title:
Metallicities of Outer Halo M31 Globular Clusters from Integrated Light
  Calcium-II Triplet Spectroscopy

abstract:
  This paper presents [Fe/H] ratios for GCs in the outer halo of the Andromeda
Galaxy, M31, based on moderate-resolution, integrated light (IL) spectroscopy
of the calcium-II triplet (CaT) lines. The CaT strengths are measured by
fitting Voigt profiles to the lines and integrating those profiles;
integrations of defined bandpasses are also considered. The [Fe/H] ratios are
determined using an empirical calibration with CaT line strength, as derived
from another sample of M31 GCs that were previously studied at high-resolution.
The [Fe/H] ratios for the new GCs reveal that the outer halo GCs are indeed
generally more metal-poor than typical inner halo GCs, though there are several
more metal-rich GCs that look to have been accreted from dwarf satellites. The
metallicities of these GCs also place important constraints on the nature of
the substructure in the outer halo and the dwarf satellites that created this
substructure.


title: Tidally-induced nonlinear resonances in EMRIs with an analogue model

abstract:
  One of the important classes of targets for the future space-based
gravitational wave observatory LISA is extreme mass ratio inspirals (EMRIs),
where long and accurate waveform modeling is necessary for detection and
characterization. When modeling the dynamics of an EMRI, several effects need
to be included, such as the modifications caused by an external tidal field.
The effects of such perturbations will generally break integrability at
resonance, and can produce significant dephasing from an unperturbed system. In
this paper, we use a Newtonian analogue of a Kerr black hole to study the
effect of an external tidal field on the dynamics and the gravitational
waveform. We have developed a numerical framework that takes advantage of the
integrability of the background system to evolve it with a symplectic splitting
integrator, and compute approximate gravitational waveforms to estimate the
time scale over which the perturbation affects the dynamics. We find that
different entry points into the resonance in phase-space can produce
substantially different dynamics. Finally, by comparing this time scale with
the inspiral time, we find tidal effects will need to be included when modeling
EMRI gravitational waves when $\varepsilon \gtrsim 300\, q^2$, where $q$ is the
small mass ratio, and $\varepsilon$ measures the strength of the external tidal
field.


title: Cost of holographic path integrals

abstract:
  We consider proposals for the cost of holographic path integrals.
Gravitational path integrals within finite radial cutoff surfaces have a
precise map to path integrals in $T\bar T$ deformed holographic CFTs. In
Nielsen's geometric formulation cost is the length of a
not-necessarily-geodesic path in a metric space of operators. Our cost
proposals differ from holographic state complexity proposals in that (1) the
boundary dual is cost, a quantity that can be `optimised' to state complexity,
(2) the set of proposals is large: all functions on all bulk subregions of any
co-dimension which satisfy the physical properties of cost, and (3) the
proposals are by construction UV-finite. The optimal path integral that
prepares a given state is that with minimal cost, and cost proposals which
reduce to the CV and CV2.0 complexity conjectures when the path integral is
optimised are found, while bounded cost proposals based on gravitational action
are not found. Related to our analysis of gravitational action-based proposals,
we study bulk hypersurfaces with a constant intrinsic curvature of a specific
value and give a Lorentzian version of the Gauss-Bonnet theorem valid in the
presence of conical singularities.


title: Stability Analysis of a Non-Unitary CFT

abstract:
  We study instability of the lowest dimension operator (\it i.e., \rm the
imaginary part of its operator dimension) in the rank-$Q$ traceless symmetric
representation of the $O(N)$ Wilson-Fisher fixed point in $D=4+\epsilon$. We
find a new semi-classical bounce solution, which gives an imaginary part to the
operator dimension of order
$O\left({{{\epsilon^{-1/2}}}}\exp\left[-\frac{N+8}{3\epsilon}F(\epsilon
Q)\right]\right)$ in the double-scaling limit where $\epsilon Q \leq
\frac{N+8}{6\sqrt{3}}$ is fixed. The form of $F(\epsilon Q)$, normalised as
$F(0)=1$, is also computed. This non-perturbative correction continues to give
the leading effect even when $Q$ is finite, indicating the instability of
operators for any values of $Q$. We also observe a phase transition at
$\epsilon Q=\frac{N+8}{6\sqrt{3}}$ associated with the condensation of bounces,
similar to the Gross-Witten-Wadia transition.


title: Ergodicity breaking transition in zero dimensions

abstract:
  It is of great current interest to establish toy models of ergodicity
breaking transitions in quantum many-body systems. Here we study a model dubbed
quantum sun model that is expected to exhibit an ergodic to nonergodic
transition in the thermodynamic limit upon tuning the coupling between an
ergodic quantum dot and distant particles with spin-1/2. The model is
effectively zero dimensional, however, a variant of the model is often referred
to as the avalanche model of ergodicity breaking transition in one-dimensional
disordered spin chains. We show that exact numerical results based on the
spectral form factor calculation accurately agree with theoretical predictions,
and can hence unambiguously confirm existence of the ergodicity breaking
transition in this model. We benchmark specific properties that represent
hallmarks of the ergodicity breaking transition in finite systems.

categories: cond-mat.stat-mech cond-mat.dis-nn cond-mat.quant-gas cond-mat.str-el quant-ph


title:
Weak Gravitational Lensing Shear Estimation with Metacalibration for the
  Roman High-Latitude Imaging Survey

abstract:
  We investigate the performance of the Metacalibration shear calibration
framework using simulated imaging data for the Nancy Grace Roman Space
Telescope (Roman) reference High-Latitude Imaging Survey (HLIS). The weak
lensing program of the Roman mission requires the mean weak lensing shear
estimate to be calibrated within about 0.03%. To reach this goal, we can test
our calibration process with various simulations and ultimately isolate the
sources of residual shear biases in order to improve our methods. In this work,
we build on the Roman HLIS image simulation pipeline in Troxel et al. 2021 to
incorporate several new realistic processing-pipeline updates necessary to more
accurately process the imaging data and calibrate the shear. We show the first
results of this calibration for six deg$^2$ of the simulated reference HLIS
using Metacalibration and compare these results to measurements on more simple,
faster Roman-like image simulations. In both cases, we neglect the impact of
blending of objects. We find that in the simplified simulations,
Metacalibration can calibrate shapes to be within $m=(-0.01\pm 0.10)$%. When
applied to the current most-realistic version of the simulations, the precision
is much lower, with estimates of $m=(-1.34\pm 0.67)$% for joint multi-band
single-epoch measurements and $m=(-1.13\pm 0.60)$% for multi-band coadd
measurements. These results are all consistent with zero within 1-2$\sigma$,
indicating we are currently limited by our simulated survey volume. Further
work on testing the shear calibration methodology is necessary at higher
precision to reach the level of the Roman requirements, in particular in the
presence of blending. Current results demonstrate, however, that the
Metacalibration method can work on undersampled space-based Roman imaging data
at levels comparable to the requirements of current weak lensing surveys.


title:
The White Dwarf Binary Pathways Survey VII: Evidence for a bi-modal
  distribution of post mass transfer systems?

abstract:
  Binary systems consisting of a white dwarf and a main-sequence companion with
orbital periods up to $\approx 100$ d are often thought to be formed through
common envelope evolution which is still poorly understood. To provide new
observational constraints on the physical processes involved in the formation
of these objects, we are conducting a large-scale survey of close binaries
consisting of a white dwarf and an A to K-type companion. Here we present three
systems with eccentric orbits and orbital periods between $\approx10-42$ d
discovered by our survey. Based on HST spectroscopy and high angular resolution
images obtained with SPHERE-IRDIS, we find that two of these systems are most
likely triple systems while the remaining one could be either a binary or a
hierarchical triple but none of them is a post common envelope binary (PCEB).
The discovery of these systems shows that our survey is capable to detect
systems with orbital periods of the order of weeks, but all six PCEBs we have
previously discovered have periods below 2.5 d. We suggest that the fact that
all of the systems we identify with periods of the order of weeks are not PCEBs
indicates a transition between two different mechanisms responsible for the
formation of very close ($\lesssim 10$ d) and somewhat wider WD+AFGK binaries:
common envelope evolution and non-conservative stable mass transfer.


title: Topological Disorder Parameter

abstract:
  We introduce a many-body topological invariant, called the topological
disorder parameter (TDP), to characterize gapped quantum phases with global
internal symmetry in (2+1)d. TDP is defined as the constant correction that
appears in the ground state expectation value of a partial symmetry
transformation applied to a connected spatial region $M$, the absolute value of
which scales generically as $\exp(-\alpha l+\gamma)$ where $l$ is the perimeter
of $M$ and $\gamma$ is the TDP. Motivated by a topological quantum field theory
interpretation of the operator, we show that $e^\gamma$ can be related to the
quantum dimension of the symmetry defect, and provide a general formula for
$\gamma$ when the entanglement Hamiltonian of the topological phase can be
described by a (1+1)d conformal field theory (CFT). A special case of TDP is
equivalent to the topological R\'enyi entanglement entropy when the symmetry is
the cyclic permutation of the replica of the gapped phase. We then investigate
several examples of lattice models of topological phases, both analytically and
numerically, in particular when the assumption of having a CFT edge theory is
not satisfied. We also consider an example of partial translation symmetry in
Wen's plaquette model and show that the result can be understood using the edge
CFT. Our results establish a new tool to detect quantum topological order.


title: Greedy Algorithms for Decision Trees with Hypotheses

abstract:
  We investigate at decision trees that incorporate both traditional queries
based on one attribute and queries based on hypotheses about the values of all
attributes. Such decision trees are similar to ones studied in exact learning,
where membership and equivalence queries are allowed. We present greedy
algorithms based on diverse uncertainty measures for construction of above
decision trees and discuss results of computer experiments on various data sets
from the UCI ML Repository and randomly generated Boolean functions. We also
study the length and coverage of decision rules derived from the decisiontrees
constructed by greedy algorithms.


title: FairFoody: Bringing in Fairness in Food Delivery

abstract:
  Along with the rapid growth and rise to prominence of food delivery
platforms, concerns have also risen about the terms of employment of the gig
workers underpinning this growth. Our analysis on data derived from a
real-world food delivery platform across three large cities from India show
that there is significant inequality in the money delivery agents earn. In this
paper, we formulate the problem of fair income distribution among agents while
also ensuring timely food delivery. We establish that the problem is not only
NP-hard but also inapproximable in polynomial time. We overcome this
computational bottleneck through a novel matching algorithm called FairFoody.
Extensive experiments over real-world food delivery datasets show FairFoody
imparts up to 10 times improvement in equitable income distribution when
compared to baseline strategies, while also ensuring minimal impact on customer
experience.


title:
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for
  Low-Resource Language Translation?

abstract:
  What can pre-trained multilingual sequence-to-sequence models like mBART
contribute to translating low-resource languages? We conduct a thorough
empirical experiment in 10 languages to ascertain this, considering five
factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning
data, (3) the amount of pre-training data in the model, (4) the impact of
domain mismatch, and (5) language typology. In addition to yielding several
heuristics, the experiments form a framework for evaluating the data
sensitivities of machine translation systems. While mBART is robust to domain
differences, its translations for unseen and typologically distant languages
remain below 3.0 BLEU. In answer to our title's question, mBART is not a
low-resource panacea; we therefore encourage shifting the emphasis from new
models to new data.


title:
Adaptive Objective Configuration in Bi-Objective Evolutionary
  Optimization for Cervical Cancer Brachytherapy Treatment Planning

abstract:
  The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary
Algorithm (MO-RV-GOMEA) has been proven effective and efficient in solving
real-world problems. A prime example is optimizing treatment plans for prostate
cancer brachytherapy, an internal form of radiation treatment, for which
equally important clinical aims from a base protocol are grouped into two
objectives and bi-objectively optimized. This use of MO-RV-GOMEA was recently
successfully introduced into clinical practice. Brachytherapy can also play an
important role in treating cervical cancer. However, using the same approach to
optimize treatment plans often does not immediately lead to clinically
desirable results. Concordantly, medical experts indicate that they use
additional aims beyond the cervix base protocol. Moreover, these aims have
different priorities and can be patient-specifically adjusted. For this reason,
we propose a novel adaptive objective configuration method to use with
MO-RV-GOMEA so that we can accommodate additional aims of this nature. Based on
results using only the base protocol, in consultation with medical experts, we
configured key additional aims. We show how, for 10 patient cases, the new
approach achieves the intended result, properly taking into account the
additional aims. Consequently, plans resulting from the new approach are
preferred by medical specialists in 8/10 cases.


title:
Learning the Dynamics of Physical Systems from Sparse Observations with
  Finite Element Networks

abstract:
  We propose a new method for spatio-temporal forecasting on arbitrarily
distributed points. Assuming that the observed system follows an unknown
partial differential equation, we derive a continuous-time model for the
dynamics of the data via the finite element method. The resulting graph neural
network estimates the instantaneous effects of the unknown dynamics on each
cell in a meshing of the spatial domain. Our model can incorporate prior
knowledge via assumptions on the form of the unknown PDE, which induce a
structural bias towards learning specific processes. Through this mechanism, we
derive a transport variant of our model from the convection equation and show
that it improves the transfer performance to higher-resolution meshes on sea
surface temperature and gas flow forecasting against baseline models
representing a selection of spatio-temporal forecasting methods. A qualitative
analysis shows that our model disentangles the data dynamics into their
constituent parts, which makes it uniquely interpretable.


title:
Velocity-dependent annihilation radiation from dark matter subhalos in
  cosmological simulations

abstract:
  We use the suite of Milky Way-like galaxies in the Auriga simulations to
determine the contribution to annihilation radiation from dark matter subhalos
in three velocity-dependent dark matter annihilation models: Sommerfeld,
p-wave, and d-wave models. We compare these to the corresponding distribution
in the velocity-independent s-wave annihilation model. For both the
hydrodynamical and dark-matter-only simulations, only in the case of the
Sommerfeld-enhanced annihilation does the total annihilation flux from subhalos
exceed the total annihilation flux from the smooth halo component within the
virial radius of the halo. Progressing from Sommerfeld to the s, p, and d-wave
models, the contribution from the smooth component of the halo becomes more
dominant, implying that for the p-wave and d-wave models the smooth component
is by far the dominant contribution to the radiation. Comparing to the Galactic
center excess observed by Fermi-LAT, for all simulated halos the emission is
dominated by the smooth halo contribution. However, it is possible that for
Sommerfeld models, extrapolation down to mass scales below the current
resolution limit of the simulation would imply a non-negligible contribution to
the gamma-ray emission from the Galactic Center region.


title:
Limits to gauge coupling in the dark sector set by the non-observation
  of instanton-induced decay of Super-Heavy Dark Matter in the Pierre Auger
  Observatory data

abstract:
  We investigate instanton-induced decay processes of super-heavy dark matter
particles $X$ produced during the inflationary epoch. Using data collected at
the Pierre Auger Observatory we derive a bound on the reduced coupling constant
of gauge interactions in the dark sector: $\alpha_X^{\rm eff} \lesssim 0.09$,
for $10^{10} < M_X/{\rm GeV} < 10^{16}$. We show that this upper limit on
$\alpha_X^{\rm eff}$ is complementary to that obtained from the non-observation
of tensor modes in the cosmic microwave background.


title: Fluctuating Hydrodynamics and Wormholes

abstract:
  We show that a recent reformulation of hydrodynamic equations for a large
class of models consisting of q-dits on a graph with short range interactions
is sufficient for understanding chaotic behavior. Any such system consists of
large subsystems coupled together by interactions whose relative strength goes
to zero with the subsystem size. In the absence of conservation laws other than
energy, the Hamiltonians of the subsystems form a complete set of commuting
operators. The hydrodynamic variables are the block diagonal matrix elements
$\rho(e(X))$ of the density matrix in the joint eigenbasis of the subsystem
Hamiltonians, averaged over energy bins. To leading order in the inverse
subsystem size, $\rho(e(X); t)$ satisfies a classical stochastic equation,
which for certain systems takes the form of a functional Fokker-Planck
equation. In such systems the time averaged spectral form factors can be
written as a two dimensional Euclidean functional integral, on a space with
multiple disconnected boundaries. The failure of factorization in this
representation is attributable to the time averaging necessary to apply the
hydrodynamic approximation. The bulk Euclidean action is purely topological. We
make tentative explorations of the special properties of the system that are
required in order to have a representation as a functional integral over
metrics.


title:
Planar Rosa : a family of quasiperiodic substitution discrete plane
  tilings with $2n$-fold rotational symmetry

abstract:
  We present Planar Rosa, a family of rhombus tilings with a $2n$-fold
rotational symmetry that are generated by a primitive substitution and that are
also discrete plane tilings, meaning that they are obtained as a projection of
a higher dimensional discrete plane. The discrete plane condition is a relaxed
version of the cut-and-project condition. We also prove that the Sub Rosa
substitution tilings with $2n$-fold rotational symmetry defined by Kari and
Rissanen do not satisfy even the weaker discrete plane condition. We prove our
results for all even $n\geq 4$. This completes our previously published results
for odd values of $n$.


title: Noisy Tensor Completion via Low-rank Tensor Ring

abstract:
  Tensor completion is a fundamental tool for incomplete data analysis, where
the goal is to predict missing entries from partial observations. However,
existing methods often make the explicit or implicit assumption that the
observed entries are noise-free to provide a theoretical guarantee of exact
recovery of missing entries, which is quite restrictive in practice. To remedy
such drawbacks, this paper proposes a novel noisy tensor completion model,
which complements the incompetence of existing works in handling the
degeneration of high-order and noisy observations. Specifically, the tensor
ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize
the underlying tensor and the observed entries, respectively. In addition, a
non-asymptotic upper bound of estimation error is provided to depict the
statistical performance of the proposed estimator. Two efficient algorithms are
developed to solve the optimization problem with convergence guarantee, one of
which is specially tailored to handle large-scale tensors by replacing the
minimization of TRNN of the original tensor equivalently with that of a much
smaller one in a heterogeneous tensor decomposition framework. Experimental
results on both synthetic and real-world data demonstrate the effectiveness and
efficiency of the proposed model in recovering noisy incomplete tensor data
compared with state-of-the-art tensor completion models.


title:
A Real-Time Region Tracking Algorithm Tailored to Endoscopic Video with
  Open-Source Implementation

abstract:
  With a video data source, such as multispectral video acquired during
administration of fluorescent tracers, extraction of time-resolved data
typically requires the compensation of motion. While this can be done manually,
which is arduous, or using off-the-shelf object tracking software, which often
yields unsatisfactory performance, we present an algorithm which is simple and
performant. Most importantly, we provide an open-source implementation, with an
easy-to-use interface for researchers not inclined to write their own code, as
well as Python modules that can be used programmatically.


title:
Convergence of Optimal Expected Utility for a Sequence of Discrete-Time
  Markets in Initially Enlarged Filtrations

abstract:
  In this paper, we extend Kreps' conjecture that optimal expected utility in
the classic Black-Scholes-Merton (BSM) economy is the limit of optimal expected
utility for a sequence of discrete-time economies in initially enlarged
filtrations converge to the BSM economy in an initially enlarged filtration in
a "strong" sense. The n-th discrete-time economy is generated by a scaled
n-step random walk, based on an unscaled random variable with mean 0, variance
1, and bounded support. Moreover, the informed insider knows each functional
generating the enlarged filtrations path-by-path. We confirm Kreps' conjecture
in initially enlarged filtrations when the consumer's utility function U has
asymptotic elasticity strictly less than one.


title: Generalized Ellis-Bronnikov wormholes in asymptotically safe gravity

abstract:
  In this paper we investigate the presence of exotic matter and the energy
conditions of the generalized Ellis-Bronnikov solutions proposed in
\cite{Kar:1995jz}, a generalization of the Ellis-Bronnikov spacetime that has
two parameters defining it ($n$ and $r_t$) in the context of asymptotically
safe gravity (ASG). These generalized solutions was a attempt to solve the
problem of exotic matter, and we investigate if the ASG cured these
problems.Further, as pointed in \cite{DuttaRoy:2019hij}, these generalized
solutions needs an extra matter beyond phantom to exist. Therefore we
investigated if the ASG brings what kind of matter these extra matter can be.
For this, we have choose the models that use the Ricci scalar, squared Ricci
and the Kretschmann scalar to define the antiscreening function. We found that
we have corrections at the throat due the ASG only for the Ricci scalar and the
squared Ricci as improvement and this only for a specific generalized wormhole
(those with $n=4$).
  For the Ricci scalar case we have solved the problem with exotic matter at
the throat of the wormhole, however, the radial energy conditions are not
satisfied at the throat, as we have in the usual case. But, we have shown that
non-exotic matter at throat implies regions where the radial energy conditions
can be satisfied, contrary to what is predicted by classical theory. For this
we have regions with extra matter (quintessence and non-exotic) beyond Phantom,
which naturally appears in the ASG context. When using the squared Ricci as
improvement, we have shown that only Phantom is allowed at the throat, but, in
this case, the radial energy conditions are always satisfied at the throat, and
for this we have regions with exotic matter with $\omega>1$ and non-exotic
matter. Once again the ASG shows naturally what kind of extra matter we have to
support this generalized solutions.


title: Towards an HPC Complementary Computing Facility

abstract:
  This Letter considers the design for computing facilities that are
complementary to the leadership class High Performance Computing (HPC)
facilities. This design envisions a future where funding agencies are
allocating greater resources for leadership class facilities and these
facilities will provide a significant part of the total compute cycles for HEP
Experiments. While a leadership class facility (LCF) may provide cycles and
advanced architectures, the facility does not necessarily provide all of the
services needed to help HEP users make the best use of the HPC facility, as
well as the services needed to provide computing for workflows that are not a
good fit for the HPC facilities. This Letter outlines some of the necessary
components of a facility designed to provide those services and capabilities.


title: Post-starburst galaxies in the centers of intermediate redshift clusters

abstract:
  We present results from MUSE spatially-resolved spectroscopy of 21
post-starburst galaxies in the centers of 8 clusters from $z\sim0.3$ to
$z\sim0.4$. We measure spatially resolved star-formation histories (SFHs), the
time since quenching ($t_Q$) and the fraction of stellar mass assembled in the
past 1.5 Gyr ($\mu_{1.5}$). The SFHs display a clear enhancement of
star-formation prior to quenching for 16 out of 21 objects, with at least 10%
(and up to $>50$%) of the stellar mass being assembled in the past 1.5 Gyr and
$t_Q$ ranging from less than 100 Myrs to $\sim800$ Myrs. By mapping $t_Q$ and
$\mu_{1.5}$, we analyze the quenching patterns of the galaxies. Most galaxies
in our sample have quenched their star-formation from the outside-in or show a
side-to-side/irregular pattern, both consistent with quenching by ram-pressure
stripping. Only three objects show an inside-out quenching pattern, all of
which are at the high-mass end of our sample. At least two of them currently
host an active galactic nucleus. In two post-starbursts, we identify tails of
ionized gas indicating that these objects had their gas stripped by ram
pressure very recently. Post-starburst features are also found in the stripped
regions of galaxies undergoing ram-pressure stripping in the same clusters,
confirming the link between these classes of objects. Our results point to
ram-pressure stripping as the main driver of fast quenching in these
environments, with active galactic nuclei playing a role at high stellar
masses.


title: Electroweak Precision Measurements in Diboson Production at CMS

abstract:
  In this contribution, I have outlined recent precision measurements of the
standard model (SM) multiboson production at CMS. A study of diboson production
at 5 TeV constitutes an important probe of the SM at a new energy, and the data
favour NNLO predictions obtained by MATRIX. A study of WZ production at 13 TeV
constitutes the most comprehensive study of WZ production to date, containing
inclusive and differential cross section measurements, charge asymmetry
measurements, constraints on the LHC proton parton distribution functions, and
constraints on anomalous values of the WWZ trilinear gauge coupling. No
evidence for new physics is found, and all the results favour SM predictions
calculated at NNLO using MATRIX.


title:
"It Feels Like Taking a Gamble": Exploring Perceptions, Practices, and
  Challenges of Using Makeup and Cosmetics for People with Visual Impairments

abstract:
  Makeup and cosmetics offer the potential for self-expression and the
reshaping of social roles for visually impaired people. However, there exist
barriers to conducting a beauty regime because of the reliance on visual
information and color variances in makeup. We present a content analysis of 145
YouTube videos to demonstrate visually impaired individuals' unique practices
before, during, and after doing makeup. Based on the makeup practices, we then
conducted semi-structured interviews with 12 visually impaired people to
discuss their perceptions of and challenges with the makeup process in more
depth. Overall, through our findings and discussion, we present novel
perceptions of makeup from visually impaired individuals (e.g., broader
representations of blindness and beauty). The existing challenges provide
opportunities for future research to address learning barriers, insufficient
feedback, and physical and environmental barriers, making the experience of
doing makeup more accessible to people with visual impairments.


title: Evidence of surface heterogeneity on active asteroid (3200) Phaethon

abstract:
  Thermal infrared emission and thermophysical modeling techniques are powerful
tools in deciphering the surface properties of asteroids. The near-Earth
asteroid (3200) Phaethon is an active asteroid with a very small perihelion
distance and is likely the source of the Geminid meteor shower. We estimate and
interpret the thermal inertia of this extraordinary asteroid using observations
that span ten distinct sightings. The variation in thermal inertia over these
sightings is inconsistent with the expected temperature-dependent thermal
inertia theorized from radiative heat transfer within the regolith. Thus, we
test whether the variation in thermal inertia can be explained by modeling a
regolith layer over bedrock and two spatially heterogeneous scenarios. We find
that the model in which Phaethon's north and south hemispheres have distinctly
different thermophysical properties can sufficiently explain the
thermal-inertias determined herein. In particular, we find that a boundary
located between latitudes -30 deg and +10 deg separates fine-grained southern
latitudes from a northern hemisphere that is dominated by coarse-grained
regolith and/or a high coverage of porous boulders. We discuss the implications
related to Phaethon's activity and potential association with 2005 UD.


title: Non-minimal non-Abelian quantum vector fields in curved spacetime

abstract:
  The quantum effective action of non-minimal vector fields with Abelian or non
Abelian gauge degrees of freedom in curved spacetime is studied. The Proca or
Yang-Mills fields are coupled to a local mass-like term acting in both
coordinate and gauge spaces. Pathologies due to gauge invariance in the
ultraviolet are avoided through the introduction of a non-Abelian version of
the Stueckelberg field. It is found that the breaking of gauge invariance
induced by the mass term affects only the tree level part of the effective
action. The ultraviolet divergent part of the effective action to one loop is
obtained using the method of covariant symbols and dimensional regularization.
Formulas are given valid for any spacetime dimension and explicit results are
shown for the two dimensional case. As already happened for the case of a
single vector field, the ultraviolet divergences are local but not of
polynomial type.


title: Canonical analysis of Kalb-Ramond-Proca duality

abstract:
  It is shown that the canonical quantization of the free massive Kalb-Ramond
Lagrangian leads to the same theory obtained from the canonical quantization of
the free Proca Lagrangian. This indicates that both Lagrangians describe the
same physical system, contrary to recent claims in the literature. The duality
in the presence of an interaction is explored in the context of perturbation
theory and beyond. It is pointed out that the equivalence between Kalb-Ramond
and Proca is rooted in an ambiguity of coordinate choices in the canonical
formalism and it is present for other spins.


title: CTs for free products

abstract:
  The fundamental group of a finite graph of groups with trivial edge groups is
a free product. We are interested in those outer automorphisms of such a free
product that permute the conjugacy classes of the vertex groups. We show that
in particular cases of interest, such as where vertex groups are themselves
finite free products of finite and cyclic groups, given such an outer
automorphism, after passing to a positive power, the outer automorphism is
represented by a particularly nice kind of relative train track map called a
CT. CTs were first introduced by Feighn and Handel for outer automorphisms of
free groups. We develop the theory of attracting laminations for and principal
automorphisms of free products. We prove that outer automorphisms of free
products satisfy an index inequality reminiscent of a result of Gaboriau,
Jaeger, Levitt and Lustig and sharpening a result of Martino. Finally, we prove
a result reminiscent of a theorem of Culler on the fixed subgroup of an
automorphism of a free product whose outer class has finite order.


title: Advancing Superconducting Magnet Diagnostics for Future Colliders

abstract:
  Future colliders will operate at increasingly high magnetic fields pushing
limits of electromagnetic and mechanical stress on the conductor [1].
Understanding factors affecting superconducting (SC) magnet performance in
challenging conditions of high mechanical stress and cryogenic temperatures is
only possible with the use of advanced magnet diagnostics. Diagnostics provide
a unique observation window into mechanical and electromagnetic processes
associated with magnet operation, and give essential feedback to magnet design,
simulations and material research activities. Development of novel diagnostic
capabilities is therefore an integral part of next-generation magnet
development. In this paper, we summarize diagnostics development needs from a
prospective of the US Magnet Development Program (MDP), and define main
research directions that could shape this field in the near future.


title:
Instability from high-order resonant chains in wide-separation massive
  planet systems

abstract:
  Diversity in the properties of exoplanetary systems arises, in part, from
dynamical evolution that occurs after planet formation. We use numerical
integrations to explore the relative role of secular and resonant dynamics in
the long-term evolution of model planetary systems, made up of three equal mass
giant planets on initially eccentric orbits. The range of separations studied
is dominated by secular processes, but intersects chains of high-order
mean-motion resonances. Over time-scales of $10^8$ orbits, the secular
evolution of the simulated systems is predominantly regular. High-order
resonant chains, however, can be a significant source of angular momentum
deficit (AMD), leading to instability. Using a time-series analysis based on a
Hilbert transform, we associate instability with broad islands of chaotic
evolution. Previous work has suggested that first-order resonances could modify
the AMD of nominally secular systems and facilitate secular chaos. We find that
higher-order resonances, when present in chains, can have similar impacts.


title:
Understanding training in superconducting accelerator magnets using
  acoustic emission diagnostics

abstract:
  Among various long-standing standing problems in superconducting accelerator
magnet development, training is one of the oldest and well-known issues
affecting magnet performance. Understanding physics behind the training process
is crucial for proper mitigation and elimination of this phenomenon in new
magnet designs. This paper discusses application of acoustic emission (AE)
diagnostics to localization and classification of transient mechanical events
in superconducting magnets and characterization of transient energy releases
responsible for premature quenching, memory effects and, ultimately, training.
Prospective of future AE diagnostics development are outlined in connection to
the goals of US Magnet Development Program.


title: Discovery of an electronic crystal in a cuprate Mott insulator

abstract:
  Copper oxide high temperature superconductors universally exhibit multiple
forms of electronically ordered phases that break the native translational
symmetry of the CuO2 planes. The interplay between these orders and the
superconducting ground state, as well as how they arise through doping a Mott
insulator, is essential to decode the mechanisms of high-temperature
superconductivity. Over the years, various forms of electronic liquid crystal
phases including charge/spin stripes and incommensurate charge-density-waves
(CDWs) were found to emerge out of a correlated metallic ground state in
underdoped cuprates. Early theoretical studies also predicted the emergence of
a Coulomb-frustrated 'charge crystal' phase in the very lightly-doped,
insulating limit of the CuO2 planes. Here, we use resonant X-ray scattering,
electron transport, and muon spin rotation measurements to fully resolve the
electronic and magnetic ground state and search for signatures of charge order
in very lightly hole-doped cuprates from the RBa2Cu3O7-d family (RBCO; R: Y or
rare earth). X-ray scattering data from RBCO films reveal a breaking of
translational symmetry more pervasive than was previously known, extending down
to the Mott limit. The ordering vector of this charge crystal state is linearly
connected to the charge-density-waves of underdoped RBCO, suggesting that the
former phase is a precursor to the latter as hole doping is increased. Most
importantly, the coexistence of charge and spin order in RBCO suggests that
this electronic symmetry-breaking state is common to the CuO2 planes in the
very lightly-doped regime. These findings bridge the gap between the Mott
insulating state and the underdoped metallic state and underscore the prominent
role of Coulomb-frustrated electronic phase separation among all cuprates.


title:
Improving stability of moving particle semi-implicit method by source
  terms based on time-scale correction of particle-level impulses

abstract:
  The aim of this paper is to investigate the unstable nature of pressure
computation focusing on incompressible flow modeling through the
projection-based particle methods. A new approach from the original viewpoint
of the momentum conservation regarding particle-level collisions, hereinafter
refered to as time-scale correction of particle-level impulses (TCPI), is
proposed to derive new source terms of pressure Poisson equation (PPE). This
results in more stable computations with drastic reduction of unphysical
pressure oscillations and more robust simulation with pressure magnitudes
almost independent to time step. Moreover, compared to other strategies, no
additional computational effort is required, its implementation is extremely
simple, and the only numerical parameter is the propagation speed of the
perturbations, of which the calibration is much more straightforward due to its
physical meaning. Simulations were carried out using moving particle
semi-implicit (MPS) method improved by the proposed approach. The comparisons
of computed results with theoretical and experimental ones confirmed the
effectiveness of the proposed approach.


title:
Prospects for the Measurement of the Standard Model Higgs Pair
  Production at the Muon Colliders

abstract:
  We study the Higgs pair production process at a muon collider using b-pair
decays of the Higgs bosons. Efficient identification and good measurement
resolution for the b-jet pair invariant mass are crucial for unearthing the
di-Higgs signal. However, the beam-induced background has potential to
drastically degrade the performance. We report on the full simulation studies
of the degradation of the reconstructed b-jet pair invariant mass in di-Higgs
events, considering only the beam-induced background in the calorimeter.
Mitigation strategies for the suppression of the beam-induced background are
underway. We also report prospects for the measurement of the Standard Model
Higgs pair production at the Muon Colliders at various benchmarks of the
collider center of mass energy and integrated luminosity using a fast
simulation program.


title: SC2: Supervised Compression for Split Computing

abstract:
  Split computing distributes the execution of a neural network (e.g., for a
classification task) between a mobile device and a more powerful edge server. A
simple alternative to splitting the network is to carry out the supervised task
purely on the edge server while compressing and transmitting the full data, and
most approaches have barely outperformed this baseline. This paper proposes a
new approach for discretizing and entropy-coding intermediate feature
activations to efficiently transmit them from the mobile device to the edge
server. We show that a efficient splittable network architecture results from a
three-way tradeoff between (a) minimizing the computation on the mobile device,
(b) minimizing the size of the data to be transmitted, and (c) maximizing the
model's prediction performance. We propose an architecture based on this
tradeoff and train the splittable network and entropy model in a knowledge
distillation framework. In an extensive set of experiments involving three
vision tasks, three datasets, nine baselines, and more than 180 trained models,
we show that our approach improves supervised rate-distortion tradeoffs while
maintaining a considerably smaller encoder size. We also release sc2bench, an
installable Python package, to encourage and facilitate future studies on
supervised compression for split computing (SC2).


title:
Encrypted Operator Computing: an alternative to Fully Homomorphic
  Encryption

abstract:
  We introduce a new approach to computation on encrypted data -- Encrypted
Operator Computing (EOC) -- as an alternative to Fully Homomorphic Encryption
(FHE). EOC can be viewed as a reversible computation performed in a transformed
(encrypted) frame of reference on transformed (encrypted) data, with both the
transformation and the data, as well as the function to be computed, hidden
from adversaries. Encryption is implemented via a fast-scrambling two-stage
cipher based on shallow -- ${\cal O}(\log n)$ depth -- random reversible
circuits of long-ranged 3-bit gates, organized in a hierarchical tree structure
[1]. Encrypted functions are expressed as a concatenation of a polynomial
number of "chips", $n$-input/$n$-output reversible functions, the outputs of
which are expressed as ordered Binary Decision Diagrams (OBDDs). OBDDs are
normal forms that only expose the functionality of the chip but hide its
precise circuit implementation. The ${\cal O}(\log n)$ depth of the cipher
allows us to prove analytically that the output OBDDs are polynomial in size,
establishing individual chips as examples of Best Possible Obfuscators
introduced by Goldwasser and Rothblum [2]. To extend single-chip security to
the concatenation of chips we add random pairs of NOT gates, which are split
apart and distributed across the system, for each recursive step in our
construction. This randomization process, which is amplified by the
nonlinearity of the cipher, scrambles the functionality of individual chips but
preserves that of the whole circuit, thus enhancing the security of the full
computation beyond that conferred by Best Possible Obfuscation of individual
chips. While the paper focuses on symmetric encryption, we also present a
generalization to public-private encryption.


title: Code Smells in Elixir: Early Results from a Grey Literature Review

abstract:
  Elixir is a new functional programming language whose popularity is rising in
the industry. However, there are few works in the literature focused on
studying the internal quality of systems implemented in this language.
Particularly, to the best of our knowledge, there is currently no catalog of
code smells for Elixir. Therefore, in this paper, through a grey literature
review, we investigate whether Elixir developers discuss code smells. Our
preliminary results indicate that 11 of the 22 traditional code smells
cataloged by Fowler and Beck are discussed by Elixir developers. We also
propose a list of 18 new smells specific for Elixir systems and investigate
whether these smells are currently identified by Credo, a well-known static
code analysis tool for Elixir. We conclude that only two traditional code
smells and one Elixir-specific code smell are automatically detected by this
tool. Thus, these early results represent an opportunity for extending tools
such as Credo to detect code smells and then contribute to improving the
internal quality of Elixir systems.


title:
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning
  for Segmentation

abstract:
  Uncertainty estimation in deep learning has become a leading research field
in medical image analysis due to the need for safe utilisation of AI algorithms
in clinical practice. Most approaches for uncertainty estimation require
sampling the network weights multiple times during testing or training multiple
networks. This leads to higher training and testing costs in terms of time and
computational resources. In this paper, we propose Layer Ensembles, a novel
uncertainty estimation method that uses a single network and requires only a
single pass to estimate predictive uncertainty of a network. Moreover, we
introduce an image-level uncertainty metric, which is more beneficial for
segmentation tasks compared to the commonly used pixel-wise metrics such as
entropy and variance. We evaluate our approach on 2D and 3D, binary and
multi-class medical image segmentation tasks. Our method shows competitive
results with state-of-the-art Deep Ensembles, requiring only a single network
and a single pass.


title:
Make the Difference! Computationally Trivial Estimators for Grouped
  Fixed Effects Models

abstract:
  Novel estimators are proposed for linear grouped fixed effects models. Rather
than predicting a single grouping of units, they deliver a collection of
groupings with the same flavor as the so-called Lasso regularization path. Mild
conditions are found that ensure their asymptotic guarantees are the same as
the so-called grouped fixed effects and post-spectral estimators (Bonhomme and
Manresa, 2015; Chetverikov and Manresa, 2021). In contrast, the new estimators
are computationally straightforward and do not require prior knowledge of the
number of groups. Monte Carlo simulations suggest good finite sample
performance. Applying the approach to real data provides new insights on the
potential network structure of the unobserved heterogeneity.


title:
Finite-Length Scaling of SC-LDPC Codes With a Limited Number of Decoding
  Iterations

abstract:
  We propose four finite-length scaling laws to predict the frame error rate
(FER) performance of spatially-coupled low-density parity-check codes under
full belief propagation (BP) decoding with a limit on the number of decoding
iterations and a scaling law for sliding window decoding, also with limited
iterations. The laws for full BP decoding provide a choice between accuracy and
computational complexity; a good balance between them is achieved by the law
that models the number of decoded bits after a certain number of BP iterations
by a time-integrated Ornstein-Uhlenbeck process. This framework is developed
further to model sliding window decoding as a race between the integrated
Ornstein-Uhlenbeck process and an absorbing barrier that corresponds to the
left boundary of the sliding window. The proposed scaling laws yield accurate
FER predictions.


title: Hyperbolic Uncertainty Aware Semantic Segmentation

abstract:
  Semantic segmentation (SS) aims to classify each pixel into one of the
pre-defined classes. This task plays an important role in self-driving cars and
autonomous drones. In SS, many works have shown that most misclassified pixels
are commonly near object boundaries with high uncertainties. However, existing
SS loss functions are not tailored to handle these uncertain pixels during
training, as these pixels are usually treated equally as confidently classified
pixels and cannot be embedded with arbitrary low distortion in Euclidean space,
thereby degenerating the performance of SS. To overcome this problem, this
paper designs a "Hyperbolic Uncertainty Loss" (HyperUL), which dynamically
highlights the misclassified and high-uncertainty pixels in Hyperbolic space
during training via the hyperbolic distances. The proposed HyperUL is model
agnostic and can be easily applied to various neural architectures. After
employing HyperUL to three recent SS models, the experimental results on
Cityscapes and UAVid datasets reveal that the segmentation performance of
existing SS models can be consistently improved.


title: Quantum algorithms from fluctuation theorems: Thermal-state preparation

abstract:
  Fluctuation theorems provide a correspondence between properties of quantum
systems in thermal equilibrium and a work distribution arising in a
non-equilibrium process that connects two quantum systems with Hamiltonians
$H_0$ and $H_1=H_0+V$. Building upon these theorems, we present a quantum
algorithm to prepare a purification of the thermal state of $H_1$ at inverse
temperature $\beta \ge 0$ starting from a purification of the thermal state of
$H_0$. The complexity of the quantum algorithm, given by the number of uses of
certain unitaries, is $\tilde {\cal O}(e^{\beta (\Delta \! A- w_l)/2})$, where
$\Delta \! A$ is the free-energy difference between $H_1$ and $H_0,$ and $w_l$
is a work cutoff that depends on the properties of the work distribution and
the approximation error $\epsilon>0$. If the non-equilibrium process is
trivial, this complexity is exponential in $\beta \|V\|$, where $\|V\|$ is the
spectral norm of $V$. This represents a significant improvement of prior
quantum algorithms that have complexity exponential in $\beta \|H_1\|$ in the
regime where $\|V\|\ll \|H_1\|$. The dependence of the complexity in $\epsilon$
varies according to the structure of the quantum systems. It can be exponential
in $1/\epsilon$ in general, but we show it to be sublinear in $1/\epsilon$ if
$H_0$ and $H_1$ commute, or polynomial in $1/\epsilon$ if $H_0$ and $H_1$ are
local spin systems. The possibility of applying a unitary that drives the
system out of equilibrium allows one to increase the value of $w_l$ and improve
the complexity even further. To this end, we analyze the complexity for
preparing the thermal state of the transverse field Ising model using different
non-equilibrium unitary processes and see significant complexity improvements.


title:
Testing the empirical relationship between Forbush decreases and cosmic
  ray diurnal anisotropy

abstract:
  The abrupt aperiodic modulation of cosmic ray (CR) flux intensity, often
referred to as Forbush decrease (FD), plays a significant role in our
understanding of the Sun-Earth electrodynamics. Accurate and precise
determination of FD magnitude and timing are among the intractable problems in
FD-based analysis. FD identification is complicated by CR diurnal anisotropy.
CR anisotropy can increase or reduce the number and amplitude of FDs. It is
therefore important to remove its contributions from CR raw data before FD
identification. Recently, an attempt was made, using a combination of Fourier
transformed technique and FD-location machine to address this. Thus, two FD
catalogs and amplitude diurnal variation (ADV) were calculated from filtered
(FD1 and ADV) and raw (FD2) CR data. In the current work, we test the empirical
relationship between FD1, FD2, ADV, and solar-geophysical characteristics. Our
analysis shows that two types of magnetic fields-interplanetary (IMF) and
geomagnetic (Dst) govern the evolution of CR flux intensity reductions.


title: Quantum Kernel Methods for Solving Differential Equations

abstract:
  We propose several approaches for solving differential equations (DEs) with
quantum kernel methods. We compose quantum models as weighted sums of kernel
functions, where variables are encoded using feature maps and model derivatives
are represented using automatic differentiation of quantum circuits. While
previously quantum kernel methods primarily targeted classification tasks, here
we consider their applicability to regression tasks, based on available data
and differential constraints. We use two strategies to approach these problems.
First, we devise a mixed model regression with a trial solution represented by
kernel-based functions, which is trained to minimize a loss for specific
differential constraints or datasets. Second, we use support vector regression
that accounts for the structure of differential equations. The developed
methods are capable of solving both linear and nonlinear systems. Contrary to
prevailing hybrid variational approaches for parametrized quantum circuits, we
perform training of the weights of the model classically. Under certain
conditions this corresponds to a convex optimization problem, which can be
solved with provable convergence to global optimum of the model. The proposed
approaches also favor hardware implementations, as optimization only uses
evaluated Gram matrices, but require quadratic number of function evaluations.
We highlight trade-offs when comparing our methods to those based on
variational quantum circuits such as the recently proposed differentiable
quantum circuits (DQC) approach. The proposed methods offer potential quantum
enhancement through the rich kernel representations using the power of quantum
feature maps, and start the quest towards provably trainable quantum DE
solvers.


title: Short and local transformations between ($\Delta+1$)-colorings

abstract:
  Recoloring a graph is about finding a sequence of proper colorings of this
graph from an initial coloring $\sigma$ to a target coloring $\eta$. Each pair
of consecutive colorings must differ on exactly one vertex. The question
becomes: is there a sequence of colorings from $\sigma$ to $\eta$?
  In this paper, we focus on $(\Delta+1)$-colorings of graphs of maximum degree
$\Delta$. Feghali, Johnson and Paulusma proved that, if both colorings are
non-frozen (i.e. we can change the color of a least one vertex), then a
quadratic recoloring sequence always exists. We improve their result by proving
that there actually exists a linear transformation.
  In addition, we prove that the core of our algorithm can be performed
locally. Informally, if we start from a coloring where there is a set of
well-spread non-frozen vertices, then we can reach any other such coloring by
recoloring only $f(\Delta)$ independent sets one after another. Moreover, these
independent sets can be computed efficiently in the LOCAL model of distributed
computing.


title:
On the Origin of Plasticity-Induced Microstructure Change under Sliding
  Contacts

abstract:
  Discrete dislocation plasticity (DDP) calculations are carried out to
investigate the response of a single crystal contacted by a rigid sinusoidal
asperity under sliding loading conditions to look for causes of microstructure
change in the dislocation structure. The mechanistic driver is identified as
the development of lattice rotations and stored energy in the subsurface, which
can be quantitatively correlated to recent tribological experimental
observations. Maps of surface slip initiation and substrate permanent
deformation obtained from DDP calculations for varying contact size and normal
load suggest ways of optimally tailoring the interface and microstructural
material properties for various frictional loads.


title: On Redundancy and Diversity in Cell-based Neural Architecture Search

abstract:
  Searching for the architecture cells is a dominant paradigm in NAS. However,
little attention has been devoted to the analysis of the cell-based search
spaces even though it is highly important for the continual development of NAS.
In this work, we conduct an empirical post-hoc analysis of architectures from
the popular cell-based search spaces and find that the existing search spaces
contain a high degree of redundancy: the architecture performance is minimally
sensitive to changes at large parts of the cells, and universally adopted
designs, like the explicit search for a reduction cell, significantly increase
the complexities but have very limited impact on the performance. Across
architectures found by a diverse set of search strategies, we consistently find
that the parts of the cells that do matter for architecture performance often
follow similar and simple patterns. By explicitly constraining cells to include
these patterns, randomly sampled architectures can match or even outperform the
state of the art. These findings cast doubts into our ability to discover truly
novel architectures in the existing cell-based search spaces, and inspire our
suggestions for improvement to guide future NAS research. Code is available at
https://github.com/xingchenwan/cell-based-NAS-analysis.


title: A Predicted Dearth of Majority Hypervolatile Ices in Oort Cloud Comets

abstract:
  We present new, ice species-specific New Horizons/Alice upper gas coma
production limits from the 01 Jan 2019 MU69/Arrokoth flyby of Gladstone et al.
(2021) and use them to make predictions about the rarity of majority
hypervolatile (CO, N$_2$, CH$_4$) ices in KBOs and Oort Cloud comets. These
predictions have a number of important implications for the study of the Oort
Cloud, including: determination of hypervolatile rich comets as the first
objects emplaced into the Oort Cloud; measurement of CO/N$_2$/CH$_4$ abundance
ratios in the proto-planetary disk from hypervolatile rich comets; and
population statistical constraints on early (< 20 Myr) planetary aggregation
driven versus later (> 50 Myr) planetary migration driven emplacement of
objects into the Oort Cloud. They imply that the phenomenon of ultra-distant
active comets like C/2017K2 (Jewitt et al. 2017, Hui et al. 2018) should be
rare, and thus not a general characteristic of all comets. They also suggest
that interstellar object 2I/Borisov did not originate in a planetary system
that was inordinately CO rich (Bodewits et al. 2020), but rather could have
been ejected onto an interstellar trajectory very early in its natal system's
history.


title:
Social-Cultural Factors in the Design of Technology for Hispanic People
  with Stroke

abstract:
  Stroke is a leading cause of serious, long-term disability in the United
States. There exist disparities in both stroke prevalence and outcomes between
people with stroke in Hispanic and Latinx communities and the general stroke
population. Current stroke technology - which aims to improve quality of life
and bring people with stroke to the most functional, independent state possible
- has shown promising results for the general stroke population, but has failed
to close the recovery outcome gap for underserved Hispanic and Latinx people
with stroke. Previous work in health education, digital health, and HRI has
improved human health outcomes by incorporating social-cultural factors, though
not for stroke. In this position paper, we aim to justify accounting for unique
cultural factors in stroke technology design for the Hispanic and Latinx
community. We review examples of successful culturally appropriate
interventions and suggest design considerations (mutually beneficial community
consultation, accommodating for barriers beforehand, building on culture, and
incorporating education of the family) to provide more culturally appropriate
design of Hispanic and Latinx stroke technology and reduce the disparity gap.


title: The Mathematics of Artificial Intelligence

abstract:
  We currently witness the spectacular success of artificial intelligence in
both science and public life. However, the development of a rigorous
mathematical foundation is still at an early stage. In this survey article,
which is based on an invited lecture at the International Congress of
Mathematicians 2022, we will in particular focus on the current "workhorse" of
artificial intelligence, namely deep neural networks. We will present the main
theoretical directions along with several exemplary results and discuss key
open problems.


title: Improved analysis of converging shock radiographs

abstract:
  We previously reported an experimental platform to induce a
spherically-convergent shock in a sample using laser-driven ablation, probed
with time-resolved x-ray radiography, and an analysis method to deduce states
along the principal shock Hugoniot simultaneously with the x-ray opacity. We
have now developed a modified method of analysis that is numerically
better-conditioned and faster, and usually provides a better representation of
the radiograph with correspondingly lower uncertainties. The previous approach
was based on optimizing parameters in a model of the density distribution as a
function of radius and time, warped to follow loci such as the shock and the
outside of the sample. The converging shock configuration can be described more
efficiently in terms of the shocked density and sound speed, expressed as
functions of the shock speed Studies of the Hugoniot from various theoretical
equations of state (EOS) indicate that, in the typical range of states explored
by these experiments, these functions can be described by low-order
polynomials. Similarly, few-parameter functions were found suitable for
representing the variation of x-ray opacity with shock pressure. This approach
was found to perform better in most cases than an alternative method based on
parameterization of the EOS.


title:
Electromagnetic interactions of reactor neutrinos and $\sin^2{\theta_W}$
  estimate in CE$\overline{\nu}$NS with different quenching factor models

abstract:
  Recently, evidence for the observation of $\sim$2 keV and below nuclear
recoils from the coherent scattering of reactor neutrinos and nucleus with a
Germanium detector has been reported. We analyze the observed data to estimate
the value of the weak mixing angle and constrain the neutrino millicharge,
magnetic moment, charge radius and anapole moment contributing to the coherent
scattering process. Currently, there is no definite model available for the
quenching factor at such low energies. To this aim, we use different models of
the quenching factor and show how does it affect the interpretation of obtained
results. We find that the obtained bounds are stronger in some cases while
comparable in others than previous bounds from the coherent scattering process.
These results are the first at a keV scale nuclear recoils. In addition, we
also provide different quenching factor functions which can be used for any new
physics phenomenology.


title: Multimodal Learning on Graphs for Disease Relation Extraction

abstract:
  Objective: Disease knowledge graphs are a way to connect, organize, and
access disparate information about diseases with numerous benefits for
artificial intelligence (AI). To create knowledge graphs, it is necessary to
extract knowledge from multimodal datasets in the form of relationships between
disease concepts and normalize both concepts and relationship types. Methods:
We introduce REMAP, a multimodal approach for disease relation extraction and
classification. The REMAP machine learning approach jointly embeds a partial,
incomplete knowledge graph and a medical language dataset into a compact latent
vector space, followed by aligning the multimodal embeddings for optimal
disease relation extraction. Results: We apply REMAP approach to a disease
knowledge graph with 96,913 relations and a text dataset of 1.24 million
sentences. On a dataset annotated by human experts, REMAP improves text-based
disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing
disease knowledge graphs with text information. Further, REMAP leverages text
information to recommend new relationships in the knowledge graph,
outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score).
Discussion: Systematized knowledge is becoming the backbone of AI, creating
opportunities to inject semantics into AI and fully integrate it into machine
learning algorithms. While prior semantic knowledge can assist in extracting
disease relationships from text, existing methods can not fully leverage
multimodal datasets. Conclusion: REMAP is a multimodal approach for extracting
and classifying disease relationships by fusing structured knowledge and text
information. REMAP provides a flexible neural architecture to easily find,
access, and validate AI-driven relationships between disease concepts.


title:
Decision Trees with Hypotheses for Recognition of Monotone Boolean
  Functions and for Sorting

abstract:
  In this paper, we consider decision trees that use both queries based on one
attribute each and queries based on hypotheses about values of all attributes.
Such decision trees are similar to ones studied in exact learning, where not
only membership but also equivalence queries are allowed. We investigate the
problem of recognition of monotone Boolean functions with $n$ variables, $n=2,
\ldots, 4$, and the problem of sorting $n$ pairwise different elements from
linearly ordered set, $n=3, \ldots, 6$. For each of these problems, we compare
the complexity of different types of optimal (relative to the depth or the
number of realizable nodes) decision trees with hypotheses. We also study the
complexity of decision trees constructed by entropy-based greedy algorithm and
analyze the length of decision rules derived from these trees.


title: Explaining Preference-driven Schedules: the EXPRES Framework

abstract:
  Scheduling is the task of assigning a set of scarce resources distributed
over time to a set of agents, who typically have preferences about the
assignments they would like to get. Due to the constrained nature of these
problems, satisfying all agents' preferences is often infeasible, which might
lead to some agents not being happy with the resulting schedule. Providing
explanations has been shown to increase satisfaction and trust in solutions
produced by AI tools. However, it is particularly challenging to explain
solutions that are influenced by and impact on multiple agents. In this paper
we introduce the EXPRES framework, which can explain why a given preference was
unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i)
an explanation generator that, based on a Mixed-Integer Linear Programming
model, finds the best set of reasons that can explain an unsatisfied
preference; and (ii) an explanation parser, which translates the generated
explanations into human interpretable ones. Through simulations, we show that
the explanation generator can efficiently scale to large instances. Finally,
through a set of user studies within J.P. Morgan, we show that employees
preferred the explanations generated by EXPRES over human-generated ones when
considering workforce scheduling scenarios.


title:
Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient
  Objects and Shadow Modeling Using RPC Cameras

abstract:
  We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end
model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF
combines some of the latest trends in neural rendering with native satellite
camera models, represented by rational polynomial coefficient (RPC) functions.
The proposed method renders new views and infers surface models of similar
quality to those obtained with traditional state-of-the-art stereo pipelines.
Multi-date images exhibit significant changes in appearance, mainly due to
varying shadows and transient objects (cars, vegetation). Robustness to these
challenges is achieved by a shadow-aware irradiance model and uncertainty
weighting to deal with transient phenomena that cannot be explained by the
position of the sun. We evaluate Sat-NeRF using WorldView-3 images from
different locations and stress the advantages of applying a bundle adjustment
to the satellite camera models prior to training. This boosts the network
performance and can optionally be used to extract additional cues for depth
supervision.


title: Gate-Shift-Fuse for Video Action Recognition

abstract:
  Convolutional Neural Networks are the de facto models for image recognition.
However 3D CNNs, the straight forward extension of 2D CNNs for video
recognition, have not achieved the same success on standard action recognition
benchmarks. One of the main reasons for this reduced performance of 3D CNNs is
the increased computational complexity requiring large scale annotated datasets
to train them in scale. 3D kernel factorization approaches have been proposed
to reduce the complexity of 3D CNNs. Existing kernel factorization approaches
follow hand-designed and hard-wired techniques. In this paper we propose
Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which
controls interactions in spatio-temporal decomposition and learns to adaptively
route features through time and combine them in a data dependent manner. GSF
leverages grouped spatial gating to decompose input tensor and channel
weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D
CNNs to convert them into an efficient and high performing spatio-temporal
feature extractor, with negligible parameter and compute overhead. We perform
an extensive analysis of GSF using two popular 2D CNN families and achieve
state-of-the-art or competitive performance on five standard action recognition
benchmarks. Code and models will be made publicly available at
https://github.com/swathikirans/GSF.


title: Neural network processing of holographic images

abstract:
  HOLODEC, an airborne cloud particle imager, captures holographic images of a
fixed volume of cloud to characterize the types and sizes of cloud particles,
such as water droplets and ice crystals. Cloud particle properties include
position, diameter, and shape. We present a hologram processing algorithm,
HolodecML, that utilizes a neural segmentation model, GPUs, and computational
parallelization. HolodecML is trained using synthetically generated holograms
based on a model of the instrument, and predicts masks around particles found
within reconstructed images. From these masks, the position and size of the
detected particles can be characterized in three dimensions. In order to
successfully process real holograms, we find we must apply a series of image
corrupting transformations and noise to the synthetic images used in training.
  In this evaluation, HolodecML had comparable position and size estimation
performance to the standard processing method, but improved particle detection
by nearly 20\% on several thousand manually labeled HOLODEC images. However,
the improvement only occurred when image corruption was performed on the
simulated images during training, thereby mimicking non-ideal conditions in the
actual probe. The trained model also learned to differentiate artifacts and
other impurities in the HOLODEC images from the particles, even though no such
objects were present in the training data set, while the standard processing
method struggled to separate particles from artifacts. The novelty of the
training approach, which leveraged noise as a means for parameterizing
non-ideal aspects of the HOLODEC detector, could be applied in other domains
where the theoretical model is incapable of fully describing the real-world
operation of the instrument and accurate truth data required for supervised
learning cannot be obtained from real-world observations.


title: First Experimental Demonstration of Optical Stochastic Cooling

abstract:
  Particle accelerators and storage rings have been transformative instruments
of discovery, and, for many applications, innovations in particle-beam cooling
have been a principal driver of that success1. Beam cooling reduces the spread
in particle positions and momenta, while keeping the number of particles
constant, and combats diffusive effects, thereby enabling particle accumulation
and the production and preservation of intense beams. In the case of particle
colliders, cooling increases the likelihood of observing rare physics events.
One of the most important conceptual and technological advances in this area
was stochastic cooling (SC), which was instrumental in the discovery of the W
and Z bosons at CERN and the top quark at Fermilab2-6. SC reduces the random
motion of the beam particles through granular sampling and correction of the
beams phase-space structure, thus bearing resemblance to a Maxwells demon. The
extension of SC from the microwave regime up to optical frequencies and
bandwidths has long been pursued as it could increase the achievable cooling
rates by three to four orders of magnitude and provide a powerful new tool for
future accelerators. First proposed nearly thirty years ago, Optical Stochastic
Cooling (OSC) replaces the conventional microwave elements of SC with
optical-frequency analogs and is, in principle, compatible with any species of
charged-particle beam7,8. Here we describe the first experimental demonstration
of OSC in a proof-of-principle experiment9 at the Fermi National Accelerator
Laboratorys Integrable Optics Test Accelerator10.


title:
A Structure-Preserving Divide-and-Conquer Method for Pseudosymmetric
  Matrices

abstract:
  We devise a spectral divide-and-conquer scheme for matrices that are
self-adjoint with respect to a given indefinite scalar product (i.e.
pseudosymmetic matrices). The pseudosymmetric structure of the matrix is
preserved in the spectral division, such that the method can be applied
recursively to achieve full diagonalization. The method is well-suited for
structured matrices that come up in computational quantum physics and
chemistry. In this application context, additional definiteness properties
guarantee a convergence of the matrix sign function iteration within two steps
when Zolotarev functions are used. The steps are easily parallelizable.
Furthermore, it is shown that the matrix decouples into symmetric definite
eigenvalue problems after just one step of spectral division.


title:
Blockchain as privacy and security solution for smart environments: A
  Survey

abstract:
  Blockchain was always associated with Bitcoin, cryptocurrencies, and digital
asset trading. However, its benefits are far beyond that. It supports
technologies like the Internet-of-Things (IoT) to pave the way for futuristic
smart environments, like smart homes, smart transportation, smart energy
trading, smart industries, smart supply chains, and more. To enable these
environments, IoT devices, machines, appliances, and vehicles, need to
intercommunicate without the need for centralized trusted parties. Blockchain
replaces these trusted parties in such trustless environments. It provides
security enforcement, privacy assurance, authentication, and other key features
to IoT ecosystems. Besides IoT-Blockchain integration, other technologies add
more benefits that attract the research community. Software-Defined Networking
(SDN), Fog, Edge, and Cloud Computing technologies, for example, play a key
role in enabling realistic IoT applications. Moreover, the integration of
Artificial Intelligence (AI) provides smart, dynamic, and autonomous
decision-making capabilities for IoT devices in smart environments. To push the
research further in this domain, we provide in this paper a comprehensive
survey that includes state-of-the-art technological integration, challenges,
and solutions for smart environments, and the role of these technologies as the
building blocks of such smart environments. We also demonstrate how the level
of integration between these technologies has increased over the years, which
brings us closer to the futuristic view of smart environments. We further
discuss the current need to provide general-purpose Blockchain platforms that
can adapt to unique design requirements of different applications and
solutions. Finally, we provide a simplified architecture of futuristic smart
environments that integrate these technologies, showing the advantage of such
integration.


title: Price Tracing: Linking Nodal Prices in Optimized Power Systems

abstract:
  Optimizing the total cost of power systems is a common tool for network
operation and planning. Besides valuable information about how to run and
possibly expand a power system, the optimization provides an optimal Locational
Marginal Price per node and time step. This price can be seen as the price of
electricity paid by consumers and purchased by suppliers, while maximizing
social welfare. Naturally, it is a direct result of the optimization problem,
and therefore does not give any information about its internal composition.
This paper shows that by applying Flow Tracing, an algorithm for tracking flows
in complex networks, it is possible to interlink Locational Marginal Prices in
a coherent mathematical way. This does not only lead to important insights into
the price structure, but also provides an intuitive decomposition and
allocation of all system costs. Then individual consumers see how much they
have to pay to individual generators and transmission lines in the power
system. This method, introduced as Price Tracing, outperforms similar
approaches provided by the literature, since the resulting cost allocations are
transparent, plausible, and consistent with the Locational Marginal Prices from
the optimization. The Price Tracing method is applied and discussed on behalf
of a power system model of Germany with a high share of renewable power. The
presented analysis and its implications can help in finding a more efficient
market design to promote renewable power supply.


title: SMARTmBOT: A ROS2-based Low-cost and Open-source Mobile Robot Platform

abstract:
  This paper introduces SMARTmBOT, an open-source mobile robot platform based
on Robot Operating System 2 (ROS2). The characteristics of the SMARTmBOT,
including low-cost, modular-typed, customizable and expandable design, make it
an easily achievable and effective robot platform to support broad robotics
research and education involving either single-robot or multi-robot systems.
The total cost per robot is approximately $210, and most hardware components
can be fabricated by a generic 3D printer, hence allowing users to build the
robots or replace any broken parts conveniently. The SMARTmBot is also equipped
with a rich range of sensors, making it competent for general task scenarios,
such as point-to-point navigation and obstacle avoidance. We validated the
mobility and function of SMARTmBOT through various robot navigation experiments
and applications with tasks including go-to-goal, pure-pursuit, line following,
and swarming. All source code necessary for reading sensors, streaming from an
embedded camera, and controlling the robot including robot navigation
controllers is available through an online repository that can be found at
https://github.com/SMARTlab-Purdue/SMARTmBOT.


title: Optothermal evolution of active colloidal matter in defocused laser trap

abstract:
  Optothermal interaction of active colloidal matter can facilitate
environmental cues which can influence the dynamics of active soft matter
systems. The optically induced thermal effect can be harnessed to study
non-equilibrium thermodynamics as well as applied to self-propel colloids and
form assemblies. In this work, we employ a defocused laser trap to form
self-evolving colloidal active matter. The optothermal interaction of the
active colloids in both focused and defocused optical trap has been
investigated to ascertain their thermophoretic behavior, which shows a
long-range attraction and a short-range repulsion between the colloids. The
optical gradient field enabled attraction and the short-range repulsion between
the active colloids have been harnessed to form re-configurable dynamic
assembly. Additionally, the assembly undergoes self-evolution as a new colloid
joins the structure. Further, we show that the incident polarization state of
the optical field can be employed as a parameter to modulate the structural
orientation of the active colloids. The simple defocused optical field enabled
assembly can serve as model to understand the collective dynamics of active
matter systems and harnessed as re-configurable microscopic engine.

categories: cond-mat.soft cond-mat.mes-hall physics.app-ph physics.optics


title:
Probing confinement in a $\mathbb{Z}_2$ lattice gauge theory on a
  quantum computer

abstract:
  Digital quantum simulators provide a table-top platform for addressing
salient questions in particle and condensed-matter physics. A particularly
rewarding target is given by lattice gauge theories (LGTs). Their constituents,
e.g., charged matter and the electric gauge field, are governed by local gauge
constraints, which are highly challenging to engineer and which lead to
intriguing yet not fully understood features such as confinement of particles.
Here, we simulate confinement dynamics in a $\mathbb{Z}_2$ LGT on a
superconducting quantum chip. We synthesize the charge--gauge-field interaction
using only 6 native two-qubit gates, enabling us to reach simulation times of
up to 25 Trotter steps. We observe how tuning a term that couples only to the
electric field confines the charges, a manifestation of the tight bond that the
local gauge constraint generates between both. Moreover, we study a different
mechanism, where a modification of the gauge constraint from a $\mathbb{Z}_2$
to $U(1)$ symmetry freezes the system dynamics. Our work showcases the dramatic
restriction that the underlying gauge constraint imposes on the dynamics of an
LGT, it illustrates how gauge constraints can be modified and protected, and it
paves the way for studying other models governed by many-body interactions.

categories: quant-ph cond-mat.str-el hep-lat nucl-th


title:
ORCA: A Network and Architecture Co-design for Offloading us-scale
  Datacenter Applications

abstract:
  Responding to the "datacenter tax" and "killer microseconds" problems for
datacenter applications, diverse solutions including Smart NIC-based ones have
been proposed. Nonetheless, they often suffer from high overhead of
communications over network and/or PCIe links. To tackle the limitations of the
current solutions, this paper proposes ORCA, a holistic network and
architecture co-design solution that leverages current RDMA and emerging
cache-coherent off-chip interconnect technologies. Specifically, ORCA consists
of four hardware and software components: (1) unified abstraction of inter- and
intra-machine communications managed by one-sided RDMA write and cache-coherent
memory write; (2) efficient notification of requests to accelerators assisted
by cache coherence; (3) cache-coherent accelerator architecture directly
processing requests received by NIC; and (4) adaptive device-to-host data
transfer for modern server memory systems consisting of both DRAM and NVM
exploiting state-of-the-art features in CPUs and PCIe. We prototype ORCA with a
commercial system and evaluate three popular datacenter applications: in-memory
key-value store, chain replication-based distributed transaction system, and
deep learning recommendation model inference. The evaluation shows that ORCA
provides 30.1~69.1% lower latency, up to 2.5x higher throughput, and 3x higher
power efficiency than the current state-of-the-art solutions.


title:
Observational constraints on the interacting dark energy - dark matter
  (IDM) model

abstract:
  Particle creation (or annihilation) mechanisms, described by either quantum
field theoretical models, or by the thermodynamics of irreversible processes,
play an important role in the evolution of the early Universe, like, for
example, in the warm inflationary scenario. Following a similar approach, based
on the thermodynamics of open systems, in the present work we investigate the
consequences of the interaction, decay, and particle generation in a
many-component Universe, containing dark energy, dark matter, radiation, and
ordinary matter, respectively. In this model, due to the interaction between
these components, and of the corresponding thermodynamical properties, the
conservation equations of different cosmological constituents are not satisfied
individually. We introduce a thermodynamic description of the Universe, in
which two novel physical aspects, the particle number balance equations, and
the creation pressures, are considered, thus making the cosmological evolution
equations thermodynamically consistent. To constrain the free parameters of the
model several observational data sets are employed, including the Planck data
sets, Riess 2020, BAO, as well as Pantheon data. By using a scaling ansatz for
the dark matter to dark energy ratio, and by imposing constraints from
Planck+Riess 2020 data, this model predicts an acceptable value for the Hubble
parameter, and thus it may provide a solution to the so-called Hubble tension
problem, much debated recently.


title: Adversarial Support Alignment

abstract:
  We study the problem of aligning the supports of distributions. Compared to
the existing work on distribution alignment, support alignment does not require
the densities to be matched. We propose symmetric support difference as a
divergence measure to quantify the mismatch between supports. We show that
select discriminators (e.g. discriminator trained for Jensen-Shannon
divergence) are able to map support differences as support differences in their
one-dimensional output space. Following this result, our method aligns supports
by minimizing a symmetrized relaxed optimal transport cost in the discriminator
1D space via an adversarial process. Furthermore, we show that our approach can
be viewed as a limit of existing notions of alignment by increasing
transportation assignment tolerance. We quantitatively evaluate the method
across domain adaptation tasks with shifts in label distributions. Our
experiments show that the proposed method is more robust against these shifts
than other alignment-based baselines.


title:
Morphological Processing of Low-Resource Languages: Where We Are and
  What's Next

abstract:
  Automatic morphological processing can aid downstream natural language
processing applications, especially for low-resource languages, and assist
language documentation efforts for endangered languages. Having long been
multilingual, the field of computational morphology is increasingly moving
towards approaches suitable for languages with minimal or no annotated
resources. First, we survey recent developments in computational morphology
with a focus on low-resource languages. Second, we argue that the field is
ready to tackle the logical next challenge: understanding a language's
morphology from raw text alone. We perform an empirical study on a truly
unsupervised version of the paradigm completion task and show that, while
existing state-of-the-art models bridged by two newly proposed models we devise
perform reasonably, there is still much room for improvement. The stakes are
high: solving this task will increase the language coverage of morphological
resources by a number of magnitudes.


title: The equivalence of two inequalities for quasisymmetric designs

abstract:
  It has been an open problem whether Hobart's inequality on the parameters of
a quasisymmetric 2-design is independent of earlier known restrictions. In this
note we show that it is equivalent to inequalities found by Neumaier and
Calderbank. We also give some more parameter sets ruled out by the
Blokhuis-Calderbank inequality.


title:
Complex Analytic Dependence on the Dielectric Permittivity in ENZ
  Materials: The Photonic Doping Example

abstract:
  Motivated by the physics literature on "photonic doping" of scatterers made
from "epsilon-near-zero" (ENZ) matrials, we consider how the scattering of
time-harmonic TM electromagnetic waves by a cylindrical ENZ region $\Omega
\times \mathbb{R}$ is affected by the presence of a "dopant" $D \subset \Omega$
in which the dielectric permittivity is not near zero. Mathematically, this
reduces to analysis of a 2D Helmholtz equation $\mathrm{div}\, (a(x)\nabla u) +
k^2 u = f$ with a piecewise-constant, complex valued coefficient $a$ that is
nearly infinite (say $a = \frac{1}{\delta}$ with $\delta \approx 0$) in $\Omega
\setminus \overline{D}.$ We show (under suitable hypotheses) that the solution
$u$ depends analytically on $\delta$ near $0$, and we give a simple PDE
characterization of the terms in its Taylor expansion. For the application to
photonic doping, it is the leading-order corrections in $\delta$ that are most
interesting: they explain why photonic doping is only mildly affected by the
presence of losses, and why it is seen even at frequencies where dielectric
permittivity is merely small. Equally important: our results include a PDE
characterization of the leading-order electric field in the ENZ region as
$\delta \to 0$, whereas the existing literature on photonic doping provides
only the leading-order magnetic field.

categories: math.AP cond-mat.mtrl-sci math-ph math.MP


title:
The Best of Both Worlds: Combining Learned Embeddings with Engineered
  Features for Accurate Prediction of Correct Patches

abstract:
  A large body of the literature on automated program repair develops
approaches where patches are automatically generated to be validated against an
oracle (e.g., a test suite). Because such an oracle can be imperfect, the
generated patches, although validated by the oracle, may actually be incorrect.
Our empirical work investigates different representation learning approaches
for code changes to derive embeddings that are amenable to similarity
computations of patch correctness identification, and assess the possibility of
accurate classification of correct patch by combining learned embeddings with
engineered features. Experimental results demonstrate the potential of learned
embeddings to empower Leopard (a patch correctness predicting framework
implemented in this work) with learning algorithms in reasoning about patch
correctness: a machine learning predictor with BERT transformer-based learned
embeddings associated with XGBoost achieves an AUC value of about 0.895 in the
prediction of patch correctness on a new dataset of 2,147 labeled patches that
we collected for the experiments. Our investigations show that deep learned
embeddings can lead to complementary/better performance when comparing against
the state-of-the-art, PATCH-SIM, which relies on dynamic information. By
combining deep learned embeddings and engineered features, Panther (the
upgraded version of Leopard implemented in this work) outperforms Leopard with
higher scores in terms of AUC, +Recall and -Recall, and can accurately identify
more (in)correct patches that cannot be predicted by the classifiers only with
learned embeddings or engineered features. Finally, we use an explainable ML
technique, SHAP, to empirically interpret how the learned embeddings and
engineered features are contributed to the patch correctness prediction.


title: Memorizing Transformers

abstract:
  Language models typically need to be trained or finetuned in order to acquire
new knowledge, which involves updating their weights. We instead envision
language models that can simply read and memorize new data at inference time,
thus acquiring new knowledge immediately. In this work, we extend language
models with the ability to memorize the internal representations of past
inputs. We demonstrate that an approximate kNN lookup into a non-differentiable
memory of recent (key, value) pairs improves language modeling across various
benchmarks and tasks, including generic webtext (C4), math papers (arXiv),
books (PG-19), code (Github), as well as formal theorems (Isabelle). We show
that the performance steadily improves when we increase the size of memory up
to 262K tokens. On benchmarks including code and mathematics, we find that the
model is capable of making use of newly defined functions and theorems during
test time.


title:
Automated Grading of Radiographic Knee Osteoarthritis Severity Combined
  with Joint Space Narrowing

abstract:
  The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a
central criteria for the use of total knee arthroplasty. However, this
assessment suffers from imprecise standards and a remarkably high inter-reader
variability. An algorithmic, automated assessment of KOA severity could improve
overall outcomes of knee replacement procedures by increasing the
appropriateness of its use. We propose a novel deep learning-based five-step
algorithm to automatically grade KOA from posterior-anterior (PA) views of
radiographs: (1) image preprocessing (2) localization of knees joints in the
image using the YOLO v3-Tiny model, (3) initial assessment of the severity of
osteoarthritis using a convolutional neural network-based classifier, (4)
segmentation of the joints and calculation of the joint space narrowing (JSN),
and (5), a combination of the JSN and the initial assessment to determine a
final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation
masks used to make the assessment, our algorithm demonstrates a higher degree
of transparency compared to typical "black box" deep learning classifiers. We
perform a comprehensive evaluation using two public datasets and one dataset
from our institution, and show that our algorithm reaches state-of-the art
performance. Moreover, we also collected ratings from multiple radiologists at
our institution and showed that our algorithm performs at the radiologist
level.
  The software has been made publicly available at
https://github.com/MaciejMazurowski/osteoarthritis-classification.


title: On $\mathbb{F}_2^\omega$-affine-exchangeable probability measures

abstract:
  For any standard Borel space $B$, let $\mathcal{P}(B)$ denote the space of
Borel probability measures on $B$. In relation to a difficult problem of Aldous
in exchangeability theory, and in connection with arithmetic combinatorics,
Austin raised the question of describing the structure of affine-exchangeable
probability measures on product spaces indexed by the vector space
$\mathbb{F}_2^\omega$, i.e., the measures in
$\mathcal{P}(B^{\mathbb{F}_2^\omega})$ that are invariant under the coordinate
permutations on $B^{\mathbb{F}_2^\omega}$ induced by all affine automorphisms
of $\mathbb{F}_2^{\omega}$. We answer this question by describing the extreme
points of the space of such affine-exchangeable measures. We prove that there
is a single structure underlying every such measure, namely, a random
infinite-dimensional cube (sampled using Haar measure adapted to a specific
filtration) on a group that is a countable power of the 2-adic integers.
Indeed, every extreme affine-exchangeable measure in
$\mathcal{P}(B^{\mathbb{F}_2^\omega})$ is obtained from a
$\mathcal{P}(B)$-valued function on this group, by a vertex-wise composition
with this random cube. The consequences of this result include a description of
the convex set of affine-exchangeable measures in
$\mathcal{P}(B^{\mathbb{F}_2^\omega})$ equipped with the vague topology (when
$B$ is a compact metric space), showing that this convex set is a Bauer
simplex. We also obtain a correspondence between affine-exchangeability and
limits of convergent sequences of (compact-metric-space valued) functions on
vector spaces $\mathbb{F}_2^n$ as $n\to\infty$. Via this correspondence, we
establish the above-mentioned group as a general limit domain valid for any
such sequence.


title:
The need for structural changes to create impactful public engagement in
  US particle physics

abstract:
  This Snowmass21 Contributed Paper addresses the structural changes that need
to occur in the many groups and organizations that intersect with the US
particle physics community to enable impactful public engagement to flourish.
The impetus for these changes should come from the particle physics community,
which should acknowledge the importance of public engagement and act on the
recommendations in this Snowmass contributed paper. Scientists have expressed
frustration at the barriers, penalties and lack of support that discourage them
from participating in public engagement. In this paper, we provide many ways to
create a supportive, enabling atmosphere for public engagement among
physicists.


title: Sound Development of Safety Supervisors

abstract:
  Safety supervisors are controllers enforcing safety properties by keeping a
system in (or returning it to) a safe state. The development of such
high-integrity components can benefit from a rigorous workflow integrating
formal design and verification. In this paper, we present a workflow for the
sound development of safety supervisors combining the best of two worlds,
verified synthesis and complete testing. Synthesis allows one to focus on
problem specification and model validation. Testing compensates for the
crossing of abstraction, formalism, and tool boundaries and is a key element to
obtain certification credit before entry into service. We establish soundness
of our workflow through a rigorous argument. Our approach is tool-supported,
aims at modern autonomous systems, and is illustrated with a collaborative
robotics example.