title:
The competent Computational Thinking test (cCTt): Development and
  validation of an unplugged Computational Thinking test for upper primary
  school

abstract:
  With the increasing importance of Computational Thinking (CT) at all levels
of education, it is essential to have valid and reliable assessments.
Currently, there is a lack of such assessments in upper primary school. That is
why we present the development and validation of the competent CT test (cCTt),
an unplugged CT test targeting 7-9 year-old students. In the first phase, 37
experts evaluated the validity of the cCTt through a survey and focus group. In
the second phase, the test was administered to 1519 students. We employed
Classical Test Theory, Item Response Theory, and Confirmatory Factor Analysis
to assess the instruments' psychometric properties. The expert evaluation
indicates that the cCTt shows good face, construct, and content validity.
Furthermore, the psychometric analysis of the student data demonstrates
adequate reliability, difficulty, and discriminability for the target age
groups. Finally, shortened variants of the test are established through
Confirmatory Factor Analysis. To conclude, the proposed cCTt is a valid and
reliable instrument, for use by researchers and educators alike, which expands
the portfolio of validated CT assessments across compulsory education. Future
assessments looking at capturing CT in a more exhaustive manner might consider
combining the cCTt with other forms of assessments.


title:
Design of acoustic diffraction plates for manipulating ultrasound in
  liquid Helium

abstract:
  Many experiments in liquid Helium, such as the optical imaging of exploding
electron bubbles, which enables research on individual particles under applied
conditions, involve the usage of ultrasound generated by piezoelectric
transducers. Previous studies either use planar transducers, which limits the
maximum sound intensity and the spatial resolution, or curved transducers,
which only allow observations at fixed foci and make it difficult to apply
uniform electric fields. In this paper, we introduce the usage of acoustic
diffraction plates in liquid Helium to amplify ultrasonic pressure oscillations
at an arbitrary set of primary foci coupled with large counts of secondary
foci, all of which can be freely moved around by changing the ultrasound
frequency. The frequency dependence also allows us to generate controlled
Faraday instabilities at the surface, which enables the generation of
multi-electron bubbles with desired parameters.


title:
New Physics searches in a low threshold scintillating argon bubble
  chamber measuring coherent elastic neutrino-nucleus scattering in reactors

abstract:
  The sensitivity to New Physics of a low threshold scintillating argon bubble
chamber measuring coherent elastic neutrino-nucleus scattering in reactors is
reported. Namely, light scalar mediators, sterile neutrino oscillations,
unitarity violation, and non-standard interactions are studied. The results
indicate that this detector could be able to set stronger constraints than
current limits set by the recent COHERENT measurements. Considering the best
scenario, a 100 kg detector located 30 m from a 2000 MW$_{th}$ reactor, a
sterile neutrino search would cover most of the space parameter allowed from
the reactor anti-neutrino anomaly fit. Unitarity violation studies could set
constraints on $\alpha_{11}$ more stringent than the current oscillation
experiments fit. A low threshold argon detector with very low backgrounds has
the potential to explore New Physics in different scenarios and set competitive
constraints.


title:
PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object
  Detection in Autonomous Driving Systems

abstract:
  Semi-supervised object detection methods are widely used in autonomous
driving systems, where only a fraction of objects are labeled. To propagate
information from the labeled objects to the unlabeled ones, pseudo-labels for
unlabeled objects must be generated. Although pseudo-labels have proven to
improve the performance of semi-supervised object detection significantly, the
applications of image-based methods to video frames result in numerous miss or
false detections using such generated pseudo-labels. In this paper, we propose
a new approach, PseudoProp, to generate robust pseudo-labels by leveraging
motion continuity in video frames. Specifically, PseudoProp uses a novel
bidirectional pseudo-label propagation approach to compensate for misdetection.
A feature-based fusion technique is also used to suppress inference noise.
Extensive experiments on the large-scale Cityscapes dataset demonstrate that
our method outperforms the state-of-the-art semi-supervised object detection
methods by 7.4% on mAP75.


title: Deep Class Incremental Learning from Decentralized Data

abstract:
  In this paper, we focus on a new and challenging decentralized machine
learning paradigm in which there are continuous inflows of data to be addressed
and the data are stored in multiple repositories. We initiate the study of data
decentralized class-incremental learning (DCIL) by making the following
contributions. Firstly, we formulate the DCIL problem and develop the
experimental protocol. Secondly, we introduce a paradigm to create a basic
decentralized counterpart of typical (centralized) class-incremental learning
approaches, and as a result, establish a benchmark for the DCIL study. Thirdly,
we further propose a Decentralized Composite knowledge Incremental Distillation
framework (DCID) to transfer knowledge from historical models and multiple
local sites to the general model continually. DCID consists of three main
components namely local class-incremental learning, collaborated knowledge
distillation among local models, and aggregated knowledge distillation from
local models to the general one. We comprehensively investigate our DCID
framework by using different implementations of the three components. Extensive
experimental results demonstrate the effectiveness of our DCID framework. The
codes of the baseline methods and the proposed DCIL will be released at
https://github.com/zxxxxh/DCIL.


title:
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep
  Reinforcement Learning of Robot Control

abstract:
  State-of-the-art reinforcement learning algorithms predominantly learn a
policy from either a numerical state vector or images. Both approaches
generally do not take structural knowledge of the task into account, which is
especially prevalent in robotic applications and can benefit learning if
exploited. This work introduces a neural network architecture that combines
relational inductive bias and visual feedback to learn an efficient position
control policy for robotic manipulation. We derive a graph representation that
models the physical structure of the manipulator and combines the robot's
internal state with a low-dimensional description of the visual scene generated
by an image encoding network. On this basis, a graph neural network trained
with reinforcement learning predicts joint velocities to control the robot. We
further introduce an asymmetric approach of training the image encoder
separately from the policy using supervised learning. Experimental results
demonstrate that, for a 2-DoF planar robot in a geometrically simplistic 2D
environment, a learned representation of the visual scene can replace access to
the explicit coordinates of the reaching target without compromising on the
quality and sample efficiency of the policy. We further show the ability of the
model to improve sample efficiency for a 6-DoF robot arm in a visually
realistic 3D environment.


title: Infrared appearance of wind-blown bubbles around young massive stars

abstract:
  Thousands of ring-like bubbles appear on infrared images of the Galaxy plane.
Most of these infrared bubbles form during expansion of HII regions around
massive stars. However, the physical effects that determine their morphology
are still under debate. Namely, the absence of the infrared emission toward the
centres of the bubbles can be explained by pushing the dust grains by stellar
radiation pressure. At the same time, small graphite grains and PAHs are not
strongly affected by the radiation pressure and must be removed by another
process. Stellar ultraviolet emission can destroy the smallest PAHs but the
photodestruction is ineffective for the large PAHs. Meanwhile, the stellar wind
can evacuate all types of grains from HII regions. In the frame of our
chemo-dynamical model we vary parameters of the stellar wind and illustrate
their influence on the morphology and synthetic infrared images of the bubbles.


title:
The Particle as a Statistical Ensemble of Events in
  Stueckelberg-Horwitz-Piron Electrodynamics

abstract:
  In classical Maxwell electrodynamics, charged particles following
deterministic trajectories are described by currents that induce fields,
mediating interactions with other particles. Statistical methods are used when
needed to treat complex particle and/or field configurations. In
Stueckelberg-Horwitz-Piron (SHP) electrodynamics, the classical trajectories
are traced out dynamically, through the evolution of a 4D spacetime event
$x^\mu (\tau)$ as $\tau$ grows monotonically. Stueckelberg proposed to
formalize the distinction between coordinate time $x^0 = ct$ (measured by
laboratory clocks) and chronology $\tau$ (the temporal ordering of event
occurrence) in order to describe antiparticles and resolve problems of
irreversibility such as grandfather paradoxes. Consequently, in SHP theory, the
elementary object is not a particle (a 4D curve in spacetime) but rather an
event (a single point along the dynamically evolving curve). Following standard
deterministic methods in classical relativistic field theory, one is led to
Maxwell-like field equations that are \hbox{$\tau$-dependent} and sourced by a
current that represents a statistical ensemble of instantaneous events
distributed along the trajectory. The width $\lambda$ of this distribution
defines a correlation time for the interactions and a mass spectrum for the
photons emitted by particles. As $\lambda$ becomes very large, the photon mass
goes to zero and the field equations become $\tau$-independent Maxwell's
equations. Maxwell theory thus emerges as an equilibrium limit of SHP, in which
$\lambda$ is larger than any other relevant time scale. Thus, statistical
mechanics is a fundamental ingredient in SHP electrodynamics, and its insights
are required to give meaning to the concept of a particle.


title: Scaled Affine Quantization of $\varphi^4_4$ in the Low Temperature Limit

abstract:
  We prove through Monte Carlo analysis that the covariant euclidean scalar
field theory, $\varphi^r_n$, where $r$ denotes the power of the interaction
term and $n = s + 1$ where $s$ is the spatial dimension and $1$ adds imaginary
time, such that $r = n = 4$ can be acceptably quantized using scaled affine
quantization and the resulting theory is nontrivial and renormalizable even at
low temperatures in the highly quantum regime.


title:
Superluminal Geometrodynamics of Braneworld Hyperdrive via Brane-Bulk
  Interaction

abstract:
  The idea of superluminal travel in the framework of General Relativity has
firstly been introduced by Miguel Alcubierre. Instead of puncturing through a
hole in spacetime as in a wormhole, this method of spacetime geometrical
modification manipulates the geometrodynamics contraction and expansion of
spacetime curvature so that the intended travelling object's hypersurface surfs
on the surrounding spacetime hyperspace. In the general relativity framework,
this feat requires exotic matter that violates the null energy condition.
However, in the braneworld framework, the bulk space may naturally support the
geometrodynamics requirement without violating the energy condition. In this
paper, we will explore in more detail mathematically, how the concept has been
derived in the framework of General Relativity, and how by embedding the
extra-dimensional terms that represent bulk space of braneworld the travelling
object's hypersurface may fly-through the bulk hyperspace. We begin by
developing the metric to formulate the energy density expression that governs
the spacetime of "flying-through-hyperspace" geometrodynamics in relation to
the necessary parameters describing the spacetime hyper-warped bubble for the
braneworld hyperdrive.


title: Coherent Smith-Purcell $\gamma$-Ray Emission

abstract:
  We investigate the Smith-Purcell emission produced by electron- or
ion-beam-driven coherent excitation of nuclei arranged in periodic crystal
lattices. The excitation and subsequent radiative decay of the nuclei can leave
the target in the initial ground state after $\gamma$-ray emission, thus
generating a coherent superposition of the far-field photon amplitude emanating
from different nuclei that results in sharp angular patterns at spectrally
narrow nuclear transition energies. We focus on Fe-57 as an example of
two-level nuclear lossy system giving rise to Smith-Purcell emission at
14.4\,keV with a characteristic delay of 1.2\,ns relative to the excitation
time. These properties enable a clean separation from faster and spectrally
broader emission mechanisms, such as bremsstrahlung. Besides its fundamental
interest, our study holds potential for the design of high-energy, narrow-band,
highly-directive photon sources, as well as a means to store energy in the form
of nuclear excitations.


title: Gromov-Hausdorff Distance and Borsuk Number

abstract:
  The aim of this paper is to demonstrate relations between Gromov-Hausdorff
distance properties and the Borsuk Conjecture. The Borsuk number of a given
bounded metric space $X$ is the infimum of cardinal numbers $n$ such that $X$
can be partitioned into $n$ smaller parts (in the sense of diameter). An exact
formula for the Gromov-Hausdorff distance between bounded metric spaces is
obtained under the assumptions that the diameter and the cardinality of one
space is less than the diameter and the Borsuk number of the other one,
respectively. Using Bacon equivalence results between Lusternik-Schnirelmann
and Borsuk Problems several corollaries are obtained.


title: Exploiting new classes of mass-dimension one fermions

abstract:
  In this paper, we advance in investigating a specific set of eigenvectors of
a linearly-independent square root of the identity matrix to obtain a new class
of spin-half mass-dimension one fermions. Such eigenvectors, after an
appropriated dual structure examination, may serve as expansion coefficients of
a local field.


title:
Measuring dependencies between variables of a dynamical system using
  fuzzy affiliations

abstract:
  A statistical, data-driven method is presented that quantifies influences
between variables of a dynamical system. The method is based on finding a
suitable representation of points by fuzzy affiliations with respect to
landmark points using the Scalable Probabilistic Approximation algorithm. This
is followed by the construction of a linear mapping between these affiliations
for different variables and forward in time. This linear mapping can be
directly interpreted in light of unidirectional dependencies and relevant
properties of it are quantified. These quantifications then serve as measures
for the influences between variables of the dynamics. The validity of the
method is demonstrated with theoretical results and on several numerical
examples, including real-world basketball player movement.


title: Submersions, immersions, and \'etale maps in diffeology

abstract:
  One of the most important challenges in diffeology is providing suitable
analogous for submersions, immersions, and \'{e}tale maps (i.e., local
diffeomorphisms) consistent with the classical versions of these maps between
manifolds. In this paper, we introduce diffeological submersions, immersions,
and \'{e}tale maps as an adaptation of these maps in diffeology by a nonlinear
approach. We explore their behaviors from different aspects in a systematized
manner with respect to plots, and especially, show that for manifolds, there is
no difference between the classical versions and diffeological versions of
these maps. Moreover, we introduce a class of diffeological spaces, called
diffeological \'etale manifolds, and establish the rank theorem for them. It is
also proved that a diffeological \'{e}tale space on a diffeological orbifold is
again a diffeological orbifold.


title: Self-similar solutions for Fuzzy Dark Matter

abstract:
  Fuzzy Dark Matter (FDM) models admit self-similar solutions which are very
different from their Cold Dark Matter (CDM) counterparts and do not converge to
the latter in the semiclassical limit. In contrast with the familiar CDM
hierarchical collapse, they correspond to an inverse-hierarchy blow-up.
Constant-mass shells start in the nonlinear regime, at early times, with small
radii and high densities, and expand to reach at late times the Hubble flow, up
to small linear perturbations. Thus, larger masses become linear first. This
blow-up approximately follows the Hubble expansion, so that the central density
contrast remains constant with time, although the width of the self-similar
profile shrinks in comoving coordinates. As in a gravitational cooling process,
matter is ejected from the central peaks through successive clumps. As in wave
systems, the velocities of the geometrical structures and of the matter do not
coincide, and matter slowly moves from one clump to the next, with intermittent
velocity bursts at the transitions. These features are best observed using the
density-velocity representation of the nonrelativistic scalar field, or the
mass-shell trajectories, than with the Husimi phase-space distribution, where
an analogue of the Heisenberg uncertainty principle blurs the resolution in the
position or velocity direction. These behaviours are due to the quantum
pressure and the wavelike properties of the Schr\"odinger equation. Although
the latter has been used as an alternative to N-body simulations for CDM, these
self-similar solutions show that the semiclassical limit needs to be handled
with care.


title:
Modification of the effective potential of $\phi^4$ theory using the
  Boltzmann factor

abstract:
  The need for a cutoff in the Lamb shift calculation suggests that high-energy
virtual photons do not interact with the real particles. In this study, we
assume that the creation of high-energy virtual particles is suppressed by a
Boltzmann factor $e^{-\beta E}$ and apply this Boltzmann factor to the
effective potential of the $\phi^4$ theory. Consequently, the effective
potential is transformed into $V(\phi) = 1/4!\phi^4 + \beta^{-4} \left \{
\phi^3 K_3(\phi) -\phi^2 K_2 (\phi) \right\}$. $K_n(x)$ is the modified Bessel
function of the second kind of order $n$. We demonstrate that the minimum of
the effective potential occurs for $\phi \ne 0$.


title:
Towards Self-Supervised Learning of Global and Object-Centric
  Representations

abstract:
  Self-supervision allows learning meaningful representations of natural
images, which usually contain one central object. How well does it transfer to
multi-entity scenes? We discuss key aspects of learning structured
object-centric representations with self-supervision and validate our insights
through several experiments on the CLEVR dataset. Regarding the architecture,
we confirm the importance of competition for attention-based object discovery,
where each image patch is exclusively attended by one object. For training, we
show that contrastive losses equipped with matching can be applied directly in
a latent space, avoiding pixel-based reconstruction. However, such an
optimization objective is sensitive to false negatives (recurring objects) and
false positives (matching errors). Careful consideration is thus required
around data augmentation and negative sample selection.


title:
Adaptive POD-DEIM correction for Turing pattern approximation in
  reaction-diffusion PDE systems

abstract:
  We investigate a suitable application of Model Order Reduction (MOR)
techniques for the numerical approximation of Turing patterns, that are
stationary solutions of reaction-diffusion PDE (RD-PDE) systems. We show that
solutions of surrogate models built by classical Proper Orthogonal
Decomposition (POD) exhibit an unstable error behaviour over the dimension of
the reduced space. To overcome this drawback, first of all, we propose a
POD-DEIM technique with a correction term that includes missing information in
the reduced models. To improve the computational efficiency, we propose an
adaptive version of this algorithm in time that accounts for the peculiar
dynamics of the RD-PDE in presence of Turing instability. We show the
effectiveness of the proposed methods in terms of accuracy and computational
cost for a selection of RD systems, i.e. FitzHugh-Nagumo, Schnackenberg and the
morphochemical DIB models, with increasing degree of nonlinearity and more
structured patterns.


title: AI for Experimental Controls at Jefferson Lab

abstract:
  The AI for Experimental Controls project is developing an AI system to
control and calibrate detector systems located at Jefferson Laboratory.
Currently, calibrations are performed offline and require significant time and
attention from experts. This work would reduce the amount of data and the
amount of time spent calibrating in an offline setting. The first use case
involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer
in Hall D. We use a combination of environmental and experimental data, such as
atmospheric pressure, gas temperature, and the flux of incident particles as
inputs to a Sequential Neural Network (NN) to recommend a high voltage setting
and the corresponding calibration constants in order to maintain consistent
gain and optimal resolution throughout the experiment. Utilizing AI in this
manner represents an initial shift from offline calibration towards near real
time calibrations performed at Jefferson Laboratory.


title:
Polar Transformation Based Multiple Instance Learning Assisting Weakly
  Supervised Image Segmentation With Loose Bounding Box Annotations

abstract:
  This study investigates weakly supervised image segmentation using loose
bounding box supervision. It presents a multiple instance learning strategy
based on polar transformation to assist image segmentation when loose bounding
boxes are employed as supervision. In this strategy, weighted smooth maximum
approximation is introduced to incorporate the observation that pixels closer
to the origin of the polar transformation are more likely to belong to the
object in the bounding box. The proposed approach was evaluated on a public
medical dataset using Dice coefficient. The results demonstrate its superior
performance. The codes are available at
\url{https://github.com/wangjuan313/wsis-polartransform}.


title:
Non-equilibrium relaxation of a trapped particle in a near-critical
  Gaussian field

abstract:
  We study the non-equilibrium relaxational dynamics of a probe particle
linearly coupled to a thermally fluctuating scalar field and subject to a
harmonic potential, which provides a cartoon for an optically trapped colloid
immersed in a fluid close to its bulk critical point. The average position of
the particle initially displaced from the position of mechanical equilibrium is
shown to feature long-time algebraic tails as the critical point of the field
is approached, the universal exponents of which are determined in arbitrary
spatial dimensions. As expected, this behavior cannot be captured by adiabatic
approaches which assume fast field relaxation. The predictions of the analytic,
perturbative approach are qualitatively confirmed by numerical simulations.


title:
Experiments on the Violation of Electromagnetic Gauge Symmetry by
  Yang-Mills Gravity Using Josephson Effects in Superconductors

abstract:
  Yang-Mills gravity is a quantum theory of gravity with translational gauge
symmetry that is based on a flat space-time. The universal coupling of all
quantum fields to quantum Yang-Mills gravity is based on the replacement of
$\p_\mu$ by the translational gauge covariant derivative $(\p_\mu
+g\phi_\mu^\nu \p_\nu)$ in the Lagrangians of non-gravitational fields. Near
the surface of the Earth, Yang-Mills gravity causes the phase gradient $\p_k
\th$ to be altered by a factor of $h_1\approx(1- g\phi)\approx 1- 7\times
10^{-10}$. In addition, the usual gauge-invariant combination of phase
gradients and vector potentials ${\bf A}$ in Josephson junctions is modified
and is no longer gauge invariant. The voltage across a Josephson junction is
thus affected by the presence of the gravitational coupling constant $g$, and
is now given by $V_{g21}\approx Q \int_1^2 [- \mbox{\boldmath$ {\bf \nabla}$}
A_0 - h_1^{-2}{\p {\bf A}}/{\p t}]\cdot d{\bf s}$. We propose an experimental
test of Yang-Mills gravity based on this effect. If one were to compare the
voltage across a Josephson junction in a laboratory at rest on Earth with that
across a junction in free fall (e.g., in the International Space Station or in
a plane maneuvering to simulate zero-gravity such as NASA's now-retired "Vomit
Comet"), Yang-Mills gravity predicts a difference on the order of 1 part in
$10^{9}$, which should be detectable as the precision of the Josephson junction
voltage standard is on the order of a few parts in $10^{10}$.


title:
A singular two-phase Stefan problem and particles interacting through
  their hitting times

abstract:
  We consider a probabilistic formulation of a singular two-phase Stefan
problem in one space dimension, which amounts to a coupled system of two
McKean-Vlasov stochastic differential equations. Our main result shows the
existence of a solution whose discontinuities obey the natural physicality
condition for the problem at hand. Thus, this work extends the recent series of
existence results for singular one-phase Stefan problems in one space dimension
that can be found in [DIRT15a], [NS19a], [HLS18], [CRSF20]. As therein, our
existence result is obtained via a large system limit of a finite particle
system approximation in the Skorokhod M1 topology. But, unlike for the
previously studied one-phase case, the free boundary herein is not monotone, so
that the large system limit is obtained by a novel argument.


title:
An error correction scheme for improved air-tissue boundary in real-time
  MRI video for speech production

abstract:
  The best performance in Air-tissue boundary (ATB) segmentation of real-time
Magnetic Resonance Imaging (rtMRI) videos in speech production is known to be
achieved by a 3-dimensional convolutional neural network (3D-CNN) model.
However, the evaluation of this model, as well as other ATB segmentation
techniques reported in the literature, is done using Dynamic Time Warping (DTW)
distance between the entire original and predicted contours. Such an evaluation
measure may not capture local errors in the predicted contour. Careful analysis
of predicted contours reveals errors in regions like the velum part of contour1
(ATB comprising of upper lip, hard palate, and velum) and tongue base section
of contour2 (ATB covering jawline, lower lip, tongue base, and epiglottis),
which are not captured in a global evaluation metric like DTW distance. In this
work, we automatically detect such errors and propose a correction scheme for
the same. We also propose two new evaluation metrics for ATB segmentation
separately in contour1 and contour2 to explicitly capture two types of errors
in these contours. The proposed detection and correction strategies result in
an improvement of these two evaluation metrics by 61.8% and 61.4% for contour1
and by 67.8% and 28.4% for contour2. Traditional DTW distance, on the other
hand, improves by 44.6% for contour1 and 4.0% for contour2.


title:
Weak diamond, weak projectivity, and transfinite extensions of simple
  artinian rings

abstract:
  We apply set-theoretic methods to study projective modules and their
generalizations over transfinite extensions of simple artinian rings R. We
prove that if R is small, then the Weak Diamond implies that projectivity of an
arbitrary module can be tested at the layer epimorphisms of R.


title: Edge and Pair Queries -- Random Graphs and Complexity

abstract:
  We investigate two types of query games played on a graph, pair queries and
edge queries. We concentrate on investigating the two associated graph
parameters for binomial random graphs, and showing that determining any of the
two parameters is NP-hard for bounded degree graphs.


title: Online Graph Learning from Social Interactions

abstract:
  Social learning algorithms provide models for the formation of opinions over
social networks resulting from local reasoning and peer-to-peer exchanges.
Interactions occur over an underlying graph topology, which describes the flow
of information and relative influence between pairs of agents. For a given
graph topology, these algorithms allow for the prediction of formed opinions.
In this work, we study the inverse problem. Given a social learning model and
observations of the evolution of beliefs over time, we aim at identifying the
underlying graph topology. The learned graph allows for the inference of
pairwise influence between agents, the overall influence agents have over the
behavior of the network, as well as the flow of information through the social
network. The proposed algorithm is online in nature and can adapt dynamically
to changes in the graph topology or the true hypothesis.


title:
Delaunay-like Triangulation of Smooth Orientable Submanifolds by L1-Norm
  Minimization

abstract:
  In this paper, we focus on one particular instance of the shape
reconstruction problem, in which the shape we wish to reconstruct is an
orientable smooth submanifold of the Euclidean space. Assuming we have as input
a simplicial complex K that approximates the submanifold (such as the Cech
complex or the Rips complex), we recast the reconstruction problem as a L1-norm
minimization problem in which the optimization variable is a chain of K.
Providing that K satisfies certain reasonable conditions, we prove that the
considered minimization problem has a unique solution which triangulates the
submanifold and coincides with the flat Delaunay complex introduced and studied
in a companion paper. Since the objective is a weighted L1-norm and the
contraints are linear, the triangulation process can thus be implemented by
linear programming.


title: Explicit isogenies of prime degree over number fields

abstract:
  We provide an explicit and algorithmic version of a theorem of Momose
classifying isogenies of prime degree of elliptic curves over number fields,
which we implement in Sage and PARI/GP. Combining this algorithm with recent
work of Box-Gajovi\'c-Goodman we determine the first instances of isogenies of
prime degree for cubic number fields, as well as for several quadratic fields
not previously known. While the correctness of the general algorithm relies on
the Generalised Riemann Hypothesis, the algorithm is unconditional for the
restricted class of semistable elliptic curves.


title:
Emergence of a hidden magnetic phase in LaFe11.8Si1.2 investigated by
  inelastic neutron scattering as a function of field and temperature

abstract:
  The NaZn13 type itinerant magnet LaFe13-xSix has seen considerable interest
due to its unique combination of large magnetocaloric effect and low
hysteresis. Here we demonstrate, with a combination of magnetometry, bespoke
microcalorimetry and inelastic neutron scattering that this is due to the
presence of paramagnetic spin fluctuations, which build up as the critical
point is approached. While thermal measurements show significant latent heat
independent changes in the heat capacity, inelastic neutron scattering reveals
the presence of broad quasielastic scattering that persists above Tc, in
addition to a finite Q quasielastic peak at Q=0.52 A$^{-1}$ (close to a 100
Bragg reflection in this system at Q = 0.54 A$^{-1}$). This finite Q
quasielastic peak appears only in the paramagnetic state and when in proximity
to the itinerant metamagnetic transition. We associate these observations with
a hidden competing phase and spin fluctuations close to the transition
temperature and magnetic field, that persist across the magnetic transition.


title:
Enabling the autofocus approach for parameter optimization in planar
  measurement geometry clinical optoacoustic imaging

abstract:
  In optoacoustic (photoacoustic) tomography, several parameters related to
tissue and detector features are needed for image formation, but they may not
be known a priori. An autofocus (AF) algorithm is generally used to estimate
these parameters. However, the algorithm works iteratively, therefore, it is
impractical for clinical imaging with systems featuring planar geometry due to
long reconstruction times.
  We have developed a fast autofocus (FAF) algorithm for optoacoustic systems
with planar geometry that is much simpler computationally than the conventional
AF algorithm. We show that the FAF algorithm required about 5 sec. to provide
accurate estimates of the speed of sound in simulated data and experimental
data obtained using an imaging system that is poised to enter the clinic. The
applicability of FAF for estimating other image formation parameters is
discussed.
  We expect the FAF algorithm to contribute decisively to the clinical use of
optoacoustic tomography systems with planar geometry.


title: Snap-Stabilizing Tasks in Anonymous Networks

abstract:
  We consider snap-stabilizing algorithms in anonymous networks.
Self-stabilizing algorithms are well known fault tolerant algorithms : a
self-stabilizing algorithm will eventually recover from arbitrary transient
faults. On the other hand, an algorithm is snap-stabilizing if it can withstand
arbitrary initial values and immediately satisfy its safety requirement. It is
a subset of self-stabilizing algorithms. Distributed tasks that are solvable
with self-stabilizing algorithms in anonymous networks have already been
characterized by Boldi and Vigna in [BV02b]. In this paper, we show how the
more demanding snap-stabilizing algorithms can be handled with standard tools
for (not stabilizing) algorithms in anonymous networks. We give a
characterization of which tasks are solvable by snap-stabilizing algorithms in
anonymous networks. We also present a snap-stabilizing version of Mazurkiewicz'
enumeration algorithm. This work exposes, from a task-equivalence point of
view, the complete correspondence in anonymous networks between self or
snap-stabilizing tasks and distributed tasks with various termination detection
requirements.


title:
Communication Layer Security in Smart Farming: A Survey on Wireless
  Technologies

abstract:
  Human population growth has driven rising demand for food that has, in turn,
imposed huge impacts on the environment. In an effort to reconcile our need to
produce more sustenance while also protecting the ecosystems of the world,
farming is becoming more reliant on smart tools and communication technologies.
Developing a smart farming framework allows farmers to make more efficient use
of inputs, thus protecting water quality and biodiversity habitat. Internet of
Things (IoT), which has revolutionized every sphere of the economy, is being
applied to agriculture by connecting on-farm devices and providing real-time
monitoring of everything from environmental conditions to market signals
through to animal health data. However, utilizing IoT means farming networks
are now vulnerable to malicious activities, mostly when wireless communications
are highly employed. With that in mind, this research aims to review different
utilized communication technologies in smart farming. Moreover, possible cyber
attacks are investigated to discover the vulnerabilities of communication
technologies considering the most frequent cyber-attacks that have been
happened.


title: Dark Energy and the Spinning Superparticle

abstract:
  We revisit the theory of background fields constructed on the BRST-algebra of
a spinning particle with $\mathcal{N}=4$ worldline supersymmetry, whose
spectrum contains the graviton but no other fields. On a generic background,
the closure of the BRST algebra implies the vacuum Einstein equations with a
cosmological constant that is undetermined. On the other hand, in the "vacuum"
background with no metric, the cohomology is given by a collection of free
scalar- and vector fields. Only certain combinations of linear excitations,
necessarily involving a vector field, can be extended beyond the linear level
with the vector field inducing an Einstein metric.


title:
Comparing Global Tourism Flows Measured by Official Census and Social
  Sensing

abstract:
  A better understanding of the behavior of tourists is strategic for improving
services in the competitive and important economic segment of global tourism.
Critical studies in the literature often explore the issue using traditional
data, such as questionnaires or interviews. Traditional approaches provide
precious information; however, they impose challenges to obtaining large-scale
data, making it hard to study worldwide patterns. Location-based social
networks (LBSNs) can potentially mitigate such issues due to the relatively low
cost of acquiring large amounts of behavioral data. Nevertheless, before using
such data for studying tourists' behavior, it is necessary to verify whether
the information adequately reveals the behavior measured with traditional data
-- considered the ground truth. Thus, the present work investigates in which
countries the global tourism network measured with an LBSN agreeably reflects
the behavior estimated by the World Tourism Organization using traditional
methods. Although we could find exceptions, the results suggest that, for most
countries, LBSN data can satisfactorily represent the behavior studied. We have
an indication that, in countries with high correlations between results
obtained from both datasets, LBSN data can be used in research regarding the
mobility of the tourists in the studied context.


title: Astrophysics with the Laser Interferometer Space Antenna

abstract:
  Laser Interferometer Space Antenna (LISA) will be a transformative experiment
for gravitational wave astronomy as it will offer unique opportunities to
address many key astrophysical questions in a completely novel way. The synergy
with ground-based and other space-based instruments in the electromagnetic
domain, by enabling multi-messenger observations, will add further to the
discovery potential of LISA. The next decade is crucial to prepare the
astrophysical community for LISA's first observations. This review outlines the
extensive landscape of astrophysical theory, numerical simulations, and
astronomical observations that are instrumental for modeling and interpreting
the upcoming LISA datastream. To this aim, the current knowledge in three main
source classes for LISA is reviewed: ultra-compact stellar-mass binaries,
massive black hole binaries, and extreme or intermediate mass ratio inspirals.
The relevant astrophysical processes and the established modeling techniques
are summarized. Likewise, open issues and gaps in our understanding of these
sources are highlighted, along with an indication of how LISA could help make
progress in the different areas. New research avenues that LISA itself, or its
joint exploitation with studies in the electromagnetic domain, will enable, are
also illustrated. Improvements in modeling and analysis approaches, such as the
combination of numerical simulations and modern data science techniques, are
discussed. This review is intended to be a starting point for using LISA as a
new discovery tool for understanding our Universe.

categories: gr-qc astro-ph.CO astro-ph.GA astro-ph.HE astro-ph.IM astro-ph.SR


title: Motivic Pontryagin classes and hyperbolic orientations

abstract:
  We introduce the notion of hyperbolic orientation of a motivic ring spectrum,
which generalises the various existing notions of orientation (by the groups
GL, SLc, SL, Sp). We show that hyperbolic orientations of eta-periodic ring
spectra correspond to theories of Pontryagin classes, much in same way that
GL-orientations of arbitrary ring spectra correspond to theories of Chern
classes. We prove that eta-periodic hyperbolically oriented cohomology theories
do not admit further characteristic classes for vector bundles, by computing
the cohomology of the \'etale classifying space BGL_n. Finally we construct the
universal hyperbolically oriented eta-periodic commutative motivic ring
spectrum, an analog of Voevodsky's cobordism spectrum MGL.


title: Registering Articulated Objects With Human-in-the-loop Corrections

abstract:
  Remotely programming robots to execute tasks often relies on registering
objects of interest in the robot's environment. Frequently, these tasks involve
articulating objects such as opening or closing a valve. However, existing
human-in-the-loop methods for registering objects do not consider articulations
and the corresponding impact to the geometry of the object, which can cause the
methods to fail. In this work, we present an approach where the registration
system attempts to automatically determine the object model, pose, and
articulation for user-selected points using a nonlinear iterative closest point
algorithm. When the automated fitting is incorrect, the operator can
iteratively intervene with corrections after which the system will refit the
object. We present an implementation of our fitting procedure and evaluate it
with a user study that shows that it can improve user performance, in measures
of time on task and task load, ease of use, and usefulness compared to a manual
registration approach. We also present a situated example that demonstrates the
integration of our method in an end-to-end system for articulating a remote
valve.


title: Counterpropagating topological and quantum Hall edge channels

abstract:
  The survival of the quantum spin Hall edge channels in presence of an
external magnetic field has been a subject of experimental and theoretical
research. The inversion of Landau levels that accommodates the quantum spin
Hall effect is destroyed at a critical magnetic field, and a trivial insulating
gap appears in the spectrum for stronger fields. In this work, we report the
absence of this transport gap in disordered two dimensional topological
insulators in perpendicular magnetic fields of up to 16 T. Instead, we observe
that a topological edge channel (from band inversion) coexists with a
counterpropagating quantum Hall edge channel for magnetic fields at which the
transition to the insulating regime is expected. For larger fields, we observe
only the quantum Hall edge channel with transverse resistance close to $h/e^2$.
By tuning the disorder using different fabrication processes, we find evidence
that this unexpected $\nu=1$ plateau originates from extended quantum Hall edge
channels along a continuous network of charge puddles at the edges of the
device.


title: Enhancing Adversarial Training with Second-Order Statistics of Weights

abstract:
  Adversarial training has been shown to be one of the most effective
approaches to improve the robustness of deep neural networks. It is formalized
as a min-max optimization over model weights and adversarial perturbations,
where the weights can be optimized through gradient descent methods like SGD.
In this paper, we show that treating model weights as random variables allows
for enhancing adversarial training through \textbf{S}econd-Order
\textbf{S}tatistics \textbf{O}ptimization (S$^2$O) with respect to the weights.
By relaxing a common (but unrealistic) assumption of previous PAC-Bayesian
frameworks that all weights are statistically independent, we derive an
improved PAC-Bayesian adversarial generalization bound, which suggests that
optimizing second-order statistics of weights can effectively tighten the
bound. In addition to this theoretical insight, we conduct an extensive set of
experiments, which show that S$^2$O not only improves the robustness and
generalization of the trained neural networks when used in isolation, but also
integrates easily in state-of-the-art adversarial training techniques like
TRADES, AWP, MART, and AVMixup, leading to a measurable improvement of these
techniques. The code is available at \url{https://github.com/Alexkael/S2O}.


title: Odd rank vector bundles in eta-periodic motivic homotopy theory

abstract:
  We observe that, in the eta-periodic motivic stable homotopy category, odd
rank vector bundles behave to some extent as if they had a nowhere vanishing
section. We discuss some consequences concerning SLc-orientations of motivic
ring spectra, and the \'etale classifying spaces of certain algebraic groups.
In particular, we compute the classifying spaces of diagonalisable groups in
the eta-periodic motivic stable homotopy category.


title:
On the role of mechanosensitive binding dynamics in the pattern
  formation of active surfaces

abstract:
  The actin cortex of an animal cell is a thin polymeric layer attached to the
inner side of the plasma membrane. It plays a key role in shape regulation and
pattern formation on the cellular and tissue scale and, in particular,
generates the contractile ring during cell division. Experimental studies
showed that the cortex is fluid-like but highly viscous on long time scales
with a mechanics that is sensitively regulated by active and passive
cross-linker molecules that tune active stress and shear viscosity. Here, we
use an established minimal model of active surface dynamics of the cell cortex
supplemented with the experimentally motivated feature of mechanosensitivity in
cross-linker binding dynamics. Performing linear stability analysis and
computer simulations, we show that cross-linker mechanosensitivity
significantly enhances the versatility of pattern formation and enables
self-organized formation of stable contractile rings.


title:
Estimating QCD uncertainties on antiproton spectra from dark-matter
  annihilation

abstract:
  In this talk, we discuss the physics modeling of antiproton spectra arising
from dark matter (DM) annihilation or decay in a model-independent manner. The
modeling of antiproton spectra contains some intrinsic uncertainties related to
QCD parton showers and hadronisation of baryons. We briefly assess the sources
of these uncertainties and their impact on antiproton energy spectra for a few
selected DM scenarios. The results are provided in tabulated form for future
analyses.


title:
Radiation hardened beam instrumentations for multi-Mega-Watt beam
  facilities

abstract:
  The radiation hardened beam instrumentations workshop was held on February
3rd and 4th, 2022 to identify the technological challenges on the beam
instrumentation for multi-Mega-Watt (MW) beam facilities. We invited a speaker
from a high energy neutrino beam facility (CERN, Fermilab and J-PARC), a
collider detector group (CERN and Fermilab), a neutron spallation source
facility (ESS, J-PARC and SNS), and a rare-isotope beam facility (FRIB).
Although, the operational beam parameter for each institution is unique due to
their different physics goals, we realize that there is a common issue on the
beam instrumentation. Therefore, it will be tackled by a collaborative R&D
effort among facilities. We summarize an output from the workshop in the paper,
and propose a possible R&D for the future radiation hardened beam
instrumentations with a global effort.


title:
Electrical Conductivity of Iron in Earth's Core from Microscopic Ohm's
  Law

abstract:
  Understanding the electronic transport properties of iron under high
temperatures and pressures is essential for constraining geophysical processes.
The difficulty of reliably measuring these properties under Earth-core
conditions calls for sophisticated theoretical methods that can support
diagnostics. We present results of the electrical conductivity within the
pressure and temperature ranges found in Earth's core from simulating
microscopic Ohm's law using time-dependent density functional theory. Our
predictions provide a new perspective on resolving discrepancies in recent
experiments.


title: The Role of ImageNet Classes in Fr\'echet Inception Distance

abstract:
  Fr\'echet Inception Distance (FID) is a metric for quantifying the distance
between two distributions of images. Given its status as a standard yardstick
for ranking models in data-driven generative modeling research, it seems
important that the distance is computed from general, "vision-related"
features. But is it? We observe that FID is essentially a distance between sets
of ImageNet class probabilities. We trace the reason to the fact that the
standard feature space, the penultimate "pre-logit" layer of a particular
Inception-V3 classifier network, is only one affine transform away from the
logits, i.e., ImageNet classes, and thus, the features are necessarily highly
specialized to them. This has unintuitive consequences for the metric's
sensitivity. For example, when evaluating a model for human faces, we observe
that, on average, FID is actually very insensitive to the facial region, and
that the probabilities of classes like "bow tie" or "seat belt" play a much
larger role. Further, we show that FID can be significantly reduced -- without
actually improving the quality of results -- by an attack that first generates
a slightly larger set of candidates, and then chooses a subset that happens to
match the histogram of such "fringe features" in the real data. We then
demonstrate that this observation has practical relevance in case of ImageNet
pre-training of GANs, where a part of the observed FID improvement turns out
not to be real. Our results suggest caution against over-interpreting FID
improvements, and underline the need for distribution metrics that are more
perceptually uniform.


title: Stellar mass and radius estimation using Artificial Intelligence

abstract:
  Estimating stellar masses and radii is a challenge for most of the stars but
their knowledge is critical for many different astrophysical fields. One of the
most extended techniques for estimating these variables are the so-called
empirical relations. In this work we propose a group of state-of-the-art AI
regression models, with the aim of studying their proficiency in estimating
stellar masses and radii. We publicly release the database, the AI models, and
an online tool for stellar mass and radius estimation to the community. We use
a sample of 726 MS stars in the literature with accurate M, R, T_eff, L, log g,
and [Fe/H]. We have split our data sample into training and testing sets and
then analyzed the different AI techniques with them. In particular, we have
experimentally evaluated the accuracy of the following models: Linear Reg.,
Bayesian Reg., Regression Trees, Random Forest, Support-Vector Reg. (SVR),
Neural Networks, kNN, and Stacking. We propose a series of experiments designed
to evaluate the accuracy of the estimations. We have also analyzed the impact
of reducing the number of inputs parameters and compared our results with those
from state-of-the-art empirical relations in the literature. We have found that
a Stacking of several regression models is the most suitable technique for
estimating masses and radii. In the case of the mass, Neural Networks also
provide precise results, and for the radius, SVR and Neural Networks work too.
When comparing with other state-of-the-art empirical relations based models,
our Stacking improves the accuracy by a factor of two for both variables. In
addition, bias is reduced to one order of magnitude in the case of the stellar
mass. Finally, we have found that using our Stacking and only T_eff and L as
input features, the accuracies obtained are slightly larger than a 5%, with a
bias approx 1.5%.


title:
Electronic and optical properties of InAs/InAs$_{0.625}$Sb$_{0.375}$
  superlattices and their application to far-infrared detectors

abstract:
  We calculate the electronic and optical properties of
InAs/InAs$_{0.625}$Sb$_{0.375}$ superlattices within relativistic density
functional theory. To have a good description of the electronic and optical
properties, the modified Becke-Johnson exchange-correlation functional is
pondered to correctly approximate the band gap. First, we analyze electronic
and optical characteristics of bulk InAs and InSb, and then we investigate the
InAs/InAs$_{0.625}$Sb$_{0.375}$ superlattice. The optical gaps deduced from the
imaginary part of the dielectric function are associated with the
characteristic interband transitions. We investigate the electronic and optical
properties of the InAs/InAs$_{0.625}$Sb$_{0.375}$ superlattice with three
lattice constants of the bulk InAs, GaSb and AlSb, respectively. It is observed
that the electronic and optical properties strongly depend on the lattice
constant. Our results support the presence of two heavy-hole bands with
increasing in-plane effective mass as we go far from the Fermi level. We notice
a considerable decrease in the energy gaps and the effective masses of the
heavy-holes in the k$_x$-k$_y$ plane compared to the bulk phases of the parent
compounds. We demonstrate that the electrons are s-orbitals delocalized in the
entire superlattice, while the holes have mainly 5p-Sb character localized in
the In(As,Sb) side of the superlattice. In the superlattice, the low-frequency
absorption spectra greatly increase when the electric field is polarized
orthogonal to the growth axis allowing the applicability of III-V compounds for
the long-wavelength infrared detectors.


title:
Search for secluded dark matter towards the Galactic Centre with the
  ANTARES neutrino telescope

abstract:
  Searches for dark matter (DM) have not provided any solid evidence for the
existence of weakly interacting massive particles in the GeV-TeV mass range.
Coincidentally, the scale of new physics is being pushed by collider searches
well beyond the TeV domain. This situation strongly motivates the exploration
of DM masses much larger than a TeV. Secluded scenarios contain a natural way
around the unitarity bound on the DM mass, via the early matter domination
induced by the mediator of its interactions with the Standard Model.
High-energy neutrinos constitute one of the very few direct accesses to energy
scales above a few TeV. An indirect search for secluded DM signals has been
performed with the ANTARES neutrino telescope using data from 2007 to 2015.
Upper limits on the DM annihilation cross section for DM masses up to 6 PeV are
presented and discussed.


title:
EFT Approach to Black Hole Scalarization and its Compatibility with
  Cosmic Evolution

abstract:
  We address the issue of black hole scalarization and its compatibility with
cosmic inflation and big bang cosmology from an effective field theory (EFT)
point of view. In practice, using a well-defined and healthy toy model which
(in part) has been broadly considered in the literature, we consider how
higher-order theories of gravity, up to cubic operators in Riemann curvature,
fit within this context. Interestingly enough, we find that already at this
minimal level, there is a non-trivial interplay between the Wilson coefficients
which are otherwise completely independent, constraining the parameter space
where scalarization may actually occur. Conclusively, we claim that the EFT
does exhibit black hole scalarization, remaining compatible with the
inflationary paradigm, and admitting General Relativity as a cosmological
attractor.


title:
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on
  Riemannian Gradient Descent With Illustrations of Speech Processing

abstract:
  This work focuses on designing low complexity hybrid tensor networks by
considering trade-offs between the model complexity and practical performance.
Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to
build an end-to-end deep learning pipeline, namely LR-TT-DNN. Secondly, a
hybrid model combining LR-TT-DNN with a convolutional neural network (CNN),
which is denoted as CNN+(LR-TT-DNN), is set up to boost the performance.
Instead of randomly assigning large TT-ranks for TT-DNN, we leverage Riemannian
gradient descent to determine a TT-DNN associated with small TT-ranks.
Furthermore, CNN+(LR-TT-DNN) consists of convolutional layers at the bottom for
feature extraction and several TT layers at the top to solve regression and
classification problems. We separately assess the LR-TT-DNN and CNN+(LR-TT-DNN)
models on speech enhancement and spoken command recognition tasks. Our
empirical evidence demonstrates that the LR-TT-DNN and CNN+(LR-TT-DNN) models
with fewer model parameters can outperform the TT-DNN and CNN+(TT-DNN)
counterparts.


title:
An annotated translation from Latin of L. Euler's 'On the motion of
  boats propelled by oars in rivers'

abstract:
  Euler considers the following problem: A boat with a perfect rudder moves at
constant speed across a stream flowing in straight fillets at assigned speeds.
Assuming that the downstream velocity of the boat equals that of the river, how
should the rudder be set so that the boat traverses a given path? He works out
various instances, one of which gives rise to a variational problem, in detail.
(From Clifford Truesdell's An idiot's fugitive essays on science: methods,
criticisms, training, circumstances.)


title:
Multifractal analysis for Markov interval maps with countably many
  branches

abstract:
  We study multifractal decompositions based on Birkhoff averages for sequences
of functions belonging to certain classes of symbolically continuous functions.
We do this for an expanding interval map with countably many branches, which we
assume can be coded by a topologically mixing countable Markov shift. This
generalises previous work on expanding maps with finitely many branches, and
expanding maps with countably many branches where the coding is assumed to be
the full shift. When the infimum of the derivative on each branch approaches
infinity in the limit, we can directly generalise the results of the full
countable shift case. However, when this does not hold, we show that there can
be different behaviour, in particular in cases where the coding has finite
topological entropy.


title:
Unconditionally energy decreasing high-order Implicit-Explicit
  Runge-Kutta methods for phase-field models with the Lipschitz nonlinearity

abstract:
  Phase field models attract much attention these years. The energy naturally
decreases along the direction of gradient flows, so it is rather significant
for numerical methods to preserve this intrinsic structure. In order to
guarantee the energy dissipation, various numerical schemes have been developed
and among them, a simple but vital approach is implicit-explicit (IMEX)
Runge-Kutta (RK) method. In this paper we prove that a class of high-order
IMEX-RK schemes unconditionally preserve the energy dissipation law for
phase-field models with Lipschitz nonlinearity. This is the first work to prove
that a high-order linear scheme can guarantee the dissipation of the original
energy unconditionally. We also obtain the error estimate to show the
convergence and accuracy. In the end, we give some IMEX RK schemes as examples.


title:
Comparing Implementations of Self-Interacting Dark Matter in the Gizmo
  and Arepo Codes

abstract:
  Self-interacting dark matter (SIDM) models have received great attention over
the past decade as solutions to the small-scale puzzles of astrophysics. Though
there are different implementations of dark matter (DM) self-interactions in
N-body codes of structure formation, there has not been a systematic study to
compare the predictions of these different implementations. We investigate the
implementation of dark matter self-interactions in two simulation codes: Gizmo
and Arepo. We begin with identical initial conditions for an isolated $10^{10}$
M$_\odot$ dark matter halo and investigate the evolution of the density and
velocity dispersion profiles in Gizmo and Arepo for SIDM cross-section over
mass of 1, 5, and 50 $\rm cm^2 g^{-1}$. Our tests are restricted to the core
expansion phase where the core density decreases and core radius increases with
time. We find better than 30% agreement between the codes for the density
profile in this phase of evolution, with the agreement improving at higher
resolution. We find that varying code-specific SIDM parameters changes the
central halo density by less than 10% outside of the convergence radius. We
argue that SIDM core formation is robust across the two different schemes and
conclude that these codes can reliably differentiate between cross-sections of
1, 5, and 50 $\rm cm^2 g^{-1}$ but finer distinctions would require further
investigation.


title: Applied electric field instead of pressure in H-based superconductors

abstract:
  In our desire to give a new suggestion for H-based superconductors
experiments we present a theoretical framework for understanding the impact of
an applied electric field on pressured hydride superconductors. We study a
material at pressure $p$, when it possesses insulator-superconductor
transition, at the respective superconducting critical temperature $T_{cr}$.
The theory shows the applied electric field penetrates the material and forces
the Cooper pairs to Bose condensate. If one applies an electric field and then
increases the temperature, the theory predicts novel critical temperature
$T^{el}_{cr}$ higher than $T_{cr}$. Therefore, the system has a higher
superconducting critical temperature if we apply an electric field instead of
increasing the pressure. The result shows that in the case of carbonaceous
sulfur hydride at $234Gpa$ and near but below critical temperature $T_c=283K$,
applying a sufficiently strong electric field, we can bring the superconducting
critical temperature close to 300K.


title: Directional Detection of Dark Matter Using Solid-State Quantum Sensing

abstract:
  Next-generation dark matter (DM) detectors searching for weakly interacting
massive particles (WIMPs) will be sensitive to coherent scattering from solar
neutrinos, demanding an efficient background-signal discrimination tool.
Directional detectors improve sensitivity to WIMP DM despite the irreducible
neutrino background. Wide-bandgap semiconductors offer a path to directional
detection in a high-density target material. A detector of this type operates
in a hybrid mode. The WIMP or neutrino-induced nuclear recoil is detected using
real-time charge, phonon, or photon collection. The directional signal,
however, is imprinted as a durable sub-micron damage track in the lattice
structure. This directional signal can be read out by a variety of atomic
physics techniques, from point defect quantum sensing to x-ray microscopy. In
this white paper, we present the detector principle and review the status of
the experimental techniques required for directional readout of nuclear recoil
tracks. Specifically, we focus on diamond as a target material; it is both a
leading platform for emerging quantum technologies and a promising component of
next-generation semiconductor electronics. Based on the development and
demonstration of directional readout in diamond over the next decade, a future
WIMP detector will leverage or motivate advances in multiple disciplines
towards precision dark matter and neutrino physics.

categories: physics.ins-det astro-ph.CO hep-ex hep-ph


title: The Long Arc of Fairness: Formalisations and Ethical Discourse

abstract:
  In recent years, the idea of formalising and modelling fairness for
algorithmic decision making (ADM) has advanced to a point of sophisticated
specialisation. However, the relations between technical (formalised) and
ethical discourse on fairness are not always clear and productive. Arguing for
an alternative perspective, we review existing fairness metrics and discuss
some common issues. For instance, the fairness of procedures and distributions
is often formalised and discussed statically, disregarding both structural
preconditions of the status quo and downstream effects of a given intervention.
We then introduce dynamic fairness modelling, a more comprehensive approach
that realigns formal fairness metrics with arguments from the ethical
discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal
metrics to quantify decision procedures and outcomes and (3) mid-term or
long-term downstream effects. By contextualising these elements of
fairness-related processes, dynamic fairness modelling explicates formerly
latent ethical aspects and thereby provides a helpful tool to navigate
trade-offs between different fairness interventions. To illustrate the
framework, we discuss an example application -- the current European efforts to
increase the number of women on company boards, e.g. via quota solutions -- and
present early technical work that fits within our framework.


title:
Fully-dynamic $\alpha + 2$ Arboricity Decomposition and Implicit
  Colouring

abstract:
  In the implicit dynamic colouring problem, the task is to maintain a
representation of a proper colouring as a dynamic graph is subject to
insertions and deletions of edges, while facilitating interspersed queries to
the colours of vertices. The goal is to use few colours, while still
efficiently handling edge-updates and responding to colour-queries. For an
n-vertex dynamic graph of arboricity $\alpha$, we present an algorithm that
maintains an implicit vertex colouring with $4\cdot2^\alpha$ colours, in
amortised poly-$(\log n)$ update time, and with $O({\alpha} log n)$ worst-case
query time. The previous best implicit dynamic colouring algorithm uses
$2^{40\alpha}$) colours, and has a more efficient update time of $O(\log^3 n)$
and the same query time of $O({\alpha} log n)$ [Henzinger et al'20].
  For graphs undergoing arboricity $\alpha$ preserving updates, we give a
fully-dynamic $\alpha+2$ arboricity decomposition in poly$(\log n,\alpha)$
time, which matches the number of forests in the best near-linear static
algorithm by Blumenstock and Fischer [2020] who obtain $\alpha+2$ forests in
near-linear time. Our construction goes via dynamic bounded out-degree
orientations, where we present a fully-dynamic explicit, deterministic,
worst-case algorithm for $\lfloor (1+\varepsilon)\alpha \rfloor + 2$ bounded
out-degree orientation with update time $O(\varepsilon^{-6}\alpha^2 \log^3 n)$.
The state-of-the-art explicit, deterministic, worst-case algorithm for bounded
out-degree orientations maintains a $\beta\cdot \alpha + \log_{\beta} n$
out-orientation in $O(\beta^2\alpha^2+\beta\alpha\log_{\beta} n)$ time
[Kopelowitz et al'13].


title:
A note on affine cones over Grassmannians and their stringy
  $E$-functions

abstract:
  We compute the stringy $E$-function of the affine cone over a Grassmannian.
If the Grassmannian is not a projective space then its cone does not admit a
crepant resolution. Nonetheless the stringy $E$-function is sometimes a
polynomial and in those cases the cone admits a noncommutative crepant
resolution. This raises the question as to whether the existence of a
noncommutative crepant resolution implies that the stringy $E$-function is a
polynomial.


title:
Embedding Earth: Self-supervised contrastive pre-training for dense land
  cover classification

abstract:
  In training machine learning models for land cover semantic segmentation
there is a stark contrast between the availability of satellite imagery to be
used as inputs and ground truth data to enable supervised learning. While
thousands of new satellite images become freely available on a daily basis,
getting ground truth data is still very challenging, time consuming and costly.
In this paper we present Embedding Earth a self-supervised contrastive
pre-training method for leveraging the large availability of satellite imagery
to improve performance on downstream dense land cover classification tasks.
Performing an extensive experimental evaluation spanning four countries and two
continents we use models pre-trained with our proposed method as initialization
points for supervised land cover semantic segmentation and observe significant
improvements up to 25% absolute mIoU. In every case tested we outperform random
initialization, especially so when ground truth data are scarse. Through a
series of ablation studies we explore the qualities of the proposed approach
and find that learnt features can generalize between disparate regions opening
up the possibility of using the proposed pre-training scheme as a replacement
to random initialization for Earth observation tasks. Code will be uploaded
soon at https://github.com/michaeltrs/DeepSatModels.


title:
Hyperbolic structures on link complements, octahedral decompositions,
  and quantum $\mathfrak{sl}_2$

abstract:
  Hyperbolic structures (equivalently, principal $\operatorname{PSL}_2(\mathbb
C)$-bundles with connection) on link complements can be described algebraically
by using the octahedral decomposition, which assigns an ideal triangulation to
any diagram of the link. The decomposition (like any ideal triangulation) gives
a set of gluing equations in shape parameters whose solutions are hyperbolic
structures. We show that these equations are closely related to a certain
presentation of the Kac-de Concini quantum group
$\mathcal{U}_q(\mathfrak{sl}_2)$ in terms of cluster algebras at $q = \xi$ a
root of unity. Specifically, we identify ratios of the shape parameters of the
octahedral decomposition with central characters of
$\mathcal{U}_\xi(\mathfrak{sl}_2)$. The quantum braiding on these characters is
known to be closely related to $\operatorname{SL}_2(\mathbb C)$-bundles on link
complements, and our work provides a geometric perspective on this
construction.


abstract:
  Concepts are key building blocks of higher level human understanding.
Explainable AI (XAI) methods have shown tremendous progress in recent years,
however, local attribution methods do not allow to identify coherent model
behavior across samples and therefore miss this essential component. In this
work, we study concept-based explanations and put forward a new definition of
concepts as low-dimensional subspaces of hidden feature layers. We novelly
apply sparse subspace clustering to discover these concept subspaces. Moving
forward, we derive insights from concept subspaces in terms of localized input
(concept) maps, show how to quantify concept relevances and lastly, evaluate
similarities and transferability between concepts. We empirically demonstrate
the soundness of the proposed Sparse Subspace Clustering for Concept Discovery
(SSCCD) method for a variety of different image classification tasks. This
approach allows for deeper insights into the actual model behavior that would
remain hidden from conventional input-level heatmaps.


title: Stochastic optimization algorithms for quantum applications

abstract:
  Hybrid classical quantum optimization methods have become an important tool
for efficiently solving problems in the current generation of NISQ computers.
These methods use an optimization algorithm that is executed in a classical
computer, which is fed with values of the objective function that are obtained
in a quantum computer. Therefore, a proper choice of optimization algorithm is
essential to achieve good performance. Here, we review the use of first-order,
second-order, and quantum natural gradient stochastic optimization methods,
which are defined in the field of real numbers, and propose new stochastic
algorithms defined in the field of complex numbers. The performance of the
methods is evaluated by means of their application to variational quantum
eigensolver, quantum control of quantum states, and quantum state estimation.
In general, complex number optimization algorithms perform better and
second-order algorithms provide a large improvement over first-order methods in
the case of variational quantum eigensolver.


title:
Integer Sets of Large Harmonic Sum Which Avoid Long Arithmetic
  Progressions

abstract:
  We give conditions under which certain digit-restricted integer sets avoid
$k$-term arithmetic progressions. These sets are reasonably efficient to
compute and therefore enable large-scale search. We identify a set with no
arithmetic progression of four terms and with harmonic sum $4.43975$, which
improves an earlier "greedy" construction.


title:
Universally Consistent Online Learning with Arbitrarily Dependent
  Responses

abstract:
  This work provides an online learning rule that is universally consistent
under processes on (X,Y) pairs, under conditions only on the X process. As a
special case, the conditions admit all processes on (X,Y) such that the process
on X is stationary. This generalizes past results which required stationarity
for the joint process on (X,Y), and additionally required this process to be
ergodic. In particular, this means that ergodicity is superfluous for the
purpose of universally consistent online learning.


title:
Variable and orbital-dependent spin-orbit field orientations in a InSb
  double quantum dot characterized via dispersive gate sensing

abstract:
  Utilizing dispersive gate sensing (DGS), we investigate the spin-orbit field
($\textbf{B}_{SO}$) orientation in a many-electron double quantum dot (DQD)
defined in an InSb nanowire. While characterizing the inter-dot tunnel
couplings, the measured dispersive signal depends on the electron charge
occupancy, as well as on the amplitude and orientation of the external magnetic
field. The dispersive signal is mostly insensitive to the external field
orientation when a DQD is occupied by a total odd number of electrons. For a
DQD occupied by a total even number of electrons, the dispersive signal is
reduced when the finite external magnetic field aligns with the effective
$\textbf{B}_{SO}$ orientation. This fact enables the identification of
$\textbf{B}_{SO}$ orientations for different DQD electron occupancies. The
$\textbf{B}_{SO}$ orientation varies drastically between charge transitions,
and is generally neither perpendicular to the nanowire nor in the chip plane.
Moreover, $\textbf{B}_{SO}$ is similar for pairs of transitions involving the
same valence orbital, and varies between such pairs. Our work demonstrates the
practicality of DGS in characterizing spin-orbit interactions in quantum dot
systems, without requiring any current flow through the device.


title:
Semiclassical spectral gaps of the 3D Neumann Laplacian with constant
  magnetic field

abstract:
  This article deals with the spectral analysis of the semiclassical Neumann
magnetic Laplacian on a smooth bounded domain in dimension three. When the
magnetic field is constant and in the semiclassical limit, we establish a
four-term asymptotic expansion of the low-lying eigenvalues, involving a
geometric quantity along the apparent contour of the domain in the direction of
the field. In particular, we prove that they are simple.


title:
Comment on "Towards a quantum notion of covariance in spherically
  symmetric loop quantum gravity"

abstract:
  A recent paper promises new constructions that may make it possible to
achieve covariance in spherically symmetric models of loop quantum gravity.
This claim is contrary to the discovery of several stubborn obstacles to
covariance uncovered in the same models during the last few years. As the
authors point out in their abstract, new progress is supposedly possible if one
takes into account crucial features of a certain partial Abelianization of the
gauge transformations of spherically symmetric gravity, published by the same
authors. In canonical form, these gauge transformations are given by
hypersurface deformations. A closer inspection of the technical calculations
shows that spherically symmetric hypersurface deformations are, in fact,
violated in the construction.


title: On the characteristic features of ionization in QED environments

abstract:
  The ionization of molecular systems is important in many chemical processes
such as electron transfer and photoelectron spectroscopy. Strong coupling
between molecules and quantized fields (e.g. inside optical cavities)
represents a new promising way to modify molecular properties in a non-invasive
way. Recently, strong light-matter coupling has shown the potential to
significantly improve the rates of hot electron-driven processes, for instance,
water splitting. In this paper, we demonstrate that inside an optical cavity
the residual interaction between an outgoing free electron and the vacuum field
is significant. We further show that, since the quantized field is also
interacting with the ionized molecule, the free electron and the molecular
system are correlated. We develop a theoretical framework to account for the
field-induced correlation and show that the free electron-field interaction has
sizeable effects on the ionization potential of typical organic molecules.


title: The state of the Lieb--Thirring conjecture

abstract:
  In 1976 Lieb and Thirring established upper bounds on sums of powers of the
negative eigenvalues of a Schr\"odinger operator in terms of semiclassical
phase-space integrals. Over the last 45 years the optimal constants in these
inequalities, the values of which were conjectured by Lieb and Thirring, have
been subject of intense investigations. We aim to review existing results.


title:
Magnetic anisotropy energies and metal-insulator transitions in
  monolayers of $\alpha$-RuCl$_3$ and OsCl$_3$ on graphene

abstract:
  Transition metal thriclorides, with $4d$ or $5d$ electrons, are materials at
the forefront of recent studies about the interplay of spin-orbit coupling and
strong Coulomb interactions. Within our first-principles calculations
(DFT+$U$+SOC) we study the effects of graphene on the electronic and magnetic
properties of the monolayers of $\alpha$-RuCl$_3$ and OsCl$_3$. Despite the
spatially inhomogeneous $n$-type doping induced by graphene, we show that the
occupancy of the upper Hubbard bands of MLs of \rucl and OsCl$_3$ can be tuned
through external electric fields, and allows the control of (i) metal-insulator
transitions, and (ii) the magnetic easy-axis and anisotropy energies. Our
findings point towards the tunning of electronic and magnetic properties of
transition metal thriclorides monolayers by using graphene and external
electronic fields.


title:
A Machine Learning Approach for Prosumer Management in Intraday
  Electricity Markets

abstract:
  Prosumer operators are dealing with extensive challenges to participate in
short-term electricity markets while taking uncertainties into account.
Challenges such as variation in demand, solar energy, wind power, and
electricity prices as well as faster response time in intraday electricity
markets. Machine learning approaches could resolve these challenges due to
their ability to continuous learning of complex relations and providing a
real-time response. Such approaches are applicable with presence of the high
performance computing and big data. To tackle these challenges, a Markov
decision process is proposed and solved with a reinforcement learning algorithm
with proper observations and actions employing tabular Q-learning. Trained
agent converges to a policy which is similar to the global optimal solution. It
increases the prosumer's profit by 13.39% compared to the well-known stochastic
optimization approach.


title:
3D large-scale fused silica microfluidic chips enabled by hybrid laser
  microfabrication for continuous-flow UV photochemical synthesis

abstract:
  We demonstrate a hybrid laser microfabrication approach, which combines the
technical merits of ultrafast laser-assisted chemical etching and carbon
dioxide laser-induced in-situ melting, for centimeter-scale and bonding-free
fabrication of 3D complex hollow microstructures in fused silica glass. With
the developed approach, large-scale fused silica microfluidic chips with
integrated 3D cascaded micromixing units can be reliably manufactured.
High-performance on-chip mixing and continuous-flow photochemical synthesis
under UV LEDs irradiation at ~280 nm were demonstrated using the manufactured
chip, indicating a powerful capability for versatile fabrication of highly
transparent all-glass microfluidic reactors for on-chip photochemical
synthesis.


title:
Physics-aware Complex-valued Adversarial Machine Learning in
  Reconfigurable Diffractive All-optical Neural Network

abstract:
  Diffractive optical neural networks have shown promising advantages over
electronic circuits for accelerating modern machine learning (ML) algorithms.
However, it is challenging to achieve fully programmable all-optical
implementation and rapid hardware deployment. Furthermore, understanding the
threat of adversarial ML in such system becomes crucial for real-world
applications, which remains unexplored. Here, we demonstrate a large-scale,
cost-effective, complex-valued, and reconfigurable diffractive all-optical
neural networks system in the visible range based on cascaded transmissive
twisted nematic liquid crystal spatial light modulators. With the assist of
categorical reparameterization, we create a physics-aware training framework
for the fast and accurate deployment of computer-trained models onto optical
hardware. Furthermore, we theoretically analyze and experimentally demonstrate
physics-aware adversarial attacks onto the system, which are generated from a
complex-valued gradient-based algorithm. The detailed adversarial robustness
comparison with conventional multiple layer perceptrons and convolutional
neural networks features a distinct statistical adversarial property in
diffractive optical neural networks. Our full stack of software and hardware
provides new opportunities of employing diffractive optics in a variety of ML
tasks and enabling the research on optical adversarial ML.


title:
Identifying Causal Effects using Instrumental Time Series: Nuisance IV
  and Correcting for the Past

abstract:
  Instrumental variable (IV) regression relies on instruments to infer causal
effects from observational data with unobserved confounding. We consider IV
regression in time series models, such as vector auto-regressive (VAR)
processes. Direct applications of i.i.d. techniques are generally inconsistent
as they do not correctly adjust for dependencies in the past. In this paper, we
propose methodology for constructing identifying equations that can be used for
consistently estimating causal effects. To do so, we develop nuisance IV, which
can be of interest even in the i.i.d. case, as it generalizes existing IV
methods. We further propose a graph marginalization framework that allows us to
apply nuisance and other IV methods in a principled way to time series. Our
framework builds on the global Markov property, which we prove holds for VAR
processes. For VAR(1) processes, we prove identifiability conditions that
relate to Jordan forms and are different from the well-known rank conditions in
the i.i.d. case (they do not require as many instruments as covariates, for
example). We provide methods, prove their consistency, and show how the
inferred causal effect can be used for distribution generalization. Simulation
experiments corroborate our theoretical results. We provide ready-to-use Python
code.


title: Extremes of the stochastic heat equation with additive L\'evy noise

abstract:
  We analyze the spatial asymptotic properties of the solution to the
stochastic heat equation driven by an additive L\'evy space-time white noise.
For fixed time $t > 0$ and space $x \in \mathbb{R}^d$ we determine the exact
tail behavior of the solution both for light-tailed and for heavy-tailed L\'evy
jump measures. Based on these asymptotics we determine for any fixed time $t>
0$ the almost-sure growth rate of the solution as $|x| \to \infty$.


title:
Cross sections of removal reactions populating weakly-bound residual
  nuclei

abstract:
  In many instances, single nucleon removal reactions from neutron-proton
asymmetric projectile nuclei populate final states in the residual nuclei that
are very weakly bound. Familiar examples include neutron removal reactions from
neutron-rich $^{11}$Be and $^{12}$Be, the latter populating the well-known
$1/2^+$ halo ground-state and $1/2^-$ excited-state of $^{11}$Be - both states
less than 1 MeV from the first neutron-decay threshold. Numerous additional
examples arise in reactions of asymmetric $p$- and $sd$-shell nuclei. The
importance of this weak residue binding upon calculated single-nucleon removal
reaction cross sections is quantified by means of model calculations that
neglect or include the dissociation degree of freedom of the residual nuclei.
The calculated removal-reaction cross sections for two representative $p$-shell
projectiles indicate that an explicit treatment of these residue break-up
effects is unnecessary and that the differences between the break-up and no
break-up calculations are small provided a consistent description of the
residue structure and density is used.


title:
Deep Convolutional Neural Network for Roadway Incident Surveillance
  Using Audio Data

abstract:
  Crash events identification and prediction plays a vital role in
understanding safety conditions for transportation systems. While existing
systems use traffic parameters correlated with crash data to classify and train
these models, we propose the use of a novel sensory unit that can also
accurately identify crash events: microphone. Audio events can be collected and
analyzed to classify events such as crash. In this paper, we have demonstrated
the use of a deep Convolutional Neural Network (CNN) for road event
classification. Important audio parameters such as Mel Frequency Cepstral
Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum
were used as feature set. Additionally, the dataset was augmented with more
sample data by the use of audio augmentation techniques such as time and pitch
shifting. Together with the feature extraction this data augmentation can
achieve reasonable accuracy. Four events such as crash, tire skid, horn and
siren sounds can be accurately identified giving indication of a road hazard
that can be useful for traffic operators or paramedics. The proposed
methodology can reach accuracy up to 94%. Such audio systems can be implemented
as a part of an Internet of Things (IoT) platform that can complement
video-based sensors without complete coverage.


title:
ROOD-MRI: Benchmarking the robustness of deep learning segmentation
  models to out-of-distribution and corrupted data in MRI

abstract:
  Deep artificial neural networks (DNNs) have moved to the forefront of medical
image analysis due to their success in classification, segmentation, and
detection challenges. A principal challenge in large-scale deployment of DNNs
in neuroimage analysis is the potential for shifts in signal-to-noise ratio,
contrast, resolution, and presence of artifacts from site to site due to
variances in scanners and acquisition protocols. DNNs are famously susceptible
to these distribution shifts in computer vision. Currently, there are no
benchmarking platforms or frameworks to assess the robustness of new and
existing models to specific distribution shifts in MRI, and accessible
multi-site benchmarking datasets are still scarce or task-specific. To address
these limitations, we propose ROOD-MRI: a platform for benchmarking the
Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and
artifacts in MRI. The platform provides modules for generating benchmarking
datasets using transforms that model distribution shifts in MRI,
implementations of newly derived benchmarking metrics for image segmentation,
and examples for using the methodology with new models and tasks. We apply our
methodology to hippocampus, ventricle, and white matter hyperintensity
segmentation in several large studies, providing the hippocampus dataset as a
publicly available benchmark. By evaluating modern DNNs on these datasets, we
demonstrate that they are highly susceptible to distribution shifts and
corruptions in MRI. We show that while data augmentation strategies can
substantially improve robustness to OOD data for anatomical segmentation tasks,
modern DNNs using augmentation still lack robustness in more challenging
lesion-based segmentation tasks. We finally benchmark U-Nets and
transformer-based models, finding consistent differences in robustness to
particular classes of transforms across architectures.


title:
Device-system Co-design of Photonic Neuromorphic Processor using
  Reinforcement Learning

abstract:
  The incorporation of high-performance optoelectronic devices into photonic
neuromorphic processors can substantially accelerate computationally intensive
operations in machine learning (ML) algorithms. However, the conventional
device design wisdom is disconnected with system optimization. We report a
device-system co-design methodology to optimize a free-space optical general
matrix multiplication (GEMM) hardware accelerator by engineering a spatially
reconfigurable array made from chalcogenide phase change materials. With a
highly-parallelized hardware emulator constructed based on experimental
information, we demonstrate the design of unit device by optimizing GEMM
calculation accuracy via reinforcement learning, including deep Q-learning
neural network, Bayesian optimization, and their cascaded approach, which show
a clear correlation between system performance metrics and physical device
specifications. Furthermore, we employ physics-aware training approaches to
deploy optimized hardware to the tasks of image classification, materials
discovery, and a closed-loop design of optical ML accelerators. The
demonstrated framework offers insights into the co-design of optoelectronic
devices and systems with reduced human-supervision and domain-knowledge
barriers.


title: Entropy-Stable Gauss Collocation Methods for Ideal Magneto-Hydrodynamics

abstract:
  In this paper, we present an entropy-stable Gauss collocation discontinuous
Galerkin (DG) method on 3D curvilinear meshes for the GLM-MHD equations: the
single-fluid magneto-hydrodynamics (MHD) equations with a generalized Lagrange
multiplier (GLM) divergence cleaning mechanism. For the continuous entropy
analysis to hold and to ensure Galilean invariance in the divergence cleaning
technique, the GLM-MHD system requires the use of non-conservative terms.
  Traditionally, entropy-stable DG discretizations have used a collocated nodal
variant of the DG method, also known as the discontinuous Galerkin spectral
element method (DGSEM) on Legendre-Gauss-Lobatto (LGL) points. Recently, Chan
et al. ("Efficient Entropy Stable Gauss Collocation Methods". SIAM -2019)
presented an entropy-stable DGSEM scheme that uses Legendre-Gauss points
(instead of LGL points) for conservation laws. Our main contribution is to
extend the discretization technique of Chan et al. to the non-conservative
GLM-MHD system.
  We provide a numerical verification of the entropy behavior and convergence
properties of our novel scheme on 3D curvilinear meshes. Moreover, we test the
robustness and accuracy of our scheme with a magneto-hydrodynamic
Kelvin-Helmholtz instability problem. The numerical experiments suggest that
the entropy-stable DGSEM on Gauss points for the GLM-MHD system is more
accurate than the LGL counterpart.


title:
Active Evaluation: Efficient NLG Evaluation with Few Pairwise
  Comparisons

abstract:
  Recent studies have shown the advantages of evaluating NLG systems using
pairwise comparisons as opposed to direct assessment. Given $k$ systems, a
naive approach for identifying the top-ranked system would be to uniformly
obtain pairwise comparisons from all ${k \choose 2}$ pairs of systems. However,
this can be very expensive as the number of human annotations required would
grow quadratically with $k$. In this work, we introduce Active Evaluation, a
framework to efficiently identify the top-ranked system by actively choosing
system pairs for comparison using dueling bandit algorithms. We perform
extensive experiments with 13 dueling bandits algorithms on 13 NLG evaluation
datasets spanning 5 tasks and show that the number of human annotations can be
reduced by 80%. To further reduce the number of human annotations, we propose
model-based dueling bandit algorithms which combine automatic evaluation
metrics with human evaluations. Specifically, we eliminate sub-optimal systems
even before the human annotation process and perform human evaluations only on
test examples where the automatic metric is highly uncertain. This reduces the
number of human annotations required further by 89%. In effect, we show that
identifying the top-ranked system requires only a few hundred human
annotations, which grow linearly with $k$. Lastly, we provide practical
recommendations and best practices to identify the top-ranked system
efficiently. Our code has been made publicly available at
https://github.com/akashkm99/duelnlg


title:
Climate Change & Computer Audition: A Call to Action and Overview on
  Audio Intelligence to Help Save the Planet

abstract:
  Among the seventeen Sustainable Development Goals (SDGs) proposed within the
2030 Agenda and adopted by all the United Nations member states, the 13$^{th}$
SDG is a call for action to combat climate change for a better world. In this
work, we provide an overview of areas in which audio intelligence -- a powerful
but in this context so far hardly considered technology -- can contribute to
overcome climate-related challenges. We categorise potential computer audition
applications according to the five elements of earth, water, air, fire, and
aether, proposed by the ancient Greeks in their five element theory; this
categorisation serves as a framework to discuss computer audition in relation
to different ecological aspects. Earth and water are concerned with the early
detection of environmental changes and, thus, with the protection of humans and
animals, as well as the monitoring of land and aquatic organisms. Aerial audio
is used to monitor and obtain information about bird and insect populations.
Furthermore, acoustic measures can deliver relevant information for the
monitoring and forecasting of weather and other meteorological phenomena. The
fourth considered element is fire. Due to the burning of fossil fuels, the
resulting increase in CO$_2$ emissions and the associated rise in temperature,
fire is used as a symbol for man-made climate change and in this context
includes the monitoring of noise pollution, machines, as well as the early
detection of wildfires. In all these areas, computer audition can help
counteract climate change. Aether then corresponds to the technology itself
that makes this possible. This work explores these areas and discusses
potential applications, while positioning computer audition in relation to
methodological alternatives.


title: Towards Sustainable Satellite Edge Computing

abstract:
  Recently, Low Earth Orbit (LEO) satellites experience rapid development and
satellite edge computing emerges to address the limitation of bent-pipe
architecture in existing satellite systems. Introducing energy-consuming
computing components in satellite edge computing increases the depth of battery
discharge. This will shorten batteries' life and influences the satellites'
operation in orbit. In this paper, we aim to extend batteries' life by
minimizing the depth of discharge for Earth observation missions. Facing the
challenges of wireless uncertainty and energy harvesting dynamics, our work
develops an online energy scheduling algorithm within an online convex
optimization framework. Our algorithm achieves sub-linear regret and the
constraint violation asymptotically approaches zero. Simulation results show
that our algorithm can reduce the depth of discharge significantly.


title:
MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse
  Data via Manifold-constrained Gaussian Processes

abstract:
  This article presents the MAGI software package for inference of dynamic
systems. The focus of MAGI is where the dynamics are modeled by nonlinear
ordinary differential equations with unknown parameters. While such models are
widely used in science and engineering, the available experimental data for
parameter estimation may be noisy and sparse. Furthermore, some system
components may be entirely unobserved. MAGI solves this inference problem with
the help of manifold-constrained Gaussian processes within a Bayesian
statistical framework, whereas the unobserved components have posed a
significant challenge for existing software. We use three realistic examples to
illustrate the functionality of MAGI. The user may choose to use the package in
any of the R, MATLAB, and Python environments.


title:
Automatic selection by penalized asymmetric Lq-norm in an
  high-dimensional model with grouped variables

abstract:
  The paper focuses on the automatic selection of the grouped explanatory
variables in an high-dimensional model, when the model errors are asymmetric.
After introducing the model and notations, we define the adaptive group LASSO
expectile estimator for which we prove the oracle properties: the sparsity and
the asymptotic normality.
  Afterwards, the results are generalized by considering the asymmetric
$L_q$-norm loss function. The theoretical results are obtained in several cases
with respect to the number of variable groups. This number can be fixed or
dependent on the sample size $n$, with the possibility that it is of the same
order as $n$. Note that these new estimators allow us to consider weaker
assumptions on the data and on the model errors than the usual ones. Simulation
study demonstrates the competitive performance of the proposed penalized
expectile regression, especially when the samples size is close to the number
of explanatory variables and model errors are asymmetrical. An application on
air pollution data is considered.