title: Archimedean Lever Leptogenesis

abstract:
  We propose that weak scale leptogenesis via $\sim 10$ TeV scale right-handed
neutrinos could be possible if their couplings had transitory larger values in
the early Universe. The requisite lifted parameters can be attained if a light
scalar $\phi$ is displaced a long distance from its origin by the thermal
population of fermions $X$ that become massive before electroweak symmetry
breaking. The fermion $X$ can be a viable dark matter candidate; for suitable
choice of parameters, the light scalar itself can be dark matter through a
misalignment mechanism. We find that a two-component DM population made up of
both $X$ and $\phi$ is a typical outcome in our framework.


title:
Gifts from anomalies: Exact results for Landau phase transitions in
  metals

abstract:
  Non-Fermi liquid phenomena arise naturally near critical points of Landau
ordering transitions in metallic systems, where strong fluctuations of a
bosonic order parameter destroy coherent quasiparticles. Despite progress in
developing controlled perturbative techniques, much of the low energy physics
of such metallic quantum critical points remains poorly understood. We
demonstrate that exact, non-perburbative results can be obtained for both
optical transport and static susceptibilities in "Hertz-Millis" theories of
Fermi surfaces coupled to critical bosons. Such models possess a large emergent
symmetry and anomaly structure, which we leverage to fix these quantities. In
particular, we show that in the infrared limit, the boson self energy at zero
wave vector, $\mathbf{q}=0$, is a constant independent of frequency, and the
real part of the optical conductivity, $\sigma(\omega)$, is purely a delta
function Drude peak with no other corrections. Therefore, further frequency
dependence in the boson self energy or optical conductivity can only come from
irrelevant operators in a clean system. Exact relations between Fermi liquid
parameters as the critical point is approached from the disordered phase are
also obtained. The absence of a universal, power law frequency dependence in
the boson self energy contrasts with previous perturbative calculations, and we
explain the origin of this discrepancy.


title:
The COS CGM Compendium. IV. Effects of Varying Ionization Backgrounds on
  Metallicity Determinations in the z < 1 Circumgalactic Medium

abstract:
  Metallicity estimates of circumgalactic gas based on absorption line
measurements typically require photoionization modeling to account for unseen
ionization states. We explore the impact of uncertainties in the extreme
ultraviolet background (EUVB) radiation on such metallicity determinations for
the z < 1 circumgalactic medium (CGM). In particular, we study how
uncertainties in the power-law slope of the EUV radiation,
$\mathrm{\alpha_{EUVB}}$, from active galactic nuclei affect metallicity
estimates in a sample of 34 absorbers with HI column densities between 15.25 <
log ($\mathrm{N_{HI}}$ / $\mathrm{cm^{-2}}$) < 17.25 and measured metal ion
column densities. We demonstrate the sensitivity of metallicity estimates to
changes in the EUV power-law slope of active galactic nuclei,
$\mathrm{\alpha_{EUVB}}$, at low redshift (z < 1), showing derived absorber
metallicities increase on average by approximately 0.3 dex as the EUV slope is
hardened from $\mathrm{\alpha_{EUVB}}$ = -2.0 to -1.4. We use Markov Chain
Monte Carlo sampling of photoionization models with $\mathrm{\alpha_{EUVB}}$ as
a free parameter to derive metallicities for these absorbers. The current
sample of absorbers does not provide a robust constraint on the slope,
$\mathrm{\alpha_{EUVB}}$, itself; we discuss how future analyses may provide
stronger constraints. Marginalizing over the uncertainty in the slope of the
background, we find the average uncertainties in the metallicity determinations
increase from 0.08 dex to 0.14 dex when switching from a fixed EUVB slope to
one that freely varies. Thus, we demonstrate that EUVB uncertainties can be
included in ionization models while still allowing for robust metallicity
inferences.


title:
The long-active afterglow of GRB 210204A: Detection of the most delayed
  flares in a Gamma-Ray Burst

abstract:
  We present results from extensive broadband follow-up of GRB 210204A over the
period of thirty days. We detect optical flares in the afterglow at 7.6 x 10^5
s and 1.1 x 10^6 s after the burst: the most delayed flaring ever detected in a
GRB afterglow. At the source redshift of 0.876, the rest-frame delay is 5.8 x
10^5 s (6.71 d). We investigate possible causes for this flaring and conclude
that the most likely cause is a refreshed shock in the jet. The prompt emission
of the GRB is within the range of typical long bursts: it shows three disjoint
emission episodes, which all follow the typical GRB correlations. This suggests
that GRB 210204A might not have any special properties that caused late-time
flaring, and the lack of such detections for other afterglows might be
resulting from the paucity of late-time observations. Systematic late-time
follow-up of a larger sample of GRBs can shed more light on such afterglow
behaviour. Further analysis of the GRB 210204A shows that the late time bump in
the light curve is highly unlikely due to underlying SNe at redshift (z) =
0.876 and is more likely due to the late time flaring activity. The cause of
this variability is not clearly quantifiable due to the lack of multi-band data
at late time constraints by the bad weather conditions. The flare of GRB
210204A is the latest flare detected to date.


title:
Cosmic evolution of low-excitation radio galaxies in the LOFAR Two-meter
  Sky Survey Deep Fields

abstract:
  Feedback from low-excitation radio galaxies (LERGs) plays a key role in the
lifecycle of massive galaxies in the local Universe; their evolution, and the
impact of these active galactic nuclei on early galaxy evolution, however,
remain poorly understood. We use a sample of 10481 LERGs from the first data
release of the LOFAR Two-meter Sky Survey Deep Fields, covering $\sim$ 25
deg$^2$, to present the first measurement of the evolution of the radio
luminosity function (LF) of LERGs out to $z\sim2.5$; this shows relatively mild
evolution. We split the LERGs into those hosted by quiescent and star-forming
galaxies, finding a new dominant population of LERGs hosted by star-forming
galaxies at high redshifts. The incidence of LERGs in quiescent galaxies shows
a steep dependence on stellar-mass out to $z \sim1.5$, consistent with local
Universe measurements of accretion occurring from cooling of hot gas haloes.
The quiescent-LERGs dominate the LFs at $z<1$, showing a strong decline in
space density with redshift, tracing that of the available host galaxies, while
there is an increase in the characteristic luminosity. The star-forming LERG LF
increases with redshift, such that this population dominates the space
densities at most radio-luminosities by $z \sim 1$. The incidence of LERGs in
star-forming galaxies shows a much weaker stellar-mass dependence, and
increases with redshift, suggesting a different fuelling mechanism compared to
their quiescent counterparts, potentially associated with the cold gas supply
present in the star-forming galaxies.


title:
Loop quantum cosmology and its gauge-covariant avatar: a weak curvature
  relationship

abstract:
  We explore the relationship between the effective dynamics in standard loop
quantum cosmology (LQC) based on holonomies and triads obtained from
gauge-fixing fluxes, and a modification of LQC based on holonomies and
gauge-covariant fluxes (referred to as gLQC). Both the models yield singularity
resolution via a bounce because of non-perturbative quantum geometric effects
resulting in a maximum for energy density. In LQC, the bounce is extremely well
captured by a $\rho^2$ term in energy density with a negative sign which
emerges as a non-perturbative modification to the classical Friedmann and
Raychaudhuri equations. But, details of such modifications in gLQC have
remained hidden due to an arduous nature of gauge-covariant flux modifications
which do not allow writing above equations in a closed form. To extract these
modifications we explore the large volume, weak curvature limit for matter with
a fixed equation of state and obtain higher order corrections to the classical
theory. We find that in the weak curvature limit of gLQC, in the post-bounce
branch, the first order correction beyond classical theory fully recovers the
form of modified Friedmann and Raychaudhuri equations of LQC. In contrast, due
to an asymmetric bounce in gLQC, the weak curvature limit of the pre-bounce
branch exhibits a novel structure with a $\rho^{3/2}$ term as a first order
correction beyond classical theory while the $\rho^2$ term appears as a second
order correction. Our work shows that gLQC has a far richer structure which
includes the form of dynamical equations with non-perturbative modifications in
LQC in its weak curvature limit. This indicates that more general loop
quantizations of cosmological sectors can reveal LQC at some truncation, and
possibly there exist a tower of potentially interesting higher order
modifications from quantum geometry which are hidden in the setting of LQC.


title:
Deforming Symmetric Product Orbifolds: A tale of moduli and higher spin
  currents

abstract:
  We analyze how deforming symmetric product orbifolds of two-dimensional
$\mathcal{N}=2$ conformal field theories by an exactly marginal operator lifts
higher spin currents present at the orbifold point. We find on the one hand
that these currents are universally lifted regardless of the underlying CFT. On
the other hand the details of the lifting are surprisingly non-universal, with
dependence on the central charge of the underlying CFT and the specific
marginal operator in use. In the context of the AdS/CFT correspondence, our
results illustrate the mechanism by which the stringy spectrum turns into a
supergravity spectrum when moving through the moduli space. They also provide
further evidence that symmetric product orbifolds of $\mathcal{N}=2$ minimal
models are holographic.


title:
First demonstration of tuning between the Kitaev and Ising limits in a
  honeycomb lattice

abstract:
  Recent observations of novel spin-orbit coupled states have generated
tremendous interest in $4d/5d$ transition metal systems. A prime example is the
$J_{\text{eff}}=\frac{1}{2}$ state in iridate materials and $\alpha$-RuCl$_{3}$
that drives Kitaev interactions. Here, by tuning the competition between
spin-orbit interaction ($\lambda_{\text{SOC}}$) and trigonal crystal field
splitting ($\Delta_\text{T}$), we restructure the spin-orbital wave functions
into a novel $\mu=\frac{1}{2}$ state that drives Ising interactions. This is
done via a topochemical reaction that converts Li$_{2}$RhO$_{3}$ to
Ag$_{3}$LiRh$_{2}$O$_{6}$, leading to an enhanced trigonal distortion and a
diminished spin-orbit coupling in the latter compound. Using perturbation
theory, we present an explicit expression for the new $\mu=\frac{1}{2}$ state
in the limit $\Delta_\text{T}\gg \lambda_{\text{SOC}}$ realized in
Ag$_{3}$LiRh$_{2}$O$_{6}$, different from the conventional
$J_\text{eff}=\frac{1}{2}$ state in the limit $\lambda_{\text{SOC}}\gg
\Delta_\text{T}$ realized in Li$_{2}$RhO$_{3}$. The change of ground state is
followed by a dramatic change of magnetism from a 6 K spin-glass in
Li$_{2}$RhO$_{3}$ to a 94 K antiferromagnet in Ag$_{3}$LiRh$_{2}$O$_{6}$. These
results open a pathway for tuning materials between the two limits and creating
a rich magnetic phase diagram.


title:
Multi-messenger observations of binary neutron star mergers in the O4
  run

abstract:
  We present realistic expectations for the number and properties of neutron
star binary mergers to be detected as multi-messenger sources during the
upcoming fourth observing run (O4) of the LIGO-Virgo-KAGRA gravitational wave
(GW) detectors, with the aim of providing guidance for the optimization of
observing strategies. Our predictions are based on a population synthesis model
which includes the GW signal-to-noise ratio, the kilonova (KN) optical and
near-infrared light curves, the relativistic jet gamma-ray burst (GRB) prompt
emission peak photon flux, and the afterglow light curves in radio, optical and
X-rays. Within our assumptions, the rate of GW events to be confidently
detected during O4 is $7.7^{+11.9}_{-5.7}$ yr$^{-1}$, 78% of which will produce
a KN, and a lower 52% will also produce a relativistic jet. The typical depth
of current optical electromagnetic search and follow up strategies is still
sufficient to detect most of the KNe in O4, but only for the first night or
two. The prospects for detecting relativistic jet emission are not promising.
While closer events (within z<0.02) will likely still have a detectable cocoon
shock breakout, most events will have their GRB emission (both prompt and
afterglow) missed unless seen under a favorably small viewing angle. This
reduces the fraction of events with detectable jets to 2% (prompt emission,
serendipitous) and 10% (afterglow, deep radio monitoring), corresponding to
detection rates of $0.17^{+0.26}_{-0.13}$ and $0.78^{+1.21}_{-0.58}$ yr$^{-1}$,
respectively. When considering a GW sub-threshold search triggered by a GRB
detection, our predicted rate of GW+GRB prompt emission detections increases up
to a more promising $0.75^{+1.16}_{-0.55}$ yr$^{-1}$.


title: An Entropic Lens on Stabilizer States

abstract:
  The $n$-qubit stabilizer states are those left invariant by a $2^n$-element
subset of the Pauli group. The Clifford group is the group of unitaries which
take stabilizer states to stabilizer states; a physically--motivated generating
set, the Hadamard, phase, and CNOT gates which comprise the Clifford gates,
imposes a graph structure on the set of stabilizers. We explicitly construct
these structures, the "reachability graphs," at $n\le5$. When we consider only
a subset of the Clifford gates, the reachability graphs separate into multiple,
often complicated, connected components. Seeking an understanding of the
entropic structure of the stabilizer states, which is ultimately built up by
CNOT gate applications on two qubits, we are motivated to consider the
restricted subgraphs built from the Hadamard and CNOT gates acting on only two
of the $n$ qubits. We show how the two subgraphs already present at two qubits
are embedded into more complicated subgraphs at three and four qubits. We argue
that no additional types of subgraph appear beyond four qubits, but that the
entropic structures within the subgraphs can grow progressively more
complicated as the qubit number increases. Starting at four qubits, some of the
stabilizer states have entropy vectors which are not allowed by holographic
entropy inequalities. We comment on the nature of the transition between
holographic and non-holographic states within the stabilizer reachability
graphs.


title: Universal cooling dynamics towards a quantum critical point

abstract:
  We investigate the loss of adiabaticity when cooling a many-body quantum
system from an initial thermal state towards a quantum critical point. The
excitation density, which quantifies the degree of adiabaticity of the
dynamics, is found to obey scaling laws in the cooling velocity as well as in
the initial and final temperatures of the cooling protocol. The scaling laws
are universal, governed by the critical exponents of the quantum phase
transition. The validity of these statements is shown analytically for a Kitaev
quantum wire coupled to Markovian baths, and subsequently argued to be valid
under rather general conditions. Our results establish that quantum critical
properties can be probed dynamically at finite temperature, without even
varying the control parameter of the quantum phase transitions.


title:
Long-range Kitaev chain in a thermal bath: Analytic techniques for
  time-dependent systems and environments

abstract:
  We construct and solve a "minimal model" with which nonequilibrium phenomena
in many-body open quantum systems can be studied analytically under
time-dependent parameter changes in the system and/or the bath. Coupling a
suitable configuration of baths to a Kitaev chain, we self-consistently derive
a Lindblad master equation which, at least in the absence of explicit time
dependencies, leads to thermalization. Using the method of Third Quantization
we derive time-evolution equations for the correlation matrix, which we relate
to the occupation of the system's quasiparticle modes. These results permit
analytic and efficient numeric descriptions of the nonequilibrium dynamics of
open Kitaev chains under a wide range of driving protocols, which in turn
facilitate the investigation of the interplay between bath-induced dissipation
and the generation of coherent excitations by nonadiabatic driving. We
advertise this minimal model of maximum simplicity for the study of
finite-temperature generalizations of Kibble-Zurek ramps, Floquet physics, and
many other nonequilibrium protocols of quantum many-body systems driven by
time-varying parameters and/or temperatures.


title:
Perfectly Balanced: Improving Transfer and Robustness of Supervised
  Contrastive Learning

abstract:
  An ideal learned representation should display transferability and
robustness. Supervised contrastive learning (SupCon) is a promising method for
training accurate models, but produces representations that do not capture
these properties due to class collapse -- when all points in a class map to the
same representation. Recent work suggests that "spreading out" these
representations improves them, but the precise mechanism is poorly understood.
We argue that creating spread alone is insufficient for better representations,
since spread is invariant to permutations within classes. Instead, both the
correct degree of spread and a mechanism for breaking this invariance are
necessary. We first prove that adding a weighted class-conditional InfoNCE loss
to SupCon controls the degree of spread. Next, we study three mechanisms to
break permutation invariance: using a constrained encoder, adding a
class-conditional autoencoder, and using data augmentation. We show that the
latter two encourage clustering of latent subclasses under more realistic
conditions than the former. Using these insights, we show that adding a
properly-weighted class-conditional InfoNCE loss and a class-conditional
autoencoder to SupCon achieves 11.1 points of lift on coarse-to-fine transfer
across 5 standard datasets and 4.7 points on worst-group robustness on 3
datasets, setting state-of-the-art on CelebA by 11.5 points.


title:
Disc dichotomy signature in the vertical distribution of [Mg/Fe] and the
  delayed gas infall scenario

abstract:
  The analysis of the APOGEE data suggests the existence of a clear distinction
between two sequences of disc stars in the [$\alpha$/Fe] vs. [Fe/H] abundance
ratio space. We aim to test if the two-infall chemical evolution models
designed to reproduce these two sequences in the solar neighbourhood are also
capable to predict the disc bimodality observed in the vertical distribution of
[Mg/Fe] in APOGEE DR16 data. Along with the predicted chemical composition of
SSPs born at different Galactic times in the solar vicinity, we provide their
maximum vertical height |zmax| above the Galactic plane computed assuming the
relation between the vertical action and stellar age in thin disc stars. The
predicted vertical distribution of the [Mg/Fe] abundance ratio is in agreement
with the one observed combining the APOGEE DR16 data and the astroNN catalogue
(stellar ages, orbital parameters) for stars younger than 8 Gyr (only
low-$\alpha$ sequence stars). Including the high-$\alpha$ disc component, the
dichotomy in the vertical [Mg/Fe] abundance distribution is reproduced
considering the observational cut in the Galactic height of |z| < 2 kpc.
However, our model predicts a too flat growth of the |zmax| as a function of
[Mg/Fe] for high-$\alpha$ objects in contrast with the median values from
APOGEE data. Possible explanations for such a tension are: i) the data sample
with |z| < 2 kpc is more likely contaminated by halo stars, causing the median
values to be kinematically hotter, ii) external perturbations such as minor
mergers could have heated up the disc, and the heating of the orbits cannot be
modelled by only scattering processes. Assuming for the data a disc dissection
based on chemistry, the observed |zmax| distributions for high-$\alpha$ and
low-$\alpha$ sequences are in good agreement with our model predictions if we
consider in the calculation the errors in the vertical action estimates.


title: Many-body parametric resonances in the driven sine-Gordon model

abstract:
  We study a quantum many-body variant of the parametric oscillator, by
investigating the driven sine-Gordon model with a modulated tunnel coupling via
a semi-classical Truncated Wigner Approximation (TWA). We first analyze the
parametric resonant regime for driving protocols that retain our model gapped,
and compare the TWA to a Time-Dependent Variational Principle (TDVP). We then
turn to a drive which closes the gap, resulting in an enhanced energy
absorption. While the TDVP approach breaks down in this regime, we can apply
TWA to explore the dynamics of the mode-resolved energy density, and the
higher-order correlations between modes in the prethermal heating regime. For
weak driving amplitude, we find an exponentially fast energy absorption in the
main resonant mode, while the heating of all remaining modes is almost
perfectly suppressed on short time scales. At later times, the highly excited
main resonance provides effective resonant driving terms for its higher
harmonics through the non-linearities in the Hamiltonian, and gives rise to an
exponentially fast heating in these particular modes. We capture the strong
correlations induced by these resonant processes by evaluating higher order
connected correlation functions. Our results can be experimentally probed in
ultracold atomic settings, with parallel one-dimensional quasi-condensates in
the presence of a modulated tunnel coupling.


title: Dynamics of a parametrized dark energy model in $f(R,T)$ gravity

abstract:
  We investigate a flat FLRW-model in $f(R,T)$-gravity, which includes the
quadratic variation in scalar curvature $R$ and the linear term of the trace of
the stress-energy tensor $T$. In turn, we establish the model has the behaviour
of the late time Universe, which is accelerated expanding and ends up in a big
rip. Using the parametrization of scale factor $a(t)$, we propose a model,
which begins with point-type singularity, i.e., the model starts with a point
of zero volume, infinite energy density and infinite temperature. The model's
behaviour is accelerated expanding at present and $\Lambda$CDM in late times.
Finally, the proposed model behaves like a quintessence dark energy model in
the present time and is consistent with standard cosmology $\Lambda$CDM in late
times.


title: In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

abstract:
  Single-task models have proven pivotal in solving specific tasks; however,
they have limitations in real-world applications where multi-tasking is
necessary and domain shifts are exhibited. Recently, instructional prompts have
shown significant improvement towards multi-task generalization; however, the
effect of instructional prompts and Multi-Task Learning (MTL) has not been
systematically studied in the biomedical domain. Motivated by this, this paper
explores the impact of instructional prompts for biomedical MTL. We introduce
the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X)
various categories. Using this meta-dataset, we propose a unified model termed
In-BoXBART, that can jointly learn all tasks of the BoX without any
task-specific modules. To the best of our knowledge, this is the first attempt
to propose a unified model in the biomedical domain and use instructions to
achieve generalization across several biomedical tasks. Experimental results
indicate that the proposed model: 1) outperforms the single-task baseline by
~3% and multi-task (without instruction) baseline by ~18% on an average, and 2)
shows ~23% improvement compared to the single-task baseline in few-shot
learning (i.e., 32 instances per task) on an average. Our analysis indicates
that there is significant room for improvement across tasks in the BoX,
implying the scope for future research direction.


title:
Energy-Efficient Data Transfer Optimization via Decision-Tree Based
  Uncertainty Reduction

abstract:
  The increase and rapid growth of data produced by scientific instruments, the
Internet of Things (IoT), and social media is causing data transfer performance
and resource consumption to garner much attention in the research community.
The network infrastructure and end systems that enable this extensive data
movement use a substantial amount of electricity, measured in terawatt-hours
per year. Managing energy consumption within the core networking infrastructure
is an active research area, but there is a limited amount of work on reducing
power consumption at the end systems during active data transfers. This paper
presents a novel two-phase dynamic throughput and energy optimization model
that utilizes an offline decision-search-tree based clustering technique to
encapsulate and categorize historical data transfer log information and an
online search optimization algorithm to find the best application and kernel
layer parameter combination to maximize the achieved data transfer throughput
while minimizing the energy consumption. Our model also incorporates an
ensemble method to reduce aleatoric uncertainty in finding optimal application
and kernel layer parameters during the offline analysis phase. The experimental
evaluation results show that our decision-tree based model outperforms the
state-of-the-art solutions in this area by achieving 117% higher throughput on
average and also consuming 19% less energy at the end systems during active
data transfers.


title: The Distribution of Values of $\frac{L'}{L}(1/2+\epsilon,\chi_D)$

abstract:
  We determine the limiting distribution of the family of values
$\frac{L'}{L}(1/2+\epsilon,\chi_D)$ as $D$ varies over fundamental
discriminants.
  Here, $0<\epsilon<\frac12$, and $\chi_D$ is the real character associated
with $D$. Moreover, we also establish an upper bound for the rate of
convergence of this family to its limiting distribution. As a consequence of
this result, we derive an asymptotic bound for the small values of
$\left|\frac{L'}{L}(1/2+\epsilon,\chi_D)\right|$.


title: Learning to Adapt Domain Shifts of Moral Values via Instance Weighting

abstract:
  Classifying moral values in user-generated text from social media is critical
in understanding community cultures and interpreting user behaviors of social
movements. Moral values and language usage can change across the social
movements; however, text classifiers are usually trained in source domains of
existing social movements and tested in target domains of new social issues
without considering the variations. In this study, we examine domain shifts of
moral values and language usage, quantify the effects of domain shifts on the
morality classification task, and propose a neural adaptation framework via
instance weighting to improve cross-domain classification tasks. The
quantification analysis suggests a strong correlation between morality shifts,
language usage, and classification performance. We evaluate the neural
adaptation framework on a public Twitter data across 7 social movements and
gain classification improvements up to 12.1\%. Finally, we release a new data
of the COVID-19 vaccine labeled with moral values and evaluate our approach on
the new target domain. For the case study of the COVID-19 vaccine, our
adaptation framework achieves up to 5.26\% improvements over neural baselines.


title: Curvature dominance DE-model in $f(R)$-gravity

abstract:
  We have probed a cosmological model in $f(R)$-gravity, which is a cubic
equation in scalar curvature $R$. The terms arise due to nonlinear $f(R)$
function are treated as energy due to curvature inspired geometry. As a result,
we find accelerating expansion in the universe, which creates an
anti-gravitating negative pressure in it. Some of the physical parameters are
solved using numerical methods. The evolution of the model are examined by the
latest observational Hubble data (46-data points) and Pantheon data (the latest
compilation of SNIa with 40 binned in the redshift range $0.014 \leqslant z
\leqslant1.62$). Some important features of the model have been discussed by
analyzing the plots of various dynamical parameters. The plots of deceleration
parameter $q$ and the Hubble parameter $H$ describe the accelerating expansion
in the evolution of the Universe at the present epoch. The transition from
deceleration to acceleration for our model is obtained at redshift $z_{tr}
\simeq 0.694069$, which is in good agreement with $\Lambda$CDM. We have also
carried out state finder analysis for our model. The analysis of specific
features of the model confirms that our model is consistent with $\Lambda$CDM
in late times.


title: Moment functions of higher rank on polynomial hypergroups

abstract:
  In this paper we consider generalized moment functions of higher order. These
functions are closely related to the well-known functions of binomial type
which have been investigated on various abstract structures. In our former
paper we investigated the properties of generalized moment functions of higher
order on commutative groups. In particular, we proved the characterization of
generalized moment functions on a commutative group as the product of an
exponential and composition of multivariate Bell polynomial and a sequence
additive functions. In the present paper we continue the study of generalized
moment function sequences of higher order in the more abstract setting, namely
we consider functions defined on a hypergroup. We characterize these functions
on the polynomial hypergroup in one variable by means of partial derivatives of
a composition of polynomials generating the polynomial hypergroup and an
analytic function. As an example, we give an explicit formula for moment
generating functions of rank at most two on the Tchebyshev hypergroup.


title: Generalized nerves of monads

abstract:
  One interpretation of the Kleisli construction (given by Miranda and related
to work of Par\'e) is as a nerve sending a monad $P$ to the Kleisli double
category of $P$. In this paper we find more general nerve constructions on the
2-categories of monads, which also give fully faithful nerve 2-functors
$\mathbf{Mnd}\left(\mathcal{K}\right)\to\mathbf{Dbl}$.


title:
Anomalous Hall conductivity control in Mn$_3$NiN antiperovskite by
  epitaxial strain along the kagome plane

abstract:
  Antiferromagnetic manganese-based nitride antiperovskites, such as Mn$_3$NiN,
hold a triangular frustrated magnetic ordering over their kagome lattice formed
by the Mn atoms along the (111)-plane. As such, frustration imposes a
non-trivial interplay between the symmetric and asymmetric magnetic
interactions, which can only reach equilibrium in a noncollinear magnetic
configuration. Consequently, the associated electronic interactions and their
possible tuning by external constraints, such as applied epitaxial strain, play
a crucial role in defining the microscopic and macroscopic properties of such
topological condensed matter systems. Thus, in the present work, we explored
and explained the effect of the epitaxial strain imposed within the
(111)-plane, in which the magnetic and crystallographic symmetry operations are
kept fixed, and only the magnitude of the ionic and electronic interactions are
tuned. We found a linear shifting in the energy of the band structure and a
linear increase/decrease of the available states near the Fermi level with the
applied strain. Concretely, the compression strain reduces the Mn-Mn distances
in the (111) kagome plane but linearly increases the separation between the
stacked kagome lattices and the available states near the Fermi level. Despite
the linear controlling of the available states across the Fermi energy, the
anomalous Hall conductivity shows a non-linear behavior where the
$\sigma_{111}$ conductivity nearly vanishes for tensile strain. On the other
hand, $\sigma_{111}$ fetches a maximum increase of 26\% about the unstrained
structure for a compression value close to $-$1.5\%.This behavior found an
explanation in the non-divergent Berry curvature within the kagome plane, which
is increased for constraining but significantly reduced for expansion strain
values...


title: Ab initio calculations of third-order elastic coefficients

abstract:
  Third-order elasticity (TOE) theory is predictive of strain-induced changes
in second-order elastic coefficients (SOECs) and can model elastic wave
propagation in stressed media. Although third-order elastic tensors have been
determined based on first principles in previous studies, their current
definition is based on an expansion of thermodynamic energy in terms of the
Lagrangian strain near the natural, or zero pressure, reference state. This
definition is inconvenient for predictions of SOECs under significant initial
stresses. Therefore, when TOE theory is necessary to study the strain
dependence of elasticity, the seismological community has resorted to an
empirical version of the theory.
  This study reviews the thermodynamic definition of the third-order elastic
tensor and proposes using an "effective" third-order elastic tensor. We extend
the ab initio approach to calculate third-order elastic tensors under finite
pressure and apply it to two cubic systems, namely, NaCl and MgO. As
applications and validations, we evaluate (a) strain-induced changes in SOECs
and (b) pressure derivatives of SOECs based on ab initio calculations. Good
agreement between third-order elasticity-based predictions and numerically
calculated values confirms the validity of our theory.


title:
Server Free Wireless Federated Learning: Architecture, Algorithm, and
  Analysis

abstract:
  We demonstrate that merely analog transmissions and match filtering can
realize the function of an edge server in federated learning (FL). Therefore, a
network with massively distributed user equipments (UEs) can achieve
large-scale FL without an edge server. We also develop a training algorithm
that allows UEs to continuously perform local computing without being
interrupted by the global parameter uploading, which exploits the full
potential of UEs' processing power. We derive convergence rates for the
proposed schemes to quantify their training efficiency. The analyses reveal
that when the interference obeys a Gaussian distribution, the proposed
algorithm retrieves the convergence rate of a server-based FL. But if the
interference distribution is heavy-tailed, then the heavier the tail, the
slower the algorithm converges. Nonetheless, the system run time can be largely
reduced by enabling computation in parallel with communication, whereas the
gain is particularly pronounced when communication latency is high. These
findings are corroborated via excessive simulations.


title: Sources of Irreproducibility in Machine Learning: A Review

abstract:
  Lately, several benchmark studies have shown that the state of the art in
some of the sub-fields of machine learning actually has not progressed despite
progress being reported in the literature. The lack of progress is partly
caused by the irreproducibility of many model comparison studies. Model
comparison studies are conducted that do not control for many known sources of
irreproducibility. This leads to results that cannot be verified by third
parties. Our objective is to provide an overview of the sources of
irreproducibility that are reported in the literature. We review the literature
to provide an overview and a taxonomy in addition to a discussion on the
identified sources of irreproducibility. Finally, we identify three lines of
further inquiry.


title: Curvature functionals on convex bodies

abstract:
  We investigate the weighted $L_p$ affine surface areas which appear in the
recently established $L_p$ Steiner formula of the $L_p$ Brunn Minkowski theory.
We show that they are valuations on the set of convex bodies and prove
isoperimetric inequalities for them. We show that they are related to $f$
divergences of the cone measures of the convex body and its polar, namely the
Kullback-Leibler divergence and the R\'enyi-divergence.


title:
Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of
  Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in
  Artificial Intelligence

abstract:
  In this meta-ethnography, we explore three different angles of Ethical AI
design and implementation in a top-down/bottom-up framework, including the
philosophical ethical viewpoint, the technical perspective, and framing through
a political lens. We will discuss the values and drawbacks of individual and
hybrid approaches within this framework. Examples of approaches include ethics
either being determined by corporations and governments (coming from the top),
or ethics being called for by the people (coming from the bottom), as well as
top-down, bottom-up, and hybrid technicalities of how AI is developed within a
moral construct, in consideration of its developers and users, with expected
and unexpected consequences and long-term impact. This investigation includes
real-world case studies, philosophical debate, and theoretical future thought
experimentation based on historical facts, current world circumstances, and
possible ensuing realities.


title: $\Upsilon$-Net: A Spatiospectral Network for Retinal OCT Segmentation

abstract:
  Automated segmentation of retinal optical coherence tomography (OCT) images
has become an important recent direction in machine learning for medical
applications. We hypothesize that the anatomic structure of layers and their
high-frequency variation in OCT images make retinal OCT a fitting choice for
extracting spectral-domain features and combining them with spatial domain
features. In this work, we present $\Upsilon$-Net, an architecture that
combines the frequency domain features with the image domain to improve the
segmentation performance of OCT images. The results of this work demonstrate
that the introduction of two branches, one for spectral and one for spatial
domain features, brings a very significant improvement in fluid segmentation
performance and allows outperformance as compared to the well-known U-Net
model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on
the average dice score. Finally, removing selected frequency ranges in the
spectral domain demonstrates the impact of these features on the fluid
segmentation outperformance.


title:
DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI
  Feedback Deep Learning

abstract:
  We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP)
which leverages standard-compliant beamforming feedback matrices to
authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique
imperfections in off-the-shelf radio circuitry, RFP techniques can identify
wireless devices directly at the physical layer, allowing low-latency
low-energy cryptography-free authentication. However, existing Wi-Fi RFP
techniques are based on software-defined radio (SDRs), which may ultimately
prevent their widespread adoption. Moreover, it is unclear whether existing
strategies can work in the presence of MU-MIMO transmitters - a key technology
in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require
SDR technologies and can be run on any low-cost Wi-Fi device to authenticate
MU-MIMO transmitters. Our key intuition is that imperfections in the
transmitter's radio circuitry percolate onto the beamforming feedback matrix,
and thus RFP can be performed without explicit channel state information (CSI)
computation. DeepCSI is robust to inter-stream and inter-user interference
being the beamforming feedback not affected by those phenomena. We extensively
evaluate the performance of DeepCSI through a massive data collection campaign
performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi
radios emit signals in different positions. Experimental results indicate that
DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The
identification accuracy remains above 82% when the device moves within the
environment. To allow replicability and provide a performance benchmark, we
pledge to share the 800 GB datasets - collected in static and, for the first
time, dynamic conditions - and the code database with the community.


title: Resource-Constrained Neural Architecture Search on Tabular Datasets

abstract:
  The best neural architecture for a given machine learning problem depends on
many factors: not only the complexity and structure of the dataset, but also on
resource constraints including latency, compute, energy consumption, etc.
Neural architecture search (NAS) for tabular datasets is an important but
under-explored problem. Previous NAS algorithms designed for image search
spaces incorporate resource constraints directly into the reinforcement
learning rewards. In this paper, we argue that search spaces for tabular NAS
pose considerable challenges for these existing reward-shaping methods, and
propose a new reinforcement learning (RL) controller to address these
challenges. Motivated by rejection sampling, when we sample candidate
architectures during a search, we immediately discard any architecture that
violates our resource constraints. We use a Monte-Carlo-based correction to our
RL policy gradient update to account for this extra filtering step. Results on
several tabular datasets show TabNAS, the proposed approach, efficiently finds
high-quality models that satisfy the given resource constraints.


title: Multi-Frame Self-Supervised Depth with Transformers

abstract:
  Multi-frame depth estimation improves over single-frame approaches by also
leveraging geometric relationships between images via feature matching, in
addition to learning appearance-based features. In this paper we revisit
feature matching for self-supervised monocular depth estimation, and propose a
novel transformer architecture for cost volume generation. We use
depth-discretized epipolar sampling to select matching candidates, and refine
predictions through a series of self- and cross-attention layers. These layers
sharpen the matching probability between pixel features, improving over
standard similarity metrics prone to ambiguities and local minima. The refined
cost volume is decoded into depth estimates, and the whole pipeline is trained
end-to-end from videos using only a photometric objective. Experiments on the
KITTI and DDAD datasets show that our DepthFormer architecture establishes a
new state of the art in self-supervised monocular depth estimation, and is even
competitive with highly specialized supervised single-frame architectures. We
also show that our learned cross-attention network yields representations
transferable across datasets, increasing the effectiveness of pre-training
strategies. Project page: https://sites.google.com/tri.global/depthformer


title: Celestial insights into the S-matrix bootstrap

abstract:
  We consider 2-2 scattering in four spacetime dimensions in Celestial
variables. Using the crossing symmetric dispersion relation (CSDR), we recast
the Celestial amplitudes in terms of crossing symmetric partial waves. These
partial waves have spurious singularities in the complex Celestial variable,
which need to be removed in local theories. The locality constraints (null
constraints) admit closed form expressions, which lead to novel bounds on
partial wave moments. These bounds allow us to quantify the degree of low spin
dominance(LSD) for scalar theories. We study a new kind of positivity that
seems to be present in a wide class of theories. We prove that this positivity
arises only in theories with a spin-0 dominance. The crossing symmetric partial
waves with spurious singularities removed, dubbed as Feynman blocks, have
remarkable properties in the Celestial variable, namely typically realness, in
the sense of Geometric Function Theory (GFT). Using GFT techniques we derive
non-projective bounds on Wilson coefficients in terms of partial wave moments.


title: Operator inequalities via accretive and dissipative transforms

abstract:
  In this article, we employ certain properties of the transform
$\mathscr{C}_{M,m}(A)=(M\mathbf1_{\mathcal{H}}-A^*)(A-m\mathbf1_{\mathcal{H}})$
to obtain new inequalities for the bounded linear operator $A$ on a complex
Hilbert space $\mathcal{H}$. In particular, we obtain new relations among
$|A|,|A^*|,|\mathfrak{R}A|$ and $|\mathfrak{I}A|$. Further numerical radius
inequalities that extend some known inequalities will be presented too.


title:
Transfer Importance Sampling $\unicode{x2013}$ How Testing Automated
  Vehicles in Multiple Test Setups Helps With the Bias-Variance Tradeoff

abstract:
  The promise of increased road safety is a key motivator for the development
of automated vehicles (AV). Yet, demonstrating that an AV is as safe as, or
even safer than, a human-driven vehicle has proven to be challenging. Should an
AV be examined purely virtually, allowing large numbers of fully controllable
tests? Or should it be tested under real environmental conditions on a proving
ground? Since different test setups have different strengths and weaknesses, it
is still an open question how virtual and real tests should be combined. On the
way to answer this question, this paper proposes transfer importance sampling
(TIS), a risk estimation method linking different test setups. Fusing the
concepts of transfer learning and importance sampling, TIS uses a scalable,
cost-effective test setup to comprehensively explore an AV's behavior. The
insights gained then allow parameterizing tests in a more trustworthy test
setup accurately reflecting risks. We show that when using a trustworthy test
setup alone is prohibitively expensive, linking it to a scalable test setup can
increase efficiency $\unicode{x2013}$ without sacrificing the result's
validity. Thus, the test setups' individual deficiencies are compensated for by
their systematic linkage.


title: Some weighted numerical radius inequalities

abstract:
  The weighted numerical radius of a Hilbert space operator has been defined
recently. This article explores other properties and uses this newly defined
numerical radius to obtain several new interesting inequalities for the
weighted numerical radius and the numerical radius. In particular, new
identities for the numerical radius, further comparisons between the numerical
radius and the real and imaginary parts, and some inequalities for sectorial
operators will be presented.


title:
Beyond the Local Volume II: Population Scaleheights and Ages of
  Ultracool Dwarfs in Deep HST/WFC3 Parallel Fields

abstract:
  Ultracool dwarfs represent a significant proportion of stars in the Milky
Way,and deep samples of these sources have the potential to constrain the
formation history and evolution of low-mass objects in the Galaxy. Until
recently, spectral samples have been limited to the local volume (d<100 pc).
Here, we analyze a sample of 164 spectroscopically-characterized ultracool
dwarfs identified by Aganze et al. (2022) in the Hubble Space Telescope WFC3
Infrared Spectroscopic Parallel (WISP) Survey and 3D-HST. We model the observed
luminosity function using population simulations to place constraints on
scaleheights, vertical velocity dispersions and population ages as a function
of spectral type. Our star counts are consistent with a power-law mass function
and constant star formation history for ultracool dwarfs, with vertical
scaleheights 249$_{-61}^{+48}$ pc for late M dwarfs, 153$_{-30}^{+56}$ pc for L
dwarfs, and 175$_{-56}^{+149}$ pc for T dwarfs. Using spatial and velocity
dispersion relations, these scaleheights correspond to disk population ages of
3.6$_{-1.0}^{+0.8}$ for late M dwarfs, 2.1$_{-0.5}^{+0.9}$ Gyr for L dwarfs,
and 2.4$_{-0.8}^{+2.4}$ Gyr for T dwarfs, which are consistent with prior
simulations that predict that L-type dwarfs are on average a younger and less
dispersed population. There is an additional 1-2 Gyr systematic uncertainty on
these ages due to variances in age-velocity relations. We use our population
simulations to predict the UCD yield in the JWST PASSAGES survey, a similar and
deeper survey to WISPS and 3D-HST, and find that it will produce a
comparably-sized UCD sample, albeit dominated by thick disk and halo sources.


title: Operator inequalities via the triangle inequality

abstract:
  This article improves the triangle inequality for complex numbers, using the
Hermite-Hadamard inequality for convex functions. Then, applications of the
obtained refinement are presented to include some operator inequalities. The
operator applications include numerical radius inequalities and operator mean
inequalities.


title:
Active Mapping via Gradient Ascent Optimization of Shannon Mutual
  Information over Continuous SE(3) Trajectories

abstract:
  The problem of active mapping aims to plan an informative sequence of sensing
views given a limited budget such as distance traveled. This paper consider
active occupancy grid mapping using a range sensor, such as LiDAR or depth
camera. State-of-the-art methods optimize information-theoretic measures
relating the occupancy grid probabilities with the range sensor measurements.
The non-smooth nature of ray-tracing within a grid representation makes the
objective function non-differentiable, forcing existing methods to search over
a discrete space of candidate trajectories. This work proposes a differentiable
approximation of the Shannon mutual information between a grid map and
ray-based observations that enables gradient ascent optimization in the
continuous space of SE(3) sensor poses. Our gradient-based formulation leads to
more informative sensing trajectories, while avoiding occlusions and
collisions. The proposed method is demonstrated in simulated and real-world
experiments in 2-D and 3-D environments.


title: Total mean curvatures of Riemannian hypersurfaces

abstract:
  We obtain a comparison formula for integrals of mean curvatures of Riemannian
hypersurfaces, via Reilly's identities. As applications we derive a number of
geometric inequalities for a convex hypersurface $\Gamma$ in a Cartan-Hadamard
manifold $M$. In particular we show that the first mean curvature integral of a
convex hypersurface $\gamma$ nested inside $\Gamma$ cannot exceed that of
$\Gamma$, which leads to a sharp lower bound for the total first mean curvature
of $\Gamma$ in terms of the volume it bounds. This monotonicity property is
extended to all mean curvature integrals when $\gamma$ is parallel to $\Gamma$,
or $M$ has constant curvature. We also characterize hyperbolic balls as
minimizers of the mean curvature integrals among balls with equal radii in
Cartan-Hadamard manifolds.


title: Design of optimization tools for quantum information theory

abstract:
  In this thesis, we present optimization tools for different problems in
quantum information theory. First, we introduce an algorithm for quantum estate
estimation. The algorithm consists of orthogonal projections on intersecting
hyperplanes, which are determined by the probability distributions and the
measurement operators. We show its performance, in both runtime and fidelity,
considering realistic errors. Second, we present a technique for certifying
quantum non-locality. Given a set of bipartite measurement frequencies, this
technique finds a Bell inequality that maximizes the gap between the local
hidden variable and the quantum value of a Bell inequality. Lastly, to study
the quantum marginal problem, we introduce an operator and develop an
algorithm, which takes as inputs a set of quantum marginals and eigenvalues,
and outputs a density matrix, if exists, compatible with the prescribed data.


title:
Short Time Existence for Coupling of Scaled Mean Curvature Flow and
  Diffusion

abstract:
  We prove a short time existence result for a system consisting of a geometric
evolution equation for a hypersurface and a parabolic equation on this evolving
hypersurface. More precisely, we discuss a mean curvature flow scaled with a
term that depends on a quantity defined on the surface coupled to a diffusion
equation for that quantity. The proof is based on a splitting ansatz, solving
both equations separately using linearization and a contraction argument. Our
result is formulated for the case of immersed hypersurfaces and yields a
uniform lower bound on the existence time that allows for small changes in the
initial value of the height function.


title:
Metallicity of Galactic RR Lyrae from Optical and Infrared Light Curves:
  II. Period-Fourier-Metallicity Relations for First Overtone RR Lyrae

abstract:
  We present new period-$\phi_{31}$-[Fe/H] relations for first overtone RRL
stars (RRc), calibrated over a broad range of metallicities ($-2.5 <
\textrm{[Fe/H]}< 0.0$) utilizing the largest currently available set of
Galactic halo field RRL with homogeneous spectroscopic metallicities. Our
relations are defined in the optical (ASAS-SN $V$-band) and, inaugurally, in
the infrared (WISE $W1$ and $W2$ bands). Our $V$-band relation can reproduce
individual RRc spectroscopic metallicities with a dispersion of 0.30 dex over
the entire metallicity range of our calibrator sample (an RMS smaller than what
we found for other relations in literature including non-linear terms). Our
infrared relation has a similar dispersion in the low and intermediate
metallicity range ($\textrm{[Fe/H]} < -0.5$) but tends to underestimate the
[Fe/H] abundance around solar metallicity. We tested our relations by measuring
both the metallicity of the Sculptor dSph and a sample of Galactic globular
clusters, rich in both RRc and RRab stars. The average metallicity we obtain
for the combined RRL sample in each cluster is within $\pm 0.08$ dex of their
spectroscopic metallicities. The infrared and optical relations presented in
this work will enable deriving reliable photometric RRL metallicities in
conditions where spectroscopic measurements are not feasible; e.g., in distant
galaxies or reddened regions (observed with upcoming Extremely Large Telescopes
and the James Webb Space Telescope), or in the large sample of new RRL that
will be discovered in large-area time-domain photometric surveys (such as LSST
and the Roman space telescope).


title: On short-time stability notions for nonlinear systems

abstract:
  This paper introduces the notions of annular settling and output annular
settling for general nonlinear systems. Also, we propose conditions for annular
short-time stability, short time boundedness with a nonzero initial state,
annular settling, and output annular settling for generalized Persidskii
systems with an essentially bounded input. These conditions are based on the
verification of linear matrix inequalities. An application to recurrent neural
networks illustrates the usefulness of the proposed notions and conditions.


title:
Navigation between states in ecological communities by taking shortcuts,
  with application to control

abstract:
  Many community ecology problems can be framed in terms of controlling the
transition from an initial state to a desired state. However, it is often
unclear what action sequence (if any) would yield the desired state. Here we
develop a simple approach for navigating to desired states, applicable when the
costs and outcomes of actions are known. We find lowest-cost action sequences
(adding a species, removing a species, changing the environment, waiting) via
A* search on a state diagram. Lowest-cost sequences usually are indirect and
leverage waiting for natural transitions caused by competitive exclusion. In
tests on simulated and empirical data across taxa, our approach provides ~50%
probability of substantial cost improvement relative to nominal approaches. As
an example, numerous successes are predicted in gut microbial communities for
removing the pathogen Clostridium difficile. This work thus provides a
conceptual foundation for efficient state transitions in species-rich
communities.


title: Prismatic Soft Actuator Augments the Workspace of Soft Continuum Robots

abstract:
  Soft robots are promising for manipulation tasks thanks to their compliance,
safety, and high degree of freedom. However, the commonly used bidirectional
continuum segment design means soft robotic manipulators only function in a
limited hemispherical workspace. This work increases a soft robotic arm's
workspace by designing, fabricating, and controlling an additional soft
prismatic actuator at the base of the soft arm. This actuator consists of
pneumatic artificial muscles and a piston, making the actuator back-driveable.
We increase the task space volume by 116\%, and we are now able to perform
manipulation tasks that were previously impossible for soft robots, such as
picking and placing objects at different positions on a surface and grabbing an
object out of a container. By combining a soft robotic arm with a prismatic
joint, we greatly increase the usability of soft robots for object
manipulation. This work promotes the use of integrated and modular soft robotic
systems for practical manipulation applications in human-centered environments.


title:
Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost
  Sensor for Dexterous Manipulation

abstract:
  Imitation learning is a promising approach to help robots acquire dexterous
manipulation capabilities without the need for a carefully-designed reward or a
significant computational effort. However, existing imitation learning
approaches require sophisticated data collection infrastructure and struggle to
generalize beyond the training distribution. One way to address this limitation
is to gather additional data that better represents the full operating
conditions. In this work, we investigate characteristics of such additional
demonstrations and their impact on performance. Specifically, we study the
effects of corrective and randomly-sampled additional demonstrations on
learning a policy that guides a five-fingered robot hand through a
pick-and-place task. Our results suggest that corrective demonstrations
considerably outperform randomly-sampled demonstrations, when the proportion of
additional demonstrations sampled from the full task distribution is larger
than the number of original demonstrations sampled from a restrictive training
distribution. Conversely, when the number of original demonstrations are higher
than that of additional demonstrations, we find no significant differences
between corrective and randomly-sampled additional demonstrations. These
results provide insights into the inherent trade-off between the effort
required to collect corrective demonstrations and their relative benefits over
randomly-sampled demonstrations. Additionally, we show that inexpensive
vision-based sensors, such as LeapMotion, can be used to dramatically reduce
the cost of providing demonstrations for dexterous manipulation tasks. Our code
is available at
https://github.com/GT-STAR-Lab/corrective-demos-dexterous-manipulation.


title:
Mass Distribution and "Mass Gap" of Compact Stellar Remnants in Binary
  Systems

abstract:
  The highest critical mass of neutron stars (NSs) was reviewed in the context
of equation of state and observationsl results/ It was predicted that the
maximum NS mass (MNS) exists in the range MNS ~ 2.2-2.9 M_Sun. However, recent
observations of gravitationsl waves and other studies had suggested the higher
mass limit of NSs, MNS ~ 3/2 M_Sun. The NS mass up to the value of MNS ~ 2
M_Sun is well understood, and with such a mass value it was meaning ful to
discuss the "mass gap" ("m-gap") between the NS and black hole (BH)
collapsars.The "m-gap" exists in between the highest mass of NS and the lowest
mass of BH collapsars (Mm-gap ~ 2-5 M_Sun). in the mass distribution, the
maximim population of NSs and BHs is located at MNS = 1.4 M_Sun and MBH = 6.7
M_Sun, respectively. However, recent ofservational results predicted filling
the "m-gap" by the compact objects. In this paper, the concept of gravidynamics
was reported to resolve the problem of peak likelihood value of gravitational
mass at Mpeak = 6.7 M_Sun and the "m-gap" (Mm-gap ~ 2-5 M_Sun). This concept
was based on a non-metric scalar-tensor model of gravitational interaction with
localizable field energy. The gravidynamics model shows the total mass (MQ) of
a compact relativistic object filled with matter of quark-gluon plasma of the
radius r* = GMQ/c2 ~ 10 km, consistent with the "m-gap". It was conceptualized
that the total measurable gravitational mass of such an extremely dense object
consists of both matter and field, which is described by scalar-tensor
components. This model is also useful for predicting the collapsars within the
"m-gap".


title:
Topology-based Phase Identification of Bulk, Interface, and Confined
  Water using Edge-Conditioned Convolutional Graph Neural Network

abstract:
  Water plays a significant role in various physicochemical and biological
processes. Understanding and identifying water phases in various systems such
as bulk, interface, and confined water is crucial in improving and engineering
state-of-the-art nano-devices. Various order parameters have been developed to
distinguish water phases, including bond-order parameters, local structure
index, and tetrahedral order parameters. These order parameters are often
developed with the assumption of homogenous bulk systems, while most
applications involve heterogeneous and non-bulk systems, thus, limiting their
generalizability. Our study develops a methodology based on the graph neural
network to distinguish water phases directly from data and to learn features
instead of predefining them. We provide comparisons between baseline methods
trained using conventional order-parameters as features, and a graph neural
network model trained using radial distance and hydrogen-bonding information
for phase classification and phase transition of water in bulk, interface, and
confined systems with continuous and discontinuous phase transitions.


title: A generative neural network model for random dot product graphs

abstract:
  We present GraphMoE, a novel neural network-based approach to learning
generative models for random graphs. The neural network is trained to match the
distribution of a class of random graphs by way of a moment estimator. The
features used for training are graphlets, subgraph counts of small order. The
neural network accepts random noise as input and outputs vector representations
for nodes in the graph. Random graphs are then realized by applying a kernel to
the representations. Graphs produced this way are demonstrated to be able to
imitate data from chemistry, medicine, and social networks. The produced graphs
are similar enough to the target data to be able to fool discriminator neural
networks otherwise capable of separating classes of random graphs.


title:
Full superconducting gap in the candidate topological superconductor
  In$_{1-x}$Pb$_x$Te for x = 0.2

abstract:
  High-pressure synthesis techniques have allowed for the growth of samples on
the indium-rich side of (Pb,In)Te, which have increased superconducting
transition temperatures compared to lead-rich compounds. In this study we
present measurements of the temperature dependence of the London penetration
depth $\Delta \lambda(T)$ in the compound In$_{0.8}$Pb$_{0.2}$Te, which shows a
bulk $T_{c,onset}$ of $\sim4.75$ K. The results indicate fully gapped BCS-like
behavior, ruling out odd-parity, topologically nontrivial $A_{2u}$ pairing;
however, odd-parity $A_{1u}$ pairing is still possible. Critical field values
measured below 1 K and other superconducting parameters are also presented.


title:
Lagrangian Motion Magnification with Double Sparse Optical Flow
  Decomposition

abstract:
  Motion magnification techniques aim at amplifying and hence revealing subtle
motion in videos. There are basically two main approaches to reach this goal,
namely via Eulerian or Lagrangian techniques. While the first one magnifies
motion implicitly by operating directly on image pixels, the Lagrangian
approach uses optical flow techniques to extract and amplify pixel
trajectories. Microexpressions are fast and spatially small facial expressions
that are difficult to detect. In this paper, we propose a novel approach for
local Lagrangian motion magnification of facial micromovements. Our
contribution is three-fold: first, we fine-tune the recurrent all-pairs field
transforms for optical flows (RAFT) deep learning approach for faces by adding
ground truth obtained from the variational dense inverse search (DIS) for
optical flow algorithm applied to the CASME II video set of faces. This enables
us to produce optical flows of facial videos in an efficient and sufficiently
accurate way. Second, since facial micromovements are both local in space and
time, we propose to approximate the optical flow field by sparse components
both in space and time leading to a double sparse decomposition. Third, we use
this decomposition to magnify micro-motions in specific areas of the face,
where we introduce a new forward warping strategy using a triangular splitting
of the image grid and barycentric interpolation of the RGB vectors at the
corners of the transformed triangles. We demonstrate the very good performance
of our approach by various examples.


title: Towards a Stronger Theory for Permutation-based Evolutionary Algorithms

abstract:
  While the theoretical analysis of evolutionary algorithms (EAs) has made
significant progress for pseudo-Boolean optimization problems in the last 25
years, only sporadic theoretical results exist on how EAs solve
permutation-based problems.
  To overcome the lack of permutation-based benchmark problems, we propose a
general way to transfer the classic pseudo-Boolean benchmarks into benchmarks
defined on sets of permutations. We then conduct a rigorous runtime analysis of
the permutation-based $(1+1)$ EA proposed by Scharnow, Tinnefeld, and Wegener
(2004) on the analogues of the \textsc{LeadingOnes} and \textsc{Jump}
benchmarks. The latter shows that, different from bit-strings, it is not only
the Hamming distance that determines how difficult it is to mutate a
permutation $\sigma$ into another one $\tau$, but also the precise cycle
structure of $\sigma \tau^{-1}$. For this reason, we also regard the more
symmetric scramble mutation operator. We observe that it not only leads to
simpler proofs, but also reduces the runtime on jump functions with odd jump
size by a factor of $\Theta(n)$. Finally, we show that a heavy-tailed version
of the scramble operator, as in the bit-string case, leads to a speed-up of
order $m^{\Theta(m)}$ on jump functions with jump size~$m$.%


title: Paths are Tur\'an-good

abstract:
  We show that among $K_{k+1}$-free $n$-vertex graphs, the Tur\'an graph
contains the most copies of any path.


title: Graded Frobenius Rings

abstract:
  In order to study graded Frobenius algebras from a ring theoretical
perspective, we introduce graded quasi-Frobenius rings, graded Frobenius rings
and a shift-version of the latter ones, and we investigate the structure and
representations of such objects. We need to revisit graded simple graded left
Artinian rings, graded semisimple rings, and to provide graded versions of
certain results concerning the Jacobson radical, the singular radical, and
their connection to finiteness conditions and injectivity. We prove a structure
result for (shift-)graded Frobenius rings.


title: Event-aided Direct Sparse Odometry

abstract:
  We introduce EDS, a direct monocular visual odometry using events and frames.
Our algorithm leverages the event generation model to track the camera motion
in the blind time between frames. The method formulates a direct probabilistic
approach of observed brightness increments. Per-pixel brightness increments are
predicted using a sparse number of selected 3D points and are compared to the
events via the brightness increment error to estimate camera motion. The method
recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the
first method to perform 6-DOF VO using events and frames with a direct
approach. By design, it overcomes the problem of changing appearance in
indirect methods. We also show that, for a target error performance, EDS can
work at lower frame rates than state-of-the-art frame-based VO solutions. This
opens the door to low-power motion-tracking applications where frames are
sparingly triggered "on demand" and our method tracks the motion in between. We
release code and datasets to the public.


title:
Investigating Positive and Negative Qualities of Human-in-the-Loop
  Optimization for Designing Interaction Techniques

abstract:
  Designers reportedly struggle with design optimization tasks where they are
asked to find a combination of design parameters that maximizes a given set of
objectives. In HCI, design optimization problems are often exceedingly complex,
involving multiple objectives and expensive empirical evaluations. Model-based
computational design algorithms assist designers by generating design examples
during design, however they assume a model of the interaction domain. Black box
methods for assistance, on the other hand, can work with any design problem.
However, virtually all empirical studies of this human-in-the-loop approach
have been carried out by either researchers or end-users. The question stands
out if such methods can help designers in realistic tasks. In this paper, we
study Bayesian optimization as an algorithmic method to guide the design
optimization process. It operates by proposing to a designer which design
candidate to try next, given previous observations. We report observations from
a comparative study with 40 novice designers who were tasked to optimize a
complex 3D touch interaction technique. The optimizer helped designers explore
larger proportions of the design space and arrive at a better solution, however
they reported lower agency and expressiveness. Designers guided by an optimizer
reported lower mental effort but also felt less creative and less in charge of
the progress. We conclude that human-in-the-loop optimization can support
novice designers in cases where agency is not critical.


title:
Slowed Recombination via Tunable Surface Energetics in Perovskite Solar
  Cells

abstract:
  Metal halide perovskite semiconductors have the potential to reach the
optoelectronic quality of meticulously grown inorganic materials, but with a
distinct advantage of being solution processable. Currently, perovskite
performance is limited by charge carrier recombination loss at surfaces and
interfaces. Indeed, the highest quality perovskite films are achieved with
molecular surface passivation, for example with n-trioctylphosphine oxide, but
these treatments are often labile and electrically insulating. As an
alternative, the formation of a thin 2D perovskite layer on the bulk 3D
perovskite reduces non-radiative energy loss while also improving device
performance. But, thus far, it has been unclear how best to design and optimize
2D/3D heterostructures and whether critical material properties, such as charge
carrier lifetime, can reach values as high as ligand-based approaches. Here, we
study perovskite devices that have exhibited power conversion efficiencies
exceeding 25% and show that 2D layers are capable of pushing beyond molecular
passivation strategies with even greater tunability. We set new benchmarks for
photoluminescence lifetime, reaching values > 30 {\mu}s, and perovskite/charge
transport layer surface recombination velocity with values < 7 cm s^{-1}. We
use X-ray spectroscopy to directly visualize how treatment with hexylammonium
bromide not only selectively targets defects at surfaces and grain boundaries,
but also forms a bandgap grading extending > 100 nm into the bulk layer. We
expect these results to be a starting point for more sophisticated engineering
of 2D/3D heterostructures with surface fields that exclusively repel charge
carriers from defective regions while also enabling efficient charge transfer.
It is likely that the precise manipulation of energy bands will enable
perovskite-based optoelectronics to operate at their theoretical performance
limits.


title: Proof of the Riemann Hypothesis

abstract: In this article we will prove the Riemann Hypothesis.


title: Identifying Ethical Issues in AI Partners in Human-AI Co-Creation

abstract:
  Human-AI co-creativity involves humans and AI collaborating on a shared
creative product as partners. In many existing co-creative systems, users
communicate with the AI using buttons or sliders. However, typically, the AI in
co-creative systems cannot communicate back to humans, limiting their potential
to be perceived as partners. This paper starts with an overview of a
comparative study with 38 participants to explore the impact of AI-to-human
communication on user perception and engagement in co-creative systems and the
results show improved collaborative experience and user engagement with the
system incorporating AI-to-human communication. The results also demonstrate
that users perceive co-creative AI as more reliable, personal and intelligent
when it can communicate with the users. The results indicate a need to identify
potential ethical issues from an engaging communicating co-creative AI. Later
in the paper, we present some potential ethical issues in human-AI co-creation
and propose to use participatory design fiction as the research methodology to
investigate the ethical issues associated with a co-creative AI that
communicates with users.


title: Timescales for Nitric Acid Desorption in Epitaxial Graphene Devices

abstract:
  This work reports the dynamics of transient hole doping in epitaxial graphene
devices by using nitric acid as an adsorbent. The timescales associated with
corresponding desorption processes are extracted from the data. The
understanding of reversible hole doping without gating is of crucial importance
to those fabricating devices with a particular functionality. Measurements of
the electrical and optical properties of several devices post-exposure were
performed with transport temperatures between 300 K and 1.5 K. Ambient
conditions are applied to non-transport measurements to replicate the most
likely laboratory conditions for handling devices using this doping method. The
relevant timescales from transport measurements are compared with results from
Raman spectroscopy measurements.


title: Wavelet Moments for Cosmological Parameter Estimation

abstract:
  Extracting non-Gaussian information from the non-linear regime of structure
formation is key to fully exploiting the rich data from upcoming cosmological
surveys probing the large-scale structure of the universe. However, due to
theoretical and computational complexities, this remains one of the main
challenges in analyzing observational data. We present a set of summary
statistics for cosmological matter fields based on 3D wavelets to tackle this
challenge. These statistics are computed as the spatial average of the complex
modulus of the 3D wavelet transform raised to a power $q$ and are therefore
known as invariant wavelet moments. The 3D wavelets are constructed to be
radially band-limited and separable on a spherical polar grid and come in three
types: isotropic, oriented, and harmonic. In the Fisher forecast framework, we
evaluate the performance of these summary statistics on matter fields from the
Quijote suite, where they are shown to reach state-of-the-art parameter
constraints on the base $\Lambda$CDM parameters, as well as the sum of neutrino
masses. We show that we can improve constraints by a factor 5 to 10 in all
parameters with respect to the power spectrum baseline.


title:
Spectroscopic assessment of short-term nitric acid doping of epitaxial
  graphene

abstract:
  This work reports information on the transience of hole doping in epitaxial
graphene devices when nitric acid is used as an adsorbent. Under vacuum
conditions, desorption processes are monitored by electrical and spectroscopic
means to extract the relevant timescales from the corresponding data. It is of
vital importance to understand the reversible nature of hole doping because
such device processing can be a suitable alternative to large-scale, metallic
gating. Most measurements are performed post-exposure at room temperature, and,
for some electrical transport measurements, at 1.5 K. Vacuum conditions are
applied to many measurements to replicate the laboratory conditions under which
devices using this doping method would be measured. The relevant timescales
from transport measurements are compared with results from X-ray photoelecton
spectroscopy and Fourier transform infrared spectroscopy measurements, with the
latter performed at ambient conditions and accompanied by calculations of the
spectra in the Reststrahlen band.


title:
Design of an arrangement of cubic magnets for a quasi-axisymmetric
  stellarator experiment

abstract:
  The usage of permanent magnets to shape the confining magnetic field of a
stellarator has the potential to reduce or eliminate the need for non-planar
coils. As a proof-of-concept for this idea, we have developed a procedure for
designing an array of cubic permanent magnets that works in tandem with a set
of toroidal-field coils to confine a stellarator plasma. All of the magnets in
the design are constrained to have identical geometry and one of three
polarization types in order to simplify fabrication while still producing
sufficient field accuracy. We present some of the key steps leading to the
design, including the geometric arrangement of the magnets around the device,
the procedure for optimizing the polarizations according to the three allowable
magnet types, and the choice of magnet types to be used. We apply these methods
to design an array of rare-Earth permanent magnets that can be paired with a
set of planar toroidal-field coils to confine a quasi-axisymmetric plasma with
a toroidal magnetic field strength of about 0.5 T on axis.


title:
MultiEarth 2022 -- Multimodal Learning for Earth and Environment
  Workshop and Challenge

abstract:
  The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022)
will be the first competition aimed at the monitoring and analysis of
deforestation in the Amazon rainforest at any time and in any weather
conditions. The goal of the Challenge is to provide a common benchmark for
multimodal information processing and to bring together the earth and
environmental science communities as well as multimodal representation learning
communities to compare the relative merits of the various multimodal learning
methods to deforestation estimation under well-defined and strictly comparable
conditions. MultiEarth 2022 will have three sub-challenges: 1) matrix
completion, 2) deforestation estimation, and 3) image-to-image translation.
This paper presents the challenge guidelines, datasets, and evaluation metrics
for the three sub-challenges. Our challenge website is available at
https://sites.google.com/view/rainforest-challenge.


title:
Momentum microscopy of Pb-intercalated graphene on SiC: charge
  neutrality and electronic structure of interfacial Pb

abstract:
  Intercalation is an established technique for tailoring the electronic
structure of epitaxial graphene. Moreover, it enables the synthesis of
otherwise unstable two-dimensional (2D) layers of elements with unique physical
properties compared to their bulk versions due to interfacial quantum
confinement. In this work, we present uniformly Pb-intercalated
quasi-freestanding monolayer graphene on SiC, which turns out to be essentially
charge neutral with an unprecedented $p$-type carrier density of only
$(5.5\pm2.5)\times10^9$ cm$^{-2}$. Probing the low-energy electronic structure
throughout the entire first surface Brillouin zone by means of momentum
microscopy, we clearly discern additional bands related to metallic 2D-Pb at
the interface. Low-energy electron diffraction further reveals a $10\times10$
Moir\'e superperiodicity relative to graphene, counterparts of which cannot be
directly identified in the available band structure data. Our experiments
demonstrate 2D interlayer confinement and associated band structure formation
of a heavy-element superconductor, paving the way towards strong spin-orbit
coupling effects or even 2D superconductivity at the graphene/SiC interface.


title:
Learning time-dependent PDE solver using Message Passing Graph Neural
  Networks

abstract:
  One of the main challenges in solving time-dependent partial differential
equations is to develop computationally efficient solvers that are accurate and
stable. Here, we introduce a graph neural network approach to finding efficient
PDE solvers through learning using message-passing models. We first introduce
domain invariant features for PDE-data inspired by classical PDE solvers for an
efficient physical representation. Next, we use graphs to represent PDE-data on
an unstructured mesh and show that message passing graph neural networks
(MPGNN) can parameterize governing equations, and as a result, efficiently
learn accurate solver schemes for linear/nonlinear PDEs. We further show that
the solvers are independent of the initial trained geometry, i.e. the trained
solver can find PDE solution on different complex domains. Lastly, we show that
a recurrent graph neural network approach can find a temporal sequence of
solutions to a PDE.


title:
Precise predictions for V+2 jet backgrounds in searches for invisible
  Higgs decays

abstract:
  We present next-to-leading order QCD and electroweak (EW) theory predictions
for $V+2\,$jet production, with $V=Z,W^{\pm}$, considering both the QCD and EW
production modes and their interference. We focus on phase-space regions where
$V+2\,$jet production is dominated by vector-boson fusion, and where these
processes yield the dominant irreducible backgrounds in searches for invisible
Higgs boson decays. Predictions at parton level are provided together with
detailed prescriptions for their implementation in experimental analyses based
on the reweighting of Monte Carlo samples. The key idea is that, exploiting
accurate data for $W+2\,$jet production in combination with a theory-driven
extrapolation to the $Z+2\,$jet process can lead to a determination of the
irreducible background at the few-percent level. Particular attention is
devoted to the estimate of the residual theoretical uncertainties due to
unknown higher-order QCD and EW effects and their correlation between the
different $V+2\,$jet processes, which is key to improve the sensitivity to
invisible Higgs decays.


title:
Bayesian Updating of Seismic Ground Failure Estimates via Causal
  Graphical Models and Satellite Imagery

abstract:
  Earthquake-induced secondary ground failure hazards, such as liquefaction and
landslides, result in catastrophic building and infrastructure damage as well
as human fatalities. To facilitate emergency responses and mitigate losses, the
U.S. Geological Survey provides a rapid hazard estimation system for
earthquake-triggered landslides and liquefaction using geospatial
susceptibility proxies and ShakeMap ground motion estimates. In this study, we
develop a generalized causal graph-based Bayesian network that models the
physical interdependencies between geospatial features, seismic ground
failures, and building damage, as well as DPMs. Geospatial features provide
physical insights for estimating ground failure occurrence while DPMs contain
event-specific surface change observations. This physics-informed causal graph
incorporates these variables with complex physical relationships in one
holistic Bayesian updating scheme to effectively fuse information from both
geospatial models and remote sensing data. This framework is scalable and
flexible enough to deal with highly complex multi-hazard combinations. We then
develop a stochastic variational inference algorithm to jointly update the
intractable posterior probabilities of unobserved landslides, liquefaction, and
building damage at different locations efficiently. In addition, a local
graphical model pruning algorithm is presented to reduce the computational cost
of large-scale seismic ground failure estimation. We apply this framework to
the September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January
2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our
algorithm.


title:
Noisy detectors with unmatched detection efficiency thwart
  identification of single photon emitters via photon coincidence correlation

abstract:
  Single photon emitters (SPEs) are often identified with a Hanbury Brown and
Twiss intensity interferometer (HBTII) consisting of a 50:50 beamsplitter and
two time-correlated single photon counters. For an SPE, the cross-correlation
of photon arrival time between the two detectors, $g^{(2)}(\tau)$, shows a
hallmark dip to zero at $\tau=0$. One common heuristic for identifying SPEs is
by measuring a system to have $g^{(2)}(0)<1/2$. Here in, we use stochastic
methods to simulate the case of a SPE observed with non-ideal detectors and
optics. We show that identification of SPEs is thwarted when the detectors have
asymmetric detection efficiency, and when detector background noise is more
than half the true SPE signal rate.


title: Deep Unlearning via Randomized Conditionally Independent Hessians

abstract:
  Recent legislation has led to interest in machine unlearning, i.e., removing
specific training samples from a predictive model as if they never existed in
the training dataset. Unlearning may also be required due to
corrupted/adversarial data or simply a user's updated privacy requirement. For
models which require no training (k-NN), simply deleting the closest original
sample can be effective. But this idea is inapplicable to models which learn
richer representations. Recent ideas leveraging optimization-based updates
scale poorly with the model dimension d, due to inverting the Hessian of the
loss function. We use a variant of a new conditional independence coefficient,
L-CODEC, to identify a subset of the model parameters with the most semantic
overlap on an individual sample level. Our approach completely avoids the need
to invert a (possibly) huge matrix. By utilizing a Markov blanket selection, we
premise that L-CODEC is also suitable for deep unlearning, as well as other
applications in vision. Compared to alternatives, L-CODEC makes approximate
unlearning possible in settings that would otherwise be infeasible, including
vision models used for face recognition, person re-identification and NLP
models that may require unlearning samples identified for exclusion. Code can
be found at https://github.com/vsingh-group/LCODEC-deep-unlearning/


title:
Universal Non-Polar Switching in Carbon-doped Transition Metal Oxides
  (TMOs) and Post TMOs

abstract:
  Transition metal oxides (TMOs) and post-TMOs (PTMOs), when doped with Carbon,
show non-volatile current-voltage (I-V) characteristics, which are both
universal and repeatable. We have shown spectroscopic evidence of the
introduction of carbon-based impurity states inside the existing larger bandgap
effectively creating a smaller bandgap which we suggest could enable Mott-like
correlation effect. Our findings indicate new insights for yet to be understood
unipolar and nonpolar resistive switching in the TMOs and PTMOs. We have shown
that device switching is not thermal-energy dependent and have developed an
electronic-dominated switching model that allows for the extreme temperature
operation (from 1.5 K to 423 K) and state retention up to 673 K for a 1-hour
bake. Importantly, we have optimized the technology in an industrial process
and demonstrated integrated 1-transistor/1-resistor (1T1R) arrays up to 1 kbit
with 47 nm devices on 300 mm wafers for advanced node CMOS-compatible
correlated electron RAM (CeRAM). These devices are shown to operate with 2 ns
write pulses and retain the memory states up to 200 C for 24 hours. The
collection of attributes shown, including scalability to state-of-the-art
dimensions, non-volatile operation to extreme low and high temperatures, fast
write, and reduced stochasticity as compared to filamentary memories such as
ReRAMs show the potential for a highly capable two-terminal back-end-of-line
non-volatile memory.


title:
Accurate detection of sepsis at ED triage using machine learning with
  clinical natural language processing

abstract:
  Sepsis is a life-threatening condition with organ dysfunction and is a
leading cause of death and critical illness worldwide. Accurate detection of
sepsis during emergency department triage would allow early initiation of lab
analysis, antibiotic administration, and other sepsis treatment protocols. The
purpose of this study was to determine whether EHR data can be extracted and
synthesized with the latest machine learning algorithms (KATE Sepsis) and
clinical natural language processing to produce accurate sepsis models, and
compare KATE Sepsis performance with existing sepsis screening protocols, such
as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using
patient encounters with triage data from 16 participating hospitals. KATE
Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were
tested in three settings. Cohort-A was a retrospective analysis on medical
records from a single Site 1. Cohort-B was a prospective analysis of Site 1.
Cohort-C was a retrospective analysis on Site 1 with 15 additional sites.
Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with
73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of
0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol
demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR.
For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of
0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all
cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR
and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and
TPR for severe sepsis and septic shock detection. KATE Sepsis provided
substantially better sepsis detection performance in triage than commonly used
screening protocols.


title:
An anticyclotomic Euler system for adjoint modular Galois
  representations

abstract:
  Let $K$ be an imaginary quadratic field and $p$ a prime split in $K$. In this
paper we construct an anticyclotomic Euler system for the adjoint
representation attached to elliptic modular forms base changed to $K$. We also
relate our Euler system to a $p$-adic $L$-function deduced from the
construction by Eischen-Wan and Eischen-Harris-Li-Skinner of $p$-adic
$L$-functions for unitary groups. This allows us to derive new cases of the
Bloch-Kato conjecture in rank zero, and a divisibility towards an Iwasawa main
conjecture.


title:
Weighted Generalized Fractional Integration by Parts and the
  Euler-Lagrange Equation

abstract:
  Integration by parts plays a crucial role in mathematical analysis, e.g.,
during the proof of necessary optimality conditions in the calculus of
variations and optimal control. Motivated by this fact, we construct a new,
right-weighted generalized fractional derivative in the Riemann-Liouville sense
with its associated integral for the recently introduced weighted generalized
fractional derivative with Mittag-Leffler kernel. We rewrite these operators
equivalently in effective series, proving some interesting properties relating
to the left and the right fractional operators. These results permit us to
obtain the corresponding integration by parts formula. With the new general
formula, we obtain an appropriate weighted Euler-Lagrange equation for dynamic
optimization, extending those existing in the literature. We end with the
application of an optimization variational problem to the quantum mechanics
framework.


title:
It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image
  Captioning by Contrastive Data Collection

abstract:
  Datasets that capture the connection between vision, language, and affection
are limited, causing a lack of understanding of the emotional aspect of human
intelligence. As a step in this direction, the ArtEmis dataset was recently
introduced as a large-scale dataset of emotional reactions to images along with
language explanations of these chosen emotions. We observed a significant
emotional bias towards instance-rich emotions, making trained neural speakers
less accurate in describing under-represented emotions. We show that collecting
new data, in the same way, is not effective in mitigating this emotional bias.
To remedy this problem, we propose a contrastive data collection approach to
balance ArtEmis with a new complementary dataset such that a pair of similar
images have contrasting emotions (one positive and one negative). We collected
260,533 instances using the proposed method, we combine them with ArtEmis,
creating a second iteration of the dataset. The new combined dataset, dubbed
ArtEmis v2.0, has a balanced distribution of emotions with explanations
revealing more fine details in the associated painting. Our experiments show
that neural speakers trained on the new dataset improve CIDEr and METEOR
evaluation metrics by 20% and 7%, respectively, compared to the biased dataset.
Finally, we also show that the performance per emotion of neural speakers is
improved across all the emotion categories, significantly on under-represented
emotions. The collected dataset and code are available at
https://artemisdataset-v2.org.


title:
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for
  Hate Speech Detection

abstract:
  Recent work has emphasized the importance of balancing competing objectives
in model training (e.g., accuracy vs. fairness, or competing measures of
fairness). Such trade-offs reflect a broader class of multi-objective
optimization (MOO) problems in which optimization methods seek Pareto optimal
trade-offs between competing goals. In this work, we first introduce a
differentiable measure that enables direct optimization of group fairness
(specifically, balancing accuracy across groups) in model training. Next, we
demonstrate two model-agnostic MOO frameworks for learning Pareto optimal
parameterizations over different groups of neural classification models. We
evaluate our methods on the specific task of hate speech detection, in which
prior work has shown lack of group fairness across speakers of different
English dialects. Empirical results across convolutional, sequential, and
transformer-based neural architectures show superior empirical accuracy vs.
fairness trade-offs over prior work. More significantly, our measure enables
the Pareto machinery to ensure that each architecture achieves the best
possible trade-off between fairness and accuracy w.r.t. the dataset, given
user-prescribed error tolerance bounds.


title: A Catalogue of Concerns for Specifying Machine Learning-Enabled Systems

abstract:
  Requirements engineering (RE) activities for Machine Learning (ML) are not
well-established and researched in the literature. Many issues and challenges
exist when specifying, designing, and developing ML-enabled systems. Adding
more focus on RE for ML can help to develop more reliable ML-enabled systems.
Based on insights collected from previous work and industrial experiences, we
propose a catalogue of 45 concerns to be considered when specifying ML-enabled
systems, covering five different perspectives we identified as relevant for
such systems: objectives, user experience, infrastructure, model, and data.
Examples of such concerns include the execution engine and telemetry for the
infrastructure perspective, and explainability and reproducibility for the
model perspective. We conducted a focus group session with eight software
professionals with experience developing ML-enabled systems to validate the
importance, quality and feasibility of using our catalogue. The feedback
allowed us to improve the catalogue and confirmed its practical relevance. The
main research contribution of this work consists in providing a validated set
of concerns grouped into perspectives that can be used by requirements
engineers to support the specification of ML-enabled systems.


title: Spanish Abstract Meaning Representation: Annotation of a General Corpus

abstract:
  The Abstract Meaning Representation (AMR) formalism, designed originally for
English, has been adapted to a number of languages. We build on previous work
proposing the annotation of AMR in Spanish, which resulted in the release of 50
Spanish AMR annotations for the fictional text "The Little Prince." In this
work, we present the first sizable, general annotation project for Spanish
Abstract Meaning Representation. Our approach to annotation makes use of
Spanish rolesets from the AnCora-Net lexicon and extends English AMR with
semantic features specific to Spanish. In addition to our guidelines, we
release an annotated corpus (586 annotations total, for 486 unique sentences)
of multiple genres of documents from the "Abstract Meaning Representation 2.0 -
Four Translations" sembank. This corpus is commonly used for evaluation of AMR
parsing and generation, but does not include gold AMRs; we hope that providing
gold annotations for this dataset can result in a more complete approach to
cross-lingual AMR parsing. Finally, we perform a disagreement analysis and
discuss the implications of our work on the adaptability of AMR to languages
other than English.