Abstract:
  In the Madelung-Bohm approach to quantum mechanics, we consider a (time
dependent) phase that depends quadratically on position and show that it leads
to a Bohm potential that corresponds to a time dependent harmonic oscillator,
provided the time dependent term in the phase obeys an Ermakov equation.


Abstract:
  Exponential random graphs are important to model the structure of real-world
complex networks. Here we solve the two-star model with degree-degree
correlations in the sparse regime. The model constraints the average
correlation between the degrees of adjacent nodes (nearest neighbors) and
between the degrees at the end-points of two-stars (next nearest neighbors). We
compute exactly the network free energy and show that this model undergoes a
first-order transition to a condensed phase. For non-negative degree
correlations between next nearest neighbors, the degree distribution inside the
condensed phase has a single peak at the largest degree, while for negative
degree correlations between next nearest neighbors the condensed phase is
characterized by a bimodal degree distribution. We calculate the degree
assortativities and show they are non-monotonic functions of the model
parameters, with a discontinuous behavior at the first-order transition. The
first-order critical line terminates at a second-order critical point, whose
location in the phase diagram can be accurately determined. Our results can
help to develop more detailed models of complex networks with correlated
degrees.


Abstract:
  We discuss the non-Abelian artificial magnetic monopoles associated with
$n$-level energy crossing in quantum systems. We found that hidden symmetries
reveal themselves as observables such as spin, charge, and other physical
degrees of freedom. We illustrated our results on concrete examples of two and
three energy-level crossing. Our results can be useful for modeling of various
phenomena in physical and biological systems.


Abstract:
  Detector-based spectral computed tomography is a recent dual-energy CT (DECT)
technology that offers the possibility of obtaining spectral information. From
this spectral data, different types of images can be derived, amongst others
virtual monoenergetic (monoE) images. MonoE images potentially exhibit
decreased artifacts, improve contrast, and overall contain lower noise values,
making them ideal candidates for better delineation and thus improved
diagnostic accuracy of vascular abnormalities.
  In this paper, we are training convolutional neural networks~(CNN) that can
emulate the generation of monoE images from conventional single energy CT
acquisitions. For this task, we investigate several commonly used
image-translation methods. We demonstrate that these methods while creating
visually similar outputs, lead to a poorer performance when used for automatic
classification of pulmonary embolism (PE). We expand on these methods through
the use of a multi-task optimization approach, under which the networks achieve
improved classification as well as generation results, as reflected by PSNR and
SSIM scores. Further, evaluating our proposed framework on a subset of the
RSNA-PE challenge data set shows that we are able to improve the Area under the
Receiver Operating Characteristic curve (AuROC) in comparison to a na\"ive
classification approach from 0.8142 to 0.8420.


Abstract:
  Let $X$ be an abstract orientable not necessarily compact CR manifold of
dimension $2n+1$, $n\geq1$, and let $L^k$ be the $k$-th tensor power of a CR
complex line bundle $L$ over $X$. Suppose that condition $Y(q)$ holds at each
point of $X$, we establish asymptotics of the heat kernel of Kohn Laplacian
with values in $L^k$. As an application, we give a heat kernel proof of Morse
inequalities on compact CR manifolds. When $X$ admits a transversal CR $\mathbb
R$-action, we also establish asymptotics of the $\mathbb R$-equivariant heat
kernel of Kohn Laplacian with values in $L^k$. As an application, we get
$\mathbb R$-equivariant Morse inequalities on compact CR manifolds with
transversal CR $\mathbb R$-action.


Abstract:
  An outlier is an observation or a data point that is far from rest of the
data points in a given dataset or we can be said that an outlier is away from
the center of mass of observations. Presence of outliers can skew statistical
measures and data distributions which can lead to misleading representation of
the underlying data and relationships. It is seen that the removal of outliers
from the training dataset before modeling can give better predictions. With the
advancement of machine learning, the outlier detection models are also
advancing at a good pace. The goal of this work is to highlight and compare
some of the existing outlier detection techniques for the data scientists to
use that information for outlier algorithm selection while building a machine
learning model.


Abstract:
  Molecular dynamics is a powerful simulation tool to explore material
properties. Most of the realistic material systems are too large to be
simulated with first-principles molecular dynamics. Classical molecular
dynamics has lower computational cost but requires accurate force fields to
achieve chemical accuracy. In this work, we develop a symmetry-adapted graph
neural networks framework, named molecular dynamics graph neural networks
(MDGNN), to construct force fields automatically for molecular dynamics
simulations for both molecules and crystals. This architecture consistently
preserves the translation, rotation and permutation invariance in the
simulations. We propose a new feature engineering method including higher order
contributions and show that MDGNN accurately reproduces the results of both
classical and first-principles molecular dynamics. We also demonstrate that
force fields constructed by the model has good transferability. Therefore,
MDGNN provides an efficient and promising option for molecular dynamics
simulations of large scale systems with high accuracy.


Abstract:
  We present an alternative approach to studying topology in open quantum
systems, relying directly on Green's functions and avoiding the need to
construct an effective non-Hermitian Hamiltonian. We define an energy-dependent
Chern number based on the eigenstates of the inverse Green's function matrix of
the system which contains, within the self-energy, all the information about
the influence of the environment, interactions, gain or losses. We explicitly
calculate this topological invariant for a system consisting of a single 2D
Dirac cone and find that it is half-integer quantized when certain assumptions
over the damping are made. Away from these conditions, which cannot or are not
usually considered within the formalism of non-Hermitian Hamiltonians, we find
that such a quantization is usually lost and the Chern number vanishes, and
that in special cases, it can change to integer quantization.


Abstract:
  We use a logical device called the Dutch Book to establish epistemic
confidence, defined as the sense of confidence \emph{in an observed} confidence
interval. This epistemic property is unavailable -- or even denied -- in
orthodox frequentist inference. In financial markets, including the betting
market, the Dutch Book is also known as arbitrage or risk-free profitable
transaction. A numerical confidence is deemed epistemic if its use as a betting
price is protected from the Dutch Book by an external agent. Theoretically, to
construct the Dutch Book, the agent must exploit unused information available
in any relevant subset. Pawitan and Lee (2021) showed that confidence is an
extended likelihood, and the likelihood principle states that the likelihood
contains all the information in the data, hence leaving no relevant subset.
Intuitively, this implies that confidence associated with the full likelihood
is protected from the Dutch Book, and hence is epistemic. Our aim is to provide
the theoretical support for this intuitive notion.


Abstract:
  The use of continuum emission radio galaxies as cosmological tracers of the
large-scale structure will soon move into a new phase. Upcoming surveys from
the Australian Square Kilometre Array Pathfinder (ASKAP), MeerKAT, and the
Square Kilometre Array project (SKA) will survey the entire available sky down
to an ~100uJy flux limit, increasing the number of detected extra-galactic
radio sources by several orders of magnitude. External data and machine
learning algorithms will also enable some low resolution radial selection
(photometric redshift binning) of the sample, increasing the cosmological
utility of the sample observed. In this paper, we discuss the flux limit
required to detect enough galaxies to decrease the shot noise term in the error
to be 10% of the total. We show how future surveys of this type will be limited
by available technology. The confusion generated by the intrinsic sizes of
galaxies may have the consequence that surveys of this type eventually reach a
hard flux limit of ~100nJy, as is predicted by the current modelling of AGN
sizes by simulations such as the Tiered Radio Extragalactic Continuum
Simulation (T-RECS). Finally, when considering the multi-tracer approach, where
galaxies are split by type to measure some bias ratio, we find that there are
not enough AGN present to achieve a reasonable level of shot noise for this
kind of measurement.


Abstract:
  Weakly-supervised anomaly detection aims at learning an anomaly detector from
a limited amount of labeled data and abundant unlabeled data. Recent works
build deep neural networks for anomaly detection by discriminatively mapping
the normal samples and abnormal samples to different regions in the feature
space or fitting different distributions. However, due to the limited number of
annotated anomaly samples, directly training networks with the discriminative
loss may not be sufficient. To overcome this issue, this paper proposes a novel
strategy to transform the input data into a more meaningful representation that
could be used for anomaly detection. Specifically, we leverage an autoencoder
to encode the input data and utilize three factors, hidden representation,
reconstruction residual vector, and reconstruction error, as the new
representation for the input data. This representation amounts to encode a test
sample with its projection on the training data manifold, its direction to its
projection and its distance to its projection. In addition to this encoding, we
also propose a novel network architecture to seamlessly incorporate those three
factors. From our extensive experiments, the benefits of the proposed strategy
are clearly demonstrated by its superior performance over the competitive
methods.


Abstract:
  We study the evolution of networks through `triplets' - three-node graphlets.
We develop a method to compute a transition matrix to describe the evolution of
triplets in temporal networks. To identify the importance of higher-order
interactions in the evolution of networks, we compare both artificial and
real-world data to a model based on pairwise interactions only. The significant
differences between the computed matrix and the calculated matrix from the
fitted parameters demonstrate that non-pairwise interactions exist for various
real-world systems in space and time, such as our data sets. Furthermore, this
also reveals that different patterns of higher-order interaction are involved
in different real-world situations.
  To test our approach, we then use these transition matrices as the basis of a
link prediction algorithm. We investigate our algorithm's performance on four
temporal networks, comparing our approach against ten other link prediction
methods. Our results show that higher-order interactions in both space and time
play a crucial role in the evolution of networks as we find our method, along
with two other methods based on non-local interactions, give the best overall
performance. The results also confirm the concept that the higher-order
interaction patterns, i.e., triplet dynamics, can help us understand and
predict the evolution of different real-world systems.


Abstract:
  Tunnel transport of interacting spin-polarized electrons through a
single-level vibrating quantum dot in external magnetic field is studied. By
using density matrix method, the current-voltage characteristics and the
dependence of conductance on temperature of a single-electron transistor were
calculated. We found that a lifting of Coulomb blockade in external magnetic
field happens in stages. The Franck-Condon steps associated with inelastic
electron tunneling in our case are doubled due to contribution of two
Zeeman-split levels in electron transport. The doubling of steps can be also
observed in the presence of Coulomb interaction. For strong electron-vibron
interaction the temperature dependence of conductance is shown to be
non-monotonic and anomalous growth of conductance maximum weakly depends both
on the Coulomb strength and the external magnetic field.


Abstract:
  With the emergence of low-cost robotic systems, such as unmanned aerial
vehicle, the importance of embedded high-performance image processing has
increased. For a long time, FPGAs were the only processing hardware that were
capable of high-performance computing, while at the same time preserving a low
power consumption, essential for embedded systems. However, the recently
increasing availability of embedded GPU-based systems, such as the NVIDIA
Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for
massively parallel embedded computing on graphics hardware. With this in mind,
we propose an approach for real-time embedded stereo processing on ARM and
CUDA-enabled devices, which is based on the popular and widely used Semi-Global
Matching algorithm. In this, we propose an optimization of the algorithm for
embedded CUDA GPUs, by using massively parallel computing, as well as using the
NEON intrinsics to optimize the algorithm for vectorized SIMD processing on
embedded ARM CPUs. We have evaluated our approach with different configurations
on two public stereo benchmark datasets to demonstrate that they can reach an
error rate as low as 3.3%. Furthermore, our experiments show that the fastest
configuration of our approach reaches up to 46 FPS on VGA image resolution.
Finally, in a use-case specific qualitative evaluation, we have evaluated the
power consumption of our approach and deployed it on the DJI Manifold 2-G
attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating
its suitability for real-time stereo processing onboard a UAV.


Abstract:
  The quantum approximate optimization algorithm (QAOA) has become a
cornerstone of contemporary quantum applications development. In QAOA, a
quantum circuit is trained -- by repeatedly adjusting circuit parameters -- to
solve a problem. Several recent findings have reported parameter concentration
effects in QAOA and their presence has become one of folklore: while
empirically observed, the concentrations have not been defined and analytical
approaches remain scarce, focusing on limiting system and not considering
parameter scaling as system size increases. We found that optimal QAOA circuit
parameters concentrate as an inverse polynomial in the problem size, providing
an optimistic result for improving circuit training. Our results are
analytically demonstrated for variational state preparations at $p=1,2$
(corresponding to 2 and 4 tunable parameters respectively). The technique is
also applicable for higher depths and the concentration effect is cross
verified numerically. Parameter concentrations allow for training on a fraction
$w < n$ of qubits to assert that these parameters are nearly optimal on $n$
qubits. Clearly this effect has significant practical importance.


Abstract:
  Unprecedented winter storms that hit across Texas in February 2021 have
caused at least 69 deaths and 4.5 million customer interruptions due to the
wide-ranging generation capacity outage and record-breaking electricity demand.
While much remains to be investigated on what, how, and why such wide-spread
power outages occurred across Texas, it is imperative for the broader macro
energy community to develop insights for policy making based on a coherent
electric grid model and data set. In this paper, we collaboratively release an
open-source extendable model that is synthetic but nevertheless provides a
realistic representation of the actual energy grid, accompanied by open-source
cross-domain data sets. This simplified synthetic model is calibrated to the
best of our knowledge based on published data resources. Building upon this
open-source synthetic grid model, researchers could quantitatively assess the
impact of various policies on mitigating the impact of such extreme events. As
an example, in this paper we critically assess several corrective measures that
could have mitigated the blackout under such extreme weather conditions. We
uncover the regional disparity of load shedding. The analysis also quantifies
the sensitivity of several corrective measures with respect to mitigating the
severity of the power outage, as measured in Energy-not-Served (ENS). This
approach and methodology are generalizable for other regions experiencing
significant energy portfolio transitions.


Abstract:
  We propose novel estimators for categorical and continuous treatments by
using an optimal covariate balancing strategy for inverse probability
weighting. The resulting estimators are shown to be consistent and
asymptotically normal for causal contrasts of interest, either when the model
explaining treatment assignment is correctly specified, or when the correct set
of bases for the outcome models has been chosen and the assignment model is
sufficiently rich. For the categorical treatment case, we show that the
estimator attains the semiparametric efficiency bound when all models are
correctly specified. For the continuous case, the causal parameter of interest
is a function of the treatment dose. The latter is not parametrized and the
estimators proposed are shown to have bias and variance of the classical
nonparametric rate. Asymptotic results are complemented with simulations
illustrating the finite sample properties. Our analysis of a data set suggests
a nonlinear effect of BMI on the decline in self reported health.


Abstract:
  Inverse propensity weighting (IPW) is a popular method for estimating
treatment effects from observational data. However, its correctness relies on
the untestable (and frequently implausible) assumption that all confounders
have been measured. This paper introduces a robust sensitivity analysis for IPW
that estimates the range of treatment effects compatible with a given amount of
unobserved confounding. The estimated range converges to the narrowest possible
interval (under the given assumptions) that must contain the true treatment
effect. Our proposal is a refinement of the influential sensitivity analysis by
Zhao, Small, and Bhattacharya (2019), which we show gives bounds that are too
wide even asymptotically. This analysis is based on new partial identification
results for Tan (2006)'s marginal sensitivity model.


Abstract:
  Traversing a continuous phase transition at a finite rate leads to the
breakdown of adiabatic dynamics and the formation of topological defects, as
predicted by the celebrated Kibble-Zurek mechanism (KZM). We investigate
universal signatures beyond the KZM, by characterizing the distribution of
vortices generated in a thermal quench leading to the formation of a
holographic superconductor. The full counting statistics of vortices is
described by a binomial distribution, in which the mean value is dictated by
the KZM and higher-order cumulants share the universal power-law scaling with
the quench time. Extreme events associated with large fluctuations no longer
exhibit a power-law behavior with the quench time and are characterized by a
universal form of the Weibull distribution for different quench rates.


Abstract:
  A common approach to tackle a combinatorial optimization problem is to first
solve a continuous relaxation and then round the obtained fractional solution.
For the latter, the framework of contention resolution schemes (or CR schemes),
introduced by Chekuri, Vondrak, and Zenklusen, is a general and successful
tool. A CR scheme takes a fractional point $x$ in a relaxation polytope, rounds
each coordinate $x_i$ independently to get a possibly non-feasible set, and
then drops some elements in order to satisfy the independence constraints.
Intuitively, a CR scheme is $c$-balanced if every element $i$ is selected with
probability at least $c \cdot x_i$.
  It is known that general matroids admit a $(1-1/e)$-balanced CR scheme, and
that this is (asymptotically) optimal. This is in particular true for the
special case of uniform matroids of rank one. In this work, we provide a simple
and explicit monotone CR scheme with a balancedness of $1 -
\binom{n}{k}\:\left(1-\frac{k}{n}\right)^{n+1-k}\:\left(\frac{k}{n}\right)^k$,
and show that this is optimal. As $n$ grows, this expression converges from
above to $1 - e^{-k}k^k/k!$. While this asymptotic bound can be obtained by
combining previously known results, these require defining an exponential-sized
linear program, as well as using random sampling and the ellipsoid algorithm.
Our procedure, on the other hand, has the advantage of being simple and
explicit. Moreover, this scheme generalizes into an optimal CR scheme for
partition matroids.


Abstract:
  Multi-component integrable generalizations of the Fokas-Lenells equation,
associated with each irreducible Hermitian symmetric space are formulated.
Description of the underlying structures associated to the integrability, such
as the Lax representation and the bi-Hamiltonian formulation of the equations
is provided. Two reductions are considered as well, one of which leads to a
nonlocal integrable model. Examples with Hermitian symmetric spaces of all
classical series of types A.III, BD.I, C.I and D.III are presented in details,
as well as possibilities for further reductions in a general form.


Abstract:
  The ultrafast hole dynamics triggered by the photoexcitation of molecular
targets is a highly correlated process even for those systems, like organic
molecules, having a weakly correlated ground state. We here provide a unifying
framework and a numerically efficient matrix formulation of state-of-the-art
non-equilibrium Green's function (NEGF) methods like second-Born as well as
$GW$ and $T$-matrix without and {\em with} exchange diagrams. Numerical
simulations are presented for a paradigmatic, exactly solvable molecular system
and the shortcomings of the established NEGF methods are highlighted. We then
develop a NEGF scheme based on the Faddeev treatment of three-particle
correlations; the exceptional improvement over established methods is explained
and demonstrated. The Faddeev NEGF scheme scales linearly with the maximum
propagation time, thereby opening prospects for femtosecond simulations of
large molecules.


Abstract:
  In the assembly process of printed circuit boards (PCB), most of the errors
are caused by solder joints in Surface Mount Devices (SMD). In the literature,
traditional feature extraction based methods require designing hand-crafted
features and rely on the tiered RGB illumination to detect solder joint errors,
whereas the supervised Convolutional Neural Network (CNN) based approaches
require a lot of labelled abnormal samples (defective solder joints) to achieve
high accuracy. To solve the optical inspection problem in unrestricted
environments with no special lighting and without the existence of error-free
reference boards, we propose a new beta-Variational Autoencoders (beta-VAE)
architecture for anomaly detection that can work on both IC and non-IC
components. We show that the proposed model learns disentangled representation
of data, leading to more independent features and improved latent space
representations. We compare the activation and gradient-based representations
that are used to characterize anomalies; and observe the effect of different
beta parameters on accuracy and on untwining the feature representations in
beta-VAE. Finally, we show that anomalies on solder joints can be detected with
high accuracy via a model trained on directly normal samples without designated
hardware or feature engineering.


Abstract:
  The quasi-spherical Szekeres dust solutions are a generalization of the
spherically symmetric Lemaitre-Tolman-Bondi dust models where the spherical
shells of constant mass are non-concentric. The quasi-spherical Szekeres dust
solutions can be considered as cosmological models and are potentially models
for the formation of primordial black holes in the early universe. Any
collapsing quasi-spherical Szekeres dust solution where an apparent horizon
covers all shell-crossings that will occur can be considered as a model for the
formation of a black hole. In this paper we will show that the apparent horizon
can be detected by a Cartan invariant. We will show that particular Cartan
invariants characterize properties of these solutions which have a physical
interpretation such as: the expansion or contraction of spacetime itself, the
relative movement of matter shells, shell-crossings and the appearance of necks
and bellies.


Abstract:
  We prove an estimate for multi-variable multiplicative character sums over
affine subspaces of $\mathbb A^n_k$, which generalize the well known estimates
for both classical Jacobi sums and one-variable polynomial multiplicative
character sums.


Abstract:
  We calculate the quasiparticle dispersion and spectral weight of the
quasiparticle that results when a hole is added to an antiferromagnetically
ordered CuO$_2$ plane of a cuprate superconductor. We also calculate the magnon
contribution to the quasiparticle spectral function. We start from a multiband
model for the cuprates considered previously [Nat. Phys. \textbf{10}, 951
(2014)]. We map this model and the operator for creation of an O hole to an
effective one-band generalized $t-J$ model, without free parameters. The
effective model is solved using the state of the art self-consistent Born
approximation. Our results reproduce all the main features of experiments. They
also reproduce qualitatively the dispersion of the multiband model, giving
better results for the intensity near wave vector $(\pi,\pi)$, in comparison
with the experiments. In contrast to what was claimed in [Nat. Phys.
\textbf{10}, 951 (2014)], we find that spin fluctuations play an essential role
in the dynamics of the quasiparticle, and hence in both its weight and
dispersion.


Abstract:
  The goal of this paper is to study the smallest brightening events observed
in the EUV quiet Sun. We use commissioning data taken by the EUI instrument
onboard the recently launched Solar Orbiter mission. On 2020 May 30, EUI was
situated at 0.556AU from the Sun. Its HRIEUV telescope 17.4nm passband reached
an exceptionally high two-pixel spatial resolution of 400km. The size and
duration of small-scale structures is determined in the HRIEUV data, while
their height is estimated from triangulation with the simultaneous SDO/AIA
data. This is the first stereoscopy of small scale brightenings at high
resolution. We observed small localised brightenings ("campfires") in a quiet
Sun region with lengthscales between 400km and 4000km and durations between 10
and 200s. The smallest and weakest of these HRIEUV brightenings have not been
observed before. Simultaneous HRILYA observations do not show localised
brightening events, but the locations of the HRIEUV events correspond clearly
to the chromospheric network. Comparison with simultaneous AIA images shows
that most events can also be identified in the 17.1nm, 19.3nm, 21.1nm, and
30.4nm passbands of AIA, although they appear weaker and blurred. DEM analysis
indicates coronal temperatures peaking at log(T)~6.1-6.15. We determined the
height of a few campfires, which is between 1000 and 5000km above the
photosphere. We conclude that "campfires" are mostly coronal in nature and are
rooted in the magnetic flux concentrations of the chromospheric network. We
interpret these events as a new extension to the flare/microflare/nanoflare
family. Given their low height, the EUI "campfires" could be a new element of
the fine structure of the transition region/low corona: apexes of small-scale
loops that are internally heated to coronal temperatures.


Abstract:
  Deciding what's next? is a fundamental problem in robotics and Artificial
Intelligence. Under belief space planning (BSP), in a partially observable
setting, it involves calculating the expected accumulated belief-dependent
reward, where the expectation is with respect to all future measurements. Since
solving this general un-approximated problem quickly becomes intractable, state
of the art approaches turn to approximations while still calculating planning
sessions from scratch. In this work we propose a novel paradigm, Incremental
BSP (iX-BSP), based on the key insight that calculations across planning
sessions are similar in nature and can be appropriately re-used. We calculate
the expectation incrementally by utilizing Multiple Importance Sampling
techniques for selective re-sampling and re-use of measurement from previous
planning sessions. The formulation of our approach considers general
distributions and accounts for data association aspects. We demonstrate how
iX-BSP could benefit existing approximations of the general problem,
introducing iML-BSP, which re-uses calculations across planning sessions under
the common Maximum Likelihood assumption. We evaluate both methods and
demonstrate a substantial reduction in computation time while statistically
preserving accuracy. The evaluation includes both simulation and real-world
experiments considering autonomous vision-based navigation and SLAM. As a
further contribution, we introduce to iX-BSP the non-integral wildfire
approximation, allowing one to trade accuracy for computational performance by
averting from updating re-used beliefs when they are "close enough". We
evaluate iX-BSP under wildfire demonstrating a substantial reduction in
computation time while controlling the accuracy sacrifice. We also provide
analytical and empirical bounds of the effect wildfire holds over the objective
value.


Abstract:
  Model-based Reinforcement Learning (MBRL) is a promising framework for
learning control in a data-efficient manner. MBRL algorithms can be fairly
complex due to the separate dynamics modeling and the subsequent planning
algorithm, and as a result, they often possess tens of hyperparameters and
architectural choices. For this reason, MBRL typically requires significant
human expertise before it can be applied to new problems and domains. To
alleviate this problem, we propose to use automatic hyperparameter optimization
(HPO). We demonstrate that this problem can be tackled effectively with
automated HPO, which we demonstrate to yield significantly improved performance
compared to human experts. In addition, we show that tuning of several MBRL
hyperparameters dynamically, i.e. during the training itself, further improves
the performance compared to using static hyperparameters which are kept fixed
for the whole training. Finally, our experiments provide valuable insights into
the effects of several hyperparameters, such as plan horizon or learning rate
and their influence on the stability of training and resulting rewards.


Abstract:
  We introduce the Probabilistic Worldbuilding Model (PWM), a new
fully-symbolic Bayesian model of semantic parsing and reasoning, as a first
step in a research program toward more domain- and task-general NLU and AI.
Humans create internal mental models of their observations which greatly aid in
their ability to understand and reason about a large variety of problems. In
PWM, the meanings of sentences, acquired facts about the world, and
intermediate steps in reasoning are all expressed in a human-readable formal
language, with the design goal of interpretability. PWM is Bayesian, designed
specifically to be able to generalize to new domains and new tasks. We derive
and implement an inference algorithm that reads sentences by parsing and
abducing updates to its latent world model that capture the semantics of those
sentences, and evaluate it on two out-of-domain question-answering datasets:
(1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be
more representative of real language but still simple enough to focus on
evaluating reasoning ability, while being robust against heuristics. Our method
outperforms baselines on both, thereby demonstrating its value as a
proof-of-concept.


Abstract:
  Video-to-speech is the process of reconstructing the audio speech from a
video of a spoken utterance. Previous approaches to this task have relied on a
two-step process where an intermediate representation is inferred from the
video, and is then decoded into waveform audio using a vocoder or a waveform
reconstruction algorithm. In this work, we propose a new end-to-end
video-to-speech model based on Generative Adversarial Networks (GANs) which
translates spoken video to waveform end-to-end without using any intermediate
representation or separate waveform synthesis algorithm. Our model consists of
an encoder-decoder architecture that receives raw video as input and generates
speech, which is then fed to a waveform critic and a power critic. The use of
an adversarial loss based on these two critics enables the direct synthesis of
raw audio waveform and ensures its realism. In addition, the use of our three
comparative losses helps establish direct correspondence between the generated
audio and the input video. We show that this model is able to reconstruct
speech with remarkable realism for constrained datasets such as GRID, and that
it is the first end-to-end model to produce intelligible speech for LRW (Lip
Reading in the Wild), featuring hundreds of speakers recorded entirely `in the
wild'. We evaluate the generated samples in two different scenarios -- seen and
unseen speakers -- using four objective metrics which measure the quality and
intelligibility of artificial speech. We demonstrate that the proposed approach
outperforms all previous works in most metrics on GRID and LRW.


Abstract:
  We use the NewHorizon simulation to study the redshift evolution of bar
properties and fractions within galaxies in the stellar masses range $M_{\star}
= 10^{7.25} - 10^{11.4} \ \rm{M}_{\odot}$ over the redshift range $z = 0.25 -
1.3$. We select disc galaxies using stellar kinematics as a proxy for galaxy
morphology. We employ two different automated bar detection methods, coupled
with visual inspection, resulting in observable bar fractions of $f_{\rm bar} =
0.070_{{-0.012}}^{{+0.018}}$ at $z\sim$ 1.3, decreasing to $f_{\rm bar} =
0.011_{{-0.003}}^{{+0.014}}$ at $z\sim$ 0.25. Only one galaxy is visually
confirmed as strongly barred in our sample. This bar is hosted by the most
massive disk and only survives from $z=1.3$ down to $z=0.7$. Such a low bar
fraction, in particular amongst Milky Way-like progenitors, highlights a
missing bars problem, shared by literally all cosmological simulations with
spatial resolution $<$100 pc to date. The analysis of linear growth rates,
rotation curves and derived summary statistics of the stellar, gas and dark
matter components suggest that galaxies with stellar masses below
$10^{9.5}-10^{10} \ \rm{M}_{\odot}$ in NewHorizon appear to be too dominated by
dark matter to bar, while more massive galaxies typically have formed large
bulges that prevent bar persistence at low redshift. This investigation
confirms that the evolution of the bar fraction puts stringent constraints on
the assembly history of baryons and dark matter onto galaxies.


Abstract:
  We study the relationship between the concept of a continuous ellipsoid
$\Theta$ cover of $\mathbb{R}^n$, which was introduced by Dahmen, Dekel, and
Petrushev, and the space of homogeneous type induced by $\Theta$. We
characterize the class of quasi-distances on $\mathbb{R}^n$ (up to equivalence)
which correspond to continuous ellipsoid covers. This places firmly continuous
ellipsoid covers as a subclass of spaces of homogeneous type on $\mathbb{R}^n$
satisfying quasi-convexity and $1$-Ahlfors-regularity.


Abstract:
  Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the
method of choice for learning with large over-parameterized models. A popular
theory for explaining why SGD works well in practice is that the algorithm has
an implicit regularization that biases its output towards a good solution.
Perhaps the theoretically most well understood learning setting for SGD is that
of Stochastic Convex Optimization (SCO), where it is well known that SGD learns
at a rate of $O(1/\sqrt{n})$, where $n$ is the number of samples. In this
paper, we consider the problem of SCO and explore the role of implicit
regularization, batch size and multiple epochs for SGD. Our main contributions
are threefold:
  (a) We show that for any regularizer, there is an SCO problem for which
Regularized Empirical Risk Minimzation fails to learn. This automatically rules
out any implicit regularization based explanation for the success of SGD.
  (b) We provide a separation between SGD and learning via Gradient Descent on
empirical loss (GD) in terms of sample complexity. We show that there is an SCO
problem such that GD with any step size and number of iterations can only learn
at a suboptimal rate: at least $\widetilde{\Omega}(1/n^{5/12})$.
  (c) We present a multi-epoch variant of SGD commonly used in practice. We
prove that this algorithm is at least as good as single pass SGD in the worst
case. However, for certain SCO problems, taking multiple passes over the
dataset can significantly outperform single pass SGD.
  We extend our results to the general learning setting by showing a problem
which is learnable for any data distribution, and for this problem, SGD is
strictly better than RERM for any regularization function. We conclude by
discussing the implications of our results for deep learning, and show a
separation between SGD and ERM for two layer diagonal neural networks.


Abstract:
  Social structures influence a variety of human behaviors including mobility
patterns, but the extent to which one individual's movements can predict
another's remains an open question. Further, latent information about an
individual's mobility can be present in the mobility patterns of both social
and non-social ties, a distinction that has not yet been addressed. Here we
develop a "colocation" network to distinguish the mobility patterns of an ego's
social ties from those of non-social colocators, individuals not socially
connected to the ego but who nevertheless arrive at a location at the same time
as the ego. We apply entropy and predictability measures to analyse and bound
the predictive information of an individual's mobility pattern and the flow of
that information from their top social ties and from their non-social
colocators. While social ties generically provide more information than
non-social colocators, we find that significant information is present in the
aggregation of non-social colocators: 3-7 colocators can provide as much
predictive information as the top social tie, and colocators can replace up to
85% of the predictive information about an ego, compared with social ties that
can replace up to 94% of the ego's predictability. The presence of predictive
information among non-social colocators raises privacy concerns: given the
increasing availability of real-time mobility traces from smartphones,
individuals sharing data may be providing actionable information not just about
their own movements but the movements of others whose data are absent, both
known and unknown individuals.


Abstract:
  The majority of existing methods for empathetic response generation rely on
the emotion of the context to generate empathetic responses. However, empathy
is much more than generating responses with an appropriate emotion. It also
often entails subtle expressions of understanding and personal resonance with
the situation of the other interlocutor. Unfortunately, such qualities are
difficult to quantify and the datasets lack the relevant annotations. To
address this issue, in this paper we propose an approach that relies on
exemplars to cue the generative model on fine stylistic properties that signal
empathy to the interlocutor. To this end, we employ dense passage retrieval to
extract relevant exemplary responses from the training set. Three elements of
human communication -- emotional presence, interpretation, and exploration, and
sentiment are additionally introduced using synthetic labels to guide the
generation towards empathy. The human evaluation is also extended by these
elements of human communication. We empirically show that these approaches
yield significant improvements in empathetic response quality in terms of both
automated and human-evaluated metrics. The implementation is available at
https://github.com/declare-lab/exemplary-empathy.


Abstract:
  The convergence of radar sensing and communication applications in the
terahertz (THz) band has been envisioned as a promising technology, since it
incorporates terabit-per-second (Tbps) data transmission and mm-level radar
sensing in a spectrum- and cost-efficient manner, by sharing both the frequency
and hardware resources. However, the joint THz radar and communication (JRC)
system faces considerable challenges, due to the peculiarities of the THz
channel and front ends. To this end, the waveform design for THz-JRC systems
with ultra-broad bandwidth is investigated in this paper. Firstly, by
considering THz-JRC systems based on the co-existence concept, where both
functions operate in a time-domain duplex (TDD) manner, a novel multi-subband
quasi-perfect (MS-QP) sequence, composed of multiple Zadoff-Chu (ZC) perfect
subsequences on different subbands, is proposed for target sensing, which
achieves accurate target ranging and velocity estimation, whilst only requiring
cost-efficient low-rate analog-to-digital converters (A/Ds) for sequence
detection. Furthermore, the root index of each ZC subsequence of the MS-QP
sequence is designed to eliminate the influence of doppler shift on the THz
radar sensing. Finally, a data-embedded MS-QP (DE-MS-QP) waveform is
constructed through time-domain extension of the MS-QP sequence, generating
null frequency points on each subband for data transmission. Unlike the THz-JRC
system in TDD manner, the proposed DE-MS-QP waveform enables simultaneous
interference-free sensing and communication, whilst inheriting all the merits
from MS-QP sequences. Numerical results validate the superiority of the
proposed waveforms in terms of sensing performance, hardware cost and flexible
resource allocation over their conventional counterparts.


Abstract:
  This paper is concerned with matching feature vectors in a one-to-one fashion
across large collections of datasets. Formulating this task as a
multidimensional assignment problem with decomposable costs (MDADC), we develop
extremely fast algorithms with time complexity linear in the number $n$ of
datasets and space complexity a small fraction of the data size. These
remarkable properties hinge on using the squared Euclidean distance as
dissimilarity function, which can reduce ${n \choose 2}$ matching problems
between pairs of datasets to $n$ problems and enable calculating assignment
costs on the fly. To our knowledge, no other method applicable to the MDADC
possesses these linear scaling and low-storage properties necessary to
large-scale applications. In numerical experiments, the novel algorithms
outperform competing methods and show excellent computational and optimization
performances. An application of feature matching to a large neuroimaging
database is presented. The algorithms of this paper are implemented in the R
package matchFeat available at https://github.com/ddegras/matchFeat.


Abstract:
  A new family of methods involving complex coefficients for the numerical
integration of differential equations is presented and analyzed. They are
constructed as linear combinations of symmetric-conjugate compositions obtained
from a basic time-symmetric integrator of order 2n (n $\ge$ 1). The new
integrators are of order 2(n + k), k = 1, 2, ..., and preserve time-symmetry up
to order 4n + 3 when applied to differential equations with real vector fields.
If in addition the system is Hamiltonian and the basic scheme is symplectic,
then they also preserve symplecticity up to order 4n + 3. We show that these
integrators are well suited for a parallel implementation, thus improving their
efficiency. Methods up to order 10 based on a 4th-order integrator are built
and tested in comparison with other standard procedures to increase the order
of a basic scheme.


Abstract:
  Predicting user positive response (e.g., purchases and clicks) probability is
a critical task in Web applications. To identify predictive features from raw
data, the state-of-the-art extreme deep factorization machine model (xDeepFM)
introduces a new interaction network to leverage feature interactions at the
vector-wise level explicitly. However, since each hidden layer in the
interaction network is a collection of feature maps, it can be viewed
essentially as an ensemble of different feature maps. In this case, only using
a single objective to minimize the prediction loss may lead to overfitting and
generate correlated errors. In this paper, an ensemble diversity enhanced
extreme deep factorization machine model (DexDeepFM) is proposed, which designs
the ensemble diversity measure in each hidden layer and considers both ensemble
diversity and prediction accuracy in the objective function. In addition, the
attention mechanism is introduced to discriminate the importance of ensemble
diversity measures with different feature interaction orders. Extensive
experiments on three public real-world datasets are conducted to show the
effectiveness of the proposed model.


Abstract:
  Several statistics-based detectors, based on unimodal matrix models, for
determining the number of sources in a field are designed. A new variance ratio
statistic is proposed, and its asymptotic distribution is analyzed. The
variance ratio detector is shown to outperform the alternatives. It is shown
that further improvements are achievable via optimally selected rotations.
Numerical experiments demonstrate the performance gains of our detection
methods over the baseline approach.


Abstract:
  Topological spin textures have attracted much attention both for fundamental
physics and spintronics applications. Among them, antiskyrmions possess a
unique spin configuration with Bloch-type and N\'eel-type domain walls due to
anisotropic Dzyaloshinskii-Moriya interaction (DMI) in the noncentrosymmetric
crystal structure. However, antiskyrmions have thus far only been observed in a
few Heusler compounds with $D_{2\mathrm{d}}$ symmetry. Here, we report a new
material Fe$_{1.9}$Ni$_{0.9}$Pd$_{0.2}$P in a different symmetry class ($S_4$
symmetry), where antiskyrmions exist over a wide temperature region including
room temperature, and transform to skyrmions upon changing magnetic field and
lamella thickness. The periodicity of magnetic textures greatly depends on
crystal thickness, and domains with anisotropic sawtooth fractals are observed
at the surface of thick crystals, which are attributed to the interplay between
dipolar interaction and DMI as governed by crystal symmetry. Our findings
provide a new arena to study antiskyrmions, and should stimulate further
research on topological spin textures and their applications.


Abstract:
  We present a hierarchical equations of motion approach, which allows a
numerically exact simulation of nonequilibrium transport in general open
quantum systems involving multiple macroscopic bosonic and fermionic
environments. The performance of the method is demonstrated for a model of a
nanosystem, which involves interacting electronic and vibrational degrees of
freedom and is coupled to fermionic and bosonic baths. The results show the
intricate interplay of electronic and vibrational degrees of freedom in this
nonequilibrium transport scenario for both voltage and thermally driven
transport processes. Furthermore, the use of importance criteria to improve the
efficiency of the method is discussed.


Abstract:
  The standard collisional model paradigm consists of a system that interacts
sequentially with identically prepared ancillas. After infinitely many
collisions, and under appropriate conditions, the system may converge to the
same state as the ancillas. This process, known as homogenization, is
independent of the ancilla initial state, being a property only of the
underlying dynamics. In this paper we extend this idea to locally identical,
but globally correlated, ancillas, and show that correlations break
homogenization. This is done numerically using a minimal qubit model, and
analytically using an exactly soluble Gaussian model. In both cases, we use
Hamiltonian graph states with cyclic graphs as the prototypical method for
building scalable many-body entangled ancillary states.


Abstract:
  In this survey article we revisit Hilbert's $19^{\text{th}}$ problem
concerning the regularity of minimizers of variational integrals. We first
discuss the classical theory (that is, the statement and resolution of
Hilbert's problem in all dimensions). We then discuss recent results concerning
the regularity of minimizers of degenerate convex functionals. Finally, we
discuss some open problems. Exercises are included for the benefit of
researchers who are entering the subject.


Abstract:
  In spaces of metrics, we investigate topological distributions of the
doubling property, the uniform disconnectedness, and the uniform perfectness,
which are the quasi-symmetrically invariant properties appearing in the
David--Semmes theorem. We show that the set of all doubling metrics and the set
of all uniformly disconnected metrics are dense in spaces of metrics on
finite-dimensional and zero-dimensional compact metrizable spaces,
respectively. Conversely, this denseness of the sets implies the
finite-dimensionality, zero-dimensionality, and the compactness of metrizable
spaces. We also determine the topological distribution of the set of all
uniformly perfect metrics in the space of metrics on the Cantor set.


Abstract:
  Understanding students' misconceptions is important for effective teaching
and assessment. However, discovering such misconceptions manually can be
time-consuming and laborious. Automated misconception discovery can address
these challenges by highlighting patterns in student data, which domain experts
can then inspect to identify misconceptions. In this work, we present a novel
method for the semi-automated discovery of problem-specific misconceptions from
students' program code in computing courses, using a state-of-the-art code
classification model. We trained the model on a block-based programming dataset
and used the learned embedding to cluster incorrect student submissions. We
found these clusters correspond to specific misconceptions about the problem
and would not have been easily discovered with existing approaches. We also
discuss potential applications of our approach and how these misconceptions
inform domain-specific insights into students' learning processes.


Abstract:
  Let $M\subset\mathbb{C}^{n+1}$ be a smooth affine hypersurface defined by the
equation $xy+p(z_1,\cdots,z_{n-1})=1$, where $p$ is a Brieskorn-Pham polynomial
and $n\geq2$. We prove that if $L\subset M$ is an orientable exact Lagrangian
submanifold, then $L$ does not admit a Riemannian metric with non-positive
sectional curvature. The key point of the proof is to establish a version of
homological mirror symmetry for the wrapped Fukaya category of $M$, from which
the finite-dimensionality of the symplectic cohomology group $\mathit{SH}^0(M)$
follows by a Hochschild cohomology computation.


Abstract:
  A direct consequence of quantization of gravity would be the existence of
gravitons. Therefore, spontaneous transition of an atom from an excited state
to a lower-lying energy state accompanied with the emission of a graviton is
expected. In this paper, we take the gravitons emitted by hydrogen and helium
in the Universe after recombination as a possible source of high frequency
background gravitational waves, and calculate the energy density spectrum.
Explicit calculations show that the most prominent contribution comes from the
$3d-1s$ transition of singly ionized helium $\mathrm{He}^{+}$, which gives a
peak in frequency at $\sim10^{13}$ Hz. Although the corresponding energy
density is too small to be detected even with state-of-the-art technology
today, we believe that the spontaneous emission of $\mathrm{He}^{+}$ is a
natural source of high frequency gravitational waves, since it is a direct
consequence if we accept that the basic quantum principles we are already
familiar with apply as well to a quantum theory of gravity.


Abstract:
  An important problem in quantum information is the practical demonstration of
non-classical long-range order on quantum computers. One of the best known
examples of a quantum system with non-classical long-range order is a
superconductor. Here we achieve Cooper pairing of qubits on a quantum computer
to represent superconducting or superfluid states. We rigorously confirm the
quantum long-range order by measuring the large $O(N)$ eigenvalue of the
two-electron reduced density matrix. The demonstration of maximal quantum
long-range order is an important step towards more complex modeling of
superconductivity and superfluidity as well as other phenomena with significant
quantum long-range order on quantum computers.


Abstract:
  Solar and stellar flares are powerful events which produce intense radiation
across the electromagnetic spectrum. Multiwavelength observations are highly
important for understanding the nature of flares, because different
flare-related processes reveal themselves in different spectral ranges. To
study the correlation between thermal and nonthermal processes in stellar
flares, we have searched the databases of Kepler (optical observations) and
XMM-Newton (soft X-rays) for the flares observed simultaneously with both
instruments; nine distinctive flares (with energies exceeding $10^{33}$ erg) on
three stars (of K-M spectral classes) have been found. We have analyzed and
compared the flare parameters in the optical and X-ray spectral ranges; we have
also compared the obtained results with similar observations of solar flares.
Most of the studied stellar flares released more energy in the optical range
than in X-rays. In one flare, X-ray emission strongly dominated, which could be
caused either by soft spectrum of energetic electrons or by a near-limb
position of this flare. The X-ray flares were typically delayed with respect to
and shorter than their optical counterparts, which is partially consistent with
the Neupert effect. Using the scaling laws based on the magnetic reconnection
theory, we have estimated the characteristic magnetic field strengths in the
stellar active regions and the sizes of these active regions as about $25-70$ G
and $250\,000-500\,000$ km, respectively. The observed stellar superflares
appear to be scaled-up versions of solar flares, with a similar underlying
mechanism and nearly the same characteristic magnetic field values, but with
much larger active region sizes.


Abstract:
  In this paper, a new method for the separation of noise categories based on
Four-Wave Mixing is presented.
  The theoretical analysis is grounded in the Gaussian Noise model and verified
by split step simulations. The noise categories react differently to the
introduced perturbations, by performing a set of perturbations the behaviour of
the different categories can be separated by means of a least-square fitting.
Given ASE is independent of the induced perturbations, it is possible to
separate noise contributions. The analysis includes constant and variable power
perturbations.
  The estimation of the noise categories is discussed from two points of view:
NSR evolution post-DSP processing, and over the power spectral density in a
notched region. The NSR estimation can only be performed at reception, whereas
the power spectral density approach can be performed along the optical link if
a high resolution Optical Spectrum Analyzer is available.
  Additionally, we perform a simple experimental verification considering of
two WaveLogic 3 transceivers for the NSR, successfully estimating the noise
contributions.


Abstract:
  Quasirandomness is a general mathematical concept meant to encapsulate
several characteristics usually satisfied by random combinatorial objects, and
which we regard as describing when a given object 'looks random'. In this
survey we explore this general concept as it applies to graphs, hypergraphs and
additive groups, making clear their many connections to each other and showing
how they can be used in order to better study these objects.


Abstract:
  We propose a learning-based robust predictive control algorithm that
compensates for significant uncertainty in the dynamics for a class of
discrete-time systems that are nominally linear with an additive nonlinear
component. Such systems commonly model the nonlinear effects of an unknown
environment on a nominal system. We optimize over a class of nonlinear feedback
policies inspired by certainty equivalent "estimate-and-cancel" control laws
pioneered in classical adaptive control to achieve significant performance
improvements in the presence of uncertainties of large magnitude, a setting in
which existing learning-based predictive control algorithms often struggle to
guarantee safety. In contrast to previous work in robust adaptive MPC, our
approach allows us to take advantage of structure (i.e., the numerical
predictions) in the a priori unknown dynamics learned online through function
approximation. Our approach also extends typical nonlinear adaptive control
methods to systems with state and input constraints even when we cannot
directly cancel the additive uncertain function from the dynamics. Moreover, we
apply contemporary statistical estimation techniques to certify the system's
safety through persistent constraint satisfaction with high probability.
Finally, we show in simulation that our method can accommodate more significant
unknown dynamics terms than existing methods.


Abstract:
  Suppose we choose a permutation $\pi$ uniformly at random from $S_n$. Let
$\mathsf{runsort}(\pi)$ be the permutation obtained by sorting the ascending
runs of $\pi$ into lexicographic order. Alexandersson and Nabawanda recently
asked if the plot of $\mathsf{runsort}(\pi)$, when scaled to the unit square
$[0,1]^2$, converges to a limit shape as $n\to\infty$. We answer their question
by showing that the measures corresponding to the scaled plots of these
permutations $\mathsf{runsort}(\pi)$ converge with probability $1$ to a
permuton (limiting probability distribution) that we describe explicitly. In
particular, the support of this permuton is $\{(x,y)\in[0,1]^2:x\leq
ye^{1-y}\}$.


Abstract:
  The gravitational wave event, GW190521 is the most massive binary black hole
merger observed by ground-based gravitational wave observatories LIGO/Virgo to
date. While the observed gravitational-wave signal is mainly in the merger and
ringdown phases, the inspiral gravitational-wave signal of GW190521-like binary
will be more visible by space-based detectors in the low-frequency band. In
addition, the ringdown gravitational-wave signal will be more loud with the
next generation (3G) of ground-based detectors in the high-frequency band,
displaying a great potential of the multiband gravitational wave observations.
In this paper, we explore the scientific potential of multiband observations of
GW190521-like binaries with milli-Hz gravitational wave observatory: LISA,
deci-Hz observatory: B-DECIGO, and (next generation of) hecto-Hz observatories:
aLIGO and ET. In the case of quasicircular evolution, the triple-band
observation by LISA, B-DECIGO and ET will provide parameter estimation errors
of the masses and spin amplitudes of component black holes at the level of
order 1% -- 10%. This would allow consistency tests of general relativity in
the strong-field at an unparalleled precision, particularly with the "B-DECIGO
+ ET" observation. In the case of eccentric evolution, the multiband
signal-to-noise ratio by "B-DECIGO + ET" observation would be larger than 100
for a five year observation prior to coalescence, even with high final
eccentricities.


Abstract:
  We describe here a new algorithm to model the water contents of the
atmosphere from GNSS slant wet delays relative to a single receiver. We first
make the assumption that the water vapor contents are mainly governed by a
scale height (exponential law), and secondly that the departures from this
decaying exponential can be mapped as a set of low degree 3D Zernike functions
(w.r.t. space) and Tchebyshev polynomials (w.r.t. time.) We give an example of
inversion with data acquired over a one day time span at the Geodesy
Observatory of Tahiti.


Abstract:
  This contribution studies the impact of the rotor-stator interaction in a
single-stage progressing cavity pump on the flow rate and pressure.
Specifically, we investigate the effect of the rotor movement on the sealings
formed with deformable stators for various speeds and pressures. Sealings are
reconstructed with the help of a geometric 3D model. We analyze the tangential
and radial deviation of the rotor from its reference path and show that the
radial deviation affects the flow rate, whereas the tangential deviation
affects the pressure dynamics. The conjectures are confirmed with a laboratory
test setup.


Abstract:
  Explosive astrophysical transients at cosmological distances can be used to
place precision tests of the basic assumptions of relativity theory, such as
Lorentz invariance, the photon zero-mass hypothesis, and the weak equivalence
principle (WEP). Signatures of Lorentz invariance violations (LIV) include
vacuum dispersion and vacuum birefringence. Sensitive searches for LIV using
astrophysical sources such as gamma-ray bursts, active galactic nuclei, and
pulsars are discussed. The most direct consequence of a nonzero photon rest
mass is a frequency dependence in the velocity of light propagating in vacuum.
A detailed representation of how to obtain a combined severe limit on the
photon mass using fast radio bursts at different redshifts through the
dispersion method is presented. The accuracy of the WEP has been well tested
based on the Shapiro time delay of astrophysical messengers traveling through a
gravitational field. Some caveats of Shapiro delay tests are discussed. In this
article, we review and update the status of astrophysical tests of fundamental
physics.


Abstract:
  In this work, we firstly apply the Train-Tensor (TT) networks to construct a
compact representation of the classical Multilayer Perceptron, representing a
reduction of up to 95% of the coefficients. A comparative analysis between
tensor model and standard multilayer neural networks is also carried out in the
context of prediction of the Mackey-Glass noisy chaotic time series and NASDAQ
index. We show that the weights of a multidimensional regression model can be
learned by means of TT network and the optimization of TT weights is a more
robust to the impact of coefficient initialization and hyper-parameter setting.
Furthermore, an efficient algorithm based on alternating least squares has been
proposed for approximating the weights in TT-format with a reduction of
computational calculus, providing a much faster convergence than the well-known
adaptive learning-method algorithms, widely applied for optimizing neural
networks.


Abstract:
  Reeb graphs are widely used in a range of fields for the purposes of
analyzing and comparing complex spaces via a simpler combinatorial object.
Further, they are closely related to extended persistence diagrams, which
largely but not completely encode the information of the Reeb graph. In this
paper, we investigate the effect on the persistence diagram of a particular
continuous operation on Reeb graphs; namely the (truncated) smoothing
operation. This construction arises in the context of the Reeb graph
interleaving distance, but separately from that viewpoint provides a
simplification of the Reeb graph which continuously shrinks small loops. We
then use this characterization to initiate the study of inverse problems for
Reeb graphs using smoothing by showing which paths in persistence diagram space
(commonly known as vineyards) can be realized by a path in the space of Reeb
graphs via these simple operations. This allows us to solve the inverse problem
on a certain family of piecewise linear vineyards when fixing an initial Reeb
graph.


Abstract:
  Nonlinearity continuation method, applied to boundary value problems for
steady-state Richards equation, gradually approaches the solution through a
series of intermediate problems. Originally, the Newton method with simple line
search algorithm was used to solve the intermediate problems. In this paper,
other solvers such as Picard and mixed Picard-Newton methods are considered,
combined with slightly modified line search approach. Numerical experiments are
performed with advanced finite volume discretizations on model and real-life
problems.


Abstract:
  Let $f$ be a Rademacher or a Steinhaus random multiplicative function. Let
$\varepsilon>0$ small. We prove that, as $x\rightarrow +\infty$, we almost
surely have $$\bigg|\sum_{\substack{n\leq x\\
P(n)>\sqrt{x}}}f(n)\bigg|\leq\sqrt{x}(\log\log x)^{1/4+\varepsilon},$$ where
$P(n)$ stands for the largest prime factor of $n$. This gives an indication of
the almost sure size of the largest fluctuations of $f$.


Abstract:
  This work proposes RaNNC (Rapid Neural Network Connector) as middleware for
automatic hybrid parallelism. In recent deep learning research, as exemplified
by T5 and GPT-3, the size of neural network models continues to grow. Since
such models do not fit into the memory of accelerator devices, they need to be
partitioned by model parallelism techniques. Moreover, to accelerate training
for huge training data, we need a combination of model and data parallelisms,
i.e., hybrid parallelism. Given a model description for PyTorch without any
specification for model parallelism, RaNNC automatically partitions the model
into a set of subcomponents so that (1) each subcomponent fits a device memory
and (2) a high training throughput for pipeline parallelism is achieved by
balancing the computation times of the subcomponents. In our experiments, we
compared RaNNC with two popular frameworks, Megatron-LM (hybrid parallelism)
and GPipe (originally proposed for model parallelism, but a version allowing
hybrid parallelism also exists), for training models with increasingly greater
numbers of parameters. In the pre-training of enlarged BERT models, RaNNC
successfully trained models five times larger than those Megatron-LM could, and
RaNNC's training throughputs were comparable to Megatron-LM's when pre-training
the same models. RaNNC also achieved better training throughputs than GPipe on
both the enlarged BERT model pre-training (GPipe with hybrid parallelism) and
the enlarged ResNet models (GPipe with model parallelism) in all of the
settings we tried. These results are remarkable, since RaNNC automatically
partitions models without any modification to their descriptions; Megatron-LM
and GPipe require users to manually rewrite the models' descriptions.


Abstract:
  Active control of heat flow is of both fundamental and applied interest in
thermal management and energy conversion. Here, we present a fluctuational
electrodynamic study of thermal radiation between twisted bilayer graphene
(TBLG), motivated by its unusual and highly tunable plasmonic properties. We
show that near-field heat flow can vary by more than 10-fold over only a few
degrees of twist, and identify special angles leading to heat flow extrema.
These special angles are dictated by the Drude weight in the intraband optical
conductivity of TBLG, and are roughly linear with the chemical potential.
Further, we observe multiband thermal transport due to the increasing role of
interband transitions as the twist angle decreases, in analogy to monolayer
graphene in a magnetic field. Our findings are understood via the surface
plasmons in TBLG, and highlight its potential for manipulating radiative heat
flow.


Abstract:
  Marangoni contraction of sessile droplets occurs when a binary mixture of
volatile liquids is placed on a high-energy surface. Although the surface is
wetted completely by the mixture and its components, a quasi-stationary
non-vanishing contact angle is observed. This seeming contradiction is caused
by Marangoni flows that are driven by evaporative depletion of the volatile
component near the edge of the droplet. Here we show that the compositional
evolution of such droplets is governed by Taylor-Aris dispersion. Breaking with
precedent, we demonstrate that Taylor-Aris dispersion can consistently be
included in a long wave expansion for the evolution of thin films. Coupled to
diffusion limited evaporation, this model quantitatively reproduces not only
the apparent shape of Marangoni contracted droplets, but also their internal
flows.


Abstract:
  We demonstrate a 2-grating free electron Mach-Zehnder interferometer
constructed in a transmission electron microscope. A symmetric binary phase
grating and condenser lens system forms two spatially separated, focused probes
at the sample which can be scanned while maintaining alignment. The two paths
interfere at a second grating, creating in constructive or destructive
interference in output beams. This interferometer has many notable features:
positionable probe beams, large path separations relative to beam width,
continuously tunable relative phase between paths, and real-time phase
information. Here we use the electron interferometer to measure the relative
phase shifts imparted to the electron probes by electrostatic potentials as
well as a demonstration of quantitative nanoscale phase imaging of a
polystyrene latex nanoparticle.


Abstract:
  This paper introduces a new controllability notion, termed partial strong
structural controllability (PSSC), on a structured system whose entries of
system matrices are either fixed zero or indeterminate, which naturally extends
the conventional strong structural controllability (SSC) and bridges the gap
between structural controllability and SSC. Dividing the indeterminate entries
into two categories, generic entries and unspecified entries, a system is PSSC,
if for almost all values of the generic entries in the parameter space except
for a set of measure zero, and any nonzero (complex) values of the unspecified
entries, the corresponding system is controllable. We highlight that this
notion generalizes the generic property embedded in the conventional structural
controllability for single-input systems. We then give algebraic and
(bipartite) graph-theoretic necessary and sufficient conditions for
single-input systems to be PSSC. Conditions for multi-input systems are
subsequently given for a special case. It is shown the established results can
induce a new maximum matching based criterion for SSC over the system bipartite
graph representations.


Abstract:
  We analyze 6.7 million case law documents to determine the presence of gender
bias within our judicial system. We find that current bias detectino methods in
NLP are insufficient to determine gender bias in our case law database and
propose an alternative approach. We show that existing algorithms' inconsistent
results are consequences of prior research's definition of biases themselves.
Bias detection algorithms rely on groups of words to represent bias (e.g.,
'salary,' 'job,' and 'boss' to represent employment as a potentially biased
theme against women in text). However, the methods to build these groups of
words have several weaknesses, primarily that the word lists are based on the
researchers' own intuitions. We suggest two new methods of automating the
creation of word lists to represent biases. We find that our methods outperform
current NLP bias detection methods. Our research improves the capabilities of
NLP technology to detect bias and highlights gender biases present in
influential case law. In order test our NLP bias detection method's
performance, we regress our results of bias in case law against U.S census data
of women's participation in the workforce in the last 100 years.


Abstract:
  This paper describes how the current lexical similarity and analogy gold
standards are built to conform to certain ideas about what the models they are
designed to evaluate are used for. Topical relevance has always been the most
important target notion for information access tools and related language
technology technologies, and while this has proven a useful starting point for
much of what information technology is used for, it does not always align well
with other uses to which technologies are being put, most notably use cases
from digital scholarship in the humanities or social sciences. This paper
argues for more systematic formulation of requirements from the digital
humanities and social sciences and more explicit description of the assumptions
underlying model design.


Abstract:
  We study mean field portfolio games in incomplete markets with random market
parameters, where each player is concerned with not only her own wealth but
also the relative performance to her competitors. We use the martingale
optimality principle approach to characterize the unique Nash equilibrium in
terms of a mean field FBSDE with quadratic growth, which is solvable under a
weak interaction assumption. Motivated by the weak interaction assumption, we
establish an asymptotic expansion result in powers of the competition
parameter. When the market parameters do not depend on the Brownian paths, we
get the Nash equilibrium in closed form. Moreover, when all the market
parameters become time-independent, we revisit the games in [21] and our
analysis shows that nonconstant equilibria do not exist in $L^\infty$, and the
constant equilibrium obtained in [21] is unique in $L^\infty$, not only in the
space of constant equilibria.


Abstract:
  Despite extensive researches on manganites owing to widespread use in modern
electronics, this class of metal oxides does not cease to surprise with its
unique properties and new phenomena. Here we have studied structural and
magnetic properties of non-Heisenberg manganite nanoparticles with a strong
spin-electron coupling. During transition from low temperature ferromagnetic to
high temperature paramagnetic state, the change in charge, valence and spin
(magnetic moment) with the localization of eg-electrons on the manganese ions
have been detected. With decrease in a temperature, the overstated effective
magnetic moment of Mn ions in paramagnetic phase is reduced dramatically
testifying to spin-dependent magnetism. The critical behavior of magnetization
with determination of critical parameters near second-order phase transition
has been studied comprehensively. Based on unusual behavior of temperature and
field dependences of magnetic entropy change under different magnetic field, an
additional influence of superparamagnetism of nanoparticles on the
magnetocaloric effect has been found.


Abstract:
  The Deep Underground Neutrino Experiment (DUNE) is a next-generation
long-baseline neutrino oscillation experiment consisting of a high-power,
broadband neutrino beam, a highly capable near detector located on site at
Fermilab, in Batavia, Illinois, and a massive liquid argon time projection
chamber (LArTPC) far detector located at the 4850L of Sanford Underground
Research Facility in Lead, South Dakota. The long-baseline physics sensitivity
calculations presented in the DUNE Physics TDR, and in a related physics paper,
rely upon simulation of the neutrino beam line, simulation of neutrino
interactions in the near and far detectors, fully automated event
reconstruction and neutrino classification, and detailed implementation of
systematic uncertainties. The purpose of this posting is to provide a
simplified summary of the simulations that went into this analysis to the
community, in order to facilitate phenomenological studies of long-baseline
oscillation at DUNE. Simulated neutrino flux files and a GLoBES configuration
describing the far detector reconstruction and selection performance are
included as ancillary files to this posting. A simple analysis using these
configurations in GLoBES produces sensitivity that is similar, but not
identical, to the official DUNE sensitivity. DUNE welcomes those interested in
performing phenomenological work as members of the collaboration, but also
recognizes the benefit of making these configurations readily available to the
wider community.


Abstract:
  A method for the detection of changes in the expectation in univariate
sequences is provided. Moving sum processes are studied. These rely on the
selection of a tuning bandwidth. Here, a framework to overcome bandwidth
selection is presented - the bandwidth adjusts gradually. For that, moving sum
processes are made dependent on both time and the bandwidth: the domain becomes
a triangle. On the triangle, paths are constructed which systematically lead to
change points. An algorithm is provided that estimates change points by
subsequent consideration of paths. Strong consistency for the number and
location of change points is shown. Simulations support estimation precision. A
companion R-package mscp is made available on CRAN.


Abstract:
  We present a Satisfiability (SAT)-based approach for building Mixed Covering
Arrays with Constraints of minimum length, referred to as the Covering Array
Number problem. This problem is central in Combinatorial Testing for the
detection of system failures. In particular, we show how to apply Maximum
Satisfiability (MaxSAT) technology by describing efficient encodings for
different classes of complete and incomplete MaxSAT solvers to compute optimal
and suboptimal solutions, respectively. Similarly, we show how to solve through
MaxSAT technology a closely related problem, the Tuple Number problem, which we
extend to incorporate constraints. For this problem, we additionally provide a
new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we
carry out on the available Mixed Covering Arrays with Constraints benchmarks
and the comparison with state-of-the-art tools confirm the good performance of
our approaches.


Abstract:
  The magnetic van der Waals crystals MnBi2Te4/(Bi2Te3)n have drawn significant
attention due to their rich topological properties and the tunability by
external magnetic field. Although the MnBi2Te4/(Bi2Te3)n family have been
intensively studied in the past few years, their close relatives, the
MnSb2Te4/(Sb2Te3)n family, remain much less explored. In this work, combining
magnetotransport measurements, angle-resolved photoemission spectroscopy, and
first principles calculations, we find that MnSb4Te7, the n = 1 member of the
MnSb2Te4/(Sb2Te3)n family, is a magnetic topological system with versatile
topological phases which can be manipulated by both carrier doping and magnetic
field. Our calculations unveil that its A-type antiferromagnetic (AFM) ground
state stays in a Z_2 AFM topological insulator phase, which can be converted to
an inversion-symmetry-protected axion insulator phase when in the ferromagnetic
(FM) state. Moreover, when this system in the FM phase is slightly carrier
doped on either the electron or hole side, it becomes a Weyl semimetal with
multiple Weyl nodes in the highest valence bands and lowest conduction bands,
which are manifested by the measured notable anomalous Hall effect. Our work
thus introduces a new magnetic topological material with different topological
phases which are highly tunable by carrier doping or magnetic field.


Abstract:
  A number of recent studies indicate that the charge conduction of the
LaAlO$_3$/SrTiO$_3$ interface at low temperature is confined to filaments which
are linked to structural domain walls in the SrTiO$_3$ with drastic
consequences for example for the temperature dependence of local transport
properties. We demonstrate that as a consequences of this current carrying
filaments on the nano-scale the magnetotransport properties of the interface
are highly anisotropic. Our magnetoresistance measurements reveal that the
magnetoresistance in different nanostructures ($<500nm$) is random in magnitude
and sign, respectively. Warming up nanostructures above the structural phase
transition temperature (105K) results in the significant change in MR. Even a
sign change of the magnetoresistance is possible. The results suggest that
domain walls that are differently oriented with respect to the surface exhibit
different respective magnetoresistance and the total magnetoresistance is a
result of a random domain wall pattern formed during the structural phase
transition in the SrTiO$_3$ at cool down.


Abstract:
  We have successfully demonstrated electrical charging using the electromotive
force (EMF) generated in a ferromagnetic metal (FM) film under ferromagnetic
resonance (FMR). In the case of Ni80Fe20 films, electrical charge due to the
EMF generated under FMR can be accumulated in a capacitor; however, the amount
of charge is saturated well below the charging limit of the capacitor.
Meanwhile in the case of Co50Fe50, electrical charge generated under FMR can be
accumulated in a capacitor and the amount of charge increases linearly with the
FMR duration time. The difference between the Ni80Fe20 and Co50Fe50 films is
due to the respective magnetic field ranges for the FMR excitation. When the FM
films were in equivalent thermal states during FMR experiments, Co50Fe50 films
could maintain FMR in a detuned condition, while Ni80Fe20 films were outside
the FMR excitation range. The EMF generation phenomenon in an FM film under FMR
can be used an energy harvesting technology by appropriately controlling the
thermal conditions of the FM film.


Abstract:
  A promising characteristic of Deep Reinforcement Learning (DRL) is its
capability to learn optimal policy in an end-to-end manner without relying on
feature engineering. However, most approaches assume a fully observable state
space, i.e. fully observable Markov Decision Processes (MDPs). In real-world
robotics, this assumption is unpractical, because of issues such as sensor
sensitivity limitations and sensor noise, and the lack of knowledge about
whether the observation design is complete or not. These scenarios lead to
Partially Observable MDPs (POMDPs). In this paper, we propose
Long-Short-Term-Memory-based Twin Delayed Deep Deterministic Policy Gradient
(LSTM-TD3) by introducing a memory component to TD3, and compare its
performance with other DRL algorithms in both MDPs and POMDPs. Our results
demonstrate the significant advantages of the memory component in addressing
POMDPs, including the ability to handle missing and noisy observation data.


Abstract:
  Recently it was proposed that microscopic models of braneworld cosmology
could be realized in the context of AdS/CFT using black hole microstates
containing an end-of-the-world brane. Motivated by a desire to establish the
microscopic existence of such microstates, which so far have been discussed
primarily in bottom-up models, we have studied similar microstates in a simpler
version of AdS/CFT. On one side, we define and study boundary states in the
charged Sachdev-Ye-Kitaev model and show that these states typically look
thermal with a certain pattern of symmetry breaking. On the other side, we
study the dimensional reduction of microstates in Einstein-Maxwell theory
featuring an end-of-the-world brane and show that they have an equivalent
description in terms of 2D Jackiw-Teitelboim gravity coupled to an
end-of-the-world particle. In particular, the same pattern of symmetry breaking
is realized in both sides of the proposed duality. These results give
significant evidence that such black hole microstates have a sensible
microscopic realization.


Abstract:
  In this paper, a hardware-optimized approach to emotion recognition based on
the efficient brain-inspired hyperdimensional computing (HDC) paradigm is
proposed. Emotion recognition provides valuable information for human-computer
interactions, however the large number of input channels (>200) and modalities
(>3) involved in emotion recognition are significantly expensive from a memory
perspective. To address this, methods for memory reduction and optimization are
proposed, including a novel approach that takes advantage of the combinatorial
nature of the encoding process, and an elementary cellular automaton. HDC with
early sensor fusion is implemented alongside the proposed techniques achieving
two-class multi-modal classification accuracies of >76% for valence and >73%
for arousal on the multi-modal AMIGOS and DEAP datasets, almost always better
than state of the art. The required vector storage is seamlessly reduced by 98%
and the frequency of vector requests by at least 1/5. The results demonstrate
the potential of efficient hyperdimensional computing for low-power,
multi-channeled emotion recognition tasks.


Abstract:
  Yet, their performance degrades in the presence of noisy labels at train
time. Inspired by the setting of learning with expert advice, where
multiplicative weights (MW) updates were recently shown to be robust to
moderate data corruptions in expert advice, we propose to use MW for
reweighting examples during neural networks optimization. We theoretically
establish the convergence of our method when used with gradient descent and
prove its advantage for label noise in 1d cases. We then validate empirically
our findings for the general case by showing that MW improves neural networks
accuracy in the presence of label noise on CIFAR-10, CIFAR-100 and Clothing1M.
We also show the impact of our approach on adversarial robustness.


Abstract:
  The estimation of the polarization $P$ of extragalactic compact sources in
Cosmic Microwave Background images is a very important task in order to clean
these images for cosmological purposes -- as, for example, to constrain the
tensor-to-scalar ratio of primordial fluctuations during inflation -- and also
to obtain relevant astrophysical information about the compact sources
themselves in a frequency range, $\nu \sim 10$--$200$ GHz, where observations
have only very recently started to be available. In this paper we propose a
Bayesian maximum a posteriori (MAP) approach estimation scheme which
incorporates prior information about the distribution of the polarization
fraction of extragalactic compact sources between 1 and 100 GHz. We apply this
Bayesian scheme to white noise simulations and to more realistic simulations
that include CMB intensity, Galactic foregrounds and instrumental noise with
the characteristics of the QUIJOTE experiment Wide Survey at 11 GHz. Using
these simulations, we also compare our Bayesian method with the frequentist
Filtered Fusion method that has been already used in WMAP data and in the
\emph{Planck} mission. We find that the Bayesian method allows us to decrease
the threshold for a feasible estimation of $P$ to levels below $\sim 100$ mJy
(as compared to $\sim 500$ mJy that was the equivalent threshold for the
frequentist Filtered Fusion). We compare the bias introduced by the Bayesian
method and find it to be small in absolute terms. Finally, we test the
robustness of the Bayesian estimator against uncertainties in the prior and in
the flux density of the sources. We find that the Bayesian estimator is robust
against moderate changes in the parameters of the prior and almost insensitive
to realistic errors in the estimated photometry of the sources.


Abstract:
  We explore the implementation of hybridly protected quantum operations
combining the merits of holonomy, dynamical decoupling approach and
dephasing-free feature based on a simple and experimentally achievable spin
model. The implementation of the quantum operations can be achieved in
different physical systems with controllable parameters. The protected quantum
operations are hence controllable, well-suited for resolving various quantum
computation tasks, such as executing quantum error-correction codes or quantum
error mitigation. Our scheme is based on experimentally achievable Hamiltonian
with reduced requirement of computational resources and thus, it brings us
closer towards realizing protected quantum operations for resolving quantum
computation tasks in near-term quantum devices.


Abstract:
  We present methods for computing the distance from a Boolean polynomial on
$m$ variables of degree $m-3$ (i.e., a member of the Reed-Muller code
$RM(m-3,m)$) to the space of lower-degree polynomials ($RM(m-4,m)$). The
methods give verifiable certificates for both the lower and upper bounds on
this distance. By applying these methods to representative lists of
polynomials, we show that the covering radius of $RM(4,8)$ in $RM(5,8)$ is 26
and the covering radius of $RM(5,9)$ in $RM(6,9)$ is between 28 and 32
inclusive, and we get improved lower bounds for higher~$m$. We also apply our
methods to various polynomials in the literature, thereby improving the known
bounds on the distance from 2-resilient polynomials to $RM(m-4,m)$.


Abstract:
  The interaction between the quantum emitter and topological photonic system
makes the photon behave in exotic ways. We here study the properties of a giant
atom coupled to two sites of a one-dimensional topological waveguide, which is
described by the Su-Schrieffer-Heeger (SSH) chain. We find that the giant atom
can act as an effective boundary and induce the chiral zero modes, which are
similar to those in the SSH model with open boundary, for the waveguide under
the periodical boundary. Except for the boundary effect, we also find that the
giant atom can lift energy degeneracy inside the energy bands of the SSH chain
and adjust spatial symmetry of the photon distributions for the states of the
dressed giant atom and waveguide. That is, the giant atom can be used to change
the properties of the topological environment. Our work may stimulate more
studies on the interaction between matter and topological environment.


Abstract:
  Atomically precise graphene nanoribbons are a promising emerging class of
designer quantum materials with electronic properties that are tunable by
chemical design. However, many challenges remain in the device integration of
these materials, especially regarding contacting strategies. We report on the
device integration of uniaxially aligned and non-aligned 9-atom wide armchair
graphene nanoribbons (9-AGNRs) in a field-effect transistor geometry using
electron beam lithography-defined graphene electrodes. This approach yields
controlled electrode geometries and enables higher fabrication throughput
compared to previous approaches using an electrical breakdown technique.
Thermal annealing is found to be a crucial step for successful device operation
resulting in electronic transport characteristics showing a strong gate
dependence. Raman spectroscopy confirms the integrity of the graphene
electrodes after patterning and of the GNRs after device integration. Our
results demonstrate the importance of the GNR-graphene electrode interface and
pave the way for GNR device integration with structurally well-defined
electrodes.


Abstract:
  Using the gauge/string duality, we model a heavy quark-antiquark pair in a
color singlet state moving through a cold medium and explore the consequences
of temperature and velocity on string breaking. We show that the string
breaking distance slowly varies with temperature and velocity away from the
critical line but could fall near it.


Abstract:
  We consider the optimal decision-making problem in a primary sample of
interest with multiple auxiliary sources available. The outcome of interest is
limited in the sense that it is only observed in the primary sample. In
reality, such multiple data sources may belong to heterogeneous studies and
thus cannot be combined directly. This paper proposes a new framework to handle
heterogeneous studies and address the limited outcome simultaneously through a
novel calibrated optimal decision making (CODA) method, by leveraging the
common intermediate outcomes in multiple data sources. Specifically, CODA
allows the baseline covariates across different samples to have either
homogeneous or heterogeneous distributions. Under a mild and testable
assumption that the conditional means of intermediate outcomes in different
samples are equal given baseline covariates and the treatment information, we
show that the proposed CODA estimator of the conditional mean outcome is
asymptotically normal and more efficient than using the primary sample solely.
In addition, the variance of the CODA estimator can be easily obtained using
the simple plug-in method due to the rate double robustness. Extensive
experiments on simulated datasets demonstrate empirical validity and improved
efficiency using CODA, followed by a real application to a MIMIC-III dataset as
the primary sample with the auxiliary data from eICU.


Abstract:
  There were over 70,000 drug overdose deaths in the USA in 2017. Almost half
of those involved the use of Opioids such as Heroin. This research supports
efforts to combat the Opioid Epidemic by further understanding factors that
lead to Heroin consumption. Previous research has debated the cause of Heroin
addiction, with some explaining the phenomenon as a transition from
prescription Opioids, and others pointing to various psycho-social factors.
This research used self-reported information about personality, demographics
and drug consumption behavior to predict Heroin consumption. By applying a
Support Vector Machine algorithm optimized with a Genetic Algorithm (GA-SVM
Hybrid) to simultaneously identify predictive features and model parameters,
this research produced several models that were more accurate in predicting
Heroin use than those produced in previous studies. Although all factors had
predictive power, these results showed that consumption of other drugs (both
prescription and illicit) were stronger predictors of Heroin use than
psycho-social factors. The use of prescription drugs as a strong predictor of
Heroin use is an important though disturbing discovery but that can help combat
Heroin use.


Abstract:
  Pretrained multilingual models are able to perform cross-lingual transfer in
a zero-shot setting, even for languages unseen during pretraining. However,
prior work evaluating performance on unseen languages has largely been limited
to low-level, syntactic tasks, and it remains unclear if zero-shot learning of
high-level, semantic tasks is possible for unseen languages. To explore this
question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018)
to 10 indigenous languages of the Americas. We conduct experiments with XLM-R,
testing multiple zero-shot and translation-based approaches. Additionally, we
explore model adaptation via continued pretraining and provide an analysis of
the dataset by considering hypothesis-only models. We find that XLM-R's
zero-shot performance is poor for all 10 languages, with an average performance
of 38.62%. Continued pretraining offers improvements, with an average accuracy
of 44.05%. Surprisingly, training on poorly translated data by far outperforms
all other methods with an accuracy of 48.72%.


Abstract:
  Insurance data can be asymmetric with heavy tails, causing inadequate
adjustments of the usually applied models. To deal with this issue,
hierarchical models for collective risk with heavy-tails of the claims
distributions that take also into account overdispersion of the number of
claims are proposed. In particular, the distribution of the logarithm of the
aggregate value of claims is assumed to follow a Student-t distribution.
Additionally, to incorporate possible overdispersion, the number of claims is
modeled as having a negative binomial distribution. Bayesian decision theory is
invoked to calculate the fair premium based on the modified absolute deviation
utility. An application to a health insurance dataset is presented together
with some diagnostic measures to identify excess variability. The variability
measures are analyzed using the marginal posterior predictive distribution of
the premiums according to some competitive models. Finally, a simulation study
is carried out to assess the predictive capability of the model and the
adequacy of the Bayesian estimation procedure.
  Keywords: Continuous ranked probability score (CRPS); decision theory;
insurance data; marginal posterior predictive; tail value at risk; value at
risk.


Abstract:
  Superadditivity relations characterize the distributions of coherence in
multipartite quantum systems. In this work, we investigate the superadditivity
relations related to the $l_1$-norm of coherence $C_{l_1}$ in multiqubit
quantum systems. Tighter superadditivity inequalities based on the $\alpha$-th
($\alpha\geqslant 1$) power of $l_1$-norm of coherence are presented for
multiqubit states under certain conditions, which include the existing results
as special cases. These superadditivity relations give rise to finer
characterization of the coherence distributions among the subsystems of a
multipartite system. A detailed example is presented.


Abstract:
  This paper investigates how to correct Chinese text errors with types of
mistaken, missing and redundant characters, which is common for Chinese native
speakers. Most existing models based on detect-correct framework can correct
mistaken characters errors, but they cannot deal with missing or redundant
characters. The reason is that lengths of sentences before and after correction
are not the same, leading to the inconsistence between model inputs and
outputs. Although the Seq2Seq-based or sequence tagging methods provide
solutions to the problem and achieved relatively good results on English
context, but they do not perform well in Chinese context according to our
experimental results. In our work, we propose a novel detect-correct framework
which is alignment-agnostic, meaning that it can handle both text aligned and
non-aligned occasions, and it can also serve as a cold start model when there
are no annotated data provided. Experimental results on three datasets
demonstrate that our method is effective and achieves the best performance
among existing published models.