title: $k$-Transmitter Watchman Routes

abstract:
  We consider the watchman route problem for a $k$-transmitter watchman:
standing at point $p$ in a polygon $P$, the watchman can see $q\in P$ if
$\overline{pq}$ intersects $P$'s boundary at most $k$ times -- $q$ is
$k$-visible to $p$. Traveling along the $k$-transmitter watchman route, either
all points in $P$ or a discrete set of points $S\subset P$ must be $k$-visible
to the watchman. We aim for minimizing the length of the $k$-transmitter
watchman route.
  We show that even in simple polygons the shortest $k$-transmitter watchman
route problem for a discrete set of points $S\subset P$ is NP-complete and
cannot be approximated to within a logarithmic factor (unless P=NP), both with
and without a given starting point. Moreover, we present a polylogarithmic
approximation for the $k$-transmitter watchman route problem for a given
starting point and $S\subset P$ with approximation ratio $O(\log^2(|S|\cdot n)
\log\log (|S|\cdot n) \log(|S|+1))$ (with $|P|=n$).


title:
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for
  Memristive Accelerators

abstract:
  In this work, PRUNIX, a framework for training and pruning convolutional
neural networks is proposed for deployment on memristor crossbar based
accelerators. PRUNIX takes into account the numerous non-ideal effects of
memristor crossbars including weight quantization, state-drift, aging and
stuck-at-faults. PRUNIX utilises a novel Group Sawtooth Regularization intended
to improve non-ideality tolerance as well as sparsity, and a novel Adaptive
Pruning Algorithm (APA) intended to minimise accuracy loss by considering the
sensitivity of different layers of a CNN to pruning. We compare our
regularization and pruning methods with other standards on multiple CNN
architectures, and observe an improvement of 13% test accuracy when
quantization and other non-ideal effects are accounted for with an overall
sparsity of 85%, which is similar to other methods


title: Physical properties of circumnuclear ionizing clusters: NGC 7742

abstract:
  We have analyzed the circumnuclear ring of the spiral galaxy NGC7742 in order
to understand its formation and evolution. We have obtained gaseous abundances,
characterized the interstellar medium of the clusters and studied the
properties of the ionizing clusters. We have also implemented a new methodology
using the red wavelength range of optical spectra, with the purpose of
understanding how star formation evolves in high metallicity environments.


title:
Statistical mechanics of Coulomb systems: From electrons and nuclei to
  atoms and molecules

abstract:
  This article is dedicated to Elliott Lieb in celebration of his 90th
birthday. I recount briefly some history of our joint work on the existence of
the thermodynamic limit for Coulomb systems and discuss, even more briefly, two
open problems of fundamental nature.


title: On expressive rule-based logics

abstract:
  We investigate a family of rule-based logics. The focus is on very expressive
languages. We provide a range of characterization results for the expressive
powers of the logics and relate them with corresponding game systems.


title:
Learning Physics through Images: An Application to Wind-Driven Spatial
  Patterns

abstract:
  For centuries, scientists have observed nature to understand the laws that
govern the physical world. The traditional process of turning observations into
physical understanding is slow. Imperfect models are constructed and tested to
explain relationships in data. Powerful new algorithms are available that can
enable computers to learn physics by observing images and videos. Inspired by
this idea, instead of training machine learning models using physical
quantities, we trained them using images, that is, pixel information. For this
work, and as a proof of concept, the physics of interest are wind-driven
spatial patterns. Examples of these phenomena include features in Aeolian dunes
and the deposition of volcanic ash, wildfire smoke, and air pollution plumes.
We assume that the spatial patterns were collected by an imaging device that
records the magnitude of the logarithm of deposition as a red, green, blue
(RGB) color image with channels containing values ranging from 0 to 255. In
this paper, we explore deep convolutional neural network-based autoencoders to
exploit relationships in wind-driven spatial patterns, which commonly occur in
geosciences, and reduce their dimensionality. Reducing the data dimension size
with an encoder allows us to train regression models linking geographic and
meteorological scalar input quantities to the encoded space. Once this is
achieved, full predictive spatial patterns are reconstructed using the decoder.
We demonstrate this approach on images of spatial deposition from a pollution
source, where the encoder compresses the dimensionality to 0.02% of the
original size and the full predictive model performance on test data achieves
an accuracy of 92%.


title: Non-spectroscopic sensing enabled by electro-optical reservoir computer

abstract:
  A fully bio-inspired apparatus to perform non-spectroscopic sensing to
discriminate, and determine the constituent concentrations of a chemical
mixture is proposed. Here, fully bio-inspired means that it is comprised of a
biomimetic sensor and a neuromorphic signal processor. The sensor is similar to
the human eye in terms of its trichromat architecture and overlapping spectral
response. The information processor is a neuromorphic system based on an
electro-optical implementation of a reservoir computer. A time-stepping signal
algorithm based on the Z-bilinear transformation has been developed to
realistically simulate the electro-optical reservoir computer; this
demonstrates the discrimination and chemical concentration determination tasks.
We believe such an apparatus offers potential benefits in areas in which
chemical composition needs to be monitored in real time, for example in
chemical processing, and food-beverage (fermentation/brewing) and environmental
monitoring.


title:
JaQuAD: Japanese Question Answering Dataset for Machine Reading
  Comprehension

abstract:
  Question Answering (QA) is a task in which a machine understands a given
document and a question to find an answer. Despite impressive progress in the
NLP area, QA is still a challenging problem, especially for non-English
languages due to the lack of annotated datasets. In this paper, we present the
Japanese Question Answering Dataset, JaQuAD, which is annotated by humans.
JaQuAD consists of 39,696 extractive question-answer pairs on Japanese
Wikipedia articles. We finetuned a baseline model which achieves 78.92% for F1
score and 63.38% for EM on test set. The dataset and our experiments are
available at https://github.com/SkelterLabsInc/JaQuAD.


title:
Who will Leave a Pediatric Weight Management Program and When? -- A
  machine learning approach for predicting attrition patterns

abstract:
  Childhood obesity is a major public health concern. Multidisciplinary
pediatric weight management programs are considered standard treatment for
children with obesity and severe obesity who are not able to be successfully
managed in the primary care setting; however, high drop-out rates (referred to
as attrition) are a major hurdle in delivering successful interventions.
Predicting attrition patterns can help providers reduce the attrition rates.
Previous work has mainly focused on finding static predictors of attrition
using statistical analysis methods. In this study, we present a machine
learning model to predict (a) the likelihood of attrition, and (b) the change
in body-mass index (BMI) percentile of children, at different time points after
joining a weight management program. We use a five-year dataset containing the
information related to around 4,550 children that we have compiled using data
from the Nemours Pediatric Weight Management program. Our models show strong
prediction performance as determined by high AUROC scores across different
tasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predicting
weight outcomes). Additionally, we report the top features predicting attrition
and weight outcomes in a series of explanatory experiments.


title: Continuous bilinear maps on Banach $\star$-algebras

abstract:
  Let $A$ be a unital Banach $\star$-algebra with unity $1$, $X$ be a Banach
space and $\phi : A \times A \to X$ be a continuous bilinear map. We
characterize the structure of $\phi$ where it satisfies any of the following
properties: $$a,b \in A, \,\,\, a b^\star = z \, \,(a^\star b=z)\Rightarrow
\phi ( a , b^\star ) = \phi ( z, 1 ) \, \, (\phi ( a^\star , b) = \phi ( z, 1
));$$ $$a,b \in A, \,\,\, a b^\star = z \, \, (a^\star b=z)\Rightarrow \phi ( a
, b^\star ) = \phi ( 1, z ) \, \, (\phi ( a^\star , b) = \phi ( 1, z )),$$
where $z\in A$ is fixed.


title:
Updated modelling and refined absolute parameters of the oscillating
  eclipsing binary AS Eri

abstract:
  We present a new study of the Algol-type eclipsing binary system AS Eri based
on the combination of the MOST and TESS light curves and a collection of very
precise radial velocities obtained with the spectrographs HERMES operating at
the Mercator telescope, La Palma, and TCES operating at the Alfred Jensch
telescope, Tautenburg. The primary component is an A3 V-type pulsating,
mass-accreting star. We fitted the light and velocity data with the package
PHOEBE, and determined the best-fitting model adopting the configuration of a
semi-detached system. The orbital period has been improved using a recent (O-C)
analysis and the phase shift detected between both light curves to the value
2.6641496 $\pm$ 0.0000001 days. The absence of any cyclic variation in the
(O-C) residuals confirms the long-term stability of the orbital period.
Furthermore, we show that the models derived for each light curve separately
entail small differences, e.g. in the temperature parameter T$_{\rm eff,2}$.
The high quality of the new solutions is illustrated by the residuals. We
obtained the following absolute component parameters: L$_1$ =
14.125~L$_{\odot}$, M$_1$ = 2.014~M$_{\odot}$, R$_1$ = 1.733~R$_{\odot}$, log
g$_1$ = 4.264, L$_2$ = 4.345~L$_{\odot}$, M$_2$ = 0.211~M$_{\odot}$, R$_2$ =
2.19~R$_{\odot}$, log g$_2$ = 3.078~ with T$_{\rm eff,2}$/T$_{\rm eff,1}$ =
0.662 $\pm$ 0.002. Although the orbital period appears to be stable on the long
term, we show that the light-curve shape is affected by a years-long modulation
which is most probably due to the magnetic activity of the cool companion.


title: Well-being in French Astrophysics

abstract:
  It has become clear that early career astrophysics researchers (doctoral
researchers, post-docs, etc) have a very diverse appreciation of their career,
with some declaring it the best job that you can have and others suffering from
overwork, harrassment and stress from the precarity of their job, and
associated difficulties. In order to establish how astrophysics researchers,
primarily in France, experience their career, we sent out a survey to
understand the impact that their job has on their well-being. 276 people
responded to the survey. Whilst around half of the respondents expressed
pleasure derived from their career, it is clear that many (early career)
researchers are suffering due to overwork, with more than a quarter saying that
they work in excess of 50 hours per week and 2\% in excess of 90 h per week.
Almost 30\% professed to having suffered harrassment or discrimination in the
course of their work. Further, whilst only 20\% had suffered mental health
issues before starting their career in astrophysics, $\sim$45\% said that they
suffered with mental health problems since starting in astrophysics. Here we
provide results from the survey as well as possible avenues to explore and a
list of recommendations to improve (early) careers in astrophysics.

categories: astro-ph.IM astro-ph.CO astro-ph.EP astro-ph.GA astro-ph.HE astro-ph.SR physics.soc-ph


title: Improving Automatic Complexity Analysis of Integer Programs

abstract:
  In earlier work, we developed an approach for automatic complexity analysis
of integer programs, based on an alternating modular inference of upper runtime
and size bounds for program parts. In this paper, we show how recent techniques
to improve automated termination analysis of integer programs (like the
generation of multiphase-linear ranking functions and control-flow refinement)
can be integrated into our approach for the inference of runtime bounds. The
power of the resulting approach is demonstrated by an extensive experimental
evaluation with our new re-implementation of the corresponding tool KoAT.


title: Exploring Multi-physics with Extremely Weak Supervision

abstract:
  Multi-physical inversion plays a critical role in geophysics. It has been
widely used to infer various physical properties (such as velocity and
conductivity), simultaneously. Among those inversion problems, some are
explicitly governed by partial differential equations (PDEs), while others are
not. Without explicit governing equations, conventional multi-physical
inversion techniques will not be feasible and data-driven inversion require
expensive full labels. To overcome this issue, we develop a new data-driven
multi-physics inversion technique with extremely weak supervision. Our key
finding is that the pseudo labels can be constructed by learning the local
relationship among geophysical properties at very sparse locations. We explore
a multi-physics inversion problem from two distinct measurements (seismic and
EM data) to three geophysical properties (velocity, conductivity, and CO$_2$
saturation). Our results show that we are able to invert for properties without
explicit governing equations. Moreover, the label data on three geophysical
properties can be significantly reduced by 50 times (from 100 down to only 2
locations).


title: Pre-Trained Language Models for Interactive Decision-Making

abstract:
  Language model (LM) pre-training has proven useful for a wide variety of
language processing tasks, but can such pre-training be leveraged for more
general machine learning problems? We investigate the effectiveness of language
modeling to scaffold learning and generalization in autonomous decision-making.
We describe a framework for imitation learning in which goals and observations
are represented as a sequence of embeddings, and translated into actions using
a policy network initialized with a pre-trained transformer LM. We demonstrate
that this framework enables effective combinatorial generalization across
different environments, such as VirtualHome and BabyAI. In particular, for test
tasks involving novel goals or novel scenes, initializing policies with
language models improves task completion rates by 43.6% in VirtualHome. We
hypothesize and investigate three possible factors underlying the effectiveness
of LM-based policy initialization. We find that sequential representations (vs.
fixed-dimensional feature vectors) and the LM objective (not just the
transformer architecture) are both important for generalization. Surprisingly,
however, the format of the policy inputs encoding (e.g. as a natural language
string vs. an arbitrary sequential encoding) has little influence. Together,
these results suggest that language modeling induces representations that are
useful for modeling not just language, but also goals and plans; these
representations can aid learning and generalization even outside of language
processing.


title:
How to find all connections in the Pantelides algorithm for delay
  differential-algebraic equations

abstract:
  The Pantelides algorithm for delay differential-algebraic equations (DDAEs)
is a method to structurally analyse such systems with the goal to detect which
equations have to be differentiated or shifted to construct a solution. In this
process, one has to detect implicit connections between equations in the
shifting graph, making it necessary to check all possible connections. The
problem of finding these efficiently remained unsolved so far. It is explored
in further detail and a reformulation is introduced. Additionally, an
algorithmic approach for its solution is presented.


title:
Multiclass learning with margin: exponential rates with no bias-variance
  trade-off

abstract:
  We study the behavior of error bounds for multiclass classification under
suitable margin conditions. For a wide variety of methods we prove that the
classification error under a hard-margin condition decreases exponentially fast
without any bias-variance trade-off. Different convergence rates can be
obtained in correspondence of different margin assumptions. With a
self-contained and instructive analysis we are able to generalize known results
from the binary to the multiclass setting.


title: The Duistermaat-Heckman formula and Chern-Schwartz-MacPherson classes

abstract:
  Let M be a smooth complex projective variety, bearing a K\"ahler symplectic
form \omega and a Hamiltonian action of a torus T, with finitely many fixed
points M^T. One standard form of the Duistermaat-Heckman theorem gives a
formula for M's Duistermaat-Heckman measure DH_T(M,\omega) as an alternating
sum of projections of cones, with overall direction determined by a Morse
decomposition of M.
  Using Victor Ginzburg's construction of Chern-Schwartz-MacPherson classes, we
show that these individual cone terms can themselves be interpreted as
Duistermaat-Heckman measures of cycles in T^*M. (This has a similar goal to the
symplectic cobordism approach of Viktor Ginzburg, Guillemin, and Karshon.) Our
approach also suggests extensions of the formula, including the Brianchon-Gram
theorem.


title:
A computational view on the non-degeneracy invariant for Enriques
  surfaces

abstract:
  For an Enriques surface $S$, the non-degeneracy invariant $\mathrm{nd}(S)$
retains information on the elliptic fibrations of $S$ and its polarizations. In
the current paper, we introduce a combinatorial version of the non-degeneracy
invariant which depends on $S$ together with a configuration of smooth rational
curves, and gives a lower bound for $\mathrm{nd}(S)$. We provide a SageMath
code that computes such combinatorial invariant and we apply it in several
examples. First we identify a new family of nodal Enriques surfaces satisfying
$\mathrm{nd}(S)=10$ which are not general and with infinite automorphism group.
We obtain lower bounds on $\mathrm{nd}(S)$ for the Enriques surfaces with eight
disjoint smooth rational curves studied by Mendes Lopes-Pardini. Finally, we
recover Dolgachev and Kond\=o's computation of the non-degeneracy invariant of
the Enriques surfaces with finite automorphism group and provide additional
information on the geometry of their elliptic fibrations.


title: Gralmonium: Granular Aluminum Nano-Junction Fluxonium Qubit

abstract:
  Mesoscopic Josephson junctions (JJs), consisting of overlapping
superconducting electrodes separated by a nanometer thin oxide layer, provide a
precious source of nonlinearity for superconducting quantum circuits and are at
the heart of state-of-the-art qubits, such as the transmon and fluxonium. Here,
we show that in a fluxonium qubit the role of the JJ can also be played by a
lithographically defined, self-structured granular aluminum (grAl)
nano-junction: a superconductor-insulator-superconductor (SIS) JJ obtained in a
single layer, zero-angle evaporation. The measured spectrum of the resulting
qubit, which we nickname gralmonium, is indistinguishable from the one of a
standard fluxonium qubit. Remarkably, the lack of a mesoscopic parallel plate
capacitor gives rise to an intrinsically large grAl nano-junction charging
energy in the range of $10-100\,\mathrm{GHz}$, comparable to its Josephson
energy $E_\text{J}$. We measure average energy relaxation times of
$T_1=10\,\mathrm{\mu s}$ and Hahn echo coherence times of
$T_2^\text{echo}=9\,\mathrm{\mu s}$. The exponential sensitivity of the
gralmonium to the $E_\text{J}$ of the grAl nano-junction provides a highly
susceptible detector. Indeed, we observe spontaneous jumps of the value of
$E_\text{J}$ on timescales from milliseconds to days, which offer a powerful
diagnostics tool for microscopic defects in superconducting materials.


title:
Tuning carrier density and phase transitions in oxide semiconductors
  using focused ion beams

abstract:
  We demonstrate spatial modification of the optical properties of thin-film
metal oxides, zinc oxide and vanadium dioxide as representatives, using a
commercial focused ion beam (FIB) system. Using a Ga+ FIB and thermal
annealing, we demonstrated variable doping of a band semiconductor, zinc oxide
(ZnO), achieving carrier concentrations from 10^18 cm-3 to 10^20 cm-3. Using
the same FIB without subsequent thermal annealing, we defect-engineered a
correlated semiconductor, vanadium dioxide (VO2), locally modifying its
insulator-to-metal transition (IMT) temperature by range of ~25 degrees C. Such
area-selective modification of metal oxides by direct writing using a FIB
provides a simple, mask-less route to the fabrication of optical structures,
especially when multiple or continuous levels of doping or defect density are
required.


title:
Predicting Cyber-Attack using Cyber Situational Awareness: The Case of
  Independent Power Producers (IPPs)

abstract:
  The increasing critical dependencies on Internetof-Things (IoT) have raised
security concerns; its application on the critical infrastructures (CIs) for
power generation has come under massive cyber-attack over the years. Prior
research efforts to understand cybersecurity from Cyber Situational Awareness
(CSA) perspective fail to critically consider the various Cyber Situational
Awareness (CSA) security vulnerabilities from a human behavioural perspective
in line with the CI. This study evaluates CSA elements to predict cyber-attacks
in the power generation sector. Data for this research article was collected
from IPPs using the survey method. The analysis method was employed through
Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the
proposed model. The results revealed negative effects on people and
cyber-attack, but significant in predicting cyber-attacks. The study also
indicated that information handling is significant and positively influences
cyber-attack. The study also reveals no mediation effect between the
association of People and Attack and Information and Attack. It could result
from an effective cyber security control implemented by the IPPs. Finally, the
study also shows no sign of network infrastructure cyber-attack predictions.
The reasons could be because managers of IPPs had adequate access policies and
security measures in place.


title: Surface modification of fly ash by mechano-chemical treatment

abstract:
  Fly ash (FA), as an industry by-product has attracted much attention as a
suitable supplier of silicon (Si) and aluminum (Al) in preparation of
geopolymer for the sustainable environment and material science applications.
Here, the effect of mechano-chemical (MC) treatment for surface modification of
FA powder was systemically investigated by analyzing the size, surface
morphology, crystal structure and dissolubility of Si and Al ions in alkali
solutions. The dissolution dynamic as a function of MC treatment time is
discussed in details and concluded with a recombination model of "grinding
effect" and "activation effect", which can be well correlated with the change
of surface morphology and crystal structure, respectively.


title: Even Simpler Deterministic Matrix Sketching

abstract:
  This paper provides a one-line proof of Frequent Directions (FD) for
sketching streams of matrices. The simpler proof arises from sketching the
covariance of the stream of matrices rather than the stream itself.


title: Predicting the impact of urban change in pedestrian and road safety

abstract:
  Increased interaction between and among pedestrians and vehicles in the
crowded urban environments of today gives rise to a negative side-effect: a
growth in traffic accidents, with pedestrians being the most vulnerable
elements. Recent work has shown that Convolutional Neural Networks are able to
accurately predict accident rates exploiting Street View imagery along urban
roads. The promising results point to the plausibility of aided design of safe
urban landscapes, for both pedestrians and vehicles. In this paper, by
considering historical accident data and Street View images, we detail how to
automatically predict the impact (increase or decrease) of urban interventions
on accident incidence. The results are positive, rendering an accuracies
ranging from 60 to 80%. We additionally provide an interpretability analysis to
unveil which specific categories of urban features impact accident rates
positively or negatively. Considering the transportation network substrates
(sidewalk and road networks) and their demand, we integrate these results to a
complex network framework, to estimate the effective impact of urban change on
the safety of pedestrians and vehicles. Results show that public authorities
may leverage on machine learning tools to prioritize targeted interventions,
since our analysis show that limited improvement is obtained with current
tools. Further, our findings have a wider application range such as the design
of safe urban routes for pedestrians or to the field of driver-assistance
technologies.


title: Retinal Vessel Segmentation with Pixel-wise Adaptive Filters

abstract:
  Accurate retinal vessel segmentation is challenging because of the complex
texture of retinal vessels and low imaging contrast. Previous methods generally
refine segmentation results by cascading multiple deep networks, which are
time-consuming and inefficient. In this paper, we propose two novel methods to
address these challenges. First, we devise a light-weight module, named
multi-scale residual similarity gathering (MRSG), to generate pixel-wise
adaptive filters (PA-Filters). Different from cascading multiple deep networks,
only one PA-Filter layer can improve the segmentation results. Second, we
introduce a response cue erasing (RCE) strategy to enhance the segmentation
accuracy. Experimental results on the DRIVE, CHASE_DB1, and STARE datasets
demonstrate that our proposed method outperforms state-of-the-art methods while
maintaining a compact structure. Code is available at
https://github.com/Limingxing00/Retinal-Vessel-Segmentation-ISBI20222.


title:
Oral cancer detection and interpretation: Deep multiple instance
  learning versus conventional deep single instance learning

abstract:
  The current medical standard for setting an oral cancer (OC) diagnosis is
histological examination of a tissue sample from the oral cavity. This process
is time consuming and more invasive than an alternative approach of acquiring a
brush sample followed by cytological analysis. Skilled cytotechnologists are
able to detect changes due to malignancy, however, to introduce this approach
into clinical routine is associated with challenges such as a lack of experts
and labour-intensive work. To design a trustworthy OC detection system that
would assist cytotechnologists, we are interested in AI-based methods that
reliably can detect cancer given only per-patient labels (minimizing annotation
bias), and also provide information on which cells are most relevant for the
diagnosis (enabling supervision and understanding). We, therefore, perform a
comparison of a conventional single instance learning (SIL) approach and a
modern multiple instance learning (MIL) method suitable for OC detection and
interpretation, utilizing three different neural network architectures. To
facilitate systematic evaluation of the considered approaches, we introduce a
synthetic PAP-QMNIST dataset, that serves as a model of OC data, while offering
access to per-instance ground truth. Our study indicates that on PAP-QMNIST,
the SIL performs better, on average, than the MIL approach. Performance at the
bag level on real-world cytological data is similar for both methods, yet the
single instance approach performs better on average. Visual examination by
cytotechnologist indicates that the methods manage to identify cells which
deviate from normality, including malignant cells as well as those suspicious
for dysplasia. We share the code as open source at
https://github.com/MIDA-group/OralCancerMILvsSIL


title: Robust Audio Anomaly Detection

abstract:
  We propose an outlier robust multivariate time series model which can be used
for detecting previously unseen anomalous sounds based on noisy training data.
The presented approach doesn't assume the presence of labeled anomalies in the
training dataset and uses a novel deep neural network architecture to learn the
temporal dynamics of the multivariate time series at multiple resolutions while
being robust to contaminations in the training dataset. The temporal dynamics
are modeled using recurrent layers augmented with attention mechanism. These
recurrent layers are built on top of convolutional layers allowing the network
to extract features at multiple resolutions. The output of the network is an
outlier robust probability density function modeling the conditional
probability of future samples given the time series history. State-of-the-art
approaches using other multiresolution architectures are contrasted with our
proposed approach. We validate our solution using publicly available machine
sound datasets. We demonstrate the effectiveness of our approach in anomaly
detection by comparing against several state-of-the-art models.


title: Cosmic Perturbations from a Rotating Field

abstract:
  Complex scalar fields charged under approximate $U(1)$ symmetries appear in
well-motivated extensions of the Standard Model. One example is the field that
contains the QCD axion field associated with the Peccei-Quinn symmetry; others
include flat directions in supersymmetric theories with baryon, lepton, or
flavor charges. These fields may take on large values and rotate in field space
in the early universe. The relevant approximate $U(1)$ symmetry ensures that
the angular direction of the complex field is light during inflation and that
the rotation is thermodynamically stable and is long-lived. These properties
allow rotating complex scalar fields to naturally serve as curvatons and
explain the observed perturbations of the universe. The scenario imprints
non-Gaussianity in the curvature perturbations, likely at a level detectable in
future large scale structure observations. The rotation can also explain the
baryon asymmetry of the universe without producing excessive isocurvature
perturbations.


title:
Modelling the spectra of the kilonova AT2017gfo -- I: The photospheric
  epochs

abstract:
  The kilonova (KN) associated with the binary neutron star (BNS) merger
GW170817 is the only known electromagnetic counterpart to a gravitational wave
source. Here we produce a sequence of radiative transfer models (using
$\textsc{tardis}$) with updated atomic data, and compare them to accurately
calibrated spectra. We use element compositions from nuclear network
calculations based on a realistic hydrodynamical simulation of a BNS merger. We
show that the blue spectrum at +1.4 days after merger requires a
nucleosynthetic trajectory with a high electron fraction. Our best-fitting
model is composed entirely of first $r$-process peak elements (Sr & Zr) and the
strong absorption feature is reproduced well by Sr$\,\textsc{ii}$ absorption.
At this epoch, we set an upper limit on the lanthanide mass fraction of
$X_{\textsc{ln}} \lesssim 5 \times 10^{-3}$. In contrast, all subsequent
spectra from $+2.4 - 6.4$ days require the presence of a modest amount of
lanthanide material ($X_{\textsc{ln}} \simeq 0.05^{+0.05}_{-0.02}$), produced
by a trajectory with $Y_{\rm e} = 0.29$. This produces lanthanide-induced line
blanketing below 6000$\,$\AA, and sufficient light $r$-process elements to
explain the persistent strong feature at $\sim 0.7 - 1.0 \, \micron$
(Sr$\,\textsc{ii}$). The composition gives good matches to the observed data,
indicating that the strong blue flux deficit results in the near-infrared (NIR)
excess. The disjoint in composition between the first epoch and all others
indicates either ejecta stratification, or the presence of two distinct
components of material. This further supports the `two-component' kilonova
model, and constrains the element composition from nucleosynthetic
trajectories. The major uncertainties lie in availability of atomic data and
the ionisation state of the expanding material.


title:
Toward the discovery of matter creation with neutrinoless double-beta
  decay

abstract:
  The discovery of neutrinoless double-beta decay could soon be within reach.
This hypothetical ultra-rare nuclear decay is a portal to new physics beyond
the Standard Model. Its observation would constitute the discovery of a
matter-creating process, corroborating leading theories of why the universe
contains more matter than antimatter, and how the forces unify at high energy
scales. It would also prove that neutrinos and anti-neutrinos are not two
distinct particles, but can transform into each other, generating their own
mass in the process. The recognition that neutrinos are not massless
necessitates an explanation and has boosted interest in neutrinoless
double-beta decay. The field is now at a turning point. A new round of
experiments is currently being prepared for the next decade to cover an
important region of parameter space. Advancements in nuclear theory are laying
the groundwork to connect the nuclear decay with the underlying new physics.
Meanwhile, the particle theory landscape continues to find new motivations for
neutrinos to be their own antiparticle. This review brings together the
experimental, nuclear theory, and particle theory aspects connected to
neutrinoless double-beta decay, to explore the path toward - and beyond - its
discovery.


title: Holographic tensor networks from hyperbolic buildings

abstract:
  We introduce a unifying framework for the construction of holographic tensor
networks, based on the theory of hyperbolic buildings. The underlying dualities
relate a bulk space to a boundary which can be homeomorphic to a sphere, but
also to more general spaces like a Menger sponge type fractal. In this general
setting, we give a precise construction of a large family of bulk regions that
satisfy complementary recovery. For these regions, our networks obey a
Ryu--Takayanagi formula. The areas of Ryu--Takayanagi surfaces are controlled
by the Hausdorff dimension of the boundary, and consistently generalize the
behavior of holographic entanglement entropy in integer dimensions to the
non-integer case. Our construction recovers HaPPY--like codes in all
dimensions, and generalizes the geometry of Bruhat--Tits trees. It also
provides examples of infinite-dimensional nets of holographic conditional
expectations, and opens a path towards the study of conformal field theory and
holography on fractal spaces.


title:
Unravelling the left-right mixing using $0\nu \beta\beta$ decay and
  collider probes

abstract:
  In the context of the minimal left-right symmetric model, we study the
interplay between current and future neutrinoless double beta
($0\nu\beta\beta$) decay experiments, long-lived particle searches at the LHC
main detectors ATLAS/CMS, and the proposed far detector MATHUSLA. The heavy
Majorana neutrino can be produced in association with an electron from the
decay of $W$ boson for a non-zero left-right mixing and subsequently decays
into another electron with the same charge and jets. Owing to the suppression
of large right-handed charged gauge boson $W_R$ mass, the heavy neutrinos could
be long-lived. We show that long-lived particle (LLP) searches for heavy
Majorana neutrinos in the same-sign dilepton channel at the LHC can be used to
extend $W_R$ boson mass reach relative to the reach of the Keung-Senjanovic
(KS) process. Finally, we show that sensitivities of LLP searches at the
high-luminosity LHC with main detectors ATLAS/CMS are competitive with those of
future $0\nu\beta\beta$ decay searches.


title:
The halo model with beyond-linear halo bias: unbiasing cosmological
  constraints from galaxy-galaxy lensing and clustering

abstract:
  We determine the error introduced in a joint halo model analysis of
galaxy-galaxy lensing and galaxy clustering observables when adopting the
standard approximation of linear halo bias. Considering the Kilo-Degree Survey,
we forecast that ignoring the non-linear halo bias would result in up to
5$\sigma$ offsets in the recovered cosmological parameters describing structure
growth, $S_8$, and the matter density parameter, $\Omega_{\mathrm{m}}$. The
direction of these offsets are shown to depend on the freedom afforded to the
halo model through other nuisance parameters. We conclude that a beyond-linear
halo bias correction must therefore be included in future cosmological halo
model analyses of large-scale structure observables on non-linear scales.


title:
Integer and fractionalized vortex lattices and off-diagonal long-range
  order

abstract:
  We analyze the implication of off-diagonal long-range order (ODLRO) for
inhomogeneous periodic field configurations and multi-component order
parameters. For single component order parameters we show that the only static,
periodic field configuration consistent with ODLRO is a vortex lattice with
integer flux in units of the flux quantum in each unit cell. For a
superconductor with $g$ degenerate components, fractional vortices are allowed.
Depending on the precise order-parameter manifold, they tend to occur in units
of $1/g$ of the flux quantum. These results are well known to emerge from the
Ginzburg-Landau or BCS theories of superconductivity. Our results imply that
they are valid even if these theories no-longer apply. Integer and fractional
vortex lattices are transparently seen to emerge as a consequence of the
macroscopic coherence and single valuedness of the condensate.


title: A Sm\"org{\aa}sbord of Skyrmions

abstract:
  We study static solutions of the standard Skyrme model with a pion mass.
Using approximately $10^5$ pseudo-random initial configurations made of single
Skyrmions in the non-symmetrized product Ansatz and an automatic detection of
repeated solutions, we find 409 local energy minimizers (Skyrmions) of the
model with baryon numbers 1 through 16, of which 385 are new. In particular, we
find new solutions for baryon numbers 5, 8, 9, 10, 11, 12, 13, 14, 15, and 16.
Our results for the number of solutions per baryon number suggest that this
number could grow either polynomially or exponentially. We identify new
families of solutions: sheets of Skyrmions in synchronized and antisynchronized
hexagonal layers (which we call graphene); chains of 2- and 3-tori; chain-like
solutions containing a hinge and many clustered Skyrmions. Contrary to common
lore, only the $B=12$ global energy minimizer is made of alpha particles or
some chunk of a cubic crystal, whereas the $B=9,11,14,15$ minimizers contain
the $B=7$ icosahedrally symmetric Skyrmion as a component. The $B=10,13,16$ are
symmetric graphene-like solutions. We find $B=5$ and $B=8$ minimizers with
numerically indistinguishable energies. The $B=8$ candidates are the chain of
two cubes, which is a chunk of the cubic Skyrme crystal and the fullerene-type
ball found originally by the rational map approximation. The $B=5$ global
minimizer is either the well-known $D_{2d}$ symmetric fullerene or a new
$C_{2v}$ symmetric solution. Finally, our findings show a large number of
solutions have no discrete symmetries or just one symmetry, contrary to the
common lore that Skyrmions are highly symmetric configurations.


title: Incorporating Sum Constraints into Multitask Gaussian Processes

abstract:
  Machine learning models can be improved by adapting them to respect existing
background knowledge. In this paper we consider multitask Gaussian processes,
with background knowledge in the form of constraints that require a specific
sum of the outputs to be constant. This is achieved by conditioning the prior
distribution on the constraint fulfillment. The approach allows for both linear
and nonlinear constraints. We demonstrate that the constraints are fulfilled
with high precision and that the construction can improve the overall
prediction accuracy as compared to the standard Gaussian process.


title: Hyperboloidal method for frequency-domain self-force calculations

abstract:
  Gravitational self-force theory is the leading approach for modeling
gravitational wave emission from small mass-ratio compact binaries. This method
perturbatively expands the metric of the binary in powers of the mass ratio.
The source for the perturbations depends on the orbital configuration,
calculational approach, and the order of the perturbative expansion. These
sources fall into three broad classes: (i) distributional, (ii) worldtube, and
(iii) unbounded support. The latter, in particular, is important for emerging
second-order (in the mass ratio) calculations. Traditional frequency domain
approaches employ the variation of parameters method and compute the
perturbation on standard time slices with numerical boundary conditions
supplied at finite radius from series expansions of the asymptotic behavior.
This approach has been very successful, but the boundary conditions
calculations are tedious, and the approach is not well suited to unbounded
sources where homogeneous solutions must be computed at all radii. This work
develops an alternative approach where hyperboloidal slices foliate the
spacetime, and compactifying coordinates simplify the boundary treatment. We
implement this approach with a multi-domain spectral solver with analytic mesh
refinement and use the scalar-field self-force on circular orbits around a
Schwarzschild black hole as an example problem. The method works efficiently
for all three source classes encountered in self-force calculations and has
distinct advantages over the traditional approach. For example, our code
efficiently computes the perturbation for orbits with extremely large orbital
radii ($r_{p}>10^5M$) or modes with very high spherical harmonic mode index
($\ell \ge 100$). Our results indicate that hyperboloidal methods can play an
essential role in self-force calculations.


title:
A Pair of Early- and Late-Forming Galaxy Cluster Samples: Implications
  for Detection of Halo Assembly Bias

abstract:
  The halo assembly bias, a phenomenon referring to dependencies of the
large-scale bias of a dark matter halo other than its mass, is a fundamental
property of our standard cosmological model. First discovered in 2005 via
high-resolution numerical simulations, it has been proven very difficult to be
detected observationally, with only a few convincing claims of detection thus
far. The main obstacle lies in finding an accurate proxy of the halo formation
time. In this study, by utilizing a constrained simulation that can faithfully
reproduce the observed structures larger than $\sim 2\,$Mpc in the local
universe, for a sample of about 630 massive clusters at $z\le 0.12$, we find
their counterpart halos in the simulation and use the mass growth history of
the matched halos to estimate the formation time of the observed clusters. This
allows us to construct a pair of early- and late-forming clusters, with similar
mass as measured via weak gravitational lensing, and large-scale bias differing
at $\gtrsim 4\sigma$ level, clearly showing the signature of assembly bias.


title:
Theory of Competing Charge Density Wave, Kekule and Antiferromagnetic
  ordered Fractional Quantum Hall states in Graphene aligned with Boron Nitride

abstract:
  We investigate spin and valley symmetry-broken fractional quantum Hall phases
within a formalism that naturally extends the paradigm of quantum Hall
ferromagnetism from integer to fractional quantum Hall states, allowing us to
construct detailed phase diagrams for a large class of multi-component states.
Motivated by recent experiments on Graphene aligned with a Boron Nitride
substrate, we predict a sequence of transitions realized by increasing the
magnetic field, starting from a sub-lattice polarized state to a valley
coherent Kekule charge density wave state and further to an anti-ferromagnetic
phase. Moreover for filling fractions such as $\nu=\pm 1/3$, we predict that
the system undergoes a transition at low fields, that not only differ by the
spin-valley orientation of the fractionally filled flavors but also by their
intrinsic fractional quantum Hall nature. This transition is from a
Laughlin-like state to a two component Halperin-like state both with a charge
density wave order. Moreover for $\nu=\pm 1/3,\pm 2/3$, we predict a "canted
Kekule density phase"(CaKD) where the spinors of integer and fractionally
occupied components have different orientations in the valley Bloch sphere, in
contrast to the Kekule state for the integer quantum Hall state at neutrality
where both occupied components have the same orientation in the valley Bloch
sphere.


title: Understanding Digital Government Transformation

abstract:
  In today's era of innovation of technological progression, digitalisation has
not only transformed individual lives but also has a prominent influence on
business activities. The world is surviving in a global yet complex
technological progression that not only changes the lives of civilians but is
also transforming the public, private, and academic spheres of life. This
research focuses on the digitalisation of governments, their challenges, and
success factors. It is found that government faces difficulties in formulating
strategies, proper planning, execution strategies, and a lack of organised
information and expertise. However, success can be achieved by working on
capabilities of the future workforce, creating leaders for tomorrow, generating
digitalisation capabilities, and bringing a purpose-driven digitalisation
before digital government transformation. Overall, the study's findings suggest
that digital government transformation creates value, enhances relations,
improves service delivery, grows economy, pushes economic activities, enhances
citizen engagement, increases the policy implementation and their efficiency,
and affects business growth positively.


title:
Stellar Companions to TESS Objects of Interest: A Test of
  Planet-Companion Alignment

abstract:
  We present a catalog of stellar companions to host stars of Transiting
Exoplanet Survey Satellite Objects of Interest (TOIs) identified from a
marginalized likelihood ratio test that incorporates astrometric data from the
Gaia Early Data Release 3 catalog (EDR3). The likelihood ratio is computed
using a probabilistic model that incorporates parallax and proper-motion
covariances and marginalizes the distances and 3D velocities of stars in order
to identify comoving stellar pairs. We find 172 comoving companions to 170
non-false-positive TOI hosts, consisting of 168 systems with two stars and 2
systems with three stars. Among the 170 TOI hosts, 54 harbor confirmed planets
that span a wide range of system architectures. We conduct an investigation of
the mutual inclinations between the stellar companion and planetary orbits
using Gaia EDR3, which is possible because transiting exoplanets must orbit
within the line of sight; thus, stellar companion kinematics can constrain
mutual inclinations. While the statistical significance of the current sample
is weak, we find that 73$^{+14}_{-20}\%$ of systems with Kepler-like
architectures ($R_{P}$ $\leq$ 4 $R_\oplus$ and $a$ $<$ 1 AU) appear to favor a
nonisotropic orientation between the planetary and companion orbits with a
typical mutual inclination $\alpha$ of 35 $\pm$ 24$^\circ$. In contrast,
65$^{+20}_{-35}\%$ of systems with close-in giants ($P$ $<$ 10 days and $R_{P}$
$>$ 4 $R_{\oplus}$) favor a perpendicular geometry ($\alpha =$ 89 $\pm$
21$^\circ$) between the planet and companion. Moreover, the close-in giants
with large stellar obliquities (planet-host misalignment) are also those that
favor significant planet-companion misalignment.


title:
Massive black holes at high redshifts from superconducting cosmic
  strings

abstract:
  The observation of quasars at high redshifts presents a mystery in the theory
of black hole formation. In order to source such objects, one often relies on
the presence of heavy seeds ($M \approx 10^{4-6} \, M_{\odot}$) in place at
early times. Unfortunately, the formation of these heavy seeds are difficult to
realize within the standard astrophysical context. Here, we investigate whether
superconducting cosmic string loops can source sufficiently strong
overdensities in the early universe to address this mystery. We review a set of
direct collapse conditions under which a primordial gas cloud will undergo
monolithic collapse into a massive black hole (forming with a mass of $M_{BH}
\approx 10^5 \, M_{\odot}$ at $z \approx 300$ in our scenario), and
systematically show how superconducting cosmic string loops can satisfy such
conditions in regions of the $G\mu-I$ parameter space.

categories: astro-ph.CO astro-ph.GA gr-qc hep-ph hep-th


title: Duals in natural characteristic

abstract:
  We introduce a derived smooth duality functor on the unbounded derived
category of smooth mod p representations of a p-adic Lie group. Using this
functor we relate various subcategories of admissible complexes.


title:
Double Inequalities for Complete Monotonicity Degrees of Remainders of
  Asymptotic Expansions of the Gamma and Digamma Functions

abstract:
  Motivated by several conjectures posed in the paper " Completely monotonic
degrees for a difference between the logarithmic and psi functions",we confirm
in this work some conjectures on completely monotonic degrees of remainders of
the asymptotic expansion of the logarithm of the gamma function and the digamma
function and we give two bounded for this degrees.


title:
Cross-Platform Difference in Facebook and Text Messages Language Use:
  Illustrated by Depression Diagnosis

abstract:
  How does language differ across one's Facebook status updates vs. one's text
messages (SMS)? In this study, we show how Facebook and SMS use differs in
psycho-linguistic characteristics and how these differences drive downstream
analyses with an illustration of depression diagnosis. We use a sample of
consenting participants who shared Facebook status updates, SMS data, and
answered a standard psychological depression screener. We quantify domain
differences using psychologically driven lexical methods and find that language
on Facebook involves more personal concerns, experiences, and content features
while the language in SMS contains more informal and style features. Next, we
estimate depression from both text domains, using a depression model trained on
Facebook data, and find a drop in accuracy when predicting self-reported
depression assessments from the SMS-based depression estimates. Finally, we
evaluate a simple domain adaption correction based on words driving the
cross-platform differences and applied it to the SMS-derived depression
estimates, resulting in significant improvement in prediction. Our work shows
the Facebook vs. SMS difference in language use and suggests the necessity of
cross-domain adaption for text-based predictions.


title:
Quark and gluon entanglement in the proton on the light cone at
  intermediate $x$

abstract:
  In QCD with $N_c$ colors the anti-symmetric valence quark color space singlet
state $\sim \epsilon_{i_1\cdots i_{N_c}} |i_1,\cdots, i_{N_c}\rangle$ of the
proton corresponds to the reduced density matrix $\rho_{ij}=(1/N_c)
\delta_{ij}$ for a single color degree of freedom. Its degenerate spectrum of
eigenvalues, $\lambda_i=1/N_c$, the purity $\mathrm{tr}~\rho^2 = 1/N_c$, and
the von~Neumann entropy $S_\mathrm{vN}=\log(N_c)$ all indicate maximal
entanglement of color. On the other hand, for $N_c\to\infty$ the spatial wave
function of the proton factorizes into valence quark wave functions determined
by a mean field (E. Witten, Nucl. Phys. B 160 (1979) p. 57) where there is no
entanglement of spatial degrees of freedom. A model calculation at $N_c=3$
using a simple three quark model light-front wave function by Brodsky and
Schlumpf, predicts percent level entanglement of spatial degrees of freedom.
Using light-cone perturbation theory we also derive the density matrix
associated with the four parton $|qqqg\rangle$ Fock state. Tracing out the
quarks, we construct the reduced density matrix for the degrees of freedom of
the gluon, which encodes its entanglement with the sources. Our expressions
provide the dependence of the density matrix on the soft cutoff $x$ for the
gluon light-cone momentum, and on the collinear and ultraviolet regulators.
Numerical results obtained in a simple approximation indicate stronger
entanglement for the gluon (with $x_g < \langle x_q\rangle$) than for quarks in
the three quark Fock state.


title:
The different structure of economic ecosystems at the scales of
  companies and countries

abstract:
  A key element to understand complex systems is the relationship between the
spatial scale of investigation and the structure of the interrelation among its
elements. When it comes to economic systems, it is now well-known that the
country-product bipartite network exhibits a nested structure, which is the
foundation of different algorithms that have been used to scientifically
investigate countries' development and forecast national economic growth.
Changing the subject from countries to companies, a significantly different
scenario emerges. Through the analysis of a unique dataset of Italian firms'
exports and a worldwide dataset comprising countries' exports, here we find
that, while a globally nested structure is observed at the country level, a
local, in-block nested structure emerges at the level of firms. Remarkably,
this in-block nestedness is statistically significant with respect to suitable
null models and the algorithmic partitions of products into blocks have a high
correspondence with exogenous product classifications. These findings lay a
solid foundation for developing a scientific approach based on the physics of
complex systems to the analysis of companies, which has been lacking until now.


title:
On the relations of stochastic convex optimization problems with
  empirical risk minimization problems on $p$-norm balls

abstract:
  In this paper, we consider convex stochastic optimization problems arising in
machine learning applications (e.g., risk minimization) and mathematical
statistics (e.g., maximum likelihood estimation). There are two main approaches
to solve such kinds of problems, namely the Stochastic Approximation approach
(online approach) and the Sample Average Approximation approach, also known as
the Monte Carlo approach, (offline approach). In the offline approach, the
problem is replaced by its empirical counterpart (the empirical risk
minimization problem). The natural question is how to define the problem sample
size, i.e., how many realizations should be sampled so that the quite accurate
solution of the empirical problem be the solution of the original problem with
the desired precision. This issue is one of the main issues in modern machine
learning and optimization. In the last decade, a lot of significant advances
were made in these areas to solve convex stochastic optimization problems on
the Euclidean balls (or the whole space). In this work, we are based on these
advances and study the case of arbitrary balls in the $\ell_p$-norms. We also
explore the question of how the parameter $p$ affects the estimates of the
required number of terms as a function of empirical risk.


title: Answering Count Queries for Genomic Data with Perfect Privacy

abstract:
  In this paper, we consider the problem of answering count queries for genomic
data subject to perfect privacy constraints. Count queries are often used in
applications that collect aggregate (population-wide) information from
biomedical Databases (DBs) for analysis, such as Genome-wide association
studies. Our goal is to design mechanisms for answering count queries of the
following form: How many users in the database have a specific set of genotypes
at certain locations in their genome? At the same time, we aim to achieve
perfect privacy (zero information leakage) of the sensitive genotypes at a
pre-specified set of secret locations. The sensitive genotypes could indicate
rare diseases and/or other health traits that one may want to keep private. We
present two local count-query mechanisms for the above problem that achieve
perfect privacy for sensitive genotypes while minimizing the expected absolute
error (or per-user error probability) of the query answer. We also derived a
lower bound of the per-user probability of error for an arbitrary query
answering mechanism that satisfies perfect privacy. We show that our mechanisms
achieve error that is close to the lower bound, and are match the lower bound
for some special cases. We numerically show that the performance of each
mechanism depends on the data prior distribution, the intersection between the
queried and sensitive data, and the strength of the correlation in the genomic
data sequence.


title: Dark matter from torsion in Friedmann cosmology

abstract:
  A cosmological model in an Einstein-Cartan framework endowed with torsion is
studied. For a torsion function assumed to be proportional to Hubble expansion
function, namely $\phi=-\alpha H$, the contribution of torsion function as a
dark matter component is studied in two different approaches. In the first one,
the total matter energy density is altered by torsion coupling $\alpha$, giving
rise to an effective dark matter and cosmological constant terms that reproduce
quite well the flat cosmic concordance model. In the second approach, starting
with just standard baryonic matter plus a cosmological constant term, it is
obtained that the coupling of torsion with baryons and cosmological constant
term naturally gives rise to a dark matter contribution, together a modified
cosmological term. In this model the dark matter sector can be interpreted as
an effective coupling of the torsion function with the ordinary baryonic matter
and cosmological constant. Finally, it is shown that both models are totally
compatible with recent cosmological data from Supernovae and Hubble parameter
measurements.


title:
Hacking the Colony: On the Disruptive Effect of Misleading Pheromone and
  How to Defend Against It

abstract:
  Ants have evolved to seek and retrieve food by leaving trails of pheromones.
This mechanism has inspired several approaches to decentralized multi-robot
coordination. However, in this paper, we show that pheromone trails are a
fragile mechanism for coordination, and can be sabotaged to starve the colony.
We introduce detractors: malicious agents that leave a misleading, but
indistinguishable, trail of food pheromone to distract and trap cooperator ants
in the nest. We analyze the effectiveness of detractors with respect to
parameters such as evaporation rate of misleading pheromone and fraction of
detractors in the colony. In addition, we propose a countermeasure to this
attack by introducing a new type of pheromone: the cautionary pheromone.
Cooperator ants secrete this type of pheromone atop existing food trails as a
warning. When the cautionary pheromone intensity exceeds the food pheromone
intensity, cooperator ants ignore overlapping food pheromone. We show that,
despite its simplicity, this defense mechanism can limit, but not nullify, the
effect of detractors. Ultimately, our work shows that pheromone-based
coordination, while effective, is also fragile.


title:
The DESI PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) Mock
  Challenge

abstract:
  The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will
provide measurements of galaxy properties, such as stellar mass ($M_*$), star
formation rate (${\rm SFR}$), stellar metallicity ($Z_{\rm MW}$), and stellar
age ($t_{\rm age, MW}$), for >10 million galaxies of the DESI Bright Galaxy
Survey. Full posterior distributions of the galaxy properties will be inferred
using state-of-the-art Bayesian spectral energy distribution (SED) modeling of
DESI spectroscopy and Legacy Surveys photometry. In this work, we present the
SED model, Bayesian inference framework, and methodology of PROVABGS.
Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI
spectra and photometry, constructed using the L-GALAXIES semi-analytic model.
We compare the inferred galaxy properties to the true galaxy properties of the
simulation using a hierarchical Bayesian framework to quantify accuracy and
precision. Overall, we accurately infer the true $M_*$, ${\rm SFR}$, $Z_{\rm
MW}$, and $t_{\rm age, MW}$ of the simulated galaxies. However, the priors on
galaxy properties induced by the SED model have a significant impact on the
posteriors. They impose a ${\rm SFR}{>}10^{-1} M_\odot/{\rm yr}$ lower bound on
${\rm SFR}$, a ${\sim}0.3$ dex bias on $\log Z_{\rm MW}$ for galaxies with low
spectral signal-to-noise, and $t_{\rm age, MW} < 8\,{\rm Gyr}$ upper bound on
stellar age. This work also demonstrates that a joint analysis of spectra and
photometry significantly improves the constraints on galaxy properties over
photometry alone and is necessary to mitigate the impact of the priors. With
the methodology presented and validated in this work, PROVABGS will maximize
information extracted from DESI observations and provide a probabilistic
value-added galaxy catalog that will extend current galaxy studies to new
regimes and unlock cutting-edge probabilistic analyses.


title:
Deep Surface Reconstruction from Point Clouds with Visibility
  Information

abstract:
  Most current neural networks for reconstructing surfaces from point clouds
ignore sensor poses and only operate on raw point locations. Sensor visibility,
however, holds meaningful information regarding space occupancy and surface
orientation. In this paper, we present two simple ways to augment raw point
clouds with visibility information, so it can directly be leveraged by surface
reconstruction networks with minimal adaptation. Our proposed modifications
consistently improve the accuracy of generated surfaces as well as the
generalization ability of the networks to unseen shape domains. Our code and
data is available at https://github.com/raphaelsulzer/dsrv-data.


title:
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding
  Attacks via Patch-agnostic Masking

abstract:
  Object detectors, which are widely deployed in security-critical systems such
as autonomous vehicles, have been found vulnerable to physical-world patch
hiding attacks. The attacker can use a single physically-realizable adversarial
patch to make the object detector miss the detection of victim objects and
completely undermines the functionality of object detection applications. In
this paper, we propose ObjectSeeker as a defense framework for building
certifiably robust object detectors against patch hiding attacks. The core
operation of ObjectSeeker is patch-agnostic masking: we aim to mask out the
entire adversarial patch without any prior knowledge of the shape, size, and
location of the patch. This masking operation neutralizes the adversarial
effect and allows any vanilla object detector to safely detect objects on the
masked images. Remarkably, we develop a certification procedure to determine if
ObjectSeeker can detect certain objects with a provable guarantee against any
adaptive attacker within the threat model. Our evaluation with two object
detectors and three datasets demonstrates a significant (~10%-40% absolute and
~2-6x relative) improvement in certified robustness over the prior work, as
well as high clean performance (~1% performance drop compared with vanilla
undefended models).


title:
A Challenge for Discrimination of Color-Singlet versus Color-Octet
  Quarkonium Production

abstract:
  The precise mechanism for production of quarkonium at hadron colliders is
still an open question. Within non-relativistic quantum chromodynamics,
quarkonium production cross sections can be factorized into short-distance,
perturbative contributions and universal, non-perturbative, long-distance
matrix elements, and then summed over quantum numbers of the heavy quark pair.
In principle, at short-distances, the heavy quark pair can be either in a
color-singlet or color-octet state, and it is desirable to establish the
relative contributions to compare with data and to make predictions in
different experimental environments. From the explicit form of the lowest-order
perturbative matrix elements for color-singlet and color-octet production, we
show that the structure of the optimal observable for discrimination on phase
space, the likelihood ratio, has strong dependence on the angular momentum
state of the heavy quark pair. This presents an obstruction for construction of
a single, robust discrimination observable that can be applied to production of
an arbitrary quarkonium state.


title:
Terrestrial Impact from the Passage of the Solar System through a Cold
  Cloud a Few Million Years Ago

abstract:
  It is expected that as the Sun travels through the interstellar medium (ISM),
there will be different filtration of Galactic Cosmic Rays (GCR) that affect
Earth. The effect of GCR on Earth's atmosphere and climate is still uncertain.
Although the interaction with molecular clouds was previously considered, the
terrestrial impact of compact cold clouds was neglected. There is overwhelming
geological evidence from 60Fe and 244Pu isotopes that Earth was in direct
contact with the ISM 2 million years ago, and the local ISM is home to several
nearby cold clouds. Here we show, with a state-of the art simulation that
incorporate all the current knowledge about the heliosphere that if the solar
system passed through a cloud such as Local Leo Cold Cloud, then the
heliosphere which protects the solar system from interstellar particles, must
have shrunk to a scale smaller than the Earth's orbit around the Sun (0.22).
Using a magnetohydrodynamic simulation that includes charge exchange between
neutral atoms and ions, we show that during the heliosphere shrinkage, Earth
was exposed to a neutral hydrogen density of up to 3000cm-3. This could have
had drastic effects on Earth's climate and potentially on human evolution at
that time, as suggested by existing data.


title: On Shilnikov attractors of three-dimensional flows and maps

abstract:
  We describe scenarios for the emergence of Shilnikov attractors, i.e. strange
attractors containing a saddle-focus with two-dimensional unstable manifold, in
the case of three-dimensional flows and maps. The presented results are
illustrated with various specific examples.


title:
Position of the centroid of a planar convex body and the centroid
  Banach-Mazur distance

abstract:
  It is well known that any planar convex body $A$ permits to inscribe an
affine-regular hexagon $H_A$. We prove that the centroid of $A$ belongs to the
homothetic image of $H_A$ with ratio $\frac{4}{21}$ and the center in the
center of $H_A$.This ratio cannot be enlarged. Moreover, we consider the
variant of the Banach-Mazur distance $\delta_{BM}^{\rm cen} (C, D)$ of two
convex bodies $C, D$ of $E^d$ with the additional requirement that the
centroids of them coincide. We prove that $\delta_{BM}^{\rm cen} (C, D) \leq
\frac{33}{8}$ for every $C, D$ of $E^2$.


title:
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network
  Sensors and its Application in Process Control

abstract:
  We present a novelty detection framework for Convolutional Neural Network
(CNN) sensors that we call Sensor-Activated Feature Extraction One-Class
Classification (SAFE-OCC). We show that this framework enables the safe use of
computer vision sensors in process control architectures. Emergent control
applications use CNN models to map visual data to a state signal that can be
interpreted by the controller. Incorporating such sensors introduces a
significant system operation vulnerability because CNN sensors can exhibit high
prediction errors when exposed to novel (abnormal) visual data. Unfortunately,
identifying such novelties in real-time is nontrivial. To address this issue,
the SAFE-OCC framework leverages the convolutional blocks of the CNN to create
an effective feature space to conduct novelty detection using a desired
one-class classification technique. This approach engenders a feature space
that directly corresponds to that used by the CNN sensor and avoids the need to
derive an independent latent space. We demonstrate the effectiveness of
SAFE-OCC via simulated control environments.


title:
Satellite-based Quantum Information Networks: Use cases, Architecture,
  and Roadmap

abstract:
  Quantum Information Networks (QIN) currently represent a major goal in the
field quantum communication technologies. Such QINs will allow connecting
quantum devices (computers, sensors, communication stations, etc) over long
distances, thus improving significantly their intrinsic processing, sensing,
and security capabilities. The core mechanism of a QIN is quantum state
teleportation, demonstrated more than two decades ago, that consumes quantum
entanglement which can be seen in this context as a new kind of network
resource. This paper is the result of the collaboration under the auspices of
the French Space agency (CNES) of academic research and a Space telecom
industry actor that has defined and now executes a long term roadmap towards
operational QINs. Here, we address the key elements of this roadmap and
describe the stage we have reached in its execution. First, we identify and
quantitatively describe use cases per activity sector as a reference for the
requirements on the QINs, including key performance targets. Second, we define
a high-level architecture of a generic QIN so as to introduce structuring
elements such as resource, layers, governance, etc. We then focus on the
architecture on the Space part to identify its main design drivers and critical
elements. A survey of the state-of-the-art of these critical elements, as well
as issues related to standardisation is then presented. Based on these
elements, we explain our 3-stage roadmap. Finally, we detail the already
concluded first step of this roadmap, that is the design of a Space-to-ground
entanglement distribution demonstrator, which relies on detailed simulations so
as to allocate efficiently the performance requirements on each subsystems. We
invite relevant entities to join our roadmap to progress together towards the
ambitious goal of operational QINs in the next decade.


title:
Propagation of cosmic rays in plasmoids of AGN jets -- implications for
  multimessenger predictions

abstract:
  After the successful detection of cosmic high-energy neutrinos, the field of
multiwavelength photon studies of active galactic nuclei (AGN) is entering an
exciting new phase. The first hint of a possible neutrino signal from the
blazar TXS 0506+056 leads to the anticipation that AGN could soon be identified
as point sources of high-energy neutrino radiation, representing another
messenger signature besides the well-established photon signature. To
understand the complex flaring behavior at multiwavelengths, a genuine
theoretical understanding needs to be developed. These observations of the
electromagnetic spectrum and neutrinos can only be interpreted fully when the
charged, relativistic particles responsible for the different emissions are
modeled properly. The description of the propagation of cosmic rays in a
magnetized plasma is a complex question that can only be answered when
analysing the transport regimes of cosmic rays in a quantitative way. In this
paper, we therefore present a quantitative analysis of the propagation regimes
of cosmic rays in the approach that is most commonly used to model non-thermal
emission signatures from blazars, i.e. the existence of a high-energy
cosmic-ray population in a relativistic plasmoid traveling along the jet axis.
In this paper, we show that in the considered energy range of high-energy
photon and neutrino emission, the transition between diffusive and ballistic
propagation takes place, significantly influencing not only the spectral energy
distribution but also the lightcurve of blazar flares.


title: Mesoscopic physics of nanomechanical systems

abstract:
  Nanomechanics has brought mesoscopic physics into the world of vibrations.
Because nanomechanical systems are small, fluctuations are significant, the
vibrations become nonlinear already for comparatively small amplitudes, and new
mechanisms of dissipation come into play. At the same time, the exquisite
control of these systems makes them a platform for studying many problems of
classical and quantum physics far from thermal equilibrium in a
well-characterized setting. This review describes, at a conceptual level, basic
theoretical ideas and explicative experiments pertaining to mesoscopic physics
of nanomechanical systems. Major applications of nanomechanics in science and
technology are also outlined. A broad range of phenomena related to the
conservative as well as dissipative nonlinearity and fluctuations are discussed
within a unifying framework. They include the linear response of single and
coupled vibrational modes as well as nonlinear effects of periodic driving.
Such driving breaks the continuous time-translation symmetry and the detailed
balance, with conspicuous consequences for fluctuations, particularly in the
presence of the driving-induced bi- and multistability. Mathematical techniques
are described in the appendices to streamline the reading, but also to provide
an introduction to the theory. The goal of the review is to show the richness
of the physics at work. The continuous experimental and theoretical advances
make nanomechanical systems a vibrant area of research, with many new phenomena
to discover.


title:
Thermal effects on tidal deformability in the last orbits of an
  inspiraling binary neutron star system

abstract:
  The study of binary neutron stars mergers by the detection of the emitted
gravitational waves is one of the most promised tools to study the properties
of dense nuclear matter at high densities. It is worth claiming that, at the
moment, strong evidence that the temperature of the stars is zero during the
last orbits before coalescing, does not exist. Nevertheless, theoretical
studies suggest that the temperature concerning the inspiral phase, could reach
even a few MeV. According to the main theory, tides transfer mechanical energy
and angular momentum to the star at the expense of the orbit, where friction
within the star converts the mechanical energy into heat. During the inspiral,
these effects are potentially detectable. Different treatments have been used
to estimate the transfer of the mechanical energy and the size of the tidal
friction, leading to different conclusions about the importance of pre-merger
tidal effects. The present work is dedicated to the study of the effect of
temperature on the tidal deformability of neutron stars during the inspiral of
a neutron star system just before the merger. We applied a class of hot
equations of state, both isothermal and adiabatic, originated from various
nuclear models. We found that even for low values of temperature ($T<1$ MeV),
the effects on the basic ingredients of tidal deformability are not negligible.
On the other hand, in the case of the adiabatic star, the thermal effects on
tidal deformability remain imperceptible, up to the value $S=0.2 \ {\rm
k}_{B}$. According to the main finding, the effect of the temperature on the
tidal deformability is indistinguishable. The consequences of the above result
are discussed and analyzed.


title:
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place
  Recognition and Localization

abstract:
  Place recognition and visual localization are particularly challenging in
wide baseline configurations. In this paper, we contribute with the
\emph{Danish Airs and Grounds} (DAG) dataset, a large collection of
street-level and aerial images targeting such cases. Its main challenge lies in
the extreme viewing-angle difference between query and reference images with
consequent changes in illumination and perspective. The dataset is larger and
more diverse than current publicly available data, including more than 50 km of
road in urban, suburban and rural areas. All images are associated with
accurate 6-DoF metadata that allows the benchmarking of visual localization
methods.
  We also propose a map-to-image re-localization pipeline, that first estimates
a dense 3D reconstruction from the aerial images and then matches query
street-level images to street-level renderings of the 3D model. The dataset can
be downloaded at: https://frederikwarburg.github.io/DAG


title: Application of an Upwind Integration Method to Plane Parallel Hall-MHD

abstract:
  We study the impact of an Upwind scheme on the numerical convergence of
simulations of the Hall and Ohmic effect in neutron stars crusts. While
simulations of these effects have explored a variety of geometries and wide
ranges of physical parameters, they are limited to relatively low values of the
Hall parameter, playing the role of the magnetic Reynolds number, which should
be not exceed a few hundred for numerical convergence.
  We study the evolution of the magnetic field in a plane-parallel Cartesian
geometry. We discretise the induction equation using a finite difference scheme
and then integrate it via the Euler forward method. Two different approaches
are used for the integration of the advective terms appearing in the equation:
a Forward Time and Central in Space (FTCS) and an Upwind scheme. We compare
them in terms of accuracy and performance. We explore the impact of the Upwind
method on convergence according to the ratio of planar to vertical field and
the Hall parameter. In the limit of a low strength planar field the use of an
Upwind scheme provides a vast improvement leading to the convergence of
simulations where the Hall parameter is 2 orders of magnitude higher than that
of the FTCS. Upwind is still better if the planar field is stronger, yet, the
difference of the maximum value of the Hall parameter reached is within a
factor of 10 or a few. Moreover, we notice if the schemes diverge their
behaviour is very different, with FTCS producing infinite energy, while the
Upwind scheme only temporarily increasing the overall magnetic field energy.
  Overall, the Upwind scheme enhances the efficiency of the simulations
allowing the exploration of environments with higher value of electric
conductivity getting us closer than before to realistic environmental
conditions of magnetars.


title: Feedback-enabled Microwave Quantum Illumination

abstract:
  A simple feedback scheme can be used to operate efficiently a
microwave-quantum-illumination device based on electro-optomechanical systems
also in regimes in which excess dissipation would, otherwise, prevent to
outperform the optimal classical illumination protocol with the same
transmitted energy.


title: Waveform inversion via reduced order modeling

abstract:
  We introduce a novel approach to waveform inversion, based on a data driven
reduced order model (ROM) of the wave operator. The presentation is for the
acoustic wave equation, but the approach can be extended to elastic or
electromagnetic waves. The data are time resolved measurements of the pressure
wave at the sensors in an active array, which probe the unknown medium with
pulses and measure the generated waves. The ROM depends nonlinearly on the data
but it can be constructed from them using numerical linear algebra methods. We
show that the ROM can be used for the inverse problem of velocity estimation.
While the full-waveform inversion approach of {nonlinear least-squares} data
fitting is challenging without low frequency information, due to multiple
minima of the objective function, the minimization of the ROM misfit function
has a better behavior, even for a poor initial guess. In fact, the ROM misfit
function is demonstrably a convex function for low-dimensional parametrizations
of the unknown velocity. We give the construction of the ROM, introduce the
inversion approach based on the ROM misfit and assess its performance with
numerical simulations.


title: Misspecification Tests on Models of Random Graphs

abstract:
  A class of models that have been widely used are the exponential random graph
(ERG) models, which form a comprehensive family of models that include
independent and dyadic edge models, Markov random graphs, and many other graph
distributions, in addition to allow the inclusion of covariates that can lead
to a better fit of the model.
  Another increasingly popular class of models in statistical network analysis
are stochastic block models (SBMs). They can be used for the purpose of
grouping nodes into communities or discovering and analyzing a latent structure
of a network. The stochastic block model is a generative model for random
graphs that tends to produce graphs containing subsets of nodes characterized
by being connected to each other, called communities.
  Many researchers from various areas have been using computational tools to
adjust these models without, however, analyzing their suitability for the data
of the networks they are studying. The complexity involved in the estimation
process and in the goodness-of-fit verification methodologies for these models
can be factors that make the analysis of adequacy difficult and a possible
discard of one model in favor of another.
  And it is clear that the results obtained through an inappropriate model can
lead the researcher to very wrong conclusions about the phenomenon studied.
  The purpose of this work is to present a simple methodology, based on
Hypothesis Tests, to verify if there is a model specification error for these
two cases widely used in the literature to represent complex networks: the ERGM
and the SBM. We believe that this tool can be very useful for those who want to
use these models in a more careful way, verifying beforehand if the models are
suitable for the data under study.


title: Optimal Strategies for the Game of Protecting a Plane in 3-D

abstract:
  A conflict between rational and autonomous agents is considered. The paper
addresses a differential game of protecting a target in the 3-D space. This
problem highlights the strong correlation between the highly dynamic scenario,
the uncertainty on the behavior of the adversary, and the online and robust
computation of state-feedback strategies which guarantee the required level of
performance of each player. This work significantly expands previous results
around this problem by providing the players' state-feedback saddle-point
strategies. Additionally, the continuously differentiable Value function of the
multi-agent differential game is obtained and it is shown to be the solution of
the Hamilton-Jacobi-Isaacs equation. Finally, the Barrier surface is explicitly
obtained and illustrative examples highlight the robustness properties and the
guarantees provided by the saddle-point strategies obtained in this paper.


title:
Proofs and Supplementary Material: Unified Characterization and
  Precoding for Non-Stationary Channels

abstract:
  This document provides the supplementary material including a comprehensive
related work, the complete proofs and extended evaluation results that support
the manuscript, "Unified Characterization and Precoding for Non-Stationary
Channels", that was accepted for publication at IEEE International Conference
on Communications (ICC) 2022. Equations (1)--(34) refer to the equations from
the main manuscript, and the Theorem, Lemma and Corollaries correspond to those
from the manuscript.


title:
Astronomical data organization, management and access in Scientific Data
  Lakes

abstract:
  The data volumes stored in telescope archives is constantly increasing due to
the development and improvements in the instrumentation. Often the archives
need to be stored over a distributed storage architecture, provided by
independent compute centres. Such a distributed data archive requires
overarching data management orchestration. Such orchestration comprises of
tools which handle data storage and cataloguing, and steering transfers
integrating different storage systems and protocols, while being aware of data
policies and locality. In addition, it needs a common Authorisation and
Authentication Infrastructure (AAI) layer which is perceived as a single entity
by end users and provides transparent data access.
  The scientific domain of particle physics also uses complex and distributed
data management systems. The experiments at the Large Hadron Collider\,(LHC)
accelerator at CERN generate several hundred petabytes of data per year. This
data is globally distributed to partner sites and users using national compute
facilities. Several innovative tools were developed to successfully address the
distributed computing challenges in the context of the Worldwide LHC Computing
Grid (WLCG).
  The work being carried out in the ESCAPE project and in the Data
Infrastructure for Open Science (DIOS) work package is to prototype a
Scientific Data Lake using the tools developed in the context of the WLCG,
harnessing different physics scientific disciplines addressing FAIR standards
and Open Data. We present how the Scientific Data Lake prototype is applied to
address astronomical data use cases. We introduce the software stack and also
discuss some of the differences between the domains.


title:
HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly
  implicits

abstract:
  Reconstruction of high-fidelity 3D objects or scenes is a fundamental
research problem. Recent advances in RGB-D fusion have demonstrated the
potential of producing 3D models from consumer-level RGB-D cameras. However,
due to the discrete nature and limited resolution of their surface
representations (e.g., point- or voxel-based), existing approaches suffer from
the accumulation of errors in camera tracking and distortion in the
reconstruction, which leads to an unsatisfactory 3D reconstruction. In this
paper, we present a method using on-the-fly implicits of Hermite Radial Basis
Functions (HRBFs) as a continuous surface representation for camera tracking in
an existing RGB-D fusion framework. Furthermore, curvature estimation and
confidence evaluation are coherently derived from the inherent surface
properties of the on-the-fly HRBF implicits, which devote to a data fusion with
better quality. We argue that our continuous but on-the-fly surface
representation can effectively mitigate the impact of noise with its robustness
and constrain the reconstruction with inherent surface smoothness when being
compared with discrete representations. Experimental results on various
real-world and synthetic datasets demonstrate that our HRBF-fusion outperforms
the state-of-the-art approaches in terms of tracking robustness and
reconstruction accuracy.


title:
Modularization, Composition, and Hierarchization of Petri Nets with
  Heraklit

abstract:
  It is known for decades that computer-based systems cannot be understood
without a concept of modularization and decomposition. We suggest a universal,
expressive, intuitively attractive composition operator for Petri nets,
combined with a refinement concept and an algebraic representation of nets and
their composition. Case studies show exemplarily, how large systems can be
composed from tiny net snippets. In the future, more field studies are needed
to better understand the consequences of the proposed ideas in the real world.


title: Modeling Meteoroid Impacts on the Juno spacecraft

abstract:
  Events which meet certain criteria from star tracker images onboard the Juno
spacecraft have been proposed to be due to interplanetary dust particle impacts
on its solar arrays. These events have been suggested to be caused by particles
with diameters larger than 10 micrometers. Here, we compare the reported event
rates to expected dust impact rates using dynamical meteoroid models for the
four most abundant meteoroid/dust populations in the inner solar system. We
find that the dust impact rates predicted by dynamical meteoroid models are not
compatible with either the Juno observations in terms of the number of star
tracker events per day, or with the variations of dust flux on Juno's solar
panels with time and position in the solar system. For example, the rate of
star tracker events on Juno's anti-sunward surfaces is the largest during a
period during which Juno is expected to experience the peak impact fluxes on
the opposite, sunward hemisphere. We also investigate the hypothesis of dust
leaving the Martian Hill sphere originating either from the surface of Mars
itself or from one of its moons. We do not find such a hypothetical source to
be able to reproduce the star tracker event rate variations observed by Juno.
We conclude that the star tracker events observed by Juno are unlikely to be
the result of instantaneous impacts from the Zodiacal Cloud.


title:
Adversarially Robust Models may not Transfer Better: Sufficient
  Conditions for Domain Transferability from the View of Regularization

abstract:
  Machine learning (ML) robustness and domain generalization are fundamentally
correlated: they essentially concern data distribution shifts under adversarial
and natural settings, respectively. On one hand, recent studies show that more
robust (adversarially trained) models are more generalizable. On the other
hand, there is a lack of theoretical understanding of their fundamental
connections. In this paper, we explore the relationship between regularization
and domain transferability considering different factors such as norm
regularization and data augmentations (DA). We propose a general theoretical
framework proving that factors involving the model function class
regularization are sufficient conditions for relative domain transferability.
Our analysis implies that "robustness" is neither necessary nor sufficient for
transferability; rather, robustness induced by adversarial training is a
by-product of such function class regularization. We then discuss popular DA
protocols and show when they can be viewed as the function class regularization
under certain conditions and therefore improve generalization. We conduct
extensive experiments to verify our theoretical findings and show several
counterexamples where robustness and generalization are negatively correlated
on different datasets.


title: Interfacial assembly of anisotropic core-shell and hollow microgels

abstract:
  Microgels, cross-linked polymers with submicrometer size, are ideal soft
model systems. While spherical microgels have been studied extensively,
anisotropic microgels have been hardly investigated. In this study, we compare
the interfacial deformation and assembly of anisotropic core-shell and hollow
microgels. The core-shell microgel consists of an elliptical core of hematite
covered with a thin silica layer and a thin shell made of PNiPAM. The hollow
microgels were obtained after a two step etching procedure of the inorganic
core. The behavior of these microgels at the oil-water interface was
investigated in a Langmuir Blodgett trough combined with ex-situ AFM. First,
the influence of the architecture of anisotropic microgels on their spreading
at the interface was investigated experimentally and by dissipative particle
dynamic simulations. Hereby, the importance of the local shell thickness on the
lateral and longitudinal interfacial deformation was highlighted as well as the
differences between the core-shell and hollow architectures. The shape of the
compression isotherms as well as the dimensions, ordering and orientation of
the microgels at the different compressions were analysed. Due to their
anisotropic shape and stiffness, both anisotropic microgels were found to
exhibit significant capillary interactions with a preferential side-to-side
assembly leading to stable microgel clusters at low interfacial coverage. Such
capillary interactions were found to decrease in the case of the more
deformable hollow anisotropic microgels. Consequently, anisotropic hollow
microgels were found to distribute more evenly at high surface pressure
compared to stiffer core-shell microgels. Our findings emphasize the complex
interplay between the colloid design, anisotropy and softness on the
interfacial assembly and the opportunities it thus offers to create more
complex ordered interfaces.


title:
The exact solution of the Axial Next-Nearest-Neighbor Ising model on
  width 2 spin chains

abstract:
  Using the transfer matrix method for Axial Next-Nearest Neighbor Ising model
without an external field on a closed chain of spins of width 2 in the
direction of interaction of only nearest neighbors and length L in the
direction of interaction of nearest neighbors and next nearest neighbors, we
found exact values of the partition function in a finite strip of length L ,
free energy, internal energy per node, heat capacity in a finite strip of
length L and in the thermodynamic limit. The entire spectrum of the 16x16
transfer matrix and the structure of all transfer matrix eigenvectors are
found. The problem of finding the spectrum of the transfer matrix is reduced to
finding the spectrum of two matrixes of size 8x8. The characteristic polynomial
of the first matrix is decomposed into two linear polynomials of multiplicity
two and two square trinomials. The characteristic polynomial of the second
matrix is decomposed into two polynomials of the fourth degree, the roots of
which are found by the Ferrari method. The free energy in the thermodynamic
limit has a very simple form and is expressed in terms of the logarithm of the
root of the quadratic equation. Two theorems on the physical characteristics of
the ANNNI model in a finite closed strip of length L and in the thermodynamic
limit are formulated and proved. An example of calculation is given for some
values of the model parameters in the thermodynamic limit, heat capacity graphs
are plotted. A comparative analysis of the heat capacity for the
double-stranded ANNNI model and the ANNNI model on the entire flat lattice is
given. The closest connection and similarity of the physical characteristics of
the two-chain model and the model on the entire flat lattice is shown.


title:
Static solutions to the spherically symmetric Einstein-Vlasov system: a
  particle-number-Casimir approach

abstract:
  Existence of spherically symmetric solutions to the Einstein-Vlasov system is
well-known. However, it is an open problem whether or not static solutions
arise as minimizers of a variational problem. Apart from being of interest in
its own right, it is the connection to non-linear stability that gives this
topic its importance. This problem was considered in \cite{Wol}, but as has
been pointed out in \cite{AK}, the paper \cite{Wol} contained serious flaws. In
this work we construct static solutions by solving the Euler-Lagrange equation
for the energy density $\rho$ as a fixed point problem. The Euler-Lagrange
equation originates from the particle number-Casimir functional introduced in
\cite{Wol}. We then define a density function $f$ on phase space which induces
the energy density $\rho$ and we show that it constitutes a static solution of
the Einstein-Vlasov system. Hence we settle rigorously parts of what the author
of \cite{Wol} attempted to prove.


title: Some recent progress on the stationary measure for the open KPZ equation

abstract:
  This note, dedicated in Harold Widom's memory, is an expanded version of a
lecture I gave in fall 2021 at the MSRI program "Universality and Integrability
in Random Matrices and Interacting Particle Systems". I will focus on the
behavior of the stationary measure for the open KPZ equation, a paradigmatic
model for interface growth in contact with boundaries. Much of this will review
elements of my joint work with A. Knizel as well as with H. Shen, as well as
subsequent works of W. Bryc, A. Kuznetsov, Y. Wang, and J. Wesolowski and of G.
Barraquand and P. Le Doussal. The basis for this advance is fundamental work of
B. Derrida, M. Evans, V. Hakim and V. Pasquier from 1993, of T. Sasamoto, M.
Uchiyama and M. Wadati from 2003, and of W. Bryc and J. Wesolowski from 2010
and 2017. I will try to explain how all of this fits together, without laboring
details for the sake of exposition.
  Though this work does not directly follow from Harold Widom's own work, it
(and a great deal of my research) is very much inspired by his and Craig
Tracy's work on ASEP.


title:
Oscillation of the remainder term in the prime number theorem of
  Beurling, "caused by a given zeta-zero"

abstract:
  Continuing previous study of the Beurling zeta function, here we prove two
results, generalizing long existing knowledge regarding the classical case of
the Riemann zeta function and some of its generalizations.
  First, we address the question of Littlewood, who asked for explicit
oscillation results provided a zeta-zero is known. We prove that given a zero
$\rho_0$ of the Beurling zeta function $\zeta_P$ for a given number system
generated by the primes $P$, the corresponding error term
$\Delta_P(x):=\psi_{P}(x)-x$, where $\psi_{P}(x)$ is the von Mangoldt summatory
function shows oscillation in any large enough interval, as large as
$(\pi/2-\varepsilon) x^{\Re \rho_0}/|\rho_0|$.
  The somewhat mysterious appearance of the constant $\pi/2$ is explained in
the study. Finally, we prove as the next main result of the paper the
following: given $\varepsilon>0$, there exists a Beurling number system with
primes $P$, such that $|\Delta_P(x)| \le (\pi/2+\varepsilon)x^{\Re
\rho_0}/|\rho_0|$.
  In this second part a nontrivial construction of a low norm sine polynomial
is coupled by the application of the wonderful recent prime random
approximation result of Broucke and Vindas, who sharpened the breakthrough
probabilistic construction due to Diamond, Montgomery and Vorhauer.