll, pitch and yaw), locations of
twenty-one key-points and gender information generated by a pre-trained neural
network. In addition, the quality of keypoint annotations has been verified by
humans for about 115,000 images. Finally, we compare the quality of the dataset
with other publicly available face datasets at similar scales.