Abstract:
  For 6-DOF (degrees of freedom) interactive virtual acoustic environments
(VAEs), the spatial rendering of diffuse late reverberation in addition to
early (specular) reflections is important. In the interest of computational
efficiency, the acoustic simulation of the late reverberation can be simplified
by using a limited number of spatially distributed virtual reverb sources (VRS)
each radiating incoherent signals. A sufficient number of VRS is needed to
approximate spatially anisotropic late reverberation, e.g., in a room with
inhomogeneous distribution of absorption at the boundaries. Here, a highly
efficient and perceptually plausible method to generate and spatially render
late reverberation is suggested, extending the room acoustics simulator RAZR
[Wendt et al., J. Audio Eng. Soc., 62, 11 (2014)]. The room dimensions and
frequency-dependent absorption coefficients at the wall boundaries are used to
determine the parameters of a physically-based feedback delay network (FDN) to
generate the incoherent VRS signals. The VRS are spatially distributed around
the listener with weighting factors representing the spatially subsampled
distribution of absorption coefficients on the wall boundaries. The minimum
number of VRS required to be perceptually distinguishable from the maximum
(reference) number of 96 VRS was assessed in a listening test conducted with a
spherical loudspeaker array within an anechoic room. For the resulting low
numbers of VRS suited for spatial rendering, optimal physically-based parameter
choices for the FDN are discussed.


Abstract:
  We present the results from an observing campaign to confirm the peculiar
motion of the supermassive black hole (SMBH) in J0437+2456 first reported in
Pesce et al. (2018). Deep observations with the Arecibo Observatory have
yielded a detection of neutral hydrogen (HI) emission, from which we measure a
recession velocity of 4910 km s$^{-1}$ for the galaxy as a whole. We have also
obtained near-infrared integral field spectroscopic observations of the
galactic nucleus with the Gemini North telescope, yielding spatially resolved
stellar and gas kinematics with a central velocity at the innermost radii
($0.1^{\prime \prime} \approx 34$ pc) of 4860 km s$^{-1}$. Both measurements
differ significantly from the $\sim$4810 km s$^{-1}$ H$_2$O megamaser velocity
of the SMBH, supporting the prior indications of a velocity offset between the
SMBH and its host galaxy. However, the two measurements also differ
significantly from one another, and the galaxy as a whole exhibits a complex
velocity structure that implies the system has recently been dynamically
disturbed. These results make it clear that the SMBH is not at rest with
respect to the systemic velocity of the galaxy, though the specific nature of
the mobile SMBH -- i.e., whether it traces an ongoing galaxy merger, a binary
black hole system, or a gravitational wave recoil event -- remains unclear.


Abstract:
  We define standardized constructions of finite fields, and standardized
generators of (multiplicative) cyclic subgroups in these fields.
  The motivation is to provide a substitute for Conway polynomials which can be
used by various software packages and in collections of mathematical data which
involve finite fields.


Abstract:
  Very recently, the construction of twist actuators from magnetorheological
gels and elastomers has been suggested. These materials consist of magnetizable
colloidal particles embedded in a soft elastic polymeric environment. The twist
actuation is enabled by a net chirality of the internal particle arrangement.
Upon magnetization by a homogeneous external magnetic field, the systems
feature an overall torsional deformation around the magnetization direction.
Starting from a discrete minimal mesoscopic model set-up we work towards a
macroscopic characterization. The two scales are linked by identifying
expressions for the macroscopic system parameters as functions of the
mesoscopic model parameters. In this way, the observed behavior of a
macroscopic system can in principle be mapped to and illustratively be
understood from an appropriate mesoscopic picture. Our results apply equally
well to corresponding soft electrorheological gels and elastomers.


Abstract:
  Radiative feedback can influence subsequent star formation. We quantify the
heating from OB stars in the local star-forming regions in the JCMT Gould Belt
survey. Dust temperatures are calculated from 450/850 micron flux ratios from
SCUBA-2 observations at the JCMT assuming a fixed dust opacity spectral index
$\beta=1.8$. Mean dust temperatures are calculated for each submillimetre clump
along with projected distances from the main OB star in the region. Temperature
vs. distance is fit with a simple model of dust heating by the OB star
radiation plus the interstellar radiation field and dust cooling through
optically thin radiation. Classifying the heating sources by spectral type,
O-type stars produce the greatest clump average temperature rises and largest
heating extent, with temperatures over 40 K and significant heating out to at
least 2.4 pc. Early-type B stars (B4 and above) produce temperatures of over 20
K and significant heating over 0.4 pc. Late-type B stars show a marginal
heating effect within 0.2 pc. For a given projected distance, there is a
significant scatter in clump temperatures that is due to local heating by other
luminous stars in the region, projection effects, or shadowing effects. Even in
these local, `low-mass' star-forming regions, radiative feedback is having an
effect on parsec scales, with 24% of the clumps heated to at least 3 K above
the 15 K base temperature expected from heating by only the interstellar
radiation field, and a mean dust temperature for heated clumps of 24 K.


Abstract:
  The software ecosystem is a trust-rich part of the world. Collaboratively,
software engineers trust major hubs in the ecosystem, such as package managers,
repository services, and programming language ecosystems. This trust, however,
is often broken by vulnerabilities, ransomware, and abuse from malignant
actors.
  But what is trust? In this paper we explore, through twelve in-depth
interviews with software engineers, how they perceive trust in their daily
work. From the interviews we conclude three things. First, software engineers
make a distinction between an adoption factor and a trust factor when selecting
a package. Secondly, while in literature mostly technical factors are
considered as the main trust factors, the software engineers in this study
conclude that organizational factors are more important. Finally, we find that
different kinds of software engineers require different views on trust, and
that it is impossible to create one unified perception of trust.
  Keywords: software ecosystem trust, empirical software engineering,
TrustSECO, external software adoption, cross-sectional exploratory interview
analysis, trust perception.


Abstract:
  In this paper, we formulate black hole solutions through extended
gravitational decoupling scheme in the framework of self-interacting
Brans-Dicke theory. The addition of a new source in the matter distribution
increases the degrees of freedom in the system of field equations.
Transformations in radial as well as temporal metric functions split the system
into two arrays. Each array includes the effects of only one source (either
seed or additional). The seed source is assumed to be a vacuum and the
corresponding system is specified through the Schwarzschild metric. In order to
construct a suitable solution of the second system, constraints are applied on
the metric potentials and energy-momentum tensor of the additional source. We
obtain three solutions corresponding to different values of the decoupling
parameter in the presence of a massive scalar field. The extra source is
classified as normal or exotic through energy conditions. It is found that two
solutions agree with the energy bounds and thus have normal matter as their
source.


Abstract:
  A famous result by R\"odl, Ruci\'nski, and Szemer\'edi guarantees a (tight)
Hamilton cycle in $k$-uniform hypergraphs $H$ on $n$ vertices with minimum
$(k-1)$-degree $\delta_{k-1}(H)\geq (1/2+o(1))n$, thereby extending Dirac's
result from graphs to hypergraphs. For graphs, much more is known; each graph
on $n$ vertices with $\delta(G)\geq (1/2+o(1))n$ contains $(1-o(1))r$
edge-disjoint Hamilton cycles where $r$ is the largest integer such that $G$
contains a spanning $2r$-regular subgraph, which is clearly asymptotically
optimal. This was proved by Ferber, Krivelevich, and Sudakov answering a
question raised by K\"uhn, Lapinskas, and Osthus.
  We extend this result to hypergraphs; every $k$-uniform hypergraph $H$ on $n$
vertices with $\delta_{k-1}(H)\geq (1/2+o(1))n$ contains $(1-o(1))r$
edge-disjoint (tight) Hamilton cycles where $r$ is the largest integer such
that $H$ contains a spanning subgraph with each vertex belonging to $kr$ edges.
In particular, this yields an asymptotic solution to a question of Glock,
K\"uhn, and Osthus.
  In fact, our main result applies to approximately vertex-regular $k$-uniform
hypergraphs with a weak quasirandom property and provides approximate
decompositions into cycle factors without too short cycles.


Abstract:
  Neural networks trained on visual data are well-known to be vulnerable to
often imperceptible adversarial perturbations. The reasons for this
vulnerability are still being debated in the literature. Recently Ilyas et al.
(2019) showed that this vulnerability arises, in part, because neural network
classifiers rely on highly predictive but brittle "non-robust" features. In
this paper we extend the work of Ilyas et al. by investigating the nature of
the input patterns that give rise to these features. In particular, we
hypothesize that in a neural network trained in a standard way, non-robust
features respond to small, "non-semantic" patterns that are typically entangled
with larger, robust patterns, known to be more human-interpretable, as opposed
to solely responding to statistical artifacts in a dataset. Thus, adversarial
examples can be formed via minimal perturbations to these small, entangled
patterns. In addition, we demonstrate a corollary of our hypothesis: robust
classifiers are more effective than standard (non-robust) ones as a source for
generating transferable adversarial examples in both the untargeted and
targeted settings. The results we present in this paper provide new insight
into the nature of the non-robust features responsible for adversarial
vulnerability of neural network classifiers.


Abstract:
  This paper focuses on building semantic maps, containing object poses and
shapes, using a monocular camera. This is an important problem because robots
need rich understanding of geometry and context if they are to shape the future
of transportation, construction, and agriculture. Our contribution is an
instance-specific mesh model of object shape that can be optimized online based
on semantic information extracted from camera images. Multi-view constraints on
the object shape are obtained by detecting objects and extracting
category-specific keypoints and segmentation masks. We show that the errors
between projections of the mesh model and the observed keypoints and masks can
be differentiated in order to obtain accurate instance-specific object shapes.
We evaluate the performance of the proposed approach in simulation and on the
KITTI dataset by building maps of car poses and shapes.


Abstract:
  Unsupervised domain adaptation (UDA) enables cross-domain learning without
target domain labels by transferring knowledge from a labeled source domain
whose distribution differs from that of the target. However, UDA is not always
successful and several accounts of `negative transfer' have been reported in
the literature. In this work, we prove a simple lower bound on the target
domain error that complements the existing upper bound. Our bound shows the
insufficiency of minimizing source domain error and marginal distribution
mismatch for a guaranteed reduction in the target domain error, due to the
possible increase of induced labeling function mismatch. This insufficiency is
further illustrated through simple distributions for which the same UDA
approach succeeds, fails, and may succeed or fail with an equal chance.
Motivated from this, we propose novel data poisoning attacks to fool UDA
methods into learning representations that produce large target domain errors.
We evaluate the effect of these attacks on popular UDA methods using benchmark
datasets where they have been previously shown to be successful. Our results
show that poisoning can significantly decrease the target domain accuracy,
dropping it to almost 0% in some cases, with the addition of only 10% poisoned
data in the source domain. The failure of these UDA methods demonstrates their
limitations at guaranteeing cross-domain generalization consistent with our
lower bound. Thus, evaluating UDA methods in adversarial settings such as data
poisoning provides a better sense of their robustness to data distributions
unfavorable for UDA.


Abstract:
  We study the problem of labelling effort for semantic segmentation of
large-scale 3D point clouds. Existing works usually rely on densely annotated
point-level semantic labels to provide supervision for network training.
However, in real-world scenarios that contain billions of points, it is
impractical and extremely costly to manually annotate every single point. In
this paper, we first investigate whether dense 3D labels are truly required for
learning meaningful semantic representations. Interestingly, we find that the
segmentation performance of existing works only drops slightly given as few as
1% of the annotations. However, beyond this point (e.g. 1 per thousand and
below) existing techniques fail catastrophically. To this end, we propose a new
weak supervision method to implicitly augment the total amount of available
supervision signals, by leveraging the semantic similarity between neighboring
points. Extensive experiments demonstrate that the proposed Semantic Query
Network (SQN) achieves state-of-the-art performance on six large-scale open
datasets under weak supervision schemes, while requiring only 1000x fewer
labeled points for training. The code is available at
https://github.com/QingyongHu/SQN.


Abstract:
  Elastic metamaterials are often designed for a single permanent function. We
explore the possibility of altering a material's function repeatedly through a
self-organization, "training" process, controlled by applied strains. We show
that the elastic function can be altered numerous times, though each new
trained task imprints a memory. This ultimately leads to material degradation
through the gradual reduction of the frequency gap in the density of states. We
also show that retraining adapts previously trained low energy modes to a new
function. As a result consecutive trained responses are realized similarly. We
show how retraining can be exploited to attain a response that would otherwise
be difficult.


Abstract:
  Spreadsheet formula prediction has been an important program synthesis
problem with many real-world applications. Previous works typically utilize
input-output examples as the specification for spreadsheet formula synthesis,
where each input-output pair simulates a separate row in the spreadsheet.
However, this formulation does not fully capture the rich context in real-world
spreadsheets. First, spreadsheet data entries are organized as tables, thus
rows and columns are not necessarily independent from each other. In addition,
many spreadsheet tables include headers, which provide high-level descriptions
of the cell data. However, previous synthesis approaches do not consider
headers as part of the specification. In this work, we present the first
approach for synthesizing spreadsheet formulas from tabular context, which
includes both headers and semi-structured tabular data. In particular, we
propose SpreadsheetCoder, a BERT-based model architecture to represent the
tabular context in both row-based and column-based formats. We train our model
on a large dataset of spreadsheets, and demonstrate that SpreadsheetCoder
achieves top-1 prediction accuracy of 42.51%, which is a considerable
improvement over baselines that do not employ rich tabular context. Compared to
the rule-based system, SpreadsheetCoder assists 82% more users in composing
formulas on Google Sheets.


Abstract:
  We investigate the Poincar\'e approach to computing 3d gravity partition
functions dual to Rational CFT. For a single genus-1 boundary, we show that for
certain infinite sets of levels, the SU(2)$_k$ WZW models provide unitary
examples for which the Poincare series is a positive linear combination of two
modular-invariant partition functions. This supports the interpretation that
the bulk gravity theory (a topological Chern-Simons theory in this case) is
dual to an average of distinct CFT's sharing the same Kac-Moody algebra. We
compute the weights of this average for all seed primaries and all relevant
values of k. We then study other WZW models, notably SU($N$)$_1$ and SU(3)$_k$,
and find that each class presents rather different features. Finally we
consider multiple genus-1 boundaries, where we find a class of seed functions
for the Poincar\'e sum that reproduces both disconnected and connected
contributions -- the latter corresponding to analogues of 3-manifold
"wormholes" -- such that the expected average is correctly reproduced.


Abstract:
  We study in detail the evaluation of the gauge invariant overlap for analytic
solutions constructed out of elements in the $KBc$ algebra in open string field
theory. We compute this gauge invariant observable using analytical and
numerical techniques based on the sliver frame $\mathcal{L}_0$ and traditional
Virasoro $L_0$ level expansions of the solutions.


Abstract:
  Three different elements, Silicon, Selenium, and Tellurium, are ion-implanted
in Gallium arsenide to form a conducting layer that serves as a back-gate to a
molecular beam epitaxy (MBE) overgrown two-dimensional electron gas (2DEG).
While the heavy ion Tellurium creates too many damages in the gallium arsenide
to form a conducting layer, both Silicon and Selenium show promising results
combined with MBE-grown high-quality 2DEGs. Similar 2DEG mobility compared to
non-implanted reference samples is achieved for both Silicon and Selenium
implanted structures. Individual contacts to the back-gate are challenging.
However, Silicon implanted structures, annealed before the MBE growth, result
in a function back-gate, and the electron density of the 2DEG is tuned via the
back-gate.


Abstract:
  We report world record high data transmission over standard optical fiber
from a single optical source. We achieve a line rate of 44.2 Terabits per
second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral
efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb
called soliton crystals that exhibit robust operation and stable generation as
well as a high intrinsic efficiency that, together with an extremely low
spacing of 48.9 GHz enables a very high coherent data modulation format of 64
QAM. We achieve error free transmission across 75 km of standard optical fiber
in the lab and over a field trial with a metropolitan optical fiber network.
This work demonstrates the ability of optical micro-combs to exceed other
approaches in performance for the most demanding practical optical
communications applications.


Abstract:
  Distributed systems become more and more important to our life. Especially in
areas like Smart Home and the Internet of Things (IoT) reliable low-power
sensor networks become increasingly important. For ensuring this there are a
lot of trust metrics. In this paper we compare a model of a distributed
low-power sensor network including one root node and the corresponding Simple
Trust Metric to the requirements from "Representation of Trust and Reputation
in Self-Managed Computing Systems" [1], the Weighted Trust Metric and the
Weighted Simple Exponential Smoothing


Abstract:
  Interactions with either environments or expert policies during training are
needed for most of the current imitation learning (IL) algorithms. For IL
problems with no interactions, a typical approach is Behavior Cloning (BC).
However, BC-like methods tend to be affected by distribution shift. To mitigate
this problem, we come up with a Robust Model-Based Imitation Learning (RMBIL)
framework that casts imitation learning as an end-to-end differentiable
nonlinear closed-loop tracking problem. RMBIL applies Neural ODE to learn a
precise multi-step dynamics and a robust tracking controller via Nonlinear
Dynamics Inversion (NDI) algorithm. Then, the learned NDI controller will be
combined with a trajectory generator, a conditional VAE, to imitate an expert's
behavior. Theoretical derivation shows that the controller network can
approximate an NDI when minimizing the training loss of Neural ODE. Experiments
on Mujoco tasks also demonstrate that RMBIL is competitive to the
state-of-the-art generative adversarial method (GAIL) and achieves at least 30%
performance gain over BC in uneven surfaces.


Abstract:
  The mean field theory is revisited in the classical and quantum mechanical
limits. Taking into account the boundary conditions at the phase transition and
the third law of the thermodynamics the physical properties of the ordered and
disordered phases were reported. The equation for the order parameter predicts
the occurrence of a saturation of $\Psi^2$ = 1 near $\Theta_S$, the temperature
below the quantum mechanical ground state is reached. The theoretical
predictions are also compared with high resolution thermal expansion data of
SrTiO$_{\text{3}}$ monocrystalline samples and other some previous results. An
excellent agreement has been found suggesting a universal behavior of the
theoretical model to describe continuous structural phase transitions.


Abstract:
  This paper deals with the dynamic factor analysis problem for an ARMA
process. To robustly estimate the number of factors, we construct a confidence
region centered in a finite sample estimate of the underlying model which
contains the true model with a prescribed probability. In this confidence
region, the problem, formulated as a rank minimization of a suitable spectral
density, is efficiently approximated via a trace norm convex relaxation. The
latter is addressed by resorting to the Lagrange duality theory, which allows
to prove the existence of solutions. Finally, a numerical algorithm to solve
the dual problem is presented. The effectiveness of the proposed estimator is
assessed through simulation studies both with synthetic and real data.


Abstract:
  The shapes of galaxy N-point correlation functions can be used as standard
rulers to constrain the distance-redshift relationship and thence the expansion
rate of the Universe. The cosmological density fields traced by late-time
galaxy formation are initially nearly Gaussian, and hence all the cosmological
information can be extracted from their 2-Point Correlation Function (2PCF) or
its Fourier-space analog the power spectrum. Subsequent nonlinear evolution
under gravity, as well as halo and then galaxy formation, generate higher-order
correlation functions. Since the mapping of the initial to the final density
field is, on large scales, invertible, it is often claimed that the information
content of the initial field's power spectrum is equal to that of all the
higher-order functions of the final, nonlinear field. This claim implies that
reconstruction of the initial density field from the nonlinear field renders
analysis of higher-order correlation functions of the latter superfluous. We
here show that this claim is false when the N-point functions are used as
standard rulers. Constraints available from joint analysis of the galaxy power
spectrum and bispectrum (Fourier-space analog of the 3-Point Correlation
Function) can, in some cases, exceed those offered by the initial power
spectrum even when the reconstruction is perfect. We provide a mathematical
justification for this claim and also demonstrate it using a large suite of
N-body simulations. In particular, we show that for the z = 0 real-space matter
field in the limit of vanishing shot noise, taking modes up to k_max = 0.2
h/Mpc, using the bispectrum alone offers a factor of two reduction in the
variance on the cosmic distance scale relative to that available from the power
spectrum.


Abstract:
  Lithium-ion batteries are ubiquitous in modern day applications ranging from
portable electronics to electric vehicles. Irrespective of the application,
reliable real-time estimation of battery state of health (SOH) by on-board
computers is crucial to the safe operation of the battery, ultimately
safeguarding asset integrity. In this paper, we design and evaluate a machine
learning pipeline for estimation of battery capacity fade - a metric of battery
health - on 179 cells cycled under various conditions. The pipeline estimates
battery SOH with an associated confidence interval by using two parametric and
two non-parametric algorithms. Using segments of charge voltage and current
curves, the pipeline engineers 30 features, performs automatic feature
selection and calibrates the algorithms. When deployed on cells operated under
the fast-charging protocol, the best model achieves a root mean squared percent
error of 0.45\%. This work provides insights into the design of scalable
data-driven models for battery SOH estimation, emphasising the value of
confidence bounds around the prediction. The pipeline methodology combines
experimental data with machine learning modelling and can be generalized to
other critical components that require real-time estimation of SOH.


Abstract:
  The memory wall bottleneck is a key challenge across many data-intensive
applications. Multi-level FeFET-based embedded non-volatile memories are a
promising solution for denser and more energy-efficient on-chip memory.
However, reliable multi-level cell storage requires careful optimizations to
minimize the design overhead costs. In this work, we investigate the interplay
between FeFET device characteristics, programming schemes, and memory array
architecture, and explore different design choices to optimize performance,
energy, area, and accuracy metrics for critical data-intensive workloads. From
our cross-stack design exploration, we find that we can store DNN weights and
social network graphs at a density of over 8MB/mm^2 and sub-2ns read access
latency without loss in application accuracy.


Abstract:
  In this research, we present a comprehensive, longitudinal empirical summary
of the R package ecosystem, including not just CRAN, but also Bioconductor and
GitHub. We analyze more than 25,000 packages, 150,000 releases, and 15 million
files across two decades, providing comprehensive counts and trends for common
metrics across packages, releases, authors, licenses, and other important
metadata. We find that the historical growth of the ecosystem has been robust
under all measures, with a compound annual growth rate of 29% for active
packages, 28% for new releases, and 26% for active maintainers. As with many
similar social systems, we find a number of highly right-skewed distributions
with practical implications, including the distribution of releases per
package, packages and releases per author or maintainer, package and maintainer
dependency in-degree, and size per package and release. For example, the top
five packages are imported by nearly 25% of all packages, and the top ten
maintainers support packages that are imported by over half of all packages. We
also highlight the dynamic nature of the ecosystem, recording both dramatic
acceleration and notable deceleration in the growth of R. From a licensing
perspective, we find a notable majority of packages are distributed under
copyleft licensing or omit licensing information entirely. The data, methods,
and calculations herein provide an anchor for public discourse and industry
decisions related to R and CRAN, serving as a foundation for future research on
the R software ecosystem and "data science" more broadly.


Abstract:
  Matched Field Processing (MFP) locates the underwater sources by matching the
received data with the replica vectors, which could be regarded as a
generalized beamformer. In this paper, the MFP method is combined with a
recently developed framework -- Graph Signal Processing (GSP) method. Following
the paradigm of GSP, a spatial adjacency matrix is constructed for the
arbitrary distributed sensors based on the Green's function, then the source is
located by utilizing the graph Fourier transform. The simulation results
illustrate that the Graph-based MFP outperforms the the conventional MFP
processors -- the Bartlett processor and the Minimum Variance processor -- for
its good accuracy and robustness.


Abstract:
  We explore whether assumptions about dust grain shape affect resulting
estimates of the composition and grain size distribution of the AU Microscopii
(AU Mic) debris disk from scattered light data collected by Lomax et al.
(2018). The near edge-on orientation of the AU Mic debris disk makes it ideal
for studying the effect of the scattering phase function (SPF) on the measured
flux ratios as a function of wavelength and projected distance. Previous
efforts to model the AU Mic debris disk have invoked a variety of dust grain
compositions and explored the effect of porosity, but did not undertake a
systematic effort to explore a full range of size distributions and
compositions to understand possible degeneracies in fitting the data. The
degree to which modelling dust grains with more realistic shapes compounds
these degeneracies has also not previously been explored. We find differences
in the grain properties retrieved depending on the grain shape model used. We
also present here our calculations of porous grains of size parameters x = 0.1
to 48 and complex refractive indices (m = n+ik) ranging from n = 1.1 to 2.43
and k = 0 to 1.0, covering multiple compositions at visible and near infrared
wavelengths such as ice, silicates, amorphous carbon, and tholins.


Abstract:
  Existing table question answering datasets contain abundant factual questions
that primarily evaluate the query and schema comprehension capability of a
system, but they fail to include questions that require complex reasoning and
integration of information due to the constraint of the associated short-form
answers. To address these issues and to demonstrate the full challenge of table
question answering, we introduce FeTaQA, a new dataset with 10K Wikipedia-based
{table, question, free-form answer, supporting table cells} pairs. FeTaQA
yields a more challenging table question answering setting because it requires
generating free-form text answers after retrieval, inference, and integration
of multiple discontinuous facts from a structured knowledge source. Unlike
datasets of generative QA over text in which answers are prevalent with copies
of short text spans from the source, answers in our dataset are human-generated
explanations involving entities and their high-level relations. We provide two
benchmark methods for the proposed task: a pipeline method based on
semantic-parsing-based QA systems and an end-to-end method based on large
pretrained text generation models, and show that FeTaQA poses a challenge for
both methods.


Abstract:
  In the context of Wilsonian Renormalization, renormalization group (RG) flows
are a set of differential equations that defines how the coupling constants of
a theory depend on an energy scale. These equations closely resemble
thermodynamical equations and how thermodynamical systems are related to
temperature. In this sense, it is natural to look for structures in the flows
that show a thermodynamics-like behaviour. The mathematical theory to study
these equations is called Dynamical Systems, and applications of that have been
used to study RG flows. For example, the classical Zamolodchikov's C-Theorem
and its higher-dimensional counterparts, that show that there is a
monotonically decreasing function along the flow and it is a property that
resembles the second-law of thermodynamics, is related to the Lyapunov function
in the context of Dynamical Systems. It can be used to rule out exotic
asymptotic behaviours like periodic flows (also known as limit cycles). We also
study bifurcation theory and index theories, which have been proposed to be
useful in the study of RG flows, the former can be used to explain couplings
crossing through marginality and the latter to extract global information about
the space the flows lives in. In this dissertation, we also look for
applications in holographic RG flows and we try to see if the structural
behaviours in holographic theories are the same as the ones in the dual field
theory side.


Abstract:
  We study the dynamics of black holes in Einstein-scalar-Gauss-Bonnet theories
that exhibit spontaneous black hole scalarization using recently introduced
methods for solving the full, non-perturbative equations of motion. For one
sign of the coupling parameter, non-spinning vacuum black holes are unstable to
developing scalar hair, while for the other, instability only sets in for black
holes with sufficiently large spin. We study scalarization in both cases,
demonstrating that there is a range of parameter space where the theory
maintains hyperbolic evolution and for which the instability saturates in a
scalarized black hole that is stable without symmetry assumptions. However,
this parameter space range is significantly smaller than the range for which
stationary scalarized black hole solutions exist. We show how different choices
for the subleading behavior of the Gauss-Bonnet coupling affect the dynamics of
the instability and the final state, or lack thereof. Finally, we present
mergers of binary black holes and demonstrate the imprint of the scalar hair in
the gravitational radiation.


Abstract:
  We study here the conditions to perform the distribution of a pure state on a
quantum network using quantum operations which can succeed with a non-zero
probability, the Stochastic Local Operation and Classical Communication (SLOCC)
operations.
  In their pioneering 2010 work, Kobayashi et al. showed how to convert any
classical network coding protocol into a quantum network coding protocol.
However, they left open whether the existence of a quantum network coding
protocol implied the existence of a classical one. Motivated by this question,
we characterize the set of distribution tasks achievable with non zero
probability for both classical and quantum networks. We develop a formalism
which encompasses both types of distribution protocols by reducing the solving
of a distribution task to the factorization of a tensor with complex
coefficients or real positive ones. Using this formalism, we examine the
equivalences and differences between both types of distribution protocols
exhibiting several elementary and fundamental relations between them as well as
concrete examples of both convergence and divergence. We answer by the negative
to the issue previously left open: some tasks are achievable in the quantum
setting, but not in the classical one. We believe this formalism to be a useful
tool for studying the extent of quantum network ability to perform multipartite
distribution tasks.


Abstract:
  We reexpress the superfluid density of a disordered superconductor obtained
by two of us earlier [Phys. Rev. B 102, 024514 (2020)] in a new highly
convergent form, and use the results to make an extensive and successful
comparison with experiment in the dirty limit for all temperatures. We point
out that there is a regime (conventional superconductor with low, but
increasing disorder) where theoretical predictions need to be confronted with
accurate experiment.


Abstract:
  A large number of consensus algorithms have been proposed. However, the
requirement of strict consistency limits their wide adoption, especially in
high-performance required systems. In this paper, we propose a weak consensus
algorithm that only maintains the consistency of relative positions between the
messages. We apply this consensus algorithm to construct a high-performance
blockchain system, called \textit{Sphinx}. We implement the system with 32k+
lines of code including all components like consensus/P2P/ledger/etc. The
evaluations show that Sphinx can reach a peak throughput of 43k TPS (with 8
full nodes), which is significantly faster than current blockchain systems such
as Ethereum given the same experimental environment. To the best of our
knowledge, we present the first weak consensus algorithm with a fully
implemented blockchain system.


Abstract:
  Hate speech on social media is a growing concern, and automated methods have
so far been sub-par at reliably detecting it. A major challenge lies in the
potentially evasive nature of hate speech due to the ambiguity and fast
evolution of natural language. To tackle this, we introduce a vectorisation
based on a crowd-sourced and continuously updated dictionary of hate words and
propose fusing this approach with standard word embedding in order to improve
the classification performance of a CNN model. To train and test our model we
use a merge of two established datasets (110,748 tweets in total). By adding
the dictionary-enhanced input, we are able to increase the CNN model's
predictive power and increase the F1 macro score by seven percentage points.


Abstract:
  This work focuses on the numerical solution of hyperbolic conservations laws
(possibly endowed with a source term) using the Active Flux method. This method
is an extension of the finite volume method. Instead of solving a Riemann
Problem, the Active Flux method uses actively evolved point values along the
cell boundary in order to compute the numerical flux. Early applications of the
method were linear equations with an available exact solution operator, and
Active Flux was shown to be structure preserving in such cases. For nonlinear
PDEs or balance laws, exact evolution operators generally are unavailable.
Here, strategies are shown how sufficiently accurate approximate evolution
operators can be designed which allow to make Active Flux structure preserving
/ well-balanced for nonlinear problems.


Abstract:
  For the minimization of state-based systems (i.e. the reduction of the number
of states while retaining the system's semantics), there are two obvious
aspects: removing unnecessary states of the system and merging redundant states
in the system. In the present article, we relate the two aspects on coalgebras
by defining an abstract notion of minimality. The abstract notion minimality
and minimization live in a general category with a factorization system. We
will find criteria on the category that ensure uniqueness, existence, and
functoriality of the minimization aspects. The proofs of these results
instantiate to those for reachability and observability minimization in the
standard coalgebra literature. Finally, we will see how the two aspects of
minimization interact and under which criteria they can be sequenced in any
order, like in automata minimization.


Abstract:
  A problem that is frequently encountered in a variety of mathematical
contexts, is to find the common invariant subspaces of a single, or set of
matrices. In a recent paper, a new method was proposed that gives an answer to
that problem. In the present article conditions are given for the more general
problem of putting set of matrices into block-diagonal form simultaneously. In
addition, two algorithms for simultaneous block triangularization and block
diagonalization of sets of matrices are presented and are applied to several
concrete examples using the symbolic manipulation system Maple.


Abstract:
  This paper presents our solution for ICDAR 2021 competition on scientific
literature parsing taskB: table recognition to HTML. In our method, we divide
the table content recognition task into foursub-tasks: table structure
recognition, text line detection, text line recognition, and box assignment.Our
table structure recognition algorithm is customized based on MASTER [1], a
robust image textrecognition algorithm. PSENet [2] is used to detect each text
line in the table image. For text linerecognition, our model is also built on
MASTER. Finally, in the box assignment phase, we associatedthe text boxes
detected by PSENet with the structure item reconstructed by table structure
prediction,and fill the recognized content of the text line into the
corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115
validation samples in the development phase, and a 96.32%TEDS score on 9,064
samples in the final evaluation phase.


Abstract:
  Recently it was suggested that certain perturbations of integrable spin
chains lead to a weak breaking of integrability in the sense that integrability
is preserved at the first order in the coupling. Here we examine this claim
using level spacing distribution. We find that the volume dependent crossover
between integrable and chaotic level spacing statistics which marks the onset
of quantum chaotic behaviour, is markedly different for weak vs. strong
breaking of integrability. In particular, for the gapless case we find that the
crossover coupling as a function of the volume $L$ scales with a $1/L^2$ law
for weak breaking as opposed to the $1/L^3$ law previously found for the strong
case.


Abstract:
  The objective of person re-identification (re-ID) is to retrieve a person's
images from an image gallery, given a single instance of the person of
interest. Despite several advancements, learning discriminative
identity-sensitive and viewpoint invariant features for robust Person
Re-identification is a major challenge owing to the large pose variation of
humans. This paper proposes a re-ID pipeline that utilizes the image generation
capability of Generative Adversarial Networks combined with pose clustering and
feature fusion to achieve pose invariant feature learning. The objective is to
model a given person under different viewpoints and large pose changes and
extract the most discriminative features from all the appearances. The pose
transformational GAN (pt-GAN) module is trained to generate a person's image in
any given pose. In order to identify the most significant poses for
discriminative feature extraction, a Pose Clustering module is proposed. The
given instance of the person is modelled in varying poses and these features
are effectively combined through the Feature Fusion Network. The final re-ID
model consisting of these 3 sub-blocks, alleviates the pose dependence in
person re-ID. Also, The proposed model is robust to occlusion, scale, rotation
and illumination, providing a framework for viewpoint invariant feature
learning. The proposed method outperforms the state-of-the-art GAN based models
in 4 benchmark datasets. It also surpasses the state-of-the-art models that
report higher re-ID accuracy in terms of improvement over baseline.


Abstract:
  In this paper, we investigate qualitative integrals (generalizations of
Sugeno integral) acting on recently introduced Dragonfly algebras. These
algebras are designed for applications in data analysis (based on fuzzy
relational compositions) when some data are unknown (e.g., missing). Unknown
data are represented by the additional dummy value. Definitions of operations
on Dragonfly algebras follow lower estimation strategy, i.e., results of
operations can be interpreted as lower estimations of results obtained when
unknown values are replaced by known ones. We define qualitative integrals on
Dragonfly algebras and show their monotonicity. We also prove results
characterizing when these integrals return known (or unknown) values. We also
present an illustrative example and hint directions of further research.


Abstract:
  The laborious process of labeling data often bottlenecks projects that aim to
leverage the power of supervised machine learning. Active Learning (AL) has
been established as a technique to ameliorate this condition through an
iterative framework that queries a human annotator for labels of instances with
the most uncertain class assignment. Via this mechanism, AL produces a binary
classifier trained on less labeled data but with little, if any, loss in
predictive performance. Despite its advantages, AL can have difficulty with
class-imbalanced datasets and results in an inefficient labeling process. To
address these drawbacks, we investigate our unsupervised instance selection
(UNISEL) technique followed by a Random Forest (RF) classifier on 10 outlier
detection datasets under low-label conditions. These results are compared to AL
performed on the same datasets. Further, we investigate the combination of
UNISEL and AL. Results indicate that UNISEL followed by an RF performs
comparably to AL with an RF and that the combination of UNISEL and AL
demonstrates superior performance. The practical implications of these findings
in terms of time savings and generalizability afforded by UNISEL are discussed.


Abstract:
  We construct a class of knot solutions of the gravitoelectromagnetic (GEM)
equations in vacuum in the linearized gravity approximation by analogy with the
Ra\~{n}ada-Hopf fields. For these solutions, the dual metric tensors of the
bi-metric geometry of the gravitational vacuum with knot perturbations are
given and the geodesic equation as a function of two complex parameters of the
GEM knots are calculated. Finally, the Landau--Lifshitz pseudo-tensor and a
scalar invariant of the GEM knots are computed.


Abstract:
  Sound event localization and detection consists of two subtasks which are
sound event detection and direction-of-arrival estimation. While sound event
detection mainly relies on time-frequency patterns to distinguish different
sound classes, direction-of-arrival estimation uses magnitude or phase
differences between microphones to estimate source directions. Therefore, it is
often difficult to jointly train these two subtasks simultaneously. We propose
a novel feature called spatial cue-augmented log-spectrogram (SALSA) with exact
time-frequency mapping between the signal power and the source
direction-of-arrival. The feature includes multichannel log-spectrograms
stacked along with the estimated direct-to-reverberant ratio and a normalized
version of the principal eigenvector of the spatial covariance matrix at each
time-frequency bin on the spectrograms. Experimental results on the DCASE 2021
dataset for sound event localization and detection with directional
interference showed that the deep learning-based models trained on this new
feature outperformed the DCASE challenge baseline by a large margin. We
combined several models with slightly different architectures that were trained
on the new feature to further improve the system performances for the DCASE
sound event localization and detection challenge.


Abstract:
  Image registration as an important basis in signal processing task often
encounter the problem of stability and efficiency. Non-learning registration
approaches rely on the optimization of the similarity metrics between the fix
and moving images. Yet, those approaches are usually costly in both time and
space complexity. The problem can be worse when the size of the image is large
or the deformations between the images are severe. Recently, deep learning, or
precisely saying, the convolutional neural network (CNN) based image
registration methods have been widely investigated in the research community
and show promising effectiveness to overcome the weakness of non-learning based
methods. To explore the advanced learning approaches in image registration
problem for solving practical issues, we present in this paper a method of
introducing attention mechanism in deformable image registration problem. The
proposed approach is based on learning the deformation field with a Transformer
framework (AiR) that does not rely on the CNN but can be efficiently trained on
GPGPU devices also. In a more vivid interpretation: we treat the image
registration problem as the same as a language translation task and introducing
a Transformer to tackle the problem. Our method learns an unsupervised
generated deformation map and is tested on two benchmark datasets. The source
code of the AiR will be released at Gitlab.


Abstract:
  Random feature methods have been successful in various machine learning
tasks, are easy to compute, and come with theoretical accuracy bounds. They
serve as an alternative approach to standard neural networks since they can
represent similar function spaces without a costly training phase. However, for
accuracy, random feature methods require more measurements than trainable
parameters, limiting their use for data-scarce applications or problems in
scientific machine learning. This paper introduces the sparse random feature
expansion to obtain parsimonious random feature models. Specifically, we
leverage ideas from compressive sensing to generate random feature expansions
with theoretical guarantees even in the data-scarce setting. In particular, we
provide generalization bounds for functions in a certain class (that is dense
in a reproducing kernel Hilbert space) depending on the number of samples and
the distribution of features. The generalization bounds improve with additional
structural conditions, such as coordinate sparsity, compact clusters of the
spectrum, or rapid spectral decay. In particular, by introducing sparse
features, i.e. features with random sparse weights, we provide improved bounds
for low order functions. We show that the sparse random feature expansions
outperforms shallow networks in several scientific machine learning tasks.


Abstract:
  A framework for the generation of synthetic time-series transmission-level
load data is presented. Conditional generative adversarial networks are used to
learn the patterns of a real dataset of hourly-sampled week-long load profiles
and generate unique synthetic profiles on demand, based on the season and type
of load required. Extensive testing of the generative model is performed to
verify that the synthetic data fully captures the characteristics of real loads
and that it can be used for downstream power system and/or machine learning
applications.


Abstract:
  The weights of a deep neural network model are optimized in conjunction with
the governing flow equations to provide a model for sub-grid-scale stresses in
a temporally developing plane turbulent jet at Reynolds number $Re_0=6\,000$.
The objective function for training is first based on the instantaneous
filtered velocity fields from a corresponding direct numerical simulation, and
the training is by a stochastic gradient descent method, which uses the adjoint
Navier--Stokes equations to provide the end-to-end sensitivities of the model
weights to the velocity fields. In-sample and out-of-sample testing on multiple
dual-jet configurations show that its required mesh density in each coordinate
direction for prediction of mean flow, Reynolds stresses, and spectra is half
that needed by the dynamic Smagorinsky model for comparable accuracy. The same
neural-network model trained directly to match filtered sub-grid-scale stresses
-- without the constraint of being embedded within the flow equations during
the training -- fails to provide a qualitatively correct prediction. The
coupled formulation is generalized to train based only on mean-flow and
Reynolds stresses, which are more readily available in experiments. The
mean-flow training provides a robust model, which is important, though a
somewhat less accurate prediction for the same coarse meshes, as might be
anticipated due to the reduced information available for training in this case.
The anticipated advantage of the formulation is that the inclusion of resolved
physics in the training increases its capacity to extrapolate. This is assessed
for the case of passive scalar transport, for which it outperforms established
models due to improved mixing predictions.


Abstract:
  Track radii Re induced by swift heavy ions are studied. The experimental
track data are analyzed in Gd2Ti2O7 pyrochlore in the range Se=6-54 keV/nm (Se
- electronic stopping power) including irradiations by low and high velocity
ions. The Analytical Thermal Spike Model (ATSM) is applied whose main features
are reviewed. The track data of Gd2Ti2O7 exhibit scaling features which mean
simple quantitative relationships with track sizes of other insulators
controlled only by a single materials parameter (MP), the melting temperature
Tm. The Re2-Se track evolution curve is described in the whole range of Se
without the application of any individual fitting parameter and MPs apart Tm;
the threshold for track formation Set=6.1/14.4 keV/nm for E<2 MeV/nucleon and
E>8 MeV/nucleon, respectively. The unique role of Tm is a highly important
limitation for the mechanism of track formation. The practical consequences of
the results are discussed with respect of the estimation of irradiation
parameters


Abstract:
  What role does culture play in determining institutions in a country? This
paper argues that the establishment of institutions is a process originating
predominantly in a nation's culture and tries to discern the role of a cultural
background in the governance of countries. We use the six Hofstede's Cultural
Dimensions and the six Worldwide Governance Indicators to test the strength of
the relationship on 94 countries between 1996 and 2019. We find that the
strongest cultural characteristics are Power Distance with negative effect on
governance and Long-Term Orientation with positive effect. We also determine
how well countries transform their cultural characteristics into institutions
using stochastic frontier analysis.


Abstract:
  We introduce the notion of adaptive synchronisation for pushdown automata, in
which there is an external observer who has no knowledge about the current
state of the pushdown automaton, but can observe the contents of the stack. The
observer would then like to decide if it is possible to bring the automaton
from any state into some predetermined state by giving inputs to it in an
\emph{adaptive} manner, i.e., the next input letter to be given can depend on
how the contents of the stack changed after the current input letter. We show
that for non-deterministic pushdown automata, this problem is
2-EXPTIME-complete and for deterministic pushdown automata, we show
EXPTIME-completeness.
  To prove the lower bounds, we first introduce (different variants of)
subset-synchronisation and show that these problems are polynomial-time
equivalent with the adaptive synchronisation problem. We then prove hardness
results for the subset-synchronisation problems. For proving the upper bounds,
we consider the problem of deciding if a given alternating pushdown system has
an accepting run with at most $k$ leaves and we provide an $n^{O(k^2)}$ time
algorithm for this problem.


Abstract:
  We provide the largest and most homogeneous sample of $\alpha$-element (Mg,
Ca, Ti) and iron abundances for field RR Lyrae (RRLs, 162 variables) by using
high-resolution spectra. The current measurements were complemented with
similar abundances available in the literature for 46 field RRLs brought to our
metallicity scale. We ended up with a sample of old (t$\ge$ 10 Gyr), low-mass
stellar tracers (208 RRLs: 169 fundamental, 38 first overtone, 1 mixed mode)
covering three dex in iron abundance (-3.00$\le$[Fe/H]$\le$0.24). We found that
field RRLs are $\sim$0.3 dex more $\alpha$-poor than typical Halo tracers in
the metal-rich regime, ([Fe/H]$\ge$-1.2) while in the metal-poor regime
([Fe/H]$\le$-2.2) they seem to be on average $\sim$0.1 dex more
$\alpha$-enhanced. This is the first time that the depletion in
$\alpha$-elements for solar iron abundances is detected on the basis of a
large, homogeneous and coeval sample of old stellar tracers. Interestingly, we
also detected a close similarity in the [$\alpha$/Fe] trend between
$\alpha$-poor, metal-rich RRLs and red giants (RGs) in the Sagittarius dwarf
galaxy as well as between $\alpha$-enhanced, metal-poor RRLs and RGs in ultra
faint dwarf galaxies. These results are supported by similar elemental
abundances for 46 field Horizontal Branch (HB) stars. These stars share with
RRLs the same evolutionary phase and the same progenitors. This evidence
further supports the key role that old stellar tracers play in constraining the
early chemical enrichment of the Halo and, in particular, in investigating the
impact that dwarf galaxies have had in the mass assembly of the Galaxy.


Abstract:
  Spatio-temporal action detection is an important and challenging problem in
video understanding. The existing action detection benchmarks are limited in
aspects of small numbers of instances in a trimmed video or low-level atomic
actions. This paper aims to present a new multi-person dataset of
spatio-temporal localized sports actions, coined as MultiSports. We first
analyze the important ingredients of constructing a realistic and challenging
dataset for spatio-temporal action detection by proposing three criteria: (1)
multi-person scenes and motion dependent identification, (2) with well-defined
boundaries, (3) relatively fine-grained classes of high complexity. Based on
these guide-lines, we build the dataset of MultiSports v1.0 by selecting 4
sports classes, collecting 3200 video clips, and annotating 37701 action
instances with 902k bounding boxes. Our datasets are characterized with
important properties of high diversity, dense annotation, and high quality. Our
Multi-Sports, with its realistic setting and detailed annotations, exposes the
intrinsic challenges of spatio-temporal action detection. To benchmark this, we
adapt several baseline methods to our dataset and give an in-depth analysis on
the action detection results in our dataset. We hope our MultiSports can serve
as a standard benchmark for spatio-temporal action detection in the future. Our
dataset website is at https://deeperaction.github.io/multisports/.


Abstract:
  We use a 1030 nm laser with 7 ps pulse duration and average power up to 100 W
to ablate pyramid-shape subwavelength structures (SWS) on alumina and sapphire.
The SWS give an effective and cryogenically robust anti-reflection coating in
the millimeter-wave band. We demonstrate average ablation rate of up to 34
mm$^3$/min and 20 mm$^3$/min for structure heights of 900 $\mu$m and 750 $\mu$m
on alumina and sapphire, respectively. These rates are a factor of 34 and 9
higher than reported previously on similar structures. We propose a model that
relates structure height to cumulative laser fluence. The model depends on the
absorption length $\delta$, which is assumed to depend on peak fluence, and on
the threshold fluence $\phi_{th}$. Using a best-fit procedure we find an
average $\delta = 630$ nm and 650 nm, and $\phi_{th} = 2.0^{+0.5}_{-0.5}$
J/cm$^2$ and $2.3^{+0.1}_{-0.1}$ J/cm$^2$ for alumina and sapphire,
respectively, for peak fluence values between 30 and 70 J/cm$^{2}$. With the
best fit values, the model and data values for cumulative fluence agree to
within 10%. Given inputs for $\delta$ and $\phi_{th}$ the model is used to
predict average ablation rates as a function of SWS height and average laser
power.


Abstract:
  A co-polycondensation reaction is discussed analytically and by Monte-Carlo
simulations where two reactive units compete for reactions with an alternating
third reactive unit, whereby irreversible reactions replace bonds which are
able to undergo exchange reactions. The resulting number average molar mass,
$M_{\text{n}}$, exhibits only one distinct peak at the stoichiometric condition
of both competitors with the alternating partner. The weight average molar
mass, $M_{\text{w}}$, reaches an additional second peak at the stoichiometric
condition between the dominating competitor and the alternating partner. Both
peaks of $M_{\text{w}}$ surround a range of compositions where a rather high
and approximately constant $M_{\text{w}}$ is obtained. The degree of
polymerization of the dominating and alternating reaction partners is rather
insensitive towards composition fluctuations if the reaction mixture remains
within this composition window. This promotes high molecular weight species and
more homogeneous weight distributions at incomplete mixing conditions. An ideal
reference case (identical reaction rates for all reactions) is solved
analytically to describe these reactions. The position of the stable
composition window and the average molar masses inside this window can be tuned
by choosing appropriate precursor molecules, reaction mixtures or post-tuning
steps at later times.


Abstract:
  The training of Generative Adversarial Networks (GANs) requires a large
amount of data, stimulating the development of new augmentation methods to
alleviate the challenge. Oftentimes, these methods either fail to produce
enough new data or expand the dataset beyond the original manifold. In this
paper, we propose a new augmentation method that guarantees to keep the new
data within the original data manifold thanks to the optimal transport theory.
The proposed algorithm finds cliques in the nearest-neighbors graph and, at
each sampling iteration, randomly draws one clique to compute the Wasserstein
barycenter with random uniform weights. These barycenters then become the new
natural-looking elements that one could add to the dataset. We apply this
approach to the problem of landmarks detection and augment the available
annotation in both unpaired and in semi-supervised scenarios. Additionally, the
idea is validated on cardiac data for the task of medical segmentation. Our
approach reduces the overfitting and improves the quality metrics beyond the
original data outcome and beyond the result obtained with popular modern
augmentation methods.


Abstract:
  RGB-infrared person re-identification is a challenging task due to the
intra-class variations and cross-modality discrepancy. Existing works mainly
focus on learning modality-shared global representations by aligning image
styles or feature distributions across modalities, while local feature from
body part and relationships between person images are largely neglected. In
this paper, we propose a Dual-level (i.e., local and global) Feature Fusion
(DF^2) module by learning attention for discriminative feature from local to
global manner. In particular, the attention for a local feature is determined
locally, i.e., applying a learned transformation function on itself. Meanwhile,
to further mining the relationships between global features from person images,
we propose an Affinities Modeling (AM) module to obtain the optimal intra- and
inter-modality image matching. Specifically, AM employes intra-class
compactness and inter-class separability in the sample similarities as
supervised information to model the affinities between intra- and
inter-modality samples. Experimental results show that our proposed method
outperforms state-of-the-arts by large margins on two widely used
cross-modality re-ID datasets SYSU-MM01 and RegDB, respectively.


Abstract:
  The coded caching problem with secrecy constraint i.e., the users should not
be able to gain any information about the content of the files that they did
not demand, is known as the secretive coded caching problem. This was proposed
by Ravindrakumar et al. in the paper titled ``Private Coded Caching'' that
appeared in \emph{ IEEE Transactions on Information Forensics and Security},
2018 and is characterised by subpacketization levels growing exponentially with
the number of users. In the context of coded caching without secrecy, coded
caching schemes at subexponential subpacketization levels are feasible by
representing the caching system in the form of a Placement Delivery Array (PDA)
and designing placement and delivery policies from it. Motivated by this, we
propose a secretive coded caching scheme with low subpacketization using PDA,
for users with dedicated caches in the centralized setting. When our scheme is
applied to a special class of PDA known as MN PDA, the scheme proposed by
Ravindrakumar et al. is recovered.


Abstract:
  Andromeda XXI (And XXI) has been proposed as a dwarf spheroidal galaxy with a
central dark matter density that is lower than expected in the Standard
$\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology. In this work, we present
dynamical observations for 77 member stars in this system, more than doubling
previous studies to determine whether this galaxy is truly a low density
outlier. We measure a systemic velocity of $v_r=-363.4\pm1.0\,{\rm kms}^{-1}$
and a velocity dispersion of $\sigma_v=6.1^{+1.0}_{-0.9}\,{\rm kms}^{-1}$,
consistent with previous work and within $1\sigma$ of predictions made within
the modified Newtonian dynamics framework. We also measure the metallicity of
our member stars from their spectra, finding a mean value of ${\rm
[Fe/H]}=-1.7\pm0.1$~dex. We model the dark matter density profile of And~XXI
using an improved version of \GravSphere, finding a central density of
$\rho_{\rm DM}({\rm 150 pc})=2.7_{-1.7}^{+2.7} \times 10^7 \,{\rm
M_\odot\,kpc^{-3}}$ at 68\% confidence, and a density at two half light radii
of $\rho_{\rm DM}({\rm 1.75 kpc})=0.9_{-0.2}^{+0.3} \times 10^5 \,{\rm
M_\odot\,kpc^{-3}}$ at 68\% confidence. These are both a factor ${\sim}3-5$
lower than the densities expected from abundance matching in $\Lambda$CDM. We
show that this cannot be explained by `dark matter heating' since And~XXI had
too little star formation to significantly lower its inner dark matter density,
while dark matter heating only acts on the profile inside the half light
radius. However, And~XXI's low density can be accommodated within $\Lambda$CDM
if it experienced extreme tidal stripping (losing $>95\%$ of its mass), or if
it inhabits a low concentration halo on a plunging orbit that experienced
repeated tidal shocks.


Abstract:
  A new non-ergodic ground-motion model (GMM) for effective amplitude spectral
($EAS$) values for California is presented in this study. $EAS$, which is
defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier
amplitude spectrum of the two horizontal components of an acceleration time
history. The main motivation for developing a non-ergodic $EAS$ GMM, rather
than a spectral acceleration GMM, is that the scaling of $EAS$ does not depend
on spectral shape, and therefore, the more frequent small magnitude events can
be used in the estimation of the non-ergodic terms.
  The model is developed using the California subset of the NGAWest2 dataset
Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic $EAS$
GMM was used as backbone to constrain the average source, path, and site
scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model:
the non-ergodic source and site terms are modeled as spatially varying
coefficients following the approach of Landwehr et al. (2016), and the
non-ergodic path effects are captured by the cell-specific anelastic
attenuation attenuation following the approach of Dawood and Rodriguez-Marek
(2013). Close to stations and past events, the mean values of the non-ergodic
terms deviate from zero to capture the systematic effects and their epistemic
uncertainty is small. In areas with sparse data, the epistemic uncertainty of
the non-ergodic terms is large, as the systematic effects cannot be determined.
  The non-ergodic total aleatory standard deviation is approximately $30$ to
$40\%$ smaller than the total aleatory standard deviation of BA18. This
reduction in the aleatory variability has a significant impact on hazard
calculations at large return periods. The epistemic uncertainty of the ground
motion predictions is small in areas close to stations and past events.


Abstract:
  Purpose. We present an approach for forecasting mental health conditions and
emotions of a given population during the COVID-19 pandemic in Argentina based
on language expressions used in social media. This approach permits
anticipating high prevalence periods in short- to medium-term time horizons.
Design. Mental health conditions and emotions are captured via markers, which
link social media contents with lexicons. First, we build descriptive timelines
for decision makers to monitor the evolution of markers, and their correlation
with crisis events. Second, we model the timelines as time series, and support
their forecasting, which in turn serve to identify high prevalence points for
the estimated markers. Findings. Results showed that different time series
forecasting strategies offer different capabilities. In the best scenario, the
emergence of high prevalence periods of emotions and mental health disorders
can be satisfactorily predicted with a neural network strategy, even when
limited data is available in early stages of a crisis (e.g., 7 days).
Originality. Although there have been efforts in the literature to predict
mental states of individuals, the analysis of mental health at the collective
level has received scarce attention. We take a step forward by proposing a
forecasting approach for analyzing the mental health of a given population (or
group of individuals) at a larger scale. Practical implications. We believe
that this work contributes to a better understanding of how psychological
processes related to crisis manifest in social media, being a valuable asset
for the design, implementation and monitoring of health prevention and
communication policies.


Abstract:
  In this work we introduce a new measure of dispersion of the eigenvalues of a
Hermitian (self-adjoint) compact operator, that we call spectral spread. Then,
we obtain several inequalities for unitarily invariant norms involving the
spectral spread of self-adjoint compact operators, that are related with
Bhatia-Davis's and Corach-Porta-Recht's work on Arithmetic-Geometric mean
inequalities, Zhan's inequality for the difference of positive compact
operators, Tao's inequalities for anti-diagonal blocks of positive compact
operators and Kittaneh's commutator inequalities for positive operators.


Abstract:
  Accurate state and uncertainty estimation is imperative for mobile robots and
self driving vehicles to achieve safe navigation in pedestrian rich
environments. A critical component of state and uncertainty estimation for
robot navigation is to perform robustly under out-of-distribution noise.
Traditional methods of state estimation decouple perception and state
estimation making it difficult to operate on noisy, high dimensional data.
Here, we describe an approach that combines the expressiveness of deep neural
networks with principled approaches to uncertainty estimation found in
recursive filters. We particularly focus on techniques that provide better
robustness to out-of-distribution noise and demonstrate applicability of our
approach on two scenarios: a simple noisy pendulum state estimation problem and
real world pedestrian localization using the nuScenes dataset. We show that our
approach improves state and uncertainty estimation compared to baselines while
achieving approximately 3x improvement in computational efficiency.


Abstract:
  We show that the Adaptive Greedy algorithm of Golovin and Krause (2011)
achieves an approximation bound of $(\ln (Q/\eta)+1)$ for Stochastic Submodular
Cover: here $Q$ is the "goal value" and $\eta$ is the smallest non-zero
marginal increase in utility deliverable by an item. (For integer-valued
utility functions, we show a bound of $H(Q)$, where $H(Q)$ is the $Q^{th}$
Harmonic number.) Although this bound was claimed by Golovin and Krause in the
original version of their paper, the proof was later shown to be incorrect by
Nan and Saligrama (2017). The subsequent corrected proof of Golovin and Krause
(2017) gives a quadratic bound of $(\ln(Q/\eta) + 1)^2$. Other previous bounds
for the problem are $56(\ln(Q/\eta) + 1)$, implied by work of Im et al. (2016)
on a related problem, and $k(\ln (Q/\eta)+1)$, due to Deshpande et al. (2016)
and Hellerstein and Kletenik (2018), where $k$ is the number of states. Our
bound generalizes the well-known $(\ln~m + 1)$ approximation bound on the
greedy algorithm for the classical Set Cover problem, where $m$ is the size of
the ground set.


Abstract:
  We prove the equivalence between two explicit expressions for two-point
Witten-Kontsevich correlators obtained by M. Bertola, B. Dubrovin, D. Yang and
by P. Zograf.


Abstract:
  Recent developments in the field of model-based RL have proven successful in
a range of environments, especially ones where planning is essential. However,
such successes have been limited to deterministic fully-observed environments.
We present a new approach that handles stochastic and partially-observable
environments. Our key insight is to use discrete autoencoders to capture the
multiple possible effects of an action in a stochastic environment. We use a
stochastic variant of Monte Carlo tree search to plan over both the agent's
actions and the discrete latent variables representing the environment's
response. Our approach significantly outperforms an offline version of MuZero
on a stochastic interpretation of chess where the opponent is considered part
of the environment. We also show that our approach scales to DeepMind Lab, a
first-person 3D environment with large visual observations and partial
observability.


Abstract:
  A randomized version of the recently developed barycenter method for
derivative--free optimization has desirable properties of a gradient search. We
developed a complex version to avoid evaluations at high--gradient points. The
method, which is also applicable to non--convex and to non--smooth functions,
is parallelizable in a natural way and shown to be robust under noisy
measurements. The goal of this paper is to present an overview of the method,
whose properties make it particularly useful in control applications.


Abstract:
  This article considers the application of Langevin dynamics to sampling and
investigates how to choose the damping parameter in Langevin dynamics for the
purpose of maximizing thoroughness of sampling. Also, it considers the
computation of measures of sampling thoroughness.


Abstract:
  While structural colors are ubiquitous in nature, saturated reds are
mysteriously absent. Hence, a longstanding problem is in fabricating
nanostructured surfaces that exhibit reflectance approaching the theoretical
limit. This limit is termed the Schrodinger red and demands sharp spectral
transitions from "stopband" to a high reflectance "passband" with total
suppression of higher-order resonances at blue and green wavelengths. Current
approaches based on metallic or dielectric nanoantennas are insufficient to
simultaneously meet these conditions. Here, for the 1st time, we designed and
fabricated tall Si nanoantenna arrays on quartz substrate to support two
partially overlapping y polarized quasi bound-state-in-the-continuum (q-BIC)
modes in the red wavelengths with sharp spectral edges. These structures
produce possibly the most saturated and brightest reds with ~80% reflectance,
exceeding the red vertex in sRGB and even the cadmium red pigment. We employed
a gradient descent algorithm with structures supporting q BIC as the starting
point. Although the current design is polarization dependent, the proposed
paradigm has enabled us to achieve the elusive structural red and the design
principle could be generalized to Schrodinger's pixels of other colors. The
design is suitable for scale up using other nanofabrication techniques for
larger area applications, such as red pixels in displays, decorative coatings,
and miniaturized spectrometers with high wavelength selectivity.


Abstract:
  Graph Neural Networks (GNNs) have achieved promising results for
semi-supervised learning tasks on graphs such as node classification. Despite
the great success of GNNs, many real-world graphs are often sparsely and
noisily labeled, which could significantly degrade the performance of GNNs, as
the noisy information could propagate to unlabeled nodes via graph structure.
Thus, it is important to develop a label noise-resistant GNN for
semi-supervised node classification. Though extensive studies have been
conducted to learn neural networks with noisy labels, they mostly focus on
independent and identically distributed data and assume a large number of noisy
labels are available, which are not directly applicable for GNNs. Thus, we
investigate a novel problem of learning a robust GNN with noisy and limited
labels. To alleviate the negative effects of label noise, we propose to link
the unlabeled nodes with labeled nodes of high feature similarity to bring more
clean label information. Furthermore, accurate pseudo labels could be obtained
by this strategy to provide more supervision and further reduce the effects of
label noise. Our theoretical and empirical analysis verify the effectiveness of
these two strategies under mild conditions. Extensive experiments on real-world
datasets demonstrate the effectiveness of the proposed method in learning a
robust GNN with noisy and limited labels.


Abstract:
  OBJECTIVE: Our objective is to evaluate the possibility of using cough audio
recordings (spontaneous or simulated) to detect sound patterns in people who
are diagnosed with COVID-19. The research question that led our work was: what
is the sensitivity and specificity of a machine learning based COVID-19 cough
classifier, using RT-PCR tests as gold standard?
  SETTING: The audio samples that were collected for this study belong to
individuals who were swabbed in the City of Buenos Aires in 20 public and 1
private facilities where RT-PCR studies were carried out on patients suspected
of COVID, and 14 out-of-hospital isolation units for patients with confirmed
COVID mild cases. The audios were collected through the Buenos Aires city
government WhatsApp chatbot that was specifically designed to address citizen
inquiries related to the coronavirus pandemic (COVID-19).
  PARTICIPANTS: The data collected corresponds to 2821 individuals who were
swabbed in the City of Buenos Aires, between August 11 and December 2, 2020.
Individuals were divided into 1409 that tested positive for COVID-19 and 1412
that tested negative. From this sample group, 52.6% of the individuals were
female and 47.4% were male. 2.5% were between the age of 0 and 20 , 61.1%
between the age of 21 and 40 , 30.3% between the age of 41 and 60 and 6.1% were
over 61 years of age.
  RESULTS: Using the dataset of 2821 individuals our results showed that the
neural network classifier was able to discriminate between the COVID-19
positive and the healthy coughs with an accuracy of 86%. This accuracy obtained
during the training process was later tested and confirmed with a second
dataset corresponding to 492 individuals.


Abstract:
  The renormalization constant $Z_J$ of the flavor-singlet axial-vector current
with a non-anticommuting $\gamma_5$ in dimensional regularization is determined
to order $\alpha_s^3$ in QCD with massless quarks. The result is obtained by
computing the matrix elements of the operators appearing in the axial-anomaly
equation $\big[\partial_{\mu} J^{\mu}_{5} \big]_{R} = \frac{\alpha_s}{4 \pi}\,
n_f\, {\displaystyle \mathrm{T}_{F}} \, \big[F \tilde{F} \big]_{R}$ between the
vacuum and a state of two (off-shell) gluons to 4-loop order. Furthermore,
through this computation, the conjectured equality between the
$\overline{\mathrm{MS}}$ renormalization constant $Z_{F\tilde{F}}$ associated
with the operator $\big[F \tilde{F} \big]_{R}$ and that of $\alpha_s$ is
verified explicitly to hold true at 4-loop order. This equality automatically
ensures a relation between the respective anomalous dimensions,
$\gamma_{{\scriptscriptstyle J}} = \frac{\alpha_s}{4 \pi}\, n_f\,
{\displaystyle \mathrm{T}_{F}} \, \gamma_{{\scriptscriptstyle FJ}} $, at order
$\alpha_s^4$ given the validity of the axial-anomaly equation which was used to
determine the non-$\overline{\mathrm{MS}}$ piece of $Z_J$ for the particular
$\gamma_5$ prescription in use.


Abstract:
  Microstructure characterization is of great value to understanding nuclear
graphite's properties and irradiation behavior. However, graphite is soft and
could be easily damaged during sample preparation. A three-step polishing
method involving mechanical polishing, ion milling and rapid oxidation is
proposed for graphite. Ion milling is adopted to remove the broken graphite
pieces produced by mechanical polishing. Rapid oxidation is then adopted to
remove irradiation-induced damage layer during ion milling. The Raman spectra
show very low G peak width and ID/IG ratio after rapid oxidation, indicating a
surface completely free from artificial defects. The micro-cracks which were
conventionally observed via a transmission electron microscope can be observed
on rapid-oxidized surface in a scanning electron microscope. By digital image
processing, the micro-cracks along with the gas-escape pores in nuclear
graphite IG-110 are statistically analyzed. Porosity's distributions on crack
(pore) size (spanning from 10 nm to 100 um) are given, which could help to
understand and simulate graphite's performances in reactors.


Abstract:
  Speakers of non-English languages often adopt loanwords from English to
express new or unusual concepts. While these loanwords may be borrowed
unchanged, speakers may also integrate the words to fit the constraints of
their native language, e.g. creating Spanish "tuitear" from English "tweet."
Linguists have often considered the process of loanword integration to be more
dependent on language-internal constraints, but sociolinguistic constraints
such as speaker background remain only qualitatively understood. We investigate
the role of social context and speaker background in Spanish speakers' use of
integrated loanwords on social media. We find first that newspaper authors use
the integrated forms of loanwords and native words more often than social media
authors, showing that integration is associated with formal domains. In social
media, we find that speaker background and expectations of formality explain
loanword and native word integration, such that authors who use more Spanish
and who write to a wider audience tend to use integrated verb forms more often.
This study shows that loanword integration reflects not only language-internal
constraints but also social expectations that vary by conversation and speaker.


Abstract:
  Small-sized systems exhibit a finite number of routes to chaos. However, in
extended systems, not all routes to complex spatiotemporal behavior have been
fully explored. Starting from the sine-Gordon model of parametrically driven
chain of damped nonlinear oscillators, we investigate a route to spatiotemporal
chaos emerging from standing waves. The route from the stationary to the
chaotic state proceeds through quasiperiodic dynamics. The standing wave
undergoes the onset of oscillatory instability, which subsequently exhibits a
different critical frequency, from which the complexity originates. A suitable
amplitude equation, valid close to the parametric resonance, makes it possible
to produce universe results. The respective phase-space structure and
bifurcation diagrams are produced in a numerical form. We characterize the
relevant dynamical regimes by means of the largest Lyapunov exponent, the power
spectrum, and the evolution of the total intensity of the wave field.


Abstract:
  Humans are highly efficient learners, with the ability to grasp the meaning
of a new concept from just a few examples. Unlike popular computer vision
systems, humans can flexibly leverage the compositional structure of the visual
world, understanding new concepts as combinations of existing concepts. In the
current paper, we study how people learn different types of visual
compositions, using abstract visual forms with rich relational structure. We
find that people can make meaningful compositional generalizations from just a
few examples in a variety of scenarios, and we develop a Bayesian program
induction model that provides a close fit to the behavioral data. Unlike past
work examining special cases of compositionality, our work shows how a single
computational approach can account for many distinct types of compositional
generalization.


Abstract:
  The Kurth solution is a particular non-isotropic steady state solution to the
gravitational Vlasov-Poisson system. It has the property that by means of a
suitable time-dependent transformation it can be turned into a family of
time-dependent solutions. Therefore, for a general steady state $Q(x,
v)=\tilde{Q}(e_Q, \beta)$, depending upon the particle energy $e_Q$ and
$\beta=\ell^2=|x\wedge v|^2$, the question arises if solutions $f$ could be
generated that are of the form \[ f(t)=\tilde{Q}\Big(e_Q(R(t), P(t), B(t)),
B(t)\Big) \] for suitable functions $R$, $P$ and $B$, all depending on $(t, r,
p_r, \beta)$ for $r=|x|$ and $p_r=\frac{x\cdot v}{|x|}$. We are going to show
that, under some mild assumptions, basically if $R$ and $P$ are independent of
$\beta$, and if $B=\beta$ is constant, then $Q$ already has to be the Kurth
solution.
  This paper is dedicated to the memory of Professor Robert Glassey.


Abstract:
  Most software maintenance and evolution tasks require developers to
understand the source code of their software systems. Software developers
usually inspect class comments to gain knowledge about program behavior,
regardless of the programming language they are using. Unfortunately, (i)
different programming languages present language-specific code commenting
notations/guidelines; and (ii) the source code of software projects often lacks
comments that adequately describe the class behavior, which complicates program
comprehension and evolution activities.
  To handle these challenges, this paper investigates the different
language-specific class commenting practices of three programming languages:
Python, Java, and Smalltalk. In particular, we systematically analyze the
similarities and differences of the information types found in class comments
of projects developed in these languages.
  We propose an approach that leverages two techniques, namely Natural Language
Processing and Text Analysis, to automatically identify various types of
information from class comments i.e., the specific types of semantic
information found in class comments. To the best of our knowledge, no previous
work has provided a comprehensive taxonomy of class comment types for these
three programming languages with the help of a common automated approach. Our
results confirm that our approach can classify frequent class comment
information types with high accuracy for Python, Java, and Smalltalk
programming languages. We believe this work can help to monitor and assess the
quality and evolution of code comments in different program languages, and thus
support maintenance and evolution tasks.


Abstract:
  We report the first isotopic composition measurements of trinitite, nuclear
detonation debris from the Trinity test, using the novel forensics technique of
decay energy spectroscopy (DES). DES measures the unique total decay energy (Q
value) of each alpha-decaying isotope in a small radioactive sample embedded in
a microcalorimeter detector. We find that DES can measure the major
alpha-decaying isotopes in small particles of trinitite with no dissolution or
chemical processing. These first measurements demonstrate the potential of DES
to provide a radiometric isotopic characterization method with sensitivity and
precision to complement traditional forensics techniques.


Abstract:
  We advocate the development of a discipline of interacting with and
extracting information from models, both mathematical (e.g. game-theoretic
ones) and computational (e.g. agent-based models). We outline some directions
for the development of a such a discipline:
  - the development of logical frameworks for the systematic formal
specification of stylized facts and social mechanisms in (mathematical and
computational) social science. Such frameworks would bring to attention new
issues, such as phase transitions, i.e. dramatical changes in the validity of
the stylized facts beyond some critical values in parameter space. We argue
that such statements are useful for those logical frameworks describing
properties of ABM.
  - the adaptation of tools from the theory of reactive systems (such as
bisimulation) to obtain practically relevant notions of two systems "having the
same behavior".
  - the systematic development of an adversarial theory of model perturbations,
that investigates the robustness of conclusions derived from models of social
behavior to variations in several features of the social dynamics. These may
include: activation order, the underlying social network, individual agent
behavior.


Abstract:
  Transformations for enhancing sparsity in the approximation of color images
by 2D atomic decomposition are discussed. The sparsity is firstly considered
with respect to the most significant coefficients in the wavelet decomposition
of the color image. The discrete cosine transform is singled out as an
effective transformation for this purpose. The enhanced feature is further
exploited by approximating the transformed arrays using an effective greedy
strategy with a separable highly redundant dictionary. The relevance of the
achieved sparsity is illustrated by a simple encoding procedure. On a set of
typical test images the compression at high quality recovery is shown to
significantly improve upon JPEG and WebP formats. The results are competitive
with those produced by the JPEG2000 standard.


Abstract:
  As job markets worldwide have become more competitive and applicant selection
criteria have become more opaque, and different (and sometimes contradictory)
information and advice is available for job seekers wishing to progress in
their careers, it has never been more difficult to determine which factors in a
r\'esum\'e most effectively help career progression. In this work we present a
novel, large scale dataset of over half a million r\'esum\'es with preliminary
analysis to begin to answer empirically which factors help or hurt people
wishing to transition to more senior roles as they progress in their career. We
find that previous experience forms the most important factor, outweighing
other aspects of human capital, and find which language factors in a r\'esum\'e
have significant effects. This lays the groundwork for future inquiry in career
trajectories using large scale data analysis and natural language processing
techniques.


Abstract:
  In this note, we consider the homogenization of the compressible
Navier-Stokes equations in a periodically perforated domain in $\mathbb{R}^3$.
Assuming that the particle size scales like $\varepsilon^3$, where
$\varepsilon>0$ is their mutual distance, and that the Mach number decreases
fast enough, we show that in the limit $\varepsilon\to 0$, the velocity and
density converge to a solution of the incompressible Navier-Stokes equations
with Brinkman term. We strongly follow the methods of H\"ofer, Kowalczik and
Schwarzacher [arXiv:2007.09031], where they proved convergence to Darcy's law
for the particle size scaling like $\varepsilon^\alpha$ with $\alpha\in (1,3)$.


Abstract:
  We present the results of a quantitative study of the phase behavior of a
model polymer chain with side spheres using two independent computer simulation
techniques. We find that the mere addition of side spheres results in key
modifications of standard polymer behavior. One obtains a novel marginally
compact phase at low temperatures, the structures in this phase are reduced in
dimensionality and are ordered, they include strands assembled into sheets and
a variety of helices, and at least one of the transitions on lowering the
temperature to access these ordered states is found to be first order. Our
model serves to partially bridge conventional polymer phases with biomolecular
phases.


Abstract:
  We study the light rings and shadows of an uniparametric family of
spherically symmetric geometries interpolating between the Schwarzschild
solution, a regular black hole, and a traversable wormhole, and dubbed as black
bounces, all of them sharing the same critical impact parameter. We consider
the ray-tracing method in order to study the impact parameter regions
corresponding to the direct, lensed, and photon ring emission, finding a
broadening of all these regions for black bounce solutions as compared to the
Schwarzschild one. Using this, we determine the optical appearance of black
bounces when illuminated by three standard toy models of optically and
geometrically thin accretion disks viewed in face-on orientation.


Abstract:
  This paper describes computer models of three interventions used for treating
refractory pulmonary hypertension (RPH). These procedures create either an
atrial septal defect, a ventricular septal defect, or, in the case of a Potts
shunt, a patent ductus arteriosus. The aim in all three cases is to generate a
right-to-left shunt, allowing for either pressure or volume unloading of the
right side of the heart in the setting of right ventricular failure, while
maintaining cardiac output. These shunts are created, however, at the expense
of introducing de-oxygenated blood into the systemic circulation, thereby
lowering the systemic arterial oxygen saturation. The models developed in this
paper are based on compartmental descriptions of human hemodynamics and oxygen
transport. An important parameter included in our models is the cross-sectional
area of the surgically created defect. Numerical simulations are performed to
compare different interventions and various shunt sizes and to assess their
impact on hemodynamic variables and oxygen saturations. We also create a model
for exercise and use it to study exercise tolerance in simulated
pre-intervention and post-intervention RPH patients.


Abstract:
  In this work, we consider the asynchronous distributed optimization problem
in which each node has its own convex cost function and can communicate
directly only with its neighbors, as determined by a directed communication
topology (directed graph or digraph). First, we reformulate the optimization
problem so that Alternating Direction Method of Multipliers (ADMM) can be
utilized. Then, we propose an algorithm, herein called Asynchronous Approximate
Distributed Alternating Direction Method of Multipliers (AsyAD-ADMM), using
finite-time asynchronous approximate ratio consensus, to solve the multi-node
convex optimization problem, in which every node performs iterative
computations and exchanges information with its neighbors asynchronously. More
specifically, at every iteration of AsyAD-ADMM, each node solves a local convex
optimization problem for one of the primal variables and utilizes a finite-time
asynchronous approximate consensus protocol to obtain the value of the other
variable which is close to the optimal value, since the cost function for the
second primal variable is not decomposable. If the individual cost functions
are convex but not necessarily differentiable, the proposed algorithm converges
at a rate of $\mathcal{O}(1/k)$, where $k$ is the iteration counter. The
efficacy of AsyAD-ADMM is exemplified via a proof-of-concept distributed
least-square optimization problem with different performance-influencing
factors investigated.


Abstract:
  We are motivated by the goal of generalist robots that can complete a wide
range of tasks across many environments. Critical to this is the robot's
ability to acquire some metric of task success or reward, which is necessary
for reinforcement learning, planning, or knowing when to ask for help. For a
general-purpose robot operating in the real world, this reward function must
also be able to generalize broadly across environments, tasks, and objects,
while depending only on on-board sensor observations (e.g. RGB images). While
deep learning on large and diverse datasets has shown promise as a path towards
such generalization in computer vision and natural language, collecting high
quality datasets of robotic interaction at scale remains an open challenge. In
contrast, "in-the-wild" videos of humans (e.g. YouTube) contain an extensive
collection of people doing interesting tasks across a diverse range of
settings. In this work, we propose a simple approach, Domain-agnostic Video
Discriminator (DVD), that learns multitask reward functions by training a
discriminator to classify whether two videos are performing the same task, and
can generalize by virtue of learning from a small amount of robot data with a
broad dataset of human videos. We find that by leveraging diverse human
datasets, this reward function (a) can generalize zero shot to unseen
environments, (b) generalize zero shot to unseen tasks, and (c) can be combined
with visual model predictive control to solve robotic manipulation tasks on a
real WidowX200 robot in an unseen environment from a single human demo.


Abstract:
  Quantum self-testing is the task of certifying quantum states and
measurements using the output statistics solely, with minimal assumptions about
the underlying quantum system. It is based on the observation that some
extremal points in the set of quantum correlations can only be achieved, up to
isometries, with specific states and measurements. Here, we present a new
approach for quantum self-testing in Bell non-locality scenarios, motivated by
the following observation: the quantum maximum of a given Bell inequality is,
in general, difficult to characterize. However, it is strictly contained in an
easy-to-characterize set: the \emph{theta body} of a vertex-weighted induced
subgraph $(G,w)$ of the graph in which vertices represent the events and edges
join mutually exclusive events. This implies that, for the cases where the
quantum maximum and the maximum within the theta body (known as the Lov\'asz
theta number) of $(G,w)$ coincide, self-testing can be demonstrated by just
proving self-testability with the theta body of $G$. This graph-theoretic
framework allows us to (i) recover the self-testability of several quantum
correlations that are known to permit self-testing (like those violating the
Clauser-Horne-Shimony-Holt (CHSH) and three-party Mermin Bell inequalities for
projective measurements of arbitrary rank, and chained Bell inequalities for
rank-one projective measurements), (ii) prove the self-testability of quantum
correlations that were not known using existing self-testing techniques (e.g.,
those violating the Abner Shimony Bell inequality for rank-one projective
measurements). Additionally, the analysis of the chained Bell inequalities
gives us a closed-form expression of the Lov\'asz theta number for a family of
well-studied graphs known as the M\"obius ladders, which might be of
independent interest in the community of discrete mathematics.


Abstract:
  We give an algorithm to determine whether a kernel sheaf over a smooth
projective curve over an algebraically closed field is semistable. The
algorithm uses symmetric powers to make destabilizing subbundles visible as
global sections.