title: How stable are Transferability Metrics evaluations?

abstract:
  Transferability metrics is a maturing field with increasing interest, which
aims at providing heuristics for selecting the most suitable source models to
transfer to a given target dataset, without fine-tuning them all. However,
existing works rely on custom experimental setups which differ across papers,
leading to inconsistent conclusions about which transferability metrics work
best. In this paper we conduct a large-scale study by systematically
constructing a broad range of 715k experimental setup variations. We discover
that even small variations to an experimental setup lead to different
conclusions about the superiority of a transferability metric over another.
Then we propose better evaluations by aggregating across many experiments,
enabling to reach more stable conclusions. As a result, we reveal the
superiority of LogME at selecting good source datasets to transfer from in a
semantic segmentation scenario, NLEEP at selecting good source architectures in
an image classification scenario, and GBC at determining which target task
benefits most from a given source model. Yet, no single transferability metric
works best in all scenarios.


title: Asymptotic Theories of Classes Defined by Forbidden Homomorphisms

abstract:
  We study the first-order almost-sure theories for classes of finite
structures that are specified by homomorphically forbidding a set $\mathcal{F}$
of finite structures. If $\mathcal{F}$ consists of undirected graphs, a full
description of these theories can be derived from the
Kolaitis-Pr\"omel-Rothschild theorem, which treats the special case where
$\mathcal{F} = \{K_n\}$. The corresponding question for finite $\mathcal{F}$ of
finite directed graphs is wide open. We present a full description of the
almost-sure theories of classes described by homomorphically forbidding finite
sets $\mathcal{F}$ of oriented trees; all of them are $\omega$-categorical. In
our proof, we establish a result of independent interest, namely that every
constraint satisfaction problem for a finite digraph has first-order
convergence, and that the corresponding asymptotic theory can be described as a
finite linear combination of $\omega$-categorical theories.


title:
Construction of Machine-Learning Interatomic Potential Under Heat Flux
  Regularization and Its Application to Power Spectrum Analysis for Silver
  Chalcogenides

abstract:
  We propose a data-driven approach for constructing machine-learning
interatomic potentials (MLIPs) trained under a regularization with the aim of
avoiding nonphysical heat flux. Specifically, we introduce a regularization
term for the heat flux into the cost function of MLIPs to be minimized. Since
the treatment of heat flux using MLIPs with regularization can be decomposed
into elemental contributions or conducted in frequency space, this approach is
expected to be useful for investigating the origin of thermal conductivity
obtained from the Green-Kubo formula. However, the strength of regularization
needs to be appropriately set because it may reduce not only the nonphysical
part but also the intrinsic heat flux one. To this end, we investigated the
conditions for constructing MLIPs that can reproduce the power spectra of heat
flux associated with the empirical interatomic potential of Ag$_2$Se, which
consists of pairwise functions and do not contain a nonphysical heat flux. The
appropriate strength could be estimated from the variation of the magnitude of
regularization term as well as root mean square errors for total potential
energy, atomic force, and virial stress with respect to the strengths, without
reference spectrum data. As an application example, we explored the differences
in power spectra between superionic and nonsuperionic conducting phases based
on the heat flux regularization to MLIPs trained with the first-principles
calculation data of Ag$_2$S. Furthermore, our results demonstrate that training
with the regularization improves the robustness of MLIPs as well as the
reduction of the nonphysical heat flux.


title:
Carleson measures and the range of a Ces\`aro-like operator acting on
  $H^\infty$

abstract:
  In this paper, by describing characterizations of Carleson type measures on
$[0,1)$, we determine the range of a Ces\`aro-like operator acting on
$H^\infty$. A special case of our result gives an answer to a question posed by
P. Galanopoulos, D. Girela and N. Merch\'an recently.


title: Re-examining Distillation For Continual Object Detection

abstract:
  Training models continually to detect and classify objects, from new classes
and new domains, remains an open problem. In this work, we conduct a thorough
analysis of why and how object detection models forget catastrophically. We
focus on distillation-based approaches in two-stage networks; the most-common
strategy employed in contemporary continual object detection work.Distillation
aims to transfer the knowledge of a model trained on previous tasks -- the
teacher -- to a new model -- the student -- while it learns the new task. We
show that this works well for the region proposal network, but that wrong, yet
overly confident teacher predictions prevent student models from effective
learning of the classification head. Our analysis provides a foundation that
allows us to propose improvements for existing techniques by detecting
incorrect teacher predictions, based on current ground-truth labels, and by
employing an adaptive Huber loss as opposed to the mean squared error for the
distillation loss in the classification heads. We evidence that our strategy
works not only in a class incremental setting, but also in domain incremental
settings, which constitute a realistic context, likely to be the setting of
representative real-world problems.


title: Restricting directions for Kakeya sets

abstract:
  We prove that the Kakeya maximal conjecture is equivalent to the
$\Omega$-Kakeya maximal conjecture. This completes a recent result in [2] where
Keleti and Math{\'e} proved that the Kakeya conjecture is equivalent to the
$\Omega$-Kakeya conjecture. Moreover, we improve concrete bound on the
Hausdorff dimension of a $\Omega$-Kakeya set : for any Bore set $\Omega$ in S
n--1 , we prove that if X $\subset$ R n contains for any e $\in$ $\Omega$ a
unit segment oriented along e then we have dX $\ge$ 6 11 d$\Omega$ + 1 where dE
denotes the Hausdorff dimension of a set E.


title:
Safe Controller for Output Feedback Linear Systems using Model-Based
  Reinforcement Learning

abstract:
  The objective of this research is to enable safety-critical systems to
simultaneously learn and execute optimal control policies in a safe manner to
achieve complex autonomy. Learning optimal policies via trial and error, i.e.,
traditional reinforcement learning, is difficult to implement in
safety-critical systems, particularly when task restarts are unavailable. Safe
model-based reinforcement learning techniques based on a barrier transformation
have recently been developed to address this problem. However, these methods
rely on full state feedback, limiting their usability in a real-world
environment. In this work, an output-feedback safe model-based reinforcement
learning technique based on a novel barrier-aware dynamic state estimator has
been designed to address this issue. The developed approach facilitates
simultaneous learning and execution of safe control policies for
safety-critical linear systems. Simulation results indicate that barrier
transformation is an effective approach to achieve online reinforcement
learning in safety-critical systems using output feedback.


title:
Ergodic control of a heterogeneous population and application to
  electricity pricing

abstract:
  We consider a control problem for a heterogeneous population composed of
customers able to switch at any time between different contracts, depending not
only on the tariff conditions but also on the characteristics of each
individual. A provider aims to maximize an average gain per time unit,
supposing that the population is of infinite size. This leads to an ergodic
control problem for a "mean-field" MDP in which the state space is a product of
simplices, and the population evolves according to a controlled linear
dynamics. By exploiting contraction properties of the dynamics in Hilbert's
projective metric, we show that the ergodic eigenproblem admits a solution.
This allows us to obtain optimal strategies, and to quantify the gap between
steady-state strategies and optimal ones. We illustrate this approach on
examples from electricity pricing, and show in particular that the optimal
policies may be cyclic-alternating between discount and profit taking stages.


title:
Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel
  Disease: A Systematic Review

abstract:
  Cerebral small vessel disease (CSVD) is a major vascular contributor to
cognitive impairment in ageing, including dementias. Imaging remains the most
promising method for in vivo studies of CSVD. To replace the subjective and
laborious visual rating approaches, emerging studies have applied
state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD
from MRI scans. We aimed to summarise published computer-aided methods to
examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB),
dilated perivascular spaces (PVS), and lacunes of presumed vascular origin.
Seventy-one classical image processing, classical machine learning, and deep
learning studies were identified. CMB and PVS have been better studied,
compared to lacunes. While good performance metrics have been achieved in local
test datasets, there have not been generalisable pipelines validated in
different research or clinical cohorts. Transfer learning and weak supervision
techniques have been applied to accommodate the limitations in training data.
Future studies could consider pooling data from multiple sources to increase
diversity, and validating the performance of the methods using both image
processing metrics and associations with clinical measures.


title:
Study of general relativistic magnetohydrodynamic accretion flow around
  black holes

abstract:
  We present a novel approach to study the global structure of steady,
axisymmetric, advective, geometrically thin, magnetohydrodynamic (MHD)
accretion flow around black holes in full general relativity (GR). Considering
ideal MHD conditions and relativistic equation of state (REoS), we solve the
governing equations to obtain all possible smooth global accretion solutions.
We examine the dynamical and thermodynamical properties of accreting matter in
terms of the flow parameters, namely energy (${\cal E}$), angular momentum
(${\cal L}$), and local magnetic fields. For a thin GRMHD flow, we observe that
toroidal component ($b^\phi$) of the magnetic fields generally dominates over
radial component ($b^r$) at the disk equatorial plane. This evidently suggests
that toroidal magnetic field indeed plays important role in regulating the disk
dynamics. We further notice that the disk remains mostly gas pressure ($p_{\rm
gas}$) dominated ($\beta = p_{\rm gas}/p_{\rm mag} > 1$, $p_{\rm mag}$ refers
magnetic pressure) except at the near horizon region, where magnetic fields
become dynamically important ($\beta \sim 1$). We observe that Maxwell stress
is developed that eventually yields angular momentum transport inside the disk.
Towards this, we calculate the viscosity parameter ($\alpha$) that appears to
be radially varying. In addition, we examine the underlying scaling relation
between $\alpha$ and $\beta$, which clearly distinguishes two domains coexisted
along the radial extent of the disk. Finally, we discuss the utility of the
present formalism in the realm of GRMHD simulation studies.


title:
Modules at boundary points, fiberwise Bergman kernels, and
  log-subharmonicity

abstract:
  In this article, we consider Bergman kernels with respect to modules at
boundary points, and obtain a log-subharmonicity property of the Bergman
kernels, which deduces a concavity property related to the Bergman kernels. As
applications, we reprove the sharp effectiveness result related to a conjecture
posed by Jonsson-Must\c{a}t\u{a} and the effectiveness result of strong
openness property of the modules at boundary points.


title: Crystallographic Groups and Calabi-Yau 3-folds of Type $\mathrm{III}_0$

abstract:
  We provide a fine classification of Gorenstein quotients of three-dimensional
abelian varieties with isolated singularities, up to biholomorphism and
homeomorphism. This refines a result of Oguiso and Sakurai about fibred
Calabi-Yau threefolds of type $\mathrm{III}_0$. Our proof relies on methods of
crystallographic group theory applied to the orbifold fundamental groups of
such quotients.


title:
Vibrational response functions for multidimensional electronic
  spectroscopy: a coherent-state approach

abstract:
  Multi-dimensional spectroscopy represents a particularly insightful tool for
investigating the interplay of nuclear and electronic dynamics, which plays an
important role in a number of photophysical processes and photochemical
reactions. Here we present a coherent state representation of the vibronic
dynamics and of the resulting response functions. Analytical expressions are
initially derived for the case of third-order response functions in an N-level
system, with ground state initialization of the oscillator (zero-temperature
limit). The results are then generalized to the case of M-th order response
functions, with arbitrary M. The formal derivation is translated into a simple
recipe, whereby the explicit analytical expressions of the response functions
can be derived directly from the Feynman diagrams. We further generalize to the
whole set of initial coherent states, which form an overcomplete basis. This
allows one in principle to derive the dependence of the response functions on
arbitrary initial states of the vibrational modes and is here applied to the
case of thermal states. Finally, a non-Hermitian Hamiltonian approach is used
to include in the above expressions the effect of vibrational relaxation.


title: Anchoring molecular rotors by on-surface synthesis

abstract:
  Single molecular rotor is an important component for constructing bot-tom-up
molecular mechanical machines and a window for shedding light on complex
physical and chemical questions about motions of organic molecules on surfaces.
Stability of each component in such a molecular construction site is a crucial
prerequisite. To realize a stable stepwise rotation of a molecule by a low
temperature scanning tunneling microscope (LT-STM), atomic scale axles is
particularly important. An ideal atomic scale axle is expected to balance
between anchoring and mobility of rotating a single molecule on a metal surface
under external excitations. In this Chapter, several chemical anchoring
strategies on how to pin a molecular rotor are tested and discussed.
Tip-induced manipulation and motion analysis are used as tools to investigate
the properties and functionality of the proposed strategies.


title: Aging effects in Schelling Segregation model

abstract:
  The Schelling model has become a paradigm in social sciences to explain the
emerge of residential spatial segregation even in the presence of high
tolerance to mixed neighborhoods by the side of citizens. In particular, we
consider a noisy constrained version of the Schelling model, in which agents
maximize its satisfaction, related to the composition of the local
neighborhood, by infinite-range movements towards satisfying vacancies. We add
to it an aging effect by making the probability of agents to move inversely
proportional to the time they have been satisfied in their present location.
This mechanism simulates the development of an emotional attachment to a
location where an agent has been satisfied for a while. The introduction of
aging has several major impacts on the model statics and dynamics: the phase
transition between a segregated and a mixed phase of the original model
disappears, and we observe segregated states with high level of agent
satisfaction even for high values of the tolerance. In addition, the new
segregated phase is dynamically characterized by a slow power-law coarsening
process and by a glassy-like dynamics in which the asymptotic time
translational invariance is broken.


title: The Cardinal Complexity of Comparison-based Online Algorithms

abstract:
  We consider ordinal online problems, i.e., those tasks that only depend on
the pairwise comparisons between elements in the input. E.g., the secretary
problem and the game of googol. The natural approach to these tasks is to use
ordinal online algorithms that at each step only consider relative ranking
among the arrived elements, without looking at the numerical values of the
input. We formally study the question of how cardinal algorithms (that can use
numerical values of the input) can improve upon ordinal algorithms.
  We give a universal construction of the input distribution for any ordinal
online problem, such that the advantage of the cardinal algorithms over the
ordinal algorithms is at most $1+\varepsilon$ for arbitrary small $\varepsilon>
0$. However, the value range of the input elements in this construction is
huge: $O\left(\frac{n^3\cdot n!}{\varepsilon}\right)\uparrow\uparrow (n-1)$ for
an input sequence of length $n$. Surprisingly, we also identify a natural
family of hardcore problems that achieve a matching advantage of $1+ \Omega
\left(\frac{1}{\log^{(c)}N}\right),$ where $\log^{(c)}N=\log\log\ldots\log N$
with $c$ iterative logs and $c$ is an arbitrary constant $c\le n-2$. We also
consider a simpler variant of the hardcore problem, which we call maximum
guessing and is closely related to the game of googol. We provide a much more
efficient construction with cardinal complexity
$O\left(\frac{1}{\varepsilon}\right)^{n-1}$ for this easier task. Finally, we
study the dependency on $n$ of the hardcore problem. We provide an efficient
construction of size $O(n)$, if we allow cardinal algorithms to have constant
factor advantage against ordinal algorithms.


title:
Stability of estimates for fundamental solutions under Feynman-Kac
  perturbations for symmetric Markov processes

abstract:
  In this paper, when a given symmetric Markov process X satisfies the
stability of global heat kernel two-sided (upper) estimates by Markov
perturbations, we give a necessary and sufficient condition on the stability of
global two-sided (upper) estimates for fundamental solution of Feynman-Kac
semigroup of X. As a corollary, under the same assumptions, a weak type global
two-sided (upper) estimates holds for the fundamental solution of Feynman-Kac
semigroup with (extended) Kato class conditions for measures. This generalizes
all known results on the stability of global integral kernel estimates by
symmetric Feynman-Kac perturbations with Kato class conditions in the framework
of symmetric Markov processes.


title: Braids, metallic ratios and periodic solutions of the $2n$-body problem

abstract:
  Periodic solutions of the planar $N$-body problem determine braids through
the trajectory of $N$ bodies. Braid types can be used to classify periodic
solutions. According to the Nielsen-Thurston classification of surface
automorphisms, braids fall into three types: periodic, reducible and
pseudo-Anosov. To a braid of pseudo-Anosov type, there is an associated stretch
factor greater than 1, and this is a conjugacy invariant of braids. In 2006,
the third author discovered a family of multiple choreographic solutions of the
planar $2n$-body problem. We prove that braids obtained from the solutions in
the family are of pseudo-Anosov type, and their stretch factors are expressed
in metallic ratios. New numerical periodic solutions of the planar $2n$-body
problem are also provided.


title: The rotation of planet-hosting stars

abstract:
  Understanding the distribution of angular momentum during the formation of
planetary systems is a key topic in astrophysics. Data from the
$\textit{Kepler}$ and $\textit{Gaia}$ missions allow to investigate whether
stellar rotation is correlated with the presence of planets around Sun-like
stars. Here, we perform a statistical analysis of the rotation period of 493
planet-hosting stars. These are matched to a control sample, without detected
planets, with similar effective temperatures, masses, radii, metallicities, and
ages. We find that planet-hosting stars rotate on average $1.63 \pm 0.40$ days
slower. The difference in rotation is statistically significant both in samples
including and not including planets confirmed by radial velocity follow-up
observations. We also analyse the dependence of rotation distribution on
various stellar and planetary properties. Our results could potentially be
explained by planet detection biases depending on the rotation period of their
host stars in both RV and transit methods. Alternatively, they could point to a
physical link between the existence of planets and stellar rotation,
emphasising the need to understand the role of angular momentum in the
formation and evolution planetary systems.


title:
Characterization of Graphene/Ionic Liquid Memristive Devices for
  Neuromorphic Systems

abstract:
  Flexible and biocompatible memristive devices are particularly attractive for
bioelectronic systems due to the interest in improving computing capabilities
and the motivation to interface electronics with biological systems including
drug delivery, neural interfaces and biosensors. Structures made of more
unorthodox, organic material can address different issues due to their
characteristics: flexibility, conformability, biocompatibility and simple and
low-cost fabrication. It has been observed that gating graphene/ionic liquid
(IL) devices leads to the formation of an electrical double layer (a thin layer
of ions with a thickness of a few nanometers) at the graphene/IL interface due
to the local potential difference which also controls the local conductivity.
This structure provides a memristive mechanism based on a dynamic p-n junction
formation along the channel. Motivated by this memristive behavior, graphene/IL
devices were assembled with the aim of demonstrating memristive behavior and
associative learning. This work investigates memristive properties of flexible
graphene/ IL devices on polymer substrates. The I-V characteristics of these
novel devices and switching mechanism are investigated. Two distinct topologies
(single input, single output and double input, single output) of devices are
manufactured and tested to mimic conditioning. It is observed that the
application of voltage pulse trains of both positive and negative polarities
increases the device conductance and allows larger currents to pass after
repetitive excitation. This characteristic was exploited to condition devices
and emulate associative learning.


title: Phonon heat capacity and self-heating normal domains in NbTiN nanostrips

abstract:
  Self-heating normal domains in thin superconducting NbTiN nanostrips were
characterized via steady-state hysteretic current-voltage characteristics
measured at different substrate temperatures. The temperature dependence and
the magnitude of the current, which sustains a domain in equilibrium at
different voltages, can only be explained with a phonon heat capacity
noticeably less than expected for 3-d Debye phonons. This reduced heat capacity
coincides with the value obtained earlier from magnetoconductance and
photoresponse studies of the same films. The rate of heat flow from electrons
at a temperature Te to phonons in the substrate at a temperature TB is
proportional to (T_e^p - T_B^p) with the exponent p~3, which differs from the
exponents for heat flows mediated by the electron-phonon interaction or by
escaping of 3-d Debye phonons via the film/substrate interface. We attribute
both findings to the effect of the mean grain size on the phonon spectrum of
thin granular NbTiN films. Our findings are significant for understanding the
thermal transport in superconducting devices exploiting thin granular films.


title: Life and death of the Bose polaron

abstract:
  Spectroscopic and interferometric measurements complement each other in
extracting the fundamental properties of quantum many-body systems. While
spectroscopy provides precise measurements of equilibrated energies,
interferometry can elucidate the dynamical evolution of the system. For an
impurity immersed in a bosonic medium, both are equally important for
understanding the quasiparticle physics of the Bose polaron. Here, we compare
the interferometric and spectroscopic timescales to the underlying dynamical
regimes of the impurity dynamics and the polaron lifetime, highlighting the
capability of the interferometric approach to clearly resolve polaron dynamics.
In particular, interferometric measurements of the coherence amplitude at
strong interactions reveal faster quantum dynamics at large repulsive
interaction strengths than at unitarity. These observations are in excellent
agreement with a short-time theoretical prediction including both the continuum
and the attractive polaron branch. For longer times, qualitative agreement with
a many-body theoretical prediction which includes both branches is obtained.
Moreover, the polaron energy is extracted from interferometric measurements of
the observed phase velocity in agreement with previous spectroscopic results
from weak to strong attractive interactions. Finally, the phase evolution
allows for the measurement of an energetic equilibration timescale, describing
the initial approach of the phase velocity to the polaron energy.
Theoretically, this is shown to lie within the regime of universal dynamics
revealing a fast initial evolution towards the formation of polarons. Our
results give a comprehensive picture of the many-body physics governing the
Bose polaron and thus validates the quasiparticle framework for further
studies.


title:
Order Selection Prophet Inequality: From Threshold Optimization to
  Arrival Time Design

abstract:
  In the classical prophet inequality, a gambler faces a sequence of items,
whose values are drawn independently from known distributions. Upon the arrival
of each item, its value is realized and the gambler either accepts it and the
game ends, or irrevocably rejects it and continues to the next item. The goal
is to maximize the value of the selected item and compete against the expected
maximum value of all items. A tight competitive ratio of $\frac{1}{2}$ is
established in the classical setting and various relaxations have been proposed
to surpass the barrier, including the i.i.d. model, the order selection model,
and the random order model.
  In this paper, we advance the study of the order selection prophet
inequality, in which the gambler is given the extra power for selecting the
arrival order of the items. Our main result is a $0.725$-competitive algorithm,
that substantially improves the state-of-the-art $0.669$ ratio by Correa, Saona
and Ziliotto~(Math. Program. 2021), achieved in the harder random order model.
Recently, Agrawal, Sethuraman and Zhang~(EC 2021) proved that the task of
selecting the optimal order is NP-hard. Despite this fact, we introduce a novel
algorithm design framework that translates the discrete order selection problem
into a continuous arrival time design problem. From this perspective, we can
focus on the arrival time design without worrying about the threshold
optimization afterwards. As a side result, we achieve the optimal $0.745$
competitive ratio by applying our algorithm to the i.i.d. model.


title: Versatility of translational quantum dynamics

abstract:
  Is the Schr\"odinger equation with the Hamiltonian
$\hat{H}=-i\hbar\frac{\partial\ }{\partial\tau}$ too simple for real-world
systems? I show that the following quantum systems are governed by this
Hamiltonian, and at any time they are in an eigenstate of its canonical
conjugate $\hat{\tau}$, which plays the role of a time operator:
  1. the measuring device and the observed system in the standard model of
ideal measurements,
  2. any closed quantum system having as subsystem an ideal clock,
  3. any quantum world which contains a sterile massless fermion in a certain
eigenstate,
  4. the quantum representation of any deterministic time-reversible dynamical
system without time loops. All properties of the dynamical system are
faithfully encoded as quantum observables.
  The energy spectrum of these systems is $\mathbb{R}$, yet there is no decay
to infinite negative energy. Two such quantum systems may be unitarily
equivalent, but the physical meaning of their observables may be very
different, allowing unlimited diversity and complexity.

categories: quant-ph math-ph math.MP physics.hist-ph


title:
Measuring a population of spin waves from the electrical noise of an
  inductively coupled antenna

abstract:
  We study how a population of spin waves can be characterized from the
analysis of the electrical microwave noise delivered by an inductive antenna
placed in its vicinity. The measurements are conducted on a synthetic
antiferromagnetic thin stripe covered by a micron-sized antenna that feeds a
spectrum analyser after amplification. The antenna noise contains two
contributions. The population of incoherent spin waves generates a fluctuating
field that is sensed by the antenna: this is the "magnon noise". The antenna
noise also contains the contribution of the electronic fluctuations: the
Johnson-Nyquist noise. The latter depends on all impedances within the
measurement circuit; this includes the antenna self-inductance. As a result,
the electronic noise contains information about the magnetic susceptibility,
though it does not inform on the absolute amplitude of the magnetic
fluctuations. For micrometer-sized systems at thermal equilibrium, the
electronic noise dominates and the pure magnon noise cannot be determined. If
in contrast the spinwave bath is not at thermal equilibrium with the
measurement circuit, and if the spinwave population can be changed then one
could measure a mode-resolved effective magnon temperature provided specific
precautions are implemented.


title: The modular Dirac equation

abstract:
  We introduce a new equation we dubbed the modular Dirac equation to see and
reconstruct a spin 1/2 particle at the center of a nearly $AdS_2$ spacetime in
the entanglement wedge reconstruction paradigm and we study hidden symmetries
of this spacetime, too. Various properties of the Dirac modular operator are
studied: a generalized Tomita-Takesaki construction, the connection to the
Schwarzian derivative of the logarithm of the modular Dirac operator, the link
with the an allowable complex metric, the connection to regenesis, we write the
corresponding Lagrangian of the modular Dirac equation and we put in
perspective some limitations of the current bulk reconstruction in the case of
unknown couplings.


title: Degradation-agnostic Correspondence from Resolution-asymmetric Stereo

abstract:
  In this paper, we study the problem of stereo matching from a pair of images
with different resolutions, e.g., those acquired with a tele-wide camera
system. Due to the difficulty of obtaining ground-truth disparity labels in
diverse real-world systems, we start from an unsupervised learning perspective.
However, resolution asymmetry caused by unknown degradations between two views
hinders the effectiveness of the generally assumed photometric consistency. To
overcome this challenge, we propose to impose the consistency between two views
in a feature space instead of the image space, named feature-metric
consistency. Interestingly, we find that, although a stereo matching network
trained with the photometric loss is not optimal, its feature extractor can
produce degradation-agnostic and matching-specific features. These features can
then be utilized to formulate a feature-metric loss to avoid the photometric
inconsistency. Moreover, we introduce a self-boosting strategy to optimize the
feature extractor progressively, which further strengthens the feature-metric
consistency. Experiments on both simulated datasets with various degradations
and a self-collected real-world dataset validate the superior performance of
the proposed method over existing solutions.


title:
Fermi-surface origin of skyrmion lattices in centrosymmetric rare-earth
  intermetallics

abstract:
  We show from first-principles that barrel-shaped structures within the Fermi
surface of the centrosymmetric intermetallic compounds GdRu$_2$Si$_2$ and
Gd$_2$PdSi$_3$ give rise to Fermi surface nesting, which determines the
strength and sign of quasi-two-dimensional Ruderman-Kittel-Kasuya-Yosida
pairwise exchange interactions between the Gd moments. This is the principal
mechanism leading to their helical single-$q$ spin-spiral ground states,
providing transition temperatures and magnetic periods in good agreement with
experiment. Using atomistic spin-dynamic simulations, we draw a direct line
between the subtleties of the three-dimensional Fermi surface topology and the
stabilization of a square skyrmion lattice in GdRu$_2$Si$_2$ at applied
magnetic fields as observed in experiment.


title: Cored Dark Matter halos in the Cosmic Neutrino Background

abstract:
  We study the impact of the interaction between DM and the cosmic neutrino
background on the evolution of galactic dark matter halos. The energy transfer
from the neutrinos to the dark matter can heat the center of the galaxy and
make it cored. This effect is efficient for the small galaxies such as the
satellite galaxies of the Milky Way and we can put conservative constraint on
the non-relativistic elastic scattering cross section as
$\sigma_{\chi\nu}\lesssim 10^{-31} {\rm cm}^2$ for 0.1 keV dark matter and 0.1
eV neutrino.


title:
The Riesz basis property of the eigenvectors connected to the
  exponential stability problem of a boundary damped tube carrying the
  stationary flow of a fluid

abstract:
  In the present paper we study the stability problem for a stretched tube
conveying an ideal fluid with boundary damping. The spectral problem concerns
operator functions of the forms \begin{equation*}
\mathcal{M}\left(\lambda\right)=\lambda^2G+\lambda
D+C\quad\text{and}\quad\mathcal{P}\left(\lambda\right)=\lambda I-T
\end{equation*} taking values in different Hilbert spaces. Thorough analysis is
made of the location and asymptotics of eigenvalues in the complex plane and
Riesz basis property of the corresponding eigenvectors. Well-posedness of the
initial-value problem for the abstract equation \begin{equation*}
\dot{{x}}\left(t\right)=Tx\left(t\right) \end{equation*} is established as well
as expansions of the solutions in terms of eigenvectors and exponential
stability of the corresponding $C_0$-semigroup.


title: Dynamic Network-Code Design for Satellite Networks

abstract:
  Internet access from space enjoys renaissance as satellites in
Mega-Constellations is no longer fictitious. Network capacity, subject to power
and computational complexity constraints among other challenges, is a major
goal in this type of networks. This work studies Network Coding in the presence
of dynamically changing network conditions. The notion of generalized acyclic
network is introduced and employed for promoting the generation of
linear-multicast network code for what is considered to be a cyclic network.
The performance of several network coding schemes, among these is the known
static network code, is evaluated by a STK simulation for a swarm of
communicating satellites, conceptually based on the Iridium system. Exploiting
the prior knowledge of the networks topology over time, new network coding
approaches are described, whose aim is to better cope with the time-varying,
dynamic behavior of the network. It is demonstrated that in all cases,
pertaining to our example network, static network codes under-perform compared
to the presented approach. In addition, an efficient test for identifying the
most appropriate coding approach is presented.


title: Circuit Model Reduction with Scaled Relative Graphs

abstract:
  Continued fractions are classical representations of complex objects (for
example, real numbers) as sums and inverses of simpler objects (for example,
integers). The analogy in linear circuit theory is a chain of series/parallel
one-ports: the port behavior is a continued fraction containing the port
behaviors of its elements. Truncating a continued fraction is a classical
method of approximation, which corresponds to deleting the circuit elements
furthest from the port. We apply this idea to chains of series/parallel
one-ports composed of arbitrary nonlinear relations. This gives a model
reduction method which automatically preserves properties such as incremental
positivity. The Scaled Relative Graph (SRG) gives a graphical representation of
the original and truncated port behaviors. The difference of these SRGs gives a
bound on the approximation error, which is shown to be competitive with
existing methods.


title: The Potential Method For Price-Formation Models

abstract:
  We consider the mean-field game price formation model introduced by Gomes and
Sa\'ude. In this MFG model, agents trade a commodity whose supply can be
deterministic or stochastic. Agents maximize profit, taking into account
current and future prices. The balance between supply and demand determines the
price. We introduce a potential function that converts the MFG into a convex
variational problem. This variational formulation is particularly suitable for
machine learning approaches. Here, we use a recurrent neural network to solve
this problem. In the last section of the paper, we compare our results with
known analytical solutions.


title: SAM-kNN Regressor for Online Learning in Water Distribution Networks

abstract:
  Water distribution networks are a key component of modern infrastructure for
housing and industry. They transport and distribute water via widely branched
networks from sources to consumers. In order to guarantee a working network at
all times, the water supply company continuously monitors the network and takes
actions when necessary -- e.g. reacting to leakages, sensor faults and drops in
water quality. Since real world networks are too large and complex to be
monitored by a human, algorithmic monitoring systems have been developed. A
popular type of such systems are residual based anomaly detection systems that
can detect events such as leakages and sensor faults. For a continuous high
quality monitoring, it is necessary for these systems to adapt to changed
demands and presence of various anomalies.
  In this work, we propose an adaption of the incremental SAM-kNN classifier
for regression to build a residual based anomaly detection system for water
distribution networks that is able to adapt to any kind of change.


title:
Disentangling Abstraction from Statistical Pattern Matching in Human and
  Machine Learning

abstract:
  The ability to acquire abstract knowledge is a hallmark of human intelligence
and is believed by many to be one of the core differences between humans and
neural network models. Agents can be endowed with an inductive bias towards
abstraction through meta-learning, where they are trained on a distribution of
tasks that share some abstract structure that can be learned and applied.
However, because neural networks are hard to interpret, it can be difficult to
tell whether agents have learned the underlying abstraction, or alternatively
statistical patterns that are characteristic of that abstraction. In this work,
we compare the performance of humans and agents in a meta-reinforcement
learning paradigm in which tasks are generated from abstract rules. We define a
novel methodology for building "task metamers" that closely match the
statistics of the abstract tasks but use a different underlying generative
process, and evaluate performance on both abstract and metamer tasks. In our
first set of experiments, we found that humans perform better at abstract tasks
than metamer tasks whereas a widely-used meta-reinforcement learning agent
performs worse on the abstract tasks than the matched metamers. In a second set
of experiments, we base the tasks on abstractions derived directly from
empirically identified human priors. We utilize the same procedure to generate
corresponding metamer tasks, and see the same double dissociation between
humans and agents. This work provides a foundation for characterizing
differences between humans and machine learning that can be used in future work
towards developing machines with human-like behavior.


title:
How Can We Develop Explainable Systems? Insights from a Literature
  Review and an Interview Study

abstract:
  Quality aspects such as ethics, fairness, and transparency have been proven
to be essential for trustworthy software systems. Explainability has been
identified not only as a means to achieve all these three aspects in systems,
but also as a way to foster users' sentiments of trust. Despite this, research
has only marginally focused on the activities and practices to develop
explainable systems. To close this gap, we recommend six core activities and
associated practices for the development of explainable systems based on the
results of a literature review and an interview study. First, we identified and
summarized activities and corresponding practices in the literature. To
complement these findings, we conducted interviews with 19 industry
professionals who provided recommendations for the development process of
explainable systems and reviewed the activities and practices based on their
expertise and knowledge. We compared and combined the findings of the
interviews and the literature review to recommend the activities and assess
their applicability in industry. Our findings demonstrate that the activities
and practices are not only feasible, but can also be integrated in different
development processes.


title:
IoT with a Soft Touch: A Modular Remote Sensing Platform for STE(A)M
  Applications

abstract:
  Besides wide attraction in the industry, IoT is being used to advance STEM
and STEAM education across a range of education levels. This work presents a
remote sensing platform, named IoT with a Soft Touch, developed to achieve two
goals. First, it aims to lower the technicality, stimulating the students to do
STE(A)M. Second, the technology is to be used in `softer' applications (e.g.,
environmental and health care), thereby aiming to attract a more diverse set of
student profiles. Students can easily build a wireless sensing device, with a
specific application in mind. The modular design of the platform and an
intuitive graphical configurator tool allows them to tailor the device's
functionality to their needs. The sensor's data is transmitted wirelessly with
LoRaWAN. The data can be viewed and analyzed on a dashboard, or the raw data
can be extracted for further processing, e.g., as part of the school's STE(A)M
curriculum. This work elaborates on the low-power and modular design
challenges, and how the platform is used in education.


title:
Using Pre-Trained Language Models for Producing Counter Narratives
  Against Hate Speech: a Comparative Study

abstract:
  In this work, we present an extensive study on the use of pre-trained
language models for the task of automatic Counter Narrative (CN) generation to
fight online hate speech in English. We first present a comparative study to
determine whether there is a particular Language Model (or class of LMs) and a
particular decoding mechanism that are the most appropriate to generate CNs.
Findings show that autoregressive models combined with stochastic decodings are
the most promising. We then investigate how an LM performs in generating a CN
with regard to an unseen target of hate. We find out that a key element for
successful `out of target' experiments is not an overall similarity with the
training data but the presence of a specific subset of training data, i.e. a
target that shares some commonalities with the test target that can be defined
a-priori. We finally introduce the idea of a pipeline based on the addition of
an automatic post-editing step to refine generated CNs.


title:
Limiting conditions of Muckenhoupt and reverse H\"older classes on
  metric measure spaces

abstract:
  The natural maximal and minimal functions commute pointwise with the
logarithm on $A_\infty$. We use this observation to characterize the spaces
$A_1$ and $RH_\infty$ on metric measure spaces with a doubling measure. As the
limiting cases of Muckenhoupt $A_p$ and reverse H\"older classes, respectively,
their behavior is remarkably symmetric. On general metric measure spaces, an
additional geometric assumption is needed in order to pass between $A_p$ and
reverse H\"older descriptions. Finally, we apply the characterization to give
simple proofs of several known properties of $A_1$ and $RH_\infty$, including a
refined Jones factorization theorem. In addition, we show a boundedness result
for the natural maximal function.


title:
Complete hyperentangled Bell states analysis for
  polarization-spatial-time-bin degrees of freedom with unity fidelity

abstract:
  Hyperentangled states can outperform their classical counterparts on solving
certain tasks. Here we present a simplified scheme for completely
distinguishing two-photon hyperentangled Bell states in polarization, spatial,
and time-bin degrees of freedom (DOFs). Unity fidelity can be achieved in
principle without strong couple limitation between photon and quantum dot (QD),
and the incomplete and imperfect QD-cavity interactions are prevented by
single-photon detectors. In addition, auxiliary photons or DOFs are not
required in our scheme. The necessary linear optical elements are fewer than
the parity-check-based one.


title: A quantum advantage for Density Functional Theory ?

abstract:
  The technological revolution brought about by quantum computers promises to
solve problems with high economical and societal impact that remain intractable
on classical computers. While several quantum algorithms have been devoted to
solve the many-body problem in quantum chemistry, the focus is on wavefunction
theory that is limited to relatively small systems, even for quantum computers,
i.e., the size of tractable systems being roughly limited by the number of
qubits available. Computations on large systems rely mainly on mean-field-type
approaches such as density functional theory, for which no quantum advantage
has been envisioned so far. In this work, we question this a priori by
investigating the benefit of quantum computers to scale up not only many-body
wavefunction methods, but also mean-field-type methods, and consequently the
all range of application of quantum chemistry.


title: Optimal Workplace Occupancy Strategies during the COVID-19 Pandemic

abstract:
  During the COVID-19 pandemic, many organizations (e.g. businesses, companies,
government facilities, etc.) have attempted to reduce infection risk by
employing partial home office strategies. However, working from home can also
reduce productivity for certain types of work and particular employees. Over
the long term, many organizations therefore face a need to balance infection
risk against productivity. Motivated by this trade-off, we model this situation
as a bi-objective optimization problem and propose a practical approach to find
trade-off (Pareto optimal) solutions. We present a new probabilistic framework
to compute the expected number of infected employees as a function of key
parameters including: the incidence level in the neighborhood of the
organization, the COVID-19 transmission rate, the number of employees, the
percentage of vaccinated employees, the testing frequency, and the contact rate
among employees. We implement the model and the optimization algorithm and
perform several numerical experiments with different parameter settings.
Furthermore, we provide an online application based on the models and
algorithms developed in this paper, which can be used to identify the optimal
workplace occupancy rate for real-world organizations.


title:
Shifted substitution in non-commutative multivariate power series with a
  view toward free probability

abstract:
  We study a particular group law on formal power series in non-commuting
variables induced by their interpretation as linear forms on a suitable graded
connected word Hopf algebra. This group law is left-linear and is therefore
associated to a pre-Lie structure on formal power series. We study these
structures and show how they can be used to recast in a group theoretic form
various identities and transformations on formal power series that have been
central in the context of non-commutative probability theory, in particular in
Voiculescu's theory of free probability.


title: WildNet: Learning Domain Generalized Semantic Segmentation from the Wild

abstract:
  We present a new domain generalized semantic segmentation network named
WildNet, which learns domain-generalized features by leveraging a variety of
contents and styles from the wild. In domain generalization, the low
generalization ability for unseen target domains is clearly due to overfitting
to the source domain. To address this problem, previous works have focused on
generalizing the domain by removing or diversifying the styles of the source
domain. These alleviated overfitting to the source-style but overlooked
overfitting to the source-content. In this paper, we propose to diversify both
the content and style of the source domain with the help of the wild. Our main
idea is for networks to naturally learn domain-generalized semantic information
from the wild. To this end, we diversify styles by augmenting source features
to resemble wild styles and enable networks to adapt to a variety of styles.
Furthermore, we encourage networks to learn class-discriminant features by
providing semantic variations borrowed from the wild to source contents in the
feature space. Finally, we regularize networks to capture consistent semantic
information even when both the content and style of the source domain are
extended to the wild. Extensive experiments on five different datasets validate
the effectiveness of our WildNet, and we significantly outperform
state-of-the-art methods. The source code and model are available online:
https://github.com/suhyeonlee/WildNet.


title:
Current-induced control of the polarization state in a polar metal based
  heterostructure SnSe/WTe$_2$

abstract:
  The concept of a polar metal proposes new approach of current-induced
polarization control for ferroelectrics. We fabricate SnSe/WTe$_2$
heterostructure to experimentally investigate charge transport between two
ferroelectric van der Waals materials with different polarization directions.
WTe$_2$ is a polar metal with out-of-plane ferroelectric polarization, while
SnSe ferroelectric semiconductor is polarized in-plane, so one should expect
complicated polarization structure at the SnSe/WTe$_2$ interface. We study
$dI/dV(V)$ curves, which demonstrate sharp symmetric drop to zero $dI/dV$
differential conductance at some threshold bias voltages $\pm V_{th}$, which
are nearly symmetric in respect to the bias sign. While the gate electric field
is too small to noticeably affect the carrier concentration, the positive and
negative threshold positions are sensitive to the gate voltage. Also,
SnSe/WTe$_2$ heterostructure shows re-entrant transition to the low-conductive
$dI/dV=0$ state for abrupt change of the bias voltage even below the threshold
values. This behavior can not be observed for single SnSe or WTe$_2$ flakes, so
we interpret it as a result of the SnSe/WTe$_2$ interface coupling. In this
case, some threshold value of the electric field at the SnSe/WTe$_2$ interface
is enough to drive 90$^\circ$ change of the initial SnSe in-plane polarization
in the overlap region. The polarization mismatch leads to the significant
interface resistance contribution, analogously to the scattering of the charge
carriers on the domain walls. Thus, we demonstrate polarization state control
by electron transport through the SnSe/WTe$_2$ interface.


title:
Computing second-order points under equality constraints: revisiting
  Fletcher's augmented Lagrangian

abstract:
  We address the problem of minimizing a smooth function under smooth equality
constraints. Under regularity assumptions on the feasible set, we consider a
smooth exact penalty function known as Fletcher's augmented Lagrangian. We
propose an algorithm to minimize the penalized cost function which reaches
$\varepsilon$-approximate second-order critical points of the original
optimization problem in at most $\mathcal{O}(\varepsilon^{-3})$ iterations.
This improves on current best theoretical bounds. Along the way, we show new
properties of Fletcher's augmented Lagrangian, which may be of independent
interest.


title: Mining Precise Test Oracle Modelled by FSM

abstract:
  Precise test oracles for reactive systems such as critical control systems
and communication protocols can be modelled with deterministic finite state
machines (FSMs). Among other roles, they serve in evaluating the correctness of
systems under test. A great number of candidate precise oracles (shortly,
candidates) can be produced at the system design phase due to uncertainties,
e.g., when interpreting their requirements expressed in ambiguous natural
languages. Selecting the proper candidate becomes challenging for an expert. We
propose a test-driven approach to assist experts in this selection task. The
approach uses a non deterministic FSM to represent the candidates, includes the
partitioning of the candidates into subsets of candidates via Boolean encodings
and requires the intervention of experts to select subsets. We perform an
empirical evaluation of the applicability of the proposed approach.


title:
Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal
  Grounding

abstract:
  Grounding temporal video segments described in natural language queries
effectively and efficiently is a crucial capability needed in
vision-and-language fields. In this paper, we deal with the fast video temporal
grounding (FVTG) task, aiming at localizing the target segment with high speed
and favorable accuracy. Most existing approaches adopt elaborately designed
cross-modal interaction modules to improve the grounding performance, which
suffer from the test-time bottleneck. Although several common space-based
methods enjoy the high-speed merit during inference, they can hardly capture
the comprehensive and explicit relations between visual and textual modalities.
In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a
commonsense-aware cross-modal alignment (CCA) framework, which incorporates
commonsense-guided visual and text representations into a complementary common
space for fast video temporal grounding. Specifically, the commonsense concepts
are explored and exploited by extracting the structural semantic information
from a language corpus. Then, a commonsense-aware interaction module is
designed to obtain bridged visual and text features by utilizing the learned
commonsense concepts. Finally, to maintain the original semantic information of
textual queries, a cross-modal complementary common space is optimized to
obtain matching scores for performing FVTG. Extensive results on two
challenging benchmarks show that our CCA method performs favorably against
state-of-the-arts while running at high speed. Our code is available at
https://github.com/ZiyueWu59/CCA.


title:
Atomistic Global Optimization X: A Python package for optimization of
  atomistic structures

abstract:
  Modelling and understanding properties of materials from first principles
require knowledge of the underlying atomistic structure. This entails knowing
the individual identity and position of all involved atoms. Obtaining such
information for macro-molecules, nano-particles, clusters, and for the surface,
interface, and bulk phases of amorphous and solid materials represents a
difficult high dimensional global optimization problem. The rise of machine
learning techniques in materials science has, however, led to many compelling
developments that may speed up such structure searches. The complexity of the
new methods have established the necessity for an efficient way of
experimenting with and assembling them into global optimization algorithms. In
this paper we introduce the Atomistic Global Optimization X (AGOX) framework
and code, as a customizable approach to building efficient global optimization
algorithms. A modular way of expressing global optimization algorithms is
described and modern programming practices are used to enable that modularity
in the freely available AGOX python package. Two examples of global
optimization problems are analyzed: One that is computationally inexpensive
which is used to showcase that AGOX enables the expression of multiple global
optimization algorithms. As the other example, AGOX is used for solving a
complex atomistic optimization problem for a metal-nitride nano-cluster
embedded in a graphene sheet as described at the density functional theory
(DFT) level.


title:
Engineering spin-orbit effects and Berry curvature by deposition of Eu
  on WSe$_{2}$

abstract:
  Motivated by recent progress in 2D spintronics, we present Eu deposited on a
1H-WSe$_2$ as a promising platform for engineering spin-orbit effects and Berry
curvature. By first-principles calculations based on density functional theory,
we show that Eu/WSe$_2$ exhibits intriguing properties such as high magnetic
anisotropy, valley-dependent polarization of spin and orbital angular momenta,
and their Rashba textures. These originate from magnetic and spin-orbit
proximity effects at the interface and the interplay between localized $4f$
magnetic moments of Eu and mobile charge carriers of WSe$_2$. We find a
pronounced anomalous Hall effect in the proposed system. Thus, we promote $4f$
rare-earth metals deposited on top of a transition-metal dichalcogenides as a
promising platform for 2D spintronics.


title: Induced Chern-Simons term by dimensional reduction

abstract:
  We derive an induced Abelian Chern-Simons (CS) term in 2+1 dimensions, by
dimensional reduction from the finite-temperature theory of a Dirac field with
both vector and axial-vector couplings to two Abelian gauge fields, in 3+1
dimensions. In our construction, the CS term emerges for the lowest Matsubara
mode of the vector Abelian field, by integrating the fermionic field, under the
assumption that the axial vector field is in a "vacuum" configuration. This
configuration is characterized by a single number, which in turn determines the
coefficient of the induced CS term for the Abelian vector field.


title: New Limit on Axion-Dark-Matter using Cold Neutrons

abstract:
  We report on a search for axion-like dark matter using a Ramsey-type
apparatus for cold neutrons. A hypothetical axion-gluon-coupling would manifest
in a neutron electric dipole moment signal oscillating in time. Twenty-four
hours of data have been analyzed in a frequency range from 23 $\mu$Hz to 1 kHz,
and no significant oscillating signal has been found. The usage of present
axion and dark-matter models allowed excluding the coupling of axions to gluons
in the mass range from $1.5 \times 10^{-20}$ to $6.6 \times 10^{-13}$ eV with a
best sensitivity of $C_G / f_a m_a = (3.1 \pm 0.2) \times 10^{12}$ GeV$^{-2}$
(95% C.L.).


title:
Ultra Local Nonlinear Unknown Input Observers for Robust Fault
  Reconstruction

abstract:
  In this paper, we present a methodology for actuator and sensor fault
estimation in nonlinear systems. The method consists in augmenting the system
dynamics with an approximated ultra-local model (a finite chain of integrators)
for the fault vector and constructing a Nonlinear Unknown Input Observer (NUIO)
for the augmented dynamics. Then, fault reconstruction is reformulated as a
robust state estimation problem in the augmented state (true state plus
fault-related state). We provide sufficient conditions that guarantee the
existence of the observer and stability of the estimation error dynamics
(asymptotic stability of the origin in the absence of faults and ISS guarantees
in the faulty case). Then, we cast the synthesis of observer gains as a
semidefinite program where we minimize the L2-gain from the model mismatch
induced by the approximated fault model to the fault estimation error. Finally,
simulations are given to illustrate the performance of the proposed
methodology.


title: Lopsided galactic disks in IllustrisTNG

abstract:
  A significant fraction of nearby late-type galaxies are lopsided. We study
the asymmetry of the stellar component in a sample of well-resolved disky
galaxies selected from the last snapshot of the Illustris TNG100 simulation
based on their flatness and rotational support. Among 1912 disks, we identify
161 objects with significant asymmetry in terms of the m=1 Fourier mode of the
stellar component within (1-2) stellar half-mass radii and describe their
properties using three representative examples. The profiles of the m=1 mode
typically increase with radius, and the corresponding phase is constant in the
asymmetric region, signifying a global distortion. Following the evolution of
the lopsided disks over time, we find that their history is rather uneventful
and the occurrence of the asymmetry is fairly recent. Only about 1/3 of the
lopsided disks experienced any strong interaction recently that could have led
to the distortion of their shape: 24% were affected by a more massive object
and 9% underwent a gas-rich merger. Still, a majority of lopsided disks show a
significant increase in their recent star formation rate. The most frequent
mechanism for the formation of lopsided disks thus seems to be asymmetric star
formation probably related to gas accretion, although the distortions in the
gas and stars are not strongly correlated. This picture is supported by the
finding that the lopsided population on average contains more gas, has higher
star formation rate, lower metallicity and bluer color than the remaining
disks. These correlations are similar to those seen in real galaxies, even
though the fraction of simulated lopsided disks (8%) is much lower than in
observations (30%). The observed correlation between the presence of the
asymmetry and a bar is not reproduced either. These discrepancies may be due to
overquenching or insufficient resolution of IllustrisTNG simulations.


title: SHiFT: An Efficient, Flexible Search Engine for Transfer Learning

abstract:
  Transfer learning can be seen as a data- and compute-efficient alternative to
training models from scratch. The emergence of rich model repositories, such as
TensorFlow Hub, enables practitioners and researchers to unleash the potential
of these models across a wide range of downstream tasks. As these repositories
keep growing exponentially, efficiently selecting a good model for the task at
hand becomes paramount. By carefully comparing various selection and search
strategies, we realize that no single method outperforms the others, and hybrid
or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first
downstream task-aware, flexible, and efficient model search engine for transfer
learning. These properties are enabled by a custom query language SHiFT-QL
together with a cost-based decision maker, which we empirically validate.
Motivated by the iterative nature of machine learning development, we further
support efficient incremental executions of our queries, which requires a
careful implementation when jointly used with our optimizations.


title: Correlation Verification for Image Retrieval

abstract:
  Geometric verification is considered a de facto solution for the re-ranking
task in image retrieval. In this study, we propose a novel image retrieval
re-ranking network named Correlation Verification Networks (CVNet). Our
proposed network, comprising deeply stacked 4D convolutional layers, gradually
compresses dense feature correlation into image similarity while learning
diverse geometric matching patterns from various image pairs. To enable
cross-scale matching, it builds feature pyramids and constructs cross-scale
feature correlations within a single inference, replacing costly multi-scale
inferences. In addition, we use curriculum learning with the hard negative
mining and Hide-and-Seek strategy to handle hard samples without losing
generality. Our proposed re-ranking network shows state-of-the-art performance
on several retrieval benchmarks with a significant margin (+12.6% in mAP on
ROxford-Hard+1M set) over state-of-the-art methods. The source code and models
are available online: https://github.com/sungonce/CVNet.


title: Towards high all-optical data writing rates in synthetic ferrimagnets

abstract:
  Although all-optical magnetization switching with fs laser pulses has
garnered much technological interest, the ultimate data rates achievable have
scarcely been investigated. Recently it has been shown that after a switching
event in a GdCo alloy, a second laser pulse arriving 7 ps later can
consistently switch the magnetization. However, it is as of yet unknown whether
the same holds in layered ferrimagnetic systems, which hold much promise for
applications. In this work we investigate the minimum time delay required
between two subsequent switching events in synthetic ferrimagnetic Co/Gd
bilayers using two fs laser pulses. We experimentally demonstrate that the
minimum time delay needed for consistent switching can be as low as 10 ps.
Moreover, we demonstrate the importance of engineering heat diffusion away from
the magnetic material, as well as control over the laser pulse power. This
behavior is reproduced using modelling, where we find that the second switch
can occur even when the magnetization is not fully recovered. We further
confirm that heat diffusion is a critical factor in reducing the time delay for
the second switch, while also confirming a critical dependence on laser power.


title:
Optimizing the Consumption of Spiking Neural Networks with Activity
  Regularization

abstract:
  Reducing energy consumption is a critical point for neural network models
running on edge devices. In this regard, reducing the number of
multiply-accumulate (MAC) operations of Deep Neural Networks (DNNs) running on
edge hardware accelerators will reduce the energy consumption during inference.
Spiking Neural Networks (SNNs) are an example of bio-inspired techniques that
can further save energy by using binary activations, and avoid consuming energy
when not spiking. The networks can be configured for equivalent accuracy on a
task through DNN-to-SNN conversion frameworks but their conversion is based on
rate coding therefore the synaptic operations can be high. In this work, we
look into different techniques to enforce sparsity on the neural network
activation maps and compare the effect of different training regularizers on
the efficiency of the optimized DNNs and SNNs.


title: On a weak topology for Hadamard spaces

abstract:
  We investigate whether existing notions of weak sequential convergence on
Hadamard spaces can be topologized, that is whether there exist corresponding
notions of weak topologies. We provide an affirmative answer on what we call
weakly proper Hadamard spaces. A notion of dual space is proposed and it is
shown that our weak topology and dual space coincide with the standard ones in
the case of a Hilbert space. We extend several results from classical
functional analysis to the setting of Hadamard spaces, and we compare our
topology to existing notions of weak topologies.


title:
Demonstration of room-temperature continuous-wave operation of
  InGaAs/AlGaAs quantum well lasers directly grown on on-axis silicon (001)

abstract:
  The room-temperature continuous-wave operation of InGaAs/AlGaAs quantum well
lasers directly grown on on-axis silicon (001) has been demonstrated. A 420nm
thick GaAs epilayer, completely free of antiphase domains, was initially grown
on the silicon substrate in a metal-organic chemical vapor deposition system.
And the other epilayers including four sets of five-period strained-layer
superlattices and the laser-structural layers were successively grown in a
molecular beam epitaxy system. The lasers were prepared as broad-stripe
Fabry-Perot ones with a stripe width of 21.5 {\mu}m and a cavity length of 1
mm. Typically, the threshold current and the corresponding threshold current
density are 186.4 mA and 867 A/cm2, respectively. The lasing wavelength is
around 980 nm and the slope efficiency is 0.097 W/A with a single-facet output
power of 22.5 mW at an injection current of 400 mA. This advancement makes the
silicon-based monolithic optoelectronic integration relevant to quantum well
lasers more promising with enhanced feasibility.


title:
Global weak solutions to the compressible Cucker-Smale-Navier-Stokes
  system in a bounded domain

abstract:
  A coupled kinetic-fluid model is investigated, which describes the dynamic
behavior of an ensemble of Cucker-Smale flocking particles interacting with a
viscous fluid in a three-dimensional bounded domain. This system consists of a
kinetic Cucker-Smale equation and a compressible Navier-Stokes system with
nonhomogeneous boundary conditions. The global existence of weak solutions to
this system with adiabatic coefficient $\gamma> {3}/{2}$ is established.


title: Value Gradient weighted Model-Based Reinforcement Learning

abstract:
  Model-based reinforcement learning (MBRL) is a sample efficient technique to
obtain control policies, yet unavoidable modeling errors often lead performance
deterioration. The model in MBRL is often solely fitted to reconstruct
dynamics, state observations in particular, while the impact of model error on
the policy is not captured by the training objective. This leads to a mismatch
between the intended goal of MBRL, enabling good policy and value learning, and
the target of the loss function employed in practice, future state prediction.
Naive intuition would suggest that value-aware model learning would fix this
problem and, indeed, several solutions to this objective mismatch problem have
been proposed based on theoretical analysis. However, they tend to be inferior
in practice to commonly used maximum likelihood (MLE) based approaches. In this
paper we propose the Value-gradient weighted Model Learning (VaGraM), a novel
method for value-aware model learning which improves the performance of MBRL in
challenging settings, such as small model capacity and the presence of
distracting state dimensions. We analyze both MLE and value-aware approaches
and demonstrate how they fail to account for exploration and the behavior of
function approximation when learning value-aware models and highlight the
additional goals that must be met to stabilize optimization in the deep
learning setting. We verify our analysis by showing that our loss function is
able to achieve high returns on the Mujoco benchmark suite while being more
robust than maximum likelihood based approaches.


title: On the deuteron relativistic charge distributions

abstract:
  We study the relativistic 2D charge distributions in the case of a spin-$1$
target. These charge distributions are based on a phase-space approach allowing
one to study their frame dependence, and hence to relate the familiar
rest-frame picture with the light-front picture developed in the last two
decades. Like in the spin-$1/2$ case, we show that relativistic kinematical
effects associated with spin are responsible for the distortions of the charge
distributions seen in a moving frame. Applying our result to the deuteron, we
observe a mild frame dependence compared to the nucleon case.


title:
A single Long Short-Term Memory network for enhancing the prediction of
  path-dependent plasticity with material heterogeneity and anisotropy

abstract:
  This study presents the applicability of conventional deep recurrent neural
networks (RNN) to predict path-dependent plasticity associated with material
heterogeneity and anisotropy. Although the architecture of RNN possesses
inductive biases toward information over time, it is still challenging to learn
the path-dependent material behavior as a function of the loading path
considering the change from elastic to elastoplastic regimes. Our attempt is to
develop a simple machine-learning-based model that can replicate elastoplastic
behaviors considering material heterogeneity and anisotropy. The basic
Long-Short Term Memory Unit (LSTM) is adopted for the modeling of plasticity in
the two-dimensional space by enhancing the inductive bias toward the past
information through manipulating input variables. Our results find that a
single LSTM based model can capture the J2 plasticity responses under both
monotonic and arbitrary loading paths provided the material heterogeneity. The
proposed neural network architecture is then used to model elastoplastic
responses of a two-dimensional transversely anisotropic material associated
with computational homogenization (FE2). It is also found that a single LSTM
model can be used to accurately and effectively capture the path-dependent
responses of heterogeneous and anisotropic microstructures under arbitrary
mechanical loading conditions.


title: On scientific understanding with artificial intelligence

abstract:
  Imagine an oracle that correctly predicts the outcome of every particle
physics experiment, the products of every chemical reaction, or the function of
every protein. Such an oracle would revolutionize science and technology as we
know them. However, as scientists, we would not be satisfied with the oracle
itself. We want more. We want to comprehend how the oracle conceived these
predictions. This feat, denoted as scientific understanding, has frequently
been recognized as the essential aim of science. Now, the ever-growing power of
computers and artificial intelligence poses one ultimate question: How can
advanced artificial systems contribute to scientific understanding or achieve
it autonomously?
  We are convinced that this is not a mere technical question but lies at the
core of science. Therefore, here we set out to answer where we are and where we
can go from here. We first seek advice from the philosophy of science to
understand scientific understanding. Then we review the current state of the
art, both from literature and by collecting dozens of anecdotes from scientists
about how they acquired new conceptual understanding with the help of
computers. Those combined insights help us to define three dimensions of
android-assisted scientific understanding: The android as a I) computational
microscope, II) resource of inspiration and the ultimate, not yet existent III)
agent of understanding. For each dimension, we explain new avenues to push
beyond the status quo and unleash the full power of artificial intelligence's
contribution to the central aim of science. We hope our perspective inspires
and focuses research towards androids that get new scientific understanding and
ultimately bring us closer to true artificial scientists.


title:
Cria\c{c}\~ao e aplica\c{c}\~ao de ferramenta para auxiliar no ensino de
  algoritmos e programa\c{c}\~ao de computadores

abstract:
  Knowledge about programming is part of the knowledge matrix that will be
required of the professionals of the future. Based on this, this work aims to
report the development of a teaching tool developed during the monitoring
program of the Algorithm and Computer Programming discipline of the University
of Fortaleza. The tool combines the knowledge acquired in the books, with a
language closer to the students, using video lessons and exercises proposed,
with all the content available on the internet. The preliminary results were
positive, with the students approving this new approach and believing that it
could contribute to a better performance in the discipline.


title: Estimating the Entropy of Linguistic Distributions

abstract:
  Shannon entropy is often a quantity of interest to linguists studying the
communicative capacity of human language. However, entropy must typically be
estimated from observed data because researchers do not have access to the
underlying probability distribution that gives rise to these data. While
entropy estimation is a well-studied problem in other fields, there is not yet
a comprehensive exploration of the efficacy of entropy estimators for use with
linguistic data. In this work, we fill this void, studying the empirical
effectiveness of different entropy estimators for linguistic distributions. In
a replication of two recent information-theoretic linguistic studies, we find
evidence that the reported effect size is over-estimated due to over-reliance
on poor entropy estimators. Finally, we end our paper with concrete
recommendations for entropy estimation depending on distribution type and data
availability.


title: Event Log Sampling for Predictive Monitoring

abstract:
  Predictive process monitoring is a subfield of process mining that aims to
estimate case or event features for running process instances. Such predictions
are of significant interest to the process stakeholders. However,
state-of-the-art methods for predictive monitoring require the training of
complex machine learning models, which is often inefficient. This paper
proposes an instance selection procedure that allows sampling training process
instances for prediction models. We show that our sampling method allows for a
significant increase of training speed for next activity prediction methods
while maintaining reliable levels of prediction accuracy.


title: Pulse-level Noise Mitigation on Quantum Applications

abstract:
  Currently available quantum computers are prone to errors. Circuit
optimization and error mitigation methods are needed to design quantum circuits
to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling
(DD) is generally used to suppress the decoherence error and different DD
strategies have been proposed. Moreover, the circuit fidelity can be improved
by pulse-level optimization, such as creating hardware-native pulse-efficient
gates. This paper implements all the popular DD sequences and evaluates their
performances on IBM quantum chips with different characteristics for various
well-known quantum applications. Also, we investigate combining DD with
pulse-level optimization method and apply them to QAOA to solve Max-Cut
problem. Based on the experimental results, we found that DD can be a benefit
for only certain types of quantum algorithms, while the combination of DD and
pulse-level optimization methods always has a positive impact. Finally, we
provide several guidelines for users to learn how to use these pulse-level
noise mitigation methods to build circuits for quantum applications with high
fidelity.


title: Distributed online estimation of biophysical neural networks

abstract:
  In this work, we propose a distributed adaptive observer for a class of
networked systems inspired by biophysical conductance-based neural network
models. Neural systems learn by adjusting intrinsic and synaptic weights in a
distributed fashion, with neuronal membrane voltages carrying information from
neighbouring neurons in the network. Using contraction analysis, we show that
this learning principle can be used to design an adaptive observer based on a
decentralized learning rule that greatly reduces the number of observer states
required for consistent exponential convergence of parameter estimates. This
novel design is relevant for biological, biomedical and neuromorphic
applications.


title: Mock theta functions and characters of N=3 superconformal modules II

abstract:
  We obtain an explicit formula for the mock theta function $\Phi^{[m,s]}$ in
the case when either $m$ or $s$ is a half of an odd integer by using the coroot
lattice of $D(2,1;a)$. This enables us, together with the recurrence formula
for $\Phi^{[m,s]}$, to know $\Phi^{[m,s]}$ for all $m$ and $s$. As its
application, we deduce explicit formulas for the character and the
supercharacter of N=3 modules obtained from the quantum Hamiltonian reduction
of $\hat{osp}(3|2)$-modules.


title:
Unit Vectors for Similar Oblate Spheroidal Coordinates and Vector
  Transformation

abstract:
  The unit vectors transformation between the Cartesian and the novel Similar
Oblate Spheroidal coordinates, and vice versa, is derived. It can help to
transform vector fields between these two types of orthogonal coordinates which
can advantageously simplify solutions of problems exhibiting oblate spheroidal
geometry. Several examples demonstrate the use of the derived relations.
Generalized sine and cosine applicable in Similar Oblate Spheroidal coordinate
system are introduced.


title: Unsupervised Learning of Accurate Siamese Tracking

abstract:
  Unsupervised learning has been popular in various computer vision tasks,
including visual object tracking. However, prior unsupervised tracking
approaches rely heavily on spatial supervision from template-search pairs and
are still unable to track objects with strong variation over a long time span.
As unlimited self-supervision signals can be obtained by tracking a video along
a cycle in time, we investigate evolving a Siamese tracker by tracking videos
forward-backward. We present a novel unsupervised tracking framework, in which
we can learn temporal correspondence both on the classification branch and
regression branch. Specifically, to propagate reliable template feature in the
forward propagation process so that the tracker can be trained in the cycle, we
first propose a consistency propagation transformation. We then identify an
ill-posed penalty problem in conventional cycle training in backward
propagation process. Thus, a differentiable region mask is proposed to select
features as well as to implicitly penalize tracking errors on intermediate
frames. Moreover, since noisy labels may degrade training, we propose a
mask-guided loss reweighting strategy to assign dynamic weights based on the
quality of pseudo labels. In extensive experiments, our tracker outperforms
preceding unsupervised methods by a substantial margin, performing on par with
supervised methods on large-scale datasets such as TrackingNet and LaSOT. Code
is available at https://github.com/FlorinShum/ULAST.


title:
$\mathbb{C}P^2$ Skyrmion Crystals in an SU(3) Magnet with a Generalized
  Dzyaloshinskii-Moriya Interaction

abstract:
  We study $\mathbb{C}P^2$ Skyrmion crystals in the ferromagnetic SU(3)
Heisenberg model with a generalization of the Dzyaloshinskii-Moriya interaction
and Zeeman term as a stabilizing mechanism. The model possesses two different
types of Skyrmion crystal with Skyrmions that can be interpreted as bound
states of two half-Skyrmions or four quarter-Skyrmions.


title: Finite M results for time delay moments in all symmetry classes

abstract:
  Within a random matrix theory approach, we obtain spectral statistics of the
Wigner time delay matrix $Q$, for arbitrary channels number $M$ and for all
symmetry classes, in fact for general Dyson parameter $\beta$. For small values
of $M$, the observables develop singularities at fractional values of $\beta$
which may be of physical consequence in situations of partial breaking of the
time-reversal symmetry. We also put forth two conjectures: one is related to
the large-$M$ expansion of joint cumulants of traces of powers of $Q$, which
generalizes and implies a previous conjecture of Cunden, Mezzadri, Vivo and
Simm; the other concerns the tail of the distribution of traces of powers of
$Q$.


title: WEcharge: democratizing EV charging infrastructure

abstract:
  The sustainable growth of EVs will have to be met with proportional growth in
EV charging infrastructure. With limited urban spaces to place new charging
stations, shrinking profitability, privately owned charging facilities need to
be shared. WEcharge will allow privately owned charging infrastructure to be
shared with public EV owners using a business model. We propose a resource
matching algorithm that takes into account incoming EV preferences, hard
constraints for such EV, and provides the best suited resource for charging. We
demonstrate the applicability of the matching model by showing a realistic case
study with a Nissan Leaf 40 kW EV and 25 company and publicly owned charging
stations (DC fast charger, AC rapid charger, level 1 and level 2 charger) in
Hasselt, Belgium. The case study shows that consumer preferences will govern
resource matching.


title:
The Hadamard product, its residual, and its dual residual in the dioid
  of counters: algorithms and implementation in C++

abstract:
  This report presents the algorithms for computing the Hadamard product, its
residual, and its dual residual between formal power series in the dioid of
counters. The algorithms have been implemented in the C++ toolbox ETVO
((Event|Time)-Variant Operators). After proving the correctness of the
algorithms, we present a user guide for the C++ implementation.


title:
Life, the Metaverse and Everything: An Overview of Privacy, Ethics, and
  Governance in Metaverse

abstract:
  The metaverse is expected to be the next major evolution phase of the
internet. The metaverse will have an impact on human society, production, and
life. In this work, we analyze the current trends and challenges that building
such a virtual environment will face. We focus on three major pillars to guide
the development of the metaverse: privacy, governance, and ethical design, to
guide the development of the metaverse. Finally, we propose a preliminary
modular-based framework for an ethical design of the metaverse.


title:
Automated Proof of Mixed Trigonometric-polynomial Inequalities in the
  Unbounded Case

abstract:
  Mixed trigonometric-polynomials frequently occur in applications in physics,
numerical analysis and engineering, the algorithm has been already proposed to
determine its sign on (0,{pi}/2]. This paper proposes a procedure to extend the
interval to (0, +{\inf}). Such generalization is by no means trivial, for the
process depends on boundedness of roots and square-free factorization of mixed
trigonometric-polynomials.


title: Feasibility of nowcasting SDG indicators: a comprehensive survey

abstract:
  The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are
vital in guiding national and global policy. However, many of the SDG
indicators used to measure progress toward those goals suffer from long
publication lags. Nowcasting has the potential to address this problem and
generate more timely estimates of those indicators. This paper provides
resources for achieving that potential by 1) carrying out a comprehensive
nowcasting feasibility survey of all SDG indicators to assess their potential
to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate
and shed light on the process of performing a nowcasting exercise. There exist
231 SDG indicators, but due to only examining Tier 1 indicators and the fact
that many indicators have multiple sub-indicators, 362 indicators and
sub-indicators were eventually surveyed. Of those 362, 150 were found highly
likely to be suitable candidates for nowcasting, 87 were found to be likely,
and 125 were found to be unsuitable.


title:
Assessing dengue fever risk in Costa Rica by using climate variables and
  machine learning techniques

abstract:
  Dengue fever is a vector-borne disease mostly endemic to tropical and
subtropical countries that affect millions every year and is considered a
significant burden for public health. Its geographic distribution makes it
highly sensitive to climate conditions. Here, we explore the effect of climate
variables using the Generalized Additive Model for location, scale, and shape
(GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported
number of dengue cases, we obtained reliable predictions. The uncertainty of
the predictions was also measured. These predictions will serve as input to
health officials to further improve and optimize the allocation of resources
prior to dengue outbreaks.


title: Average Error of the Prime Number Theorem

abstract:
  In this paper, we will give some estimation for the average error of the
prime number theorem.


title: Satellite Monitoring of Terrestrial Plastic Waste

abstract:
  Plastic waste is a significant environmental pollutant that is difficult to
monitor. We created a system of neural networks to analyze spectral, spatial,
and temporal components of Sentinel-2 satellite data to identify terrestrial
aggregations of waste. The system works at continental scale. We evaluated
performance in Indonesia and detected 374 waste aggregations, more than double
the number of sites found in public databases. The same system deployed across
twelve countries in Southeast Asia identifies 996 subsequently confirmed waste
sites. For each detected site, we algorithmically monitor waste site footprints
through time and cross-reference other datasets to generate physical and social
metadata. 19% of detected waste sites are located within 200 m of a waterway.
Numerous sites sit directly on riverbanks, with high risk of ocean leakage.


title:
Bayesian approach for limited-aperture inverse acoustic scattering with
  total variation prior

abstract:
  In this work, we apply the Bayesian approach for the acoustic scattering
problem to reconstruct the shape of a sound-soft obstacle using the
limited-aperture far-field measure data. A novel total variation prior is
assigned to the shape parameterization form. This prior is imposed on the
Fourier coefficients of the parameterized form of the obstacle. Extensive
numerical tests are provided to illustrate the numerical performance.


title: Identifying Security Risks in NFT Platforms

abstract:
  This paper examines the effects of inherent risks in the emerging technology
of non-fungible tokens and proposes an actionable set of solutions for
stakeholders in this ecosystem and observers. Web3 and NFTs are a fast-growing
300 billion dollar economy with some clear, highly publicized harms that came
to light recently. We set out to explore the risks to understand their nature
and scope, and if we could find ways to mitigate them. In due course of
investigation, we recap the background of the evolution of the web from a
client-server model to the rise of Web2.0 tech giants in the early 2000s. We
contrast how the Web3 movement is trying to re-establish the independent style
of the early web. In our research we discover a primary set of risks and harms
relevant to the ecosystem, and classify them into a simple taxonomy while
addressing their mitigations with solutions. We arrive at a set of solutions
that are a combination of processes to be adopted, and technological changes or
improvements to be incorporated into the ecosystem, to implement risk
mitigations. By linking mitigations to individual risks, we are confident our
recommendations will improve the security maturity of the growing Web3
ecosystem. We are not endorsing, or recommending specifically any particular
product or service in our solution set. Nor are we compensated or influenced in
any way by these companies to list these products in our research. The
evaluations of products in our research have to simply be viewed as suggested
improvements.


title:
Training the next generation of computational scientists through a new
  undergraduate course

abstract:
  We introduce a newly designed undergraduate-level interdisciplinary course in
scientific computing that aims to prepare students as the next generation of
research-oriented computational scientists and engineers. The course offers
students opportunities to explore a diverse set of projects and develop the
necessary programming skills to implement ideas and algorithms within high
performance computing environments. The training includes how to think about,
formulate, organize, and implement programs in scientific computing. The
emphasis of the course is on problem solving within a wide range of
applications in science and engineering.


title: Towards a New Science of Disinformation

abstract:
  How can we best address the dangerous impact that deep learning-generated
fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal
and societal life? We foresee that the availability of cheap deepfake
technology will create a second wave of disinformation where people will
receive specific, personalized disinformation through different channels,
making the current approaches to fight disinformation obsolete. We argue that
fake media has to be seen as an upcoming cybersecurity problem, and we have to
shift from combating its spread to a prevention and cure framework where users
have available ways to verify, challenge, and argue against the veracity of
each piece of media they are exposed to. To create the technologies behind this
framework, we propose that a new Science of Disinformation is needed, one which
creates a theoretical framework both for the processes of communication and
consumption of false content. Key scientific and technological challenges
facing this research agenda are listed and discussed in the light of
state-of-art technologies for fake media generation and detection, argument
finding and construction, and how to effectively engage users in the prevention
and cure processes.


title:
Symmetry effects on the static and dynamic properties of coupled
  magnetic oscillators

abstract:
  The effect of symmetry on the resonance spectra of antiferromagnetically
coupled oscillators has attracted new interest with the discovery of
symmetry-breaking induced anti-crossings. Here, we experimentally characterise
the resonance spectrum of a synthetic antiferromagnet Pt/CoFeB/Ru/CoFeB/Pt,
where we are able to independently tune the effective magnetisation of the two
coupled magnets. To model our results we apply the mathematical methods of
group theory to the solutions of the Landau Lifshitz Gilbert equation. This
general approach, usually applied to quantum mechanical systems, allows us to
identify the main features of the resonance spectrum in terms of symmetry
breaking and to make a direct comparison with crystal antiferromagnets.


title:
Fore-aft clearance controls how three-dimensional confinement affects
  micropropulsion

abstract:
  Systems of active particles are often affected by confinement due to nearby
boundaries. Recently, there has been interest in the effect of confinement by
complex three dimensional geometries, as might occur in structured environments
such as porous media, foams, gels, or biological tissues and ducts. The effects
of confinement for particles moving along boundaries has been extensively
studied, but in three dimensions active particles move not only parallel to
boundaries, but also towards or away from boundaries. The consequences of this
fore-aft clearance is less well understood. Swimmers that actively remodel
their environment create an ideal situation to study the effect of clearance,
since they maintain a steady clearance while translating. By numerically
studying the locomotion of the bacterium Helicobacter pylori, which de-gels
surrounding gastric mucus to make a co-moving pocket of fluid around itself, we
show that the effect of three-dimensional confinement is controlled by
clearance, rather than distance from a parallel boundary. Analytical
calculations show that the effect of clearance can be understood in terms of
flow structures, such as the generic pusher and puller flows of active
particles, indicating that our results should apply to a wide range of confined
active particles.


title:
Rheotaxis of chiral bacteria: from single-cell behavior to a
  population-level description

abstract:
  Due to their morphology, the dynamics of bacteria suspended in media can
exhibit complex behaviors. In the presence of a shear, swimming bacteria
experience a drift perpendicular to the shear plane. This drift, termed
rheotaxis, is studied here semi-analytically, numerically and experimentally.
We find the dependency of bacterial orientation and bacterial speed on the
shear rate, in the presence of rotational diffusion. This enables us to show
that the drift speed of bacteria perpendicular to the shear is predominantly
due to bacterial propulsion, and not rheotactic forces. Comparing the drift
speed of bacteria and diffusion leads to the definition of a P\'{e}clet number.
The rheotactic effect increases with the shear, reaching a plateau at very
large P\'{e}clet numbers, in good agreement with experiments of rheotaxis
performed in microfluidic droplets.


title: On totally disconnected subsets of chainable continua

abstract:
  We prove that each hereditarily disconnected Polish subspace of a Suslinian
chainable continuum is zero-dimensional at some point. It follows that every
chainable continuum which contains Erd\H{o}s space has uncountably-many
pairwise disjoint subcontinua. This answers a question of J. Krzempek.


title:
An Inverse Optimal Control Approach for Trajectory Prediction of
  Autonomous Race Cars

abstract:
  This paper proposes an optimization-based approach to predict trajectories of
autonomous race cars. We assume that the observed trajectory is the result of
an optimization problem that trades off path progress against acceleration and
jerk smoothness, and which is restricted by constraints. The algorithm predicts
a trajectory by solving a parameterized nonlinear program (NLP) which contains
path progress and smoothness in cost terms. By observing the actual motion of a
vehicle, the parameters of prediction are updated by means of solving an
inverse optimal control problem that contains the parameters of the predicting
NLP as optimization variables. The algorithm therefore learns to predict the
observed vehicle trajectory in a least-squares relation to measurement data and
to the presumed structure of the predicting NLP. This work contributes with an
algorithm that allows for accurate and interpretable predictions with sparse
data. The algorithm is implemented on embedded hardware in an autonomous
real-world race car that is competing in the challenge Roborace and analyzed
with respect to recorded data.


title: Logical coherence in Bayesian simultaneous three-way hypothesis tests

abstract:
  This paper studies whether Bayesian simultaneous three-way hypothesis tests
can be logically coherent. Two types of results are obtained. First, under the
standard error-wise constant loss, only for a limited set of models can a Bayes
simultaneous test be logically coherent. Second, if more general loss functions
are used, then it is possible to obtain Bayes simultaneous tests that are
always logically coherent. An explicit example of such a loss function is
provided.