title: Conservativity spectra and generalized Ignatiev model

abstract:
  We study a generalization of the notion of conservativity spectrum of an
arithmetical theory to a language with transfinitely many truth definitions. We
establish a correspondence of conservativity spectra and points of a
generalized Ignatied model introduced and studied by D. Fern\'andez-Duque and
J. Joosten. We also prove the so-called Schmerl formulas for iterated
reflection principles of predicative strength.


title:
Anomalous fluctuations of renewal-reward processes with heavy-tailed
  distributions

abstract:
  For renewal-reward processes with a power-law decaying waiting time
distribution, anomalously large probabilities are assigned to atypical values
of the asymptotic processes. Previous works have reveals that this anomalous
scaling causes a singularity in the corresponding large deviation function. In
order to further understand this problem, we study in this article the scaling
of variance in several renewal-reward processes: counting processes with two
different power-law decaying waiting time distributions and a Knudsen gas (a
heat conduction model). Through analytical and numerical analyses of these
models, we find that the variances show an anomalous scaling when the exponent
of the power law is -3. For a counting process with the power-law exponent
smaller than -3, this anomalous scaling does not take place: this indicates
that the processes only fluctuate around the expectation with an error that is
compatible with a standard large deviation scaling. In this case, we argue that
anomalous scaling appears in higher order cumulants. Finally, many-body
particles interacting through soft-core interactions with the boundary
conditions employed in the Knudsen gas are studied using numerical simulations.
We observe that the variance scaling becomes normal even though the power-law
exponent in the boundary conditions is -3.


title: On the persistence of spatial analyticity for the Beam Equation

abstract:
  Persistence of spatial analyticity is studied for solution of the beam
equation $ u_{tt} + \left(m+\Delta^2\right) u + |u|^{p-1}u = 0$ on $\mathbb R^n
\times \mathbb R$. In particular, for a class of analytic initial data with a
uniform radius of analyticity $\sigma_0$, we obtain an asymptotic lower bound
$\sigma(t) \ge c/\sqrt t$ on the uniform radius of analyticity $\sigma(t)$ of
solution $u(\cdot, t)$, as $t \rightarrow \infty.$


title:
Deep vanishing point detection: Geometric priors make dataset variations
  vanish

abstract:
  Deep learning has improved vanishing point detection in images. Yet, deep
networks require expensive annotated datasets trained on costly hardware and do
not generalize to even slightly different domains, and minor problem variants.
Here, we address these issues by injecting deep vanishing point detection
networks with prior knowledge. This prior knowledge no longer needs to be
learned from data, saving valuable annotation efforts and compute, unlocking
realistic few-sample scenarios, and reducing the impact of domain changes.
Moreover, the interpretability of the priors allows to adapt deep networks to
minor problem variations such as switching between Manhattan and non-Manhattan
worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform --
mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping
lines to great circles whose intersections denote vanishing points.
Experimentally, we ablate our choices and show comparable accuracy to existing
models in the large-data setting. We validate our model's improved data
efficiency, robustness to domain changes, adaptability to non-Manhattan
settings.


title:
Scanning gate microscopy imaging of the supercurrent distribution in a
  planar Josephson junction

abstract:
  We theoretically investigate the mapping of the supercurrent distribution in
a planar superconductor-normal-superconductor junction in the presence of a
perpendicular magnetic field via the scanning gate microscopy technique. We
find that the change of the critical current induced by the scanning probe tip
reflects the distribution of counter-propagating supercurrents aligned in
Josephson vortices provided that the flux in the junction is set close to
Fraunhofer pattern maxima. Instead, when the magnetic field drives the junction
to a supercurrent minimum in the Fraunhofer pattern, the superconducting phase
adapts, and the tip always increases the supercurrent. The perpendicular
magnetic field leads to the formation of Josephson vortices, whose extension
depends on the current circulation direction. We show that this leads to an
asymmetric supercurrent distribution in the junction and that this can be
revealed by scanning gate microscopy. We explain our findings on the basis of
numerical calculations for both short- and long-junction limits and provide a
semiclassical theory for the observed phenomena.


title: MIMO-GAN: Generative MIMO Channel Modeling

abstract:
  We propose generative channel modeling to learn statistical channel models
from channel input-output measurements. Generative channel models can learn
more complicated distributions and represent the field data more faithfully.
They are tractable and easy to sample from, which can potentially speed up the
simulation rounds. To achieve this, we leverage advances in GAN, which helps us
learn an implicit distribution over stochastic MIMO channels from observed
measurements. In particular, our approach MIMO-GAN implicitly models the
wireless channel as a distribution of time-domain band-limited impulse
responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe
high-consistency in capturing power, delay and spatial correlation statistics
of the underlying channel. In particular, we observe MIMO-GAN achieve errors of
under 3.57 ns average delay and -18.7 dB power.


title:
New Lower Bound for the Radius of Analyticity of Solutions to the fifth
  order KdV-BBM model

abstract:
  We show that the uniform radius of spatial analyticity $\sigma(t)$ of
solutions at time $t$ to the fifth order KdV-BBM equation cannot decay faster
than $1/ \sqrt{t}$ for large $t$, given initial data that is analytic with
fixed radius $\sigma_0$. This improves a recent result by Belayneh, Tegegn and
the third author \cite{BTT21}, where they obtained a $1/t$ decay of $\sigma(t)$
for large time $t$.


title:
Broad band flux-density monitoring of radio sources with the Onsala twin
  telescopes

abstract:
  Context and aims: The Onsala twin telescopes (OTT) are two 13 m telescopes
located at the Onsala Space Observatory in Sweden. With dual linear polarized
broad-band (3-14 GHz) receivers, they are part of the next generation Very Long
Baseline Interferometry (VLBI) Global Observing System (VGOS) for geodesy and
astrometry. In addition to purely geodetic data products, VGOS will regularly
produce full-polarisation images of hundreds of radio sources. These rich
monitoring data will be valuable for both astronomy and geodesy. In this pilot
study we aim to monitor 10 bright radio sources to search for flares or similar
activity, and to verify the instrument calibration on long (months) and short
(hours) time scales. Method: We observed and analysed 91 short (<30 min)
sessions spanning 7 months. We monitored seven potentially variable radio
sources (0059+581, 0552+398, 1144+402, 1156+295, 1617+229, 3C418, OJ287) and
three reference calibrators (3C147, 3C286, 3C295). We used the Common Astronomy
Software Applications (CASA) package to fringe-fit, bandpass-correct and scale
the data to obtain flux densities in the four standard VGOS bands: 3.0-3.5 GHz
(band 1), 5.2-5.7 GHz (band 2), 6.3-6.8 GHz (band 3), and 10.2-10.7 GHz (band
4). Results: We obtain simultaneous multi-frequency light curves for ten radio
sources. A bright multi-frequency flare is observed in the radio source
0059+581. OJ287 and 1156+295 show significant long-term variability.
Conclusions: After correcting for instrumental biases, we determine the
empirical flux density uncertainty as $\sim$5%. Future refined analysis methods
will allow regular monitoring of more and fainter sources.


title: An Invitation to Coarse Groups

abstract:
  In this monograph we lay the foundation for a theory of coarse groups and
coarse actions. Coarse groups are group objects in the category of coarse
spaces, and can be thought of as sets with operations that satisfy the group
axioms "up to uniformly bounded error". In the first part of this work, we
develop the theory of coarse homomorphisms, quotients, and subgroups, and prove
that coarse versions of the Isomorphism Theorems hold true. We also initiate
the study of coarse actions and show how they relate to the fundamental
observation of Geometric Group Theory.
  In the second part we explore a selection of specialized topics, such as the
study of coarse group structures on set-groups, groups of coarse automorphisms
and spaces of controlled maps. Here the main aim is to show how the theory of
coarse groups connects with classical subjects. These include: number theory;
the study of bi\=/invariant metrics on groups; quasimorphisms and stable
commutator length; ${\rm Out}(F_n)$; topological group actions.


title: On the complexity of the word problem of the R. Thompson group V

abstract:
  We analyze the proof by Lehnert and Schweitzer that the word problem of the
Thompson group V is co-context-free, and we show that this word problem is the
complement of the cyclic closure of a union of reverse deterministic
context-free languages. For certain finite generating sets, this word problem
is the complement of the cyclic closure of the union of four deterministic
context-free languages. It follows that the deterministic time-complexity of
the word problem of $V$ is at most quadratic.


title: Triangular modular curves of low genus

abstract:
  Triangular modular curves are a generalization of modular curves that arise
from quotients of the upper half-plane by congruence subgroups of hyperbolic
triangle groups. These curves also arise naturally as Belyi maps with monodromy
$\text{PGL}_2(\mathbb{F}_q)$ or $\text{PSL}_2(\mathbb{F}_q)$. We present a
computational approach to enumerate triangular modular curves of low genus, and
we carry out this enumeration up to genus 2.


title: Towards a Roadmap on Software Engineering for Responsible AI

abstract:
  Although AI is transforming the world, there are serious concerns about its
ability to behave and make decisions responsibly. Many ethical regulations,
principles, and frameworks for responsible AI have been issued recently.
However, they are high level and difficult to put into practice. On the other
hand, most AI researchers focus on algorithmic solutions, while the responsible
AI challenges actually crosscut the entire engineering lifecycle and components
of AI systems. To close the gap in operationalizing responsible AI, this paper
aims to develop a roadmap on software engineering for responsible AI. The
roadmap focuses on (i) establishing multi-level governance for responsible AI
systems, (ii) setting up the development processes incorporating
process-oriented practices for responsible AI systems, and (iii) building
responsible-AI-by-design into AI systems through system-level architectural
style, patterns and techniques.


title:
Linear and nonlinear waves in quantum plasmas with arbitrary degeneracy
  of electrons

abstract:
  The purpose of this review is to revisit recent results in the literature
where quantum plasmas with arbitrary degeneracy degree are considered. This is
different from a frequent approach, where completely degeneracy is assumed in
dense plasmas. The general reasoning in the reviewed works is to take a
numerical coefficient in from of the Bohm potential term in quantum fluids, in
order to fit the linear waves from quantum kinetic theory in the long
wavelength limit. Moreover, the equation of state for the ideal Fermi gas is
assumed, for arbitrary degeneracy degree. The quantum fluid equations allow the
expedite derivation of weakly nonlinear equations from reductive perturbation
theory. In this way, quantum Korteweg - de Vries and quantum Zakharov -
Kuznetsov equations are derived, together with the conditions for bright and
dark soliton propagation. Quantum ion-acoustic waves in unmagnetized and
magnetized plasmas, together with magnetosonic waves, have been obtained for
arbitrary degeneracy degree. The conditions for the application of the models,
and the physical situations where the mixed dense - dilute systems exist, have
been identified.


title:
Kepler Pixel Project -- background RR Lyrae stars in the primary Kepler
  mission field of view

abstract:
  In this paper we describe a project we initiated to investigate individual
pixels in the downloaded Kepler apertures in order to find objects in the
background of the main targets with variable brightness. In the first paper of
this series we discovered and investigated 547 short-period eclipsing binaries
(Bienias et al. 2021). Here we present the independent discovery of 26 new RR
Lyrae stars in the Kepler background pixels obtained during the primary
mission, and provide continuous and precise photometry for these objects.
Twenty-one of these stars were already noted by Gaia or the Pan-STARRS survey.
This new population of dominantly faint and distant RR Lyrae stars increases by
50% and complements nicely the 52 already known main target RR Lyrae stars in
the original Kepler field. Despite their faintness, the four-year
quasi-uninterrupted light curves of these stars allow an unprecedented view of
these faint halo objects. We present an analysis of the light curves of the new
RR Lyrae sample, verify their classification using Fourier parameters, and
discuss the properties of these newly found pulsating variable stars. Most
notably, this is the first time that such faint RR Lyrae stars have been
investigated with the help of a photometric data set with outstanding cadence
and precision. Interestingly, these objects share the properties of their
brighter siblings in terms of sub-class characteristics, additional mode
content, and modulation occurrence rates.


title:
Less is More: Summary of Long Instructions is Better for Program
  Synthesis

abstract:
  Despite the success of large pre-trained language models (LMs) such as Codex,
they show below-par performance on the larger and more complicated programming
related questions. We show that LMs benefit from the summarized version of
complicated questions. Our findings show that superfluous information often
present in problem description such as human characters, background stories,
names (which are included to help humans in understanding a task) does not help
models in understanding a task. To this extent, we create a meta-dataset from
the frequently used APPS dataset for the program synthesis task. Our
meta-dataset consists of human and synthesized summary of the long and
complicated programming questions. Experimental results on Codex show that our
proposed approach outperforms baseline by 8.13% on an average in terms of
strict accuracy. Our analysis shows that summary significantly improve
performance for introductory (9.86%) and interview (11.48%) related programming
questions. However, it shows improvement by a small margin (~2%) for
competitive programming questions, implying the scope for future research
direction.


title:
Superconducting order parameter in UTe$_2$ determined by Knight shift
  measurement

abstract:
  This study investigates the spin susceptibility in U-based superconductor
UTe$_2$ in the superconducting (SC) state by using Knight shift measurements
for a magnetic field $H$ along the $a$ axis, which is the magnetic easy axis of
UTe$_2$. Although a tiny anomaly ascribed to the SC diamagnetic effect was
observed just below the SC transition temperature $T_{\rm c}$, the $a$-axis
Knight shift in the SC state shows no significant decrease, following the
extrapolation from the normal-state temperature dependence. This indicates that
the spin susceptibility is nearly unchanged below $T_{\rm c}$. Considering the
previous Knight shift results for $H \parallel b$ and $H \parallel c$, the
dominant SC state is determined to be $B_{\rm 3u}$ in the spin-triplet pairing,
which is consistent with the spin anisotropy in the normal state. The present
result shows that UTe$_2$ is a spin-triplet superconductor with spin degrees of
freedom.


title: Long-time existence for systems of quasilinear wave equations

abstract:
  We consider quasilinear wave equations in $(1+3)$-dimensions where the
nonlinearity $F(u,u',u'')$ is permitted to depend on the solution rather than
just its derivatives. For scalar equations, if $(\partial_u^2 F)(0,0,0)=0$,
almost global existence was established by Lindblad. We seek to show a related
almost global existence result for coupled systems of such equations. To do so,
we will rely upon a variant of the $r^p$-weighted local energy estimate of
Dafermos and Rodnianski that includes a ghost weight akin to those used by
Alinhac. The decay that is needed to close the argument comes from space-time
Klainerman-Sobolev type estimates from the work of Metcalfe, Tataru, and
Tohaneanu.


title:
NELA-Local: A Dataset of U.S. Local News Articles for the Study of
  County-level News Ecosystems

abstract:
  In this paper, we present a dataset of over 1.4M online news articles from
313 local U.S. news outlets published over 20 months (between April 4th, 2020
and December 31st, 2021). These outlets cover a geographically diverse set of
communities across the United States. In order to estimate characteristics of
the local audience, included with this news article data is a wide range of
county-level metadata, including demographics, 2020 Presidential Election vote
shares, and community resilience estimates from the U.S. Census Bureau. The
NELA-Local dataset can be found at:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K.


title: Sparsification Lower Bound for Linear Spanners in Directed Graphs

abstract:
  For $\alpha \ge 1$, $\beta \ge 0$, and a graph $G$, a spanning subgraph $H$
of $G$ is said to be an $(\alpha, \beta)$-spanner if $\dist(u, v, H) \le \alpha
\cdot \dist(u, v, G) + \beta$ holds for any pair of vertices $u$ and $v$. These
type of spanners, called \emph{linear spanners}, generalizes \emph{additive
spanners} and \emph{multiplicative spanners}. Recently, Fomin, Golovach,
Lochet, Misra, Saurabh, and Sharma initiated the study of additive and
multiplicative spanners for directed graphs (IPEC $2020$). In this article, we
continue this line of research and prove that \textsc{Directed Linear Spanner}
parameterized by the number of vertices $n$ admits no polynomial compression of
size $\calO(n^{2 - \epsilon})$ for any $\epsilon > 0$ unless $\NP \subseteq
\coNP/poly$. We show that similar results hold for \textsc{Directed Additive
Spanner} and \textsc{Directed Multiplicative Spanner} problems. This
sparsification lower bound holds even when the input is a directed acyclic
graph and $\alpha, \beta$ are \emph{any} computable functions of the distance
being approximated.


title:
Gas phase metallicity determinations in nearby AGNs with SDSS-IV MaNGA:
  evidence of metal poor accretion

abstract:
  We derive the metallicity (traced by the O/H abundance) of the Narrow Line
Region ( NLR) of 108 Seyfert galaxies as well as radial metallicity gradients
along their galaxy disks and of these of a matched control sample of no active
galaxies. In view of that, observational data from the SDSS-IV MaNGA survey and
strong emission-line calibrations taken from the literature were considered.
The metallicity obtained for the NLRs %each Active Galactic Nucleus (AGN) was
compared to the value derived from the extrapolation of the radial oxygen
abundance gradient, obtained from \ion{H}{ii} region estimates along the galaxy
disk, to the central part of the host galaxies. We find that, for most of the
objects ($\sim 80\,\%$), the NLR metallicity is lower than the extrapolated
value, with the average difference ($<D>$) between these estimates ranging from
0.16 to 0.30 dex. We suggest that $<D>$ is due to the accretion of metal-poor
gas to the AGN that feeds the nuclear supermassive black hole (SMBH), which is
drawn from a reservoir molecular and/or neutral hydrogen around the SMBH.
Additionally, we look for correlations between $D$ and the electron density
($N_{\rm e}$), [\ion{O}{iii}]$\lambda$5007 and H$\alpha$ luminosities,
extinction coefficient ($A_{V})$ of the NLRs, as well as the stellar mass
($M_{*}$) of the host galaxies. Evidences of an inverse correlation between the
$D$ and the parameters $N_{\rm e}$, $M_{*}$ and $A_{\rm v}$ were found.


title:
Laplacian Filters for Integral Equations: Further Developments and Fast
  Algorithms

abstract:
  This paper extends the concept of Laplacian filtered quasi-Helmholtz
decompositions we have recently introduced, to the basis-free projector-based
setting. This extension allows the discrete analyses of electromagnetic
integral operators spectra without passing via an explicit Loop-Star
decomposition as previously done. We also present a fast scheme for the
evaluation of the filters in quasi linear complexity in the total number of
unknowns. Together with the fact that only a logarithmic number of these
filters are required for solving the h-refinement breakdown of electric field
integral equation, this results in an effective preconditioner that rivals
Calder\'on strategies in performance without relying on barycentric
refinements. Numerical results confirm the theoretically predicted behavior and
the effectiveness of the approach.


title: Comment on "Consistent hydrodynamics of ferrofluids"

abstract:
  We demonstrate that the theory presented by A. Fang (Phys. Fluids, v. 34,
013319, 2022) violates the law of conservation of angular momentum, and is
therefore false.


title:
Compositional dependence of direct transition energies in
  Si$_x$Ge$_{1-x-y}$Sn$_y$ alloys lattice-matched to Ge/GaAs

abstract:
  Si$_x$Ge$_{1-x-y}$Sn$_y$ ternary alloys are a candidate material system for
use in solar cells and other optoelectronic devices. We report on the direct
transition energies and structural properties of Ge-rich
Si$_x$Ge$_{1-x-y}$Sn$_y$ alloys with six different compositions up to 10 % Si
and 3 % Sn, lattice-matched to Ge or GaAs substrates. The direct transitions
occurring between 0.9 and 5.0 eV were investigated using spectroscopic
ellipsometry (SE), and the resulting data was used to obtain the dielectric
functions of the Si$_x$Ge$_{1-x-y}$Sn$_y$n layer by fitting a multi-layer
model. Values for the $E_0$, $E_1$, $\Delta_1$, $E_0'$ and $E_2$ transition
energies were then found by differentiating these dielectric functions to
extract the locations of critical points. Structurally, the composition of the
samples was measured using energy-dispersive X-ray measurements (EDX). The
lattice constants predicted from these compositions are in good agreement with
reciprocal space maps obtained through X-ray diffraction (XRD). The results
confirm that a 1 eV direct absorption edge can be achieved using relatively low
Si and Sn fractions ($<$ 10 % and $<$ 3 % respectively), while the
higher-energy critical points show smaller shifts relative to Ge and match
results previously observed or predicted in the literature.


title: A Reachability Index for Recursive Label-Concatenated Graph Queries

abstract:
  Reachability queries checking the existence of a path from a source node to a
target node are fundamental operators for querying and processing graph data.
Current approaches for index-based evaluation of reachability queries either
focus on plain reachability or constraint-based reachability with alternation
only. In this paper, for the first time we study the problem of index-based
processing for recursive label-concatenated reachability queries, referred to
as RLC queries. These queries check the existence of a path that can satisfy
the constraint defined by a concatenation of at most k edge labels under the
Kleene plus. Many practical graph database and network analysis applications
exhibit RLC queries. However, their evaluation remains prohibitive in current
graph database engines.
  We introduce the RLC index, the first reachability index to efficiently
process RLC queries. The RLC index checks whether the source vertex can reach
an intermediate vertex that can also reach the target vertex under a recursive
label-concatenated constraint. We propose an indexing algorithm to build the
RLC index, which guarantees the soundness and the completeness of query
execution and avoids recording redundant index entries. Comprehensive
experiments on real-world graphs show that the RLC index can significantly
reduce both the offline processing cost and the memory overhead of transitive
closure while improving query processing up to six orders of magnitude over
online traversals. Finally, our open-source implementation of the RLC index
significantly outperforms current mainstream graph engines for evaluating RLC
queries.


title:
Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces
  from Modified High Dimensional Neural Networks for Applications in Energy
  Storage

abstract:
  Designing new heterostructure electrodes has many challenges associated with
interface engineering. Demanding simulation resources and lack of
heterostructure databases continue to be a barrier in understanding the
chemistry and mechanics of complex interfaces using simulations.
Mixed-dimensional heterostructures composed of two-dimensional (2D) and
three-dimensional (3D) materials are undisputed next-generation materials for
engineered devices due to their changeable properties. The present work
computationally investigates the interface between 2D graphene and 3D tin (Sn)
systems with density functional theory (DFT) method and uses the
computationally demanding simulation data to develop machine learning (ML)
based potential energy surface (PES). Developed PES can be used to model
graphene-tin interface systems from ML with close to DFT accuracies. To develop
PES, high dimensional neural networks (HDNN) that rely on atom-centered
symmetry function to represent structural information have been used. HDNN are
modified to train on the total energies of the interface system rather than
atomic energies. The performance of modified HDNN trained on 5789 interface
structures of graphene|Sn is tested on unfamiliar interfaces of the same
material pair with varying levels of structural deviations from the training
dataset, including new Sn bulk phase, defects, and interfacial atomic
diffusion. The highest RMSE obtained for the prediction on test interfaces is
0.458 eV/atom. The modified HDNN approach is shown to be more accurate in
predicting energies of complex polymorphing interfaces such as graphene and
tin. Improved accuracy in ML-based modeling approach promises cost-effective
means of designing interfaces in heterostructure energy storage systems with
higher cycle life and stability.


title: On the effect of boundary vibration on Mucus Mobilization

abstract:
  In this study, we investigate the mobilization of mucus in a cylindrical tube
of constant cross-section subjected to a Small Amplitude Oscillatory Shear
(SAOS) assuming the viscoelastic behavior of mucus is described by the
Oldroyd-B constitutive equation. The Laplace transform method was adopted to
derive expressions for the velocity profile, average velocity, instantaneous
flowrate and mean flowrate (i.e., mobilization) of the mucus within the tube.
Additionally, a 2D finite element model (FEM) was developed using COMSOL
Multiphysics software to verify the accuracy of the derived analytical solution
considering both a Newtonian and an Oldroyd-B fluid. Furthermore, sensitivity
studies were performed to evaluate the influence of the vibration frequency,
vibration amplitude, mean relaxation time, and zero-shear viscosity on mucus
mobilization. Results prove that the analytical and numerical results are
within acceptable error tolerance and thus in excellent agreement. Besides, the
parametric studies indicate a 48%, 57%, and 343% improvement in mucus
mobilization when the mean relaxation time, vibration amplitude, and vibration
frequency, respectively were increased by a factor of 6. Conversely, the mucus
mobilization decreased by 25% when the zero-shear viscosity was increased by a
factor of 6. In general, this study confirms that mucus mobilization in a tube
can be improved by increasing the magnitude of vibration amplitude and
vibration frequency. Similarly, the larger the magnitude of mucus mean
relaxation time the better the mucus mobilization when the tube is subjected to
boundary vibrations. Finally, mucus mobilization decreases as the magnitude of
mucus viscosity increases.


title:
The Sabatier principle for Battery Anodes: Chemical Kinetics and
  Reversible Electrodeposition at Heterointerfaces

abstract:
  How surface chemistry influences reactions occurring thereupon has been a
long-standing question of broad scientific and technological interest for
centuries. Recently, it has re-emerged as a critical question in a
subdiscipline of chemistry - electrochemistry at heterointerphases, where the
answers have implications for both how, and in what forms, humanity stores the
rising quantities of renewable electric power generated from solar and wind
installations world-wide. Here we consider the relation between the surface
chemistry at such interphases and the reversibility of electrochemical
transformations at a rechargeable battery electrode. Conventional wisdom holds
that stronger chemical interaction between the metal deposits and electrode
promotes reversibility. We report instead that a moderate strength of chemical
interaction between the deposit and the substrate, neither too weak nor too
strong, enables highest reversibility and stability of the plating/stripping
redox processes at a battery anode. Analogous to the empirical Sabatier
principle for chemical heterogeneous catalysis, our finding arises from the
confluence of competing processes - one driven by electrochemistry and the
other by chemical alloying. Based on experimental evaluation of metal
plating/stripping systems in battery anodes of contemporary interest, we show
that such knowledge provides a powerful tool for designing key materials in
highly reversible electrochemical energy storage technologies based on
earth-abundant, low-cost metals.


title:
Interaction of Vertical Convection with an Electromagnetically Forced
  Flow

abstract:
  Heat and momentum transfer of low-Prandtl-number fluid ($Pr=0.029$) in a
closed rectangular cavity ($100\times60\times10$ mm$^3$) heated at one side and
cooled at the opposite side are analyzed. The electromagnetic forces into the
liquid metal are generated by the travelling magnetic field inductor and
directed towards buoyancy forces. Large eddy simulations are performed with the
Grashof number $Gr$ from $1.9\cdot 10^5$ to $7.6\cdot 10^7$ and the
electromagnetic forcing parameter $F$ from $2.6\cdot 10^4$ to $2.6\cdot10^6$.
An experimental validation of the simulation results of vertical convection and
electromagnetically driven flow using GaInSn alloy has been performed. Three
types of flow patterns are obtained for different interaction parameters $ N =
F / Gr $: counterclockwise flow, clockwise flow, and coexistence of two
vortices. Analysis of the Reynolds number shows that the transition zone from
natural convection to electromagnetic stirring lies in the range
$0.02<F/Gr<0.07$ and two braking modes are found. The transition point between
the convective heat transfer regimes is found for $ F / Gr $ around 1. The
analysis of isotherms deformation showed that in such convective systems it is
possible to achieve minimum deviation of the isotherm shape from a straight
line in the range of $ 0.05 <F/Gr <0.2 $.


title:
Interpretation of the micromorphic elastic moduli by harmonic
  decomposition and homogenization of a porous microstructure

abstract:
  Micromorphic theories became an established tool to model size effects in
materials like dispersion, localization phenomena or apparently size dependent
properties. However, the large number of constitutive parameters hinders the
usage of the full micromorphic theory, which has 18 constitutive parameters
already in the isotropic linear elastic case. Although it is clear that these
parameters are related to predicted size effects, the individual meaning of
single parameters has been rather unclear.
  The present work tries to elucidate the interpretation of these parameters.
For this purpose, a harmonic decomposition is applied to the governing
equations of micromorphic theory. The harmonic modes are interpreted at the
microscale using a homogenization method for a simple volume element with
spherical pore. The resulting boundary-value problem at the microscale is
solved analytically using spherical harmonics resulting in closed-form
expressions for all of the 18 parameters. These values are used to predict the
size effect in torsion of slender foam specimens. The predictions are compared
with respective experimental results from literature.


title:
CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer
  Learning

abstract:
  Generating artistic portraits is a challenging problem in computer vision.
Existing portrait stylization models that generate good quality results are
based on Image-to-Image Translation and require abundant data from both source
and target domains. However, without enough data, these methods would result in
overfitting. In this work, we propose CtlGAN, a new few-shot artistic portraits
generation model with a novel contrastive transfer learning strategy. We adapt
a pretrained StyleGAN in the source domain to a target artistic domain with no
more than 10 artistic faces. To reduce overfitting to the few training
examples, we introduce a novel Cross-Domain Triplet loss which explicitly
encourages the target instances generated from different latent codes to be
distinguishable. We propose a new encoder which embeds real faces into Z+ space
and proposes a dual-path training strategy to better cope with the adapted
decoder and eliminate the artifacts. Extensive qualitative, quantitative
comparisons and a user study show our method significantly outperforms
state-of-the-arts under 10-shot and 1-shot settings and generates high quality
artistic portraits. The code will be made publicly available.


title: The Search for a Counterpart to NuSTAR J053449+2126.0

abstract:
  Tumer et al. 2022, ATel #15171, have recently reported the discovery of an
X-ray source, NuSTAR J053449+2126.0, during a calibration observation which
took place on 25 April 2020. We scan the Zwicky Transient Facility (ZTF) alerts
and archival photometry to determine the nature of the source. Palomar
Gattini-IR is searched as well. We identify no obvious counterpart candidate.
Follow-up X-ray and optical studies are needed to determine the true
counterpart.


title: A Model of Job Parallelism for Latency Reduction in Large-Scale Systems

abstract:
  Processing computation-intensive jobs at multiple processing cores in
parallel is essential in many real-world applications. In this paper, we
consider an idealised model for job parallelism in which a job can be served
simultaneously by $d$ distinct servers. The job is considered complete when the
total amount of work done on it by the $d$ servers equals its size. We study
the effect of parallelism on the average delay of jobs. Specifically, we
analyze a system consisting of $n$ parallel processor sharing servers in which
jobs arrive according to a Poisson process of rate $n \lambda$ ($\lambda <1$)
and each job brings an exponentially distributed amount of work with unit mean.
Upon arrival, a job selects $d$ servers uniformly at random and joins all the
chosen servers simultaneously. We show by a mean-field analysis that, for fixed
$d \geq 2$ and large $n$, the average occupancy of servers is $O(\log
(1/(1-\lambda)))$ as $\lambda \to 1$ in comparison to $O(1/(1-\lambda))$
average occupancy for $d=1$. Thus, we obtain an exponential reduction in the
response time of jobs through parallelism. We make significant progress towards
rigorously justifying the mean-field analysis.


title:
Scientific and Technological Information Oriented Semantics-adversarial
  and Media-adversarial Cross-media Retrieval

abstract:
  Cross-media retrieval of scientific and technological information is one of
the important tasks in the cross-media study. Cross-media scientific and
technological information retrieval obtain target information from massive
multi-source and heterogeneous scientific and technological resources, which
helps to design applications that meet users' needs, including scientific and
technological information recommendation, personalized scientific and
technological information retrieval, etc. The core of cross-media retrieval is
to learn a common subspace, so that data from different media can be directly
compared with each other after being mapped into this subspace. In subspace
learning, existing methods often focus on modeling the discrimination of
intra-media data and the invariance of inter-media data after mapping; however,
they ignore the semantic consistency of inter-media data before and after
mapping and media discrimination of intra-semantics data, which limit the
result of cross-media retrieval. In light of this, we propose a scientific and
technological information oriented Semantics-adversarial and Media-adversarial
Cross-media Retrieval method (SMCR) to find an effective common subspace.
Specifically, SMCR minimizes the loss of inter-media semantic consistency in
addition to modeling intra-media semantic discrimination, to preserve semantic
similarity before and after mapping. Furthermore, SMCR constructs a basic
feature mapping network and a refined feature mapping network to jointly
minimize the media discriminative loss within semantics, so as to enhance the
feature mapping network's ability to confuse the media discriminant network.
Experimental results on two datasets demonstrate that the proposed SMCR
outperforms state-of-the-art methods in cross-media retrieval.


title:
Generic Lithography Modeling with Dual-band Optics-Inspired Neural
  Networks

abstract:
  Lithography simulation is a critical step in VLSI design and optimization for
manufacturability. Existing solutions for highly accurate lithography
simulation with rigorous models are computationally expensive and slow, even
when equipped with various approximation techniques. Recently, machine learning
has provided alternative solutions for lithography simulation tasks such as
coarse-grained edge placement error regression and complete contour prediction.
However, the impact of these learning-based methods has been limited due to
restrictive usage scenarios or low simulation accuracy. To tackle these
concerns, we introduce an dual-band optics-inspired neural network design that
considers the optical physics underlying lithography. To the best of our
knowledge, our approach yields the first published via/metal layer contour
simulation at 1nm^2/pixel resolution with any tile size. Compared to previous
machine learning based solutions, we demonstrate that our framework can be
trained much faster and offers a significant improvement on efficiency and
image quality with 20X smaller model size. We also achieve 85X simulation
speedup over traditional lithography simulator with 1% accuracy loss.


title:
Conditional Measurement Density Estimation in Sequential Monte Carlo via
  Normalizing Flow

abstract:
  Tuning of measurement models is challenging in real-world applications of
sequential Monte Carlo methods. Recent advances in differentiable particle
filters have led to various efforts to learn measurement models through neural
networks. But existing approaches in the differentiable particle filter
framework do not admit valid probability densities in constructing measurement
models, leading to incorrect quantification of the measurement uncertainty
given state information. We propose to learn expressive and valid probability
densities in measurement models through conditional normalizing flows, to
capture the complex likelihood of measurements given states. We show that the
proposed approach leads to improved estimation performance and faster training
convergence in a visual tracking experiment.


title:
Real Spectra and Phase Transition of Skin Effect in Nonreciprocal
  Systems

abstract:
  We study the one-dimensional nonreciprocal lattices with real nearest
neighboring hopping and find that the energy spectra under open boundary
conditions can be entirely real or imaginary without imposing any symmetries.
We further investigate the spectral properties and the non-Hermitian skin
effect in the one-dimensional mosaic lattices with real nonreciprocal hopping
introduced at equally spaced sites. The eigenenergies of such lattices undergo
a real-complex-imaginary or real-complex transition as the nonreciprocity
varies. Moreover, the skin effect exhibits phase transitions depending on the
period of the mosaic nonreciprocity. The bulk states are abruptly shifted from
one end of the lattice to the opposite one by crossing the critical points,
accompanied by the closing and reopening of point gaps in the spectra under
periodic boundary conditions. The phase diagrams of the transition are
presented and the critical boundaries are analytically determined. Our work
unveils the intriguing properties of the energy spectrum and skin effect in
non-Hermitian systems.


title:
An Independently Learnable Hierarchical Model for Bilateral
  Control-Based Imitation Learning Applications

abstract:
  Recently, motion generation by machine learning has been actively researched
to automate various tasks. Imitation learning is one such method that learns
motions from data collected in advance. However, executing long-term tasks
remains challenging. Therefore, a novel framework for imitation learning is
proposed to solve this problem. The proposed framework comprises upper and
lower layers, where the upper layer model, whose timescale is long, and lower
layer model, whose timescale is short, can be independently trained. In this
model, the upper layer learns long-term task planning, and the lower layer
learns motion primitives. The proposed method was experimentally compared to
hierarchical RNN-based methods to validate its effectiveness. Consequently, the
proposed method showed a success rate equal to or greater than that of
conventional methods. In addition, the proposed method required less than 1/20
of the training time compared to conventional methods. Moreover, it succeeded
in executing unlearned tasks by reusing the trained lower layer.


title:
Presolar grain dynamics: creating nucleosynthetic variations through a
  combination of drag and viscous evolution

abstract:
  Meteoritic studies of solar system objects show evidence of nucleosynthetic
heterogeneities that are inherited from small presolar grains (< 10 $\mu$m)
formed in stellar environments external to our own. The initial distribution
and subsequent evolution of these grains are currently unconstrained. Using 3D,
gas-dust simulations, we find that isotopic variations on the order of those
observed in the solar system can be generated and maintained by drag and
viscosity. Small grains are dragged radially outwards without size/density
sorting by viscous expansion and backreaction, enriching the outer disc with
presolar grains. Meanwhile large aggregates composed primarily of silicates
drift radially inwards due to drag, further enriching the relative portion of
presolar grains in the outer disc and diluting the inner disc. The late
accumulation of enriched aggregates outside Jupiter could explain some of the
isotopic variations observed in solar system bodies, such as the enrichment of
supernovae derived material in carbonaceous chondrites. We also see evidence
for isotopic variations in the inner disc that may hold implications for
enstatite and ordinary chondrites that formed closer to the Sun. Initial
heterogeneities in the presolar grain distribution that are not continuously
reinforced are dispersed by diffusion, radial surface flows, and/or planetary
interactions over the entire lifetime of the disc. For younger, more massive
discs we expect turbulent diffusion to be even more homogenising, suggesting
that dust evolution played a more central role in forming the isotopic
anomalies in the solar system than originally thought.


title:
Elliptical skyrmions: theory and nucleation by a magnetic tip in an
  antiskyrmion-hosting material

abstract:
  Motivated by experimental findings in [ 1 ] and [ 2 ], we find an elliptical
skyrmion can be nucleated in a material with a Dzyaloshinskii-Moriya
interaction (DMI) that supports antiskyrmions. The DMI favors the elongation of
the skyrmion along a given direction so that it is characterized by two size
parameters. We derive analytical properties of the elliptical skyrmion using
the Belavin-Polyakov (BP) pure exchange model. We show that the DMI which
typically favors isotropic skyrmions or antiskyrmions, respectively, can also
support elliptical antiskyrmions or skyrmions, respectively. Using the real
material parameters extracted from [1] and including all relevant interactions,
numerical computations indicate that the elliptical skyrmion can be nucleated
from the stripe or labyrinth domain state by a magnetic force microscope tip in
a thin film with a DMI favoring antiskyrmions that consists of at least a few
atomic layers.


title:
A probabilistic view on rupture predictability: all earthquakes evolve
  similarly

abstract:
  Ruptures of the largest earthquakes can last between a few seconds and
several minutes. An early assessment of the final earthquake size is essential
for early warning systems. However, it is still unclear when in the rupture
history this final size can be predicted. Here we introduce a probabilistic
view of rupture evolution - how likely is the event to become large - allowing
for a clear and well-founded answer with implications for earthquake physics
and early warning. We apply our approach to real time magnitude estimation
based on either moment rate functions or broadband teleseismic P arrivals. In
both cases, we find strong and principled evidence against early rupture
predictability because differentiation between differently sized ruptures only
occurs once half of the rupture has been observed. Even then, it is impossible
to foresee future asperities. Our results hint towards a universal initiation
behavior for small and large ruptures.


title: Scaling in Local Optimal Paths Cracks

abstract:
  How local cracks can contribute to the global cracks landscape is a goal of
several scientific topics, for example, how bottlenecks can impact the
robustness of traffic into a city? In one direction, cracks from cascading
failures into networks were generated using a modified Optimal Path-Cracking
(OPC) model proposed by Andrade et al \cite{Andrade2009}. In this model, we
broke links of maximum energies from optimal paths between two sites with
internal (euclidean) distances $l$ in networks with linear size $L$. Each link
of this network has an energy value that scales with a power-law that can be
controlled using a parameter of the disorder $\beta$. Using finite-size scaling
and the exponents from percolation theory we found that the mass of the cracked
links on local optimal paths scales with a power-law $l^{0.4}$ as a separable
equation from $L$ and that can be independent of the disorder parameter.


title: Intrinsic randomness under general quantum measurements

abstract:
  Quantum measurements can produce randomness arising from the uncertainty
principle. When measuring a state with von Neumann measurements, the intrinsic
randomness can be quantified by the quantum coherence of the state on the
measurement basis. Unlike projection measurements, there are additional and
possibly hidden degrees of freedom in apparatus for generic measurements. We
propose an adversary scenario for general measurements with arbitrary input
states, based on which, we characterize the intrinsic randomness.
Interestingly, we discover that under certain measurements, such as the
symmetric and information-complete measurement, all states have nonzero
randomness, inspiring a new design of source-independent random number
generators without state characterization. Furthermore, our results show that
intrinsic randomness can quantify coherence under general measurements, which
generalizes the result in the standard resource theory of state coherence.


title: Cloud-convection feedback in brown dwarfs atmosphere

abstract:
  Numerous observational evidence has suggested the presence of active
meteorology in the atmospheres of brown dwarfs. A near-infrared brightness
variability has been observed. Clouds have a major role in shaping the thermal
structure and spectral properties of these atmospheres. The mechanism of such
variability is still unclear and both 1D and global circulation model cannot
fully study this topics due to resolution. In this study, a convective
resolving model is coupled to grey-band radiative transfer in order to study
the coupling between the convective atmosphere and the variability of clouds
over a large temperature range with a domain of several hundreds of kilometers.
Six types of clouds are considered, with microphysics including settling. The
clouds are radiatively active using Rosseland mean coefficient. Radiative cloud
feedback can drive spontaneous atmospheric variability in both temperature and
cloud structure, as modeled for the first time in three dimensions. Silicate
clouds have the most effect of the thermal structure with the generation of a
secondary convective layer in some cases, depending on the assumed particle
size. Iron and Aluminum clouds also have a substantial impact on the
atmosphere. Thermal spectra were computed, and we find the strongest effect of
clouds is the smoothing of spectral features at optical wavelengths. Compared
to observed L and T dwarfs on color-magnitude diagram, the simulated
atmospheres are redder for most of the cases. The simulations with the presence
of cloud holes are closer to the observations.


title: Equilibrium states of Burgers and KdV equations

abstract:
  We simulate KdV and dissipation-less Burgers equations using delta-correlated
random noise as initial condition. We observe that the energy fluxes of the two
equations remain zero throughout, thus indicating their equilibrium nature. We
characterize the equilibrium states using Gaussian probability distribution for
the real space field, and using Boltzmann distribution for the modal energy. We
show that the single soliton of the KdV equation too exhibits zero energy flux,
hence it is in equilibrium. We argue that the energy flux is a good measure for
ascertaining whether a system is in equilibrium or not.

categories: cond-mat.stat-mech hep-th nlin.SI physics.flu-dyn


title:
Anomalous conduction and second sound in the Fermi-Pasta-Ulam-Tsingou
  chain: wave-turbulence approach

abstract:
  One-dimensional particle chains are fundamental models to explain anomalous
thermal conduction in low-dimensional solids like nanotubes and nanowires. In
these systems the thermal energy is carried by phonons, i.e. propagating
lattice oscillations that interact via nonlinear resonance. The average energy
transfer between the phonons is described by the wave kinetic equation (WKE),
derived directly from the microscopic dynamics. Here, we use the spatially
nonhomogeneous WKE of the prototypical $\beta-$ Fermi-Pasta-Ulam-Tsingou (FPUT)
model, equipped with thermostats able to set different temperatures at the two
ends. Our main findings are as follows: (i) The anomalous scaling of the
conductivity with the system size, in close agreement with the known results
from the microscopic dynamics, is due to a nontrivial interplay between high
and low wavenumbers. (ii) The high-wavenumber phonons relax to local
thermodynamic equilibrium transporting energy diffusively, {\it \`a la
Fourier}. (iii) The low-wavenumber phonons are nearly noninteracting and
transfer energy ballistically; this latter phenomenon is the analogous of the
second sound emission, observed for example in superfluids.


title: Convergence of Kac--Moody Eisenstein Series over a function field

abstract:
  We establish everywhere convergence in a natural domain for Eisenstein series
on a symmetrizable Kac--Moody group over a function field. Our method is
different from that of the affine case which does not directly generalize. In
comparison with the analogous result over the real numbers, everywhere
convergence is achieved without any additional condition on the root system.


title:
Inelastic processes in strontium-hydrogen collisions and their impact on
  non-LTE calculations

abstract:
  Inelastic processes rate coefficients for low-energy Sr + H, Sr$^+$ + H$^-$,
Sr$^+$ + H and Sr$^{2+}$ + H$^-$ collisions are calculated using multichannel
quantum model approach. 31 scattering channels of SrH$^+$ and 17 scattering
channels of SrH are considered. The partial cross sections and the partial rate
coefficients are hence calculated for 1202 partial processes in total. Using
new quantum data for Sr II + H I collisions, we updated model atom of Sr II and
performed the non-local thermodynamic equilibrium (non-LTE) calculations. We
provide the non-LTE abundance corrections for the Sr II resonance lines in two
grids of model atmospheres, which are applicable to very metal-poor ([Fe/H]
$\le -2$) dwarfs and giants.


title:
Extended vehicle energy dataset (eVED): an enhanced large-scale dataset
  for deep learning on vehicle trip energy consumption

abstract:
  This work presents an extended version of the Vehicle Energy Dataset (VED),
which is a openly released large-scale dataset for vehicle energy consumption
analysis. Compared with its original version, the extended VED (eVED) dataset
is enhanced with accurate vehicle trip GPS coordinates, serving as a basis to
associate the VED trip records with external information, e.g., road speed
limit and intersections, from accessible map services to accumulate attributes
that is essential in analyzing vehicle energy consumption. In particularly, we
calibrate all the GPS trace records in the original VED data, upon which we
associated the VED data with attributes extracted from the Geographic
Information System (QGIS), the Overpass API, the Open Street Map API, and
Google Maps API. The associated attributes include 12,609,170 records of road
elevation, 12,203,044 of speed limit, 12,281,719 of speed limit with direction
(in case the road is bi-directional), 584,551 of intersections, 429,638 of bus
stop, 312,196 of crossings, 195,856 of traffic signals, 29,397 of stop signs,
5,848 of turning loops, 4,053 of railway crossings (level crossing), 3,554 of
turning circles, and 2,938 of motorway junctions. With the accurate GPS
coordinates and enriched features of the vehicle trip record, the obtained eVED
dataset can provide a precise and abundant medium to feed a learning engine,
especially a deep learning engine that is more demanding on data sufficiency
and richness. Moreover, our software work for data calibration and enrichment
can be reused to generate further vehicle trip datasets for specific user
cases, contributing to deep insights into vehicle behaviors and traffic
dynamics analyses. We anticipate that the eVED dataset and our data enrichment
software can serve the academic and industrial automotive section as apparatus
in developing future technologies.


title: Lifestyle and personal wellness in particle physics research activities

abstract:
  Finding a balance between professional responsibilities and personal
priorities is a great challenge of contemporary life and particularly within
the HEPAC community. Failure to achieve a proper balance often leads to
different degrees of mental and physical issues and affects work performance.
In this paper, we discuss some of the main causes that lead to the imbalance
between work and personal life in our academic field. We present some
recommendations in order to establish mechanisms to create a healthier and more
equitable work environment, for the different members of our community at the
different levels of their careers.


title: Coverage Optimization of Camera Network for Continuous Deformable Object

abstract:
  In this paper, a deformable object is considered for cameras deployment with
the aim of visual coverage. The object contour is discretized into sampled
points as meshes, and the deformation is represented as continuous trajectories
for the sampled points. To reduce the computational complexity, some feature
points are carefully selected representing the continuous deformation process,
and the visual coverage for the deformable object is transferred to cover the
specific feature points. In particular, the vertexes of a rectangle that can
contain the entire deformation trajectory of every sampled point on the object
contour are chosen as the feature points. An improved wolf pack algorithm is
then proposed to solve the optimization problem. Finally, simulation results
are given to demonstrate the effectiveness of the proposed deployment method of
camera network.


title:
A Frequency-Agnostic RIS-based solution to control the Smart Radio
  Propagation Environment

abstract:
  The disruptive reconfigurable intelligent surface (RIS) technology is
steadily gaining relevance as a key element in future 6G networks. However, a
one-size-fits-all RIS hardware design is yet to be defined due to many
practical considerations. A major roadblock for currently available RISs is
their inability to concurrently operate at multiple carrier frequencies, which
would lead to redundant installations to support multiple radio access
technologies (RATs). In this paper, we introduce FABRIS, a novel and practical
multi-frequency RIS design. FABRIS is able to dynamically operate across
different radio frequencies (RFs) by means of frequency-tunable antennas as
unit cells with virtually no performance degradation when conventional
approaches to RIS design and optimization fail. Remarkably, our design
preserves a sufficiently narrow beamwidth as to avoid generating signal leakage
in unwanted directions and a sufficiently high antenna efficiency in terms of
scattering parameters. Indeed, FABRIS selects the RIS configuration that
maximizes the signal at the intended target user equipment (UE) while
minimizing leakage to non-intended neighboring UEs. Numerical results and
full-wave simulations validate our proposed approach against a naive
implementation that does not consider signal leakage resulting from
multi-frequency antenna arrays.


title:
Cross-scale excitability in networks of synaptically-coupled quadratic
  integrate-and-fire neurons

abstract:
  From the action potentials of neurons and cardiac cells to the amplification
of calcium signals in oocytes, excitability is a hallmark of many biological
signalling processes. In recent years, excitability in single cells has been
related to multiple-timescale dynamics through canards, special solutions which
determine the effective thresholds of the all-or-none responses. However, the
emergence of excitability in large populations remains an open problem. Here,
we show that the mechanism of excitability in large networks and mean-field
descriptions of coupled quadratic integrate-and-fire (QIF) cells mirrors that
of the individual components. We initially exploit the Ott-Antonsen ansatz to
derive low-dimensional dynamics for the coupled network and use it to describe
the structure of canards via slow periodic forcing. We demonstrate that the
thresholds for onset and offset of population firing can be found in the same
way as those of the single cell. We combine theoretical analysis and numerical
computations to develop a novel and comprehensive framework for excitability in
large populations, applicable not only to models amenable to Ott-Antonsen
reduction, but also to networks without a closed-form mean-field limit, in
particular sparse networks.


title: Measurability of functionals and of ideal point forecasts

abstract:
  The ideal probabilistic forecast for a random variable $Y$ based on an
information set $\mathcal{F}$ is the conditional distribution of $Y$ given
$\mathcal{F}$. In the context of point forecasts aiming to specify a functional
$T$ such as the mean, a quantile or a risk measure, the ideal point forecast is
the respective functional applied to the conditional distribution. This paper
provides a theoretical justification why this ideal forecast is actually a
forecast, that is, an $\mathcal{F}$-measurable random variable. To that end,
the appropriate notion of measurability of $T$ is clarified and this
measurability is established for a large class of practically relevant
functionals, including elicitable ones. More generally, the measurability of
$T$ implies the measurability of any point forecast which arises by applying
$T$ to a probabilistic forecast. Similar measurability results are established
for proper scoring rules, the main tool to evaluate the predictive accuracy of
probabilistic forecasts.


title:
A spectroscopic survey of infrared 1-4{\mu}m spectra in regions of
  prominent solar coronal emission lines of Fe XIII, Si X, and Si IX

abstract:
  The infrared solar spectrum contains a wealth of physical data about the Sun
and is being explored using modern detectors and technology with new
ground-based solar telescopes. One such instrument will be the ground-based
Cryogenic Near-IR Spectro-Polarimeter of the Daniel K. Inouye Solar Telescope
that will be capable of sensitive imaging of the faint infrared solar coronal
spectra with full Stokes I, Q, U, and V polarization states. Highly ionized
magnetic dipole emission lines have been observed in galaxies and the solar
corona. Quantifying the accuracy of spectral inversion procedures requires a
precise spectroscopic calibration of observations. A careful interpretation of
the spectra around prominent magnetic dipole lines is essential for deriving
physical parameters, and particularly, for quantifying the off-limb solar
coronal observations from DKIST. In this work, we aim to provide an analysis of
the spectral regions around the infrared coronal emission lines of Fe XIII
1074.68 nm, Fe XIII 1079.79 nm, Si X 1430.10 nm, and Si IX 3934.34 nm, aligning
with the goal of identifying solar photospheric and telluric lines that will
help facilitate production of reliable inversions and data products from four
sets of solar coronal observations. The outputs will be integrated in the
processing pipeline to produce Level-2 science-ready data that will be made
available to DKIST observers.


title: Adversarial Learned Fair Representations using Dampening and Stacking

abstract:
  As more decisions in our daily life become automated, the need to have
machine learning algorithms that make fair decisions increases. In fair
representation learning we are tasked with finding a suitable representation of
the data in which a sensitive variable is censored. Recent work aims to learn
fair representations through adversarial learning. This paper builds upon this
work by introducing a novel algorithm which uses dampening and stacking to
learn adversarial fair representations. Results show that that our algorithm
improves upon earlier work in both censoring and reconstruction.


title:
Building Domain-Specific Machine Learning Workflows: A Conceptual
  Framework for the State-of-the-Practice

abstract:
  Domain experts are increasingly employing machine learning to solve their
domain-specific problems. This article presents six key challenges that a
domain expert faces in transforming their problem into a computational
workflow, and then into an executable implementation. These challenges arise
out of our conceptual framework which presents the "route" of options that a
domain expert may choose to take while developing their solution.
  To ground our conceptual framework in the state-of-the-practice, this article
discusses a selection of available textual and graphical workflow systems and
their support for these six challenges. Case studies from the literature in
various domains are also examined to highlight the tools used by the domain
experts as well as a classification of the domain-specificity and machine
learning usage of their problem, workflow, and implementation.
  The state-of-the-practice informs our discussion of the six key challenges,
where we identify which challenges are not sufficiently addressed by available
tools. We also suggest possible research directions for software engineering
researchers to increase the automation of these tools and disseminate
best-practice techniques between software engineering and various scientific
domains.


title:
Economic evaluation of stochastic home energy management systems in a
  realistic rolling horizon setting

abstract:
  Home energy management systems (HEMSs) are expected to become a crucial part
of future smart grids. However, there is a limited number of studies that
comprehensively assess the potential economic benefits of HEMS for consumers
under real market conditions and which take account of consumers' capabilities.
In this study, a new optimization-based HEMS controller is presented to operate
a photovoltaic and battery system. The HEMS controller considers the consumers'
electrical load uncertainty by integrating multivariate probabilistic
forecasting methods and a stochastic optimization in a rolling horizon. As a
case study, a comprehensive simulation study is designed to emulate the
operation of a real HEMS using real data from nine Danish homes over different
seasons under real-time retail prices. The optimization-based control
strategies are compared with a default (naive) control strategy that encourages
self consumption. Simulation results show that seasonality in the consumers'
load and electricity prices have a significant impact on the performance of the
control strategies. A combination of optimization-based and naive control
strategy presents the best overall results.


title: The effect of uncertainty on building blocks in molecules

abstract:
  Probabilities to find a chosen number of electrons in flexible domains of
space are calculated for highly correlated wave functions. Quantum mechanics
can produce higher probabilities for chemically relevant arrangements of
electrons in these regions. However, the probability to have a given
arrangement, e.g., that corresponding to chemical formulas (bonds or atoms), is
low although being often maximal. Like in valence bond theory, it is useful to
consider alternative distributions of the electrons. Exchanges of electrons
should be considered not only between atoms, but also between other types of
regions, like those attributed to lone pairs. It is useful to have definitions
flexible enough to allow the user to find the reference representations he
considers most relevant. We tentatively suggest a tool (the effective number of
parties) to help one make the choice.


title: Phonon renormalization and Pomeranchuk instability in the Holstein model

abstract:
  The Holstein model with dispersionless Einstein phonons is one of the
simplest models describing electron-phonon interactions in condensed matter. A
naive extrapolation of perturbation theory in powers of the relevant
dimensionless electron-phonon coupling $\lambda_0$ suggests that at zero
temperature the model exhibits a Pomeranchuk instability characterized by a
divergent uniform compressibility at a critical value of $\lambda_0$ of order
unity. In this work, we re-examine this problem using modern functional
renormalization group (RG) methods. For dimensions $d > 3$ we find that the RG
flow of the Holstein model indeed exhibits a tricritical fixed point associated
with a Pomeranchuk instability. This non-Gaussian fixed point is ultraviolet
stable and is closely related to the well-known ultraviolet stable fixed point
of $\phi^3$-theory above six dimensions. To realize the Pomeranchuk critical
point in the Holstein model at fixed density both the electron-phonon coupling
$\lambda_0$ and the adiabatic ratio $\omega_0 / \epsilon_F$ have to be
fine-tuned to assume critical values of order unity, where $\omega_0$ is the
phonon frequency and $\epsilon_F$ is the Fermi energy. On the other hand, for
dimensions $d \leq 3$ we find that the RG flow of the Holstein model does not
have any critical fixed points. This rules out a quantum critical point
associated with a Pomeranchuk instability in $d \leq 3$.


title:
Understanding motivations and characteristics of financially-motivated
  cybercriminals

abstract:
  Background: Cyber offences, such as hacking, malware creation and
distribution, and online fraud, present a substantial threat to organizations
attempting to safeguard their data and information. By understanding the
evolving characteristics and motivations of individuals involved in these
activities, and the threats that they may pose, cyber security practitioners
will be better placed to understand and assess current threats to their systems
and the range of socio-technical mitigations that may best reduce these. Aim:
The reported work-in-progress aims to explore the extent to which findings from
prior academic literature regarding the characteristics and motivations of
offenders engaging in financially-motivated, cyber-dependent crime are
supported by the contemporary experiences and perspectives of practitioners
currently working in the cyber crime field. Method: A targeted, online survey
was developed consisting of both closed and open-ended questions relating to
current cyber threats and the characteristics and motivations of offenders
engaged in these activities. Sixteen practitioners working in law
enforcement-related domains in the cyber crime field completed the survey,
providing a combination of qualitative and quantitative data for analysis.


title:
Initiation of Alfv\'enic turbulence by Alfven wave collisions: A
  numerical study

abstract:
  In the framework of compressional magnetohydrodynamics (MHD), we numerically
studied the commonly accepted presumption that the Alfv\'enic turbulence is
generated by the collisions between counter-propagating Alfv\'en waves (AWs).
In the conditions typical for the low-beta solar corona and inner solar wind,
we launched two counter-propagating AWs in the three-dimensional simulation box
and analyzed polarization and spectral properties of perturbations generated
before and after AW collisions. The observed post-collisional perturbations
have different polarizations and smaller cross-field scales than the original
waves, which supports theoretical scenarios with direct turbulent cascades.
However, contrary to theoretical expectations, the spectral transport is
strongly suppressed at the scales satisfying the classic critical balance of
incompressional MHD. Instead, a modified critical balance can be established by
colliding AWs with significantly shorter perpendicular scales. We discuss
consequences of these effects for the turbulence dynamics and turbulent heating
of compressional plasmas. In particular, solar coronal loops can be heated by
the strong turbulent cascade if the characteristic widths of the loop
substructures are more than ten times smaller than the loop width. The revealed
new properties of AW collisions have to be incorporated in the theoretical
models of AW turbulence and related applications.


title: Context-Aware Drift Detection

abstract:
  When monitoring machine learning systems, two-sample tests of homogeneity
form the foundation upon which existing approaches to drift detection build.
They are used to test for evidence that the distribution underlying recent
deployment data differs from that underlying the historical reference data.
Often, however, various factors such as time-induced correlation mean that
batches of recent deployment data are not expected to form an i.i.d. sample
from the historical data distribution. Instead we may wish to test for
differences in the distributions conditional on \textit{context} that is
permitted to change. To facilitate this we borrow machinery from the causal
inference domain to develop a more general drift detection framework built upon
a foundation of two-sample tests for conditional distributional treatment
effects. We recommend a particular instantiation of the framework based on
maximum conditional mean discrepancies. We then provide an empirical study
demonstrating its effectiveness for various drift detection problems of
practical interest, such as detecting drift in the distributions underlying
subpopulations of data in a manner that is insensitive to their respective
prevalences. The study additionally demonstrates applicability to
ImageNet-scale vision problems.


title: The Structured Abstain Problem and the Lov\'asz Hinge

abstract:
  The Lov\'asz hinge is a convex surrogate recently proposed for structured
binary classification, in which $k$ binary predictions are made simultaneously
and the error is judged by a submodular set function. Despite its wide usage in
image segmentation and related problems, its consistency has remained open. We
resolve this open question, showing that the Lov\'asz hinge is inconsistent for
its desired target unless the set function is modular. Leveraging a recent
embedding framework, we instead derive the target loss for which the Lov\'asz
hinge is consistent. This target, which we call the structured abstain problem,
allows one to abstain on any subset of the $k$ predictions. We derive two link
functions, each of which are consistent for all submodular set functions
simultaneously.


title:
Diagnosing Circumburst Environment with Multiband Gamma-Ray Burst Radio
  Afterglows

abstract:
  It has been widely recognized that gamma-ray burst (GRB) afterglows arise
from interactions between GRB outflow and circumburst medium, while their
evolution follows the behaviors of relativistic shock waves. Assuming the
distribution of circumburst medium follows a general power-law form, that is,
$n = A_{\ast} R^{-k}$, where $R$ denotes the distance from the burst, it is
obvious that the value of density-distribution index $k$ can affect the
behaviors of the afterglow. In this paper, we analyze the temporal and spectral
behaviors of GRB radio afterglows with arbitrary $k$-values. In the radio band,
a standard GRB afterglow produced by forward shock exhibits a late-time flux
peak, and the relative peak fluxes as well as peak times at different
frequencies show dependencies on $k$. Thus with multi-band radio peak
observations, one can determine the density profile of circumburst medium by
comparing the relations between peak flux/time and frequency at each observing
band. Also, the effects of trans-relativistic shock waves, as well as jets in
afterglows are discussed. By analyzing 31 long and 1 short GRBs with multi-band
data of radio afterglows, we find that nearly half of them can be explained
with uniform interstellar medium ($k=0$), $\sim 1/5$ can be constrained to
exhibiting stellar wind environment ($k=2$), while less than $\sim 1/3$ samples
show $0< k< 2$.


title: Cutoff in the Bernoulli-Laplace model with $O(n)$ swaps

abstract:
  This paper considers the $(n,k)$-Bernoulli--Laplace model in the case when
there are two urns, the total number of red and white balls is the same, and
the number of selections $k$ at each step is on the same asymptotic order as
the number of balls $n$ in each urn. Our main focus is on the large-time
behavior of the corresponding Markov chain tracking the number of red balls in
a given urn. Under reasonable assumptions on the asymptotic behavior of the
ratio $k/n$ as $n\rightarrow \infty$, cutoff in the total variation distance is
established. A cutoff window is also provided. These results, in particular,
partially resolve an open problem posed by Eskenazis and Nestoridi.


title:
Artificial Intelligence Enables Real-Time and Intuitive Control of
  Prostheses via Nerve Interface

abstract:
  Objective: The next generation prosthetic hand that moves and feels like a
real hand requires a robust neural interconnection between the human minds and
machines. Methods: Here we present a neuroprosthetic system to demonstrate that
principle by employing an artificial intelligence (AI) agent to translate the
amputee's movement intent through a peripheral nerve interface. The AI agent is
designed based on the recurrent neural network (RNN) and could simultaneously
decode six degree-of-freedom (DOF) from multichannel nerve data in real-time.
The decoder's performance is characterized in motor decoding experiments with
three human amputees. Results: First, we show the AI agent enables amputees to
intuitively control a prosthetic hand with individual finger and wrist
movements up to 97-98% accuracy. Second, we demonstrate the AI agent's
real-time performance by measuring the reaction time and information throughput
in a hand gesture matching task. Third, we investigate the AI agent's long-term
uses and show the decoder's robust predictive performance over a 16-month
implant duration. Conclusion & significance: Our study demonstrates the
potential of AI-enabled nerve technology, underling the next generation of
dexterous and intuitive prosthetic hands.


title:
Modeling the obsolescence of research literature in disciplinary
  journals through the age of their cited references

abstract:
  There are different citation habits in the research fields that influence the
obsolescence of the research literature. We analyze the distinctive
obsolescence of research literature in disciplinary journals in eight
scientific subfields based on cited references distribution, as a synchronous
approach. We use both Negative Binomial (NB) and Poisson distributions to
capture this obsolescence. The corpus being examined is published in 2019 and
covers 22,559 papers citing 872,442 references. Moreover, three measures to
analyze the tail of the distribution are proposed: (i) cited reference survival
rate, (ii) cited reference mortality rate, and (iii) cited reference
percentile. These measures are interesting because the tail of the distribution
collects the behavior of the citations at the time when the document starts to
get obsolete in the sense that it is little cited (used). As main conclusion,
the differences observed in obsolescence are so important even between
disciplinary journals in the same subfield, that it would be necessary to use
some measure for the tail of the citation distribution, such as those proposed
in this paper, when analyzing in an appropriate way the long time impact of a
journal.


title: Complexity Reduction of Learned In-Loop Filtering in Video Coding

abstract:
  In video coding, in-loop filters are applied on reconstructed video frames to
enhance their perceptual quality, before storing the frames for output.
Conventional in-loop filters are obtained by hand-crafted methods. Recently,
learned filters based on convolutional neural networks that utilize attention
mechanisms have been shown to improve upon traditional techniques. However,
these solutions are typically significantly more computationally expensive,
limiting their potential for practical applications. The proposed method uses a
novel combination of sparsity and structured pruning for complexity reduction
of learned in-loop filters. This is done through a three-step training process
of magnitude-guidedweight pruning, insignificant neuron identification and
removal, and fine-tuning. Through initial tests we find that network parameters
can be significantly reduced with a minimal impact on network performance.


title:
A Necessary and Sufficient Condition for ISS of Impulsive Systems via a
  Time-Varying Lyapunov Function

abstract:
  We propose a time-varying ISS-Lyapunov function for impulsive systems over
Banach spaces which provides a necessary and sufficient condition for ISS. Our
result applies to a broad class of impulsive systems including simultaneous
unstable continuous and discrete dynamics.


title:
Topology-Preserving Shape Reconstruction and Registration via Neural
  Diffeomorphic Flow

abstract:
  Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed
distance functions learned through deep neural nets. Recently DIFs-based
methods have been proposed to handle shape reconstruction and dense point
correspondences simultaneously, capturing semantic relationships across shapes
of the same class by learning a DIFs-modeled shape template. These methods
provide great flexibility and accuracy in reconstructing 3D shapes and
inferring correspondences. However, the point correspondences built from these
methods do not intrinsically preserve the topology of the shapes, unlike
mesh-based template matching methods. This limits their applications on 3D
geometries where underlying topological structures exist and matter, such as
anatomical structures in medical images. In this paper, we propose a new model
called Neural Diffeomorphic Flow (NDF) to learn deep implicit shape templates,
representing shapes as conditional diffeomorphic deformations of templates,
intrinsically preserving shape topologies. The diffeomorphic deformation is
realized by an auto-decoder consisting of Neural Ordinary Differential Equation
(NODE) blocks that progressively map shapes to implicit templates. We conduct
extensive experiments on several medical image organ segmentation datasets to
evaluate the effectiveness of NDF on reconstructing and aligning shapes. NDF
achieves consistently state-of-the-art organ shape reconstruction and
registration results in both accuracy and quality. The source code is publicly
available at https://github.com/Siwensun/Neural_Diffeomorphic_Flow--NDF.


title: Counterfactual Inference of Second Opinions

abstract:
  Automated decision support systems that are able to infer second opinions
from experts can potentially facilitate a more efficient allocation of
resources; they can help decide when and from whom to seek a second opinion. In
this paper, we look at the design of this type of support systems from the
perspective of counterfactual inference. We focus on a multiclass
classification setting and first show that, if experts make predictions on
their own, the underlying causal mechanism generating their predictions needs
to satisfy a desirable set invariant property. Further, we show that, for any
causal mechanism satisfying this property, there exists an equivalent mechanism
where the predictions by each expert are generated by independent
sub-mechanisms governed by a common noise. This motivates the design of a set
invariant Gumbel-Max structural causal model where the structure of the noise
governing the sub-mechanisms underpinning the model depends on an intuitive
notion of similarity between experts which can be estimated from data.
Experiments on both synthetic and real data show that our model can be used to
infer second opinions more accurately than its non-causal counterpart.


title: Graph Neural Networks for Multiparallel Word Alignment

abstract:
  After a period of decrease, interest in word alignments is increasing again
for their usefulness in domains such as typological research, cross-lingual
annotation projection, and machine translation. Generally, alignment algorithms
only use bitext and do not make use of the fact that many parallel corpora are
multiparallel. Here, we compute high-quality word alignments between multiple
language pairs by considering all language pairs together. First, we create a
multiparallel word alignment graph, joining all bilingual word alignment pairs
in one graph. Next, we use graph neural networks (GNNs) to exploit the graph
structure. Our GNN approach (i) utilizes information about the meaning,
position, and language of the input words, (ii) incorporates information from
multiple parallel sentences, (iii) adds and removes edges from the initial
alignments, and (iv) yields a prediction model that can generalize beyond the
training sentences. We show that community detection provides valuable
information for multiparallel word alignment. Our method outperforms previous
work on three word-alignment datasets and on a downstream task.


title:
Unraveled Multilevel Transformation Networks for Predicting
  Sparsely-Observed Spatiotemporal Dynamics

abstract:
  In this paper, we address the problem of predicting complex, nonlinear
spatiotemporal dynamics when available data is recorded at irregularly-spaced
sparse spatial locations. Most of the existing deep learning models for
modeling spatiotemporal dynamics are either designed for data in a regular grid
or struggle to uncover the spatial relations from sparse and irregularly-spaced
data sites. We propose a deep learning model that learns to predict unknown
spatiotemporal dynamics using data from sparsely-distributed data sites. We
base our approach on Radial Basis Function (RBF) collocation method which is
often used for meshfree solution of partial differential equations (PDEs). The
RBF framework allows us to unravel the observed spatiotemporal function and
learn the spatial interactions among data sites on the RBF-space. The learned
spatial features are then used to compose multilevel transformations of the raw
observations and predict its evolution in future time steps. We demonstrate the
advantage of our approach using both synthetic and real-world climate data.


title:
Learning Representation for Bayesian Optimization with Collision-free
  Regularization

abstract:
  Bayesian optimization has been challenged by datasets with large-scale,
high-dimensional, and non-stationary characteristics, which are common in
real-world scenarios. Recent works attempt to handle such input by applying
neural networks ahead of the classical Gaussian process to learn a latent
representation. We show that even with proper network design, such learned
representation often leads to collision in the latent space: two points with
significantly different observations collide in the learned latent space,
leading to degraded optimization performance. To address this issue, we propose
LOCo, an efficient deep Bayesian optimization framework which employs a novel
regularizer to reduce the collision in the learned latent space and encourage
the mapping from the latent space to the objective value to be Lipschitz
continuous. LOCo takes in pairs of data points and penalizes those too close in
the latent space compared to their target space distance. We provide a rigorous
theoretical justification for LOCo by inspecting the regret of this
dynamic-embedding-based Bayesian optimization algorithm, where the neural
network is iteratively retrained with the regularizer. Our empirical results
demonstrate the effectiveness of LOCo on several synthetic and real-world
benchmark Bayesian optimization tasks.


title:
Occlusion Fields: An Implicit Representation for Non-Line-of-Sight
  Surface Reconstruction

abstract:
  Non-line-of-sight reconstruction (NLoS) is a novel indirect imaging modality
that aims to recover objects or scene parts outside the field of view from
measurements of light that is indirectly scattered off a directly visible,
diffuse wall. Despite recent advances in acquisition and reconstruction
techniques, the well-posedness of the problem at large, and the recoverability
of objects and their shapes in particular, remains an open question. The
commonly employed Fermat path criterion is rather conservative with this
regard, as it classifies some surfaces as unrecoverable, although they
contribute to the signal.
  In this paper, we use a simpler necessary criterion for an opaque surface
patch to be recoverable. Such piece of surface must be directly visible from
some point on the wall, and it must occlude the space behind itself. Inspired
by recent advances in neural implicit representations, we devise a new
representation and reconstruction technique for NLoS scenes that unifies the
treatment of recoverability with the reconstruction itself. Our approach, which
we validate on various synthetic and experimental datasets, exhibits
interesting properties. Unlike memory-inefficient volumetric representations,
ours allows to infer adaptively tessellated surfaces from time-of-flight
measurements of moderate resolution. It can further recover features beyond the
Fermat path criterion, and it is robust to significant amounts of
self-occlusion. We believe that this is the first time that these properties
have been achieved in one system that, as an additional benefit, is trainable
and hence suited for data-driven approaches.


title: Thin Set Versions of Hindman's Theorem

abstract:
  In this paper we examine the reverse mathematical strength of a variation of
Hindman's Theorem HT constructed by essentially combining HT with the Thin Set
Theorem TS to obtain a principle which we call thin-HT. thin-HT says that every
coloring $c: \mathbb{N} \to \mathbb{N}$ has an infinite set $S \subseteq
\mathbb{N}$ whose finite sums are thin for $c$, meaning that there is an $i$
with $c(s) \neq i$ for all $s \in S$. We show that there is a computable
instance of thin-HT such that every solution computes $\emptyset'$, as is the
case with HT (see Blass, Hirst, and Simpson 1987). In analyzing this proof, we
deduce that thin-HT implies $ACA_0$ over $RCA_0 + I\Sigma^0_2$. On the other
hand, using Rumyantsev and Shen's computable version of the Lov\'asz Local
Lemma, we show that there is a computable instance of the restriction of
thin-HT to sums of exactly 2 elements such that any solution has diagonally
noncomputable degree relative to $\emptyset'$. Hence there is a computable
instance of this restriction of thin-HT with no $\Sigma^0_2$ solution.


title:
On optimal coordinated dispatch for heterogeneous storage fleets with
  partial availability

abstract:
  This paper addresses the problem of optimal scheduling of an aggregated power
profile (during a coordinated discharging or charging operation) by means of a
heterogeneous fleet of storage devices subject to availability constraints.
Devices have heterogeneous initial levels of energy, power ratings and
efficiency; moreover, the fleet operates without cross-charging of the units.
An explicit feedback policy is proposed to compute a feasible schedule whenever
one exists and scalable design procedures to achieve maximum time to failure or
minimal unserved energy in the case of unfeasible aggregated demand profiles.
Finally, a time-domain characterization of the set of feasible demand profiles
using aggregate constraints is proposed, suitable for optimization problems
where the aggregate population behaviour is of interest.


title:
A Liouville type result and quantization effects on the system $-\Delta
  u = u J'(1-|u|^{2})$ for a potential convex near zero

abstract:
  We consider a Ginzburg-Landau type equation in $\R^2$ of the form $-\Delta u
= u J'(1-|u|^{2})$ with a potential function $J$ satisfying weak conditions
allowing for example a zero of infinite order in the origin. We extend in this
context the results concerning quantization of finite potential solutions of
H.Brezis, F.Merle, T.Rivi\`ere from \cite{BMR} who treat the case when $J$
behaves polinomially near 0, as well as a result of Th. Cazenave, found in the
same reference, and concerning the form of finite energy solutions.


title:
Another approach to weighted inequalities for a superposition of Copson
  and Hardy operators

abstract:
  In this paper, we present a solution to the inequality $$ \bigg(
\int_0^{\infty} \bigg( \int_x^{\infty} \bigg( \int_0^t h \bigg)^q w(t)\,dt
\bigg)^{r / q} u(x)\,ds \bigg)^{1/r}\leq C \, \bigg( \int_0^{\infty} h^p v
\bigg)^{1 / p}, \quad h \in {\mathfrak M}^+(0,\infty), $$ using a combination
of reduction techniques and discretization. Here $1 \le p < \infty$, $0 < q ,\,
r < \infty$ and $u,\,v,\,w$ are weight functions on $(0,\infty)$.


title: Dense lineability and spaceability in certain subsets of $\ell_{\infty}$

abstract:
  We investigate dense lineability and spaceability of subsets of $\ell_\infty$
with a prescribed number of accumulation points. We prove that the set of all
bounded sequences with exactly countably many accumulation points is densely
lineable in $\ell_\infty$, thus complementing a recent result of Papathanasiou
who proved the same for the sequences with continuum many accumulation points.
We also prove that these sets are spaceable. We then consider the same problems
for the set of bounded non-convergent sequences with a finite number of
accumulation points. We prove that such set is densely lineable in
$\ell_\infty$ and that it is nevertheless not spaceable. The said problems are
also studied in the setting of ideal convergence and in the space
$\mathbb{R}^\omega$.


title:
Neutron stars as photon double-lenses: constraining resonant conversion
  into ALPs

abstract:
  Axion-photon conversion is a prime mechanism to detect axion-like particles
that share a coupling to the photon. We point out that in the vicinity of
neutron stars with strong magnetic fields, magnetars, the effective photon mass
receives comparable but opposite contributions from free electrons and the
radiation field. This leads to an energy-dependent resonance condition for
conversion that can be met for arbitrary light axions and leveraged when using
systems with detected radio component. Using the magnetar SGR J1745-2900 as an
exemplary source, we demonstrate that sensitivity to $|g_{a\gamma}| \sim
10^{-12}\,\rm{GeV^{-1}}$ or better can be gained for $m_a \lesssim 10^{-6}\,\rm
eV$, with the potential to improve current constraints on the axion-photon
coupling by more than one order of magnitude over a broad mass range. With
growing insights into the physical conditions of magnetospheres of magnetars,
the method hosts the potential to become a serious competitor to future
experiments such as ALPS-II and IAXO in the search for axion-like particles.


title: Two relations for the antisymmetrizer in the Hecke algebra

abstract:
  We prove two relations for the antisymmetrizer in the Hecke algebra and
derive certain restrictions imposed by these relations on unitary
representations of the Hecke algebra on tensor powers of the space ${\mathbb
C}^n$.


title: Dark matter mass loss in galaxy flybys: dependence on impact parameter

abstract:
  Galaxy flybys, interactions where two independent halos inter-penetrate but
detach at a later time and do not merge, occur frequently at lower redshifts.
These interactions can significantly impact the evolution of individual
galaxies - from the mass loss and shape transformation to the emergence of
tidal features and formation of morphological disc structures. The main focus
of this paper is on the dark matter mass loss of the secondary, intruder
galaxy, with the goal of determining a functional relationship between the
impact parameter and dark matter mass loss. Series of N-body simulations of
typical galaxy flybys (10:1 mass ratio) with differing impact parameters show
that the dark matter halo leftover mass of the intruder galaxy follows a
logarithmic growth law with impact parameter, regardless of the way the total
halo mass is estimated. The lost mass then, clearly, follows the exponential
decay law. The stellar component stretches faster as the impact parameter
decreases, following the exponential decay law with impact parameter.
Functional dependence on impact parameter in all cases seems universal, but the
fitting parameters are likely sensitive to the interaction parameters and
initial conditions (e.g. the mass ratio of interacting galaxies, initial
relative velocity of the intruder galaxy, interaction duration). While typical
flybys, investigated here, could not be the sole culprit behind the formation
of ultra-diffuse or dark matter deficient galaxies, they can still contribute
significantly. Rare, atypical and stronger flybys are worth further exploring.


title: On the chromatic number of 2-dimensional spheres

abstract:
  In 1976 Simmons conjectured that every coloring of a 2-dimensional sphere of
radius strictly greater than $1/2$ in three colors has a couple of
monochromatic points at the distance 1 apart. We prove this conjecture.


title:
Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient
  Medical Image Segmentation

abstract:
  With the development of deep convolutional neural networks, medical image
segmentation has achieved a series of breakthroughs in recent years. However,
the higher-performance convolutional neural networks always mean numerous
parameters and high computation costs, which will hinder the applications in
clinical scenarios. Meanwhile, the scarceness of large-scale annotated medical
image datasets further impedes the application of high-performance networks. To
tackle these problems, we propose Graph Flow, a comprehensive knowledge
distillation framework, for both network-efficiency and annotation-efficiency
medical image segmentation. Specifically, our core Graph Flow Distillation
transfer the essence of cross-layer variations from a well-trained cumbersome
teacher network to a non-trained compact student network. In addition, an
unsupervised Paraphraser Module is designed to purify the knowledge of the
teacher network, which is also beneficial for the stabilization of training
procedure. Furthermore, we build a unified distillation framework by
integrating the adversarial distillation and the vanilla logits distillation,
which can further refine the final predictions of the compact network.
Extensive experiments conducted on Gastric Cancer Segmentation Dataset and
Synapse Multi-organ Segmentation Dataset demonstrate the prominent ability of
our method which achieves state-of-the-art performance on these
different-modality and multi-category medical image datasets. Moreover, we
demonstrate the effectiveness of our Graph Flow through a new semi-supervised
paradigm for dual efficient medical image segmentation. Our code will be
available at Graph Flow.