title: Multilepton Signatures from Dark Matter at the LHC

abstract:
  Leptonic signatures of Dark Matter (DM) are one of the cleanest ways to
discover such a secluded form of matter at high energy colliders. We explore
the full parameter space relevant to multi-lepton (2- and 3-lepton) signatures
at the Large Hadron Collider (LHC) from representative minimal consistent
models with scalar and fermion DM. In our analysis, we suggest a new
parametrisation of the model parameter spaces in terms of the DM mass and mass
differences between DM and its multiplet partners. This parametrisation allows
us to explore properties of DM models in their whole parameter space. This
approach is generic and quite model-independent since the mass differences are
related to the couplings of the DM to the Standard Model (SM) sector. We
establish the most up-to-date LHC limits on the inert 2-Higgs Doublet Model
(i2HDM) and Minimal Fermion DM (MFDM) model parameter spaces, by using the
complementary information stemming from 2- and 3-lepton signatures. We provide
a map of LHC efficiencies and cross-section limits for such 2- and 3-lepton
signatures allowing one to easily make model-independent reinterpretations of
LHC results for analogous classes of models. We also present combined
constraints from the LHC, DM relic density and direct search experiments
indicating the current status of the i2HDM and MFDM model.


title:
Infusing nontrivial topology in massive Dirac fermions with scalar
  potential: Emergence of Scalar Hall Effect

abstract:
  We present a simplified way to access and manipulate the topology of massive
Dirac fermions by tuning the scalar potential. We show systematically how a
distribution of scalar potential can manipulate the signature of the gap or the
mass term as well as the dispersion leading to a band inversion via inverse
Klein tunnelling. In two dimensions this emergent mechanism, which we refer to
as the Scalar Hall Effect, facilitates a direct drive of non-trivial
topological invariants, e.g. the Chern number, and thus opens new possibilities
for manipulating physical observables which originate from the nontrivial
topology.


title:
Indistinguishable asymptotic pairs and multidimensional Sturmian
  configurations

abstract:
  Two asymptotic configurations on a full $\mathbb{Z}^d$-shift are
indistinguishable if for every finite pattern the associated sets of
occurrences in each configuration coincide up to a finitely supported
permutation of $\mathbb{Z}^d$. We prove that indistinguishable asymptotic pairs
satisfying a "flip condition" are characterized by their pattern complexity on
finite connected supports. Furthermore, we prove that uniformly recurrent
indistinguishable asymptotic pairs satisfying the flip condition are described
by codimension-one (dimension of the internal space) cut and project schemes,
which symbolically correspond to multidimensional Sturmian configurations.
Together the two results provide a generalization to $\mathbb{Z}^d$ of the
characterization of Sturmian sequences by their factor complexity $n+1$. Many
open questions are raised by the current work and are listed in the
introduction.


title: Bayesian Integrals on Toric Varieties

abstract:
  We explore the positive geometry of statistical models in the setting of
toric varieties. Our focus lies on models for discrete data that are
parameterized in terms of Cox coordinates. We develop a geometric theory for
computations in Bayesian statistics, such as evaluating marginal likelihood
integrals and sampling from posterior distributions. These are based on a
tropical sampling method for evaluating Feynman integrals in physics. We here
extend that method from projective spaces to arbitrary toric varieties.


title: Asymmetric harmonic oscillator

abstract:
  The solution of one--dimensional asymmetric quantum harmonic oscillator is
presented. The asymmetry can be realized, for example, by using two springs,
one spring is glued with the mass, and the second spring is freely connected
with the mass in the equilibrium point and it is located inside or outside the
first spring which acts on the mass only from the contact point on the right.
We study the spectrum of a quantum harmonic oscillator, which has a spring
constant $ k_-$ to the left of the equilibrium position and a spring constant $
k_ +$ to the right of the equilibrium position. In the presented case the
contact point of the second string is the equilibrium point of the first
string. The explicit form of eigenfunctions, the way to calculate the
eigenvalues and the properties of the eigenfunctions are discussed.


title: Illposedness of $C^{2}$ vortex patches

abstract:
  It is well known that vortex patches are wellposed in $C^{1,\alpha}$ if
$0<\alpha <1$. In this paper, we prove the illposedness of $C^{2}$ vortex
patches. The setup is to consider the vortex patches in Sobolev spaces
$W^{2,p}$ where the curvature of the boundary is $L^p$ integrable. In this
setting, we show the persistence of $W^{2,p}$ regularity when $1<p <\infty$ and
construct $C^{2}$ initial patch data for which the curvature of the patch
boundary becomes unbounded immediately for $t>0$. The key ingredient is the
evolution equation for the curvature, the dominant term in which turns out to
be linear and dispersive.


title:
Adamantanes as white-light emitters: Controlling arrangement and
  functionality by external Coulomb forces

abstract:
  Functionalized adamantane molecular cluster materials show highly transient
nonlinear optical properties of currently unclear structural origin. Several
interaction mechanisms in compounds comprising molecular clusters, their inter-
and intramolecular interactions as well as the interplay of their electronic
systems and vibrations of their backbone are viable concepts to explain these
nonlinear optical properties. We show that transient Coulomb forces also have
to be considered as they can lead to intramolecular structure transformations
and intermolecular rearrangements in the crystal. Both strongly influence the
nonlinear optical properties. Moreover, selective bromine functionalization can
trigger a photochemical rearrangement of the molecules. The structure and
chemical bonding within the compounds are investigated in dependence on the
laser irradiation at different stages of their nonlinear emission by electron
diffraction and electron energy loss spectroscopy. The transient structural and
chemical states observed are benchmarked by similar observations during
electron irradiation, which makes quantification of structural changes possible
and allows the correlation with first principles calculations. The
functionalization and its subsequent usage to exploit photochemical effects can
either enhance two-photon absorption or facilitate white-light emission rather
than second-harmonic generation.


title:
On $\tau$-tilting modules over trivial extensions of gentle tree
  algebras

abstract:
  We show that trivial extensions of gentle tree algebras are exactly Brauer
tree algebras without exceptional vertex. As a consequence, the number of
support $\tau$-tilting modules over the trivial extension $T(A)$ of a gentle
tree algebra $A$ depends only on the number of simple $A$-modules.


title:
Persistence of spectral projections for stochastic operators on large
  tensor products

abstract:
  In this paper it is proved that for families of stochastic operators on a
countable tensor product, depending smoothly on parameters, any spectral
projection persists smoothly, where smoothness is defined using norms based on
ideas of Dobrushin. A rigorous perturbation theory for families of stochastic
operators with spectral gap is thereby created. It is illustrated by deriving
an effective slow 2-state dynamics for a 3-state probabilistic cellular
automaton. Some further potential applications are discussed.


title: A 3D Phase Space Analysis of Scalar Field Potentials

abstract:
  In this study, we present the phase-space analysis of Quintessence models
specified by the choice of two potentials, namely the Recliner potential and
what we call the broken exponential-law potential, which is a new proposal.
Using a dynamical system analysis we provide a systematic study of the
cosmological evolution of the two models and their properties. We find new
scaling solutions characterised by a constant ratio between the energy density
of the scalar field and that of the matter component. These solutions are of
high interest in light of the possibility to alleviate the coincidence problem.
Additionally, the models also show attractor solutions. We finally construct
concrete models built using a double potential according to which one potential
realises the early-time scaling regime and the second one allows to exit this
regime and to enter in the epoch of cosmic acceleration driven by a
scalar-field dominated attractor point.


title:
Resonant dynamics of strongly interacting SU($n$) fermionic atoms in a
  synthetic flux ladder

abstract:
  We theoretically study the dynamics of $n$-level spin-orbit coupled
alkaline-earth fermionic atoms with SU($n$) symmetric interactions. We consider
three dimensional lattices with tunneling along one dimension, and the internal
levels treated as a synthetic dimension, realizing an $n$-leg flux ladder.
Laser driving is used to couple the internal levels and to induce an effective
magnetic flux through the ladder. We focus on the dense and strongly
interacting regime, where in the absence of flux the system behaves as a Mott
insulator with suppressed motional dynamics. At integer and fractional ratios
of the laser Rabi frequency to the onsite interactions, the system exhibits
resonant features in the dynamics. These resonances occur when interactions
help overcome kinetic constraints upon the tunneling of atoms, thus enabling
motion. Different resonances allow for the development of complex chiral
current patterns. The resonances resemble the ones appearing in the
longitudinal Hall resistance when the magnetic field is varied. We characterize
the dynamics by studying the system's long-time relaxation behavior as a
function of flux, number of internal levels $n$, and interaction strength. We
observe a series of non-trivial pre-thermal plateaus caused by the emergence of
resonant processes at successive orders in perturbation theory. We discuss
protocols to observe the predicted phenomena under current experimental
conditions.


title: Electric Motor Design Optimization: A Convex Surrogate Modeling Approach

abstract:
  This paper instantiates a convex electric powertrain design optimization
framework, bridging the gap between high-level powertrain sizing and low-level
components design. We focus on the electric motor and transmission of electric
vehicles, using a scalable convex motor model based on surrogate modeling
techniques. Specifically, we first select relevant motor design variables and
evaluate high-fidelity samples according to a predefined sampling plan. Second,
using the sample data, we identify a convex model of the motor, which predicts
its losses as a function of the operating point and the design parameters. We
also identify models of the remaining components of the powertrain, namely a
battery and a fixed-gear transmission. Third, we frame the minimum-energy
consumption design problem over a drive cycle as a second-order conic program
that can be efficiently solved with optimality guarantees. Finally, we showcase
our framework in a case study for a compact family car and compute the optimal
motor design and transmission ratio. We validate the accuracy of our models
with a high-fidelity simulation tool and calculate the drift in battery energy
consumption. We show that our model can capture the optimal operating line and
the error in battery energy consumption is low. Overall, our framework can
provide electric motor design experts with useful starting points for further
design optimization.


title:
Comparisons of Type Ia Supernova Light Curves in the UV and Optical with
  the Swift Ultra-Violet/Optical Telescope

abstract:
  We examine the light curve parameters of 97 nearby Type Ia supernovae in the
ultraviolet and optical using observations from the Swift Ultra-Violet/Optical
Telescope. Our light curve models used a linear combinations of templates,
which were based on Functional Principal Component Analysis of the optical
photometry of SNe Ia. The weights for each principal component used in the fit
capture certain aspects of the light curve properties. We find that there is
little difference in the overall variability of principal component scores
across filters. We also find correlations between certain filters and PC
components, indicating that the UV and optical properties seem to be tied
together. This is a promising step in UV SNe Ia analysis, and suggests that UV
light curves may be used to infer optical properties, paving the way for future
cosmological studies.


title: Kazakov-Migdal model on the Graph and Ihara Zeta Function

abstract:
  We propose the Kazakov-Migdal model on graphs and show that, when the
parameters of this model are appropriately tuned, the partition function is
represented by the unitary matrix integral of an extended Ihara zeta function,
which has a series expansion by all non-collapsing Wilson loops with their
lengths as weights. The partition function of the model is expressed in two
different ways according to the order of integration. A specific unitary matrix
integral can be performed at any finite $N$ thanks to this duality. We exactly
evaluate the partition function of the parameter-tuned Kazakov-Migdal model on
an arbitrary graph in the large $N$ limit and show that it is expressed by the
infinite product of the Ihara zeta functions of the graph.


title:
Aspirations and Practice of Model Documentation: Moving the Needle with
  Nudging and Traceability

abstract:
  Machine learning models have been widely developed, released, and adopted in
numerous applications. Meanwhile, the documentation practice for machine
learning models often falls short of established practices for traditional
software components, which impedes model accountability, inadvertently abets
inappropriate or misuse of models, and may trigger negative social impact.
Recently, model cards, a template for documenting machine learning models, have
attracted notable attention, but their impact on the practice of model
documentation is unclear. In this work, we examine publicly available model
cards and other similar documentation. Our analysis reveals a substantial gap
between the suggestions made in the original model card work and the content in
actual documentation. Motivated by this observation and literature on fields
such as software documentation, interaction design, and traceability, we
further propose a set of design guidelines that aim to support the
documentation practice for machine learning models including (1) the
collocation of documentation environment with the coding environment, (2)
nudging the consideration of model card sections during model development, and
(3) documentation derived from and traced to the source. We designed a
prototype tool named DocML following those guidelines to support model
development in computational notebooks. A lab study reveals the benefit of our
tool to shift the behavior of data scientists towards documentation quality and
accountability.


title:
Multi-band D-TRILEX approach to materials with strong electronic
  correlations

abstract:
  We present the multi-band dual triply irreducible local expansion (D-TRILEX)
approach to interacting electronic systems and discuss its numerical
implementation. This method is designed for a self-consistent description of
multi-orbital systems that can also have several atoms in the unit cell. The
current implementation of the D-TRILEX approach is able to account for the
frequency- and channel-dependent long-ranged electronic interactions. We show
that our method is accurate when applied to small multi-band systems such as
the Hubbard-Kanamori dimer. Calculations for the extended Hubbard, the
two-orbital Hubbard-Kanamori, and the bilayer Hubbard models are also
discussed.


title: Atomically-thin Single-photon Sources for Quantum Communication

abstract:
  To date, quantum communication widely relies on attenuated lasers for secret
key generation. In future quantum networks fundamental limitations resulting
from their probabilistic photon distribution must be overcome by using
deterministic quantum light sources. Confined excitons in monolayers of
transition metal dichalcogenides (TMDCs) constitute a novel type of emitter for
quantum light generation. These atomically-thin solid-state sources show
appealing prospects for large-scale and low-cost device integration, meeting
the demands of quantum information technologies. Here, we pioneer the practical
suitability of TMDC devices in quantum communication. We employ a WSe$_2$
monolayer single-photon source to emulate the BB84 protocol in a quantum key
distribution (QKD) setup and achieve raw key rates of up to 66.95 kHz and
antibunching values down to 0.034 - a performance competitive with QKD
experiments using semiconductor quantum dots or color centers in diamond. Our
work opens the route towards wider applications of quantum information
technologies using TMDC single-photon sources.


title: On chains associated with abstract key polynomials

abstract:
  In this paper, for a henselian valued field $(K, v)$ of arbitrary rank and an
extension $w$ of $v$ to $K(X),$ we use abstract key polynomials for $w$ to give
a connection between complete sets, saturated distinguished chains and Okutsu
frames. Further, for a valued field $(K, v),$ we also obtain a close connection
between complete set of ABKPs for $w$ and Maclane-Vaqui\'e chains of $w.$


title: Naturally reductive $(\alpha_1, \alpha_2)$ metrics

abstract:
  Let $F$ be a homogeneous $(\alpha_1,\alpha_2)$ metric on the reductive
homogeneous manifold $G/H$. Firstly, we characterize the natural reductiveness
of $F$ as a local $f$-product between naturally reductive Riemannian metrics.
Secondly, we prove the equivalence among several properties of $F$ for its mean
Berwald curvature and S-curvature. Finally, we find an explicit flag curvature
formula when $F$ is naturally reductive.


title: Dynamic grain models via fast heuristics for diagram representations

abstract:
  The present paper introduces a mathematical model for studying dynamic grain
growth. In particular, we show how characteristic measurements, grain volumes,
centroids, and central second-order moments at discrete moments in time can be
turned quickly into a continuous description of the grain growth process in
terms of geometric diagrams (which largely generalize the well-known Voronoi
and Laguerre tessellations). We evaluate the computational behavior of our
algorithm on real-world data.


title:
Stochastic Finite Volume Method for Uncertainty Quantification of
  Transient Flow in Gas Pipeline Networks

abstract:
  We develop a weakly intrusive framework to simulate the propagation of
uncertainty in solutions of generic hyperbolic partial differential equation
systems on graph-connected domains with nodal coupling and boundary conditions.
The method is based on the Stochastic Finite Volume (SFV) approach, and can be
applied for uncertainty quantification (UQ) of the dynamical state of fluid
flow over actuated transport networks. The numerical scheme has specific
advantages for modeling intertemporal uncertainty in time-varying boundary
parameters, which cannot be characterized by strict upper and lower (interval)
bounds. We describe the scheme for a single pipe, and then formulate the
controlled junction Riemann problem (JRP) that enables the extension to general
network structures. We demonstrate the method's capabilities and performance
characteristics using a standard benchmark test network.


title:
Realizability in tropical geometry and unobstructedness of Lagrangian
  submanifolds

abstract:
  We say that a tropical subvariety $V\subset \mathbb R^n$ is $B$-realizable if
it can be lifted to an analytic subset of $(\Lambda^*)^n$. When $V$ is a smooth
curve or hypersurface, there always exists a Lagrangian submanifold lift
$L_V\subset (\mathbb C^*)^n$. We prove that whenever $L_V$ has well-defined
Floer cohomology, we can find for each point of $V$ a Lagrangian torus brane
whose Lagrangian intersection Floer cohomology with $L_V$ is non-vanishing. As
a consequence, whenever $L_V$ is a Lagrangian submanifold that can be made
unobstructed by a bounding cochain, the tropical subvariety $V$ is
$B$-realizable by applying mirror symmetry.
  As an application, we show that the Lagrangian lift of a genus zero tropical
curve is unobstructed, thereby giving a purely symplectic argument for Nishinou
and Siebert's proof that genus-zero tropical curves are $B$-realizable. We also
prove that tropical curves inside tropical abelian surfaces are $B$-realizable.


title:
A Systematic Comparison of Simulation Software for Robotic Arm
  Manipulation using ROS2

abstract:
  Simulation software is a powerful tool for robotics research, allowing the
virtual representation of the real world. However with the rise of the Robot
Operating System (ROS), there are new simulation software packages that have
not been compared within the literature. This paper proposes a systematic
review of simulation software that are compatible with ROS version 2. The focus
is research in robotics arm manipulation as it represents the most often used
robotic application in industry and their future applicability to digital
twins. For this, we thus benchmark simulation software under similar
parameters, tasks and scenarios, and evaluate them in terms of their capability
for long-term operations, success at completing a task, repeatability and
resource usage. We find that there is no best simulation software overall, but
two simulation packages (Ignition and Webots) have higher stability than other
while, in terms of resources usage, PyBullet and Coppeliasim consume less than
their competitors.


title: Are fast scramblers good thermal baths?

abstract:
  The Sachdev-Ye-Kitaev (SYK$_{4}$) model has attracted attention for its fast
scrambling properties and its thermalization rate that is set only by the
temperature. In this work we ask the question of whether the SYK$_{4}$ model is
also a good thermal bath, in the sense that it allows a system coupled to it to
thermalize. We address this question by considering the dynamics of a system of
$N$ random non-interacting Majorana fermions coupled to an SYK$_{4}$ bath with
$M$ Majorana fermions that we solve with Keldysh techniques in the limit of
$M\gg N\gg 1$. We compare this nonequilibrium setting with a conventional bath
made of free non-interacting degrees of freedom with a continous spectrum. We
show that the SYK$_{4}$ bath is more efficient in thermalising the system at
weak coupling, due to its enhanced density of states at low frequency, while at
strong system-bath couplings both type of environments give rise to a similar
time scale for thermalisation.


title:
Absence of Structure Resonances in SIS100 Tune Quadrant for Heavy Ion
  Fast Extraction

abstract:
  The understanding and avoidance of space charge induced resonances is of
utmost importance for long storage times in synchrotrons, as they can lead to
halo generation and subsequent beam loss. This report discusses the absence of
structure resonances for heavy-ion operation in SIS100 in the tune quadrant
foreseen for the fast extraction mode, $18.5 < Q_{x,y} < 19$ . Simulations of
beam losses for the duration of the SIS100 accumulation plateau at nominal
transverse space charge conditions with a maximum tune shift of $\Delta
Q^{SC}_y = -0.3$ supplement the discussion.


title: Label Augmentation with Reinforced Labeling for Weak Supervision

abstract:
  Weak supervision (WS) is an alternative to the traditional supervised
learning to address the need for ground truth. Data programming is a practical
WS approach that allows programmatic labeling data samples using labeling
functions (LFs) instead of hand-labeling each data point. However, the existing
approach fails to fully exploit the domain knowledge encoded into LFs,
especially when the LFs' coverage is low. This is due to the common data
programming pipeline that neglects to utilize data features during the
generative process. This paper proposes a new approach called reinforced
labeling (RL). Given an unlabeled dataset and a set of LFs, RL augments the
LFs' outputs to cases not covered by LFs based on similarities among samples.
Thus, RL can lead to higher labeling coverage for training an end classifier.
The experiments on several domains (classification of YouTube comments, wine
quality, and weather prediction) result in considerable gains. The new approach
produces significant performance improvement, leading up to +21 points in
accuracy and +61 points in F1 scores compared to the state-of-the-art data
programming approach.


title:
Singularity formation in asymptotically safe cosmology with
  inhomogeneous equation of state

abstract:
  Interest in cosmological singularities has remarkably grown in recent times,
particularly on future singularities with the discovery of late-time
acceleration of the universe and dark energy. While such explorations have
previously been done in various modified gravity and quantum gravitationally
inspired cosmologies (besides standard general relativistic cosmology), no such
an endeavour has been taken up till now in the realms of renormalization group
approaches to cosmology and we have hence took up on this journey. In this
work, we consider the formation of cosmological singularities in an
asymptotically safe cosmology where the cut off scale is proportional to the
Hubble parameter. We consider a well motivated inhomogeneous form of the
equation of state(EOS) as well. We firstly delve into some basics of this
cosmology and show that such a scenario permits a transition between phantom
and quintessence forms of universal evolution. We then show that one can have
Type I - Type IV singularities in such a cosmology for various version of the
EOS and that the singularities can occur both in finite and infinite time. The
conditions in which these singularities occur is significantly different than
how they occur in the standard cosmology, with the formulations being even more
involved. Interestingly this difference comes about without the need of any
free parameters in the cosmological models, which is usually what one observes
when one goes for the same pursuit via various modified gravity/ QG inspired
cosmological approaches. Furthermore, we showed that usual singularity removal
methods like conformal anomaly effects and f(R) gravity effects do not offer
much hope for singularity removal in this cosmology.


title: Fair Algorithm Design: Fair and Efficacious Machine Scheduling

abstract:
  Motivated by a plethora of practical examples where bias is induced by
automated-decision making algorithms, there has been strong recent interest in
the design of fair algorithms. However, there is often a dichotomy between
fairness and efficacy: fair algorithms may proffer low social welfare solutions
whereas welfare optimizing algorithms may be very unfair. This issue is
exemplified in the machine scheduling problem where, for $n$ jobs, the social
welfare of any fair solution may be a factor $\Omega(n)$ worse than the optimal
welfare. In this paper, we prove that this dichotomy between fairness and
efficacy can be overcome if we allow for a negligible amount of bias: there
exist algorithms that are both "almost perfectly fair" and have a constant
factor efficacy ratio, that is, are guaranteed to output solutions that have
social welfare within a constant factor of optimal welfare. Specifically, for
any $\epsilon>0$, there exist mechanisms with efficacy ratio
$\Theta(\frac{1}{\epsilon})$ and where no agent is more than an $\epsilon$
fraction worse off than they are in the fairest possible solution (given by an
algorithm that does not use personal or type data). Moreover, these bicriteria
guarantees are tight and apply to both the single machine case and the multiple
machine case. The key to our results are the use of Pareto scheduling
mechanisms. These mechanisms, by the judicious use of personal or type data,
are able to exploit Pareto improvements that benefit every individual; such
Pareto improvements would typically be forbidden by fair scheduling algorithms
designed to satisfy standard statistical measures of group fairness. We
anticipate this paradigm, the judicious use of personal data by a fair
algorithm to greatly improve performance at the cost of negligible bias, has
wider application.


title:
Receptive Field Analysis of Temporal Convolutional Networks for Monaural
  Speech Dereverberation

abstract:
  Speech dereverberation is often an important requirement in robust speech
processing tasks. Supervised deep learning (DL) models give state-of-the-art
performance for single-channel speech dereverberation. Temporal convolutional
networks (TCNs) are commonly used for sequence modelling in speech enhancement
tasks. A feature of TCNs is that they have a receptive field (RF) dependant on
the specific model configuration which determines the number of input frames
that can be observed to produce an individual output frame. It has been shown
that TCNs are capable of performing dereverberation of simulated speech data,
however a thorough analysis, especially with focus on the RF is yet lacking in
the literature. This paper analyses dereverberation performance depending on
the model size and the RF of TCNs. Experiments using the WHAMR corpus which is
extended to include room impulse responses (RIRs) with larger T60 values
demonstrate that a larger RF can have significant improvement in performance
when training smaller TCN models. It is also demonstrated that TCNs benefit
from a wider RF when dereverberating RIRs with larger RT60 values.


title: Radiative Effects in the Scalar Sector of Vector Leptoquark Models

abstract:
  Gauge models with massive vector leptoquarks at the TeV scale provide a
successful framework for addressing the B-physics anomalies. Among them, the
4321 model has been considered as the low-energy limit of some complete
theories of flavor. In this work, we study the renormalization group evolution
of this model, laying particular emphasis on the scalar sector. We find that,
despite the asymptotic freedom of the gauge couplings, Landau poles can arise
at relatively low scale due to the fast running of quartic couplings. Moreover,
we discuss the possibility of radiative electroweak symmetry breaking, and
characterize the fine-tuning associated with the hierarchy between the
electroweak scale and the additional TeV-scale scalars. Finally, the idea of
scalar fields unification is explored, motivated by ultraviolet embeddings of
the 4321 model.


title:
Impact of Field Errors of Quadrupole Units on Beam Losses at SIS100
  Injection

abstract:
  This report discusses heavy-ion beam losses due to the nominal transverse
space charge and the field imperfections in SIS100 during accumulation based on
recent cold measurements of the first 10 series quadrupole units. They are
compared to the (stamped) First-of-Series quadrupole unit #2.


title:
Low-order wavefront control using a Zernike sensor through Lyot
  coronagraphs for exoplanet imaging

abstract:
  Combining large segmented space telescopes, coronagraphy and wavefront
control methods is a promising solution to produce a dark hole (DH) region in
the coronagraphic image of an observed star and study planetary companions. The
thermal and mechanical evolution of such a high-contrast facility leads to
wavefront drifts that degrade the DH contrast during the observing time, thus
limiting the ability to retrieve planetary signals. Lyot-style coronagraphs are
starlight suppression systems that remove the central part of the image for an
unresolved observed star, the point spread function, with an opaque focal plane
mask (FPM). When implemented with a flat mirror containing an etched pinhole,
the mask rejects part of the starlight through the pinhole which can be used to
retrieve information about low-order aberrations. We propose an active control
scheme using a Zernike wavefront sensor (ZWFS) to analyze the light rejected by
the FPM, control low-order aberrations, and stabilize the DH contrast. The
concept formalism is first presented before characterizing the sensor behavior
in simulations and in laboratory. We then perform experimental tests to
validate a wavefront control loop using a ZWFS on the HiCAT testbed. By
controlling the first 11 Zernike modes, we show a decrease in wavefront error
standard deviation by a factor of up to 9 between open- and closed-loop
operations using the ZWFS. In the presence of wavefront perturbations, we show
the ability of this control loop to stabilize a DH contrast around 7x10^-8 with
a standard deviation of 7x10^-9. Active control with a ZWFS proves a promising
solution in Lyot coronagraphs with an FPM-filtered beam to control and
stabilize low-order wavefront aberrations and DH contrast for exoplanet imaging
with future space missions.


title: On Helical Surfaces with a Constant Ratio of Principal Curvatures

abstract:
  We determine all helical surfaces in three-dimensional Euclidean space which
possess a constant ratio $a:=\kappa_1/\kappa_2$ of principal curvatures (CRPC
surfaces), thus providing the first explicit CRPC surfaces beyond the known
rotational ones. A key ingredient in the successful determination of these
surfaces is the proper choice of generating profiles. We employ the contours
for parallel projection orthogonal to the helical axis. This has the advantage
that the CRPC property can be nicely expressed with the help of the involution
of conjugate surface tangents. The arising ordinary differential equation has
an explicit parametric solution, which forms the basis for a further study and
classification of the possible shapes and the singularities arising for $a>0$.


title: Seshadri constants on abelian surfaces

abstract:
  So far, Seshadri constants on abelian surfaces are completely understood only
in the cases of Picard number one and on principally polarized abelian surfaces
with real multiplication. Beyond that, there are partial results for products
of elliptic curves. In this paper, we show how to compute the Seshadri constant
of any nef line bundle on any abelian surface over the complex numbers. We
develop an effective algorithm depending only on the basis of the
N\'eron-Severi group to compute not only the Seshadri constants but also the
numerical data of their Seshadri curves. Access to the Seshadri curves allows
us to plot Seshadri functions and better understand their structure. We show
that already in the case of Picard number two the complexity of Seshadri
functions can vary to a great degree. Our results indicate that aside from
finitely many cases the complexity of the Seshadri function is at least as high
as in the Cantor function.


title:
Random Graph Embedding and Joint Sparse Regularization for Multi-label
  Feature Selection

abstract:
  Multi-label learning is often used to mine the correlation between variables
and multiple labels, and its research focuses on fully extracting the
information between variables and labels. The $\ell_{2,1}$ regularization is
often used to get a sparse coefficient matrix, but the problem of
multicollinearity among variables cannot be effectively solved. In this paper,
the proposed model can choose the most relevant variables by solving a joint
constraint optimization problem using the $\ell_{2,1}$ regularization and
Frobenius regularization. In manifold regularization, we carry out a random
walk strategy based on the joint structure to construct a neighborhood graph,
which is highly robust to outliers. In addition, we give an iterative algorithm
of the proposed method and proved the convergence of this algorithm. The
experiments on the real-world data sets also show that the comprehensive
performance of our method is consistently better than the classical method.


title: Fairness in Maximal Covering Facility Location Problems

abstract:
  This paper provides a general mathematical programming based framework to
incorporate fairness measures from the facilities' perspective to Discrete and
Continuous Maximal Covering Location Problems. The main ingredients to
construct a function measuring fairness in this problem are the use of: (1)
ordered weighted averaging operators, a family of aggregation criteria very
popular to solve multiobjective combinatorial optimization problems, and (2)
$\alpha$-fairness operators which allow to generalize most of the equity
measures. A general mathematical programming model is derived which captures
the notion of fairness in maximal covering location problems. The models are
firstly formulated as Mixed Integer Non-Linear programming problems for both
the discrete and the continuous frameworks. Suitable Mixed Integer Second Order
Cone programming reformulations are derived using geometric properties of the
problem. Finally, the paper concludes with the results obtained on an extensive
battery of computational experiments. The obtained results support the
convenience of the proposed approach.


title: CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite

abstract:
  Context: Cryptographic APIs are often misused in real-world applications.
Therefore, many cryptographic API misuse detection tools have been introduced.
However, there exists no established reference benchmark for a fair and
comprehensive comparison and evaluation of these tools. While there are
benchmarks, they often only address a subset of the domain or were only used to
evaluate a subset of existing misuse detection tools. Objective: To fairly
compare cryptographic API misuse detection tools and to drive future
development in this domain, we will devise such a benchmark. Openness and
transparency in the generation process are key factors to fairly generate and
establish the needed benchmark. Method: We propose an approach where we derive
the benchmark generation methodology from the literature which consists of
general best practices in benchmarking and domain-specific benchmark
generation. A part of this methodology is transparency and openness of the
generation process, which is achieved by pre-registering this work. Based on
our methodology we design CamBench, a fair "Cryptographic API Misuse Detection
Tool Benchmark Suite". We will implement the first version of CamBench limiting
the domain to Java, the JCA, and static analyses. Finally, we will use CamBench
to compare current misuse detection tools and compare CamBench to related
benchmarks of its domain.


title: $L_\infty$-algebra of braided electrodynamics

abstract:
  Using the recently developed formalism of braided noncommutative field
theory, we construct an explicit example of braided electrodynamics, that is, a
noncommutative $U(1)$ gauge theory coupled to a Dirac fermion. We construct the
braided $L_\infty$-algebra of this field theory and apply the formalism to
obtain the braided equations of motion, action functional and conserved matter
current. The braided deformation leads to a modification of the charge
conservation. Finally, the Feynman integral appearing in the one-loop
contribution to the vacuum polarization diagram is calculated. There are no
non-planar diagrams, but the UV/IR mixing appears nevertheless. We comment on
this unexpected result.


title:
Empirical Dust Attenuation Model Leads to More Realistic UVJ Diagram for
  TNG100 Galaxies

abstract:
  Dust attenuation varies substantially from galaxy to galaxy and as of yet
cannot be reproduced from first principles in theoretical models. In Nagaraj et
al. (2022), we developed the first Bayesian population model of dust
attenuation as a function of stellar population properties and projected galaxy
shape, built on spectral energy distribution (SED) fits of nearly 30,000
galaxies in the 3D-HST grism survey with broadband photometric coverage from
the rest-frame UV to IR. In this paper, we apply the model to galaxies from the
large-volume cosmological simulation TNG100. We produce a UVJ diagram and
compare it with one obtained in previous work by applying approximate radiative
transfer to the simulated galaxies. We find that the UVJ diagram based on our
empirical model is in better agreement with observations than the previous
effort, especially in the number density of dusty star forming galaxies. We
also construct the intrinsic dust-free UVJ diagram for TNG and 3D-HST galaxies
at z ~ 1, finding qualitative agreement but residual differences at the 10-20%
level. These differences can be largely attributed to the finding that TNG
galaxies have, on average, 29% younger stellar populations and 0.28 dex higher
metallicities than observed galaxies.


title: Is Speech Pathology a Biomarker in Automatic Speaker Verification?

abstract:
  With the advancements in deep learning (DL) and an increasing interest in
data-driven speech processing methods, a major challenge for speech data
scientists in the healthcare domain is the anonymization of pathological
speech, which is a required step to be able to make them accessible as a public
training resource. In this paper, we investigate pathological speech data and
compare their speaker verifiability with that of healthy individuals. We
utilize a large pathological speech corpus of more than 2,000 test subjects
with various speech and voice disorders from different ages and apply DL-based
automatic speaker verification (ASV) techniques. As a result, we obtained a
mean equal error rate (EER) of 0.86% with a standard deviation of 0.16%, which
is a factor of three lower than comparable healthy speech databases. We further
perform detailed analyses of external influencing factors on ASV such as age,
pathology, recording environment, and utterance length, to explore their
respective effect. Our findings indicate that speech pathology is a potential
biomarker in ASV. This is potentially of high interest for the anonymization of
pathological speech data.


title: Sample-based observability of linear discrete-time systems

abstract:
  In this work, sample-based observability of linear discrete-time systems is
studied. That is, we consider the case where the system output measurements are
not available at every time instance. It is shown that some discrete-time
systems exhibit particular behaviors that lead to pathological sampling.
Depending on the characteristics of the system, different sampling schemes are
developed that allow the system state to be reconstructed.


title:
Revisiting Markovian Generative Architectures for Efficient
  Task-Oriented Dialog Systems

abstract:
  Recently, Transformer based pretrained language models (PLMs), such as GPT2
and T5, have been leveraged to build generative task-oriented dialog (TOD)
systems. A drawback of existing PLM-based models is their non-Markovian
architectures across turns, i.e., the whole history is used as the conditioning
input at each turn, which brings inefficiencies in memory, computation and
learning. In this paper, we propose to revisit Markovian Generative
Architectures (MGA), which have been used in previous LSTM-based TOD systems,
but not studied for PLM-based systems. Experiments on MultiWOZ2.1 show the
efficiency advantages of the proposed Markovian PLM-based systems over their
non-Markovian counterparts, in both supervised and semi-supervised settings.


title:
Exploiting the Right: Inferring Ideological Alignment in Online
  Influence Campaigns Using Shared Images

abstract:
  This work advances investigations into the visual media shared by agents in
disinformation campaigns by characterizing the images shared by accounts
identified by Twitter as being part of such campaigns. Using images shared by
US politicians' Twitter accounts as a baseline and training set, we build
models for inferring the ideological presentation of accounts using the images
they share. Results show that, while our models recover the expected bimodal
ideological distribution of US politicians, we find that, on average, four
separate influence campaigns -- attributed to Iran, Russia, China, and
Venezuela -- all present conservative ideological presentations in the images
they share. Given that prior work has shown Twitter accounts used by Russian
disinformation agents are ideologically diverse in the text and news they
share, these image-oriented findings provide new insights into potential axes
of coordination and suggest these accounts may not present consistent
ideological positions across modalities.


title:
DMCNet: Diversified Model Combination Network for Understanding
  Engagement from Video Screengrabs

abstract:
  Engagement is an essential indicator of the Quality-of-Learning Experience
(QoLE) and plays a major role in developing intelligent educational interfaces.
The number of people learning through Massively Open Online Courses (MOOCs) and
other online resources has been increasing rapidly because they provide us with
the flexibility to learn from anywhere at any time. This provides a good
learning experience for the students. However, such learning interface requires
the ability to recognize the level of engagement of the students for a holistic
learning experience. This is useful for both students and educators alike.
However, understanding engagement is a challenging task, because of its
subjectivity and ability to collect data. In this paper, we propose a variety
of models that have been trained on an open-source dataset of video
screengrabs. Our non-deep learning models are based on the combination of
popular algorithms such as Histogram of Oriented Gradient (HOG), Support Vector
Machine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up Robust
Features (SURF). The deep learning methods include Densely Connected
Convolutional Networks (DenseNet-121), Residual Network (ResNet-18) and
MobileNetV1. We show the performance of each models using a variety of metrics
such as the Gini Index, Adjusted F-Measure (AGF), and Area Under receiver
operating characteristic Curve (AUC). We use various dimensionality reduction
techniques such as Principal Component Analysis (PCA) and t-Distributed
Stochastic Neighbor Embedding (t-SNE) to understand the distribution of data in
the feature sub-space. Our work will thereby assist the educators and students
in obtaining a fruitful and efficient online learning experience.


title:
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic
  Segmentation for Lung Adenocarcinoma

abstract:
  Lung cancer is the leading cause of cancer death worldwide, and
adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential
value of the histopathology images can promote precision medicine in oncology.
Tissue segmentation is the basic upstream task of histopathology image
analysis. Existing deep learning models have achieved superior segmentation
performance but require sufficient pixel-level annotations, which is
time-consuming and expensive. To enrich the label resources of LUAD and to
alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call
for the outstanding weakly-supervised semantic segmentation (WSSS) techniques
for histopathology images of LUAD. Participants have to design the algorithm to
segment tumor epithelial, tumor-associated stroma and normal tissue with only
patch-level labels. This challenge includes 10,091 patch-level annotations (the
training set) and over 130 million labeled pixels (the validation and test
sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated
by a pathologist-in-the-loop pipeline with the help of AI models and checked by
the label review board. Among 532 registrations, 28 teams submitted the results
in the test phase with over 1,000 submissions. Finally, the first place team
achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919).
According to the technical reports of the top-tier teams, CAM is still the most
popular approach in WSSS. Cutmix data augmentation has been widely adopted to
generate more reliable samples. With the success of this challenge, we believe
that WSSS approaches with patch-level annotations can be a complement to the
traditional pixel annotations while reducing the annotation efforts. The entire
dataset has been released to encourage more researches on computational
pathology in LUAD and more novel WSSS techniques.


title: Non-equilibrium dynamics of fluctuations in an ultra-cold atomic mixture

abstract:
  We investigate an ultra-cold mixture of Bose gases interacting via
spin-changing collisions by studying the dynamics of spin fluctuations. The
experimental implementation employs $^{23}$Na and $^{7}$Li atoms, which are
prepared out of equilibrium across a wide range of initial conditions. We
identify three regimes in the dynamics of the system for different initial
states: a long-lived metastable regime, an instability range with strong growth
of fluctuations, and a fast relaxing regime approaching thermal equilibrium.
Theoretical modelling of the data allows us to reconstruct effective potentials
which characterize the different dynamical regimes of the system.


title:
The Impact of Cross-Lingual Adjustment of Contextual Word
  Representations on Zero-Shot Transfer

abstract:
  Large pre-trained multilingual models such as mBERT and XLM-R enabled
effective cross-lingual zero-shot transfer in many NLP tasks. A cross-lingual
adjustment of these models using a small parallel corpus can potentially
further improve results. This is a more data efficient method compared to
training a machine-translation system or a multi-lingual model from scratch
using only parallel data. In this study, we experiment with zero-shot transfer
of English models to four typologically different languages (Spanish, Russian,
Vietnamese, and Hindi) and three NLP tasks (QA, NLI, and NER). We carry out a
cross-lingual adjustment of an off-the-shelf mBERT model. We confirm prior
finding that this adjustment makes embeddings of semantically similar words
from different languages closer to each other, while keeping unrelated words
apart. However, from the paired-differences histograms introduced in our work
we can see that the adjustment only modestly affects the relative distances
between related and unrelated words. In contrast, fine-tuning of mBERT on
English data (for a specific task such as NER) draws embeddings of both related
and unrelated words closer to each other. The cross-lingual adjustment of mBERT
improves NLI in four languages and NER in two languages, while QA performance
never improves and sometimes degrades. When we fine-tune a cross-lingual
adjusted mBERT for a specific task (e.g., NLI), the cross-lingual adjustment of
mBERT may still improve the separation between related and related words, but
this works consistently only for the XNLI task. Our study contributes to a
better understanding of cross-lingual transfer capabilities of large
multilingual language models and of effectiveness of their cross-lingual
adjustment in various NLP tasks.


title:
Normal and Reverse Annual Modulations of Elastic WIMP-Nucleus Scattering
  Signals

abstract:
  Following our earlier work on the 3-dimensional effective velocity
distribution of Galactic WIMPs (not only impinging on our detectors but also)
scattering off target nuclei, in this paper, we demonstrate the normal and a
"reverse" annual modulations of elastic WIMP-nucleus scattering signals, which
could be observed in direct Dark Matter detection experiments. Our simulations
show that, once the WIMP mass is as light as only a few tens GeV, the event
number and the accumulated recoil energy of WIMP-induced scattering events off
both of light and heavy target nuclei would indeed be maximal (minimal) in
summer (winter). However, once the WIMP mass is as heavy as a few hundreds GeV,
the event number and the accumulated recoil energy of WIMP scattering events
off heavy nuclei would inversely be minimal in summer. Understandably, for an
intermediate WIMP mass, the event number and the accumulated recoil energy of
scattering events off some middle-mass nuclei would show an approximately
uniform time dependence.


title:
Constraining a possible time-variation of the speed of light along with
  the fine-structure constant using strong gravitational lensing and Type Ia
  supernovae observations

abstract:
  The possible time variation of the fundamental constants of nature has been
an active subject of research since the large-number hypothesis was proposed by
Dirac. In this paper, we propose a new method to investigate a possible time
variation of the speed of light ($c$) along with the fine-structure constant
($\alpha$) using Strong Gravitational Lensing (SGL) and Type Ia Supernovae (SNe
Ia) observations. We assume a general approach to describe the mass
distribution of lens-type galaxies, the one in favor of the power-law index
model (PLAW). We also consider the runaway dilaton model to describe a possible
time-variation of $\alpha$. In order to explore the results deeply, we split
the SGL sample into five sub-samples according to the lens stellar velocity
dispersion and three sub-samples according to lens redshift. The results
suggest that it is reasonable to treat the systems separately, but no strong
indication of varying $c$ was found.


title: The chromatic number of (P_5, HVN )-free graphs

abstract:
  Let $G$ be a graph. We use $\chi(G)$ and $\omega(G)$ to denote the chromatic
number and clique number of $G$ respectively. A $P_5$ is a path on 5 vertices,
and an $HVN$ is a $K_4$ together with one more vertex which is adjacent to
exactly two vertices of $K_4$. In this paper, we show that if $G$ is $(P_5,
\textit{HVN})$-free, then $\chi(G)\leq \omega(G)+3$. This generalizes some
results on $\chi$-bounded problem of $P_5$-free graphs, and this upper bound is
almost sharp as there are many $(P_5, K_4)$-free graphs with chromatic number
5.


title: Population Diversity Leads to Short Running Times of Lexicase Selection

abstract:
  In this paper we investigate why the running time of lexicase parent
selection is empirically much lower than its worst-case bound of O(N*C). We
define a measure of population diversity and prove that high diversity leads to
low running times O(N + C) of lexicase selection. We then show empirically that
genetic programming populations evolved under lexicase selection are diverse
for several program synthesis problems, and explore the resulting differences
in running time bounds.


title: A Scattering Amplitude for Massive Particles in AdS

abstract:
  In this paper, we propose a conformally covariant momentum space
representation of CFT correlation functions. We call it the AdS S-matrix. This
representation has the property that it reduces to the S-matrix in the flat
space limit. The flat space limit in question is taken by keeping all the
particle masses fixed as the operator conformal dimensions go to infinity along
with the AdS radius $\mathtt{R}$. We give Feynman-like rules to compute the AdS
S-matrix in $1/ \mathtt{R}$ perturbation theory. Moreover, we relate it to the
Mellin space representation of the conformal correlators in $1/ \mathtt{R}$
perturbation theory.


title: Equal-time approach to real-time dynamics of quantum fields

abstract:
  We employ the equal-time formulation of quantum field theory to derive
effective kinetic theories, first for a weakly coupled non-relativistic Bose
gas, and then for a strongly correlated system of self-interacting N-component
fields. Our results provide the link between state-of-the-art measurements of
equal-time effective actions using quantum simulator platforms, as employed in
Refs. [1, 2], and observables underlying effective kinetic or hydrodynamic
descriptions. New non-perturbative approximation schemes can be developed and
certified this way, where the a priori time-local formulation of the equal-time
effective action has crucial advantages over the conventional closed-time-path
approach which is non-local in time.


title:
Determination of the complex-valued elastic moduli of polymers by
  electrical impedance spectroscopy for ultrasound applications

abstract:
  A method is presented for the determination of complex-valued compression and
shear elastic moduli of polymers for ultrasound applications. The resulting
values, which are scarcely reported in the literature, are found with
uncertainties typically around 1 % (real part) and 6 % (imaginary part). The
method involves a setup consisting of a cm-radius, mm-thick polymer ring glued
concentrically to a disk-shaped piezoelectric transducer. The ultrasound
electrical impedance spectrum of the transducer is computed numerically and
fitted to measured values as an inverse problem in a wide frequency range,
typically from 500 Hz to 5 MHz, both on and off resonance. The method was
validated experimentally by ultrasonic through-transmission around 1.9 MHz.
Experimentally, the method is arguably simple and low cost, and it is not
limited to specific geometries and crystal symmetries. Moreover, by involving
off-resonance frequencies, it allows for determining the imaginary parts of the
elastic moduli, equivalent to attenuation coefficients. Finally, the method has
no obvious frequency limitations before severe attenuation sets in above 100
MHz.


title:
Evidence for Jahn-Teller-driven metal-insulator transition in strained
  SrCrO3 from first principles calculations

abstract:
  Using density-functional theory (DFT) and its extension to DFT+$U$, we
propose a possible scenario for a strain-induced metal-insulator transition
which has been reported recently in thin films of SrCrO$_3$. The
metal-insulator transition involves the emergence of a Jahn-Teller (JT)
distortion similar to the case of the related rare-earth vanadates, which also
exhibit a nominal $d^2$ occupation of the transition metal cation. Our
calculations indicate that, for realistic values of the Hubbard $U$ parameter,
the unstrained system exhibts a C-type antiferromagnetically ordered ground
state, that is already rather close to a JT instability. However, the emergence
of the JT distortion is disfavored by the large energetic overlap of the
$d_{xz}$/$d_{yz}$ band with the lower lying $d_{xy}$ band. Tensile epitaxial
strain lowers the energy of the $d_{xy}$ band relative to $d_{xz}$/$d_{yz}$ and
thus brings the system closer to the nominal filling of
$d_{xy}^1(d_{xz}d_{yz})^1$. The JT distortion then lifts the degeneracy between
the $d_{xz}$ and $d_{yz}$ orbitals and thus allows to open up a gap in the
electronic band structure.


title: Grand canonical ensembles of sparse networks and Bayesian inference

abstract:
  Maximum entropy network ensembles have been very successful in modelling
sparse network topologies and in solving challenging inference problems.
However the sparse maximum entropy network models proposed so far have fixed
number of nodes and are typically not exchangeable. Here we consider
hierarchical models for exchangeable networks in the sparse limit, i.e. with
the total number of links scaling linearly with the total number of nodes. The
approach is grand canonical, i.e. the number of nodes of the network is not
fixed a priori: it is finite but can be arbitrarily large. In this way the
grand canonical network ensembles circumvent the difficulties in treating
infinite sparse exchangeable networks which according to the Aldous-Hoover
theorem must vanish. The approach can treat networks with given degree
distribution or networks with given distribution of latent variables. When only
a subgraph induced by a subset of nodes is known, this model allows a Bayesian
estimation of the network size and the degree sequence (or the sequence of
latent variables) of the entire network which can be used for network
reconstruction.

categories: cond-mat.dis-nn cs.SI math.ST physics.soc-ph stat.TH


title:
Vortex jets generated by edge defects in current-carrying superconductor
  thin strips

abstract:
  At sufficiently large transport currents $I_\mathrm{tr}$, a defect at the
edge of a superconducting strip acts as a gate for the vortices entering into
it. These vortices form a jet, which is narrow near the defect and expands due
to the repulsion of vortices as they move to the opposite edge of the strip,
giving rise to a transverse voltage $V_\perp$. Here, relying upon the equation
of vortex motion under competing vortex-vortex and $I_\mathrm{tr}$-vortex
interactions, we derive the vortex jet shapes in narrow ($\xi\ll
w\lesssim\lambda_\mathrm{eff}$) and wide ($w\gg\lambda_\mathrm{eff}$) strips
[$\xi$: coherence length, $w$: strip width, $\lambda_\mathrm{eff}$: effective
penetration depth]. We predict a nonmonotonic dependence
$V_\perp(I_\mathrm{tr})$ which can be measured with Hall voltage leads placed
on the line $V_1V_2$ at a small distance $l$ apart from the edge defect and
which changes its sign upon $l\rightarrow -l$ reversal. For narrow strips, we
compare the theoretical predictions with experiment, by fitting the
$V_\perp(I_\mathrm{tr},l)$ data for $1\,\mu$m-wide MoSi strips with single edge
defects milled by a focused ion beam at distances $l = 16$-$80$\,nm from the
line $V_1V_2$. For wide strips, the derived magnetic-field dependence of the
vortex jet shape is in line with the recent experimental observations for
vortices moving in Pb bridges with a narrowing. Our findings are augmented with
the time-dependent Ginzburg-Landau simulations which reproduce the calculated
vortex jet shapes and the $V_\perp(I_\mathrm{tr},l)$ maxima. Furthermore, with
increase of $I_\mathrm{tr}$, the numerical modeling unveils the evolution of
vortex jets to vortex rivers, complementing the analytical theory in the entire
range of $I_\mathrm{tr}$.


title:
Repulsive torques alone trigger crystallization of constant speed active
  particles

abstract:
  We investigate the possibility for self-propelled particles to crystallize
without reducing their intrinsic speed. We illuminate how, in the absence of
any force, the competition between self-propulsion and repulsive torques
determine the macroscopic phases of constant-speed active particles. This
minimal model expands upon existing approaches for an improved understanding of
crystallization of active matter.


title:
Anomalous U(1) Gauge Bosons and String Physics at the Forward Physics
  Facility

abstract:
  We show that experiments at the Forward Physics Facility, planned to operate
near the ATLAS interaction point during the LHC high-luminosity era, will be
able to probe predictions of Little String Theory by searching for anomalous
U(1) gauge bosons living in the bulk. The interaction of the abelian broken
gauge symmetry with the Standard Model is generated at the one-loop level
through kinetic mixing with the photon. Gauge invariant generation of mass for
the U(1) gauge boson proceeds via the Higgs mechanism in spontaneous symmetry
breaking, or else through anomaly-cancellation appealing to Stueckelberg-mass
terms. We demonstrate that FASER2 will be able to probe string scales over
roughly two orders of magnitude: 10^5 < M_s/TeV < 10^7.


title:
The Initial-Final Mass Relation of White Dwarfs: A Tool to Calibrate the
  Third Dredge-up

abstract:
  The initial mass-final mass relationship (IFMR) of white dwarfs (WD)
represents a crucial benchmark for stellar evolution models, especially for the
efficiency of mixing episodes and mass loss during the asymptotic giant branch
(AGB) phase. In this study, we argue that such relation offers the opportunity
of constraining the third dredge-up (3DU), with important consequences for
chemical yields. The results are discussed in light of recent studies that have
identified a kink in the IFMR for initial masses close to $2\,M_{\odot}$.
Adopting a physically-sound approach in which the efficiency $\lambda$ of the
3DU varies as a function of core and envelope masses, we calibrate $\lambda$ in
solar-metallicity TP-AGB models in order to reproduce the final masses of their
WD progeny, over a the range of initial masses $0.9 \le M_{\rm i}/M_{\odot} \le
6$. In particular, we find that in low-mass stars with $1.4 \le M_{\rm
i}/M_{\odot} \le 2.0$ the efficiency is small, $\lambda \le 0.3$, it steeply
rises to about $\lambda \simeq 0.65$ in intermediate-mass stars with $2.0 \le
M_{\rm i}/M_{\odot} \le 4.0$, and then it drops in massive TP-AGB stars with
$4.0 \le M_{\rm i}/M_{\odot} \le 6.0$. Our study also suggests that a second
kink may show up in the IFMR at the transition between the most massive carbon
stars and those that are dominated by hot-bottom burning.


title:
Hybrid Neural Network Augmented Physics-based Models for Nonlinear
  Filtering

abstract:
  In this paper we present a hybrid neural network augmented physics-based
modeling (APBM) framework for Bayesian nonlinear latent space estimation. The
proposed APBM strategy allows for model adaptation when new operation
conditions come into play or the physics-based model is insufficient (or
incomplete) to properly describe the latent phenomenon. One advantage of the
APBMs and our estimation procedure is the capability of maintaining the
physical interpretability of estimated states. Furthermore, we propose a
constraint filtering approach to control the neural network contributions to
the overall model. We also exploit assumed density filtering techniques and
cubature integration rules to present a flexible estimation strategy that can
easily deal with nonlinear models and high-dimensional latent spaces. Finally,
we demonstrate the efficacy of our methodology by leveraging a target tracking
scenario with nonlinear and incomplete measurement and acceleration models,
respectively.


title: Matrix Entanglement

abstract:
  In gauge/gravity duality, matrix degrees of freedom on the gauge theory side
play important roles for the emergent geometry. In this paper, we discuss how
the entanglement on the gravity side can be described as the entanglement
between matrix degrees of freedom. Our approach, which we call 'matrix
entanglement', is different from 'target-space entanglement' proposed and
discussed recently by several groups. We consider several classes of quantum
states to which our approach can play important roles. When applied to fuzzy
sphere, matrix entanglement can be used to define the usual spatial
entanglement in two-brane or five-brane world-volume theory nonperturbatively
in a regularized setup. Another application is to a small black hole in AdS5*S5
that can evaporate without being attached to a heat bath, for which our
approach suggests a gauge theory origin of the Page curve. The confined degrees
of freedom in the partially-deconfined states play the important roles.


title:
Convergence and Analytic Decomposition of Quantum Cohomology of Toric
  Bundles

abstract:
  We prove that the equivariant big quantum cohomology QH^*_T(E) of the total
space of a toric bundle E \to B converges provided that the big quantum
cohomology QH^*(B) converges. The proof is based on Brown's mirror theorem for
toric bundles. It has been observed by Coates, Givental and Tseng that the
quantum connection of E splits into copies of that of B. Under the assumption
that QH^*(B) is convergent, we construct a decomposition of the quantum
D-module of E into a direct sum of that of B, which is analytic with respect to
parameters of QH^*_T(E). In particular, we obtain an analytic decomposition for
the equivariant/non-equivariant big quantum cohomology of E.


title:
Regularity of one-dimensional first-order mean field games and the
  planning problem

abstract:
  We study the regularity of the one-dimensional, local, first-order mean field
games system and the planning problem, assuming a Hamiltonian of superlinear
growth, with a non-separated, strictly monotone dependence on the density. We
improve upon the existing literature by obtaining two regularity results. The
first is the existence of classical solutions without the need to assume
blow-up of the cost function near small densities. The second result is the
interior smoothness of weak solutions without the need to assume neither
blow-up of the cost function nor that the initial density be bounded away from
zero. Our approach relies on the elliptic structure of the system and
displacement convexity estimates. In particular, we apply displacement
convexity methods to obtain both global and local a priori lower bounds on the
density.


title:
The contribution of magnetized galactic outflows to extragalactic
  Faraday rotation

abstract:
  Galactic outflows driven by star formation and active galactic nuclei blow
bubbles into their local environments, causing galactic magnetic fields to be
carried into intergalactic space. We explore the redshift-dependent effect of
these magnetized bubbles on the Faraday Rotation Measure (RM) of extragalactic
radio sources. Using the IllustrisTNG cosmological simulations, we separate the
contribution from magnetic bubbles from that of the volume-filling magnetic
component expected to be due to the seed field originating in the Early
Universe. We use this separation to extract the redshift dependence of each
component and to compare TNG model predictions with observation measurements of
the NRAO VLA Sky Survey (NVSS). We find that magnetized bubbles provide a
sizeable contribution to the extragalactic RM, with redshift-independent
$\langle |{\rm RM}| \rangle \simeq 13$ rad/m$^2$ for sources at redshifts $z\ge
2$. This is close to the mean residual RM of $16$ rad/m$^2$ found from NVSS
data in this redshift range. Using the IllustrisTNG simulations, we also
evaluate a simple model for the contribution to residual RM from individual
host galaxies and show that this contribution is negligible at high-redshift.
While the contribution from magnetic bubbles in the IllustrisTNG model is
currently compatible with observational measurements of residual RM, the
next-generation RM sky surveys, which will be free from the wrapping
uncertainty, have larger statistics and better sensitivity should be able to
observe predicted flat contribution from magnetic bubbles at large redshifts.
This should allow to experimentally probe magnetic bubbles and check models of
galaxy feedback in cosmological simulations.


title: Unified entropies and quantum speed limits for nonunitary dynamics

abstract:
  We discuss a class of quantum speed limits (QSLs) based on unified quantum
($\alpha,\mu$)-entropy for nonunitary physical processes. The bounds depend on
both the Schatten speed and the smallest eigenvalue of the evolved state, and
the two-parametric unified entropy. We specialize these results to quantum
channels and non-Hermitian evolutions. In the first case, the QSL depends on
the Kraus operators related to the quantum channel, while in the second case
the speed limit is recast in terms of the non-Hermitian Hamiltonian. To
illustrate these findings, we consider a single-qubit state evolving under the
(i) amplitude damping channel, and (ii) the nonunitary dynamics generated by a
parity-time-reversal symmetric non-Hermitian Hamiltonian. The QSL is nonzero at
earlier times, while becomes loose as the smallest eigenvalue of the evolved
state approaches zero. Furthermore, we investigate the interplay between
unified entropies and speed limits for the reduced dynamics of quantum
many-body systems. The unified entropy is upper bounded by the quantum
fluctuations of the Hamiltonian of the system, while the QSL is nonzero when
entanglement is created by the nonunitary evolution. Finally, we apply these
results to the XXZ model and verify that the QSL asymptotically decreases as
the system size increases. Our results find applications to nonequilibrium
thermodynamics, quantum metrology, and equilibration of many-body systems.


title: Data-heterogeneity-aware Mixing for Decentralized Learning

abstract:
  Decentralized learning provides an effective framework to train machine
learning models with data distributed over arbitrary communication graphs.
However, most existing approaches toward decentralized learning disregard the
interaction between data heterogeneity and graph topology. In this paper, we
characterize the dependence of convergence on the relationship between the
mixing weights of the graph and the data heterogeneity across nodes. We propose
a metric that quantifies the ability of a graph to mix the current gradients.
We further prove that the metric controls the convergence rate, particularly in
settings where the heterogeneity across nodes dominates the stochasticity
between updates for a given node. Motivated by our analysis, we propose an
approach that periodically and efficiently optimizes the metric using standard
convex constrained optimization and sketching techniques. Through comprehensive
experiments on standard computer vision and NLP benchmarks, we show that our
approach leads to improvement in test performance for a wide range of tasks.


title:
BEHM-GAN: Bandwidth Extension of Historical Music using Generative
  Adversarial Networks

abstract:
  Audio bandwidth extension aims to expand the spectrum of narrow-band audio
signals. Although this topic has been broadly studied during recent years, the
particular problem of extending the bandwidth of historical music recordings
remains an open challenge. This paper proposes BEHM-GAN, a model based on
generative adversarial networks, as a practical solution to this problem. The
proposed method works with the complex spectrogram representation of audio and,
thanks to a dedicated regularization strategy, can effectively extend the
bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is
designed to be applied as a second step after denoising the recording to
suppress any additive disturbances, such as clicks and background noise. We
train and evaluate the method using solo piano classical music. The proposed
method outperforms the compared baselines in both objective and subjective
experiments. The results of a formal blind listening test show that BEHM-GAN
significantly increases the perceptual sound quality in early-20th-century
gramophone recordings. For several items, there is a substantial improvement in
the mean opinion score after enhancing historical recordings with the proposed
bandwidth-extension algorithm. This study represents a relevant step toward
data-driven music restoration in real-world scenarios.


title:
Where Do You Want To Invest? Predicting Startup Funding From Freely,
  Publicly Available Web Information

abstract:
  We consider in this paper the problem of predicting the ability of a startup
to attract investments using freely, publicly available data. Information about
startups on the web usually comes either as unstructured data from news, social
networks, and websites or as structured data from commercial databases, such as
Crunchbase. The possibility of predicting the success of a startup from
structured databases has been studied in the literature and it has been shown
that initial public offerings (IPOs), mergers and acquisitions (M\&A) as well
as funding events can be predicted with various machine learning techniques. In
such studies, heterogeneous information from the web and social networks is
usually used as a complement to the information coming from databases. However,
building and maintaining such databases demands tremendous human effort. We
thus study here whether one can solely rely on readily available sources of
information, such as the website of a startup, its social media activity as
well as its presence on the web, to predict its funding events. As illustrated
in our experiments, the method we propose yields results comparable to the ones
making also use of structured data available in private databases.


title: Benchmarking CASPT3 Vertical Excitation Energies

abstract:
  Based on 280 reference vertical transition energies of various natures
(singlet, triplet, valence, Rydberg, $n\to\pi^*$, $\pi\to\pi^*$, and double
excitations) extracted from the QUEST database, we assess the accuracy of
third-order multireference perturbation theory, CASPT3, in the context of
molecular excited states. When one applies the disputable
ionization-potential-electron-affinity (IPEA) shift, we show that CASPT3
provides a similar accuracy as its second-order counterpart, CASPT2, with the
same mean absolute error of $0.11$ eV. However, as already reported, we also
observe that the accuracy of CASPT3 is almost insensitive to the IPEA shift,
irrespective of the transition type and system size, with a small reduction of
the mean absolute error to $0.09$ eV when the IPEA shift is switched off.


title:
Evolving Modular Soft Robots without Explicit Inter-Module Communication
  using Local Self-Attention

abstract:
  Modularity in robotics holds great potential. In principle, modular robots
can be disassembled and reassembled in different robots, and possibly perform
new tasks. Nevertheless, actually exploiting modularity is yet an unsolved
problem: controllers usually rely on inter-module communication, a practical
requirement that makes modules not perfectly interchangeable and thus limits
their flexibility. Here, we focus on Voxel-based Soft Robots (VSRs),
aggregations of mechanically identical elastic blocks. We use the same neural
controller inside each voxel, but without any inter-voxel communication, hence
enabling ideal conditions for modularity: modules are all equal and
interchangeable. We optimize the parameters of the neural controller-shared
among the voxels-by evolutionary computation. Crucially, we use a local
self-attention mechanism inside the controller to overcome the absence of
inter-module communication channels, thus enabling our robots to truly be
driven by the collective intelligence of their modules. We show experimentally
that the evolved robots are effective in the task of locomotion: thanks to
self-attention, instances of the same controller embodied in the same robot can
focus on different inputs. We also find that the evolved controllers generalize
to unseen morphologies, after a short fine-tuning, suggesting that an inductive
bias related to the task arises from true modularity.


title:
Central limit theorem over non-linear functionals of empirical measures:
  beyond the iid setting

abstract:
  The central limit theorem is, with the strong law of large numbers, one of
the two fundamental limit theorems in probability theory. Benjamin Jourdain and
Alvin Tse have extended to non-linear functionals of the empirical measure of
independent and identically distributed random vectors the central limit
theorem which is well known for linear functionals. The main tool permitting
this extension is the linear functional derivative, one of the notions of
derivation on the Wasserstein space of probability measures that have recently
been developed. The purpose of this work is to generalize what has been done by
Jourdain and Tse: provide a Central Limit Theorem for non-linear functionals of
independent and non equidistributed random vectors such as the successive
values of an ergodic Markov chain.


title:
Solar cell efficiency, diode factor and interface recombination:
  insights from photoluminescence

abstract:
  Metastable defects can decisively influence the diode factor and thus the
efficiency of a solar cell. The diode factor is also influenced by the doping
level and the recombination mechanisms in the solar cell. Here we quantify how
the various parameters change the diode factor by photoluminescence
measurements and simulations. In addition, we show that backside recombination
reduces the open circuit voltage in CuInSe2 solar cells by more than 40 mV.
Passivation by a Ga gradient is shown to be as efficient as a passivation by
dielectric layers. Increased backside recombination reduces the diode factor,
not because of less metastable defect transformation but because of a sublinear
increase in photo generated carriers with excitation. This reduction in diode
factor is unwanted, since the increased recombination reduces the voltage. A
higher doping level, on the other hand, reduces the diode factor, thereby
increasing the fill factor, and at the same time increases the voltage.


title: Clockwork mirror neutron

abstract:
  In this paper we address the hierarchy in the neutron-anti-neutron
($n-\bar{n}$) oscillation through clockwork mechanism (CW) with a set of mirror
neutrons acting as clockwork gears. This is achieved by coupling Standard Model
(SM) neutrons to the zeroth gear while having an explicit mirror baryon number
($\bar{B}$) breaking term at the $N^{th}$ one. The explicit $\bar{B}$ breaking
term induces neutron oscillation in the mirror world which propagates to the SM
baryon number ($B$) violation through the CW gears. This mirror world is a
Standard Model singlet and possess the $\bar{B}$ symmetry that prohibits its
decay to SM matter fields. This symmetry is broken to the combination,
$B-\bar{B}$, by a scalar field to generate the clockwork. Without introducing
large hierarchies among the mirror neutrons, we predict the time period of
neutron-anti-neutron oscillation in our world of $\tau_{n\bar{n}} \gtrsim 10^8
s$, which is within the reach of current experiments and the upcoming ones.
This mechanism also predicts neutron to mirror neutron transition along with
the emission of SM particles, given the phase space is favourable. This could
be searched for at neutron stars and will place bounds on the symmetry breaking
mechanism in the mirror world. The current constraint on neutron-mirror neutron
oscillation, coming from neutron stars, is shown to be satisfied, in the
parameter space which predicts a detectable neutron-anti-neutron oscillation in
our world. This scenario also predicts double neutron annihilation to produce
two mirror neutrons which could also be tested via the luminosity measurements
of neutron stars.


title: Implications of New CDF-II $W$ Boson Mass on Two Higgs Doublet Model

abstract:
  We present the implications of the recent measurement of $W$ boson at CDF II
on two Higgs doublet model. In the analysis, we impose theoretical bounds such
as vacuum stability and perturbative unitarity, and several experimental
constraints. In addition, we take into account the measurement of
$\sin^2(m_Z)_{\rm MS}$ on top of the CDF $W$ boson mass to investigate how the
$S$ and $T$ parameters are determined. Using the results, we show how the
parameter space is constrained, and compare it with the one based on the PDG
average of $m_W$.


title:
Bounding the difference between model predictive control and neural
  networks

abstract:
  There is a growing debate on whether the future of feedback control systems
will be dominated by data-driven or model-driven approaches. Each of these two
approaches has their own complimentary set of advantages and disadvantages,
however, only limited attempts have, so far, been developed to bridge the gap
between them. To address this issue, this paper introduces a method to bound
the worst-case error between feedback control policies based upon model
predictive control (MPC) and neural networks (NNs). This result is leveraged
into an approach to automatically synthesize MPC policies minimising the
worst-case error with respect to a NN. Numerical examples highlight the
application of the bounds, with the goal of the paper being to encourage a more
quantitative understanding of the relationship between data-driven and
model-driven control.


title:
Multilingual Language Model Adaptive Fine-Tuning: A Study on African
  Languages

abstract:
  Multilingual pre-trained language models (PLMs) have demonstrated impressive
performance on several downstream tasks on both high resourced and
low-resourced languages. However, there is still a large performance drop for
languages unseen during pre-training, especially African languages. One of the
most effective approaches to adapt to a new language is language adaptive
fine-tuning (LAFT) -- fine-tuning a multilingual PLM on monolingual texts of a
language using the same pre-training objective. However, African languages with
large monolingual texts are few, and adapting to each of them individually
takes large disk space and limits the cross-lingual transfer abilities of the
resulting models because they have been specialized for a single language. In
this paper, we perform multilingual adaptive fine-tuning (MAFT) on 17
most-resourced African languages and three other high-resource languages widely
spoken on the African continent -- English, French, and Arabic to encourage
cross-lingual transfer learning. Additionally, to further specialize the
multilingual PLM, we removed vocabulary tokens from the embedding layer that
corresponds to non-African writing scripts before MAFT, thus reducing the model
size by around 50\%. Our evaluation on two multilingual PLMs (AfriBERTa and
XLM-R) and three NLP tasks (NER, news topic classification, and sentiment
classification) shows that our approach is competitive to applying LAFT on
individual languages while requiring significantly less disk space. Finally, we
show that our adapted PLM also improves the zero-shot cross-lingual transfer
abilities of parameter efficient fine-tuning methods.


title: Sequential Snapping and Pathways in a Mechanical Metamaterial

abstract:
  Materials which feature bistable elements, hysterons, exhibit memory effects.
Often these hysterons are difficult to observe or control directly. Here we
introduce a mechanical metamaterial in which slender elements, interacting with
pushers, act as mechanical hysterons. We show how we can tune the hysteron
properties and pathways under cyclic compression by the geometric design of
these elements and how we can tune the pathways of a given sample by tilting
one of the boundaries. Furthermore, we investigate the effect of the coupling
of a global shear mode to the hysterons, as an example of the interactions
between hysteron and non-hysteron degrees of freedom. We hope our work will
inspire further studies on designer matter with targeted pathways.


title:
Iterative PDE-constrained optimization for seismic full-waveform
  inversion

abstract:
  This paper presents a novel numerical method for the Newton seismic
full-waveform inversion (FWI). The method is based on the full-space approach,
where the state, adjoint state, and control variables are optimized
simultaneously. Each Newton step is formulated as a PDE-constrained
optimization problem, which is cast in the form of the Karush-Kuhn-Tucker (KKT)
system of linear algebraic equitations. The KKT system is solved inexactly with
a preconditioned Krylov solver. We introduced two preconditioners: the one
based on the block-triangular factorization and its variant with an inexact
block solver. The method was benchmarked against the standard truncated Newton
FWI scheme on a part of the Marmousi velocity model. The algorithm demonstrated
a considerable runtime reduction compared to the standard FWI. Moreover, the
presented approach has a great potential for further acceleration. The central
result of this paper is that it establishes the feasibility of Newton-type
optimization of the KKT system in application to the seismic FWI.


title: A Bangert-Hingston Theorem for Starshaped Hypersurfaces

abstract:
  Let $Q$ be a closed manifold with non-trivial first Betti number and $\Sigma
\subseteq T^*Q$ a generic starshaped hypersurface. We prove that the number of
geometrically distinct Reeb orbits of period at most $T$ on $\Sigma$ grows at
least logarithmically in $T$ whenever $Q$ admits a non-trivial $S^1$-action.


title:
Quantization and non-quantization of energy for higher-dimensional
  Ginzburg--Landau vortices

abstract:
  Given a family of critical points $u_{\epsilon}:M^n\to\mathbb{C}$ for the
complex Ginzburg--Landau energies \begin{align*}
  &E_\epsilon(u)=\int_{M}\left(\frac{|du|^2}{2}+\frac{(1-|u|^2)^2}{4\epsilon^2}\right),
\end{align*} on a manifold $M$, with natural energy growth
$E_{\epsilon}(u_{\epsilon})=O(|\log\epsilon| )$, it is known that the vorticity
sets $\{|u_\epsilon|\leq \frac{1}{2}\}$ converge subsequentially to the support
of a stationary, rectifiable $(n-2)$-varifold $V$ in the interior,
characterized as the concentrated portion of the limit $\lim_{\epsilon\to 0}
\frac{e_\epsilon(u_\epsilon)}{\pi|\log\epsilon| }$ of the normalized energy
measures.
  When $n=2$ or the solutions $u_{\epsilon}$ are energy-minimizing, it is known
moreover that this varifold $V$ is integral; i.e., the $(n-2)$-density
$\Theta_{n-2}(|V|,x)$ of $|V|$ takes values in $\mathbb{N}$ at $|V|$-a.e. $x\in
M$. In the present paper, we show that for a general family of critical points
with $E_{\epsilon}(u_{\epsilon})=O(|\log\epsilon| )$ in dimension $n\geq 3$,
this energy quantization phenomenon only holds where the density is less than
$2$: namely, we prove that the density $\Theta_{n-2}(|V|,x)$ of the limit
varifold takes values in $\{1\}\cup [2,\infty)$ at $|V|$-a.e. $x\in M$, and
show that this is sharp, in the sense that for any $n\geq 3$ and $\theta\in
\{1\}\cup [2,\infty)$, there exists a family of critical points $u_{\epsilon}$
for $E_{\epsilon}$ in the ball $B_1^n(0)$ with concentration varifold $V$ given
by an $(n-2)$-plane with density $\theta$.


title:
Multiplication tables and word-hyperbolicity in free products of
  semigroups, monoids, and groups

abstract:
  This article studies the properties of word-hyperbolic semigroups and
monoids, i.e. those having context-free multiplication tables with respect to a
regular combing, as defined by Duncan & Gilman. In particular, the preservation
of word-hyperbolicity under taking free products is considered. Under mild
conditions on the semigroups involved, satisfied e.g. by monoids or regular
semigroups, we prove that the semigroup free product of two word-hyperbolic
semigroups is again word-hyperbolic. Analogously, with a mild condition on the
uniqueness of representation for the identity element, satisfied e.g. by
groups, we prove that the monoid free product of two word-hyperbolic monoids is
word-hyperbolic. The methods are language-theoretically general, and apply
equally well to semigroups, monoids, or groups with a
$\mathbf{C}$-multiplication table, where $\mathbf{C}$ is any reversal-closed
super-$\operatorname{AFL}$, in the sense of Greibach. In particular, we deduce
that the free product of two groups with $\operatorname{ET0L}$ resp. indexed
multiplication tables again has an $\operatorname{ET0L}$ resp. indexed
multiplication table.


title: Statistical analysis of random objects via metric measure Laplacians

abstract:
  In this paper, we consider a certain convolutional Laplacian for metric
measure spaces and investigate its potential for the statistical analysis of
complex objects. The spectrum of that Laplacian serves as a signature of the
space under consideration and the eigenvectors provide the principal directions
of the shape, its harmonics. These concepts are used to assess the similarity
of objects or understand their most important features in a principled way
which is illustrated in various examples. Adopting a statistical point of view,
we define a mean spectral measure and its empirical counterpart. The
corresponding limiting process of interest is derived and statistical
applications are discussed.


title: Normal Forms in Differential Galois Theory for the Classical Groups

abstract:
  Let $G$ be a classical group of dimension $d$ and let
$\boldsymbol{a}=(a_1,\dots,a_d)$ be differential indeterminates over a
differential field $F$ of characteristic zero with algebraically closed field
of constants $C$. Further let $A(\boldsymbol{a})$ be a generic element in the
Lie algebra $\mathfrak{g}(F\langle \boldsymbol{a} \rangle)$ of $G$ obtained
from parametrizing a basis of $\mathfrak{g}$ with the indeterminates
$\boldsymbol{a}$. It is known (cf. work by Juan) that the differential Galois
group of $\boldsymbol{y}'=A(\boldsymbol{a})\boldsymbol{y}$ over $F\langle
\boldsymbol{a} \rangle$ is $G(C)$. In this paper we construct a differential
field extension $\mathcal{L}$ of $F\langle \boldsymbol{a} \rangle$ such that
the field of constants of $\mathcal{L}$ is $C$, the differential Galois group
of $\boldsymbol{y}'=A(\boldsymbol{a})\boldsymbol{y}$ over $\mathcal{L}$ is
still the full group $G(C)$ and $A(\boldsymbol{a})$ is gauge equivalent over
$\mathcal{L}$ to a matrix in normal form which we introduced in work by Seiss.
We also consider specializations of the coefficients of $A(\boldsymbol{a})$.


title: Global Structures from the Infrared

abstract:
  Quantum field theories with identical local dynamics can admit different
choices of global structure, leading to different partition functions and
spectra of extended operators. Such choices can be reformulated in terms of a
topological field theory in one dimension higher, the symmetry TFT. In this
paper we show that this TFT can be reconstructed from a careful analysis of the
infrared Coulomb-like phases. In particular, the TFT matches between the UV and
the IR. This provides a purely field theoretical counterpart of several recent
results obtained via geometric engineering in various string/M/F theory setups
for theories in four and five dimensions that we confirm and extend.


title: Quantum clustering and jet reconstruction at the LHC

abstract:
  Clustering is one of the most frequent problems in many domains, in
particular, in particle physics where jet reconstruction is central in
experimental analyses. Jet clustering at the CERN's Large Hadron Collider is
computationally expensive and the difficulty of this task is expected to
increase with the upcoming High-Luminosity LHC (HL-LHC). In this paper, we
study the case in which quantum computing algorithms might improve jet
clustering by considering two novel quantum algorithms which may speed up the
classical jet clustering algorithms. The first one is a quantum subroutine to
compute a Minkowski-based distance between two data points, whereas the second
one consists of a quantum circuit to track the maximum into a list of unsorted
data. The latter algorithm could be of value beyond particle physics, for
instance in statistics. When one or both of these algorithms are implemented
into the classical versions of well-known clustering algorithms (K-means,
Affinity Propagation and $k_T$-jet) we obtain efficiencies comparable to those
of their classical counterparts. Even more, we achieve an exponential speed-up
in data dimensionality, when the distance algorithm is applied and an
exponential speed-up in data length, when the maximum is selected through the
quantum routine.


title:
High-energy emissions of light mesons plus heavy flavor at the LHC and
  the Forward Physics Facility

abstract:
  The study of the dynamics of strong interactions in the high-energy regime is
a core line of frontier researches at the LHC as well as at new-generation
colliding facilities. Here, the enhancement of energy logarithms due to
diffractive semi-hard final states spoils the convergence of the perturbative
series in the QCD running coupling, thus calling for an improvement of the pure
collinear factorization that accounts for an all-order resummation of these
large logarithmic contributions. Motivated by the recent discovery that
inclusive emissions of heavy-flavored particles allow for clear signals of a
stabilization of high-energy resummed differential distributions under
higher-order corrections and scale variations, we provide novel predictions of
rapidity and azimuthal-angle observables for the inclusive hadroproduction of a
light meson ($\eta$ or $\pi^\pm$) in association with a heavy-flavored hadron
($\Lambda_c$ or $b$-flavored hadron). We calculate our observables in a hybrid
high-energy and collinear factorization framework, where next-to-leading
BFKL-resummed partonic cross sections are convoluted with collinear parton
distributions and fragmentation functions. We consider kinematic ranges
typically covered by acceptances of LHC detectors, and new ones coming from the
combined tag of an ultra-forward particle at the future Forward Physics
Facility (FPF) and of a central one at ATLAS via a tight timing-coincidence
setup. By performing a detailed study on uncertainties associated to collinear
inputs via a replica-driven analysis, as well as on ones intrinsically coming
from the high-energy resummation, we highlight the challenges and the steps
required to gauge the feasibility of precision studies of our processes.


title: SpoofGAN: Synthetic Fingerprint Spoof Images

abstract:
  A major limitation to advances in fingerprint spoof detection is the lack of
publicly available, large-scale fingerprint spoof datasets, a problem which has
been compounded by increased concerns surrounding privacy and security of
biometric data. Furthermore, most state-of-the-art spoof detection algorithms
rely on deep networks which perform best in the presence of a large amount of
training data. This work aims to demonstrate the utility of synthetic (both
live and spoof) fingerprints in supplying these algorithms with sufficient data
to improve the performance of fingerprint spoof detection algorithms beyond the
capabilities when training on a limited amount of publicly available real
datasets. First, we provide details of our approach in modifying a
state-of-the-art generative architecture to synthesize high quality live and
spoof fingerprints. Then, we provide quantitative and qualitative analysis to
verify the quality of our synthetic fingerprints in mimicking the distribution
of real data samples. We showcase the utility of our synthetic live and spoof
fingerprints in training a deep network for fingerprint spoof detection, which
dramatically boosts the performance across three different evaluation datasets
compared to an identical model trained on real data alone. Finally, we
demonstrate that only 25% of the original (real) dataset is required to obtain
similar detection performance when augmenting the training dataset with
synthetic data.


title:
What Formulation Should One Choose for Modeling a 3D HTS Motor Pole with
  Ferromagnetic Materials?

abstract:
  We discuss the relevance of several finite-element formulations for nonlinear
systems containing high-temperature superconductors (HTS) and ferromagnetic
materials (FM), in the context of a 3D motor pole model. The formulations are
evaluated in terms of their numerical robustness and efficiency. We propose a
coupled h-phi-a-formulation as an optimal choice, modeling the problem with an
a-formulation in the FM and an h-phi-formulation in the remaining domains.
While maintaining a low number of degrees of freedom, the h-phi-a-formulation
guarantees a robust resolution and strongly reduces the number of iterations
required for handling the nonlinearities of HTS and FM compared to standard
formulations.