title:
Echo-aware Adaptation of Sound Event Localization and Detection in
  Unknown Environments

abstract:
  Our goal is to develop a sound event localization and detection (SELD) system
that works robustly in unknown environments. A SELD system trained on known
environment data is degraded in an unknown environment due to environmental
effects such as reverberation and noise not contained in the training data.
Previous studies on related tasks have shown that domain adaptation methods are
effective when data on the environment in which the system will be used is
available even without labels. However adaptation to unknown environments
remains a difficult task. In this study, we propose echo-aware feature
refinement (EAR) for SELD, which suppresses environmental effects at the
feature level by using additional spatial cues of the unknown environment
obtained through measuring acoustic echoes. FOA-MEIR, an impulse response
dataset containing over 100 environments, was recorded to validate the proposed
method. Experiments on FOA-MEIR show that the EAR effectively improves SELD
performance in unknown environments.


title: Decentralized Verifiable Mail-in Ballot Counting for Postal Voting

abstract:
  As computer vision is prevalently used for mail-in ballot processing and
counting, it becomes a point of centralized trust in postal voting. We propose
DVote, a prototype system of postal voting that provides decentralized trust in
computer vision. With blockchain and layer-2 technologies, DVote decentralizes
the computation and model training of computer vision to a group of scrutineers
that hold the AnyTrust assumption, i.e., at least one member is honest.
Consequently, the computational integrity is anchored to the trustworthiness of
a large public blockchain such as Ethereum.


title: Make Every Word Count: Adaptive BA with Fewer Words

abstract:
  Byzantine Agreement is a key component in many distributed systems. While
Dolev and Reischuk have proven a long time ago that quadratic communication
complexity is necessary for worst-case runs, the question of what can be done
in practically common runs with fewer failures remained open. In this paper we
present the first Byzantine Broadcast algorithm with $O(n(f+1))$ communication
complexity, where $0\leq f\leq t$ is the actual number of process failures in a
run. And for BA with strong unanimity, we present the first optimal-resilience
algorithm that has linear communication complexity in the failure-free case and
a quadratic cost otherwise.


title:
Multi-view and Multi-modal Event Detection Utilizing Transformer-based
  Multi-sensor fusion

abstract:
  We tackle a challenging task: multi-view and multi-modal event detection that
detects events in a wide-range real environment by utilizing data from
distributed cameras and microphones and their weak labels. In this task,
distributed sensors are utilized complementarily to capture events that are
difficult to capture with a single sensor, such as a series of actions of
people moving in an intricate room, or communication between people located far
apart in a room. For sensors to cooperate effectively in such a situation, the
system should be able to exchange information among sensors and combines
information that is useful for identifying events in a complementary manner.
For such a mechanism, we propose a Transformer-based multi-sensor fusion
(MultiTrans) which combines multi-sensor data on the basis of the relationships
between features of different viewpoints and modalities. In the experiments
using a dataset newly collected for this task, our proposed method using
MultiTrans improved the event detection performance and outperformed
comparatives.


title: Infinitesimal deformations of parabolic connections and parabolic opers

abstract:
  We compute the infinitesimal deformations of quadruples of the form $$(X, S,
E_*, D),$$ where $(X, S)$ is a compact Riemann surface with $n$ marked points,
$E_*$ is a parabolic vector bundle on $X$ with parabolic structure over $S$,
and $D$ is a parabolic connection on $E_*$. Using it we compute the
infinitesimal deformations of $(X, S, D)$, where $D$ is a parabolic SL(r,
C)-oper on $(X, S)$. It is shown that the monodromy map, from the moduli space
of triples $(X, S, D)$, where $D$ is a parabolic SL(r, C)-oper on $(X, S)$, to
the SL(r, C)-character variety of X - S, is an immersion.


title: Slice-reducible conformal Killing tensors, photon surfaces and shadows

abstract:
  We generalize our recent method for constructing Killing tensors of the
second rank to conformal Killing tensors. The method is intended for foliated
spacetimes of arbitrary dimension $m$, which have a set of conformal Killing
vectors. It applies to foliations of a more general structure than in previous
literature. The basic idea is to start with reducible Killing tensors in slices
constructed from a set of conformal Killing vectors and the induced metric, and
then lift them to the whole manifold. Integrability conditions are derived that
ensure this, and a constructive lifting procedure is presented. The resulting
conformal Killing tensor may be irreducible. It is shown that subdomains of
foliation slices suitable for the method are fundamental photon surfaces if
some additional photon region inequality is satisfied. Thus our procedure also
opens the way to obtain a simple general analytical expression for the boundary
of the gravitational shadow. We apply this technique to electrovacuum, and
${\cal N}=2,\,4,\,8$ supergravity black holes, providing a new easy way to
establish the existence of exact and conformal Killing tensors.


title: Pressure tuning the Jahn-Teller transition temperature in NaNiO$_2$

abstract:
  NaNiO$_2$ is a layered material consisting of alternating layers of NaO$_6$
and Jahn-Teller-active NiO$_6$ edge-sharing octahedra. At ambient pressure it
undergoes a broad phase transition from a monoclinic to rhombohedral structure
between $\sim$465 K and $\sim$495 K, associated with the loss of long-range
orbital ordering. In this work, we present the results of a neutron powder
diffraction study on powdered NaNiO$_2$ as a function of pressure and
temperature from ambient pressure to $\sim$5 GPa between 290 K and 490 K. The
290 K and 460 K isothermal compressions remained in the monoclinic phase up to
the maximum pressures studied, whereas the 490 K isotherm was mixed-phase
throughout. The unit-cell volume was fitted to a 2nd-order Birch-Murnaghan
equation of state, with $B=113(1)$ GPa. We observe at 490 K that the fraction
of Jahn-Teller-distorted phase increases with increasing pressure, from
67.8(6)% at 0.71(2) GPa to 80.2(9)% at 4.20(6) GPa. Using this observation, in
conjunction with neutron diffraction measurements at 490 K from 5.46(9) GPa to
0.342(13) GPa, we show that the Jahn-Teller transition temperature increases
with pressure. Our results are used to present a structural
pressure-temperature phase diagram for NaNiO$_2$. To our knowledge, this is the
first diffraction study of the effect of pressure on the Jahn-Teller transition
temperature in materials with edge-sharing Jahn-Teller-distorted octahedra, and
the first variable-pressure study on a JT-active edge-sharing nickelate.


title:
Energy Efficient Dual-Functional Radar-Communication: Rate-Splitting
  Multiple Access, Low-Resolution DACs, and RF Chain Selection

abstract:
  Dual-Functional Radar-Communication systems enhance the benefits of
communications and radar sensing by jointly implementing these on the same
hardware platform and using the common RF resources. An important and latest
concern to be addressed in designing such Dual-Functional Radar-Communication
systems is maximizing the energy-efficiency. In this paper, we consider a
Dual-Functional Radar-Communication system performing simultaneous multi-user
communications and radar sensing, and investigate the energy-efficiency
behaviour with respect to active transmission elements. Specifically, we
formulate a problem to find the optimal precoders and the number of active RF
chains for maximum energy-efficiency by taking into consideration the power
consumption of low-resolution Digital-to-Analog Converters on each RF chain
under communications and radar performance constraints. We consider
Rate-Splitting Multiple Access to perform multi-user communications with
perfect and imperfect Channel State Information at Transmitter. The formulated
non-convex optimization problem is solved by means of a novel algorithm. We
demonstrate by numerical results that Rate Splitting Multiple Access achieves
an improved energy-efficiency by employing a smaller number of RF chains
compared to Space Division Multiple Access, owing to its generalized structure
and improved interference management capabilities.


title:
Efficient computation of the volume of a polytope in high-dimensions
  using Piecewise Deterministic Markov Processes

abstract:
  Computing the volume of a polytope in high dimensions is computationally
challenging but has wide applications. Current state-of-the-art algorithms to
compute such volumes rely on efficient sampling of a Gaussian distribution
restricted to the polytope, using e.g. Hamiltonian Monte Carlo. We present a
new sampling strategy that uses a Piecewise Deterministic Markov Process. Like
Hamiltonian Monte Carlo, this new method involves simulating trajectories of a
non-reversible process and inherits similar good mixing properties. However,
importantly, the process can be simulated more easily due to its piecewise
linear trajectories - and this leads to a reduction of the computational cost
by a factor of the dimension of the space. Our experiments indicate that our
method is numerically robust and is one order of magnitude faster (or better)
than existing methods using Hamiltonian Monte Carlo. On a single core
processor, we report computational time of a few minutes up to dimension 500.


title: Scanning gradiometry with a single spin quantum magnetometer

abstract:
  Here, we demonstrate a gradiometry technique that significantly enhances the
measurement sensitivity of such static fields, leading to new opportunities in
the imaging of weakly magnetic systems. Our method relies on the mechanical
oscillation of a single nitrogen-vacancy center at the tip of a scanning
diamond probe, which up-converts the local spatial gradients into ac magnetic
fields enabling the use of sensitive ac quantum protocols. We show that
gradiometry provides important advantages over static field imaging: (i) an
order-of-magnitude better sensitivity, (ii) a more localized and sharper image,
and (iii) a strong suppression of field drifts. We demonstrate the capabilities
of gradiometry by imaging the nanotesla fields appearing above topographic
defects and atomic steps in an antiferromagnet, direct currents in a graphene
device, and para- and diamagnetic metals.


title: A Graphical Approach for Friedman Test: Moments Approach

abstract:
  Friedman test is a nonparametric method that proposed for analyzing data from
a randomized complete block design as a robust alternative to parametric method
and widely applied in many fields such as agriculture, biology, business,
education, and medicine. After the null hypothesis of no treatment effects is
rejected, the post-hoc pairwise comparisons must be applied to identify where
the differences occur. As the number of groups increases, the number of
required comparisons becomes large and this may increase the type I error. The
aim of this study is twofold. The main aim is to suggest expression that
facilitates the plotting Friedman test by gathering the test and pairwise
comparisons in one simple step. The second aim is to derive the sampling
distribution of the suggested expression by utilizing method of moments that
helps in obtaining the decision limit. An application and simulation study are
carried out to show the advantage of the suggested method and to compute the
empirical type I error. The results are of great value where the proposed
method makes huge reduction in the number of required tests to show where the
discrepancies occur, holds the type I error close to the nominal value and
provides visual, deep insight and understanding where the treatment effects
occur.


title:
Systematic Study of LED Stimulated Recovery of Radiation Damage in
  Optical Materials

abstract:
  The radiation damage in optical materials mostly manifests itself as the loss
of optical transmittance. The optical materials recover from radiation damage
to some extent when the radiation exposure is stopped. The recovery is at a
faster rate in the presence of stimulating light. On the other hand, a
systematic study of the dynamics of the recovery as a function of the
stimulating light parameters such as its wavelength, intensity and exposure
duration and method has not been performed in detail yet. We established an LED
recovery station which provides pulsed and continuous light at various
wavelengths at custom geometries. We irradiated soda lime glass samples at a
rate of 87.5 Gy/min to a total dose of 3.5 kGy and 7.0 kGy. The optical
transmittance of the samples were then measured in 200 nm - 1500 nm range for
an extended period of time. The recovery from radiation damage is improved,
both in terms of timing and quantity, as the wavelength of the stimulating
light decreases. Around 50 % improvement was measured both in recovery rate and
the permanent damage when UV LED with a wavelength of 396 nm was used for
stimulation. The trend is such that wavelengths deeper in the UV range would
result in faster and more effective recovery from radiation damage. The LED
stimulated recovery technique from radiation damage is a feasible
implementation for the optical active media of radiation and particle detectors
which operate in high radiation environments.


title:
Bjorken flow revisited: analytic and numerical solutions in flat
  space-time coordinates

abstract:
  In this work we provide analytic and numerical solutions for the Bjorken
flow, a standard benchmark in relativistic hydrodynamics providing a simple
model for the bulk evolution of matter created in collisions between heavy
nuclei. We consider relativistic gases of both massive and massless particles,
working in a ( 2 + 1 ) and ( 3 + 1 ) Minkowski space-time coordinate system.
The numerical results from a recently developed lattice kinetic scheme show
excellent agreement with the analytic solutions.


title: Quantifying the Effects of Data Augmentation

abstract:
  We provide results that exactly quantify how data augmentation affects the
convergence rate and variance of estimates. They lead to some unexpected
findings: Contrary to common intuition, data augmentation may increase rather
than decrease uncertainty of estimates, such as the empirical prediction risk.
Our main theoretical tool is a limit theorem for functions of randomly
transformed, high-dimensional random vectors. The proof draws on work in
probability on noise stability of functions of many variables. The pathological
behavior we identify is not a consequence of complex models, but can occur even
in the simplest settings -- one of our examples is a linear ridge regressor
with two parameters. On the other hand, our results also show that data
augmentation can have real, quantifiable benefits.


abstract:
  Gradient almost para-Ricci-like solitons on para-Sasaki-like Riemannian
$\Pi$-manifolds are studied. It is proved that these objects have constant
soliton coefficients. For the soliton under study is shown that the
corresponding scalar curvatures of the considered both metrics are equal and
constant and its Ricci tensor is a constant multiple of the vertical component.
Explicit example of a 3-dimensional para-Sasaki-like Riemannian $\Pi$-manifold
is provided in support of the proved assertions.


title: FORT: Right-proving and Attribute-blinding Self-sovereign Authentication

abstract:
  Nowadays, there is a plethora of services that are provided and paid for
online, like video streaming subscriptions, car or parking sharing, purchasing
tickets for events, etc. Online services usually issue tokens directly related
to the identities of their users after signing up into their platform, and the
users need to authenticate using the same credentials each time they are
willing to use the service. Likewise, when using in-person services like going
to a concert, after paying for this service the user usually gets a ticket
which proves that he/she has the right to use that service. In both scenarios,
the main concerns are the centralization of the systems, and that they do not
ensure customers' privacy. The involved Service Providers are Trusted Third
Parties, authorities that offer services and handle private data about users.
In this paper, we design and implement FORT, a decentralized system that allows
customers to prove their right to use specific services (either online or
in-person) without revealing sensitive information. To achieve decentralization
we propose a solution where all the data is handled by a Blockchain. We
describe and uniquely identify users' rights using Non-Fungible Tokens (NFTs),
and possession of these rights is demonstrated by using Zero-Knowledge Proofs,
cryptographic primitives that allow us to guarantee customers' privacy.
Furthermore, we provide benchmarks of FORT which show that our protocol is
efficient enough to be used in devices with low computing resources, like
smartphones or smartwatches, which are the kind of devices commonly used in our
use case scenario.


title:
A comprehensive study of the inclusive production of negative pions in
  p+p collisions for interaction energies from 3 GeV to 13 TeV covering the
  non-perturbative sector of the Strong Interaction

abstract:
  Over the past 60 years a rich sample of experimental results concerning the
inclusive production of $\pi^-$ mesons has been obtained spanning a range from
about 3 GeV to 13 TeV in interaction energy. This paper attempts a
model-independent overview of these results with the aim at obtaining an
internally consistent data description on a dense grid over the three inclusive
variables transverse momentum, rapidity or Feynman $x_F$ and interaction
energy. The study concentrates on the non-perturbative sector of the strong
interaction by limiting the transverse momenta to $p_T <$ 1.3 GeV/c. The
three-dimensional interpolation which is mandatory and necessary for this aim
is shown to provide a controlled systematic precision of better than 5%. This
accuracy allows for a critical inspection of each of the 40 experiments
concerned in turn. It also allows precision tests of some of the physics
concepts developed around inclusive processes like energy scaling, "thermal"
production and the evolution of transverse momenta.


title:
{\mu}SR investigation of the Fe-doped Ca$_{3}$Ru$_{2}$O$_{7}$ polar
  metal

abstract:
  Ca$_{3}$Ru$_{2}$O$_{7}$ is a polar metal that belongs to the class of
multiferroic magnetic materials. Here, tiny amounts of Fe doping in the Ru
sites bring about dramatic changes in the electronic and magnetic properties
and generate a complex H-T phase diagram. To date, not much is known about the
ground state of such a system in the absence of magnetic field. By performing
muon-spin spectroscopy (${\mu}$SR) measurements in 5% Fe-doped
Ca$_{3}$Ru$_{2}$O$_{7}$ single crystals, we investigate its electronic
properties at a local level. Transverse-field ${\mu}$SR results indicate a very
sharp normal-to-antiferromagnetic transition at T$_{N}$ = 79.7(1) K, with a
width of only 1 K. Zero-field ${\mu}$SR measurements in the magnetically
ordered state allow us to determine the local fields B$_{i}$ at the muon
implantation sites. By symmetry, muons stopping close to the RuO$_{2}$ planes
detect only the weak nuclear dipolar fields, while those stopping next to
apical oxygens sense magnetic fields as high as 150 mT. In remarkable agreement
with the nominal Fe-doping, a $\sim$ 6% minority of the these muons feel
slightly lower fields, reflecting a local magnetic frustration induced by iron
ions. Finally, B$_{i}$ shows no significant changes across the
metal-to-insulator transition, close to 40 K. We ascribe this surprising lack
of sensitivity to the presence of crystal twinning.


title:
Non-monotonic heat dissipation phenomenon in close-packed 2D and 3D
  hotspot system

abstract:
  Transient heat dissipation in close-packed quasi-2D nanoline and 3D
nanocuboid hotspot systems is studied based on phonon Boltzmann transport
equation. Different from previous intuitive understanding of micro/nano scale
heat conduction, it is found that the heat dissipation efficiency is not
monotonic when the distance between adjacent nanoscale heat sources decreases.
The heat dissipation efficiency reaches the highest value when the distance
between adjacent nanoscale heat sources is comparable to the phonon mean free
path. It is a competition result of two thermal transport processes:
quasiballistic transport when phonons escape from the nanoscale heat source and
the scattering among phonons originating from adjacent nanoscale heat source.


title:
Non-Gaussian random matrices determine the stability of Lotka-Volterra
  communities

abstract:
  The eigenvalue spectrum of a random matrix often only depends on the first
and second moments of its elements, but not on the specific distribution from
which they are drawn. The validity of this universality principle is often
assumed without proof in applications. In this letter, we offer a pertinent
counterexample in the context of the generalised Lotka-Volterra equations.
Using dynamic mean-field theory, we derive the statistics of the interactions
between species in an evolved ecological community. We then show that the full
statistics of these interactions, beyond those of a Gaussian ensemble, are
required to correctly predict the eigenvalue spectrum and therefore stability.
Consequently, the universality principle fails in this system. Our findings
connect two previously disparate ways of modelling complex ecosystems: Robert
May's random matrix approach and the generalised Lotka-Volterra equations. We
show that the eigenvalue spectra of random matrices can be used to deduce the
stability of dynamically constructed (or `feasible') communities, but only if
the emergent non-Gaussian statistics of the interactions between species are
taken into account.


title: How liquid-liquid phase separation induces active spreading

abstract:
  The interplay between phase separation and wetting of multicomponent mixtures
is ubiquitous in nature and technology and recently gained significant
attention across scientific disciplines, due to the discovery of biomolecular
condensates. It is well understood that sessile droplets, undergoing phase
separation in a static wetting configuration, exhibit microdroplet nucleation
at their contact lines, forming an oil ring during later stages. However, very
little is known about the dynamic counterpart, when phase separation occurs in
a non-equilibrium wetting configuration, i.e., spreading droplets. Here we
report that liquid-liquid phase separation strongly couples to the spreading
motion of three-phase contact lines. Thus, the classical Cox-Voinov law is not
applicable anymore, because phase separation adds an active spreading force
beyond the capillary driving. Intriguingly, we observe that spreading starts
well before any visible nucleation of microdroplets in the main droplet. Using
high-speed ellipsometry, we further demonstrate that surface forces cause an
even earlier nucleation in the wetting precursor film around the droplet,
initiating the observed wetting transition. We expect our findings to enrich
the fundamental understanding of phase separation processes that involve
dynamical contact lines and/or surface forces, with implications in a wide
range of applications, from oil recovery or inkjet printing to material
synthesis and biomolecular condensates.


title: Real-time observables from Euclidean thermal correlation functions

abstract:
  In this work we apply a local quantum field theory (QFT) approach in order to
analyse the connection between real-time observables and Euclidean thermal
correlation functions. In particular, using data generated from the functional
renormalisation group (FRG) in the quark-meson model, we demonstrate that
in-medium effects can be directly extracted from the spatial momentum
dependence of the Euclidean propagators, in contrast to conventional
approaches, which rely on the reconstruction from different Matsubara
frequencies. As an application, we determine the analytic features that arise
from the discrete spectral contribution to the pion correlation function, and
calculate the non-perturbative shear viscosity arising from these states.


title:
Dissociative recombination and rotational transitions of D$_2^+$ in
  collisions with slow electrons

abstract:
  Rate coefficients for dissociative recombination and state-to-state
rotational transitions of the D$_{2}^{+}$ ion induced by collisions with very
low-energy electrons have been reported following our previous studies on
HD$^{+}$ and H$_{2}^{+}$ [9,10]. The same molecular structure data sets,
excitations ($N_{i}^{+} \rightarrow$ $N_{f}^{+}=N_{i}^{+}+2$ for $N_{i}^{+}=0$
to $10$) and de-excitations ($N_{i}^{+}$ $\rightarrow$ $N_{f}^{+}=N_{i}^{+}-2$,
for $N_{i}^{+}=2$ to $10$) were used for collision energies ranging from $0.01$
meV to $0.3$ eV. Isotopic effects for dissociative recombination and rotational
transitions of the vibrationally relaxed targets are presented.


title:
Modelling the semantics of text in complex document layouts using graph
  transformer networks

abstract:
  Representing structured text from complex documents typically calls for
different machine learning techniques, such as language models for paragraphs
and convolutional neural networks (CNNs) for table extraction, which prohibits
drawing links between text spans from different content types. In this article
we propose a model that approximates the human reading pattern of a document
and outputs a unique semantic representation for every text span irrespective
of the content type they are found in. We base our architecture on a graph
representation of the structured text, and we demonstrate that not only can we
retrieve semantically similar information across documents but also that the
embedding space we generate captures useful semantic information, similar to
language models that work only on text sequences.


title:
Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via
  Nonlinear Neighborhood Aggregators

abstract:
  Graph neural networks (GNNs) have achieved great success in many graph
learning tasks. The main aspect powering existing GNNs is the multi-layer
network architecture to learn the nonlinear graph representations for the
specific learning tasks. The core operation in GNNs is message propagation in
which each node updates its representation by aggregating its neighbors'
representations. Existing GNNs mainly adopt either linear neighborhood
aggregation (mean,sum) or max aggregator in their message propagation. (1) For
linear aggregators, the whole nonlinearity and network's capacity of GNNs are
generally limited due to deeper GNNs usually suffer from over-smoothing issue.
(2) For max aggregator, it usually fails to be aware of the detailed
information of node representations within neighborhood. To overcome these
issues, we re-think the message propagation mechanism in GNNs and aim to
develop the general nonlinear aggregators for neighborhood information
aggregation in GNNs. One main aspect of our proposed nonlinear aggregators is
that they provide the optimally balanced aggregators between max and mean/sum
aggregations. Thus, our aggregators can inherit both (i) high nonlinearity that
increases network's capacity and (ii) detail-sensitivity that preserves the
detailed information of representations together in GNNs' message propagation.
Promising experiments on several datasets show the effectiveness of the
proposed nonlinear aggregators.


title:
MultiRes-NetVLAD: Augmenting Place Recognition Training with
  Low-Resolution Imagery

abstract:
  Visual Place Recognition (VPR) is a crucial component of 6-DoF localization,
visual SLAM and structure-from-motion pipelines, tasked to generate an initial
list of place match hypotheses by matching global place descriptors. However,
commonly-used CNN-based methods either process multiple image resolutions after
training or use a single resolution and limit multi-scale feature extraction to
the last convolutional layer during training. In this paper, we augment NetVLAD
representation learning with low-resolution image pyramid encoding which leads
to richer place representations. The resultant multi-resolution feature pyramid
can be conveniently aggregated through VLAD into a single compact
representation, avoiding the need for concatenation or summation of multiple
patches in recent multi-scale approaches. Furthermore, we show that the
underlying learnt feature tensor can be combined with existing multi-scale
approaches to improve their baseline performance. Evaluation on 15
viewpoint-varying and viewpoint-consistent benchmarking datasets confirm that
the proposed MultiRes-NetVLAD leads to state-of-the-art Recall@N performance
for global descriptor based retrieval, compared against 11 existing techniques.
Source code is publicly available at
https://github.com/Ahmedest61/MultiRes-NetVLAD.


title:
Morphological effects of leading-edge serrations on the acoustic
  signatures of mixed flow fan analyzed using novel CFD-informed methods

abstract:
  Leading-edge (LE) noise is a common source of broadband noise for fans that
can be suppressed using appended LE serrations. We conduct an integrated study
of the morphological effects of interval, length, and inclination angle of
owl-inspired LE serrations on the aeroacoustic characteristics of a mixed flow
fan using experiments, computational fluid dynamics (CFD), and the Ffowcs
Williams-Hawkings (FWH) analogy. A novel method for surface noise strength
(SNS) visualization was developed based on the FWH analogy with large-eddy
simulations, and a CFD-informed index SAPG is proposed to evaluate the severity
of flow separation with pressure gradient, which are verified to be effective
in examining the acoustic sources and chordwise separation. Acoustic
measurements show the robust tradeoff solving capability of the serrations
under various morphologies and the SNS visualizations indicate that the
separation-induced LE noise is suppressed considerably. One-third octave
analyses suggest that extending serration length can lower separation noise
more effectively than shrinking the interval over 100-3k Hz. A smaller interval
is more desirable while an optimal length exists in association with tonal
noise. Moreover, small inclination angles (below 20 degrees) enable the
deceleration of oncoming flows with stagnation relieved, and consequently,
further suppress the LE noise, by a flow-buffering effect. Heavy inclination
angles (over 40 degrees) induce an additional tip vortex, causing
high-coherence turbulence impingement noise and resulting in a drastic increase
in broadband noise at frequencies exceeding 4k Hz. Our study thus clarifies the
morphological effects of LE serrations on aeroacoustic signatures of rotary
devices while providing useful methods for acoustic analyses.


title:
The effect of hydrogen on the multiscale mechanical behaviour of a
  La(Fe,Mn,Si)13-based magnetocaloric material

abstract:
  Magnetocaloric cooling offers the potential to improve the efficiency of
refrigeration devices and hence cut the significant CO2 emissions associated
with cooling processes. A critical issue in deployment of this technology is
the mechanical degradation of the magnetocaloric material during processing and
operation, leading to limited service-life. The mechanical properties of
hydrogenated La(Fe,Mn,Si)13-based magnetocaloric material are studied using
macroscale bending tests of polycrystalline specimens and in situ micropillar
compression tests of single crystal specimens. The impact of hydrogenation on
the mechanical properties are quantified. Understanding of the
deformation/failure mechanisms is aided by characterization with transmission
electron microscopy and atom probe tomography to reveal the arrangement of
hydrogen atoms in the crystal lattice. Results indicate that the intrinsic
strength of this material is ~3-6 GPa and is dependent on the crystal
orientation. Single crystals under compressive load exhibit shearing along
specific crystallographic planes. Hydrogen deteriorates the strength of
La(Fe,Mn,Si)13 through promotion of transgranular fracture. The weakening
effect of hydrogen on single crystals is anisotropic; it is significant upon
shearing parallel to the {111} crystallographic planes but is negligible when
the shear plane is {001}-oriented. APT analysis suggests that this is
associated with the close arrangement of hydrogen atoms on {222} planes.


title:
Analysis and approximations of an optimal control problem for the
  Allen-Cahn equation

abstract:
  The scope of this paper is the analysis and approximation of an optimal
control problem related to the Allen-Cahn equation. A tracking functional is
minimized subject to the Allen-Cahn equation using distributed controls that
satisfy point-wise control constraints. First and second order necessary and
sufficient conditions are proved. The lowest order discontinuous Galerkin - in
time - scheme is considered for the approximation of the control to state and
adjoint state mappings. Under a suitable restriction on maximum size of the
temporal and spatial discretization parameters $k$, $h$ respectively in terms
of the parameter $\epsilon$ that describes the thickness of the interface
layer, a-priori estimates are proved with constants depending polynomially upon
$1/ \epsilon$. Unlike to previous works for the uncontrolled Allen-Cahn problem
our approach does not rely on a construction of an approximation of the
spectral estimate, and as a consequence our estimates are valid under low
regularity assumptions imposed by the optimal control setting. These estimates
are also valid in cases where the solution and its discrete approximation do
not satisfy uniform space-time bounds independent of $\epsilon$. These
estimates and a suitable localization technique, via the second order condition
(see
\cite{Arada-Casas-Troltzsch_2002,Casas-Mateos-Troltzsch_2005,Casas-Raymond_2006,Casas-Mateos-Raymond_2007}),
allows to prove error estimates for the difference between local optimal
controls and their discrete approximation as well as between the associated
state and adjoint state variables and their discrete approximations


title:
Personalization Trade-offs in Designing a Dialogue-based Information
  System for Support-Seeking of Sexual Violence Survivors

abstract:
  The lack of reliable, personalized information often complicates sexual
violence survivors' support-seeking. Recently, there is an emerging approach to
conversational information systems for support-seeking of sexual violence
survivors, featuring personalization with wide availability and anonymity.
However, a single best solution might not exist as sexual violence survivors
have different needs and purposes in seeking support channels. To better
envision conversational support-seeking systems for sexual violence survivors,
we explore personalization trade-offs in designing such information systems. We
implement a high-fidelity prototype dialogue-based information system through
four design workshop sessions with three professional caregivers and
interviewed with four self-identified survivors using our prototype. We then
identify two forms of personalization trade-offs for conversational
support-seeking systems: (1) specificity and sensitivity in understanding users
and (2) relevancy and inclusiveness in providing information. To handle these
trade-offs, we propose a reversed approach that starts from designing
information and inclusive tailoring that considers unspecified needs,
respectively.


title:
The Diamond (111) Surface Reconstruction and Epitaxial Graphene
  Interface

abstract:
  The evolution of the diamond (111) surface as it undergoes reconstruction and
subsequent graphene formation is investigated with angle-resolved photoemission
spectroscopy, low energy electron diffraction, and complementary density
functional theory calculations. The process is examined starting at the
C(111)-(2x1) surface reconstruction that occurs following detachment of the
surface adatoms at 920 {\deg}C, and continues through to the liberation of the
reconstructed surface atoms into a free-standing monolayer of epitaxial
graphene at temperatures above 1000 {\deg}C. Our results show that the
C(111)-(2x1) surface is metallic as it has electronic states that intersect the
Fermi-level. This is in strong agreement with a symmetrically {\pi}-bonded
chain model and should contribute to resolving the controversies that exist in
the literature surrounding the electronic nature of this surface. The graphene
formed at higher temperatures exists above a newly formed C(111)-(2\times1)
surface and appears to have little substrate interaction as the Dirac-point is
observed at the Fermi-level. Finally, we demonstrate that it is possible to
hydrogen terminate the underlying diamond surface by means of plasma processing
without removing the graphene layer, forming a graphene-semiconductor
interface. This could have particular relevance for doping the graphene formed
on the diamond (111)surface via tuneable substrate interactions as a result of
changing the terminating species at the diamond-graphene interface by plasma
processing.


title:
The application of geographic information systems in schools around the
  world: a retrospective analysis

abstract:
  The article is devoted to the problem of incorporation geographic information
systems (GIS) in world school practice. The authors single out the stages of
GIS application in school geographical education based on the retrospective
analysis of the scientific literature. The first stage (late 70s - early 90s of
the 20th century) is the beginning of the first educational GIS programs and
partnership agreements between schools and universities. The second stage (mid
90s of the 20th century - the beginning of the 21st century) comprises the
distribution of GIS-educational programs in European and Australian schools
with the involvement of leading developers of GIS-packages (ESRI, Intergraph,
MapInfo Corp., etc.). The third stage (2005-2012) marks the spread of the GIS
school education in Eastern Europe, Asia, Africa and Latin America; on the
fourth stage (from 2012 to the present) geographic information systems emerge
in school curricula in most countries. The haracteristics of the
GIS-technologies development stages are given considering the GIS didactic
possibilities for the study of school geography, as well as highlighting their
advantages and disadvantages.


title: Gaussian Mixture Convolution Networks

abstract:
  This paper proposes a novel method for deep learning based on the analytical
convolution of multidimensional Gaussian mixtures. In contrast to tensors,
these do not suffer from the curse of dimensionality and allow for a compact
representation, as data is only stored where details exist. Convolution kernels
and data are Gaussian mixtures with unconstrained weights, positions, and
covariance matrices. Similar to discrete convolutional networks, each
convolution step produces several feature channels, represented by independent
Gaussian mixtures. Since traditional transfer functions like ReLUs do not
produce Gaussian mixtures, we propose using a fitting of these functions
instead. This fitting step also acts as a pooling layer if the number of
Gaussian components is reduced appropriately. We demonstrate that networks
based on this architecture reach competitive accuracy on Gaussian mixtures
fitted to the MNIST and ModelNet data sets.


title:
Study of visible light scintillations in liquid argon and its mixtures
  with methane

abstract:
  In liquid argon TPCs for dark matter search and neutrino detection
experiments, primary scintillations are used as a prompt signal of particle
scattering, being intensively produced in the vacuum ultraviolet (VUV) due to
excimer emission mechanism. On the other hand, there were indications on the
production of visible light scintillations in liquid argon, albeit at a much
lower intensity, the origin of which is still not clear. The closely related
issue is visible light scintillations in liquid argon doped with methane, the
interest in which is due to the possible use in neutron veto detectors for
those experiments. In this work we study in detail the properties of such
scintillations in pure liquid argon and its mixtures with methane. In
particular, the absolute photon yield of visible light scintillations in pure
liquid argon was measured to be about 200 and 90 photon/MeV for X-rays and
alpha particles respectively. In liquid argon doped with methane the photon
yield dropped down significantly, by about an order of magnitude at a methane
content varying from 0.01 to 1%, and then almost did not change when further
increasing the methane content up to 10%. There are firm indications that the
mechanism of visible light scintillations in liquid argon and its mixtures with
methane is that of neutral bremsstrahlung of primary ionization electrons.


title:
PerFED-GAN: Personalized Federated Learning via Generative Adversarial
  Networks

abstract:
  Federated learning is gaining popularity as a distributed machine learning
method that can be used to deploy AI-dependent IoT applications while
protecting client data privacy and security. Due to the differences of clients,
a single global model may not perform well on all clients, so the personalized
federated learning method, which trains a personalized model for each client
that better suits its individual needs, becomes a research hotspot. Most
personalized federated learning research, however, focuses on data
heterogeneity while ignoring the need for model architecture heterogeneity.
Most existing federated learning methods uniformly set the model architecture
of all clients participating in federated learning, which is inconvenient for
each client's individual model and local data distribution requirements, and
also increases the risk of client model leakage. This paper proposes a
federated learning method based on co-training and generative adversarial
networks(GANs) that allows each client to design its own model to participate
in federated learning training independently without sharing any model
architecture or parameter information with other clients or a center. In our
experiments, the proposed method outperforms the existing methods in mean test
accuracy by 42% when the client's model architecture and data distribution vary
significantly.


title: Terahertz control of photoluminescence emission in few-layer InSe

abstract:
  A promising route for the development of opto-elelctronic technology is to
use terahertz radiation to modulate the optical properties of semiconductors.
Here we demonstrate the dynamical control of photoluminescence (PL) emission in
few-layer InSe using picosecond terahertz pulses. We observe a strong PL
quenching (up to 50%) after the arrival of the terahertz pulse followed by a
reversible recovery of the emission on the time scale of 50ps at T =10K.
Microscopic calculations reveal that the origin of the photoluminescence
quenching is the terahertz absorption by photo-excited carriers: this leads to
a heating of the carriers and a broadening of their distribution, which reduces
the probability of bimolecular electron-hole recombination and, therefore, the
luminescence. By numerically evaluating the Boltzmann equation, we are able to
clarify the individual roles of optical and acoustic phonons in the subsequent
cooling process. The same PL quenchingmechanismis expected in other van
derWaals semiconductors and the effectwill be particularly strong for materials
with low carrier masses and long carrier relaxation time, which is the case for
InSe. This work gives a solid background for the development of opto-electronic
applications based on InSe, such as THz detectors and optical modulators.


title:
Tackling A Class of Hard Subset-Sum Problems: Integration of Lattice
  Attacks with Disaggregation Techniques

abstract:
  Subset-sum problems belong to the NP class and play an important role in both
complexity theory and knapsack-based cryptosystems, which have been proved in
the literature to become hardest when the so-called density approaches one.
Lattice attacks, which are acknowledged in the literature as the most effective
methods, fail occasionally even when the number of unknown variables is of
medium size. In this paper we propose a modular disaggregation technique and a
simplified lattice formulation based on which two lattice attack algorithms are
further designed. We introduce the new concept "jump points" in our
disaggregation technique, and derive inequality conditions to identify superior
jump points which can more easily cut-off non-desirable short integer
solutions. Empirical tests have been conducted to show that integrating the
disaggregation technique with lattice attacks can effectively raise success
ratios to 100% for randomly generated problems with density one and of
dimensions up to 100. Finally, statistical regressions are conducted to test
significant features, thus revealing reasonable factors behind the empirical
success of our algorithms and techniques proposed in this paper.


title: Description of random fields by systems of conditional distributions

abstract:
  In this paper, we consider the direct and inverse problems of the description
of random fields by various systems of finite-dimensional (as well as
one-point) probability distributions parameterized by boundary conditions. In
the majority of cases, we provide necessary and sufficient conditions for the
system to be a conditional distribution of a (unique) random field. The
exception is Dobrushin-type systems for which only sufficient conditions are
known. Also, we discuss the possible applications of the considered systems.


title:
Retarded room temperature Hamaker coefficients between bulk elemental
  metals

abstract:
  The exact Lifshitz theory is utilized for the systematic calculation of the
retarded room temperature Hamaker coefficients between 26 identical isotropic
elemental metals embedded in vacuum or pure water. The full spectral method,
complemented with a Drude model low frequency extrapolation, is employed for
metals with input from extended-in-frequency dielectric data, while the simple
spectral method is employed for water with input from three dielectric
representations. The accuracy of common Lifshitz approximations is quantified.
A novel compact semi-empirical expression is proposed for the
separation-dependence of the Hamaker coefficient that is demonstrated to be
very accurate for all combinations.


title:
Analysis of Complex Survival Data: a tutorial using the Shiny MSM.app
  application

abstract:
  The development of applications for obtaining interpretable results in a
simple and summarized manner in multi-state models is a research field with
great potential, namely in terms of using open source tools that can be easily
implemented in biomedical applications. In this tutorial, we introduce MSM.app,
an interactive web application using the Shiny package for the R language. In
the following sections, we present the main functionalities of the MSM.app and
an explanation of the outputs obtained for better understanding, independent of
the statistical knowledge of users.


title:
The use of AR elements in the study of foreign languages at the
  university

abstract:
  The article deals with the analysis of the impact of the using AR technology
in the study of a foreign language by university students. It is stated out
that AR technology can be a good tool for learning a foreign language. The use
of elements of AR in the course of studying a foreign language, in particular
in the form of virtual excursions, is proposed. Advantages of using AR
technology in the study of the German language are identified, namely: the
possibility of involvement of different channels of information perception, the
integrity of the representation of the studied object, the faster and better
memorization of new vocabulary, the development of communicative foreign
language skills. The ease and accessibility of using QR codes to obtain
information about the object of study from open Internet sources is shown. The
results of a survey of students after virtual tours are presented. A
reorientation of methodological support for the study of a foreign language at
universities is proposed. Attention is drawn to the use of AR elements in order
to support students with different learning styles (audio, visual,
kinesthetic).


title:
Quantum network security dependent on connection density between trusted
  nodes

abstract:
  In this paper we estimate how introduction of additional connections between
trusted nodes through one, two and so on (i.e. connection density) to a quantum
network with serial connection of trusted nodes affects its security. We
provide proper scaling of failure probability of authentication and quantum key
distribution protocols to the level of the whole quantum network. Expressions
of the failure probability dependent on the total number of connected nodes
between users and connection density for given mean failure probability of each
element are derived. The result provides explicit trade-off between increase of
the key transport security and consequent increase of spent resources. We
believe that obtained result may be useful for both design of future network
and optimization of existing ones.


title: Selection Strategies for Commonsense Knowledge

abstract:
  Selection strategies are broadly used in first-order logic theorem proving to
select those parts of a large knowledge base that are necessary to proof a
theorem at hand. Usually, these selection strategies do not take the meaning of
symbol names into account. In knowledge bases with commonsense knowledge,
symbol names are usually chosen to have a meaning and this meaning provides
valuable information for selection strategies. We introduce the vector-based
selection strategy, a purely statistical selection technique for commonsense
knowledge based on word embeddings. We compare different commonsense knowledge
selection techniques for the purpose of theorem proving and demonstrate the
usefulness of vector-based selection with a case study.


title:
Triangulating Instrumental Variable, confounder adjustment and
  Difference-in-Difference methods for comparative effectiveness research in
  observational data

abstract:
  Observational studies can play a useful role in assessing the comparative
effectiveness of competing treatments. In a clinical trial the randomization of
participants to treatment and control groups generally results in well-balanced
groups with respect to possible confounders, which makes the analysis
straightforward. However, when analysing observational data, the potential for
unmeasured confounding makes comparing treatment effects much more challenging.
Causal inference methods such as the Instrumental Variable and Prior Even Rate
Ratio approaches make it possible to circumvent the need to adjust for
confounding factors that have not been measured in the data or measured with
error. Direct confounder adjustment via multivariable regression and Propensity
score matching also have considerable utility. Each method relies on a
different set of assumptions and leverages different data. In this paper, we
describe the assumptions of each method and assess the impact of violating
these assumptions in a simulation study. We propose the prior outcome augmented
Instrumental Variable method that leverages data from before and after
treatment initiation, and is robust to the violation of key assumptions.
Finally, we propose the use of a heterogeneity statistic to decide two or more
estimates are statistically similar, taking into account their correlation. We
illustrate our causal framework to assess the risk of genital infection in
patients prescribed Sodium-glucose Co-transporter-2 inhibitors versus
Dipeptidyl Peptidase-4 inhibitors as second-line treatment for type 2 diabets
using observational data from the Clinical Practice Research Datalink.


title: Quotient graphs of symmetrically rigid frameworks

abstract:
  A natural problem in combinatorial rigidity theory concerns the determination
of the rigidity or flexibility of bar-joint frameworks in $\mathbb{R}^d$ that
admit some non-trivial symmetry. When $d=2$ there is a large literature on this
topic. In particular, it is typical to quotient the symmetric graph by the
group and analyse the rigidity of symmetric, but otherwise generic frameworks,
using the combinatorial structure of the appropriate group-labelled quotient
graph. However, mirroring the situation for generic rigidity, little is known
combinatorially when $d\geq 3$. Nevertheless in the periodic case, a key result
of Borcea and Streinu characterises when a quotient graph can be lifted to a
rigid periodic framework in $\mathbb{R}^d$. We develop an analogous theory for
symmetric frameworks in $\mathbb{R}^d$. The results obtained apply to all
finite and infinite 2-dimensional point groups, and then in arbitrary dimension
they concern a wide range of infinite point groups, sufficiently large finite
groups and groups containing translations and rotations. For the case of finite
groups we also derive results concerning the probability of assigning group
labels to a quotient graph so that the resulting lift is symmetrically rigid in
$\mathbb{R}^d$.


title:
Evaluating the Construct Validity of Text Embeddings with Application to
  Survey Questions

abstract:
  Text embedding models from Natural Language Processing can map text data
(e.g. words, sentences, documents) to supposedly meaningful numerical
representations (a.k.a. text embeddings). While such models are increasingly
applied in social science research, one important issue is often not addressed:
the extent to which these embeddings are valid representations of constructs
relevant for social science research. We therefore propose the use of the
classic construct validity framework to evaluate the validity of text
embeddings. We show how this framework can be adapted to the opaque and
high-dimensional nature of text embeddings, with application to survey
questions. We include several popular text embedding methods (e.g. fastText,
GloVe, BERT, Sentence-BERT, Universal Sentence Encoder) in our construct
validity analyses. We find evidence of convergent and discriminant validity in
some cases. We also show that embeddings can be used to predict respondent's
answers to completely new survey questions. Furthermore, BERT-based embedding
techniques and the Universal Sentence Encoder provide more valid
representations of survey questions than do others. Our results thus highlight
the necessity to examine the construct validity of text embeddings before
deploying them in social science research.


title:
Domain Adaptation of low-resource Target-Domain models using
  well-trained ASR Conformer Models

abstract:
  In this paper, we investigate domain adaptation for low-resource Automatic
Speech Recognition (ASR) of target-domain data, when a well-trained ASR model
trained with a large dataset is available. We argue that in the encoder-decoder
framework, the decoder of the well-trained ASR model is largely tuned towards
the source-domain, hurting the performance of target-domain models in vanilla
transfer-learning. On the other hand, the encoder layers of the well-trained
ASR model mostly capture the acoustic characteristics. We, therefore, propose
to use the embeddings tapped from these encoder layers as features for a
downstream Conformer target-domain model and show that they provide significant
improvements. We do ablation studies on which encoder layer is optimal to tap
the embeddings, as well as the effect of freezing or updating the well-trained
ASR model's encoder layers. We further show that applying Spectral Augmentation
(SpecAug) on the proposed features (this is in addition to default SpecAug on
input spectral features) provides a further improvement on the target-domain
performance. For the LibriSpeech-100-clean data as target-domain and SPGI-5000
as a well-trained model, we get 30% relative improvement over baseline.
Similarly, with WSJ data as target-domain and LibriSpeech-960 as a well-trained
model, we get 50% relative improvement over baseline.


title: Preferential Sampling for Bivariate Spatial Data

abstract:
  Preferential sampling provides a formal modeling specification to capture the
effect of bias in a set of sampling locations on inference when a
geostatistical model is used to explain observed responses at the sampled
locations. In particular, it enables modification of spatial prediction
adjusted for the bias. Its original presentation in the literature addressed
assessment of the presence of such sampling bias while follow on work focused
on regression specification to improve spatial interpolation under such bias.
All of the work in the literature to date considers the case of a univariate
response variable at each location, either continuous or modeled through a
latent continuous variable. The contribution here is to extend the notion of
preferential sampling to the case of bivariate response at each location. This
exposes sampling scenarios where both responses are observed at a given
location as well as scenarios where, for some locations, only one of the
responses is recorded. That is, there may be different sampling bias for one
response than for the other. It leads to assessing the impact of such bias on
co-kriging. It also exposes the possibility that preferential sampling can bias
inference regarding dependence between responses at a location. We develop the
idea of bivariate preferential sampling through various model specifications
and illustrate the effect of these specifications on prediction and dependence
behavior. We do this both through simulation examples as well as with a
forestry dataset that provides mean diameter at breast height (MDBH) and trees
per hectare (TPH) as the point-referenced bivariate responses.


title: Colored HOMFLY-PT polynomials of quasi-alternating $3$-braid knots

abstract:
  Obtaining a closed-form expression for the colored HOMFLY-PT polynomials of
knots from $3$-strand braids carrying arbitrary $SU(N)$ representation is a
challenging problem. In this paper, we confine our interest to twisted
generalized hybrid weaving knots which we denote hereafter by
$\hat{Q}_3(m_1,-m_2,n,\ell)$. This family of knots not only generalizes the
well-known class of weaving knots but also contains an infinite family of
quasi-alternating knots. Interestingly, we obtain a closed-form expression for
the HOMFLY-PT polynomial of $\hat{Q}_3(m_1,-m_2,n,\ell)$ using a modified
version of the Reshitikhin-Turaev method. In addition, we compute the exact
coefficients of the Jones polynomials and the Alexander polynomials of
quasi-alternating knots $\hat{Q}_3(1,-1,n,\pm 1)$. For these homologically-thin
knots, such coefficients are known to be the ranks of their Khovanov and link
Floer homologies, respectively. We also show that the asymptotic behaviour of
the coefficients of the Alexander polynomial is trapezoidal. On the other hand,
we compute the $[r]$-colored HOMFLY-PT polynomials of quasi alternating knots
for small values of $r$. Remarkably, the study of the determinants of certain
twisted weaving knots leads to establish a connection with enumerative geometry
related to $m^{th}$ Lucas numbers, denoted hereafter as $L_{m,2n}$. At the end,
we verify that the reformulated invariants satisfy Ooguri-Vafa conjecture and
we express certain BPS integers in terms of hyper-geometric functions ${}_2
{\bf F}_1\left[a,b, c;z\right]$.


title: Astrophysical sources and acceleration mechanisms

abstract:
  Multi-messenger astronomy provides for the observation of the same
astronomical event with different kind of telescopes at the same time: optical
observations, X-rays, gamma-ray bursts, neutrinos and, most recently,
gravitational waves are just few examples of the several points of view from
which an astronomical event can be observed and analyzed. Cosmic rays play an
important role in multi-messenger astronomy and, for this reason, it is
important to deepen the study of their sources and to understand the mechanisms
behind their acceleration in astronomical environments.


title:
Linearization and Identification of Multiple-Attractors Dynamical System
  through Laplacian Eigenmaps

abstract:
  Dynamical Systems (DS) are fundamental to the modeling and understanding of
time evolving phenomena, and find application in physics, biology and control.
As determining an analytical description of the dynamics is often difficult,
data-driven approaches are preferred for identifying and controlling nonlinear
DS with multiple equilibrium points. Identification of such DS has been treated
largely as a supervised learning problem. Instead, we focus on a unsupervised
learning scenario where we know neither the number nor the type of dynamics. We
propose a Graph-based spectral clustering method that takes advantage of a
velocity-augmented kernel to connect data-points belonging to the same
dynamics, while preserving the natural temporal evolution. We study the
eigenvectors and eigenvalues of the Graph Laplacian and show that they form a
set of orthogonal embedding spaces, one for each sub-dynamics. We prove that
there always exist a set of 2-dimensional embedding spaces in which the
sub-dynamics are linear, and n-dimensional embedding where they are
quasi-linear. We compare the clustering performance of our algorithm to Kernel
K-Means, Spectral Clustering and Gaussian Mixtures and show that, even when
these algorithms are provided with the true number of sub-dynamics, they fail
to cluster them correctly. We learn a diffeomorphism from the Laplacian
embedding space to the original space and show that the Laplacian embedding
leads to good reconstruction accuracy and a faster training time through an
exponential decaying loss, compared to the state of the art
diffeomorphism-based approaches.


title: Enumeration of corner polyhedra and 3-connected Schnyder labelings

abstract:
  We show that corner polyhedra and 3-connected Schnyder labelings join the
growing list of planar structures that can be set in exact correspondence with
(weighted) models of quadrant walks via a bijection due to Kenyon, Miller,
Sheffield and Wilson.
  Our approach leads to a first polynomial time algorithm to count these
structures, and to the determination of their exact asymptotic growth
constants: the number $p_n$ of corner polyhedra and $s_n$ of 3-connected
Schnyder woods of size $n$ respectively satisfy $(p_n)^{1/n}\to 9/2$ and
$(s_n)^{1/n}\to 16/3$ as $n$ goes to infinity.
  While the growth rates are rational, like in the case of previously known
instances of such correspondences, the exponent of the asymptotic polynomial
correction to the exponential growth does not appear to follow from the now
standard Denisov-Wachtel approach, due to a bimodal behavior of the step set of
the underlying tandem walk. However a heuristic argument suggests that these
exponents are $-1-\pi/\arccos(9/16)\approx -4.23$ for $p_n$ and
$-1-\pi/\arccos(22/27)\approx -6.08$ for $s_n$, which would imply that the
associated series are not D-finite.


title: Comparing machine learning techniques for predicting glassy dynamics

abstract:
  In the quest to understand how structure and dynamics are connected in
glasses, a number of machine learning based methods have been developed that
predict dynamics in supercooled liquids. These methods include both
increasingly complex machine learning techniques, and increasingly
sophisticated descriptors used to describe the environment around particles. In
many cases, both the chosen machine learning technique and choice of structural
descriptors are varied simultaneously, making it hard to quantitatively compare
the performance of different machine learning approaches. Here, we use three
different machine learning algorithms -- linear regression, neural networks,
and GNNs -- to predict the dynamic propensity of a glassy binary hard-sphere
mixture using as structural input a recursive set of order parameters recently
introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we
show, when these advanced descriptors are used, all three methods predict the
dynamics with nearly equal accuracy. However, the linear regression is orders
of magnitude faster to train making it by far the method of choice.


title: Foliations and stable maps

abstract:
  This paper is part of an ongoing series of works on the study of foliations
on algebraic varieties via derived algebraic geometry. We focus here on the
specific case of globally defined vector fields and the global behaviour of
their algebraic integral curves. For a smooth and proper variety $X$ with a
global vector field $\nu$, we consider the induced vector field $\nu_{g,n}$ on
the derived stack of stable maps, of genus $g$ with $n$ marked points, to $X$.
When $(g,n)$ is either $(0,2)$ or $(1,0)$, the derived stack of zeros of
$\nu_{g,n}$ defines a proper \emph{moduli of algebraic trajectories} of $\nu$.
When $(g,n)=(0,2)$ algebraic trajectories behave very much like rational
algebraic paths from one zero of $\nu$ to another, and in particular they can
be composed. This composition is represented by the usual gluing maps in
Gromov-Witten theory, and we use it give three categorical constructions, of
different categorical levels, related, in a certain sense, by
decategorification. In order to do this, in particular, we have to deal with
virtual fundamental classes of non-quasi-smooth derived stacks. When
$(g,n)=(1,0)$, zeros of $\nu_{1,0}$ might be thought as algebraic analogues of
periodic orbits of vector fields on smooth real manifolds. In particular, we
propose a Zeta function counting the zeros of $\nu_{1,0}$, that we like to
think of as an algebraic version of Ruelle's dynamical Zeta function. We
conclude the paper with a brief indication on how to extend these results to
the case of general one dimensional foliation $F$, by considering the derived
stack of $F$-equivariant stable maps.


title: A note on tempered measures

abstract:
  The relation between tempered distributions and measures is analysed and
clarified. While this is straightforward for positive measures, it is
surprisingly subtle for signed or complex measures.


title: Discrete Poisson hardcore 1D model and reinfections

abstract:
  Modeling reinfections in epidemics appeared of importance during the recent
stages of the Covid-19 pandemic (2021-22); they were frequent, especially due
to the Delta and Omicron strains. The classical Poisson distribution describes
reinfections without the impact of immunity. However, immunity is important
here, and a hardcore lattice version of the Poisson distribution is needed, a
discrete variant of the so-called Matern II process in 1D. Combinatorially,
segments of one or several different lengths (protective immunity intervals)
are placed in a bigger segment (the epidemic cycle) with spaces between them.
The role of the edges is significant: the duration of immunity is comparable
with that of the epidemic cycles. We provide exact formulas for the
corresponding distributions, the lattice one and its continuous limit.


title:
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate
  Heterogeneous Graph Neural Network

abstract:
  Heterogeneous Graph Neural Network (HGNN) has been successfully employed in
various tasks, but we cannot accurately know the importance of different design
dimensions of HGNNs due to diverse architectures and applied scenarios.
Besides, in the research community of HGNNs, implementing and evaluating
various tasks still need much human effort. To mitigate these issues, we first
propose a unified framework covering most HGNNs, consisting of three
components: heterogeneous linear transformation, heterogeneous graph
transformation, and heterogeneous message passing layer. Then we build a
platform Space4HGNN by defining a design space for HGNNs based on the unified
framework, which offers modularized components, reproducible implementations,
and standardized evaluation for HGNNs. Finally, we conduct experiments to
analyze the effect of different designs. With the insights found, we distill a
condensed design space and verify its effectiveness.


title:
Refined horoball counting and conformal measure for Kleinian group
  actions

abstract:
  Parabolic fixed points form a countable dense subset of the limit set of a
non-elementary geometrically finite Kleinian group with at least one parabolic
element. Given such a group, one may associate a standard set of pairwise
disjoint horoballs, each tangent to the boundary at a parabolic fixed point.
The diameter of such a horoball can be thought of as the `inverse cost' of
approximating an arbitrary point in the limit set by the associated parabolic
point. A result of Stratmann and Velani allows one to count horoballs of a
given size and, roughly speaking, for small $r>0$ there are $r^{-\delta}$ many
horoballs of size approximately $r$, where $\delta$ is the Poincar\'e exponent
of the group. We investigate localisations of this result, where we seek to
count horoballs of size approximately $r$ inside a given ball $B(z,R)$. Roughly
speaking, if $r \lesssim R^2$, then we obtain an analogue of the
Stratmann-Velani result (normalised by the Patterson-Sullivan measure of
$B(z,R)$). However, for larger values of $r$, the count depends in a subtle way
on $z$. Our counting results have several applications, especially to the
geometry of conformal measures supported on the limit set. For example, we
compute or estimate several `fractal dimensions' of certain $s$-conformal
measures for $s>\delta$ and use this to examine continuity properties of
$s$-conformal measures at $s=\delta$.


title:
Incorporating Texture Information into Dimensionality Reduction for
  High-Dimensional Images

abstract:
  High-dimensional imaging is becoming increasingly relevant in many fields
from astronomy and cultural heritage to systems biology. Visual exploration of
such high-dimensional data is commonly facilitated by dimensionality reduction.
However, common dimensionality reduction methods do not include spatial
information present in images, such as local texture features, into the
construction of low-dimensional embeddings. Consequently, exploration of such
data is typically split into a step focusing on the attribute space followed by
a step focusing on spatial information, or vice versa. In this paper, we
present a method for incorporating spatial neighborhood information into
distance-based dimensionality reduction methods, such as t-Distributed
Stochastic Neighbor Embedding (t-SNE). We achieve this by modifying the
distance measure between high-dimensional attribute vectors associated with
each pixel such that it takes the pixel's spatial neighborhood into account.
Based on a classification of different methods for comparing image patches, we
explore a number of different approaches. We compare these approaches from a
theoretical and experimental point of view. Finally, we illustrate the value of
the proposed methods by qualitative and quantitative evaluation on synthetic
data and two real-world use cases.


title: Conformal Surface Defects in Maxwell Theory are Trivial

abstract:
  We consider a free Maxwell field in four dimensions in the presence of a
codimension two defect. Reflection positive, codimension two defects which
preserve conformal symmetry in this context are very limited. We show only
generalized free fields can appear in the defect operator product expansion of
the bulk Maxwell field; in particular correlation functions of these defect
operators can be evaluated via Wick's Theorem.


title:
Nonleptonic two-body decays of $\Lambda_b\rightarrow \Lambda_c \pi,
  \Lambda_cK$ in the perturbative QCD approach

abstract:
  We study the color-allowed $\Lambda_b\rightarrow \Lambda_c \pi, \Lambda_cK$
decays in the perturbative QCD approach (PQCD) to lowest order in strong
coupling constant $\alpha_s$. Both the factorizable and nonfactorizable
contributions are taken into account in our calculations. It is found that
these processes are dominated by the factorizable contributions, while the
important nonfactorizable contributions can enhance the branching ratios by
about 30$\%$. The decay branching ratios are predicted to be
$\mathcal{B}(\Lambda_b\rightarrow \Lambda_c \pi)=6.7^{+4.1}_{-3.8}\times
10^{-3}$ and $\mathcal{B}(\Lambda_b\rightarrow \Lambda_c
K)=5.0^{+4.0}_{-3.0}\times 10^{-4}$, where the uncertainties arise from the
baryon light-cone distribution amplitudes (LCDAs), the heavy charm quark and
charmed baryon masses, and the hard scales. It is shown that the asymmetry
parameters in the two decays are approximately equal to -1, which is expected
as in the heavy quark limit and the soft meson limit. Our predictions are
consistent with the recent experimental data within errors. The obtained
numerical results are also compared to those in the other theoretical
approaches when they are available.


title:
Churn modeling of life insurance policies via statistical and machine
  learning methods -- Analysis of important features

abstract:
  Life assurance companies typically possess a wealth of data covering multiple
systems and databases. These data are often used for analyzing the past and for
describing the present. Taking account of the past, the future is mostly
forecasted by traditional statistical methods. So far, only a few attempts were
undertaken to perform estimations by means of machine learning approaches. In
this work, the individual contract cancellation behavior of customers within
two partial stocks is modeled by the aid of various classification methods.
Partial stocks of private pension and endowment policy are considered. We
describe the data used for the modeling, their structured and in which way they
are cleansed. The utilized models are calibrated on the basis of an extensive
tuning process, then graphically evaluated regarding their goodness-of-fit and
with the help of a variable relevance concept, we investigate which features
notably affect the individual contract cancellation behavior.


title:
Exploring the deep atmospheres of HD 209458b and WASP-43b using a
  non-gray general circulation model

abstract:
  Simulations with a 3D general circulation model (GCM) suggest that one
potential driver behind the observed radius inflation in hot Jupiters may be
the downward advection of energy from the highly irradiated photosphere into
the deeper layers. Here, we compare dynamical heat transport within the
non-inflated hot Jupiter WASP-43b and the canonical inflated hot Jupiter HD
209458b, with similar effective temperatures. We investigate to what extent the
radiatively driven heating and cooling in the photosphere (at pressures smaller
than 1 bar) influence the deeper temperature profile (at pressures between 1 to
700 bar). Our simulations with the new non-gray 3D radiation-hydrodynamical
model expeRT/MITgcm show that the deep temperature profile of WASP-43b is
associated with a relatively cold adiabat. The deep layers of HD 209458b,
however, do not converge and remain nearly unchanged regardless of whether a
cold or a hot initial state is used. Furthermore, we show that different flow
structures in the deep atmospheric layers arise. There, we find that WASP-43b
exhibits a deep equatorial jet, driven by the relatively fast tidally locked
rotation of this planet (0.81 days), as compared to HD 209458b (3.47 days).
However, by comparing simulations with different rotation periods, we find that
the resulting flow structures only marginally influence the temperature
evolution in the deep atmosphere, which is almost completely dominated by
radiative heating and cooling. Furthermore, we find that the evolution of
deeper layers can influence the 3D temperature structure in the photosphere of
WASP-43b. Thus, dayside emission spectra of WASP-43b may shed more light onto
the dynamical processes occurring at greater depths.


title:
Taylor expansions and Pad\'e approximants for cumulants of conserved
  charge fluctuations at non-vanishing chemical potentials

abstract:
  Using high statistics datasets generated in (2+1)-flavor QCD calculations at
finite temperature we present results for low order cumulants of net
baryon-number fluctuations at non-zero values of the baryon chemical potential.
We calculate Taylor expansions for the pressure (zeroth order cumulant), net
baryon-number density (first order cumulant) and the variance of the
distribution on net-baryon number fluctuations (second order cumulant). We
obtain series expansions from an eighth order expansion of the pressure and
compare these to diagonal Pad\'e approximants. This allows us to estimate the
range of values for the baryon chemical potential in which these expansions are
reliable. We find $\mu_B/T\le 2.5$, $2.0$ and $1.5$ for the zeroth, first and
second order cumulants, respectively. We furthermore, construct estimators for
the radius of convergence of the Taylor series of the pressure. In the vicinity
of the pseudo-critical temperature, $T_{pc}\simeq 156.5$ MeV, we find $\mu_B/T
\gtrsim\ 2.9$ at vanishing strangeness chemical potential and somewhat larger
values for strangeness neutral matter. These estimates are temperature
dependent and range from $\mu_B/T \gtrsim\ 2.2$ at $T=135$ MeV to $\mu_B/T\
\gtrsim\ 3.2$ at $T=165$ MeV. The estimated radius of convergences is the same
for any higher order cumulant.


title:
Identification, mass and age of primary red clump stars from spectral
  features derived with the LAMOST DR7

abstract:
  Although red clump (RC) stars are easy to identify due to their stability of
luminosity and color, about 20-50% are actually red giant branch (RGB) stars in
the same location on the HR diagram. In this paper, a sample of 210,504 spectra
for 184 318 primary RC (PRC) stars from the LAMOST DR7 is identified, which has
a purity of higher than 90 percent. The RC and the RGB stars are successfully
distinguished through LAMOST spectra(R~1800 and SNR>10) by adopting the XGBoost
ensemble learning algorithm, and the secondary RC stars are also removed. The
SHapley Additive exPlanations (SHAP) value is used to explain the top features
that the XGBoost model selected. The features are around Fe5270, MgH & MgIb,
Fe4957, Fe4207, Cr5208, and CN, which can successfully distinguish RGB and RC
stars. The XGBoost is also used to estimate the ages and masses of PRC stars by
training their spectra with Kepler labeled asteroseismic parameters. The
uncertainties of mass and age are 13 and 31 percent, respectively. Verifying
the feature attribution model, we find the age-sensitive elements XGBoost gets
are consistent with the literature. Distances of the PRC stars are derived by
$K_{S}$ absolute magnitude calibrated by Gaia EDR3, which has an uncertainty of
about 6 percent and shows the stars mainly locate at the Galactic disk. We also
test the XGBoost with R$\sim$250, which is the resolution of the Chinese Space
Station Telescope(CSST) under construction, it is still capable of finding
sensitive features to distinguish RC and RGB.


title:
Finding Machine-Learning Surrogates for Electronic Structures without
  Training

abstract:
  A myriad of phenomena in materials science and chemistry rely on
quantum-level simulations of the electronic structure in matter. While moving
to larger length and time scales has been a pressing issue for decades, such
large-scale electronic structure calculations are still challenging despite
modern software approaches and advances in high-performance computing. The
silver lining in this regard is the use of machine learning to accelerate
electronic structure calculations -- this line of research has recently gained
growing attention. The grand challenge therein is finding a suitable
machine-learning model during a process called hyperparameter optimization.
This, however, causes a massive computational overhead in addition to that of
data generation. We accelerate the construction of machine-learning surrogate
models by roughly two orders of magnitude by circumventing excessive training
during the hyperparameter optimization phase. We demonstrate our workflow for
Kohn-Sham density functional theory, the most popular computational method in
materials science and chemistry.


title: Categorical symmetries of T-duality

abstract:
  Topological T-duality correspondences are higher categorical objects that can
be classified by a strict Lie 2-group. In this article we compute the
categorical automorphism group of this 2-group; hence, the higher symmetries of
topological T-duality. We prove that the categorical automorphism group is a
non-central categorical extension of the integral split pseudo-orthogonal
group. We show that its splits over several subgroups, and that its k-invariant
is 2-torsion.


title:
Testing the boundaries: Normalizing Flows for higher dimensional data
  sets

abstract:
  Normalizing Flows (NFs) are emerging as a powerful class of generative
models, as they not only allow for efficient sampling, but also deliver, by
construction, density estimation. They are of great potential usage in High
Energy Physics (HEP), where complex high dimensional data and probability
distributions are everyday's meal. However, in order to fully leverage the
potential of NFs it is crucial to explore their robustness as data
dimensionality increases. Thus, in this contribution, we discuss the
performances of some of the most popular types of NFs on the market, on some
toy data sets with increasing number of dimensions.


title:
Task-oriented Scheduling for Networked Control Systems: An Age of
  Information-Aware Implementation on Software-defined Radios

abstract:
  Networked control systems (NCSs) are feedback control loops that are closed
over a communication network. Emerging applications, such as telerobotics,
drones and autonomous driving are the most prominent examples of such systems.
Regular and timely information sharing between the components of NCSs is
essential, as stale information can lead to performance degradation or even
physical damage. In this work, we consider multiple heterogeneous NCSs that
transmit their system state over a shared physical wireless channel towards a
gateway node. We conduct a comprehensive experimental study on selected MAC
protocols using software-defined radios with state-of-the-art (SotA) solutions
that have been designed to increase information freshness and control
performance. As a significant improvement over the SotA, we propose a
contention-free algorithm that is able to outperform the existing solutions by
combining their strengths in one protocol. In addition, we propose a new metric
called normalized mean squared error and demonstrate its effectiveness as
utility for scheduling in a case study with multiple inverted pendulums. From
our experimental study and results, we observe that a control-aware
prioritization of the sub-systems contributes to minimizing the negative
effects of information staleness on control performance. In particular, as the
number of devices increases, the benefit of control-awareness to the quality of
control stands out when compared to protocols that focus solely on maximizing
information freshness.


title: A bound on the dissociation number

abstract:
  The dissociation number ${\rm diss}(G)$ of a graph $G$ is the maximum order
of a set of vertices of $G$ inducing a subgraph that is of maximum degree at
most $1$. Computing the dissociation number of a given graph is algorithmically
hard even when restricted to subcubic bipartite graphs. For a graph $G$ with
$n$ vertices, $m$ edges, $k$ components, and $c_1$ induced cycles of length $1$
modulo $3$, we show ${\rm diss}(G)\geq n-\frac{1}{3}\Big(m+k+c_1\Big)$.
Furthermore, we characterize the extremal graphs in which every two cycles are
vertex-disjoint.


title: Heroes in orientations of chordal graphs

abstract:
  We characterize all digraphs $H$ such that orientations of chordal graphs
with no induced copy of $H$ have bounded dichromatic number.


title:
Continuous phase transition from a chiral spin state to collinear
  magnetic order in a zigzag chain with Kitaev interactions

abstract:
  Quantum spin systems can break time reversal symmetry by developing
spontaneous magnetization or spin chirality. However, collinear magnets and
chiral spin states are invariant under different symmetries, implying that the
order parameter of one phase vanishes in the other. We show how to construct
one-dimensional anisotropic spin models that exhibit a "Landau-forbidden"
continuous phase transition between such states. As a concrete example, we
focus on a zigzag chain with bond-dependent exchange and six-spin interactions.
Using a combination of exact solutions, effective field theories, and numerical
simulations, we show that the transition between the chiral and magnetic phases
has an emergent U(1) symmetry. The excitations governing the transition from
the chiral phase can be pictured as mobile defects in a $\mathbb Z_2$ flux
configuration which bind fermionic modes. We briefly discuss extensions to two
dimensions and analogies with deconfined quantum criticality. Our results
suggest new prospects for unconventional phase transitions involving chiral
spin states.


title:
Non-Abelian chiral spin liquid on spin-1 kagome lattice: truncation of
  an exact Hamiltonian and numerical optimization

abstract:
  We search for short-range Hamiltonians of finite spin-1 kagome systems,
maximizing the overlaps with lattice Moore-Read states. Our starting point is
an exact, long-range parent Hamiltonian for such a state on a finite plane,
obtained from conformal field theory. A truncation procedure is applied to it,
which retains only short-range terms and makes it easy to define the
Hamiltonian on a torus. Finally, the remaining coefficients are optimized, to
yield maximized overlaps between exact diagonalization results and model ground
states. In the best cases, these overlaps exceed 0.9 and 0.8 for the three
lowest states of 12- and 18-site systems, respectively, suggesting that the
obtained Hamiltonians are good parent Hamiltonians for a non-Abelian
topological order.


title: Circuit Extraction for ZX-diagrams can be #P-hard

abstract:
  The ZX-calculus is a graphical language for reasoning about quantum
computation using ZX-diagrams, a certain flexible generalisation of quantum
circuits that can be used to represent linear maps from $m$ to $n$ qubits for
any $m,n \geq 0$. Some applications for the ZX-calculus, such as quantum
circuit optimisation and synthesis, rely on being able to efficiently translate
a ZX-diagram back into a quantum circuit of comparable size. While several
sufficient conditions are known for describing families of ZX-diagrams that can
be efficiently transformed back into circuits, it has previously been
conjectured that the general problem of circuit extraction is hard. That is,
that it should not be possible to efficiently convert an arbitrary ZX-diagram
describing a unitary linear map into an equivalent quantum circuit. In this
paper we prove this conjecture by showing that the circuit extraction problem
is #P-hard, and so is itself at least as hard as strong simulation of quantum
circuits. In addition to our main hardness result, which relies specifically on
the circuit representation, we give a representation-agnostic hardness result.
Namely, we show that any oracle that takes as input a ZX-diagram description of
a unitary and produces samples of the output of the associated quantum
computation enables efficient probabilistic solutions to NP-complete problems.


title: A Review on Methods and Applications in Multimodal Deep Learning

abstract:
  Deep Learning has implemented a wide range of applications and has become
increasingly popular in recent years. The goal of multimodal deep learning
(MMDL) is to create models that can process and link information using various
modalities. Despite the extensive development made for unimodal learning, it
still cannot cover all the aspects of human learning. Multimodal learning helps
to understand and analyze better when various senses are engaged in the
processing of information. This paper focuses on multiple types of modalities,
i.e., image, video, text, audio, body gestures, facial expressions, and
physiological signals. Detailed analysis of the baseline approaches and an
in-depth study of recent advancements during the last five years (2017 to 2021)
in multimodal deep learning applications has been provided. A fine-grained
taxonomy of various multimodal deep learning methods is proposed, elaborating
on different applications in more depth. Lastly, main issues are highlighted
separately for each domain, along with their possible future research
directions.


title:
An Integrated Optimization and Machine Learning Models to Predict the
  Admission Status of Emergency Patients

abstract:
  This work proposes a framework for optimizing machine learning algorithms.
The practicality of the framework is illustrated using an important case study
from the healthcare domain, which is predicting the admission status of
emergency department (ED) patients (e.g., admitted vs. discharged) using
patient data at the time of triage. The proposed framework can mitigate the
crowding problem by proactively planning the patient boarding process. A large
retrospective dataset of patient records is obtained from the electronic health
record database of all ED visits over three years from three major locations of
a healthcare provider in the Midwest of the US. Three machine learning
algorithms are proposed: T-XGB, T-ADAB, and T-MLP. T-XGB integrates extreme
gradient boosting (XGB) and Tabu Search (TS), T-ADAB integrates Adaboost and
TS, and T-MLP integrates multi-layer perceptron (MLP) and TS. The proposed
algorithms are compared with the traditional algorithms: XGB, ADAB, and MLP, in
which their parameters are tunned using grid search. The three proposed
algorithms and the original ones are trained and tested using nine data groups
that are obtained from different feature selection methods. In other words, 54
models are developed. Performance was evaluated using five measures: Area under
the curve (AUC), sensitivity, specificity, F1, and accuracy. The results show
that the newly proposed algorithms resulted in high AUC and outperformed the
traditional algorithms. The T-ADAB performs the best among the newly developed
algorithms. The AUC, sensitivity, specificity, F1, and accuracy of the best
model are 95.4%, 99.3%, 91.4%, 95.2%, 97.2%, respectively.


title:
Synergism between B and Nb improves fire resistance in microalloyed
  steels

abstract:
  The development of new fire-resistant steels represents a challenge in
materials science and engineering of utmost importance. Alloying elements such
as Nb and Mo are generally used to improve the strength at both room- and
high-temperatures due to, for example, the formation of precipitates and harder
microconstituents. In this study we show alternatively that the addition of
small amounts of boron in Nb-microalloyed steels may play a crucial role in
maintaining the mechanical properties at high temperatures. The 66\,\%
yield-strength criteria for fire resistance is achieved at $\approx
574$\,{\deg}C for a boron steel, whereas without boron this value reaches
$\approx 460$\,{\deg}C, a remarkable boron-induced mechanical strengthening
enhancement. DFT calculations show that boron additions can lower the vacancy
formation energy when compared to pure ferrite and, for Nb-B steels, there is a
further 24\,\% reduction, suggesting that the boron-niobium combination acts as
an effective pinning-based strengthening agent.


title:
Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable
  Evaluation

abstract:
  Extracting pitch information from music recordings is a challenging but
important problem in music signal processing. Frame-wise transcription or
multi-pitch estimation aims for detecting the simultaneous activity of pitches
in polyphonic music recordings and has recently seen major improvements thanks
to deep-learning techniques, with a variety of proposed network architectures.
In this paper, we realize different architectures based on CNNs, the U-net
structure, and self-attention components. We propose several modifications to
these architectures including self-attention modules for skip connections,
recurrent layers to replace the self-attention, and a multi-task strategy with
simultaneous prediction of the degree of polyphony. We compare variants of
these architectures in different sizes for multi-pitch estimation, focusing on
Western classical music beyond the piano-solo scenario using the MusicNet and
Schubert Winterreise datasets. Our experiments indicate that most architectures
yield competitive results and that larger model variants seem to be beneficial.
However, we find that these results substantially depend on randomization
effects and the particular choice of the training-test split, which questions
the claim of superiority for particular architectures given only small
improvements. We therefore investigate the influence of dataset splits in the
presence of several movements of a work cycle (cross-version evaluation) and
propose a best-practice splitting strategy for MusicNet, which weakens the
influence of individual test tracks and suppresses overfitting to specific
works and recording conditions. A final evaluation on a mixed dataset suggests
that improvements on one specific dataset do not necessarily generalize to
other scenarios, thus emphasizing the need for further high-quality multi-pitch
datasets in order to reliably measure progress in music transcription tasks.


title: OKVIS2: Realtime Scalable Visual-Inertial SLAM with Loop Closure

abstract:
  Robust and accurate state estimation remains a challenge in robotics,
Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial Simultaneous
Localisation and Mapping (VI-SLAM) getting commoditised. Here, a full VI-SLAM
system is introduced that particularly addresses challenges around long as well
as repeated loop-closures. A series of experiments reveals that it achieves and
in part outperforms what state-of-the-art open-source systems achieve. At the
core of the algorithm sits the creation of pose-graph edges through
marginalisation of common observations, which can fluidly be turned back into
landmarks and observations upon loop-closure. The scheme contains a realtime
estimator optimising a bounded-size factor graph consisting of observations,
IMU pre-integral error terms, and pose-graph edges -- and it allows for
optimisation of larger loops re-using the same factor-graph asynchronously when
needed.


title:
Geometric representation of the weighted harmonic mean of $n$ positive
  values and potential uses

abstract:
  This paper is dedicated to the analysis and detailed study of a procedure to
generate both the weighted arithmetic and harmonic means of $n$ positive real
numbers. Together with this interpretation, we prove some relevant properties
that will allow us to define numerical approximation methods in several
dimensions adapted to discontinuities.


title: Automated galaxy-galaxy strong lens modelling: no lens left behind

abstract:
  The distribution of dark and luminous matter can be mapped around galaxies
that gravitationally lens background objects into arcs or Einstein rings. New
surveys will soon observe hundreds of thousands of galaxy lenses, and current,
labour-intensive analysis methods will not scale up to this challenge. We
instead develop a fully automatic, Bayesian method which we use to fit a sample
of 59 lenses imaged by the Hubble Space Telescope in uniform conditions. We set
out to \textit{leave no lens behind} and focus on ways in which automated fits
fail in a small handful of lenses, describing adjustments to the pipeline that
allows us to infer accurate lens models. Our pipeline ultimately fits {\em all}
59 lenses in our sample, with a high success rate key because catastrophic
outliers would bias large samples with small statistical errors. Machine
Learning techniques might further improve the two most difficult steps:
subtracting foreground lens light and initialising a first, approximate lens
model. After that, increasing model complexity is straightforward. We find a
mean $\sim1\%$ measurement precision on the measurement of the Einstein radius
across the lens sample which {\em does not degrade with redshift} up to at
least $z=0.7$ -- in stark contrast to other techniques used to study galaxy
evolution, like stellar dynamics. Our \texttt{PyAutoLens} software is open
source, and is also installed in the Science Data Centres of the ESA Euclid
mission.


title:
Classical simulation of quantum circuits with partial and graphical
  stabiliser decompositions

abstract:
  Recent developments in classical simulation of quantum circuits make use of
clever decompositions of chunks of magic states into sums of efficiently
simulable stabiliser states. We show here how, by considering certain
non-stabiliser entangled states which have more favourable decompositions, we
can speed up these simulations. This is made possible by using the ZX-calculus,
which allows us to easily find instances of these entangled states in the
simplified diagram representing the quantum circuit to be simulated. We
additionally find a new technique of partial stabiliser decompositions that
allow us to trade magic states for stabiliser terms. With this technique we
require only $2^{\alpha t}$ stabiliser terms, where $\alpha\approx 0.396$, to
simulate a circuit with T-count $t$. This matches the $\alpha$ found by Qassim
et al., but whereas they only get this scaling in the asymptotic limit, ours
applies for a circuit of any size. Our method builds upon a recently proposed
scheme for simulation combining stabiliser decompositions and optimisation
strategies implemented in the software QuiZX. With our techniques we manage to
reliably simulate 50-qubit 1400 T-count hidden shift circuits in a couple of
minutes on a consumer laptop.