title: Abelian pole systems and Riemann-Schottky type systems

abstract:
  In this survey of works on a characterization of Jacobians and Prym varieties
among indecomposable principally polarized abelian varieties via the soliton
theory we focus on a certain circle of ideas and methods which show that the
characterization of Jacobians as ppav whose Kummer variety admits a trisecant
line and the Pryms as ppav whose Kummer variety admits a pair of symmetric
quadrisecants can be seen as an abelian version of pole systems arising in the
theory of elliptic solutions to the basic soliton hierarchies. We present also
recent results in this direction on the characterization of Jacobians of curves
with involution, which were motivated by the theory of two-dimensional
integrable hierarchies with symmetries.


title:
Less is More: Supporting Developers in Vulnerability Detection during
  Code Review

abstract:
  Reviewing source code from a security perspective has proven to be a
difficult task. Indeed, previous research has shown that developers often miss
even popular and easy-to-detect vulnerabilities during code review. Initial
evidence suggests that a significant cause may lie in the reviewers' mental
attitude and common practices. In this study, we investigate whether and how
explicitly asking developers to focus on security during a code review affects
the detection of vulnerabilities. Furthermore, we evaluate the effect of
providing a security checklist to guide the security review. To this aim, we
conduct an online experiment with 150 participants, of which 71% report to have
three or more years of professional development experience. Our results show
that simply asking reviewers to focus on security during the code review
increases eight times the probability of vulnerability detection. The presence
of a security checklist does not significantly improve the outcome further,
even when the checklist is tailored to the change under review and the existing
vulnerabilities in the change. These results provide evidence supporting the
mental attitude hypothesis and call for further work on security checklists'
effectiveness and design. Data and materials:
https://doi.org/10.5281/zenodo.6026291


title:
New Constraint on the Local Relic Neutrino Background Overdensity with
  the First KATRIN Data Runs

abstract:
  We report on the direct cosmic relic neutrino background search from the
first two science runs of the KATRIN experiment in 2019. Beta-decay electrons
from a high-purity molecular tritium gas source are analyzed by a
high-resolution MAC-E filter around the kinematic endpoint at 18.57 keV. The
analysis is sensitive to a local relic neutrino overdensity of 9.7e10 (1.1e11)
at a 90% (95%) confidence level. A fit of the integrated electron spectrum over
a narrow interval around the kinematic endpoint accounting for relic neutrino
captures in the Tritium source reveals no significant overdensity. This work
improves the results obtained by the previous kinematic neutrino mass
experiments at Los Alamos and Troitsk. We furthermore update the projected
final sensitivity of the KATRIN experiment to <1e10 at 90% confidence level, by
relying on updated operational conditions.


title: Super-regular Steiner 2-designs

abstract:
  A design is additive under an abelian group $G$ (briefly, $G$-additive) if,
up to isomorphism, its point set is contained in $G$ and the elements of each
block sum up to zero. The only known Steiner 2-designs that are $G$-additive
for some $G$ have block size which is either a prime power or a prime power
plus one. Indeed they are the point-line designs of the affine spaces
$AG(n,q)$, the point-line designs of the projective planes $PG(2,q)$, and the
point-line designs of the projective spaces $PG(n,2)$. In the attempt to find
new examples, possibly with a block size which is neither a prime power nor a
prime power plus one, we look for Steiner 2-designs which are strictly
$G$-additive (the point set is exactly $G$) and $G$-regular (any translate of
any block is a block as well) at the same time. These designs will be
called\break "$G$-super-regular". Our main result is that there are infinitely
many values of $v$ for which there exists a super-regular, and therefore
additive, $2$-$(v,k,1)$ design whenever $k$ is neither singly even nor of the
form $2^n3\geq12$. The case $k\equiv2$ (mod 4) is a definite exception whereas
$k=2^n3\geq12$ is at the moment a possible exception. We also find
super-regular $2$-$(p^n,p,1)$ designs with $p\in\{5,7\}$ and $n\geq3$ which are
not isomorphic to the point-line design of $AG(n,p)$.


title:
Adjoint-aided inference of Gaussian process driven differential
  equations

abstract:
  Linear systems occur throughout engineering and the sciences, most notably as
differential equations. In many cases the forcing function for the system is
unknown, and interest lies in using noisy observations of the system to infer
the forcing, as well as other unknown parameters. In differential equations,
the forcing function is an unknown function of the independent variables
(typically time and space), and can be modelled as a Gaussian process (GP). In
this paper we show how the adjoint of a linear system can be used to
efficiently infer forcing functions modelled as GPs, after using a truncated
basis expansion of the GP kernel. We show how exact conjugate Bayesian
inference for the truncated GP can be achieved, in many cases with
substantially lower computation than would be required using MCMC methods. We
demonstrate the approach on systems of both ordinary and partial differential
equations, and by testing on synthetic data, show that the basis expansion
approach approximates well the true forcing with a modest number of basis
vectors. Finally, we show how to infer point estimates for the non-linear model
parameters, such as the kernel length-scales, using Bayesian optimisation.


title: Statistical Tools for Imaging Atmospheric Cherenkov Telescopes

abstract:
  The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled
the sky in the teraelectronvolt regime, initiating the so-called "TeV
revolution", at the beginning of the new millennium. This revolution was also
facilitated by the implementation and adaptation of statistical tools for
analyzing the shower images collected by these telescopes and inferring the
properties of the astrophysical sources that produce such events. Image
reconstruction techniques, background discrimination, and signal-detection
analyses are just a few of the pioneering studies applied in recent decades in
the analysis of IACTs data. This (succinct) review has the intent of
summarizing the most common statistical tools that are used for analyzing data
collected with IACTs, focusing on their application in the full analysis chain,
including references to existing literature for a deeper examination.


title:
Are Fairness Perceptions Shaped by Income Inequality? Evidence from
  Latin America

abstract:
  A common assumption in the literature is that the actual level of income
inequality shapes individuals' beliefs about whether the income distribution is
fair ("fairness views," for short). However, individuals do not directly
observe income inequality (which often leads to large misperceptions), nor do
they consider all inequities to be unfair. In this paper, we empirically assess
the link between objective measures of income inequality and fairness views in
a context of high but decreasing income inequality. To do this, we combine
opinion poll data with harmonized data from household surveys of 18 Latin
American countries from 1997-2015. We find a strong and statistically
significant relationship between income inequality and unfairness views across
countries and over time. Unfairness views evolved in the same direction as
income inequality for 17 out of the 18 countries in our sample. We find that
individuals who are older, unemployed, and left-wing are, on average, more
likely to perceive the income distribution as very unfair. Finally, we find
that fairness views and income inequality have predictive power for
individuals' self-reported propensity to mobilize and protest independent of
each other, suggesting that these two variables capture different channels
through which changes in the income distribution can affect social unrest.


title:
Stability Analysis of Recurrent Neural Networks by IQC with Copositive
  Mutipliers

abstract:
  This paper is concerned with the stability analysis of the recurrent neural
networks (RNNs) by means of the integral quadratic constraint (IQC) framework.
The rectified linear unit (ReLU) is typically employed as the activation
function of the RNN, and the ReLU has specific nonnegativity properties
regarding its input and output signals. Therefore, it is effective if we can
derive IQC-based stability conditions with multipliers taking care of such
nonnegativity properties. However, such nonnegativity (linear) properties are
hardly captured by the existing multipliers defined on the positive
semidefinite cone. To get around this difficulty, we loosen the standard
positive semidefinite cone to the copositive cone, and employ copositive
multipliers to capture the nonnegativity properties. We show that, within the
framework of the IQC, we can employ copositive multipliers (or their inner
approximation) together with existing multipliers such as Zames-Falb
multipliers and polytopic bounding multipliers, and this directly enables us to
ensure that the introduction of the copositive multipliers leads to better (no
more conservative) results. We finally illustrate the effectiveness of the
IQC-based stability conditions with the copositive multipliers by numerical
examples.


title:
Stochastic Contextual Dueling Bandits under Linear Stochastic
  Transitivity Models

abstract:
  We consider the regret minimization task in a dueling bandits problem with
context information. In every round of the sequential decision problem, the
learner makes a context-dependent selection of two choice alternatives (arms)
to be compared with each other and receives feedback in the form of noisy
preference information. We assume that the feedback process is determined by a
linear stochastic transitivity model with contextualized utilities (CoLST), and
the learner's task is to include the best arm (with highest latent
context-dependent utility) in the duel. We propose a computationally efficient
algorithm, $\texttt{CoLSTIM}$, which makes its choice based on imitating the
feedback process using perturbed context-dependent utility estimates of the
underlying CoLST model. If each arm is associated with a $d$-dimensional
feature vector, we show that $\texttt{CoLSTIM}$ achieves a regret of order
$\tilde O( \sqrt{dT})$ after $T$ learning rounds. Additionally, we also
establish the optimality of $\texttt{CoLSTIM}$ by showing a lower bound for the
weak regret that refines the existing average regret analysis. Our experiments
demonstrate its superiority over state-of-art algorithms for special cases of
CoLST models.


title:
A Nonlinear Proportional Integral Disturbance Observer and Motion
  Control Technique for Permanent Magnet Synchronous Motors

abstract:
  In this paper, we present a Nonlinear-Proportional Integrator (N-PI)
disturbance observer (DOB) to enhance the motion tracking of the performance of
a surface-mounted Permanent Magnet Synchronous Motor (SPMSM) in rapidly speed
varying regions. By presenting an N-PI-DOB for load torque estimation with
torque modulation technique, we show that the tracking error dynamics of
angular position/velocity are coupled with tracking errors of currents loop and
estimation errors. After analyzing disturbances of currents tracking error
dynamics, we design the N-PI-DOB and Lyapunov-based nonlinear currents
controller to enhance the motion tracking performances. With these N-PI-DOBs
and motion controllers, we analyze the stability of motion tracking error
dynamics and estimation error dynamics. We experimentally perform a comparative
study with/without the N-PI-DOB to verify the effectiveness of the proposed
method in the condition of the unknown load torque and rapidly speed-varying.


title: Exploring Structural Sparsity in Neural Image Compression

abstract:
  Neural image compression have reached or out-performed traditional methods
(such as JPEG, BPG, WebP). However,their sophisticated network structures with
cascaded convolution layers bring heavy computational burden for practical
deployment. In this paper, we explore the structural sparsity in neural image
compression network to obtain real-time acceleration without any specialized
hardware design or algorithm. We propose a simple plug-in adaptive binary
channel masking(ABCM) to judge the importance of each convolution channel and
introduce sparsity during training. During inference, the unimportant channels
are pruned to obtain slimmer network and less computation. We implement our
method into three neural image compression networks with different entropy
models to verify its effectiveness and generalization, the experiment results
show that up to 7x computation reduction and 3x acceleration can be achieved
with negligible performance drop.


title:
Symmetry-induced many-body quantum interference in chaotic bosonic
  systems: an augmented Truncated Wigner method

abstract:
  Although highly successful, the Truncated Wigner Approximation (TWA) does not
account for genuine many-body quantum interference between different solutions
of the mean-field equations of a bosonic many-body (MB) system. This renders
the TWA essentially classical, where a large number of particles formally takes
the role of the inverse of Planck's constant $\hbar$. The failure to describe
genuine interference phenomena, such as localization and scarring in Fock
space, can be seen as a virtue of this quasiclassical method, which thereby
allows one to identify genuine quantum effects when being compared with "exact"
quantum calculations that do not involve any a priori approximation. A rather
prominent cause for such quantum effects that are not accounted for by the TWA
is the constructive interference between the contributions of symmetry-related
trajectories, which would occur in the presence of discrete symmetries provided
the phase-space distribution of the initial state and the observable to be
evaluated feature a strong localization about the corresponding symmetry
subspaces. Here we show how one can conceive an augmented version of the TWA
which can account for this particular effect. This augmented TWA effectively
amounts to complementing conventional TWA calculations by separate Truncated
Wigner simulations that are restricted to symmetric subspaces and involve
weight factors that account for the dynamical stability of sampling
trajectories with respect to perpendicular deviations from those subspaces. We
illustrate the validity of this method at pre- as well as post-Ehrenfest time
scales in prototypical Bose-Hubbard systems displaying chaotic classical
dynamics, where it also reveals the existence of additional MB interference
effects.


title: Ahlfors regular conformal dimension and Gromov-Hausdorff convergence

abstract:
  We prove that the Ahlfors regular conformal dimension is upper semicontinuous
with respect to Gromov-Hausdorff convergence when restricted to the class of
uniformly perfect, uniformly quasi-selfsimilar metric spaces. A corollary is
the upper semicontinuity of the Ahlfors regular conformal dimension of limit
sets of discrete, quasiconvex-cocompact group of isometries of uniformly
bounded codiameter of $\delta$-hyperbolic metric spaces under equivariant
pointed Gromov-Hausdorff convergence of the spaces.


title: Reproducibility in Optimization: Theoretical Framework and Limits

abstract:
  We initiate a formal study of reproducibility in optimization. We define a
quantitative measure of reproducibility of optimization procedures in the face
of noisy or error-prone operations such as inexact or stochastic gradient
computations or inexact initialization. We then analyze several convex
optimization settings of interest such as smooth, non-smooth, and
strongly-convex objective functions and establish tight bounds on the limits of
reproducibility in each setting. Our analysis reveals a fundamental trade-off
between computation and reproducibility: more computation is necessary (and
sufficient) for better reproducibility.


title:
Missing Data Imputation and Acquisition with Deep Hierarchical Models
  and Hamiltonian Monte Carlo

abstract:
  Variational Autoencoders (VAEs) have recently been highly successful at
imputing and acquiring heterogeneous missing data and identifying outliers.
However, within this specific application domain, existing VAE methods are
restricted by using only one layer of latent variables and strictly Gaussian
posterior approximations. To address these limitations, we present HH-VAEM, a
Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian
Monte Carlo with automatic hyper-parameter tuning for improved approximate
inference. Our experiments show that HH-VAEM outperforms existing baselines in
the tasks of missing data imputation, supervised learning and outlier
identification with missing features. Finally, we also present a sampling-based
approach for efficiently computing the information gain when missing features
are to be acquired with HH-VAEM. Our experiments show that this sampling-based
approach is superior to alternatives based on Gaussian approximations.


title: Threshold detection statistics of bosonic states

abstract:
  In quantum photonics, threshold detectors, distinguishing between vacuum and
one or more photons, such as superconducting nanowires and avalanche
photodiodes, are routinely used to measure Fock and Gaussian states of light.
Despite being the standard measurement scheme, there is no general closed form
expression for measurement probabilities with threshold detectors, unless
accepting coarse approximations or combinatorially scaling summations. Here, we
present new matrix functions to fill this gap. We develop the Bristolian and
the loop Torontonian functions for threshold detection of Fock and displaced
Gaussian states, respectively, and connect them to each other and to existing
matrix functions. By providing a unified picture of bosonic statistics for most
quantum states of light, we provide novel tools for the design and analysis of
photonic quantum technologies.


title: Quantum transduction with microwave and optical entanglement

abstract:
  Quantum transduction refers to the coherent conversion between microwave and
optical states, which can be achieved by quantum teleportation if given high
fidelity microwave-optical entanglement, namely entanglement-based quantum
transduction. Reliable microwave-optical entanglement can be generated using
various platforms. In this paper, we base the discussion on
piezo-optomechanical system and make the teleportation induced conversion
scheme more concrete in the framework of quantum channel theory. By comparing
the quantum capacity between the entanglement-based conversion channel and the
traditional direct quantum transduction channel, we show entanglement-based
scheme indeed admits a positive transduction rate when the direct quantum
transduction has zero quantum capacity. Given two piezo-optomechanical systems,
we also investigate the generation of microwave-microwave entanglement from
entanglement swapping within continuous variable and discrete variable
settings, showing the potentials of directly connecting microwave quantum
processor by microwave-microwave quantum teleportation.


title:
An Experimental Proof of Concept for Integrated Sensing and
  Communications Waveform Design

abstract:
  The integration of sensing and communication (ISAC) functionalities have
recently gained significant research interest as a hardware-, power-, spectrum-
and cost- efficient solution. This experimental work focuses on a
dual-functional radar sensing and communication framework where a single
radiation waveform, either omnidirectional or directional, can realize both
radar sensing and communication functions. We study a trade-off approach that
can balance the performance of communications and radar sensing. We design an
orthogonal frequency division multiplexing (OFDM) based multi-user multiple
input multiple output (MIMO) software-defined radio (SDR) testbed to validate
the dual-functional model. We carry out over-the-air experiments to investigate
the optimal trade-off factor to balance the performance for both functions. On
the radar performance, we measure the output beampatterns of our transmission
to examine their similarity to simulation based beampatterns. On the
communication side, we obtain bit error rate (BER) results from the testbed to
show the communication performance using the dual-functional waveform. Our
experiment reveals that the dual-functional approach can achieve comparable BER
performance with pure communication-based solutions while maintaining fine
radar beampatterns simultaneously.


title:
A modular implementation of an effective interaction approach for
  harmonically trapped fermions in 1D

abstract:
  We introduce a generic and accessible implementation of an exact
diagonalization method for studying few-fermion models. Our aim is to provide a
testbed for the newcomers to the field as well as a stepping stone for trying
out novel optimizations and approximations. This user guide consists of a
description of the algorithm, and several examples in varying orders of
sophistication. In particular, we exemplify our routine using an
effective-interaction approach that fixes the low-energy physics. We benchmark
this approach against the existing data, and show that it is able to deliver
state-of-the-art numerical results at a significantly reduced computational
cost.


title:
Hyperpolarization read-out through rapidly rotating fields in the zero-
  and low-field regime

abstract:
  An integral part of para-hydrogen induced polarization (PHIP) methods is the
conversion of nuclear singlet order into observable magnetization. In this
study polarisation transfer to a heteronucleus is achieved through a selective
rotation of the proton singlet-triplet states driven by a combination of a
rotating magnetic field and a weak bias field. Surprisingly we find that
efficient polarisation transfer driven by a STORM (Singlet-Triplet Oscillations
through Rotating Magnetic fields) pulse in the presence of $\mu$T bias fields
requires rotation frequencies on the order of several kHz. The rotation
frequencies therefore greatly exceed any of the internal frequencies of typical
zero- to ultralow field experiments. We further show that the rotational
direction of the rotating field is not arbitrary and greatly influences the
final transfer efficiency. Some of these aspects are demonstrated
experimentally by considering hyperpolarised (1-$^{13}$C)fumarate. In addition,
we provide numerical simulations highlighting the resilience of the STORM pulse
against disruptive quadrupolar coupling partners. In contrast to most of the
existing methods, the STORM procedure therefore represents a promising
candidate for quadrupolar decoupled polarisation transfer in PHIP experiments.


title: Renormalization group study of marginal ferromagnetism

abstract:
  When studying the collective motion of biological groups, one useful
theoretical framework is that of ferromagnetic systems, in which the alignment
interactions are a surrogate of the effective imitation among the individuals.
In this context, the experimental discovery of scale-free correlations of speed
fluctuations in starling flocks poses a challenge to the common statistical
physics wisdom, as in the ordered phase of standard ferromagnetic models with
$\mathrm{O}(n)$ symmetry, the modulus of the order parameter has finite
correlation length. To make sense of this anomaly a novel ferromagnetic theory
has been proposed, where the bare confining potential has zero second
derivative (i.e.\ it is marginal) along the modulus of the order parameter. The
marginal model exhibits a zero-temperature critical point, where the modulus
correlation length diverges, hence allowing to boost both correlation and
collective order by reducing the temperature. Here, we derive an effective
field theory describing the marginal model close to the $T=0$ critical point
and calculate the renormalization group equations at one loop within a momentum
shell approach. We discover a non-trivial scenario, as the cubic and quartic
vertices do not vanish in the infrared limit, while the coupling constants
effectively regulating the exponents $\nu$ and $\eta$ have upper critical
dimension $d_c=2$, so that in three dimensions the critical exponents acquire
their free values, $\nu=1/2$ and $\eta=0$.


title: New hard benchmark functions for global optimization

abstract:
  In this paper, we present some new unimodal, multimodal, and noise test
functions to assess the performance of global optimization algorithms. All the
test functions are multidimensional problems. The 2-dimension landscape of the
proposed functions has been graphically presented in 3D space to show their
geometry, however these functions are more complicated in dimensions greater
than 3. To show the hardness of these functions, we have made an experimental
study with some powerful algorithms such as CEC competition winners: LSHADE,
MadDe, and LSHADE-SPACMA algorithms. Besides the novel algorithm, Tangent
search algorithm (TSA) and its modified Tangent search algorithm (mTSA) were
also used in the experimental study. The results found demonstrate the hardness
of the proposed functions. The code sources of the proposed test functions are
available on Matlab Exchange website.
https://www.mathworks.com/matlabcentral/fileexchange/106450-new-hard-benchmark-functions-for-global-optimization?s_tid=srchtitle


title: Magnetic field strength in cosmic web filaments

abstract:
  We used the Rotation Measure (RM) catalogue derived from the LOFAR Two-metre
Sky Survey Data Release 2 (LoTSS DR2) at 144-MHz to measure the evolution with
redshift of the extragalactic RM (RRM: Residual RM) and the polarization
fraction ($p$) of sources in low density environments. We also measured the
same at 1.4-GHz by cross-matching with the NRAO VLA Sky Survey RM catalogue. We
find that RRM versus redshift is flat at 144-MHz, but, once redshift-corrected,
it shows evolution at high significance. Also $p$ evolves with redshift with a
decrement by a factor of $\sim$8 at $z\sim2$. Comparing the 144-MHz and 1.4-GHz
data, we find that the observed RRM and $p$ are most likely to have an origin
local to the source at 1.4-GHz, while a cosmic web filament origin is favoured
at 144-MHz. If we attribute the entire signal to filaments, we infer a mean
rest frame RRM per filament of RRM_{0,f} = 0.71 \pm 0.07 rad m^{-2} and a
magnetic field per filament of B_f = 32 \pm 3 nG. This is in agreement with
estimates obtained with a complementary method based on synchrotron emission
stacking, and with cosmological simulations if primordial magnetic fields are
amplified by astrophysical source field seeding. The measurement of an
RRM_{0,f} supports the presence of diffuse baryonic gas in filaments. We also
estimated a conservative upper limit of the filament magnetic turbulence of
\sigma_{ RRM_{0,f}} =0.039 \pm 0.001 rad m^{-2}, concluding that the ordered
magnetic field component dominates in filaments.


title: A Strongly First-Order Electroweak Phase Transition from Loryons

abstract:
  We study the effect of BSM particles receiving most of their mass from their
coupling to the Higgs boson ("Loryons") on the electroweak phase transition.
The existence of BSM Loryons would imply that electroweak symmetry must be
non-linearly realized in the effective theory of the Standard Model. Since, by
definition, Loryons are strongly coupled to the Higgs, they are expected to
have a significant effect on the Higgs effective potential and thereby the
electroweak phase transition. We show that the BSM Loryon parameter space
viable under current experimental and theoretical constraints overlaps heavily
with the parameter space in which a strongly first-order phase transition is
predicted. The portion of the experimentally allowed parameter space which
gives a strongly first-order phase transition is significantly larger for
Loryons as compared to non-Loryons.


title:
Full off-shell predictions and $b$-jet definitions for
  $t\bar{t}b\bar{b}$ at NLO in QCD

abstract:
  In this proceeding we present predictions for the complete NLO QCD
corrections to the production of $t\bar{t}b\bar{b}$ in the dilepton decay
channel of the top quark at the LHC with $\sqrt{s}=13$ TeV. The calculation is
performed considering all the full off-shell effects. Moreover, the various
sources of uncertainty for this process are studied in detail. We then compare
these predictions to experimental measurements. We additionally investigate the
impact of the $b$-quark initiated subprocesses.


title:
Anti-windup-like Compensator Synthesis for Discrete-Time Quantized
  Control Systems

abstract:
  This paper addresses the problem of designing an anti-windup like compensator
for discrete-time linear control systems with quantized input. The proposed
compensator provides a correction signal proportional to the quantization error
that fed to the controller. The compensator is designed to ensure that
solutions to the closed-loop systems converge in finite time into a compact set
containing the origin that can be tuned by the designer. A numerically
tractable algorithm with feasibility guarantees is provided for the design of
the compensator. The proposed results are illustrated on an academic example
and an open-loop unstable aircraft system.


title: Task Modifiers for HTN Planning and Acting

abstract:
  The ability of an agent to change its objectives in response to unexpected
events is desirable in dynamic environments. In order to provide this
capability to hierarchical task network (HTN) planning, we propose an extension
of the paradigm called task modifiers, which are functions that receive a task
list and a state and produce a new task list. We focus on a particular type of
problems in which planning and execution are interleaved and the ability to
handle exogenous events is crucial. To determine the efficacy of this approach,
we evaluate the performance of our task modifier implementation in two
environments, one of which is a simulation that differs substantially from
traditional HTN domains.


title: Zeroth-Order Randomized Subspace Newton Methods

abstract:
  Zeroth-order methods have become important tools for solving problems where
we have access only to function evaluations. However, the zeroth-order methods
only using gradient approximations are $n$ times slower than classical
first-order methods for solving n-dimensional problems. To accelerate the
convergence rate, this paper proposes the zeroth order randomized subspace
Newton (ZO-RSN) method, which estimates projections of the gradient and Hessian
by random sketching and finite differences. This allows us to compute the
Newton step in a lower dimensional subspace, with small computational costs. We
prove that ZO-RSN can attain lower iteration complexity than existing zeroth
order methods for strongly convex problems. Our numerical experiments show that
ZO-RSN can perform black-box attacks under a more restrictive limit on the
number of function queries than the state-of-the-art Hessian-aware zeroth-order
method.


title: Distance Estimation and Animal Tracking for Wildlife Camera Trapping

abstract:
  The ongoing biodiversity crysis calls for accurate estimation of animal
density and abundance to identify, for example, sources of biodiversity decline
and effectiveness of conservation interventions. Camera traps together with
abundance estimation methods are often employed for this purpose. The necessary
distances between camera and observed animal are traditionally derived in a
laborious, fully manual or semi-automatic process. Both approaches require
reference image material, which is both difficult to acquire and not available
for existing datasets. In this study, we propose a fully automatic approach to
estimate camera-to-animal distances, based on monocular depth estimation (MDE),
and without the need of reference image material. We leverage state-of-the-art
relative MDE and a novel alignment procedure to estimate metric distances. We
evaluate the approach on a zoo scenario dataset unseen during training. We
achieve a mean absolute distance estimation error of only 0.9864 meters at a
precision of 90.3% and recall of 63.8%, while completely eliminating the
previously required manual effort for biodiversity researchers. The code will
be made available.


title:
Three-dimensional monopole-free CP$^{N-1}$ models: Behavior in the
  presence of a quartic potential

abstract:
  We investigate the phase diagram and the nature of the phase transitions in a
three-dimensional model characterized by a global SU($N$) symmetry, a local
U(1) symmetry, and the absence of monopoles. It represents a natural
generalization of the gauge monopole-free (MF) CP$^{N-1}$ model, in which the
fixed-length constraint (London limit) is relaxed. We have performed Monte
Carlo simulations for $N=2$ and 25, observing a finite-temperature transition
in both cases, related to the condensation of a local gauge-invariant order
parameter. For $N=2$ results for the MF model are consistent with a weak
first-order transition. A continuous transition would be possible only if
scaling corrections were anomalously large. For $N=25$ the results in the
general MF model are also consistent with a first-order transition, that
becomes weaker as the size of the field-length fluctuations decreases.


title:
Triggered single-photon generation and resonance fluorescence in
  ultra-low loss integrated photonic circuits

abstract:
  A central requirement for photonic quantum information processing systems
lies in the combination of nonclassical light sources and low-loss,
phase-stable optical modes. While substantial progress has been made separately
towards ultra-low loss, $\leq1$ dB/m, chip-scale photonic circuits and high
brightness single-photon sources, integration of these technologies has
remained elusive. Here, we report a significant advance towards this goal, in
the hybrid integration of a quantum emitter single-photon source with a
wafer-scale, ultra-low loss silicon nitride photonic integrated circuit. We
demonstrate triggered and pure single-photon emission directly into a
Si$_3$N$_4$ photonic circuit with $\approx1$ dB/m propagation loss at a
wavelength of $\approx920$ nm. These losses are more than two orders of
magnitude lower than reported to date for any photonic circuit with on-chip
quantum emitter sources, and $>50$ % lower than for any prior
foundry-compatible integrated quantum photonic circuit, to the best of our
knowledge. Using these circuits we report the observation of resonance
fluorescence in the strong drive regime, a milestone towards integrated
coherent control of quantum emitters. These results constitute an important
step forward towards the creation of scaled chip-integrated photonic quantum
information systems.


title: Coasian Dynamics under Informational Robustness

abstract:
  This paper studies durable good monopoly without commitment under an
informationally robust objective. A seller cannot commit to future prices and
does not know the information arrival process available to a representative
buyer. We consider the case where the seller chooses prices to maximize her
profit guarantee against a time-consistent worst-case information structure. In
the gap case, the solution to this model is payoff-equivalent to a particular
known-values environment, immediately delivering a sharp characterization of
the equilibrium price paths. Furthermore, for a large class of environments,
arbitrary (possibly time-inconsistent) information arrival processes would not
lower the seller's profit as long as these prices are chosen. We call a price
path with this property a reinforcing solution. As certain versions of the
informationally robust objective under limited commitment may very well involve
time-inconsistency, we posit that the notion of a reinforcing solution may be
useful for researchers seeking to tractably analyze these settings. To
highlight the non-triviality of these conclusions, we comment that while the
analogy to known values can hold under an equilibrium selection in the no-gap
case, it does not hold more generally.


title:
Outside Looking In: Approaches to Content Moderation in End-to-End
  Encrypted Systems

abstract:
  In this paper, we assess existing technical proposals for content moderation
in End-to-End Encryption (E2EE) services. First, we explain the various tools
in the content moderation toolbox, how they are used, and the different phases
of the moderation cycle, including detection of unwanted content. We then lay
out a definition of encryption and E2EE, which includes privacy and security
guarantees for end-users, before assessing current technical proposals for the
detection of unwanted content in E2EE services against those guarantees.
  We find that technical approaches for user-reporting and meta-data analysis
are the most likely to preserve privacy and security guarantees for end-users.
Both provide effective tools that can detect significant amounts of different
types of problematic content on E2EE services, including abusive and harassing
messages, spam, mis- and disinformation, and CSAM, although more research is
required to improve these tools and better measure their effectiveness.
Conversely, we find that other techniques that purport to facilitate content
detection in E2EE systems have the effect of undermining key security
guarantees of E2EE systems.


title: Electromagnetic and strong isospin breaking in light meson masses

abstract:
  We study electromagnetic as well as strong isospin breaking effects in the
isospin mass splittings of light pseudoscalar and vector mesons. To this end we
employ a coupled system of quark Dyson-Schwinger and meson Bethe-Salpeter
equations whose interaction kernels contain gluon, pion and photon exchange
interactions. In bound states, QCD-induced isospin breaking is manifest on
different levels. On the one hand, a different explicit up- and down-quark mass
directly affects the propagators of the constituent quarks. On the other hand,
it leads to different interaction kernels within the isospin multiplets. In
addition, electromagnetic isospin breaking is induced via a photon exchange
diagram. Using the kaon iso-doublet and the charged pion masses as input to
determine the up, down and strange quark masses we find for the pion, kaon and
rho meson mass splittings different patterns each. In particular, our results
provide evidence that the effects from two sources of mass splittings, the
different quark masses and the different quark charges, do not add up linearly.


title: Irreversibility and the Arrow of Time

abstract:
  Within the general formalism of quantum theory irreversibility and the arrow
of time in the evolution of various physical systems are studied. Irreversible
behavior often manifests itself in the guise of entropy production. This
motivates me to begin this paper with a brief review of quantum-mechanical
entropy, a subject that Elliott Lieb has made outstanding contributions to,
followed by an enumeration of examples of irreversible behavior and of an arrow
of time analyzed in later sections. Subsequently, a derivation of the laws of
thermodynamics from (quantum) statistical mechanics, and, in particular, of the
Second Law of thermodynamics, in the forms given to it by Clausius and Carnot,
is presented. In a third part, results on diffusive (Brownian) motion of a
quantum particle interacting with a quasi-free quantum-mechanical heat bath are
reviewed. This is followed by an outline of a theory of friction by emission of
Cherenkov radiation of sound waves in a system consisting of a particle moving
through a Bose- Einstein condensate and interacting with it. In what may be the
most important section of this paper, the fundamental arrow of time inherent in
Quantum Mechanics is discussed.


title:
IoTMonitor: A Hidden Markov Model-based Security System to Identify
  Crucial Attack Nodes in Trigger-action IoT Platforms

abstract:
  With the emergence and fast development of trigger-action platforms in IoT
settings, security vulnerabilities caused by the interactions among IoT devices
become more prevalent. The event occurrence at one device triggers an action in
another device, which may eventually contribute to the creation of a chain of
events in a network. Adversaries exploit the chain effect to compromise IoT
devices and trigger actions of interest remotely just by injecting malicious
events into the chain. To address security vulnerabilities caused by
trigger-action scenarios, existing research efforts focus on the validation of
the security properties of devices or verification of the occurrence of certain
events based on their physical fingerprints on a device. We propose IoTMonitor,
a security analysis system that discerns the underlying chain of event
occurrences with the highest probability by observing a chain of physical
evidence collected by sensors. We use the Baum-Welch algorithm to estimate
transition and emission probabilities and the Viterbi algorithm to discern the
event sequence. We can then identify the crucial nodes in the trigger-action
sequence whose compromise allows attackers to reach their final goals. The
experiment results of our designed system upon the PEEVES datasets show that we
can rebuild the event occurrence sequence with high accuracy from the
observations and identify the crucial nodes on the attack paths.


title: For LISA. A piano-based sonification project of gravitational waves

abstract:
  In the paper we discuss the sonification of the simulated gravitational wave
data for the future LISA space mission. First, we introduce the LISA project
and its output. Then, we present Einstein's Sonata, a multimedia project
devoted to the artistic public display of the LISA data. Einstein's Sonata
features as its main element a music composition for prepared piano, Periplo
del latte. The latter results from a sonification strategy mapping astronomical
data onto music. We thus detail a four-stage sonification procedure, that
features two data preprocessing stages, a mapping into an abstract control
space and finally automatic notation generation.


title:
Wave function Ansatz (but Periodic) Networks and the Homogeneous
  Electron Gas

abstract:
  We design a neural network Ansatz for variationally finding the ground-state
wave function of the Homogeneous Electron Gas, a fundamental model in the
physics of extended systems of interacting fermions. We study the
spin-polarised and paramagnetic phases with 7, 14 and 19 electrons over a broad
range of densities from $r_s=1$ to $r_s=100$, obtaining similar or higher
accuracy compared to a state-of-the-art iterative backflow baseline even in the
challenging regime of very strong correlation. Our work extends previous
applications of neural network Ans\"{a}tze to molecular systems with methods
for handling periodic boundary conditions, and makes two notable changes to
improve performance: splitting the pairwise streams by spin alignment and
generating backflow coordinates for the orbitals from the network. This
contribution establishes neural network models as flexible and high precision
Ans\"{a}tze for periodic electronic systems, an important step towards
applications to crystalline solids.


title: Graph Spectrum Based Seismic Survey Design

abstract:
  Randomized sampling techniques have become increasingly useful in seismic
data acquisition and processing, allowing practitioners to achieve dense
wavefield reconstruction from a substantially reduced number of field samples.
However, typical designs studied in the low-rank matrix recovery and
compressive sensing literature are difficult to achieve by standard industry
hardware. For practical purposes, a compromise between stochastic and
realizable samples is needed. In this paper, we propose a deterministic and
computationally cheap tool to alleviate randomized acquisition design, prior to
survey deployment and large-scale optimization. We consider universal and
deterministic matrix completion results in the context of seismology, where a
bipartite graph representation of the source-receiver layout allows for the
respective spectral gap to act as a quality metric for wavefield
reconstruction. We provide realistic survey design scenarios to demonstrate the
utility of the spectral gap for successful seismic data acquisition via
low-rank and sparse signal recovery.


title:
Turbulence via intermolecular potential: Viscosity and transition range
  of the Reynolds number

abstract:
  Turbulence in fluids is an ubiquitous phenomenon, characterized by
spontaneous transition of a smooth, laminar flow to rapidly changing, chaotic
dynamics. In 1883, Reynolds experimentally demonstrated that, in an initially
laminar flow of water, turbulent motions emerge without any measurable external
disturbance. To this day, turbulence remains a major unresolved phenomenon in
fluid mechanics; in particular, there is a lack of a mathematical model where
turbulent dynamics emerge naturally from a laminar flow. Recently, we proposed
a new theory of turbulence in gases, according to which turbulent motions are
created in an inertial gas flow by the mean field effect of the intermolecular
potential. In the current work, we investigate the effect of viscosity in our
turbulence model, by numerically simulating the air flow at normal conditions
in a straight pipe for different values of the Reynolds number. We find that
the transition between the laminar and turbulent flows in our model occurs
without any deliberate perturbations as the Reynolds number increases from 2000
to 4000. As the simulated flow becomes turbulent, the decay rate of the time
averaged Fourier spectrum of the kinetic energy in our model approaches
Kolmogorov's inverse five-thirds law. Both results are consistent with
experiments and observations.


title: Analyzing Medical Data with Process Mining: a COVID-19 Case Study

abstract:
  The recent increase in the availability of medical data, possible through
automation and digitization of medical equipment, has enabled more accurate and
complete analysis on patients' medical data through many branches of data
science. In particular, medical records that include timestamps showing the
history of a patient have enabled the representation of medical information as
sequences of events, effectively allowing to perform process mining analyses.
In this paper, we will present some preliminary findings obtained with
established process mining techniques in regard of the medical data of patients
of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19.
We show that process mining techniques are able to reconstruct a model of the
ICU treatments for COVID patients.


title: Symbolic Comparison of Geometric Quantities in GeoGebra

abstract:
  Comparison of geometric quantities usually means obtaining generally true
equalities of different algebraic expressions of a given geometric figure.
Today's technical possibilities already support symbolic proofs of a
conjectured theorem, by exploiting computer algebra capabilities of some
dynamic geometry systems as well. We introduce GeoGebra's new feature, the
Compare command, that helps the users in experiments in planar geometry. We
focus on automatically obtaining conjectures and their proofs at the same time,
including not just equalities but inequalities too. Our contribution can
already be successfully used to support teaching geometry classes at secondary
level, by getting several well-known and some previously unpublished result
within seconds on a modern personal computer.


title: Automated Discovery of Geometrical Theorems in GeoGebra

abstract:
  We describe a prototype of a new experimental GeoGebra command and tool,
Discover, that analyzes geometric figures for salient patterns, properties, and
theorems. This tool is a basic implementation of automated discovery in
elementary planar geometry. The paper focuses on the mathematical background of
the implementation, as well as methods to avoid combinatorial explosion when
storing the interesting properties of a geometric figure.


title:
Reinforcement Learning with Sparse Rewards using Guidance from Offline
  Demonstration

abstract:
  A major challenge in real-world reinforcement learning (RL) is the sparsity
of reward feedback. Often, what is available is an intuitive but sparse reward
function that only indicates whether the task is completed partially or fully.
However, the lack of carefully designed, fine grain feedback implies that most
existing RL algorithms fail to learn an acceptable policy in a reasonable time
frame. This is because of the large number of exploration actions that the
policy has to perform before it gets any useful feedback that it can learn
from. In this work, we address this challenging problem by developing an
algorithm that exploits the offline demonstration data generated by a
sub-optimal behavior policy for faster and efficient online RL in such sparse
reward settings. The proposed algorithm, which we call the Learning Online with
Guidance Offline (LOGO) algorithm, merges a policy improvement step with an
additional policy guidance step by using the offline demonstration data. The
key idea is that by obtaining guidance from - not imitating - the offline data,
LOGO orients its policy in the manner of the sub-optimal policy, while yet
being able to learn beyond and approach optimality. We provide a theoretical
analysis of our algorithm, and provide a lower bound on the performance
improvement in each learning episode. We also extend our algorithm to the even
more challenging incomplete observation setting, where the demonstration data
contains only a censored version of the true state observation. We demonstrate
the superior performance of our algorithm over state-of-the-art approaches on a
number of benchmark environments with sparse rewards and censored state.
Further, we demonstrate the value of our approach via implementing LOGO on a
mobile robot for trajectory tracking and obstacle avoidance, where it shows
excellent performance.


title: Reducing Redundancy in the Bottleneck Representation of the Autoencoders

abstract:
  Autoencoders are a type of unsupervised neural networks, which can be used to
solve various tasks, e.g., dimensionality reduction, image compression, and
image denoising. An AE has two goals: (i) compress the original input to a
low-dimensional space at the bottleneck of the network topology using an
encoder, (ii) reconstruct the input from the representation at the bottleneck
using a decoder. Both encoder and decoder are optimized jointly by minimizing a
distortion-based loss which implicitly forces the model to keep only those
variations of input data that are required to reconstruct the and to reduce
redundancies. In this paper, we propose a scheme to explicitly penalize feature
redundancies in the bottleneck representation. To this end, we propose an
additional loss term, based on the pair-wise correlation of the neurons, which
complements the standard reconstruction loss forcing the encoder to learn a
more diverse and richer representation of the input. We tested our approach
across different tasks: dimensionality reduction using three different dataset,
image compression using the MNIST dataset, and image denoising using fashion
MNIST. The experimental results show that the proposed loss leads consistently
to superior performance compared to the standard AE loss.


title:
Non-Fermi liquid to Fermi liquid crossover inside a valley-polarized
  nematic state

abstract:
  The interplay between strong correlations and non-trivial topology in twisted
moir\'e systems can give rise to a rich landscape of ordered states that
intertwine the spin, valley, and charge degrees of freedom. In this paper, we
investigate the properties of a metal that undergoes a quantum phase transition
to a valley-polarized nematic state. Besides breaking the threefold rotational
symmetry of the triangular moir\'e superlattice, this type of order also breaks
twofold rotational and time-reversal symmetries. At zero temperature, the
ordered state displays a pseudo-Goldstone mode due to the existence of a
dangerously irrelevant coupling $\lambda$ in the $6$-state clock model that
describes the valley-polarized nematic quantum critical point. Using a
two-patch model, we compute the fermionic self-energy to show that down to very
low energies, the Yukawa-like coupling between the pseudo-Goldstone mode and
the electronic degrees of freedom promotes the emergence of non-Fermi liquid
behavior. Below a crossover energy scale $\Omega^{*}\sim\lambda^{3/2}$, Fermi
liquid behavior is recovered. We discuss possible experimental manifestations
of this behavior, as well as the applicability of these results to other
non-trivial nematic states, such as the spin-polarized nematic phase.


title: Security of EV-Charging Protocols

abstract:
  The field of electric vehicle charging involves a complex combination of
actors, devices, networks, and protocols. These protocols are being developed
without a clear focus on security. In this paper, we give an overview of the
main roles and protocols in use in the Netherlands. We describe a clear
attacker model and security requirements, show that in light of this many of
the protocols have security issues, and provide suggestions on how to address
these issues. The most important conclusion is the need for end-to-end security
for data in transit and long-term authenticity for data at rest. In addition,
we highlight the need for improved authentication of the EV driver, e.g. by
using banking cards. For the communication links we advise mandatory use of
TLS, standardization of TLS options and configurations, and improved
authentication using TLS client certificates.


title:
A Local Geometric Interpretation of Feature Extraction in Deep
  Feedforward Neural Networks

abstract:
  In this paper, we present a local geometric analysis to interpret how deep
feedforward neural networks extract low-dimensional features from
high-dimensional data. Our study shows that, in a local geometric region, the
optimal weight in one layer of the neural network and the optimal feature
generated by the previous layer comprise a low-rank approximation of a matrix
that is determined by the Bayes action of this layer. This result holds (i) for
analyzing both the output layer and the hidden layers of the neural network,
and (ii) for neuron activation functions with non-vanishing gradients. We use
two supervised learning problems to illustrate our results: neural network
based maximum likelihood classification (i.e., softmax regression) and neural
network based minimum mean square estimation. Experimental validation of these
theoretical results will be conducted in our future work.


title:
General embedded cluster protocol for accurate modeling of oxygen
  vacancies in metal-oxides

abstract:
  The O vacancy (Ov) formation energy, $E_\textrm{Ov}$, is an important
property of a metal-oxide, governing its performance in applications such as
fuel cells or heterogeneous catalysis. These defects are routinely studied with
density functional theory (DFT). However, it is well-recognized that standard
DFT formulations (e.g. the generalized gradient approximation) are insufficient
for modeling the Ov, requiring higher levels of theory. The embedded cluster
method offers a promising approach to compute $E_\textrm{Ov}$ accurately,
giving access to all electronic structure methods. Central to this approach is
the construction of quantum(-mechanically treated) clusters placed within
suitable embedding environments. Unfortunately, current approaches to
constructing the quantum clusters either require large system sizes, preventing
application of high-level methods, or require significant manual input,
preventing investigations of multiple systems simultaneously. In this work, we
present a systematic and general quantum cluster design protocol that can
determine small converged quantum clusters for studying the Ov in metal-oxides
with accurate methods such as local coupled cluster with single, double and
perturbative triple excitations [CCSD(T)]. We apply this protocol to study the
Ov in the bulk and surface planes of rutile TiO2 and rocksalt MgO, producing
the first accurate and well-converged determinations of $E_\textrm{Ov}$ with
this method. These reference values are used to benchmark exchange-correlation
functionals in DFT and we find that all studied functionals underestimate
$E_\textrm{Ov}$, with the average error decreasing along the rungs of Jacob's
ladder. This protocol is automatable for high-throughput calculations and can
be generalized to study other point defects or adsorbates.


title:
Offline Reinforcement Learning with Realizability and Single-policy
  Concentrability

abstract:
  Sample-efficiency guarantees for offline reinforcement learning (RL) often
rely on strong assumptions on both the function classes (e.g.,
Bellman-completeness) and the data coverage (e.g., all-policy concentrability).
Despite the recent efforts on relaxing these assumptions, existing works are
only able to relax one of the two factors, leaving the strong assumption on the
other factor intact. As an important open problem, can we achieve
sample-efficient offline RL with weak assumptions on both factors?
  In this paper we answer the question in the positive. We analyze a simple
algorithm based on the primal-dual formulation of MDPs, where the dual
variables (discounted occupancy) are modeled using a density-ratio function
against offline data. With proper regularization, we show that the algorithm
enjoys polynomial sample complexity, under only realizability and single-policy
concentrability. We also provide alternative analyses based on different
assumptions to shed light on the nature of primal-dual algorithms for offline
RL.


title:
A closer look at predicting turbulence statistics of arbitrary moments
  when based on a non-modelled symmetry approach

abstract:
  A recent Letter by Oberlack et al. [Phys. Rev. Lett. 128, 024502 (2022)]
claims to have derived new symmetry-induced solutions of the non-modelled
statistical Navier-Stokes equations of turbulent channel flow. A high accuracy
match to DNS data for all streamwise moments up to order 6 is presented, both
in the region of the channel-center and in the inertial sublayer close to the
wall. Here we will show that the findings and conclusions in that study are
highly misleading, as they give the impression that a significant breakthrough
in turbulence research has been achieved. But, unfortunately, this is not the
case. Besides trivial and misleading aspects, we will demonstrate that even
basic turbulence-relevant correlations as the Reynolds-stress cannot be fitted
to data using the proposed symmetry-induced scaling laws. The Lie-group
symmetry method as used by Oberlack et al. cannot bypass the closure problem of
turbulence. It is just another assumption-based method that requires modelling
and is not, as claimed, a first-principle method that leads directly to
solutions. Next to PRL, two more papers by Oberlack et al. are called out for
correction or a retraction.


title:
A very low central oxygen mass in the peculiar type Ia SN 2010lp:
  further diversity at the low-luminosity end of SNe Ia

abstract:
  A nebular spectrum of the peculiar, low-luminosity type Ia supernova 2010lp
is modelled in order to estimate the composition of the inner ejecta and to
illuminate the nature of this event. Despite having a normally declining light
curve, SN 2010lp was similar spectroscopically to SN 1991bg at early times.
However, it showed a very unusual double-peaked [OI]
$\lambda\lambda\,6300,6363$ emission at late times (Taubenberger et al. 2013).
Modelling of the nebular spectrum suggests that a very small amount of oxygen
($\sim0.05$ M$_{\odot}$), expanding at very low speed ($\sim2000$ km/s) is
sufficient to reproduce the observed emission. The rest of the nebula is not
too dissimilar from SN 1991bg, except that SN 2010lp is slightly more luminous.
The double-peaked [OI] emission suggests that SN 2010lp may be consistent with
the merger or collision of two low-mass white dwarfs. The low end of the SN Ia
luminosity sequence is clearly populated by diverse events, where different
channels may contribute.


title: A designer approach to $f(Q)$ gravity and cosmological implications

abstract:
  We investigate the evolution of linear perturbations in the Symmetric
Teleparallel Gravity, namely $f(Q)$ gravity, for which we design the $f(Q)$
function to match specific expansion histories. We consider different
evolutions of the effective dark energy equation of state, $w_Q(a)$, which
includes $w_Q=-1$, a constant $w_Q \neq -1$ and a fast varying equation of
state. We identify clear patterns in the effective gravitational coupling,
which accordingly modifies the linear growth of large scale structures. We
provide theoretical predictions for the product of the growth rate $\tilde{f}$
and the root mean square of matter fluctuations $\sigma_8$, namely
$\tilde{f}\sigma_8$ and for the sign of the cross-correlation power spectrum of
the galaxy fluctuations and the cosmic microwave background radiation
anisotropies. These properties can be used to distinguish the $f(Q)$ gravity
from the standard cosmological model using accurate cosmological observations.


title:
Properties of the ionisation glitch II. Seismic signature of the
  structural perturbation

abstract:
  In the present paper, we aim to constrain the properties of the ionisation
region in a star from the oscillation frequency variation (a so-called glitch)
caused by rapid structural variations in this very region. In particular, we
seek to avoid the use of calibration based on stellar models thus providing a
truly independent estimate of these properties. These include both the helium
abundance and other physical quantities that can have a significant impact on
the oscillation frequencies such as the electronic degeneracy parameter or the
extent of the ionisation region.
  Taking as a starting point our first paper, we applied structural
perturbations of the ionisation zone to the wave equation for radial
oscillations in an isentropic region. The resulting glitch model is thus able
to exploit the information contained in the fast frequency oscillation caused
by the helium ionisation but also in the slow trend accompanying that of
hydrogen. This information can directly be expressed in terms of parameters
related respectively to the helium abundance, electronic degeneracy and extent
of the ionisation region.
  Using a Bayesian inference, we show that a substantial recovery of the
properties at the origin of the glitch is possible. A degeneracy between the
helium abundance and the electronic degeneracy is found to exist, which
particularly affects the helium estimate. Extending the method to cases where
the glitch is subject to contamination (e.g. surface effects), we noted the
importance of the slow glitch trend associated with hydrogen ionisation. We
propose using a Gaussian process to disentangle the frequency glitch from
surface effects.


title: Point-Level Region Contrast for Object Detection Pre-Training

abstract:
  In this work we present point-level region contrast, a self-supervised
pre-training approach for the task of object detection. This approach is
motivated by the two key factors in detection: localization and recognition.
While accurate localization favors models that operate at the pixel- or
point-level, correct recognition typically relies on a more holistic,
region-level view of objects. Incorporating this perspective in pre-training,
our approach performs contrastive learning by directly sampling individual
point pairs from different regions. Compared to an aggregated representation
per region, our approach is more robust to the change in input region quality,
and further enables us to implicitly improve initial region assignments via
online knowledge distillation during training. Both advantages are important
when dealing with imperfect regions encountered in the unsupervised setting.
Experiments show point-level region contrast improves on state-of-the-art
pre-training methods for object detection and segmentation across multiple
tasks and datasets, and we provide extensive ablation studies and
visualizations to aid understanding. Code will be made available.


title:
Sharper Rates for Separable Minimax and Finite Sum Optimization via
  Primal-Dual Extragradient Methods

abstract:
  We design accelerated algorithms with improved rates for several fundamental
classes of optimization problems. Our algorithms all build upon techniques
related to the analysis of primal-dual extragradient methods via relative
Lipschitzness proposed recently by [CST21].
  (1) Separable minimax optimization. We study separable minimax optimization
problems $\min_x \max_y f(x) - g(y) + h(x, y)$, where $f$ and $g$ have
smoothness and strong convexity parameters $(L^x, \mu^x)$, $(L^y, \mu^y)$, and
$h$ is convex-concave with a $(\Lambda^{xx}, \Lambda^{xy},
\Lambda^{yy})$-blockwise operator norm bounded Hessian. We provide an algorithm
with gradient query complexity $\tilde{O}\left(\sqrt{\frac{L^{x}}{\mu^{x}}} +
\sqrt{\frac{L^{y}}{\mu^{y}}} + \frac{\Lambda^{xx}}{\mu^{x}} +
\frac{\Lambda^{xy}}{\sqrt{\mu^{x}\mu^{y}}} +
\frac{\Lambda^{yy}}{\mu^{y}}\right)$. Notably, for convex-concave minimax
problems with bilinear coupling (e.g.\ quadratics), where $\Lambda^{xx} =
\Lambda^{yy} = 0$, our rate matches a lower bound of [ZHZ19].
  (2) Finite sum optimization. We study finite sum optimization problems
$\min_x \frac{1}{n}\sum_{i\in[n]} f_i(x)$, where each $f_i$ is $L_i$-smooth and
the overall problem is $\mu$-strongly convex. We provide an algorithm with
gradient query complexity $\tilde{O}\left(n + \sum_{i\in[n]}
\sqrt{\frac{L_i}{n\mu}} \right)$. Notably, when the smoothness bounds
$\{L_i\}_{i\in[n]}$ are non-uniform, our rate improves upon accelerated SVRG
[LMH15, FGKS15] and Katyusha [All17] by up to a $\sqrt{n}$ factor.
  (3) Minimax finite sums. We generalize our algorithms for minimax and finite
sum optimization to solve a natural family of minimax finite sum optimization
problems at an accelerated rate, encapsulating both above results up to a
logarithmic factor.


title:
Unconditionally secure digital signatures implemented in an 8-user
  quantum network

abstract:
  The ability to know and verifiably demonstrate the origins of messages can
often be as important as encrypting the message itself. Here we present an
experimental demonstration of an unconditionally secure digital signature (USS)
protocol implemented for the first time, to the best of our knowledge, on a
fully connected quantum network without trusted nodes. Our USS protocol is
secure against forging, repudiation and messages are transferrable. We show the
feasibility of unconditionally secure signatures using only bi-partite
entangled states distributed throughout the network and experimentally evaluate
the performance of the protocol in real world scenarios with varying message
lengths.


title: Cosmic rays density fluctuations with turbulence universal spectrum -8/3

abstract:
  An exact turbulence universal scaling law-8/3 for the density fluctuations of
cosmic ray (CR) is obtained on the basis of a new analytical compressible
turbulence theory and known two-fluid model of the CR dynamics. It is shown
that the origin of this scaling law may be due to the breaking of the nonlinear
simple waves in CR medium near the scale of their Larmor radii as for the space
plasma of solar wind and magnetosheath.


title: Dimensionally Consistent Learning with Buckingham Pi

abstract:
  In the absence of governing equations, dimensional analysis is a robust
technique for extracting insights and finding symmetries in physical systems.
Given measurement variables and parameters, the Buckingham Pi theorem provides
a procedure for finding a set of dimensionless groups that spans the solution
space, although this set is not unique. We propose an automated approach using
the symmetric and self-similar structure of available measurement data to
discover the dimensionless groups that best collapse this data to a lower
dimensional space according to an optimal fit. We develop three data-driven
techniques that use the Buckingham Pi theorem as a constraint: (i) a
constrained optimization problem with a non-parametric input-output fitting
function, (ii) a deep learning algorithm (BuckiNet) that projects the input
parameter space to a lower dimension in the first layer, and (iii) a technique
based on sparse identification of nonlinear dynamics (SINDy) to discover
dimensionless equations whose coefficients parameterize the dynamics. We
explore the accuracy, robustness and computational complexity of these methods
as applied to three example problems: a bead on a rotating hoop, a laminar
boundary layer, and Rayleigh-B\'enard convection.


title: On-the-fly 3D metrology of volumetric additive manufacturing

abstract:
  Additive manufacturing techniques are revolutionizing product development by
enabling fast turnaround from design to fabrication. However, the throughput of
the rapid prototyping pipeline remains constrained by print optimization,
requiring multiple iterations of fabrication and ex-situ metrology. Despite the
need for a suitable technology, robust in-situ shape measurement of an entire
print is not currently available with any additive manufacturing modality.
Here, we address this shortcoming by demonstrating fully simultaneous 3D
metrology and printing. We exploit the dramatic increase in light scattering by
a photoresin during gelation for real-time 3D imaging of prints during
tomographic volumetric additive manufacturing. Tomographic imaging of the light
scattering density in the build volume yields quantitative, artifact-free 3D +
time models of cured objects that are accurate to below 1% of the size of the
print. By integrating shape measurement into the printing process, our work
paves the way for next-generation rapid prototyping with real-time defect
detection and correction.


title:
FCM-DNN: diagnosing coronary artery disease by deep accuracy Fuzzy
  C-Means clustering model

abstract:
  Cardiovascular disease is one of the most challenging diseases in middle-aged
and older people, which causes high mortality. Coronary artery disease (CAD) is
known as a common cardiovascular disease. A standard clinical tool for
diagnosing CAD is angiography. The main challenges are dangerous side effects
and high angiography costs. Today, the development of artificial
intelligence-based methods is a valuable achievement for diagnosing disease.
Hence, in this paper, artificial intelligence methods such as neural network
(NN), deep neural network (DNN), and Fuzzy C-Means clustering combined with
deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac
magnetic resonance imaging (CMRI) dataset. The original dataset is used in two
different approaches. First, the labeled dataset is applied to the NN and DNN
to create the NN and DNN models. Second, the labels are removed, and the
unlabeled dataset is clustered via the FCM method, and then, the clustered
dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second
clustering and modeling, the training process is improved, and consequently,
the accuracy is increased. As a result, the proposed FCM-DNN model achieves the
best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5
clusters for healthy subjects and 5 clusters for sick subjects, through the
10-fold cross-validation technique compared to the NN and DNN models reaching
the accuracies of 92.18% and 99.63%, respectively. To the best of our
knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset
using artificial intelligence methods. The results confirm that the proposed
FCM-DNN model can be helpful for scientific and research centers.


title:
Scattering approach for calculating one-loop effective action and vacuum
  energy

abstract:
  An approach for calculating one-loop effective actions and vacuum energies in
nonrelativistic quantum field theory is suggested. The starting point is to
regard one-loop effective actions and vacuum energies in quantum field theory
and scattering phase shifts and amplitudes in quantum mechanics as spectral
functions of an operator. Different spectral functions of the same operator may
belong to different fields of physics. Methods for calculating various spectral
functions can be interconverted through transform relations among spectral
functions. In principle, all methods for calculating scattering phase shifts
and amplitudes can be converted to methods for the calculation of the one-loop
effective actions and vacuum energies. As an example, we convert the Born
approximation in quantum mechanics to a method for calculating one-loop
effective actions and vacuum energies.


title: Multi-modal unsupervised brain image registration using edge maps

abstract:
  Diffeomorphic deformable multi-modal image registration is a challenging task
which aims to bring images acquired by different modalities to the same
coordinate space and at the same time to preserve the topology and the
invertibility of the transformation. Recent research has focused on leveraging
deep learning approaches for this task as these have been shown to achieve
competitive registration accuracy while being computationally more efficient
than traditional iterative registration methods. In this work, we propose a
simple yet effective unsupervised deep learning-based {\em multi-modal} image
registration approach that benefits from auxiliary information coming from the
gradient magnitude of the image, i.e. the image edges, during the training. The
intuition behind this is that image locations with a strong gradient are
assumed to denote a transition of tissues, which are locations of high
information value able to act as a geometry constraint. The task is similar to
using segmentation maps to drive the training, but the edge maps are easier and
faster to acquire and do not require annotations. We evaluate our approach in
the context of registering multi-modal (T1w to T2w) magnetic resonance (MR)
brain images of different subjects using three different loss functions that
are said to assist multi-modal registration, showing that in all cases the
auxiliary information leads to better results without compromising the runtime.


title:
A survey of unsupervised learning methods for high-dimensional
  uncertainty quantification in black-box-type problems

abstract:
  Constructing surrogate models for uncertainty quantification (UQ) on complex
partial differential equations (PDEs) having inherently high-dimensional
$\mathcal{O}(10^{\ge 2})$ stochastic inputs (e.g., forcing terms, boundary
conditions, initial conditions) poses tremendous challenges. The curse of
dimensionality can be addressed with suitable unsupervised learning techniques
used as a pre-processing tool to encode inputs onto lower-dimensional subspaces
while retaining its structural information and meaningful properties. In this
work, we review and investigate thirteen dimension reduction methods including
linear and nonlinear, spectral, blind source separation, convex and non-convex
methods and utilize the resulting embeddings to construct a mapping to
quantities of interest via polynomial chaos expansions (PCE). We refer to the
general proposed approach as manifold PCE (m-PCE), where manifold corresponds
to the latent space resulting from any of the studied dimension reduction
methods. To investigate the capabilities and limitations of these methods we
conduct numerical tests for three physics-based systems (treated as
black-boxes) having high-dimensional stochastic inputs of varying complexity
modeled as both Gaussian and non-Gaussian random fields to investigate the
effect of the intrinsic dimensionality of input data. We demonstrate both the
advantages and limitations of the unsupervised learning methods and we conclude
that a suitable m-PCE model provides a cost-effective approach compared to
alternative algorithms proposed in the literature, including recently proposed
expensive deep neural network-based surrogates and can be readily applied for
high-dimensional UQ in stochastic PDEs.


title: A bijection for Delannoy paths

abstract:
  We exhibit a bijection between central Delannoy $n$-paths, that is, lattice
paths from the origin to $(n,n)$ with steps $E=(1,0), \,N=(0,1),\,D=(1,1)$ and
the lattice paths from the origin to $(n+1,n)$ where the only restriction on
the steps is that they have finite nonnegative slope.


title:
Semantic Segmentation of Anaemic RBCs Using Multilevel Deep
  Convolutional Encoder-Decoder Network

abstract:
  Pixel-level analysis of blood images plays a pivotal role in diagnosing
blood-related diseases, especially Anaemia. These analyses mainly rely on an
accurate diagnosis of morphological deformities like shape, size, and precise
pixel counting. In traditional segmentation approaches, instance or
object-based approaches have been adopted that are not feasible for pixel-level
analysis. The convolutional neural network (CNN) model required a large dataset
with detailed pixel-level information for the semantic segmentation of red
blood cells in the deep learning domain. In current research work, we address
these problems by proposing a multi-level deep convolutional encoder-decoder
network along with two state-of-the-art healthy and Anaemic-RBC datasets. The
proposed multi-level CNN model preserved pixel-level semantic information
extracted in one layer and then passed to the next layer to choose relevant
features. This phenomenon helps to precise pixel-level counting of healthy and
anaemic-RBC elements along with morphological analysis. For experimental
purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and
Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks,
relevant, complete blood count (CBC), and morphology reports for performance
evaluation. The proposed model results were evaluated using crossmatch analysis
with ground truth mask by finding IoU, individual training, validation, testing
accuracies, and global accuracies using a 05-fold training procedure. This
model got training, validation, and testing accuracies as 0.9856, 0.9760, and
0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an
Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311,
0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.


title:
Stratospheric Balloons as a Complement to the Next Generation of
  Astronomy Missions

abstract:
  Observations that require large physical instrument dimensions and/or a
considerable amount of cryogens, as it is for example the case for high spatial
resolution far infrared astronomy, currently still face technological limits
for their execution from space. The high cost and finality of space missions
furthermore call for a very low risk approach and entail long development
times. For certain spectral regions, prominently including the mid- to
far-infrared as well as parts of the ultraviolet, stratospheric balloons offer
a flexible and affordable complement to space telescopes, with short
development times and comparatively good observing conditions. Yet, the entry
burden to use balloon-borne telescopes is high, with research groups typically
having to shoulder part of the infrastructure development as well. Aiming to
ease access to balloon-based observations, we present the efforts towards a
community-accessible balloon-based observatory, the European Stratospheric
Balloon Observatory (ESBO). ESBO aims at complementing space-based and airborne
capabilities over the next 10-15 years and at adding to the current landscape
of scientific ballooning activities by providing a service-centered
infrastructure for broader astronomical use, performing regular flights and
offering an operations concept that provides researchers with a similar
proposal-based access to observation time as practiced on ground-based
observatories. We present details on the activities planned towards the goal of
ESBO, the current status of the STUDIO (Stratospheric UV Demonstrator of an
Imaging Observatory) prototype platform and mission, as well as selected
technology developments with extensibility potential to space missions
undertaken for STUDIO.


title: Experimental observation of thermalisation with noncommuting charges

abstract:
  Quantum simulators have recently enabled experimental observations of quantum
many-body systems' internal thermalisation. Often, the global energy and
particle number are conserved, and the system is prepared with a well-defined
particle number - in a microcanonical subspace. However, quantum evolution can
also conserve quantities, or charges, that fail to commute with each other.
Noncommuting charges have recently emerged as a subfield at the intersection of
quantum thermodynamics and quantum information. Until now, this subfield has
remained theoretical. We initiate the experimental testing of its predictions,
with a trapped-ion simulator. We prepare 6-15 spins in an approximate
microcanonical subspace, a generalisation of the microcanonical subspace for
accommodating noncommuting charges, which cannot necessarily have well-defined
nontrivial values simultaneously. We simulate a Heisenberg evolution using
laser-induced entangling interactions and collective spin rotations. The
noncommuting charges are the three spin components. We find that small
subsystems equilibrate to near a recently predicted non-Abelian thermal state.
This work bridges quantum many-body simulators to the quantum thermodynamics of
noncommuting charges, whose predictions can now be tested.


title:
Correlating Gravitational Wave and Gamma-ray Signals from Primordial
  Black Holes

abstract:
  Asteroid-mass primordial black holes (PBH) can explain the observed dark
matter abundance while being consistent with the current indirect detection
constraints. These PBH can produce gamma-ray signals from Hawking radiation
that are within the sensitivity of future measurements by the AMEGO and
e-ASTROGAM experiments. PBH which give rise to such observable gamma-ray
signals have a cosmic origin from large primordial curvature fluctuations.
There must then be a companion, stochastic gravitational wave (GW) background
produced by the same curvature fluctuations. We demonstrate that the resulting
GW signals will be well within the sensitivity of future detectors such as
LISA, DECIGO, BBO, and the Einstein Telescope. The multi-messenger signal from
the observed gamma-rays and GW will allow a precise measurement of the
primordial curvature perturbation that produces the PBH. Indeed, we argue that
the resulting correlation between the two types of observations can provide a
smoking-gun signal of PBH.


title: Hidden Quantum Criticality and Entanglement in Quench Dynamics

abstract:
  Entanglement exhibits universal behavior near the ground-state critical point
where correlations are long-ranged and the thermodynamic entropy is vanishing.
On the other hand, a quantum quench imparts extensive energy and results in a
build-up of entropy, hence no critical behavior is expected at long times. In
this work, we present a new paradigm in the quench dynamics of integrable spin
chains which exhibit a ground-state order-disorder phase transition at a
critical line. Specifically, we consider a quench along the critical line which
displays a volume-law behavior of the entropy and exponentially decaying
correlations; however, we show that quantum criticality is hidden in
higher-order correlations and becomes manifest via measures such as the mutual
information and logarithmic negativity. Furthermore, we showcase the
scale-invariance of the R\'{e}nyi mutual information between disjoint regions
as further evidence for genuine critical behavior. We attribute the emerging
universality to the vanishing effective temperature of the soft mode in spite
of the quench. Our results are amenable to an experimental realization on
different quantum simulator platforms, particularly the Rydberg simulators.

categories: cond-mat.quant-gas cond-mat.stat-mech physics.atom-ph quant-ph


title: Higher-Group Symmetries and Weak Gravity Conjecture Mixing

abstract:
  In four-dimensional axion electrodynamics, a Chern-Simons coupling of the
form $\theta F \wedge F$ leads to a higher-group global symmetry between
background gauge fields. At the same time, such a Chern-Simons coupling leads
to a mixing between the Weak Gravity Conjectures for the axion and the gauge
field, so that the charged excitations of a Weak Gravity Conjecture-satisfying
axion string will also satisfy the Weak Gravity Conjecture for the gauge field.
In this paper, we argue that these higher-group symmetries and this phenomenon
of Weak Gravity Conjecture mixing are related to one another. We show that this
relationship extends to supergravities in 5, 6, 7, 8, 9, and 10 dimensions, so
higher-dimensional supergravity is endowed with precisely the structure needed
to ensure consistency with emergent higher-group symmetries and with the Weak
Gravity Conjecture. We further argue that a similar mixing of Weak Gravity
Conjectures can occur in two-term Chern-Simons theories or in theories with
kinetic mixing, though the connection with higher-group symmetries here is more
tenuous, and accordingly the constraints on effective field theory are not as
sharp.


title: Non-standard neutrino cosmology dilutes the lensing anomaly

abstract:
  Despite the impressive success of the standard cosmological model, several
anomalies defy its triumph. Among them is the so-called lensing anomaly: the
Planck satellite observes stronger CMB gravitational lensing than expected. The
role of neutrinos in this anomaly has been mostly overlooked, despite their key
role in CMB lensing, because in the standard scenario they tend to increase the
tension. Here, we show that this strongly depends on the assumed neutrino
equation of state. We demonstrate that if neutrinos have yet undiscovered
long-range interactions, the lensing pattern is significantly affected,
rendering the lensing anomaly as a pure statistical fluctuation. Our results
thus open up a window to link anomalous CMB lensing with present and future
cosmological, astrophysical, and laboratory measurements of neutrino
properties.


title:
Large and Small Non-extremal Black Holes, Thermodynamic Dualities, and
  the Swampland

abstract:
  In this paper we discuss black hole solutions parametrized by their entropy
$\mathcal{S}$ and temperature $\mathcal{T}$ in gravitational effective
theories. We are especially interested in the analysis of the boundary regions
in the $\mathcal{T}-\mathcal{S}$ diagram, i.e. large/small values of entropy
and temperature, and their relation to Swampland constraints. To explore this
correlation, we couple the gravitational theories to scalar fields and connect
limits of thermodynamic quantities of black holes to scalar field excursions in
the corresponding solutions. Whenever the scalar fields traverse an infinite
field distance, the Swampland Distance Conjecture allows for a reformulation in
terms of {\it entropy-} or {\it temperature-distance}. The effective theories
with scalars we investigate are Einstein-Maxwell-dilaton theory as well as
$\mathcal{N} = 2$ supergravity in four dimensions. The relation of the latter
to type II string theory compactified on Calabi-Yau 3-folds often allows for a
direct identification of the corresponding light tower of states. These setups
also point towards various dualities between asymptotic regions of the black
hole solution. In the context of $\mathcal{N} = 2$ supergravity black holes,
these {\it thermodynamic dualities} have an interpretation in terms of T- and
S-dualities along the internal directions and their natural action on
Kaluza-Klein and winding states.


title: A covariant formulation of relativistic mechanics

abstract:
  Accretion disks surrounding compact objects, and other environmental factors,
deviate satellites from geodetic motion. Unfortunately, setting up the
equations of motion for such relativistic trajectories is not as simple as in
Newtonian mechanics. The principle of general (or Lorentz) covariance and the
mass-shell constraint make it difficult to parametrize physically adequate
4-forces. Here, we propose a solution to this old problem. We apply our
framework to several conservative and dissipative forces. In particular, we
propose covariant formulations for Hooke's law and the constant force, and
compute the drag due to gravitational and hard-sphere collisions in dust, gas
and radiation media. We recover and covariantly extend known forces such as
Epstein drag, Chandrasekhar's dynamical friction and Poynting-Robertson drag.
Variable-mass effects are also considered, namely Hoyle-Lyttleton accretion and
the variable-mass rocket. We conclude with two applications: 1. The
free-falling spring. We find that Hooke's law corrects the deviation equation
by an effective Anti-de Sitter tidal force; 2. Black hole infall with drag. We
numerically compute some trajectories on a Schwarzschild background supporting
a dust-like accretion disk.


title:
The Triangulum Extended (TREX) Survey: The Stellar Disk Dynamics of M33
  as a Function of Stellar Age

abstract:
  Triangulum, M33, is a low mass, relatively undisturbed spiral galaxy that
offers a new regime in which to test models of dynamical heating. In spite of
its proximity, the dynamical heating history of M33 has not yet been well
constrained. In this work, we present the TREX Survey, the largest stellar
spectroscopic survey across the disk of M33. We present the stellar disk
kinematics as a function of age to study the past and ongoing dynamical heating
of M33. We measure line of sight velocities for ~4,500 disk stars. Using a
subset, we divide the stars into broad age bins using Hubble Space Telescope
and Canada-France-Hawaii-Telescope photometric catalogs: massive main sequence
stars and helium burning stars (~80 Myr), intermediate mass asymptotic branch
stars (~1 Gyr), and low mass red giant branch stars (~4 Gyr). We compare the
stellar disk dynamics to that of the gas using existing HI, CO, and Halpha
kinematics. We find that the disk of M33 has relatively low velocity dispersion
(~16 km/s), and unlike in the Milky Way and Andromeda galaxies, there is no
strong trend in velocity dispersion as a function of stellar age. The youngest
disk stars are as dynamically hot as the oldest disk stars and are dynamically
hotter than predicted by most M33 like low mass simulated analogs in Illustris.
The velocity dispersion of the young stars is highly structured, with the large
velocity dispersion fairly localized. The cause of this high velocity
dispersion is not evident from the observations and simulated analogs presented
here.


title: Quark and gluon form factors in four-loop QCD

abstract:
  We compute the photon-quark and Higgs-gluon form factors to four-loop order
within massless perturbative Quantum Chromodynamics. Our results constitute
ready-to-use building blocks for N${}^4$LO cross sections for Drell-Yan
processes and gluon-fusion Higgs boson production at the LHC. We present
complete analytic expressions for both form factors and show several of the
most complicated master integrals.


title: Collisions of localized shocks and quantum circuits

abstract:
  We study collisions between localized shockwaves inside a black hole
interior. We give a holographic boundary description of this process in terms
of the overlap of two growing perturbations in a shared quantum circuit. The
perturbations grow both exponentially as well as ballistically. Due to a
competition between different physical effects, the circuit analysis shows
dependence on the transverse locations and exhibits four regimes of
qualitatively different behaviors. On the gravity side we study properties of
the post-collision geometry, using exact calculations in simple setups and
estimations in more general circumstances. We show that the circuit analysis
offers intuitive and surprisingly accurate predictions about gravity
computations involving non-linear features of general relativity.


title: The LPM Effect in sequential bremsstrahlung: $1/N_c^2$ corrections

abstract:
  An important question concerning in-medium high-energy parton showers in a
quark-gluon plasma or other QCD medium is whether consecutive splittings of the
partons in a given shower can be treated as quantum mechanically independent,
or whether the formation times for two consecutive splittings instead have
significant overlap. Various previous calculations of the effect of overlapping
formation times have either (i) restricted attention to a soft bremsstrahlung
limit, or else (ii) used the large-$N_c$ limit (where $N_c{=}3$ is the number
of quark colors). In this paper, we make a first study of the accuracy of the
large-$N_c$ limit used by those calculations of overlap effects that avoid a
soft bremsstrahlung approximation. Specifically, we calculate the $1/N_c^2$
correction to previous $N_c{=}\infty$ results for overlap $g \to gg \to ggg$ of
two consecutive gluon splittings $g \to gg$. At order $1/N_c^2$, there is
interesting and non-trivial color dynamics that must be accounted for during
the overlap of the formation times.


title: Pilot-WINGS: An extended MUSE view of the structure of Abell 370

abstract:
  We investigate the strong-lensing cluster Abell 370 (A370) using a wide
Integral Field Unit (IFU) spectroscopic mosaic from the Multi-Unit
Spectroscopic Explorer (MUSE). IFU spectroscopy provides significant insight
into the structure and mass content of galaxy clusters, yet IFU-based cluster
studies focus almost exclusively on the central Einstein-radius region.
Covering over 14 arcmin$^2$, the new MUSE mosaic extends significantly beyond
the A370 Einstein radius, providing, for the first time, a detailed look at the
cluster outskirts. Combining these data with wide-field, multi-band Hubble
Space Telescope (HST) imaging from the BUFFALO project, we analyse the
distribution of objects within the cluster and along the line of sight.
Identifying 416 cluster galaxies, we use kinematics to trace the radial mass
profile of the halo, providing a mass estimate independent from the lens model.
We also measure radially-averaged properties of the cluster members, tracking
their evolution as a function of infall. Thanks to the high spatial resolution
of our data, we identify six cluster members acting as galaxy-galaxy lenses,
which constrain localized mass distributions beyond the Einstein radius.
Finally, taking advantage of MUSE's 3D capabilities, we detect and analyse
multiple spatially extended overdensities outside of the cluster that influence
lensing-derived halo mass estimates. We stress that much of this work is only
possible thanks to the robust, extended IFU coverage, highlighting its
importance even in less optically dense cluster regions. Overall, this work
showcases the power of combining HST+MUSE, and serves as the initial step
towards a larger and wider program targeting several clusters.


title:
Gate tunable anomalous Hall effect at (111) LaAlO$_3$/SrTiO$_3$
  interface

abstract:
  We present the theoretical prediction of a gate tunable anomalous Hall effect
(AHE) in an oxide interface as a hallmark of spin-orbit coupling. The observed
AHE at low-temperatures in the presence of an external magnetic field emerges
from a complex structure of the Berry curvature of the electrons on the Fermi
surface and strongly depends on the orbital character of the occupied bands. A
detailed picture of the results comes from a multiband low-energy model with a
generalized Rashba interaction that supports characteristic out-of-plane spin
and orbital textures. We discuss strategies for optimizing the intrinsic AHE in
(111) SrTiO$_3$ heterostructure interfaces.


title: Higgs-Dilaton Inflation in Einstein-Cartan gravity

abstract:
  We study the phenomenology of the Higgs-Dilaton model in the context of
Einstein-Cartan gravity, focusing on the separate impact of the Holst and
Nieh-Yan terms on the inflationary observables. Using analytical and numerical
techniques, we show the predictions of these scenarios to display an
attractor-like behaviour intrinsically related to the curvature of the
field-space manifold in the metric formulation of the theory. Beyond that, the
analysis of the Nieh-Yan case reveals the existence of an additional attractor
solution induced by a cubic pole in the inflaton kinetic term that becomes
relevant at large dilaton couplings. This constitutes a unique feature of the
Einstein-Cartan formulation as compared to the metric and Palatini
counterparts.


title: Empirical Constraints on the Nucleosynthesis of Nitrogen

abstract:
  We derive empirical constraints on the nucleosynthetic yields of nitrogen by
incorporating N enrichment into our previously developed and empirically tuned
multi-zone galactic chemical evolution model. We adopt a
metallicity-independent ("primary") N yield from massive stars and a
metallicity-dependent ("secondary") N yield from AGB stars. In our model,
galactic radial zones do not evolve along the observed [N/O]-[O/H] relation,
but first increase in [O/H] at roughly constant [N/O], then move upward in
[N/O] via secondary N production. By $t\approx5$ Gyr, the model approaches an
equilibrium [N/O]-[O/H] relation, which traces the radial oxygen gradient. We
find good agreement with the [N/O]-[O/H] trend observed in extra-galactic
systems if we adopt an IMF-averaged massive star yield
$y_\text{N}^\text{CC}=3.6\times10^{-4}$, consistent with predictions for
rapidly rotating progenitors, and a fractional AGB yield that is linear in mass
and metallicity
$y_\text{N}^\text{AGB}=(9\times10^{-4})(M_*/M_\odot)(Z_*/Z_\odot)$. This model
reproduces the [N/O]-[O/H] relation found for Milky Way stars in the APOGEE
survey, and it reproduces (though imperfectly) the trends of stellar [N/O] with
age and [O/Fe]. The metallicity-dependent yield plays the dominant role in
shaping the gas-phase [N/O]-[O/H] relation, but the AGB time-delay is required
to match the APOGEE stellar age and [O/Fe] trends. If we add $\sim$40\%
oscillations to the star formation rate, the model reproduces the scatter in
gas-phase [N/O] vs. [O/H] observed in external galaxies by MaNGA. We also
construct models using published AGB yields and examine their empirical
successes and shortcomings. For all AGB yields we consider, simple stellar
populations release half their N after only $\sim$250 Myr.