title:
Beyond i.i.d. in the Resource Theory of Asymmetry: An
  Information-Spectrum Approach for Quantum Fisher Information

abstract:
  Energetic coherence is a valuable resource for various operations, including
precise measurement of time and acceleration of quantum manipulations.
Recently, it has been shown that the theory of energetic coherence, called the
resource theory of asymmetry, has a thermodynamic structure, as do entanglement
theory and quantum thermodynamics. In the so-called i.i.d. regime, where
identical copies of a state are converted into identical copies of another
state, the convertibility is entirely determined by the amount of energetic
coherence measured by the quantum Fisher information (QFI). This fact means
that QFI takes the place of entropy in thermodynamics and entanglement entropy
in entanglement theory. So far, such a correspondence between energetic
coherence and entropy is known only in the i.i.d. regime. In entanglement
theory and quantum thermodynamics in the non-i.i.d. regime, entropy-related
quantities based on the information-spectrum method reveal a non-equilibrium
thermodynamics structure. Still, it has been an open question whether a similar
structure exists for energetic coherence. In this Letter, we solve this problem
by constructing an information-spectrum method applicable to the quantum Fisher
information content. As a result, we establish a general theory of the
pure-state convertibility in the non-i.i.d. regime of the resource theory of
asymmetry. The given theory reveals a non-equilibrium thermodynamic structure
of energetic coherence in the non-i.i.d. regime, based on QFI rather than
entropy.


title: Beta-decay implications for the W-boson mass anomaly

abstract:
  We point out the necessity to consider $\beta$-decay observables in
resolutions of the $W$-boson anomaly in the Standard Model Effective Field
Theory that go beyond pure oblique corrections. We demonstrate that present
global analyses that explain the $W$-boson mass anomaly predict a large,
percent-level, violation of first-row CKM unitarity. We investigate what
solutions to the $W$-boson mass anomaly survive after including $\beta$-decay
constraints.


title:
Gaussian dispersion analysis in the time domain: efficient conversion
  with Pad\'e approximants

abstract:
  We present an approach for adapting the Gaussian dispersion analysis (GDA) of
optical materials to time-domain simulations. Within a GDA model, the imaginary
part of a measured dielectric function is presented as a sum of Gaussian
absorption terms. Such a simple model is valid for materials where
inhomogeneous broadening is substantially larger than the homogeneous
linewidth. The GDA model is the essential broadband approximation for the
dielectric function of many glasses, polymers, and other natural and artificial
materials with disorder. However, efficient implementation of this model in
time-domain full-wave electromagnetic solvers has never been fully achieved. We
start with a causal form of an isolated oscillator with Gaussian-type
absorption - Causal Dawson-Gauss oscillator. Then, we derive explicit
analytical formulas to implement the Gaussian oscillator in a finite-difference
time-domain (FDTD) solver with minimal use of memory and floating point
operations. The derivation and FDTD implementation employ our generalized
dispersive material (GDM) model - a universal, modular approach to describing
optical dispersion with Pad\'e approximants. We share the FDTD prototype codes
that include automated generation of the approximants and a universal FDTD
dispersion implementation that employs various second-order accurate numerical
schemes. The codes can be used with non-commercial solvers and commercial
software for time-domain simulations of light propagation in dispersive media,
which are experimentally characterized with GDA models.


title: Deep Equilibrium Optical Flow Estimation

abstract:
  Many recent state-of-the-art (SOTA) optical flow models use finite-step
recurrent update operations to emulate traditional algorithms by encouraging
iterative refinements toward a stable flow estimation. However, these RNNs
impose large computation and memory overheads, and are not directly trained to
model such stable estimation. They can converge poorly and thereby suffer from
performance degradation. To combat these drawbacks, we propose deep equilibrium
(DEQ) flow estimators, an approach that directly solves for the flow as the
infinite-level fixed point of an implicit layer (using any black-box solver),
and differentiates through this fixed point analytically (thus requiring $O(1)$
training memory). This implicit-depth approach is not predicated on any
specific model, and thus can be applied to a wide range of SOTA flow estimation
model designs. The use of these DEQ flow estimators allows us to compute the
flow faster using, e.g., fixed-point reuse and inexact gradients, consumes
$4\sim6\times$ times less training memory than the recurrent counterpart, and
achieves better results with the same computation budget. In addition, we
propose a novel, sparse fixed-point correction scheme to stabilize our DEQ flow
estimators, which addresses a longstanding challenge for DEQ models in general.
We test our approach in various realistic settings and show that it improves
SOTA methods on Sintel and KITTI datasets with substantially better
computational and memory efficiency.


title:
Photothermal responsivity of van der Waals material-based nanomechanical
  resonators

abstract:
  Nanomechanical resonators made from van der Waals materials (vdW NMRs)
provide a new tool for sensing absorbed laser power. The photothermal response
of vdW NMRs, quantified from the resonant frequency shifts induced by optical
absorption, is enhanced when incorporated in a Fabry-Perot (FP) interferometer.
Along with the enhancement comes the dependence of the photothermal response on
NMR displacement, which lacks investigation. Here, we address the knowledge gap
by studying electromotively driven niobium diselenide drumheads fabricated on
highly reflective substrates. We use a FP-mediated absorptive heating model to
explain the measured variations of the photothermal response. The model
predicts a higher magnitude and tuning range of photothermal responses on
few-layer and monolayer NbSe$_{2}$ drumheads, which outperform other clamped
vdW drum-type NMRs at a laser wavelength of $532\,$nm. Further analysis of the
model shows that both the magnitude and tuning range of NbSe$_{2}$ drumheads
scale with thickness, establishing a displacement-based framework for building
bolometers using FP-mediated vdW NMRs.


title:
Network Onion Divergence: Network representation and comparison using
  nested configuration models with fixed connectivity, correlation and
  centrality patterns

abstract:
  Random networks, constrained to reproduce specific features of networks, are
often used to represent and analyze network data as well as their mathematical
descriptions. Chief among them, the configuration model constrains random
networks by their degree distribution and is foundational to many areas of
network science. However, these representations are often selected based on
intuition or mathematical and computational simplicity rather than on
statistical evidence. To evaluate the quality of a network representation we
need to consider both the amount of information required by a random network
model as well as the probability of recovering the original data when using the
model as a generative process. To this end, we calculate the approximate size
of network ensembles generated by the popular configuration model and its
generalizations that include degree-correlations and centrality layers based on
the onion decomposition. We then apply minimum description length as a model
selection criterion and also introduce the Network Onion Divergence: model
selection and network comparison over a nested family of configuration models
with differing level of structural details. Using over 100 empirical sets of
network data, we find that a simple Layered Configuration Model offers the most
compact representation of the majority of real networks. We hope that our
results will continue to motivate the development of intricate random network
models that help capture network structure beyond the simple degree
distribution.


title:
Constraining runaway dilaton models using joint gravitational-wave and
  electromagnetic observations

abstract:
  With the advent of gravitational-wave astronomy it has now been possible to
constrain modified theories of gravity that were invoked to explain the dark
energy. In a class of dilaton models, distances to cosmic sources inferred from
electromagnetic and gravitational wave observations would differ due to the
presence of a friction term. In such theories, the ratio of the Newton's
constant to the fine structure constant varies with time. In this paper we
explore the degree to which it will be possible to test such models. If
collocated sources (e.g. supernovae and binary neutron star mergers), but not
necessarily multimessengers, can be identified by electromagnetic telescopes
and gravitational-wave detectors one can probe if light and gravitational
radiation are subject to the same laws of propagation over cosmological
distances. This helps in constraining the variation of Newton's constant
relative to fine-structure constant. The next generation of gravitational wave
detectors, such as the Cosmic Explorer and Einstein Telescope, in tandem with
the Vera Rubin Observatory and gamma ray observatories such as the Fermi Space
Observatory will be able to detect or constrain such variations at the level of
a few parts in 100. We apply this method to GW170817 with distances inferred by
the LIGO and Virgo detectors and the observed Kilonova.


title: VSA: Learning Varied-Size Window Attention in Vision Transformers

abstract:
  Attention within windows has been widely explored in vision transformers to
balance the performance, computation complexity, and memory footprint. However,
current models adopt a hand-crafted fixed-size window design, which restricts
their capacity of modeling long-term dependencies and adapting to objects of
different sizes. To address this drawback, we propose
\textbf{V}aried-\textbf{S}ize Window \textbf{A}ttention (VSA) to learn adaptive
window configurations from data. Specifically, based on the tokens within each
default window, VSA employs a window regression module to predict the size and
location of the target window, i.e., the attention area where the key and value
tokens are sampled. By adopting VSA independently for each attention head, it
can model long-term dependencies, capture rich context from diverse windows,
and promote information exchange among overlapped windows. VSA is an
easy-to-implement module that can replace the window attention in
state-of-the-art representative models with minor modifications and negligible
extra computational cost while improving their performance by a large margin,
e.g., 1.1\% for Swin-T on ImageNet classification. In addition, the performance
gain increases when using larger images for training and test. Experimental
results on more downstream tasks, including object detection, instance
segmentation, and semantic segmentation, further demonstrate the superiority of
VSA over the vanilla window attention in dealing with objects of different
sizes. The code will be released
https://github.com/ViTAE-Transformer/ViTAE-VSA.


title:
Reparametrization Symmetry of Local Entropy Production on a Dynamical
  Horizon

abstract:
  Recently, it has been shown that for a dynamical black hole in any higher
derivative theory of gravity, one could construct a spatial entropy current,
characterizing the in/outflow of entropy at every point on the horizon, as long
as the dynamics of the amplitude is small enough. However, the construction is
very much dependent on how we choose the spatial slicing of the horizon along
its null generators. In this note, we have shown that though both the entropy
density and the spatial entropy current change non-trivially under a
reparametrization of the null generator, the net entropy production, which is
given by the `time' derivative of entropy density plus the divergence of the
spatial current is invariant. We have explicitly verified this claim for the
particular case of dynamical black holes Einstein-Gauss-Bonnet theory.


title:
Quasi-spherical hydrodynamic adiabatic accretion in a Keplerian
  potential

abstract:
  We present an exact $\gamma=5/3$ spherical accretion model which modifies the
Bondi boundary condition of $\rho \to const.$ as $r\to \infty$ to one where
$\rho \to 0$ as $r \to \infty$. This change allows for simple power law
solutions on the density and infall velocity fields, ranging from a cold empty
free-fall condition where pressure tends to zero, to a hot hydrostatic
equilibrium limit with no infall velocity. As in the case of the Bondi
solution, a maximum accretion rate appears. As in the $\gamma=5/3$ case of the
Bondi solution, no sonic radius appears, this time however, because the flow is
always characterised by a constant Mach number. This number equals 1 for the
case of the maximum accretion rate, diverges towards the cold empty state, and
becomes subsonic towards the hydrostatic equilibrium limit. Deviations from
sphericity are then explored through an analytic perturbative analysis. The
perturbed solution yields a rich phenomenology through density and radial
velocity fields in terms of Legendre polynomials, which we begin to explore for
simple angular velocity boundary conditions having zeros on the plane and pole.
Infall/outflow solutions appear for the sub-sonic parameter region, closely
resembling the outputs of recent numerical experiments. The strong density
gradients in these cases result in significant pressure gradients which
accelerate the polar outflows to many times the local escape velocity, well
within the validity range of the perturbative analysis. Our results could
complement our understanding of the origin of outflows in a variety of
astrophysical settings surrounding infall situations, through purely
hydrodynamical physics.

categories: astro-ph.HE astro-ph.GA physics.class-ph physics.flu-dyn


title:
Pulsar skips: Understanding variations in the regular periods of
  rotating neutron stars

abstract:
  Pulsars are spinning neutron stars with very regular periods. These pulsars
have, however, had instances where they exhibit a change in their periods.
Older theories have shown that older pulsars have a tendency to skip and speed
up. Newer theories have been created, due to the discovery that younger X-ray
pulsars exhibit the same skips. The older theories explain that the core of the
pulsar is a superfluid with a differential rotation and the core will
occasionally exhibit solid properties to catch the crust of the pulsar and
speed it up. The newer quantum mechanical theory states that quantum particle
packets, called the strange nuggets, slam into the side of the pulsar to add
angular momentum to the pulsar and then release it later.


title:
Comment on "Scaling properties of background- and
  chiral-magnetically-driven charge separation in heavy ion collisions at
  $\sqrt{s}_{\rm NN}=200$ GeV (arXiv:2203.10029)"

abstract:
  In a recent preprint arXiv:2203.10029, the authors argue for a multiplicity
scaling of the squared inverse width of the $R_{\Psi_{2}}$ distribution to
claim a positive signal for the chiral magnetic effect from STAR's isobar data,
in stark contradiction to the conclusion reached by the STAR Collaboration.
This Comment points out the fallacy of their arguments and reinforces the STAR
conclusion.


title: Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion

abstract:
  We present a framework for modeling interactional communication in dyadic
conversations: given multimodal inputs of a speaker, we autoregressively output
multiple possibilities of corresponding listener motion. We combine the motion
and speech audio of the speaker using a motion-audio cross attention
transformer. Furthermore, we enable non-deterministic prediction by learning a
discrete latent representation of realistic listener motion with a novel
motion-encoding VQ-VAE. Our method organically captures the multimodal and
non-deterministic nature of nonverbal dyadic interactions. Moreover, it
produces realistic 3D listener facial motion synchronous with the speaker (see
video). We demonstrate that our method outperforms baselines qualitatively and
quantitatively via a rich suite of experiments. To facilitate this line of
research, we introduce a novel and large in-the-wild dataset of dyadic
conversations. Code, data, and videos available at
https://evonneng.github.io/learning2listen/.


title: Deconstructing {\" U}nsal-Yaffe Reconfinement

abstract:
  In the UY reconfined phase on a lattice the thermal trace is over states
transforming in all $SU(N)/Z(N)$ irreducible representations as opposed to only
over $SU(N)$ singlets in the standard formulation. As $N\to\infty$, on a finite
lattice, the usual Hilbert space becomes orthogonal to the deformed one.
Concerns about the extended UY proposal for large $N$ Eguchi-Kawai reduction
are raised.


title: Neural Space-filling Curves

abstract:
  We present Neural Space-filling Curves (SFCs), a data-driven approach to
infer a context-based scan order for a set of images. Linear ordering of pixels
forms the basis for many applications such as video scrambling, compression,
and auto-regressive models that are used in generative modeling for images.
Existing algorithms resort to a fixed scanning algorithm such as Raster scan or
Hilbert scan. Instead, our work learns a spatially coherent linear ordering of
pixels from the dataset of images using a graph-based neural network. The
resulting Neural SFC is optimized for an objective suitable for the downstream
task when the image is traversed along with the scan line order. We show the
advantage of using Neural SFCs in downstream applications such as image
compression. Code and additional results will be made available at
https://hywang66.github.io/publication/neuralsfc.


title:
Revisiting Consistency Regularization for Semi-supervised Change
  Detection in Remote Sensing Images

abstract:
  Remote-sensing (RS) Change Detection (CD) aims to detect "changes of
interest" from co-registered bi-temporal images. The performance of existing
deep supervised CD methods is attributed to the large amounts of annotated data
used to train the networks. However, annotating large amounts of remote sensing
images is labor-intensive and expensive, particularly with bi-temporal images,
as it requires pixel-wise comparisons by a human expert. On the other hand, we
often have access to unlimited unlabeled multi-temporal RS imagery thanks to
ever-increasing earth observation programs. In this paper, we propose a simple
yet effective way to leverage the information from unlabeled bi-temporal images
to improve the performance of CD approaches. More specifically, we propose a
semi-supervised CD model in which we formulate an unsupervised CD loss in
addition to the supervised Cross-Entropy (CE) loss by constraining the output
change probability map of a given unlabeled bi-temporal image pair to be
consistent under the small random perturbations applied on the deep feature
difference map that is obtained by subtracting their latent feature
representations. Experiments conducted on two publicly available CD datasets
show that the proposed semi-supervised CD method can reach closer to the
performance of supervised CD even with access to as little as 10% of the
annotated training data. Code available at https://github.com/wgcban/SemiCD


title: On perfect powers that are sum of two balancing numbers

abstract:
  Let $B_k$ denote the $k^{th}$ term of balancing sequence. In this paper we
find all positive integer solutions of the Diophantine equation $B_n+B_m = x^q$
in variables $(m, n,x,q)$ under the assumption $n\equiv m \pmod 2$.
Furthermore, we study the Diophantine equation \[B_n^{3}\pm B_m^{3} = x^q\]
with positive integer $q\geq 3$ and $\gcd(B_n, B_m) =1$.


title: Non-Stationary Fluctuations via Boltzmann-Gibbs Principle

abstract:
  Conjecture II.3.6 of Spohn in [Spohn '91] and Lecture 7 of Jensen-Yau in
[Jensen-Yau '99] ask for a general derivation of universal fluctuations of
hydrodynamic limits in large-scale stochastic interacting particle systems.
However, the past few decades have witnessed only minimal progress according to
[Goncalves-Landim-Milanes '17]. In this paper, we develop a general method for
deriving the so-called Boltzmann-Gibbs principle for a general family of
non-integrable and non-stationary interacting particle systems, thereby
responding to Spohn and Jensen-Yau. Most importantly, our method depends mostly
on local and dynamical, and thus more general/universal, features of the model.
This contrasts with previous works, which rely on global and non-universal
assumptions on invariant measures or initial measures of the model. As a
concrete application of the method, we derive the KPZ equation as a large-scale
limit of the height functions for a family of non-stationary and non-integrable
exclusion processes with an environment-dependent asymmetry. This establishes a
first result to Big Picture Question 1.6 in [AimPLKPZ] for non-stationary and
non-integrable "speed-change" models that have also been of interest beyond KPZ
[De Masi-Presutti-Spohn-Wick '86, Funaki '18, Funaki-Handa-Uchiyama '91,
Komoriya '98].


title:
Efficient reverse engineering of one-qubit filter functions with
  dynamical invariants

abstract:
  We derive an integral expression for the filter-transfer function of an
arbitrary one-qubit gate through the use of dynamical invariant theory and
Hamiltonian reverse engineering. We use this result to define a cost functional
which can be efficiently optimized to produce one-qubit control pulses that are
robust against specified frequency bands of the noise power spectral density.
We demonstrate the utility of our result by generating optimal control pulses
that are designed to suppress broadband detuning and pulse amplitude noise. We
report an order of magnitude improvement in gate fidelity in comparison with
known composite pulse sequences. More broadly, we also use the same theoretical
framework to prove the robustness of nonadiabatic geometric quantum gates under
specific error models and control constraints.


title:
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data
  Augmentation

abstract:
  A novel approach of data augmentation based on irregular superpixel
decomposition is proposed. This approach called SuperpixelGridMasks permits to
extend original image datasets that are required by training stages of machine
learning-related analysis architectures towards increasing their performances.
Three variants named SuperpixelGridCut, SuperpixelGridMean and
SuperpixelGridMix are presented. These grid-based methods produce a new style
of image transformations using the dropping and fusing of information.
Extensive experiments using various image classification models and datasets
show that baseline performances can be significantly outperformed using our
methods. The comparative study also shows that our methods can overpass the
performances of other data augmentations. Experimental results obtained over
image recognition datasets of varied natures show the efficiency of these new
methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are
publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks


title:
Comparative analysis of machine learning and numerical modeling for
  combined heat transfer in Polymethylmethacrylate

abstract:
  This study compares different methods to predict the simultaneous effects of
conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA)
sample. PMMA is a kind of polymer utilized in various sensors and actuator
devices. One-dimensional combined heat transfer is considered in numerical
analysis. Computer implementation was obtained for the numerical solution of
governing equation with the implicit finite difference method in the case of
discretization. Kirchhoff transformation was used to get data from a non-linear
equation of conductive heat transfer by considering monochromatic radiation
intensity and temperature conditions applied to the PMMA sample boundaries. For
Deep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM)
method was introduced to find accurate results in the least processing time
than the numerical method. A recent study derived the combined heat transfers
and their temperature profiles for the PMMA sample. Furthermore, the transient
temperature profile is validated by another study. A comparison proves a
perfect agreement. It shows the temperature gradient in the primary positions
that makes a spectral amount of conductive heat transfer from a PMMA sample. It
is more straightforward when they are compared with the novel DNN method.
Results demonstrate that this artificial intelligence method is accurate and
fast in predicting problems. By analyzing the results from the numerical
solution it can be understood that the conductive and radiative heat flux is
similar in the case of gradient behavior, but it is also twice in its amount
approximately. Hence, total heat flux has a constant value in an approximated
steady state condition. In addition to analyzing their composition, ROC curve
and confusion matrix were implemented to evaluate the algorithm performance.


title:
3D Convolutional Networks for Action Recognition: Application to Sport
  Gesture Recognition

abstract:
  3D convolutional networks is a good means to perform tasks such as video
segmentation into coherent spatio-temporal chunks and classification of them
with regard to a target taxonomy. In the chapter we are interested in the
classification of continuous video takes with repeatable actions, such as
strokes of table tennis. Filmed in a free marker less ecological environment,
these videos represent a challenge from both segmentation and classification
point of view. The 3D convnets are an efficient tool for solving these problems
with window-based approaches.


title:
Investigating Temporal Convolutional Neural Networks for Satellite Image
  Time Series Classification

abstract:
  Satellite Image Time Series (SITS) of the Earth's surface provide detailed
land cover maps, with their quality in the spatial and temporal dimensions
consistently improving. These image time series are integral for developing
systems that aim to produce accurate, up-to-date land cover maps of the Earth's
surface. Applications are wide-ranging, with notable examples including
ecosystem mapping, vegetation process monitoring and anthropogenic land-use
change tracking. Recently proposed methods for SITS classification have
demonstrated respectable merit, but these methods tend to lack native
mechanisms that exploit the temporal dimension of the data; commonly resulting
in extensive data pre-processing prohibitively long training times. To overcome
these shortcomings, this paper seeks to study and enhance the newly proposed
method for SITS classification from literature; namely Temporal CNNs.
Comprehensive experiments are carried out on two benchmark SITS datasets with
the results demonstrating that Temporal CNNs display a superior or competitive
performance to the benchmark algorithms for both datasets. Investigations into
the Temporal CNNs architecture also highlighted the non-trivial task of
optimising the model for a new dataset.


title:
CapillaryX: A Software Design Pattern for Analyzing Medical Images in
  Real-time using Deep Learning

abstract:
  Recent advances in digital imaging, e.g., increased number of pixels
captured, have meant that the volume of data to be processed and analyzed from
these images has also increased. Deep learning algorithms are state-of-the-art
for analyzing such images, given their high accuracy when trained with a large
data volume of data. Nevertheless, such analysis requires considerable
computational power, making such algorithms time- and resource-demanding. Such
high demands can be met by using third-party cloud service providers. However,
analyzing medical images using such services raises several legal and privacy
challenges and does not necessarily provide real-time results. This paper
provides a computing architecture that locally and in parallel can analyze
medical images in real-time using deep learning thus avoiding the legal and
privacy challenges stemming from uploading data to a third-party cloud
provider. To make local image processing efficient on modern multi-core
processors, we utilize parallel execution to offset the resource-intensive
demands of deep neural networks. We focus on a specific medical-industrial case
study, namely the quantifying of blood vessels in microcirculation images for
which we have developed a working system. It is currently used in an
industrial, clinical research setting as part of an e-health application. Our
results show that our system is approximately 78% faster than its serial system
counterpart and 12% faster than a master-slave parallel system architecture.


title:
Machine Learning-Based Automated Thermal Comfort Prediction: Integration
  of Low-Cost Thermal and Visual Cameras for Higher Accuracy

abstract:
  Recent research is trying to leverage occupants' demand in the building's
control loop to consider individuals' well-being and the buildings' energy
savings. To that end, a real-time feedback system is needed to provide data
about occupants' comfort conditions that can be used to control the building's
heating, cooling, and air conditioning (HVAC) system. The emergence of thermal
imaging techniques provides an excellent opportunity for contactless data
gathering with no interruption in occupant conditions and activities. There is
increasing attention to infrared thermal camera usage in public buildings
because of their non-invasive quality in reading the human skin temperature.
However, the state-of-the-art methods need additional modifications to become
more reliable. To capitalize potentials and address some existing limitations,
new solutions are required to bring a more holistic view toward non-intrusive
thermal scanning by leveraging the benefit of machine learning and image
processing. This research implements an automated approach to collect and
register simultaneous thermal and visual images and read the facial temperature
in different regions. This paper also presents two additional investigations.
First, through utilizing IButton wearable thermal sensors on the forehead area,
we investigate the reliability of an in-expensive thermal camera (FLIR Lepton)
in reading the skin temperature. Second, by studying the false-color version of
thermal images, we look into the possibility of non-radiometric thermal images
for predicting personalized thermal comfort. The results shows the strong
performance of Random Forest and K-Nearest Neighbor prediction algorithms in
predicting personalized thermal comfort. In addition, we have found that
non-radiometric images can also indicate thermal comfort when the algorithm is
trained with larger amounts of data.


title:
Towards a Geodesic Flow Bundle Formalism of General Relativity:
  Riemannian case

abstract:
  Gravity is a phenomenon which arises due to the space-time geometry. The main
equations that describe gravity are the Einstein equations. To understand the
consequences of these field equations we need to calculate the free particle
worldlines to the geometries, which solve these field equations e.g. the
Schwarzschild metric solves the Einstein equations and we would need to solve
the geodesic equations to this metric. If we were to describe the space-time
geometry in terms of geodesics instead of the metric, we could skip the step of
solving for the metric and solve for the geodesics directly. In this work we
have developed a formalism doing that: we use the bundle of the arclength
parametrized geodesics (geodesic flow bundle GFB) from all points in the
manifold to describe a Riemannian geometry. Our formalism uses characteristic
properties of smooth Riemannian geometries, infinitesimal spherical triangles
as generating elements, to solve geometric problems. We relate the geodesic
flow bundle to Gaussian curvature and develop a method to calculate the
geodesics geometrically starting from a Gaussian curvature field. The result
amounts to a generalization of the cosine- and sine-laws for constant curvature
to varying curvature fields. In this work we restrict ourselves to the
Riemannian case and positive curvature fields. We expand the triangulation
problem in power series and calculate the main result up to second order. The
method itself could be extended to treat more generic cases in particular
pseudo-Riemannian geometries which relate directly to Einsteins equations.
  We test our results against the sphere and perform consistency checks for a
number of examples of varying curvature fields. As a by-product we generalize
the notion of integration to products and derive a relation analogue to the
main theorem of calculus, for product integrals.


title:
Intelligent Spatial Interpolation-based Frost Prediction Methodology
  using Artificial Neural Networks with Limited Local Data

abstract:
  The weather phenomenon of frost poses great threats to agriculture. Since it
damages the crops and plants from upstream of the supply chain, the potential
impact of frosts is significant for agriculture-related industries. As recent
frost prediction methods are based on on-site historical data and sensors,
extra development and deployment time are required for data collection in any
new site. The aim of this article is to eliminate the dependency on on-site
historical data and sensors for frost prediction methods. In this article, a
frost prediction method based on spatial interpolation is proposed. The models
use climate data from existing weather stations, digital elevation models
surveys, and normalized difference vegetation index data to estimate a target
site's next hour minimum temperature. The proposed method utilizes ensemble
learning to increase the model accuracy. Ensemble methods include averaging and
weighted averaging. Climate datasets are obtained from 75 weather stations
across New South Wales and Australian Capital Territory areas of Australia. The
models are constructed with five-fold validation, splitting the weather
stations into five testing dataset folds. For each fold, the other stations act
as training datasets. After the models are constructed, three experiments are
conducted. The first experiment compares the results generated by models
between different folds. Then, the second experiment compares the accuracy of
different methods. The final experiment reveals the effect of available
stations on the proposed models. The results show that the proposed method
reached a detection rate up to 92.55%. This method could be implemented as an
alternative solution when on-site historical datasets are scarce.


title:
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in
  X-ray Coronary Angiography

abstract:
  Although robust PCA has been increasingly adopted to extract vessels from
X-ray coronary angiography (XCA) images, challenging problems such as
inefficient vessel-sparsity modelling, noisy and dynamic background artefacts,
and high computational cost still remain unsolved. Therefore, we propose a
novel robust PCA unrolling network with sparse feature selection for
super-resolution XCA vessel imaging. Being embedded within a patch-wise
spatiotemporal super-resolution framework that is built upon a pooling layer
and a convolutional long short-term memory network, the proposed network can
not only gradually prune complex vessel-like artefacts and noisy backgrounds in
XCA during network training but also iteratively learn and select the
high-level spatiotemporal semantic information of moving contrast agents
flowing in the XCA-imaged vessels. The experimental results show that the
proposed method significantly outperforms state-of-the-art methods, especially
in the imaging of the vessel network and its distal vessels, by restoring the
intensity and geometry profiles of heterogeneous vessels against complex and
dynamic backgrounds.


title:
IOP-FL: Inside-Outside Personalization for Federated Medical Image
  Segmentation

abstract:
  Federated learning (FL) allows multiple medical institutions to
collaboratively learn a global model without centralizing all clients data. It
is difficult, if possible at all, for such a global model to commonly achieve
optimal performance for each individual client, due to the heterogeneity of
medical data from various scanners and patient demographics. This problem
becomes even more significant when deploying the global model to unseen clients
outside the FL with new distributions not presented during federated training.
To optimize the prediction accuracy of each individual client for critical
medical tasks, we propose a novel unified framework for both Inside and Outside
model Personalization in FL (IOP-FL). Our inside personalization is achieved by
a lightweight gradient-based approach that exploits the local adapted model for
each client, by accumulating both the global gradients for common knowledge and
local gradients for client-specific optimization. Moreover, and importantly,
the obtained local personalized models and the global model can form a diverse
and informative routing space to personalize a new model for outside FL
clients. Hence, we design a new test-time routing scheme inspired by the
consistency loss with a shape constraint to dynamically incorporate the models,
given the distribution information conveyed by the test data. Our extensive
experimental results on two medical image segmentation tasks present
significant improvements over SOTA methods on both inside and outside
personalization, demonstrating the great potential of our IOP-FL scheme for
clinical practice. Code will be released at https://github.com/med-air/IOP-FL.


title: Face recognition with small and large size databases

abstract:
  This paper presents experimental results using the ORL (40 people) and FERET
(994 people) databases. The ORL database can be useful for securing
applications where few users attempting to access are expected. This is the
case, for instance, of a PDA or PC where the password is the face of the user.
On the other hand, the FERET database is useful for studying those situations
where the number of authorized users is around a thousand people.


title: Hand Geometry Based Recognition with a MLP Classifier

abstract:
  This paper presents a biometric recognition system based on hand geometry. We
describe a database specially collected for research purposes, which consists
of 50 people and 10 different acquisitions of the right hand. This database can
be freely downloaded. In addition, we describe a feature extraction procedure
and we obtain experimental results using different classification strategies
based on Multi Layer Perceptrons (MLP). We have evaluated identification rates
and Detection Cost Function (DCF) values for verification applications.
Experimental results reveal up to 100% identification and 0% DCF


title: U-Net and its variants for Medical Image Segmentation : A short review

abstract:
  The paper is a short review of medical image segmentation using U-Net and its
variants. As we understand going through a medical images is not an easy job
for any clinician either radiologist or pathologist. Analysing medical images
is the only way to perform non-invasive diagnosis. Segmenting out the regions
of interest has significant importance in medical images and is key for
diagnosis. This paper also gives a bird eye view of how medical image
segmentation has evolved. Also discusses challenge's and success of the deep
neural architectures. Following how different hybrid architectures have built
upon strong techniques from visual recognition tasks. In the end we will see
current challenges and future directions for medical image segmentation(MIS).


title: AI for human assessment: What do professional assessors need?

abstract:
  We present our case study that aims to help professional assessors make
decisions in human assessment, in which they conduct interviews with assessees
and evaluate their suitability for certain job roles. Our workshop with two
industrial assessors revealed that a computational system that can extract
nonverbal cues of assesses from interview videos would be beneficial to
assessors in terms of supporting their decision making. In response, we
developed such a system based on an unsupervised anomaly detection algorithm
using multimodal behavioral features such as facial keypoints, pose, head pose,
and gaze. Moreover, we enabled the system to output how much each feature
contributed to the outlierness of the detected cues with the purpose of
enhancing its interpretability. We then conducted a preliminary study to
examine the validity of the system's output by using 20 actual assessment
interview videos and involving the two assessors. The results suggested the
advantages of using unsupervised anomaly detection in an interpretable manner
by illustrating the informativeness of its outputs for assessors. Our approach,
which builds on top of the idea of separation of observation and interpretation
in human-AI teaming, will facilitate human decision making in highly contextual
domains, such as human assessment, while keeping their trust in the system.


title:
Simultaneous Multiple-Prompt Guided Generation Using Differentiable
  Optimal Transport

abstract:
  Recent advances in deep learning, such as powerful generative models and
joint text-image embeddings, have provided the computational creativity
community with new tools, opening new perspectives for artistic pursuits.
Text-to-image synthesis approaches that operate by generating images from text
cues provide a case in point. These images are generated with a latent vector
that is progressively refined to agree with text cues. To do so, patches are
sampled within the generated image, and compared with the text prompts in the
common text-image embedding space; The latent vector is then updated, using
gradient descent, to reduce the mean (average) distance between these patches
and text cues. While this approach provides artists with ample freedom to
customize the overall appearance of images, through their choice in generative
models, the reliance on a simple criterion (mean of distances) often causes
mode collapse: The entire image is drawn to the average of all text cues,
thereby losing their diversity. To address this issue, we propose using
matching techniques found in the optimal transport (OT) literature, resulting
in images that are able to reflect faithfully a wide diversity of prompts. We
provide numerous illustrations showing that OT avoids some of the pitfalls
arising from estimating vectors with mean distances, and demonstrate the
capacity of our proposed method to perform better in experiments, qualitatively
and quantitatively.


title:
Full Electroweak O(alpha) corrections to Higgs boson production
  processes with the beam polarization at the International Linear Collider

abstract:
  We present the full O(alpha) electroweak radiative corrections to 9 Higgs
boson production with the beam polarization at the International Linear
Collider(ILC). The computation is performed with the help of GRACE-Loop. We
compared the full O(alpha) electroweak radiative corrections with the
factorized initial state radiation(ISR) effects to estimate the pure weak
corrections. This reveals the pure weak corrections are not negligible and
should be taken into account for the precision measurements of the Higgs boson
property at the ILC experiments.


title:
AB/BA analysis: A framework for estimating keyword spotting recall
  improvement while maintaining audio privacy

abstract:
  Evaluation of keyword spotting (KWS) systems that detect keywords in speech
is a challenging task under realistic privacy constraints. The KWS is designed
to only collect data when the keyword is present, limiting the availability of
hard samples that may contain false negatives, and preventing direct estimation
of model recall from production data. Alternatively, complementary data
collected from other sources may not be fully representative of the real
application. In this work, we propose an evaluation technique which we call
AB/BA analysis. Our framework evaluates a candidate KWS model B against a
baseline model A, using cross-dataset offline decoding for relative recall
estimation, without requiring negative examples. Moreover, we propose a
formulation with assumptions that allow estimation of relative false positive
rate between models with low variance even when the number of false positives
is small. Finally, we propose to leverage machine-generated soft labels, in a
technique we call Semi-Supervised AB/BA analysis, that improves the analysis
time, privacy, and cost. Experiments with both simulation and real data show
that AB/BA analysis is successful at measuring recall improvement in
conjunction with the trade-off in relative false positive rate.


title: Predictive analytics for appointment bookings

abstract:
  One of the service providers in the financial service sector, who provide
premium service to the customers, wanted to harness the power of data analytics
as data mining can uncover valuable insights for better decision making.
Therefore, the author aimed to use predictive analytics to discover crucial
factors that will affect the customers' showing up for their appointment and
booking the service. The first model predicts whether a customer will show up
for the meeting, while the second model indicates whether a customer will book
a premium service. Both models produce accurate results with more than a 75%
accuracy rate, thus providing a more robust model for implementation than gut
feeling and intuition. Finally, this paper offers a framework for resource
planning using the predicted demand.


title:
Research on Domain Information Mining and Theme Evolution of Scientific
  Papers

abstract:
  In recent years, with the increase of social investment in scientific
research, the number of research results in various fields has increased
significantly. Cross-disciplinary research results have gradually become an
emerging frontier research direction. There is a certain dependence between a
large number of research results. It is difficult to effectively analyze
today's scientific research results when looking at a single research field in
isolation. How to effectively use the huge number of scientific papers to help
researchers becomes a challenge. This paper introduces the research status at
home and abroad in terms of domain information mining and topic evolution law
of scientific and technological papers from three aspects: the semantic feature
representation learning of scientific and technological papers, the field
information mining of scientific and technological papers, and the mining and
prediction of research topic evolution rules of scientific and technological
papers.


title: Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis

abstract:
  Previous deep learning based Computer Aided Diagnosis (CAD) system treats
multiple views of the same lesion as independent images. Since an ultrasound
image only describes a partial 2D projection of a 3D lesion, such paradigm
ignores the semantic relationship between different views of a lesion, which is
inconsistent with the traditional diagnosis where sonographers analyze a lesion
from at least two views. In this paper, we propose a multi-task framework that
complements Benign/Malignant classification task with lesion recognition (LR)
which helps leveraging relationship among multiple views of a single lesion to
learn a complete representation of the lesion. To be specific, LR task employs
contrastive learning to encourage representation that pulls multiple views of
the same lesion and repels those of different lesions. The task therefore
facilitates a representation that is not only invariant to the view change of
the lesion, but also capturing fine-grained features to distinguish between
different lesions. Experiments show that the proposed multi-task framework
boosts the performance of Benign/Malignant classification as two sub-tasks
complement each other and enhance the learned representation of ultrasound
images.


title:
Enhancing Non-mass Breast Ultrasound Cancer Classification With
  Knowledge Transfer

abstract:
  Much progress has been made in the deep neural network (DNN) based diagnosis
of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is
less investigated because of the limited data. Based on the insight that mass
data is sufficient and shares the same knowledge structure with non-mass data
of identifying the malignancy of a lesion based on the ultrasound image, we
propose a novel transfer learning framework to enhance the generalizability of
the DNN model for non-mass BUS with the help of mass BUS. Specifically, we
train a shared DNN with combined non-mass and mass data. With the prior of
different marginal distributions in input and output space, we employ two
domain alignment strategies in the proposed transfer learning framework with
the insight of capturing domain-specific distribution to address the issue of
domain shift. Moreover, we propose a cross-domain semantic-preserve data
generation module called CrossMix to recover the missing distribution between
non-mass and mass data that is not presented in training data. Experimental
results on an in-house dataset demonstrate that the DNN model trained with
combined data by our framework achieves a 10% improvement in AUC on the
malignancy prediction task of non-mass BUS compared to training directly on
non-mass data.


title:
Inductive Biases for Object-Centric Representations in the Presence of
  Complex Textures

abstract:
  Understanding which inductive biases could be helpful for the unsupervised
learning of object-centric representations of natural scenes is challenging. We
use neural style transfer to generate datasets where objects have complex
textures while still retaining ground-truth annotations. We find that methods
that use a single module to reconstruct both the shape and visual appearance of
each object learn more useful representations and achieve better object
separation. In addition, we observe that adjusting the latent space size is not
sufficient to improve segmentation performance. Finally, the downstream
usefulness of the representations is significantly more strongly correlated
with segmentation quality than with reconstruction accuracy.


title: Probing Quadratic Gravity with the Event Horizon Telescope

abstract:
  Quadratic gravity constitutes a prototypical example of a perturbatively
renormalizable quantum theory of the gravitational interactions. In this work,
we construct the associated phase space of static, spherically symmetric, and
asymptotically flat spacetimes. It is found that the Schwarzschild geometry is
embedded in a rich solution space comprising horizonless, naked singularities
and wormhole solutions. Characteristically, the deformed solutions follow the
Schwarzschild solution up outside of the photon sphere while they differ
substantially close to the center of gravity. We then carry out an analytic
analysis of observable signatures accessible to the Event Horizon Telescope,
comprising the size of the black hole shadow as well as the radiation emitted
by infalling matter. On this basis, we argue that it is the brightness within
the shadow region which constrains the phase space of solutions. Our work
constitutes the first step towards bounding the phase space of black hole type
solutions with a clear quantum gravity interpretation based on observational
data.


title:
The Escape of Globular Clusters from the Satellite Dwarf Galaxies of the
  Milky Way

abstract:
  Using numerical simulations, we have studied the escape of globular clusters
(GCs) from the satellite dwarf spheroidal galaxies (dSphs) of the Milky Way
(MW). We start by following the orbits of a large sample of GCs around dSphs in
the presence of the MW potential field. We then obtain the fraction of GCs
leaving their host dSphs within a Hubble Time. We model dSphs by a Hernquist
density profile with masses between $10^7\,\mathrm{M}_{\odot}$ and $7\times
10^9\,\mathrm{M}_{\odot}$. All dSphs lie on the Galactic disc plane, but they
have different orbital eccentricities and apogalactic distances. We compute the
escape fraction of GCs from 13 of the most massive dSphs of the MW, using their
realistic orbits around the MW (as determined by Gaia). The escape fraction of
GCs from 13 dSphs is in the range $12\%$ to $93\%$. The average escape time of
GCs from these dSphs was less than 8 $\,\mathrm{Gyrs}$, indicating that the
escape process of GCs from dSphs was over. We then adopt a set of
observationally-constrained density profiles for specific case of the Fornax
dSph. According to our results, the escape fraction of GCs shows a negative
correlation with both the mass and the apogalactic distance of the dSphs, as
well as a positive correlation with the orbital eccentricity of dSphs. In
particular, we find that the escape fraction of GCs from the Fornax dSph is
between $13\%$ and $38\%$. Finally, we observe that when GCs leave their host
dSphs, their final orbit around the MW does not differ much from their host
dSphs.


title: New Constraints on the Composition and Initial Speed of CNEOS 2014-01-08

abstract:
  We study the newly released light curve from the fireball of the first
interstellar meteor CNEOS 2014-01-08. The measured velocity and three observed
flares down to an altitude of $18.7 \mathrm{\; km}$ imply ambient ram pressure
in the range of $113-194$ MPa when the meteor disintegrated. The required yield
strength is $\gtrsim 20$ times higher than stony meteorites and $\gtrsim 2$
times larger than iron meteorites. The implied slowdown in the atmosphere
suggests an initial speed of about $66.5 \; {\rm km~s^{-1}}$, strengthening the
case for an interstellar origin of this meteor and making it an outlier
relative to the velocity dispersion of local stars.


title:
The dust-to-gas mass ratio of luminous galaxies as a function of their
  metallicity at cosmic noon

abstract:
  We aim to quantify the relation between the dust-to-gas (DTG) mass ratio and
gas-phase metallicity of $z=$2.1-2.5 luminous galaxies and contrast this
high-redshift relation against analogous constraints at $z=$0. We present a
sample of 10 star-forming main-sequence galaxies in the redshift range
$2.1<z<2.5$ with rest-optical emission-line information available from the
MOSDEF survey and with ALMA 1.2 millimeter and CO J$=$3--2 follow-up
observations. The galaxies have stellar masses ranging from $10^{10.3}$ to
$10^{10.6}\,\rm{M}_\odot$ and cover a range in star-formation rate from 35 to
145 $\rm{M}_\odot\,\rm{yr}^{-1}$. We calculate the gas-phase oxygen abundance
of these galaxies from rest-optical nebular emission lines (8.4 < $12 +
\log{(\rm{O/H})} < 8.8$, corresponding to 0.5-1.25 Z$_\odot$). We estimate the
dust and H2 masses (using a metallicity dependent CO-to-H2 conversion factor)
of the galaxies from the 1.2 mm and CO J$=$3-2 observations, respectively, from
which we estimate a DTG. We find that the galaxies in this sample follow the
earlier observed trends between CO line luminosity and dust-continuum
luminosity from $z=0$ to $z=3$, extending such trends to fainter galaxies at
$2.1<z<2.5$ than observed to date. We find no second-order metallicity
dependence in the CO - dust-continuum luminosity relation for the galaxies
presented in this work. The DTG of main-sequence galaxies at $2.1<z<2.5$ are
consistent with an increase in DTG with gas-phase metallicity. Galaxies at
$z=$2.1-2.5 are furthermore consistent with the DTG-metallicity relation found
at $z=$0, providing relevant constraints for galaxy formation models. These
results furthermore imply that the metallicity of galaxies should be taken into
account when estimating cold-gas masses from dust-continuum emission,
especially relevant when studying metal-poor low-mass or high-redshift
galaxies. [abridged]


title: Unveiling dark fifth forces with linear cosmology

abstract:
  We initiate the exploration of the cosmology of dark fifth forces: new forces
acting solely on Dark Matter. We focus on long range interactions which lead to
an effective violation of the Equivalence Principle on cosmological scales. At
the microscopic level, the dark fifth force can be realized by a light scalar
with mass smaller than the Hubble constant today ($\lesssim
10^{-33}\,\text{eV}$) coupled to Dark Matter. We study the behavior of the
background cosmology and linear perturbations in such a Universe. At the
background level, the new force modifies the evolution of the Dark Matter
energy density and thus the Hubble flow. At linear order, it modifies the
growth of matter perturbations and generates relative density and velocity
perturbations between Dark Matter and baryons that grow over time. We derive
constraints from current CMB and BAO data, bounding the strength of the dark
fifth force to be less than a percent of gravity. These are the strongest
constraints to date. We present potential implications of this scenario for the
Hubble tension and discuss how our results are modified if the light scalar
mediator accounts for the observed density of the Dark Energy. Finally, we
comment on the interplay between our constraints and searches for violations of
the Equivalence Principle in the visible sector.


title:
Mixed Mode Asteroseismology of Red Giant Stars Through the Luminosity
  Bump

abstract:
  Most current models of low mass red giant stars do not reproduce the observed
position of the red giant branch luminosity bump, a diagnostic of the maximum
extent of the convective envelope during the first dredge up. Global
asteroseismic parameters, the large frequency separation and frequency of
maximum oscillation power, measured for large samples of red giants, show that
modeling convective overshoot below the convective envelope helps match the
modeled luminosity bump positions to observations. However, these global
parameters cannot be used to probe envelope overshoot in a star-by-star manner.
Red giant mixed modes, which behave like acoustic modes at the surface and like
gravity modes in the core, contain important information about the interior
structure of the star, especially near the convective boundary. Therefore,
these modes may be used to probe interior processes, such as overshoot. Using a
grid of red giant models with varying mass, metallicity, surface gravity,
overshoot treatment, and amount of envelope overshoot, we find that changing
the overshoot amplitude (and prescription) of overshoot below the convection
zone in red giant stellar models results in significant differences in the
evolution of the models' dipole mixed-mode oscillation frequencies, the average
mixed mode period spacing, $\langle \Delta P \rangle$, and gravity mode phase
offset term, $\epsilon_g$.


title:
Luminous Supernovae: Unveiling a Population Between Superluminous and
  Normal Core-collapse Supernovae

abstract:
  Stripped-envelope core-collapse supernovae can be divided into two broad
classes: the common Type Ib/c supernovae (SNe Ib/c), powered by the radioactive
decay of $^{56}$Ni, and the rare superluminous supernovae (SLSNe), most likely
powered by the spin-down of a magnetar central engine. Up to now, the
intermediate regime between these two populations has remained mostly
unexplored. Here, we present a comprehensive study of 40 \textit{luminous
supernovae} (LSNe), SNe with peak magnitudes of $M_r = -19$ to $-20$ mag, bound
by SLSNe on the bright end and by SNe Ib/c on the dim end. Spectroscopically,
LSNe appear to form a continuum between Type Ic SNe and SLSNe. Given their
intermediate nature, we model the light curves of all LSNe using a combined
magnetar plus radioactive decay model and find that they are indeed
intermediate, not only in terms of their peak luminosity and spectra, but also
in their rise times, power sources, and physical parameters. We sub-classify
LSNe into distinct groups that are either as fast-evolving as SNe Ib/c or as
slow-evolving as SLSNe, and appear to be either radioactively or magnetar
powered, respectively. Our findings indicate that LSNe are powered by either an
over-abundant production of $^{56}$Ni or by weak magnetar engines, and may
serve as the missing link between the two populations.


title:
Observed Extra Mixing Trends in Red Giants are Reproduced by the Reduced
  Density Ratio in Thermohaline Zones

abstract:
  Observations show an almost ubiquitous presence of extra mixing in low-mass
upper giant branch stars. The most commonly invoked explanation for this is the
thermohaline instability. One dimensional stellar evolution models include
prescriptions for thermohaline mixing, but our ability to make direct
comparisons between models and observations has thus far been limited. Here, we
propose a new framework to facilitate direct comparison: Using carbon to
nitrogen measurements from the SDSS-IV APOGEE survey as a probe of mixing and a
fluid parameter known as the reduced density ratio from one dimensional stellar
evolution programs, we compare the observed amount of extra mixing on the upper
giant branch to predicted trends from three-dimensional fluid dynamics
simulations. By applying this method, we are able to place empirical
constraints on the efficiency of mixing across a range of masses and
metallicities. We find that the observed amount of extra mixing is strongly
correlated with the reduced density ratio and that trends between reduced
density ratio and fundamental stellar parameters are robust across choices for
modeling prescription. We show that stars with available mixing data tend to
have relatively low density ratios, which should inform the regimes selected
for future simulation efforts. Finally, we show that there is increased mixing
at low values of the reduced density ratio, which is consistent with current
hydrodynamical models of the thermohaline instability. The introduction of this
framework sets a new standard for theoretical modeling efforts, as validation
for not only the amount of extra mixing, but trends between the degree of extra
mixing and fundamental stellar parameters is now possible.


title: Islands in Kerr-de Sitter spacetime and their flat limit

abstract:
  We use quantum extremal island method to study the information paradox on
certain cosmological setups known as three dimensional Kerr-de Sitter
spacetimes. To do so, we couple an auxiliary flat bath system to this spacetime
in timelike singularity and measure entropy of hawking radiation in its
asymptotic regions where the gravity is weak. We show that adding island
regions to the entanglement wedge of radiation causes its entropy to obey Page
curve. The boundary of island i.e. quantum extremal surface is located outside
the cosmological horizon in the region connected directly to the bath. Taking
the flat-space limit from the location of island and its related calculation in
the Kerr-de Sitter sapcetime results in the flat-space cosmology (FSC) island,
scrambling time and also Page time that were obtained in our previous paper. We
repeat the same calculation for the pure de Sitter spacetime and show that our
setup which neglects the effect of backreaction, leads also to a quantum
extremal surface outside the cosmological horizon. We calculate the scrambling
time and confirm the idea that pure de Sitter spacetime is a fast scrambler.


title:
Topological order and entanglement dynamics in the measurement-only XZZX
  quantum code

abstract:
  We examine the dynamics of a $(1+1)$-dimensional measurement-only circuit
defined by the stabilizers of the [[5,1,3]] quantum error correcting code
interrupted by single-qubit Pauli measurements. The code corrects arbitrary
single-qubit errors and it stabilizes an area law entangled state with a $D_2 =
\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry protected topological (SPT) order,
as well as a symmetry breaking (SB) order from a two-fold bulk degeneracy. The
Pauli measurements break the topological order and induce a phase transition
into a trivial area law phase. Allowing more than one type of Pauli measurement
increases the measurement-induced frustration, and the SPT and SB order can be
broken either simultaneously or separately at nonzero measurement rate. This
yields a rich phase diagram and unanticipated critical behavior at the phase
transitions. Although the correlation length exponent $\nu=\tfrac43$ and the
dynamical critical exponent $z=1$ are consistent with bond percolation, the
prefactor of the logarithmic entanglement growth may take non-integer multiples
of the percolation value. Remarkably, we identify a robust transient scaling
regime for the purification dynamics of $L$ qubits. It reveals a modified
dynamical critical exponent $z^*\neq z$, which is observable up to times $t\sim
L^{z^*}$ and is reminiscent of the relaxation of critical systems into a
prethermal state.


title:
The GLEAMing of the first supermassive black holes: II. A new sample of
  high-redshift radio galaxy candidates

abstract:
  While unobscured and radio-quiet active galactic nuclei are regularly being
found at redshifts $z > 6$, their obscured and radio-loud counterparts remain
elusive. We build upon our successful pilot study, presenting a new sample of
low-frequency-selected candidate high-redshift radio galaxies (HzRGs) over a
sky area twenty times larger. We have refined our selection technique, in which
we select sources with curved radio spectra between 72-231 MHz from the
GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey. In
combination with the requirements that our GLEAM-selected HzRG candidates have
compact radio morphologies and be undetected in near-infrared $K_{\rm s}$-band
imaging from the Visible and Infrared Survey Telescope for Astronomy
Kilo-degree Infrared Galaxy (VIKING) survey, we find 51 new candidate HzRGs
over a sky area of approximately 1200 deg$^2$. Our sample also includes two
sources from the pilot study: the second-most distant radio galaxy currently
known, at $z=5.55$, with another source potentially at $z \sim 8$. We present
our refined selection technique and analyse the properties of the sample. We
model the broadband radio spectra between 74 MHz and 9 GHz by supplementing the
GLEAM data with both publicly available data and new observations from the
Australia Telescope Compact Array at 5.5 and 9 GHz. In addition, deep $K_{\rm
s}$-band imaging from the High-Acuity Widefield $K$-band Imager (HAWK-I) on the
Very Large Telescope and from the Southern Herschel Astrophysical Terahertz
Large Area Survey Regions $K_{\rm s}$-band Survey (SHARKS) is presented for
five sources. We discuss the prospects of finding very distant radio galaxies
in our sample, potentially within the epoch of reionisation at $z \gtrsim 6.5$.


title: Active Learning Helps Pretrained Models Learn the Intended Task

abstract:
  Models can fail in unpredictable ways during deployment due to task
ambiguity, when multiple behaviors are consistent with the provided training
data. An example is an object classifier trained on red squares and blue
circles: when encountering blue squares, the intended behavior is undefined. We
investigate whether pretrained models are better active learners, capable of
disambiguating between the possible tasks a user may be trying to specify.
Intriguingly, we find that better active learning is an emergent property of
the pretraining process: pretrained models require up to 5 times fewer labels
when using uncertainty-based active learning, while non-pretrained models see
no or even negative benefit. We find these gains come from an ability to select
examples with attributes that disambiguate the intended behavior, such as rare
product categories or atypical backgrounds. These attributes are far more
linearly separable in pretrained model's representation spaces vs
non-pretrained models, suggesting a possible mechanism for this behavior.


title: Constraining equation of state groups from $g$-mode asteroseismology

abstract:
  Buoyancy-restored modes inside neutron stars depend sensitively on both the
microphysical (e.g., composition and entropy gradients) and macrophysical
(e.g., stellar mass and radius) properties of the star. Asteroseismology
efforts for $g$-modes are therefore particularly promising avenues for
recovering information concerning the nuclear equation of state. In this work
it is shown that the overall low-temperature $g$-space consists of multiple
groups corresponding to different classes of equation of state (e.g., hadronic
vs. hybrid). This is in contrast to the case of pressure-driven modes, for
example, which tend to follow a universal relation regardless of microphysical
considerations. Using a wide library of currently-viable equations of state,
perturbations of static, stratified stars are calculated in general relativity
to demonstrate in particular how $g$-space groupings can be classified
according to the mean mass density, temperature, central speed of sound, and
tidal deformability. Considering present and future observations regarding
gravitational waves, accretion outbursts, quasi-periodic oscillations, and
precursor flashes from gamma-ray bursts, it is shown how one might determine
which group the $g$-modes belong to.


title:
Beyond binarity in A stars II. Disentangling the four stars in the
  vicinity of the triple HIP 87813 within the quintuple system HJ2814

abstract:
  A-stars are the progenitors of about half of the white dwarfs (WDs) that
currently exist. The connection between the multiplicity of A-stars and that of
WDs is not known and both multiplicities are still poorly explored. We are in
the process of obtaining tight constraints on a sample of 108 southern A-type
stars that are part of the nearby VAST sample \citep{DeRosa14} by conducting
near-infrared interferometric follow-up observations to the (twenty) stars
among them which have large $Gaia$-$Hipparcos$ accelerations. In this paper, we
combine spectroscopy, adaptive optics imaging, NIR interferometry and
$Gaia$-$Hipparcos$ astrometry in order to disentangle the stars in the
complicated HIP 87813 = HJ2814A system. We show that (i) a previously
discovered faint star that is separated by 2" from the A star is actually a
background source; (ii) the $Gaia$-$Hipparcos$ acceleration is caused by a
newly discovered $0.74 M_{\odot}$ star that was missed in previous AO images
and we solve for its $P \approx 60 \text{ yrs}$ astrometric orbit; (iii) by
combining previously obtained spectra we show that the A star has a very close
$0.85 M_{\odot}$ companion on a 13.4-day period orbit. The radial velocity
curve combined with NIR interferometry constrains its orbit allowing
Kozai-Lidov oscillations in the hierarchical triple to be ruled out. The system
HJ2814 is one of only about fifteen known 5+ systems with an A star primary,
and will result in a system of between two to five bound WDs within around a
Hubble time.


title:
Training variational quantum circuits with CoVaR: covariance root
  finding with classical shadows

abstract:
  Exploiting near-term quantum computers and achieving practical value is a
considerable and exciting challenge. Most prominent candidates as variational
algorithms typically aim to find the ground state of a Hamiltonian by
minimising a single classical (energy) surface which is sampled from by a
quantum computer. Here we introduce a method we call CoVaR, an alternative
means to exploit the power of variational circuits: We find eigenstates by
finding joint roots of a polynomially growing number of properties of the
quantum state as covariance functions between the Hamiltonian and an operator
pool of our choice. The most remarkable feature of our CoVaR approach is that
it allows us to fully exploit the extremely powerful classical shadow
techniques, i.e., we simultaneously estimate a very large number $>10^4-10^7$
of covariances. We randomly select covariances and estimate analytical
derivatives at each iteration applying a stochastic Levenberg-Marquardt step
via a large but tractable linear system of equations that we solve with a
classical computer. We prove that the cost in quantum resources per iteration
is comparable to a standard gradient estimation, however, we observe in
numerical simulations a very significant improvement by many orders of
magnitude in convergence speed. CoVaR is directly analogous to stochastic
gradient-based optimisations of paramount importance to classical machine
learning while we also offload significant but tractable work onto the
classical processor.


title: KANT: A tool for Grounding and Knowledge Management

abstract:
  The intelligent robotics community usually organizes knowledge into symbolic
and sub-symbolic levels. These two levels establish the set of symbols and
rules for manipulating knowledge based on their (symbol system - dictionary).
Thus, the correspondences -- Grounding or knowledge representation -- require
specific software techniques for anchoring continuous and discrete state
variables between these two levels. This paper presents the design and
evaluation of an Open Source tool called KANT(Knowledge mAnagemeNT) to let
different components of the system architecture controlling the robot query,
save, edit, and delete the data from the Knowledge Base without having to worry
about the type and the implementation of the source data. Using KANT,
components managing subsymbolic information can smoothly interact with symbolic
components. Besides, implementation mechanisms used in KANT, such as the use of
in-memory and non-SQL databases, improve the performance of the knowledge
management systems in ROS middleware, as shown by the evaluations presented in
this work.


title: Tuning skyrmions in B20 compounds by 4d and 5d doping

abstract:
  Skyrmion stabilization in novel magnetic systems with the B20 crystal
structure is reported here, primarily based on theoretical results. The focus
is on the effect of alloying on the 3d sublattice of the B20 structure by
substitution of heavier 4d and 5d elements, with the ambition to tune the
spin-orbit coupling and its influence on magnetic interactions.
State-of-the-art methods based on density functional theory are used to
calculate both isotropic and anisotropic exchange interactions. Significant
enhancement of the Dzyaloshinskii-Moriya interaction is reported for 5d-doped
FeSi and CoSi, accompanied by a large modification of the spin stiffness and
spiralization. Micromagnetic simulations coupled to atomistic spin-dynamics and
ab initio magnetic interactions reveal a helical ground state and field-induced
skyrmions for all these systems. Especially small skyrmions $\sim$50 nm are
predicted for Co$_{0.75}$Os$_{0.25}$Si, compared to $\sim$148 nm for
Fe$_{0.75}$Co$_{0.25}$Si. Convex-hull analysis suggests that all B20 compounds
considered here are structurally stable at elevated temperatures and should be
possible to synthesize. This prediction is confirmed experimentally by
synthesis and structural analysis of the Ru-doped CoSi systems discussed here,
both in powder and in single-crystal forms.


title:
Tracking a beam of electrons from the low solar corona into
  interplanetary space with the Low Frequency Array, Parker Solar Probe and 1
  au spacecraft

abstract:
  Type III radio bursts are the result of plasma emission from mildly
relativistic electron beams propagating from the low solar corona into the
heliosphere where they can eventually be detected in situ if they align with
the location of a heliospheric spacecraft. Here we observe a type III radio
burst from 0.1-16 MHz using the Parker Solar Probe (PSP) FIELDS Radio Frequency
Spectrometer (RFS), and from 10-80 MHz using the Low Frequency Array (LOFAR).
This event was not associated with any detectable flare activity but was part
of an ongoing noise storm that occurred during PSP encounter 2. A deprojection
of the LOFAR radio sources into 3D space shows that the type III radio burst
sources were located on open magnetic field from 1.6-3 $R_\odot$ and originated
from a specific active region near the East limb. Combining PSP/RFS
observations with WIND/WAVES and Solar Terrestrial Relations Observatory
(STEREO)/WAVES, we reconstruct the type III radio source trajectory in the
heliosphere interior to PSP's position, assuming ecliptic confinement. An
energetic electron enhancement is subsequently detected in situ at the STEREO-A
spacecraft at compatible times although the onset and duration suggests the
individual burst contributes a subset of the enhancement. This work shows
relatively small-scale flux emergence in the corona can cause the injection of
electron beams from the low corona into the heliosphere, without needing a
strong solar flare. The complementary nature of combined ground and space-based
radio observations, especially in the era of PSP, is also clearly highlighted
by this study.


title:
Is the resonance $X_0(2900)$ a ground-state or radially excited scalar
  tetraquark $[ud][\overline{c}\overline{s}]$?

abstract:
  We investigate properties of the ground-state and first radially excited
four-quark mesons $X_0$ and $X_0^{\prime}$ with a diquark-antidiquark structure
$[ud][\overline{c}\overline{s}]$ and spin-parities $J^{\mathrm{P} }=0^{+}$. Our
aim is to reveal whether or not one of these states can be identified with the
resonance $X_0(2900)$, recently discovered by the LHCb collaboration. We model
$X_0$ and $X_0^{\prime}$ as tetraquarks composed of either axial-vector or
scalar diquark and antidiquark pairs. Their spectroscopic parameters are
computed by employing the QCD two-point sum rule method and including into
analysis vacuum condensates up to dimension $ 15$. For an axial-axial structure
of $X_0^{(\prime)}$, we find partial widths of the decays $X_0^{(\prime)} \to
D^{-}K^{+}$ and $X_0^{(\prime)} \to D^{0}K^{0}$, and estimate full widths of
the states $X_0^{(\prime)}$. To this end, we calculate the strong couplings at
the vertices $ X_0^{(\prime)}DK $ in the framework of the light-cone sum rule
method. We use also technical approaches of the soft-meson approximation
necessary to analyze tetraquark-meson-meson vertices. Obtained results $m=(2545
\pm 160)~ \mathrm{MeV}$ and $m^{\prime}=(3320 \pm 120)~\mathrm{MeV}$
[$m_{\mathrm{S} }=(2663 \pm 110)~\mathrm{MeV}$ and
$m_{\mathrm{S}}^{\prime}=(3325 \pm 85)~ \mathrm{MeV}$ for a scalar-scalar
current] for the masses of the particles $ X_0$ and $X_0^{\prime}$, as well as
estimates for their full widths $ \Gamma_{0}=(140 \pm 29)~\mathrm{MeV}$ and
$\Gamma_{0}^{\prime}=(110 \pm 25)~ \mathrm{MeV}$ allow us to interpret none of
them as the resonance $X_0(2900)$ . At the same time, these predictions provide
important information about ground-state and radially excited
diquark-antidiquark structures $X_0$ and $ X_0^{\prime}$, which should be
objects of future experimental and theoretical studies.


title: DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

abstract:
  Coreset selection, which aims to select a subset of the most informative
training samples, is a long-standing learning problem that can benefit many
downstream tasks such as data-efficient learning, continual learning, neural
architecture search, active learning, etc. However, many existing coreset
selection methods are not designed for deep learning, which may have high
complexity and poor generalization ability to unseen representations. In
addition, the recently proposed methods are evaluated on models, datasets, and
settings of different complexities. To advance the research of coreset
selection in deep learning, we contribute a comprehensive code library, namely
DeepCore, and provide an empirical study on popular coreset selection methods
on CIFAR10 and ImageNet datasets. Extensive experiment results show that,
although some methods perform better in certain experiment settings, random
selection is still a strong baseline.


title:
Influence of the presence of multiple resonances on material parameter
  determination using broadband ferromagnetic resonance spectroscopy

abstract:
  The influence of the presence of multiple resonances in ferromagnetic
resonance spectra on extracted material parameters is investigated using
numerical simulations. Our results show that the systematic error of assuming
an incorrect number of resonances for a material can lead to the extraction of
material parameters that significantly deviate from any of the true material
parameters. When noise is present in experimental ferromagnetic resonance
spectra increasing the frequency range of the broadband characterization can
significantly reduce the error-margins when the data is analyzed assuming the
correct number of resonances present in the material. For the cases
investigated in this study it was found that analyzing the data using a single
resonance results in extracted gyromagnetic ratios and effective magnetization
parameters that are consistent with the weighted average of the true material
parameters. We further provide a cautionary example regarding the extraction of
the inhomogeneous linewidth broadening and damping parameters of materials that
contain an unknown number of resonances.


title:
Common envelope evolution in born-again planetary nebulae -- Shaping the
  H-deficient ejecta of A30

abstract:
  Born-again planetary nebulae (PNe) are extremely rare cases in the evolution
of solar-like stars. It is commonly accepted that their central stars (CSPN)
experienced a very late thermal pulse (VLTP), ejecting H-deficient material
inside the evolved H-rich PN. Given the short duration of this event and the
fast subsequent evolution of the CSPN, details of the mass ejection are
unknown. We present the first morpho-kinematic model of the H-deficient
material surrounding a born-again PN, namely A30. New San Pedro M\'{a}rtir
observations with the Manchester Echelle Spectrograph were recently obtained to
map its inner regions of A30 which are interpreted by means of the software
SHAPE in conjunction with HST WFC3 images. The SHAPE morpho-kinematic model
that best reproduces the observations is composed by a disrupted disk tilted
37$^\circ$ with respect to the line of sight and a pair of orthogonal opposite
bipolar ejections. We confirm previous suggestions that the structures closer
to the CSPN present the highest expansion velocities, that is, the disrupted
disk expands faster than the farther bipolar features. We propose that the
current physical structure and abundance discrepancy of the H-deficient clumps
around the CSPN of A30 can be explained by a common envelope (CE) phase
following the VLTP event. Our proposed scenario is also compared with other
known born-again PNe (A58, A78, HuBi1 and the Sakurai's Object).


title:
Spot the Difference: A Novel Task for Embodied Agents in Changing
  Environments

abstract:
  Embodied AI is a recent research area that aims at creating intelligent
agents that can move and operate inside an environment. Existing approaches in
this field demand the agents to act in completely new and unexplored scenes.
However, this setting is far from realistic use cases that instead require
executing multiple tasks in the same environment. Even if the environment
changes over time, the agent could still count on its global knowledge about
the scene while trying to adapt its internal representation to the current
state of the environment. To make a step towards this setting, we propose Spot
the Difference: a novel task for Embodied AI where the agent has access to an
outdated map of the environment and needs to recover the correct layout in a
fixed time budget. To this end, we collect a new dataset of occupancy maps
starting from existing datasets of 3D spaces and generating a number of
possible layouts for a single environment. This dataset can be employed in the
popular Habitat simulator and is fully compliant with existing methods that
employ reconstructed occupancy maps during navigation. Furthermore, we propose
an exploration policy that can take advantage of previous knowledge of the
environment and identify changes in the scene faster and more effectively than
existing agents. Experimental results show that the proposed architecture
outperforms existing state-of-the-art models for exploration on this new
setting.


title: Encoding beyond cosmological horizons in de Sitter JT gravity

abstract:
  Black hole event horizons and cosmological event horizons share many
properties, making it natural to ask whether our recent advances in
understanding black holes generalize to cosmology. To this end, we discuss a
paradox that occurs if observers can access what lies beyond their cosmological
horizon in the same way that they can access what lies beyond a black hole
horizon. In particular, distinct observers with distinct horizons may encode
the same portion of spacetime, violating the no-cloning theorem of quantum
mechanics. This paradox is due precisely to the observer-dependence of the
cosmological horizon -- the sharpest difference from a black hole horizon --
although we will argue that the gravity path integral avoids the paradox in
controlled examples.


title: CGC: Contrastive Graph Clustering for Community Detection and Tracking

abstract:
  Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods.


title: Inclusion unlocks the creative potential of gender diversity in teams

abstract:
  Diversity in teams can boost creativity, and gender diversity was shown to be
a contributor to collective creativity. We show that gender diversity requires
inclusion to lead to benefits in creativity by analyzing teams in 4011 video
game projects. Recording data on the weighted network from past collaborations,
we developed four measures of inclusion, depending on a lack of segregation,
strong ties across genders, and the incorporation of women into the core of the
team s network. We found that gender diversity without inclusion does not
contribute to creativity, while with maximal inclusion one standard deviation
change in diversity results in .04 to .09 standard deviation change in
creativity, depending on the measure of inclusion. To reap creative benefits of
diversity, developer firms need to include 23 percent or more female developers
(as opposed to the 15 percent mean female proportion) and include them in the
team along all dimensions. Inclusion at low diversity has a negative effect. By
analyzing the sequences of diversity and inclusion across games within firms,
we found that adding diversity first, and developing inclusion later can lead
to higher diversity and inclusion, compared to adding female developers with
already existing cross-gender ties to the team.


title:
How large are the monomers of dust aggregates in planet-forming disks?:
  Insights from quantitative optical and near-infrared polarimetry

abstract:
  Context: The size of the constituent particles (monomers) of dust aggregates
is one of the most uncertain parameters directly affecting collisional growth
of aggregates in planet-forming disks. Despite its importance, the monomer size
has not yet been meaningfully constrained by disk observations. Aims: We
attempt to derive the monomer size from optical and near-infrared (IR)
polarimetric observations of planet-forming disks. Methods: We perform a
comprehensive parameter survey on the degree of linear polarization of light
scattered by dust aggregates, using an exact numerical method called the
$T$-matrix method. We investigate the effect of the monomer size, aggregate
size, porosity, and composition on the degree of polarization. The obtained
results are then compared with observed polarization fractions of several
planet-forming disks at optical and near-IR wavelengths. Results: It is shown
that the degree of polarization of aggregates depends sensitively on the
monomer size unless the monomer size parameter is smaller than one or two.
Comparing the simulation results with the disk observations, we find that the
monomer radius is no greater than $0.4~\mu$m. The inferred monomer size is
therefore similar to subunit sizes of the solar system dust aggregates and the
maximum size of interstellar grains. Conclusions: Optical and near-IR
quantitative polarimetry will provide observational grounds on the initial
conditions for dust coagulation and thereby planetesimal formation in
planet-forming disks.


title: Multiplicative Ehresmann connections

abstract:
  We develop the theory of multiplicative Ehresmann connections for Lie
groupoid submersions covering the identity, as well as their infinitesimal
counterparts. We construct obstructions to the existence of such connections,
and we prove existence for several interesting classes of Lie groupoids and Lie
algebroids, including all proper Lie groupoids. We show that many notions from
the theory of principal bundle connections have analogues in this general
setup, including connections 1-forms, curvature 2-forms, Bianchi identity, etc.
In [17] we provide a non-trivial application of the results obtained here to
construct local models in Poisson geometry and to obtain linearization results
around Poisson submanifolds.


title: Entropy of labeled versus unlabeled networks

abstract:
  The structure of a network is an unlabeled graph, yet graphs in most models
of complex networks are labeled by meaningless random integers. Is the
associated labeling noise always negligible, or can it overpower the
network-structural signal? To address this question, we introduce and consider
the sparse unlabeled versions of popular network models, and compare their
entropy against the original labeled versions. We show that labeled and
unlabeled Erdos-Renyi graphs are entropically equivalent, even though their
degree distributions are very different. The labeled and unlabeled versions of
the configuration model may have different prefactors in their leading entropy
terms, although this remains conjectural. Our main results are upper and lower
bounds for the entropy of labeled and unlabeled one-dimensional random
geometric graphs. We show that their unlabeled entropy is negligible in
comparison with the labeled entropy. These results imply that in sparse
networks the entropy of meaningless labeling may dominate the entropy of the
network structure, suggesting a need for a thorough reexamination of the
statistical foundations of network modeling.

categories: physics.soc-ph cond-mat.stat-mech cs.SI math.PR physics.data-an


title: Generalized Paley graphs equienergetic with their complements

abstract:
  We consider generalized Paley graphs $\Gamma(k,q)$, generalized Paley sum
graphs $\Gamma^+(k,q)$, and their corresponding complements $\bar \Gamma(k,q)$
and $\bar \Gamma^+(k,q)$, for $k=3,4$. Denote by $\Gamma = \Gamma^*(k,q)$
either $\Gamma(k,q)$ or $\Gamma^+(k,q)$. We compute the spectra of
$\Gamma(3,q)$ and $\Gamma(4,q)$ and from them we obtain the spectra of
$\Gamma^+(3,q)$ and $\Gamma^+(4,q)$ also. Then we show that, in the
non-semiprimitive case, the spectrum of $\Gamma(3,p^{3\ell})$ and
$\Gamma(4,p^{4\ell})$ with $p$ prime can be recursively obtained, under certain
arithmetic conditions, from the spectrum of the graphs $\Gamma(3,p)$ and
$\Gamma(4,p)$ for any $\ell \in \mathbb{N}$, respectively. Using the spectra of
these graphs we give necessary and sufficient conditions on the spectrum of
$\Gamma^*(k,q)$ such that $\Gamma^*(k,q)$ and $\bar \Gamma^*(k,q)$ are
equienergetic for $k=3,4$. In a previous work we have classified all bipartite
regular graphs $\Gamma_{bip}$ and all strongly regular graphs $\Gamma_{srg}$
which are complementary equienergetic, i.e.\@ $\{\Gamma_{bip},
\bar{\Gamma}_{bip}\}$ and $\{\Gamma_{srg}, \bar{\Gamma}_{srg}\}$ are
equienergetic pairs of graphs. Here we construct infinite pairs of
equienergetic non-isospectral regular graphs $\{\Gamma, \bar \Gamma\}$ which
are neither bipartite nor strongly regular.


title: How does environment affect the morphology of radio AGN?

abstract:
  Galaxies hosting Active Galactic Nuclei (AGN) with bent radio jets are used
as tracers of dense environments, such as galaxy groups and clusters. The
assumption behind using these jets is that they are bent under ram pressure
from a dense, gaseous medium through which the host galaxy moves. However,
there are many AGN in groups and clusters with jets that are not bent, which
leads us to ask: why are some AGN jets affected so much by their environment
while others are seemingly not? We present the results of an environmental
study on a sample of 185 AGN with bent jets and 191 AGN with unbent jets in
which we characterize their environments by searching for neighboring galaxies
using a Friends-of-Friends algorithm. We find that AGN with bent jets are
indeed more likely to reside in groups and clusters, while unbent AGN are more
likely to exist in singles or pairs. When considering only AGN in groups of 3
or more galaxies, we find that bent AGN are more likely to exist in halos with
more galaxies than unbent AGN. We also find that unbent AGN are more likely
than bent AGN to be the brightest group galaxy. Additionally, groups hosting
AGN with bent jets have a higher density of galaxies than groups hosting unbent
AGN. Curiously, there is a population of AGN with bent jets that are in
seemingly less dense regions of space, indicating they may be embedded in a
cosmic web filament. Overall, our results indicate that bent doubles are more
likely to exist in in larger, denser, and less relaxed environments than unbent
doubles, potentially linking a galaxy's radio morphology to its environment.


title:
Multilevel Picard approximations for high-dimensional decoupled
  forward-backward stochastic differential equations

abstract:
  Backward stochastic differential equations (BSDEs) appear in numeruous
applications. Classical approximation methods suffer from the curse of
dimensionality and deep learning-based approximation methods are not known to
converge to the BSDE solution. Recently, Hutzenthaler et al. (arXiv:2108.10602)
introduced a new approximation method for BSDEs whose forward diffusion is
Brownian motion and proved that this method converges with essentially optimal
rate without suffering from the curse of dimensionality. The central object of
this article is to extend this result to general forward diffusions. The main
challenge is that we need to establish convergence in temporal-spatial H\"older
norms since the forward diffusion cannot be sampled exactly in general.


title:
An Experiment of Randomized Hints on an Axiom of Infinite-Valued
  Lukasiewicz Logic

abstract:
  In this paper, we present an experiment of our randomized hints strategy of
automated reasoning for yielding Axiom(5) from Axiom(1)(2)(3)(4) of
Infinite-Valued Lukasiewicz Logic. In the experiment, we randomly generated a
set of hints with size ranging from 30 to 60 for guiding hyper-resolution based
search by the theorem prover OTTER. We have successfully found the most useful
hints list (with 30 clauses) among 150 * 6 hints lists. Also, we discuss a
curious non-linear increase of generated clauses in deducing Axiom(5) by
applying our randomized hints strategy.


title: Magnetar bursts due to Alfv\'{e}n wave nonlinear breakout

abstract:
  The most common form of magnetar activity is short X-ray bursts, with
durations from milliseconds to seconds, and luminosities ranging from $10^{36}$
to $10^{43}\ {\rm erg}\,{\rm s}^{-1}$. Recently, an X-ray burst from the
galactic magnetar SGR 1935+2154 was detected to be coincident with two fast
radio burst (FRB) like events from the same source, providing evidence that
FRBs may be linked to magnetar bursts. Using fully 3D force-free
electrodynamics simulations, we show that such magnetar bursts may be produced
by Alfv\'{e}n waves launched from localized magnetar quakes: a wave packet
propagates to the outer magnetosphere, becomes nonlinear, and escapes the
magnetosphere, forming an ultra-relativistic ejecta. The ejecta pushes open the
magnetospheric field lines, creating current sheets behind it. Magnetic
reconnection can happen at these current sheets, leading to plasma energization
and X-ray emission. The angular size of the ejecta can be compact, $\lesssim
0.5$ sr if the quake launching region is small, $\lesssim 5\times 10^{-3}$ sr
at the stellar surface. We discuss implications for the FRBs and the coincident
X-ray burst from SGR 1935+2154.