title: Anticanonical models of smoothings of cyclic quotient singularities

abstract:
  Given a surface cyclic quotient singularity $Q\in Y$, it is an open problem
to determine all smoothings of $Y$ that admit an anticanonical model and to
compute it. In [HTU], Hacking, Tevelev, and Urz\'ua studied certain irreducible
components of the versal deformation space of $Y$, and within these components,
they found one parameter smoothings $\mathcal{Y} \to \mathbb{A}^1$ that admit
an anticanonical model and proved that they have canonical singularities.
Moreover, they compute explicitly the anticanonical models that have terminal
singularities using Mori's division algorithm [M02]. We study one parameter
smoothings in these components that admit an anticanonical model with canonical
but non-terminal singularities with the goal of classifying them completely. We
identify certain class of "diagonal" smoothings where the total space is a
toric threefold and we construct the anticanonical model explicitly using the
toric MMP.


title:
Regularization-based Pruning of Irrelevant Weights in Deep Neural
  Architectures

abstract:
  Deep neural networks exploiting millions of parameters are nowadays the norm
in deep learning applications. This is a potential issue because of the great
amount of computational resources needed for training, and of the possible loss
of generalization performance of overparametrized networks. We propose in this
paper a method for learning sparse neural topologies via a regularization
technique which identifies non relevant weights and selectively shrinks their
norm, while performing a classic update for relevant ones. This technique,
which is an improvement of classical weight decay, is based on the definition
of a regularization term which can be added to any loss functional regardless
of its form, resulting in a unified general framework exploitable in many
different contexts. The actual elimination of parameters identified as
irrelevant is handled by an iterative pruning algorithm. We tested the proposed
technique on different image classification and Natural language generation
tasks, obtaining results on par or better then competitors in terms of sparsity
and metrics, while achieving strong models compression.


title:
Daily Generation Scheduling : Decomposition Methods to Solve the
  Hydraulic Problems

abstract:
  Short-term hydro-generation management poses a non-convex or even
non-continuous optimization problem. For this reason, the problem of
systematically obtaining feasible and economically satisfying solutions has not
yet been completely solved. Two decomposition methods, which, as far as we
know, have not been applied in this field, are here proposed : $\bullet$ the
first is based on a decomposition by prediction method and the coordination is
a primal-dual relaxation algorithm, $\bullet$ handling the dynamic constraints
by duality, the second achieves a price decomposition by an Augmented
Lagrangian technique. Numerical tests show the efficiency of these algorithms.
They will enable the process in use at Electricit{\'e} de France to be
improved.


title:
General Nilpotent and Solvable Approximations of Almost-Riemannian
  Structures

abstract:
  It is first shown that the nilpotent or the solvable approximation of an
almost-Riemannian structure at a singular point is always a linear
almost-Riemannian structure on a Lie group or a homogeneous space. The generic
properties of almost-Riemannian structures are then investigated in all
dimensions and the generic nilpotent and solvable approximations are
identified.


title:
A Comparative Study of Pre-trained Encoders for Low-Resource Named
  Entity Recognition

abstract:
  Pre-trained language models (PLM) are effective components of few-shot named
entity recognition (NER) approaches when augmented with continued pre-training
on task-specific out-of-domain data or fine-tuning on in-domain data. However,
their performance in low-resource scenarios, where such data is not available,
remains an open question. We introduce an encoder evaluation framework, and use
it to systematically compare the performance of state-of-the-art pre-trained
representations on the task of low-resource NER. We analyze a wide range of
encoders pre-trained with different strategies, model architectures,
intermediate-task fine-tuning, and contrastive learning. Our experimental
results across ten benchmark NER datasets in English and German show that
encoder performance varies significantly, suggesting that the choice of encoder
for a specific low-resource scenario needs to be carefully evaluated.


title: Empirical Bayes inference for the block maxima method

abstract:
  The block maxima method is one of the most popular approaches for extreme
value analysis with independent and identically distributed observations in the
domain of attraction of an extreme value distribution. The lack of a rigorous
study on the Bayesian inference in this context has limited its use for
statistical analysis of extremes. In this paper we propose an empirical Bayes
procedure for inference on the block maxima law and its related quantities. We
show that the posterior distributions of the tail index of the data
distribution and of the return levels (representative of future extreme
episodes) satisfy a number of important theoretical properties. These guarantee
the reliability of posterior-based inference and extend to the posterior
predictive distribution, the key tool in Bayesian probabilistic forecasting.
Posterior computations are readily obtained via an efficient adaptive
Metropolis-Hasting type of algorithm. Simulations show its excellent
inferential performances already with modest sample sizes. The utility of our
proposal is showcased analysing extreme winds generated by hurricanes in the
Atlantic basin.


title:
Comparison of Two Methods for Calculating Magnetic Helicity in the Solar
  Corona

abstract:
  Duo to the large magnetic Reynolds number, the magnetic helicity originating
from the solar interior can be carried away through the photosphere into the
corona. However, the relationship between the accumulated magnetic helicity
flux through the photosphere and the magnetic helicity in the corona is still
unclear. By selecting 36 newly emerging active regions in the 23rd solar cycle,
we apply optical flow methods to derive the accumulated magnetic helicity
through the photosphere ($H_m^p$) by using the sequential longitudinal
magnetograms, use nonlinear force-free field extrapolation to obtain the 3D
coronal magnetic field, and adopt finite volume methods to calculate the
instantaneous relative magnetic helicity in the corona ($H_m^c$) by using
vector magnetograms. It is found that the local correlation tracking
(LCT)-based $H_m^p$ is larger than $H_m^c$ in $1"$, and that the Differential
Affine Velocity Estimator-based $H_m^p$ is more consistent with $H_m^c$ than
the LCT-based $H_m^p$. $H_m^p$ is more consistent with $H_m^c$ in evaluation
from $2"$ than from $1"$. Moreover, $H_m^c - H_m^p$ systematically shows
consistency with the Hemispheric Helicity Rule (over 55\%), no matter which
resolution and method are used. These estimations suggest that the consistency
of $H_m^c$ and $H_m^p$ is partly dependent on the resolution of the
magnetograms and the calculation methods.


title: T- Hop: Tensor representation of paths in graph convolutional networks

abstract:
  We describe a method for encoding path information in graphs into a 3-d
tensor. We show a connection between the introduced path representation scheme
and powered adjacency matrices. To alleviate the heavy computational demands of
working with the 3-d tensor, we propose to apply dimensionality reduction on
the depth axis of the tensor. We then describe our the reduced 3-d matrix can
be parlayed into a plausible graph convolutional layer, by infusing it into an
established graph convolutional network framework such as MixHop.


title: Compact Objects in Alternative Gravities

abstract:
  We address neutron stars and black holes in alternative gravities, after
recalling their basic properties in General Relativity. Among the plethora of
interesting alternative gravities we here focus on an interesting set of
scalar-tensor theories. We discuss the phenomenon of spontaneous scalarization,
that is matter induced for neutron stars and curvature induced for black holes.
Along with other relevant physical properties, we address the quasi-normal
modes of these compact objects. In particular, we consider \textit{universal
relations} of neutron stars to largely reduce the dependence on the equation of
state, and we briefly address the shadow of black holes.


title:
Nonlinear optical response of ferroelectric oxides: first-principles
  calculations within the time-domain and the frequency-domain

abstract:
  The second and third order nonlinear susceptibilities of the ferroelectric
oxides LiNbO$_3$, LiTaO$_3$, and KNbO$_3$ are calculated from first principles.
Two distinct methodologies are compared, one approach is based on a
perturbative approach within the frequency-domain, another on the
time-evolution of the electric polarization. The frequency dependence of the
second harmonic coefficients of the ferroelectric phase of LiNbO$_3$ calculated
within the two approaches is in excellent agreement. This is further validated
by experimental data for LiNbO$_3$ and LiTaO$_3$, measured for an incident
range of photon energies between 0.78 eV and 1.6 eV. The real-time based
approach is furthermore employed to estimate the third order nonlinear
susceptibilities of all investigated ferroelectric oxides. We further show that
the quasiparticle effects, considered by means of a scissors-shift in
combination with the the computationally efficient independent particle
approximation, result in a shift all spectral features towards higher energies
and decrease the magnitude of the optical nonlinearities. The energy of the
main resonances in the hyperpolarizabilities suggests that the spectra can be
understood by multi-photon adsorption within the fundamental bandgap for all
investigated materials.


title:
Multi-Objective Yield Optimization for Electrical Machines using Machine
  Learning

abstract:
  This work deals with the design optimization of electrical machines under the
consideration of manufacturing uncertainties. In order to efficiently quantify
the uncertainty, blackbox machine learning methods are employed. A
multi-objective optimization problem is formulated, maximizing simultaneously
the reliability, i.e., the yield, and further performance objectives, e.g., the
costs. A permanent magnet synchronous machine is modeled and simulated in
commercial finite element simulation software. Four approaches for solving the
multi-objective optimization problem are described and numerically compared,
namely: epsilon-constraint scalarization, weighted sum scalarization, a
multi-start weighted sum approach and a genetic algorithm.


title: A Dual Sensor Computational Camera for High Quality Dark Videography

abstract:
  Videos captured under low light conditions suffer from severe noise. A
variety of efforts have been devoted to image/video noise suppression and made
large progress. However, in extremely dark scenarios, extensive photon
starvation would hamper precise noise modeling. Instead, developing an imaging
system collecting more photons is a more effective way for high-quality video
capture under low illuminations. In this paper, we propose to build a
dual-sensor camera to additionally collect the photons in NIR wavelength, and
make use of the correlation between RGB and near-infrared (NIR) spectrum to
perform high-quality reconstruction from noisy dark video pairs. In hardware,
we build a compact dual-sensor camera capturing RGB and NIR videos
simultaneously. Computationally, we propose a dual-channel multi-frame
attention network (DCMAN) utilizing spatial-temporal-spectral priors to
reconstruct the low-light RGB and NIR videos. In addition, we build a
high-quality paired RGB and NIR video dataset, based on which the approach can
be applied to different sensors easily by training the DCMAN model with
simulated noisy input following a physical-process-based CMOS noise model. Both
experiments on synthetic and real videos validate the performance of this
compact dual-sensor camera design and the corresponding reconstruction
algorithm in dark videography.


title:
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement
  Learning Approach

abstract:
  In real-world decision optimization, often multiple competing objectives must
be taken into account. Following classical reinforcement learning, these
objectives have to be combined into a single reward function. In contrast,
multi-objective reinforcement learning (MORL) methods learn from vectors of
per-objective rewards instead. In the case of multi-policy MORL, sets of
decision policies for various preferences regarding the conflicting objectives
are optimized. This is especially important when target preferences are not
known during training or when preferences change dynamically during
application. While it is, in general, straightforward to extend a
single-objective reinforcement learning method for MORL based on linear
scalarization, solutions that are reachable by these methods are limited to
convex regions of the Pareto front. Non-linear MORL methods like Thresholded
Lexicographic Ordering (TLO) are designed to overcome this limitation.
Generalized MORL methods utilize function approximation to generalize across
objective preferences and thereby implicitly learn multiple policies in a
data-efficient manner, even for complex decision problems with high-dimensional
or continuous state spaces. In this work, we propose \textit{generalized
Thresholded Lexicographic Ordering} (gTLO), a novel method that aims to combine
non-linear MORL with the advantages of generalized MORL. We introduce a deep
reinforcement learning realization of the algorithm and present promising
results on a standard benchmark for non-linear MORL and a real-world
application from the domain of manufacturing process control.


title: Lower semicontinuity in $GSBD$ for nonautonomous surface integrals

abstract:
  We provide a sufficient condition for lower semicontinuity of nonautonomous
noncoercive surface energies defined on the space of $GSBD^p$ functions, whose
dependence on the $x$-variable is $W^{1,1}$ or even $BV$: the notion of
nonautonomous symmetric joint convexity, which extends the analogous definition
devised for autonomous integrands in \cite{FPS} where the conservativeness of
the approximating vector fields is assumed. This condition allows to extend to
our setting a nonautonomous chain formula in $SBV$ obtained in \cite{ACDD}, and
this is a key tool in the proof of the lower semicontinuity result. This new
joint convexity can be checked explicitly for some classes of surface energies
arising from variational models of fractures in inhomogeneous materials.


title:
The difference between port-Hamiltonian, passive and positive real
  descriptor systems

abstract:
  The relation between passive and positive real systems has been extensively
studied in the literature. In this paper, we study their connection to the more
recently used notion of port-Hamiltonian descriptor systems. It is well-known
that port-Hamiltonian systems are passive and that passive systems are positive
real. Hence it is studied under which assumptions the converse implications
hold. Furthermore, the relationship between passivity, KYP inequalities and
a~finite available storage is investigated.


title: TRUE: Re-evaluating Factual Consistency Evaluation

abstract:
  Grounded text generation systems often generate text that contains factual
inconsistencies, hindering their real-world applicability. Automatic factual
consistency evaluation may help alleviate this limitation by accelerating
evaluation cycles, filtering inconsistent outputs and augmenting training data.
While attracting increasing attention, such evaluation metrics are usually
developed and evaluated in silo for a single task or dataset, slowing their
adoption. Moreover, previous meta-evaluation protocols focused on system-level
correlations with human annotations, which leave the example-level accuracy of
such metrics unclear. In this work, we introduce TRUE: a comprehensive survey
and assessment of factual consistency metrics on a standardized collection of
existing texts from diverse tasks, manually annotated for factual consistency.
Our standardization enables an example-level meta-evaluation protocol that is
more actionable and interpretable than previously reported correlations,
yielding clearer quality measures. Across diverse state-of-the-art metrics and
11 datasets we find that large-scale NLI and question
generation-and-answering-based approaches achieve strong and complementary
results. We recommend those methods as a starting point for model and metric
developers, and hope TRUE will foster progress towards even better evaluation
methods.


title:
Double Nonstationarity: Blind Extraction of Independent Nonstationary
  Vector/Component from Nonstationary Mixtures -- Algorithms

abstract:
  In this article, nonstationary mixing and source models are combined for
developing new fast and accurate algorithms for Independent Component or Vector
Extraction (ICE/IVE), one of which stands for a new extension of the well-known
FastICA. This model allows for a moving source-of-interest (SOI) whose
distribution on short intervals can be (non-)circular (non-)Gaussian. A
particular Gaussian source model assuming tridiagonal covariance matrix
structures is proposed. It is shown to be beneficial in the frequency-domain
speaker extraction problem. The algorithms are verified in simulations. In
comparison to the state-of-the-art algorithms, they show superior performance
in terms of convergence speed and extraction accuracy.


title: Ischemic Stroke Lesion Segmentation Using Adversarial Learning

abstract:
  Ischemic stroke occurs through a blockage of clogged blood vessels supplying
blood to the brain. Segmentation of the stroke lesion is vital to improve
diagnosis, outcome assessment and treatment planning. In this work, we propose
a segmentation model with adversarial learning for ischemic lesion
segmentation. We adopt U-Net with skip connection and dropout as segmentation
baseline network and a fully connected network (FCN) as discriminator network.
Discriminator network consists of 5 convolution layers followed by leaky-ReLU
and an upsampling layer to rescale the output to the size of the input map.
Training a segmentation network along with an adversarial network can detect
and correct higher order inconsistencies between the segmentation maps produced
by ground-truth and the Segmentor. We exploit three modalities (CT, DPWI, CBF)
of acute computed tomography (CT) perfusion data provided in ISLES 2018
(Ischemic Stroke Lesion Segmentation) for ischemic lesion segmentation. Our
model has achieved dice accuracy of 42.10% with the cross-validation of
training and 39% with the testing data.


title: Arthur's Conjectures and the Orbit Method for Real Reductive Groups

abstract:
  The first half of this article is expository -- I will review, with examples,
the main statements of the Langlands classification and Arthur's conjectures
for real reductive groups as formulated by Adams, Barbasch, and Vogan. In the
second half, I will turn my attention to the Orbit Method, a conjectural scheme
for classifying irreducible unitary representations of a real reductive group.
I will give a definition of the Orbit Method in the case when the group is
complex. The main input is the theory of unipotent ideals and Harish-Chandra
bimodules, developed in arXiv:2108.03453. I will show that the Orbit Method I
define is related to Arthur's conjectures via a natural duality map. Finally, I
will sketch a possible generalization of this Orbit Method for arbitrary real
groups.


title:
A simple catch: thermal fluctuations enable hydrodynamic trapping of
  microrollers by obstacles

abstract:
  In order to leverage colloidal swimmers in microfluidic and drug-delivery
applications, it is crucial to understand their interaction with obstacles.
Previous studies have shown that this interaction can result in the
hydrodynamic trapping in orbits around cylindrical obstacles, where the
trapping strength heavily depends on the obstacle geometry and the flow field
of the swimmer, and that Brownian motion is needed to escape the trap. Here, we
use both experiments and simulations to investigate the interaction of driven
microrollers with cylindrical obstacles. Microrollers have a unique flow field
and a prescribed propulsion direction; the flow field that drives their motion
is quite different than previously-studied swimmers. We observe
curvature-dependent hydrodynamic trapping of these microrollers in the wake of
an obstacle, and show explicitly the mechanism for this trapping. More
interestingly, we find that these rollers need Brownian motion to both leave
$\mathit{and}$ enter the trap. By tuning both the obstacle curvature and the
Debye length of the roller suspension, the trapping strength can be tuned over
three orders of magnitude. Our findings suggest that the trapping of
fluid-pumping swimmers is generic and encourages the study of swimmers with
other flow fields and their interaction with obstacles.


title: An octonionic construction of $E_8$ and the Lie algebra magic square

abstract:
  We give a new construction of the Lie algebra of type $E_8$, in terms of
$3\times3$ matrices, such that the Lie bracket has a natural description as the
matrix commutator. This leads to a new interpretation of the Freudenthal-Tits
magic square of Lie algebras, acting on themselves by commutation.


title:
Oscillation growth in mixed traffic flow of human driven vehicles and
  automated vehicles: Experimental study and simulation

abstract:
  This paper reports an experimental study on oscillation growth in mixed
traffic flow of automated vehicles (AVs) and human driven vehicles (HVs). The
leading vehicle moves with constant speed in the experiment. The following
vehicles consist of six developable AVs and different number of HVs. Thus, the
market penetration rate (MPR) of AVs decreases with the increase of platoon
size. The AVs are homogeneously distributed in the platoon. The constant time
gap car-following policy is adopted for the AVs and the gap is set to 1.5 s.
The experiment shows that in the 7-vehicle-platoon, the oscillations grow only
slightly. In the 10-vehicle-platoon, the AVs could still significantly suppress
the growth of oscillations. With the further decrease of MPR of AVs in the 13-
and 20-vehicle-platoon, the AVs become having no significant impact on
oscillation growth. On the other hand, with the decrease of MPR of AVs, average
density of the vehicles and flow rate of the platoon increase, which
demonstrates a trade-off between traffic stability and throughput under the
given setup of AVs. The simulation study is also carried out, which exhibits
good agreement with the experiment. Finally, sensitivity analysis of the
parameters in the AV upper-level control algorithm has been performed, which is
expected to guide future experiment design.


title:
Team \'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe
  for predicting Eye-Tracking features using Pretrained Language Models

abstract:
  Eye-Tracking data is a very useful source of information to study cognition
and especially language comprehension in humans. In this paper, we describe our
systems for the CMCL 2022 shared task on predicting eye-tracking information.
We describe our experiments with pretrained models like BERT and XLM and the
different ways in which we used those representations to predict four
eye-tracking features. Along with analysing the effect of using two different
kinds of pretrained multilingual language models and different ways of pooling
the tokenlevel representations, we also explore how contextual information
affects the performance of the systems. Finally, we also explore if factors
like augmenting linguistic information affect the predictions. Our submissions
achieved an average MAE of 5.72 and ranked 5th in the shared task. The average
MAE showed further reduction to 5.25 in post task evaluation.


title: Thermal insulation and heat guiding using nanopatterned MoS2

abstract:
  In the modern electronics overheating is one of the major reasons for device
failure. Overheating causes irreversible damage to circuit components and can
also lead to fire, explosions, and injuries. Accordingly, in the advent of 2D
material-based electronics, an understanding of their thermal properties in
addition to their electric ones is crucial to enable efficient transfer of
excess heat away from the electronic components. In this work we propose
structures based on free-standing, few-layer, nanopatterned MoS2 that insulate
and guide heat in the in-plane direction. We arrive at these designs via a
thorough study of the in-plane thermal conductivity as a function of thickness,
porosity, and temperature in both pristine and nanopatterned MoS2 membranes.
Two-laser Raman thermometry was employed to measure the thermal conductivities
of a set of free-standing MoS2 flakes with diameters greater than 20 um and
thicknesses from 5 to 40 nm, resulting in values from 30 to 85 W/mK,
respectively. After nanopatterning a square lattice of 100-nm diameter holes
with a focused ion beam we have obtained a greater than 10-fold reduction of
the thermal conductivities for the period of 500 nm and values below 1 W/mK for
the period of 300 nm. The results were supported by equilibrium molecular
dynamic simulations for both pristine and nanopatterned MoS2. The selective
patterning of certain areas results in extremely large difference in thermal
conductivities within the same material. Exploitation of this effect enabled
for the first time thermal insulation and heat guiding in the few-layer MoS2.
The patterned regions act as high thermal resistors: we obtained a thermal
resistance of 4x10-6 m2K/W with only four patterned lattice periods of 300 nm,
highlighting the significant potential of MoS2 for thermal management
applications.


title: Topological polarization networking in uniaxial ferroelectrics

abstract:
  Discovery of topological polarization textures has put ferroelectrics at the
frontier of topological matter science. High-symmetry ferroelectric oxide
materials allowing for freedom of the polarization vector rotation offer a
fertile ground for emergent topological polar formations, like vortices,
skyrmions, merons, and Hopfions. It has been commonly accepted that uniaxial
ferroelectrics do not belong in the topological universe because strong
anisotropy imposes insurmountable energy barriers for topological excitations.
Here we show that uniaxial ferroelectrics provide unique opportunity for the
formation of topological polarization networks comprising branching intertwined
domains with opposite counterflowing polarization. We report that they host the
topological state of matter: a crisscrossing structure of topologically
protected colliding head-to-head and tail-to-tail polarization domains, which
for decades has been considered impossible from the electrostatic viewpoint.
The domain wall interfacing the counterflowing domains is a multiconnected
surface, propagating through the whole volume of the ferroelectric.


title:
Radial evolution of thermal and suprathermal electron populations in the
  slow solar wind from 0.13 to 0.5 au : Parker Solar Probe Observations

abstract:
  We develop and apply a bespoke fitting routine to a large volume of solar
wind electron distribution data measured by Parker Solar Probe (PSP) over its
first five orbits, covering radial distances from 0.13 to 0.5 au. We
characterise the radial evolution of the electron core, halo and strahl
populations in the slow solar wind during these orbits. The fractional
densities of these three electron populations provide evidence for the growth
of the combined suprathermal halo and strahl populations from 0.13 to 0.17 au.
Moreover, the growth in the halo population is not matched by a decrease of the
strahl population at these distances, as has been reported for previous
observations at distances greater than 0.3 au. We also find that the halo is
negligible at small heliocentric distances. The fractional strahl density
remains relatively constant ~1 % below 0.2 au, suggesting that the rise in the
relative halo density is not solely due to the transfer of strahl electrons
into the halo.

categories: astro-ph.SR astro-ph.EP physics.plasm-ph physics.space-ph


title:
Fast evaluation of B-spline functions and rendering of multiple B-spline
  curves using linear-time algorithm for computing the Bernstein-B\'{e}zier
  coefficients of B-spline functions

abstract:
  A new differential-recurrence relation for the B-spline functions of the same
degree is proved. From this relation, a recursive method of computing the
coefficients of B-spline functions of degree $m$ in the Bernstein-B\'{e}zier
form is derived. Its complexity is proportional to the number of coefficients
in the case of coincident boundary knots. This means that, asymptotically, the
algorithm is optimal. In other cases, the complexity is increased by at most
$O(m^3)$. When the Bernstein-B\'{e}zier coefficients of B-spline basis
functions are known, it is possible to compute any B-spline function in linear
time with respect to its degree by performing the geometric algorithm proposed
recently by the authors. Using a similar approach, one can also convert a
$d$-dimensional B-spline curve of degree $m$ over one knot span to a B\'{e}zier
curve in $O(m^2)$ time and then evaluate it in $O(md)$ time. Since one only
needs to convert each knot span once, this algorithm scales well when
evaluating the B-spline curve at multiple points, e.g., in order to render it.
When evaluating many B-spline curves at multiple points, such approach has
lower computational complexity than using the de Boor-Cox algorithm. The
problem of finding the coefficients of the B-spline functions in the power
basis can be solved similarly.


title:
Local convergence rates of the least squares estimator with applications
  to transfer learning

abstract:
  Convergence properties of empirical risk minimizers can be conveniently
expressed in terms of the associated population risk. To derive bounds for the
performance of the estimator under covariate shift, however, pointwise
convergence rates are required. Under weak assumptions on the design
distribution, it is shown that the least squares estimator (LSE) over
$1$-Lipschitz functions is also minimax rate optimal with respect to a weighted
uniform norm, where the weighting accounts in a natural way for the
non-uniformity of the design distribution. This moreover implies that although
least squares is a global criterion, the LSE turns out to be locally adaptive.
We develop a new indirect proof technique that establishes the local
convergence behavior based on a carefully chosen local perturbation of the LSE.
These local rates are then used to construct a rate-optimal estimator for
transfer learning under covariate shift.


title:
Rota-Baxter operators on Clifford semigroups and the Yang-Baxter
  equation

abstract:
  In this paper, we introduce the theory of Rota-Baxter operators on Clifford
semigroups, useful tools for obtaining dual weak braces, i.e., triples
$\left(S,+,\circ\right)$ where $\left(S,+\right)$ and $\left(S,\circ\right)$
are Clifford semigroups such that $a\circ\left(b+c\right) = a\circ b - a
+a\circ c$ and $a\circ a^- = -a+a$, for all $a,b,c\in S$. To each algebraic
structure is associated a set-theoretic solution of the Yang-Baxter equation
that has a behaviour near to the bijectivity and non-degeneracy. Drawing from
the theory of Clifford semigroups, we provide methods for constructing dual
weak braces and deepen some structural aspects, including the notion of ideal.


title:
Sol-gel transition induced by alumina nanoparticles in a model pulmonary
  surfactant

abstract:
  Inhaled airborne particles smaller than 100 nm entering the airways have been
shown to deposit in significant amount in the alveolar region of the lungs. The
interior of the alveoli is covered with a ~ 1 micron thick lining fluid, called
pulmonary surfactant. Inhaled nanoparticles are susceptible to interact with
the lung fluid and modify pulmonary functions. Here we evaluate the structural
and rheological properties of the pulmonary surfactant substitute Curosurf
which is administered to premature babies for the treatment of respiratory
distress syndrome. Curosurf is considered a reliable model of endogenous
pulmonary surfactant in terms of composition, structure and function. Using
active microrheology based on magnetically actuated wires, we find that
Curosurf dispersions exhibit a Newtonian behavior at lipid concentration from 0
to 80 g L-1, and that the viscosity follows the Krieger-Dougherty law observed
for a wide variety of colloids. Upon addition of 40 nm alumina nanoplatelets, a
significant change of the Curosurf rheology is noticed. The dispersions then
enter a soft solid phase characterized by an infinite viscosity and a non-zero
equilibrium elastic modulus. The sol-gel transition induced by the
nanoparticles is interpreted as the result of the alumina/vesicle interaction,
which are illustrated by transmission electron microscopy. It also suggests a
potential toxicity associated with the modification of the lung fluid
structural and dynamical properties.


title:
The observable properties of cool winds from galaxies, AGN, and star
  clusters -- II. 3D models for the multiphase wind of M82

abstract:
  Galactic winds are a crucial player in galaxy formation and evolution, but
observations of them have proven extraordinarily difficult to interpret,
leaving large uncertainties even in basic quantities such as mass outflow
rates. Part of this uncertainty arises from the relatively simplistic models to
which complex wind observations are often fit, which inevitably discard much of
the available information. Here we present an analysis of the wind of the
nearby dwarf starburst galaxy M82 using a semi-analytic model that is able to
take advantage of the full three-dimensional information present in
position-position-velocity data cubes measured in the Hi 21 cm line, the CO 2-1
line, and the Ha line. Our best-fitting model produces position-dependent
spectra in good agreement with the observations, and shows that the total wind
mass flux in the atomic and molecular phases is approx 10 M_sun yr-1
(corresponding to a mass loading factor of about 2 - 3), with less than a
factor of two uncertainty; the mass flux in the warm ionised phase is more
poorly constrained, and may be comparable to or smaller than this. At least
over the few kpc off the plane for which we trace the outflow, it appears to be
a wind escaping the galaxy, rather than a fountain that falls back. Our fits
require that clouds of cool gas entrained into the wind expand only modestly,
suggesting they are magnetically confined. Finally, we demonstrate that
attempts to model the wind using simplifying assumptions such as instantaneous
acceleration and a constant terminal wind speed can yield significantly
erroneous results.


title: HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

abstract:
  Monocular omnidirectional depth estimation is receiving considerable research
attention due to its broad applications for sensing 360{\deg} surroundings.
Existing approaches in this field suffer from limitations in recovering small
object details and data lost during the ground-truth depth map acquisition. In
this paper, a novel monocular omnidirectional depth estimation model, namely
HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder)
architecture whose modules are efficiently designed to mitigate distortion and
computational cost, without performance degradation. Firstly, we design a
feature pyramid network based on the HNet block to extract high-resolution
features near the edges. The performance is further improved, benefiting from a
self and cross attention layer and spatial/temporal patches in the Transformer
encoder and decoder, respectively. Besides, a spatial residual block is
employed to reduce the number of parameters. By jointly passing the deep
features extracted from an input image at each backbone block, along with the
raw depth maps predicted by the transformer encoder-decoder, through a context
adjustment layer, our model can produce resulting depth maps with better visual
quality than the ground-truth. Comprehensive ablation studies demonstrate the
significance of each individual module. Extensive experiments conducted on
three datasets; Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE
can achieve state-of-the-art performance for 360{\deg} monocular depth
estimation.


title: Cover times in dynamical systems

abstract:
  Given a one-dimensional dynamical system we study its cover time, which
quantifies the rate at which orbits become dense in the state space. Using
transfer operator tools for dynamical systems with holes and inducing
techniques, for a wide class of uniformly hyperbolic and non-uniformly
hyperbolic systems we obtain an asymptotic formula for the expected cover time
in terms of the decay rate of the measure of the ball of minimum measure.
Applications include the Gauss map and Manneville-Pomeau maps.


title:
VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for
  Low-Power Embedded Devices

abstract:
  Edge-computing requires high-performance energy-efficient embedded systems.
Fixed-function or custom accelerators, such as FFT or FIR filter engines, are
very efficient at implementing a particular functionality for a given set of
constraints. However, they are inflexible when facing application-wide
optimizations or functionality upgrades. Conversely, programmable cores offer
higher flexibility, but often with a penalty in area, performance, and, above
all, energy consumption. In this paper, we propose VWR2A, an architecture that
integrates high computational density and low power memory structures (i.e.,
very-wide registers and scratchpad memories). VWR2A narrows the energy gap with
similar or better performance on FFT kernels with respect to an FFT
accelerator. Moreover, VWR2A flexibility allows to accelerate multiple kernels,
resulting in significant energy savings at the application level.


title: Certified Reduced Basis Method for the Damped Wave Equations on Networks

abstract:
  In this paper we present a reduced basis method which yields
structure-preservation and a tight a posteriori error bound for the simulation
of the damped wave equations on networks. The error bound is based on the
exponential decay of the energy inside the system and therefore allows for
sharp bounds without the need of regularization parameters. The fast
convergence of the reduced solution to the truth solution as well as the
tightness of the error bound are verified numerically using an academic network
as example.


title: FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

abstract:
  Although remarkable progress has been made by the existing federated learning
(FL) platforms to provide fundamental functionalities for development, these
platforms cannot well tackle the challenges brought by the heterogeneity of FL
scenarios from both academia and industry. To fill this gap, in this paper, we
propose a flexible federated learning platform, named FederatedScope, for
handling various types of heterogeneity in FL. Considering both flexibility and
extensibility, FederatedScope adopts an event-driven architecture to frame an
FL course into event-handler pairs: the behaviors of participants are described
in handlers, and triggered by events of message passing or meeting certain
conditions in training. For a new FL application, developers only need to
specify the adopted FL algorithm by defining new types of events and the
corresponding handling functions based on participants' behaviors, which would
be automatically executed in an asynchronous way for balancing effectiveness
and efficiency in FederatedScope. Meanwhile, towards an easy-to-use platform,
FederatedScope provides rich built-in algorithms, including personalization,
federated aggregation, privacy protection, and privacy attack, for users to
conveniently customize participant-specific training, fusing, aggregating, and
protecting. Besides, a federated hyperparameter optimization module is
integrated into FederatedScope for users to automatically tune their FL systems
for resolving the unstable issues brought by heterogeneity. We conduct a series
of experiments on the provided easy-to-use and comprehensive FL benchmarks to
validate the correctness and efficiency of FederatedScope. We have released
FederatedScope for users on https://github.com/alibaba/FederatedScope to
promote research and industrial deployment of federated learning in a variety
of real-world applications.


title: Primitives of continuous functions via polynomials

abstract:
  We present an elementary self-contained folkloristic proof, using limits of
primitives of Bernstein polynomials, for the existence of primitive functions
of continuous functions defined on the unit interval.


title:
Are binary black hole mergers and long gamma-ray bursts drawn from the
  same black hole population?

abstract:
  This paper compares the population of BBH mergers detected by LIGO/Virgo with
selected long GRB world models convolved with a delay function (LGRBs are used
as a tracer of stellar mass BH formation). The comparison involves the redshift
distribution and the fraction of LGRBs required to produce the local rate of
BBH mergers. We find that BBH mergers and LGRBs cannot have the same formation
history, unless BBHs mergers have a long coalescence time of several Gyr. This
would imply that BHs born during the peak of long GRB formation at redshift z =
2 - 3 merge within the horizon of current GW interferometers. We also show that
LGRBs are more numerous than BBH mergers, so that most of them do not end their
lives in BBH mergers. We interpret these results as an indication that BBH
mergers and LGRBs constitute two distinct populations of stellar mass BHs, with
LGRBs being more frequent than BBH mergers. We speculate that the descendants
of LGRBs may resemble galactic High-Mass X-Ray Binaries more than BBH mergers.
We finally discuss the possible existence of a sub-population of fast-spinning
LGRBs descendants among BBH mergers, showing that this population, if it
exists, is expected to become dominant beyond redshift z = 1, leading to a
change in the observed properties of BBH mergers.


title: Integer circulant determinants of order 16

abstract: We solve Olga Taussky-Todd's circulant problem in the case of order 16.


title:
Compactly generated tensor t-structures on the derived category of a
  Noetherian scheme

abstract:
  We introduce a tensor compatibility condition for t-structures. For any
Noetherian scheme $X$, we prove that there is a one-to-one correspondence
between the set of filtrations of Thomason subsets and the set of aisles of
compactly generated tensor compatible t-structures on the derived category of
$X$. This generalizes the earlier classification of compactly generated
t-structures for commutative rings to schemes. Hrbek and Nakamura have
reformulated the famous telescope conjecture for t-structures. As an
application of our main theorem, we prove that a tensor version of the
conjecture is true for separated Noetherian schemes.


title: Sub-Hardy Hilbert spaces in the non-commutative unit row-ball

abstract:
  In the classical Hardy space theory of square-summable Taylor series in the
complex unit disk there is a circle of ideas connecting Szeg\"o's theorem,
factorization of positive semi-definite Toeplitz operators, non-extreme points
of the convex set of contractive analytic functions, de Branges--Rovnyak spaces
and the Smirnov class of ratios of bounded analytic functions in the disk. We
extend these ideas to the multi-variable and non-commutative setting of the
full Fock space, identified as the \emph{free Hardy space} of square-summable
power series in several non-commuting variables. As an application, we prove a
Fej\'er-Riesz style theorem for non-commutative rational functions.


title:
Global smooth solutions to the 2D isentropic and irrotational Chaplygin
  gases with a class of large initial data

abstract:
  In this paper, we establish the global existence of smooth solutions to the
2-dimensional (2D) compressible isentropic irrotational Euler equations for
Chaplygin gases with the short pulse initial data introduced by Christodoulou.
This is related to the Majda's conjecture on the non-formation of shock waves
of solutions from smooth initial data for multi-dimensional nonlinear symmetric
systems which are totally linearly degenerate. The main ingredients of our
analysis consist of showing the positivity of the inverse foliation density
near the outermost conic surface for all time and solving a global Goursat
problem inside the outermost cone. To overcome the difficulties due to the
slower time decay rate of the solutions to the 2D wave equation and the
largeness of the solution, we introduce some new auxiliary energies. It is
noted that the methods and results in the paper can be extended to general 2D
quasilinear wave equations satisfying corresponding null conditions for short
pulse initial data.


title: Structure-Aware Motion Transfer with Deformable Anchor Model

abstract:
  Given a source image and a driving video depicting the same object type, the
motion transfer task aims to generate a video by learning the motion from the
driving video while preserving the appearance from the source image. In this
paper, we propose a novel structure-aware motion modeling approach, the
deformable anchor model (DAM), which can automatically discover the motion
structure of arbitrary objects without leveraging their prior structure
information. Specifically, inspired by the known deformable part model (DPM),
our DAM introduces two types of anchors or keypoints: i) a number of motion
anchors that capture both appearance and motion information from the source
image and driving video; ii) a latent root anchor, which is linked to the
motion anchors to facilitate better learning of the representations of the
object structure information. Moreover, DAM can be further extended to a
hierarchical version through the introduction of additional latent anchors to
model more complicated structures. By regularizing motion anchors with latent
anchor(s), DAM enforces the correspondences between them to ensure the
structural information is well captured and preserved. Moreover, DAM can be
learned effectively in an unsupervised manner. We validate our proposed DAM for
motion transfer on different benchmark datasets. Extensive experiments clearly
demonstrate that DAM achieves superior performance relative to existing
state-of-the-art methods.


title: Extended Higher Herglotz function \textup{II}

abstract:
  Very recently, Radchenko and Zagier revived the theory of Herglotz functions.
The main goal of the article is to show that one of the formulas on page 220 of
Ramanujan's Lost Notebook actually lives in the realms of this theory. As a
consequence of our general theorem, we derive an interesting identity analogous
to Ramanujan's formula for $\zeta(2m+1)$. We also introduce a character
analogue of the Herglotz function and initiate its theory by obtaining an
elegant functional equation governed by it.


title:
On one isoperimetric problem on the Lobachevsky hyperbolic plane with
  left-invariant Finsler structure

abstract:
  We deal with one isoperimetric problem on the Finsler hyperbolic plane. The
space is defined as the Lie group of proper affine transformations of the line
with a left-invariant Finsler structure. To state the problem, we use the
left-invariant volume form. The optimal isoperimetric loops are found in terms
of convex trigonometry functions. We also propose a generalization of the
isoperimetric inequality in parametric form.


title: Landmarks and Regions: A Robust Approach to Data Extraction

abstract:
  We propose a new approach to extracting data items or field values from
semi-structured documents. Examples of such problems include extracting
passenger name, departure time and departure airport from a travel itinerary,
or extracting price of an item from a purchase receipt. Traditional approaches
to data extraction use machine learning or program synthesis to process the
whole document to extract the desired fields. Such approaches are not robust to
format changes in the document, and the extraction process typically fails even
if changes are made to parts of the document that are unrelated to the desired
fields of interest. We propose a new approach to data extraction based on the
concepts of landmarks and regions. Humans routinely use landmarks in manual
processing of documents to zoom in and focus their attention on small regions
of interest in the document. Inspired by this human intuition, we use the
notion of landmarks in program synthesis to automatically synthesize extraction
programs that first extract a small region of interest, and then automatically
extract the desired value from the region in a subsequent step. We have
implemented our landmark-based extraction approach in a tool LRSyn, and show
extensive evaluation on documents in HTML as well as scanned images of invoices
and receipts. Our results show that our approach is robust to various types of
format changes that routinely happen in real-world settings.


title:
Hermite-type modifications of BOBYQA for optimization with some partial
  derivatives

abstract:
  In this work we propose two Hermite-type optimization methods, Hermite least
squares and Hermite BOBYQA, specialized for the case that some partial
derivatives of the objective function are available and others are not. The
main objective is to reduce the number of objective function calls by
maintaining the convergence properties. Both methods are modifications of
Powell's derivative-free BOBYQA algorithm. But instead of (underdetermined)
interpolation for building the quadratic subproblem in each iteration, the
training data is enriched with first and -- if possible -- second order
derivatives and then (weighted) least squares regression is used. Proofs for
global convergence are discussed and numerical results are presented. Further,
the applicability is verified for a realistic test case in the context of yield
optimization. Numerical tests show that the Hermite least squares approach
outperforms classic BOBYQA if half or more partial derivatives are available.
In addition, the Hermite-type approaches achieve more robustness and thus
better performance in case of noisy objective functions.


title: Machine Learning and Deep Learning -- A review for Ecologists

abstract:
  The popularity of Machine learning (ML), Deep learning (DL), and Artificial
intelligence (AI) has sharply risen in recent years. Despite their spike in
popularity, the inner workings of ML and DL algorithms are perceived as opaque,
and their relationship to classical data analysis tools remains debated. It is
often assumed that ML and DL excel primarily at making predictions. Recently,
however, they have been increasingly used for classical analytical tasks
traditionally covered by statistical models. Moreover, recent reviews on ML
have focused exclusively on DL, missing out on synthesizing the wealth of ML
algorithms with different advantages and general principles. Here, we provide a
comprehensive overview of ML and DL, starting with their historical
developments, their algorithm families, their differences from traditional
statistical tools, and universal ML principles. We then discuss why and when ML
and DL excel at prediction tasks, and where they could offer alternatives to
traditional statistical methods for inference, highlighting current and
emerging applications for ecological problems. Finally, we summarize emerging
trends, particularly scientific and causal ML, explainable AI, and responsible
AI that may significantly impact ecological data analysis in the future.


title: A Model of Vector-like Leptons for the Muon $g-2$ and the $W$ Boson Mass

abstract:
  We consider a simple extension of the Standard Model (SM) with a vector-like
lepton and a local $U(1)'$ symmetry, motivated by the recent experimental
anomalies in the muon $g-2$ and the $W$ boson mass. The $U(1)'$ symmetry is
spontaneously broken by the VEVs of the dark Higgs scalar and the second Higgs
doublet, giving rise to the mixing between the muon and the vector-like lepton.
As a result, we obtain the desirable corrections to the muon $g-2$ and the $W$
boson mass simultaneously, dominantly due to the $Z'$ gauge interactions for
the vector-like lepton and the second Higgs doublet, respectively. We also
discuss the consistency of the model with the $Z$ boson decay width, the Higgs
couplings and the dilepton bounds.


title:
The Nature of Reactive and Non-reactive Trajectories for a Three
  Dimensional Caldera Potential Energy Surface

abstract:
  We used for the first time the method of periodic orbit dividing surfaces in
a non-integrable Hamiltonian system with three degrees of freedom. We have
studied the structure of these four dimensional objects in the five dimensional
phase space. This method enabled us to detect the reactive and non-reactive
trajectories in a three dimensional Caldera potential energy surface. We
distinguished four distinct types of trajectory behavior. Two of the types of
trajectories could only occur in a three dimensional Caldera potential energy
surface and not in a two dimensional surface, and we have shown that this is a
result of homoclinic intersections. These homoclinic intersections were
analyzed with the method of Lagrangian descriptors. Finally, we were able to
detect and describe the phenomenon of dynamical matching in the three
dimensional Caldera potential energy surface, which is an important mechanism
for understanding the reaction dynamics of organic molecules.


title: Quantum state transfer on integral oriented circulant graphs

abstract:
  An oriented circulant graph is called integral if all eigenvalues of its
Hermitian adjacency matrix are integers. The main purpose of this paper is to
investigate the existence of perfect state transfer ($\PST$ for short) and
multiple state transfer ($\MST$ for short) on integral oriented circulant
graphs. Specifically, a characterization of $\PST$ (or $\MST$) on integral
oriented circulant graphs is provided. As an application, we also obtain a
closed-form expression for the number of integral oriented circulant graphs
with fixed order having $\PST$ (or $\MST$).


title:
Exploring the Pareto front of multi-objective COVID-19 mitigation
  policies using reinforcement learning

abstract:
  Infectious disease outbreaks can have a disruptive impact on public health
and societal processes. As decision making in the context of epidemic
mitigation is hard, reinforcement learning provides a methodology to
automatically learn prevention strategies in combination with complex epidemic
models. Current research focuses on optimizing policies w.r.t. a single
objective, such as the pathogen's attack rate. However, as the mitigation of
epidemics involves distinct, and possibly conflicting criteria (i.a.,
prevalence, mortality, morbidity, cost), a multi-objective approach is
warranted to learn balanced policies. To lift this decision-making process to
real-world epidemic models, we apply deep multi-objective reinforcement
learning and build upon a state-of-the-art algorithm, Pareto Conditioned
Networks (PCN), to learn a set of solutions that approximates the Pareto front
of the decision problem. We consider the first wave of the Belgian COVID-19
epidemic, which was mitigated by a lockdown, and study different deconfinement
strategies, aiming to minimize both COVID-19 cases (i.e., infections and
hospitalizations) and the societal burden that is induced by the applied
mitigation measures. We contribute a multi-objective Markov decision process
that encapsulates the stochastic compartment model that was used to inform
policy makers during the COVID-19 epidemic. As these social mitigation measures
are implemented in a continuous action space that modulates the contact matrix
of the age-structured epidemic model, we extend PCN to this setting. We
evaluate the solution returned by PCN, and observe that it correctly learns to
reduce the social burden whenever the hospitalization rates are sufficiently
low. In this work, we thus show that multi-objective reinforcement learning is
attainable in complex epidemiological models and provides essential insights to
balance complex mitigation policies.


title:
Robust and Efficient Parameter Estimation for Discretely Observed
  Stochastic Processes

abstract:
  In various practical situations, we encounter data from stochastic processes
which can be efficiently modelled by an appropriate parametric model for
subsequent statistical analyses. Unfortunately, the most common estimation and
inference methods based on the maximum likelihood (ML) principle are
susceptible to minor deviations from assumed model or data contamination due to
their well known lack of robustness. Since the alternative non-parametric
procedures often lose significant efficiency, in this paper, we develop a
robust parameter estimation procedure for discretely observed data from a
parametric stochastic process model which exploits the nice properties of the
popular density power divergence measure in the framework of minimum distance
inference. In particular, here we define the minimum density power divergence
estimators (MDPDE) for the independent increment and the Markov processes. We
establish the asymptotic consistency and distributional results for the
proposed MDPDEs in these dependent stochastic process set-ups and illustrate
their benefits over the usual ML estimator for common examples like Poisson
process, drifted Brownian motion and auto-regressive models.


title: Interplay between Pair Density Wave and a Nested Fermi Surface

abstract:
  We show that spontaneous time-reversal-symmetry (TRS) breaking can naturally
arise from the interplay between pair density wave (PDW) ordering at multiple
momenta and nesting of Fermi surfaces (FS). Concretely, we consider
pair-density-wave superconductivity on a hexagonal lattice with nested FS at
$3/4$ electron filling, which is related to a recently discovered
superconductor CsV$_3$Sb$_5$. Because of nesting of the FS, each momentum $\bk$
on the FS has at least two counterparts $-\bk\pm\mathbf{Q}_{\alpha}$
($\alpha=1,2,3$) on the FS to form finite momentum ($\pm\mathbf{Q}_{\alpha}$)
Cooper pairs, resulting in a TRS and inversion broken PDW state with stable
Bogoliubov Fermi pockets. Various spectra, including (local) density of states,
electron spectral function and the effect of quasi-particle interference, have
been investigated. The partial melting of the PDW will give rise to
$4\times{}4$ and $\frac{4}{\sqrt{3}}\times\frac{4}{\sqrt{3}}$ CDW orders, in
addition to the $2\times2$ CDW. Possible implications to real materials such as
CsV$_3$Sb$_5$ and future experiments have been further discussed.


title:
Assessing the communication gap between AI models and healthcare
  professionals: explainability, utility and trust in AI-driven clinical
  decision-making

abstract:
  This paper contributes with a pragmatic evaluation framework for explainable
Machine Learning (ML) models for clinical decision support. The study revealed
a more nuanced role for ML explanation models, when these are pragmatically
embedded in the clinical context. Despite the general positive attitude of
healthcare professionals (HCPs) towards explanations as a safety and trust
mechanism, for a significant set of participants there were negative effects
associated with confirmation bias, accentuating model over-reliance and
increased effort to interact with the model. Also, contradicting one of its
main intended functions, standard explanatory models showed limited ability to
support a critical understanding of the limitations of the model. However, we
found new significant positive effects which repositions the role of
explanations within a clinical context: these include reduction of automation
bias, addressing ambiguous clinical cases (cases where HCPs were not certain
about their decision) and support of less experienced HCPs in the acquisition
of new domain knowledge.


title:
Type-II Seesaw Triplet Scalar and Its VEV Effects on Neutrino Trident
  Scattering and W mass

abstract:
  In Type-II seesaw model, an electroweak triplet scalar field $\Delta$ with a
non-zero vacuum expectation value (vev) $v_\Delta$ is introduced to facilitate
the generation of small neutrino masses. A non-zero $v_\Delta$ also affects the
W mass through the electroweak $\rho$ parameter, making it to be less than 1 as
predicted by standard model (SM). The component fields in $\Delta$ come along
introduce additional contributions to reduce the SM rare neutrino trident
scattering cross section. These fields also induce new processes not existed in
SM, such as $l_i \to \bar{ l_j} l_k l_l$ and $l_i \to l_j \gamma$. There are
severe constraints on these processes which limit the effects on neutrino
trident scattering and the $\rho$ parameter and therefore the W mass. The newly
measured W mass by CDF makes the central value of $\rho$ parameter to be larger
than 1, even larger than previously expected. Combining neutrinoless double
beta decay, direct neutrino mass and oscillation data, we find a lower limit
for $v_\Delta$ as a function of the triplet scalar mass $m_\Delta$, $v_\Delta >
(6.3 \sim 8.4) \mathrm{eV} (100 \mathrm{GeV}/m_\Delta)$. To have significant
effect on $\rho$ in this model, $v_\Delta$ needs to be in the range of a GeV or
so. However this implies a very small $m_\Delta$ which is ruled out by data. We
conclude that the effect of triplet vev $v_\Delta$ on the W mass can be
neglected. We also find that at 3$\sigma$ level, the deviation of the ratio for
Type-II Seesaw to SM neutrino trident scattering cross section predictions is
reduced to be below 1, but is restricted to be larger than 0.98.


title:
Rudin Extension Theorems on Product Spaces, Turning Bands, and Random
  Fields on Balls cross Time

abstract:
  Characteristic functions that are radially symmetric have a dual
interpretation, as they can be used as the isotropic correlation functions of
spatial random fields. Extensions of isotropic correlation functions from balls
into $d$-dimensional Euclidean spaces, $\R^{d}$, have been understood after
Rudin. Yet, extension theorems on product spaces are elusive, and a
counterexample provided by Rudin on rectangles suggest that the problem is
quite challenging. This paper provides extension theorem for multiradial
characteristic functions that are defined in balls embedded in $\R^d$ cross,
either $\R^{\dd}$ or the unit sphere $\S^{\dd}$ embedded in $\R^{\dd+1}$, for
any two positive integers $d$ and $\dd$. We then examine Turning Bands
operators that provide bijections between the class of multiradial correlation
functions in given product spaces, and multiradial correlations in product
spaces having different dimensions. The combination of extension theorems with
Turning Bands provides a connection with random fields that are defined in
balls cross linear or circular time.


title:
Information in probability: Another information-theoretic proof of a
  finite de Finetti theorem

abstract:
  We recall some of the history of the information-theoretic approach to
deriving core results in probability theory and indicate parts of the recent
resurgence of interest in this area with current progress along several
interesting directions. Then we give a new information-theoretic proof of a
finite version of de Finetti's classical representation theorem for
finite-valued random variables. We derive an upper bound on the relative
entropy between the distribution of the first $k$ in a sequence of $n$
exchangeable random variables, and an appropriate mixture over product
distributions. The mixing measure is characterised as the law of the empirical
measure of the original sequence, and de Finetti's result is recovered as a
corollary. The proof is nicely motivated by the Gibbs conditioning principle in
connection with statistical mechanics, and it follows along an appealing
sequence of steps. The technical estimates required for these steps are
obtained via the use of a collection of combinatorial tools known within
information theory as `the method of types.'


title: Perfect state transfer in neighborhood coronas

abstract:
  The neighborhood corona $G \star H$ is the graph obtained by taking one copy
$G$ and $|G|$ copies of $H$, and joining each vertex of the $j$th copy of $H$
to all neighbors of $v_{j}$ in $G$. In this paper, we study the state transfer
of neighborhood coronas related to the adjacency matrix. Concretely, we provide
some necessary conditions under which the neighborhood corona $G \star H$
admits perfect state transfer, and obtain a new family of graphs with pretty
good state transfer based on neighborhood coronas.


title: A Bayesian Decision Support System in Energy Systems Planning

abstract:
  Gaussian Process (GP) emulators are widely used to approximate complex
computer model behaviour across the input space. Motivated by the problem of
coupling computer models, recently progress has been made in the theory of the
analysis of networks of connected GP emulators. In this paper, we combine these
recent methodological advances with classical state-space models to construct a
Bayesian decision support system. This approach gives a coherent probability
model that produces predictions with the measure of uncertainty in terms of two
first moments and enables the propagation of uncertainty from individual
decision components.
  This methodology is used to produce a decision support tool for a UK county
council considering low carbon technologies to transform its infrastructure to
reach a net-zero carbon target. In particular, we demonstrate how to couple
information from an energy model, a heating demand model, and gas and
electricity price time-series to quantitatively assess the impact on
operational costs of various policy choices and changes in the energy market.


title: Pareto Conditioned Networks

abstract:
  In multi-objective optimization, learning all the policies that reach
Pareto-efficient solutions is an expensive process. The set of optimal policies
can grow exponentially with the number of objectives, and recovering all
solutions requires an exhaustive exploration of the entire state space. We
propose Pareto Conditioned Networks (PCN), a method that uses a single neural
network to encompass all non-dominated policies. PCN associates every past
transition with its episode's return. It trains the network such that, when
conditioned on this same return, it should reenact said transition. In doing so
we transform the optimization problem into a classification problem. We recover
a concrete policy by conditioning the network on the desired Pareto-efficient
solution. Our method is stable as it learns in a supervised fashion, thus
avoiding moving target issues. Moreover, by using a single network, PCN scales
efficiently with the number of objectives. Finally, it makes minimal
assumptions on the shape of the Pareto front, which makes it suitable to a
wider range of problems than previous state-of-the-art multi-objective
reinforcement learning algorithms.


title: Schwartz-Zippel for multilinear polynomials mod N

abstract:
  We derive a tight upper bound on the probability over
$\mathbf{x}=(x_1,\dots,x_\mu) \in \mathbb{Z}^\mu$ uniformly distributed in $
[0,m)^\mu$ that $f(\mathbf{x}) = 0 \bmod N$ for any $\mu$-linear polynomial $f
\in \mathbb{Z}[X_1,\dots,X_\mu]$ co-prime to $N$. We show that for
$N=p_1^{r_1},...,p_\ell^{r_\ell}$ this probability is bounded by $\frac{\mu}{m}
+ \prod_{i=1}^\ell I_{\frac{1}{p_i}}(r_i,\mu)$ where $I$ is the regularized
beta function. Furthermore, we provide an inverse result that for any target
parameter $\lambda$ bounds the minimum size of $N$ for which the probability
that $f(\mathbf{x}) \equiv 0 \bmod N$ is at most $2^{-\lambda} +
\frac{\mu}{m}$. For $\mu =1$ this is simply $N \geq 2^\lambda$. For $\mu \geq
2$, $\log_2(N) \geq 8 \mu^{2}+ \log_2(2 \mu)\cdot \lambda$ the probability that
$f(\mathbf{x}) \equiv 0 \bmod N$ is bounded by $2^{-\lambda} +\frac{\mu}{m}$.
We also present a computational method that derives tighter bounds for specific
values of $\mu$ and $\lambda$. For example, our analysis shows that for
$\mu=20$, $\lambda = 120$ (values typical in cryptography applications), and
$\log_2(N)\geq 416$ the probability is bounded by $ 2^{-120}+\frac{20}{m}$. We
provide a table of computational bounds for a large set of $\mu$ and $\lambda$
values.


title: Bounds on bilinear forms with Kloosterman sums

abstract:
  We prove new bounds on bilinear forms with Kloosterman sums, complementing
and improving a series of results by \'E. Fouvry, E. Kowalski and Ph. Michel
(2014), V. Blomer, \'E. Fouvry, E. Kowalski, Ph. Michel and D. Mili\'cevi\'c
(2017), E. Kowalski, Ph. Michel and W. Sawin (2019, 2020) and I. E. Shparlinski
(2019). These improvements rely on new estimates for Type II bilinear forms
with incomplete Kloosterman sums. We also establish new estimates for bilinear
forms with one variable from an arbitrary set by introducing techniques from
additive combinatorics over prime fields. As a typical application, an estimate
for higher moments of averages of Kloosterman sums is also given.


title: Answering Count Queries with Explanatory Evidence

abstract:
  A challenging case in web search and question answering are count queries,
such as \textit{"number of songs by John Lennon"}. Prior methods merely answer
these with a single, and sometimes puzzling number or return a ranked list of
text snippets with different numbers. This paper proposes a methodology for
answering count queries with inference, contextualization and explanatory
evidence. Unlike previous systems, our method infers final answers from
multiple observations, supports semantic qualifiers for the counts, and
provides evidence by enumerating representative instances. Experiments with a
wide variety of queries show the benefits of our method. To promote further
research on this underexplored topic, we release an annotated dataset of 5k
queries with 200k relevant text spans.


title:
High-Performance Microwave Frequency Standard Based on Sympathetically
  Cooled Ions

abstract:
  The ion microwave frequency standard is a candidate for the next generation
of microwave frequency standard with the potential for very wide applications.
The Dick effect and second-order Doppler frequency shift (SODFS) limit the
performance of ion microwave frequency standards. The introduction of
sympathetic cooling technology can suppress the Dick effect and SODFS and
improve the stability and accuracy of the frequency standard. However, the
sympathetically-cooled ion microwave frequency standard has seldom been studied
before. This paper reports the first sympathetically-cooled ion microwave
frequency standard in a Paul trap. Using laser-cooled
${}^{40}\mathrm{{Ca}}^{+}$ as coolant ions, ${}^{113}\mathrm{{Cd}}^{+}$ ion
crystal is cooled to below 100 mK and has a coherence lifetime of over 40 s.
The short-term frequency stability reached $3.48 \times 10^{-13}/\tau^{1/2}$,
which is comparable to that of the mercury ion frequency standard. Its
uncertainty is $1.5\times 10^{-14}$, which is better than that of directly
laser-cooled cadmium ion frequency standard.


title:
Pyramid Grafting Network for One-Stage High Resolution Saliency
  Detection

abstract:
  Recent salient object detection (SOD) methods based on deep neural network
have achieved remarkable performance. However, most of existing SOD models
designed for low-resolution input perform poorly on high-resolution images due
to the contradiction between the sampling depth and the receptive field size.
Aiming at resolving this contradiction, we propose a novel one-stage framework
called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to
extract features from different resolution images independently and then graft
the features from transformer branch to CNN branch. An attention-based
Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine
broken detailed information more holistically, guided by different source
feature during decoding process. Moreover, we design an Attention Guided Loss
(AGL) to explicitly supervise the attention matrix generated by CMGM to help
the network better interact with the attention from different models. We
contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD,
containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the
largest dataset in both quantity and resolution for high-resolution SOD task,
which can be used for training and testing in future research. Sufficient
experiments on UHRSD and widely-used SOD datasets demonstrate that our method
achieves superior performance compared to the state-of-the-art methods.


title: Resources for Turkish Natural Language Processing: A critical survey

abstract:
  This paper presents a comprehensive survey of corpora and lexical resources
available for Turkish. We review a broad range of resources, focusing on the
ones that are publicly available. In addition to providing information about
the available linguistic resources, we present a set of recommendations, and
identify gaps in the data available for conducting research and building
applications in Turkish Linguistics and Natural Language Processing.


title:
Learning-based Lossless Point Cloud Geometry Coding using Sparse
  Representations

abstract:
  Most point cloud compression methods operate in the voxel or octree domain
which is not the original representation of point clouds. Those representations
either remove the geometric information or require high computational power for
processing. In this paper, we propose a context-based lossless point cloud
geometry compression that directly processes the point representation.
Operating on a point representation allows us to preserve geometry correlation
between points and thus to obtain an accurate context model while significantly
reduce the computational cost. Specifically, our method uses a sparse
convolution neural network to estimate the voxel occupancy sequentially from
the x,y,z input data. Experimental results show that our method outperforms the
state-of-the-art geometry compression standard from MPEG with average rate
savings of 52% on a diverse set of point clouds from four different datasets.


title:
From CNNs to Vision Transformers -- A Comprehensive Evaluation of Deep
  Learning Models for Histopathology

abstract:
  While machine learning is currently transforming the field of histopathology,
the domain lacks a comprehensive evaluation of state-of-the-art models based on
essential but complementary quality requirements beyond a mere classification
accuracy. In order to fill this gap, we conducted an extensive evaluation by
benchmarking a wide range of classification models, including recent vision
transformers, convolutional neural networks and hybrid models comprising
transformer and convolutional models. We thoroughly tested the models on five
widely used histopathology datasets containing whole slide images of breast,
gastric, and colorectal cancer and developed a novel approach using an
image-to-image translation model to assess the robustness of a cancer
classification model against stain variations. Further, we extended existing
interpretability methods to previously unstudied models and systematically
reveal insights of the models' classification strategies that allow for
plausibility checks and systematic comparisons. The study resulted in specific
model recommendations for practitioners as well as putting forward a general
methodology to quantify a model's quality according to complementary
requirements that can be transferred to future model architectures.


title:
SAL-CNN: Estimate the Remaining Useful Life of Bearings Using
  Time-frequency Information

abstract:
  In modern industrial production, the prediction ability of the remaining
useful life (RUL) of bearings directly affects the safety and stability of the
system. Traditional methods require rigorous physical modeling and perform
poorly for complex systems. In this paper, an end-to-end RUL prediction method
is proposed, which uses short-time Fourier transform (STFT) as preprocessing.
Considering the time correlation of signal sequences, a long and short-term
memory network is designed in CNN, incorporating the convolutional block
attention module, and understanding the decision-making process of the network
from the interpretability level. Experiments were carried out on the 2012PHM
dataset and compared with other methods, and the results proved the
effectiveness of the method.


title: Root structures of polynomials with sparse exponents

abstract:
  For real polynomials with (sparse) exponents in some fixed set, \[
\Psi(t)=x+y_1t^{k_1}+\ldots +y_L t^{k_L}, \] we analyse the types of root
structures that might occur as the coefficients vary. We first establish a
stratification of roots into tiers, each containing roots of comparable sizes.
We then show that there exists a suitable small parameter $\epsilon>0$ such
that, for any root $w\in \mathbb{C}$, $B(w,\epsilon|w|)$ contains at most $L$
roots, counted with multiplicity. Our analysis suggests the consideration of a
rough factorisation of the original polynomial and we establish the closeness
of the corresponding root structures: there exists a covering of the roots by
balls wherein a) each ball contains the same number of roots of the original
polynomial and of its rough factorisation and b) the balls are strongly
separated.


title:
Energetics and scattering of gravitational two-body systems at fourth
  post-Minkowskian order

abstract:
  Upcoming observational runs of the LIGO-Virgo-KAGRA collaboration, and future
gravitational-wave (GW) detectors on the ground and in space, require waveform
models more accurate than currently available. High-precision waveform models
can be developed by improving the analytical description of compact binary
dynamics and completing it with numerical-relativity (NR) information. Here, we
assess the accuracy of the recent results for the fourth post-Minkowskian (PM)
conservative dynamics through comparisons with NR simulations for the
circular-orbit binding energy and for the scattering angle. We obtain that the
4PM dynamics gives better agreement with NR than the 3PM dynamics, and that
while the 4PM approximation gives comparable results to the third
post-Newtonian (PN) approximation for bound orbits, it performs better for
scattering encounters. Furthermore, we incorporate the 4PM results in
effective-one-body (EOB) Hamiltonians, which improves the disagreement with NR
over the 4PM-expanded Hamiltonian from $\sim 40\%$ to $\sim 10\%$, or $\sim
3\%$ depending on the EOB gauge, for an equal-mass binary, two GW cycles before
merger. Finally, we derive a 4PN-EOB Hamiltonian for hyperbolic orbits, and
compare its predictions for the scattering angle to NR, and to the scattering
angle of a 4PN-EOB Hamiltonian computed for elliptic orbits.


title: On the Duflo-Serganova functor for the queer Lie superalgebra

abstract:
  We study the Duflo-Serganova functor $\operatorname{DS}_x$ for the queer Lie
superalgebra $\mathfrak{q}_n$ and for all odd $x$ with $[x,x]$ semisimple. For
the case when the rank of $x$ is $1$ we give a formula for multiplicities in
terms of the arc diagram attached to $\lambda$. Further, we prove that
$\operatorname{DS}_x(L)$ is semisimple if $L$ is a simple finite-dimensional
module and $x$ is of rank $1$ satisfying $x^2=0$.


title:
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of
  Lexical Gaps in Kinship

abstract:
  This paper describes a method to enrich lexical resources with content
relating to linguistic diversity, based on knowledge from the field of lexical
typology. We capture the phenomenon of diversity through the notions of lexical
gap and language-specific word and use a systematic method to infer gaps
semi-automatically on a large scale. As a first result obtained for the domain
of kinship terminology, known to be very diverse throughout the world, we
publish a lexico-semantic resource consisting of 198 domain concepts, 1,911
words, and 37,370 gaps covering 699 languages. We see potential in the use of
resources such as ours for the improvement of a variety of cross-lingual NLP
tasks, which we demonstrate through a downstream application for the evaluation
of machine translation systems.


title:
The Northern Cross Fast Radio Burst project - II. Monitoring of
  repeating FRB 20180916B, 20181030A, 20200120E and 20201124A

abstract:
  In this work we report the results of a nineteen-month Fast Radio Burst
observational campaign carried out with the North-South arm of the Medicina
Northern Cross radio telescope at 408~MHz in which we monitored four repeating
sources: FRB20180916B, FRB20181030A, FRB20200120E and FRB20201124A. We present
the current state of the instrument and the detection and characterisation of
three bursts from FRB20180916B. Given our observing time, our detections are
consistent with the event number we expect from the known burst rate ($2.7 \pm
1.9$ above our 10$\sigma$, 38~Jy~ms detection threshold) in the 5.2 day active
window of the source, further confirming the source periodicity. We detect no
bursts from the other sources. We turn this result into a 95\% confidence level
lower limit on the slope of the differential fluence distribution $\alpha$ to
be $\alpha > 2.1$ and $\alpha > 2.2$ for FRB20181030A and FRB20200120E
respectively. Given the known rate for FRB20201124A, we expect $1.0 \pm 1.1$
bursts from our campaign, consistent with our non-detection.


title:
Performance Metrics for Communication Systems with Forward Error
  Correction

abstract:
  We revisit performance metrics for optical communication systems with FEC. We
illustrate the concept of universality and discuss the most widespread
performance thresholds. Finally, we show by example how to include FEC into
transmission experiments.


title:
A Novel Channel Identification Architecture for mmWave Systems Based on
  Eigen Features

abstract:
  Millimeter wave (mmWave) communication technique has been developed rapidly
because of many advantages of high speed, large bandwidth, and ultra-low delay.
However, mmWave communications systems suffer from fast fading and frequent
blocking. Hence, the ideal communication environment for mmWave is line of
sight (LOS) channel. To improve the efficiency and capacity of mmWave system,
and to better build the Internet of Everything (IoE) service network, this
paper focuses on the channel identification technique in line-of- sight (LOS)
and non-LOS (NLOS) environments. Considering the limited computing ability of
user equipments (UEs), this paper proposes a novel channel identification
architecture based on eigen features, i.e. eigenmatrix and eigenvector (EMEV)
of channel state information (CSI). Furthermore, this paper explores clustered
delay line (CDL) channel identification with mmWave, which is defined by the
3rd generation partnership project (3GPP). Ther experimental results show that
the EMEV based scheme can achieve identification accuracy of 99.88% assuming
perfect CSI. In the robustness test, the maximum noise can be tolerated is SNR=
16 dB, with the threshold acc \geq 95%. What is more, the novel architecture
based on EMEV feature will reduce the comprehensive overhead by about 90%.


title:
Standing waves and global well-posedness for the 2d Hartree equation
  with a point interaction

abstract:
  We study a class of two-dimensional non-linear Schr\"odinger equations with
point-like singular perturbation and Hartree non-linearity. The point-like
singular perturbation of the free Laplacian induces appropriate perturbed
Sobolev spaces that are necessary for the study of ground states and evolution
flow. We include in our treatment both mass sub-critical and mass critical
Hartree non-linearities. Our analysis is two-fold: we establish existence,
symmetry, and regularity of ground states, and we demonstrate the
well-posedness of the associated Cauchy problem in the singular perturbed
energy space. The first goal, unlike other treatments emerging in parallel with
the present work, is achieved by a non-trivial adaptation of the standard
properties of Schwartz symmetrisation for the modified Weinstein functional.
This produces, among others, modified Gagliardo-Nirenberg type inequalities
that allow to efficiently control the non-linearity and obtain well-posedness
by energy methods. The evolution flow is proved to be global in time in the
defocusing case, and in the focusing and mass sub-critical case. It is also
global in the focusing and mass critical case, for initial data that are
suitably small in terms of the best Gagliardo-Nirenberg constant.


title:
Black hole perturbations in higher-order scalar-tensor theories: initial
  value problem and dynamical stability

abstract:
  We propose a physically sensible formulation of initial value problem for
black hole perturbations in higher-order scalar-tensor theories. As a first
application, we study monopole perturbations around stealth Schwarzschild
solutions in a shift- and reflection-symmetric subclass of degenerate
higher-order scalar-tensor (DHOST) theories. In particular, we investigate the
time evolution of the monopole perturbations by solving a two-dimensional wave
equation and analyze the Vishveshwara's classical scattering experiment, i.e.,
the time evolution of a Gaussian wave packet. As a result, we confirm that
stealth Schwarzschild solutions in the DHOST theory are dynamically stable
against the monopole perturbations with the wavelength comparable or shorter
than the size of the black hole horizon. We also find that the damped
oscillations at the late time do not show up unlike the ringdown phase in the
standard case of general relativity. Moreover, we investigate the
characteristic curves of the monopole perturbations as well as a static
spherically symmetric solution with monopole hair.


title:
Fractional Modelling and Optimal Control of COVID-19 Transmission in
  Portugal

abstract:
  A fractional-order compartmental model was recently used to describe real
data of the first wave of the COVID-19 pandemic in Portugal [Chaos Solitons
Fractals 144 (2021), Art. 110652]. Here, we modify that model in order to
correct time dimensions and use it to investigate the third wave of COVID-19
that occurred in Portugal from December 2020 to February 2021, and that has
surpassed all previous waves, both in number and consequences. A new fractional
optimal control problem is then formulated and solved, with vaccination and
preventive measures as controls. A cost-effectiveness analysis is carried out,
and the obtained results are discussed.


title:
What do complexity measures measure? Correlating and validating
  corpus-based measures of morphological complexity

abstract:
  We present an analysis of eight measures used for quantifying morphological
complexity of natural languages. The measures we study are corpus-based
measures of morphological complexity with varying requirements for corpus
annotation. We present similarities and differences between these measures
visually and through correlation analyses, as well as their relation to the
relevant typological variables. Our analysis focuses on whether these
`measures' are measures of the same underlying variable, or whether they
measure more than one dimension of morphological complexity. The principal
component analysis indicates that the first principal component explains 92.62
% of the variation in eight measures, indicating a strong linear dependence
between the complexity measures studied.


title:
Fisher matrix for the angular power spectrum of multi-tracer galaxy
  surveys

abstract:
  Redshift evolution and peculiar velocities break the isotropy of cosmological
surveys with respect to the directions parallel and transverse to the line of
sight, limiting the accuracy of the Fourier representation to small areas and
redshift ranges. In contrast to the Fourier space power spectrum, the full
information about the two-point function of tracers of large-scale structure is
encapsulated in the redshift-dependent angular power spectrum $C_\ell^{ij}
(z_i,z_j)$ for the tracer species $i$ and $j$ at the redshift slices $z_i$ and
$z_j$, expressed in harmonic space. In this paper we derive semi-analytical
expressions for the multi-tracer Fisher matrix of angular power spectra, in
real and in redshift space, which are exact in the linear regime of structure
formation. Our expressions can be used to forecast the constraining power of
galaxy surveys with many tracers and a large number of redshift slices, for
which the derivation of the Fisher matrix from numerically evaluated covariance
matrices may not be feasible or practical.


title: Elastic properties of Yukawa crystals

abstract:
  We study elastic properties of solid Yukawa systems. Elastic moduli and
effective shear modulus of body-centered cubic (bcc) and face-centered cubic
(fcc) lattices are obtained from electrostatic energies of deformed crystals.
For the bcc lattice our results are well consistent with previous calculations
and improve them, while results for the fcc lattice are new. We have also
obtained an analytical expression of the elastic moduli for the weak
polarization and constructed a convenient approximation for the higher
polarization.


title: Forecasting new diseases in low-data settings using transfer learning

abstract:
  Recent infectious disease outbreaks, such as the COVID-19 pandemic and the
Zika epidemic in Brazil, have demonstrated both the importance and difficulty
of accurately forecasting novel infectious diseases. When new diseases first
emerge, we have little knowledge of the transmission process, the level and
duration of immunity to reinfection, or other parameters required to build
realistic epidemiological models. Time series forecasts and machine learning,
while less reliant on assumptions about the disease, require large amounts of
data that are also not available in early stages of an outbreak. In this study,
we examine how knowledge of related diseases can help make predictions of new
diseases in data-scarce environments using transfer learning. We implement both
an empirical and a theoretical approach. Using empirical data from Brazil, we
compare how well different machine learning models transfer knowledge between
two different disease pairs: (i) dengue and Zika, and (ii) influenza and
COVID-19. In the theoretical analysis, we generate data using different
transmission and recovery rates with an SIR compartmental model, and then
compare the effectiveness of different transfer learning methods. We find that
transfer learning offers the potential to improve predictions, even beyond a
model based on data from the target disease, though the appropriate source
disease must be chosen carefully. While imperfect, these models offer an
additional input for decision makers during pandemic response.


title:
A multi-messenger study of the blazar PKS 0735+178: a new major neutrino
  source candidate

abstract:
  PKS 0735+178 is a bright radio and $\gamma$-ray blazar that is possibly
associated with multiple neutrino events observed by the IceCube, Baikal,
Baksan, and KM3NeT neutrino telescopes. The source was found to undergo a major
flaring activity in $\gamma$-ray, X-ray, ultraviolet (UV) and optical bands. We
present a long-term detailed study of this peculiar blazar to investigate the
temporal and spectral changes in the multi-wavelength emission when the
neutrino events were observed. The analysis of Swift-XRT snapshots reveal a
flux variability of more than a factor 2 in about $5\times10^3$ seconds during
the observation on December 17, 2021. In the $\gamma$-ray band, the source was
in its historical highest flux level at the time of the arrival of the
neutrinos. The observational comparison between PKS 0735+178 and other neutrino
source candidates, such as TXS 0506+056, PKS 1424+240, and GB6 J1542+6129,
shows that all these sources share similar spectral energy distributions, very
high radio and $\gamma$-ray powers, and parsec scale jet properties. Moreover,
PKS 0735+178, like all the others, is a masquerading BL Lac. We perform
comprehensive modelling of the multiwavelength emission from PKS 0735+178
within one-zone lepto-hadronic models considering both internal and external
photon fields and estimate the expected accompanying neutrino flux. The most
optimistic scenario invokes a jet with luminosity close to the Eddington value
and the interactions of $\sim$ PeV protons with an external UV photon field.
This scenario predicts $\sim 0.067$ muon and antimuon neutrinos over the
observed 3-week flare. Our results are consistent with the detection of one
very-high-energy neutrino like IceCube-211208A.


title: Surfactancy in a tadpole model of proteins

abstract:
  We model the environment of eukaryotic nuclei by representing macromolecules
by only their entropic properties, with globular molecules represented by
spherical colloids and flexible molecules by polymers. We put particular focus
on proteins with both globular and intrinsically disordered regions, which we
represent with `tadpole' constructed by grafting single polymers and colloids
together. In Monte Carlo simulations we find these tadpoles support phase
separation via depletion flocculation, and demonstrate several surfactant
behaviours, including being found preferentially at interfaces and forming
micelles in single phase solution. Furthermore, the model parameters can be
tuned to give a tadpole a preference for either bulk phase. However, we find
entropy too weak to drive these behaviours by itself at biological
concentrations.


title: Local Rationalizability and Choice Consistency

abstract:
  We introduce a new notion of rationalizability where the rationalizing
relation may depend on the set of feasible alternatives. More precisely, we say
that a choice function is locally rationalizable if it is rationalized by a
family of rationalizing relations such that a strict preference between two
alternatives in some feasible set is preserved when removing other
alternatives. We then show that a choice function is locally rationalizable if
and only if it satisfies Sen's $\gamma$ and give similar characterizations for
local rationalizability via transitive, PIP-transitive, and quasi-transitive
relations. Local rationalizability is realized via families of revealed
preference relations that are sandwiched in between the base relation and the
revealed preference relation of a choice function. We demonstrate the adequacy
of our results for analyzing and constructing consistent social choice
functions by giving simple characterizations of the top cycle and the uncovered
set using transitive and quasi-transitive local rationalizability.


title: Distortions produced in optical homodyne tomography

abstract:
  An analysis of the homodyne tomography process that is often used to
determine the Wigner functions of quantum optical states is performed to
consider the effects of the spatiotemporal degrees of freedom. The homodyne
tomography process only reproduces the part of the Wigner functional of the
input state, that is associated with the mode of the local oscillator. All the
other degrees of freedom are traced out. The analysis demonstrates how this
reduction in the degrees of freedom introduces distortions in the observed
Wigner function. As examples, we consider coherent states, Fock states and
squeezed vacuum states.


title: Quantum sensing with diamond NV centers under megabar pressures

abstract:
  Megabar pressures are of crucial importance for cutting-edge studies of
condensed matter physics and geophysics. With the development of diamond anvil
cell, laboratory studies of high pressure have entered the megabar era for
decades. However, it is still challenging to implement in-situ magnetic sensing
under ultrahigh pressures. Here, we demonstrate optically detected magnetic
resonance of diamond nitrogen-vacancy (NV) centers, a promising quantum sensor
of strain and magnetic fields, up to 1.4 Mbar. We quantify the reduction and
blueshifts of NV fluorescence under high pressures. We demonstrate coherent
manipulation of NV electron spins and extend its working pressure to the
megabar region. These results shed new light on our understanding of diamond NV
centers and will benefit quantum sensing under extreme conditions.


title:
Curvilinear spin-wave dynamics beyond the thin-shell approximation:
  Magnetic nanotubes as a case study

abstract:
  Surface curvature of magnetic systems can lead to many static and dynamic
effects which are not present in flat systems of the same material. These
emergent magnetochiral effects can lead to frequency nonreciprocity of spin
waves, which has been shown to be a bulk effect of dipolar origin and is
related to a curvature-induced symmetry breaking in the magnetic volume
charges. So far, such effects have been investigated theoretically mostly for
thin shells, where the spatial profiles of the spin waves can be assumed to be
homogeneous along the thickness. Here, using a finite-element dynamic-matrix
approach, we investigate the transition of the spin-wave spectrum from thin to
thick curvilinear shells, at the example of magnetic nanotubes in the vortex
state. With increasing thickness, we observe the appearance of higher-order
radial modes which are strongly hybridized and resemble the
perpendicular-standing-waves (PSSWs) in flat films. Along with an increasing
dispersion asymmetry, we uncover the curvature-induced non-reciprocity of the
mode profiles. This is explained in a very simple picture general for thick
curvilinear shells, considering the inhomogeneity of the emergent geometric
volume charges along the thickness of the shell. Such curvature-induced
mode-profile asymmetry also leads to non-reciprocal hybridization which can
facilitate unidirectional spin-wave propagation. With that, we also show how
curvature allows for nonlinear three-wave splitting of a higher-order radial
mode into secondary modes which can also propagate unidirectionally. We believe
that our study provides a significant contribution to the understanding of the
spin-wave dynamics in curvilinear magnetic systems, but also advertises these
for novel magnonic applications.