title:
Exploring X-ray variability with unsupervised machine learning I.
  Self-organizing maps applied to XMM-Newton data

abstract:
  XMM-Newton provides unprecedented insight into the X-ray Universe, recording
variability information for hundreds of thousands of sources. Manually
searching for interesting patterns in light curves is impractical, requiring an
automated data-mining approach for the characterization of sources.
  Straightforward fitting of temporal models to light curves is not a sure way
to identify them, especially with noisy data. We used unsupervised machine
learning to distill a large data set of light-curve parameters, revealing its
clustering structure in preparation for anomaly detection and subsequent
searches for specific source behaviors (e.g., flares, eclipses).
  Self-organizing maps (SOMs) achieve dimensionality reduction and clustering
within a single framework. They are a type of artificial neural network trained
to approximate the data with a two-dimensional grid of discrete interconnected
units, which can later be visualized on the plane. We trained our SOM on
temporal-only parameters computed from more than 100,000 detections from the
EXTraS catalog.
  The resulting map reveals that about 2500 most variable sources are clustered
based on temporal characteristics. We find distinctive regions of the SOM map
associated with flares, eclipses, dips, linear light curves, and others. Each
group contains sources that appear similar by eye. We single out a handful of
interesting sources for further study.
  The condensed view of our dataset provided by SOMs allowed us to identify
groups of similar sources, speeding up manual characterization by orders of
magnitude. Our method also highlights problems with fitting simple temporal
models to light curves and can be used to mitigate them to an extent. This will
be crucial for fully exploiting the high data volume expected from upcoming
X-ray surveys, and may also help with interpreting supervised classification
models.


title: A recommender system for automatic picking of subsurface formation tops

abstract:
  Geoscience domain experts traditionally correlate formation tops in the
subsurface using geophysical well logs (known as well-log correlation) by-hand.
Based on individual well log interpretation and well-to-well comparisons, these
correlations are done in the context of depositional models within a
stratigraphic framework. Recently, many researchers have focused on automatic
well-log correlation using a variety of warping algorithms that measure well
similarity, and both unsupervised and supervised machine learning methods that
assign categorical labels based on known tops in many other wells. These
methods require a standardized suite of digital well logs (i.e. gamma ray logs
for every well) along with the depth to the top of the formations, which might
not be available in many cases. Herein, we propose a method that does not use
geophysical well logs for correlation, but rather uses already picked tops in
multiple wells to recommend the depth to the remaining unpicked tops in the
wells. This recommender system calculates the depth to all formation tops in
all the wells for two different datasets in two different basins. The Teapot
Dome dataset is composed of lithostratigraphic formation tops, and the
Mannville Group dataset is composed of sequence-stratigraphic (representing
multiple lithologic groups within a stratigraphic unit) formation tops. For the
demonstration, mean absolute error and root mean squared error of four-fold
cross-validation compares the recommender system predictions to the ground
truth human interpretations. The recommender system is competitive and often
outperforms state of the art spline interpolation methods. Lastly, increasing
the size of the training dataset decreases the prediction error, and that
variance in error decreases with increasing formation tops picked in each
formation and well for the lithostratigraphic top picks.


title: An Optimal Algorithm for Product Structure in Planar Graphs

abstract:
  The \emph{Product Structure Theorem} for planar graphs (Dujmovi\'c et al.\
\emph{JACM}, \textbf{67}(4):22) states that any planar graph is contained in
the strong product of a planar $3$-tree, a path, and a $3$-cycle. We give a
simple linear-time algorithm for finding this decomposition as well as several
related decompositions. This improves on the previous $O(n\log n)$ time
algorithm (Morin.\ \emph{Algorithmica}, \textbf{85}(5):1544--1558).


title: Graph Data Augmentation for Graph Machine Learning: A Survey

abstract:
  Data augmentation has recently seen increased interest in graph machine
learning given its ability of creating extra training data and improving model
generalization. Despite this recent upsurge, this area is still relatively
underexplored, due to the challenges brought by complex, non-Euclidean
structure of graph data, which limits the direct analogizing of traditional
augmentation operations on other types of data. In this paper, we present a
comprehensive and systematic survey of graph data augmentation that summarizes
the literature in a structured manner. We first categorize graph data
augmentation operations based on the components of graph data they modify or
create. Next, we introduce recent advances in graph data augmentation,
separating by their learning objectives and methodologies. We conclude by
outlining currently unsolved challenges as well as directions for future
research. Overall, this paper aims to clarify the landscape of existing
literature in graph data augmentation and motivate additional work in this
area. We provide a GitHub repository
(https://github.com/zhao-tong/graph-data-augmentation-papers) with a reading
list that will be continuously updated.


title: Geometric Dilations and Operator Annuli

abstract:
  Fix 1<R. The dilation theory for the quantum annulus, consisting of those
invertible Hilbert space operators T such that the norm of T and its inverse
are both at most R is determined. The proof technique involves a geometric
approach to dilation that applies to other well known dilation theorems. The
dilation theory for the quantum annulus is compared, and contrasted, with the
dilation theory for other canonical operator annuli.


title:
Renormalons in a scalar self interacting theory: thermal, thermomagnetic
  and thermoelectric corrections for all values of temperature

abstract:
  In this article we revisit the discussion of renormalons in the frame of a
scalar self-interacting $\lambda \phi^{4}$ theory in the presence of
thermomagnetic effects, i.e magnetic and thermal effects. Our results for the
evolution of the residues is now given by an explicit analytic expression,
valid for all values of temperature, without the necessity of separating the
discussion in a low- and a high temperature region analyses. We carry out the
same discussion for the case of an external constant electric field, obtaining
also in this case an analytic expression for the whole range o temperature. In
both cases, the location of the poles in the Borel plane does not change with
respect to the vacuum case. Their residues, however, acquire a dependence on
temperature and the external field. Results are presented for the evolution of
residues in the thermomagnetic and thermoelectric cases. We show a comparison
with our previous results in the thermomagnetic case, presenting also in detail
the mathematical techniques needed for our analytic expressions to be valid in
the whole range of temperature.


title: Nonadiabatic instanton rate theory beyond the golden-rule limit

abstract:
  Fermi's golden rule describes the leading-order behaviour of the reaction
rate as a function of the diabatic coupling. Its asymptotic $(\hbar \rightarrow
0)$ limit is the semiclassical golden-rule instanton rate theory, which
rigorously approximates nuclear quantum effects, lends itself to efficient
numerical computation and gives physical insight into reaction mechanisms.
However the golden rule by itself becomes insufficient as the strength of the
diabatic coupling increases, so higher-order terms must be additionally
considered. In this work we give a first-principles derivation of the
next-order term beyond the golden rule, represented as a sum of three
components. Two of them lead to new instanton pathways that extend the
golden-rule case and, among other factors, account for the effects of
recrossing on the full rate. The remaining component derives from the
equilibrium partition function and accounts for changes in potential energy
around the reactant and product wells due to diabatic coupling. The new
semiclassical theory demands little computational effort beyond a golden-rule
instanton calculation. It makes it possible to rigorously assess the accuracy
of the golden-rule approximation and sets the stage for future work on general
semiclassical nonadiabatic rate theories.


title:
How do Magnetic Field Models Affect Astrophysical Limits on Light
  Axion-like Particles? An X-ray Case Study with NGC 1275

abstract:
  Axion-like particles (ALPs) are a well-motivated extension to the standard
model of particle physics, and X-ray observations of cluster-hosted AGN
currently place the most stringent constraints on the ALP coupling to
electromagnetism, $g_{a \gamma}$, for very light ALPs ($m_a\lesssim10^{-11}$
eV). We revisit limits obtained by Reynolds et al. (2020) using Chandra X-ray
grating spectroscopy of NGC 1275, the central AGN in the Perseus cluster,
examining the impact of the X-ray spectral model and magnetic field model. We
also present a new publicly available code, ALPro, which we use to solve the
ALP propagation problem. We discuss evidence for turbulent magnetic fields in
Perseus and show that it can be important to resolve the magnetic field
structure on scales below the coherence length. We re-analyse the NGC 1275
X-ray spectra using an improved data reduction and baseline spectral model. We
find the limits are insensitive to whether a partially covering absorber is
used in the fits. At low $m_a$ ($m_a\lesssim10^{-13}$ eV), we find marginally
weaker limits on $g_{a \gamma}$ (by $0.1-0.3$ dex) with different magnetic
field models, compared to Model B from Reynolds et al. (2020). A Gaussian
random field (GRF) model designed to mimic $\sim50$ kpc scale coherent
structures also results in only slightly weaker limits. We conclude that the
existing Model B limits are robust assuming that $\beta_{\rm pl}\approx100$,
and are insensitive to whether cell-based or GRF methods are used. However,
astrophysical uncertainties regarding the strength and structure of cluster
magnetic fields persist, motivating high sensitivity RM observations and
tighter constraints on the radial profile of $\beta_{\rm pl}$.


title: Training neural networks using monotone variational inequality

abstract:
  Despite the vast empirical success of neural networks, theoretical
understanding of the training procedures remains limited, especially in
providing performance guarantees of testing performance due to the non-convex
nature of the optimization problem. Inspired by a recent work of (Juditsky &
Nemirovsky, 2019), instead of using the traditional loss function minimization
approach, we reduce the training of the network parameters to another problem
with convex structure -- to solve a monotone variational inequality (MVI). The
solution to MVI can be found by computationally efficient procedures, and
importantly, this leads to performance guarantee of $\ell_2$ and
$\ell_{\infty}$ bounds on model recovery accuracy and prediction accuracy under
the theoretical setting of training one-layer linear neural network. In
addition, we study the use of MVI for training multi-layer neural networks and
propose a practical algorithm called \textit{stochastic variational inequality}
(SVI), and demonstrates its applicability in training fully-connected neural
networks and graph neural networks (GNN) (SVI is completely general and can be
used to train other types of neural networks). We demonstrate the competitive
or better performance of SVI compared to the stochastic gradient descent (SGD)
on both synthetic and real network data prediction tasks regarding various
performance metrics.


title:
Frequency dependence and vertical structure of ocean surface kinetic
  energy from global high-resolution models and surface drifter observations

abstract:
  The geographical variability, frequency content, and vertical structure of
near-surface oceanic kinetic energy (KE) are important for air-sea interaction,
marine ecosystems, operational oceanography, pollutant tracking, and
interpreting remotely sensed velocity measurements. Here, KE in high-resolution
global simulations (HYbrid Coordinate Ocean Model; HYCOM, and Massachusetts
Institute of Technology general circulation model; MITgcm), at the sea surface
(0 m) and 15 m, are respectively compared with KE from undrogued and drogued
surface drifters. Global maps and zonal averages are computed for low-frequency
(periods longer than 2 days), near-inertial, diurnal, and semi-diurnal bands.
In the low-frequency band, near the equator, both models exhibit KE values that
are too low relative to drifters. MITgcm near-inertial KE is too low, while
HYCOM near-inertial KE lies closer to drifter KE, probably due to more
frequently updated atmospheric forcing. In the semi-diurnal band, MITgcm KE is
too high, while HYCOM lies closer to drifters, likely due to the inclusion of a
parameterized topographic internal wave drag. We assess the KE vertical
structure by considering the ratio of zonally averaged KE in 0 m/15 m model
results and undrogued/drogued drifter results. Over most latitudes and
frequency bands, model ratios track the drifter ratio to within error bars. All
frequency bands except the semi-diurnal band display measurable vertical
structure. Latitudinal dependence in the vertical structure is greatest in the
diurnal and low-frequency bands. As in a previous comparison of HYCOM and
MITgcm to current meter observations, HYCOM generally displays larger spatial
correlations with the drifter observations than MITgcm does.


title: Effects of the order parameter anisotropy on the vortex lattice in UPt3

abstract:
  We have used small-angle neutron scattering to determine the vortex lattice
phase diagram in the topological superconductor UPt3 for the applied magnetic
field along the crystalline c-axis. A triangular vortex lattice is observed
throughout the superconducting state, but with an orientation relative to the
hexagonal basal place that changes with field and temperature. At low
temperature, in the chiral B phase, the vortex lattice undergoes a
non-monotonic rotation with increasing magnetic field. The rotation amplitude
decreases with increasing temperature and vanishes before reaching the A phase.
Within the A phase an abrupt +/-15 degree vortex lattice rotation was
previously reported by Huxley et al., Nature 406, 160-164 (2000). The complex
phase diagram may be understood from competing effects of the superconducting
order parameter, the symmetry breaking field, and the Fermi surface anisotropy.
The low-temperature rotated phase, centered around 0.8 T, reported by Avers et
al., Nature Physics 16, 531-535 (2020), can be attributed directly to the
symmetry breaking field.


title:
Exciton-polarons in the presence of strongly correlated electronic
  states in a MoSe$_2$/WSe$_2$ moir\'e superlattice

abstract:
  Two-dimensional moir\'e materials provide a highly tunable platform to
investigate strongly correlated electronic states. Such emergent many-body
phenomena can be optically probed in moir\'e systems created by stacking two
layers of transition metal dichalcogenide semiconductors: optically injected
excitons can interact with itinerant carriers occupying narrow moir\'e bands to
form exciton-polarons sensitive to strong correlations. Here, we investigate
the behaviour of excitons dressed by a Fermi sea localised by the moir\'e
superlattice of a molybdenum diselenide (MoSe$_2$) / tungsten diselenide
(WSe$_2$) twisted hetero-bilayer. At a multitude of fractional fillings of the
moir\'e lattice, we observe ordering of both electrons and holes into stable
correlated electronic states. Magneto-optical measurements reveal extraordinary
Zeeman splittings of the exciton-polarons due to exchange interactions in the
correlated hole phases, with a maximum close to the correlated state at one
hole per site. The temperature dependence of the Zeeman splitting reveals
antiferromagnetic ordering of the correlated holes across a wide range of
fractional fillings. Our results illustrate the nature of exciton-polarons in
the presence of strongly correlated electronic states and reveal the rich
potential of the MoSe$_2$/WSe$_2$ platform for investigations of Fermi-Hubbard
and Bose-Hubbard physics.


title:
Ray-transfer functions for camera simulation of 3D scenes with hidden
  lens design

abstract:
  Combining image sensor simulation tools (e.g., ISETCam) with physically based
ray tracing (e.g., PBRT) offers possibilities for designing and evaluating
novel imaging systems as well as for synthesizing physically accurate, labeled
images for machine learning. One practical limitation has been simulating the
optics precisely: Lens manufacturers generally prefer to keep lens design
confidential. We present a pragmatic solution to this problem using a black box
lens model in Zemax; such models provide necessary optical information while
preserving the lens designer's intellectual property. First, we describe and
provide software to construct a polynomial ray transfer function that
characterizes how rays entering the lens at any position and angle subsequently
exit the lens. We implement the ray-transfer calculation as a camera model in
PBRT and confirm that the PBRT ray-transfer calculations match the Zemax lens
calculations for edge spread functions and relative illumination.


title:
Higher rank parabolic geometries with essential automorphisms and
  nonvanishing curvature

abstract:
  We construct infinite families of regular normal Cartan geometries with
nonvanishing curvature and essential automorphisms on closed manifolds for many
higher rank parabolic model geometries. To do this, we use particular elements
of the kernel of the Kostant Laplacian to construct homogeneous Cartan
geometries of the desired type, giving a global realization of an elegant local
construction due to Kruglikov and The, and then modify these homogeneous
geometries to make their base manifolds compact. As a demonstration, we apply
the construction to quaternionic contact structrures of mixed signature, among
other examples.


title:
Improving English to Sinhala Neural Machine Translation using
  Part-of-Speech Tag

abstract:
  The performance of Neural Machine Translation (NMT) depends significantly on
the size of the available parallel corpus. Due to this fact, low resource
language pairs demonstrate low translation performance compared to high
resource language pairs. The translation quality further degrades when NMT is
performed for morphologically rich languages. Even though the web contains a
large amount of information, most people in Sri Lanka are unable to read and
understand English properly. Therefore, there is a huge requirement of
translating English content to local languages to share information among
locals. Sinhala language is the primary language in Sri Lanka and building an
NMT system that can produce quality English to Sinhala translations is
difficult due to the syntactic divergence between these two languages under low
resource constraints. Thus, in this research, we explore effective methods of
incorporating Part of Speech (POS) tags to the Transformer input embedding and
positional encoding to further enhance the performance of the baseline English
to Sinhala neural machine translation model.


title: Curriculum optimization for low-resource speech recognition

abstract:
  Modern end-to-end speech recognition models show astonishing results in
transcribing audio signals into written text. However, conventional data
feeding pipelines may be sub-optimal for low-resource speech recognition, which
still remains a challenging task. We propose an automated curriculum learning
approach to optimize the sequence of training examples based on both the
progress of the model while training and prior knowledge about the difficulty
of the training examples. We introduce a new difficulty measure called
compression ratio that can be used as a scoring function for raw audio in
various noise conditions. The proposed method improves speech recognition Word
Error Rate performance by up to 33% relative over the baseline system


title: BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

abstract:
  While reinforcement learning (RL) has made great advances in scalability,
exploration and partial observability are still active research topics. In
contrast, Bayesian RL (BRL) provides a principled answer to both state
estimation and the exploration-exploitation trade-off, but struggles to scale.
To tackle this challenge, BRL frameworks with various prior assumptions have
been proposed, with varied success. This work presents a
representation-agnostic formulation of BRL under partially observability,
unifying the previous models under one theoretical umbrella. To demonstrate its
practical significance we also propose a novel derivation, Bayes-Adaptive Deep
Dropout rl (BADDr), based on dropout networks. Under this parameterization, in
contrast to previous work, the belief over the state and dynamics is a more
scalable inference problem. We choose actions through Monte-Carlo tree search
and empirically show that our method is competitive with state-of-the-art BRL
methods on small domains while being able to solve much larger ones.


title:
Hermitian-Einstein metrics on stable vector bundles over compact
  K\"ahler orbifolds

abstract:
  For a holomorphic vector bundle over a compact K\"ahler orbifold, the slope
stability of the bundle is shown to be equivalent to the existence of a
Hermitian-Einstein metric or to the properness of a certain functional
introduced by Donaldson.


title:
Simulation and Parameterization of Longitudinal Development in Extensive
  Air Showers for Different Hadronic Interaction Models

abstract:
  The simulation analysis of the Extensive Air Showers (EAS) was executed by
exploring the longitudinal development employing the AIRES system (version
19.04.00) for several hadronic interaction models (SIBYLL, QGSJET, and EPOS)
for high energies. The simulation was performed for different high energies
(10^17, 10^18, and 10^19) eV and two dissimilar primary particles, proton as
well iron nuclei, with several zenith angles values (0^o, 10^o, and 30^o). The
shower size of longitudinal development was parameterized using the sigmoidal
function (Boltzmann model) and gave a new four parameters as functions of the
primary energy between the energy extent (10^17-10^19) eV. The comparison among
the acquired results data (the parameterized number of shower particles) along
with the experimental results (Pierre Auger experiment) had offered a
fascinating matching for the primary proton at the fixed primary energy 10^19
eV for vertical EAS showers.


title:
Resolved frustrated tunneling ionization in asymmetrical fast
  oscillation of above-threshold ionization spectrum

abstract:
  Tunneling ionization is one of the fundamental electron dynamics, which has
wide applications in ultrafast physics. When frustrated tunneling ionization
(FTI) is considered, the tunneling rate is not equivalent to ionization rate.
However, it is hard to resolve the effects of FTI and direct tunneling
ionization (DTI) in ionization spectrum experimentally. Here we report the
first observation of the asymmetrical fast oscillation in above-threshold
ionization (ATI) spectrum of Argon as function of carrier-envelope phase (CEP),
to the best of our knowledge. Simulation results identify that in the
experimental ATI spectrum, the {\pi}/5 oscillation originates from the quantum
interference of electrons in FTI, while DTI is responsible for the asymmetry.
Our results provide clear evidence to resolve the effects of direct tunneling
and FTI in a new physical regime.


title: Combinatorial part of the cohomology of the nearby fibre

abstract:
  Let $f: X \to S$ be a unipotent degeneration of projective complex manifolds
of dimension $n$ over a disc such that the reduction of the central fibre
$Y=f^{-1}(0)$ is simple normal crossings, and let $X_\infty$ be the canonical
nearby fibre. Building on the work of Kontsevich, Tschinkel, Mikhalkin and
Zharkov, I introduce a sheaf of graded algebras $\Lambda^\bullet$ on the dual
intersection complex $Y$, denoted $\Delta_X$. When $f$ is a Type III Kulikov
degeneration of $K3$ surfaces, the sheaf $\Lambda^1$ recovers singular affine
structure glued from singular affine strucutres on pseudo-fans of
anti-canonical pairs previously studied in the liturature. I show that there
exists a map $H^q(\Delta_X, \Lambda^p) \to \mathrm{gr}^W_{2q} H^{p+q}(X_\infty,
\mathbb{Q})$, where $W$ is the monodromy weight filtration, commuting with the
ogarithm of the monodromy on the right, and a certain coboundary map defined
purely in terms of sheaves $\Lambda^p$ on the left. This map is injective
whenever there exists a class $\omega \in H^2(Y)$ such that its restriction on
each component $Y_i$ satisfies Lefschetz property and Hodge-Riemann bilinear
relations with respect to classes lying in the span of cycle classes of strata
of $Y_i$. In addition, existence of such class implies $\mathrm{dim}
H^q(\Delta_X, \Lambda^p) = \mathrm{dim} H^p(\Delta_X, \Lambda^q) = \dim
H^{n-q}(\Delta_X, \Lambda^{n-p})$. I check that this condition is satisfied for
Kulikov degenerations in $(-1)$-form.


title:
Infant-phase reddening by surface Fe-peak elements in a normal Type Ia
  Supernova

abstract:
  Type Ia Supernovae are thermonuclear explosions of white dwarf stars. They
play a central role in the chemical evolution of the Universe and are an
important measure of cosmological distances. However, outstanding questions
remain about their origins. Despite extensive efforts to obtain natal
information from their earliest signals, observations have thus far failed to
identify how the majority of them explode. Here, we present infant-phase
detections of SN 2018aoz from a brightness of -10.5 absolute AB magnitudes --
the lowest luminosity early Type Ia signals ever detected -- revealing a
hitherto unseen plateau in the $B$-band that results in a rapid redward color
evolution between 1.0 and 12.4 hours after the estimated epoch of first light.
The missing $B$-band flux is best-explained by line-blanket absorption from
Fe-peak elements in the outer 1% of the ejected mass. The observed $B-V$ color
evolution of the SN also matches the prediction from an over-density of Fe-peak
elements in the same outer 1% of the ejected mass, whereas bluer colors are
expected from a purely monotonic distribution of Fe-peak elements. The presence
of excess nucleosynthetic material in the extreme outer layers of the ejecta
points to enhanced surface nuclear burning or extended sub-sonic mixing
processes in some normal Type Ia Supernova explosions.


title:
Deep Transfer Learning on Satellite Imagery Improves Air Quality
  Estimates in Developing Nations

abstract:
  Urban air pollution is a public health challenge in low- and middle-income
countries (LMICs). However, LMICs lack adequate air quality (AQ) monitoring
infrastructure. A persistent challenge has been our inability to estimate AQ
accurately in LMIC cities, which hinders emergency preparedness and risk
mitigation. Deep learning-based models that map satellite imagery to AQ can be
built for high-income countries (HICs) with adequate ground data. Here we
demonstrate that a scalable approach that adapts deep transfer learning on
satellite imagery for AQ can extract meaningful estimates and insights in LMIC
cities based on spatiotemporal patterns learned in HIC cities. The approach is
demonstrated for Accra in Ghana, Africa, with AQ patterns learned from two US
cities, specifically Los Angeles and New York.


title:
Band filling effects on the emergence of magnetic skyrmions: Pd/Fe and
  Pd/Co bilayers on Ir(111)

abstract:
  Structurally similar transition metal systems can have widely differing
magnetic properties. A prime example of this is found for bilayers on Ir(111),
where Pd/Fe/Ir(111) ground state has a well-established noncollinear spin
texture, while Pd/Co/Ir(111) present a ferromagnetic (FM) single-domain. To
unravel the origins of these different magnetic behaviors, an investigation of
Pd/Fe and Pd/Co bilayers on Ir(111), is here performed. Based on the obtained
\textit{ab-initio} electronic structure, exchange coupling parameters
($J_{ij}$) and Dzyaloshinskii-Moriya interactions (DMI), we demonstrate that,
although in Pd/Co/Ir(111) the DMI is significant, two ingredients play a key
role on the origin of noncollinearity in Pd/Fe/Ir(111): the presence of
magnetic frustrations and a much more in-plane DMI, both with a long-range
influence. The $J_{ij}$ and DMI behaviors in both systems can be explained in
terms of a simple rigid-band-like model. Also, by performing spin-dynamics
simulations with the magnetic parameters tuned from the Pd/Co/Ir(111) to
Pd/Fe/Ir(111) \textit{ab-initio} values, we could find conditions for the
emergence of skyrmionic phases in the originally FM Pd/Co/Ir(111).


title:
Developing Imperceptible Adversarial Patches to Camouflage Military
  Assets From Computer Vision Enabled Technologies

abstract:
  Convolutional neural networks (CNNs) have demonstrated rapid progress and a
high level of success in object detection. However, recent evidence has
highlighted their vulnerability to adversarial attacks. These attacks are
calculated image perturbations or adversarial patches that result in object
misclassification or detection suppression. Traditional camouflage methods are
impractical when applied to disguise aircraft and other large mobile assets
from autonomous detection in intelligence, surveillance and reconnaissance
technologies and fifth generation missiles. In this paper we present a unique
method that produces imperceptible patches capable of camouflaging large
military assets from computer vision-enabled technologies. We developed these
patches by maximising object detection loss whilst limiting the patch's colour
perceptibility. This work also aims to further the understanding of adversarial
examples and their effects on object detection algorithms.


title: Warped compactifications and holographic duality

abstract:
  We review warped compactifications of superstring theory with some attention
to the limit in which these resemble "bottom-up" phenomenological models. In
addition to some discussion of the original Klebanov-Witten and
Klebanov-Strassler set-ups, we also touch on various generalizations of the
geometry that have been considered. Various other systems with a holographic
duality are also briefly reviewed. The point of this latter exploration is to
illustrate how far beyond the standard AdS(5) x S(5) set-up things have moved
over the years.


title: Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study

abstract:
  Robotic practitioners generally approach the vision-based SLAM problem
through discrete-time formulations. This has the advantage of a consolidated
theory and very good understanding of success and failure cases. However,
discrete-time SLAM needs tailored algorithms and simplifying assumptions when
high-rate and/or asynchronous measurements, coming from different sensors, are
present in the estimation process. Conversely, continuous-time SLAM, often
overlooked by practitioners, does not suffer from these limitations. Indeed, it
allows integrating new sensor data asynchronously without adding a new
optimization variable for each new measurement. In this way, the integration of
asynchronous or continuous high-rate streams of sensor data does not require
tailored and highly-engineered algorithms, enabling the fusion of multiple
sensor modalities in an intuitive fashion. On the down side, continuous time
introduces a prior that could worsen the trajectory estimates in some
unfavorable situations. In this work, we aim at systematically comparing the
advantages and limitations of the two formulations in vision-based SLAM. To do
so, we perform an extensive experimental analysis, varying robot type, speed of
motion, and sensor modalities. Our experimental analysis suggests that,
independently of the trajectory type, continuous-time SLAM is superior to its
discrete counterpart whenever the sensors are not time-synchronized. In the
context of this work, we developed, and open source, a modular and efficient
software architecture containing state-of-the-art algorithms to solve the SLAM
problem in discrete and continuous time.


title:
Statistical Analyses in the case of an Italian nurse accused of
  murdering patients

abstract:
  Suspicions about medical murder sometimes arise due to a surprising or
unexpected series of events, such as an apparently unusual number of deaths
among patients under the care of a particular nurse. But also a single
disturbing event might trigger suspicion about a particular nurse, and this
might then lead to investigation of events which happened when she was thought
to be present. In either case, there is a statistical challenge of
distinguishing event clusters that arise from criminal acts from those that
arise coincidentally from other causes. We show that an apparently striking
association between a nurse's presence and a high rate of deaths in a hospital
ward can easily be completely spurious. In short: in a medium-care hospital
ward where many patients are suffering terminal illnesses, and deaths are
frequent, most deaths occur in the morning. Most nurses are on duty in the
morning, too. There are less deaths in the afternoon, and even less at night;
correspondingly, less nurses are on duty in the afternoon, even less during the
night. Consequently, a full time nurse works the most hours when the most
deaths occur. The death rate is higher when she is present than when she is
absent.


title: Computing list homomorphisms in geometric intersection graphs

abstract:
  A homomorphism from a graph $G$ to a graph $H$ is an edge-preserving mapping
from $V(G)$ to $V(H)$. Let $H$ be a fixed graph with possible loops. In the
list homomorphism problem, denoted by \textsc{LHom}($H$), the instance is a
graph $G$, whose every vertex is equipped with a subset of $V(H)$, called list.
We ask whether there exists a homomorphism from $G$ to $H$, such that every
vertex from $G$ is mapped to a vertex from its list.
  We study the complexity of the \textsc{LHom}($H$) problem in intersection
graphs of various geometric objects. In particular, we are interested in
answering the question for what graphs $H$ and for what types of geometric
objects, the \textsc{LHom}($H$) problem can be solved in time subexponential in
the number of vertices of the instance.
  We fully resolve this question for string graphs, i.e., intersection graphs
of continuous curves in the plane. Quite surprisingly, it turns out that the
dichotomy exactly coincides with the analogous dichotomy for graphs excluding a
fixed path as an induced subgraph [Okrasa, Rz\k{a}\.zewski, STACS 2021].
  Then we turn our attention to subclasses of string graphs, defined as
intersections of fat objects. We observe that the (non)existence of
subexponential-time algorithms in such classes is closely related to the size
$\mathrm{mrc}(H)$ of a maximum reflexive clique in $H$, i.e., maximum number of
pairwise adjacent vertices, each of which has a loop. We study the maximum
value of $\mathrm{mrc}(H)$ that guarantees the existence of a
subexponential-time algorithm for \textsc{LHom}($H$) in intersection graphs of
(i) convex fat objects, (ii) fat similarly-sized objects, and (iii) disks. In
the first two cases we obtain optimal results, by giving matching algorithms
and lower bounds.
  Finally, we discuss possible extensions of our results to weighted
generalizations of \textsc{LHom}($H$).


title:
Implementing Spiking Neural Networks on Neuromorphic Architectures: A
  Review

abstract:
  Recently, both industry and academia have proposed several different
neuromorphic systems to execute machine learning applications that are designed
using Spiking Neural Networks (SNNs). With the growing complexity on design and
technology fronts, programming such systems to admit and execute a machine
learning application is becoming increasingly challenging. Additionally,
neuromorphic systems are required to guarantee real-time performance, consume
lower energy, and provide tolerance to logic and memory failures. Consequently,
there is a clear need for system software frameworks that can implement machine
learning applications on current and emerging neuromorphic systems, and
simultaneously address performance, energy, and reliability. Here, we provide a
comprehensive overview of such frameworks proposed for both, platform-based
design and hardware-software co-design. We highlight challenges and
opportunities that the future holds in the area of system software technology
for neuromorphic computing.


title: Word Embeddings for Automatic Equalization in Audio Mixing

abstract:
  In recent years, machine learning has been widely adopted to automate the
audio mixing process. Automatic mixing systems have been applied to various
audio effects such as gain-adjustment, stereo panning, equalization, and
reverberation. These systems can be controlled through visual interfaces,
providing audio examples, using knobs, and semantic descriptors. Using semantic
descriptors or textual information to control these systems is an effective way
for artists to communicate their creative goals. Furthermore, sometimes artists
use non-technical words that may not be understood by the mixing system, or
even a mixing engineer. In this paper, we explore the novel idea of using word
embeddings to represent semantic descriptors. Word embeddings are generally
obtained by training neural networks on large corpora of written text. These
embeddings serve as the input layer of the neural network to create a
translation from words to EQ settings. Using this technique, the machine
learning model can also generate EQ settings for semantic descriptors that it
has not seen before. We perform experiments to demonstrate the feasibility of
this idea. In addition, we compare the EQ settings of humans with the
predictions of the neural network to evaluate the quality of predictions. The
results showed that the embedding layer enables the neural network to
understand semantic descriptors. We observed that the models with embedding
layers perform better those without embedding layers, but not as good as human
labels.


title:
ROME III. The Arecibo Search for Auroral Emissions and Star-Planet
  Interactions at 5 GHz

abstract:
  Despite many attempts to detect radio emissions from magnetized exoplanets, a
reproducible, unambiguous detection remains elusive. On the other hand, the
search for periodic radio emissions from exoplanet host stars that may be
induced by star-planet interactions in, for example, hot Jupiter systems, has
only recently begun. In this third paper of the ROME (Radio Observations of
Magnetized Exoplanets) series, we present the results of a targeted radio
survey of 17 nearby systems that host exoplanet, brown dwarf, and low-mass
stellar companions conducted with the Arecibo radio telescope at $\sim$5 GHz.
This GHz-frequency survey has the greatest frequency coverage of any to date,
while providing mJy-level sensitivity over $<$1 s integration times. Neither
auroral radio emissions from the substellar targets, nor exoplanet-induced
stellar radio bursts were detected. These results are considered within the
context of observed brown dwarf radio emissions, which support magnetospheric
phenomenon similar to that found at Jupiter. We also describe the orbital phase
coverage of our data for systems that may feature star-planet interactions, and
briefly examine our results within the context of other searches for
star-planet interactions within the same systems. Finally, as several of our
targets are within their respective systems' habitable zones, we consider the
implications of our survey on the search for technosignatures within those
systems.


title: Attributable-Watermarking of Speech Generative Models

abstract:
  Generative models are now capable of synthesizing images, speeches, and
videos that are hardly distinguishable from authentic contents. Such
capabilities cause concerns such as malicious impersonation and IP theft. This
paper investigates a solution for model attribution, i.e., the classification
of synthetic contents by their source models via watermarks embedded in the
contents. Building on past success of model attribution in the image domain, we
discuss algorithmic improvements for generating user-end speech models that
empirically achieve high attribution accuracy, while maintaining high
generation quality. We show the trade off between attributability and
generation quality under a variety of attacks on generated speech signals
attempting to remove the watermarks, and the feasibility of learning robust
watermarks against these attacks.


title:
The Effects of Interactive AI Design on User Behavior: An Eye-tracking
  Study of Fact-checking COVID-19 Claims

abstract:
  We conducted a lab-based eye-tracking study to investigate how the
interactivity of an AI-powered fact-checking system affects user interactions,
such as dwell time, attention, and mental resources involved in using the
system. A within-subject experiment was conducted, where participants used an
interactive and a non-interactive version of a mock AI fact-checking system and
rated their perceived correctness of COVID-19 related claims. We collected
web-page interactions, eye-tracking data, and mental workload using NASA-TLX.
We found that the presence of the affordance of interactively manipulating the
AI system's prediction parameters affected users' dwell times, and
eye-fixations on AOIs, but not mental workload. In the interactive system,
participants spent the most time evaluating claims' correctness, followed by
reading news. This promising result shows a positive role of interactivity in a
mixed-initiative AI-powered system.


title:
Error estimation and adaptivity for stochastic collocation finite
  elements Part II: multilevel approximation

abstract:
  A multilevel adaptive refinement strategy for solving linear elliptic partial
differential equations with random data is recalled in this work. The strategy
extends the a posteriori error estimation framework introduced by Guignard and
Nobile in 2018 (SIAM J. Numer. Anal, 56, 3121--3143) to cover problems with a
nonaffine parametric coefficient dependence. A suboptimal, but nonetheless
reliable and convenient implementation of the strategy involves approximation
of the decoupled PDE problems with a common finite element approximation space.
Computational results obtained using such a single-level strategy are presented
in part I of this work (Bespalov, Silvester and Xu, arXiv:2109.07320). Results
obtained using a potentially more efficient multilevel approximation strategy,
where meshes are individually tailored, are discussed herein. The codes used to
generate the numerical results are available online.


title:
Dynamic Service Provisioning in the Edge-cloud Continuum with Provable
  Guarantees

abstract:
  We consider a hierarchical edge-cloud architecture in which services are
provided to mobile users as chains of virtual network functions. Each service
has specific computation requirements and target delay performance, which
require placing the corresponding chain properly and allocating a suitable
amount of computing resources. Furthermore, chain migration may be necessary to
meet the services' target delay, or convenient to keep the service provisioning
cost low. We tackle such issues by formalizing the problem of optimal chain
placement and resource allocation in the edge-cloud continuum, taking into
account migration, bandwidth, and computation costs. Specifically, we first
envision an algorithm that, leveraging resource augmentation, addresses the
above problem and provides an upper bound to the amount of resources required
to find a feasible solution. We use this algorithm as a building block to
devise an efficient approach targeting the minimum-cost solution, while
minimizing the required resource augmentation. Our results, obtained through
trace-driven, large-scale simulations, show that our solution can provide a
feasible solution by using half the amount of resources required by
state-of-the-art alternatives.


title: SGPT: GPT Sentence Embeddings for Semantic Search

abstract:
  GPT transformers are the largest language models available, yet semantic
search is dominated by BERT transformers. We present SGPT-BE and SGPT-CE for
applying GPT models as Bi-Encoders or Cross-Encoders to symmetric or asymmetric
search.
  SGPT-BE produces semantically meaningful sentence embeddings by contrastive
fine-tuning of only bias tensors and a novel pooling method. A 5.8 billion
parameter SGPT-BE outperforms the best available sentence embeddings by 6%
setting a new state-of-the-art on BEIR. It outperforms the concurrently
proposed OpenAI Embeddings of the 175B Davinci endpoint, which fine-tunes
250,000 times more parameters.
  SGPT-CE uses log probabilities from GPT models without any fine-tuning. A 6.1
billion parameter SGPT-CE sets an unsupervised state-of-the-art on BEIR. It
beats the supervised state-of-the-art on 7 datasets, but significantly loses on
other datasets. We show how this can be alleviated by adapting the prompt.
  SGPT-BE and SGPT-CE performance scales with model size. Yet, increased
latency, storage and compute costs should be considered. Code, models and
result files are freely available at https://github.com/Muennighoff/sgpt.


title:
LArQL: A phenomenological model for treating light and charge generation
  in liquid argon

abstract:
  Experimental data shows that both ionization charge and scintillation light
in LAr depend on the deposited energy density ($dE/dx$) and electric field
($\mathcal{E}$). Moreover, free ionization charge and scintillation light are
anticorrelated, complementary at a given ($dE/dx$, $\mathcal{E}$) pair. We
present LArQL, a phenomenological model that provides the anticorrelation
between light and charge and its dependence on the deposited energy as well as
on the electric field applied. It modifies the Birks' charge model considering
the contribution from the escape electrons at null and low electric fields, and
reconciles with Birks' model prediction at higher fields. Deviations from
current Birks' model are observed for LArTPCs operating at low $\mathcal{E}$
and for heavily ionizing particles. The LArQL model presents a satisfactory
description at $dE/dx$ and field ranges for interacting particles in LArTPCs
and fits well the available data. Improvements via data sets compilation and
global fits are also interesting features of the model.


title: ST-MoE: Designing Stable and Transferable Sparse Expert Models

abstract:
  Scale has opened new frontiers in natural language processing -- but at a
high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have
been proposed as an energy efficient path to even larger and more capable
language models. But advancing the state-of-the-art across a broad set of
natural language tasks has been hindered by training instabilities and
uncertain quality during fine-tuning. Our work focuses on these issues and acts
as a design guide. We conclude by scaling a sparse model to 269B parameters,
with a computational cost comparable to a 32B dense encoder-decoder Transformer
(Stable and Transferable Mixture-of-Experts or ST-MoE-32B). For the first time,
a sparse model achieves state-of-the-art performance in transfer learning,
across a diverse set of tasks including reasoning (SuperGLUE, ARC Easy, ARC
Challenge), summarization (XSum, CNN-DM), closed book question answering
(WebQA, Natural Questions), and adversarially constructed tasks (Winogrande,
ANLI R3).


title:
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational
  Methods

abstract:
  We consider Ising models on the hypercube with a general interaction matrix
$J$, and give a polynomial time sampling algorithm when all but $O(1)$
eigenvalues of $J$ lie in an interval of length one, a situation which occurs
in many models of interest. This was previously known for the Glauber dynamics
when *all* eigenvalues fit in an interval of length one; however, a single
outlier can force the Glauber dynamics to mix torpidly. Our general result
implies the first polynomial time sampling algorithms for low-rank Ising models
such as Hopfield networks with a fixed number of patterns and Bayesian
clustering models with low-dimensional contexts, and greatly improves the
polynomial time sampling regime for the antiferromagnetic/ferromagnetic Ising
model with inconsistent field on expander graphs. It also improves on previous
approximation algorithm results based on the naive mean-field approximation in
variational methods and statistical physics.
  Our approach is based on a new fusion of ideas from the MCMC and variational
inference worlds. As part of our algorithm, we define a new nonconvex
variational problem which allows us to sample from an exponential reweighting
of a distribution by a negative definite quadratic form, and show how to make
this procedure provably efficient using stochastic gradient descent. On top of
this, we construct a new simulated tempering chain (on an extended state space
arising from the Hubbard-Stratonovich transform) which overcomes the obstacle
posed by large positive eigenvalues, and combine it with the SGD-based sampler
to solve the full problem.


title:
Pulse based Variational Quantum Optimal Control for hybrid quantum
  computing

abstract:
  This work studies pulse based variational quantum algorithms (VQAs), which
are designed to determine the ground state of a quantum mechanical system by
combining classical and quantum hardware. In contrast to more standard gate
based methods, pulse based methods aim to directly optimize the laser pulses
interacting with the qubits, instead of using some parametrized gate based
circuit. Using the mathematical formalism of optimal control, these laser
pulses are optimized. This method has been used in quantum computing to
optimize pulses for quantum gate implementations, but has only recently been
proposed for full optimization in VQAs. Pulse based methods have several
advantages over gate based methods such as faster state preparation, simpler
implementation and more freedom in moving through the state space. Based on
these ideas, we present the development of a novel adjoint based variational
method. This method can be tailored towards and applied in neutral atom quantum
computers. This method of pulse based variational quantum optimal control is
able to approximate molecular ground states of simple molecules up to chemical
accuracy and is able to compete with the gate based variational quantum
eigensolver in terms of total number of quantum evaluations. The total
evolution time $T$ and the form of the control Hamiltonian $H_c$ are important
factors in the convergence behavior to the ground state energy, both having
influence on the quantum speed limit and the controllability of the system.


title: Proofs, Circuits, and Communication

abstract:
  We survey lower-bound results in complexity theory that have been obtained
via newfound interconnections between propositional proof complexity, boolean
circuit complexity, and query/communication complexity. We advocate for the
theory of total search problems (TFNP) as a unifying language for these
connections and discuss how this perspective suggests a whole programme for
further research.


title:
Combining Varied Learners for Binary Classification using Stacked
  Generalization

abstract:
  The Machine Learning has various learning algorithms that are better in some
or the other aspect when compared with each other but a common error that all
algorithms will suffer from is training data with very high dimensional feature
set. This usually ends up algorithms into generalization error that deplete the
performance. This can be solved using an Ensemble Learning method known as
Stacking commonly termed as Stacked Generalization. In this paper we perform
binary classification using Stacked Generalization on high dimensional
Polycystic Ovary Syndrome dataset and prove the point that model becomes
generalized and metrics improve significantly. The various metrics are given in
this paper that also point out a subtle transgression found with Receiver
Operating Characteristic Curve that was proved to be incorrect.


title:
Symmetry of terminating series representations of the Askey-Wilson
  polynomials

abstract:
  In this paper, we explore the symmetric nature of the terminating basic
hypergeometric series representations of the Askey--Wilson polynomials and the
corresponding terminating basic hypergeometric transformations that these
polynomials satisfy. In particular we identify and classify the set of 4 and 7
equivalence classes of terminating balanced ${}_4\phi_3$ and terminating
very-well poised ${}_8W_7$ basic hypergeometric series which are connected with
the Askey--Wilson polynomials. We study the inversion properties of these
equivalence classes and also identify the connection of both sets of
equivalence classes with the symmetric group $S_6$, the symmetry group of the
terminating balanced ${}_4\phi_3$. We then use terminating balanced
${}_4\phi_3$ and terminating very-well poised ${}_8W_7$ transformations to give
a broader interpretation of Watson's $q$-analog of Whipple's theorem and its
converse. We give a broad description of the symmetry structure of the
terminating basic hypergeometric series representations of the Askey--Wilson
polynomials.


title:
Mechanical response to tension and torque of molecular chains via
  statistically interacting particles associated with extension, contraction,
  twist, and supercoiling

abstract:
  A methodology for the statistical mechanical analysis of polymeric chains
under tension introduced previously is extended to include torque. The response
of individual bonds between monomers or of entire groups of monomers to a
combination of tension and torque involves, in the framework of this method of
analysis, the (thermal or mechanical) activation of a specific mix of
statistically interacting particles carrying quanta of extension or contraction
and quanta of twist or supercoiling. The methodology, which is elucidated in
applications of increasing complexity, is capable of describing the conversion
between twist chirality and plectonemic chirality in quasistatic processes. The
control variables are force or extension and torque or linkage (a combination
of twist and writhe). The versatility of this approach is demonstrated in two
applications relevant and promising for double-stranded DNA under controlled
tension and torque. One application describes conformational transformations
between (native) B-DNA, (underwound) S-DNA, and (overwound) P-DNA in accord
with experimental data. The other application describes how the conversion
between a twisted chain and a supercoiled chain accommodates variations of
linkage and excess length in a buckling transition.


title:
Machine learning models and facial regions videos for estimating heart
  rate: a review on Patents, Datasets and Literature

abstract:
  Estimating heart rate is important for monitoring users in various
situations. Estimates based on facial videos are increasingly being researched
because it makes it possible to monitor cardiac information in a non-invasive
way and because the devices are simpler, requiring only cameras that capture
the user's face. From these videos of the user's face, machine learning is able
to estimate heart rate. This study investigates the benefits and challenges of
using machine learning models to estimate heart rate from facial videos,
through patents, datasets, and articles review. We searched Derwent Innovation,
IEEE Xplore, Scopus, and Web of Science knowledge bases and identified 7 patent
filings, 11 datasets, and 20 articles on heart rate, photoplethysmography, or
electrocardiogram data. In terms of patents, we note the advantages of
inventions related to heart rate estimation, as described by the authors. In
terms of datasets, we discovered that most of them are for academic purposes
and with different signs and annotations that allow coverage for subjects other
than heartbeat estimation. In terms of articles, we discovered techniques, such
as extracting regions of interest for heart rate reading and using Video
Magnification for small motion extraction, and models such as EVM-CNN and
VGG-16, that extract the observed individual's heart rate, the best regions of
interest for signal extraction and ways to process them.


title: On a weak Lie type for vectors

abstract:
  In the symbol space for differential operators, we discuss a scalar type for
change of local coordinates, using vector valued distributions. In particular,
we discuss P-convexity for hypoelliptic operators.


title:
Exact time-dependent solutions of a Fisher-KPP-like equation obtained
  with nonclassical symmetry analysis

abstract:
  We consider a family of exact solutions to a nonlinear reaction-diffusion
model, constructed using nonclassical symmetry analysis. In a particular limit,
the mathematical model approaches the well-known Fisher-KPP model, which means
that it is related to various applications including cancer progression, wound
healing and ecological invasion. The exact solution is mathematically
interesting since exact solutions of the Fisher-KPP model are rare, and often
restricted to long-time travelling wave solutions for special values of the
travelling wave speed.


title:
Graph Convolutional Networks for Multi-modality Medical Imaging:
  Methods, Architectures, and Clinical Applications

abstract:
  Image-based characterization and disease understanding involve integrative
analysis of morphological, spatial, and topological information across
biological scales. The development of graph convolutional networks (GCNs) has
created the opportunity to address this information complexity via graph-driven
architectures, since GCNs can perform feature aggregation, interaction, and
reasoning with remarkable flexibility and efficiency. These GCNs capabilities
have spawned a new wave of research in medical imaging analysis with the
overarching goal of improving quantitative disease understanding, monitoring,
and diagnosis. Yet daunting challenges remain for designing the important
image-to-graph transformation for multi-modality medical imaging and gaining
insights into model interpretation and enhanced clinical decision support. In
this review, we present recent GCNs developments in the context of medical
image analysis including imaging data from radiology and histopathology. We
discuss the fast-growing use of graph network architectures in medical image
analysis to improve disease diagnosis and patient outcomes in clinical
practice. To foster cross-disciplinary research, we present GCNs technical
advancements, emerging medical applications, identify common challenges in the
use of image-based GCNs and their extensions in model interpretation,
large-scale benchmarks that promise to transform the scope of medical image
studies and related graph-driven medical research.


title: Discovering Fine-Grained Semantics in Knowledge Graph Relations

abstract:
  When it comes to comprehending and analyzing multi-relational data, the
semantics of relations are crucial. Polysemous relations between different
types of entities, that represent multiple semantics, are common in real-world
relational datasets represented by knowledge graphs. For numerous use cases,
such as entity type classification, question answering and knowledge graph
completion, the correct semantic interpretation of these relations is
necessary. In this work, we provide a strategy for discovering the different
semantics associated with abstract relations and deriving many sub-relations
with fine-grained meaning. To do this, we leverage the types of the entities
associated with the relations and cluster the vector representations of
entities and relations. The suggested method is able to automatically discover
the best number of sub-relations for a polysemous relation and determine their
semantic interpretation, according to our empirical evaluation.


title:
Expansion for a fundamental solution of Laplace's equation in flat-ring
  cyclide coordinates

abstract:
  We derive an expansion for the fundamental solution of Laplace's equation in
flat-ring coordinates in three-dimensional Euclidean space. This expansion is a
double series of products of functions that are harmonic in the interior and
exterior of "flat rings". These internal and external flat-ring harmonic
functions are expressed in terms of simply-periodic Lam\'e functions. In a
limiting case we obtain the expansion of the fundamental solution in toroidal
coordinates.


title: Debiaser Beware: Pitfalls of Centering Regularized Transport Maps

abstract:
  Estimating optimal transport (OT) maps (a.k.a. Monge maps) between two
measures $P$ and $Q$ is a problem fraught with computational and statistical
challenges. A promising approach lies in using the dual potential functions
obtained when solving an entropy-regularized OT problem between samples $P_n$
and $Q_n$, which can be used to recover an approximately optimal map. The
negentropy penalization in that scheme introduces, however, an estimation bias
that grows with the regularization strength. A well-known remedy to debias such
estimates, which has gained wide popularity among practitioners of regularized
OT, is to center them, by subtracting auxiliary problems involving $P_n$ and
itself, as well as $Q_n$ and itself. We do prove that, under favorable
conditions on $P$ and $Q$, debiasing can yield better approximations to the
Monge map. However, and perhaps surprisingly, we present a few cases in which
debiasing is provably detrimental in a statistical sense, notably when the
regularization strength is large or the number of samples is small. These
claims are validated experimentally on synthetic and real datasets, and should
reopen the debate on whether debiasing is needed when using entropic optimal
transport.


title: Diffusive regimes in a two-dimensional chiral fluid

abstract:
  We study the experimental behavior of diffusion in a chiral flow (produced in
this case by a set of identical disk-shaped rotors). Results display in general
-- but not always -- strong anisotropic diffusion, represented here by an
antisymmetric two-dimensional tensor. In particular, we show chiral diffusion
is, strikingly, very slowly aging. Moreover, we show that the same chiral fluid
may feature either strong super-diffusive behavior, quasi-normal diffusion, or
weak sub-diffusion, depending on the flow chiral vorticity value. Moreover,
off-diagonal diffusion coefficients (odd diffusion coefficient) change sign
according to this scheme as well. Therefore, the chiral fluid appears to have a
strikingly complex and self regulated diffusive behavior.


title: Hierarchical Sensitivity Parity

abstract:
  In this work we present a new framework for modelling portfolio dynamics and
how to incorporate this information in the portfolio selection process. We
define drivers for asset and portfolio dynamics, and their optimal selection.
We introduce the new Commonality Principle, which gives a solution for the
optimal selection of portfolio drivers as being the common drivers. Asset
dynamics are modelled by PDEs and approximated with Neural Networks, and
sensitivities of portfolio constituents with respect to portfolio common
drivers are obtained via Automatic Adjoint Differentiation (AAD). Information
of asset dynamics is incorporated via sensitivities into the portfolio
selection process. Portfolio constituents are projected into a hypersurface,
from a vector space formed by the returns of common drivers of the portfolio.
The commonality principle allows for the necessary geometric link between the
hyperplane formed by portfolio constituents in a traditional setup with no
exogenous information, and the hypersurface formed by the vector space of
common portfolio drivers, so that when portfolio constituents are projected
into this hypersurface, the representations of idiosyncratic risks from the
hyperplane are kept at most in this new subspace, while systematic risks
representations are added via exogenous information as part of this common
drivers vector space. We build a sensitivity matrix, which is a similarity
matrix of the projections in this hypersurface, and can be used to optimize for
diversification on both, idiosyncratic and systematic risks, which is not
contemplated on the literature. Finally, we solve the convex optimization
problem for optimal diversification by applying a hierarchical clustering to
the sensitivity matrix, avoiding quadratic optimizers for the matrix
properties, and we reach over-performance in all experiments with respect to
all other out-of-sample methods.


title: FLAME: Federated Learning Across Multi-device Environments

abstract:
  Federated Learning (FL) enables distributed training of machine learning
models while keeping personal data on user devices private. While we witness
increasing applications of FL in the area of mobile sensing, such as
human-activity recognition, FL has not been studied in the context of a
multi-device environment (MDE), wherein each user owns multiple data-producing
devices. With the proliferation of mobile and wearable devices, MDEs are
increasingly becoming popular in ubicomp settings, therefore necessitating the
study of FL in them. FL in MDEs is characterized by high non-IID-ness across
clients, complicated by the presence of both user and device heterogeneities.
Further, ensuring efficient utilization of system resources on FL clients in a
MDE remains an important challenge. In this paper, we propose FLAME, a
user-centered FL training approach to counter statistical and system
heterogeneity in MDEs, and bring consistency in inference performance across
devices. FLAME features (i) user-centered FL training utilizing the time
alignment across devices from the same user; (ii) accuracy- and
efficiency-aware device selection; and (iii) model personalization to devices.
We also present an FL evaluation testbed with realistic energy drain and
network bandwidth profiles, and a novel class-based data partitioning scheme to
extend existing HAR datasets to a federated setup. Our experiment results on
three multi-device HAR datasets show that FLAME outperforms various baselines
by 4.8-33.8% higher F-1 score, 1.02-2.86x greater energy efficiency, and up to
2.02x speedup in convergence to target accuracy through fair distribution of
the FL workload.


title:
Peanut harmonic expansion for a fundamental solution of Laplace's
  equation in flat-ring coordinates

abstract:
  We derive an expansion for the fundamental solution of Laplace's equation in
flat-ring cyclide coordinates in three-dimensional Euclidean space. This
expansion is a double series of products of functions that are harmonic in the
interior and exterior of coordinate surfaces which are peanut shaped and
orthogonal to surfaces which are flat-rings. These internal and external peanut
harmonic functions are expressed in terms of Lam\'e-Wangerin functions. Using
the expansion for the fundamental solution, we derive an addition theorem for
the azimuthal Fourier component in terms of the odd-half-integer degree
Legendre function of the second kind as an infinite series in Lam\'e-Wangerin
functions. We also derive integral identities over the Legendre function of the
second kind for a product of three Lam\'e-Wangerin functions. In a limiting
case we obtain the expansion of the fundamental solution in spherical
coordinates.


title: Dynamical double black holes and their host cluster properties

abstract:
  We investigate the relationship between the global properties of star
clusters and their double black hole (DBH) populations. We use the code {\tt
NBODY6} to evolve a suite of star cluster models with an initial mass of
$\mathcal{O}(10^4)$M$_\odot$ and varying initial parameters. We conclude that
cluster metallicity plays the most significant role in determining the lifespan
of a cluster, while the initial half-mass radius is dominant in setting the
rate of BH exchange interactions in the central cluster regions. We find that
the mass of interacting BHs, rather than how frequently their interactions with
other BHs occur, is more crucial in the thermal expansion and eventual
evaporation of the cluster. We formulate a novel approach to easily quantify
the degree of BH-BH dynamical activity in each model. We report 12 in-cluster
and three out-of-cluster (after ejection from the cluster) DBH mergers, of
different types (inspiral, eccentric, hierarchical) across the ten $N$-body
models presented. Our DBH merger efficiency is 3--4$\times10^{-5}$ mergers per
M$_\odot$. We note the cluster initial density plays the most crucial role in
determining the number of DBH mergers, with the potential presence of a
transitional density point (between 1.2-3.8$\times10^3$M$_\odot$/pc$^3$) below
which the number of in-cluster mergers increases with cluster density and above
which the increased stellar density acts to prevent in-cluster BH mergers. The
importance of the history of dynamical interactions within the cluster in
setting up the pathways to ejected DBH mergers is also discussed. Our findings
show a broad match with observed LIGO-Virgo DBH mergers.


title: Quantisation-aware Precoding for MU-MIMO with Limited-capacity Fronthaul

abstract:
  Base stations in 5G and beyond use advanced antenna systems (AASs), where
multiple passive antenna elements and radio units are integrated into a single
box. A critical bottleneck of such a system is the digital fronthaul between
the AAS and baseband unit (BBU), which has limited capacity. In this paper, we
study an AAS used for precoded downlink transmission over a multi-user
multiple-input multiple-output (MU-MIMO) channel. First, we present the
baseline quantization-unaware precoding scheme created when a precoder is
computed at the BBU and then quantized to be sent over the fronthaul. We
propose a new precoding design that is aware of the fronthaul quantization. We
formulate an optimization problem to minimize the mean squared error at the
receiver side. We rewrite the problem to utilize mixed-integer programming to
solve it. The numerical results manifest that our proposed precoding greatly
outperforms quantization-unaware precoding in terms of sum rate.


title: On Guiding Visual Attention with Language Specification

abstract:
  While real world challenges typically define visual categories with language
words or phrases, most visual classification methods define categories with
numerical indices. However, the language specification of the classes provides
an especially useful prior for biased and noisy datasets, where it can help
disambiguate what features are task-relevant. Recently, large-scale multimodal
models have been shown to recognize a wide variety of high-level concepts from
a language specification even without additional image training data, but they
are often unable to distinguish classes for more fine-grained tasks. CNNs, in
contrast, can extract subtle image features that are required for fine-grained
discrimination, but will overfit to any bias or noise in datasets. Our insight
is to use high-level language specification as advice for constraining the
classification evidence to task-relevant features, instead of distractors. To
do this, we ground task-relevant words or phrases with attention maps from a
pretrained large-scale model. We then use this grounding to supervise a
classifier's spatial attention away from distracting context. We show that
supervising spatial attention in this way improves performance on
classification tasks with biased and noisy data, including about 3-15%
worst-group accuracy improvements and 41-45% relative improvements on fairness
metrics.


title:
Acceleration and adiabatic expansion of multi-state fluorescence from a
  nanofocus

abstract:
  Since Purcell's seminal report 75 years ago, electromagnetic resonators have
been used to control light-matter interactions to make brighter radiation
sources and unleash unprecedented control over quantum states of light and
matter. Indeed, optical resonators such as microcavities and plasmonic
nanostructures offer excellent control but only over a limited spectral range.
Strategies to tune both emission and the resonator are often required, which
preclude the possibility of enhancing multiple transitions simultaneously. In
this letter, we report a more than 590-fold radiative emission enhancement
across the telecommunications emission band of Erbium-ions in silica using a
single non-resonant plasmonic waveguide. Our plasmonic waveguide uses a novel
reverse nanofocusing approach to efficiently collect emission, making these
devices brighter than all non-plasmonic control samples considered. Remarkably,
the high broadband Purcell factor allows us to resolve the Stark-split electric
dipole transitions, which are typically only observed under cryogenic
conditions. Simultaneous Purcell enhancement of multiple quantum states is of
interest for photonic quantum networks as well as on-chip data communications.


title: "Back to the future" projections for COVID-19 surges

abstract:
  We argue that information from countries who had earlier COVID-19 surges can
be used to inform another country's current model, then generating what we call
back-to-the-future (BTF) projections. We show that these projections can be
used to accurately predict future COVID-19 surges prior to an inflection point
of the daily infection curve. We show, across 12 different countries from all
populated continents around the world, that our method can often predict future
surges in scenarios where the traditional approaches would always predict no
future surges. However, as expected, BTF projections cannot accurately predict
a surge due to the emergence of a new variant. To generate BTF projections, we
make use of a matching scheme for asynchronous time series combined with a
response coaching SIR model.

categories: q-bio.PE physics.data-an q-bio.QM stat.ME


title:
An explanation for the similar appearance of two identities involving
  Dickson polynomials

abstract:
  This article explains the similar appearance of two polynomial identities
involving Dickson polynomials in char. 2, one found by Abhyankar, Cohen, and
Zieve, and the other found by the author.


title:
A Distributed Algorithm for Measure-valued Optimization with Additive
  Objective

abstract:
  We propose a distributed nonparametric algorithm for solving measure-valued
optimization problems with additive objectives. Such problems arise in several
contexts in stochastic learning and control including Langevin sampling from an
unnormalized prior, mean field neural network learning and Wasserstein gradient
flows. The proposed algorithm comprises a two-layer alternating direction
method of multipliers (ADMM). The outer-layer ADMM generalizes the Euclidean
consensus ADMM to the Wasserstein consensus ADMM, and to its
entropy-regularized version Sinkhorn consensus ADMM. The inner-layer ADMM turns
out to be a specific instance of the standard Euclidean ADMM. The overall
algorithm realizes operator splitting for gradient flows in the manifold of
probability measures.


title: Desingularization and p-Curvature of Recurrence Operators

abstract:
  Linear recurrence operators in characteristic $p$ are classified by their
$p$-curvature. For a recurrence operator $L$, denote by $\chi(L)$ the
characteristic polynomial of its $p$-curvature. We can obtain information about
the factorization of $L$ by factoring $\chi(L)$. The main theorem of this paper
gives an unexpected relation between $\chi(L)$ and the true singularities of
$L$. An application is to speed up a fast algorithm for computing $\chi(L)$ by
desingularizing $L$ first. Another contribution of this paper is faster
desingularization.


title: The Waring Rank of Ternary Cubic Forms

abstract:
  We provide conditions on the coefficients of a ternary cubic form that
determine its Waring rank.


title:
Design of EMG-driven Musculoskeletal Model for Volitional Control of a
  Robotic Ankle Prosthesis

abstract:
  Existing robotic lower-limb prostheses use autonomous control to address
cyclic, locomotive tasks, but they are inadequate to operate the prosthesis for
daily activities that are non-cyclic and unpredictable. To address this
challenge, this study aims to design a novel electromyography (EMG)-driven
musculoskeletal model for volitional control of a robotic ankle-foot
prosthesis. This controller places the user in continuous control of the
device, allowing them to freely manipulate the prosthesis behavior at will. The
Hill-type muscle model was used to model a dorsiflexor and a plantarflexor,
which functioned around a virtual ankle joint. The model parameters were
determined by fitting the model prediction to the experimental data collected
from an able-bodied subject. EMG signals recorded from ankle agonist and
antagonist muscle pair were used to activate the virtual muscle models. This
model was validated via offline simulations and real-time prosthesis control.
Additionally, the feasibility of the proposed prosthesis control on assisting
the user's functional tasks was demonstrated. The present control may further
improve the function of robotic prosthesis for supporting versatile activities
in individuals with lower-limb amputations.


title: Handling Imbalanced Datasets Through Optimum-Path Forest

abstract:
  In the last decade, machine learning-based approaches became capable of
performing a wide range of complex tasks sometimes better than humans,
demanding a fraction of the time. Such an advance is partially due to the
exponential growth in the amount of data available, which makes it possible to
extract trustworthy real-world information from them. However, such data is
generally imbalanced since some phenomena are more likely than others. Such a
behavior yields considerable influence on the machine learning model's
performance since it becomes biased on the more frequent data it receives.
Despite the considerable amount of machine learning methods, a graph-based
approach has attracted considerable notoriety due to the outstanding
performance over many applications, i.e., the Optimum-Path Forest (OPF). In
this paper, we propose three OPF-based strategies to deal with the imbalance
problem: the $\text{O}^2$PF and the OPF-US, which are novel approaches for
oversampling and undersampling, respectively, as well as a hybrid strategy
combining both approaches. The paper also introduces a set of variants
concerning the strategies mentioned above. Results compared against several
state-of-the-art techniques over public and private datasets confirm the
robustness of the proposed approaches.


title:
A Formal Safety Characterization of Advanced Driver Assist Systems in
  the Car-Following Regime with Scenario-Sampling

abstract:
  The capability to follow a lead-vehicle and avoid rear-end collisions is one
of the most important functionalities for human drivers and various Advanced
Driver Assist Systems (ADAS). Existing safety performance justification of the
car-following systems either relies on simple concrete scenarios with biased
surrogate metrics or requires a significantly long driving distance for risk
observation and inference. In this paper, we propose a guaranteed unbiased and
sampling efficient scenario-based safety evaluation framework inspired by the
previous work on $\epsilon\delta$-almost safe set quantification. The proposal
characterizes the complete safety performance of the test subject in the
car-following regime. The performance of the proposed method is also
demonstrated in challenging cases including some widely adopted car-following
decision-making modules and the commercially available Openpilot driving stack
by CommaAI.


title:
Prior image-based medical image reconstruction using a style-based
  generative adversarial network

abstract:
  Computed medical imaging systems require a computational reconstruction
procedure for image formation. In order to recover a useful estimate of the
object to-be-imaged when the recorded measurements are incomplete, prior
knowledge about the nature of object must be utilized. In order to improve the
conditioning of an ill-posed imaging inverse problem, deep learning approaches
are being actively investigated for better representing object priors and
constraints. This work proposes to use a style-based generative adversarial
network (StyleGAN) to constrain an image reconstruction problem in the case
where additional information in the form of a prior image of the sought-after
object is available. An optimization problem is formulated in the intermediate
latent-space of a StyleGAN, that is disentangled with respect to meaningful
image attributes or "styles", such as the contrast used in magnetic resonance
imaging (MRI). Discrepancy between the sought-after and prior images is
measured in the disentangled latent-space, and is used to regularize the
inverse problem in the form of constraints on specific styles of the
disentangled latent-space. A stylized numerical study inspired by MR imaging is
designed, where the sought-after and the prior image are structurally similar,
but belong to different contrast mechanisms. The presented numerical studies
demonstrate the superiority of the proposed approach as compared to classical
approaches in the form of traditional metrics.


title: When, Why, and Which Pretrained GANs Are Useful?

abstract:
  The literature has proposed several methods to finetune pretrained GANs on
new datasets, which typically results in higher performance compared to
training from scratch, especially in the limited-data regime. However, despite
the apparent empirical benefits of GAN pretraining, its inner mechanisms were
not analyzed in-depth, and understanding of its role is not entirely clear.
Moreover, the essential practical details, e.g., selecting a proper pretrained
GAN checkpoint, currently do not have rigorous grounding and are typically
determined by trial and error.
  This work aims to dissect the process of GAN finetuning. First, we show that
initializing the GAN training process by a pretrained checkpoint primarily
affects the model's coverage rather than the fidelity of individual samples.
Second, we explicitly describe how pretrained generators and discriminators
contribute to the finetuning process and explain the previous evidence on the
importance of pretraining both of them. Finally, as an immediate practical
benefit of our analysis, we describe a simple recipe to choose an appropriate
GAN checkpoint that is the most suitable for finetuning to a particular target
task. Importantly, for most of the target tasks, Imagenet-pretrained GAN,
despite having poor visual quality, appears to be an excellent starting point
for finetuning, resembling the typical pretraining scenario of discriminative
computer vision models.


title: Improving Intrinsic Exploration with Language Abstractions

abstract:
  Reinforcement learning (RL) agents are particularly hard to train when
rewards are sparse. One common solution is to use intrinsic rewards to
encourage agents to explore their environment. However, recent intrinsic
exploration methods often use state-based novelty measures which reward
low-level exploration and may not scale to domains requiring more abstract
skills. Instead, we explore natural language as a general medium for
highlighting relevant abstractions in an environment. Unlike previous work, we
evaluate whether language can improve over existing exploration methods by
directly extending (and comparing to) competitive intrinsic exploration
baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These
language-based variants outperform their non-linguistic forms by 45-85% across
13 challenging tasks from the MiniGrid and MiniHack environment suites.


title:
Experimental analysis of quantum annealers and hybrid solvers using
  benchmark optimization problems

abstract:
  This paper studies the Hamiltonian Cycle Problem (HCP) and the Traveling
Salesman Problem (TSP) on D-Wave's quantum systems. Initially, motivated by the
fact that most libraries present their benchmark instances in terms of
adjacency matrices, we develop a novel matrix formulation for the HCP and TSP
Hamiltonians, which enables the seamless and automatic integration of benchmark
instances in quantum platforms. our extensive experimental tests have led us to
some interesting conclusions. D-Wave's {\tt Advantage\_system4.1} is more
efficient than {\tt Advantage\_system1.1} both in terms of qubit utilization
and quality of solutions. Finally, we experimentally establish that D-Wave's
Hybrid solvers always provide a valid solution to a problem, without violating
the QUBO constraints, even for arbitrarily big problems, of the order of $120$
nodes. When solving TSP instances, the solutions produced by the quantum
annealer are often invalid, in the sense that they violate the topology of the
graph. To address this use we advocate the use of \emph{min-max normalization}
for the coefficients of the TSP Hamiltonian. Finally, we present a thorough
mathematical analysis on the precise number of constraints required to express
the HCP and TSP Hamiltonians. This analysis, explains quantitatively why,
almost always, running incomplete graph instances requires more qubits than
complete instances. It turns out that incomplete graph require more quadratic
constraints than complete graphs, a fact that has been corroborated by a series
of experiments.


title:
Defining the quantum workforce landscape: a review of global quantum
  education initiatives

abstract:
  Rapid advances in quantum technology have exacerbated the shortage of a
diverse, inclusive, and sustainable quantum workforce. National governments and
industries are developing strategies for education, training, and workforce
development to accelerate the commercialization of quantum technologies. In
this paper, we report the existing state of the quantum workforce as well as
several learning pathways to nurture the talent pipeline between academia and
industry. We provide a comprehensive guide of various educational initiatives
accessible throughout the world, such as online courses, conferences, seminars,
games, and community-focused networks, that facilitate quantum training and
upskill the talent needed to develop a better quantum future.


title: Invariance of knot lattice homology and homotopy

abstract:
  Links of singularity and generalized algebraic links are ways of constructing
three-manifolds and smooth links inside them from potentially singular complex
algebraic surfaces and complex curves inside them. We prove that knot lattice
homology is an invariant of the smooth knot type of a generalized algebraic
knot in a rational homology sphere. In that case, knot lattice homology can be
realized as the cellular homology of a doubly filtered homotopy type, which is
itself invariant. Along the way, we show that the topological link type of a
generalized algebraic link determines the nested singularity type.


title:
Enhanced DeepONet for Modeling Partial Differential Operators
  Considering Multiple Input Functions

abstract:
  Machine learning, especially deep learning is gaining much attention due to
the breakthrough performance in various cognitive applications. Recently,
neural networks (NN) have been intensively explored to model partial
differential equations as NN can be viewed as universal approximators for
nonlinear functions. A deep network operator (DeepONet) architecture was
proposed to model the general non-linear continuous operators for partial
differential equations (PDE) due to its better generalization capabilities than
existing mainstream deep neural network architectures. However, existing
DeepONet can only accept one input function, which limits its application. In
this work, we explore the DeepONet architecture to extend it to accept two or
more input functions. We propose new Enhanced DeepONet or EDeepONet high-level
neural network structure, in which two input functions are represented by two
branch DNN sub-networks, which are then connected with output truck network via
inner product to generate the output of the whole neural network. The proposed
EDeepONet structure can be easily extended to deal with multiple input
functions. Our numerical results on modeling two partial differential equation
examples shows that the proposed enhanced DeepONet is about 7X-17X or about one
order of magnitude more accurate than the fully connected neural network and is
about 2X-3X more accurate than a simple extended DeepONet for both training and
test.


title:
COVID-19 and Science Communication: The Recording and Reporting of
  Disease Mortality

abstract:
  The ongoing COVID-19 pandemic has brought science to the fore of the public
discourse and considering the complexity of the issues involved, with it also
the challenge of effective and informative science communication. A
particularly contentious topic, in that it is both highly emotional in and of
itself, as well as in that it sits at the nexus of the decision-making process
regarding the handling of the pandemic, which has effected lockdowns, social
behaviour measures, business closures, and others, concerns the recording and
the reporting of the disease mortality. To clarify a point which has caused
much controversy and anger in the public debate, the first part of the present
article discusses the very fundamentals underlying the issue of causative
attribution with regards to mortality, lays out the foundations of the
statistical means of mortality estimation, and concretizes these by analysing
the recording and reporting practices adopted in England, and their widespread
misrepresentations. The second part of the article is empirical in nature. I
present data and an analysis of how COVID-19 mortality has been reported in the
mainstream media in the UK and the USA, including a comparative analysis both
across the two countries as well as across different media outlets. The
findings clearly demonstrate a uniform and worrying lack of understanding of
the relevant technical subject matter by the media in both countries. Of
particular interest is the finding that with a remarkable regularity
($\rho>0.998$) the greater the number of articles a media outlet published on
COVID-19 mortality, the greater the \emph{proportion} of its articles
misrepresented the disease mortality figures.


title:
Rethinking Machine Learning Robustness via its Link with the
  Out-of-Distribution Problem

abstract:
  Despite multiple efforts made towards robust machine learning (ML) models,
their vulnerability to adversarial examples remains a challenging problem that
calls for rethinking the defense strategy. In this paper, we take a step back
and investigate the causes behind ML models' susceptibility to adversarial
examples. In particular, we focus on exploring the cause-effect link between
adversarial examples and the out-of-distribution (OOD) problem. To that end, we
propose an OOD generalization method that stands against both adversary-induced
and natural distribution shifts. Through an OOD to in-distribution mapping
intuition, our approach translates OOD inputs to the data distribution used to
train and test the model. Through extensive experiments on three benchmark
image datasets of different scales (MNIST, CIFAR10, and ImageNet) and by
leveraging image-to-image translation methods, we confirm that the adversarial
examples problem is a special case of the wider OOD generalization problem.
Across all datasets, we show that our translation-based approach consistently
improves robustness to OOD adversarial inputs and outperforms state-of-the-art
defenses by a significant margin, while preserving the exact accuracy on benign
(in-distribution) data. Furthermore, our method generalizes on naturally OOD
inputs such as darker or sharper images


title:
Direct Measurement of Storage and Loss Behavior in AFM Force-Distance
  Experiments Using the Modified Fourier Transformation

abstract:
  Force-distance curve experiments are commonly performed in Atomic Force
Microscopy (AFM) to obtain the viscoelastic characteristics of materials, such
as the storage and loss moduli or compliances. The classic methods used to
obtain these characteristics consist of fitting a viscoelastic material model
to the experimentally obtained AFM data. Here, we demonstrate a new method that
utilizes the modified discrete Fourier transform to approximate the storage and
loss behavior of a material directly from the data, without the need for a fit.
Additionally, one may still fit a model to the resulting storage and loss
behavior if a parameterized description of the material is desired. In contrast
to fitting the data to a model chosen a priori, departing from a model-free
description of the material's frequency behavior guides the selection of the
model, such that the user may choose the one that is most appropriate for the
particular material under study. To this end, we also include modified Fourier
domain descriptions of commonly used viscoelastic models.


title: Symphony: Composing Interactive Interfaces for Machine Learning

abstract:
  Interfaces for machine learning (ML), information and visualizations about
models or data, can help practitioners build robust and responsible ML systems.
Despite their benefits, recent studies of ML teams and our interviews with
practitioners (n=9) showed that ML interfaces have limited adoption in
practice. While existing ML interfaces are effective for specific tasks, they
are not designed to be reused, explored, and shared by multiple stakeholders in
cross-functional teams. To enable analysis and communication between different
ML practitioners, we designed and implemented Symphony, a framework for
composing interactive ML interfaces with task-specific, data-driven components
that can be used across platforms such as computational notebooks and web
dashboards. We developed Symphony through participatory design sessions with 10
teams (n=31), and discuss our findings from deploying Symphony to 3 production
ML projects at Apple. Symphony helped ML practitioners discover previously
unknown issues like data duplicates and blind spots in models while enabling
them to share insights with other stakeholders.


title: Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

abstract:
  Classification and regression employing a simple Deep Neural Network (DNN)
are investigated to perform touch localization on a tactile surface using
ultrasonic guided waves. A robotic finger first simulates the touch action and
captures the data to train a model. The model is then validated with data from
experiments conducted with human fingers. The localization root mean square
errors (RMSE) in time and frequency domains are presented. The proposed method
provides satisfactory localization results for most human-machine interactions,
with a mean error of 0.47 cm and standard deviation of 0.18 cm and a computing
time of 0.44 ms. The classification approach is also adapted to identify
touches on an access control keypad layout, which leads to an accuracy of 97%
with a computing time of 0.28 ms. These results demonstrate that DNN-based
methods are a viable alternative to signal processing-based approaches for
accurate and robust touch localization using ultrasonic guided waves.


title: SKaMPI-OpenSHMEM: Measuring OpenSHMEM Communication Routines

abstract:
  Benchmarking is an important challenge in HPC, in particular, to be able to
tune the basic blocks of the software environment used by applications. The
communication library and distributed run-time environment are among the most
critical ones. In particular, many of the routines provided by communication
libraries can be adjusted using parameters such as buffer sizes and
communication algorithm. As a consequence, being able to measure accurately the
time taken by these routines is crucial in order to optimize them and achieve
the best performance. For instance, the SKaMPI library was designed to measure
the time taken by MPI routines, relying on MPI's two-sided communication model
to measure one-sided and two-sided peer-to-peer communication and collective
routines. In this paper, we discuss the benchmarking challenges specific to
OpenSHMEM's communication model, mainly to avoid inter-call pipelining and
overlapping when measuring the time taken by its routines. We extend SKaMPI for
OpenSHMEM for this purpose and demonstrate measurement algorithms that address
OpenSHMEM's communication model in practice. Scaling experiments are run on the
Summit platform to compare different benchmarking approaches on the SKaMPI
benchmark operations. These show the advantages of our techniques for more
accurate performance characterization.


title:
Measuring the Virial Factor in SDSS DR5 Quasars with Redshifted H$\beta$
  and Fe ii Broad Emission Lines

abstract:
  Under the hypothesis of gravitational redshift induced by the central
supermassive black hole, and based on line widths and shifts of redward shifted
H$\beta$ and Fe ii broad emission lines for a sample of 1973 $z<0.8$ SDSS DR5
quasars, we measured the virial factor in determining supermassive black hole
masses, usually estimated by the reverberation mapping (RM) method or the
relevant secondary methods. The virial factor had been believed to be from the
geometric effect of broad-line region. The measured virial factor of Fe ii is
larger than that of H$\beta$ for 98% of these quasars. The virial factor is
very different from object to object and for different emission lines. For most
of these quasars, the virial factor of H$\beta$ is larger than these averages
that were usually used in determining the masses of black holes. There are
three positive correlations among the measured virial factor of H$\beta$,
dimensionless accretion rate and Fe ii/H$\beta$ line ratio. A positive
three-dimensional correlation is found among these three quantities, and this
correlation indicates that the virial factor is likely dominated by the
dimensionless accretion rate and metallicity. A negative correlation is found
between the redward shift of H$\beta$ and the scaled size of broad-line region
radius in units of the gravitational radius of black hole. This negative
correlation will be expected naturally if the redward shift of H$\beta$ is
mainly from the gravity of black hole. Radiation pressure from accretion disk
may be a significant contributor to the virial factor.