title:
Numerical calculation of Green's function and momentum distribution for
  spin-polarized fermions by path integral molecular dynamics

abstract:
  Most recently, path integral molecular dynamics (PIMD) has been successfully
applied to perform simulations of identical bosons and fermions by B. Hirshberg
et al.. In this work, we demonstrate that PIMD can be developed to calculate
Green's function and extract momentum distribution for spin-polarized fermions.
In particular, we show that the momentum distribution calculated by PIMD has
potential application to numerous quantum systems, such as cold atom simulation
of Mott insulator in Fermi-Hubbard model.

categories: cond-mat.quant-gas cond-mat.stat-mech physics.chem-ph physics.comp-ph quant-ph


title:
Comment on "Black Box Prediction Methods in Sports Medicine Deserve a
  Red Card for Reckless Practice: A Change of Tactics is Needed to Advance
  Athlete Care"

abstract:
  In this paper we examine the claims made by Bullock et. al. on the
applicability of black-box injury risk approaches in the sports injury domain.
Overall, we agree that transparency is necessary for Machine Learning models to
be useful in this field. However, there are areas of research that address
precisely the concerns of the authors and strongly temper their conclusions. In
the following we look at how these issues are being tackled by the Machine
Learning community.


title:
Explainable Deep Learning Algorithm for Distinguishing Incomplete
  Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging

abstract:
  Background and Objective: Incomplete Kawasaki disease (KD) has often been
misdiagnosed due to a lack of the clinical manifestations of classic KD.
However, it is associated with a markedly higher prevalence of coronary artery
lesions. Identifying coronary artery lesions by echocardiography is important
for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to
KD, coronavirus disease 2019, currently causing a worldwide pandemic, also
manifests with fever; therefore, it is crucial at this moment that KD should be
distinguished clearly among the febrile diseases in children. In this study, we
aimed to validate a deep learning algorithm for classification of KD and other
acute febrile diseases.
  Methods: We obtained coronary artery images by echocardiography of children
(n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks
(VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the
collected data.
  Results: SE-ResNext50 showed the best performance in terms of accuracy,
specificity, and precision in the classification. SE-ResNext50 offered a
precision of 76.35%, a sensitivity of 82.64%, and a specificity of 58.12%.
  Conclusions: The results of our study suggested that deep learning algorithms
have similar performance to an experienced cardiologist in detecting coronary
artery lesions to facilitate the diagnosis of KD.


title: Hospital-Agnostic Image Representation Learning in Digital Pathology

abstract:
  Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer
subtypes. The difference in procedures to acquire WSIs at various trial sites
gives rise to variability in the histopathology images, thus making consistent
diagnosis challenging. These differences may stem from variability in image
acquisition through multi-vendor scanners, variable acquisition parameters, and
differences in staining procedure; as well, patient demographics may bias the
glass slide batches before image acquisition. These variabilities are assumed
to cause a domain shift in the images of different hospitals. It is crucial to
overcome this domain shift because an ideal machine-learning model must be able
to work on the diverse sources of images, independent of the acquisition
center. A domain generalization technique is leveraged in this study to improve
the generalization capability of a Deep Neural Network (DNN), to an unseen
histopathology image set (i.e., from an unseen hospital/trial site) in the
presence of domain shift. According to experimental results, the conventional
supervised-learning regime generalizes poorly to data collected from different
hospitals. However, the proposed hospital-agnostic learning can improve the
generalization considering the low-dimensional latent space representation
visualization, and classification accuracy results.


title: Zero-shot Blind Image Denoising via Implicit Neural Representations

abstract:
  Recent denoising algorithms based on the "blind-spot" strategy show
impressive blind image denoising performances, without utilizing any external
dataset. While the methods excel in recovering highly contaminated images, we
observe that such algorithms are often less effective under a low-noise or real
noise regime. To address this gap, we propose an alternative denoising strategy
that leverages the architectural inductive bias of implicit neural
representations (INRs), based on our two findings: (1) INR tends to fit the
low-frequency clean image signal faster than the high-frequency noise, and (2)
INR layers that are closer to the output play more critical roles in fitting
higher-frequency parts. Building on these observations, we propose a denoising
algorithm that maximizes the innate denoising capability of INRs by penalizing
the growth of deeper layer weights. We show that our method outperforms
existing zero-shot denoising methods under an extensive set of low-noise or
real-noise scenarios.


title:
A deep learning framework for the detection and quantification of drusen
  and reticular pseudodrusen on optical coherence tomography

abstract:
  Purpose - To develop and validate a deep learning (DL) framework for the
detection and quantification of drusen and reticular pseudodrusen (RPD) on
optical coherence tomography scans.
  Design - Development and validation of deep learning models for
classification and feature segmentation.
  Methods - A DL framework was developed consisting of a classification model
and an out-of-distribution (OOD) detection model for the identification of
ungradable scans; a classification model to identify scans with drusen or RPD;
and an image segmentation model to independently segment lesions as RPD or
drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with
a self-reported diagnosis of age-related macular degeneration (AMD) and 250
UKBB controls. Drusen and RPD were manually delineated by five retina
specialists. The main outcome measures were sensitivity, specificity, area
under the ROC curve (AUC), kappa, accuracy and intraclass correlation
coefficient (ICC).
  Results - The classification models performed strongly at their respective
tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans
classifier, the OOD model, and the drusen and RPD classification model). The
mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively,
compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that
the model's sensitivity was close to human performance.
  Conclusions - The models achieved high classification and segmentation
performance, similar to human performance. Application of this robust framework
will further our understanding of RPD as a separate entity from drusen in both
research and clinical settings.


title: Higher Gauging and Non-invertible Condensation Defects

abstract:
  We discuss invertible and non-invertible topological condensation defects
arising from gauging a discrete higher-form symmetry on a higher codimensional
manifold in spacetime, which we define as higher gauging. A $q$-form symmetry
is called $p$-gaugeable if it can be gauged on a codimension-$p$ manifold in
spacetime. We focus on 1-gaugeable 1-form symmetries in general 2+1d QFT, and
gauge them on a surface in spacetime. The universal fusion rules of the
resulting invertible and non-invertible condensation surfaces are determined.
In the special case of 2+1d TQFT, every (invertible and non-invertible) 0-form
global symmetry, including the $\mathbb{Z}_2$ electromagnetic symmetry of the
$\mathbb{Z}_2$ gauge theory, is realized from higher gauging. We further
compute the fusion rules between the surfaces, the bulk lines, and lines that
only live on the surfaces, determining some of the most basic data for the
underlying fusion 2-category. We emphasize that the fusion "coefficients" in
these non-invertible fusion rules are generally not numbers, but rather 1+1d
TQFTs. Finally, we discuss examples of non-invertible symmetries in
non-topological 2+1d QFTs such as the free $U(1)$ Maxwell theory and QED.


title:
Constraining cosmology with machine learning and galaxy clustering: the
  CAMELS-SAM suite

abstract:
  As the next generation of large galaxy surveys come online, it is becoming
increasingly important to develop and understand the machine learning tools
that analyze big astronomical data. Neural networks are powerful and capable of
probing deep patterns in data, but must be trained carefully on large and
representative data sets. We developed and generated a new `hump' of the
Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project:
CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100
$h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and
$\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy
formation over a broad range of astrophysical parameters. As a proof-of-concept
for the power of this vast suite of simulated galaxies in a large volume and
broad parameter space, we probe the power of simple clustering summary
statistics to marginalize over astrophysics and constrain cosmology using
neural networks. We use the two-point correlation function, count-in-cells, and
the Void Probability Function, and probe non-linear and linear scales across
$0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around
3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect
of various galaxy selections, galaxy sampling, and choice of clustering
statistics on these constraints. We additionally explore how these clustering
statistics constrain and inform key stellar and galactic feedback parameters in
the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of
CAMELS, and offers great potential to many applications of machine learning in
astrophysics: https://camels-sam.readthedocs.io.


title: Valley hydrodynamics in gapped graphene

abstract:
  Recent experiments have revealed that novel nonequilibrium states consistent
with the hydrodynamic description of electrons are realized in ultrapure
graphene, which hosts the valley degrees of freedom. Here, we formulate a
theory of electron hydrodynamics including dissipation processes of the valley
angular momentum by employing the concept of micropolar fluids. As a result,
our theory proposes a novel strategy to generate a valley polarization by the
microrotation. We uncover that the rotational viscosity induces longitudinal
valley currents which are second order in electric fields.


title: How Cosmic Rays Mediate the Evolution of the Interstellar Medium

abstract:
  We explore the impact of diffusive cosmic rays (CRs) on the evolution of the
interstellar medium (ISM) under varying assumptions of supernova explosion
environment. In practice, we systematically vary the relative fractions of
supernovae (SN) occurring in star-forming high-density gas and those occurring
in random locations decoupled from star-forming gas to account for SN from
run-away stars or explosions in regions that have been cleared by prior SN,
stellar winds, or radiation. We explore various mixed models by adjusting these
fractions relative to each other. We find that in the simple system of a
periodic stratified gas layer the ISM structure will evolve to one of two
solutions: a "peak driving" state where warm gas is volume filling or a
"thermal runaway" state where hot gas is volume filling. CR pressure and
transport are important factors that strongly influence the solution state the
ISM reaches and have the ability to flip the ISM between solutions. Observable
signatures such as gamma ray emission and HI gas are explored. We find that
gamma ray luminosity from pion decay is largely consistent with observations
for a range of model parameters. The thickness of the HI gas layer may be too
compact, however, this may be due to a large cold neutral fraction of midplane
gas. The volume fraction of hot gas evolves to stable states in both solutions,
but neither settles to a Milky Way-like configuration, suggesting that
additional physics which is omitted here (e.g. a cosmological circum-galactic
medium, radiation transport, or spectrally resolved and spatially varying CR
transport) may be required.


title: Texturify: Generating Textures on 3D Shape Surfaces

abstract:
  Texture cues on 3D objects are key to compelling visual representations, with
the possibility to create high visual fidelity with inherent spatial
consistency across different views. Since the availability of textured 3D
shapes remains very limited, learning a 3D-supervised data-driven method that
predicts a texture based on the 3D input is very challenging. We thus propose
Texturify, a GAN-based method that leverages a 3D shape dataset of an object
class and learns to reproduce the distribution of appearances observed in real
images by generating high-quality textures. In particular, our method does not
require any 3D color supervision or correspondence between shape geometry and
images to learn the texturing of 3D objects. Texturify operates directly on the
surface of the 3D objects by introducing face convolutional operators on a
hierarchical 4-RoSy parametrization to generate plausible object-specific
textures. Employing differentiable rendering and adversarial losses that
critique individual views and consistency across views, we effectively learn
the high-quality surface texturing distribution from real-world images.
Experiments on car and chair shape collections show that our approach
outperforms state of the art by an average of 22% in FID score.


title: The diversity of spiral galaxies explained

abstract:
  An extensive catalog of spatially-resolved galaxy rotation curves and
multi-band optical light profiles for 1752 observed spiral galaxies is
assembled to explore the drivers of diversity in galaxy structural parameters,
rotation curve shapes, and stellar mass profiles. Similar data were extracted
from the NIHAO galaxy simulations to identify any differences between
observations and simulations. Several parameters, including the inner slope "S"
of a rotation curve (RC), were tested for diversity. Two distinct populations
are found in observed and simulated galaxies; (i) blue, low mass spirals with
stellar mass M* < 10^9.3 Msol and roughly constant "S", and (ii) redder, more
massive and more diverse spirals with rapidly increasing "S". In all cases, the
value of "S" seems equally contributed by the baryonic and non-baryonic (dark)
matter. Diversity is shown to increase mildly with mass. Numerical simulations
reproduce well most baryon-dominated galaxy parameter distributions, such as
the inner stellar mass profile slope and baryonic scaling relations, but they
struggle to match the full diversity of observed galaxy rotation curves
(through "S") and most dark-matter-dominated parameters. To reproduce
observations, the error broadening of the simulation's intrinsic spread of RC
metrics would have to be tripled. The differences in various projections of
observed and simulated scaling relations may reflect limitations of current
sub-grid physics models to fully capture the complex nature of galaxies. For
instance, AGNs are shown to have a significant effect on the shapes of
simulated RCs. The inclusion of AGN feedback brings simulated and observed
inner RC shapes into closer agreement.


title:
Neutron Star Heating in Dark Matter Models for the Muon $g-2$
  Discrepancy

abstract:
  The observed value of the muon magnetic dipole moment, which deviates from
the Standard Model prediction by $4.2\sigma$, can be explained in models with
weakly-interacting massive particles (WIMPs) coupled to muons. However, a
considerable range of parameter space of such models will remain unexplored in
the future LHC experiments and dark matter (DM) direct searches. In this work
we discuss the temperature observation of neutron stars (NSs) as a promising
way to probe such models given that WIMPs are efficiently captured by NSs
through DM-muon or spin-dependent DM-nucleon scattering. The captured WIMPs
eventually annihilate in the star core and heat the NS. This effect can be
observed in old NSs as it keeps the NS surface temperature at a few thousand K
at most, which is much higher than the predicted values of the standard NS
cooling theory for NSs older than $\sim 10^7$ years. We consider two classes of
representative models, where the DM couples or does not couple to the Higgs
field at tree level, and show that the maximal DM heating is realized in both
scenarios.


title:
Reconstructing the Assembly of Massive Galaxies. I: The Importance of
  the Progenitor Effect in the Observed Properties of Quiescent Galaxies at
  $z\approx 2$

abstract:
  We study the relationship between the morphology and star formation history
(SFH) of 361 quiescent galaxies (QGs) at redshift $\langle
z_{obs}\rangle\approx 2$, with stellar mass $\log M_*\ge10.3$, selected with
the UVJ technique. Taking advantage of panchromatic photometry covering the
rest-frame UV-to-NIR spectral range ($\approx40$ bands), we reconstruct the
non-parametric SFH of the galaxies with the fully Bayesian SED fitting code
Prospector. We find that the half-light radius $R_e$, observed at $z_{obs}$,
depends on the formation redshift of the galaxies, $z_{form}$, and that this
relationship depends on stellar mass. At $\log M_*<11$, the relationship is
consistent with $R_e\propto(1+z_{form})^{-1}$, in line with the expectation
that the galaxies' central density depends on the cosmic density at the time of
their formation, i.e. the "progenitor effect". At $\log M_*>11$, the
relationship between $R_e$ and $z_{form}$ flattens, suggesting that mergers
become increasingly important for the size growth of more massive galaxies
after they quenched. We also find that the relationship between $z_{form}$ and
galaxy compactness similarly depends on stellar mass. While no clear trend is
observed for QGs with $\log M_*>11$, lower-mass QGs that formed earlier, i.e.
with larger $z_{form}$, have larger central stellar mass surface densities,
both within the $R_e$ ($\Sigma_e$) and central 1 kpc ($\Sigma_{1kpc}$), and
also larger $M_{1kpc}/M_*$, the fractional mass within the central 1 kpc. These
trends between $z_{form}$ and compactness, however, essentially disappear, if
the progenitor effect is removed by normalizing the stellar density with the
cosmic density at $z_{form}$. Our findings highlight the importance of
reconstructing the SFH of galaxies before attempting to infer their intrinsic
structural evolution.


title: Non-Binary Polar Codes for Spread-Spectrum Modulations

abstract:
  This paper proposes a new coded modulation scheme for reliable transmission
of short data packets at very low signal-to-noise ratio, combining cyclic code
shift keying modulation and non-binary polar coding. We consider non-binary
polar codes defined over Galois fields, and propose a new design methodology,
aimed at optimizing the choice of the kernel coefficients. Numerical results
show that the system performance is close to the achievable limits in the
finite blocklength regime.


title: Unexplored outflows in nearby low luminosity AGNs: the case of NGC 1052

abstract:
  Outflows play a central role in galaxy evolution shaping the properties of
galaxies. Understanding outflows and their effects in low luminosity AGNs, such
as LINERs, is essential (e.g. they are a numerous AGN population in the local
Universe). We obtained VLT/MUSE and GTC/MEGARA optical IFS-data for NGC1052,
the prototypical LINER. The stars are distributed in a dynamically hot disc,
with a centrally peaked velocity dispersion map and large observed velocity
amplitudes. The ionised gas, probed by the primary component is detected up to
$\sim$30arcsec ($\sim$3.3 kpc) mostly in the polar direction with blue and red
velocities ($\mid$V$\mid$$<$250 km/s). The velocity dispersion map shows a
notable enhancement ($\sigma$$>$90 km/s) crossing the galaxy along the major
axis of rotation in the central 10arcsec. The secondary component has a bipolar
morphology, velocity dispersion larger than 150 km/s and velocities up to 660
km/s. A third component is detected but not spatially resolved. The maps of the
NaD absorption indicate optically thick neutral gas with a velocity field
consistent with a slow rotating disc ($\Delta$V = 77$\pm$12 km/s) but the
velocity dispersion map is off-centred without any counterpart in the flux map.
We found evidence of an ionised gas outflow with mass of 1.6$\pm$0.6 $\times$
10$^{5}$ Msun, and mass rate of 0.4$\pm$0.2 Msun/yr. The outflow is propagating
in a cocoon of gas with enhanced turbulence and might be triggering the onset
of kpc-scale buoyant bubbles (polar emission). Taking into account the energy
and kinetic power of the outflow (1.3$\pm$0.9 $\times$ 10$^{53}$ erg and
8.8$\pm$3.5 $\times$ 10$^{40}$ erg/s, respectively) as well as its alignment
with both the jet and the cocoon, and that the gas is collisionally ionised, we
consider that the outflow is jet-powered, although some contribution from the
AGN is possible.


title:
Proof of a three-loop relation between the Regge limits of four-point
  amplitudes in N=4 SYM and N=8 supergravity

abstract:
  A previously proposed all-loop-orders relation between the Regge limits of
four-point amplitudes of N=4 supersymmetric Yang-Mills theory and N=8
supergravity is established at the three-loop level. We show that the Regge
limit of known expressions for the amplitudes obtained using generalized
unitarity simplifies in both cases to a (modified) sum over three-loop ladder
and crossed-ladder scalar diagrams. This in turn is consistent with the result
obtained using the eikonal representation of the four-point gravity amplitude.
A possible exact three-loop relation between four-point amplitudes is also
considered.


title:
KiDS+VIKING+GAMA: Halo occupation distributions and correlations of
  satellite numbers with a new halo model of the galaxy-matter bispectrum for
  galaxy-galaxy-galaxy lensing

abstract:
  Halo models and halo occupation distributions (HODs) are important tools to
model the galaxy and matter distribution. We present and assess a new method
for constraining the parameters of HODs using the gravitational lensing shear
around galaxy pairs, galaxy-galaxy-galaxy-lensing (G3L). In contrast to
galaxy-galaxy-lensing, G3L is sensitive to correlations between the per-halo
numbers of galaxies from different populations. We use G3L to probe these
correlations and test the default hypothesis that they are negligible. We
derive a halo model for G3L and validate it with realistic mock data from the
Millennium Simulation and a semi-analytic galaxy model. Then, we analyse public
data from the Kilo-Degree Survey (KiDS), the VISTA Infrared Kilo-Degree Galaxy
Survey (VIKING) and data from the Galaxy And Mass Assembly Survey (GAMA) to
infer the HODs of galaxies at $z<0.5$ in five different stellar mass bins
between $10^{8.5}h^{-2} M_\odot$ and $10^{11.5}h^{-2} M_\odot$ and two colours
(red and blue), as well as correlations between satellite numbers. The analysis
recovers the true HODs in the simulated data within the $68\%$ credibility
range. The inferred HODs vary significantly with colour and stellar mass. There
is also strong evidence ($>3\sigma$) for correlations, increasing with halo
mass, between the numbers of red and blue satellites and galaxies with stellar
masses below $10^{10} \Msun. Possible causes of these correlations are the
selection of similar galaxies in different samples, the survey flux limit, or
physical mechanisms like a fixed ratio between the satellite numbers of
distinct populations. The decorrelation for halos with smaller masses is
probably an effect of shot noise by low-occupancy halos. The inferred HODs can
be used to complement galaxy-galaxy-lensing or galaxy clustering HOD studies or
as input to cosmological analyses and improved mock galaxy catalogues.


title:
Spinning Black Holes with Scalar Hair and Horizonless Compact Objects
  within and beyond General Relativity

abstract:
  The last years have brought upon us a golden age of observational
gravitational physics. The several observations by the LIGO/Virgo/KAGRA
collaboration about gravitational waves and by the EHT collaboration about the
shadow and lensing of light around the supermassive black hole in the centre of
M87 will point the scientific community in the correct direction to find an
answer to the Kerr hypothesis. In order to follow that direction, the
systematic construction and analysis of the physical properties of solutions
within General Relativity with additional fields or within modified theories of
gravity is necessary. In this thesis, we shall provide such construction and
analysis for compact objects within (complex-)Einstein-Klein-Gordon theory with
various scalar potentials and within a particular scalar-tensor theory -- the
shift-symmetric Horndesky theory. After a brief introduction to some key topics
that shall be useful throughout this thesis, we present a discussion about the
horizon geometry of Kerr black holes with and without scalar hair. We follow up
with the construction and study of the same hairy solutions discussed in the
previous chapter but with higher azimuthal harmonic indexes. In the following
two chapters, we introduce a different scalar potential based on the Quantum
Chromodynamic axion potential and obtain and study both horizonless compact
objects and black holes. We then go to the shift-symmetric Horndeski theory,
where we perform similar constructions and analyses to the ones already
mentioned. Lastly, we derive a relation between the radial stability of
light-rings and timelike circular orbits around them. We follow up with a study
on how efficient it is the conversion of gravitational energy in radiation as a
timelike particle falls towards all compact objects studied throughout this
thesis. We end with some conclusions and remarks.


title:
Deep VLT photometry of the faint stellar system in the Large Magellanic
  Cloud periphery YMCA-1

abstract:
  We present FORS2@VLT follow-up photometry of YMCA-1, a recently discovered
stellar system located 13\degr~from the Large Magellanic Cloud (LMC) center.
The deep color-magnitude diagram (CMD) reveals a well-defined main sequence
(MS) and a handful of stars in the post-MS evolutionary phases. We analyse the
YMCA-1 CMD by means of the automated isochrone matching package {\tt ASteCA}
and model its radial density profile with a Plummer function. We find that
YMCA-1 is an old ($11.7^{+1.7}_{-1.3}$~Gyr), metal-intermediate ([Fe/H] $\simeq
-1.12^{+0.21}_{-0.13}$~dex), compact (r$_{\rm h} = 3.5 \pm 0.5$ pc), low-mass
(M $= 10^{2.45 \pm 0.02} M_{\odot}$) and low-luminosity (M$_V = -0.47 \pm
0.57$~mag) stellar system. The estimated distance modulus ($\mu_0 =
18.72^{+0.15}_{-0.17}$~mag), corresponding to about 55~kpc, suggests that
YMCA-1 is associated to the LMC, but we cannot discard the scenario in which it
is a Milky Way satellite. The structural parameters of YMCA-1 are remarkably
different compared with those of the 15 known old LMC globular clusters. In
particular, it resides in a transition region of the M$_V$-r$_h$ plane, in
between the ultra-faint dwarf galaxies and the classical old clusters, and
close to SMASH-1, another faint stellar system recently discovered in the LMC
surroundings.


title: Shock interactions for the Burgers-Hilbert Equation

abstract:
  This paper provides an asymptotic description of a solution to the
Burgers-Hilbert equation in a neighborhood of a point where two shocks
interact. The solution is obtained as the sum of a function with $H^2$
regularity away from the shocks plus a corrector term having an asymptotic
behavior like |x|ln|x| close to each shock. A key step in the analysis is the
construction of piecewise smooth solutions with a single shock for a general
class of initial data.


title:
The $Hb\bar{b}$ vertex at four loops and hard matching coefficients in
  SCET for various currents

abstract:
  We compute the four-loop corrections to the Higgs-bottom vertex within
massless QCD and present analytic results for all color structures. The
infrared poles of the renormalized form factor agree with the predicted
four-loop pattern. Furthermore, we use the results for the Higgs-bottom,
photon-quark, and Higgs-gluon form factors to provide hard matching
coefficients in soft-collinear effective theory up to four-loop accuracy.


title: Finite Temperature Description of an Interacting Bose Gas

abstract:
  We derive the equation of state of a Bose gas with contact interactions using
relativistic quantum field theory. The calculation accounts for both thermal
and quantum corrections up to 1-loop order. We work in the
Hartree-Fock-Bogoliubov approximation and follow Yukalov's prescription of
introducing two chemical potentials, one for the condensed phase and another
one for the excited phase, to circumvent the well-known Hohenberg-Martin
dilemma. As a check on the formalism, we take the non-relativistic limit and
reproduce known non-relativistic results. Finally, we translate our results to
the hydrodynamical, two-fluid model for finite-temperature superfluids. Our
results are relevant for the phenomenology of Bose-Einstein Condensate and
superfluid dark matter candidates, as well as the color-flavor locking phase of
quark matter in neutron stars.


title:
Physical Regimes of Electrostatic Wave-Wave nonlinear interactions
  generated by an Electron Beam Propagation in Background Plasma

abstract:
  Electron-beam plasma interaction has long been a topic of great interest. The
validities of Quasi-Linear (QL) theory and Weak Turbulence (WT) theory are
limited by the requirement of sufficiently dense mode spectrum and small wave
amplitude. In this paper, by performing a large number of high resolution
two-dimensional (2D) particle-in-cell (PIC) simulations and using analytical
theories, we extensively studied the collective processes of a mono-energetic
electron beam emitted from a thermionic cathode propagating through a cold
plasma. We show that initial two-stream instability between the beam and
background cold electrons is saturated by wave trapping rather than QL theory.
Further evolution occurs due to strong wave-wave nonlinear processes. We show
that the beam-plasma interaction can be classified into four different physical
regimes in the parameter space for the plasma and beam parameters. The
differences between the different regimes are analyzed in detail. For the first
time, we identified a new regime in strong Langmuir turbulence featured by what
we call Electron Modulational Instability (EMI) that creates a local Langmuir
wave packet faster than ion frequency ({\omega}_pi) and ions initially do not
respond to EMI in the initial growing stage. On a longer timescale, the action
of the ponderomotive force produces very strong ion density perturbations so
that the beam-plasma wave interaction stops being resonant. Consequently, in
this EMI regime beam-plasma interaction is a periodic burst (intermittent)
process. The beams are strongly scattered, and the Langmuir wave spectrum is
significantly broadened, which gives rise to the strong heating of bulk
electrons. Some interesting phenomena in the strong turbulent regime are also
discussed


title: Hybrid cosmological attractors

abstract:
  We construct $\alpha$-attractor versions of hybrid inflation models. In these
models, the potential of the inflaton field $\varphi$ is uplifted by the
potential of the second field $\chi$. This uplifting ends due to a tachyonic
instability with respect to the field $\chi$, which appears when $\varphi$
becomes smaller than some critical value $\varphi_{c}$. In the large $N$ limit,
these models have the standard universal $\alpha$-attractor predictions. In
particular, $n_{s }= 1- {2 \over N}$ for the exponential attractors. However,
in some special cases the large $N$ limit is reached only beyond the horizon,
for $N \gtrsim 60$. This may change predictions for the cosmological
observations. For any fixed $N$, in the limit of large uplift $V_{\rm up}$, or
in the limit of large $\varphi_{c}$, we find another attractor prediction, $
n_s = 1$. By changing the parameters $V_{\rm up}$ and $\varphi_{c}$ one can
continuously interpolate between the two attractor predictions $n_{s }= 1- {2
\over N}$ and $n_{s} = 1$.


title: OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses

abstract:
  Dataset bias and spurious correlations can significantly impair
generalization in deep neural networks. Many prior efforts have addressed this
problem using either alternative loss functions or sampling strategies that
focus on rare patterns. We propose a new direction: modifying the network
architecture to impose inductive biases that make the network robust to dataset
bias. Specifically, we propose OccamNets, which are biased to favor simpler
solutions by design. OccamNets have two inductive biases. First, they are
biased to use as little network depth as needed for an individual example.
Second, they are biased toward using fewer image locations for prediction.
While OccamNets are biased toward simpler hypotheses, they can learn more
complex hypotheses if necessary. In experiments, OccamNets outperform or rival
state-of-the-art methods run on architectures that do not incorporate these
inductive biases. Furthermore, we demonstrate that when the state-of-the-art
debiasing methods are combined with OccamNets results further improve.


title:
Electron Modulation Instability in the Strong Turbulent Regime for
  Electron Beam Propagation in Background Plasma

abstract:
  We study collective processes for an electron beam propagating through a
background plasma using simulations and analytical theory. A new regime where
the instability of a Langmuir wave packet can grow locally much faster than ion
frequency ({\omega}_pi) is clearly identified. The key feature of this new
regime is an Electron Modulational Instability that rapidly creates a local
Langmuir wave packet, which in its turn produces local charge separation and
strong ion density perturbations because of the action of the ponderomotive
force, such that the beam-plasma wave interaction stops being resonant. Three
evolution stages of the process and observed periodic burst features are
discussed. Different physical regimes in the plasma and beam parameter space
are clearly demonstrated for the first time.


title: Inhomogeneity within Local Interstellar Clouds

abstract:
  Analysis of interstellar absorption lines observed in high-resolution {\em
HST} spectra of nearby stars provides temperatures, turbulent velocities, and
kinetic properties of warm interstellar clouds. Previous studies identified 15
warm partially ionized clouds within about 10~pc of the Sun and measured their
mean thermal and kinematic properties. A new analysis of 100 interstellar
velocity components reveals a wide range of temperatures and turbulent
velocities within the Local Interstellar Cloud (LIC) and other nearby clouds.
These variations appear to be random with Gaussian distributions. We find no
trends of these properties with stellar distance, angle from the Galactic
Center, the main source of EUV radiation (the star $\epsilon$~CMa), the center
of the LIC, or the direction of inflowing interstellar matter into the
heliosphere. The spatial scale for temperature variations in the LIC is likely
smaller than 5,100~au, a distance that the Sun will traverse in 1,000 years.
Essentially all velocity components align with known warm clouds. We find that
within 4~pc of the Sun, space is completely filled with partially ionized
clouds, but at larger distances space is only partially filled with partially
ionized clouds, indicating that fully ionized inter-cloud gas fills the voids.
We find that the neutral hydrogen number density in the LIC and likely other
warm clouds in the CLIC is about 0.10~cm$^{-3}$ rather than the 0.20~cm$^{-3}$
density that may be representative of only the immediate environment of the
LIC. The 3,000--12,000~K temperature range for the gas is consistent with the
predictions of theoretical models of the WNM and WIM, but the high degree of
inhomogeneity within clouds argues against simple theoretical models. Finally,
we find evidence for a shock in the sight line to the star AD~Leo.


title:
Universal signatures of singularity-resolving physics in photon rings of
  black holes and horizonless objects

abstract:
  Within quantum-gravity approaches and beyond, different mechanisms for
singularity resolution in black holes exist. Under a set of assumptions that we
spell out in detail, these mechanisms leave their imprint in shadow images of
spherically symmetric black holes. We find that even current EHT accuracy is
sufficient to place nontrivial constraints on the scale of new physics within
one modified spacetime, if the EHT measurement of M87* is combined with an
independent measurement of the black-hole mass. In other spacetimes, increased
accuracy is required that the next-generation EHT may deliver. We show how the
combination of $n=1$ and $n=2$ photon rings is a powerful probe of the
spacetime geometry of regular black holes, even when considering astrophysical
uncertainties in accretion disks. Further, we generate images containing a
localized emission region, inspired by the idea of hotspots in accretion flows.
Finally, we investigate the photon-ring structure of a horizonless object,
which is characterized by either two or no photon spheres. We show how photon
rings annihilate each other, when there is no photon sphere in the spacetime.


title:
Nonlocal conductance spectroscopy of Andreev bound states in
  gate-defined InAs/Al nanowires

abstract:
  The charge character of Andreev bound states (ABSs) in a three-terminal
semiconductor-superconductor hybrid nanowire was measured using local and
nonlocal tunneling spectroscopy. The device is fabricated using an epitaxial
InAs/Al two-dimensional heterostructure with several gate-defined side probes.
ABSs are found to oscillate around zero as a function of gate voltage, with
modifications of their charge consistent with theoretical expectations for the
total Bardeen-Cooper- Schrieffer (BCS) charge of ABSs.


title: Mean field sparse optimal control of systems with additive white noise

abstract:
  We analyze the problem of controlling a multi-agent system with additive
white noise through parsimonious interventions on a selected subset of the
agents (leaders). For such a controlled system with a SDE constraint, we
introduce a rigorous limit process towards an infinite dimensional optimal
control problem constrained by the coupling of a system of ODE for the leaders
with a McKean-Vlasov-type SDE, governing the dynamics of the prototypical
follower. The latter is, under some assumptions on the distribution of the
initial data, equivalent with a (nonlinear parabolic) PDE-ODE system. The
derivation of the limit mean-field optimal control problem is achieved by
linking the mean-field limit of the governing equations together with the
$\Gamma$-limit of the cost functionals for the finite dimensional problems.


title:
Double sampling and semiparametric methods for informatively missing
  data

abstract:
  Missing data arise almost ubiquitously in applied settings, and can pose a
substantial threat to the validity of statistical analyses. In the context of
comparative effectiveness research, such as in large observational databases
(e.g., those derived from electronic health records), outcomes may be missing
not at random with respect to measured covariates. In this setting, we propose
a double sampling method, in which outcomes are obtained via intensive
follow-up on a subsample of subjects for whom data were initially missing. We
describe assumptions under which the joint distribution of confounders,
treatment, and outcome is identified under this design, and derive efficient
estimators of the average treatment effect under a nonparametric model, as well
as a model assuming outcomes were initially missing at random. We compare these
in simulations to an approach that adaptively selects an estimator based on
evidence of violation of the missing at random assumption. We also show that
the proposed double sampling design can be extended to handle arbitrary
coarsening mechanisms, and derive consistent, asymptotically normal, and
nonparametric efficient estimators of any smooth full data functional of
interest, and prove that these estimators often are multiply robust.


title: Dynamics of non-Gaussian fluctuations in model A

abstract:
  Motivated by the experimental search for the QCD critical point we perform
simulations of a stochastic field theory with purely relaxational dynamics
(model A). We verify the expected dynamic scaling of correlation functions.
Using a finite size scaling analysis we obtain the dynamic critical exponent
$z=2.026(56)$. We investigate time dependent correlation functions of higher
moments $M^n(t)$ of the order parameter $M(t)$ for $n=1,2,3,4$. We obtain
dynamic scaling with the same critical exponent $z$ for all $n$, but the
relaxation constant depends on $n$. We also study the relaxation of $M^n(t)$
after a quench, where the simulation is initialized in the high temperature
phase, and the dynamics is studied at the critical temperature $T_c$. We find
that the evolution does not follow simple scaling with the dynamic exponent
$z$, and that it involves an early time rise followed by late stage relaxation.


title: The Anomalous Hall Effect

abstract:
  This article reviews the main contributions to the anomalous Hall effect and
its resurgence in the past three decades, which has been accompanied by the
rise of topological phenomena and topological materials. I will show that the
anomalous Hall effect stems from the interplay of magnetism, spin-orbit
coupling, disorder scattering and driving electric fields, it spans the
classical, quantum and relativistic worlds, and has generated controversy for
nearly one and a half centuries. It has become the smoking gun for detecting
magnetic order and, extraordinarily, it continues to reveal new physics, with
several novel varieties reported in the past decade.


title: Quantum Collapse of a Thin Shell Revisited

abstract:
  There are several possible choices of the time parameter for the canonical
description of a self-gravitating thin shell, but quantum thories built on
different time parameters lead to unitarily inequivalent descriptions. We
compare the quantum collapse of a thin dust shell in two different times {\it
viz.,} the time coordinate in the interior of the shell (originally addressed
in \cite{hajicek92a}) and the time coordinate of the comoving observer (proper
time). In each case, we obtain exact solutions to the Wheeler-DeWitt equation
requiring only a finite and well behaved $U(1)$ current. The two quantum
theories are complementary and each highlights the role played by the Planck
mass: stationary states of positive energy in interior time exist only if the
shell rest mass in smaller than the Planck mass. In proper time they exist only
when the shell rest mass is {\it greater} than the Planck mass. In coordinate
time there are both scattering states and bound states with a well defined
energy spectrum. In the proper time description there are only bound states,
whose spectrum we determine.


title:
On monogenity of certain pure number fields defined by $x^{2^u\cdot
  3^v\cdot 5^t}-m$

abstract:
  Let $K = \mathbb{Q} (\alpha) $ be a pure number field generated by a root
$\alpha$ of a monic irreducible polynomial $ F(x) = x^{2^u\cdot 3^v\cdot
5^t}-m$, with $ m \neq \pm 1 $ a square free rational integer, $u$, $v$ and $t$
three positive integers. In this paper, we study the monogenity of $K$. We
prove that if $m\not\equiv 1\md4$, $m\not\equiv \pm 1\md9$, and $m\not\in\{\pm
1, \pm 7\}\md{25}$, then $K$ is monogenic. But if {$m\equiv 1\md{4}$} or
$m\equiv 1\md9$ or $m\equiv -1\md9$ and $u=2k$ for some odd integer $k$ or
$u\ge 2$ and $m\equiv 1\md{25}$ or $m\equiv -1\md{25}$ and $u=2k$ for some odd
integer $k$ or $u=v=1$ and $m\equiv \pm 82\md{5^4}$, then $K$ is not monogenic.


title:
AAAI SSS-22 Symposium on Closing the Assessment Loop: Communicating
  Proficiency and Intent in Human-Robot Teaming

abstract:
  The proposed symposium focuses understanding, modeling, and improving the
efficacy of (a) communicating proficiency from human to robot and (b)
communicating intent from a human to a robot. For example, how should a robot
convey predicted ability on a new task? How should it report performance on a
task that was just completed? How should a robot adapt its proficiency criteria
based on human intentions and values?
  Communities in AI, robotics, HRI, and cognitive science have addressed
related questions, but there are no agreed upon standards for evaluating
proficiency and intent-based interactions. This is a pressing challenge for
human-robot interaction for a variety of reasons. Prior work has shown that a
robot that can assess its performance can alter human perception of the robot
and decisions on control allocation. There is also significant evidence in
robotics that accurately setting human expectations is critical, especially
when proficiency is below human expectations. Moreover, proficiency assessment
depends on context and intent, and a human teammate might increase or decrease
performance standards, adapt tolerance for risk and uncertainty, demand
predictive assessments that affect attention allocation, or otherwise reassess
or adapt intent.


title:
Revealing the Charge Transfer Dynamics Between Singlet Fission Molecule
  and Hybrid Perovskite Nanocrystals

abstract:
  Singlet fission process has gained considerable attention because of its
potential to enhance photovoltaic efficiency and break the Shockley Queisser
limit. In photovoltaic devices perovskite materials have shown tremendous
progress in the last decade. Therefore combining the singlet fission materials
in perovskite devices can lead to a drastic enhancement in their performance.
To reveal the applicability of singlet fission processes in perovskite
materials we have investigated the charge transfer dynamics from an SF active
material 910 bis phenylethynyl anthracene to CH3NH3PbBr3 perovskite
nanocrystals using the transient absorption spectroscopy. We observed a
significant charge transfer from the coupled triplet state of BPEA to
conduction band of CH3NH3PbBr3 in picosecond timescale. The observation of
shortened lifetime in a mixture of BPEA and CH3NH3PbBr3 nanocrytals confirms
the significant charge transfer between these systems. Our study reveals the
charge transfer mechanism in singlet fission perovskite composite which will
help to develop an advanced photovoltaic system.


title:
Block designs with $\gcd(r,\lambda)=1$ admitting flag-transitive
  automorphism groups

abstract:
  In this paper, we present a classification of $2$-designs with
$\gcd(r,\lambda)=1$ admitting flag-transitive automorphism groups. If $G$ is a
flag-transitive automorphism group of a non-trivial $2$-design $\mathcal{D}$
with $\gcd(r,\lambda)=1$, then either $(\mathcal{D},G)$ is one of the known
examples described in this paper, or $\mathcal{D}$ has $q = p^{d}$ points with
$p$ prime and $G$ is a subgroup of $A\Gamma L_{1}(q)$.


title:
Predicting LyC emission of galaxies using their physical and Ly$\alpha$
  emission properties

abstract:
  The primary difficulty in understanding the sources and processes that
powered cosmic reionization is that it is not possible to directly probe the
ionizing Lyman Continuum (LyC) radiation at that epoch as those photons have
been absorbed by the intervening neutral hydrogen in the IGM on their way to
us. It is therefore imperative to build a model to accurately predict LyC
emission using other properties of galaxies in the reionization era.
  In recent years, studies have shown that the LyC emission from galaxies may
be correlated to their Lya emission. Here, we study this correlation by
analyzing thousands of galaxies at high-z in the SPHINX cosmological
simulation. We post-process these galaxies with the Lya radiative transfer code
RASCAS and analyze the Lya - LyC connection.
  We find that the Lya and LyC luminosities are strongly correlated with each
other, although with dispersion. There is a positive correlation between Lya
and LyC escape fractions in the brightest Lya emitters (>$10^{41}$ erg/s),
similar to the recent observational studies. However, when we also include
fainter Lya emitters (LAEs), the correlation disappears, which suggests that
the observed relationship may be driven by selection effects. We also find that
bright LAEs are dominant contributors to reionization ($> 10^{40}$ erg/s
galaxies contribute $> 90\%$ of LyC emission). Finally, we build predictive
models using multivariate linear regression where we use the physical and the
Lya properties of simulated galaxies to predict their intrinsic and escaping
LyC luminosities with a high degree of accuracy. We find that the most
important galaxy properties to predict the escaping LyC luminosity of a galaxy
are its escaping Lya luminosity, gas mass, gas metallicity, and SFR.
  These models can be very useful to predict LyC emissions from galaxies and
can help us identify the sources of reionization.


title:
Imaging Conductivity from Current Density Magnitude using Neural
  Networks

abstract:
  Conductivity imaging represents one of the most important tasks in medical
imaging. In this work we develop a neural network based reconstruction
technique for imaging the conductivity from the magnitude of the internal
current density. It is achieved by formulating the problem as a relaxed
weighted least-gradient problem, and then approximating its minimizer by
standard fully connected feedforward neural networks. We derive bounds on two
components of the generalization error, i.e., approximation error and
statistical error, explicitly in terms of properties of the neural networks
(e.g., depth, total number of parameters, and the bound of the network
parameters). We illustrate the performance and distinct features of the
approach on several numerical experiments. Numerically, it is observed that the
approach enjoys remarkable robustness with respect to the presence of data
noise.


title: Remarks on the anisotropic Calder\'{o}n problem

abstract:
  We show uniqueness results for the anisotropic Calder\'{o}n problem stated on
transversally anisotropic manifolds. Moreover, we give a convexity result for
the range of Dirichlet-to-Neumann maps on general Riemannian manifolds near the
zero potential. Finally, we present results for Calder\'{o}n type inverse
problems associated to semilinear elliptic equations on general Riemannian
manifolds.


title:
Efficient Table-based Function Approximation on FPGAs using Interval
  Splitting and BRAM Instantiation

abstract:
  This paper proposes a novel approach for the generation of memory-efficient
table-based function approximation circuits for FPGAs. Given a function f(x) to
be approximated in a given interval [x0,x0+a] and a maximum approximation error
Ea, the goal is to determine a function table implementation with a minimized
memory footprint, i.e., number of entries that need to be stored. Rather than
state-of-the-art work performing an even sampling of the given interval by
so-called breakpoints and using linear interpolation between two adjacent
breakpoints to determine f(x) at the maximum error bound, first, we propose
three interval-splitting algorithms to reduce the required memory footprint
drastically based on the observation that in sub-intervals of low gradient, a
coarser sampling grid may be assumed to satisfy the maximum interpolation error
bound. Experiments on elementary mathematical functions show that a large
fraction in memory footprint may be saved. Second, a hardware architecture
implementing the sub-interval selection, breakpoint lookup and interpolation at
a latency of just 9 clock cycles is introduced. Third, within each generated
circuit design, BRAMs are automatically instantiated rather than synthesizing
the reduced footprint function table using LUT primitives providing an
additional degree of resource efficiency.


title: Ramanujan congruences for overpartitions with restricted odd differences

abstract:
  We investigate Ramanujan congruences for the function which counts the
overpartitions of n with restricted odd differences. In particular, we show
that only one such congruence exists. Our method involves using the theory of
modular forms to prove a more general theorem which bounds the number of primes
possible for Ramanujan congruences in certain eta-quotients. This generalizes
work done by Jonah Sinick. We also provide two congruences modulo 5 for this
function.


title: CHORE: Contact, Human and Object REconstruction from a single RGB image

abstract:
  While most works in computer vision and learning have focused on perceiving
3D humans from single images in isolation, in this work we focus on capturing
3D humans interacting with objects. The problem is extremely challenging due to
heavy occlusions between human and object, diverse interaction types and depth
ambiguity. In this paper, we introduce CHORE, a novel method that learns to
jointly reconstruct human and object from a single image. CHORE takes
inspiration from recent advances in implicit surface learning and classical
model-based fitting. We compute a neural reconstruction of human and object
represented implicitly with two unsigned distance fields, and additionally
predict a correspondence field to a parametric body as well as an object pose
field. This allows us to robustly fit a parametric body model and a 3D object
template, while reasoning about interactions. Furthermore, prior pixel-aligned
implicit learning methods use synthetic data and make assumptions that are not
met in real data. We propose a simple yet effective depth-aware scaling that
allows more efficient shape learning on real data. Our experiments show that
our joint reconstruction learned with the proposed strategy significantly
outperforms the SOTA. Our code and models will be released to foster future
research in this direction.


title: Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models

abstract:
  Web-based phishing attacks nowadays exploit popular cloud web hosting
services and apps such as Google Sites and Typeform for hosting their attacks.
Since these attacks originate from reputable domains and IP addresses of the
cloud services, traditional phishing detection methods such as IP reputation
monitoring and blacklisting are not very effective. Here we investigate the
effectiveness of deep learning models in detecting this class of cloud-based
phishing attacks. Specifically, we evaluate deep learning models for three
phishing detection methods--LSTM model for URL analysis, YOLOv2 model for logo
analysis, and triplet network model for visual similarity analysis. We train
the models using well-known datasets and test their performance on phishing
attacks in the wild. Our results qualitatively explain why the models succeed
or fail. Furthermore, our results highlight how combining results from the
individual models can improve the effectiveness of detecting cloud-based
phishing attacks.


title:
Aperiodic subshifts of finite type on groups which are not finitely
  generated

abstract:
  We provide an example of a non-finitely generated group which admits a
nonempty strongly aperiodic SFT. Furthermore, we completely characterize the
groups with this property in terms of their finitely generated subgroups and
the roots of their conjugacy classes.


title:
Predicting and Explaining Mobile UI Tappability with Vision Modeling and
  Saliency Analysis

abstract:
  We use a deep learning based approach to predict whether a selected element
in a mobile UI screenshot will be perceived by users as tappable, based on
pixels only instead of view hierarchies required by previous work. To help
designers better understand model predictions and to provide more actionable
design feedback than predictions alone, we additionally use ML interpretability
techniques to help explain the output of our model. We use XRAI to highlight
areas in the input screenshot that most strongly influence the tappability
prediction for the selected region, and use k-Nearest Neighbors to present the
most similar mobile UIs from the dataset with opposing influences on
tappability perception.


title: Superconductivity and Local Inversion-Symmetry Breaking

abstract:
  Inversion and time reversal are essential symmetries for the structure of
Cooper pairs in superconductors. The loss of one or both leads to modifications
to this structure and can change the properties of the superconducting phases
in profound ways. Lacking inversion, superconductivity in noncentrosymmetric
materials has become an important topic, in particular, in the context of
topological superconductivity as well as unusual magnetic and magneto-electric
properties. Recently, crystal structures with local, but not global
inversion-symmetry breaking have attracted attention, as superconductivity can
exhibit phenomena not naively expected in centrosymmetric materials. After
introducing the concept of locally noncentrosymmetric crystals and different
material realizations, we discuss consequences of such local symmetry breaking
on the classification, the expected and, in parts, already observed
phenomenology of unconventional superconductivity, and possible topological
superconducting phases.


title: Federated Cross Learning for Medical Image Segmentation

abstract:
  Federated learning (FL) can collaboratively train deep learning models using
isolated patient data owned by different hospitals for various clinical
applications, including medical image segmentation. However, a major problem of
FL is its performance degradation when dealing with the data that are not
independently and identically distributed (non-iid), which is often the case in
medical images. In this paper, we first conduct a theoretical analysis on the
FL algorithm to reveal the problem of model aggregation during training on
non-iid data. With the insights gained through the analysis, we propose a
simple and yet effective method, federated cross learning (FedCross), to tackle
this challenging problem. Unlike the conventional FL methods that combine
multiple individually trained local models on a server node, our FedCross
sequentially trains the global model across different clients in a round-robin
manner, and thus the entire training procedure does not involve any model
aggregation steps. To further improve its performance to be comparable with the
centralized learning method, we combine the FedCross with an ensemble learning
mechanism to compose a federated cross ensemble learning (FedCrossEns) method.
Finally, we conduct extensive experiments using a set of public datasets. The
experimental results show that the proposed FedCross training strategy
outperforms the mainstream FL methods on non-iid data. In addition to improving
the segmentation performance, our FedCrossEns can further provide a
quantitative estimation of the model uncertainty, demonstrating the
effectiveness and clinical significance of our designs. Source code will be
made publicly available after paper publication.


title:
PDE-constrained shape registration to characterize biological growth and
  morphogenesis from imaging data

abstract:
  We propose a PDE-constrained shape registration algorithm that captures the
deformation and growth of biological tissue from imaging data. Shape
registration is the process of evaluating optimum alignment between pairs of
geometries through a spatial transformation function. We start from our
previously reported work, which uses 3D tensor product B-spline basis functions
to interpolate 3D space. Here, the movement of the B-spline control points,
composed with an implicit function describing the shape of the tissue, yields
the total deformation gradient field. The deformation gradient is then split
into growth and elastic contributions. The growth tensor captures addition of
mass, i.e. growth, and evolves according to a constitutive equation which is
usually a function of the elastic deformation. Stress is generated in the
material due to the elastic component of the deformation alone. The result of
the registration is obtained by minimizing a total energy functional which
includes: a distance measure reflecting similarity between the shapes, and the
total elastic energy accounting for the growth of the tissue. We apply the
proposed shape registration framework to study zebrafish embryo epiboly process
and tissue expansion during skin reconstruction surgery. We anticipate that our
PDE-constrained shape registration method will improve our understanding of
biological and medical problems in which tissues undergo extreme deformations
over time.


title:
The unitary dependence theory for understanding quantum circuits and
  states

abstract:
  We develop a unitary dependence theory to characterize the behaviors of
quantum circuits and states in terms of how quantum gates manipulate qubits and
determine their measurement probabilities. A qubit has dependence on a 1-qubit
unitary gate if its measurement probabilities depend on the parameters of the
gate. A 1-qubit unitary creates such a dependence onto the target qubit, and a
CNOT gate copies all the dependences from the control qubit to the target
qubit. The complete dependence picture of the output state details the
connections qubits may have when being manipulated or measured. Compared to the
conventional entanglement description of quantum circuits and states, the
dependence picture offers more practical information, easier generalization to
many-qubit systems, and better robustness upon partitioning of the system. The
unitary dependence theory is a useful tool for understanding quantum circuits
and states that is based on the practical ideas of how manipulations and
measurements are affected by elementary gates.


title:
A Comprehensive Framework based on Dynamic and Steady State Analysis to
  Evaluate Power System Resiliency to Extreme Weather Conditions

abstract:
  Power system robustness against high impact low probability events is
becoming a major concern. To depict distinct phases of a system response during
these disturbances, an irregular polygon model is derived from the conventional
trapezoid model and the model is analytically investigated for transmission
system performance, based on which resiliency metrics are developed for the
same. Furthermore, the system resiliency to windstorm is evaluated on the IEEE
reliability test system (RTS) by performing steady state and dynamic security
assessment incorporating protection modelling and corrective action schemes
using the software Power System Simulator for Engineering (PSS/E). Based on the
results of steady state and dynamic analysis, modified resiliency metrics are
quantified. Finally, this paper quantifies the interdependency of operational
and infrastructure resiliency as they cannot be considered as discrete
characteristics of the system.


title: $\mathcal{R}^2$ Quantum Corrected Scalar Field Inflation

abstract:
  String theory enjoys an elevated role among quantum gravity theories, since
it seems to be the most consistent UV completion of general relativity and the
Standard Model. However, it is hard to verify the existence of this underlying
theory on terrestrial accelerators. One way to probe string theory is to study
its imprints on the low-energy effective inflationary Lagrangian, which are
quantified in terms of high energy correction terms. It is highly likely, thus,
to find higher order curvature terms combined with string moduli, that is
scalar fields, since both these types of interactions and matter fields appear
in string theory. In this work we aim to stress the probability that the
inflationary dynamics are controlled by the synergy of scalar fields and higher
order curvature terms. Specifically, we shall consider a well motivated quantum
corrected canonical scalar field theory, with the quantum corrections being of
the $\mathcal{R}^2$ type. The reason for choosing minimally coupled scalar
theory is basically because if scalar fields are evaluated in their vacuum
configuration, they will either be minimally coupled or conformally coupled.
Here we choose the former case, and the whole study shall be performed in the
string frame (Jordan frame), in contrast to similar studies in the literature
where the Einstein frame two scalar theory is considered. We derive the field
equations of the quantum-corrected theory at leading order and we present the
form the slow-roll indices obtain for the quantum corrected theory. We
exemplify our theoretical framework by using the quadratic inflation model, and
as we show, the $\mathcal{R}^2$ quantum corrected quadratic inflation model
produces a viable inflationary phenomenology, in contrast with the simple
quadratic inflation model.


title: Improving Voice Trigger Detection with Metric Learning

abstract:
  Voice trigger detection is an important task, which enables activating a
voice assistant when a target user speaks a keyword phrase. A detector is
typically trained on speech data independent of speaker information and used
for the voice trigger detection task. However, such a speaker independent voice
trigger detector typically suffers from performance degradation on speech from
underrepresented groups, such as accented speakers. In this work, we propose a
novel voice trigger detector that can use a small number of utterances from a
target speaker to improve detection accuracy. Our proposed model employs an
encoder-decoder architecture. While the encoder performs speaker independent
voice trigger detection, similar to the conventional detector, the decoder
predicts a personalized embedding for each utterance. A personalized voice
trigger score is then obtained as a similarity score between the embeddings of
enrollment utterances and a test utterance. The personalized embedding allows
adapting to target speaker's speech when computing the voice trigger score,
hence improving voice trigger detection accuracy. Experimental results show
that the proposed approach achieves a 38% relative reduction in a false
rejection rate (FRR) compared to a baseline speaker independent voice trigger
model.


title: Relations with a fixed interval exchange transformation

abstract:
  We study the group of all interval exchange transformations (IETs). We show
that for every IET $S$, there exists a dense open set of admissible IETs that
share a relation with $S$. This is an extension of a result published by
Dahmani, Fujiwara and Guirardel in 2013: the group generated by a generic pair
of elements of IET([0;1[) is not free (assuming a suitable condition on the
underlying permutation).
  Key words: interval exchange transformations, free group of rank 2, interval
exchange transformations with flips.


title: An answer to a question of J.W. Cannon and S.G. Wayment

abstract:
  Solving R.J. Daverman's problem, V. Krushkal described sticky Cantor sets in
$\mathbb R^N$ for $N\geqslant 4$; these sets cannot be isotoped off of itself
by small ambient isotopies. Using Krushkal sets, we answer a question of J.W.
Cannon and S.G. Wayment (1970). Namely, for $N\geqslant 4$ we construct
compacta $X\subset \mathbb R^N$ with the following two properties: some
sequence $\{ X_i \subset \mathbb R^N \setminus X, \ i\in\mathbb N \}$ converges
homeomorphically to $X$, but there is no uncountable family of pairwise
disjoint sets $Y_\alpha \subset \mathbb R^N$ each of which is embedded
equivalently to $X$.


title: Robust Active Visual Perching with Quadrotors on Inclined Surfaces

abstract:
  Autonomous Micro Aerial Vehicles are deployed for a variety tasks including
surveillance and monitoring. Perching and staring allow the vehicle to monitor
targets without flying, saving battery power and increasing the overall mission
time without the need to frequently replace batteries. This paper addresses the
Active Visual Perching (AVP) control problem to autonomously perch on inclined
surfaces up to $90^\circ$. Our approach generates dynamically feasible
trajectories to navigate and perch on a desired target location, while taking
into account actuator and Field of View (FoV) constraints. By replanning in
mid-flight, we take advantage of more accurate target localization increasing
the perching maneuver's robustness to target localization or control errors. We
leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility
between planning objectives and the visual sensing constraint during the
planned maneuver. Furthermore, we experimentally identify the corresponding
boundary conditions that maximizes the spatio-temporal target visibility during
the perching maneuver. The proposed approach works on-board in real-time with
significant computational constraints relying exclusively on cameras and an
Inertial Measurement Unit (IMU). Experimental results validate the proposed
approach and shows the higher success rate as well as increased target
interception precision and accuracy with respect to a one-shot planning
approach, while still retaining aggressive capabilities with flight envelopes
that include large excursions from the hover position on inclined surfaces up
to 90$^\circ$, angular speeds up to 750~deg/s, and accelerations up to
10~m/s$^2$.


title:
Deviatoric Stress Driven Transient Melting Below the Glass Transition
  Temperature in Shocked Polymers

abstract:
  The relaxation of polymers around and below their glass transition
temperature is governed by a range of correlated unit processes with a wide
range of timescales. The fast deformation rates of shock loading can negate a
significant fraction of these processes resulting in the dynamical glass
transition in rubbers. In this letter we report the inverse, a transient
melting of glassy polymer under shock loading. The large deviatoric stresses
near the shock front induce fast transitions in backbone dihedral angles and a
stress relaxation characteristic of polymer melts. This is followed by the
slower relaxation expected for glasses.


title:
Electrostatic Brakes Enable Individual Joint Control of Underactuated,
  Highly Articulated Robots

abstract:
  Highly articulated organisms serve as blueprints for incredibly dexterous
mechanisms, but building similarly capable robotic counterparts has been
hindered by the difficulties of developing electromechanical actuators with
both the high strength and compactness of biological muscle. We develop a
stackable electrostatic brake that has comparable specific tension and weight
to that of muscles and integrate it into a robotic joint. Compared to
electromechanical motors, our brake-equipped joint is four times lighter and
one thousand times more power efficient while exerting similar holding torques.
Our joint design enables a ten degree-of-freedom robot equipped with only one
motor to manipulate multiple objects simultaneously. We also show that the use
of brakes allows a two-fingered robot to perform in-hand re-positioning of an
object 45% more quickly and with 53% lower positioning error than without
brakes. Relative to fully actuated robots, our findings suggest that robots
equipped with such electrostatic brakes will have lower weight, volume, and
power consumption yet retain the ability to reach arbitrary joint
configurations.


title: Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks

abstract:
  Blockchain is rapidly emerging as an important class of network application,
with a unique set of trust, security and transparency properties. In a
blockchain system, participants record and update the `server-side' state of an
application as blocks of a replicated, immutable ledger using a consensus
protocol over the Internet. Mining blocks has become lucrative in recent years;
e.g., a miner receives over USD 200,000 per mined block in Bitcoin today. A key
factor affecting mining rewards, is the latency of broadcasting blocks over the
network. In this paper, we consider the problem of topology design for
optimizing mining rewards in a wide-area blockchain network that uses a
Proof-of-Work protocol for consensus. Contrary to general wisdom that a faster
network is always better for miners, we show a counter intuitive result where a
slower network is actually beneficial to some miners. This is because competing
miners must choose neighbors that not only decrease their own latency to
others, but also ensure that the latency between other miners do not decrease
because of itself. We formalize this problem, and provide both theoretical
analysis and experimental results to support our claim.


title:
Quadratic Approximation Manifold for Mitigating the Kolmogorov Barrier
  in Nonlinear Projection-Based Model Order Reduction

abstract:
  A quadratic approximation manifold is presented for performing nonlinear,
projection-based, model order reduction (PMOR). It constitutes a departure from
the traditional affine subspace approximation that is aimed at mitigating the
Kolmogorov barrier for nonlinear PMOR, particularly for convection-dominated
transport problems. It builds on the data-driven approach underlying the
traditional construction of projection-based reduced-order models (PROMs); is
application-independent; is linearization-free; and therefore is robust for
highly nonlinear problems. Most importantly, this approximation leads to
quadratic PROMs that deliver the same accuracy as their traditional
counterparts using however a much smaller dimension -- typically, $n_2 \sim
\sqrt n_1$, where $n_2$ and $n_1$ denote the dimensions of the quadratic and
traditional PROMs, respectively. The computational advantages of the proposed
high-order approach to nonlinear PMOR over the traditional approach are
highlighted for the detached-eddy simulation-based prediction of the Ahmed body
turbulent wake flow, which is a popular CFD benchmark problem in the automotive
industry. For a fixed accuracy level, these advantages include: a reduction of
the total offline computational cost by a factor greater than five; a reduction
of its online wall clock time by a factor greater than 32; and a reduction of
the wall clock time of the underlying high-dimensional model by a factor
greater than two orders of magnitude.


title: Local connectedness of boundaries for relatively hyperbolic groups

abstract:
  Let $(\Gamma,\mathbb{P})$ be a relatively hyperbolic group pair that is
relatively one ended. Then the Bowditch boundary of $(\Gamma,\mathbb{P})$ is
locally connected. Bowditch previously established this conclusion under the
additional assumption that all peripheral subgroups are finitely presented,
either one or two ended, and contain no infinite torsion subgroups. We remove
these restrictions; we make no restriction on the cardinality of $\Gamma$ and
no restriction on the peripheral subgroups $P \in \mathbb{P}$.


title:
BeeTS: Smart Distributed Sensor Tuple Spaces combined with Agents using
  Bluetooth and IP Broadcasting

abstract:
  Most Internet-of-Things (IoT) devices and smart sensors are connected via the
Internet using IP communication driectly accessed by a server that collect
sensor information periodically or event-based. Although, Internet access is
widely available, there are places that are not covered and WLAN and mobile
cell communication requires a descent amount of power not always available.
Finally, the spatial context (the environment in which the sensor or devices is
situated) is not considered (or lost) by Internet connectivity. In this work,
smart devices communicate connectionless and ad-hoc by using low-energy
Bluetooth broadcasting available in any smartphone and in most embedded
computers, e.g., the Raspberry PI devices. Bi-directional connectionless
communication is established via the advertisements and scanning modes. The
communication nodes can exchange data via functional tuples using a tuple space
service on each node. Tuple space access is performed by simple evenat-based
agents. Mobile devices act as tuple carriers that can carry tuples between
different locations. Additionally, UDP-based Intranet communication can be used
to access tuple spaces on a wider spatial range. The Bluetooth Low Energy Tuple
Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device
communication with a spatial context.


title:
Extremals on three-dimensional Lie groups with asymmetric polyhedral
  Finsler structures

abstract:
  A polyhedral Finsler structure on a differentiable manifold $M$ is a real
valued continuous function $F$ on its tangent bundle $TM$ such that the
restriction of $F$ to each tangent space $T_xM$ is an asymmetric norm such that
its closed unit ball is a polyhedron that contains the origin of $T_xM$ in its
interior. In this work we study extremals on three-dimensional connected Lie
groups $G$ endowed with a left invariant polyhedral Finsler structure. We use
the Pontryagin's Maximal Principle (PMP) to find curves on the cotangent bundle
of the group, such that its projections on $G$ are extremals. Let $\mathfrak g$
and $\mathfrak g^\ast$ be the Lie algebra of $G$ and its dual space
respectively. We represent this problem as a control system $\mathfrak a^\prime
(t)= -\mathrm{ad}^\ast(u(t))(\mathfrak a(t))$ of Euler-Arnold type equation,
where $u(t)$ is a measurable control in the unit sphere of $\mathfrak g$ and
$\mathfrak a(t)$ is an absolutely continuous curve in $\mathfrak g^\ast$. A
solution $(u(t), \mathfrak a(t))$ of this control system is a Pontryagin
extremal and $\mathfrak a(t)$ is its vertical part. In this work we show that
for a fixed vertical part of the Pontryagin extremal $\mathfrak a(t)$, the
uniqueness of $u(t)$ such that $(u(t),\mathfrak a(t))$ is a Pontryagin extremal
can be studied through an asymptotic curvature of $\mathfrak a(t)$.


title:
Aggregating human judgment probabilistic predictions of COVID-19
  transmission, burden, and preventative measures

abstract:
  Aggregated human judgment forecasts for COVID-19 targets of public health
importance are accurate, often outperforming computational models. Our work
shows aggregated human judgment forecasts for infectious agents are timely,
accurate, and adaptable, and can be used as tool to aid public health decision
making during outbreaks.


title:
The Extended and Asymmetric Extended Krylov Subspace in
  Moment-Matching-Based Order Reduction of Large Circuit Models

abstract:
  The rapid growth of circuit complexity has rendered Model Order Reduction
(MOR) a key enabler for the efficient simulation of large circuit models. MOR
techniques based on moment-matching are well established due to their
simplicity and computational performance in the reduction process. However,
moment-matching methods based on the ordinary Krylov subspace are usually
inadequate to accurately approximate the original circuit behavior, and at the
same time do not produce reduced-order models as compact as needed. In this
paper, we present a moment-matching method which utilizes the extended and the
asymmetric extended Krylov subspace (EKS and AEKS), while it allows the
parallel computation of the transfer function in order to deal with circuits
that have many terminals. The proposed method can handle large-scale regular
and singular circuits and generate accurate and efficient reduced-order models
for circuit simulation. Experimental results on industrial IBM power grids
demonstrate that the EKS method can achieve an error reduction up to 85.28%
over a standard Krylov subspace method, while the AEKS method greatly reduces
the runtime of EKS, introducing a negligible overhead in the reduction error.


title: Hausdorff limits of submanifolds of symplectic and contact manifolds

abstract:
  We study sequences of immersions respecting bounds coming from Riemannian
geometry and apply the ensuing results to the study of sequences of
submanifolds of symplectic and contact manifolds. This allows us to study the
subtle interaction between the Hausdorff metric and the Lagrangian Hofer and
spectral metrics. In the process, we get proofs of metric versions of the
nearby Lagrangian conjecture and of the Viterbo conjecture on the spectral
norm. We also get $C^0$-rigidity results in the vein of the Gromov-Eliashberg
theorem for a vast class of important submanifolds of symplectic and contact
manifolds-even when no Riemannian bounds are present.


title: Generalized Numerical Radius Inequalities for Schatten p-Norms

abstract:
  In this paper, we present various inequalities for generalized numerical
radius of $2 \times 2$ block matrices for Schatten p-norm. Moreover, we give a
refinement of the triangle inequality for the Schatten p-generalized numerical
radius.


title:
Combining Spectral and Self-Supervised Features for Low Resource Speech
  Recognition and Translation

abstract:
  Self-Supervised Learning (SSL) models have been successfully applied in
various deep learning-based speech tasks, particularly those with a limited
amount of data. However, the quality of SSL representations depends highly on
the relatedness between the SSL training domain(s) and the target data domain.
On the contrary, spectral feature (SF) extractors such as log Mel-filterbanks
are hand-crafted non-learnable components, and could be more robust to domain
shifts. The present work examines the assumption that combining non-learnable
SF extractors to SSL models is an effective approach to low resource speech
tasks. We propose a learnable and interpretable framework to combine SF and SSL
representations. The proposed framework outperforms significantly both baseline
and SSL models on Automatic Speech Recognition (ASR) and Speech Translation
(ST) tasks on three low resource datasets. We additionally design a mixture of
experts based combination model. This last model reveals that the relative
contribution of SSL models over conventional SF extractors is very small in
case of domain mismatch between SSL training set and the target language data.


title: Configuration Path Control

abstract:
  Reinforcement learning methods often produce brittle policies -- policies
that perform well during training, but generalize poorly beyond their direct
training experience, thus becoming unstable under small disturbances. To
address this issue, we propose a method for stabilizing a control policy in the
space of configuration paths. It is applied post-training and relies purely on
the data produced during training, as well as on an instantaneous
control-matrix estimation. The approach is evaluated empirically on a planar
bipedal walker subjected to a variety of perturbations. The control policies
obtained via reinforcement learning are compared against their stabilized
counterparts. Across different experiments, we find two- to four-fold increase
in stability, when measured in terms of the perturbation amplitudes. We also
provide a zero-dynamics interpretation of our approach.


title:
Equivalence of coupled parametric oscillator dynamics to Lagrange
  multiplier primal-dual optimization

abstract:
  There has been a recent surge of interest in physics-based solvers for
combinatorial optimization problems. We present a dynamical solver for the
Ising problem that is comprised of a network of coupled parametric oscillators
and show that it implements Lagrange multiplier constrained optimization. We
show that the pump depletion effect, which is intrinsic to parametric
oscillators, enforces binary constraints and enables the system's continuous
analog variables to converge to the optimal binary solutions to the
optimization problem. Moreover, there is an exact correspondence between the
equations of motion for the coupled oscillators and the update rules in the
primal-dual method of Lagrange multipliers. Though our analysis is performed
using electrical LC oscillators, it can be generalized to any system of coupled
parametric oscillators. We simulate the dynamics of the coupled oscillator
system and demonstrate that the performance of the solver on a set of benchmark
problems is comparable to the best-known results obtained by digital algorithms
in the literature.


title: "Does it come in black?" CLIP-like models are zero-shot recommenders

abstract:
  Product discovery is a crucial component for online shopping. However,
item-to-item recommendations today do not allow users to explore changes along
selected dimensions: given a query item, can a model suggest something similar
but in a different color? We consider item recommendations of the comparative
nature (e.g. "something darker") and show how CLIP-based models can support
this use case in a zero-shot manner. Leveraging a large model built for
fashion, we introduce GradREC and its industry potential, and offer a first
rounded assessment of its strength and weaknesses.


title:
Generative Enriched Sequential Learning (ESL) Approach for Molecular
  Design via Augmented Domain Knowledge

abstract:
  Deploying generative machine learning techniques to generate novel chemical
structures based on molecular fingerprint representation has been well
established in molecular design. Typically, sequential learning (SL) schemes
such as hidden Markov models (HMM) and, more recently, in the sequential deep
learning context, recurrent neural network (RNN) and long short-term memory
(LSTM) were used extensively as generative models to discover unprecedented
molecules. To this end, emission probability between two states of atoms plays
a central role without considering specific chemical or physical properties.
Lack of supervised domain knowledge can mislead the learning procedure to be
relatively biased to the prevalent molecules observed in the training data that
are not necessarily of interest. We alleviated this drawback by augmenting the
training data with domain knowledge, e.g. quantitative estimates of the
drug-likeness score (QEDs). As such, our experiments demonstrated that with
this subtle trick called enriched sequential learning (ESL), specific patterns
of particular interest can be learnt better, which led to generating de novo
molecules with ameliorated QEDs.


title:
Artificial SA-I, RA-I and RA-II/Vibrotactile Afferents for Tactile
  Sensing of Texture

abstract:
  Robot touch can benefit from how humans perceive tactile textural
information, from the stimulation mode to which tactile channels respond, then
the tactile cues and encoding. Using a soft biomimetic tactile sensor (the
TacTip) based on the physiology of the dermal-epidermal boundary, we construct
two biomimetic tactile channels based on slowly-adapting SA-I and
rapidly-adapting RA-I afferents, and introduce an additional sub-modality for
vibrotactile information with an embedded microphone interpreted as an
artificial RA-II channel. These artificial tactile channels are stimulated
dynamically with a set of 13 artificial rigid textures comprising raised-bump
patterns on a rotating drum that vary systematically in roughness. Methods
employing spatial, spatio-temporal and temporal codes are assessed for texture
classification insensitive to stimulation speed. We find: (i) spatially-encoded
frictional cues provide a salient representation of texture; (ii) a simple
transformation of spatial tactile features to model natural afferent responses
improves the temporal coding; and (iii) the harmonic structure of induced
vibrations provides a pertinent code for speed-invariant texture
classification. Just as human touch relies on an interplay between
slowly-adapting (SA-I), rapidly-adapting (RA-I) and vibrotactile (RA-II)
channels, this tripartite structure may be needed for future robot applications
with human-like dexterity, from prosthetics to materials testing, handling and
manipulation.


title: Local lens rigidity for manifolds of Anosov type

abstract:
  The lens data of a Riemannian manifold with boundary is the collection of
lengths of geodesics with endpoints on the boundary together with their
incoming and outgoing vectors. We show that negatively-curved Riemannian
manifolds with strictly convex boundary are locally lens rigid in the following
sense: if $g_0$ is such a metric, then any metric $g$ sufficiently close to
$g_0$ and with same lens data is isometric to $g_0$, up to a
boundary-preserving diffeomorphism. More generally, we consider the same
problem for a wider class of metrics with strictly convex boundary, called
metrics of Anosov type. We prove that the same rigidity result holds within
that class in dimension $2$ and in any dimension, further assuming that the
curvature is non-positive.


title:
Method of Winsorized Moments for Robust Fitting of Truncated and
  Censored Lognormal Distributions

abstract:
  When constructing parametric models to predict the cost of future claims,
several important details have to be taken into account: (i) models should be
designed to accommodate deductibles, policy limits, and coinsurance factors,
(ii) parameters should be estimated robustly to control the influence of
outliers on model predictions, and (iii) all point predictions should be
augmented with estimates of their uncertainty. The methodology proposed in this
paper provides a framework for addressing all these aspects simultaneously.
Using payment-per-payment and payment-per-loss variables, we construct the
adaptive version of method of winsorized moments (MWM) estimators for the
parameters of truncated and censored lognormal distribution. Further, the
asymptotic distributional properties of this approach are derived and compared
with those of the maximum likelihood estimator (MLE) and method of trimmed
moments (MTM) estimators. The latter being a primary competitor to MWM.
Moreover, the theoretical results are validated with extensive simulation
studies and risk measure sensitivity analysis. Finally, practical performance
of these methods is illustrated using the well-studied data set of 1500 U.S.
indemnity losses. With this real data set, it is also demonstrated that the
composite models do not provide much improvement in the quality of predictive
models compared to a stand-alone fitted distribution specially for truncated
and censored sample data.


title:
Gaussian matrix product states cannot efficiently describe critical
  systems

abstract:
  Gaussian fermionic matrix product states (GfMPS) form a class of ansatz
quantum states for 1d systems of noninteracting fermions. We show, for a simple
critical model of free hopping fermions, that: (i) any GfMPS approximation to
its ground state must have bond dimension scaling superpolynomially with the
system size, whereas (ii) there exists a non-Gaussian fermionic MPS
approximation to this state with polynomial bond dimension. This proves that,
in general, imposing Gaussianity at the level of the tensor network may
significantly alter its capability to efficiently approximate critical Gaussian
states. We also provide numerical evidence that the required bond dimension is
subexponential, and thus can still be simulated with moderate resources.


title:
Parametric Channel Model Estimation for Large Intelligent Surface-Based
  Transceiver-assisted Communication System

abstract:
  The number of connected mobile devices and the amount of data traffic through
these devices are expected to grow many-fold in future communication networks.
To support the scale of this huge data traffic, more and more base stations and
wireless terminals are required to be deployed in existing networks.
Nevertheless, practically deploying a large number of base stations having
massive antenna arrays will substantially increase the hardware cost and power
consumption of the network. A promising approach for enhancing the coverage and
rate of wireless communication systems is the large intelligent surface-based
transceiver (LISBT), which uses a spatially continuous surface for signal
transmission and receiving. A typical LIS consists of a planar array having a
large number of reflecting metamaterial elements (e.g., low-cost printed
dipoles), each of which could act as a phase shift. It is also considered to be
a cost effective and energy efficient solution. Accurate channel state
information (CSI) in LISBT-assisted wireless communication systems is critical
for achieving these goals. In this paper, we propose a channel estimation
scheme based on the physical parameters of the system. that requires only five
pilot signals to perfectly estimate the channel parameters assuming there is no
noise at the receiver. In the presence of noise, we propose an iterative
estimation algorithm that decreases the channel estimation error due to noise.
The proposed scheme's training overhead and computational cost do not grow with
the number of antennas, unlike previous work on enormous multiple-input
multiple-output (MIMO). The channel estimate scheme based on the physical
properties of the Large intelligent surface-based transceiver (LISBT)-assisted
wireless communication systems is the subject of our future study.


title:
Learning Optimal K-space Acquisition and Reconstruction using
  Physics-Informed Neural Networks

abstract:
  The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred
the development of various acceleration methods, typically through
heuristically undersampling the MRI measurement domain known as k-space.
Recently, deep neural networks have been applied to reconstruct undersampled
k-space data and have shown improved reconstruction performance. While most of
these methods focus on designing novel reconstruction networks or new training
strategies for a given undersampling pattern, e.g., Cartesian undersampling or
Non-Cartesian sampling, to date, there is limited research aiming to learn and
optimize k-space sampling strategies using deep neural networks. This work
proposes a novel optimization framework to learn k-space sampling trajectories
by considering it as an Ordinary Differential Equation (ODE) problem that can
be solved using neural ODE. In particular, the sampling of k-space data is
framed as a dynamic system, in which neural ODE is formulated to approximate
the system with additional constraints on MRI physics. In addition, we have
also demonstrated that trajectory optimization and image reconstruction can be
learned collaboratively for improved imaging efficiency and reconstruction
performance. Experiments were conducted on different in-vivo datasets (e.g.,
brain and knee images) acquired with different sequences. Initial results have
shown that our proposed method can generate better image quality in accelerated
MRI than conventional undersampling schemes in Cartesian and Non-Cartesian
acquisitions.


title: Adversarial Robustness through the Lens of Convolutional Filters

abstract:
  Deep learning models are intrinsically sensitive to distribution shifts in
the input data. In particular, small, barely perceivable perturbations to the
input data can force models to make wrong predictions with high confidence. An
common defense mechanism is regularization through adversarial training which
injects worst-case perturbations back into training to strengthen the decision
boundaries, and to reduce overfitting. In this context, we perform an
investigation of 3x3 convolution filters that form in adversarially-trained
models. Filters are extracted from 71 public models of the linf-RobustBench
CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from
models built on the same architectures but trained without robust
regularization. We observe that adversarially-robust models appear to form more
diverse, less sparse, and more orthogonal convolution filters than their normal
counterparts. The largest differences between robust and normal models are
found in the deepest layers, and the very first convolution layer, which
consistently and predominantly forms filters that can partially eliminate
perturbations, irrespective of the architecture. Data & Project website:
https://github.com/paulgavrikov/cvpr22w_RobustnessThroughTheLens


title: PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

abstract:
  The ubiquitous presence of printed circuit boards (PCBs) in modern electronic
systems and embedded devices makes their integrity a top security concern. To
take advantage of the economies of scale, today's PCB design and manufacturing
are often performed by suppliers around the globe, exposing them to many
security vulnerabilities along the segmented PCB supply chain. Moreover, the
increasing complexity of the PCB designs also leaves ample room for numerous
sneaky board-level attacks to be implemented throughout each stage of a PCB's
lifetime, threatening many electronic devices. In this paper, we propose
PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework
that can identify a wide spectrum of board-level malicious modifications.
PDNPulse leverages the fact that the PDN's characteristics are inevitably
affected by modifications to the PCB, no matter how minuscule. By detecting
changes to the PDN impedance profile and using the Frechet distance-based
anomaly detection algorithms, PDNPulse can robustly and successfully discern
malicious modifications across the system. Using PDNPulse, we conduct extensive
experiments on seven commercial-off-the-shelf PCBs, covering different design
scales, different threat models, and seven different anomaly types. The results
confirm that PDNPulse creates an effective security asymmetry between attack
and defense.


title: Considerations for Multilingual Wikipedia Research

abstract:
  English Wikipedia has long been an important data source for much research
and natural language machine learning modeling. The growth of non-English
language editions of Wikipedia, greater computational resources, and calls for
equity in the performance of language and multimodal models have led to the
inclusion of many more language editions of Wikipedia in datasets and models.
Building better multilingual and multimodal models requires more than just
access to expanded datasets; it also requires a better understanding of what is
in the data and how this content was generated. This paper seeks to provide
some background to help researchers think about what differences might arise
between different language editions of Wikipedia and how that might affect
their models. It details three major ways in which content differences between
language editions arise (local context, community and governance, and
technology) and recommendations for good practices when using multilingual and
multimodal data for research and modeling.