title:
A Coarse-to-fine Morphological Approach With Knowledge-based Rules and
  Self-adapting Correction for Lung Nodules Segmentation

abstract:
  The segmentation module which precisely outlines the nodules is a crucial
step in a computer-aided diagnosis(CAD) system. The most challenging part of
such a module is how to achieve high accuracy of the segmentation, especially
for the juxtapleural, non-solid and small nodules. In this research, we present
a coarse-to-fine methodology that greatly improves the thresholding method
performance with a novel self-adapting correction algorithm and effectively
removes noisy pixels with well-defined knowledge-based principles. Compared
with recent strong morphological baselines, our algorithm, by combining dataset
features, achieves state-of-the-art performance on both the public LIDC-IDRI
dataset (DSC 0.699) and our private LC015 dataset (DSC 0.760) which closely
approaches the SOTA deep learning-based models' performances. Furthermore,
unlike most available morphological methods that can only segment the isolated
and well-circumscribed nodules accurately, the precision of our method is
totally independent of the nodule type or diameter, proving its applicability
and generality.


title: Multi-modal data generation with a deep metric variational autoencoder

abstract:
  We present a deep metric variational autoencoder for multi-modal data
generation. The variational autoencoder employs triplet loss in the latent
space, which allows for conditional data generation by sampling in the latent
space within each class cluster. The approach is evaluated on a multi-modal
dataset consisting of otoscopy images of the tympanic membrane with
corresponding wideband tympanometry measurements. The modalities in this
dataset are correlated, as they represent different aspects of the state of the
middle ear, but they do not present a direct pixel-to-pixel correlation. The
approach shows promising results for the conditional generation of pairs of
images and tympanograms, and will allow for efficient data augmentation of data
from multi-modal sources.


title:
Uniform stabilization of Boussinesq systems in critical
  $\mathbf{L}^q$-based Sobolev and Besov spaces by finite dimensional interior
  localized feedback controls

abstract:
  We consider the d-dimensional Boussinesq system defined on a sufficiently
smooth bounded domain, with homogeneous boundary conditions, and subject to
external sources, assumed to cause instability. The initial conditions for both
fluid and heat equations are taken of low regularity. We then seek to uniformly
stabilize such Boussinesq system in the vicinity of an unstable equilibrium
pair, in the critical setting of correspondingly low regularity spaces, by
means of explicitly constructed, feedback controls, which are localized on an
arbitrarily small interior subdomain. In addition, they will be minimal in
number, and of reduced dimension: more precisely, they will be of dimension
$(d-1)$ for the fluid component and of dimension $1$ for the heat component.
The resulting space of well-posedness and stabilization is a suitable, tight
Besov space for the fluid velocity component (close to $\mathbf{L}^3(\Omega$)
for $ d = 3 $) and the space $L^q(\Omega$) for the thermal component, $ q > d
$. Thus, this paper may be viewed as an extension of \cite{LPT.1}, where the
same interior localized uniform stabilization outcome was achieved by use of
finite dimensional feedback controls for the Navier-Stokes equations, in the
same Besov setting.


title:
Revisiting the Dark Matter Interpretation of Excess Rates in
  Semiconductors

abstract:
  In light of recent results from low-threshold dark matter detectors, we
revisit the possibility of a common dark matter origin for multiple excesses
across numerous direct detection experiments, with a focus on the excess rates
in semiconductor detectors. We explore the interpretation of the low-threshold
calorimetric excess rates above 40 eV in the silicon SuperCDMS Cryogenic Phonon
Detector and above 100 eV in the germanium EDELWEISS Surface detector as
arising from a common but unknown origin, and demonstrate a compatible fit for
the observed energy spectra in both experiments, which follow a power law of
index $\alpha = 3.43^{+0.11}_{-0.06}$. Despite the intriguing scaling of the
normalization of these two excess rates with approximately the square of the
mass number $A^2$, we argue that the possibility of common origin by dark
matter scattering via nuclear recoils is strongly disfavored, even allowing for
exotic condensed matter effects in an as-yet unmeasured kinematic regime. We
also investigate the possibility of inelastic nuclear scattering by cosmic ray
neutrons, solar neutrinos, and photons as the origin, and quantitatively
disfavor all three based on known fluxes of particles.


title: Signatures of supermassive black hole binaries on maser systems

abstract:
  We illustrate a novel signature of black hole binaries from their effect on
the kinematics of water maser emission in their environments. With the help of
simulations, we establish the condition for clumps to mase based on their
coherence lengths calibrated to those of the known maser galaxy NGC 4258. This
is then used to identify masing clumps around a binary black hole system, and
quantify the kinematic and spectral differences relative to the single black
hole case. For some generic circumstances, blue-shifted masers around a binary
black hole are found to preferentially follow the Keplerian rotation curve
observed in the single black hole case. The redshifted ones, however, are found
to visibly deviate from this relation, and also display more scatter with a
tendency towards lower absolute values of the velocity along the line-of-sight.
The spectrum of the masers as a function of line-of-sight velocity also shows a
double peaked structure, reminiscent of recent observations of systems such as
Mrk 1. Our results motivate future prospects for identifying binary black hole
candidates with the help of water maser emissions.


title:
Scanning disk rings and winds in CO at 0.01-10 au: a high-resolution
  $M$-band spectroscopy survey with IRTF-iSHELL

abstract:
  We present an overview and first results from a $M$-band spectroscopic survey
of planet-forming disks performed with iSHELL on IRTF, using two slits that
provide resolving power R $\approx$ 60,000-92,000 (5-3.3 km/s). iSHELL provides
a nearly complete coverage at 4.52-5.24 $\mu$m in one shot, covering $>50$
lines from the R and P branches of $^{12}$CO and $^{13}$CO for each of multiple
vibrational levels, and providing unprecedented information on the excitation
of multiple emission and absorption components. Some of the most notable new
findings of this survey are: 1) the detection of two CO Keplerian rings at $<2$
au (in HD 259431), 2) the detection of H${_2}$O ro-vibrational lines at 5
$\mu$m (in AS 205 N), and 3) the common kinematic variability of CO lines over
timescales of 1-14 years. By homogeneously analyzing this survey together with
a previous VLT-CRIRES survey of cooler stars, we discuss a unified view of CO
spectra where emission and absorption components scan the disk surface across
radii from a dust-free region within dust sublimation out to $\approx10$ au. We
classify two fundamental types of CO line shapes interpreted as emission from
Keplerian rings (double-peak lines) and a disk surface plus a low-velocity part
of a wind (triangular lines), where CO excitation reflects different emitting
regions (and their gas-to-dust ratio) rather than just the irradiation
spectrum. A disk+wind interpretation for the triangular lines naturally
explains several properties observed in CO spectra, including the line
blue-shifts, line shapes that turn into narrow absorption at high inclinations,
and the frequency of disk winds as a function of stellar type.


title: Old Phase Remnants in First Order Phase Transitions

abstract:
  First order phase transitions (FOPTs) are usually described by the nucleation
and expansion of new phase bubbles in the old phase background. While the
dynamics of new phase bubbles have been extensively studied, a comprehensive
treatment of the shrinking old phase remnants remained undeveloped. We present
a novel formalism for remnant statistics in FOPTs and perform the first
analytical calculations of their distribution. By shifting to the reverse time
description, we identify the shrinking remnants with expanding old phase
bubbles, allowing a quantitative evolution and determination of the population
statistics. Our results not only provide essential input for cosmological
FOPT-induced soliton/primordial black hole formation scenarios, but can also be
readily applied to generic FOPTs.


title:
Asteroseismic inference of the near-core magnetic field strength in the
  main sequence B star HD 43317

abstract:
  About 10 per cent of intermediate- and high-mass dwarf stars are observed to
host a strong large-scale magnetic field at their surface, which is thought to
be of fossil field origin. However, there are few inferences as to the magnetic
field strength and geometry within the deep interiors of stars. Considering
that massive stars harbour a convective core whilst on the main sequence,
asteroseismology of gravity (g) modes is able to provide constraints on their
core masses, yet it has so far not been used to probe the strength of their
interior magnetic fields. Here we use asteroseismology to constrain an upper
limit for the magnetic field strength in the near-core region of the pulsating
and magnetic B star HD 43317, based on the expected interaction of a magnetic
field and its g modes. We find a magnetic field strength of order $5 \times
10^5$ G is sufficient to suppress high-radial order g modes and reproduce the
observed frequency spectrum of HD 43317, which contains only high-frequency g
modes. This result is the first inference of the magnetic field strength inside
a main-sequence star.


title:
The dark side of galaxy stellar populations I: The stellar-to-halo mass
  relation and the velocity dispersion - halo mass relation

abstract:
  The growth and properties of galaxies are thought to be closely connected to
the ones of their host dark matter halos. Despite the importance of this
so-called galaxy-halo connection, the potential role of dark matter halos in
regulating observed galaxy properties remains yet to be fully understood. In
this work, we derive the ages, metallicites and [Mg/Fe] abundances from optical
spectra from the Sloan Digital Sky Survey of nearby central galaxies, and study
them in terms of their host halos. We investigate how the scatter in the
stellar-to-halo mass relation and the velocity dispersion - halo mass relation
correlates with these stellar population parameters. In addition, we also study
the differences when distinguishing between different galaxy morphologies and
environments. We find that the ages and chemical enrichment of galaxies are not
fully determined by their stellar masses or velocity dispersion, but also
depend on the mass of the host halos. Our findings suggest that the velocity
dispersion is the best proxy of the stellar population parameters with halo
mass playing a secondary yet noticeable role. We interpret that the origin of
the correlation between the scatter of these relations and the ages and
metallicities might be related to different halo formation times.


title: Supertranslation-Invariant Dressed Lorentz Charges

abstract:
  We present an explicit formula for Lorentz boosts and rotations that commute
with BMS supertranslations in asymptotically flat spacetimes. Key to the
construction is the use of infrared regularizations and of a unitary
transformation that makes observables commute with the soft degrees of freedom.
We explicitly verify that our charges satisfy the Lorentz algebra and we check
that they are consistent with expectations by evaluating them on the
supertranslated Minkowski space and on the boosted Kerr black hole.


title:
MUSE sneaks a peek at extreme ram-pressure stripping events -- V.
  Towards a complete view of the galaxy cluster A1367

abstract:
  We present an analysis of the kinematics and ionization conditions in a
sample composed of seven star-forming galaxies undergoing ram-pressure
stripping in the A1367 cluster, and the galaxy ESO137-001 in the Norma cluster.
MUSE observations of two new galaxies in this sample, CGCG097-073 and
CGCG097-079, are also presented. This sample is characterized by homogeneous
integral field spectroscopy with MUSE and by a consistent selection based on
the presence of ionised gas tails. The ratio [OI]/H${\alpha}$ is consistently
elevated in the tails of these objects compared to what observed in unperturbed
galaxy disks, an ubiquitous feature which we attribute to shocks or turbulent
phenomena in the stripped gas. Compact star-forming regions are observed in
only $\approx$ 50% of the tails, implying that specific (currently unknown)
conditions are needed to trigger star formation inside the stripped gas.
Focusing on the interface regions between the interstellar and intracluster
medium, we observe different line ratios that we associate to different stages
of the stripping process, with galaxies at an early stage of perturbation
showing more prominent signatures of elevated star formation. Our analysis thus
demonstrates the power of a well selected and homogeneous sample to infer
general properties arising from ram-pressure stripping inside local clusters.


title: The evolution of the heaviest super-massive black-holes in jetted AGNs

abstract:
  We present the space density evolution, from z=1.5 up to z=5.5, of the most
massive (M$\geq10^9$M$_{\odot}$) black holes hosted in jetted Active Galactic
Nuclei(AGNs). The analysis is based on a sample of 380 luminosity-selected
($\lambda$L$_{1350}\geq10^{46}$ erg s$^{-1}$ and P$_{5\text{GHz}}\geq10^{27}$ W
Hz$^{-1}$) Flat Spectrum Radio Quasars (FSRQs) obtained from the Cosmic Lens
All Sky Survey (CLASS). These sources are known to be face-on jetted AGNs (i.e.
blazars) and can be exploited to infer the abundance of all the (misaligned)
jetted AGNs, using a geometrical argument. We then compare the space density of
the most massive SMBHs hosted in jetted AGNs with those present in the total
population (mostly composed by non-jetted AGNs). We find that the space density
has a peak at $z\sim3$, which is significantly larger than the value observed
in the total AGN population with similar optical/UV luminosities ($z\sim2.2$),
but not as extreme as the value previously inferred from X-ray selected blazars
($z\gtrsim4$). The jetted fraction (jetted AGNs/total AGNs) is overall
consistent with the estimates in the local Universe (10--20\%) and at high
redshift, assuming Lorentz bulk factors $\Gamma\approx5$. Finally, we find a
marginal decrease in the jetted fraction at high redshifts (by a factor of
$\sim2$). All these evidences point toward a different evolutionary path in the
jetted AGNs compared to the total AGN population.


title: Exceptional dynamics of interacting spin liquids

abstract:
  We show that interactions in quantum spin liquids can result in non-Hermitian
phenomenology that differs qualitatively from mean-field expectations. We
demonstrate this in two prominent cases through the effects of phonons and
disorder on a Kitaev honeycomb model. Using analytic and numerical
calculations, we show the generic appearance of exceptional points and rings
depending on the symmetry of the system. Their existence is reflected in
dynamical observables including the dynamic structure function measured in
neutron scattering. The results point to new phenomenological features in
realizable spin liquids that must be incorporated into the analysis of
experimental data.


title: Holographic Realization of the Prime Number Quantum Potential

abstract:
  We report the first experimental realization of the prime number quantum
potential $V_N(x)$, defined as the potential entering the single-particle
Schr\"{o}dinger Hamiltonian with eigenvalues given by the first $N$ prime
numbers. We use holographic optical traps and, in particular, a spatial light
modulator to tailor the potential to the desired shape. As a further
application, we also implement a potential with lucky numbers, a sequence of
integers generated by a different sieve than the familiar Eratosthenes's sieve
used for the primes.
  Our results pave the way towards the realization of quantum potentials with
arbitrary sequences of integers as energy levels and show, in perspective, the
possibility to set up quantum systems for arithmetic manipulations or
mathematical tests involving prime numbers.


title: New bounds on axion-like particles from MicroBooNE

abstract:
  Neutrino experiments lie at the edge of the intensity frontier and therefore
can be exploited to search for new light particles weakly coupled to the
visible sector. In this work we derive new constraints on axion-like particles
(ALPs) using data from the MicroBooNE experiment, from a search for $e^+ e^-$
pairs pointing in the direction of the NuMI absorber. In particular, we
consider the addition of higher-dimensional effective operators coupling the
ALP to the electroweak gauge bosons. These would induce $K\to \pi a$ from kaon
decay at rest in the NuMI absorber, as well as ALP decays into pairs of leptons
or photons. We discuss in detail and compare various results obtained for the
decay width $K\rightarrow \pi a$ in previous literature. For the operator
involving the Higgs, MicroBooNE already sets competitive bounds (comparable to
those of NA62) for ALP masses between 100 and 200 MeV. We also compute the
expected sensitivities from the full NuMI dataset recorded at MicroBooNE. Our
results show that a search for a $a\to\gamma\gamma$ signal may be able to
improve over current constraints from beam-dump experiments on the operator
involving the ALP coupling to the $W$.


title: Normalization of ZZ instanton amplitudes in minimal string theory

abstract:
  We use insights from string field theory to analyze and cure the divergences
in the cylinder diagram in minimal string theory with both boundaries lying on
a ZZ brane. We focus on theories with worldsheet matter consisting of the
$(2,p)$ minimal model plus Liouville theory, with total central charge 26,
together with the usual $bc$-ghosts. The string field theory procedure gives a
finite, purely imaginary normalization constant for non-perturbative effects in
minimal string theory, or doubly non-perturbative effects in JT gravity. We
find precise agreement with the prediction from the dual double-scaled
one-matrix integral. We also make a few remarks about the extension of this
result to the more general $(p',p)$ minimal string.


title: Physical Properties of the Host Galaxies of Ca-rich Transients

abstract:
  Calcium-rich (Ca-rich) transients are a new class of supernovae (SNe) that
are known for their comparatively rapid evolution, modest peak luminosities,
and strong nebular calcium emission lines. Currently, the progenitor systems of
Ca-rich transients remain unknown. Although they exhibit spectroscopic
properties not unlike core-collapse Type Ib/c SNe, nearly half are found in the
outskirts of their host galaxies that are predominantly elliptical, suggesting
a closer connection to the older stellar populations of SNe Ia. In this paper,
we present a compilation of publicly available multiwavelength observations of
all known and/or suspected host galaxies of Ca-rich transients ranging from FUV
to IR, and use these data to characterize their stellar populations with
prospector. We estimate several galaxy parameters including integrated star
formation rate, stellar mass, metallicity, and age. For nine host galaxies, the
observations are sensitive enough to obtain nonparametric star formation
histories, from which we recover SN rates and estimate probabilities that the
Ca-rich transients in each of these host galaxies originated from a
core-collapse vs. Type Ia-like explosion. Our work supports the notion that the
population of Ca-rich transients do not come exclusively from core-collapse
explosions, and must either be only from white dwarf stars or a mixed
population of white dwarf stars with other channels, potentially including
massive star explosions. Additional photometry and explosion site spectroscopy
of larger samples of Ca-rich host galaxies will improve these estimates and
better constrain the ratio of white dwarf vs. massive star progenitors of
Ca-rich transients.


title:
Nonresonant Searches for Axion-Like Particles in Vector Boson Scattering
  Processes at the LHC

abstract:
  We propose a new search for Axion-Like Particles (ALPs), targeting Vector
Boson Scattering (VBS) processes at the LHC. We consider nonresonant
ALP-mediated VBS, where the ALP participates as an off-shell mediator. This
process occurs whenever the ALP is too light to be produced resonantly, and it
takes advantage of the derivative nature of ALP interactions with the
electroweak Standard Model bosons. We study the production of $ZZ$, $Z\gamma$,
$W^\pm \gamma$, $W^\pm Z$ and $W^\pm W^\pm$ pairs with large diboson invariant
masses in association with two jets. Working in a gauge-invariant framework,
upper limits on ALP couplings to electroweak bosons are obtained from a
reinterpretation of Run 2 public CMS VBS analyses. The constraints inferred on
ALP couplings to $ZZ$, $Z\gamma$ and $W^\pm W^\pm$ pairs are very competitive
for ALP masses up to 100 GeV. They have the advantage of being independent of
the ALP coupling to gluons and of the ALP decay width. Simple projections for
LHC Run 3 and HL-LHC are also calculated, demonstrating the power of future
dedicated analyses at ATLAS and CMS.


title:
Scars from protected zero modes and beyond in $U(1)$ quantum link and
  quantum dimer models

abstract:
  We demonstrate the presence of anomalous high-energy eigenstates, or
many-body scars, in $U(1)$ quantum link and quantum dimer models on square and
rectangular lattices. In particular, we consider the paradigmatic
Rokhsar-Kivelson Hamiltonian $H=\mathcal{O}_{\mathrm{kin}} + \lambda
\mathcal{O}_{\mathrm{pot}}$ where $\mathcal{O}_{\mathrm{pot}}$
($\mathcal{O}_{\mathrm{kin}}$) is defined as a sum of terms on elementary
plaquettes that are diagonal (off-diagonal) in the computational basis. Both
these interacting models possess an exponentially large number of mid-spectrum
zero modes in system size at $\lambda=0$ that are protected by an index theorem
preventing any mixing with the nonzero modes at this coupling. We classify
different types of scars for $|\lambda| \lesssim \mathcal{O}(1)$ both at zero
and finite winding number sectors complementing and significantly generalizing
our previous work [Banerjee and Sen, Phys. Rev. Lett. 126, 220601 (2021)]. The
scars at finite $\lambda$ show a rich variety with those that are composed
solely from the zero modes of $\mathcal{O}_{\mathrm{kin}}$, those that contain
an admixture of both the zero and the nonzero modes of
$\mathcal{O}_{\mathrm{kin}}$, and finally those composed solely from the
nonzero modes of $\mathcal{O}_{\mathrm{kin}}$. We give analytic expressions for
certain "lego scars" for the quantum dimer model on rectangular lattices where
one of the linear dimensions can be made arbitrarily large, with the building
blocks (legos) being composed of emergent singlets and other more complicated
entangled structures.


title: Belle II sensitivity to long-lived dark photons

abstract:
  In this letter we point out that the Belle II experiment has a unique
sensitivity to visibly decaying long-lived dark photons. Concentrating on the
signatures with a single high energy photon in association with a displaced
pair of charged particles, we find that Belle II will be able to probe large
regions of parameter space that cannot be covered by any other running or
proposed experimental facility. While the signature with charged muons or pions
in the final state is expected to be background-free after all selections are
applied, the case of final state electrons is more involved and requires an
in-depth study. We discuss possible ways to further suppress backgrounds and
the corresponding experimental prospects.


title:
New constraints on extended Higgs sectors from the trilinear Higgs
  coupling

abstract:
  The trilinear Higgs coupling $\lambda_{hhh}$ is crucial for determining the
structure of the Higgs potential and for probing possible effects of physics
beyond the Standard Model (SM). Focusing on the Two-Higgs-Doublet Model as a
concrete example, we identify parameter regions in which $\lambda_{hhh}$ is
significantly enhanced with respect to the SM. Taking into account all relevant
corrections up to the two-loop level, we show that already current experimental
bounds on $\lambda_{hhh}$ rule out significant parts of the parameter space
that would otherwise be unconstrained. We illustrate the interpretation of the
results on $\lambda_{hhh}$ for a benchmark scenario. Similar results are
expected for wide classes of models with extended Higgs sectors.


title: $T\overline{T}$-deformed free energy of the Airy model

abstract:
  Sharpening the holographic correspondence of Jackiw-Teitelboim (JT) gravity
and its dual matrix model description at a finite radial cutoff $\lambda$
through the $T\overline{T}$ deformation is of interest. To proceed, we simplify
the problem by considering the Airy model and deform Airy correlators in the
same way as in $T\overline{T}$-deformed JT gravity. We use those correlators to
compute the annealed and quenched free energies for both $\lambda > 0$ and
$\lambda < 0$ from an integral representation of the replica trick. At the
leading order in $\lambda$ and low temperatures, we confirm that the genus-zero
quenched free energy monotonically decreases as a function of temperature when
perturbation theory is valid. We then study the all-genus quenched free energy
at low temperatures, where we discover and discuss subtleties due to
non-perturbative effects in the Airy model, as well as the contributions from
the non-perturbative branch under the $T\overline{T}$ deformation.


title:
Probing emergent QED in quantum spin ice via Raman scattering of
  phonons: shallow inelastic scattering and pair production

abstract:
  We present a new mechanism for Raman scattering of phonons, which is based on
the linear magnetoelastic coupling present in non-Kramers magnetic ions. This
provides a direct coupling of Raman-active phonons to the magnet's
quasiparticles. We propose to use this mechanism to probe the emergent magnetic
monopoles, electric charges, and photons of the emergent quantum
electrodynamics (eQED) of the U(1) quantum spin liquid known as quantum spin
ice. Detecting this eQED in candidate rare-earth pyrochlore materials, or
indeed signatures of topological magnetic phases more generally, is a
challenging task. We show that the Raman scattering cross-section of the
phonons directly yields relevant information, with the broadening of the phonon
linewidth, which we compute, exhibiting a characteristic frequency dependence
reflecting the two-particle density of states of the emergent excitations.
Remarkably, we find that the Raman linewidth is sensitive to the details of the
symmetry fractionalisation and hence can reveal information about the
projective implementation of symmetry in the quantum spin liquid, thereby
providing a diagnostic for a $\pi$-flux phase. The Raman scattering of the
phonons thus provides a useful experimental tool to probe the fractionalisation
in quantum spin liquids that turns out closely to mirror pair production in
quantum electrodynamics and the deep inelastic scattering of quantum
chromodynamics. Indeed, the difference to the latter is conceptual more than
technical: the partons (quarks) emerge from the hadrons at high energies due to
asymptotic freedom, while those in eQED arise from fractionalisation of the
spins at low energies.


title: Distal systems in topological dynamics and ergodic theory

abstract:
  We generalize a result of Lindenstrauss on the interplay between measurable
and topological dynamics which shows that every separable ergodic measurably
distal dynamical system has a minimal distal model. We show that such a model
can, in fact, be chosen completely canonically. The construction is performed
by going through the Furstenberg--Zimmer tower of a measurably distal system
and showing that at each step, there is a simple and canonical distal minimal
model. This hinges on a new characterization of isometric extensions in
topological dynamics.


title: Selecting Seed Words for Wordle using Character Statistics

abstract:
  Wordle, a word guessing game rose to global popularity in the January of
2022. The goal of the game is to guess a five-letter English word within six
tries. Each try provides the player with hints by means of colour changing
tiles which inform whether or not a given character is part of the solution as
well as, in cases where it is part of the solution, whether or not it is in the
correct placement. Numerous attempts have been made to find the best starting
word and best strategy to solve the daily wordle. This study uses character
statistics of five-letter words to determine the best three starting words.


title:
Hole Spectral Function of a Chiral Spin Liquid in the Triangular Lattice
  Hubbard Model

abstract:
  Quantum spin liquids are fascinating phases of matter, hosting fractionalized
spin excitations and unconventional long-range quantum entanglement. These
exotic properties, however, also render their experimental characterization
challenging and finding ways to diagnose quantum spin liquids is therefore a
pertinent challenge. Here, we numerically compute the spectral function of a
single hole doped into the half-filled Hubbard model on the triangular lattice
using techniques based on matrix product states. At half filling the system has
been proposed to realize a chiral spin liquid at intermediate interaction
strength, surrounded by a magnetically ordered phase at strong interactions and
a superconducting/metallic phase at weak interactions. We find that the spectra
of these phases exhibit distinct signatures. By developing appropriate parton
mean-field descriptions, we gain insight into the relevant low energy features.
While the magnetic phase is characterized by a dressed hole moving through the
ordered spin background, we find indications of spinon dynamics in the chiral
spin liquid. Our results suggest that the hole spectral function, as measured
by Angle-Resolved Photoemission Spectroscopy (ARPES), provides a useful tool to
characterize quantum spin liquids.


title:
Scaling of the quantum approximate optimization algorithm on
  superconducting qubit based hardware

abstract:
  Quantum computers may provide good solutions to combinatorial optimization
problems by leveraging the Quantum Approximate Optimization Algorithm (QAOA).
The QAOA is often presented as an algorithm for noisy hardware. However,
hardware constraints limit its applicability to problem instances that closely
match the connectivity of the qubits. Furthermore, the QAOA must outpace
classical solvers. Here, we investigate swap strategies to map dense problems
into linear, grid and heavy-hex coupling maps. A line-based swap strategy works
best for linear and two-dimensional grid coupling maps. Heavy-hex coupling maps
require an adaptation of the line swap strategy. By contrast, three-dimensional
grid coupling maps benefit from a different swap strategy. Using known entropic
arguments we find that the required gate fidelity for dense problems lies deep
below the fault-tolerant threshold. We also provide a methodology to reason
about the execution-time of QAOA. Finally, we present a QAOA Qiskit Runtime
program and execute the closed-loop optimization on cloud-based quantum
computers with transpiler settings optimized for QAOA. This work highlights
some obstacles to improve to make QAOA competitive, such as gate fidelity, gate
speed, and the large number of shots needed. The Qiskit Runtime program gives
us a tool to investigate such issues at scale on noisy superconducting qubit
hardware.


title:
Deletion Inference, Reconstruction, and Compliance in Machine
  (Un)Learning

abstract:
  Privacy attacks on machine learning models aim to identify the data that is
used to train such models. Such attacks, traditionally, are studied on static
models that are trained once and are accessible by the adversary. Motivated to
meet new legal requirements, many machine learning methods are recently
extended to support machine unlearning, i.e., updating models as if certain
examples are removed from their training sets, and meet new legal requirements.
However, privacy attacks could potentially become more devastating in this new
setting, since an attacker could now access both the original model before
deletion and the new model after the deletion. In fact, the very act of
deletion might make the deleted record more vulnerable to privacy attacks.
  Inspired by cryptographic definitions and the differential privacy framework,
we formally study privacy implications of machine unlearning. We formalize
(various forms of) deletion inference and deletion reconstruction attacks, in
which the adversary aims to either identify which record is deleted or to
reconstruct (perhaps part of) the deleted records. We then present successful
deletion inference and reconstruction attacks for a variety of machine learning
models and tasks such as classification, regression, and language models.
Finally, we show that our attacks would provably be precluded if the schemes
satisfy (variants of) Deletion Compliance (Garg, Goldwasser, and Vasudevan,
Eurocrypt' 20).


title: The trace of $T_2$ takes no repeated values

abstract:
  We prove that the trace of the Hecke operator $T_2$ acting on the vector
space of cusp forms of level one takes no repeated values, except for 0, which
only occurs when the space is trivial.


title: Zoology of non-Hermitian spectra and their graph topology

abstract:
  We uncover the very rich graph topology of generic bounded non-Hermitian
spectra, distinct from the topology of conventional band invariants and complex
spectral winding. The graph configuration of complex spectra are characterized
by the algebraic structures of their corresponding energy dispersions, drawing
new intimate links between combinatorial graph theory, algebraic geometry and
non-Hermitian band topology. Spectral graphs that are conformally related
belong to the same equivalence class, and are characterized by emergent
symmetries not necessarily present in the physical Hamiltonian. The simplest
class encompasses well-known examples such as the Hatano-Nelson and
non-Hermitian SSH models, while more sophisticated classes represent novel
multi-component models with interesting spectral graphs resembling stars,
flowers, and insects. With recent rapid advancements in metamaterials,
ultracold atomic lattices and quantum circuits, it is now feasible to not only
experimentally realize such esoteric spectra, but also investigate the
non-Hermitian flat bands and anomalous responses straddling transitions between
different spectral graph topologies.


title:
On learning Whittle index policy for restless bandits with scalable
  regret

abstract:
  Reinforcement learning is an attractive approach to learn good resource
allocation and scheduling policies based on data when the system model is
unknown. However, the cumulative regret of most RL algorithms scales as $\tilde
O(\mathsf{S} \sqrt{\mathsf{A} T})$, where $\mathsf{S}$ is the size of the state
space, $\mathsf{A}$ is the size of the action space, $T$ is the horizon, and
the $\tilde{O}(\cdot)$ notation hides logarithmic terms. Due to the linear
dependence on the size of the state space, these regret bounds are
prohibitively large for resource allocation and scheduling problems. In this
paper, we present a model-based RL algorithm for such problem which has
scalable regret. In particular, we consider a restless bandit model, and
propose a Thompson-sampling based learning algorithm which is tuned to the
underlying structure of the model. We present two characterizations of the
regret of the proposed algorithm with respect to the Whittle index policy.
First, we show that for a restless bandit with $n$ arms and at most $m$
activations at each time, the regret scales either as $\tilde{O}(mn\sqrt{T})$
or $\tilde{O}(n^2 \sqrt{T})$ depending on the reward model. Second, under an
additional technical assumption, we show that the regret scales as
$\tilde{O}(n^{1.5} \sqrt{T})$. We present numerical examples to illustrate the
salient features of the algorithm.


title: Hyperbolic Lattice for Scalar Field Theory in AdS$_3$

abstract:
  We construct a tessellation of AdS$_3$, by extending the equilateral
triangulation of AdS$_2$ on the Poincar\'{e} disk based on the $(2,3,7)$
triangle group, suitable for studying strongly coupled phenomena and the
AdS/CFT correspondence. A Hamiltonian form conducive to the study of dynamics
and quantum computation is presented. We show agreement between lattice
calculations and analytic results for the free scalar theory and find evidence
of a second order critical transition for $\phi^4$ theory using Monte Carlo
simulations. Applications of this AdS Hamiltonian formulation to real time
evolution and quantum computing are discussed.


title:
Particle energization in colliding subcritical collisionless shocks
  investigated in the laboratory

abstract:
  Colliding collisionless shocks appear in a great variety of astrophysical
phenomena and are thought to be possible sources of particle acceleration in
the Universe. To investigate the detailed dynamics of this phenomenon, we have
performed a dedicated laboratory experiment. We have generated two
counter-streaming subcritical collisionless magnetized shocks by irradiating
two teflon (CF$_2$) targets with 100 J, 1 ns laser beams on the LULI2000 laser
facility. The interaction region between the plasma flows was pre-filled with a
low density background hydrogen plasma and initialized with an externally
applied homogeneous magnetic field perpendicular to the shocks. We report here
on measurements of the plasma density and temperature during the formation of
the supercritical shocks, their transition to subcritical, and final
interpenetration. We have also modeled the macroscopic evolution of the system
via hydrodynamic simulations and the microphysics at play during the
interaction via Particle-In-Cell simulations. The main goal was to understand
what was the effect of the second shock on particle energization. We found that
in the presence of two shocks the ambient ions reach energies around 1.5 times
of the ones obtained with single shocks. Both the presence of the downstream
zone of the second shock and of the downstream zone common for the two shocks
play a role in the different energization: the characteristics of the
perpendicular electric fields in the two areas allow, indeed, certain particles
to keep being accelerated or to avoid being decelerated.


title: Reward-Respecting Subtasks for Model-Based Reinforcement Learning

abstract:
  To achieve the ambitious goals of artificial intelligence, reinforcement
learning must include planning with a model of the world that is abstract in
state and time. Deep learning has made progress in state abstraction, but,
although the theory of time abstraction has been extensively developed based on
the options framework, in practice options have rarely been used in planning.
One reason for this is that the space of possible options is immense and the
methods previously proposed for option discovery do not take into account how
the option models will be used in planning. Options are typically discovered by
posing subsidiary tasks such as reaching a bottleneck state, or maximizing a
sensory signal other than the reward. Each subtask is solved to produce an
option, and then a model of the option is learned and made available to the
planning process. The subtasks proposed in most previous work ignore the reward
on the original problem, whereas we propose subtasks that use the original
reward plus a bonus based on a feature of the state at the time the option
stops. We show that options and option models obtained from such
reward-respecting subtasks are much more likely to be useful in planning and
can be learned online and off-policy using existing learning algorithms. Reward
respecting subtasks strongly constrain the space of options and thereby also
provide a partial solution to the problem of option discovery. Finally, we show
how the algorithms for learning values, policies, options, and models can be
unified using general value functions.


title:
Canonical and Non-canonical Inflation in the light of the recent
  BICEP/Keck results

abstract:
  We discuss implications of the latest BICEP/Keck data release for
inflationary models, with particular emphasis on scalar fields with
non-canonical Lagrangians of the type ${\cal L} = X^\alpha - V(\phi)$. The
observational upper bound on the tensor-to-scalar ratio, $r \leq 0.036$,
implies that monotonically increasing convex potentials are now ruled out in
the canonical framework. This includes the simplest classic inflationary
potentials such as $\frac{1}{2}m^2 \phi^2$ and $\lambda \phi^4$, as well as the
whole family of monomial power law potentials $V(\phi) \sim \phi^p$. Instead,
the current observations strongly favour asymptotically flat plateau
potentials. However, working in the non-canonical framework, we demonstrate
that the classic monomial potentials, as well as the Higgs potential with its
Standard Model self-coupling, can easily be accommodated by the CMB data.
Similarly we show that the inverse power law potential $V(\phi) \sim
\phi^{-p}$, which leads to power law inflation in the non-canonical framework,
also satisfies the latest CMB bounds. Significantly, $V(\phi)$ can originate
from Planck scale initial values $V(\phi) \simeq \, m_p^4$ in non-canonical
models while in plateau-like canonical inflation the initial value of the
potential is strongly suppressed $V_{\rm plat}(\phi) \leq 10^{-10} \, m_p^4$.
This has bearing on the issue of initial conditions for inflation and allows
for the equipartition of the kinetic and potential terms in non-canonical
models. Equipartition initial conditions can also be accommodated in simple
extensions to plateau potentials such as the Margarita model of inflation which
is discussed in this paper.


title:
Spectrum of weighted composition operators. Part VIII. Lower
  semi-Fredholm and Fredholm spectra of weighted compositions on $C(K)$, case
  of non-invertible surjections

abstract:
  We characterize lower semi-Fredholm and Fredholm of weighted composition
operators on $C(K)$ in the case when the corresponding map is an open
surjection of the compact space $K$ onto itself. The obtained criterions
involve the notion of almost homeomorphisms of compact spaces.


title:
Locally Random P-adic Alloy Codes with Channel Coding Theorems for
  Distributed Coded Tensors

abstract:
  Tensors, i.e., multi-linear functions, are a fundamental building block of
machine learning algorithms. In order to train on large data-sets, it is common
practice to distribute the computation amongst workers. However, stragglers and
other faults can severely impact the performance and overall training time. A
novel strategy to mitigate these failures is the use of coded computation. We
introduce a new metric for analysis called the typical recovery threshold,
which focuses on the most likely event and provide a novel construction of
distributed coded tensor operations which are optimal with this measure. We
show that our general framework encompasses many other computational schemes
and metrics as a special case. In particular, we prove that the recovery
threshold and the tensor rank can be recovered as a special case of the typical
recovery threshold when the probability of noise, i.e., a fault, is equal to
zero, thereby providing a noisy generalization of noiseless computation as a
serendipitous result. Far from being a purely theoretical construction, these
definitions lead us to practical random code constructions, i.e., locally
random p-adic alloy codes, which are optimal with respect to the measures. We
analyze experiments conducted on Amazon EC2 and establish that they are faster
and more numerically stable than many other benchmark computation schemes in
practice, as is predicted by theory.

categories: cs.IT cs.DC cs.LG cs.NA cs.SC math.IT math.NA


title: Property tau in positive characteristic

abstract:
  We prove a quantitative equidistribution statement for certain adelic
homogeneous subsets in positive characteristic. As an application, we describe
a proof of property tau for arithmetic groups in this context.


title: A Scaling Limit for Line and Surface Defects

abstract:
  We study symmetry-breaking line defects in the Wilson-Fisher theory with
$O(2N+1)$ global symmetry near four dimensions and symmetry-preserving surface
defects in a cubic model with $O(2N)$ global symmetry near six dimensions. We
introduce a scaling limit inspired by the large charge expansion in Conformal
Field Theory. Using this, we compute the beta function for the defect coupling
which allows to identify the corresponding Defect Conformal Field Theories. We
also compute the correlation function of two parallel defects as well as
correlation functions of certain defect operators with large charge under the
surviving symmetry.


title: New Bounds on the Size of Binary Codes with Large Minimum Distance

abstract:
  Let A(n, d) denote the maximum number of codewords in a binary code of length
n and minimum Hamming distance d. Deriving upper and lower bounds on A(n, d)
have been a subject for extensive research in coding theory. In this paper, we
examine upper and lower bounds on A(n, d) in the high-minimum distance regime,
in particular, when $d = n/2 - \Theta(\sqrt{n})$. We will first provide a lower
bound based on a cyclic construction for codes of length $n= 2^m -1$ and show
that $A(n, d= n/2 - 2^{c-1}\sqrt{n}) \geq n^c$, where c is an integer with $1
\leq c \leq m/2-1$. With a Fourier-analytic view of Delsarte's linear program,
novel upper bounds on $A(n, n/2 - \sqrt{n})$ and $A(n, n/2 - 2 \sqrt{n})$ are
obtained, and, to the best of the authors' knowledge, are the first upper
bounds scaling polynomially in n for the regime with $d = n/2 -
\Theta(\sqrt{n})$.


abstract:
  We completely classify K-stability of log del Pezzo hypersurfaces with index
2.


title: Random Gegenbauer Features for Scalable Kernel Methods

abstract:
  We propose efficient random features for approximating a new and rich class
of kernel functions that we refer to as Generalized Zonal Kernels (GZK). Our
proposed GZK family, generalizes the zonal kernels (i.e., dot-product kernels
on the unit sphere) by introducing radial factors in their Gegenbauer series
expansion, and includes a wide range of ubiquitous kernel functions such as the
entirety of dot-product kernels as well as the Gaussian and the recently
introduced Neural Tangent kernels. Interestingly, by exploiting the reproducing
property of the Gegenbauer polynomials, we can construct efficient random
features for the GZK family based on randomly oriented Gegenbauer kernels. We
prove subspace embedding guarantees for our Gegenbauer features which ensures
that our features can be used for approximately solving learning problems such
as kernel k-means clustering, kernel ridge regression, etc. Empirical results
show that our proposed features outperform recent kernel approximation methods.


title:
Nonlinear structural stability and linear dynamic instability of
  transonic steady-states to a hydrodynamic model for semiconductors

abstract:
  For unipolar hydrodynamic model of semiconductor device represented by
Euler-Poisson equations, when the doping profile is supersonic, the existence
of steady transonic shock solutions and C-smooth steady transonic solutions for
Euler-Poisson Equations were established in [27] and [41], respectively. In
this paper we further study the nonlinear structural stability and the linear
dynamic instability of these steady transonic solutions. When the C^1-smooth
transonic steady-states pass through the sonic line, they produce singularities
for the system, and cause some essential difficulty in the proof of structural
stability. For any relaxation time, by means of elaborate singularity analysis,
we first investigate the structural stability of the C^1-smooth transonic
steady-states, once the perturbations of the initial data and the doping
profiles are small enough. Moreover, when the relaxation time is large enough,
under the condition that the electric field is positive at the shock location,
we prove that the transonic shock steady-states are structurally stable with
respect to small perturbations of the supersonic doping profile. Furthermore,
we show the linearly dynamic instability for these transonic shock
steady-states provided that the electric field is suitable negative. The proofs
for the structural stability results are based on singularity analysis, a
monotonicity argument on the shock position and the downstream density, and the
stability analysis of supersonic and subsonic solutions. The linear dynamic
instability of the steady transonic shock for Euler-Poisson equations can be
transformed to the ill-posedness of a free boundary problem for the
Klein-Gordon equation. By using a nontrivial transformation and the shooting
method, we prove that the linearized problem has a transonic shock solution
with exponential growths. These results enrich and develop the existing
studies.


title: Admissible extensions of subtheories of second order arithmetic

abstract:
  In this paper we study admissible extensions of several theories T of reverse
mathematics. The idea is that in such an extension the structure M = (N,S,\in)
of the natural numbers N and collection of sets of natural numbers S has to
obey the axioms of T while simultaneously one also has a set-theoretic world
with transfinite levels erected on top of M governed by the axioms of
Kripke-Platek set theory, KP.


title:
Current Studies and Applications of Shuffled Frog Leaping Algorithm: A
  Review

abstract:
  Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread
algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a
population-based metaheuristic algorithm that combines the benefits of memetics
with particle swarm optimization. It has been used in various areas, especially
in engineering problems due to its implementation easiness and limited
variables. Many improvements have been made to the algorithm to alleviate its
drawbacks, whether they were achieved through modifications or hybridizations
with other well-known algorithms. This paper reviews the most relevant works on
this algorithm. An overview of the SFLA is first conducted, followed by the
algorithm's most recent modifications and hybridizations. Next, recent
applications of the algorithm are discussed. Then, an operational framework of
SLFA and its variants is proposed to analyze their uses on different cohorts of
applications. Finally, future improvements to the algorithm are suggested. The
main incentive to conduct this survey to provide useful information about the
SFLA to researchers interested in working on the algorithm's enhancement or
application


title:
Studying the mixed transmission in a community with age heterogeneity:
  COVID-19 as a case study

abstract:
  COVID-19 has been prevalent worldwide for about 2 years now and has brought
unprecedented challenges to our society. Before vaccines were available, the
main disease intervention strategies were non-pharmaceutical. Starting December
2020, in Ontario, Canada, vaccines were approved for administering to
vulnerable individuals and gradually expanded to all individuals above the age
of 12. As the vaccine coverage reached a satisfactory level among the eligible
population, normal social activities resumed and schools reopened starting
September 2021. However, when schools reopen for in-person learning, children
under the age of 12 are unvaccinated and are at higher risks of contracting the
virus. We propose an age-stratified model based on the age and vaccine
eligibility of the individuals. We fit our model to the data in Ontario, Canada
and obtain a good fitting result. The results show that a relaxed between-group
contact rate may trigger future epidemic waves more easily than an increased
within-group contact rate. An increasing mixed contact rate of the older group
quickly amplifies the daily incidence numbers for both groups whereas an
increasing mixed contact rate of the younger group mainly leads to future waves
in the younger group alone. The results indicate the importance of accelerating
vaccine rollout for younger individuals in mitigating disease spread.


title:
Competing Charge/Spin-Stripe and Correlated Metal Phases in Trilayer
  Nickelates (Pr$_{1-x}$La$_x$)$_4$Ni$_3$O$_8$

abstract:
  Low-valent nickelates R$_{n+1}$Ni$_n$O$_{2n+2}$ (R = rare earth) containing
Ni$^{1+}$ (d$^{9}$) with a quasi-two-dimensional (quasi-2D) square planar
coordination geometry possess structural and electronic properties that are
similar to those of high-Tc cuprates, including superconductivity itself in the
doped infinite layer ($n = \infty$) RNiO$_2$ system. Within this
R$_{n+1}$Ni$_n$O$_{2n+2}$ nickelate family, the crystallographic isomorphs
Pr$_4$Ni$_3$O$_8$ and La$_4$Ni$_3$O$_8$ exhibit singularly different ground
states: Pr$_4$Ni$_3$O$_8$ is metallic and La$_4$Ni$_3$O$_8$ is a charge- and
spin-stripe ordered insulator. To explore and understand the ground state
evolution from metallic Pr$_4$Ni$_3$O$_8$ to stripe-ordered La$_4$Ni$_3$O$_8$
in the R$_4$Ni$_3$O$_8$ family, we have grown a series of isovalent-substituted
single crystals (Pr$_{1-x}$La$_x$)$_4$Ni$_3$O$_8$. Combining thermodynamic,
transport, magnetic, and synchrotron X-ray single crystal diffraction
measurements, we reveal a transition between metallic and stripe-insulator
phase regions, with a putative quantum phase transition at x = 0.4. We propose
two possible models for (Pr$_{1-x}$La$_x$)$_4$Ni$_3$O$_8$: an electronically
inhomogeneous system that could serve as a candidate for exploring quantum
Griffiths phase physics and a homogeneous system with a putative quantum
critical point at the phase boundary.


title: Universal Spam Detection using Transfer Learning of BERT Model

abstract:
  Deep learning transformer models become important by training on text data
based on self-attention mechanisms. This manuscript demonstrated a novel
universal spam detection model using pre-trained Google's Bidirectional Encoder
Representations from Transformers (BERT) base uncased models with four datasets
by efficiently classifying ham or spam emails in real-time scenarios. Different
methods for Enron, Spamassain, Lingspam, and Spamtext message classification
datasets, were used to train models individually in which a single model was
obtained with acceptable performance on four datasets. The Universal Spam
Detection Model (USDM) was trained with four datasets and leveraged
hyperparameters from each model. The combined model was finetuned with the same
hyperparameters from these four models separately. When each model using its
corresponding dataset, an F1-score is at and above 0.9 in individual models. An
overall accuracy reached 97%, with an F1 score of 0.96. Research results and
implications were discussed.


title: A Ranking Game for Imitation Learning

abstract:
  We propose a new framework for imitation learning - treating imitation as a
two-player ranking-based Stackelberg game between a $\textit{policy}$ and a
$\textit{reward}$ function. In this game, the reward agent learns to satisfy
pairwise performance rankings within a set of policies, while the policy agent
learns to maximize this reward. This game encompasses a large subset of both
inverse reinforcement learning (IRL) methods and methods which learn from
offline preferences. The Stackelberg game formulation allows us to use
optimization methods that take the game structure into account, leading to more
sample efficient and stable learning dynamics compared to existing IRL methods.
We theoretically analyze the requirements of the loss function used for ranking
policy performances to facilitate near-optimal imitation learning at
equilibrium. We use insights from this analysis to further increase sample
efficiency of the ranking game by using automatically generated rankings or
with offline annotated rankings. Our experiments show that the proposed method
achieves state-of-the-art sample efficiency and is able to solve previously
unsolvable tasks in the Learning from Observation (LfO) setting.


title:
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust
  Model Debugging

abstract:
  State-of-the-art machine learning models are commonly (pre-)trained on large
benchmark datasets. These often contain biases, artifacts, or errors that have
remained unnoticed in the data collection process and therefore fail in
representing the real world truthfully. This can cause models trained on these
datasets to learn undesired behavior based upon spurious correlations, e.g.,
the existence of a copyright tag in an image. Concept Activation Vectors (CAV)
have been proposed as a tool to model known concepts in latent space and have
been used for concept sensitivity testing and model correction. Specifically,
class artifact compensation (ClArC) corrects models using CAVs to represent
data artifacts in feature space linearly. Modeling CAVs with filters of linear
models, however, causes a significant influence of the noise portion within the
data, as recent work proposes the unsuitability of linear model filters to find
the signal direction in the input, which can be avoided by instead using
patterns. In this paper we propose Pattern Concept Activation Vectors (PCAV)
for noise-robust concept representations in latent space. We demonstrate that
pattern-based artifact modeling has beneficial effects on the application of
CAVs as a means to remove influence of confounding features from models via the
ClArC framework.


title: MAML and ANIL Provably Learn Representations

abstract:
  Recent empirical evidence has driven conventional wisdom to believe that
gradient-based meta-learning (GBML) methods perform well at few-shot learning
because they learn an expressive data representation that is shared across
tasks. However, the mechanics of GBML have remained largely mysterious from a
theoretical perspective. In this paper, we prove that two well-known GBML
methods, MAML and ANIL, as well as their first-order approximations, are
capable of learning common representation among a set of given tasks.
Specifically, in the well-known multi-task linear representation learning
setting, they are able to recover the ground-truth representation at an
exponentially fast rate. Moreover, our analysis illuminates that the driving
force causing MAML and ANIL to recover the underlying representation is that
they adapt the final layer of their model, which harnesses the underlying task
diversity to improve the representation in all directions of interest. To the
best of our knowledge, these are the first results to show that MAML and/or
ANIL learn expressive representations and to rigorously explain why they do so.


title:
Self-supervised Speaker Recognition Training Using Human-Machine
  Dialogues

abstract:
  Speaker recognition, recognizing speaker identities based on voice alone,
enables important downstream applications, such as personalization and
authentication. Learning speaker representations, in the context of supervised
learning, heavily depends on both clean and sufficient labeled data, which is
always difficult to acquire. Noisy unlabeled data, on the other hand, also
provides valuable information that can be exploited using self-supervised
training methods. In this work, we investigate how to pretrain speaker
recognition models by leveraging dialogues between customers and smart-speaker
devices. However, the supervisory information in such dialogues is inherently
noisy, as multiple speakers may speak to a device in the course of the same
dialogue. To address this issue, we propose an effective rejection mechanism
that selectively learns from dialogues based on their acoustic homogeneity.
Both reconstruction-based and contrastive-learning-based self-supervised
methods are compared. Experiments demonstrate that the proposed method provides
significant performance improvements, superior to earlier work. Dialogue
pretraining when combined with the rejection mechanism yields 27.10% equal
error rate (EER) reduction in speaker recognition, compared to a model without
self-supervised pretraining.


title: There aren't that many Morava E-theories

abstract:
  Let $k$ be a perfect field of characteristic $p$. Associated to any
(1-dimensional, commutative) formal group law of finite height $n$ over $k$
there is a complex oriented cohomology theory represented by a spectrum denoted
$E(n)$ and commonly referred to as Morava $E$-theory. These spectra are known
to admit $E_\infty$-structures, and the dependence of the $E_\infty$-structure
on the choice of formal group law has been well studied (cf.\ [GH], [R], [L],
Section 5, [PV]). In this note we show that the underlying homotopy type of
$E(n)$ is independent of the choice of formal group law.


title: Optimizing Warfarin Dosing using Deep Reinforcement Learning

abstract:
  Warfarin is a widely used anticoagulant, and has a narrow therapeutic range.
Dosing of warfarin should be individualized, since slight overdosing or
underdosing can have catastrophic or even fatal consequences. Despite much
research on warfarin dosing, current dosing protocols do not live up to
expectations, especially for patients sensitive to warfarin. We propose a deep
reinforcement learning-based dosing model for warfarin. To overcome the issue
of relatively small sample sizes in dosing trials, we use a Pharmacokinetic/
Pharmacodynamic (PK/PD) model of warfarin to simulate dose-responses of virtual
patients. Applying the proposed algorithm on virtual test patients shows that
this model outperforms a set of clinically accepted dosing protocols by a wide
margin.


title:
Targeted-BEHRT: Deep learning for observational causal inference on
  longitudinal electronic health records

abstract:
  Observational causal inference is useful for decision making in medicine when
randomized clinical trials (RCT) are infeasible or non generalizable. However,
traditional approaches fail to deliver unconfounded causal conclusions in
practice. The rise of "doubly robust" non-parametric tools coupled with the
growth of deep learning for capturing rich representations of multimodal data,
offers a unique opportunity to develop and test such models for causal
inference on comprehensive electronic health records (EHR). In this paper, we
investigate causal modelling of an RCT-established null causal association: the
effect of antihypertensive use on incident cancer risk. We develop a dataset
for our observational study and a Transformer-based model, Targeted BEHRT
coupled with doubly robust estimation, we estimate average risk ratio (RR). We
compare our model to benchmark statistical and deep learning models for causal
inference in multiple experiments on semi-synthetic derivations of our dataset
with various types and intensities of confounding. In order to further test the
reliability of our approach, we test our model on situations of limited data.
We find that our model provides more accurate estimates of RR (least sum
absolute error from ground truth) compared to benchmarks for risk ratio
estimation on high-dimensional EHR across experiments. Finally, we apply our
model to investigate the original case study: antihypertensives' effect on
cancer and demonstrate that our model generally captures the validated null
association.


title:
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless
  Network Communication Mode

abstract:
  The wireless network communication mode represented by the Internet of
vehicles (IoV) has been widely used. However, due to the limitations of
traditional network architecture, resource scheduling in wireless network
environment is still facing great challenges. This paper focuses on the
allocation of bandwidth resources in the virtual network environment. This
paper proposes a bandwidth aware multi domain virtual network embedding
algorithm (BA-VNE). The algorithm is mainly aimed at the problem that users
need a lot of bandwidth in wireless communication mode, and solves the problem
of bandwidth resource allocation from the perspective of virtual network
embedding (VNE). In order to improve the performance of the algorithm, we
introduce particle swarm optimization (PSO) algorithm to optimize the
performance of the algorithm. In order to verify the effectiveness of the
algorithm, we have carried out simulation experiments from link bandwidth,
mapping cost and virtual network request (VNR) acceptance rate. The final
results show that the proposed algorithm is better than other representative
algorithms in the above indicators.


title: Lines on $p$-adic and real cubic surfaces

abstract:
  We study lines on smooth cubic surfaces over the field of $p$-adic numbers,
from a theoretical and computational point of view. Segre showed that the
possible counts of such lines are $0,1,2,3,5,7,9,15$ or $27$. We show that each
of these counts is achieved. Probabilistic aspects are also investigated by
sampling both $p$-adic and real cubic surfaces from different distributions and
estimating the probability of each count. We link this to recent results on
probabilistic enumerative geometry. Some experimental results on the Galois
groups attached to $p$-adic cubic surfaces are also discussed.


title:
Microacoustic metagratings at ultra-high frequencies fabricated by
  two-photon lithography

abstract:
  The recently proposed bianisotropic acoustic metagratings offer promising
opportunities for passive acoustic wavefront manipulation, which is of
particular interest in flat acoustic lenses and ultrasound imaging at
ultra-high frequency ultrasound. Despite this fact, acoustic metagratings have
never been scaled to MHz frequencies that are common in ultrasound imaging. One
of the greatest challenges is the production of complex structures of
microscopic size. Owing to two-photon polymerization, a novel fabrication
technique from the view of acoustic metamaterials, it is now possible to
precisely manufacture sub-wavelength structures in this frequency range.
However, shrinking in size poses another challenge; the increasing
thermoviscous effects lead to considerable losses, which must be taken into
account in the design. In this work we propose three microacoustic metagrating
designs refracting a normally incident wave towards -35$^{\circ}$ at 2 MHz. In
order to develop metaatoms insensitive to thermoviscous effects we use shape
optimization techniques incorporating the linearized Navier-Stokes equations
discretized with finite element method. We report for the first time
microscopic acoustic metamaterials manufactured using two-photon polymerization
and, subsequently, experimentally verify their effectively using a capacitive
micromachined ultrasonic transducer as source and an optical microphone as a
detector in a range from 1.8 MHz to 2.2 MHz. We demonstrate not just that a
microacoustic metagrating can effectively redirect the normally incident wave
despite the thermoviscous losses, but also that it being only 0.29$\lambda$
thick can allocate 90% of the transmitted energy in the $-$1st diffraction
order.


title:
Scalar Bound States of $D^{\ast}\bar{D}^{\ast}$ and
  $B^{\ast}\bar{B}^{\ast}$ in the Bethe-Salpeter Formalism

abstract:
  We study the scalar bound states of $D^{\ast}\bar{D}^{\ast}$ and
$B^{\ast}\bar{B}^{\ast}$ in the Bethe-Salpeter formalism, with the effective
interaction kernel extracted from the chiral perturbative theory and the heavy
quark effective theory in the ladder approximation and the covariant
instantaneous approximation. The results show that, in the scalar case ($J=0$),
there can only exist $I = 0$ bound states for parameters in proper range, while
there cannot exist the $I = 1$ bound states in the whole reasonable parameter
range, due to more constraints arising from our definition of the
Bethe-Salpeter wavefunction.


title: Approximation Algorithms for ROUND-UFP and ROUND-SAP

abstract:
  We study ROUND-UFP and ROUND-SAP, two generalizations of the classical BIN
PACKING problem that correspond to the unsplittable flow problem on a path
(UFP) and the storage allocation problem (SAP), respectively. We are given a
path with capacities on its edges and a set of tasks where for each task we are
given a demand and a subpath. In ROUND-UFP, the goal is to find a packing of
all tasks into a minimum number of copies (rounds) of the given path such that
for each copy, the total demand of tasks on any edge does not exceed the
capacity of the respective edge. In ROUND-SAP, the tasks are considered to be
rectangles and the goal is to find a non-overlapping packing of these
rectangles into a minimum number of rounds such that all rectangles lie
completely below the capacity profile of the edges.
  We show that in contrast to BIN PACKING, both the problems do not admit an
asymptotic polynomial-time approximation scheme (APTAS), even when all edge
capacities are equal. However, for this setting, we obtain asymptotic
$(2+\varepsilon)$-approximations for both problems. For the general case, we
obtain an $O(\log\log n)$-approximation algorithm and an
$O(\log\log\frac{1}{\delta})$-approximation under $(1+\delta)$-resource
augmentation for both problems. For the intermediate setting of the no
bottleneck assumption (i.e., the maximum task demand is at most the minimum
edge capacity), we obtain absolute $12$- and asymptotic
$(16+\varepsilon)$-approximation algorithms for ROUND-UFP and ROUND-SAP,
respectively.


title:
DeepStability: A Study of Unstable Numerical Methods and Their Solutions
  in Deep Learning

abstract:
  Deep learning (DL) has become an integral part of solutions to various
important problems, which is why ensuring the quality of DL systems is
essential. One of the challenges of achieving reliability and robustness of DL
software is to ensure that algorithm implementations are numerically stable. DL
algorithms require a large amount and a wide variety of numerical computations.
A naive implementation of numerical computation can lead to errors that may
result in incorrect or inaccurate learning and results. A numerical algorithm
or a mathematical formula can have several implementations that are
mathematically equivalent, but have different numerical stability properties.
Designing numerically stable algorithm implementations is challenging, because
it requires an interdisciplinary knowledge of software engineering, DL, and
numerical analysis. In this paper, we study two mature DL libraries PyTorch and
Tensorflow with the goal of identifying unstable numerical methods and their
solutions. Specifically, we investigate which DL algorithms are numerically
unstable and conduct an in-depth analysis of the root cause, manifestation, and
patches to numerical instabilities. Based on these findings, we launch, the
first database of numerical stability issues and solutions in DL. Our findings
and provide future references to developers and tool builders to prevent,
detect, localize and fix numerically unstable algorithm implementations. To
demonstrate that, using {\it DeepStability} we have located numerical stability
issues in Tensorflow, and submitted a fix which has been accepted and merged
in.


title: Linear Response Theory of Berry Curvature and Quantum Metric in Solids

abstract:
  The quantum geometric properties of a Bloch state in momentum space are
usually described by the Berry curvature and quantum metric. In realistic
gapped materials where interactions and disorder render the Bloch state not a
viable starting point, we generalize these concepts by introducing dressed
Berry curvature and quantum metric at finite temperature, in which the effect
of many-body interactions can be included perturbatively. These quantities are
extracted from the charge polarization susceptibility caused by linearly or
circularly polarized electric fields, whose spectral functions can be measured
from momentum-resolved exciton or infrared absorption rate. As a concrete
example, we investigate Chern insulators in the presence of impurity
scattering, whose results suggest that the quantum geometric properties are
protected by the energy gap against many-body interactions.


title: The evolution of ultra-massive carbon oxygen white dwarfs

abstract:
  Ultra-massive white dwarfs ($\rm M_{WD} \gtrsim 1.05\, M_{\odot}$) are
considered powerful tools to study type Ia supernovae explosions, merger
events, the occurrence of physical processes in the Super Asymptotic Giant
Branch (SAGB) phase, and the existence of high magnetic fields. Traditionally,
ultra-massive white dwarfs are expected to harbour oxygen-neon (ONe) cores.
However, new observations and recent theoretical studies suggest that the
progenitors of some ultra-massive white dwarfs can avoid carbon burning,
leading to the formation of ultra-massive white dwarfs harbouring carbon-oxygen
(CO) cores. Here we present a set of ultra-massive white dwarf evolutionary
sequences with CO cores for a wide range of metallicity and masses. We take
into account the energy released by latent heat and phase separation during the
crystallization process and by $^{22}$Ne sedimentation. Realistic chemical
profiles resulting from the full computation of progenitor evolution are
considered. We compare our CO ultra-massive white dwarf models with ONe models.
We conclude that CO ultra-massive white dwarfs evolve significantly slower than
their ONe counterparts mainly for three reasons: their larger thermal content,
the effect of crystallization, and the effect of $^{22}$Ne sedimentation. We
also provide colors in several photometric bands on the basis of new model
atmospheres. These CO ultra-massive white dwarf models, together with the ONe
ultra-massive white dwarf models, provide an appropriate theoretical framework
to study the ultra-massive white dwarf population in our Galaxy.


title: Scalarization of Chern-Simons Kerr Black Hole Solutions and Wormholes

abstract:
  Chern-Simons gravity rotating Kerr-type black hole solutions are revisited
from the point of view of their scalarization, namely examining the back
reaction of pseudoscalar axion fields on the rotating geometry. To lowest order
in an angular momentum expansion, and a long range approximation for both the
axion field configuration and its back reaction onto the metric, such solutions
had been discussed for the first time in the context of string theory. In this
work, we extend the analysis to give analytic expressions for slowly rotating
black holes outside the horizon, which formally include an all order expansion
in inverse powers of the radial distance from the centre of the black hole,
which allows to approach arbitrarily close the horizon. We also discuss in some
detail the way Chern-Simons gravity violates the energy conditions, which leads
to secondary axionic hair of the rotating black hole solutions. This discussion
is extended to studies of wormhole solutions, which we construct in this
article via the thin-shell procedure and we demonstrate that the presence of
the axion field enforces the two black hole solutions to be counter-rotating
and for slow rotation, there is no backreaction of the Cotton tensor on the
thin-shell of the wormhole.


title: A crawling robot driven by a folded self-sustained oscillator

abstract:
  Locomotive robots that do not rely on electronics and/or electromagnetic
components will open up new perspectives and applications for robotics.
However, these robots usually involve complicated and tedious fabrication
processes, limiting their applications. Here, we develop an easy-to-fabricate
crawling robot by embedding simple control and actuation into origami-inspired
mechanisms through folding, eliminating the need for discrete electronics and
transducers. Our crawling robot locomotes through directional friction
propelled by an onboard origami self-sustained oscillator, which generates
periodic actuation from a single source of constant power. The crawling robot
is lightweight (~ 3.8 gram), ultra low-cost (~ US $1), nonmagnetic, and
electronic-free; it may enable practical applications in extreme environments,
e.g., large radiation or magnetic fields. The robot can be fabricated through a
monolithic origami-inspired folding-based method with universal materials,
i.e., sheet materials and conductive threads. This rapid design and fabrication
approach enables the programmable assembly of various mechanisms within this
manufacturing paradigm, laying the foundation for autonomous, untethered robots
without requiring electronics.


title: Random Ferns for Semantic Segmentation of PolSAR Images

abstract:
  Random Ferns -- as a less known example of Ensemble Learning -- have been
successfully applied in many Computer Vision applications ranging from keypoint
matching to object detection. This paper extends the Random Fern framework to
the semantic segmentation of polarimetric synthetic aperture radar images. By
using internal projections that are defined over the space of Hermitian
matrices, the proposed classifier can be directly applied to the polarimetric
covariance matrices without the need to explicitly compute predefined image
features. Furthermore, two distinct optimization strategies are proposed: The
first based on pre-selection and grouping of internal binary features before
the creation of the classifier; and the second based on iteratively improving
the properties of a given Random Fern. Both strategies are able to boost the
performance by filtering features that are either redundant or have a low
information content and by grouping correlated features to best fulfill the
independence assumptions made by the Random Fern classifier. Experiments show
that results can be achieved that are similar to a more complex Random Forest
model and competitive to a deep learning baseline.


title: Bayesian homodyne and heterodyne tomography

abstract:
  Continuous-variable (CV) photonic states are of increasing interest in
quantum information science, bolstered by features such as deterministic
resource state generation and error correction via bosonic codes.
Data-efficient characterization methods will prove critical in the fine-tuning
and maturation of such CV quantum technology. Although Bayesian inference
offers appealing properties -- including uncertainty quantification and
optimality in mean-squared error -- Bayesian methods have yet to be
demonstrated for the tomography of arbitrary CV states. Here we introduce a
complete Bayesian quantum state tomography workflow capable of inferring
generic CV states measured by homodyne or heterodyne detection, with no
assumption of Gaussianity. As examples, we demonstrate our approach on
experimental coherent, thermal, and cat state data, obtaining excellent
agreement between our Bayesian estimates and theoretical predictions. Our
approach lays the groundwork for Bayesian estimation of highly complex CV
quantum states in emerging quantum photonic platforms, such as quantum
communications networks and sensors.


title: Measures on perfect e-free PAC fields

abstract:
  We construct measures on definable sets in $e$-free perfect PAC fields, as
well as on perfect PAC fields whose absolute Galois groups are free pro-$p$ of
finite rank. We deduce the definable amenability of all groups definable in
such fields. As a corollary, we additionally prove the definable amenability of
all groups definable in perfect $\omega$-free PAC fields via ultralimit
measures.


title:
Scribble-based Boundary-aware Network for Weakly Supervised Salient
  Object Detection in Remote Sensing Images

abstract:
  Existing CNNs-based salient object detection (SOD) heavily depends on the
large-scale pixel-level annotations, which is labor-intensive, time-consuming,
and expensive. By contrast, the sparse annotations become appealing to the
salient object detection community. However, few efforts are devoted to
learning salient object detection from sparse annotations, especially in the
remote sensing field. In addition, the sparse annotation usually contains
scanty information, which makes it challenging to train a well-performing
model, resulting in its performance largely lagging behind the fully-supervised
models. Although some SOD methods adopt some prior cues to improve the
detection performance, they usually lack targeted discrimination of object
boundaries and thus provide saliency maps with poor boundary localization. To
this end, in this paper, we propose a novel weakly-supervised salient object
detection framework to predict the saliency of remote sensing images from
sparse scribble annotations. To implement it, we first construct the
scribble-based remote sensing saliency dataset by relabelling an existing
large-scale SOD dataset with scribbles, namely S-EOR dataset. After that, we
present a novel scribble-based boundary-aware network (SBA-Net) for remote
sensing salient object detection. Specifically, we design a boundary-aware
module (BAM) to explore the object boundary semantics, which is explicitly
supervised by the high-confidence object boundary (pseudo) labels generated by
the boundary label generation (BLG) module, forcing the model to learn features
that highlight the object structure and thus boosting the boundary localization
of objects. Then, the boundary semantics are integrated with high-level
features to guide the salient object detection under the supervision of
scribble labels.


title: Variational discretization of the Navier-Stokes-Fourier system

abstract:
  This paper presents the variational discretization of the compressible
Navier-Stokes-Fourier system, in which the viscosity and the heat conduction
terms are handled within the variational approach to nonequilibrium
thermodynamics as developed by one of the authors. In a first part, we review
the variational framework for the Navier-Stokes-Fourier (NSF) system in the
smooth setting. In a second part, we review a discrete exterior calculus based
on discrete diffeomorphisms then proceed to establish the spatially discretized
variational principle for the NSF system through the use of this discrete
exterior calculus, which yields a semi-discrete nonholonomic variational
principle, as well as semi-discrete evolution equations. In order to avoid
important technical difficulties, further treatment of the phenomenological
constraint is needed. In a third part we discretize in time the spatial
variational principle underlying the NSF system by extending previous work of
the authors, which at last yields a nonholonomic variational integrator for the
NSF system, as well as fully discrete evolution equations.


title: From anomalous to classical diffusion in a non-linear heat equation

abstract:
  In this paper, we consider the heat equation with the natural polynomial
non-linear term; and with two different cases in the diffusion term. The first
case (anomalous diffusion) concerns the fractional Laplacian operator with
parameter $1<\alpha <2$, while, the second case (classical diffusion) involves
the classical Laplacian operator. When $\alpha \to 2$, we prove the uniform
convergence of the solutions of the anomalous diffusion case to a solution of
the classical diffusion case. Moreover, we rigorous derive a convergence rate,
which was experimentally exhibit in previous related works.


title: Comprehensive analysis of TESS Full Orbital Phase Curve of WASP-121b

abstract:
  We present the full phase curve analysis of the ultrahot Jupiter WASP-121b
($R_p \simeq 1.865 R_J, M_p \simeq 1.184 M_J $) using observations from the
Transiting Exoplanet Survey Satellite (TESS) and a comparison between our
results with previous studies on this target. Our comprehensive phase curve
model includes primary transit, secondary eclipse, thermal emission,
reflection, and ellipsoidal tidal distortion, which are jointly fit to extract
the information of all parameters simultaneously from the data sets. We also
evaluated and calculated the amplitude of Doppler beaming to be $\sim 2$ ppm,
but given the precision of the photometric data, we found it to be
insignificant. After removing the instrumental systematic noise, we reliably
detect the secondary eclipse with a depth of $489_{-10}^{+16}$
parts-per-million (ppm), dominated by thermal emission. Using the TESS
bandpass, we measure the dayside $2941_{-150}^{+61} K$ and nightside
$2236_{-97}^{+38} K $ temperatures of WASP-121b. We find that a hotspot is well
aligned with the substellar point, leading to the conclusion that there is an
inefficient heat distribution from the dayside to the nightside. Our estimated
geometric albedo, $A_g = 0.069_{-0.02}^{+0.06}$, suggest that WASP-121b has a
low geometric albedo. Finally, our estimated amplitude of the ellipsoidal
variation signal is in agreement with the predictions of the theoretical
expectations.


title: Filtration and breathability of nonwoven fabrics used in washable masks

abstract:
  This study explores nonwoven and woven fabrics to improve upon the
performance of the widespread all-cotton mask, and examines the effect of
layering, machine washing and drying on their filtration and breathability for
submicron and supermicron particles. Individual materials were evaluated for
their quality factor, Q, which combines filtration efficiency and
breathability. Filtration was tested against particles 0.5 to 5 micron
aerodynamic diameter. Nonwoven polyester and nonwoven polypropylene (craft
fabrics, medical masks, and medical wraps) showed higher quality factors than
woven materials (flannel cotton, Kona cotton, sateen cotton). Materials with
meltblown nonwoven polypropylene filtered best, especially against submicron
particles. Subsequently, we combined high performing fabrics into multi-layer
sets, evaluating the sets quality factors before and after our washing
protocol, which included machine washing, machine drying, and isopropanol soak.
Sets incorporating meltblown nonwoven polypropylene designed for filtration
(Filti and surgical mask) degraded significantly post-wash in the submicron
range where they excelled prior to washing (Q = 57 and 79 at 1 micron,
respectively, degraded to Q = 10 and 15 post-wash). Washing caused lesser
quality degradation in sets incorporating spunbond non-woven polypropylene or
medical wraps (Q = 12 to 24 pre-wash, Q = 8 to 10 post-wash). Post-wash quality
factors are similar for all multi-layer sets in this study, and higher than
Kona quilting cotton (Q = 6). Washed multi-layer sets filtered 12-42 percent of
0.5 micron, 27-76 percent of 1 micron, 58-96 percent of 2.8 micron, and 72-100
percent of 4.2 micron particles. The measured filtration and pressure drop of
both the homogeneous and heterogeneous multi-layer fabric combinations agreed
with the estimations from the layering model.


title: The Long Filament of PSR J2030+4415

abstract:
  New X-ray and optical observations shed light on the remarkable X-ray
filament of the Gamma-ray pulsar PSR~J2030+4415. Images of the associated
H$\alpha$ bow shock's evolution over the past decade compared with its velocity
structure provide an improved kinematic distance of $\sim$0.5kpc. These
velocities also imply that the pulsar spin axis lies $\sim 15^\circ$ from the
proper motion axis which is close to the plane of the sky. The multi-bubble
shock structure indicates that the bow shock stand-off was compressed to a
small value $\sim 20-30$y ago when the pulsar broke through the bow shock to
its present bubble. This compression allowed multi-TeV pulsar $e^\pm$ to escape
to the external ISM, lighting up an external magnetic field structure as the
`filament'. The narrow filament indicates excellent initial confinement and the
full $15^\prime$ ($2.2$~pc=7~lt-y) projected length of the filament indicates
rapid $e^\pm$ propagation to its end. Spectral variation along the filament
suggests that the injected particle energy evolved during the break-through
event.


title:
Electron correlations due to pair spin-orbit interaction in 2D electron
  systems

abstract:
  We show that the pair spin-orbit interaction (PSOI) created by the Coulomb
fields of interacting electrons leads to the appearance of an unconventional
correlated electronic state in two-dimensional materials with the large Rashba
effect. The effect manifests itself at sufficiently strong PSOI as a sharp peak
in the structure factor, indicating a tendency to form a striped structure with
a spatial scale determined by the competition between the Coulomb repulsion and
the PSOI-induced attraction of electrons. Above the critical value of PSOI, the
system becomes unstable with respect to the charge density fluctuations on this
scale.


title:
A simple proof of non-explosion for measure solutions of the
  Keller-Segel equation

abstract:
  We give a simple proof, relying on a {\it two-particles} moment computation,
that there exists a global weak solution to the $2$-dimensional
parabolic-elliptic Keller-Segel equation when starting from any initial measure
$f_0$ such that $f_0(\mathbb{R}^2)< 8 \pi$.


title: A 20 Gbps PAM4 Data Transmitter ASIC for Particle Physics Experiments

abstract:
  We present the design and test results of a novel data transmitter ASIC
operating up to 20.48 Gbps with 4-level Pulse-Amplitude-Modulation (PAM4) for
particle physics experiments. This ASIC, named GBS20, is fabricated in a 65 nm
CMOS technology. Two serializers share a 5.12 GHz Phase Locked Loop (PLL)
clock. The outputs from the serializers are combined into a PAM4 signal that
directly drives a Vertical-Cavity-Surface-Emitting-Laser (VCSEL). The input
data channels, each at 1.28 Gbps, are scrambled with an internal 27-1
Pseudo-Random Binary Sequence (PRBS), which also serves as a frame aligner.
GBS20 is tested to work at 10.24 and 20.48 Gbps with a VCSEL-based
Transmitter-Optical-Subassembly (TOSA). The power consumption of GBS20 is below
238 mW and reduced to 164 mW in the low-power mode.


title:
Modeling of V graded In(x)Ga(1-x)N solar cells: comparison of strained
  and relaxed features

abstract:
  The optical properties of V graded InGaN solar cells are studied. Graded
InGaN well structures with the indium composition increasing then decreasing in
a V shaped pattern have been designed. Through polarization doping, this
naturally creates alternating p-type and n-type regions. Separate structures
are designed by varying the indium alloy profile from GaN to maximum indium
concentrations ranging from 20% to 80%, while maintaining a constant overall
structure thicknesses of 100 nm. The solar cell parameters under fully strained
and relaxed conditions are considered. The results show that a maximum
efficiency of 5.5%, under fully strained condition occurs at x=60%. Solar cell
efficiency under relaxed conditions increases to a maximum of 8.3% at 90%.
While Vegards law predicts the bandgap under relaxed conditions, a Vegard like
law is empirically determined from the output of Nextnano for varying In
compositions in order to calculate solar cell parameters under strain.


title: Homotopy groups of cubical sets

abstract:
  We define and study homotopy groups of cubical sets. To this end, we give
four definitions of homotopy groups of a cubical set, prove that they are
equivalent, and further that they agree with their topological analogues via
the geometric realization functor. We also provide purely combinatorial proofs
of several classical theorems, including: product preservation, commutativity
of higher homotopy groups, the long exact sequence of a fibration, and
Whitehead's theorem.
  This is a companion paper to our "Cubical setting for discrete homotopy
theory, revisited" in which we apply these results to study the homotopy theory
of simple graphs.


title:
Physics-based tissue simulator to model multicellular systems: A study
  of liver regeneration and hepatocellular carcinoma recurrence

abstract:
  We present a multiagent-based model that captures the interactions between
different types of cells with their microenvironment, and enables the analysis
of the emergent global behavior during tissue regeneration and tumor
development. Using this model, we are able to reproduce the temporal dynamics
of healthy epithelial cells and cancer cells, as well as the evolution of their
three-dimensional spatial distributions. By tuning the characteristics of the
individual patients, our model reproduces a variety of spatial patterns of
tissue regeneration and tumor growth, resembling those found in clinical
imaging or biopsies. By correlating these spatial characteristics with in
silico treatment results, the model provides a framework to assess treatment
combinations in different scenarios based on patient-specific experimental
data. In order to calibrate and validate our model we study the process of
liver regeneration after surgical hepactectomy in different degrees. In the
clinical context, our model is able to to predict the recurrence of a
hepatocellular carcinoma after a $70\%$ partial hepatectomy. The outcomes of
our simulations are in remarkable agreement with experimental and clinical
observations. By fitting the model parameters to specific patient factors, it
might well become a useful platform for hypotheses testing in treatments
protocols.


title:
Causal survival analysis under competing risks using longitudinal
  modified treatment policies

abstract:
  Longitudinal modified treatment policies (LMTP) have been recently developed
as a novel method to define and estimate causal parameters that depend on the
natural value of treatment. LMTPs represent an important advancement in causal
inference for longitudinal studies as they allow the non-parametric definition
and estimation of the joint effect of multiple categorical, numerical, or
continuous exposures measured at several time points. We extend the LMTP
methodology to problems in which the outcome is a time-to-event variable
subject to right-censoring and competing risks. We present identification
results and non-parametric locally efficient estimators that use flexible
data-adaptive regression techniques to alleviate model misspecification bias,
while retaining important asymptotic properties such as $\sqrt{n}$-consistency.
We present an application to the estimation of the effect of the
time-to-intubation on acute kidney injury amongst COVID-19 hospitalized
patients, where death by other causes is taken to be the competing event.


title:
Maximizing Audio Event Detection Model Performance on Small Datasets
  Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation
  Study

abstract:
  An Xception model reaches state-of-the-art (SOTA) accuracy on the ESC-50
dataset for audio event detection through knowledge transfer from ImageNet
weights, pretraining on AudioSet, and an on-the-fly data augmentation pipeline.
This paper presents an ablation study that analyzes which components contribute
to the boost in performance and training time. A smaller Xception model is also
presented which nears SOTA performance with almost a third of the parameters.


title: What does cosmology tell us about the mass of thermal-relic dark matter?

abstract:
  The presence of light thermally coupled dark matter affects early expansion
history and production of light elements during the Big Bang Nucleosynthesis.
Specifically, dark matter that annihilates into Standard Model particles can
modify the effective number of light species in the universe $N_\mathrm{eff}$,
as well as the abundance of light elements created buring BBN. These quantities
in turn affect the cosmic microwave background (CMB) anisotropy. We present the
first joint analysis of small-scale temperature and polarization CMB anisotropy
from Atacama Cosmology Telescope (ACT) and South Pole Telescope (SPT), together
with Planck data and the recent primordial abundance measurements of helium and
deuterium to place comprehensive bounds on the mass of light thermal-relic dark
matter. We consider a range of models, including dark matter that couples to
photons and Standard-Model neutrinos. We find that the combination of ACT, SPT,
and Planck generally leads to the most stringent mass constraint for dark
matter that couples to neutrinos, improving the lower limit by 40%-80%, with
respect to previous Planck analyses. On the other hand, the addition of ACT and
SPT leads to a slightly weaker bound on electromagnetically coupled particles,
due to a shift in the preferred values of $Y_\mathrm{p}$ and $N_\mathrm{eff}$
driven by the ground based experiments. Combining all CMB measurements with
primordial abundance measurements, we rule out masses below $\sim$4 MeV at 95%
confidence, for all models. We show that allowing for new relativistic species
can weaken the mass bounds for dark matter that couples to photons by up to an
order of magnitude or more. Finally, we discuss the reach of the next
generation of the CMB experiments in terms of probing the mass of the thermal
relic dark matter.


title: Cubical setting for discrete homotopy theory, revisited

abstract:
  We construct a functor associating a cubical set to a (simple) graph. We show
that cubical sets arising in this way are Kan complexes, and that the A-groups
of a graph coincide with the homotopy groups of the associated Kan complex.
  We use this to prove a conjecture of Babson, Barcelo, de Longueville, and
Laubenbacher from 2006, and a strong version of the Hurewicz theorem in
discrete homotopy theory.


title:
Local majority-with-inertia rule can explain global consensus dynamics
  in a network coordination game

abstract:
  We study how groups reach consensus by varying communication network
structure and individual incentives. In 342 networks of seven individuals,
single opinionated "leaders" can drive decision outcomes, but do not accelerate
consensus formation, whereas conflicting opinions slow consensus. While
networks with more links reach consensus faster, this advantage disappears
under conflict. Unopinionated individuals make choices consistent with a local
majority rule combined with "inertia" favouring their previous choice, while
opinionated individuals favour their preferred option but yield under high peer
or time pressure. Simulations show these individual rules can account for group
patterns, and allow rapid consensus while preventing deadlocks.