title:
Quantifying extinction probabilities of endangered species for
  phylogenetic conservation prioritization may not be as sensitive as might be
  feared

abstract:
  In this study we are concerned with the general problem of choosing from a
set of endangered species T a subset S of k species to protect as a priority.
Here, the interest to protect the species of S is assessed by the resulting
expected phylogenetic diversity (ePD) of the set T, a widely used criterion for
measuring the expected amount of evolutionary history associated with T. We
consider that the survival of the protected species is assured and, on the
contrary, that there is a risk of extinction for the unprotected species. The
problem is easy to solve by a greedy type method if the extinction
probabilities of the unprotected species are known but these probabilities are
generally not easy to quantify. We show in this note that the choice of the
precise values attributed to the extinction probabilities-provided it respects
the rank of imperilment of each species-is not as decisive as might be feared
for the considered problem. The values of these probabilities have a clear
impact on the selection of the species to be protected but a little impact on
the resulting ePD. More precisely, if T1 and T2 are the two optimal subsets of
species corresponding to two scenarios (two different sets of probabilities)
the ePDs of T1 and T2 , calculated with the probabilities of the first
scenario-or with the probabilities of the second scenario-are not very
different.


title:
Thermal evolution of skyrmion formation mechanism in chiral multilayer
  films

abstract:
  Magnetic skyrmions form in chiral multilayers from the shrinking or fission
of elongated stripe textures. Here we report an experimental and theoretical
study of the temperature dependence of this stripe-to-skyrmion transition in
Co/Pt-based multilayers. Field-reversal magnetometry and Lorentz microscopy
experiments over 100 - 350 K establish the increased efficacy of
stripe-to-skyrmion fission at higher temperatures - driven primarily by the
thermal evolution of key magnetic interactions - thereby enhancing skyrmion
density. Atomistic calculations elucidate that the energy barrier to fission
governs the thermodynamics of the skyrmion formation. Our results establish a
mechanistic picture of the stripe-to-skyrmion transition and advance the use of
thermal knobs for efficient skyrmion generation.


title:
Bayesian Nonparametric Vector Autoregressive Models via a Logit
  Stick-breaking Prior: an Application to Child Obesity

abstract:
  Overweight and obesity in adults are known to be associated with risks of
metabolic and cardiovascular diseases. Because obesity is an epidemic,
increasingly affecting children, it is important to understand if this
condition persists from early life to childhood and if different patterns of
obesity growth can be detected. Our motivation starts from a study of obesity
over time in children from South Eastern Asia. Our main focus is on clustering
obesity patterns after adjusting for the effect of baseline information.
Specifically, we consider a joint model for height and weight patterns taken
every 6 months from birth. We propose a novel model that facilitates clustering
by combining a vector autoregressive sampling model with a dependent logit
stick-breaking prior. Simulation studies show the superiority of the model to
capture patterns, compared to other alternatives. We apply the model to the
motivating dataset, and discuss the main features of the detected clusters. We
also compare alternative models with ours in terms of predictive performances.


title: Efficient Fully Distributed Federated Learning with Adaptive Local Links

abstract:
  Nowadays, data-driven, machine and deep learning approaches have provided
unprecedented performance in various complex tasks, including image
classification and object detection, and in a variety of application areas,
like autonomous vehicles, medical imaging and wireless communications.
Traditionally, such approaches have been deployed, along with the involved
datasets, on standalone devices. Recently, a shift has been observed towards
the so-called Edge Machine Learning, in which centralized architectures are
adopted that allow multiple devices with local computational and storage
resources to collaborate with the assistance of a centralized server. The
well-known federated learning approach is able to utilize such architectures by
allowing the exchange of only parameters with the server, while keeping the
datasets private to each contributing device. In this work, we propose a fully
distributed, diffusion-based learning algorithm that does not require a central
server and propose an adaptive combination rule for the cooperation of the
devices. By adopting a classification task on the MNIST dataset, the efficacy
of the proposed algorithm over corresponding counterparts is demonstrated via
the reduction of the number of collaboration rounds required to achieve an
acceptable accuracy level in non- IID dataset scenarios.


title: On the non-archimedean Monge-Amp\`ere equation in mixed characteristic

abstract:
  Let X be a smooth projective variety over a complete discretely valued field
of mixed characteristic. We solve non-archimedean Monge-Amp\`ere equations on X
assuming resolution and embedded resolution of singularities and subadditivity
for +-test ideals on models of X. We follow the variational approach of
Boucksom, Favre, and Jonsson proving the continuity of the plurisubharmonic
envelope of a continuous metric on an ample line bundle on X. We replace the
use of multiplier ideals in equicharacteristic zero by the use of +-test ideals
introduced in mixed characteristic by Hacon, Lamarche, and Schwede.


title:
Higher differentiability results for solutions to a class of
  non-homogeneouns elliptic problems under sub-quadratic growth conditions

abstract:
  We prove a sharp higher differentiability result for local minimizers of
functionals of the form $$\mathcal{F}\left(w,\Omega\right)=\int_{\Omega}\left[
F\left(x,Dw(x)\right)-f(x)\cdot w(x)\right]dx$$ with non-autonomous integrand
$F(x,\xi)$ which is convex with respect to the gradient variable, under
$p$-growth conditions, with $1<p<2$. The main novelty here is that the results
are obtained assuming that the partial map $x\mapsto D_\xi F(x,\xi)$ has weak
derivatives in some Lebesgue space $L^q$ and the datum $f$ is assumed to belong
to a suitable Lebesgue space $L^r$.
  We also prove that it is possible to weaken the assumption on the datum $f$
and on the map $x\mapsto D_\xi F(x,\xi)$, if the minimizers are assumed to be a
priori bounded.


title: Critical points of degenerate polyconvex energies

abstract:
  We study critical and stationary, i.e. critical with respect to both inner
and outer variations, points of polyconvex functionals of the form $f(X) =
g(\det(X))$, for $X \in \mathbb{R}^{2\times 2}$. In particular, we show that
critical points $u \in Lip(\Omega,\mathbb{R}^2)$ with $\det(Du) \neq 0$ a.e.
have locally constant determinant except in a relatively closed set of measure
zero, and that stationary points have constant determinant almost everywhere.
This is deduced from a more general result concerning solutions $u \in
Lip(\Omega,\mathbb{R}^n)$, $\Omega \subset \mathbb{R}^n$ to the linearized
problem $curl(\beta Du) = 0$. We also present some generalization of the
original result to higher dimensions and assuming further regularity on
solutions $u$. Finally, we show that the differential inclusion associated to
stationarity with respect to polyconvex energies as above is rigid.


title:
Mining Latent Relationships among Clients: Peer-to-peer Federated
  Learning with Adaptive Neighbor Matching

abstract:
  In federated learning (FL), clients may have diverse objectives, merging all
clients' knowledge into one global model will cause negative transfers to local
performance. Thus, clustered FL is proposed to group similar clients into
clusters and maintain several global models. Nevertheless, current clustered FL
algorithms require the assumption of the number of clusters, they are not
effective enough to explore the latent relationships among clients. However, we
take advantage of peer-to-peer (P2P) FL, where clients communicate with
neighbors without a central server and propose an algorithm that enables
clients to form an effective communication topology in a decentralized manner
without assuming the number of clusters. Additionally, the P2P setting will
release the concerns caused by the central server in centralized FL, such as
reliability and communication bandwidth problems. In our method, 1) we present
two novel metrics for measuring client similarity, applicable under P2P
protocols; 2) we devise a two-stage algorithm, in the first stage, an efficient
method to enable clients to match same-cluster neighbors with high confidence
is proposed; 3) then in the second stage, a heuristic method based on
Expectation Maximization under the Gaussian Mixture Model assumption of
similarities is used for clients to discover more neighbors with similar
objectives. We make a theoretical analysis of how our work is superior to the
P2P FL counterpart and extensive experiments show that our method outperforms
all P2P FL baselines and has comparable or even superior performance to
centralized cluster FL. Moreover, results show that our method is much
effective in mining latent cluster relationships under various heterogeneity
without assuming the number of clusters and it is effective even under low
communication budgets.


title:
Geometrize and conquer: the Klein-Gordon and Dirac equations in Doubly
  Special Relativity

abstract:
  In this work we discuss the deformed relativistic wave equations, namely the
Klein--Gordon and Dirac equations in a Doubly Special Relativity scenario. We
employ what we call a geometric approach, based on the geometry of a curved
momentum space, which should be seen as complementary to the more spread
algebraic one. In this frame we are able to rederive well-known algebraic
expressions, as well as to treat yet unresolved issues, to wit, the explicit
relation between both equations, the discrete symmetries for Dirac particles,
the fate of covariance, and the formal definition of a Hilbert space for the
Klein--Gordon case.


title: A formal moment map on $\textrm{Diff}_0(M)$

abstract:
  In the framework of deformation quantization, we obtain a deformation of
Donaldson moment map on $\textrm{Diff}_0(M)$, the connected component of the
group of diffeomorphisms of a symplectic manifold $(M,\omega)$ admitting
another symplectic structure $\chi$.


title:
Electronic pair alignment and roton feature in the warm dense electron
  gas

abstract:
  The study of matter under extreme densities and temperatures as they occur
e.g. in astrophysical objects and nuclear fusion applications has emerged as
one of the most active frontiers in physics, material science, and related
disciplines. In this context, a key quantity is given by the dynamic structure
factor $S(\mathbf{q},\omega)$, which is probed in scattering experiments -- the
most widely used method of diagnostics at these extreme conditions. In addition
to its crucial importance for the study of warm dense matter, the modelling of
such dynamic properties of correlated quantum many-body systems constitutes one
of the most fundamental theoretical challenges of our time. Here we report a
hitherto unexplained \emph{roton feature} in $S(\mathbf{q},\omega)$ of the warm
dense electron gas, and introduce a microscopic explanation in terms of a new
\emph{electronic pair alignment} model. This new paradigm will be highly
important for the understanding of warm dense matter, and has a direct impact
on the interpretation of scattering experiments. Moreover, we expect our
results to give unprecedented insights into the dynamics of a number of
correlated quantum many-body systems such as ultracold helium, dipolar
supersolids, and bilayer heterostructures.


title:
Selected topics in diffraction with protons and nuclei: past, present,
  and future

abstract:
  We review a broad range of phenomena in diffraction in the context of
hadron-hadron, hadron-nucleus collisions and deep inelastic
lepton-proton/nucleus scattering focusing on the interplay between the
perturbative QCD and non-perturbative models. We discuss inclusive diffraction
in DIS, phenomenology of dipole models, resummation and parton saturation at
low $x$, hard diffractive production of vector mesons, inelastic diffraction in
hadron-hadron scattering, formalism of color fluctuations, inclusive coherent
and incoherent diffraction as well as soft and hard diffraction phenomena in
hadron-hadron/nucleus and photon-nucleus collisions. For each topic we review
key results from the past and present experiments including HERA and the LHC.
Finally, we identify the remaining open questions, which could be addressed in
the continuing experiments, in particular in photon-induced reactions at the
LHC and the future Electron-Ion Collider (EIC) in the US, Large Hadron electron
Collider (LHeC) and Future Circular Collider (FCC) at CERN.


title:
Cell segmentation from telecentric bright-field transmitted light
  microscopic images using a Residual Attention U-Net: a case study on HeLa
  line

abstract:
  Living cell segmentation from bright-field light microscopic images is
challenging due to the image complexity and temporal changes in the living
cells. Recently developed deep learning (DL)-based methods became popular in
medical and microscopic image segmentation tasks due to their success and
promising outcomes. The main objective of this paper is to develop a deep
learning, UNet-based method to segment the living cells of the HeLa line in
bright-field transmitted light microscopy. To find the most suitable
architecture for our datasets, we have proposed a residual attention U-Net and
compared it with an attention and a simple U-Net architecture. The attention
mechanism highlights the remarkable features and suppresses activations in the
irrelevant image regions. The residual mechanism overcomes with vanishing
gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524,
and 0.9530 for the simple, attention, and residual attention U-Net,
respectively. We achieved the most accurate semantic segmentation results in
the Mean-IoU and Dice metrics by applying the residual and attention mechanisms
together. The watershed method applied to this best - Residual Attention -
semantic segmentation result gave the segmentation with the specific
information for each cell.


title:
A hybrid multi-phase field model to describe cohesive failure in
  orthotropic materials, assessed by modeling failure mechanisms in wood

abstract:
  Fracture mechanics is crucial for many fields of engineering, as precisely
predicting failure of structures and parts is required for efficient designs.
The simulation of failure processes is, from a mechanical and a numerical point
of view, challenging, especially for inhomogeneous materials, where the
microstructure influences crack initiation and propagation and leads to complex
crack patterns. The phase field method for fracture is a promising approach to
encounter such materials since it is able to describe complex fracture
phenomena like crack kinking, branching and coalescence. Moreover, it is a
largely mesh independent approach, given that the mesh is homogenous around the
crack. However, the broadly used formulation of the phase field method is
limited to isotropic materials and does not account for preferable fracture
planes defined through the material's microstructure. In this work, the method
is expanded to take orthotropic constitutive behavior and preferable directions
of crack propagation into account. We show that by using a stress-based split
and multiple phase field variables with preferable fracture planes, in
combination with a hybrid phase field approach, a general framework can be
found for simulating anisotropic, inhomogeneous materials. The stress-based
split is based on fictitious crack faces and is, herein, expanded to
anisotropic materials. Furthermore, a novel hybrid approach is used, where the
degradation of the sound material is performed based on a smooth traction free
crack boundary condition, which proves to be the main driving factor for
recovering observed crack patterns. This is shown by means of a detailed
analysis of two examples: a wooden single edge notched plate and a wood board
with a single knot and complex fiber directions. In both cases, the proposed
novel hybrid phase field approach can realistically reproduce complex failure
modes.


title:
Efficient distributed matrix-free multigrid methods on locally refined
  meshes for FEM computations

abstract:
  This work studies three multigrid variants for matrix-free finite-element
computations on locally refined meshes: geometric local smoothing, geometric
global coarsening, and polynomial global coarsening. We have integrated the
algorithms into the same framework-the open-source finite-element library
deal.II-, which allows us to make fair comparisons regarding their
implementation complexity, computational efficiency, and parallel scalability
as well as to compare the measurements with theoretically derived performance
models. Serial simulations and parallel weak and strong scaling on up to
147,456 CPU cores on 3,072 compute nodes are presented. The results obtained
indicate that global coarsening algorithms show a better parallel behavior for
comparable smoothers due to the better load balance particularly on the
expensive fine levels. In the serial case, the costs of applying hanging-node
constraints might be significant, leading to advantages of local smoothing,
even though the number of solver iterations needed is slightly higher.


title: Weakly admissible locus and Newton stratification in p-adic Hodge theory

abstract:
  The basic admissible locus $\mathcal{F}(G, \mu, b)^a$ inside the flag variety
$\mathcal{F}(G, \mu)$, attached to a reductive group $G$ with a minuscule
cocharacter $\mu$ of $G$, is a $p$-adic analogue of the complex analytic period
spaces. It has an algebraic approximation $\mathcal{F}(G, \mu, b)^{wa}$ inside
the flag variety, called the weakly admissible locus. On the flag variety
$\mathcal{F}(G, \mu)$, we have the Newton stratification which has the
admissible locus as its unique open stratum. In this paper, we study the
relation between the Newton strata and the weakly admissible locus. We show
that $\mathcal{F}(G, \mu, b)^{wa}$ is maximal (in the sense that it's a union
of Newton strata) is equivalent to $(G, \mu)$ weakly fully HN-decomposable,
it's also equivalent to the condition that the Newton stratification is finer
than the Harder-Narasimhan stratification. These equivalent conditions are
generalizations of the fully HN-decomposable condition and the weakly
accessible condition. Moreover, we give a criterion to determine whether a
Newton stratum is completely contained in the weakly admissible locus involving
$G$-bundles as extensions of $M$-bundles over the Fargues-Fontaine curve, where
$M$ is a Levi subgroup of $G$. When $G=\mathrm{GL}_n$, we also give a
combinatorial inductive criterion to determine whether a vector bundle over the
Fargues-Fontaine curve is an extension of two given vector bundles.


title:
Effect of Render Resolution on Gameplay Experience, Performance, and
  Simulator Sickness in Virtual Reality Games

abstract:
  Higher resolution is one of the main directions and drivers in the
development of virtual reality (VR) head-mounted displays (HMDs). However,
given its associated higher cost, it is important to determine the benefits of
having higher resolution on user experience. For non-VR games, higher
resolution is often thought to lead to a better experience, but it is
unexplored in VR games. This research aims to investigate the resolution
tradeoff in gameplay experience, performance, and simulator sickness (SS) for
VR games, particularly first-person shooter (FPS) games. To this end, we
designed an experiment to collect gameplay experience, SS, and player
performance data with a popular VR FPS game, Half-Life: Alyx. Our results
indicate that 2K resolution is an important threshold for an enhanced gameplay
experience without affecting performance and increasing SS levels. Moreover,
the resolution from 1K to 4K has no significant difference in player
performance. Our results can inform game developers and players in determining
the type of HMD they want to use to balance the tradeoff between costs and
benefits and achieve a more optimal experience.


title:
Coded Caching and Spatial Multiplexing Gain Trade-off in Dynamic MISO
  Networks

abstract:
  The global caching gain of multi-antenna coded caching techniques can be also
mostly achieved in dynamic network setups, where the cache contents of users
are dictated by a central server, and each user can freely join or leave the
network at any moment. In the dynamic setup, users are assigned to a limited
set of caching profiles and the non-uniformness in the number of users assigned
to each profile is compensated during the delivery phase by either adding
phantom users for multicasting or serving a subset of users with unicast
transmissions. In this paper, we perform a thorough analysis and provide
closed-form representations of the achievable degrees of freedom (DoF) in such
hybrid schemes, and assess the inherent trade-off between the global caching
and spatial multiplexing gains caused by either adding phantom users or serving
parts of the data through unicasting.


title:
Active Intelligent Reflecting Surface Assisted Secure Air-to-Ground
  Communication with UAV Jittering

abstract:
  Unmanned Aerial Vehicles (UAV)-enabled communication is a promising solution
for secure air-to-ground (A2G) networks due to the additional secure degrees of
freedom afforded by mobility. However, the jittering characteristics caused by
the random airflow and the body vibration of the UAV itself have a
non-negligible impact on the performance of UAV communication. Considering the
impact of UAV jittering, this paper propose a robust and secure transmission
design assisted by an novel active intelligent reflecting surface (IRS),where
the reflecting elements in IRS not only adjust the phase shift but also amplify
the amplitude of signals. Specifically, under the worst-case secrecy rate
constraints, we aim to minimize the transmission power by the robust joint
design of active IRS's reflecting coefficient and beamforming at the UAV-borne
base station (UBS). However,it is challenging to solve the joint optimization
problem due to its non-convexity. To tackle this problem, the non-convex
problem is reformulated with linear approximation for the channel variations
and linear matrix inequality transformed by S-procedure and Schur's complement.
Then, we decouple this problem into two sub-problems, namely, passive
beamforming and active IRS's reflecting coefficient optimization, and solve
them through alternate optimization (AO). Finally, the numerical results
demonstrate the potential of active IRS on power saving under secure
transmission constraints and the impact of UAV jittering.


title:
Increasing the accuracy and resolution of precipitation forecasts using
  deep generative models

abstract:
  Accurately forecasting extreme rainfall is notoriously difficult, but is also
ever more crucial for society as climate change increases the frequency of such
extremes. Global numerical weather prediction models often fail to capture
extremes, and are produced at too low a resolution to be actionable, while
regional, high-resolution models are hugely expensive both in computation and
labour. In this paper we explore the use of deep generative models to
simultaneously correct and downscale (super-resolve) global ensemble forecasts
over the Continental US. Specifically, using fine-grained radar observations as
our ground truth, we train a conditional Generative Adversarial Network --
coined CorrectorGAN -- via a custom training procedure and augmented loss
function, to produce ensembles of high-resolution, bias-corrected forecasts
based on coarse, global precipitation forecasts in addition to other relevant
meteorological fields. Our model outperforms an interpolation baseline, as well
as super-resolution-only and CNN-based univariate methods, and approaches the
performance of an operational regional high-resolution model across an array of
established probabilistic metrics. Crucially, CorrectorGAN, once trained,
produces predictions in seconds on a single machine. These results raise
exciting questions about the necessity of regional models, and whether
data-driven downscaling and correction methods can be transferred to data-poor
regions that so far have had no access to high-resolution forecasts.


title: Input-specific Attention Subnetworks for Adversarial Detection

abstract:
  Self-attention heads are characteristic of Transformer models and have been
well studied for interpretability and pruning. In this work, we demonstrate an
altogether different utility of attention heads, namely for adversarial
detection. Specifically, we propose a method to construct input-specific
attention subnetworks (IAS) from which we extract three features to
discriminate between authentic and adversarial inputs. The resultant detector
significantly improves (by over 7.5%) the state-of-the-art adversarial
detection accuracy for the BERT encoder on 10 NLU datasets with 11 different
adversarial attack types. We also demonstrate that our method (a) is more
accurate for larger models which are likely to have more spurious correlations
and thus vulnerable to adversarial attack, and (b) performs well even with
modest training sets of adversarial examples.


title: NavDreams: Towards Camera-Only RL Navigation Among Humans

abstract:
  Autonomously navigating a robot in everyday crowded spaces requires solving
complex perception and planning challenges. When using only monocular image
sensor data as input, classical two-dimensional planning approaches cannot be
used. While images present a significant challenge when it comes to perception
and planning, they also allow capturing potentially important details, such as
complex geometry, body movement, and other visual cues. In order to
successfully solve the navigation task from only images, algorithms must be
able to model the scene and its dynamics using only this channel of
information. We investigate whether the world model concept, which has shown
state-of-the-art results for modeling and learning policies in Atari games as
well as promising results in 2D LiDAR-based crowd navigation, can also be
applied to the camera-based navigation problem. To this end, we create
simulated environments where a robot must navigate past static and moving
humans without colliding in order to reach its goal. We find that
state-of-the-art methods are able to achieve success in solving the navigation
problem, and can generate dream-like predictions of future image-sequences
which show consistent geometry and moving persons. We are also able to show
that policy performance in our high-fidelity sim2real simulation scenario
transfers to the real world by testing the policy on a real robot. We make our
simulator, models and experiments available at
https://github.com/danieldugas/NavDreams.


title:
Anti-Stokes Photoluminescence from CsPbBr3 Nanostructures Embedded in a
  Cs$_4$PbBr$_6$ Crystal

abstract:
  Lead halide perovskites possess high photoluminescence (PL) efficiency and
strong electron-phonon interactions, and therefore the optical cooling using
up-conversion PL has been expected. We investigate anti-Stokes PL from
green-luminescent Cs$_4$PbBr$_6$, whose origin is attributable to CsPbBr3
nanostructures embedded in a Cs$_4$PbBr$_6$ crystal. Because of the high
transparency, low refractive index, and high stability of Cs$_4$PbBr$_6$, the
green PL displays high external quantum efficiency without photo-degradation.
Time-resolved PL spectroscopy reveals the excitonic behaviors in recombination
process. The shape of the PL spectrum is almost independent of excitation
photon energy, which means that the spectral width is determined by homogeneous
broadening. We demonstrate that the phonon-assisted process dominates the
Urbach tail of optical absorption and anti-Stokes PL at room temperature.
Anti-Stokes PL is observed down to 70 K. We determine the temperature
dependence of the Urbach energy and estimate the strength of the
electron-phonon coupling. Our spectroscopic data show that CsPbBr3
nanostructures have potentially useful features for optical cooling.


title: Lane detection with Position Embedding

abstract:
  Recently, lane detection has made great progress in autonomous driving. RESA
(REcurrent Feature-Shift Aggregator) is based on image segmentation. It
presents a novel module to enrich lane feature after preliminary feature
extraction with an ordinary CNN. For Tusimple dataset, there is not too
complicated scene and lane has more prominent spatial features. On the basis of
RESA, we introduce the method of position embedding to enhance the spatial
features. The experimental results show that this method has achieved the best
accuracy 96.93% on Tusimple dataset.


title: TRAPUM discovery of thirteen new pulsars in NGC 1851 using MeerKAT

abstract:
  We report the discovery of 13 new pulsars in the globular cluster NGC 1851 by
the TRAPUM Large Survey Project using the MeerKAT radio telescope. The
discoveries consist of six isolated millisecond pulsars (MSPs) and seven binary
pulsars, of which six are MSPs and one is mildly recycled. For all the pulsars,
we present the basic kinematic, astrometric, and orbital parameters, where
applicable, as well as their polarimetric properties, when these are
measurable. Two of the binary MSPs (PSR J0514-4002D and PSR J0514-4002E) are in
wide and extremely eccentric (e > 0.7) orbits with a heavy white dwarf and a
neutron star as their companion, respectively. With these discoveries, NGC 1851
is now tied with M28 as the cluster with the third largest number of known
pulsars (14). Its pulsar population shows remarkable similarities with that of
M28, Terzan 5 and other clusters with comparable structural parameters. The
newly-found pulsars are all located in the innermost regions of NGC 1851 and
will likely enable, among other things, detailed studies of the cluster
structure and dynamics.


title: Koopman-Based Neural Lyapunov Functions for General Attractors

abstract:
  Koopman spectral theory has grown in the past decade as a powerful tool for
dynamical systems analysis and control. In this paper, we show how recent
data-driven techniques for estimating Koopman-Invariant subspaces with neural
networks can be leveraged to extract Lyapunov certificates for the underlying
system. In our work, we specifically focus on systems with a limit-cycle,
beyond just an isolated equilibrium point, and use Koopman eigenfunctions to
efficiently parameterize candidate Lyapunov functions to construct
forward-invariant sets under some (unknown) attractor dynamics. Additionally,
when the dynamics are polynomial and when neural networks are replaced by
polynomials as a choice of function approximators in our approach, one can
further leverage Sum-of-Squares programs and/or nonlinear programs to yield
provably correct Lyapunov certificates. In such a polynomial case, our
Koopman-based approach for constructing Lyapunov functions uses significantly
fewer decision variables compared to directly formulating and solving a
Sum-of-Squares optimization problem.


title: Domain-Generalized Textured Surface Anomaly Detection

abstract:
  Anomaly detection aims to identify abnormal data that deviates from the
normal ones, while typically requiring a sufficient amount of normal data to
train the model for performing this task. Despite the success of recent anomaly
detection methods, performing anomaly detection in an unseen domain remain a
challenging task. In this paper, we address the task of domain-generalized
textured surface anomaly detection. By observing normal and abnormal surface
data across multiple source domains, our model is expected to be generalized to
an unseen textured surface of interest, in which only a small number of normal
data can be observed during testing. Although with only image-level labels
observed in the training data, our patch-based meta-learning model exhibits
promising generalization ability: not only can it generalize to unseen image
domains, but it can also localize abnormal regions in the query image. Our
experiments verify that our model performs favorably against state-of-the-art
anomaly detection and domain generalization approaches in various settings.


title:
Magnetodipolar interaction and quasiparticles delocalization in
  disordered quantum magnets

abstract:
  It is well-known that disordered quantum magnets usually host localized
elementary excitations in gapped phases. Here we show that long-range
magnetodipolar interaction can lead to quasiparticles delocalization in the
three-dimensional case, similarly to the phenomenon previously described for
localized vibrational modes in disordered solids. Employing renormalization
group-like ideas, the average eigenmode occupation number is found to scale
with the system size, which also leads to sub-diffusive dynamical properties of
the system.


title: A combination between VQ and covariance matrices for speaker recognition

abstract:
  This paper presents a new algorithm for speaker recognition based on the
combination between the classical Vector Quantization (VQ) and Covariance
Matrix (CM) methods. The combined VQ-CM method improves the identification
rates of each method alone, with comparable computational burden. It offers a
straightforward procedure to obtain a model similar to GMM with full covariance
matrices. Experimental results also show that it is more robust against noise
than VQ or CM alone.


title:
On the existence of derivations as square roots of generators of
  state-symmetric quantum Markov semigroups

abstract:
  Cipriani and Sauvageot have shown that for any generator $L$ of a tracially
symmetric quantum Markov semigroup on a C*-algebra $\mathcal{A}$ there exists a
densely defined derivation $\delta$ from $\mathcal{A}$ to a Hilbert bimodule
$H$ such that $L=\delta^*\circ \bar{\delta}$. Here we show that this
construction of a derivation can in general not be generalised to quantum
Markov semigroups that are symmetric with respect to a non-tracial state. In
particular we show that all derivations to Hilbert bimodules can be assumed to
have a concrete form, and then we use this form to show that in the
finite-dimensional case the existence of such a derivation is equivalent to the
existence of a positive matrix solution of a system of linear equations. We
solve this system of linear equations for concrete examples using Mathematica
to complete the proof.


title: The SuSAv2 model for inelastic neutrino-nucleus scattering

abstract:
  The susperscaling model SuSAv2, already available for charged-current
neutrino-nucleus cross sections in the quasielastic region, is extended to the
full inelastic regime. In the model the resonance production and deep inelastic
reactions are described through the extension to the neutrino sector of the
SuSAv2 inelastic model developed for ($e,e'$) reactions, which combines
phenomenological structure functions with a nuclear scaling function. This work
also compares two different descriptions of the $\Delta$ resonance region, one
based on a global scaling function for the full inelastic spectrum and the
other on a semi-phenomenological $\Delta$ scaling function extracted from
($e,e'$) data for this specific region and updated with respect to previous
work. The results of the model are tested against ($e,e'$) data on $^{12}$C,
$^{16}$O, $^{40}$Ca and $^{40}$Ar and applied to the study of the charged
current inclusive neutrino cross-section on $^{12}$C and $^{40}$Ar measured by
the T2K, MicroBooNE, ArgoNEUT and MINERvA experiments, thus covering several
kinematical regions.


title: Optical Network Design for 4G LTE

abstract:
  The number of mobile users is increasing rapidly, 3GPP initiated a new
technology 4G Long Term Evolution (LTE). LTE is an enabling technology as a
solution to the problem of network capacity and quality in areas that have a
high demand like Sleman. The quality of LTE networks are supported by backbone
and distribution network. This paper describes the designing of fiber optic
network as the LTE backbone network in Sleman. The backbone network requires
G-652 optical cable along 85 km with a ring topology and WDM-STM64 technology.
The distribution network uses GPON technology and the type of G984 optical
cable along 61.35 km. The minimum of power received at the end-point in the
optisystem simulation is -25 dBm and -26 dBm through the calculation. This
value is acceptable, since it is above the minimum of power received (Receiver
Sensitivity) -28 dBm which refers to the standard parameters of G.984.2. The
average value of rise time is 69 ps, this value is still below the maximum
allowable value of rise time 70 ps. Average BER of backbone link is 5x10-4.


title:
Deep Channel Prediction: A DNN Framework for Receiver Design in
  Time-Varying Fading Channels

abstract:
  In time-varying fading channels, channel coefficients are estimated using
pilot symbols that are transmitted every coherence interval. For channels with
high Doppler spread, the rapid channel variations over time will require
considerable bandwidth for pilot transmission, leading to poor throughput. In
this paper, we propose a novel receiver architecture using deep recurrent
neural networks (RNNs) that learns the channel variations and thereby reduces
the number of pilot symbols required for channel estimation. Specifically, we
design and train an RNN to learn the correlation in the time-varying channel
and predict the channel coefficients into the future with good accuracy over a
wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training
methodology enables accurate channel prediction through the use of techniques
such as teacher-force training, early-stop, and reduction of learning rate on
plateau. Also, the robustness of prediction for different Dopplers and SNRs is
achieved by adapting the number of predictions into the future based on the
Doppler and SNR. Numerical results show that good bit error performance is
achieved by the proposed receiver in time-varying fading channels. We also
propose a data decision driven receiver architecture using RNNs that further
reduces the pilot overhead while maintaining good bit error performance.


title: Self-supervised HDR Imaging from Motion and Exposure Cues

abstract:
  Recent High Dynamic Range (HDR) techniques extend the capabilities of current
cameras where scenes with a wide range of illumination can not be accurately
captured with a single low-dynamic-range (LDR) image. This is generally
accomplished by capturing several LDR images with varying exposure values whose
information is then incorporated into a merged HDR image. While such approaches
work well for static scenes, dynamic scenes pose several challenges, mostly
related to the difficulty of finding reliable pixel correspondences.
Data-driven approaches tackle the problem by learning an end-to-end mapping
with paired LDR-HDR training data, but in practice generating such HDR
ground-truth labels for dynamic scenes is time-consuming and requires complex
procedures that assume control of certain dynamic elements of the scene (e.g.
actor pose) and repeatable lighting conditions (stop-motion capturing). In this
work, we propose a novel self-supervised approach for learnable HDR estimation
that alleviates the need for HDR ground-truth labels. We propose to leverage
the internal statistics of LDR images to create HDR pseudo-labels. We
separately exploit static and well-exposed parts of the input images, which in
conjunction with synthetic illumination clipping and motion augmentation
provide high quality training examples. Experimental results show that the HDR
models trained using our proposed self-supervision approach achieve performance
competitive with those trained under full supervision, and are to a large
extent superior to previous methods that equally do not require any
supervision.


title: Energy Efficient Placement of ML-Based Services in IoT Networks

abstract:
  The Internet of Things (IoT) is gaining momentum in its quest to bridge the
gap between the physical and the digital world. The main goal of the IoT is the
creation of smart environments and self-aware things that help to facilitate a
variety of services such as smart transport, climate monitoring, e-health, etc.
Huge volumes of data are expected to be collected by the connected
sensors/things, which in traditional cases are processed centrally by large
data centers in the core network that will inevitably lead to excessive
transportation power consumption as well as added latency overheads. Instead,
fog computing has been proposed by researchers from industry and academia to
extend the capability of the cloud right to the point where the data is
collected at the sensing layer. This way, primitive tasks that can be hosted in
IoT sensors do not need to be sent all the way to the cloud for processing. In
this paper we propose energy efficient embedding of machine learning (ML)
models over a cloud-fog network using a Mixed Integer Linear Programming (MILP)
optimization model. We exploit virtualization in our framework to provide
service abstraction of Deep Neural Networks (DNN) layers that can be composed
into a set of VMs interconnected by virtual links. We constrain the number of
VMs that can be processed at the IoT layer and study the impact on the
performance of the cloud fog approach.


title: Effective Actions for Regge Piecewise Flat Quantum Gravity

abstract:
  We review the construction of the path integral and the corresponding
effective action for the Regge formulation of General Relativity under the
assumption that the short-distance structure of the spacetime is not a smooth
4-manifold, but a picewise linear manifold based on a triangulation of a smooth
4-manifold. We point out that the exponentially damped 4-volume path-integral
measure does not give a finite path integral, although it can be used for the
construction of the perturbative effective action. We modify the 4-volume
measure by multiplying it by an inverse power of the product of the
edge-lengths such that the new measure gives a finite path integral while it
retains all the nice features of the unmodified measure.


title:
Wider or Deeper Neural Network Architecture for Acoustic Scene
  Classification with Mismatched Recording Devices

abstract:
  In this paper, we present a robust and low complexity system for Acoustic
Scene Classification (ASC), the task of identifying the scene of an audio
recording. We first construct an ASC baseline system in which a novel
inception-residual-based network architecture is proposed to deal with the
mismatched recording device issue. To further improve the performance but still
satisfy the low complexity model, we apply two techniques: ensemble of multiple
spectrograms and channel reduction on the ASC baseline system. By conducting
extensive experiments on the benchmark DCASE 2020 Task 1A Development dataset,
we achieve the best model performing an accuracy of 69.9% and a low complexity
of 2.4M trainable parameters, which is competitive to the state-of-the-art ASC
systems and potential for real-life applications on edge devices.


title:
Resource allocation optimization using artificial intelligence methods
  in various computing paradigms: A Review

abstract:
  With the advent of smart devices, the demand for various computational
paradigms such as the Internet of Things, fog, and cloud computing has
increased. However, effective resource allocation remains challenging in these
paradigms. This paper presents a comprehensive literature review on the
application of artificial intelligence (AI) methods such as deep learning (DL)
and machine learning (ML) for resource allocation optimization in computational
paradigms. To the best of our knowledge, there are no existing reviews on
AI-based resource allocation approaches in different computational paradigms.
The reviewed ML-based approaches are categorized as supervised and
reinforcement learning (RL). Moreover, DL-based approaches and their
combination with RL are surveyed. The review ends with a discussion on open
research directions and a conclusion.


title: Information conduction and convection in the Vicsek model

abstract:
  Physical interactions generally respect certain symmetries, such as
reciprocity and energy conservation, which survive in coarse grained isothermal
descriptions. Active many-body systems usually break such symmetries
intrinsically, on the particle level, so that their collective behavior is
often more naturally interpreted as a result of information exchange. Here, we
study numerically how information spreads from a "leader" particle through an
initially aligned flock, described by the Vicsek model. In the low-speed limit
of a static spin lattice, we find purely conductive spreading, reminiscent of
heat transfer. Swarm motility and heterogeneity can break reciprocity and spin
conservation. But what seems more consequential for the swarm response is that
the dispersion relation acquires a significant convective contribution along
the leader's direction of motion.


title: Charge order breaks time-reversal symmetry in CsV$_3$Sb$_5$

abstract:
  The recently discovered vanadium-based kagome metals $A$V$_{3}$Sb$_{5}$
($A$~=~K,~Rb,~Cs) exhibit superconductivity at low-temperatures and charge
density wave (CDW) order at high-temperatures. A prominent feature of the
charge ordered state in this family is that it breaks time-reversal symmetry
(TRSB), which is connected to the underlying topological nature of the band
structure. In this work, a powerful combination of zero-field and high-field
muon-spin rotation/relaxation is used to study the signatures of TRSB of the
charge order in CsV$_3$Sb$_5$, as well as its anisotropic character. By
tracking the temperature evolution of the in-plane and out-of-plane components
of the muon-spin polarization, an enhancement of the internal field width
sensed by the muon-spin ensemble was observed below $T_{\rm TRSB}=T_{\rm
CDW}\simeq95$~K. Additional increase of the internal field width, accompanied
by a change of the local field direction at the muon site from the $ab$-plane
to the $c$-axis, was detected below $T^\ast\simeq30$~K. Remarkably, this
two-step feature becomes well pronounced when a magnetic field of 8~T is
applied along the crystallographic $c-$axis, thus indicating a field-induced
enhancement of the electronic response at the CDW transition. These results
point to a TRSB in CsV$_3$Sb$_5$ by charge order with an onset of
${\simeq}~95$~K, followed by an enhanced electronic response below
${\simeq}~30$~K. The observed two-step transition is discussed within the
framework of different charge-order instabilities, which, in accordance with
density functional theory calculations, are nearly degenerate in energy.


title:
Trust and Reliance in XAI -- Distinguishing Between Attitudinal and
  Behavioral Measures

abstract:
  Trust is often cited as an essential criterion for the effective use and
real-world deployment of AI. Researchers argue that AI should be more
transparent to increase trust, making transparency one of the main goals of
XAI. Nevertheless, empirical research on this topic is inconclusive regarding
the effect of transparency on trust. An explanation for this ambiguity could be
that trust is operationalized differently within XAI. In this position paper,
we advocate for a clear distinction between behavioral (objective) measures of
reliance and attitudinal (subjective) measures of trust. However, researchers
sometimes appear to use behavioral measures when intending to capture trust,
although attitudinal measures would be more appropriate. Based on past
research, we emphasize that there are sound theoretical reasons to keep trust
and reliance separate. Properly distinguishing these two concepts provides a
more comprehensive understanding of how transparency affects trust and
reliance, benefiting future XAI research.


title:
Solving the Quispel-Roberts-Thompson maps using Kajiwara-Noumi-Yamada's
  representation of elliptic curves

abstract:
  It is well known that the dynamical system determined by a
Quispel-Roberts-Thompson map (a QRT map) preserves a pencil of biquadratic
polynomial curves on $CP^1 \times CP^1$ and that the system can be solved as a
translation on the elliptic fiber to which the initial point belongs. However,
this procedure is rather complicated to handle, especially in the normalization
process. In this paper, we present a method to construct the solution directly
in terms of the Weierstrass sigma function, using Kajiwara-Noumi-Yamada's
parametric representation of elliptic curves.


title:
Generalized Phase-Space Techniques to Explore Quantum Phase Transitions
  in Critical Quantum Spin Systems

abstract:
  We apply the generalized Wigner function formalism to detect and characterize
a range of quantum phase transitions in several cyclic, finite-length,
spin-$\frac{1}{2}$ one-dimensional spin-chain models, viz., the Ising and
anisotropic $XY$ models in a transverse field, and the $XXZ$ anisotropic
Heisenberg model. We make use of the finite system size to provide an
exhaustive exploration of each system's single-site, bipartite and
multi-partite correlation functions. In turn, we are able to demonstrate the
utility of phase-space techniques in witnessing and characterizing first-,
second- and infinite-order quantum phase transitions, while also enabling an
in-depth analysis of the correlations present within critical systems. We also
highlight the method's ability to capture other features of spin systems such
as ground-state factorization and critical system scaling. Finally, we
demonstrate the generalized Wigner function's utility for state verification by
determining the state of each system and their constituent sub-systems at
points of interest across the quantum phase transitions, enabling interesting
features of critical systems to be intuitively analyzed.


title: Autofocus for Event Cameras

abstract:
  Focus control (FC) is crucial for cameras to capture sharp images in
challenging real-world scenarios. The autofocus (AF) facilitates the FC by
automatically adjusting the focus settings. However, due to the lack of
effective AF methods for the recently introduced event cameras, their FC still
relies on naive AF like manual focus adjustments, leading to poor adaptation in
challenging real-world conditions. In particular, the inherent differences
between event and frame data in terms of sensing modality, noise, temporal
resolutions, etc., bring many challenges in designing an effective AF method
for event cameras. To address these challenges, we develop a novel event-based
autofocus framework consisting of an event-specific focus measure called event
rate (ER) and a robust search strategy called event-based golden search (EGS).
To verify the performance of our method, we have collected an event-based
autofocus dataset (EAD) containing well-synchronized frames, events, and focal
positions in a wide variety of challenging scenes with severe lighting and
motion conditions. The experiments on this dataset and additional real-world
scenarios demonstrated the superiority of our method over state-of-the-art
approaches in terms of efficiency and accuracy.


title: Ion-scale transition of plasma turbulence: Pressure-strain effect

abstract:
  We investigate properties of solar wind-like plasma turbulence using direct
numerical simulations. We analyze the transition from large,
magnetohydrodynamic (MHD) scales to the ion characteristic ones using
two-dimensional hybrid (fluid electrons, kinetic ions) simulations. To capture
and quantify turbulence properties, we apply the Karman-Howarth-Monin (KHM)
equation for compressible Hall MHD (extended by considering the plasma pressure
as a tensor quantity) to the numerical results. The KHM analysis indicates that
the transition from MHD to ion scales (the so called ion break in the power
spectrum) results from a combination of an onset of Hall physics and of an
effective dissipation owing to the pressure-strain energy-exchange channel and
resistivity. We discuss the simulation results in the context of the solar
wind.


title:
CollaChain: A BFT Collaborative Middleware for Decentralized
  Applications

abstract:
  The sharing economy is centralizing services, leading to misuses of the
Internet. We can list growing damages of data hacks, global outages and even
the use of data to manipulate their owners. Unfortunately, there is no
decentralized web where users can interact peer-to-peer in a secure way.
Blockchains incentivize participants to individually validate every transaction
and impose their block to the network. As a result, the validation of smart
contract requests is computationally intensive while the agreement on a unique
state does not make full use of the network. In this paper, we propose
Collachain, a new byzantine fault tolerant blockchain compatible with the
largest ecosystem of DApps that leverages collaboration. First, the
pariticipants executing smart contracts collaborate to validate the
transactions, hence halving the number of validations required by modern
blockchains (e.g., Ethereum, Libra). Second, the participants in the consensus
collaborate to combine their block proposal into a superblock, hence improving
throughput as the system grows to hundreds of nodes. In addition, Collachain
offers the possibility to its users to interact securely with each other
without downloading the blockchain, hence allowing interactions via mobile
devices. Collachain is effective at outperforming the Concord and Quorum
blockchains and its throughput peaks at 4500 TPS under a Twitter DApp
(Decentralized Application) workload. Finally, we demonstrate Collachain's
scalability by deploying it on 200 nodes located in 10 countries over 5
continents.


title: Proportional Budget Allocations: Towards a Systematization

abstract:
  We contribute to the programme of lifting proportionality axioms from the
multi-winner voting setting to participatory budgeting. We define novel
proportionality axioms for participatory budgeting and test them on known
proportionality-driven rules such as Phragm\'en and Rule X. We investigate
logical implications among old and new axioms and provide a systematic overview
of proportionality criteria in participatory budgeting.


title: Differential equations for the closed geometric crystal chains

abstract:
  We present two types of systems of differential equations that can be derived
from a set of discrete integrable systems which we call the closed geometric
crystal chains. One is a kind of extended Lotka-Volterra systems, and the other
seems to be generally new but reduces to a previously known system in a special
case. Both equations have Lax representations associated with what are known as
the loop elementary symmetric functions, which were originally introduced to
describe products of affine type A geometric crystals for symmetric tensor
representations. Examples of the derivations of the continuous time Lax
equations from a discrete time one are described in detail, where a novel
method of taking a continuum limit by assuming asymptotic behaviors of the
eigenvalues of the Lax matrix in Puiseux series expansions is used.


title:
A convergent SAV scheme for Cahn--Hilliard equations with dynamic
  boundary conditions

abstract:
  The Cahn-Hilliard equation is one of the most common models to describe phase
separation processes in mixtures of two materials. For a better description of
short-range interactions between the material and the boundary, various dynamic
boundary conditions for this equation have been proposed. Recently, a family of
models using Cahn-Hilliard-type equations on the boundary of the domain to
describe adsorption processes was analysed (cf. Knopf, Lam, Liu, Metzger,
ESAIM: Math. Model. Numer. Anal., 2021). This family of models includes the
case of instantaneous adsorption processes studied by Goldstein, Miranville,
and Schimperna (Physica D, 2011) as well as the case of vanishing adsorption
rates which was investigated by Liu and Wu (Arch. Ration. Mech. Anal., 2019).
In this paper, we are interested in the numerical treatment of these models and
propose an unconditionally stable, linear, fully discrete finite element scheme
based on the scalar auxiliary variable approach. Furthermore, we establish the
convergence of discrete solutions towards suitable weak solutions of the
original model. Thereby, when passing to the limit, we are able to remove the
auxiliary variables introduced in the discrete setting completely. Finally, we
present simulations based on the proposed linear scheme and compare them to
results obtained using a stable, non-linear scheme to underline the
practicality of our scheme.


title:
Analytical discrete ordinates for the three-dimensional radiative
  radiative equation in the half space

abstract:
  In one-dimensional transport theory, the method of analytical discrete
ordinates (ADO) is known to be a concise and fast numerical scheme to solve the
radiative transport equation. However, the extension of ADO to three dimensions
has been restricted to the case of isotropic scattering. In this paper, we
extend ADO to the case of the three-dimensional radiative transport equation
with anisotropic scattering. This extension is made possible by the technique
of rotated reference frames. The radiative transport equation with constant
absorption and scattering coefficients in the half space is solved by the
extended ADO in three dimensions. Moreover, we show that even an analytical
solution in the half space is obtained by extending the singular eigenfunction
approach with rotated reference frames.


title:
Learning based Channel Estimation and Phase Noise Compensation in
  Doubly-Selective Channels

abstract:
  In this letter, we propose a learning based channel estimation scheme for
orthogonal frequency division multiplexing (OFDM) systems in the presence of
phase noise in doubly-selective fading channels. Two-dimensional (2D)
convolutional neural networks (CNNs) are employed for effective training and
tracking of channel variation in both frequency as well as time domain. The
proposed network learns and estimates the channel coefficients in the entire
time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In
order to make the network robust to phase noise (PN) impairment, a novel
training scheme where the training data is rotated by random phases before
being fed to the network is employed. Further, using the estimated channel
coefficients, a simple and effective PN estimation and compensation scheme is
devised. Numerical results demonstrate that the proposed network and PN
compensation scheme achieve robust OFDM performance in the presence of phase
noise.


title:
The BP Dependency Function: a Generic Measure of Dependence between
  Random Variables

abstract:
  Measuring and quantifying dependencies between random variables (RV's) can
give critical insights into a data-set. Typical questions are: `Do underlying
relationships exist?', `Are some variables redundant?', and `Is some target
variable $Y$ highly or weakly dependent on variable $X$?' Interestingly,
despite the evident need for a general-purpose measure of dependency between
RV's, common practice of data analysis is that most data analysts use the
Pearson correlation coefficient (PCC) to quantify dependence between RV's,
while it is well-recognized that the PCC is essentially a measure for linear
dependency only. Although many attempts have been made to define more generic
dependency measures, there is yet no consensus on a standard, general-purpose
dependency function. In fact, several ideal properties of a dependency function
have been proposed, but without much argumentation. Motivated by this, in this
paper we will discuss and revise the list of desired properties and propose a
new dependency function that meets all these requirements. This general-purpose
dependency function provides data analysts a powerful means to quantify the
level of dependence between variables. To this end, we also provide Python code
to determine the dependency function for use in practice.

categories: stat.ML cs.LG math.PR math.ST stat.ME stat.TH


title:
Towards explaining the generalization gap in neural networks using
  topological data analysis

abstract:
  Understanding how neural networks generalize on unseen data is crucial for
designing more robust and reliable models. In this paper, we study the
generalization gap of neural networks using methods from topological data
analysis. For this purpose, we compute homological persistence diagrams of
weighted graphs constructed from neuron activation correlations after a
training phase, aiming to capture patterns that are linked to the
generalization capacity of the network. We compare the usefulness of different
numerical summaries from persistence diagrams and show that a combination of
some of them can accurately predict and partially explain the generalization
gap without the need of a test set. Evaluation on two computer vision
recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap
prediction when compared against state-of-the-art methods.


title: School-age Vaccination, School Openings and Covid-19 diffusion

abstract:
  Do school openings trigger Covid-19 diffusion when school-age vaccination is
available? We investigate this question using a unique geo-referenced high
frequency database on school openings, vaccinations, and Covid-19 cases from
the Italian region of Sicily. The analysis focuses on the change of Covid-19
diffusion after school opening in a homogeneous geographical territory. The
identification of causal effects derives from a comparison of the change in
cases before and after school opening in 2020/21, when vaccination was not
available, and in 2021/22, when the vaccination campaign targeted individuals
of age 12-19 and above 19. The results indicate that, while school opening
determined an increase in the growth rate of Covid-19 cases in 2020/2021, this
effect has been substantially reduced by school-age vaccination in 2021/2022.
In particular, we find that an increase of approximately 10% in the vaccination
rate of school-age population reduces the growth rate of Covid-19 cases after
school opening by approximately 1.4%. In addition, a counterfactual simulation
suggests that a permanent no vaccination scenario would have implied an
increase of 19% in ICU beds occupancy.


title: Microscopic black holes as probes for quantum gravity

abstract:
  One of the main goals of contemporary theoretical physics is to find the
quantum theory of gravity. There are various working hypotheses, mostly
operating in the regime of high-energy physics well above the reach of particle
accelerators. So far, strong experimental or observational evidence to guide
the theory is missing. A possible consequence of quantum gravity and quantum
spacetime that is often discussed is the vacuum dispersion effect. In this
paper, we consider a different line of quantum space phenomenology, the
behaviour of microscopic black holes. Even though their exact nature is
unknown, some of their features are very model-independent, allowing us to draw
conclusions about their role in the current cosmological models.


title:
On Covering Smooth Manifolds with Overlapping Simplicies: An inductive
  Characterization of Q-matrices

abstract:
  This paper is concerned with a covering problem of smooth manifolds of
dimension $n-1$ using a specific arrangement, called Q-arrangement, of $2^{n}$
$n$-simplices which could be degenerate and are allowed to overlap (preventing
therefore the immediate use of simplicial homology). We leverage the underlying
inductive nature of the problem to give a (non-constructive) topological
characterization. We show that for low dimensions such characterization reduces
to studying the local geometry around the specific points serving to form the
simplices, solving thereby the problem for $n \leq 3$. The motivation of such
work comes from a relatively old, yet unsolved, problem that originated in the
optimization community: under which conditions on the $n\times n$ matrix $M$,
does the so called linear complementarity problem given by $w - M z = q$,
$w,z\geq 0$, and $w.z = 0$, have a solution $(w,z)$ for all vectors $q\in
\mathbb{R}^n$. If the latter property holds, the matrix $M$ is said to be a
Q-matrix.


title: On wide-angle photo- and electroproduction of pions to twist-3 accuracy

abstract:
  The wide-angle photo- and electroproduction of pions is investigated within
the handbag mechanism in which the $\gamma^{(*)} N\to \pi N'$ amplitudes
factorize into subprocess amplitudes,$\gamma^{(*)} q\to \pi q'$, and form
factors representing $1/x$-moments of generalized parton distributions (GPDs).
The subprocess is calculated to twist-3 accuracy taking into account both the
2- and 3-body Fock components of the pion. The cross-sections are compared to
experiment and spin-effects are discussed.


title: DR.VIC: Decomposition and Reasoning for Video Individual Counting

abstract:
  Pedestrian counting is a fundamental tool for understanding pedestrian
patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian
counting, crossline crowd counting et al.) either only focus on the image-level
counting or are constrained to the manual annotation of lines. In this work, we
propose to conduct the pedestrian counting from a new perspective - Video
Individual Counting (VIC), which counts the total number of individual
pedestrians in the given video (a person is only counted once). Instead of
relying on the Multiple Object Tracking (MOT) techniques, we propose to solve
the problem by decomposing all pedestrians into the initial pedestrians who
existed in the first frame and the new pedestrians with separate identities in
each following frame. Then, an end-to-end Decomposition and Reasoning Network
(DRNet) is designed to predict the initial pedestrian count with the density
estimation method and reason the new pedestrian's count of each frame with the
differentiable optimal transport. Extensive experiments are conducted on two
datasets with congested pedestrians and diverse scenes, demonstrating the
effectiveness of our method over baselines with great superiority in counting
the individual pedestrians. Code: https://github.com/taohan10200/DRNet.


title:
The Impact of Partial Packet Recovery on the Inherent Secrecy of Random
  Linear Coding

abstract:
  This paper considers a source, which employs random linear coding (RLC) to
encode a message, a legitimate destination, which can recover the message if it
gathers a sufficient number of coded packets, and an eavesdropper. The
probability of the eavesdropper accumulating enough coded packets to recover
the message, known as the intercept probability, has been studied in the
literature. In our work, the eavesdropper does not abandon its efforts to
obtain the source message if RLC decoding has been unsuccessful; instead, it
employs partial packet recovery (PPR) offline in an effort to repair
erroneously received coded packets before it attempts RLC decoding again.
Results show that PPR-assisted RLC decoding marginally increases the intercept
probability, compared to RLC decoding, when the channel conditions are good.
However, as the channel conditions deteriorate, PPR-assisted RLC decoding
significantly improves the chances of the eavesdropper recovering the source
message, even if the eavesdropper experiences similar or worse channel
conditions than the destination.


title: Binary Morphological Neural Network

abstract:
  In the last ten years, Convolutional Neural Networks (CNNs) have formed the
basis of deep-learning architectures for most computer vision tasks. However,
they are not necessarily optimal. For example, mathematical morphology is known
to be better suited to deal with binary images. In this work, we create a
morphological neural network that handles binary inputs and outputs. We propose
their construction inspired by CNNs to formulate layers adapted to such images
by replacing convolutions with erosions and dilations. We give explainable
theoretical results on whether or not the resulting learned networks are indeed
morphological operators. We present promising experimental results designed to
learn basic binary operators, and we have made our code publicly available
online.


title: Real-time Object Detection for Streaming Perception

abstract:
  Autonomous driving requires the model to perceive the environment and (re)act
within a low latency for safety. While past works ignore the inevitable changes
in the environment after processing, streaming perception is proposed to
jointly evaluate the latency and accuracy into a single metric for video online
perception. In this paper, instead of searching trade-offs between accuracy and
speed like previous works, we point out that endowing real-time models with the
ability to predict the future is the key to dealing with this problem. We build
a simple and effective framework for streaming perception. It equips a novel
DualFlow Perception module (DFP), which includes dynamic and static flows to
capture the moving trend and basic detection feature for streaming prediction.
Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to
generate adaptive weights for objects with different moving speeds. Our simple
method achieves competitive performance on Argoverse-HD dataset and improves
the AP by 4.9% compared to the strong baseline, validating its effectiveness.
Our code will be made available at https://github.com/yancie-yjr/StreamYOLO.


title:
Real-time Rendering and Editing of Scattering Effects for Translucent
  Objects

abstract:
  The photorealistic rendering of the transparent effect of translucent objects
is a hot research topic in recent years. A real-time photorealistic rendering
and material dynamic editing method for the diffuse scattering effect of
translucent objects is proposed based on the bidirectional surface scattering
reflectance function's (BSSRDF) Dipole approximation. The diffuse scattering
material function in the Dipo le approximation is decomposed into the product
form of the shape-related function and the translucent material-related
function through principal component analysis; using this decomposition
representation, under the real-time photorealistic rendering framework of
pre-radiative transmission and the scattering transmission to realize real-time
editing of translucent object materials under various light sources. In
addition, a method for quadratic wavelet compression of precomputed radiative
transfer data in the spatial domain is also proposed. Using the correlation of
surface points in the spatial distribution position, on the premise of ensuring
the rendering quality, the data is greatly compressed and the rendering is
efficiently improved. The experimental results show that the method in this
paper can generate a highly realistic translucent effect and ensure the
real-time rendering speed.


title: A characteristic polynomial of the Seidel matrix over $\mathbb{F}_3$

abstract:
  In this paper, we consider the condition when the characteristic polynomials
of the Seidel matrix of the graphs are decomposed into products of linear
polynomials over $\mathbb{F}_3$. We also show the equality over $\mathbb{F}_3$
between the characteristic polynomial of the Seidel matrix of a graph and the
characteristic polynomial of the adjacency matrix of that graph.


title:
Towards Semi-Supervised Deep Facial Expression Recognition with An
  Adaptive Confidence Margin

abstract:
  Only parts of unlabeled data are selected to train models for most
semi-supervised learning methods, whose confidence scores are usually higher
than the pre-defined threshold (i.e., the confidence margin). We argue that the
recognition performance should be further improved by making full use of all
unlabeled data. In this paper, we learn an Adaptive Confidence Margin (Ada-CM)
to fully leverage all unlabeled data for semi-supervised deep facial expression
recognition. All unlabeled samples are partitioned into two subsets by
comparing their confidence scores with the adaptively learned confidence margin
at each training epoch: (1) subset I including samples whose confidence scores
are no lower than the margin; (2) subset II including samples whose confidence
scores are lower than the margin. For samples in subset I, we constrain their
predictions to match pseudo labels. Meanwhile, samples in subset II participate
in the feature-level contrastive objective to learn effective facial expression
features. We extensively evaluate Ada-CM on four challenging datasets, showing
that our method achieves state-of-the-art performance, especially surpassing
fully-supervised baselines in a semi-supervised manner. Ablation study further
proves the effectiveness of our method. The source code is available at
https://github.com/hangyu94/Ada-CM.


title:
Cognitive Workload Associated with Different Conceptual Modeling
  Approaches in Information Systems

abstract:
  Conceptual models visually represent entities and relationships between them
in an information system. Effective conceptual models should be simple while
communicating sufficient information. This trade-off between model complexity
and clarity is crucial to prevent failure of information system development.
Past studies have found that more expressive models lead to higher performance
on tasks measuring a user s deep understanding of the model and attributed this
to lower experience of cognitive workload associated with these models. This
study examined this hypothesis by measuring users EEG brain activity while they
completed a task with different conceptual models. 30 participants were divided
into two groups: One group used a low ontologically expressive model (LOEM),
and the other group used a high ontologically expressive model (HOEM).
Cognitive workload during the task was quantified using EEG Engagement Index,
which is a ratio of brain activity power in beta as opposed to the sum of alpha
and theta frequency bands. No significant difference in cognitive workload was
found between the LOEM and HOEM groups indicating equal amounts of cognitive
processing required for understanding of both models. The main contribution of
this study is the introduction of neurophysiological measures as an objective
quantification of cognitive workload in the field of conceptual modeling and
information systems.


title: Asymptotics of non-local perimeters

abstract:
  We introduce a notion of non-local perimeter which is defined through an
arbitrary positive Borel measure on $\mathbb{R}^d$ which integrates the
function $1\wedge |x|$. Such definition of non-local perimeter encompasses a
wide range of perimeters which have been already studied in the literature,
including fractional perimeters and anisotropic fractional perimeters. The main
part of the article is devoted to the study of the asymptotic behaviour of
non-local perimeters. As direct applications we recover well-known convergence
results for fractional perimeters and anisotropic fractional perimeters


title: How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs

abstract:
  We aim to understand how actions are performed and identify subtle
differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a
method which recognizes adverbs across different actions. However, such
fine-grained annotations are difficult to obtain and their long-tailed nature
makes it challenging to recognize adverbs in rare action-adverb compositions.
Our approach therefore uses semi-supervised learning with multiple adverb
pseudo-labels to leverage videos with only action labels. Combined with
adaptive thresholding of these pseudo-adverbs we are able to make efficient use
of the available data while tackling the long-tailed distribution.
Additionally, we gather adverb annotations for three existing video retrieval
datasets, which allows us to introduce the new tasks of recognizing adverbs in
unseen action-adverb compositions and unseen domains. Experiments demonstrate
the effectiveness of our method, which outperforms prior work in recognizing
adverbs and semi-supervised works adapted for adverb recognition. We also show
how adverbs can relate fine-grained actions.


title: Surface Patches with Rounded Corners

abstract:
  We analyze surface patches with a corner that is rounded in the sense that
the partial derivatives at that point are antiparallel. Sufficient conditions
for $G^1$ smoothness are given, which, up to a certain degenerate case, are
also necessary. Further, we investigate curvature integrability and present
examples


title:
Robust Text Line Detection in Historical Documents: Learning and
  Evaluation Methods

abstract:
  Text line segmentation is one of the key steps in historical document
understanding. It is challenging due to the variety of fonts, contents, writing
styles and the quality of documents that have degraded through the years.
  In this paper, we address the limitations that currently prevent people from
building line segmentation models with a high generalization capacity. We
present a study conducted using three state-of-the-art systems Doc-UFCN,
dhSegment and ARU-Net and show that it is possible to build generic models
trained on a wide variety of historical document datasets that can correctly
segment diverse unseen pages. This paper also highlights the importance of the
annotations used during training: each existing dataset is annotated
differently. We present a unification of the annotations and show its positive
impact on the final text recognition results. In this end, we present a
complete evaluation strategy using standard pixel-level metrics, object-level
ones and introducing goal-oriented metrics.


title: Verifying Outsourced Computation in an Edge Computing Marketplace

abstract:
  An edge computing marketplace could enable IoT devices (Outsourcers) to
outsource computation to any participating node (Contractors) in their
proximity. In return, these nodes receive a reward for providing computation
resources. In this work, we propose a scheme that verifies the integrity of
arbitrary deterministic functions and is resistant to both dishonest
Outsourcers and Contractors who try to maximize their expected payoff. We
tested our verification scheme with state-of-the-art pre-trained Convolutional
Neural Network models designed for object detection. On all devices, our
verification scheme causes less than 1ms computational overhead and a
negligible network bandwidth overhead of at most 84 bytes per frame. Our
implementation can also perform our verification scheme's tasks parallel to the
object detection to eliminate any latency overhead. Compared to other proposed
verification schemes, our scheme resists a comprehensive set of protocol
violations without sacrificing performance.


title:
Characteristics of small protoplanetary disc warps in kinematic
  observations

abstract:
  Many circumstellar discs appear to have misaligned central regions that give
rise to shadows seen in scattered light observations. Small warps ($<20^\circ$
misalignment) are probably more common but are also more difficult to detect
than the large misalignments studied previously. We present the characteristics
of CO emission that may be used to identify a small disc warp, found from
synthetic $^{13}$CO maps of a model misaligned circumbinary disc. The spectra
are not symmetrical, so fitting a Keplerian model is not appropriate and can
hide a warp or lead to spurious features such as spirals appearing in the
residuals. We quantify the observed warp structure by fitting sinusoids to
concentric annuli of the disc. From this we can trace the radial variation of
the peak velocity and of the azimuth of the peak velocity, i.e., the twist. At
near face-on inclinations, these radial profiles reveal the warp structure. The
twist remains detectable at moderate inclinations (${i_{\rm outer~disc}\lesssim
35^{\circ}}$) in the absence of radial flows but the measured inclination must
be accurate to $\lesssim 5^{\circ}$ to allow detection of the radial variation.
The observed twist does not provide a direct measure of the warp structure
because of its dependence on optical depth. The warp causes broad asymmetries
in the channel maps that span several channels and that are distinct from
localised features caused by embedded planets and gravitational instability. We
suspect that kinematic evidence of warps may have been missed and we suggest a
few examples where the data may be revisited.


title: Functionals with extrema at reproducing kernels

abstract:
  We show that certain monotone functionals on the Hardy spaces and convex
functionals on the Bergman spaces are maximized at the normalized reproducing
kernels among the functions of norm $1$, thus proving the contractivity
conjecture of Pavlovi\'c and of Brevig, Ortega-Cerd\`a, Seip and Zhao and the
Wehrl-type entropy conjecture for the $SU(1,1)$ group of Lieb and Solovej,
respectively.


title: Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation

abstract:
  Given the hyper-spectral imaging unique potentials in grasping the polymer
characteristics of different materials, it is commonly used in sorting
procedures. In a practical plastic sorting scenario, multiple plastic flakes
may overlap which depending on their characteristics, the overlap can be
reflected in their spectral signature. In this work, we use hyper-spectral
imaging for the segmentation of three types of plastic flakes and their
possible overlapping combinations. We propose an intuitive and simple
multi-label encoding approach, bitfield encoding, to account for the
overlapping regions. With our experiments, we show that the bitfield encoding
improves over the baseline single-label approach and we further demonstrate its
potential in predicting multiple labels for overlapping classes even when the
model is only trained with non-overlapping classes.


title:
Bayesian Physics-Informed Neural Networks for seismic tomography based
  on the eikonal equation

abstract:
  The high cost of acquiring sufficient amount of seismic data for training has
limited the use of machine learning in seismic tomography. To overcome this
issue, in this work, eikonal equation is used as a a prior knowledge in the
learning framework to establish the constraint relationship between inverse
velocity and reconstruct traveltime. Besides, to mitigate the effects due to
noisy data and quantify the uncertainty in the prediction, Bayesian
Physics-Informed Neural Networks (BPINN) is used and a general Bayesian
inference algorithm Stein Variational Gradient Descent (SVGD) is used to
facilitate the training process. The proposed method was tested on a synthetic
velocity model with different noise level in the data. The results show the
velocity model can be estimated accurately and the traveltime can be well
approximated. The predictive uncertainty offered by BPINN can provide
reasonable uncertainty assessments in the predictive results


title:
An Extensible Logic Embedding Tool for Lightweight Non-Classical
  Reasoning

abstract:
  The logic embedding tool provides a procedural encoding for non-classical
reasoning problems into classical higher-order logic. It is extensible and can
support an increasing number of different non-classical logics as reasoning
targets. When used as a pre-processor or library for higher-order theorem
provers, the tool admits off-the-shelf automation for logics for which
otherwise few to none provers are currently available.


title:
Low-energy electron microscopy intensity-voltage data -- factorization,
  sparse sampling, and classification

abstract:
  Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves
provides hyperspectral images of surfaces, which can be used to identify the
surface type, but are difficult to analyze. Here, we demonstrate the use of an
algorithm for factorizing the data into spectra and concentrations of
characteristic components (FSC3) for identifying distinct physical surface
phases. Importantly, FSC3 is an unsupervised and fast algorithm. As example
data we use experiments on the growth of praseodymium oxide or ruthenium oxide
on ruthenium single crystal substrates, both featuring a complex distribution
of coexisting surface components, varying in both chemical composition and
crystallographic structure. With the factorization result a sparse sampling
method is demonstrated, reducing the measurement time by 1-2 orders of
magnitude, relevant for dynamic surface studies. The FSC3 concentrations are
providing the features for a support vector machine (SVM) based supervised
classification of the types. Here, specific surface regions which have been
identified structurally, via their diffraction pattern, as well as chemically
by complementary spectro-microscopic techniques, are used as training sets. A
reliable classification is demonstrated on both exemplary LEEM I-V datasets.


title:
Low-$p_T$ direct-photon production in Au$+$Au collisions at
  $\sqrt{s_{_{NN}}}=39$ and 62.4 GeV

abstract:
  The measurement of direct photons from Au$+$Au collisions at
$\sqrt{s_{_{NN}}}=39$ and 62.4 GeV in the transverse-momentum range $0.4<p_T<3$
Gev/$c$ is presented by the PHENIX collaboration at the Relativistic Heavy Ion
Collider. A significant direct-photon yield is observed in both collision
systems. A universal scaling is observed when the direct-photon $p_T$ spectra
for different center-of-mass energies and for different centrality selections
at $\sqrt{s_{_{NN}}}=62.4$ GeV is scaled with $(dN_{\rm ch}/d\eta)^{\alpha}$
for $\alpha=1.21{\pm}0.04$. This scaling also holds true for direct-photon
spectra from Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV measured earlier
by PHENIX, as well as the spectra from Pb$+$Pb at $\sqrt{s_{_{NN}}}=2760$ GeV
published by ALICE. The scaling power $\alpha$ seems to be independent of
$p_T$, center of mass energy, and collision centrality. The spectra from
different collision energies have a similar shape up to $p_T$ of 2 GeV/$c$. The
spectra have a local inverse slope $T_{\rm eff}$ increasing with $p_T$ of
$0.174\pm0.018$ GeV/$c$ in the range $0.4<p_T<1.3$ GeV/$c$ and increasing to
$0.289\pm0.024$ GeV/$c$ for $0.9<p_T<2.1$ GeV/$c$. The observed similarity of
low-$p_T$ direct-photon production from $\sqrt{s_{_{NN}}}= 39$ to 2760 GeV
suggests a common source of direct photons for the different collision energies
and event centrality selections, and suggests a comparable space-time evolution
of direct-photon emission.


title:
Epidemic processes on self-propelled particles: continuum and
  agent-based modelling

abstract:
  Most spreading processes require spatial proximity between agents. The
stationary state of spreading dynamics in a population of mobile agents thus
depends on the interplay between the time and length scales involved in the
epidemic process and their motion in space. We analyze the steady properties
resulting from such interplay in a simple model describing epidemic spreading
(modeled as a Susceptible-Infected-Susceptible process) on self-propelled
particles (performing Run-and-Tumble motion). Focusing our attention on the
diffusive long-time regime, we find that the agents' motion changes
qualitatively the nature of the epidemic transition characterized by the
emergence of a macroscopic fraction of infected agents. Indeed, the transition
becomes of the mean-field type for agents diffusing in one, two and three
dimensions, while, in the absence of motion, the epidemic outbreak depends on
the dimension of the underlying static network determined by the agents' fixed
locations. The insights obtained from a continuum description of the system are
validated by numerical simulations of an agent-based model. Our work aims at
bridging soft active matter physics and theoretical epidemiology, and may be of
interest for researchers in both communities.


title:
Relativistic and QED corrections to one-bond indirect nulcear spin-spin
  couplings in X$_2^{2+}$ and X$_3^{2+}$ ions (X = Zn, Cd, Hg)

abstract:
  The indirect nuclear spin-spin coupling tensor, $\mathbf J$, between mercury
nuclei in Hg-containing systems can be of the order of few kHz and one of the
largest measured. We conduct an analysis of the physics behind the electronic
mechanisms that contribute to the one- and two-bond couplings $^n {\mathbf
J}_{\mathrm{Hg}-\mathrm{Hg}}$ ($n=1, 2$). We performed calculations for
$J$-couplings in X$_2^{2+}$ and $X_3^{2+}$ ions ($X$ = Zn, Cd, Hg), within
polarization propagator theory, using the random phase approximation (RPA) and
the pure zeroth order approximation (PZOA), with Dirac-Hartree-Fock (DHF) and
Dirac-Kohn-Sham (DKS) orbitals, both at four-component and ZORA levels. We show
that the "paramagnetic-like" mechanism contribute with more than 99.98\% to the
total isotropic component of the coupling tensor. By means of an analysis of
the molecular and atomic orbitals involved in the total value of the response
function, we find that the $s$-type valence atomic orbitals have a predominant
role in the description of the coupling. This fact allows us to develop an
effective model from which quantum electrodynamics (QED) effects on
$J$-coupling in the aforementioned ions can be estimated. The estimated QED
corrections were found in the interval $(0.7; ~ 1.7)$\% of the total
relativistic effect on isotropic one-bond $^1 {\mathbf J}$ coupling and from
the interval $(-0.2; ~ -0.4)$\%, in Zn-containing ions, to $(-0.8; ~ -1.2)$\%,
in Hg-containing ions, of the total isotropic coupling constant in the studied
systems. We also show that estimated QED corrections cast a visible dependence
on the nuclear charge $Z$ of each atom $X$ in the form of a power-law $\propto
Z^5$.


title: The hyperonic star in relativistic mean-field model

abstract:
  The neutron star as a supernova remnant is attracting high attention recently
due to the gravitation wave detection and precise measurements about its mass
and radius. In the inner core region of the neutron star, the strangeness
degrees of freedom, such as the hyperons, can be present, which is also named
as a hyperonic star. In this work, the neutron star consisting of nucleons and
leptons, and the hyperonic star including the hyperons will be reviewed in the
framework of the relativistic mean-field (RMF) model. The popular non-linear
and density-dependent RMF parametrizations in the market will be adopted to
investigate the role of strangeness baryons in a hyperonic star on its mass,
radius, tidal deformability, and other properties. Finally, the magnitudes of
the coupling strengths between mesons and hyperons also will be discussed,
which can generate the massive hyperonic star with present RMF parameter sets,
when the vector coupling constants are strong.


title: Dynamical Dark Energy in Minimally Modified Gravity

abstract:
  Minimally modified gravity is a class of models with only the two tensor
degrees of freedom as in general relativity. Using the framework with auxiliary
constraints these models can maintain a dynamical cosmological background. The
form of the constraints is thereby restricted by the requirement of dynamical
dark energy and the avoidance of a breakdown of perturbation theory. Studying
the linear perturbations around the FLRW background the results are, however,
quite insensitive to the details of the constraints leading to a modified
effective gravitational constant or a non-vanishing sound speed for dust.


title: Modular Metric Spaces

abstract:
  We give a short introduction to the theory of modular metric spaces. This is
a corrected version of the paper [1], which had some errors. We are grateful to
V. V. Chistyakov for bringing these to our attention.


title: Li-Yau inequalities for the Helfrich functional and applications

abstract:
  We prove a general Li-Yau inequality for the Helfrich functional where the
spontaneous curvature enters with a singular volume type integral. In the
physically relevant cases, this term can be converted into an explicit energy
threshold that guarantees embeddedness. We then apply our result to the
spherical case of the variational Canham-Helfrich model. If the infimum energy
is not too large, we show existence of smoothly embedded minimizers.
Previously, existence of minimizers was only known in the class of immersed
bubble trees.


title:
Analyses of Some Structural Properties on a Class of Hierarchical
  Scale-free Networks

abstract:
  Hierarchical networks actually have many applications in the real world.
Firstly, we propose a new class of hierarchical networks with scale-free and
fractal structure, which are the networks with triangles compared to
traditional hierarchical networks. Secondly, we study the precise results of
some structural properties to derive small-world effect and scale-free feature.
Thirdly, it is found that the constructed network is sparse through the average
degree and density. Fourthly, it is also demonstrated the degree distributions
of hub nodes and the bottom nodes are the power law and exponential,
respectively. Finally, we prove that clustering coefficient with a definite
value z tends to stabilize at a lower bound as t iterates to a certain number,
and the average distance of G_{t}^{z} has a increasing relationship along with
the value of lnN_{t}.


title:
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D
  Medical Images

abstract:
  The lack of annotated datasets is a major challenge in training new
task-specific supervised AI algorithms as manual annotation is expensive and
time-consuming. To address this problem, we present MONAI Label, a free and
open-source platform that facilitates the development of AI-based applications
that aim at reducing the time required to annotate 3D medical image datasets.
Through MONAI Label researchers can develop annotation applications focusing on
their domain of expertise. It allows researchers to readily deploy their apps
as services, which can be made available to clinicians via their preferred
user-interface. Currently, MONAI Label readily supports locally installed
(3DSlicer) and web-based (OHIF) frontends, and offers two Active learning
strategies to facilitate and speed up the training of segmentation algorithms.
MONAI Label allows researchers to make incremental improvements to their
labeling apps by making them available to other researchers and clinicians
alike. Lastly, MONAI Label provides sample labeling apps, namely DeepEdit and
DeepGrow, demonstrating dramatically reduced annotation times.


title: Ethereum Fraud Detection with Heterogeneous Graph Neural Networks

abstract:
  While transactions with cryptocurrencies such as Ethereum are becoming more
prevalent, fraud and other criminal transactions are not uncommon. Graph
analysis algorithms and machine learning techniques detect suspicious
transactions that lead to phishing in large transaction networks. Many graph
neural network (GNN) models have been proposed to apply deep learning
techniques to graph structures. Although there is research on phishing
detection using GNN models in the Ethereum transaction network, models that
address the scale of the number of vertices and edges and the imbalance of
labels have not yet been studied. In this paper, we compared the model
performance of GNN models on the actual Ethereum transaction network dataset
and phishing reported label data to exhaustively compare and verify which GNN
models and hyperparameters produce the best accuracy. Specifically, we
evaluated the model performance of representative homogeneous GNN models which
consider single-type nodes and edges and heterogeneous GNN models which support
different types of nodes and edges. We showed that heterogeneous models had
better model performance than homogeneous models. In particular, the RGCN model
achieved the best performance in the overall metrics.


title: Terms of Lucas sequences having a large smooth divisor

abstract:
  We show that the $Kn$--smooth part of $a^n-1$ for an integer $a>1$ is
$a^{o(n)}$ for most positive integers $n$.


title:
Values of Coordinated Residential Space Heating in Demand Response
  Provision

abstract:
  Demand-side response from space heating in residential buildings can
potentially provide a huge amount of flexibility for the power system,
particularly with deep electrification of the heat sector. In this context,
this paper presents a novel distributed control strategy to coordinate space
heating across numerous residential households with diversified thermal
parameters. By employing an iterative algorithm under the game-theoretical
framework, each household adjusts its own heating schedule through demand shift
and thermal comfort compensation with the purpose of achieving individual cost
savings, whereas the aggregate peak demand is effectively shaved on the system
level. Additionally, an innovative thermal comfort model which considers both
the temporal and spatial differences in customised thermal comfort requirements
is proposed. Through a series of case studies, it is demonstrated that the
proposed space heating coordination strategy can facilitate effective energy
arbitrage for individual buildings, driving a 13.96% reduction in system
operational cost and 28.22% peak shaving. Moreover, the superiority of the
proposed approach in thermal comfort maintenance is numerically analysed based
on the proposed thermal comfort quantification model.