Abstract:
  The objective of this paper is to study the stability of a linear
one-dimensional thermoelastic Bresse system in a bounded domain, where the
coupling is given through the first component of the Bresse model with the heat
conduction of Gurtin-Pipkin type. Two kinds of coupling are considered; the
first coupling is of order one with respect to space variable, and the second
one is of order zero. We state the well-posedness and show the polynomial
stability of the systems, where the decay rates depend on the smoothness of
initial data. Moreover, in case of coupling of order one, we prove the
equivalence between the exponential stability and some new conditions on the
parameters of the system. However, when the coupling is of order zero, we prove
the non-exponential stability independently of the parameters of the system.
Applications to the corresponding particular Timoshenko models are also given,
where we prove that both couplings lead to the exponential stability if and
only if some conditions on the parameters of the systems are satisfied, and
both couplings guarantee the polynomial stability independently of the
parameters of the systems. The proof is based on the semigroup theory and a
combination of the energy method and the frequency domain approach.


Abstract:
  Advanced communication protocols are critical to enable the coexistence of
autonomous robots with humans. Thus, the development of explanatory
capabilities is an urgent first step toward autonomous robots. This survey
provides an overview of the various types of "explainability" discussed in
machine learning research. Then, we discuss the definition of "explainability"
in the context of autonomous robots (i.e., explainable autonomous robots) by
exploring the question "what is an explanation?" We further conduct a research
survey based on this definition and present some relevant topics for future
research.


Abstract:
  Brain image analysis has advanced substantially in recent years with the
proliferation of neuroimaging datasets acquired at different resolutions. While
research on brain image super-resolution has undergone a rapid development in
the recent years, brain graph super-resolution is still poorly investigated
because of the complex nature of non-Euclidean graph data. In this paper, we
propose the first-ever deep graph super-resolution (GSR) framework that
attempts to automatically generate high-resolution (HR) brain graphs with N'
nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR)
graphs with N nodes where N < N'. First, we formalize our GSR problem as a node
feature embedding learning task. Once the HR nodes' embeddings are learned, the
pairwise connectivity strength between brain ROIs can be derived through an
aggregation rule based on a novel Graph U-Net architecture. While typically the
Graph U-Net is a node-focused architecture where graph embedding depends mainly
on node attributes, we propose a graph-focused architecture where the node
feature embedding is based on the graph topology. Second, inspired by graph
spectral theory, we break the symmetry of the U-Net architecture by
super-resolving the low-resolution brain graph structure and node content with
a GSR layer and two graph convolutional network layers to further learn the
node embeddings in the HR graph. Third, to handle the domain shift between the
ground-truth and the predicted HR brain graphs, we incorporate adversarial
regularization to align their respective distributions. Our proposed AGSR-Net
framework outperformed its variants for predicting high-resolution functional
brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub
at https://github.com/basiralab/AGSR-Net.


Abstract:
  This article discusses the decoding of Gabidulin codes and shows how to
extend the usual decoder to any supercode of a Gabidulin code at the cost of a
significant decrease of the decoding radius. Using this decoder, we provide
polynomial time attacks on the rank-metric encryption schemes RAMESSES and
LIGA.


Abstract:
  We present a method for path optimization for ground vehicles in off-road
environments at high speeds. This path optimization considers the kinematic
constraints of the vehicle. By thinking in the actuator space we can represent
these constraints as limits in the space rather than derived properties of the
path. In this paper we present a actuator space approach to path optimization
for off-road ground vehicles. This is done by representing and operation on the
path as a list of steering angles over the path length. This transforms the set
of kinematic constraints into constraints on the steering angle. We then put
this path into a gradient descent solver. This produced paths that are
kinematically feasible and optimized in accordance with our cost function.
Finally, we tested the system both in simulation and on an off-road vehicle at
speeds of 5 m/s.


Abstract:
  Machine intelligence is very successful at standard recognition tasks when
having high-quality training data. There is still a significant gap between
machine-level pattern recognition and human-level concept learning. Humans can
learn under uncertainty from only a few examples and generalize these concepts
to solve new problems. The growing interest in explainable machine
intelligence, requires experimental environments and diagnostic tests to
analyze weaknesses in existing approaches to drive progress in the field. In
this paper, we discuss existing diagnostic tests and test data sets such as
CLEVR, CLEVERER, CLOSURE, CURI, Bongard-LOGO, V-PROM, and present our own
experimental environment: The KANDINSKYPatterns, named after the Russian artist
Wassily Kandinksy, who made theoretical contributions to compositivity, i.e.
that all perceptions consist of geometrically elementary individual components.
This was experimentally proven by Hubel &Wiesel in the 1960s and became the
basis for machine learning approaches such as the Neocognitron and the even
later Deep Learning. While KANDINSKYPatterns have computationally controllable
properties on the one hand, bringing ground truth, they are also easily
distinguishable by human observers, i.e., controlled patterns can be described
by both humans and algorithms, making them another important contribution to
international research in machine intelligence.


Abstract:
  We introduce languages of higher-dimensional automata (HDAs) and develop some
of their properties. To this end, we define a new category of precubical sets,
uniquely naturally isomorphic to the standard one, and introduce a notion of
event consistency. HDAs are then finite, labeled, event-consistent precubical
sets with distinguished subsets of initial and accepting cells. Their languages
are sets of interval orders closed under subsumption; as a major technical step
we expose a bijection between interval orders and a subclass of HDAs. We show
that any finite subsumption-closed set of interval orders is the language of an
HDA, that languages of HDAs are closed under binary unions and parallel
composition, and that bisimilarity implies language equivalence.


Abstract:
  In this paper, we attack a few-shot open-set recognition (FSOSR) problem,
which is a combination of few-shot learning (FSL) and open-set recognition
(OSR). It aims to quickly adapt a model to a given small set of labeled samples
while rejecting unseen class samples. Since OSR requires rich data and FSL
considers closed-set classification, existing OSR and FSL methods show poor
performances in solving FSOSR problems. The previous FSOSR method follows the
pseudo-unseen class sample-based methods, which collect pseudo-unseen samples
from the other dataset or synthesize samples to model unseen class
representations. However, this approach is heavily dependent on the composition
of the pseudo samples. In this paper, we propose a novel unknown class sample
detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on
the transformation consistency, our method measures the difference between the
transformed prototypes and a modified prototype set. The modified set is
composed by replacing a query feature and its predicted class prototype.
SnaTCHer rejects samples with large differences to the transformed prototypes.
Our method alters the unseen class distribution estimation problem to a
relative feature transformation problem, independent of pseudo-unseen class
samples. We investigate our SnaTCHer with various prototype transformation
methods and observe that our method consistently improves unseen class sample
detection performance without closed-set classification reduction.


Abstract:
  We present a dead reckoning strategy for increased resilience to position
estimation failures on multirotors, using only data from a low-cost IMU and
novel, bio-inspired airflow sensors. The goal is challenging, since low-cost
IMUs are subject to large noise and drift, while 3D airflow sensing is made
difficult by the interference caused by the propellers and by the wind. Our
approach relies on a deep-learning strategy to interpret the measurements of
the bio-inspired sensors, a map of the wind speed to compensate for
position-dependent wind, and a filter to fuse the information and generate a
pose and velocity estimate. Our results show that the approach reduces the
drift with respect to IMU-only dead reckoning by up to an order of magnitude
over 30 seconds after a position sensor failure in non-windy environments, and
it can compensate for the challenging effects of turbulent, and spatially
varying wind.


Abstract:
  We present a SE(3)-equivariant graph neural network (GNN) approach that
directly predicting the formation factor and effective permeability from
micro-CT images. FFT solvers are established to compute both the formation
factor and effective permeability, while the topology and geometry of the pore
space are represented by a persistence-based Morse graph. Together, they
constitute the database for training, validating, and testing the neural
networks. While the graph and Euclidean convolutional approaches both employ
neural networks to generate low-dimensional latent space to represent the
features of the micro-structures for forward predictions, the SE(3) equivariant
neural network is found to generate more accurate predictions, especially when
the training data is limited. Numerical experiments have also shown that the
new SE(3) approach leads to predictions that fulfill the material frame
indifference whereas the predictions from classical convolutional neural
networks (CNN) may suffer from spurious dependence on the coordinate system of
the training data. Comparisons among predictions inferred from training the CNN
and those from graph convolutional neural networks (GNN) with and without the
equivariant constraint indicate that the equivariant graph neural network seems
to perform better than the CNN and GNN without enforcing equivariant
constraints.


Abstract:
  The non-detection of zero-metallicity stars in ultra-faint dwarf galaxies
(UFDs) can be used to constrain the Initial Mass Function (IMF) of the first
(PopIII) stars by means of a statistical comparison between available data and
predictions from chemical evolution models. To this end we develop a model that
follows the formation of isolated UFDs, calibrated to best reproduce the
available data for the best studied system: Bo\"otes I. Our statistical
approach shows that UFDs are the best suitable systems to study the
implications of the persisting non-detection of zero-metallicity stars on the
PopIII IMF, i.e. its shape, the minimum mass ($m_{min}$), and the
characteristic mass ($m_{ch}$). We show that accounting for the incomplete
sampling of the IMF is essential to compute the expected number of long-lived
PopIII stars in inefficiently star-forming UFDs. By simulating the Color
Magnitude Diagram of Bo\"otes I, and thus take into account the mass-range of
the observed stars, we can obtain even tighter constrains on $m_{min}$. By
exploiting the 96 stars with measured metallicities ($\rm i < 19$) in the UFDs
represented by our model, we demonstrate that: $m_{ch} > 1 \rm M_{\odot}$ or
$m_{min} > 0.8 \rm M_{\odot}$ at $99\%$ confidence level. This means that a
present day IMF for PopIII stars is excluded by our model, and a top-heavy
PopIII IMF is strongly favoured. We can limit $m_{min} > 0.8 \rm M_{\odot}$
independent of the PopIII IMF shape by targeting the four UFDs Bo\"otes I,
Hercules, Leo IV and Eridanus II with future generation instruments, such as
ELT/MOSAIC ($\rm i < 25$), which can provide samples of >10\,000 stars.


Abstract:
  Leveraging coherent light-matter interaction in solids is a promising new
direction towards control and functionalization of quantum materials, to
potentially realize regimes inaccessible in equilibrium and stabilize new or
useful states of matter. We show how driving the strongly spin-orbit coupled
proximal Kitaev magnet $\alpha$-RuCl$_3$ with circularly-polarized light can
give rise to a novel ligand-mediated magneto-electric effect that both
photo-induces a large dynamical effective magnetic field and dramatically
alters the interplay of competing isotropic and anisotropic exchange
interactions. We propose that tailored light pulses can nudge the material
towards the elusive Kitaev quantum spin liquid as well as probe competing
magnetic instabilities far from equilibrium, and predict that the transient
competition of magnetic exchange processes can be readily observed via
pump-probe spectroscopy.


Abstract:
  We present DaLAJ 1.0, a Dataset for Linguistic Acceptability Judgments for
Swedish, comprising 9 596 sentences in its first version; and the initial
experiment using it for the binary classification task. DaLAJ is based on the
SweLL second language learner data, consisting of essays at different levels of
proficiency. To make sure the dataset can be freely available despite the GDPR
regulations, we have sentence-scrambled learner essays and removed part of the
metadata about learners, keeping for each sentence only information about the
mother tongue and the level of the course where the essay has been written. We
use the normalized version of learner language as the basis for the DaLAJ
sentences, and keep only one error per sentence. We repeat the same sentence
for each individual correction tag used in the sentence. For DaLAJ 1.0 we have
used four error categories (out of 35 available in SweLL), all connected to
lexical or word-building choices. Our baseline results for the binary
classification show an accuracy of 58% for DaLAJ 1.0 using BERT embeddings. The
dataset is included in the SwedishGlue (Swe. SuperLim) benchmark. Below, we
describe the format of the dataset, first experiments, our insights and the
motivation for the chosen approach to data sharing.


Abstract:
  Emission-line variability studies have the potential to provide key
information about the structures of the near-nuclear outflow regions of AGN.
Here we present a VLT/Xshooter spectrum of the nucleus of the ULIRG F01004-2237
that was taken in August 2018, about 8 yr after a major flare in its integrated
optical emission. Compared with our WHT/ISIS spectrum from September 2015, the
broad, red wings of the emission lines most closely associated with the flaring
event, including HeII(4686), NIII(4100,4640) and HeI(5876), have substantially
declined in flux. In contrast, the broad, blue wings that dominate the [OIII],
[NeIII], [NeV] and [OI] forbidden lines have increased in flux by a factor 1.4
- 4.4 (depending on the line). Moreover, the [FeVII](6087) line is detected in
the new spectrum for the first time. We interpret these results in terms of a
light echo from the outflowing warm gas: the direct emission from the flaring
event is continuing to fade, but due to light travel time effects we are only
now observing the impact of the flare on the emission from the extended outflow
region. Unless the outflow is confined to a small range of angles close to our
line of sight, these observations imply that the outflow must be relatively
compact (r<50 pc). In terms of the nature of the flare event, we speculate that
the properties of the F01004-2237 flare may be the result of a tidal disruption
event (TDE) occurring in an object with pre-existing AGN activity.


Abstract:
  Group testing allows saving chemical reagents, analysis time, and costs, by
testing pools of samples instead of individual samples. We introduce a class of
group testing protocols with small dilution, suited to operate even at high
prevalence ($5\%-10\%$), and maximizing the fraction of samples classified
positive/negative within the first round of tests. Precisely, if the tested
group has exactly one positive sample then the protocols identify it without
further individual tests. The protocols also detect the presence of two or more
positives in the group, in which case a second round could be applied to
identify the positive individuals. With a prevalence of $5\%$ and maximum
dilution 6, with 100 tests we classify 242 individuals, $92\%$ of them in one
round and $8\%$ requiring a second individual test. In comparison, the
Dorfman's scheme can test 229 individuals with 100 tests, with a second round
for $18.5\%$ of the individuals.


Abstract:
  Cosmological models with an inhomogeneous viscous dark fluid, coupled with
dark matter in the Friedmann- Robertson-Walker (FRW) flat universe, are
considered. The influence of thermal effects caused by Hawking radiation on the
visible horizon is studied, in connection with the classified type I and type
III singularities which are known to occur within a finite amount of time.
Allowance of thermal effects implies that a transition to a type II singularity
can take place, in a finite time. We take into account a bulk viscosity of the
dark fluid, observing the equation of state in the case of radiation, and find
that there is a qualitative change in the singular universe of type I: it may
pass into a singularity of type III, or it may avoid the singularity at all.


Abstract:
  Axisymmetric and nonaxisymmetric patterns in the cubic-quintic
Swift-Hohenberg equation posed on a disk with Neumann boundary conditions are
studied via numerical continuation and bifurcation analysis. Axisymmetric
localized solutions in the form of spots and rings known from earlier studies
persist and snake in the usual fashion until they begin to interact with the
boundary. Depending on parameters, including the disk radius, these states may
or may not connect to the branch of domain-filling target states. Secondary
instabilities of localized axisymmetric states may create multi-arm localized
structures that grow and interact with the boundary before broadening into
domain filling states. High azimuthal wavenumber wall states referred to as
daisy states are also found. Secondary bifurcations from these states include
localized daisies, i.e., wall states localized in both radius and angle.
Depending on parameters, these states may snake much as in the one-dimensional
Swift-Hohenberg equation, or invade the interior of the domain, yielding states
referred to as worms, or domain-filling stripes.


Abstract:
  We propose a monocular depth estimator SC-Depth, which requires only
unlabelled videos for training and enables the scale-consistent prediction at
inference time. Our contributions include: (i) we propose a geometry
consistency loss, which penalizes the inconsistency of predicted depths between
adjacent views; (ii) we propose a self-discovered mask to automatically
localize moving objects that violate the underlying static scene assumption and
cause noisy signals during training; (iii) we demonstrate the efficacy of each
component with a detailed ablation study and show high-quality depth estimation
results in both KITTI and NYUv2 datasets. Moreover, thanks to the capability of
scale-consistent prediction, we show that our monocular-trained deep networks
are readily integrated into the ORB-SLAM2 system for more robust and accurate
tracking. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in
KITTI, and it generalizes well to the KAIST dataset without additional
training. Finally, we provide several demos for qualitative evaluation.


Abstract:
  The perfect fluid solutions admitting a group G$_3$ of isometries acting on
orbits S$_2$ whose curvature has a gradient which is tangent to the fluid flow
(T-models) are studied from a thermodynamic approach. All the admissible
thermodynamic schemes are obtained, and the solutions compatible with the
generic ideal gas equation of state are studied in detail. The possible
physical interpretation of some previously known T-models is also analyzed.


Abstract:
  A set of vertices in a hypergraph is called an independent set if no
hyperedge is completely contained inside the set. Given a hypergraph, computing
its largest size independent set is an NP-hard problem.
  In this work, we study the independent set problem on hypergraphs in a
natural semi-random family of instances. Our semi-random model is inspired by
the Feige-Kilian model [FK01]. This popular model has also been studied in the
works of [FK01, Ste17, MMT20] etc. McKenzie, Mehta, and Trevisan [MMT20] gave
algorithms for computing independent sets in such a semi-random family of
graphs. The algorithms by McKenzie et al. [MMT20] are based on rounding a
"crude-SDP". We generalize their results and techniques to hypergraphs for an
analogous family of hypergraph instances. Our algorithms are based on rounding
the "crude-SDP" of McKenzie et al. [MMT20], augmented with "Lasserre/SoS like"
hierarchy of constraints. Analogous to the results of McKenzie et al. [MMT20],
we study the ranges of input parameters where we can recover the planted
independent set or a large independent set.


Abstract:
  In this paper, we present QPack, a benchmark for NISQ era quantum computers
using QAOA algorithms. Unlike other evaluation metrics in the field, this
benchmark evaluates not only one, but multiple important aspects of quantum
computing hardware: the maximum problem size a quantum computer can solve, the
required run-time, as well as the achieved accuracy. This benchmark will also
be available for quantum simulators, where it is able to assess the run-time
and required memory that a simulator needs to run the application. The
applications MaxCut, dominating set and traveling salesman are included to
provide variation in resource requirements. We also discuss the design aspects
that are taken in consideration for an optimal benchmark, with critical quantum
benchmark requirements in mind. An implementation strategy is presented,
providing practical metrics. The measurement results show how QAOA can be
optimized using classical optimization algorithms, suggesting the use of a
global/local optimizer hybrid implementation. The implementation of the MaxCut,
dominating set and traveling salesman problem show that different QAOA
applications use a wide variety of quantum resources. This will allow for a
diverse benchmark that promotes optimal design considerations, avoiding
hardware implementations for specific applications.


Abstract:
  Weyl semimetals are playing a major role in condensed matter physics due to
exotic topological properties, and their coexistence with ferromagnetism may
lead to enhanced spin-related phenomena. Here, the inverse spin Hall effect
(ISHE) in the ferromagnetic Weyl-semimetal Heusler alloy Co$_2$MnGa was
investigated at room temperature by means of electrical spin injection in
lateral spin valve structures. Spin transport properties such as spin
polarization and spin diffusion length in this material were precisely
extracted in order to estimate the spin Hall angle $\theta_{\textrm{SH}}$,
which was found to be $-0.19\pm0.04$ and is among the highest reported for a
ferromagnet. Although this value is on the same order of magnitude of known
heavy metals, the significantly higher resistivity of Co$_2$MnGa implies an
improvement on the magnitude of detection voltages, while its ferromagnetic
nature allows controlling the intensity of SHE through the magnetization
direction. It was also shown that Onsager's reciprocity does not hold for this
system, which is in part attributable to a different spin-dependent Hall
conductivity for spin-up and spin-down carriers.


Abstract:
  Neural networks are prone to catastrophic forgetting when trained
incrementally on different tasks. In order to prevent forgetting, most existing
methods retain a small subset of previously seen samples, which in turn can be
used for joint training with new tasks. While this is indeed effective, it may
not always be possible to store such samples, e.g., due to data protection
regulations. In these cases, one can instead employ generative models to create
artificial samples or features representing memories from previous tasks.
Following a similar direction, we propose GenIFeR (Generative Implicit Feature
Replay) for class-incremental learning. The main idea is to train a generative
adversarial network (GAN) to generate images that contain realistic features.
While the generator creates images at full resolution, the discriminator only
sees the corresponding features extracted by the continually trained
classifier. Since the classifier compresses raw images into features that are
actually relevant for classification, the GAN can match this target
distribution more accurately. On the other hand, allowing the generator to
create full resolution images has several benefits: In contrast to previous
approaches, the feature extractor of the classifier does not have to be frozen.
In addition, we can employ augmentations on generated images, which not only
boosts classification performance, but also mitigates discriminator overfitting
during GAN training. We empirically show that GenIFeR is superior to both
conventional generative image and feature replay. In particular, we
significantly outperform the state-of-the-art in generative replay for various
settings on the CIFAR-100 and CUB-200 datasets.


Abstract:
  The recently discovered kagome metal series $A$V$_3$Sb$_5$ ($A$=K, Rb, Cs)
exhibits topologically nontrivial band structures, chiral charge order and
superconductivity, presenting a unique platform for realizing exotic electronic
states. The nature of the superconducting state and the corresponding pairing
symmetry are key questions that demand experimental clarification. Here, using
a technique based on the tunneling diode oscillator, the magnetic penetration
depth $\Delta\lambda(T)$ of CsV$_3$Sb$_5$ was measured down to 0.07 K. A clear
exponential behavior in $\Delta\lambda(T)$ with marked deviations from a $T$ or
$T^2$ temperature dependence is observed at low temperatures, indicating a
deficiency of nodal quasiparticles. Temperature dependence of the superfluid
density and electronic specific heat can be described by two-gap $s$-wave
superconductivity, consistent with the presence of multiple Fermi surfaces in
CsV$_3$Sb$_5$. These results evidence nodeless superconductivity in
CsV$_3$Sb$_5$ under ambient pressure, and constrain the allowed pairing
symmetry.


Abstract:
  Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.


Abstract:
  The aim of this paper is to establish new pointwise regularity results for
solutions to degenerate second order partial differential equations with a
Kolmogorov-type operator of the form $$\mathscr{L} :=\sum_{i,j=1}^m
\partial^2_{x_i x_j } +\sum_{i,j=1}^N b_{ij}x_j\partial_{x_i}-\partial_t, $$
where $(x,t) \in \mathbb{R}^{N+1}$, $1 \leq m \le N$ and the matrix
$B:=(b_{ij})_{i,j=1,\ldots,N}$ has real constant entries. In particular, we
show that if the modulus of $L^p$-mean oscillation of $\mathscr{L} u$ at the
origin is Dini, then the origin is a Lebesgue point of continuity in $L^p$
average for the second order derivatives $\partial^2_{x_i x_j} u$,
$i,j=1,\ldots,m$, and the Lie derivative $\left(\sum_{i,j=1}^N
b_{ij}x_j\partial_{x_i}-\partial_t\right)u$. Moreover, we are able to provide a
Taylor-type expansion up to second order with estimate of the rest in $L^p$
norm. The proof is based on decay estimates, which we achieve by contradiction,
blow-up and compactness results.


Abstract:
  The goal of this paper is to develop a modeling framework that captures the
inter-decision dynamics between mobility service providers (MSPs) and travelers
that can be used to optimize and analyze policies/regulations related to MSPs.
To meet this goal, the paper proposes a tri-level mathematical programming
model with a public-sector decision maker (regulator) at the highest level, the
MSP in the middle level, and travelers at the lowest level. The regulator aims
to maximize social welfare via implementing regulations, policies, plans,
transit service designs, etc. The MSP aims to maximize profit by adjusting its
service designs. Travelers aim to maximize utility by changing their modes and
routes. The travelers' decisions depend on the regulator and MSP's decisions
while the MSP decisions themselves depend on the regulator's decisions. To
solve the tri-level mathematical program, the study employs Bayesian
optimization (BO) within a simulation-optimization solution approach. At the
lowest level, the solution approach includes an agent-based transportation
system simulation model to capture travelers' behavior subject to specific
decisions made by the regulator and MSP. The agent-based transportation
simulation model includes a mode choice model, a road network, a transit
network, and an MSP providing automated mobility-on-demand (AMOD) service with
shared rides. The modeling and solution approaches are applied to Munich,
Germany in order to validate the model. The case study investigates the tolls
and parking costs the city administration should set, as well as changes in the
public transport budget and a limitation of the AMOD fleet size. Best policy
settings are derived for two social welfare definitions, in both of which the
AMOD fleet size is not regulated as the shared-ride AMOD service provides
significant value to travelers in Munich.


Abstract:
  We consider MCMC methods for learning equivalence classes of sparse Gaussian
DAG models when $p = e^{o(n)}$. The main contribution of this work is a rapid
mixing result for a random walk Metropolis-Hastings algorithm, which we prove
using a canonical path method. It reveals that the complexity of Bayesian
learning of sparse equivalence classes grows only polynomially in $n$ and $p$,
under some common high-dimensional assumptions. Further, a series of
high-dimensional consistency results is obtained by the path method, including
the strong selection consistency of an empirical Bayes model for structure
learning and the consistency of a greedy local search on the restricted search
space. Rapid mixing and slow mixing results for other structure-learning MCMC
methods are also derived. Our path method and mixing time results yield crucial
insights into the computational aspects of high-dimensional structure learning,
which may be used to develop more efficient MCMC algorithms.


Abstract:
  The present epoch of accelerated cosmic expansion is supposed to be driven by
an unknown constituent called dark energy, which in the standard model takes
the form of a cosmological constant, characterized by a constant equation of
state w=-1. An interesting perspective over the role and nature of dark energy
can be achieved by drawing a parallel with a previous epoch of accelerated
expansion, inflation, which we assume to be driven by a single scalar field,
the inflaton. Since the Planck satellite has constrained the value of $n_s$
away from 1, the inflaton cannot be identified with a pure cosmological
constant, as is also suggested by the fact that inflation ended. Thus, it is
interesting to verify whether a hypothetical observer would have been able to
measure the deviation of the w of the inflaton from -1. To do so, we consider a
class of single-field slow-roll inflationary models dubbed HSR{i}, where the
hierarchy of Hubble slow-roll parameters is truncated at the i-th order. The
models are tested through a MCMC analysis based on combinations of the latest
Planck and BICEP2/Keck data sets, and the resulting chains are converted into
sets of allowed evolution histories of w. HSR{1} is excluded observationally
since it would predict that $n_s=1$, in contrast with the recent Planck
constraints, while we find that HSR{2} would prefer w>-1, but is disfavoured by
the addition of the BICEP2/Keck data. The overall best description for the data
is provided by HSR{3}, which yields a 68% upper bound of 1+w<0.0014. Therefore,
if the current era of accelerated expansion happens to have the same equation
of state as inflation during the observable epoch, then current and upcoming
cosmological observations will not be able to detect that w$\neq$-1. This
provides a cautionary tale for drawing conclusions about the nature of dark
energy on the basis of the non-observation of a deviation from w=-1.


Abstract:
  The decision forest model is a recently proposed nonparametric choice model
that is capable of representing any discrete choice model and in particular,
can be used to represent non-rational customer behavior. In this paper, we
study the problem of finding the assortment that maximizes expected revenue
under the decision forest model. This problem is of practical importance
because it allows a firm to tailor its product offerings to profitably exploit
deviations from rational customer behavior, but at the same time is challenging
due to the extremely general nature of the decision forest model. We approach
this problem from a mixed-integer optimization perspective and propose three
different formulations. We theoretically compare these formulations in
strength, and analyze when these formulations are integral in the special case
of a single tree. We propose a methodology for solving these problems at a
large-scale based on Benders decomposition, and show that the Benders
subproblem can be solved efficiently by primal-dual greedy algorithms when the
master solution is fractional for two of our formulations, and in closed form
when the master solution is binary for all of our formulations. Using
synthetically generated instances, we demonstrate the practical tractability of
our formulations and our Benders decomposition approach, and their edge over
heuristic approaches.


Abstract:
  Bell's theorem shows that no hidden-variable model can explain the
measurement statistics of a quantum system shared between two parties, thus
ruling out a classical (local) understanding of nature. In this work we
demonstrate that by relaxing the positivity restriction in the hidden-variable
probability distribution it is possible to derive quasiprobabilistic Bell
inequalities whose sharp upper bound is written in terms of a negativity
witness of said distribution. This provides an analytic solution for the amount
of negativity necessary to violate the CHSH inequality by an arbitrary amount,
therefore revealing the amount of negativity required to emulate the quantum
statistics in a Bell test.


Abstract:
  In a previous paper it was shown how to calculate the ground-state energy
density $E$ and the $p$-point Green's functions $G_p(x_1,x_2,...,x_p)$ for the
$PT$-symmetric quantum field theory defined by the Hamiltonian density
$H=\frac{1}{2}(\nabla\phi)^2+\frac{1}{2}\phi^2(i\phi)^\varepsilon$ in
$D$-dimensional Euclidean spacetime, where $\phi$ is a pseudoscalar field. In
this earlier paper $E$ and $G_p(x_1,x_2,...,x_p)$ were expressed as
perturbation series in powers of $\varepsilon$ and were calculated to first
order in $\varepsilon$. (The parameter $\varepsilon$ is a measure of the
nonlinearity of the interaction rather than a coupling constant.) This paper
extends these perturbative calculations to the Euclidean Lagrangian $L=
\frac{1}{2}(\nabla\phi)^2+\frac{1}{2}\mu^2\phi^2+\frac{1}{2}
g\mu_0^2\phi^2\big(i\mu_0^{1-D/2}\phi\big)^\varepsilon-iv\phi$, which now
includes renormalization counterterms that are linear and quadratic in the
field $\phi$. The parameter $g$ is a dimensionless coupling strength and
$\mu_0$ is a scaling factor having dimensions of mass. Expressions are given
for the one-, two, and three-point Green's functions, and the renormalized
mass, to higher-order in powers of $\varepsilon$ in $D$ dimensions ($0\leq
D\leq2$). Renormalization is performed perturbatively to second order in
$\varepsilon$ and the structure of the Green's functions is analyzed in the
limit $D\to 2$. A sum of the most divergent terms is performed to {\it all}
orders in $\varepsilon$. Like the Cheng-Wu summation of leading logarithms in
electrodynamics, it is found here that leading logarithmic divergences combine
to become mildly algebraic in form. Future work that must be done to complete
the perturbative renormalization procedure is discussed.


Abstract:
  We propose a nanoscale rotor embedded between two ferromagnetic electrodes
that is driven by spin injection. The spin-rotation coupling allows this
nanorotor to continuously receive angular momentum from an injected spin under
steady current flow between ferromagnetic electrodes in an antiparallel
magnetization configuration. We develop a quantum theory of this angular
momentum transfer and show that a relaxation process from a precession state
into a sleeping top state is crucial for the efficient driving of the nanorotor
by solving the master equation. Our work clarifies a general strategy for
efficient driving of a nanorotor.


Abstract:
  The control of far-from-equilibrium physical systems, including active
materials, has emerged as an important area for the application of
reinforcement learning (RL) strategies to derive control policies for physical
systems. In active materials, non-linear dynamics and long-range interactions
between particles prohibit closed-form descriptions of the system's dynamics
and prevent explicit solutions to optimal control problems. Due to fundamental
challenges in solving for explicit control strategies, RL has emerged as an
approach to derive control strategies for far-from-equilibrium active matter
systems. However, an important open question is how the mathematical structure
and the physical properties of the active matter systems determine the
tractability of RL for learning control policies. In this work, we show that RL
can only find good strategies to the canonical active matter task of mixing for
systems that combine attractive and repulsive particle interactions. Using
mathematical results from dynamical systems theory, we relate the availability
of both interaction types with the existence of hyperbolic dynamics and the
ability of RL to find homogeneous mixing strategies. In particular, we show
that for drag-dominated translational-invariant particle systems, hyperbolic
dynamics and, therefore, mixing requires combining attractive and repulsive
interactions. Broadly, our work demonstrates how fundamental physical and
mathematical properties of dynamical systems can enable or constrain
reinforcement learning-based control.


Abstract:
  Optical coherence tomography (OCT) is a prevalent imaging technique for
retina. However, it is affected by multiplicative speckle noise that can
degrade the visibility of essential anatomical structures, including blood
vessels and tissue layers. Although averaging repeated B-scan frames can
significantly improve the signal-to-noise-ratio (SNR), this requires longer
acquisition time, which can introduce motion artifacts and cause discomfort to
patients. In this study, we propose a learning-based method that exploits
information from the single-frame noisy B-scan and a pseudo-modality that is
created with the aid of the self-fusion method. The pseudo-modality provides
good SNR for layers that are barely perceptible in the noisy B-scan but can
over-smooth fine features such as small vessels. By using a fusion network,
desired features from each modality can be combined, and the weight of their
contribution is adjustable. Evaluated by intensity-based and structural
metrics, the result shows that our method can effectively suppress the speckle
noise and enhance the contrast between retina layers while the overall
structure and small blood vessels are preserved. Compared to the single
modality network, our method improves the structural similarity with low noise
B-scan from 0.559 +\- 0.033 to 0.576 +\- 0.031.


Abstract:
  Hyperspectral image (HSI) with narrow spectral bands can capture rich
spectral information, but it sacrifices its spatial resolution in the process.
Many machine-learning-based HSI super-resolution (SR) algorithms have been
proposed recently. However, one of the fundamental limitations of these
approaches is that they are highly dependent on image and camera settings and
can only learn to map an input HSI with one specific setting to an output HSI
with another. However, different cameras capture images with different spectral
response functions and bands numbers due to the diversity of HSI cameras.
Consequently, the existing machine-learning-based approaches fail to learn to
super-resolve HSIs for a wide variety of input-output band settings. We propose
a single Meta-Learning-Based Super-Resolution (MLSR) model, which can take in
HSI images at an arbitrary number of input bands' peak wavelengths and generate
SR HSIs with an arbitrary number of output bands' peak wavelengths. We leverage
NTIRE2020 and ICVL datasets to train and validate the performance of the MLSR
model. The results show that the single proposed model can successfully
generate super-resolved HSI bands at arbitrary input-output band settings. The
results are better or at least comparable to baselines that are separately
trained on a specific input-output band setting.


Abstract:
  We study the surface elastic response of pure Ni, the random alloy FeNiCr and
an average FeNiCr alloy in terms of the surface lattice Green's function. We
propose a scheme for computing per-site Green's function and study their
per-site variations. The average FeNiCr alloy accurately reproduces the mean
Green's function of the full random alloy. Variation around this mean is
largest near the edge of the surface Brillouin-zone and decays as $q^{-2}$ with
wavevector $q$ towards the $\Gamma$-point. We also present expressions for the
continuum surface Green's function of anisotropic solids of finite and infinite
thickness and show that the atomistic Green's function approaches continuum
near the $\Gamma$-point. Our results are a first step towards efficient contact
calculations and Peierls-Nabarro type models for dislocations in high-entropy
alloys.


Abstract:
  We present a study of optical and near-infrared (NIR) spectra along with the
light curves of SN 2013ai. These data range from discovery until 380 days after
explosion. SN 2013ai is a fast declining type II supernova (SN II) with an
unusually long rise time; $18.9\pm2.7$d in $V$ band and a bright $V$ band peak
absolute magnitude of $-18.7\pm0.06$ mag. The spectra are dominated by hydrogen
features in the optical and NIR. The spectral features of SN 2013ai are unique
in their expansion velocities, which when compared to large samples of SNe II
are more than 1,000 kms faster at 50 days past explosion. In addition, the long
rise time of the light curve more closely resembles SNe IIb rather than SNe II.
If SN 2013ai is coeval with a nearby compact cluster we infer a progenitor ZAMS
mass of $\sim$17 M$_\odot$. After performing light curve modeling we find that
SN 2013ai could be the result of the explosion of a star with little hydrogen
mass, a large amount of synthesized $^{56}$Ni, 0.3-0.4 M$_\odot$, and an
explosion energy of $2.5-3.0\times10^{51}$ ergs. The density structure and
expansion velocities of SN 2013ai are similar to that of the prototypical SN
IIb, SN 1993J. However, SN 2013ai shows no strong helium features in the
optical, likely due to the presence of a dense core that prevents the majority
of $\gamma$-rays from escaping to excite helium. Our analysis suggests that SN
2013ai could be a link between SNe II and stripped envelope SNe.


Abstract:
  The gravitational shock waves have provided crucial insights into
entanglement structures of black holes in the AdS/CFT correspondence. Recent
progress on the soft hair physics suggests that these developments from
holography may also be applicable to geometries beyond negatively curved
spacetime. In this work, we derive a remarkably simple thermodynamic relation
which relates the gravitational shock wave to a microscopic area deformation.
Our treatment is based on the covariant phase space formalism and is applicable
to any Killing horizon in generic static spacetime which is governed by
arbitrary covariant theory of gravity. The central idea is to probe the
gravitational shock wave, which shifts the horizon in the $u$ direction, by the
Noether charge constructed from a vector field which shifts the horizon in the
$v$ direction. As an application, we illustrate its use for the Gauss-Bonnet
gravity. We also derive a simplified form of the gravitational scattering
unitary matrix and show that its leading-order contribution is nothing but the
exponential of the horizon area: $\mathcal{U}=\exp(i \text{Area})$.


Abstract:
  We consider the popular and classical method of alternating projections for
finding a point in the intersection of two closed sets. By situating the
algorithm in a metric space, equipped only with well-behaved geodesics and
angles (in the sense of Alexandrov), we are able to highlight the two key
geometric ingredients in a standard intuitive analysis of local linear
convergence. The first is a transversality-like condition on the intersection;
the second is a convexity-like condition on one set: "uniform approximation by
geodesics".


Abstract:
  Image denoising is one of the most critical problems in mobile photo
processing. While many solutions have been proposed for this task, they are
usually working with synthetic data and are too computationally expensive to
run on mobile devices. To address this problem, we introduce the first Mobile
AI challenge, where the target is to develop an end-to-end deep learning-based
image denoising solution that can demonstrate high efficiency on smartphone
GPUs. For this, the participants were provided with a novel large-scale dataset
consisting of noisy-clean image pairs captured in the wild. The runtime of all
models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali
GPU capable of accelerating floating-point and quantized neural networks. The
proposed solutions are fully compatible with any mobile GPU and are capable of
processing 480p resolution images under 40-80 ms while achieving high fidelity
results. A detailed description of all models developed in the challenge is
provided in this paper.


Abstract:
  We extend the theorem of Liouville on integration in finite terms to include
dilogarithmic integrals. The results provide a necessary and sufficient
condition for an element of the base field to have an antiderivative in a field
extension generated by transcendental elementary functions and dilogarithmic
integrals. We also study algebraic independence of certain dilogarithmic
integrals.


Abstract:
  In this work, an efficient physics-constrained deep learning model is
developed for solving multiphase flow in 3D heterogeneous porous media. The
model fully leverages the spatial topology predictive capability of
convolutional neural networks, and is coupled with an efficient
continuity-based smoother to predict flow responses that need spatial
continuity. Furthermore, the transient regions are penalized to steer the
training process such that the model can accurately capture flow in these
regions. The model takes inputs including properties of porous media, fluid
properties and well controls, and predicts the temporal-spatial evolution of
the state variables (pressure and saturation). While maintaining the continuity
of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing
training cost, and the decomposition results in an increased number of training
data samples and better training efficiency. Additionally, a surrogate model is
separately constructed as a postprocessor to calculate well flow rate based on
the predictions of state variables from the deep learning model. We use the
example of CO2 injection into saline aquifers, and apply the
physics-constrained deep learning model that is trained from physics-based
simulation data and emulates the physics process. The model performs prediction
with a speedup of ~1400 times compared to physics-based simulations, and the
average temporal errors of predicted pressure and saturation plumes are 0.27%
and 0.099% respectively. Furthermore, water production rate is efficiently
predicted by a surrogate model for well flow rate, with a mean error less than
5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow
in porous media, the physics-constrained deep learning model can become an
efficient predictive model for computationally demanding inverse problems or
other coupled processes.


Abstract:
  A Simultaneous Localization and Mapping (SLAM) system must be robust to
support long-term mobile vehicle and robot applications. However, camera and
LiDAR based SLAM systems can be fragile when facing challenging illumination or
weather conditions which degrade their imagery and point cloud data. Radar,
whose operating electromagnetic spectrum is less affected by environmental
changes, is promising although its distinct sensing geometry and noise
characteristics bring open challenges when being exploited for SLAM. % However,
there are still open challenges since most existing visual and LiDAR SLAM
systems do not operate in bad weathers. This paper studies the use of a
Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor
environments. We propose a full radar SLAM system, including a novel radar
motion tracking algorithm that leverages radar geometry for reliable feature
tracking. It also optimally compensates motion distortion and estimates pose by
joint optimization. Its loop closure component is designed to be simple yet
efficient for radar imagery by capturing and exploiting structural information
of the surrounding environment. % while a scheme to reject ambiguous loop
closure candidates is also designed specifically for radar. Extensive
experiments on three public radar datasets, ranging from city streets and
residential areas to countryside and highways, show competitive accuracy and
reliability performance of the proposed radar SLAM system compared to the
state-of-the-art LiDAR, vision and radar methods. The results show that our
system is technically viable in achieving reliable SLAM in extreme weather
conditions, e.g. heavy snow and dense fog, demonstrating the promising
potential of using radar for all-weather localization and mapping.


Abstract:
  In this technical report, we propose an algorithm, called Lex2vec that
exploits lexical resources to inject information into word embeddings and name
the embedding dimensions by means of knowledge bases. We evaluate the optimal
parameters to extract a number of informative labels that is readable and has a
good coverage for the embedding dimensions.


Abstract:
  The dependence of the current density on the phase difference is investigated
considering the layered superconducting structures of a SIS'IS type. To
simplify the calculations, the quasiclassical equations for the Green's
functions in a \textit{t}-representation are derived. An order parameter is
considered as a piecewise constant function. To consider the general case, no
restrictions on the dielectric layer transparency and the thickness of the
intermediate layer are imposed. It was found that a new analytical expression
for the current-phase relation can be used with the aim to obtain a number of
previously known results arising in particular cases.


Abstract:
  It has been unknown in kinetic theory whether the linearized Boltzmann or
Landau equation with soft potentials admits a spectral gap in the spatially
inhomogeneous setting. Most of existing works indicate a negative answer
because the spectrum of two linearized self-adjoint collision operators is
accumulated to the origin in case of soft interactions. In the paper we rather
prove it in an affirmative way when the space domain is bounded with an inflow
boundary condition. The key strategy is to introduce a new Hilbert space with
an exponential weight function that involves the inner product of space and
velocity variables and also has the strictly positive upper and lower bounds.
The action of the transport operator on such space-velocity dependent weight
function induces an extra non-degenerate relaxation dissipation in large
velocity that can be employed to compensate the degenerate spectral gap and
hence give the exponential decay for solutions in contrast with the
sub-exponential decay in either the spatially homogeneous case or the case of
torus domain. The result reveals a new insight of hypocoercivity for kinetic
equations with soft potentials in the specified situation.


Abstract:
  We derive the lower bound on the absolute scale of lightest neutrino mass for
normal hierarchy and inverted hierarchy pattern of light neutrinos by studying
the new physics contributions to charged lepton flavour violations in the
framework of a TeV scale left-right symmetric model. In the model, the fermion
sector comprises the usual quarks and leptons plus a fermion singlet per
generation and the scalar sector consists of isospin doublets, triplets and a
bidoublet. The framework allows large light-heavy neutrino mixing where the
light neutrino mass formula is governed by a natural type-II seesaw mechanism,
unlike the generic type-II seesaw dominance which assumes suppressed
light-heavy neutrino mixing. We demonstrate how sizeable loop-induced
contribution to light neutrino mass is kept under control such that the light
neutrino mass formula is dominantly explained by the type-II seesaw mechanism.
We examine the heavy neutrino contributions with large light-heavy neutrino
mixing to charged lepton flavour violating processes like $\mu \to e \gamma$,
$\mu \to 3 e$ and $\mu \to e$ conversion inside a nucleus. We present a
complementary study between neutrinoless double beta decay and charged lepton
flavour violation taking into account single beta decay bound, double beta
decay bound and cosmology bounds on the sum of light neutrino masses.


Abstract:
  How effectively do we adhere to nudges and interventions that help us control
our online browsing habits? If we have a temporary lapse and disable the
behavior change system, do we later resume our adherence, or has the dam
broken? In this paper, we investigate these questions through log analyses of
8,000+ users on HabitLab, a behavior change platform that helps users reduce
their time online. We find that, while users typically begin with
high-challenge interventions, over time they allow themselves to slip into
easier and easier interventions. Despite this, many still expect to return to
the harder interventions imminently: they repeatedly choose to be asked to
change difficulty again on the next visit, declining to have the system save
their preference for easy interventions.


Abstract:
  Reading text aloud is an important feature for modern computer applications.
It not only facilitates access to information for visually impaired people, but
is also a pleasant convenience for non-impaired users. In this article, the
state of the art of speech synthesis is presented separately for
mel-spectrogram generation and vocoders. It concludes with an overview of
available data sets for English and German with a discussion of the
transferability of the good speech synthesis results from English to German
language.


Abstract:
  The FCC-ee offers powerful opportunities for direct or indirect evidence for
physics beyond the Standard Model, via a combination of high precision
measurements and searches for forbidden and rare processes and feebly coupled
particles. A key element of FCC-ee physics program is the measurement of the Z
lineshape from a total of $5\times 10^{12}$ Z bosons and a beam-energy
calibration with relative uncertainty of $10^{-6}$. With this exceptionally
large event sample, five orders of magnitude larger than that accumulated
during the whole LEP1 operation at the Z pole, the defining parameters -
$m_{\rm Z}$, $\Gamma_{\rm Z}$, $N_\nu$, $\sin^2\theta_{\rm W}^{\rm eff}$,
$\alpha_{\rm S}(m_{\rm Z}^2)$, and $\alpha_{\rm QED}(m^2_{\rm Z})$ - can be
extracted with a leap in accuracy of up to two orders of magnitude with respect
to the current state of the art. The ultimate goal that experimental and theory
systematic errors match the statistical accuracy (4\,keV on the Z mass and
width, $3\times 10^{-6}$ on $\sin^2\theta_{\rm W}^{\rm eff}$, a relative
$3\times 10^{-5}$ on $\alpha_{\rm QED}$, and less than 0.0001 on $\alpha_{\rm
S}$) leads to highly demanding requirements on collider operation, beam
instrumentation, detector design, computing facilities, theoretical
calculations, and Monte Carlo event generators. Such precise measurements also
call for innovative analysis methods, which require a joint effort and
understanding between theorists, experimenters, and accelerator teams.


Abstract:
  Reliable empirical models such as those used in software effort estimation or
defect prediction are inherently dependent on the data from which they are
built. As demands for process and product improvement continue to grow, the
quality of the data used in measurement and prediction systems warrants
increasingly close scrutiny. In this paper we propose a taxonomy of data
quality challenges in empirical software engineering, based on an extensive
review of prior research. We consider current assessment techniques for each
quality issue and proposed mechanisms to address these issues, where available.
Our taxonomy classifies data quality issues into three broad areas: first,
characteristics of data that mean they are not fit for modeling; second, data
set characteristics that lead to concerns about the suitability of applying a
given model to another data set; and third, factors that prevent or limit data
accessibility and trust. We identify this latter area as of particular need in
terms of further research.


Abstract:
  In this work, we studied the impact of galaxy morphology on photometric
redshift (photo-$z$) probability density functions (PDFs). By including galaxy
morphological parameters like the radius, axis-ratio, surface brightness and
the S\'ersic index in addition to the $ugriz$ broadbands as input parameters,
we used the machine learning photo-$z$ algorithm ANNz2 to train and test on
galaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey.
Metrics like the continuous ranked probability score (CRPS), probability
integral transform (PIT), Bayesian odds parameter, and even the width and
height of the PDFs were evaluated, and the results were compared when different
number of input parameters were used during the training process. We find
improvements in the CRPS and width of the PDFs when galaxy morphology has been
added to the training, and the improvement is larger especially when the number
of broadband magnitudes are lacking.


Abstract:
  We examine an intriguing possibility that a single field is responsible for
both inflation and dark matter, focussing on the minimal set-up where inflation
is driven by a scalar coupling to curvature. We study in detail the reheating
process in this framework, which amounts mainly to particle production in a
quartic potential, and distinguish thermal and non-thermal dark matter options.
In the non-thermal case, the reheating is impeded by backreaction and
rescattering, making this possibility unrealistic. On the other hand,
thermalized dark matter is viable, yet the unitarity bound forces the inflaton
mass into a narrow window close to half the Higgs mass.


Abstract:
  We consider a nonnegative potential $W:\mathbb{R}^2\rightarrow\mathbb{R}$
invariant under the action of the rotation group $C_N$ of the regular polygon
with $N$ sides, $N\geq 3$. We assume that $W$ has $N$ nondegenerate zeros and
prove the existence of a $N$-junction solution to the vector Allen-Cahn
equation. The proof is variational and is based on sharp lower and upper bounds
for the energy and on a new pointwise estimate for vector minimizers.


Abstract:
  Deep generative models are known to be able to model arbitrary probability
distributions. Among these, a recent deep generative model, dubbed sliceGAN,
proposed a new way of using the generative adversarial network (GAN) to capture
the micro-structural characteristics of a two-dimensional (2D) slice and
generate three-dimensional (3D) volumes with similar properties. While 3D
micrographs are largely beneficial in simulating diverse material behavior,
they are often much harder to obtain than their 2D counterparts. Hence,
sliceGAN opens up many interesting directions of research by learning the
representative distribution from 2D slices, and transferring the learned
knowledge to generate arbitrary 3D volumes. However, one limitation of sliceGAN
is that latent space steering is not possible. Hence, we combine sliceGAN with
AdaIN to endow the model with the ability to disentangle the features and
control the synthesis.


Abstract:
  Starting with unitary quantum dynamics, we investigate how to add quantum
measurements. Quantum measurements have four essential components: the
furcation, the witness production, an alignment projection, and the actual
choice decision. The first two components still lie in the domain of unitary
quantum dynamics. The decoherence concept explains the third contribution. It
can be based on the requirement that witnesses reaching the end of time on the
wave function side and the conjugate one have to be identical. In this way, it
also stays within the quantum dynamics domain. The surjection hypothesis
explains the actual choice decision. It is based on a two boundary
interpretation applied to the complete quantum universe. It offers a simple way
to reduce these seemingly random projections to purely deterministic unitary
quantum dynamics, eliminating the measurement problem.


Abstract:
  It has been known for decades that a magnetic field can deflect phonons as
they flow in response to a thermal gradient, producing a thermal Hall effect.
Several recent experiments have revealed ratios of the phonon Hall conductivity
to the phonon longitudinal conductivity in oxide dielectrics that are larger
than $10^{-3}$ when phonon mean-free-paths exceed phonon wavelengths. At the
same time $\kappa_{H}/\kappa_{L}$ is not strongly temperature dependent. We
argue that these two properties together imply a mechanism related to phonon
scattering from defects that break time-reversal symmetry, and we show that
Lorentz forces acting on charged defects produce substantial skew-scattering
amplitudes, and related thermal Hall effects that are consistent with recent
observations.


Abstract:
  The ongoing exponential rise in recording capacity calls for new approaches
for analysing and interpreting neural data. Effective dimensionality has
emerged as an important property of neural activity across populations of
neurons, yet different studies rely on different definitions and
interpretations of this quantity. Here we focus on intrinsic and embedding
dimensionality, and discuss how they might reveal computational principles from
data. Reviewing recent works, we propose that the intrinsic dimensionality
reflects information about the latent variables encoded in collective activity,
while embedding dimensionality reveals the manner in which this information is
processed. We conclude by highlighting the role of network models as an ideal
substrate for testing more specifically various hypotheses on the computational
principles reflected through intrinsic and embedding dimensionality.


Abstract:
  We introduce three new analytical and semi-analytical tools that allow one to
determine the topological character of impurity Shiba chains. The analytical
methods are based on calculating the effective Green's function of an infinite
embedded chain using the T-matrix formalism and describing the chain as a {\it
line impurity}. We thus provide a solution to the longstanding size-effects
problem affecting the only general alternative method, the numerical
tight-binding analysis. As an example we consider a chain of magnetic
impurities deposited on an s-wave superconducting substrate with Rashba
spin-orbit and we calculate its topological phase diagram as a function of the
magnetic impurity strength and the chemical potential. We find a perfect
agreement between all our new techniques and a numerical analysis.


Abstract:
  We show that the $\imath$Hall algebra of the Jordan quiver is a polynomial
ring in infinitely many generators and obtain transition relations among
several generating sets. We establish a ring isomorphism from this $\imath$Hall
algebra to the ring of symmetric functions in two parameters $t, \theta$, which
maps the $\imath$Hall basis to a class of (modified) inhomogeneous
Hall-Littlewood ($\imath$HL) functions. The (modified) $\imath$HL functions
admit a formulation via raising and lowering operators. We formulate and prove
Pieri rules for (modified) $\imath$HL functions. The modified $\imath$HL
functions specialize at $\theta=0$ to the modified HL functions; they
specialize at $\theta=1$ to the deformed universal characters of type C, which
further specialize at $(t=0, \theta =1)$ to the universal characters of type C.


Abstract:
  In this article we propose a distributed collision avoidance scheme for
multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive
control (NMPC),where other agents in the system are considered as dynamic
obstacles with respect to the ego agent. Our control scheme operates at a low
level and commands roll, pitch and thrust signals at a high frequency, each
agent broadcasts its predicted trajectory to the other ones, and we propose an
obstacle prioritization scheme based on the shared trajectories to allow
up-scaling of the system. The NMPC problem is solved using an ad hoc solver
where PANOC is combined with an augmented Lagrangian method to compute
collision-free trajectories. We evaluate the proposed scheme in several
challenging laboratory experiments for up to ten aerial agents, in dense aerial
swarms.


Abstract:
  Deep learning has shown great promise for CT image reconstruction, in
particular to enable low dose imaging and integrated diagnostics. These merits,
however, stand at great odds with the low availability of diverse image data
which are needed to train these neural networks. We propose to overcome this
bottleneck via a deep reinforcement learning (DRL) approach that is integrated
with a style-transfer (ST) methodology, where the DRL generates the anatomical
shapes and the ST synthesizes the texture detail. We show that our method bears
high promise for generating novel and anatomically accurate high resolution CT
images at large and diverse quantities. Our approach is specifically designed
to work with even small image datasets which is desirable given the often low
amount of image data many researchers have available to them.


Abstract:
  While transient photoluminescence measurements are a very popular tool to
monitor the charge-carrier dynamics in the field of halide perovskite
photovoltaics, interpretation of data obtained on multilayer samples is highly
challenging due to the superposition of various effects that modulate the
charge-carrier concentration in the perovskite layer and thereby the measured
PL. These effects include bulk and interfacial recombination, charge transfer
to electron or hole transport layers and capacitive charging or discharging.
Here, numerical simulations with Sentauraus TCAD, analytical solutions and
experimental data with a dynamic range of ~7 orders of magnitude on a variety
of different sample geometries from perovskite films on glass to full devices
are combined to present an improved understanding of this method. A
presentation of the decay time of the TPL decay that follows from taking the
derivative of the photoluminescence at every time is proposed. Plotting this
decay time as a function of the time-dependent quasi-Fermi level splitting
enables distinguishing between the different contributions of radiative and
non-radiative recombination as well as charge extraction and capacitive effects
to the decay.


Abstract:
  Using the Large Binocular Telescope (LBT)/Multi-Object Dual Spectrograph
(MODS), we have obtained optical spectroscopy of one of the most metal-poor
dwarf star-forming galaxies (SFG) in the local Universe, J2229+2725. This
galaxy with a redshift z=0.0762 was selected from the Data Release 16 (DR16) of
the Sloan Digital Sky Survey (SDSS). Its properties derived from the LBT
observations are most extreme among SFGs in several ways. Its oxygen abundance
12+logO/H=7.085+/-0.031 is among the lowest ever observed for a SFG. With its
very low metallicity, an absolute magnitude Mg=-16.39 mag, a low stellar mass
Mstar=9.1x10^6 Msun and a very low mass-to-light ratio Mstar/Lg~0.0166 (in
solar units), J2229+2725 deviates strongly from the luminosity-metallicity
relation defined by the bulk of the SFGs in the SDSS. J2229$+$2725 has a very
high specific star-formation rate sSFR~75 Gyr^-1, indicating very active
ongoing star formation. Three other features of J2229+2725 are most striking,
being the most extreme among lowest-metallicity SFGs: 1) a ratio
O32=I([OIII]5007)/I([OII]3727)~53, 2) an equivalent width of the Hbeta emission
line EW(Hbeta) of 577A, and 3) an electron number density of ~1000 cm^-3. These
properties imply that the starburst in J2229+2725 is very young. Using the
extremely high O32 in J2229+2725, we have improved the strong-line calibration
for the determination of oxygen abundances in the most metal-deficient
galaxies, in the range 12 + logO/H<7.3.


Abstract:
  Understanding the origins of unconventional superconductivity has been a
major focus of condensed matter physics for many decades. While many questions
remain unanswered, experiments have found that the systems with the highest
critical temperatures tend to be layered materials where superconductivity
occurs in two-dimensional (2D) structures. However, to what extent the
remarkable stability of these strongly correlated 2D superfluids is related to
their reduced dimensionality is still an open question. In this work, we use
dilute gases of ultracold fermionic atoms as a model system to directly observe
the influence of dimensionality on strongly interacting fermionic superfluids.
We achieve this by measuring the superfluid gap of a strongly correlated
quasi-2D Fermi gas over a wide range of interaction strengths and comparing the
results to recent measurements in 3D Fermi gases. We find that the superfluid
gap follows the same universal function of the interaction strength in both
systems, which suggests that there is no inherent difference in the stability
of fermionic superfluidity between two- and three-dimensional quantum gases.
Finally, we compare our data to results from solid state systems and find a
similar relation between the interaction strength and the gap for a wide range
of two- and three-dimensional superconductors.


Abstract:
  Supervised methods for object delineation in remote sensing require labeled
ground-truth data. Gathering sufficient high quality ground-truth data is
difficult, especially when targets are of irregular shape or difficult to
distinguish from background or neighboring objects. Tree crown delineation
provides key information from remote sensing images for forestry, ecology, and
management. However, tree crowns in remote sensing imagery are often difficult
to label and annotate due to irregular shape, overlapping canopies, shadowing,
and indistinct edges. There are also multiple approaches to annotation in this
field (e.g., rectangular boxes vs. convex polygons) that further contribute to
annotation imprecision. However, current evaluation methods do not account for
this uncertainty in annotations, and quantitative metrics for evaluation can
vary across multiple annotators. In this paper, we address these limitations by
developing an adaptation of the Rand index for weakly-labeled crown delineation
that we call RandCrowns. Our new RandCrowns evaluation metric provides a method
to appropriately evaluate delineated tree crowns while taking into account
imprecision in the ground-truth delineations. The RandCrowns metric
reformulates the Rand index by adjusting the areas over which each term of the
index is computed to account for uncertain and imprecise object delineation
labels. Quantitative comparisons to the commonly used intersection over union
method shows a decrease in the variance generated by differences among multiple
annotators. Combined with qualitative examples, our results suggest that the
RandCrowns metric is more robust for scoring target delineations in the
presence of uncertainty and imprecision in annotations that are inherent to
tree crown delineation.


Abstract:
  This report classifies emission inhomogeneities that manifest in InGaN
quantum well blue light-emitting diodes grown by plasma-assisted molecular beam
epitaxy on free-standing GaN substrates. By a combination of spatially resolved
electroluminescence and cathodoluminescence measurements, atomic force
microscopy, scanning electron microscopy and hot wet KOH etching, the
identified inhomogeneities are found to fall in four categories. Labeled here
as type I through IV, they are distinguishable by their size, density, energy,
intensity, radiative and electronic characteristics and chemical etch pits
which correlates them with dislocations. Type I exhibits a blueshift of about
120 meV for the InGaN quantum well emission attributed to a perturbation of the
active region, which is related to indium droplets that form on the surface in
the metal-rich InGaN growth condition. Specifically, we attribute the blueshift
to a decreased growth rate of and indium incorporation in the InGaN quantum
wells underneath the droplet which is postulated to be the result of reduced
incorporated N species due to increased N$_{2}$ formation. The location of
droplets are correlated with mixed type dislocations for type I defects. Types
II through IV are due to screw dislocations, edge dislocations, and dislocation
bunching, respectively, and form dark spots due to leakage current and
nonradiative recombination.


Abstract:
  We consider the problem of estimating a particular type of linear
non-Gaussian model. Without resorting to the overcomplete Independent Component
Analysis (ICA), we show that under some mild assumptions, the model is uniquely
identified by a hybrid method. Our method leverages the advantages of
constraint-based methods and independent noise-based methods to handle both
confounded and unconfounded situations. The first step of our method uses the
FCI procedure, which allows confounders and is able to produce asymptotically
correct results. The results, unfortunately, usually determine very few
unconfounded direct causal relations, because whenever it is possible to have a
confounder, it will indicate it. The second step of our procedure finds the
unconfounded causal edges between observed variables among only those adjacent
pairs informed by the FCI results. By making use of the so-called Triad
condition, the third step is able to find confounders and their causal
relations with other variables. Afterward, we apply ICA on a notably smaller
set of graphs to identify remaining causal relationships if needed. Extensive
experiments on simulated data and real-world data validate the correctness and
effectiveness of the proposed method.


Abstract:
  Deceptive news posts shared in online communities can be detected with NLP
models, and much recent research has focused on the development of such models.
In this work, we use characteristics of online communities and authors -- the
context of how and where content is posted -- to explain the performance of a
neural network deception detection model and identify sub-populations who are
disproportionately affected by model accuracy or failure. We examine who is
posting the content, and where the content is posted to. We find that while
author characteristics are better predictors of deceptive content than
community characteristics, both characteristics are strongly correlated with
model performance. Traditional performance metrics such as F1 score may fail to
capture poor model performance on isolated sub-populations such as specific
authors, and as such, more nuanced evaluation of deception detection models is
critical.


Abstract:
  We consider skew-product maps over circle rotations $x\mapsto x+\alpha$ (mod
1) with factors that take values in SL(2,R). This includes maps of almost
Mathieu type. In numerical experiments, with $\alpha$ the inverse golden mean,
Fibonacci iterates of maps from the almost Mathieu family exhibit asymptotic
scaling behavior that is reminiscent of critical phase transitions. In a
restricted setup that is characterized by a symmetry, we prove that critical
behavior indeed occurs and is universal in an open neighborhood of the almost
Mathieu family. This behavior is governed by a periodic orbit of a
renormalization transformation. An extension of this transformation is shown to
have a second periodic orbit as well, and we present some evidence that this
orbit attracts supercritical almost Mathieu maps.


Abstract:
  This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find
forced wins during trick-taking in the card game Skat; for some one of the most
interesting card games for three players. It combines efficient partial
information game-tree search with knowledge representation and reasoning. This
worst-case analysis, initiated after a small number of tricks, leads to a
prioritized choice of cards. We provide variants of KBPS for the declarer and
the opponents, and an approximation to find a forced win against most worlds in
the belief space. Replaying thousands of expert games, our evaluation indicates
that the AIs with the new algorithms perform better than humans in their play,
achieving an average score of over 1,000 points in the agreed standard for
evaluating Skat tournaments, the extended Seeger system.


Abstract:
  Learning the structure of dependence relations between variables is a
pervasive issue in the statistical literature. A directed acyclic graph (DAG)
can represent a set of conditional independences, but different DAGs may encode
the same set of relations and are indistinguishable using observational data.
Equivalent DAGs can be collected into classes, each represented by a partially
directed graph known as essential graph (EG). Structure learning directly
conducted on the EG space, rather than on the allied space of DAGs, leads to
theoretical and computational benefits. Still, the majority of efforts in the
literature has been dedicated to Gaussian data, with less attention to methods
designed for multivariate categorical data. We then propose a Bayesian
methodology for structure learning of categorical EGs. Combining a constructive
parameter prior elicitation with a graph-driven likelihood decomposition, we
derive a closed-form expression for the marginal likelihood of a categorical EG
model. Asymptotic properties are studied, and an MCMC sampler scheme developed
for approximate posterior inference. We evaluate our methodology on both
simulated scenarios and real data, with appreciable performance in comparison
with state-of-the-art methods.


Abstract:
  We focus on a convolutional neural network (CNN), which has recently been
utilized for fluid flow analyses, from the perspective on the influence of
various operations inside it by considering some canonical regression problems
with fluid flow data. We consider two types of CNN-based fluid flow analyses;
1. CNN metamodeling and 2. CNN autoencoder. For the first type of CNN with
additional scalar inputs, which is one of the common forms of CNN for fluid
flow analysis, we investigate the influence of input placements in the CNN
training pipeline. As an example, estimation of force coefficients of an
inclined flat plate and two side-by-side cylinders in laminar flows is
considered. We find that care should be taken for the placement of additional
scalar inputs depending on the problem setting and the complexity of flows that
users handle. We then discuss the influence of various parameters and
operations on the CNN performance, with the utilization of autoencoder (AE). A
two-dimensional turbulence is considered for the demonstration of AE. The
results of AE highly rely on the decaying nature. Investigation on the
influence of padding operation at a convolutional layer is also performed. The
zero padding shows reasonable ability compared to other methods which account
for the boundary conditions assumed in the numerical data. Moreover, the effect
of the dimensional reduction/extension methods inside CNN is also examined. The
CNN model is robust against the difference in dimension reduction operations,
while it is sensitive to the dimensional extension methods. The findings of
this paper will help us better design a CNN architecture for practical fluid
flow analysis


Abstract:
  Analytical formulas are presented for simplified but useful qubit geometries
that predict surface dielectric loss when its thickness is much less than the
metal thickness, the limiting case needed for real devices. These formulas can
thus be used to precisely predict loss and optimize the qubit layout.
Surprisingly, a significant fraction of surface loss comes from the small wire
that connects the Josephson junction to the qubit capacitor. Tapering this wire
is shown to significantly lower its loss. Also predicted are the size and
density of the two-level state (TLS) spectrum from individual surface
dissipation sites.


Abstract:
  As subjective artistic creations, artistic paintings carry emotion of their
creators. Emotions expressed in paintings and emotion aroused in spectators by
paintings are two kinds of emotions that scholars have paid attention to.
Traditional studies on emotions expressed by paintings are mainly conducted
from qualitative perspectives, with neither quantitative output on the
emotional values of a painting, nor exploration of trends in the expression of
emotion in art history. In this research we threat facial expressions in
paintings as an artistic characteristics of art history and employ cognitive
computation technology to identify the facial emotions in paintings and to
investigate the quantitative measures of paintings from three emotion-related
aspects: the spatial and temporal patterns of painting emotions in art history,
the gender difference on the emotion of paintings and the color preference
associated with emotions. We discovered that the emotion of happiness has a
growing trend from ancient to modern times in paintings history, and men and
women have different facial expressions patterns along time. As for color
preference, artists with different culture backgrounds had similar association
preferences between colors and emotions.


Abstract:
  We present the first study on the amplification of magnetic fields by the
turbulent dynamo in the highly subsonic regime, with Mach numbers ranging from
$10^{-3}$ to $0.4$. We find that for the lower Mach numbers the saturation
efficiency of the dynamo, $(E_{\mathrm{mag}}/E_{\mathrm{kin}})_{\mathrm{sat}}$,
increases as the Mach number decreases. Even in the case when injection of
energy is purely through longitudinal forcing modes,
$(E_{\mathrm{mag}}/E_{\mathrm{kin}})_{\mathrm{sat}}$ $\gtrsim 10^{-2}$ at a
Mach number of $10^{-3}$. We apply our results to magnetic field amplification
in the early Universe and predict that a turbulent dynamo can amplify
primordial magnetic fields to $\gtrsim$ $10^{-16}$ Gauss on scales up to 0.1 pc
and $\gtrsim$ $10^{-13}$ Gauss on scales up to 100 pc. This produces fields
compatible with lower limits of the intergalactic magnetic field inferred from
blazar $\gamma$-ray observations.


Abstract:
  The theory of standard pentads is the theory aims to construct a graded Lie
algebra whose local part consists of a given Lie algebra and its
representation. In other words, using standard pentads, we can embed given Lie
algebra and its representation into a larger graded Lie algebra. As special
cases of Lie algebras associated with standard pentads, we have the notion of
PC Lie algebras. Our aim of this paper is to show that the class of PC Lie
algebras contains the class of Kac-Moody algebras, that is, to show that the
notion of PC Lie algebras is an extension of Kac-Moody algebras.


Abstract:
  Power packet dispatching system has been proposed for smart power management
in the form of discretized packet. In this paper, we discuss the routing
optimization of power packets on the network of power routers. We propose a
cost metric for the power packet delivery by circuit analysis of the router
network. Using the metric, we formulate the optimization problem as a general
shortest path problem from a source node to a load node. The result of
numerical simulations shows that the proposed algorithm can allocate
distributed power sources to load demands and identify the optimal path for the
power delivery.


Abstract:
  Multidimensional arrays (NDArrays) are a central abstraction in modern
scientific computing environments. Unfortunately, they can make reasoning about
programs harder as the number of different array shapes used in an execution of
a program is usually very large, and they rarely appear explicitly in program
text. To make things worse, many operators make implicit assumptions about the
shapes of their inputs: array addition is commonly enriched with broadcasting
semantics, while matrix multiplication assumes that the lengths of contracted
dimensions are equal. Because precise reasoning about shapes is crucial to
write correct programs using NDArrays, and because shapes are often hard to
infer from a quick glance at the program, we developed Tensors Fitting
Perfectly, a static analysis tool that reasons about NDArray shapes in Swift
for TensorFlow programs by synthesizing a set of shape constraints from an
abstract interpretation of the program. It can both (1) check for possible
inconsistencies, and (2) provide direct insights about the shapes of
intermediate values appearing in the program, including via a mechanism called
shape holes. The static analysis works in concert with optional runtime
assertions to improve the productivity of program authors.


Abstract:
  We say that the families $\mathcal F_1,\ldots, \mathcal F_{s+1}$ of
$k$-element subsets of $[n]$ are cross-dependent if there are no pairwise
disjoint sets $F_1,\ldots, F_{s+1}$, where $F_i\in \mathcal F_i$ for each $i$.
The rainbow version of the Erd\H os Matching Conjecture due to Aharoni and
Howard and independently to Huang, Loh and Sudakov states that $\min_{i}
|\mathcal F_i|\le \max\big\{{n\choose k}-{n-s\choose k}, {(s+1)k-1\choose
k}\big\}$. In this paper, we prove this conjecture for $n>3e(s+1)k$ and
$s>10^7$. One of the main tools in the proof is a concentration inequality due
to Frankl and the author.


Abstract:
  The task of inducing, via continuous static state-feedback, an asymptotically
stable heteroclinic orbit in a nonlinear control system is considered in this
paper. The main motivation comes from the problem of ensuring convergence to a
so-called point-to-point maneuver in an underactuated mechanical system.
Namely, to a smooth curve in its state--control space, which is consistent with
the system dynamics and connects two (linearly) stabilizable equilibrium
points. The proposed method uses a particular parameterization, together with a
state projection onto the maneuver as to combine two linearization techniques
for this purpose: the Jacobian linearization at the equilibria on the
boundaries and a transverse linearization along the orbit. This allows for the
computation of stabilizing control gains offline by solving a semidefinite
programming problem. The resulting nonlinear controller, which simultaneously
asymptotically stabilizes both the orbit and the final equilibrium, is
time-invariant, locally Lipschitz continuous, requires no switching, and has a
familiar feedforward plus feedback--like structure. The method is also
complemented by synchronization function--based arguments for planning such
maneuvers for mechanical systems with one degree of underactuation. Numerical
simulations of the non-prehensile manipulation task of a ball rolling between
two points upon the "butterfly" robot demonstrates the efficacy of the
synthesis.


Abstract:
  Currently, the world is in the midst of a severe global pandemic, which has
affected all aspects of people's lives. As a result, there is a deluge of
COVID-related digital media articles published in the United States, due to the
disparate effects of the pandemic. This large volume of information is
difficult to consume by the audience in a reasonable amount of time. In this
paper, we develop a Natural Language Processing (NLP) pipeline that is capable
of automatically distilling various digital articles into manageable pieces of
information, while also modelling the progression topics discussed over time in
order to aid readers in rapidly gaining holistic perspectives on pressing
issues (i.e., the COVID-19 pandemic) from a diverse array of sources. We
achieve these goals by first collecting a large corpus of COVID-related
articles during the onset of the pandemic. After, we apply unsupervised and
semi-supervised learning procedures to summarize articles, then cluster them
based on their similarities using the community detection methods. Next, we
identify the topic of each cluster of articles using the BART algorithm.
Finally, we provide a detailed digital media analysis based on the NLP-pipeline
outputs and show how the conversation surrounding COVID-19 evolved over time.


Abstract:
  Demand response (DR) refers to change in electricity consumption pattern of
customers during on-peak hours in lieu of financial gains to reduce stress on
distribution systems. Existing dynamic price models have not provided adequate
success to price-based demand response (PBDR) programs. It happened as these
models have raised typical socio-economic problems pertaining to cross-subsidy,
free-riders, social inequity, assured profit of utilities, financial gains and
comfort of customers, etc. This paper presents a new dynamic price model for
PBDR in distribution systems which aims to overcome some of the above mentioned
problems of the existing price models. The main aim of the developed price
model is to overcome the problems of cross-subsidy and free-riders of the
existing price models for widespread acceptance, deployment and efficient
utilization of PBDR programs in contemporary distribution systems. Proposed
price model generates demand-linked price signal that imposes different price
signals to different customers during on-peak hours and remains static
otherwise. This makes proposed model a class apart from other existing models.
The novelty of the proposed model lies in the fact that the financial benefits
and penalties pertaining to DR are self-adjusted among customers while
preserving social equity and profit of the utility. Such an ideology has not
been yet addressed in the literature. Detailed investigation of application
results on a standard test bench reveals that the proposed model equally cares
regarding the interests of both customers and utility. For economic assessment,
a comparison of the proposed price model with the existing pricing models is
also performed.


Abstract:
  A quantum internet connects remote quantum processors that need interact and
exchange quantum signals over a long distance through photonic channels.
However, these quantum nodes are usually composed of quantum systems with
emitted photons unsuitable for long-distance transmission. Therefore, quantum
wavelength conversion to telecom is crucial for long-distance quantum networks
based on optical fiber. Here we propose wavelength conversion devices for
single-photon polarization qubits using continuous variable quantum
teleportation, which can efficiently convert qubits between near-infrared
(780/795 nm suitable for interacting with atomic quantum nodes) and telecom
wavelength (1300-1500 nm suitable for long-distance transmission). The
teleportation uses entangled photon sources (i.e., non-degenerate two-mode
squeezed state) that can be generated by four-wave mixing in rubidium atomic
vapor cells, with a diamond configuration of atomic transitions. The entangled
fields can be emitted in two orthogonal polarizations with locked relative
phase, making them especially suitable for interfacing with single-photon
polarization qubits. Our work paves the way for the realization of
long-distance quantum networks.


Abstract:
  Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but
have been found to struggle at out-of-distribution compositional
generalization. While specialized model architectures and pre-training of
seq2seq models have been proposed to address this issue, the former often comes
at the cost of generality and the latter only shows limited success. In this
paper, we study the impact of intermediate representations on compositional
generalization in pre-trained seq2seq models, without changing the model
architecture at all, and identify key aspects for designing effective
representations. Instead of training to directly map natural language to an
executable form, we map to a reversible or lossy intermediate representation
that has stronger structural correspondence with natural language. The
combination of our proposed intermediate representations and pre-trained models
is surprisingly effective, where the best combinations obtain a new
state-of-the-art on CFQ (+14.8 accuracy points) and on the template-splits of
three text-to-SQL datasets (+15.0 to +19.4 accuracy points). This work
highlights that intermediate representations provide an important and
potentially overlooked degree of freedom for improving the compositional
generalization abilities of pre-trained seq2seq models.


Abstract:
  This version removed by arXiv administrators because the submitter did not
have the right to agree to the license at the time of submission


Abstract:
  An elimination tree for a connected graph $G$ is a rooted tree on the
vertices of $G$ obtained by choosing a root $x$ and recursing on the connected
components of $G-x$ to produce the subtrees of $x$. Elimination trees appear in
many guises in computer science and discrete mathematics, and they encode many
interesting combinatorial objects, such as bitstrings, permutations and binary
trees. We apply the recent Hartung-Hoang-M\"utze-Williams combinatorial
generation framework to elimination trees, and prove that all elimination trees
for a chordal graph $G$ can be generated by tree rotations using a simple
greedy algorithm. This yields a short proof for the existence of Hamilton paths
on graph associahedra of chordal graphs. Graph associahedra are a general class
of high-dimensional polytopes introduced by Carr, Devadoss, and Postnikov,
whose vertices correspond to elimination trees and whose edges correspond to
tree rotations. As special cases of our results, we recover several classical
Gray codes for bitstrings, permutations and binary trees, and we obtain a new
Gray code for partial permutations. Our algorithm for generating all
elimination trees for a chordal graph $G$ can be implemented in time
$\mathcal{O}(m+n)$ per generated elimination tree, where $m$ and $n$ are the
number of edges and vertices of $G$, respectively. If $G$ is a tree, we improve
this to a loopless algorithm running in time $\mathcal{O}(1)$ per generated
elimination tree. We also prove that our algorithm produces a Hamilton cycle on
the graph associahedron of $G$, rather than just Hamilton path, if the graph
$G$ is chordal and 2-connected. Moreover, our algorithm characterizes
chordality, i.e., it computes a Hamilton path on the graph associahedron of $G$
if and only if $G$ is chordal.


Abstract:
  We study how publicity, in the form of highlighting in the science press,
affects the citations of research papers. After a brief review of prior work,
we analyze papers published in Physical Review Letters (PRL) that are
highlighted across eight different platforms. Using multiple linear regression
we identify how each platform contributes to citations. We also analyze how
frequently the highlighted papers end up in the top 1% cited papers in their
field. We find that the strongest predictors of medium-term citation impact --
up to 7 years post-publication -- are Viewpoints in Physics, followed by
Research Highlights in Nature, Editors' Suggestions in PRL, and Research
Highlights in Nature Physics. Our key conclusions are that (a) highlighting for
importance identifies a citation advantage, which (b) is stratified according
to the degree of vetting during peer review (internal and external to the
journal). This implies that we can view highlighting platforms as predictors of
citation accrual, with varying degrees of strength that mirror each platform's
vetting level.