title:
End-to-End Learned Block-Based Image Compression with Block-Level Masked
  Convolutions and Asymptotic Closed Loop Training

abstract:
  Learned image compression research has achieved state-of-the-art compression
performance with auto-encoder based neural network architectures, where the
image is mapped via convolutional neural networks (CNN) into a latent
representation that is quantized and processed again with CNN to obtain the
reconstructed image. CNN operate on entire input images. On the other hand,
traditional state-of-the-art image and video compression methods process images
with a block-by-block processing approach for various reasons. Very recently,
work on learned image compression with block based approaches have also
appeared, which use the auto-encoder architecture on large blocks of the input
image and introduce additional neural networks that perform intra/spatial
prediction and deblocking/post-processing functions. This paper explores an
alternative learned block-based image compression approach in which neither an
explicit intra prediction neural network nor an explicit deblocking neural
network is used. A single auto-encoder neural network with block-level masked
convolutions is used and the block size is much smaller (8x8). By using
block-level masked convolutions, each block is processed using reconstructed
neighboring left and upper blocks both at the encoder and decoder. Hence, the
mutual information between adjacent blocks is exploited during compression and
each block is reconstructed using neighboring blocks, resolving the need for
explicit intra prediction and deblocking neural networks. Since the explored
system is a closed loop system, a special optimization procedure, the
asymptotic closed loop design, is used with standard stochastic gradient
descent based training. The experimental results indicate competitive image
compression performance.


title:
BEFANA: A Tool for Biodiversity-Ecosystem Functioning Assessment by
  Network Analysis

abstract:
  BEFANA is a free and open-source software tool for ecological network
analysis and visualisation. It is adapted to ecologists' needs and allows them
to study the topology and dynamics of ecological networks as well as apply
selected machine learning algorithms. BEFANA is implemented in Python, and
structured as an ordered collection of interactive computational notebooks. It
relies on widely used open-source libraries, and aims to achieve simplicity,
interactivity, and extensibility. BEFANA provides methods and implementations
for data loading and preprocessing, network analysis and interactive
visualisation, modelling with experimental data, and predictive modelling with
machine learning. We showcase BEFANA through a concrete example of a detrital
soil food web of agricultural grasslands, and demonstrate all of its main
components and functionalities.


title:
Proposed Search for a wind of Axion-like-particles using the
  Gravitational Wave Interferometers

abstract:
  If ALPs exist, the refractive index of light will be modified when light
traverses a region where an ALP field is present. We propose to measure the
refractive index of light and its angular dependence as the Earth rotates with
respect to the direction of the ALP wind. The LIGO Interferometers are
sensitive to differences in the refractive index of the light circulating in
the two arms, and such differences were recorded during the S5 run, February
2006 to July 2007. They are due in part to the horizontal tidal gradients when
they are aligned with one of the arms. In addition a very strong modulation was
observed at twice the Earth's orbital frequency as can be seen in Fig.1. This
can be understood if the difference in the refractive index between the two
arms depends on the angle between the light propagation vector and the
direction of the ALP wind. We present the data as a function of the Earth's
motion, and discuss the magnitude of the observed refractive index, n-1 ~
10e-20.


title:
Interpretation of galaxy rotation curves from primordial black holes in
  4D Einstein-Gauss-Bonnet gravity

abstract:
  We develop a novel approach to the dark matter halos in the context of 4
dimensional Einstein--scalar-Gauss-Bonnet gravity. This approach gives a
possible interpretation of the problem of the galaxy rotation curves. Moreover,
the Gauss-Bonnet coupling describes the interior structure of the galaxies,
while there is a presence of a scalar field $\phi $ in the galaxy edges. This
can provide an interesting interpretation for the functional coupling $f(\phi
)$. We discuss how this comparison can naturally drive the observed percentages
of \ matter and dark matter in the Universe. The effective mass range\ in our
model is $10^{-2}kg-10^{3}kg$, which is in good agreement with the constraints
on primordial black holes.


title: Symmetry Breaking of Universal type and Particular types

abstract:
  The concepts of symmetry and its breakdown are investigated in two different
terms according to whether the resulting asymmetry is universal or only
obtained for a special configuration: we shall illustrate this by considering
in the first case an example from the standard model of particles with some
consequences for cosmological scenarios related to inflation and the problem of
the cosmological constant, and in the second case we consider an example from
specific solutions for particle dynamics and an example for a toy model of
entangled spins.


title: GAM(L)A: An econometric model for interpretable Machine Learning

abstract:
  Despite their high predictive performance, random forest and gradient
boosting are often considered as black boxes or uninterpretable models which
has raised concerns from practitioners and regulators. As an alternative, we
propose in this paper to use partial linear models that are inherently
interpretable. Specifically, this article introduces GAM-lasso (GAMLA) and
GAM-autometrics (GAMA), denoted as GAM(L)A in short. GAM(L)A combines
parametric and non-parametric functions to accurately capture linearities and
non-linearities prevailing between dependent and explanatory variables, and a
variable selection procedure to control for overfitting issues. Estimation
relies on a two-step procedure building upon the double residual method. We
illustrate the predictive performance and interpretability of GAM(L)A on a
regression and a classification problem. The results show that GAM(L)A
outperforms parametric models augmented by quadratic, cubic and interaction
effects. Moreover, the results also suggest that the performance of GAM(L)A is
not significantly different from that of random forest and gradient boosting.


title: Panoptic segmentation with highly imbalanced semantic labels

abstract:
  We describe here the panoptic segmentation method we devised for our
participation in the CoNIC: Colon Nuclei Identification and Counting Challenge
at ISBI 2022. Key features of our method are a weighted loss specifically
engineered for semantic segmentation of highly imbalanced cell types, and a
state-of-the art nuclei instance segmentation model, which we combine in a
Hovernet-like architecture.


title: Optical Flow Based Motion Detection for Autonomous Driving

abstract:
  Motion detection is a fundamental but challenging task for autonomous
driving. In particular scenes like highway, remote objects have to be paid
extra attention for better controlling decision. Aiming at distant vehicles, we
train a neural network model to classify the motion status using optical flow
field information as the input. The experiments result in high accuracy,
showing that our idea is viable and promising. The trained model also achieves
an acceptable performance for nearby vehicles. Our work is implemented in
PyTorch. Open tools including nuScenes, FastFlowNet and RAFT are used.
Visualization videos are available at
https://www.youtube.com/playlist?list=PLVVrWgq4OrlBnRebmkGZO1iDHEksMHKGk .


title:
Minimum Resolution of the Minkowski, Schwarzschild and Kerr Differential
  Modules

abstract:
  Our recent arXiv preprints and published papers on the solution of the
Riemann-Lanczos and Weyl-Lanczos problems have brought our attention on the
importance of revisiting the algebraic structure of the Bianchi identities in
Riemannian geometry. We also discovered in the meantime that, in our first book
of 1978, we had already used a new way for studying the various compatibility
conditions (CC) of an operator that may not be necessarily formally integrable
(FI) in order to construct canonical formally exact differential sequences on
the jet level. The purpose of this paper is to prove that the combination of
these two facts clearly shows the specific importance of the Spencer operator
and the Spencer $\delta$-cohomology, totally absent from mathematical physics
today. The results obtained are unavoidable because they only depend on
elementary combinatorics and diagram chasing. They also provide for the first
time the purely intrinsic interpretation of the respective numbers of
successive first, second, third and higher order generating CC. However, if
they of course agree with the linearized Killing operator over the Minkowski
metric, they largely disagree with recent publications on the respective
numbers of generating CC for the linearized Killing operator over the
Schwarzschild and Kerr metrics. Many similar examples are illustrating these
new techniques, providing in particular the only symbol existing in the
literature which is 2-ayclic witout being of finite type, contrary to the
conformal situation.


title: Effective Communications for 6G: Challenges and Opportunities

abstract:
  This article studies effective communication, one of the three forms
identified by Weaver and Shannon, as an enabler for the upcoming 6G use cases.
The envisioned tactile, holographic, and multi-sensory communications require
bandwidths in the order of terabits per second and latencies in the order of
microseconds for an immersive experience. We argue that a theoretical framework
for transporting information tailored to end-users' goals is necessary to
support such applications. Different from the recently emerging discussions
focusing on the meaning of exchanged messages, we focus on using these messages
to take actions in the desired way. We highlight the essential characteristics
of distributed knowledge accumulation as a facilitator for this upcoming
paradigm, and discuss the challenges of making effective communications a
reality and the potential opportunities for future research to address these
challenges. In a real-life use case, we showcase the potential reduction in the
number of bits transferred owing to the transferred accumulated knowledge.


title:
Accelerating Extremum Seeking Convergence by Richardson Extrapolation
  Methods

abstract:
  In this paper, we propose the concept of accelerated convergence that has
originally been developed to speed up the convergence of numerical methods for
extremum seeking (ES) loops. We demonstrate how the dynamics of ES loops may be
analyzed to extract structural information about the generated output of the
loop. This information is then used to distil the limit of the loop without
having to wait for the system to converge to it.


title:
Magnetic black holes with generalized ModMax model of nonlinear
  electrodynamics

abstract:
  Recently Bandos, Lechner, Sorokin, and Townsend [Phys. Rev. D \textbf{102},
121703 (2020)] proposed Modified Maxwell (ModMax) model of nonlinear
duality-invariant conformal electrodynamics. Here, Generalized ModMax
(GenModMax) model of nonlinear electrodynamics coupled to general relativity is
studied. The metric and mass functions, and their asymptotic as
$r\rightarrow\infty$ and $r\rightarrow 0$ of a magnetic black hole are
obtained. Corrections to the Reissner--Nordstr\"{o}m solution are found and we
show that for some model parameters the black hole is regular. The Hawking
temperature and heat capacity of black holes are calculated and phase
transitions are investigated. We demonstrate that black holes are not stable
for certain model parameters.


title:
A Machine Learning Generative Method for Automating Antenna Design and
  Optimization

abstract:
  To facilitate the antenna design with the aid of computer, one of the
practices in consumer electronic industry is to model and optimize antenna
performances with a simplified antenna geometric scheme. Traditional antenna
modeling requires profound prior knowledge of electromagnetics in order to
achieve a good design which satisfies the performance specifications from both
antenna and product designs. The ease of handling multidimensional optimization
problems and the less dependence on domain knowledge and experience are the key
to achieve the popularity of simulation driven antenna design and optimization
for the industry. In this paper, we introduce a flexible geometric scheme with
the concept of mesh network that can form any arbitrary shape by connecting
different nodes. For such problems with high dimensional parameters, we propose
a machine learning based generative method to assist the searching of optimal
solutions. It consists of discriminators and generators. The discriminators are
used to predict the performance of geometric models, and the generators to
create new candidates that will pass the discriminators. Moreover, an
evolutionary criterion approach is proposed for further improving the
efficiency of our method. Finally, not only optimal solutions can be found, but
also the well trained generators can be used to automate future antenna design
and optimization. For a dual resonance antenna design with wide bandwidth, our
proposed method is in par with Trust Region Framework and much better than the
other mature machine learning algorithms including the widely used Genetic
Algorithm and Particle Swarm Optimization. When there is no wide bandwidth
requirement, it is better than Trust Region Framework.


title: Lorentz and gauge invariance of quantum space

abstract:
  Motivated by generalized uncertainty principle, we derive a discrete picture
of the space that respects Lorentz symmetry as well as gauge symmetry through
setting an equivalency between linear GUP correction term and electromagnetic
interaction term in Dirac equation. We derived a wavefunction solution that
satisfies this equivalency. This discreteness may explain the crystal and
quasicrystal structures observed in nature at different energy scales.


title: Exploring Linear Feature Disentanglement For Neural Networks

abstract:
  Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved
great success in neural networks (NNs). Due to the complex non-linear
characteristic of samples, the objective of those activation functions is to
project samples from their original feature space to a linear separable feature
space. This phenomenon ignites our interest in exploring whether all features
need to be transformed by all non-linear functions in current typical NNs,
i.e., whether there exists a part of features arriving at the linear separable
feature space in the intermediate layers, that does not require further
non-linear variation but an affine transformation instead. To validate the
above hypothesis, we explore the problem of linear feature disentanglement for
neural networks in this paper. Specifically, we devise a learnable mask module
to distinguish between linear and non-linear features. Through our designed
experiments we found that some features reach the linearly separable space
earlier than the others and can be detached partly from the NNs. The explored
method also provides a readily feasible pruning strategy which barely affects
the performance of the original model. We conduct our experiments on four
datasets and present promising results.


title:
Viscosity solutions of Hamilton-Jacobi equation in $RCD(K,\infty)$
  spaces and applications to large deviations

abstract:
  The aim of this paper is twofold. - In the setting of $RCD(K,\infty)$ metric
measure spaces, we derive uniform gradient and Laplacian contraction estimates
along solutions of the viscous approximation of the Hamilton-Jacobi equation.
We use these estimates to prove that, as the viscosity tends to zero, solutions
of this equation converge to the evolution driven by the Hopf-Lax formula, in
accordance with the smooth case. - We then use such convergence to study the
small-time Large Deviation Principle for both the heat kernel and the Brownian
motion: we obtain the expected behaviour under the additional assumption that
the space is proper.


title:
BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning
  in Sentiment Analysis

abstract:
  Aspect-based sentiment analysis (ABSA) task aims to associate a piece of text
with a set of aspects and meanwhile infer their respective sentimental
polarities. Up to now, the state-of-the-art approaches are built upon
fine-tuning of various pre-trained language models. They commonly aim to learn
the aspect-specific representation in the corpus. Unfortunately, the aspect is
often expressed implicitly through a set of representatives and thus renders
implicit mapping process unattainable unless sufficient labeled examples.
  In this paper, we propose to jointly address aspect categorization and
aspect-based sentiment subtasks in a unified framework. Specifically, we first
introduce a simple but effective mechanism that collaborates the semantic and
syntactic information to construct auxiliary-sentences for the implicit aspect.
Then, we encourage BERT to learn the aspect-specific representation in response
to the automatically constructed auxiliary-sentence instead of the aspect
itself. Finally, we empirically evaluate the performance of the proposed
solution by a comparative study on real benchmark datasets for both ABSA and
Targeted-ABSA tasks. Our extensive experiments show that it consistently
achieves state-of-the-art performance in terms of aspect categorization and
aspect-based sentiment across all datasets and the improvement margins are
considerable.


title:
Switching transformations for control of opinion patterns in signed
  networks: application to dynamic task allocation

abstract:
  We propose a new design method to control opinion patterns on signed networks
of agents making decisions about two options and to switch the network from any
opinion pattern to a new desired one. Our method relies on switching
transformations, which switch the sign of an agent's opinion at a stable
equilibrium by flipping the sign of its local interactions with its neighbors.
The global dynamical behavior of the switched network can be predicted
rigorously when the original, and thus the witched, networks are structurally
balanced. Structural balance ensures that the network dynamics are monotone,
which makes the study of the basin of attraction of the various opinion
patterns amenable to rigorous analysis through monotone systems theory. We
illustrate the utility of the approach through scenarios motivated by
multi-robot coordination and dynamic task allocation.


title: Three-partite vertex model and knot invariants

abstract:
  This work is dedicated to the consideration of the construction of a
representation of braid group generators from vertex models with $N$-states,
which provides a great way to study the knot invariant. An algebraic formula is
proposed for the knot invariant when different spins $(N-1)/2$ are located on
all components of the knot. The work summarizes procedure outputting braid
generator representations from three-partite vertex model. This representation
made it possible to study the invariant of a knot with multi-colored links,
where the components of the knot have different spins. The formula for the
invariant of knot with a multi-colored link is studied from the point of view
of the braid generators obtained from the $R$-matrices of three-partite vertex
models. The resulting knot invariant $5_2$ corresponds to the Jones polynomial
and HOMFLY-PT.


title:
Analysis and Petrov-Galerkin numerical approximation for variable
  coefficient two-sided fractional diffusion, advection, reaction equations

abstract:
  In this paper we investigate the variable coefficient two-sided fractional
diffusion, advection, reaction equations on a bounded interval. It is known
that the fractional diffusion operator may lose coercivity due to the variable
coefficient, which makes both the mathematical and numerical analysis
challenging. To resolve this issue, we design appropriate test and trial
functions to prove the inf-sup condition of the variable coefficient fractional
diffusion, advection, reaction operators in suitable function spaces. Based on
this property, we prove the well-posedness and regularity of the solutions, as
well as analyze the Petrov-Galerkin approximation scheme for the proposed
model. Numerical experiments are presented to substantiate the theoretical
findings and to compare the behaviors of different models.


title:
GJ 229B: Solving the Puzzle of the First Known T-Dwarf with the APOLLO
  Retrieval Code

abstract:
  GJ 229B was the first T-dwarf to be discovered in 1995, and its spectrum has
been more thoroughly observed than most other brown dwarfs. Yet a full
spectroscopic analysis of its atmosphere has not been done with modern
techniques. This spectrum has several peculiar features, and recent dynamical
estimates of GJ 229B's mass and orbit have disagreed widely, both of which
warrant closer investigation. With a separation of tens of AU from its host
star, GJ 229B falls near the border of the planet and stellar population
formation regimes, so its atmosphere could provide clues to formation processes
for intermediate objects of this type. In an effort to resolve these questions,
we performed retrievals on published spectra of GJ 229B over a wide range of
wavelengths (0.5-5.1 ${\rm \mu m}$) using the open-source APOLLO code. Based on
these retrievals, we present a more precise mass estimate of $41.6\pm3.3\, M_J$
and an effective temperature estimate of $869_{-7}^{+5}$ K, which are more
consistent with evolutionary models for brown dwarfs and suggest an older age
for the system of $>$1.0 Gyr. We also present retrieved molecular abundances
for the atmosphere, including replicating the previously-observed high CO
abundance, and discuss their implications for the formation and evolution of
this object. This retrieval effort will give us insight into how to study other
brown dwarfs and directly-imaged planets, including those observed with JWST
and other next-generation telescopes.


title: Probing Higgs $CP$ properties at the CEPC

abstract:
  In the Circular Electron Positron Collider (CEPC), a measurement of the Higgs
$CP$ mixing through $e^{+} e^{-} \rightarrow Z H \rightarrow \mu^{+} \mu^{-}
H(\rightarrow b \bar{b} / c \bar{c} / g g)$ process is presented, with $5.6\
\mbox{ab}^{-1}$ $e^{+} e^{-}$ collision data at the center-of-mass energy of
$240\ \mathrm{GeV}$. In this study, the $CP$-violating parameter
$\hat{\alpha}_{A \tilde{Z}}$ is constrained between the region of $ -8.27\times
10^{-2}$ and $8.09 \times 10^{-2}$ and $\hat{\alpha}_{Z \tilde{Z}}$ between
$-2.15 \times 10^{-2}$ and $2.02 \times 10^{-2}$ at $95\%$ confidence level.
This study demonstrates the great potential of probing Higgs $CP$ properties at
the CEPC.


title: Scale Fragilities in Localized Consensus Dynamics

abstract:
  We consider distributed consensus in networks where the agents have
integrator dynamics of order two or higher ($n\ge 2$). We assume all feedback
to be localized in the sense that each agent has a bounded number of neighbors
and consider a scaling of the network through the addition of agents {in a
modular manner, i.e., without re-tuning controller gains upon addition}. We
show that standard consensus algorithms, {which rely on relative state
feedback, }are subject to what we term scale fragilities, meaning that
stability is lost as the network scales. For high-order agents ($n\ge 3$), we
prove that no consensus algorithm with fixed gains can achieve consensus in
networks of any size. That is, while a given algorithm may allow a small
network to converge, it causes instability if the network grows beyond a
certain finite size. This holds in families of network graphs whose algebraic
connectivity, that is, the smallest non-zero Laplacian eigenvalue, is
decreasing towards zero in network size (e.g. all planar graphs). For
second-order consensus ($n = 2$) we prove that the same scale fragility applies
to directed graphs that have a complex Laplacian eigenvalue approaching the
origin (e.g. directed ring graphs). The proofs for both results rely on
Routh-Hurwitz criteria for complex-valued polynomials and hold true for general
directed network graphs. We survey classes of graphs subject to these scale
fragilities, discuss their scaling constants, and finally prove that a
sub-linear scaling of nodal neighborhoods can suffice to overcome the issue.


title: CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation

abstract:
  Recent advances in self-supervised contrastive learning yield good
image-level representation, which favors classification tasks but usually
neglects pixel-level detailed information, leading to unsatisfactory transfer
performance to dense prediction tasks such as semantic segmentation. In this
work, we propose a pixel-wise contrastive learning method called CP2
(Copy-Paste Contrastive Pretraining), which facilitates both image- and
pixel-level representation learning and therefore is more suitable for
downstream dense prediction tasks. In detail, we copy-paste a random crop from
an image (the foreground) onto different background images and pretrain a
semantic segmentation model with the objective of 1) distinguishing the
foreground pixels from the background pixels, and 2) identifying the composed
images that share the same foreground.Experiments show the strong performance
of CP2 in downstream semantic segmentation: By finetuning CP2 pretrained models
on PASCAL VOC 2012, we obtain 78.6% mIoU with a ResNet-50 and 79.5% with a
ViT-S.


title:
A systematic study about orbit flips of test particles caused by
  eccentric von Zeipel-Lidov-Kozai effects

abstract:
  The problem of orbit flips caused by eccentric von Zeipel-Lidov-Kozai effects
is systematically investigated by means of three approaches, including
Poincar\'e sections, dynamical system theory (periodic orbits and invariant
manifolds) and perturbation treatments. Poincar\'e sections show that orbit
flips are due to the existence of islands of libration centered at inclination
of $90^{\circ}$, dynamical system theory shows that orbit flips are due to the
existence of polar periodic orbits and invariant manifolds, and perturbative
treatments indicate that orbit flips are due to the libration of a certain
critical argument. Using these approaches, the boundaries of flipping regions
in the entire parameter space are produced and they are in excellent agreement
with each other. Through analysis, the essence of flipping orbits is reached:
(a) flipping orbits are a kind of quasi-periodic trajectories around polar
periodic orbits and invariant manifolds at the same level of Hamiltonian
provide boundaries of flipping regions, and (b) flipping orbits are a kind of
resonant trajectories and resonant width measures the size of flipping regions.


title:
Milliarcsecond astrometry for the Galilean moons using stellar
  occultations

abstract:
  A stellar occultation occurs when a Solar System object passes in front of a
star for an observer. This technique allows the determination of sizes and
shapes of the occulting body with kilometer precision. Also, this technique
constrains the occulting body's positions, albedos, densities, etc. In the
context of the Galilean moons, these events can provide their best ground-based
astrometry, with uncertainties in the order of 1 mas ($\sim$ 3 km at Jupiter's
distance during opposition). We organized campaigns and successfully observed a
stellar occultation by Io (JI) in 2021, one by Ganymede (JIII) in 2020, and one
by Europa (JII) in 2019, with stations in North and South America. Also, we
re-analyzed two previously published events, one by Europa in 2016 and another
by Ganymede in 2017. Then, we fit the known 3D shape of the occulting satellite
and determine its center of figure. That resulted in astrometric positions with
uncertainties in the milliarcsecond level. The positions obtained from these
stellar occultations can be used together with dynamical models to ensure
highly accurate orbits of the Galilean moons. These orbits can help plan future
space probes aiming at the Jovian system, such as JUICE by ESA and Europa
Clipper by NASA, and allow more efficient planning of flyby maneuvers.


title: On the use of multilayer Laue lenses with X-ray Free Electron Lasers

abstract:
  Multilayer Laue lenses were used for the first time to focus x-rays from an
X-ray Free Electron Laser (XFEL). In an experiment, which was performed at the
European XFEL, we demonstrated focusing to a spot size of a few tens of
nanometers. A series of runs in which the number of pulses per train was
increased from 1 to 2, 3, 4, 5, 6, 7, 10, 20 and 30 pulses per train, all with
a pulse separation of 3.55 us, was done using the same set of lenses. The
increase in the number of pulses per train was accompanied with an increase of
x-ray intensity (transmission) from 9% to 92% at 5 pulses per train, and then
the transmission was reduced to 23.5 % when the pulses were increased further.
The final working condition was 30 pulses per train and 23.5% transmission.
Only at this condition we saw that the diffraction efficiency of the MLLs
changed over the course of a pulse train, and this variation was reproducible
from train to train. We present the procedure to align and characterize these
lenses and discuss challenges working with the pulse trains from this unique
x-ray source.


title: Resetting photons

abstract:
  Starting from a frequency diffusion process for a tagged photon which
simulates relaxation to the Planck law, we introduce a resetting where photons
lower their frequency at random times. We consider two versions, one where the
resetting to low frequency is independent of the existing frequency and a
second case where the reduction in frequency scales with the original
frequency. The result is a nonlinear Markov process where the stationary
distribution modifies the Planck law by abundance of low-frequency occupation.
The physical relevance of such photon resetting processes can be found in
explorations of nonequilibrium effects, e.g., via random expansions of a
confined plasma or photon gas or via strongly inelastic scattering with matter.


title:
Location- and Orientation-aware Millimeter Wave Beam Selection for
  Multi-Panel Antenna Devices

abstract:
  While initial beam alignment (BA) in millimeter-wave networks has been
thoroughly investigated, most research assumes a simplified terminal model
based on uniform linear/planar arrays with isotropic antennas. Devices with
non-isotropic antenna elements need multiple panels to provide good spherical
coverage, and exhaustive search over all beams of all the panels leads to
unacceptable overhead. This paper proposes a location- and orientation-aware
solution that manages the initial BA for multi-panel devices. We present three
different neural network structures that provide efficient BA with a wide range
of training dataset sizes, complexity, and feedback message sizes. Our proposed
methods outperform the generalized inverse fingerprinting and hierarchical
panel-beam selection methods for two considered edge and edge-face antenna
placement designs.


title: Uncertainty in wave hindcasts in the North Atlantic Ocean

abstract:
  Accurate ocean surface wave knowledge is crucial for ship design. With the
significant advancements of model physics and numerical resources, the recent
numerical wave hindcast data has a potential to provide environmental
conditions for wave load estimation in the ship design process. This study aims
to quantify model uncertainty in the state of art numerical wave hindcast
products to get insight into the application of wave hindcast for ship design.
The model uncertainty is deduced based on the comparison with the wave buoys in
the North Atlantic as well as the inter-model comparison with four wave model
products. The multiple wave buoys distributed over the Northwest Atlantic and
Northeast Atlantic are considered as wave buoy arrays and used for model
evaluations. All the four models in general showed very good and similar
accuracy in the estimation of the significant wave height $H_s$. The model
$H_s$, however, deviates from buoys and also deviate among each other for the
extreme wave conditions of $H_s$>10. It is also found that there is
disagreement for the mean wave period $T_{m02}$ between numerical wave models
and the wave buoys. More specifically, the models underestimate $T_{m02}$ for
extreme wave conditions of $H_s$>10 in the Northeast Atlantic. The joint
probability density function (JPD) of the significant wave height $H_s$ and
mean wave period $T_{m02}$ for the extreme wave conditions are also derived.
Among the JPDs, the total number of samples that satisfies the extreme wave
conditions of $H_s$>10 and 8<$T_{m02}$<14 varied by 50% although the mean value
of the four model products is close to the observation. This study indicates
further model improvement is necessary to more accurately reproduce the extreme
conditions. For the model improvement as well as the model evaluation, more
measurements of the high sea states and their wave spectra information are
warranted.


title:
Twisted logarithmic complexes of positively weighted homogeneous
  divisors

abstract:
  For a rank 1 local system on the complement of a divisor on a complex
manifold, its cohomology is calculated by the twisted meromorphic de Rham
complex. Assume the divisor is everywhere positively weighted homogeneous. We
show that this complex is quasi-isomorphic to the twisted logarithmic
subcomplex if the local system is sufficiently general. In the constant local
system case we prove that the quasi-isomorphism does not hold if there is an
integral roots of the Bernstein-Sato polynomial which is strictly smaller than
$-1$, or if there is a non-zero torsion element with weighted degree 1 in the
Brieskorn modules. We consider also a stronger version of the comparison
theorem, and give a simple proof in the hyperplane arrangement case as an
immediate corollary of higher cohomology vanishing associated with
Castelnuovo-Mumford regularity.


title: Spin-controlled quantum interference of levitated nano rotors

abstract:
  We describe how to prepare an electrically levitated nanodiamond in a
superposition of orientations via microwave driving of a single embedded
nitrogen-vacancy (NV) center. Suitably aligning the magnetic field with the NV
center can serve to reach the regime of ultrastrong coupling between the NV and
the diamond rotation, enabling single-spin control of the particle's
three-dimensional orientation. We derive the effective spin-oscillator
Hamiltonian for small amplitude rotation about the equilibrium configuration
and develop a protocol to create and observe quantum superpositions of the
particle orientation. We discuss the impact of decoherence and argue that our
proposal can be realistically implemented with near-future technology.


title:
Haar-type stochastic Galerkin formulations for hyperbolic systems with
  Lipschitz continuous flux function

abstract:
  This work is devoted to the Galerkin projection of highly nonlinear random
quantities. The dependency on a random input is described by Haar-type wavelet
systems. The classical Haar sequence has been used by Pettersson, Iaccarino,
Nordstroem (2014) for a hyperbolic stochastic Galerkin formulation of the
one-dimensional Euler equations. This work generalizes their approach to
several multi-dimensional systems with Lipschitz continuous and non-polynomial
flux functions. Theoretical results are illustrated numerically by a genuinely
multidimensional CWENO reconstruction.


title: A Bayesian Approach for Shaft Centre Localisation in Journal Bearings

abstract:
  It has been shown that ultrasonic techniques work well for online measuring
of circumferential oil film thickness profile in journal bearings;
unfortunately, they can be limited by their measuring range and unable to
capture details of the film all around the bearing circumference. Attempts to
model the film thickness over the full range of the bearing rely on
deterministic approaches, which assume the observations to be true with
absolute certainty. Unaccounted uncertainties of the film thickness may lead to
a cascade of inaccurate predictions for subsequent calculations of hydrodynamic
parameters. In the present work, a probabilistic framework is proposed to model
the film thickness with Gaussian Processes. The results are then used to
estimate the location of the bearing shaft under various operational
conditions. A further step in the process involves using the newly-constructed
dataset to generate likelihood maps displaying the probable location of the
shaft centre, given the bearing rotational speed and applied static load. The
results offer the possibility to visualise the confidence of the predictions
and allow the true location to be found within an area of high probability
within the bearing's bore.


title:
Continuous Detection, Rapidly React: Unseen Rumors Detection based on
  Continual Prompt-Tuning

abstract:
  Since open social platforms allow for a large and continuous flow of
unverified information, rumors can emerge unexpectedly and spread quickly.
However, existing rumor detection (RD) models often assume the same training
and testing distributions and cannot cope with the continuously changing social
network environment. This paper proposes a Continual Prompt-Tuning RD (CPT-RD)
framework, which avoids catastrophic forgetting of upstream tasks during
sequential task learning and enables knowledge transfer between domain tasks.
To avoid forgetting, we optimize and store task-special soft-prompt for each
domain. Furthermore, we also propose several strategies to transfer knowledge
of upstream tasks to deal with emergencies and a task-conditioned prompt-wise
hypernetwork (TPHNet) to consolidate past domains, enabling bidirectional
knowledge transfer. Finally, CPT-RD is evaluated on English and Chinese RD
datasets and is effective and efficient compared to state-of-the-art baselines,
without data replay techniques and with only a few parameter tuning.


title: Liouville conformal field theory on Riemann surface with boundaries

abstract:
  In this note, we give a unified rigorous construction for the Liouville
conformal field theory on compact Riemann surface with boundaries for
$\gamma\in (0,2]$ and prove a certain type of Markov property. This note will
serve as a reference for a program leading to the conformal bootstrap for
Riemann surface with boundaries and several related projects.


title:
Convolutional Neural Network to Restore Low-Dose Digital Breast
  Tomosynthesis Projections in a Variance Stabilization Domain

abstract:
  Digital breast tomosynthesis (DBT) exams should utilize the lowest possible
radiation dose while maintaining sufficiently good image quality for accurate
medical diagnosis. In this work, we propose a convolution neural network (CNN)
to restore low-dose (LD) DBT projections to achieve an image quality equivalent
to a standard full-dose (FD) acquisition. The proposed network architecture
benefits from priors in terms of layers that were inspired by traditional
model-based (MB) restoration methods, considering a model-based deep learning
approach, where the network is trained to operate in the variance stabilization
transformation (VST) domain. To accurately control the network operation point,
in terms of noise and blur of the restored image, we propose a loss function
that minimizes the bias and matches residual noise between the input and the
output. The training dataset was composed of clinical data acquired at the
standard FD and low-dose pairs obtained by the injection of quantum noise. The
network was tested using real DBT projections acquired with a physical
anthropomorphic breast phantom. The proposed network achieved superior results
in terms of the mean normalized squared error (MNSE), training time and noise
spatial correlation compared with networks trained with traditional data-driven
methods. The proposed approach can be extended for other medical imaging
application that requires LD acquisitions.


title:
The Equilibrium Temperature of Planets on Eccentric Orbits: Time Scales
  and Averages

abstract:
  From estimates of the near-surface heat capacity of planets it is shown that
the thermal time scale is larger than the orbital period in the presence of a
global ocean that is well-mixed to a depth of 100 m, or of an atmosphere with a
pressure of several tens of bars. As a consequence, the temperature
fluctuations of such planets on eccentric orbits are damped. The average
temperature should be calculated by taking the temporal mean of the irradiation
over an orbit, which increases with $1/\sqrt{1-e^2}$. This conclusion is
independent of the orbital distance and valid for Sun-like stars; the damping
is even stronger for low-mass main sequence hosts.


title:
Explainable Misinformation Detection Across Multiple Social Media
  Platforms

abstract:
  In this work, the integration of two machine learning approaches, namely
domain adaptation and explainable AI, is proposed to address these two issues
of generalized detection and explainability. Firstly the Domain Adversarial
Neural Network (DANN) develops a generalized misinformation detector across
multiple social media platforms DANN is employed to generate the classification
results for test domains with relevant but unseen data. The DANN-based model, a
traditional black-box model, cannot justify its outcome, i.e., the labels for
the target domain. Hence a Local Interpretable Model-Agnostic Explanations
(LIME) explainable AI model is applied to explain the outcome of the DANN mode.
To demonstrate these two approaches and their integration for effective
explainable generalized detection, COVID-19 misinformation is considered a case
study. We experimented with two datasets, namely CoAID and MiSoVac, and
compared results with and without DANN implementation. DANN significantly
improves the accuracy measure F1 classification score and increases the
accuracy and AUC performance. The results obtained show that the proposed
framework performs well in the case of domain shift and can learn
domain-invariant features while explaining the target labels with LIME
implementation enabling trustworthy information processing and extraction to
combat misinformation effectively.


title:
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented
  Multi-level Cross-attentional Masked Autoencoder

abstract:
  Unsupervised anomaly detection (UAD) aims to find anomalous images by
optimising a detector using a training set that contains only normal images.
UAD approaches can be based on reconstruction methods, self-supervised
approaches, and Imagenet pre-trained models. Reconstruction methods, which
detect anomalies from image reconstruction errors, are advantageous because
they do not rely on the design of problem-specific pretext tasks needed by
self-supervised approaches, and on the unreliable translation of models
pre-trained from non-medical datasets. However, reconstruction methods may fail
because they can have low reconstruction errors even for anomalous images. In
this paper, we introduce a new reconstruction-based UAD approach that addresses
this low-reconstruction error issue for anomalous images. Our UAD approach, the
memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE),
is a transformer-based approach, consisting of a novel memory-augmented
self-attention operator for the encoder and a new multi-level cross-attention
operator for the decoder. MemMC-MAE masks large parts of the input image during
its reconstruction, reducing the risk that it will produce low reconstruction
errors because anomalies are likely to be masked and cannot be reconstructed.
However, when the anomaly is not masked, then the normal patterns stored in the
encoder's memory combined with the decoder's multi-level cross-attention will
constrain the accurate reconstruction of the anomaly. We show that our method
achieves SOTA anomaly detection and localisation on colonoscopy and Covid-19
Chest X-ray datasets.


title:
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from
  Echocardiography

abstract:
  Left ventricular (LV) function is an important factor in terms of patient
management, outcome, and long-term survival of patients with heart disease. The
most recently published clinical guidelines for heart failure recognise that
over reliance on only one measure of cardiac function (LV ejection fraction) as
a diagnostic and treatment stratification biomarker is suboptimal. Recent
advances in AI-based echocardiography analysis have shown excellent results on
automated estimation of LV volumes and LV ejection fraction. However, from
time-varying 2-D echocardiography acquisition, a richer description of cardiac
function can be obtained by estimating functional biomarkers from the complete
cardiac cycle. In this work we propose for the first time an AI approach for
deriving advanced biomarkers of systolic and diastolic LV function from 2-D
echocardiography based on segmentations of the full cardiac cycle. These
biomarkers will allow clinicians to obtain a much richer picture of the heart
in health and disease. The AI model is based on the 'nn-Unet' framework and was
trained and tested using four different databases. Results show excellent
agreement between manual and automated analysis and showcase the potential of
the advanced systolic and diastolic biomarkers for patient stratification.
Finally, for a subset of 50 cases, we perform a correlation analysis between
clinical biomarkers derived from echocardiography and CMR and we show excellent
agreement between the two modalities.


title:
Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis
  in Passive Optical Networks

abstract:
  We propose a deep learning approach based on an autoencoder for identifying
and localizing fiber faults in passive optical networks. The experimental
results show that the proposed method detects faults with 97% accuracy,
pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.


title:
Machine Learning based Data Driven Diagnostic and Prognostic Approach
  for Laser Reliability Enhancement

abstract:
  In this paper, a data-driven diagnostic and prognostic approach based on
machine learning is proposed to detect laser failure modes and to predict the
remaining useful life (RUL) of a laser during its operation. We present an
architecture of the proposed cognitive predictive maintenance framework and
demonstrate its effectiveness using synthetic data.


title: Machine Learning based Laser Failure Mode Detection

abstract:
  Laser degradation analysis is a crucial process for the enhancement of laser
reliability. Here, we propose a data-driven fault detection approach based on
Long Short-Term Memory (LSTM) recurrent neural networks to detect the different
laser degradation modes based on synthetic historical failure data. In
comparison to typical threshold-based systems, attaining 24.41% classification
accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms
classical machine learning (ML) models namely Random Forest (RF), K-Nearest
Neighbours (KNN) and Logistic Regression (LR).


title: Multi-dimensional Jordan chain and Navier-Stokes Equation

abstract:
  Multi-dimensional Jordan chain is presented. It is shown that it admits the
finite-component reductions to the Navier-Stokes equation and other important
equations of continuous media theory.


title:
A modified surface kinetic model for calcium and strontium isotope
  fractionation during calcite precipitation

abstract:
  The Ca and Sr isotope fractionation factors ($\mathrm{\Delta^{44/40}Ca}$ and
$\mathrm{\Delta^{88/86}Sr}$), as well as the bulk Sr partition coefficient
($K$), depend on calcite precipitation rates in both experimental and natural
settings. These processes are expected to be controlled by surface kinetics.
Ten years ago, Depaolo (2011) proposed a surface reaction model that
successfully explained the kinetic effects on $\mathrm{\Delta^{44/40}Ca}$ and
$K$. With new observations of $\mathrm{\Delta^{88/86}Sr}$, the limitation of
this model emerges. Here, we develop a modified surface reaction model with
separate contributions from two underlying precipitation mechanisms, namely
spiral growth and surface nucleation. We derive the most representative model
by synthesizing general forms with simple but justifiable assumptions of
surface reaction kinetics. Our modified model successfully explains
observations of the precipitation rate dependence of
$\mathrm{\Delta^{44/40}Ca}$, $\mathrm{\Delta^{88/86}Sr}$, and $K$, as well as
their correlations. Our findings suggest that surface reactions during calcite
precipitation solely regulate these parameters, regardless of diffusion. The
revised model can be readily integrated with existing stoichiometric models and
offers important implications for interpreting carbonate stable Ca and Sr
isotope compositions, as well as the partitioning and isotope fractionation of
other trace elements during carbonate precipitation.


title:
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based
  Motion Segmentation

abstract:
  Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting
apparent motion of objects with microsecond resolution, and shows great
application potential in monitoring and other fields. However, the output event
stream of existing DVS inevitably contains background activity noise (BA noise)
due to dark current and junction leakage current, which will affect the
temporal correlation of objects, resulting in deteriorated motion estimation
performance. Particularly, the existing filter-based denoising methods cannot
be directly applied to suppress the noise in event stream, since there is no
spatial correlation. To address this issue, this paper presents a novel
progressive framework, in which a Motion Estimation (ME) module and an Event
Denoising (ED) module are jointly optimized in a mutually reinforced manner.
Specifically, based on the maximum sharpness criterion, ME module divides the
input event into several segments by adaptive clustering in a motion
compensating warp field, and captures the temporal correlation of event stream
according to the clustered motion parameters. Taking temporal correlation as
guidance, ED module calculates the confidence that each event belongs to real
activity events, and transmits it to ME module to update energy function of
motion segmentation for noise suppression. The two steps are iteratively
updated until stable motion segmentation results are obtained. Extensive
experimental results on both synthetic and real datasets demonstrate the
superiority of our proposed approaches against the State-Of-The-Art (SOTA)
methods.


title: GBEM: Galerkin Boundary Element Method for 3-D Capacitance Extraction

abstract:
  For modern IC design, electromagnetic coupling among interconnect wires plays
an increasingly important role in signoff analysis. The requirement of fast and
accurate capacitance extraction is becoming more and more urgent.The critical
step of extracting capacitance among interconnect wires is solving electric
field. However, due to the high computational complexity, solving electric
field is extreme timing-consuming. To improve computational efficiency, we
propose a Galerkin boundary element method (GBEM) to extract capacitance. The
advantage of this method is that it can greatly reduce the number of boundary
elements on the premise of ensuring that the error is small enough. As a
consequence, the matrix order of the discretization equation will also
decrease. The experiments in this paper have proved this advantage of our
algorithm. Moreover, we have took advantage of some mathematical theorems in
this paper. Our attempt shows that there will be more connections between the
capacitance extraction and some mathematical conception so that we can use more
mathematical tools to solve the problems of capacitance extraction.


title: Graph spatial sampling

abstract:
  We develop lagged Metropolis-Hastings walk for sampling from simple
undirected graphs according to given stationary sampling probabilities. We
explain how to apply the technique together with designed graphs for sampling
of units-in-space. We illustrate that the proposed graph spatial sampling
approach can be more flexible for improving the design efficiency compared to
the existing spatial sampling methods.


title:
A conservative multiscale method for stochastic highly heterogeneous
  flow

abstract:
  In this paper, we propose a local model reduction approach for subsurface
flow problems in stochastic and highly heterogeneous media. To guarantee the
mass conservation, we consider the mixed formulation of the flow problem and
aim to solve the problem in a coarse grid to reduce the complexity of a
large-scale system. We decompose the entire problem into a training and a
testing stage, namely the offline coarse-grid multiscale basis generation stage
and online simulation stage with different parameters. In the training stage, a
parameter-independent and small-dimensional multiscale basis function space is
constructed, which includes the media, source and boundary information. The key
part of the basis generation stage is to solve some local problems defined
specially. With the parameter-independent basis space, one can efficiently
solve the concerned problems corresponding to different samples of permeability
field in a coarse grid without repeatedly constructing a multiscale space for
each new sample. A rigorous analysis on convergence of the proposed method is
proposed. In particular, we consider a generalization error, where bases
constructed with one source will be used to a different source. In the
numerical experiments, we apply the proposed method for both single-phase and
twophase flow problems. Simulation results for both 2D and 3D representative
models demonstrate the high accuracy and impressive performance of the proposed
model reduction techniques.


title: Sensitivity to Dark Sector Scales from Gravitational Wave Signatures

abstract:
  We consider gravitational wave signals produced by a first-order phase
transition in a theory with a generic renormalizable thermal effective
potential of power law form. We find the frequency and amplitude of the
gravitational wave signal can be related in a straightforward manner to the
parameters of the thermal effective potential. This leads to a general
conclusion; if the mass of the dark Higgs is less than 1% of the dark Higgs
vacuum expectation value, then the gravitational wave signal will be
unobservable at all upcoming and planned gravitational wave observatories.


title: Path large deviations for the kinetic theory of weak turbulence

abstract:
  We consider a generic Hamiltonian system of nonlinear interacting waves with
3-wave interactions. In the kinetic regime of wave turbulence, which assumes
weak nonlinearity and large system size, the relevant observable associated
with the wave amplitude is the empirical spectral density that appears as the
natural precursor of the spectral density, or spectrum, for finite system size.
Following classical derivations of the Peierls equation for the moment
generating function of the wave amplitudes in the kinetic regime, we propose a
large deviation estimate for the dynamics of the empirical spectral density,
where the number of admissible wavenumbers, which is proportional to the volume
of the system, appears as the natural large deviation parameter. The large
deviation stochastic Hamiltonian that quantifies the minus of the log
probability of a trajectory is computed within the kinetic regime which assumes
the Random Phase approximation for weak nonlinearity. We compare this
Hamiltonian with the one for a system of modes interacting in a mean-field way
with the empirical spectrum. Its relationship with the Random Phase and
Amplitude approximation is discussed. Moreover, for the specific case when no
forces and dissipation are present, a few fundamental properties of the large
deviation dynamics are investigated. We show that the latter conserves total
energy and momentum, as expected for a 3-wave interacting systems. In addition,
we compute the equilibrium quasipotential and check that global detailed
balance is satisfied at the large deviation level. Finally, we discuss briefly
some physical applications of the theory.


title: Deformations of some local Calabi-Yau manifolds

abstract:
  We study deformations of certain crepant resolutions of isolated rational
Gorenstein singularities. After a general discussion of the deformation theory,
we specialize to dimension three and consider examples which are log
resolutions as well as small resolutions. We obtain some partial results on the
classification of canonical threefold singularities that admit good crepant
resolutions. Finally, we study a non-crepant example, the blowup of a small
resolution whose exceptional set is a smooth curve.


title:
Spectral Characteristics of Schr\"odinger Operators Generated by Product
  Systems

abstract:
  We study ergodic Schr\"odinger operators defined over product dynamical
systems in which one factor is periodic and the other factor is either a
subshift over a finite alphabet or an irrational rotation of the circle. In the
case in which one factor is a Boshernitzan subshift, we prove that either the
resulting operators are periodic or the resulting spectra must be Cantor sets.
The main ingredient is a suitable stability result for Boshernitzan's criterion
under taking products. We also discuss the stability of purely singular
continuous spectrum, which, given the zero-measure spectrum result, amounts to
stability results for eigenvalue exclusion. In particular, we examine
situations in which the existing criteria for the exclusion of eigenvalues are
stable under periodic perturbations. As a highlight of this, we show that any
simple Toeplitz subshift over a binary alphabet exhibits uniform absence of
eigenvalues on the hull for any periodic perturbation whose period is
commensurate with the coding sequence. In the case of a full shift, we give an
effective criterion to compute exactly the spectrum of a random Anderson model
perturbed by a potential of period two, and we further show that the naive
generalization of this criterion does not hold for period three. Next, we
consider quasi-periodic potentials with potentials generated by trigonometric
polynomials with periodic background. We show that the quasiperiodic cocycle
induced by passing to blocks of period length is subcritical when the coupling
constant is small and supercritical when the coupling constant is large. Thus,
the spectral type is absolutely continuous for small coupling and pure point
(for a.e.\ frequency and phase) when the coupling is large.


title:
Plasticity Neural Network Based on Astrocytic Influence at Critical
  Periods, Synaptic Competition and Compensation by Current and Mnemonic Brain
  Plasticity and Synapse Formation

abstract:
  Based on the RNN frame, we accomplished the model construction, formula
derivation and algorithm testing for PNN. We elucidated the mechanism of PNN
based on the latest MIT research on synaptic compensation, and also grounded
our study on the basis of findings of the Stanford research, which suggested
that synapse formation is important for competition in dendrite morphogenesis.
The influence of astrocytic impacts on brain plasticity and synapse formation
is an important mechanism of our Neural Network at critical periods or the end
of critical periods.In the model for critical periods, the hypothesis is that
the best brain plasticity so far affects current brain plasticity and the best
synapse formation so far affects current synapse formation.Furthermore, PNN
takes into account the mnemonic gradient informational synapse formation, and
brain plasticity and synapse formation change frame of NN is a new method of
Deep Learning.The question we proposed is whether the promotion of neuroscience
and brain cognition was achieved by model construction, formula derivation or
algorithm testing. We resorted to the Artificial Neural Network (ANN),
evolutionary computation and other numerical methods for hypotheses, possible
explanations and rules, rather than only biological tests which include
cutting-edge imaging and genetic tools.And it has no ethics of animal testing.


title: Network Meta-Analysis: A Statistical Physics Perspective

abstract:
  Network meta-analysis (NMA) is a technique used in medical statistics to
combine evidence from multiple medical trials. NMA defines an inference and
information processing problem on a network of treatment options and trials
connecting the treatments. We believe that statistical physics can offer useful
ideas and tools for this area, including from the theory of complex networks,
stochastic modelling and simulation techniques. The lack of a unique source
that would allow physicists to learn about NMA effectively is a barrier to
this. In this article we aim to present the `NMA problem' and existing
approaches to it coherently and in a language accessible to statistical
physicists. We also summarise existing points of contact between statistical
physics and NMA, and describe our ideas of how physics might make a difference
for NMA in the future. The overall goal of the article is to attract physicists
to this interesting, timely and worthwhile field of research.


title:
Rapid direct growth of graphene on single-crystalline diamond using
  nickel as catalyst

abstract:
  Although theoretical investigations indicate that the successful combination
of graphene and diamond would give interesting properties, only a limited
number of reports dealing with the subject have been published. Here, we
present a rapid thermal process (RTP) which involves Ni as metal catalyst for a
direct growth of graphene on diamond at a temperature of 800 $^{\circ}$C for 1
min. The process operates at a lower temperature and for a shorter duration
than what has previously been reported. Thin Ni films with different
thicknesses were deposited on top of single crystalline diamond. After RTP, the
coverage of monolayer graphene was found to be around 20% shown by the
intensity ratio between the 2D- and G peak using Raman spectroscopy on 50-nm
thick Ni films. In addition, x-ray photoelectron spectroscopy and atomic force
microscopy analysis were conducted for additional investigation. For electrical
characterization, Hall-effect measurements were performed at temperatures
between 80 and 360 K. The sheet carrier concentration, sheet resistivity and
Hall mobility were at room temperature found to be $1\times 10^{15} cm^{-2}$,
$75$ ${\Omega}/square$ and $79$ $cm^2/Vs$ respectively.


title:
The Stanford Drone Dataset is More Complex than We Think: An Analysis of
  Key Characteristics

abstract:
  Several datasets exist which contain annotated information of individuals'
trajectories. Such datasets are vital for many real-world applications,
including trajectory prediction and autonomous navigation. One prominent
dataset currently in use is the Stanford Drone Dataset (SDD). Despite its
prominence, discussion surrounding the characteristics of this dataset is
insufficient. We demonstrate how this insufficiency reduces the information
available to users and can impact performance. Our contributions include the
outlining of key characteristics in the SDD, employment of an
information-theoretic measure and custom metric to clearly visualize those
characteristics, the implementation of the PECNet and Y-Net trajectory
prediction models to demonstrate the outlined characteristics' impact on
predictive performance, and lastly we provide a comparison between the SDD and
Intersection Drone (inD) Dataset. Our analysis of the SDD's key characteristics
is important because without adequate information about available datasets a
user's ability to select the most suitable dataset for their methods, to
reproduce one another's results, and to interpret their own results are
hindered. The observations we make through this analysis provide a readily
accessible and interpretable source of information for those planning to use
the SDD. Our intention is to increase the performance and reproducibility of
methods applied to this dataset going forward, while also clearly detailing
less obvious features of the dataset for new users.


title: Evidence of Amazon rainforest dieback in CMIP6 models

abstract:
  Amazon forest dieback is seen as a potential tipping point under climate
change. These concerns are partly based-on an early coupled climate-carbon
cycle simulation, that produced unusually strong drying and warming in
Amazonia. In contrast, the 5th generation Earth System Models (CMIP5) produced
few examples of Amazon dieback under climate change. Here we examine results
from seven 6th generation models (CMIP6) which include vegetation dynamics, and
in some cases interactive forest fires. Although these models typically project
increases in area-mean forest carbon across Amazonia under CO2-induced climate
change, five of the seven models also produce abrupt reductions in vegetation
carbon which indicate localised dieback events. The Northern South America
region (NSA), which contains most of the rainforest, is especially vulnerable
in the models. These dieback events, some of which are mediated by fire, are
preceded by an increase in the amplitude of the seasonal cycle in near surface
temperature, which is consistent with more extreme dry seasons. Based-on the
ensemble mean of the detected dieback events we estimate that 7 +/-5% of the
NSA region will experience abrupt downward shifts in vegetation carbon per
degree of global warming above 1.5 degrees.


title:
Solar rotation elements $i, \Omega$ and period determined using sunspot
  observations by Ru{\dj}er Bo\v{s}kovi\'c in 1777

abstract:
  Ru{\dj}er Bo\v{s}kovi\'c developed methods for determination of solar
rotation elements: the solar equator inclination i, the longitude of the node
{\Omega} and the period of solar rotation. In his last work Opera pertinentia
ad opticam et astronomiam, published in 1785, in the chapter Opuscule II he
described his methods, the formulae with figure descriptions and an example for
calculation of the solar rotation elements with detailed numerical explanation
using his own observations performed in September 1777. The original numerical
procedure was performed using logarithmic formulae. In present work we give a
description of the original results of R. Bo\v{s}kovi\'c and compare them with
our recalculated values.


title: Rough Solutions of the Relativistic Euler Equations

abstract:
  We prove that the time of classical existence of smooth solutions to the
relativistic Euler equations can be bounded from below in terms of norms that
measure the "(sound) wave-part" of the data in Sobolev space and
"transport-part" in higher regularity Sobolev space and H\"older spaces. The
solutions are allowed to have nontrivial vorticity and entropy. Our main result
is that the Sobolev norm $H^{2+}$ of the variables in the "wave-part" and the
H\"older norm $C^{0,0+}$ of the variables in the "transport-part" can be
controlled in terms of initial data for short times. We note that the Sobolev
norm assumption $H^{2+}$ is the optimal result for the variables in the
"wave-part." Compared to low regularity results for quasilinear wave equations
and the 3D non-relativistic compressible Euler equations, the main new
challenge of the paper is that when controlling the acoustic geometry and
bounding the wave equation energies, we must deal with the difficulty that the
vorticity and entropy gradient are four-dimensional space-time vectors
satisfying a space-time div-curl-transport system, where the space-time
div-curl part is not elliptic. To overcome this difficulty, we show that the
space-time div-curl systems imply elliptic div-curl-transport systems on
constant-time hypersurfaces plus error terms that involve favorable
differentiations and contractions with respect to the four-velocity. By using
these structures, we are able to adequately control the vorticity and entropy
gradient with the help of energy estimates for transport equations, elliptic
estimates, Schauder estimates, and Littlewood-Paley theory.


title:
Correlation between the solar magnetic field strength and the millimeter
  brightness temperature

abstract:
  Images of the Sun at millimeter wavelengths obtained by ALMA show a
significant correspondence with the magnetograms. In this paper, we investigate
this correspondence by comparing ALMA full-disk solar image taken at 1.2 mm
with a SDO/HMI magnetogram and analyze their correlation. It is found that
chromospheric network and active regions show a positive correlation where
brightness temperature is increasing with the line-of-sight magnetic field
strength, while sunspots have a negative correlation. Quiet Sun regions do not
show any dependence of the brightness temperature with the magnetic field.
Thermal bremsstrahlung is given as the best explanation for the observed
correlations.


title:
On p-value combination of independent and frequent signals: asymptotic
  efficiency and Fisher ensemble

abstract:
  Combining p-values to integrate multiple effects is of long-standing interest
in social science and biomedical research. In this paper, we focus on
revisiting a classical scenario closely related to meta-analysis, which
combines a relatively small (finite and fixed) number of p-values while the
sample size for generating each p-value is large (asymptotically goes to
infinity). We evaluate a list of traditional and recently developed modified
Fisher's methods to investigate their asymptotic efficiencies and finite-sample
numerical performance. The result concludes Fisher and adaptively weighted
Fisher method to have top performance and complementary advantages across
different proportions of true signals. Finally, we propose an ensemble method,
namely Fisher ensemble, to combine the two top-performing Fisher-related
methods using a robust truncated Cauchy ensemble approach. We show that Fisher
ensemble achieves asymptotic Bahadur optimality and integrates the strengths of
Fisher and adaptively weighted Fisher methods in simulations. We subsequently
extend Fisher ensemble to a variant with emphasized power for concordant effect
size directions. A transcriptomic meta-analysis application confirms the
theoretical and simulation conclusions, generates intriguing biomarker and
pathway findings and demonstrates strengths and strategy of using proposed
Fisher ensemble methods.


title:
Global existence and blow-up for a stochastic transport equation with
  non-local velocity

abstract:
  In this paper we investigate a non-linear and non-local one dimensional
transport equation under random perturbations on the real line. We first
establish a local-in-time theory, i.e., existence, uniqueness and blow-up
criterion for pathwise solutions in Sobolev spaces $H^{s}$ with $s>3$.
Thereafter, we give a complete picture of the long time behavior of the
solutions based on the type of noise we consider. On one hand, we identify a
family of noises such that blow-up can be prevented with probability $1$,
guaranteeing the existence and uniqueness of global solutions almost surely. On
the other hand, in the particular linear noise case, we show that singularities
occur in finite time with positive probability, and we derive lower bounds of
these probabilities. To conclude, we introduce the notion of stability of
exiting times and show that one cannot improve the stability of the exiting
time and simultaneously improve the continuity of the dependence on initial
data.


title: Variants of the de Jong fundamental group

abstract:
  For a rigid space $X$, we answer two questions of de Jong about the category
$\mathbf{Cov}^\mathrm{adm}_X$ of coverings which are locally in the admissible
topology on $X$ the disjoint union of finite etale coverings: we show that this
class is different from the one used by de Jong, but still gives a tame
infinite Galois category. In addition, we prove that the objects of
$\mathbf{Cov}^\mathrm{et}_X$ (with the analogous definition) correspond
precisely to locally constant sheaves for the pro-etale topology defined by
Scholze.


title:
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling
  and Correction

abstract:
  Federated learning (FL) allows multiple clients to collectively train a
high-performance global model without sharing their private data. However, the
key challenge in federated learning is that the clients have significant
statistical heterogeneity among their local data distributions, which would
cause inconsistent optimized local models on the client-side. To address this
fundamental dilemma, we propose a novel federated learning algorithm with local
drift decoupling and correction (FedDC). Our FedDC only introduces lightweight
modifications in the local training phase, in which each client utilizes an
auxiliary local drift variable to track the gap between the local model
parameter and the global model parameters. The key idea of FedDC is to utilize
this learned local drift variable to bridge the gap, i.e., conducting
consistency in parameter-level. The experiment results and analysis demonstrate
that FedDC yields expediting convergence and better performance on various
image classification tasks, robust in partial participation settings, non-iid
data, and heterogeneous clients.


title:
COSTARICA estimator for rollback-less systems handling in iterative
  co-simulation algorithms

abstract:
  Co-simulation is widely used in the industry due to the emergence of modular
dynamical models made up of interconnected, black-boxed systems. Several
co-simulation algorithms have been developed, each with different properties
and different levels of accuracy and robustness. Among them, the most accurate
and reliable ones are the iterative ones, although they have a main drawback in
common: the involved systems are required to be capable of rollback. The latter
denotes the ability of a system to integrate over a co-simulation time step
that has already been simulated. Non-rollback-capable system can only go
forward in time and every integrated step is definitive. In practice, the
industrial modelling and simulation platforms rarely produce rollback-capable
systems. This paper proposes a solution that slightly changes the co-simulation
methodology and that enables to use iterative co-simulation methods on a
modular model which contains non-rollback-capable systems in case the latter
represent ordinary differential equations. The idea is to replace such a system
by a simplified version, which is used to estimate the results of the
integrations instead of integrating the real system. Once the co-simulation
method's surrogate iterations on these estimators predict the convergence on
the co-simulation step, the non-rollback-capable systems genuinely integrate
the step using the estimated solution on the other systems before moving
forward, transforming the iterative co-simulation method into a non-iterative
one.

categories: eess.SY cs.CE cs.NA cs.SY math.DS math.NA


title: Modelling mechanically dominated vasculature development

abstract:
  Vascular networks play a key role in the development, function, and survival
of many organisms, facilitating transport of nutrients and other critical
factors within and between systems. The development of these vessel networks
has been thoroughly explored in a variety of in vivo, in vitro and in silico
contexts. However, the role of interactions between the growing vasculature and
its environment remains largely unresolved, particularly concerning mechanical
effects. Motivated by this gap in understanding, we develop a computational
framework that is tailored to exploring the role of the mechanical environment
on the formation of vascular networks. Here, we describe, document, implement,
and explore an agent-based modelling framework, resolving the growth of
individual vessels and seeking to capture phenomenology and intuitive
qualitative mechanisms. In our explorations, we demonstrate that such a model
can successfully reproduce familiar network structures, whilst highlighting the
roles that mechanical influences could play in vascular development. For
instance, we illustrate how an external substrate could act as an effective
shared memory for the periodic regrowth of vasculature. We also observe the
emergence of a nuanced collective behaviour and clustered vessel growth, which
results from mechanical characteristics of the external environment.


title: Exploring and Evaluating Image Restoration Potential in Dynamic Scenes

abstract:
  In dynamic scenes, images often suffer from dynamic blur due to superposition
of motions or low signal-noise ratio resulted from quick shutter speed when
avoiding motions. Recovering sharp and clean results from the captured images
heavily depends on the ability of restoration methods and the quality of the
input. Although existing research on image restoration focuses on developing
models for obtaining better restored results, fewer have studied to evaluate
how and which input image leads to superior restored quality. In this paper, to
better study an image's potential value that can be explored for restoration,
we propose a novel concept, referring to image restoration potential (IRP).
Specifically, We first establish a dynamic scene imaging dataset containing
composite distortions and applied image restoration processes to validate the
rationality of the existence to IRP. Based on this dataset, we investigate
several properties of IRP and propose a novel deep model to accurately predict
IRP values. By gradually distilling and selective fusing the degradation
features, the proposed model shows its superiority in IRP prediction. Thanks to
the proposed model, we are then able to validate how various image restoration
related applications are benefited from IRP prediction. We show the potential
usages of IRP as a filtering principle to select valuable frames, an auxiliary
guidance to improve restoration models, and even an indicator to optimize
camera settings for capturing better images under dynamic scenarios.


title:
Binary recurrences, Jacobi symbol criterion, Integer points on elliptic
  curves

abstract:
  In 1987, Ming Luo showed that the Fibonacci number $U_n$ is Triangular if and
only if $n=\pm1,2,4,8,10$. For this, he established a Jacobi Symbol Criterion.
Moreover, he observed this problem is equivalent to finding all integer points
on two elliptic curves. In this paper, we prove a Jacobi Symbol Criterion for
more general families of binary recurrences. In addition, applying the
criterion, we determine all integer points on the elliptic curves
$y^2=5x^2(x+3)^2+4(-1)^n$.


title:
Simultaneous Inference of Neutron Star Equation of State and Hubble
  Constant with a Population of Merging Neutron Stars

abstract:
  We develop a method for implementing a proposal on utilizing knowledge of
neutron star (NS) equation of state (EoS) for inferring the Hubble constant
from a population of binary neutron star (BNS) mergers. This method is useful
in exploiting BNSs as standard sirens when their redshifts are unavailable.
Gravitational wave (GW) signals from compact object binaries provide a direct
measurement of their luminosity distances, but not the redshifts. Unlike in the
past, we employ a realistic EoS parameterization in a Bayesian framework to
simultaneously measure the Hubble constant and refine the constraints on the
EoS parameters. The uncertainty in the redshift depends on the uncertainty in
EoS and mass parameters estimated from GW data. Combining the inferred BNS
redshifts with the corresponding luminosity distances, one constructs a
redshift-distance relationship and deduces the Hubble constant from it. Here,
we show that in the Cosmic Explorer era, one can measure the Hubble constant to
a precision of $\sim 3\%$ (with a $90\%$ credible interval) with a realistic
distribution of thousand BNSs, while allowing for uncertainties in their EoS
parameters. The methodology implemented in this work demonstrates a
comprehensive algorithm to infer NS EoS and the Hubble constant by
simultaneously combining GW observations from merging NSs, choosing a simple
population model of NS masses and keeping the merger rate of NSs constant. This
method can be immediately extended to incorporate merger rate, population
properties, and different cosmological parameters.


title: Deep Learning-based Imaging in Radio Interferometry

abstract:
  The sparse layouts of radio interferometers result in an incomplete sampling
of the sky in Fourier space which leads to artifacts in the reconstructed
images. Cleaning these systematic effects is essential for the scientific use
of radiointerferometric images. Established reconstruction methods are often
time-consuming, require expert-knowledge, and suffer from a lack of
reproducibility. We have developed a prototype Deep Learning-based method that
generates reproducible images in an expedient fashion. To this end, we take
advantage of the efficiency of Convolutional Neural Networks to reconstruct
image data from incomplete information in Fourier space. The Neural Network
architecture is inspired by super-resolution models that utilize residual
blocks. Using simulated data of radio galaxies that are composed of Gaussian
components we train Deep Learning models whose reconstruction capability is
quantified using various measures. The reconstruction performance is evaluated
on clean and noisy input data by comparing the resulting predictions with the
true source images. We find that source angles and sizes are well reproduced,
while the recovered fluxes show substantial scatter, albeit not worse than
existing methods without fine-tuning. Finally, we propose more advanced
approaches using Deep Learning that include uncertainty estimates and a concept
to analyze larger images.


title:
Linear convergence of a policy gradient method for finite horizon
  continuous time stochastic control problems

abstract:
  Despite its popularity in the reinforcement learning community, a provably
convergent policy gradient method for general continuous space-time stochastic
control problems has been elusive. This paper closes the gap by proposing a
proximal gradient algorithm for feedback controls of finite-time horizon
stochastic control problems. The state dynamics are continuous time nonlinear
diffusions with controlled drift and possibly degenerate noise, and the
objectives are nonconvex in the state and nonsmooth in the control. We prove
under suitable conditions that the algorithm converges linearly to a stationary
point of the control problem, and is stable with respect to policy updates by
approximate gradient steps. The convergence result justifies the recent
reinforcement learning heuristics that adding entropy regularization or a
fictitious discount factor to the optimization objective accelerates the
convergence of policy gradient methods. The proof exploits careful regularity
estimates of backward stochastic differential equations.


title:
Adiabatic spin dynamics and effective exchange interactions from
  constrained tight-binding electronic structure theory: beyond the Heisenberg
  regime

abstract:
  We consider an implementation of the adiabatic spin dynamics approach in a
tight-binding description of the electronic structure. The adiabatic
approximation for spin-degrees of freedom assumes that the faster electronic
degrees of freedom are always in a quasi-equilibrium state, which significantly
reduces the numerical complexity in comparison to the full electron dynamics.
Non-collinear magnetic configurations are stabilized by a constraining field,
which allows to directly obtain the effective magnetic field from the negative
of the constraining field. While the dynamics are shown to conserve energy, we
demonstrate that adiabatic spin dynamics does not conserve the total spin
angular momentum when the lengths of the magnetic moments are allowed to
change, which is confirmed by numerical simulations. Furthermore, we develop a
method to extract an effective two-spin exchange interaction from the energy
curvature tensor of non-collinear states. We demonstrate the effect of
non-collinearity on this effective exchange and limitations due to multi-spin
interactions in strongly non-collinear configurations beyond the regime where
the Heisenberg model is valid. The relevance of the results are discussed with
respect to experimental pump-probe experiments that follow the ultra-fast
dynamics of magnetism.


title: Quotients of skew morphisms of cyclic groups

abstract:
  A skew morphism of a finite group $B$ is a permutation $\varphi$ of $B$ that
preserves the identity element of $B$ and has the property that for every $a\in
B$ there exists a positive integer $i_a$ such that $\varphi(ab) =
\varphi(a)\varphi^{i_a}(b)$ for all $b\in B$. The problem of classifying skew
morphisms for all finite cyclic groups is notoriously hard, with no such
classification available up to date. In this paper, we show that each skew
morphism $\varphi$ of a finite cyclic group is closely related to a specific
skew morphism of the cyclic group of order $|\langle \varphi \rangle|$, called
a quotient of $\varphi$. We then use this relationship to prove new theorems
about skew morphisms of cyclic groups. In particular, we classify all skew
morphisms for cyclic groups of orders $4p$, $8p$, and $16p$, where $p$ is
prime, and for all cyclic groups whose order is a product of three distinct
primes. We also develop an algorithm for finding skew morphisms of cyclic
groups, and implement this algorithm in MAGMA to obtain a census of all skew
morphisms of cyclic groups of order up to $161$.
  During the preparation of this paper we noticed a few flaws in Section 5 of
the paper Cyclic complements and skew morphisms of groups [J. Algebra 453
(2016), 68-100]. We propose and prove weaker versions of the problematic
original assertions (namely Lemma 5.3(b), Theorem 5.6 and Corollary 5.7), and
show that our modifications can be used to fix all consequent proofs (in the
aforementioned paper) that use at least one of those problematic assertions.


title: Tilings of a Honeycomb Strip and Higher Order Fibonacci Numbers

abstract:
  In this paper we explore two types of tilings of a honeycomb strip and derive
some closed form formulas for the number of tilings. Furthermore, we obtain
some new identities involving tribonacci numbers, Padovan numbers and
Narayana's cow sequence and provide combinatorial proofs for several known
identities about those numbers.


title: Lattice QCD study of inclusive semileptonic decays of heavy mesons

abstract:
  We present an ab initio study of inclusive semileptonic decays of heavy
mesons from lattice QCD. Our approach is based on a recently proposed method,
that allows one to address the study of these decays from the analysis of
smeared spectral functions extracted from four-point correlators on the
lattice, where the smearing is defined in terms of the phase-space integration
relevant to the inclusive decays. We present results obtained from gauge-field
ensembles from the JLQCD and ETM collaborations, and discuss their relation
with theoretical predictions from the operator-product expansion.


title: Relaxation in one-dimensional tropical sandpile

abstract:
  Relaxation in the tropical sandpile model is a process of deforming a
tropical hypersurface towards a finite collection of points. We show that, in
the one-dimensional case, a relaxation terminates after a finite number of
steps. We present an experimental evidence suggesting that the number of such
steps obeys a power law.


title:
Listening to Affected Communities to Define Extreme Speech: Dataset and
  Experiments

abstract:
  Building on current work on multilingual hate speech (e.g., Ousidhoum et al.
(2019)) and hate speech reduction (e.g., Sap et al. (2020)), we present
XTREMESPEECH, a new hate speech dataset containing 20,297 social media passages
from Brazil, Germany, India and Kenya. The key novelty is that we directly
involve the affected communities in collecting and annotating the data - as
opposed to giving companies and governments control over defining and
combatting hate speech. This inclusive approach results in datasets more
representative of actually occurring online speech and is likely to facilitate
the removal of the social media content that marginalized communities view as
causing the most harm. Based on XTREMESPEECH, we establish novel tasks with
accompanying baselines, provide evidence that cross-country training is
generally not feasible due to cultural differences between countries and
perform an interpretability analysis of BERT's predictions.


title: Plasmonic and photonic enhancement of chiral near fields

abstract:
  A chiral near field with a highly contorted electromagnetic field builds a
bridge to match the chiral molecules and light wavelengths with large size
differences. It significantly enhances the circular dichroism of chiral
molecules and has great prospects in chirality sensing, detection, trapping,
and other chirality-related applications. Surface plasmons feature outstanding
light-trapping and electromagnetic-field-concentrating abilities. Plasmonic
chiral nanostructures facilitate light manipulation to generate superchiral
near fields. Meanwhile, the nanophotonic structures have attracted significant
interest to obtain strong chiral fields due to their unique electromagnetic
resonant properties. During the interaction of light and chiral materials, the
chiral near field not only bridges the light and chiral molecules but is also
responsible for the optical activities. This paper reviews state-of-the-art
studies on chiral near field enhancement using plasmonic and photonic
nanostructures. We review the principle of chiral electromagnetic fields and
the development of plasmonic and photonic nanostructures for near field
enhancement. The properties and applications of enhanced chiral near fields for
chiral molecule detection, spin-orbit angular interaction, and the generation
of the chiral optical force are examined. Finally, we discuss current
challenges and provide a brief outlook of this field.


title:
Monitoring and mapping of crop fields with UAV swarms based on
  information gain

abstract:
  Monitoring crop fields to map features like weeds can be efficiently
performed with unmanned aerial vehicles (UAVs) that can cover large areas in a
short time due to their privileged perspective and motion speed. However, the
need for high-resolution images for precise classification of features (e.g.,
detecting even the smallest weeds in the field) contrasts with the limited
payload and ight time of current UAVs. Thus, it requires several flights to
cover a large field uniformly. However, the assumption that the whole field
must be observed with the same precision is unnecessary when features are
heterogeneously distributed, like weeds appearing in patches over the field. In
this case, an adaptive approach that focuses only on relevant areas can perform
better, especially when multiple UAVs are employed simultaneously. Leveraging
on a swarm-robotics approach, we propose a monitoring and mapping strategy that
adaptively chooses the target areas based on the expected information gain,
which measures the potential for uncertainty reduction due to further
observations. The proposed strategy scales well with group size and leads to
smaller mapping errors than optimal pre-planned monitoring approaches.


title:
Weak ferromagnetism linked to the high-temperature spiral phase of
  YBaCuFeO5

abstract:
  The layered perovskite YBaCuFeO5 is a rare example of cycloidal spiral magnet
whose ordering temperature Tspiral can be tuned far beyond room temperature by
adjusting the degree of Cu2+/Fe3+ chemical disorder in the structure. This
unusual property qualifies this material as one of the most promising
spin-driven multiferroic candidates. However, very little is known about the
response of the spiral to magnetic fields, crucial for magnetoelectric
cross-control applications. Using bulk magnetization and neutron powder
diffraction measurements under magnetic fields up to 9 T we report here the
first temperature-magnetic field phase diagram of this material. Besides
revealing a strong stability of the spiral state, our data uncover the presence
of weak ferromagnetism coexisting with the spiral modulation. Since
ferromagnets can be easily manipulated with magnetic fields, this observation
opens new perspectives for the control of the spiral orientation, directly
linked to the polarization direction, as well as for a possible future use of
this material in technological applications.


title:
Inter- and Intra-Goal SDG Target Interactions in the Philippine Context:
  A Two-Method Approach

abstract:
  In 2015, the United Nations developed 17 Sustainable Development Goals (SDGs)
with 169 targets for a more sustainable future by 2030. This study seeks to
evaluate and analyze intra- and inter-goal SDG target interactions within the
Philippine context to determine what to mitigate and what to prioritize. To
evaluate all 14196 target interactions, two methods are employed. First,
experts with over five years of SDG-related experience evaluated interactions
using the 7-point scale. Second, official indicator data is run through a
Spearman rank correlation, with resulting coefficients serving as interaction
scores. Interaction scores are then interpreted to be indivisible, cancelling,
or consistent. Targets are modelled as nodes and interactions as edges. With
1256 evaluated interactions under expert evaluation and 1914 under official
indicator data, results were integrated to formulate recommendations for
concerned parties. This includes the mitigation of self-conflicting target
interactions under SDG 3 'Good Health and Well-Being' with an emphasis on
targets 3.6 'Halve traffic accident deaths' and 3.9 'Reduce pollution-related
deaths'. These targets as well as 8.2 'Economic productivity', and 16.1 'Reduce
violence' also have multiple negative interactions that need to be mitigated.
Targets that reinforce their corresponding SDGs should be prioritized,
including 1.1 'Eradicate extreme poverty', 1.2 'Halve poverty proportions', 3.2
'End preventable deaths', 3.5 'Prevent substance abuse', 3.9 'Reduce
pollution-related deaths', 3.D 'EW risk reduction', 4.2 'Early education', 4.B
'Higher education scholarships', and 6.2 'Sanitation and hygiene'. 'Beautiful'
targets (no negative interactions) should also be prioritized by score, namely,
3.D 'Early warning systems for health risks', 8.A 'Trade support aid', 15.B
'Sustainable forest management', and 17.1 'Domestic revenue collection'.


title:
Impact of counteracting vehicles on the characteristics of a smart city
  transport system

abstract:
  The development of smart city transport systems, including self-driving cars,
leads to an increase in the threat of hostile interference in the processes of
vehicle control. This interference may disrupt the normal functioning of the
transport system, and, if is performed covertly, the system can be negatively
affected for a long period of time. This paper develops a simulation stochastic
cellular automata model of traffic on a circular two-lane road based on the
Sakai-Nishinari-Fukui-Schadschneider (S-NFS) rules. In the presented model, in
addition to ordinary vehicles, there are covertly counteracting vehicles; their
task is to reduce the quantity indicators (such as traffic flux) of the
transport system using special rules of behavior. Three such rules are
considered and compared: two lane-changing rules and one slow-down rule. It is
shown that such counteracting vehicles can affect the traffic flow, mainly in
the region of the maximum of the fundamental diagram, that is, at average
values of the vehicle density. In free-flowing traffic or in a traffic jam, the
influence of the counteracting vehicle is negligible regardless of its rules of
behavior.


title: Hilbert schemes over quotient rings via relative marked bases

abstract:
  In this paper, we introduce the notion of {\em relative marked bases} over
quasi-stable ideals, together with constructive methods and a functorial
interpretation. This notion allows to develop computational methods for the
study of Hilbert schemes over quotients of polynomial rings for which we obtain
the following main results. When the quotient rings are Cohen-Macaulay on
quasi-stable ideals (for instance, Clements-Lindstr\"om rings), we describe an
open cover of the Hilbert schemes by means of suitable changes of variables.
When the quotient rings are Macaulay-Lex on quasi-stable ideals (for instance,
Clements-Lindstr\"om rings), we investigate the lex-point of the Hilbert
schemes and find some examples in which this point is singular.


title: Racial Disparities in the Enforcement of Marijuana Violations in the US

abstract:
  Racial disparities in US drug arrest rates have been observed for decades,
but their causes and policy implications are still highly contested. Some
scholars have argued that these disparities largely reflect differences in drug
use between racial groups, while others have hypothesized that discriminatory
enforcement policies and police practices may play a significant role. We
analyze racial disparities in the enforcement of marijuana violations in the
US. Using data from the National Incident-Based Reporting System (NIBRS) and
the National Survey on Drug Use and Health (NSDUH) programs, we investigate
whether marijuana usage and purchasing behaviors can explain the racial
disparities. We examine potential driving mechanisms for these disparities and
the extent to which county-level socioeconomic factors correlate with
county-level disparity. We find that the significant racial disparities in
reported incidents and arrests cannot be explained by differences in marijuana
days-of-use alone. Differences in frequency and the location of buying
marijuana partially explain the observed disparities. We observe an increase in
racial disparities across most counties over the last decade, with the greatest
increases in states that legalized the use of marijuana within this timeframe.
Income, high school graduation rate, and rate of employment positively
correlate with higher racial disparity, while the rate of incarceration is
negatively correlated. We discuss the implications of the observed racial
disparities in the context of algorithmic fairness.