title:
The VANDELS survey: a measurement of the average Lyman-continuum escape
  fraction of star-forming galaxies at z=3.5

abstract:
  We present a study designed to measure the average LyC escape fraction
($\langle f_{\rm esc}\rangle$) of star-forming galaxies at z=3.5. We assemble a
sample of 148 galaxies from the VANDELS survey at $3.35\leq z_{\rm
spec}\leq3.95$, selected to minimize line-of-sight contamination of their
photometry. For this sample, we use ultra-deep, ground-based, $U-$band imaging
and HST $V-$band imaging to robustly measure the distribution of
$\mathcal{R_{\rm obs}}$ $=(L_{\rm LyC}/L_{\rm UV})_{\rm obs}$. We then model
the distribution as a function of $\langle f_{\rm esc}\rangle$, carefully
accounting for attenuation by dust, and the IGM (and CGM). A maximum likelihood
fit to the $\mathcal{R_{\rm obs}}$ distribution returns a best-fitting value of
$\langle f_{\rm esc}\rangle =0.07\pm0.02$, a result confirmed using an
alternative Bayesian inference technique (both exclude $\langle f_{\rm
esc}\rangle=0.0$ at $> 3\sigma$). By splitting our sample in two, we find
evidence that $\langle f_{\rm esc}\rangle$ is positively correlated with
Ly$\alpha$ equivalent width, with high and low sub-samples returning best fits
of $\langle f_{\rm esc}\rangle=0.12^{+0.06}_{-0.04}$ and $\langle f_{\rm esc}
\rangle=0.02^{+0.02}_{-0.01}$, respectively. In contrast, we find evidence that
$\langle f_{\rm esc}\rangle$ is anti-correlated with intrinsic UV luminosity
and UV dust attenuation; with low UV luminosity and dust attenuation
sub-samples returning best fits in the range $0.10 \leq \langle f_{\rm
esc}\rangle \leq 0.22$. We do not find evidence for a clear correlation between
$f_{\rm esc}$ and galaxy stellar mass, suggesting it is not a primary indicator
of leakage. Although larger samples are needed to further explore these trends,
they suggest that it is entirely plausible that the low dust and metallicity
galaxies found at z > 6 will display the $\langle f_{\rm esc}\rangle\geq0.1$
required to drive reionization.


title: Entanglement estimation in tensor network states via sampling

abstract:
  We introduce a method for extracting meaningful entanglement measures of
tensor network states in general dimensions. Current methods require the
explicit reconstruction of the density matrix, which is highly demanding, or
the contraction of replicas, which requires an effort exponential in the number
of replicas and which is costly in terms of memory. In contrast, our method
requires the stochastic sampling of matrix elements of the classically
represented reduced states with respect to random states drawn from simple
product probability measures constituting frames. Even though not corresponding
to physical operations, such matrix elements are straightforward to calculate
for tensor network states, and their moments provide the R\'enyi entropies and
negativities as well as their symmetry-resolved components. We test our method
on the one-dimensional critical XX chain and the two-dimensional toric code.
Although the cost is exponential in the subsystem size, it is sufficiently
moderate so that - in contrast with other approaches - accurate results can be
obtained on a personal computer for relatively large subsystem sizes.


title: Three-point energy correlators and the celestial block expansion

abstract:
  We study the three-point energy correlator (EEEC), defined as a matrix
element of a product of three energy detectors at different locations on the
celestial sphere. Lorentz symmetry implies that the EEEC can be decomposed into
special functions called celestial blocks. We compute three-point celestial
blocks in an expansion around the collinear limit, where the three detectors
approach each other on the celestial sphere. The leading term is a traditional
$d{-}2$-dimensional four-point conformal block, and thus the collinear EEEC
behaves like a conformally-invariant four-point function in $d{-}2$ dimensions.
We obtain the coefficients of the conformal block decomposition for the
collinear EEEC at leading nontrivial order in weakly-coupled $\mathcal{N}=4$
SYM and QCD. These data allow us to make certain all-orders predictions for the
collinear EEEC in various kinematic limits, including the OPE limit and the
double lightcone limit. We also study Ward identities satisfied by the EEEC and
compute contact terms in the EEEC in weakly-coupled $\mathcal{N}=4$ SYM.
Finally, we study the celestial block expansion of the EEEC in planar
$\mathcal{N}=4$ SYM at strong coupling, determining celestial block
coefficients to leading and first subleading order at large $\lambda$.


title: The non-thermal secondary CMB anisotropies from radio galaxy lobes

abstract:
  Current and upcoming high angular resolution and multi-frequency experiments
are well poised to explore the rich landscape of secondary CMB anisotropies. In
this context, we compute for the first time, the power spectrum of CMB
fluctuations from plasma in a cosmological distribution of evolving lobes of
giant radio galaxies. We, also, explicitly take into account the non-thermal
electron distribution, as opposed to thermal distribution, which has important
implications for the inference of the CMB angular power spectrum. The
relativistic particles are fed from the central supermassive blackholes via
radio jets leading to radio lobes expanding to megaparsec scales into the
intergalactic medium. Using a model of radio galaxy, we model the energetics
and the pressure of the non-thermal electrons. We calculate the mean global
non-thermal y-distortion, \ynt. For observationally reasonable distribution of
the jet luminosities in the range of $10^{45}-10^{47}$ ergs$^{-1}$, we find
\ynt to be less than $10^{-5}$, and hence not violating the COBE limit as
previously claimed. Using the unique spectral dependence of the non-thermal SZ,
we show that a detection of \ynt can be within reach at the level of $\gtrsim
5\sigma$ from a future PIXIE-like experiment. The total non-thermal SZ power
spectrum, $C^{NT}_\ell$, from the radio lobes is shown to peak at $\ell \sim
3000$ with an amplitude $\sim 1\%$ of thermal SZ power spectrum from galaxy
clusters, and is also within the reach for a PIXIE like experiment. Finally, we
show that a detection of the $C^{NT}_\ell$ with a PIXIE-like experiment can
lead to $\sim 5\sigma$ constraint on the mass dependence of the jet luminosity
with the constraint becoming, at least, ten times better for the proposed more
ambitious CMB-HD survey. This will, further, lead to the tightest constraint on
the central black hole mass -to- host halo mass scaling relations.


title: Machine Explanations and Human Understanding

abstract:
  Explanations are hypothesized to improve human understanding of machine
learning models and achieve a variety of desirable outcomes, ranging from model
debugging to enhancing human decision making. However, empirical studies have
found mixed and even negative results. An open question, therefore, is under
what conditions explanations can improve human understanding and in what way.
Using adapted causal diagrams, we provide a formal characterization of the
interplay between machine explanations and human understanding, and show how
human intuitions play a central role in enabling human understanding.
Specifically, we identify three core concepts of interest that cover all
existing quantitative measures of understanding in the context of human-AI
decision making: task decision boundary, model decision boundary, and model
error. Our key result is that without assumptions about task-specific
intuitions, explanations may potentially improve human understanding of model
decision boundary, but they cannot improve human understanding of task decision
boundary or model error. To achieve complementary human-AI performance, we
articulate possible ways on how explanations need to work with human
intuitions. For instance, human intuitions about the relevance of features
(e.g., education is more important than age in predicting a person's income)
can be critical in detecting model error. We validate the importance of human
intuitions in shaping the outcome of machine explanations with empirical
human-subject studies. Overall, our work provides a general framework along
with actionable implications for future algorithmic development and empirical
experiments of machine explanations.


title:
On the Relation Between Asymptotic Charges, the Failure of Peeling and
  Late-time Tails

abstract:
  The last few years have seen considerable mathematical progress concerning
the asymptotic structure of gravitational radiation in dynamical, astrophysical
spacetimes. In this paper, we distil some of the key ideas from recent works
and assemble them in a new way in order to make them more accessible to the
wider general relativity community. We also announce some new physical findings
in this process.
  First, we introduce the conserved $f(r)$-modified Newman--Penrose charges on
asymptotically flat spacetimes, and we show that these charges provide a
dictionary that relates asymptotics of massless, general spin fields in
different regions: Asymptotic behaviour near $i^+$ ("late-time tails") can be
read off from asymptotic behaviour towards $\mathcal I^+$, and, similarly,
asymptotic behaviour towards $\mathcal I^+$ can be read off from asymptotic
behaviour near $i^-$ or $\mathcal I^-$.
  Using this dictionary, we then explain how: (I) the quadrupole approximation
for a system of $N$ infalling masses from $i^-$ causes the "peeling property
towards $\mathcal I^+$" to be violated, and (II) this failure of peeling
results in deviations from the usual predictions for tails in the late-time
behaviour of gravitational radiation: Instead of the Price's law rate
$r\Psi^{[4]}|_{\mathcal I^+}\sim u^{-6}$ as~$u\to\infty$, we predict that
$r\Psi^{[4]}|_{\mathcal I^+}\sim u^{-4}$, with the coefficient of this latter
decay rate being a multiple of the monopole and quadrupole moments of the
matter distribution in the infinite past.


title: First plastic drop distributions in strained disordered crystals

abstract:
  We derive exact probability distributions for the strain ($\epsilon$) at
which the first plastic drop event occurs in uniformly strained disordered
crystals, with quenched disorder introduced through polydispersity in particle
sizes. We identify the first plastic drop in the system with the first contact
breaking event and characterize these events numerically as well as
theoretically. Our theoretical results are corroborated with numerical
simulations of quasistatic volumetric strain applied to disordered
near-crystalline configurations of athermal soft particles. We develop a
general technique to determine the $distribution$ of strains at which the first
contact breaking events occur, through an exact mapping between the cumulative
distribution of such events and the volume of a convex polytope whose dimension
is determined by the number of defects $N_d$ in the system. An exact numerical
computation of this polytope volume for systems with small numbers of defects
displays a remarkable match with the distribution of strains generated through
direct numerical simulations. Finally, we derive the distribution of strains at
which the first plastic failure occurs, assuming that individual contact
breaking events are uncorrelated, which accurately reproduces distributions
obtained from direct numerical simulations.


title: The non-Gaussian likelihood of weak lensing power spectra

abstract:
  The power spectrum of weak lensing fluctuations has a non-Gaussian
distribution due to its quadratic nature. On small scales the Central Limit
Theorem acts to Gaussianize this distribution but non-Gaussianity in the signal
due to gravitational collapse is increasing and the functional form of the
likelihood is unclear. Analyses have traditionally assumed a Gaussian
likelihood with non-linearity incorporated into the covariance matrix; here we
provide the theory underpinning this assumption. We calculate, for the first
time, the leading-order correction to the distribution of angular power spectra
from non-Gaussianity in the underlying signal and study the transition to
Gaussianity. Our expressions are valid for an arbitrary number of correlated
maps and correct the Wishart distribution in the presence of weak (but
otherwise arbitrary) non-Gaussianity in the signal. Surprisingly, the resulting
distribution is not equivalent to an Edgeworth expansion. The leading-order
effect is to broaden the covariance matrix by the usual trispectrum term, with
residual skewness sourced by the trispectrum and the square of the bispectrum.
Using lognormal lensing maps we demonstrate that our likelihood is uniquely
able to model both large and mildly non-linear scales. We provide
easy-to-compute statistics to quantify the size of the non-Gaussian
corrections. We show that the full non-Gaussian likelihood can be accurately
modelled as a Gaussian on small, non-linear scales. On large angular scales
non-linearity in the lensing signal imparts a negligible correction to the
likelihood, which takes the Wishart form in the full-sky case. Our formalism is
equally applicable to any kind of projected field.


title:
Berry phase in the rigid rotor: the emergent physics of odd
  antiferromagnets

abstract:
  The rigid rotor is a classic problem in quantum mechanics, describing the
dynamics of a rigid body with its centre of mass held fixed. The configuration
space of this problem is $SO(3)$, the space of all rotations in three
dimensions. This is a topological space with two types of closed loops: trivial
loops that can be adiabatically shrunk to a point and non-trivial loops that
cannot. In the traditional formulation of the problem, stationary states are
periodic over both types of closed loops. However, periodicity conditions may
change if Berry phases are introduced. We argue that time-reversal-symmetry
allows for only one new possibility -- a Berry phase of $\pi$ attached to all
non-trivial loops. We derive the corresponding stationary states by exploiting
the connection between $SO(3)$ and $SU(2)$ spaces. The solutions are
anti-periodic over any non-trivial loop, i.e., stationary states reverse sign
under a $2\pi$ rotation about any axis. Remarkably, this framework is realized
in the low-energy physics of certain quantum magnets. The magnets must satisfy
the following conditions: (a) the classical ground states are unpolarized,
carrying no net magnetization, (b) the set of classical ground states is
indexed by $SO(3)$, and (c) the product $N\times S$ is a half-integer, where
$N$ is the number of spins and $S$ is the spin quantum number. We demonstrate
this result in a family of Heisenberg antiferromagnets defined on polygons with
an odd number of vertices. At each vertex, we have a spin-$S$ moment that is
coupled to its nearest neighbours. In the classical limit, these magnets have
coplanar ground states. Their quantum spectra, at low energies, correspond to
`spherical top' and `symmetric top' rigid rotors. For integer values of $S$, we
recover traditional rigid rotor spectra. With half-integer-$S$, we obtain rotor
spectra with a Berry phase of $\pi$.


title: Turnpike in optimal control of PDEs, ResNets, and beyond

abstract:
  The \emph{turnpike property} in contemporary macroeconomics asserts that if
an economic planner seeks to move an economy from one level of capital to
another, then the most efficient path, as long as the planner has enough time,
is to rapidly move stock to a level close to the optimal stationary or constant
path, then allow for capital to develop along that path until the desired term
is nearly reached, at which point the stock ought to be moved to the final
target. Motivated in part by its nature as a resource allocation strategy, over
the past decade, the turnpike property has also been shown to hold for several
classes of partial differential equations arising in mechanics. When formalized
mathematically, the turnpike theory corroborates the insights from economics:
for an optimal control problem set in a finite-time horizon, optimal controls
and corresponding states, are close (often exponentially), during most of the
time, except near the initial and final time, to the optimal control and
corresponding state for the associated stationary optimal control problem. In
particular, the former are mostly constant over time. This fact provides a
rigorous meaning to the asymptotic simplification that some optimal control
problems appear to enjoy over long time intervals, allowing the consideration
of the corresponding stationary problem for computing and applications. We
review a slice of the theory developed over the past decade --the
controllability of the underlying system is an important ingredient, and can
even be used to devise simple turnpike-like strategies which are nearly
optimal--, and present several novel applications, including, among many
others, the characterization of Hamilton-Jacobi-Bellman asymptotics, and
stability estimates in deep learning via residual neural networks.


title:
Kinematics and Feedback in H II regions in the Dwarf Starburst Galaxy IC
  10

abstract:
  We present a survey of the central region of the nearest starburst galaxy, IC
10, using the W. M. Keck Observatory Keck Cosmic Web Imager (KCWI) at high
spectral and spatial resolution. We map the central starburst of IC 10 to
sample the kinematic and ionization properties of the individual star-forming
regions. Using the low spectral resolution mode of KCWI we map the oxygen
abundance and with the high spectral resolution mode we identify 46 individual
H II regions. These H II regions have an average radius of 4.0 pc, star
formation rate $\sim1.3\times10^{-4}$ M$_\odot$ yr$^{-1}$, and velocity
dispersion $\sim$16 km s$^{-1}$. None of the H II regions appear to be
virialized ($\rm \alpha_{vir}>>1$), and, on average, they show evidence of
ongoing expansion. IC 10's H II regions are offset from the star forming region
size-luminosity scaling relationships, as well as Larson's Law that relates
size and velocity dispersion. We investigate the balance of inward and outward
pressure, $\rm P_{in}$ and $\rm P_{out}$, finding $\rm P_{out}>P_{in}$ in 89%
of H II regions, indicating feedback driven expansion even in these low mass H
II regions. We find warm gas pressure ($\rm P_{gas}$) provides the dominant
contribution to the outward pressure ($\rm P_{out}$). This counteracts the
inward pressure which is dominated by turbulence in the surrounding gas rather
than self-gravity. Five H II regions show evidence of outflows which are most
likely supported by either stellar winds (2 regions) or champagne flows (3
regions). These observations provide new insights into the state of the
star-forming regions in IC 10 and negative feedback from low mass clusters.


title:
SIBELIUS-DARK: a galaxy catalogue of the Local Volume from a constrained
  realisation simulation

abstract:
  We present SIBELIUS-DARK, a constrained realisation simulation of the local
volume to a distance of 200~Mpc from the Milky Way. SIBELIUS-DARK is the first
study of the \textit{Simulations Beyond The Local Universe} (SIBELIUS) project,
which has the goal of embedding a model Local Group-like system within the
correct cosmic environment. The simulation is dark-matter-only, with the galaxy
population calculated using the semi-analytic model of galaxy formation,
GALFORM. We demonstrate that the large-scale structure that emerges from the
SIBELIUS constrained initial conditions matches well the observational data.
The inferred galaxy population of SIBELIUS-DARK also match well the
observational data, both statistically for the whole volume and on an
object-by-object basis for the most massive clusters. For example, the $K$-band
number counts across the whole sky, and when divided between the northern and
southern Galactic hemispheres, are well reproduced by SIBELIUS-DARK. We find
that the local volume is somewhat unusual in the wider context of $\Lambda$CDM:
it contains an abnormally high number of supermassive clusters, as well as an
overall large-scale underdensity at the level of $\approx 5$\% relative to the
cosmic mean. However, whilst rare, the extent of these peculiarities does not
significantly challenge the $\Lambda$CDM model. SIBELIUS-DARK is the most
comprehensive constrained realisation simulation of the local volume to date,
and with this paper we publicly release the halo and galaxy catalogues at
$z=0$, which we hope will be useful to the wider astronomy community.


title:
Local normal approximations and probability metric bounds for the
  matrix-variate $T$ distribution and its application to Hotelling's $T$
  statistic

abstract:
  In this paper, we develop local expansions for the ratio of the centered
matrix-variate $T$ density to the centered matrix-variate normal density with
the same covariances. The approximations are used to derive upper bounds on
several probability metrics (such as the total variation and Hellinger
distance) between the corresponding induced measures.


title:
Face2PPG: An unsupervised pipeline for blood volume pulse extraction
  from faces

abstract:
  Photoplethysmography (PPG) signals have become a key technology in many
fields such as medicine, well-being, or sports. Our work proposes a set of
pipelines to extract remote PPG signals (rPPG) from the face, robustly,
reliably, and in a configurable manner. We identify and evaluate the possible
choices in the critical steps of unsupervised rPPG methodologies. We evaluate a
state-of-the-art processing pipeline in six different datasets, incorporating
important corrections in the methodology that ensure reproducible and fair
comparisons. In addition, we extend the pipeline by proposing three novel
ideas; 1) a new method to stabilize the detected face based on a rigid mesh
normalization; 2) a new method to dynamically select the different regions in
the face that provide the best raw signals, and 3) a new RGB to rPPG
transformation method called Orthogonal Matrix Image Transformation (OMIT)
based on QR decomposition, that increases robustness against compression
artifacts. We show that all three changes introduce noticeable improvements in
retrieving rPPG signals from faces, obtaining state-of-the-art results compared
with unsupervised, non-learning-based methodologies, and in some databases,
very close to supervised, learning-based methods. We perform a comparative
study to quantify the contribution of each proposed idea. In addition, we
depict a series of observations that could help in future implementations.


title: Quantum epistemology and falsification of random generators

abstract:
  The operational axiomatization of quantum theory can be regarded as a set of
six epistemological rules for falsifying propositions of the theory. In
particular, the Purification postulate-the only one that is not shared with
classical theory-allows falsification of random-sequences generators, a task
unfeasible classically.


title: Outer approximations of classical multi-network correlations

abstract:
  We propose a framework, named the postselected inflation framework, to obtain
converging outer approximations of the sets of probability distributions that
are compatible with classical multi-network scenarios. Here, a network is a
bilayer directed acyclic graph with a layer of sources of classical randomness,
a layer of agents, and edges specifying the connectivity between the agents and
the sources. A multi-network scenario is a list of such networks, together with
a specification of subsets of agents using the same strategy. An outer
approximation of the set of multi-network correlations provides means to
certify the infeasibility of a list of agent outcome distributions. We
furthermore show that the postselected inflation framework is mathematically
equivalent to the standard inflation framework: in that respect, our results
allow to gain further insights into the convergence proof of the inflation
hierarchy of Navascu\`es and Wolfe [arXiv:1707.06476], and extend it to the
case of multi-network scenarios.


title: Teaching Networks to Solve Optimization Problems

abstract:
  Leveraging machine learning to optimize the optimization process is an
emerging field which holds the promise to bypass the fundamental computational
bottleneck caused by traditional iterative solvers in critical applications
requiring near-real-time optimization. The majority of existing approaches
focus on learning data-driven optimizers that lead to fewer iterations in
solving an optimization. In this paper, we take a different approach and
propose to replace the iterative solvers altogether with a trainable parametric
set function that outputs the optimal arguments/parameters of an optimization
problem in a single feed-forward. We denote our method as, Learning to Optimize
the Optimization Process (LOOP). We show the feasibility of learning such
parametric (set) functions to solve various classic optimization problems,
including linear/nonlinear regression, principal component analysis,
transport-based core-set, and quadratic programming in supply management
applications. In addition, we propose two alternative approaches for learning
such parametric functions, with and without a solver in the-LOOP. Finally, we
demonstrate the effectiveness of our proposed approach through various
numerical experiments.


title:
Hierarchical Dependency Constrained Tree Augmented Naive Bayes
  Classifiers for Hierarchical Feature Spaces

abstract:
  The Tree Augmented Naive Bayes (TAN) classifier is a type of probabilistic
graphical model that constructs a single-parent dependency tree to estimate the
distribution of the data. In this work, we propose two novel Hierarchical
dependency-based Tree Augmented Naive Bayes algorithms, i.e. Hie-TAN and
Hie-TAN-Lite. Both methods exploit the pre-defined parent-child
(generalisation-specialisation) relationships between features as a type of
constraint to learn the tree representation of dependencies among features,
whilst the latter further eliminates the hierarchical redundancy during the
classifier learning stage. The experimental results showed that Hie-TAN
successfully obtained better predictive performance than several other
hierarchical dependency constrained classification algorithms, and its
predictive performance was further improved by eliminating the hierarchical
redundancy, as suggested by the higher accuracy obtained by Hie-TAN-Lite.


title: Fermionic vacuum polarization induced by a non-Abelian vortex

abstract:
  In this paper, we analyze the fermionic condensate (FC) and the vacuum
expectation value (VEV) of the energy-momentum tensor associated with an
isospin-$1/2$ charged massive fermionic field induced by the presence of a
$SU(2)$ vortex, taking into account the effect of the conical geometry produced
by this object. We consider the vortex as an idealized topological defect,
i.e., very thin, straight and carrying a magnetic flux running along its core.
Besides the direct coupling of the fermionic field with the iso-vector gauge
field, we also admit the coupling with the scalar sector of the non-Abelian
vortex system, expressed as a vector in the three-dimensional isospace. Due to
this interaction, the FC is expressed as the sum of two contributions
associated with the two different effective masses for the $\pm 1/2$ fermionic
components of the isospin operator, $\tau^3/2$. The VEV of the energy-tensor
also presents a similar structure. The vacuum energy density is equal to the
radial and axial stresses. As to the azimuthal one, it is expressed in terms of
the radial derivative of energy-density. Regarding to the magnetic flux, both,
the FC and the VEV of the energy-momentum tensor, can be positive or negative.
Another interesting consequence of the interaction with the bosonic sector, the
FC and VEV of the energy-momentum tensor, present different intensity for
different values of the ratio between the scalar coupling constant and the mass
of the fermionic field. This is a new feature that the system presents.


title: The Lamplighter Group

abstract:
  We are following [4]. Nevertheless we are interested only in claryfication
that the lamplighter group can be realized as a 2--states Mealy machine.


title: A Lagrangian Duality Approach to Active Learning

abstract:
  We consider the batch active learning problem, where only a subset of the
training data is labeled, and the goal is to query a batch of unlabeled samples
to be labeled so as to maximally improve model performance. We formulate the
learning problem using constrained optimization, where each constraint bounds
the performance of the model on labeled samples. Considering a primal-dual
approach, we optimize the primal variables, corresponding to the model
parameters, as well as the dual variables, corresponding to the constraints. As
each dual variable indicates how significantly the perturbation of the
respective constraint affects the optimal value of the objective function, we
use it as a proxy of the informativeness of the corresponding training sample.
Our approach, which we refer to as Active Learning via Lagrangian dualitY, or
ALLY, leverages this fact to select a diverse set of unlabeled samples with the
highest estimated dual variables as our query set. We show, via numerical
experiments, that our proposed approach performs similarly to or better than
state-of-the-art active learning methods in a variety of classification and
regression tasks. We also demonstrate how ALLY can be used in a generative mode
to create novel, maximally-informative samples. The implementation code for
ALLY can be found at https://github.com/juanelenter/ALLY.


title:
Learning Similarity Metrics for Volumetric Simulations with Multiscale
  CNNs

abstract:
  Simulations that produce three-dimensional data are ubiquitous in science,
ranging from fluid flows to plasma physics. We propose a similarity model based
on entropy, which allows for the creation of physically meaningful ground truth
distances for the similarity assessment of scalar and vectorial data, produced
from transport and motion-based simulations. Utilizing two data acquisition
methods derived from this model, we create collections of fields from numerical
PDE solvers and existing simulation data repositories, and highlight the
importance of an appropriate data distribution for an effective training
process. Furthermore, a multiscale CNN architecture that computes a volumetric
similarity metric (VolSiM) is proposed. To the best of our knowledge this is
the first learning method inherently designed to address the challenges arising
for the similarity assessment of high-dimensional simulation data.
Additionally, the tradeoff between a large batch size and an accurate
correlation computation for correlation-based loss functions is investigated,
and the metric's invariance with respect to rotation and scale operations is
analyzed. Finally, the robustness and generalization of VolSiM is evaluated on
a large range of test data, as well as a particularly challenging turbulence
case study, that is close to potential real-world applications.


title:
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and
  Loopy Belief Propagation in JAX

abstract:
  PGMax is an open-source Python package for easy specification of discrete
Probabilistic Graphical Models (PGMs) as factor graphs, and automatic
derivation of efficient and scalable loopy belief propagation (LBP)
implementation in JAX. It supports general factor graphs, and can effectively
leverage modern accelerators like GPUs for inference. Compared with existing
alternatives, PGMax obtains higher-quality inference results with
orders-of-magnitude inference speedups. PGMax additionally interacts seamlessly
with the rapidly growing JAX ecosystem, opening up exciting new possibilities.
Our source code, examples and documentation are available at
https://github.com/vicariousinc/PGMax.


title:
Deuterium fractionation as a multi-phase component tracer in the
  Galactic Centre

abstract:
  The Central Molecular Zone (CMZ) contains most of the mass of our Galaxy but
its star formation rate is one order of magnitude lower than in the Galactic
disc. This is likely related to the fact that the bulk of the gas in the CMZ is
in a warm ($>$100 K) and turbulent phase with little material in the
pre-stellar phase. We present in this Letter observations of deuterium
fractionation (D/H ratios) of HCN, HNC, HCO$^{+}$, and N$_{2}$H$^{+}$ towards
the CMZ molecular cloud G+0.693-0.027. These observations clearly show, for the
first time, the presence of a colder, denser, and less turbulent narrow
component, with a line width of $\sim$9 km s$^{-1}$, in addition to the warm,
less dense and turbulent broad component with a line width of $\sim$20 km
s$^{-1}$. The very low D/H ratio $\le$6$\times$10$^{-5}$ for HCO$^{+}$ and
N$_{2}$H$^{+}$, close to the cosmic value ($\sim$2.5$\times$10$^{-5}$), and the
high D/H ratios $>$4$\times$10$^{-4}$ for HCN and HNC derived for the broad
component, confirm the presence of high-temperatures deuteration routes for
nitriles. For the narrow component we have derived D/H ratios $>$10$^{-4}$ and
excitation temperatures of $7$ K for all molecules, suggesting kinetic
temperatures $\le$30 K and H$_2$ densities $\ge$5$\times$10$^{4}$ cm$^{-3}$, at
least one order of magnitude larger than for the broad component. The method
presented in this Letter allows to identify clouds on the verge of star
formation, i.e. under pre-stellar conditions, towards the CMZ. This method can
also be used for the identification of such clouds in external galaxies.


title:
Disentangle Saliency Detection into Cascaded Detail Modeling and Body
  Filling

abstract:
  Salient object detection has been long studied to identify the most visually
attractive objects in images/videos. Recently, a growing amount of approaches
have been proposed all of which rely on the contour/edge information to improve
detection performance. The edge labels are either put into the loss directly or
used as extra supervision. The edge and body can also be learned separately and
then fused afterward. Both methods either lead to high prediction errors near
the edge or cannot be trained in an end-to-end manner. Another problem is that
existing methods may fail to detect objects of various sizes due to the lack of
efficient and effective feature fusion mechanisms. In this work, we propose to
decompose the saliency detection task into two cascaded sub-tasks, \emph{i.e.},
detail modeling and body filling. Specifically, the detail modeling focuses on
capturing the object edges by supervision of explicitly decomposed detail label
that consists of the pixels that are nested on the edge and near the edge. Then
the body filling learns the body part which will be filled into the detail map
to generate more accurate saliency map. To effectively fuse the features and
handle objects at different scales, we have also proposed two novel multi-scale
detail attention and body attention blocks for precise detail and body
modeling. Experimental results show that our method achieves state-of-the-art
performances on six public datasets.


title:
Physical Zero-knowledge Proofs for Flow Free, Hamiltonian Cycles, and
  Many-to-many k-disjoint Covering Paths

abstract:
  In this paper we describe protocols which use a standard deck of cards to
provide a perfectly sound zero-knowledge proof for Hamiltonian cycles and Flow
Free puzzles. The latter can easily be extended to provide a protocol for a
zero-knowledge proof of many-to-many k-disjoint path coverings.


title: X-ray Tomographic Micro-Particle Velocimetry in Porous Media

abstract:
  Fluid flow through intricate confining geometries often exhibits complex
behaviors, certainly in porous materials, e.g. in groundwater flows or the
operation of filtration devices and porous catalysts. However, it has remained
extremely challenging to measure 3D flow fields in such micrometer-scale
geometries. Here, we introduce a new 3D velocimetry approach for optically
opaque porous materials, based on time-resolved X-ray micro-computed tomography
(micro-CT). We imaged the movement of X-ray tracing micro-particles in creeping
flows through the pores of a sandpack and a porous filter, using
laboratory-based micro-CT at frame rates of tens of seconds and voxel sizes of
12 micron. For both experiments, fully three-dimensional velocity fields were
determined based on thousands of individual particle trajectories, showing a
good match to computational fluid dynamics simulations. Error analysis was
performed by investigating a realistic simulation of the experiments. The
method has the potential to measure complex, unsteady 3D flows in porous media
and other intricate microscopic geometries. This could cause a breakthrough in
the study of fluid dynamics in a range of scientific and industrial application
fields.


title: Financial Vision Based Reinforcement Learning Trading Strategy

abstract:
  Recent advances in artificial intelligence (AI) for quantitative trading have
led to its general superhuman performance in significant trading performance.
However, the potential risk of AI trading is a "black box" decision. Some AI
computing mechanisms are complex and challenging to understand. If we use AI
without proper supervision, AI may lead to wrong choices and make huge losses.
Hence, we need to ask about the AI "black box", including why did AI decide to
do this or not? Why can people trust AI or not? How can people fix their
mistakes? These problems also highlight the challenges that AI technology can
explain in the trading field.


title: Asymptotic spectral properties of the Hilbert $L$-matrix

abstract:
  We study asymptotic spectral properties of the generalized Hilbert $L$-matrix
\[
  L_{n}(\nu)=\left(\frac{1}{\max(i,j)+\nu}\right)_{i,j=0}^{n-1}, \] for large
order $n$. First, for general $\nu\neq0,-1,-2,\dots$, we deduce the asymptotic
distribution of eigenvalues of $L_{n}(\nu)$ outside the origin. Second, for
$\nu>0$, asymptotic formulas for small eigenvalues of $L_{n}(\nu)$ are derived.
Third, in the classical case $\nu=1$, we also prove asymptotic formulas for
large eigenvalues of $L_{n}\equiv L_{n}(1)$. I particular, we obtain an
asymptotic expansion of $\|L_{n}\|$ improving Wilf's formula for the best
constant in truncated Hardy's inequality.


title:
Gravitational-Wave and X-ray Probes of the Neutron Star Equation of
  State

abstract:
  Neutron stars are a remarkable marriage of Einstein's theory of general
relativity with nuclear physics. Their interiors harbor extreme matter that
cannot be probed in the laboratory. At such high densities and pressures, their
cores may consist predominantly of exotic matter such as free quarks or
hyperons. Gravitational wave observations from the Laser Interferometer
Gravitational-wave Observatory (LIGO) and from other interferometers, and X-ray
observations from the Neutron Star Interior Composition Explorer (NICER), are
beginning to pierce through the veil. These observations provide information
about neutron star cores, and therefore, about the physics that makes such
objects possible. In this review, we discuss what we have learned about the
physics of neutron stars from gravitational wave and X-ray observations. We
focus on what has been observed with certainty and what should be observable in
the near future, with an eye out for the physics that these new observations
will teach us.


title: Structure formation in mimetic gravity

abstract:
  Mimetic theory of gravity was proposed as an alternative description for the
dark matter (DM) puzzle. It was argued that the mimetic field can encode a
dynamical longitudinal degree of freedom which can play the role of mimetic DM.
In this paper, we disclose the effects of an extra longitudinal degree of
freedom on the evolution of perturbations in the framework of mimetic gravity.
We consider a flat Friedmann-Robertson-Walker (FRW) background and explore the
linear perturbations by adopting the spherically symmetric collapse formalism.
By suitably choosing the potential of the mimetic field, we are able to solve
the perturbed field equations in the linear regime and derive the density
contrast $\delta_{m}$ in terms of the redshift parameter z. We observe that
$\delta_{m}$ starts growing from its initial value at the early stages of the
universe and as the universe expands, it grows slower compared to the standard
cosmology. This may due to the extra degree of freedom of the gravitational
field which affects the growth of perturbations. We then consider the effects
of this potential on the density abundance and the deceleration parameter. We
find out that mimetic potential can play the role of dark energy (DE) and
affects the dynamics of matter perturbations and cosmological parameters.


title:
Regime identification for stratified wakes from limited measurements: a
  library-based sparse regression formulation

abstract:
  Bluff body wakes in stratified fluids are known to exhibit a rich range of
dynamic behavior that can be categorized into different regimes based on
Reynolds number ($Re$) and Froude number ($Fr$). Topological differences in
wake structure across these different regimes have been clarified recently
through the use of Dynamic Mode Decomposition (DMD) on Direct Numerical
Simulation (DNS) and laboratory data for a sphere in a stratified fluid for
$Re\in [200,1000]$ and $Fr\in[0.5,16]$. In this work, we attempt to identify
the dynamic regime from limited measurement data in a stratified wake with
(nominally) unknown $Re$ and $Fr$. A large database of candidate basis
functions is compiled by pooling the DMD modes obtained in prior DNS. A sparse
model is built using the Forward Regression with Orthogonal Least Squares
(FROLS) algorithm, which sequentially identifies DMD modes that best represent
the data and calibrates their amplitude and phase. After calibration, the
velocity field can be reconstructed using a weighted combination of the
dominant DMD modes. The dynamic regime for the measurements is estimated via a
projection-weighted average of $Re$ and $Fr$ corresponding to the identified
modes. Regime identification is carried out from a limited number of 2D
velocity snapshots from numerical and experimental datasets, as well as 3 point
measurements in the wake of the body. A metric to assess confidence is
introduced based on the observed predictive capability. This approach holds
promise for the implementation of data-driven fluid pattern classifiers.


title: Modular lattices of finite length (Part B)

abstract:
  Part B (of a project involving four Parts) is about "bases of lines", a
concept introduced by C. Herrmann and the author in the late 80's. Bases of
lines attempt to describe a given modular lattice in a geometric way akin to
how projective geometries describe complemented modular lattices. This e.g.
yields the result that each modular lattice L of finite length d(L), and having
s(L) many maximal congruences, has at least 2d(L)-s(L) many join-irreducible
elements. Furthermore, an algorithm is proposed that calculates, in a
compressed way, the (full) submodule lattice Sub(W) of any (sufficiently known)
finite R-module W.


title:
IoT Malware Detection Architecture using a Novel Channel Boosted and
  Squeezed CNN

abstract:
  Interaction between devices, people, and the Internet has given birth to a
new digital communication model, the Internet of Things (IoT). The seamless
network of these smart devices is the core of this IoT model. However, on the
other hand, integrating smart devices to constitute a network introduces many
security challenges. These connected devices have created a security blind
spot, where cybercriminals can easily launch an attack to compromise the
devices using malware proliferation techniques. Therefore, malware detection is
considered a lifeline for the survival of IoT devices against cyberattacks.
This study proposes a novel IoT Malware Detection Architecture (iMDA) using
squeezing and boosting dilated convolutional neural network (CNN). The proposed
architecture exploits the concepts of edge and smoothing, multi-path dilated
convolutional operations, channel squeezing, and boosting in CNN. Edge and
smoothing operations are employed with split-transform-merge (STM) blocks to
extract local structure and minor contrast variation in the malware images. STM
blocks performed multi-path dilated convolutional operations, which helped
recognize the global structure of malware patterns. Additionally, channel
squeezing and merging helped to get the prominent reduced and diverse feature
maps, respectively. Channel squeezing and boosting are applied with the help of
STM block at the initial, middle and final levels to capture the texture
variation along with the depth for the sake of malware pattern hunting. The
proposed architecture has shown substantial performance compared with the
customized CNN models. The proposed iMDA has achieved Accuracy: 97.93%,
F1-Score: 0.9394, Precision: 0.9864, MCC: 0. 8796, Recall: 0.8873, AUC-PR:
0.9689 and AUC-ROC: 0.9938.


title: Schlieren and BOS velocimetry of a round turbulent helium jet in air

abstract:
  Seedless velocimetry is gaining interest in many industrial and research
applications. We report on a comparative study of time-resolved optical
velocimetry using traditional, mirror-type knife-edge schlieren optics versus
Background-Oriented Schlieren (BOS) of subsonic round turbulent helium jets in
air at Red = 5,890 and 11,300. Digital images with 1024 pixels streamwise
resolution (0 < x/d < 200) were captured at 6000 frames/s in large ensembles.
Velocimetry was performed on these results by digital image correlation (DIC)
using OpenPIV software, and by streak-schlieren analysis of x-t diagrams
(kymography). Limited PIV data were also collected for verification of the
schlieren velocimetry results. Both BOS and traditional schlieren show partial
success in measuring the mean-flow helium jet self-similarity in terms of the
1/x decay of centerline velocity, Gaussian-shaped radial velocity profiles, and
linear spreading rate of the jet. Visualized turbulent eddies, used as tracers
in schlieren velocimetry, are observed to last longer than is necessary for
this purpose in the present helium jets. Also, the measured convective velocity
appears to be sufficiently robust to sum to the jet mean velocity in some of
the results. Kymography yields better overall results than DIC, which we
attribute to kymography's spatiotemporal "spectrum" of jet velocities, enabling
the discrimination of fast eddies near the jet centerline from slower ones near
the jet periphery. DIC and other analysis methods suffer from a path-averaging
bias which negatively affects the results. The reduction of kymographic data
for velocimetry was done manually and also by a Fourier-transform
image-feature-orientation code, both yielding equivalent results.


title: Random Linear Network Coding in NOMA Optical Wireless Networks

abstract:
  Optical wireless communication (OWC) has the potential to provide high
communication speeds that support the massive use of the Internet that is
expected in the near future. In OWC, optical access points (APs) are deployed
on the celling to serve multiple users. In this context, efficient multiple
access schemes are required to share the resources among the users and align
multi-user interference. Recently, non-orthogonal multiple access (NOMA) has
been studied to serve multiple users simultaneously using the same resources,
while a different power level is allocated to each user. Despite the acceptable
performance of NOMA, users might experience a high packet loss due to high
noise, which results from the use of successive interference cancelation (SIC).
In this work, random linear network coding (RLNC) is proposed to enhance the
performance of NOMA in an optical wireless network where users are divided into
multicast groups, and each group contains users that slightly differ in their
channel gains. Moreover, a fixed power allocation (FPA) strategy is considered
among these groups to avoid complexity. The performance of the proposed scheme
is evaluated in terms of total packet success probability. The results show
that the proposed scheme is more suitable for the network considered compared
to other benchmark schemes such as traditional NOMA and orthogonal transmission
schemes. Moreover, the total packet success probability is highly affected by
the level of power allocated to each group in all the scenarios.


title: A Mini-Block Natural Gradient Method for Deep Neural Networks

abstract:
  The training of deep neural networks (DNNs) is currently predominantly done
using first-order methods. Some of these methods (e.g., Adam, AdaGrad, and
RMSprop, and their variants) incorporate a small amount of curvature
information by using a diagonal matrix to precondition the stochastic gradient.
Recently, effective second-order methods, such as KFAC, K-BFGS, Shampoo, and
TNT, have been developed for training DNNs, by preconditioning the stochastic
gradient by layer-wise block-diagonal matrices. Here we propose and analyze the
convergence of an approximate natural gradient method, mini-block Fisher (MBF),
that lies in between these two classes of methods. Specifically, our method
uses a block-diagonal approximation to the Fisher matrix, where for each layer
in the DNN, whether it is convolutional or feed-forward and fully connected,
the associated diagonal block is also block-diagonal and is composed of a large
number of mini-blocks of modest size. Our novel approach utilizes the
parallelism of GPUs to efficiently perform computations on the large number of
matrices in each layer. Consequently, MBF's per-iteration computational cost is
only slightly higher than it is for first-order methods. Finally, the
performance of our proposed method is compared to that of several baseline
methods, on both Auto-encoder and CNN problems, to validate its effectiveness
both in terms of time efficiency and generalization power.


title:
A stabilized formulation for the solution of the incompressible unsteady
  Stokes equations in the frequency domain

abstract:
  A stabilized finite element method is introduced for the simulation of
time-periodic creeping flows, such as those found in the cardiorespiratory
systems. The new technique, which is formulated in the frequency rather than
time domain, strictly uses real arithmetics and permits the use of similar
shape functions for pressure and velocity for ease of implementation. It
involves the addition of the Laplacian of pressure to the continuity equation
with a complex-valued stabilization parameter that is derived systematically
from the momentum equation. The numerical experiments show the excellent
accuracy and robustness of the proposed method in simulating flows in complex
and canonical geometries for a wide range of conditions. The present method
significantly outperforms a traditional solver in terms of both computational
cost and scalability, which lowers the overall solution turnover time by
several orders of magnitude.


title: Comet Fading Begins Beyond Saturn

abstract:
  The discovery probability of long-period comets (LPCs) passing near the Sun
is highest during their first passage and then declines, or fades, during
subsequent return passages. Comet fading is largely attributed to
devolatilization and fragmentation via thermal processing within 2-3 au of the
Sun (1 au being the Earth-Sun distance). Here our numerical simulations show
that comet observing campaigns miss vast numbers of LPCs making returning
passages through the Saturn region (near 10 au) because these comets fade
during prior, even more distant passages exterior to Saturn and thus elude
detection. Consequently, comet properties significantly evolve at solar
distances much larger than previously considered, and this offers new insights
into the physical and dynamical properties of LPCs, both near and far from
Earth.


title:
Non-equivalence of quasilinear dynamical systems and their statistical
  closures

abstract:
  It is widely believed that statistical closure theories for dynamical systems
provide statistics equivalent to those of the governing dynamical equations
from which the former are derived. Here, we demonstrate counterexamples in the
context of the widely used mean-field quasilinear (QL) approximation applied to
2D fluid dynamical systems. We compare statistics of QL numerical simulations
with those obtained by direct statistical simulation via a cumulant expansion
closed at second order (CE2). We observe that, though CE2 is an exact
statistical closure for QL dynamics, its predictions disagree with the
statistics of the QL solution for identical parameter values. These
disagreements are linked to instabilities of the second cumulant dynamics
within CE2 that are unavailable in the QL equations.


title:
The Fornax3D project: The environmental impact on gas metallicity
  gradients in Fornax cluster galaxies

abstract:
  The role played by environment in galaxy evolution is a current debate in
astronomy. The degree to which environment can alter, re-shape, or drive galaxy
evolution is a topic of discussion in both fronts, observations and
simulations. This paper analyses the gas metallicity gradients for a sample of
10 Fornax cluster galaxies observed with MUSE as part of the Fornax3D project.
Detailed maps of emission lines allowed a precise determination of gas
metallicity and metallicity gradients.
  The integrated gas metallicity of our Fornax cluster galaxies show slightly
higher metallicities (~0.045 dex) in comparison to a control sample. In
addition, we find signs of a mass and metallicity segregation from the center
to the outskirts of the cluster.
  By comparing our Fornax cluster metallicity gradients with a control sample
we find a general median offset of ~0.04 dex/Re, with 8 of our galaxies showing
flatter or more positive gradients.
  We find no systematic difference between the gradients of recent and
intermediate infallers when considering the projected distance of each galaxy
to the cluster center.
  To identify the origin of the observed offset in the metallicity gradients,
we perform a similar analysis with data from the TNG50 simulation. We identify
12 subhalos in Fornax-like clusters and compare their metallicity gradients
with a control sample of field subhalos. This exercise also shows a flattening
in the metallicity gradients for galaxies in Fornax-like halos, with a median
offset of ~0.05 dex/Re We also analyse the merger history, Mach numbers (M),
and ram pressure stripping of our TNG50 sample. We conclude that the observed
flattening in metallicity gradients is likely due to a combination of galaxies
traveling at supersonic velocities (M>1) that are experiencing high ram
pressure stripping and flybys.


title:
Independent Policy Gradient for Large-Scale Markov Potential Games:
  Sharper Rates, Function Approximation, and Game-Agnostic Convergence

abstract:
  We examine global non-asymptotic convergence properties of policy gradient
methods for multi-agent reinforcement learning (RL) problems in Markov
potential games (MPG). To learn a Nash equilibrium of an MPG in which the size
of state space and/or the number of players can be very large, we propose new
independent policy gradient algorithms that are run by all players in tandem.
When there is no uncertainty in the gradient evaluation, we show that our
algorithm finds an $\epsilon$-Nash equilibrium with $O(1/\epsilon^2)$ iteration
complexity which does not explicitly depend on the state space size. When the
exact gradient is not available, we establish $O(1/\epsilon^5)$ sample
complexity bound in a potentially infinitely large state space for a
sample-based algorithm that utilizes function approximation. Moreover, we
identify a class of independent policy gradient algorithms that enjoys
convergence for both zero-sum Markov games and Markov cooperative games with
the players that are oblivious to the types of games being played. Finally, we
provide computational experiments to corroborate the merits and the
effectiveness of our theoretical developments.


title: Analysis of the generalised Aw-Rascle model

abstract:
  We consider the multi-dimensional generalization of the Aw-Rascle system for
vehicular traffic. For an arbitrary large class of initial data and the
periodic domain, we prove the existence of global-in-time measure-valued
solutions. Moreover, using the relative energy technique, we show that the
measure-valued solutions coincide with the classical solutions as long as the
latter exist.


title: Facebook Shadow Profiles

abstract:
  Data is often at the core of digital products and services, especially when
related to online advertising. This has made data protection and privacy a
major policy concern. When surfing the web, consumers leave digital traces that
can be used to build user profiles and infer preferences. We quantify the
extent to which Facebook can track web behavior outside of their own platform.
The network of engagement buttons, placed on third-party websites, lets
Facebook follow users as they browse the web. Tracking users outside its core
platform enables Facebook to build shadow profiles. For a representative sample
of US internet users, 52 percent of websites visited, accounting for 40 percent
of browsing time, employ Facebook's tracking technology. Small differences
between Facebook users and non-users are largely explained by differing user
activity. The extent of shadow profiling Facebook may engage in is similar on
privacy-sensitive domains and across user demographics, documenting the
possibility for indiscriminate tracking.


title: Untrimmed Action Anticipation

abstract:
  Egocentric action anticipation consists in predicting a future action the
camera wearer will perform from egocentric video. While the task has recently
attracted the attention of the research community, current approaches assume
that the input videos are "trimmed", meaning that a short video sequence is
sampled a fixed time before the beginning of the action. We argue that, despite
the recent advances in the field, trimmed action anticipation has a limited
applicability in real-world scenarios where it is important to deal with
"untrimmed" video inputs and it cannot be assumed that the exact moment in
which the action will begin is known at test time. To overcome such
limitations, we propose an untrimmed action anticipation task, which, similarly
to temporal action detection, assumes that the input video is untrimmed at test
time, while still requiring predictions to be made before the actions actually
take place. We design an evaluation procedure for methods designed to address
this novel task, and compare several baselines on the EPIC-KITCHENS-100
dataset. Experiments show that the performance of current models designed for
trimmed action anticipation is very limited and more research on this task is
required.


title:
Comparison of all atom and united atom models for thermal transport
  calculations of amorphous polyethylene

abstract:
  Polymer simulations routinely employ models with different molecular
resolutions. United atom (UA) models are one such example, where groups of
certain atoms in a molecule are clustered into superatoms. Although their
computational simplicity makes them particularly attractive for studying a wide
range of polymer properties, the missing degrees of freedom in UA models can
impact certain properties that are intimately linked to localized vibrations,
such as the heat capacity and the thermal transport coefficient $\kappa$. In
contrast, the numerically exhausting all atom (AA) models produce results that
better match experimental data. In this work, we systematically investigate and
compare $\kappa$ obtained from an AA and a UA models for an amorphous
polyethylene system. The results indicate that the UA description may not be a
suitable model for evaluating thermal transport, since it underestimates
$\kappa$ in comparison to an AA description and the experimental value. The
coarse-graining leads to the softer interactions and its presence is
highlighted in a weaker mechanical response from the UA model, thus also
underestimates $\kappa$. We further consolidate our findings by extracting the
bonded and the nonbonded contributions to $\kappa$ within the framework of the
single chain energy transfer model.


title:
A Novel Ontology-guided Attribute Partitioning Ensemble Learning Model
  for Early Prediction of Cognitive Deficits using Quantitative Structural MRI
  in Very Preterm Infants

abstract:
  Structural magnetic resonance imaging studies have shown that brain
anatomical abnormalities are associated with cognitive deficits in preterm
infants. Brain maturation and geometric features can be used with machine
learning models for predicting later neurodevelopmental deficits. However,
traditional machine learning models would suffer from a large
feature-to-instance ratio (i.e., a large number of features but a small number
of instances/samples). Ensemble learning is a paradigm that strategically
generates and integrates a library of machine learning classifiers and has been
successfully used on a wide variety of predictive modeling problems to boost
model performance. Attribute (i.e., feature) bagging method is the most
commonly used feature partitioning scheme, which randomly and repeatedly draws
feature subsets from the entire feature set. Although attribute bagging method
can effectively reduce feature dimensionality to handle the large
feature-to-instance ratio, it lacks consideration of domain knowledge and
latent relationship among features. In this study, we proposed a novel
Ontology-guided Attribute Partitioning (OAP) method to better draw feature
subsets by considering domain-specific relationship among features. With the
better partitioned feature subsets, we developed an ensemble learning
framework, which is referred to as OAP Ensemble Learning (OAP-EL). We applied
the OAP-EL to predict cognitive deficits at 2 year of age using quantitative
brain maturation and geometric features obtained at term equivalent age in very
preterm infants. We demonstrated that the proposed OAP-EL approach
significantly outperformed the peer ensemble learning and traditional machine
learning approaches.


title: ns-3 and 5G-LENA Extensions to Support Dual-Polarized MIMO

abstract:
  MIMO spatial multiplexing is an essential feature to increase the
communication data rates in current and future cellular systems. Currently, the
ns-3 lte module leverages an abstraction model for 2x2 MIMO with spatial
multiplexing of two streams; while mmwave and nr modules were lacking the
spatial multiplexing option until this work, since the ns-3 models were not
supporting the usage of multiple antennas for spatial multiplexing and an
abstraction model such as the one used in the lte module is not suitable for
the mmWave frequencies. In this paper, we propose, implement and evaluate
models for ns-3 and the nr module to enable Dual-Polarized MIMO (DP-MIMO). The
proposed extension for the ns-3 supports multiple antennas for DP-MIMO with
spatial multiplexing of two streams and can be used by any ns-3 module that is
compatible with the ns-3 antenna array-based models, such as nr and mmwave
modules. We leverage this ns-3 extension to model DP-MIMO by exploiting
dual-polarized antennas and their orthogonality under line-of-sight conditions,
as it happens at high-frequency bands, to send the two data streams. The
proposed model does not rely on abstraction, as the MIMO model in the ns-3 lte
module, and can thus model more realistically the propagation differences of
the two streams, correlation, inter-stream interference, and allows design and
evaluation of the rank adaptation algorithms. Additionally, we propose and
evaluate an adaptive rank adaptation scheme and compare it with a fixed scheme.
The developed DP-MIMO spatial multiplexing models for the ns-3 simulator and
the nr module are openly available.


title: Generative multitask learning mitigates target-causing confounding

abstract:
  We propose a simple and scalable approach to causal representation learning
for multitask learning. Our approach requires minimal modification to existing
ML systems, and improves robustness to prior probability shift. The improvement
comes from mitigating unobserved confounders that cause the targets, but not
the input. We refer to them as target-causing confounders. These confounders
induce spurious dependencies between the input and targets. This poses a
problem for the conventional approach to multitask learning, due to its
assumption that the targets are conditionally independent given the input. Our
proposed approach takes into account the dependency between the targets in
order to alleviate target-causing confounding. All that is required in addition
to usual practice is to estimate the joint distribution of the targets to
switch from discriminative to generative classification, and to predict all
targets jointly. Our results on the Attributes of People and Taskonomy datasets
reflect the conceptual improvement in robustness to prior probability shift.


title: Machine Learning in Heterogeneous Porous Materials

abstract:
  The "Workshop on Machine learning in heterogeneous porous materials" brought
together international scientific communities of applied mathematics, porous
media, and material sciences with experts in the areas of heterogeneous
materials, machine learning (ML) and applied mathematics to identify how ML can
advance materials research. Within the scope of ML and materials research, the
goal of the workshop was to discuss the state-of-the-art in each community,
promote crosstalk and accelerate multi-disciplinary collaborative research, and
identify challenges and opportunities. As the end result, four topic areas were
identified: ML in predicting materials properties, and discovery and design of
novel materials, ML in porous and fractured media and time-dependent phenomena,
Multi-scale modeling in heterogeneous porous materials via ML, and Discovery of
materials constitutive laws and new governing equations. This workshop was part
of the AmeriMech Symposium series sponsored by the National Academies of
Sciences, Engineering and Medicine and the U.S. National Committee on
Theoretical and Applied Mechanics.


title: Vortex solutions in the presence of Dark Portals

abstract:
  Among the many attempts to extend the Standard Model (SM) in order to
understand the nature of dark matter (DM), the proposal of existence of hidden
sectors weakly coupled to the visible particles has been extensively studied.
We analyze in this work two models in which gauge and scalar hidden fields
interact with those in the visible sector which, for simplicity we take as an
Abelian Higgs model. In one of them, both sectors correspond to spontaneously
broken gauge theories and hence have classical vortex solutions. Introducing
three different portals between the two sectors, we analyze the dependence of
the model dynamics on the portal parameters and discuss the physical
implications in connection with dark matter and gravitational radiation issues.
  The hidden sector in the second model consists of an uncharged scalar and a
U(1) gauge field. The connection with the visible Abelian Higgs sector is in
this case provided by the interaction between the two scalars and also by the
coupling between the hidden scalar and the visible squared field strength. This
model can be seen as an extension of previously studied ones both in high
energy and in condensed matter physics and the solutions that we find indicate
that it could be relevant in connection to establishing the dark matter relic
density.


title: Simplified Graph Convolution with Heterophily

abstract:
  Graph convolutional networks (GCNs) (Kipf & Welling, 2017) attempt to extend
the success of deep learning in modeling image and text data to graphs.
However, like other deep models, GCNs comprise repeated nonlinear
transformations of inputs and are therefore time and memory intensive to train.
Recent work has shown that a much simpler and faster model, Simple Graph
Convolution (SGC) (Wu et al., 2019), is competitive with GCNs in common graph
machine learning benchmarks. The use of graph data in SGC implicitly assumes
the common but not universal graph characteristic of homophily, wherein nodes
link to nodes which are similar. Here we show that SGC is indeed ineffective
for heterophilous (i.e., non-homophilous) graphs via experiments on synthetic
and real-world datasets. We propose Adaptive Simple Graph Convolution (ASGC),
which we show can adapt to both homophilous and heterophilous graph structure.
Like SGC, ASGC is not a deep model, and hence is fast, scalable, and
interpretable. We find that our non-deep method often outperforms
state-of-the-art deep models at node classification on a benchmark of
real-world datasets. The SGC paper questioned whether the complexity of graph
neural networks is warranted for common graph problems involving homophilous
networks; our results suggest that this question is still open even for more
complicated problems involving heterophilous networks.


title:
Optimal Evaluation of Symmetry-Adapted $n$-Correlations Via Recursive
  Contraction of Sparse Symmetric Tensors

abstract:
  We present a comprehensive analysis of an algorithm for evaluating
high-dimensional polynomials that are invariant under permutations and
rotations. The key bottleneck is the contraction of a high-dimensional
symmetric and sparse tensor with a specific sparsity pattern that is directly
related to the symmetries imposed on the polynomial. We propose an explicit
construction of a recursive evaluation strategy and show that it is optimal in
the limit of infinite polynomial degree.


title:
Utility of Optical See-Through Head Mounted Displays in Augmented
  Reality-Assisted Surgery: A systematic review

abstract:
  This article presents a systematic review of optical see-through head mounted
display (OST-HMD) usage in augmented reality (AR) surgery applications from
2013 to 2020. Articles were categorised by: OST-HMD device, surgical
speciality, surgical application context, visualisation content, experimental
design and evaluation, accuracy and human factors of human-computer
interaction. 91 articles fulfilled all inclusion criteria. Some clear trends
emerge. The Microsoft HoloLens increasingly dominates the field, with
orthopaedic surgery being the most popular application (28.6\%). By far the
most common surgical context is surgical guidance (n=58) and segmented
preoperative models dominate visualisation (n = 40). Experiments mainly involve
phantoms (n = 43) or system setup (n = 21), with patient case studies ranking
third (n = 19), reflecting the comparative infancy of the field. Experiments
cover issues from registration to perception with very different accuracy
results. Human factors emerge as significant to OST-HMD utility. Some factors
are addressed by the systems proposed, such as attention shift away from the
surgical site and mental mapping of 2D images to 3D patient anatomy. Other
persistent human factors remain or are caused by OST-HMD solutions, including
ease of use, comfort and spatial perception issues. The significant upward
trend in published articles is clear, but such devices are not yet established
in the operating room and clinical studies showing benefit are lacking. A
focused effort addressing technical registration and perceptual factors in the
lab coupled with design that incorporates human factors considerations to solve
clear clinical problems should ensure that the significant current research
efforts will succeed.


title:
Stellar populations and star formation histories of the most extreme
  [OIII] emitters at $z=1.3-3.7$

abstract:
  As the James Webb Space Telescope approaches scientific operation, there is
much interest in exploring the redshift range beyond that accessible with
Hubble Space Telescope imaging. Currently, the only means to gauge the presence
of such early galaxies is to age-date the stellar population of systems in the
reionisation era. As a significant fraction of $z\simeq7-8$ galaxies are
inferred from Spitzer photometry to have extremely intense [OIII] emission
lines, it is commonly believed these are genuinely young systems that formed at
redshifts $z<10$, consistent with a claimed rapid rise in the star formation
density at that time. Here we study a spectroscopically-confirmed sample of
extreme [OIII] emitters at $z=1.3-3.7$, using both dynamical masses estimated
from [OIII] line widths and rest-frame UV to near-infrared photometry to
illustrate the dangers of assuming such systems are genuinely young. For the
most extreme of our intermediate redshift line emitters, we find dynamical
masses $10-100$ times that associated with a young stellar population mass,
which are difficult to explain solely by the presence of additional dark matter
or gaseous reservoirs. Adopting nonparametric star formation histories, we show
how the near-infrared photometry of a subset of our sample reveals an
underlying old ($>100$ Myr) population whose stellar mass is $\simeq40$ times
that associated with the starburst responsible for the extreme line emission.
Without adequate rest-frame near-infrared photometry we argue it may be
premature to conclude that extreme line emitters in the reionisation era are
low mass systems that formed at redshifts below $z\simeq10$.


title:
A "no-drift" runaway pile-up of pebbles in protoplanetary disks II.
  Characteristics of the resulting planetesimal belt

abstract:
  Forming planetesimals from pebbles is a major challenge in our current
understanding of planet formation. In a protoplanetary disk, pebbles drift
inward near the disk midplane via gas drag and they may enter a dead zone. In
this context, we identified that the backreaction of the drag of pebbles onto
the gas could lead to a runaway pile-up of pebbles, the so-called no-drift
mechanism. We improve upon the previous study of the no-drift mechanism by
investigating the nature and characteristics of the resultant planetesimal
belt. We performed 1D diffusion-advection simulations of drifting pebbles in
the outer region of a dead zone by including the backreaction to the radial
drift of pebbles and including planetesimal formation via the streaming
instability. We considered the parameters that regulate gas accretion and
vertical stirring of pebbles in the disk midplane. In this study, the
pebble-to-gas mass flux ($F_{\rm p/g}$) was fixed as a parameter. We find that
planetesimals initially form within a narrow ring whose width expands as
accumulating pebbles radially diffuse over time. The system finally reaches a
steady-state where the width of the planetesimal belt no longer changes. A
non-negligible total mass of planetesimals (more than one Earth mass) is formed
for a disk having $F_{\rm p/g} \gtrsim 0.1$ for more than $\sim 10-100$ kyr
with nominal parameters: a gas mass flux of $\gtrsim10^{-8} {\rm M}_\oplus$/yr,
$\tau_{\rm s} \simeq 0.01-0.1$, $\alpha_{\rm mid} \lesssim 10^{-4}$, and
$\alpha_{\rm acc} \simeq 10^{-3}-10^{-2}$ at $r \lesssim 10$ au, where $r$,
$\tau_{\rm s}$, $\alpha_{\rm mid}$, and $\alpha_{\rm acc}$ are the heliocentric
distance, the Stokes number, and the parameters in a dead zone controlling the
efficiencies of vertical turbulent diffusion of pebbles (i.e., scale height of
pebbles) and gas accretion of the $\alpha$-disk (i.e., gas surface density),
respectively.


title:
Electronic, Magnetic and Vibrational Properties of Single Layer Aluminum
  Oxide

abstract:
  The structural, magnetic, vibrational and electronic properties of single
layer aluminum oxide (AlO2) are investigated by performing state-of-the-art
first-principles calculations. Total energy optimization and phonon
calculations reveal that aluminum oxide forms a distorted octahedral structure
(1T'-AlO2) in its single layer limit. It is also shown that surfaces of
1T'-AlO2 display magnetic behavior originating from the O atoms. While the
ferromagnetic (FM) state is the most favorable magnetic order for 1T'-AlO2,
transformation to a dynamically stable antiferromagnetic (AFM) state upon a
slight distortion in the crystal structure is also possible. It is also shown
that Raman activities (350-400 cm^-1) obtained from the vibrational spectrum
can be utilized to distinguish the possible magnetic phases of the crystal
structure. Electronically, both FM and the AFM phases are semiconductors with
an indirect band gap and they can form a type-III vdW heterojunction with
graphene-like ultra-thin materials. Moreover, it is predicted that presence of
oxygen defects that inevitably occur during synthesis and production do not
alter the magnetic state, even at high vacancy density. Apparently, ultra-thin
1T'-AlO2 with its stable crystal structure, semiconducting nature and robust
magnetic state is a quite promising material for nanoscale device applications.


title:
Using a Language Model in a Kiosk Recommender System at Fast-Food
  Restaurants

abstract:
  Kiosks are a popular self-service option in many fast-food restaurants, they
save time for the visitors and save labor for the fast-food chains. In this
paper, we propose an effective design of a kiosk shopping cart recommender
system that combines a language model as a vectorizer and a neural
network-based classifier. The model performs better than other models in
offline tests and exhibits performance comparable to the best models in A/B/C
tests.


title: A Neural Phillips Curve and a Deep Output Gap

abstract:
  Many problems plague the estimation of Phillips curves. Among them is the
hurdle that the two key components, inflation expectations and the output gap,
are both unobserved. Traditional remedies include creating reasonable proxies
for the notable absentees or extracting them via some form of assumptions-heavy
filtering procedure. I propose an alternative route: a Hemisphere Neural
Network (HNN) whose peculiar architecture yields a final layer where components
can be interpreted as latent states within a Neural Phillips Curve. There are
benefits. First, HNN conducts the supervised estimation of nonlinearities that
arise when translating a high-dimensional set of observed regressors into
latent states. Second, computations are fast. Third, forecasts are economically
interpretable. Fourth, inflation volatility can also be predicted by merely
adding a hemisphere to the model. Among other findings, the contribution of
real activity to inflation appears severely underestimated in traditional
econometric specifications. Also, HNN captures out-of-sample the 2021 upswing
in inflation and attributes it first to an abrupt and sizable disanchoring of
the expectations component, followed by a wildly positive gap starting from
late 2020. HNN's gap unique path comes from dispensing with unemployment and
GDP in favor of an amalgam of nonlinearly processed alternative tightness
indicators -- some of which are skyrocketing as of early 2022.


title: The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness

abstract:
  A rate-distortion-perception (RDP) tradeoff has recently been proposed by
Blau and Michaeli and also Matsumoto. Focusing on the case of perfect realism,
which coincides with the problem of distribution-preserving lossy compression
studied by Li et al., a coding theorem for the RDP tradeoff that allows for a
specified amount of common randomness between the encoder and decoder is
provided. The existing RDP tradeoff is recovered by allowing for the amount of
common randomness to be infinite. The quadratic Gaussian case is examined in
detail.


title: Unified Characterization and Precoding for Non-Stationary Channels

abstract:
  Modern wireless channels are increasingly dense and mobile making the channel
highly non-stationary. The time-varying distribution and the existence of joint
interference across multiple degrees of freedom (e.g., users, antennas,
frequency and symbols) in such channels render conventional precoding
sub-optimal in practice, and have led to historically poor characterization of
their statistics. The core of our work is the derivation of a high-order
generalization of Mercer's Theorem to decompose the non-stationary channel into
constituent fading sub-channels (2-D eigenfunctions) that are jointly
orthogonal across its degrees of freedom. Consequently, transmitting these
eigenfunctions with optimally derived coefficients eventually mitigates any
interference across these dimensions and forms the foundation of the proposed
joint spatio-temporal precoding. The precoded symbols directly reconstruct the
data symbols at the receiver upon demodulation, thereby significantly reducing
its computational burden, by alleviating the need for any complementary
decoding. These eigenfunctions are paramount to extracting the second-order
channel statistics, and therefore completely characterize the underlying
channel. Theory and simulations show that such precoding leads to
${>}10^4{\times}$ BER improvement (at 20dB) over existing methods for
non-stationary channels.


title: First principles study of the T-center in Silicon

abstract:
  The T-center in silicon is a well-known carbon-based color center that has
been recently considered for quantum technology applications. Using first
principles computations, we show that the excited state is formed by a
defect-bound exciton made of a localized defect state occupied by an electron
to which a hole is bound. The localized state is of strong carbon \textit{p}
character and reminiscent of the localization of the unpaired electron in the
ethyl radical molecule. The radiative lifetime for the defect-bound exciton is
calculated to be on the order of $\mu$s, much longer than other quantum defects
such as the NV center in diamond and in agreement with experiments. The longer
lifetime is associated with the small transition dipole moment as a result of
the very different nature of the localized and delocalized states forming the
defect-bound exciton. Finally, we use first principles calculations to assess
the stability of the T-center. We find the T-center to be stable against
decomposition into simpler defects when keeping the stoichiometry fixed.
However, we identify that the T-center is easily prone to (de)hydrogenation and
so requires very precise annealing conditions (temperature and atmosphere) to
be efficiently formed.


title: Time-varying harmonic models for voice signal analysis

abstract:
  Assessment of voice signals has long been performed with the assumption of
periodicity as this facilitates analysis. Near periodicity of normal voice
signals makes short-time harmonic modeling an appealing choice to extract vocal
feature parameters. For dysphonic voice, however, a fixed harmonic structure
could be too constrained as it strictly enforces periodicity in the model.
Slight variation in amplitude or frequency in the signal may cause the model to
misrepresent the observed signal. To address these issues, this paper presents
a time-varying harmonic model, which allows its fundamental frequency and
harmonic amplitudes to be polynomial functions of time. The model decouples the
slow deviations of frequency and amplitude from fast irregular vocal fold
vibratory behaviors such as subharmonics and diplophonia. The time-varying
model is shown to track the frequency and amplitude modulations present in
voice with severe tremor. This reduces the sensitivity of the model-based
harmonics-to-noise ratio measures to slow frequency and amplitude variations
while maintaining its sensitivity to increase in turbulent noise or the
presence of irregular vibration. Other uses of the model include the vocal
tract filter estimation and the rates of frequency and intensity changes. These
use cases are experimentally demonstrated along with the modeling accuracy.


title: Approximate isomorphism of randomization pairs

abstract:
  We study approximate $\aleph_0$-categoricity of theories of beautiful pairs
of randomizations, in the sense of continuous logic.
  This leads us to disprove a conjecture of Ben Yaacov, Berenstein and Henson,
by exhibiting $\aleph_0$-categorical, $\aleph_0$-stable metric theories $Q$ for
which the corresponding theory $Q_P$ of beautiful pairs is not approximately
$\aleph_0$-categorical, i.e., has separable models that are not isomorphic even
up to small perturbations of the smaller model of the pair. The theory $Q$ of
randomized infinite vector spaces over a finite field is such an example.
  On the positive side, we show that the theory of beautiful pairs of
randomized infinite sets is approximately $\aleph_0$-categorical. We also prove
that a related stronger property, which holds in that case, is stable under
various natural constructions, and formulate our guesswork for the general
case.


title: Multi-model Ensemble Analysis with Neural Network Gaussian Processes

abstract:
  Multi-model ensemble analysis integrates information from multiple climate
models into a unified projection. However, existing integration approaches
based on model averaging can dilute fine-scale spatial information and incur
bias from rescaling low-resolution climate models. We propose a statistical
approach, called NN-GPR, using Gaussian process regression (GPR) with an
infinitely wide deep neural network based covariance function. NN-GPR requires
no assumptions about the relationships between models, no interpolation to a
common grid, no stationarity assumptions, and automatically downscales as part
of its prediction algorithm. Model experiments show that NN-GPR can be highly
skillful at surface temperature and precipitation forecasting by preserving
geospatial signals at multiple scales and capturing inter-annual variability.
Our projections particularly show improved accuracy and uncertainty
quantification skill in regions of high variability, which allows us to cheaply
assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a
regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced
climate models show that NN-GPR produces similar, overall climatologies to the
model ensemble while better capturing fine scale spatial patterns. Finally, we
compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can
rival the performance of RCMs using only global model data as input.


title: Source Matching and Rewriting

abstract:
  A typical compiler flow relies on a uni-directional sequence of
translation/optimization steps that lower the program abstract representation,
making it hard to preserve higher-level program information across each
transformation step. On the other hand, modern ISA extensions and hardware
accelerators can benefit from the compiler's ability to detect and raise
program idioms to acceleration instructions or optimized library calls.
Although recent works based on Multi-Level IR (MLIR) have been proposed for
code raising, they rely on specialized languages, compiler recompilation, or
in-depth dialect knowledge. This paper presents Source Matching and Rewriting
(SMR), a user-oriented source-code-based approach for MLIR idiom matching and
rewriting that does not require a compiler expert's intervention. SMR uses a
two-phase automaton-based DAG-matching algorithm inspired by early work on
tree-pattern matching. First, the idiom Control-Dependency Graph (CDG) is
matched against the program's CDG to rule out code fragments that do not have a
control-flow structure similar to the desired idiom. Second, candidate code
fragments from the previous phase have their Data-Dependency Graphs (DDGs)
constructed and matched against the idiom DDG. Experimental results show that
SMR can effectively match idioms from Fortran (FIR) and C (CIL) programs while
raising them as BLAS calls to improve performance.


title:
Dynamic Heterogeneous Distribution Regression Panel Models, with an
  Application to Labor Income Processes

abstract:
  We consider estimation of a dynamic distribution regression panel data model
with heterogeneous coefficients across units. The objects of interest are
functionals of these coefficients including linear projections on unit level
covariates. We also consider predicted actual and stationary distributions of
the outcome variable. We investigate how changes in initial conditions or
covariate values affect these objects. Coefficients and their functionals are
estimated via fixed effect methods, which are debiased to deal with the
incidental parameter problem. We propose a cross-sectional bootstrap method for
uniformly valid inference on function-valued objects. This avoids coefficient
re-estimation and is shown to be consistent for a large class of data
generating processes. We employ PSID annual labor income data to illustrate
various important empirical issues we can address. We first predict the impact
of a reduction in income on future income via hypothetical tax policies.
Second, we examine the impact on the distribution of labor income from
increasing the education level of a chosen group of workers. Finally, we
demonstrate the existence of heterogeneity in income mobility, which leads to
substantial variation in individuals' incidences to be trapped in poverty. We
also provide simulation evidence confirming that our procedures work well.


title: The final Kasner regime inside black holes with scalar or vector hair

abstract:
  The final (close to the singularity) dynamical behavior of the metric inside
black holes with massive charged scalar or vector hair is analyzed for general
anisotropic and inhomogeneous initial conditions. These solutions are relevant
to a holographic realization of superconductivity. It is shown that the
dynamics falls within the scope of the "cosmological billiard" description and
that in both cases, the corresponding hyperbolic billiard region has infinite
volume so that the system ultimately settles down to a final Kasner regime. For
massive vector hair, the conclusion holds because the longitudinal mode plays
the same role as a scalar field. There exists, however, a measure-zero subset
of solutions characterized by vanishing longitudinal modes that exhibit a
chaotic behavior with an infinite number of BKL oscillations as one goes to the
singularity.


title:
Counting problems from the viewpoint of ergodic theory: from primitive
  integer points to simple closed curves

abstract:
  In her thesis, Mirzakhani showed that the number of simple closed geodesics
of length $\leq L$ on a closed, connected, oriented hyperbolic surface $X$ of
genus $g$ is asymptotic to $L^{6g-6}$ times a constant depending on the
geometry of $X$. In this survey we give a detailed account of Mirzakhani's
proof of this result aimed at non-experts. We draw inspiration from classic
primitive lattice point counting results in homogeneous dynamics. The focus is
on understanding how the general principles that drive the proof in the case of
lattices also apply in the setting of hyperbolic surfaces.


title: A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

abstract:
  We study the risk-sensitive exponential cost MDP formulation and develop a
trajectory-based gradient algorithm to find the stationary point of the cost
associated with a set of parameterized policies. We derive a formula that can
be used to compute the policy gradient from (state, action, cost) information
collected from sample paths of the MDP for each fixed parameterized policy.
Unlike the traditional average-cost problem, standard stochastic approximation
theory cannot be used to exploit this formula. To address the issue, we
introduce a truncated and smooth version of the risk-sensitive cost and show
that this new cost criterion can be used to approximate the risk-sensitive cost
and its gradient uniformly under some mild assumptions. We then develop a
trajectory-based gradient algorithm to minimize the smooth truncated estimation
of the risk-sensitive cost and derive conditions under which a sequence of
truncations can be used to solve the original, untruncated cost problem.


title:
Mean curvature flow of graphs in Generalized Robertson-Walker spacetimes
  with perpendicular Neumann boundary condition

abstract:
  We prove the longtime existence for the mean curvature flow problem with a
perpendicular Neumann boundary condition in a Generalized Robertson Walker
(GRW) spacetime that obeys the null convergence condition. In addition, we
prove that the metric of such a solution is conformal to the one of the leaf of
the GRW in asymptotic time. Furthermore, if the initial hypersurface is mean
convex, then the evolving hypersurfaces remain mean convex during the flow.


title:
14-moment maximum-entropy modelling of collisionless ions for Hall
  thruster discharges

abstract:
  Ions in Hall thruster devices are often characterized by a low
collisionality. In the presence of acceleration fields and azimuthal electric
field waves, this results in strong deviations from thermodynamic equilibrium,
introducing kinetic effects. This work investigates the application of the
14-moment maximum-entropy model to this problem. This method consists in a set
of 14 PDEs for the density, momentum, pressure tensor components, heat flux and
fourth-order moment associated to the particle velocity distribution function.
The model is applied to the study of collisionless ion dynamics in a Hall
thruster-like configuration, and its accuracy is assessed against different
models, including the kinetic solution. Three test cases are considered: a
purely axial acceleration problem, the problem of ion-wave trapping and finally
the evolution of ions in the axial-azimuthal plane. Most of this work considers
ions only, and the coupling with electrons is removed by prescribing reasonable
values of the electric field. This allows us to obtain a direct comparison
among different ion models. However, the possibility to run self-consistent
plasma simulations is also briefly discussed, considering quasi-neutral or
multi-fluid models. The maximum-entropy system appears to be a robust and
accurate option for the considered test cases. The accuracy is improved over
the simpler pressureless gas model (cold ions) and the Euler equations for gas
dynamics, while the computational cost shows to remain much lower than direct
kinetic simulations.


title: Direct detection of charged particles with SiPMs

abstract:
  The direct response of Silicon PhotoMultipliers being traversed by a MIP
charged particle have been studied in a systematic way for the first time.
Using beam test data, time resolution and the crosstalk probability have been
measured. A characterization of the SiPM by means of a laser beam is also
reported. The results obtained for different sensors indicate a measured time
resolution around 40-70 ps. Although particles are expected to traverse only
one SPAD per event, crosstalk measurements on different sensors indicate an
unexpected higher value with respect to the one related to the sensor noise.


title: Logical Reasoning for Task Oriented Dialogue Systems

abstract:
  In recent years, large pretrained models have been used in dialogue systems
to improve successful task completion rates. However, lack of reasoning
capabilities of dialogue platforms make it difficult to provide relevant and
fluent responses, unless the designers of a conversational experience spend a
considerable amount of time implementing these capabilities in external rule
based modules. In this work, we propose a novel method to fine-tune pretrained
transformer models such as Roberta and T5. to reason over a set of facts in a
given dialogue context. Our method includes a synthetic data generation
mechanism which helps the model learn logical relations, such as comparison
between list of numerical values, inverse relations (and negation), inclusion
and exclusion for categorical attributes, and application of a combination of
attributes over both numerical and categorical values, and spoken form for
numerical values, without need for additional training dataset. We show that
the transformer based model can perform logical reasoning to answer questions
when the dialogue context contains all the required information, otherwise it
is able to extract appropriate constraints to pass to downstream components
(e.g. a knowledge base) when partial information is available. We observe that
transformer based models such as UnifiedQA-T5 can be fine-tuned to perform
logical reasoning (such as numerical and categorical attributes' comparison)
over attributes that been seen in training time (e.g., accuracy of 90\%+ for
comparison of smaller than $k_{\max}$=5 values over heldout test dataset).


title:
High Spin-Chern-Number Insulator in $\alpha$-Antimonene with a Hidden
  Topological Phase

abstract:
  In investigating the topological electronic structures of monolayer
$\alpha$-phase group V elements, we uncover a new topological phase, which is
invisible in the symmetry-based topological quantum chemistry (TQC) as well as
symmetry indicators (SIs). Since $\alpha$ phase As and Sb share the same band
representations at high-symmetry points, they are both trivial insulators in
terms of TQC and SIs. We demonstrate, however, that there is a topological
phase transition between As and Sb that involves a band-gap closing at two
$k$-points on the high-symmetry $\rm{X}$-$\rm{\Gamma}$-$\rm{X}$ line. In the
absence of spin-orbit coupling (SOC), As is a trivial insulator, while Sb is a
Dirac semimetal with four Dirac points (DPs) located away from the
high-symmetry lines. Inclusion of $S_z$-conserved SOC gaps out the Dirac points
and induces a nontrivial Berry curvature and drives Sb into a high spin Chern
number topological phase. The band structure of $\alpha$-Bi differs from that
of Sb by a band inversion at $\Gamma$, transforming Bi into a Z$_2$ topological
insulator. Our study shows that quantized spin Hall conductivity can serve as a
topological invariant beyond Z$_2$ for characterizing topological phases.


title: Counting unitaries of T-depth one

abstract:
  We show that the number of T-depth one unitaries on $n$ qubits is $
\sum_{m=1}^{n}\tfrac{1}{m!}\prod_{k=0}^{m-1}(4^n/2^k - 2^k) \times \#
\mathcal{C}_n, $ where $\#\mathcal{C}_n$ is the size of the $n$-qubit Clifford
group, that is the number of unitaries of T-depth zero. The number of T-depth
one unitaries on $n$ qubits grows as $2^{\Omega(n^2)} \cdot \# \mathcal{C}_n$.