Abstract:
  This paper proposes a novel framework for addressing the challenge of
autonomous overtaking and obstacle avoidance, which incorporates the overtaking
path planning into Gaussian Process-based model predictive control (GPMPC).
Compared with the conventional control strategies, this approach has two main
advantages. Firstly, combining Gaussian Process (GP) regression with a nominal
model allows for learning from model mismatch and unmodeled dynamics, which
enhances a simple model and delivers significantly better results. Due to the
approximation for propagating uncertainties, we can furthermore satisfy the
constraints and thereby safety of the vehicle is ensured. Secondly, we convert
the geometric relationship between the ego vehicle and other obstacle vehicles
into the constraints. Without relying on a higherlevel path planner, this
approach substantially reduces the computational burden. In addition, we
transform the state constraints under the model predictive control (MPC)
framework into a soft constraint and incorporate it as relaxed barrier function
into the cost function, which makes the optimizer more efficient. Simulation
results reveal the usefulness of the proposed approach.


Abstract:
  Given a positive integer $ r $, the $ r $-color size-Ramsey number of a graph
$ H $, denoted by $ \hat{R}(H, r) $, is the smallest integer $ m $ for which
there exists a graph $ G $ with $ m $ edges such that, in any edge coloring of
$ G $ with $ r $ colors, $G$ contains a monochromatic copy of $ H $. Haxell,
Kohayakawa and \L uczak showed that the size-Ramsey number of a cycle $ C_n $
is linear in $ n $ i.e. $ \hat{R}(C_n, r) \leq c_rn $, for some constant $ c_r
$. Their proof, however, is based on the Szemer\'edi's regularity lemma and so
no specific constant $ c_r $ is known. Javadi, Khoeini, Omidi and Pokrovskiy
gave an alternative proof for this result which avoids using of the regularity
lemma. Indeed, they proved that if $ n $ is even, then $ c_r $ is exponential
in $ r $ and if $ n $ is odd, then $ c_r $ is doubly exponential in $ r $.
  \noindent In this paper, we improve the bound $c_r$ and prove that $c_r$ is
polynomial in $r$ when $n$ is even and is exponential in $r$ when $n$ is odd.
We also prove that in the latter case, it cannot be improved to a polynomial
bound in $r$. More precisely, we prove that there are some positive constants
$c_1,c_2$ such that for every even integer $n$, we have $c_1r^2n\leq
\hat{R}(C_n,r)\leq c_2r^{120}(\log^2 r)n$ and for every odd integer $n$, we
have
  $c_1 2^{r}n \leq \hat{R}(C_n, r)\leq c_22^{16 r^2+2\log r}n $.


Abstract:
  Tearing modes in tokamaks typically rotate while small and then lock at a
fixed location when larger. Research on present-day devices has focused almost
exclusively on stabilisation of rotating modes, as it has been considered
imperative to avoid locked modes. However, in larger devices, such as those
contemplated for tokamak reactors, the locking occurs at a smaller island size,
and the island can be safely stabilised after locking. The stabilisation of
small locked modes can be performed at lower wave power and broader deposition
compared to rotating islands. On large devices, it thus becomes surprisingly
advantageous to allow the mode to grow and lock naturally before stabilising
it. Calculations indicate that the ITER international megaproject would be best
stabilised through this approach.


Abstract:
  We present results from high signal-to-noise optical spectropolarimetric
observations of the Seyfert 1 galaxies NGC783 and Mrk509 in the 3500-7000 A
range. We find complex structure in the polarized emission for both objects. In
particular, Position Angle (PA) changes across the Balmer lines show a
distinctive 'M'-shaped profile that had not been observed in this detail
before, but could represent a common trait in Seyfert 1 galaxies. In fact,
while this shape is observed in all Balmer lines in NGC3783, Mrk509 transitions
into a 'M'-shaped PA profile for higher transitions lines. We have modeled the
observed profiles using the STOKES radiative transfer code and assuming that
the scattering region is co-spatial with the BLR and outflowing. The results
give compelling new evidence for the presence of nuclear winds in these two
Seyfert 1 galaxies.


Abstract:
  A core goal of functional neuroimaging is to study how the environment is
processed in the brain. The mainstream paradigm involves concurrently measuring
a broad spectrum of brain responses to a small set of environmental features
preselected with reference to previous studies or a theoretical framework. As a
complement, we invert this approach by allowing the investigator to record the
modulation of a preselected brain response by a broad spectrum of environmental
features. Our approach is optimal when theoretical frameworks or previous
empirical data are impoverished. By using a prespecified closed-loop design,
the approach addresses fundamental challenges of reproducibility and
generalisability in brain research. These conditions are particularly acute
when studying the developing brain, where our theories based on adult brain
function may fundamentally misrepresent the topography of infant cognition and
where there are substantial practical challenges to data acquisition. Our
methodology employs machine learning to map modulation of a neural feature
across a space of experimental stimuli. Our method collects, processes and
analyses EEG brain data in real-time; and uses a neuro-adaptive Bayesian
optimisation algorithm to adjust the stimulus presented depending on the prior
samples of a given participant. Unsampled stimuli can be interpolated by
fitting a Gaussian process regression along the dataset. We show that our
method can automatically identify the face of the infant's mother through
online recording of their Nc brain response to a face continuum. We can
retrieve model statistics of individualised responses for each participant,
opening the door for early identification of atypical development. This
approach has substantial potential in infancy research and beyond for improving
power and generalisability of mapping the individual cognitive topography of
brain function.


Abstract:
  In this comment on the article by Alonso-Serrano and Liska (arXiv:
2008.04805) a formal resemblance between their expression of curvature scale
and the scale dependent on the matter energy-momentum ambiguity of Finkelstein
et al (JMP, 2001) is pointed out. Physical significance of this observation is
also discussed.


Abstract:
  We numerically and theoretically investigate how the softness of particles
affects the rheology of sheared dilute granular gases. We find that the kinetic
theory predicts the deviation of the flow curve from the Bagnold scaling, and
it works well below a certain shear rate when we compare with the simulation
results. It is also found that there is no theoretical solution above this
shear rate, which is because the energy loss due to inelastic collisions cannot
be balanced with the energy injection by the shear.


Abstract:
  We present an intimate connection among the following fields:
  (a) distributed local algorithms: coming from the area of computer science,
  (b) finitary factors of iid processes: coming from the area of analysis of
randomized processes,
  (c) descriptive combinatorics: coming from the area of combinatorics and
measure theory.
  In particular, we study locally checkable labellings in grid graphs from all
three perspectives. Most of our results are for the perspective (b) where we
prove time hierarchy theorems akin to those known in the field (a) [Chang,
Pettie FOCS 2017]. This approach that borrows techniques from the fields (a)
and (c) implies a number of results about possible complexities of finitary
factor solutions. Among others, it answers three open questions of [Holroyd et
al. Annals of Prob. 2017] or the more general question of [Brandt et al. PODC
2017] who asked for a formal connection between the fields (a) and (b). In
general, we hope that our treatment will help to view all three perspectives as
a part of a common theory of locality, in which we follow the insightful paper
of [Bernshteyn 2020+] .


Abstract:
  We compute the low-energy excitation spectrum and the dynamical spin
structure factor of the Kitaev-Heisenberg-Gamma model through a variational
approach based on the exact fractionalized excitations of the pure Kitaev
honeycomb model. This novel approach reveals the physical reason for the
asymmetric stability of the Kitaev spin liquid phases around the ferromagnetic
and antiferromagnetic Kitaev limits. Moreover, we demonstrate that the
fractionalized excitations form bound states in specific regions of each Kitaev
spin liquid phase and that certain phase transitions induced by Heisenberg and
Gamma interactions are driven by the condensation of such a bound state.
Remarkably, this bound state appears as a sharp mode in the dynamical spin
structure factor, while its condensation patterns at the appropriate phase
transitions provide a simple explanation for the magnetically ordered phases
surrounding each Kitaev spin liquid phase.


Abstract:
  Finding Bertram's optimal trading strategy for a pair of cointegrated assets
following the Ornstein--Uhlenbeck price difference process can be formulated as
an unconstrained convex optimization problem for maximization of expected
profit per unit of time. This model is generalized to the form where the
riskiness of profit, measured by its per-time-unit volatility, is controlled
(e.g. in case of existence of limits on riskiness of trading strategies imposed
by regulatory bodies). The resulting optimization problem need not be convex.
In spite of this undesirable fact, it is demonstrated that the problem is still
efficiently solvable. In addition, the problem that parameters of the price
difference process are never known exactly and are imprecisely estimated from
an observed finite sample is investigated (recalling that this problem is
critical for practice). It is shown how the imprecision affects the optimal
trading strategy by quantification of the loss caused by the imprecise estimate
compared to a theoretical trader knowing the parameters exactly. The main
results focus on the geometric and optimization-theoretic viewpoint of the
risk-bounded trading strategy and the imprecision resulting from the
statistical estimates.


Abstract:
  In this note, we show that certain dual canonical basis elements of
$\mathbb{C}[SL_m]$ are positive when evaluated on $k$-positive matrices,
matrices whose minors of size $k \times k$ and smaller are positive. Skandera
showed that all dual canonical basis elements of $\mathbb{C}[SL_m]$ can be
written in terms of Kazhdan-Lusztig immanants, which were introduced by Rhoades
and Skandera. We focus on the basis elements which are expressed in terms of
Kazhdan-Lusztig immanants indexed by 1324- and 2143-avoiding permutations. This
extends previous work of the authors on Kazhdan-Lusztig immanants and uses
similar tools, namely Lewis Carroll's identity (also known as the
Desnanot-Jacobi identity).


Abstract:
  The information describing the conditions of a system or a person is
constantly evolving and may become obsolete and contradict other information. A
database, therefore, must be consistently updated upon the acquisition of new
valid observations that contradict obsolete ones contained in the database. In
this paper, we propose a novel approach for dealing with the information
obsolescence problem. Our approach aims to detect, in real-time, contradictions
between observations and then identify the obsolete ones, given a
representation model. Since we work within an uncertain environment
characterized by the lack of information, we choose to use a Bayesian network
as our representation model and propose a new approximate concept,
$\epsilon$-Contradiction. The new concept is parameterised by a confidence
level of having a contradiction in a set of observations. We propose a
polynomial-time algorithm for detecting obsolete information. We show that the
resulting obsolete information is better represented by an AND-OR tree than a
simple set of observations. Finally, we demonstrate the effectiveness of our
approach on a real elderly fall-prevention database and showcase how this tree
can be used to give reliable recommendations to doctors. Our experiments give
systematically and substantially very good results.


Abstract:
  Exploratory analysis of time series data can yield a better understanding of
complex dynamical systems. Granger causality is a practical framework for
analysing interactions in sequential data, applied in a wide range of domains.
In this paper, we propose a novel framework for inferring multivariate Granger
causality under nonlinear dynamics based on an extension of self-explaining
neural networks. This framework is more interpretable than other
neural-network-based techniques for inferring Granger causality, since in
addition to relational inference, it also allows detecting signs of
Granger-causal effects and inspecting their variability over time. In
comprehensive experiments on simulated data, we show that our framework
performs on par with several powerful baseline methods at inferring Granger
causality and that it achieves better performance at inferring interaction
signs. The results suggest that our framework is a viable and more
interpretable alternative to sparse-input neural networks for inferring Granger
causality.


Abstract:
  We consider an elliptic boundary value problem with unilateral constraints
and subdifferential boundary conditions. The problem describes the heat
transfer in a domain $D\subset\R^d$ and its weak formulation is in the form of
a hemivariational inequality for the temperature field, denoted by $\cP$. We
associate to Problem $\cP$ an optimal control problem, denoted by $\cQ$. Then,
using appropriate Tykhonov triples, governed by a nonlinear operator $G$ and a
convex $\wK$, we provide results concerning the well-posedness of problems
$\cP$ and $\cQ$. Our main results are Theorems 14 and 18, together with their
corollaries. Their proofs are based on arguments of compactness, lower
semicontinuity and pseudomonotonicity. Moreover, we consider three relevant
perturbations of the heat transfer boundary valued problem which lead to
penalty versions of Problem $\cP$, constructed with particular choices of $G$
and $\wK$. We prove that Theorems 14 and 18 as well as their corollaries can be
applied in the study of these problems, in order to obtain various convergence
results.


Abstract:
  We study a quantum network that distributes entangled quantum states to
multiple sets of users that are connected to the network. Each user is
connected to a switch of the network via a link. All the links of the network
generate bipartite Bell-state entangled states in each time-slot with certain
probabilities, and each end node stores one qubit of the entanglement generated
by the link. To create shared entanglements for a set of users, measurement
operations are performed on qubits of link-level entanglements on a set of
related links, and these operations are probabilistic in nature and are
successful with certain probabilities. Requests arrive to the system seeking
shared entanglements for different sets of users. Each request is for the
creation of shared entanglements for a fixed set of users using link-level
entanglements on a fixed set of links. Requests are processed according to
First-Come-First-Served service discipline and unserved requests are stored in
buffers. Once a request is selected for service, measurement operations are
performed on qubits of link-level entanglements on related links to create a
shared entanglement. For given set of request arrival rates and link-level
entanglement generation rates, we obtain necessary conditions for the stability
of queues of requests. In each time-slot, the scheduler has to schedule
entanglement swapping operations for different sets of users to stabilize the
network. Next, we propose a Max-Weight scheduling policy and show that this
policy stabilizes the network for all feasible arrival rates. We also provide
numerical results to support our analysis. The analysis of a single quantum
switch that creates multipartite entanglements for different sets of users is a
special case of our work.


Abstract:
  In many applications free surface flow through rigid porous media has to be
modeled. Examples refer to coastal engineering applications as well as
geotechnical or biomedical applications. Albeit the frequent applications,
slight inconsistencies in the formulation of the governing equations can be
found in the literature. The main goal of this paper is to identify these
differences and provide a quantitative assessment of different approaches.
Following a review of the different formulations, simulation results obtained
from three alternative formulations are compared with experimental and
numerical data. Results obtained by 2D and 3D test cases indicate that the
predictive differences returned by the different formulations remain small for
most applications, in particular for small porous Reynolds number ReP < 5000.
Thus it seems justified to select a formulation that supports an efficient
algorithm and coding structure.


Abstract:
  This work is motivated by an application in an industrial context, where the
activity of sensors is recorded at a high frequency. The objective is to
automatically detect abnormal measurement behaviour. Considering the sensor
measures as functional data, we are formally interested in detecting outliers
in a multivariate functional data set. Due to the heterogeneity of this data
set, the proposed contaminated mixture model both clusters the multivariate
functional data into homogeneous groups and detects outliers. The main
advantage of this procedure over its competitors is that it does not require us
to specify the proportion of outliers. Model inference is performed through an
Expectation-Conditional Maximization algorithm, and the BIC criterion is used
to select the number of clusters. Numerical experiments on simulated data
demonstrate the high performance achieved by the inference algorithm. In
particular, the proposed model outperforms competitors. Its application on the
real data which motivated this study allows us to correctly detect abnormal
behaviours.


Abstract:
  The present level of proliferation of fake, biased, and propagandistic
content online has made it impossible to fact-check every single suspicious
claim or article, either manually or automatically. Thus, many researchers are
shifting their attention to higher granularity, aiming to profile entire news
outlets, which makes it possible to detect likely "fake news" the moment it is
published, by simply checking the reliability of its source. Source factuality
is also an important element of systems for automatic fact-checking and "fake
news" detection, as they need to assess the reliability of the evidence they
retrieve online. Political bias detection, which in the Western political
landscape is about predicting left-center-right bias, is an equally important
topic, which has experienced a similar shift towards profiling entire news
outlets. Moreover, there is a clear connection between the two, as highly
biased media are less likely to be factual; yet, the two problems have been
addressed separately. In this survey, we review the state of the art on media
profiling for factuality and bias, arguing for the need to model them jointly.
We further discuss interesting recent advances in using different information
sources and modalities, which go beyond the text of the articles the target
news outlet has published. Finally, we discuss current challenges and outline
future research directions.


Abstract:
  It is well known that deep learning models have a propensity for fitting the
entire training set even with random labels, which requires memorization of
every training sample. In this paper, we investigate the memorization effect in
adversarial training (AT) for promoting a deeper understanding of capacity,
convergence, generalization, and especially robust overfitting of adversarially
trained classifiers. We first demonstrate that deep networks have sufficient
capacity to memorize adversarial examples of training data with completely
random labels, but not all AT algorithms can converge under the extreme
circumstance. Our study of AT with random labels motivates further analyses on
the convergence and generalization of AT. We find that some AT methods suffer
from a gradient instability issue, and the recently suggested complexity
measures cannot explain robust generalization by considering models trained on
random labels. Furthermore, we identify a significant drawback of memorization
in AT that it could result in robust overfitting. We then propose a new
mitigation algorithm motivated by detailed memorization analyses. Extensive
experiments on various datasets validate the effectiveness of the proposed
method.


Abstract:
  The design, integration, testing, and launch of the first Finnish satellite
Aalto-1 is briefly presented in this paper. Aalto-1, a three-unit CubeSat,
launched into Sun-synchronous polar orbit at an altitude of approximately 500
km, is operational since June 2017. It carries three experimental payloads:
Aalto Spectral Imager (AaSI), Radiation Monitor (RADMON), and Electrostatic
Plasma Brake (EPB). AaSI is a hyperspectral imager in visible and near-infrared
(NIR) wavelength bands, RADMON is an energetic particle detector and EPB is a
de-orbiting technology demonstration payload. The platform was designed to
accommodate multiple payloads while ensuring sufficient data, power, radio,
mechanical and electrical interfaces. The design strategy of platform and
payload subsystems consists of in-house development and commercial subsystems.
The CubeSat Assembly, Integration & Test (AIT) followed Flatsat --
Engineering-Qualification Model (EQM) -- Flight Model (FM) model philosophy for
qualification and acceptance.
  The paper briefly describes the design approach of platform and payload
subsystems, their integration and test campaigns, and spacecraft launch. The
paper also describes the ground segment & services that were developed by the
Aalto-1 team.


Abstract:
  The Zeeman effect is of limited utility for probing the magnetism of the
quiet solar chromosphere. The Hanle effect in some spectral lines is sensitive
to such magnetism, but the interpretation of the scattering polarization
signals requires taking into account that the chromospheric plasma is highly
inhomogeneous and dynamic (i.e., that the magnetic field is not the only cause
of symmetry breaking). Here we investigate the reliability of a well-known
formula for mapping the azimuth of chromospheric magnetic fields directly from
the scattering polarization observed in the \ion{Ca}{2}~8542~\AA\, line, which
is typically in the saturation regime of the Hanle effect. To this end, we use
the Stokes profiles of the \ion{Ca}{2}~8542~\AA\, line computed with the PORTA
radiative transfer code in a three-dimensional (3D) model of the solar
chromosphere, degrading them to mimic spectropolarimetric observations for a
range of telescope apertures and noise levels. The simulated observations are
used to obtain the magnetic field azimuth at each point of the field of view,
which we compare with the actual values within the 3D model. We show that,
apart from intrinsic ambiguities, the method provides solid results. Their
accuracy depends more on the noise level than on the telescope diameter.
Large-aperture solar telescopes, like DKIST and EST, are needed to achieve the
required noise-to-signal ratios using reasonable exposure times.


Abstract:
  I propose a practical procedure based on bias correction and sample splitting
to calculate confidence intervals for functionals of generic kernel methods,
i.e. nonparametric estimators learned in a reproducing kernel Hilbert space
(RKHS). For example, an analyst may desire confidence intervals for functionals
of kernel ridge regression. I propose a bias correction that mirrors kernel
ridge regression. The framework encompasses (i) evaluations over discrete
domains, (ii) derivatives over continuous domains, (iii) treatment effects of
discrete treatments, and (iv) incremental treatment effects of continuous
treatments. For the target quantity, whether it is (i)-(iv), I prove root-n
consistency, Gaussian approximation, and semiparametric efficiency by finite
sample arguments. I show that the classic assumptions of RKHS learning theory
also imply inference.


Abstract:
  Let $X$ be a symmetric, isotropic random vector in $\mathbb{R}^m$ and let
$X_1...,X_n$ be independent copies of $X$. We show that under mild assumptions
on $\|X\|_2$ (a suitable thin-shell bound) and on the tail-decay of the
marginals $\langle X,u\rangle$, the random matrix $A$, whose columns are
$X_i/\sqrt{m}$ exhibits a Gaussian-like behaviour in the following sense: for
an arbitrary subset of $T\subset \mathbb{R}^n$, the distortion $\sup_{t \in T}
| \|At\|_2^2 - \|t\|_2^2 |$ is almost the same as if $A$ were a Gaussian
matrix.
  A simple outcome of our result is that if $X$ is a symmetric, isotropic,
log-concave random vector and $n \leq m \leq c_1(\alpha)n^\alpha$ for some
$\alpha>1$, then with high probability, the extremal singular values of $A$
satisfy the optimal estimate: $1-c_2(\alpha) \sqrt{n/m} \leq \lambda_{\rm min}
\leq \lambda_{\rm max} \leq 1+c_2(\alpha) \sqrt{n/m}$.


Abstract:
  Modern, torque-controlled service robots can regulate contact forces when
interacting with their environment. Model Predictive Control (MPC) is a
powerful method to solve the underlying control problem, allowing to plan for
whole-body motions while including different constraints imposed by the robot
dynamics or its environment. However, an accurate model of the
robot-environment is needed to achieve a satisfying closed-loop performance.
Currently, this necessity undermines the performance and generality of MPC in
manipulation tasks. In this work, we combine an MPC-based whole-body controller
with two adaptive schemes, derived from online system identification and
adaptive control. As a result, we enable a general mobile manipulator to
interact with unknown environments, without any need for re-tuning parameters
or pre-modeling the interacting objects. In combination with the MPC
controller, the two adaptive approaches are validated and benchmarked with a
ball-balancing manipulator in door opening and object lifting tasks.


Abstract:
  Modern neural text generation systems can produce remarkably fluent and
grammatical texts. While earlier language models suffered from repetition and
syntactic errors, the errors made by contemporary models are often semantic,
narrative, or discourse failures.
  To facilitate research of these complex error types, we introduce a new
structured, crowdsourced error annotation schema called Scarecrow. The error
categories used in Scarecrow -- such as redundancy, commonsense errors, and
incoherence -- were identified by combining expert analysis with several pilot
rounds of ontology-free crowd annotation to arrive at a schema which covers the
error phenomena found in real machine generated text.
  We use Scarecrow to collect 13k annotations of 1.3k human and machine
generate paragraphs of English language news text, amounting to over 41k spans
each labeled with its error category, severity, a natural language explanation,
and antecedent span (where relevant). We collect annotations for text generated
by state-of-the-art systems with varying known performance levels, from GPT-2
Small through the largest GPT-3. We isolate several factors for detailed
analysis, including parameter count, training data, and decoding technique. Our
results show both expected and surprising differences across these settings.
These findings demonstrate the value of Scarecrow annotations in the assessment
of current and future text generation systems. We release our complete
annotation toolkit and dataset at https://yao-dou.github.io/scarecrow/.


Abstract:
  Automated event extraction in social science applications often requires
corpus-level evaluations: for example, aggregating text predictions across
metadata and unbiased estimates of recall. We combine corpus-level evaluation
requirements with a real-world, social science setting and introduce the
IndiaPoliceEvents corpus--all 21,391 sentences from 1,257 English-language
Times of India articles about events in the state of Gujarat during March 2002.
Our trained annotators read and label every document for mentions of police
activity events, allowing for unbiased recall evaluations. In contrast to other
datasets with structured event representations, we gather annotations by posing
natural questions, and evaluate off-the-shelf models for three different tasks:
sentence classification, document ranking, and temporal aggregation of target
events. We present baseline results from zero-shot BERT-based models fine-tuned
on natural language inference and passage retrieval tasks. Our novel
corpus-level evaluations and annotation approach can guide creation of similar
social-science-oriented resources in the future.


Abstract:
  We study the properties of homogeneous and isotropic turbulence in higher
spatial dimensions through the lens of chaos and predictability using numerical
simulations. We employ both direct numerical simulations (DNS) and numerical
calculations of the Eddy Damped Quasi Normal Markovian (EDQNM) closure
approximation. Our closure results show a remarkable transition to a
non-chaotic regime above critical dimension $d_c\approx 5.88$. We relate these
results to the properties of the energy cascade as a function of spatial
dimension in the context of the idea of a critical dimension for turbulence
where Kolmogorov's 1941 theory becomes exact.


Abstract:
  We develop a novel approach for confidently accelerating inference in the
large and expensive multilayer Transformers that are now ubiquitous in natural
language processing (NLP). Amortized or approximate computational methods
increase efficiency, but can come with unpredictable performance costs. In this
work, we present CATs -- Confident Adaptive Transformers -- in which we
simultaneously increase computational efficiency, while guaranteeing a
specifiable degree of consistency with the original model with high confidence.
Our method trains additional prediction heads on top of intermediate layers,
and dynamically decides when to stop allocating computational effort to each
input using a meta consistency classifier. To calibrate our early prediction
stopping rule, we formulate a unique extension of conformal prediction. We
demonstrate the effectiveness of this approach on four classification and
regression tasks.


Abstract:
  The Merino-Welsh conjectures say that subject to conditions, there is an
inequality among the Tutte-polynomial evaluations $T(M;2,0)$, $T(M;0,2)$, and
$T(M;1,1)$. We present three results on a Merino-Welsh conjecture. These
results are "inconsequential" in the sense that although they imply a version
of the conjecture for many matroids, they seem to be dead ends.


Abstract:
  Let $G$ be the Klein Four-group and let $k$ be an arbitrary field of
characteristic 2. A classification of indecomposable $kG$-modules is known. We
calculate the relative cohomology groups $H_\{chi}^i(G,N)$ for every
indecomposable $kG$-module $N$, where $\{chi}$ is the set of proper subgroups
in $G$. This extends work of Pamuk and Yalcin to cohomology with non-trivial
coefficients. We also show that all cup products in strictly positive degree in
$H_\{chi}^*(G,k)$ are trivial.


Abstract:
  In this paper, we propose in a Hilbertian setting a second-order
time-continuous dynamic system with fast convergence guarantees to solve
structured convex minimization problems with an affine constraint. The system
is associated with the augmented Lagrangian formulation of the minimization
problem. The corresponding dynamics brings into play three general time-varying
parameters, each with specific properties, and which are respectively
associated with viscous damping, extrapolation and temporal scaling. By
appropriately adjusting these parameters, we develop a Lyapunov analysis which
provides fast convergence properties of the values and of the feasibility gap.
These results will naturally pave the way for developing corresponding
accelerated ADMM algorithms, obtained by temporal discretization.


Abstract:
  The underlying theme of this article is a class of sequences in metric
structures satisfying a much weaker kind of Cauchy condition, namely
quasi-Cauchy sequences (introduced in \cite{bc}) that has been used to define
several new concepts in recent articles \cite{PDSPNA2, PDSPNA1}. We first
consider a weaker notion of precompactness based on the idea of quasi-Cauchy
sequences and establish several results including a new characterization of
compactness in metric spaces. Next we consider associated idea of continuity,
namely, ward continuous functions \cite{caka}, as this class of functions
strictly lies between the classes of continuous and uniformly continuous
functions and mainly establish certain coincidence results. Finally a new class
of Lipschitz functions called "quasi-Cauchy Lipschitz functions" is introduced
following the line of investigations in \cite{Beer1,Beer2,Beer3,g1} and again
several coincidence results are proved. The motivation behind such kind of
Lipschitz functions is ascertained by the observation that every real valued
ward continuous function defined on a metric space can be uniformly
approximated by real valued quasi-Cauchy Lipschitz functions.


Abstract:
  We use numerical relativity to study the merger and ringdown stages of
"superkick" binary black hole systems (those with equal mass and anti-parallel
spins). We find a universal way to describe the mass and current quadrupole
gravitational waves emitted by these systems during the merger and ringdown
stage: (i) The time evolutions of these waves are insensitive to the
progenitor's parameters (spins) after being normalized by their own peak
values. (ii) The peak values, which encode all the spin information of the
progenitor, can be consistently fitted to formulas inspired by post-Newtonian
theory. We find that the universal evolution of the mass quadrupole wave can be
accurately modeled by the so-called Backwards One-Body (BOB) model. However,
the BOB model, in its present form, leads to a lower waveform match and a
significant parameter-estimation bias for the current quadrupole wave. We also
decompose the ringdown signal into seven overtones, and study the dependence of
mode amplitudes on the progenitor's parameters. Such dependence is found to be
insensitive to the overtone index (up to a scaling factor). Finally, we use the
Fisher matrix technique to investigate how the ringdown waveform can be at
least as important for parameter estimation as the inspiral stage. Assuming the
Cosmic Explorer, we find the contribution of ringdown portion dominates as the
total mass exceeds ~ 250 solar mass. For massive BBH systems, the accuracy of
parameter measurement is improved by incorporating the information of ringdown
-- the ringdown sector gives rise to a different parameter correlation from
inspiral stage, hence the overall parameter correlation is reduced in the full
signal.


Abstract:
  We compute the quasinormal frequencies of $d$-dimensional spherically
symmetric black holes with leading string $\alpha'$ corrections for tensorial
gravitational perturbations in the highly damped regime. We solve
perturbatively the master differential equation and we compute the monodromies
of the master perturbation variable (analytically continued to the complex
plane) in different contours, in order to obtain the quasinormal mode spectra.
We proceed analogously for the quasinormal modes of test scalar fields.
Differently than in Einstein gravity, we obtain distinct results for the two
cases.


Abstract:
  In this work, a new model of effective lift-off threshold of oxide/metal
target is presented. The influence of nonlinear processes in the oxide layer on
its removal from the metallic samples using picosecond laser was investigated.
Nonlinear and saturable absorption in the layer was incorporated into modeling
for prediction of effective laser lift-off threshold fluence change with
varying peak intensities in z-scan type experiment for the first time. The new
model coincides well with the experimental results.


Abstract:
  This article introduces new acceleration methods for fixed-point iterations.
Extrapolations are computed using two or three mappings alternately and a new
type of step length is proposed with good properties for nonlinear
applications. The methods require no problem-specific adaptation and are
especially efficient in high-dimensional contexts. Their computation uses few
objective function evaluations, no matrix inversion and little extra memory. A
convergence analysis is followed by eight applications including gradient
descent acceleration for constrained and unconstrained optimization.
Performances are on par with or better than competitive alternatives. The
algorithm is available as the Julia package SpeedMapping.jl.


Abstract:
  We report electron transport studies on InSb-Al hybrid
semiconductor-superconductor nanowire devices. Tunnelling spectroscopy is used
to measure the evolution of subgap states while varying magnetic field and
voltages applied to various nearby gates. At magnetic fields between 0.7 and
0.9 T, the differential conductance contains large zero bias peaks (ZBPs) whose
height reaches values on the order 2e2/h. We investigate these ZBPs for large
ranges of gate voltages in different devices. We discuss possible
interpretations in terms of disorder-induced subgap states, Andreev bound
states and Majorana zero modes.


Abstract:
  For the Stokes system in the half space, Kang [Math.~Ann.~2005] showed that a
solution generated by a compactly supported, H\"older continuous boundary flux
may have unbounded normal derivatives near the boundary. In this paper we first
prove explicit global pointwise estimates of the above solution, showing in
particular that it has finite global energy and its derivatives blow up
everywhere on the boundary away from the flux. We then use the above solution
as a profile to construct solutions of the Navier-Stokes equations which also
have finite global energy and unbounded normal derivatives due to the flux. Our
main tool is the pointwise estimates of the Green tensor of the Stokes system
proved by us in \cite{Green} (arXiv:2011.00134). We also examine the Stokes
flows generated by dipole bumps boundary flux, and identify the regions where
the normal derivatives of the solutions tend to positive or negative infinity
near the boundary.


Abstract:
  In this paper we show a deterministic parallel all-pairs shortest paths
algorithm for real-weighted directed graphs. The algorithm has
$\tilde{O}(nm+(n/d)^3)$ work and $\tilde{O}(d)$ depth for any depth parameter
$d\in [1,n]$. To the best of our knowledge, such a trade-off has only been
previously described for the real-weighted single-source shortest paths problem
using randomization [Bringmann et al., ICALP'17]. Moreover, our result improves
upon the parallelism of the state-of-the-art randomized parallel algorithm for
computing transitive closure, which has $\tilde{O}(nm+n^3/d^2)$ work and
$\tilde{O}(d)$ depth [Ullman and Yannakakis, SIAM J. Comput. '91]. Our APSP
algorithm turns out to be a powerful tool for designing efficient planar graph
algorithms in both parallel and sequential regimes.
  One notable ingredient of our parallel APSP algorithm is a simple
deterministic $\tilde{O}(nm)$-work $\tilde{O}(d)$-depth procedure for computing
$\tilde{O}(n/d)$-size hitting sets of shortest $d$-hop paths between all pairs
of vertices of a real-weighted digraph. Such hitting sets have also been called
$d$-hub sets. Hub sets have previously proved especially useful in designing
parallel or dynamic shortest paths algorithms and are typically obtained via
random sampling. Our procedure implies, for example, an $\tilde{O}(nm)$-time
deterministic algorithm for finding a shortest negative cycle of a
real-weighted digraph. Such a near-optimal bound for this problem has been so
far only achieved using a randomized algorithm [Orlin et al., Discret. Appl.
Math. '18].


Abstract:
  The interaction of Older adults with robots requires effective feedback to
keep them aware of the state of the interaction for optimum interaction
quality. This study examines the effect of different feedback modalities in a
table setting robot assistant for elder care. Two different feedback modalities
(visual and auditory) and their combination were evaluated for three complexity
levels. The visual feedback included the use of LEDs and a GUI screen. The
auditory feedback included alerts (beeps) and verbal commands. The results
revealed that the quality of interaction was influenced mainly by the feedback
modality, and complexity had less influence. The verbal feedback was
significantly preferable and increased the involvement of the participants
during the experiment. The combination of LED lights and verbal commands
increased participants' understanding contributing to the quality of
interaction.


Abstract:
  This paper shows that all immersed totally geodesic suborbifolds of
arithmetic hyperbolic orbifolds correspond to finite order subgroups of the
commensurator. We call such totally geodesic suborbifolds fc-subspaces (finite
centraliser subspaces) and use them to produce an arithmeticity criterion for
hyperbolic lattices. We show that a hyperbolic orbifold $M$ is arithmetic if
and only if it has infinitely many fc-subspaces, and provide examples of
non-arithmetic orbifolds that contain non-fc subspaces. Finally, we study
arithmetic properties of orbifolds that descend to their totally geodesic
suborbifolds and deduce some information about their geodesic embeddings. It
follows that certain arithmetic hyperbolic orbifolds cannot be geodesically
immersed in any other arithmetic ones.


Abstract:
  We study the low temperature quench dynamics of the two-dimensional Potts
model in the limit of large number of states, q >> 1. We identify a
q-independent crossover temperature (the pseudo spinodal) below which no
high-temperature metastability stops the curvature driven coarsening process.
At short length scales, the latter is decorated by freezing for some lattice
geometries, notably the square one. With simple analytic arguments we evaluate
the relevant time-scale in the coarsening regime, which turns out to be of
Arrhenius form and independent of q for large q. Once taken into account
dynamic scaling is universal.


Abstract:
  A new analytic approach to investigate the zero-temperature time evolution of
the Jaynes-Cummings system with cavity losses is developed. With the realistic
coupling between the cavity and the environment assumed, a simple master
equation is derived, leading to the explicit analytic solution for the resonant
case. This solution is suitable for the analyses not only on the single
excitation states but also on many excitation states, which enables us to
investigate the photon coherent state and to observe sharp collapses and
revivals under dissipation. For the off-resonant case, on the other hand, the
present study presents an analytic, systematic method instead. We examine the
small and large detuning limits and discuss the condition where the widely-used
phenomenological treatment is justified. Explicit evaluations of the time
evolutions for various initial states with finite detuning are also presented.


Abstract:
  The goal of group testing is to efficiently identify a few specific items,
called positives, in a large population of items via tests. A test is an action
on a subset of items which returns positive if the subset contains at least one
positive and negative otherwise. In non-adaptive group testing, all tests are
fixed in advance and can be performed in parallel. In this work, we consider
non-adaptive group testing with consecutive positives in which the items are
linearly ordered and the positives are consecutive in that order.
  We present two contributions here. The first is the direct use of a binary
code to construct measurement matrices compared to the use of Gray code in the
state-of-the-art work, which is a rearrangement of the binary code, when the
maximum number of consecutive positives is known. This leads to a reduction in
decoding time in practice. The second one is efficient designs to identify
positives when the number of consecutive positives is known. To the best of our
knowledge, this setting has not been surveyed yet. Our simulations verify the
efficiency of our proposed designs. In particular, it only requires up to $300$
tests to identify up to $100$ positives in a set of $2^{32} \approx
4.3\mathrm{B}$ items in less than $300$ nanoseconds. When the maximum number of
consecutive positives is known, the simulations validate the superiority of our
proposed design in decoding compared to the state-of-the-art work. Moreover,
when the number of consecutive positives is known, the number of tests and the
decoding time are almost reduced half.


Abstract:
  To remain aware of the fast-evolving cyber threat landscape, open-source
Cyber Threat Intelligence (OSCTI) has received growing attention from the
community. Commonly, knowledge about threats is presented in a vast number of
OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI
gathering and management platforms, however, have primarily focused on
isolated, low-level Indicators of Compromise. On the other hand, higher-level
concepts (e.g., adversary tactics, techniques, and procedures) and their
relationships have been overlooked, which contain essential knowledge about
threat behaviors that is critical to uncovering the complete threat scenario.
To bridge the gap, we propose SecurityKG, a system for automated OSCTI
gathering and management. SecurityKG collects OSCTI reports from various
sources, uses a combination of AI and NLP techniques to extract high-fidelity
knowledge about threat behaviors, and constructs a security knowledge graph.
SecurityKG also provides a UI that supports various types of interactivity to
facilitate knowledge graph exploration.


Abstract:
  We study Einstein-Gauss-Bonnet gravity coupled to a bumblebee field which
leads to a spontaneous Lorentz symmetry breaking in the gravitational sector.
We obtain an exact black hole solution and a cosmological solution in four
dimensional spacetime by a regularization scheme. We also obtain a
Schwarzschild-like bumblebee black hole solution in $D$-dimensional spacetime.
We find that the bumblebee field doesn't affect the locations of the black hole
horizon, but only affects the gravitational potential. That is, its
gravitational potential has a minimum value(negative) in the black hole
interior and has a positive value $1+\ell$ at short distance $r\rightarrow0$.
If the constant $\ell$ is large enough, then this kind of black hole is
practically free from the singularity problem. The thermodynamics and phase
transition are also studied. In a cosmological context, it is interesting that
the Gauss-Bonnet term has no effect on the conservation of energy equation. A
late-time expansion of de Sitter universe can be replicated in an empty space.
The Gauss-Bonnet term and the bumblebee field can both actually act as a form
of dark energy.


Abstract:
  This article discusses cointegration tests for nonlinear cointegration in the
presence of variance breaks in the errors. We build on approaches of Cavaliere
and Taylor (2006, Journal of Time Series Analysis) for heteroskedastic
cointegration tests and of Choi and Saikkonen (2010, Econometric Theory) for
nonlinear cointegration tests. We propose a bootstrap test and prove its
consistency. A Monte Carlo study shows the approach to have appealing finite
sample properties and to work better than an approach using subresiduals. We
provide an empirical application to the environmental Kuznets curves (EKC),
finding that the cointegration tests do not reject the EKC hypothesis in most
cases.


Abstract:
  Training the large deep neural networks that dominate NLP requires large
datasets. Many of these are collected automatically or via crowdsourcing, and
may exhibit systematic biases or annotation artifacts. By the latter, we mean
correlations between inputs and outputs that are spurious, insofar as they do
not represent a generally held causal relationship between features and
classes; models that exploit such correlations may appear to perform a given
task well, but fail on out of sample data. In this paper we propose methods to
facilitate identification of training data artifacts, using new hybrid
approaches that combine saliency maps (which highlight important input
features) with instance attribution methods (which retrieve training samples
influential to a given prediction). We show that this proposed training-feature
attribution approach can be used to uncover artifacts in training data, and use
it to identify previously unreported artifacts in a few standard NLP datasets.
We execute a small user study to evaluate whether these methods are useful to
NLP researchers in practice, with promising results. We make code for all
methods and experiments in this paper available.


Abstract:
  We study the problem of approximating the cone of positive semidefinite (PSD)
matrices with a cone that can be described by smaller-sized PSD constraints.
Specifically, we ask the question: "how closely can we approximate the set of
unit-trace $n \times n$ PSD matrices, denoted by $D$, using at most $N$ number
of $k \times k$ PSD constraints?" In this paper, we prove lower bounds on $N$
to achieve a good approximation of $D$ by considering two constructions of an
approximating set. First, we consider the unit-trace $n \times n$ symmetric
matrices that are PSD when restricted to a fixed set of $k$-dimensional
subspaces in $\mathbb{RR}^n$. We prove that if this set is a good approximation
of $D$, then the number of subspaces must be at least exponentially large in
$n$ for any $k = o(n)$. % Second, we show that any set $S$ that approximates
$D$ within a constant approximation ratio must have superpolynomial
$\mathbf{S}_+^k$-extension complexity. To be more precise, if $S$ is a constant
factor approximation of $D$, then $S$ must have $\mathbf{S}_+^k$-extension
complexity at least $\exp( C \cdot \min \{ \sqrt{n}, n/k \})$ where $C$ is some
absolute constant. In addition, we show that any set $S$ such that $D \subseteq
S$ and the Gaussian width of $D$ is at most a constant times larger than the
Gaussian width of $D$ must have $\mathbf{S}_+^k$-extension complexity at least
$\exp( C \cdot \min \{ n^{1/3}, \sqrt{n/k} \})$. These results imply that the
cone of $n \times n$ PSD matrices cannot be approximated by a polynomial number
of $k \times k$ PSD constraints for any $k = o(n / \log^2 n)$. These results
generalize the recent work of Fawzi on the hardness of polyhedral
approximations of $\mathbf{S}_+^n$, which corresponds to the special case with
$k=1$.


Abstract:
  Following Johnsen and Verdure (2013), we can associate to any linear code $C$
an abstract simplicial complex and in turn, a Stanley-Reisner ring $R_C$. The
ring $R_C$ is a standard graded algebra over a field and its projective
dimension is precisely the dimension of $C$. Thus $R_C$ admits a graded minimal
free resolution and the resulting graded Betti numbers are known to determine
the generalized Hamming weights of $C$. The question of purity of the minimal
free resolution of $R_C$ was considered by Ghorpade and Singh (2020) when $C$
is the generalized Reed-Muller code. They showed that the resolution is pure in
some cases and it is not pure in many other cases. Here we give a complete
characterization of the purity of graded minimal free resolutions of
Stanley-Reisner rings associated to generalized Reed-Muller codes of an
arbitrary order.


Abstract:
  In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19)
challenge, we have proposed three transformer-based question-answering systems
using BERT, ALBERT, and T5 models. Since the CORD-19 dataset is unlabeled, we
have evaluated the question-answering models' performance on two labeled
questions answers datasets \textemdash CovidQA and CovidGQA. The BERT-based QA
system achieved the highest F1 score (26.32), while the ALBERT-based QA system
achieved the highest Exact Match (13.04). However, numerous challenges are
associated with developing high-performance question-answering systems for the
ongoing COVID-19 pandemic and future pandemics. At the end of this paper, we
discuss these challenges and suggest potential solutions to address them.


Abstract:
  In this review we discuss recent advances in the self-assembly of
self-propelled colloidal particles and highlight some of the most exciting
results in this field with a specific focus on dry active matter. We explore
this phenomenology through the lens of the complexity of the colloidal building
blocks. We begin by considering the behavior of isotropic spherical particles.
We then discuss the case of amphiphilic and dipolar Janus particles. Finally,
we show how the geometry of the colloids and/or the directionality of their
interactions can be used to control the physical properties of the assembled
active aggregates, and suggest possible strategies on how to exploit activity
as a tunable driving force for self-assembly. The unique properties of active
colloids lend promise for the design of the next generation of functional,
environment-sensing microstructures able to perform specific tasks in an
autonomous and targeted manner.


Abstract:
  The characteristics of high loss rate, resource constraint, being eager for
good security haven't been fully considered in the existing key establishment
protocols of wireless sensor networks. Analyzing the key establishing problem
from the MAC and physical layers, existing protocols are not practicable enough
due to their overlong agreement packets and single round key establishment. To
mitigate the impact of these problems, a group of design principles for secure
sensor networks has been presented and TinyAKE, an authenticated key transport
protocol based on lightweight certificate, is proposed in this paper. The
security of TinyAKE are proved with the theory of indistinguishability,
meanwhile, the correctness is also proved, the performance is analyzed and
compared with the existing similar protocols. Finally TinyAKE is implemented in
the TinyOS with TinyECC. Our evaluation shows that TinyAKE is a more
practicable and trustable authenticated key establishment protocol than
existing protocols. The experimental result shows that the key transport with
certificate mechanism is feasible in WSNs. Moreover, the simulation results
show that the optimal number of repeated negotiation is one when the secure
connectivity rate of TinyAKE is improved by using the repeated key negotiation.


Abstract:
  Generating natural speech with diverse and smooth prosody pattern is a
challenging task. Although random sampling with phone-level prosody
distribution has been investigated to generate different prosody patterns, the
diversity of the generated speech is still very limited and far from what can
be achieved by human. This is largely due to the use of uni-modal distribution,
such as single Gaussian, in the prior works of phone-level prosody modelling.
In this work, we propose a novel approach that models phone-level prosodies
with GMM based mixture density network (GMM-MDN). Experiments on the LJSpeech
dataset demonstrate that phone-level prosodies can precisely control the
synthetic speech and GMM-MDN can generate more natural and smooth prosody
pattern than a single Gaussian. Subjective evaluations further show that the
proposed approach not only achieves better naturalness, but also significantly
improves the prosody diversity in synthetic speech without the need of manual
control.


Abstract:
  In a spatial-temporal model, structural change and/or spatial heterogeneity
can easily affect estimation of parameters. Following the spatial-temporal
model in [1], we develop a nonparametric procedure for test-ing the presence of
structural change and spatial heterogeneity using bootstrap techniques and the
forward search algorithm. The time series bootstrap can filter the effect of
temporary structural change in the con-struction of a confidence interval for
the temporal parameter. The forward search will also facilitate the
construction of a robust confidence interval for the spatial parameter. These
confidence intervals are then used in deciding on the null hypothesis that
there is no structural change/spatial heterogeneity. Simulation studies
illustrate the ability of the proposed test procedure in detecting presence of
structural change and spatial heterogeneity under certain conditions.


Abstract:
  In this paper we show that a $\rho$-Einstein solitons conformal to a
pseudo-Euclidean space, invariant under the action of the pseudo-orthogonal
group with zero scalar curvature is stady and consequently flat. How
application of the results obtained we present an explicit example for a the
question proposed by Kazdan in [17].


Abstract:
  We calculate the production of hypothetical millicharged particles (MCPs) of
sub-GeV masses by the J-PARC proton beam in the framework of T2K and future
T2HK neutrino oscillation experiments. Concentrating on the region of model
parameter space, where an MCP can hit the near neutrino detector twice, we
adopt this background-free signature to estimate the sensitivity of T2K and
T2HK experiments to MCPs. We find that a previously inaccessible in direct
searches region of charges 5$\times$$10^{-4}$-$10^{-2}$ $e$ for MCP masses
0.1-0.5 GeV can be probed.


Abstract:
  Consider a weighted branching process generated by a point process on
$[0,1]$, whose atoms sum up to one. Then the weights of all individuals in any
given generation sum up to one, as well. We define a nested occupancy scheme in
random environment as the sequence of balls-in-boxes schemes (with random
probabilities) in which boxes of the $j$th level, $j=1,2,\ldots$ are identified
with the $j$th generation individuals and the hitting probabilities of boxes
are identified with the corresponding weights. The collection of balls is the
same for all generations, and each ball starts at the root and moves along the
tree of the weighted branching process according to the following rule:
transition from a mother box to a daughter box occurs with probability given by
the ratio of the daughter and mother weights.
  Assuming that there are $n$ balls, we give a full classification of regimes
of the a.s.\ convergence for the number of occupied (ever hit) boxes in the
$j$th level, properly normalized, as $n$ and $j=j_n$ grow to $\infty$. Here,
$(j_n)_{n\in\mathbb N}$ is a sequence of positive numbers growing
proportionally to $\log n$. We call such levels late, for the nested occupancy
scheme gets extinct in the levels which grow super-logarithmically in $n$ in
the sense that each occupied box contains one ball. Also, in some regimes we
prove the strong laws of large numbers (a) for the number of the $j$th level
boxes which contain at least $k$ balls, $k\geq 2$ and (b) under the assumption
that the mean number of the first level boxes is finite, for the number of
empty boxes in the $j$th level.


Abstract:
  This paper presents a novel method for obtaining the probability wave of
breaking ($P_b$) of deep water, dominant wind-sea waves (that is, waves made of
the energy within $\pm$30\% of the peak wave frequency) derived from Gaussian
wave field theory. For a given input wave spectrum we demonstrate how it is
possible to derive a joint probability density function between wave phase
speed ($c$) and horizontal orbital velocity at wave crest ($u$) from which a
model for $P_b$ can be obtained. A non-linear kinematic wave breaking criterion
consistent with the Gaussian framework is further proposed. Our model would
allow, therefore, for application of the classical wave breaking criterion
(that is, wave breaking occurs if $u/c > 1$) in spectral wave models which, to
the authors' knowledge, has not been done to date. Our results show that the
proposed theoretical model has errors in the same order of magnitude as six
other historical models when assessed using three field datasets. With
optimization of the proposed model's single free parameter, it can become the
best performing model for specific datasets. Although our results are
promising, additional, more complete wave breaking datasets collected in the
field are needed to comprehensively assess the present model, especially in
regards to the dependence on phenomena such as direct wind forcing, long wave
modulation and wave directionality.


Abstract:
  Various proxy metrics for test quality have been defined in order to guide
developers when writing tests. Code coverage is particularly well established
in practice, even though the question of how coverage relates to test quality
is a matter of ongoing debate. Mutation testing offers a promising alternative:
Artificial defects can identify holes in a test suite, and thus provide
concrete suggestions for additional tests. Despite the obvious advantages of
mutation testing, it is not yet well established in practice. Until recently,
mutation testing tools and techniques simply did not scale to complex systems.
Although they now do scale, a remaining obstacle is lack of evidence that
writing tests for mutants actually improves test quality. In this paper we aim
to fill this gap: By analyzing a large dataset of almost 15 million mutants, we
investigate how these mutants influenced developers over time, and how these
mutants relate to real faults. Our analyses suggest that developers using
mutation testing write more tests, and actively improve their test suites with
high quality tests such that fewer mutants remain. By analyzing a dataset of
past fixes of real high-priority faults, our analyses further provide evidence
that mutants are indeed coupled with real faults. In other words, had mutation
testing been used for the changes introducing the faults, it would have
reported a live mutant that could have prevented the bug.


Abstract:
  NLP systems rarely give special consideration to numbers found in text. This
starkly contrasts with the consensus in neuroscience that, in the brain,
numbers are represented differently from words. We arrange recent NLP work on
numeracy into a comprehensive taxonomy of tasks and methods. We break down the
subjective notion of numeracy into 7 subtasks, arranged along two dimensions:
granularity (exact vs approximate) and units (abstract vs grounded). We analyze
the myriad representational choices made by 18 previously published number
encoders and decoders. We synthesize best practices for representing numbers in
text and articulate a vision for holistic numeracy in NLP, comprised of design
trade-offs and a unified evaluation.


Abstract:
  We investigate the dust attenuation in both stellar populations and ionized
gas in kpc-scale regions in nearby galaxies, using integral field spectroscopy
data from MaNGA MPL-9. We identify star-forming (HII) and diffuse ionized gas
(DIG) regions from MaNGA datacubes. From the stacked spectrum of each region,
we measure the stellar attenuation, $E(B-V)_{\rm star}$, using the technique
developed by Li et al.(2020), as well as the gas attenuation, $E(B-V)_{\rm
gas}$, from the Balmer decrement. We then examine the correlation of
$E(B-V)_{\rm star}$, $E(B-V)_{\rm gas}$, $E(B-V)_{\rm gas}-E(B-V)_{\rm star}$
and $E(B-V)_{\rm star}/E(B-V)_{\rm gas}$ with 16 regional/global properties,
and for regions with different $\rm H{\alpha}$ surface brightnesses
($\Sigma_{\rm H\alpha}$). We find a stronger correlation between $E(B-V)_{\rm
star}$ and $E(B-V)_{\rm gas}$ in regions of higher $\Sigma_{\rm H\alpha}$.
Luminosity-weighted age ($t_L$) is found to be the property that is the most
strongly correlated with $E(B-V)_{\rm star}$, and consequently with
$E(B-V)_{\rm gas}-E(B-V)_{\rm star}$ and $E(B-V)_{\rm star}/E(B-V)_{\rm gas}$.
At fixed $\Sigma_{\rm H\alpha}$, $\log_{10}t_L$ is linearly and negatively
correlated with $E(B-V)_{\rm star}/E(B-V)_{\rm gas}$ at all ages. Gas-phase
metallicity and ionization level are important for the attenuation in the gas.
Our results indicate that the ionizing source for DIG regions is likely
distributed in the outer-skirt of galaxies, while for HII regions our results
can be well explained by the two-component dust model of Charlot & Fall (2000).


Abstract:
  We study the principal core g-mode oscillation in hybrid stars containing
quark matter and find that they have an unusually large frequency range
($\approx$ 200 - 600 Hz) compared to ordinary neutron stars or self-bound quark
stars of the same mass. Theoretical arguments and numerical calculations that
trace this effect to the difference in the behaviour of the equilibrium and
adiabatic sound speeds in the mixed phase of quarks and nucleons are provided.
We propose that the sensitivity of core g-mode oscillations to non-nucleonic
matter in neutron stars could be due to the presence of a mixed quark-nucleon
phase. Based on our analysis, we conclude that for binary mergers where one or
both components may be a hybrid star, the fraction of tidal energy pumped into
resonant g-modes in hybrid stars can exceed that of a normal neutron star by a
factor of 2-3, although resonance occurs during the last stages of inspiral. A
self-bound star, on the other hand, has a much weaker tidal overlap with the
g-mode. The cumulative tidal phase error in hybrid stars, $\Delta\phi\cong$ 0.5
rad, is comparable to that from tides in ordinary neutron stars, presenting a
challenge in distinguishing between the two cases. However, should the
principal g-mode be excited to sufficient amplitude for detection in a
post-merger remnant with quark matter in its interior, its frequency would be a
possible indication for the existence of non-nucleonic matter in neutron stars.


Abstract:
  In this work we present the Scaled QUantum IDentifier (SQUID), an open-source
framework for exploring hybrid Quantum-Classical algorithms for classification
problems. The classical infrastructure is based on PyTorch and we provide a
standardized design to implement a variety of quantum models with the
capability of back-propagation for efficient training. We present the structure
of our framework and provide examples of using SQUID in a standard binary
classification problem from the popular MNIST dataset. In particular, we
highlight the implications for scalability for gradient-based optimization of
quantum models on the choice of output for variational quantum models.


Abstract:
  We study reactive synthesis of systems interacting with environments using
infinite alphabets. Register automata and transducers are popular formalisms
for specifying and modelling such systems. They extend finite-state automata by
adding registers to store data values and to compare the incoming data values
against stored ones. Synthesis from nondeterministic or universal register
automata is undecidable in general. Its register-bounded variant, where
additionally a bound on the number of registers in a sought transducer is
given, is however known to be decidable for universal register automata which
can compare data for equality. In this paper, we generalise this result. We
introduce the notion of $\omega$-regularly approximable data domains, and show
that register-bounded synthesis from universal register automata on such
domains is decidable. Importantly, the data domain ($\mathbb{N}$, =, <) with
natural order is $\omega$-regularly approximable, and its closer examination
reveals that the synthesis problem is decidable in time doubly exponential in
the number of registers, matching the known complexity of the equality-only
case ($\mathbb{N}$, =). We then introduce a notion of reducibility between data
domains which we exploit to show decidability of synthesis over, e.g., the
domains ($\mathbb{N}^d$, $=^d$, $<^d$) of tuples of numbers equipped with the
component-wise partial order and ($\Sigma^*$, =, $\prec$) of finite strings
with the prefix relation.


Abstract:
  With the development of end-to-end control based on deep learning, it is
important to study new system modeling techniques to realize dynamics modeling
with high-dimensional inputs. In this paper, a novel Koopman-based deep
convolutional network, called CKNet, is proposed to identify latent dynamics
from raw pixels. CKNet learns an encoder and decoder to play the role of the
Koopman eigenfunctions and modes, respectively. The Koopman eigenvalues can be
approximated by eigenvalues of the learned state transition matrix. The
deterministic convolutional Koopman network (DCKNet) and the variational
convolutional Koopman network (VCKNet) are proposed to span some subspace for
approximating the Koopman operator respectively. Because CKNet is trained under
the constraints of the Koopman theory, the identified latent dynamics is in a
linear form and has good interpretability. Besides, the state transition and
control matrices are trained as trainable tensors so that the identified
dynamics is also time-invariant. We also design an auxiliary weight term for
reducing multi-step linearity and prediction losses. Experiments were conducted
on two offline trained and four online trained nonlinear forced dynamical
systems with continuous action spaces in Gym and Mujoco environment
respectively, and the results show that identified dynamics are adequate for
approximating the latent dynamics and generating clear images. Especially for
offline trained cases, this work confirms CKNet from a novel perspective that
we visualize the evolutionary processes of the latent states and the Koopman
eigenfunctions with DCKNet and VCKNet separately to each task based on the same
episode and results demonstrate that different approaches learn similar
features in shapes.


Abstract:
  The availability of Open Government Data (OGD) provides means for citizens to
understand and follow governmental policies and decisions, showing evidence of
how the latter have contributed to both the place they live in and their lives.
In such a scenario, one of the proposals is the use of visualizations to
support the process of data analysis and interpretation. Herein, we present the
use of three different visualization tools, a commercial one and two academic
ones, applied to two specific Brazilian cases: the implementation of the Drink
Driving Law and the construction of a new overpass in an important city avenue.
Our focus was on the analysis of how visualization could help in the
identification of the effects of such traffic public policies. As our main
contributions, we present details on the effects of the observed policies, as
well as new cases showing how visualization tools can assist users to interpret
OGD.


Abstract:
  Probabilistic logic programming is a major part of statistical relational
artificial intelligence, where approaches from logic and probability are
brought together to reason about and learn from relational domains in a setting
of uncertainty. However, the behaviour of statistical relational
representations across variable domain sizes is complex, and scaling inference
and learning to large domains remains a significant challenge. In recent years,
connections have emerged between domain size dependence, lifted inference and
learning from sampled subpopulations. The asymptotic behaviour of statistical
relational representations has come under scrutiny, and projectivity was
investigated as the strongest form of domain-size dependence, in which query
marginals are completely independent of the domain size.
  In this contribution we show that every probabilistic logic program under the
distribution semantics is asymptotically equivalent to an acyclic probabilistic
logic program consisting only of determinate clauses over probabilistic facts.
We conclude that every probabilistic logic program inducing a projective family
of distributions is in fact everywhere equivalent to a program from this
fragment, and we investigate the consequences for the projective families of
distributions expressible by probabilistic logic programs.
  To facilitate the application of classical results from finite model theory,
we introduce the abstract distribution semantics, defined as an arbitrary
logical theory over probabilistic facts. This bridges the gap to the
distribution semantics underlying probabilistic logic programming. In this
representation, determinate logic programs correspond to quantifier-free
theories, making asymptotic quantifier elimination results available for the
setting of probabilistic logic programming.
  This paper is under consideration for acceptance in TPLP.


Abstract:
  We theoretically show that the three-dimensional (3D) topological insulator
(TI)/thin-film ferromagnetic metal (FMM) bilayer structure is possible to be a
quantum anomalous Hall (QAH) insulator with a wide global band gap. Studying
the band structure and the weight distributions of eigenstates, we demonstrate
that the attachment of a metallic thin-film on the 3DTI can shift the
topologically non-trivial state into the metal layers due to the hybridization
of bands around the original Dirac point. By introducing the magnetic exchange
interaction in the thin-film layers, we compute the anomalous Hall conductivity
and magnetic anisotropy of the bilayer structure to suggest the appearance of
wider gap realizing QAH effect than usual materials, such as magnetically doped
thin-films of 3DTI and 3DTI/ferromagnetic insulator heterostructures. Our
results indicate that the 3DTI/thin-film FMM bilayer structure may implement
the QAH effect even at room temperature, which will pave a way to the
experimental realization of other exotic topological quantum phenomena.


Abstract:
  The physical picture of interacting magnetic islands provides a useful
paradigm for certain plasma dynamics in a variety of physical environments,
such as the solar corona, the heliosheath, and the Earth's magnetosphere. In
this work, we derive an island kinetic equation to describe the evolution of
the island distribution function (in area and in flux of islands) subject to a
collisional integral designed to account for the role of magnetic reconnection
during island mergers. This equation is used to study the inverse transfer of
magnetic energy through the coalescence of magnetic islands in 2D. We solve our
island kinetic equation numerically for three different types of initial
distribution: delta-distribution, Gaussian and power-law distribution. The time
evolution of several key quantities is found to agree well with our analytical
predictions: magnetic energy decays as $\tilde t^{-1}$, the number of islands
decreases as $\tilde t^{-1}$, and the averaged area of islands grows as $\tilde
t$, where $\tilde t$ is the time normalized to the characteristic reconnection
time scale of islands. General properties of the distribution function and the
magnetic energy spectrum are also studied.


Abstract:
  Time-dependent driving of quantum systems has emerged as a powerful tool to
engineer exotic phases far from thermal equilibrium; when the drive is periodic
this is called Floquet engineering. The presence of many-body interactions can
lead to runaway heating, so that generic systems are believed to heat up until
they reach a featureless infinite-temperature state. Finding mechanisms to slow
down or even avoid this heat death is a major goal -- one such mechanism is to
drive toward an even distribution of electrons in momentum space. Here we show
how such a mechanism avoids the heat death for a charge-density-wave chain in a
strong dc electric field; minibands with nontrivial distribution functions
develop as the current is prematurely driven to zero. We also show how the
field strength tunes between positive, negative, or close-to-infinite effective
temperatures for each miniband. These results suggest that nontrivial
metastable distribution functions should be realized in the prethermal regime
of quantum systems coupled to slow bosonic modes.


Abstract:
  This paper introduces an efficient algorithm for the sequential positioning
(or nested dissection) of two planar interfaces in an arbitrary polyhedron,
such that, after each truncation, the respectively remaining polyhedron admits
a prescribed volume. This task, among others, is frequently encountered in the
numerical simulation of three-phase flows when resorting to the geometric
Volume-of-Fluid method. For two-phase flows, the recent work of Kromer & Bothe
(doi.org/10.1016/j.jcp.2021.110776) addresses the positioning of a single plane
by combining an implicit bracketing of the sought position with up to
third-order derivatives of the volume fraction. An analogous application of
their highly efficient root-finding scheme to three-material configurations
requires computing the volume of a twice truncated arbitrary polyhedron. The
present manuscript achieves this by recursive application of the Gaussian
divergence theorem in appropriate form, which allows to compute the volume as a
sum of quantities associated to the faces of the original polyhedron. With a
suitable choice of the coordinate origin, accounting for the sequential
character of the truncation, the volume parametrization becomes co-moving with
respect to the planes. This eliminates the necessity to establish topological
connectivity and tetrahedron decomposition after each truncation. After a
detailed mathematical description of the concept, we conduct a series of
carefully designed numerical experiments to assess the performance in terms of
polyhedron truncations. The high efficiency of the two-phase positioning
persists for sequential application, thereby being robust with respect to input
data and possible intersection topologies. In comparison to an existing
decomposition-based approach, the number of truncations was reduced by up to an
order of magnitude.


Abstract:
  In this paper, we present a method of clothes retargeting; generating the
potential poses and deformations of a given 3D clothing template model to fit
onto a person in a single RGB image. The problem is fundamentally ill-posed as
attaining the ground truth data is impossible, i.e., images of people wearing
the different 3D clothing template model at exact same pose. We address this
challenge by utilizing large-scale synthetic data generated from physical
simulation, allowing us to map 2D dense body pose to 3D clothing deformation.
With the simulated data, we propose a semi-supervised learning framework that
validates the physical plausibility of the 3D deformation by matching with the
prescribed body-to-cloth contact points and clothing silhouette to fit onto the
unlabeled real images. A new neural clothes retargeting network (CRNet) is
designed to integrate the semi-supervised retargeting task in an end-to-end
fashion. In our evaluation, we show that our method can predict the realistic
3D pose and deformation field needed for retargeting clothes models in
real-world examples.


Abstract:
  Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental
analysis technique used to determine the chemical composition of target
samples, such as in industrial analysis of metals or in space exploration.
Recently, there has been a rise in the use of machine learning (ML) techniques
for LIBS data processing. However, ML for LIBS is challenging as: (i) the
predictive models must be lightweight since they need to be deployed in highly
resource-constrained and battery-operated portable LIBS systems; and (ii) since
these systems can be remote, the models must be able to self-adapt to any
domain shift in input distributions which could be due to the lack of different
types of inputs in training data or dynamic environmental/sensor noise. This
on-device retraining of model should not only be fast but also unsupervised due
to the absence of new labeled data in remote LIBS systems. We introduce a
lightweight multi-layer perceptron (MLP) model for LIBS that can be adapted
on-device without requiring labels for new input data. It shows 89.3% average
accuracy during data streaming, and up to 2.1% better accuracy compared to an
MLP model that does not support adaptation. Finally, we also characterize the
inference and retraining performance of our model on Google Pixel2 phone.


Abstract:
  Hosono obtained sharper estimates of the Ohsawa--Takegoshi $L^2$-extention
theorem by allowing the constant depending on the weight function for a domain
in $\mathbb{C}$. In this article, we show the higher dimensional case of
sharper estimates of the Ohsawa--Takegoshi $L^2$-extention theorem. To prove
the higher dimensional case of them, we establish an analogue of
Berndtsson--Lempert type $L^2$-extension theorem by using the pluricomplex
Green functions with poles along subvarieties. As a special case, we consider
the sharper estimates in terms of the Azukawa pseudometric and show that the
higher dimensional case of sharper estimate provides the $L^2$-minimum
extension for radial case.


Abstract:
  The notion of quasi $f$-ideals was first presented in $[14]$ which generalize
the idea of $f$-ideals. In this paper, we give the complete characterization of
quasi $f$-ideals of degree greater or equal to $2$. Additionally, we show that
the property of being quasi $f$-ideals remains the same after taking the Newton
complementary dual of a squarefree monomial ideal $I$ provided that the minimal
generating set of $I$ is perfect.


Abstract:
  We propose a novel procedure, which combines agglomerative hierarchical
clustering and a test of overidentifying restrictions for selecting valid
instrumental variables (IV) from a large set of candidate IVs. Some of these
IVs may be invalid in the sense that they fail the exclusion restriction. We
show that if the largest group of IVs is valid, our method achieves oracle
properties. Compared with existing methods, our method can deal with weak
instruments, multiple endogenous regressors and heterogeneous treatment
effects. In simulations, we show that our method outperforms the two closest
methods, the Hard Thresholding method and the Confidence Interval method.l the
instruments are strong. Also, our method works well when some of the candidate
instruments are weak, outperforming HT and CIM. We apply our method to the
estimation of the effect of immigration on wages in the US.


Abstract:
  Let $X\subset \mathbb{P}^r$ be an integral and non-degenerate variety. Let
$\sigma _{a,b}(X)\subseteq \mathbb{P}^r$, $(a,b)\in \mathbb{N}^2$, be the join
of $a$ copies of $X$ and $b$ copies of the tangential variety of $X$. Using the
classical Alexander-Hirschowitz theorem (case $b=0$) and a recent paper by H.
Abo and N. Vannieuwenhoven (case $a=0$) we compute $\dim \sigma _{a,b}(X)$ in
many cases when $X$ is the $d$-Veronese embedding of $\mathbb{P}^n$. This is
related to certain additive decompositions of homogeneous polynomials. We give
a general theorem proving that $\dim \sigma _{0,b}(X)$ is the expected one when
$X=Y\times \mathbb{P}^1$ has a suitable Segre-Veronese style embedding in
$\mathbb{P}^r$. As a corollary we prove that if $d_i\ge 3$, $1\le i \le n$, and
$(d_1+1)(d_2+1)\ge 38$ the tangential variety of $(\mathbb{P}^1)^n$ embedded by
$|\mathcal{O} _{(\mathbb{P} ^1)^n}(d_1,\dots ,d_n)|$ is not defective and a
similar statement for $\mathbb{P}^n\times \mathbb{P}^1$. For an arbitrary $X$
and an ample line bundle $L$ on $X$ we prove the existence of an integer $k_0$
such that for all $t\ge k_0$ the tangential variety of $X$ with respect to
$|L^{\otimes t}|$ is not defective.


Abstract:
  Establishing cause-effect relationships from observational data often relies
on untestable assumptions. It is crucial to know whether, and to what extent,
the conclusions drawn from non-experimental studies are robust to potential
unmeasured confounding. In this paper, we focus on the average causal effect
(ACE) as our target of inference. We build on the work of Franks et al.
(2019)and Robins (2000) by specifying non-identified sensitivity parameters
that govern a contrast between the conditional (on measured covariates)
distributions of the outcome under treatment (control) between treated and
untreated individuals. We use semiparametric theory to derive the
non-parametric efficient influence function of the ACE, for fixed sensitivity
parameters. We use this influence function to construct a one-step
bias-corrected estimator of the ACE. Our estimator depends on semiparametric
models for the distribution of the observed data; importantly, these models do
not impose any restrictions on the values of sensitivity analysis parameters.
We establish sufficient conditions ensuring that our estimator has root-n
asymptotics. We use our methodology to evaluate the causal effect of smoking
during pregnancy on birth weight. We also evaluate the performance of
estimation procedure in a simulation study.


Abstract:
  We introduce a semi-parametric method to simultaneously infer both the drift
and volatility functions of a discretely observed scalar diffusion. We
introduce spline bases to represent these functions and develop a Markov chain
Monte Carlo algorithm to infer, a posteriori, the coefficients of these
functions in the spline basis. A key innovation is that we use spline bases to
model transformed versions of the drift and volatility functions rather than
the functions themselves. The output of the algorithm is a posterior sample of
plausible drift and volatility functions that are not constrained to any
particular parametric family. The flexibility of this approach provides
practitioners a powerful investigative tool, allowing them to posit parametric
models to better capture the underlying dynamics of their processes of
interest. We illustrate the versatility of our method by applying it to
challenging datasets from finance, paleoclimatology, and astrophysics. In view
of the parametric diffusion models widely employed in the literature for those
examples, some of our results are surprising since they call into question some
aspects of these models.


Abstract:
  We present GIGANTES, the most extensive and realistic void catalog suite ever
released -- containing over 1 billion cosmic voids covering a volume larger
than the observable Universe, more than 20 TB of data, and created by running
the void finder VIDE on QUIJOTE's halo simulations. The expansive and detailed
GIGANTES suite, spanning thousands of cosmological models, opens up the study
of voids, answering compelling questions: Do voids carry unique cosmological
information? How is this information correlated with galaxy information?
Leveraging the large number of voids in the GIGANTES suite, our Fisher
constraints demonstrate voids contain additional information, critically
tightening constraints on cosmological parameters. We use traditional void
summary statistics (void size function, void density profile) and the void
auto-correlation function, which independently yields an error of
$0.13\,\mathrm{eV}$ on $\sum\,m_{\nu}$ for a 1 $h^{-3}\mathrm{Gpc}^3$
simulation, without CMB priors. Combining halos and voids we forecast an error
of $0.09\,\mathrm{eV}$ from the same volume. Extrapolating to next generation
multi-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman Space
Telescope, we expect voids should yield an independent determination of
neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly
built for intensive machine learning exploration. We illustrate this by
training a neural network to perform likelihood-free inference on the void size
function. Cosmology problems provide an impetus to develop novel deep learning
techniques, leveraging the symmetries embedded throughout the universe from
physical laws, interpreting models, and accurately predicting errors. With
GIGANTES, machine learning gains an impressive dataset, offering unique
problems that will stimulate new techniques.


Abstract:
  The Large Hadron Collider (LHC) at CERN is undergoing a major upgrade with
the goal of increasing the luminosity as more statistics are needed for
precision measurements. The presented work pertains to the corresponding
upgrade of the ALICE Muon Trigger (MTR) Detector, now named the Muon Identifier
(MID). Previously operated in a triggered readout manner, this detector has
transitioned to continuous readout with time-delimited data payloads. However,
this results in data rates much higher than the previous operation and hence a
new Online-Offline (O2) computing system is also being developed for real-time
data processing to reduce the storage requirements. Part of the O2 System is
based on FPGA technology and is known as the Common Readout Unit (CRU). Being
common to many detectors necessitates the development of custom user logic per
detector. This work concerns the development of the ALICE MID user logic which
will interface to the core CRU firmware and perform the required data
processing. It presents the development of a conceptual design and a prototype
for the user logic. The resulting prototype shows the ability to meet the
established requirements in an effective and optimized manner. Additionally,
the modular design approach employed, allows for more features to be easily
introduced.


Abstract:
  An extension of Riemmann's geometry into a direction dependent geometric
structure is usually described by Finsler's geometry. Historically, this
construction was motivated by the well-known Riemann's quartic length element
example. Quite surprisingly, the same quartic expression emerges in solid-state
electrodynamics as a basic dispersion relation -- covariant Fresnel equation.
Consequently, Riemann's quartic length expression can be interpreted as a
mathematical model of a well-established physics phenomena. In this paper, we
present various examples of Riemann's quartic that demonstrate that Finsler's
geometry is too restrictive even in the case of a positive definite Euclidean
signature space. In the case of the spaces endowed with an indefinite
(Minkowski) signature, there are much more singular hypersurfaces where the
strong axioms of Finsler's geometry are broken down. We propose a weaker
definition of Finsler's structure that is required to be satisfied only on open
subsets of the tangent bundle. We exhibit the characteristic singular
hypersurfaces related to Riemann's quartic and briefly discuss their possible
physical interpretation.


Abstract:
  We present an algorithm for data-driven reachability analysis that estimates
finite-horizon forward reachable sets for general nonlinear systems using level
sets of a certain class of polynomials known as Christoffel functions. The
level sets of Christoffel functions are known empirically to provide good
approximations to the support of probability distributions: the algorithm uses
this property for reachability analysis by solving a probabilistic relaxation
of the reachable set computation problem. We also provide a guarantee that the
output of the algorithm is an accurate reachable set approximation in a
probabilistic sense, provided that a certain sample size is attained. We also
investigate three numerical examples to demonstrate the algorithm's
capabilities, such as providing non-convex reachable set approximations and
detecting holes in the reachable set.


Abstract:
  We consider the cubic and quintic nonlinear Schr\"{o}dinger equations (NLS)
under the $\mathbb{R}^{d}$ and $\mathbb{T}^{d}$ energy-supercritical setting.
Via a newly developed unified scheme, we prove the unconditional uniqueness for
solutions to NLS at critical regularity for all dimensions. Thus, together with
[18,19], the unconditional uniqueness problems for $H^{1}$-critical and
$H^{1}$-supercritical cubic and quintic NLS are completely and uniformly
resolved at critical regularity for these domains. One application of our
theorem is to prove that defocusing blowup solutions of the type in [54] is the
only possible $C([0,T);\dot{H}^{s_{c}})$ solution if exist in these domains.


Abstract:
  We present TNCR, a new table dataset with varying image quality collected
from free websites. The TNCR dataset can be used for table detection in scanned
document images and their classification into 5 different classes. TNCR
contains 9428 high-quality labeled images. In this paper, we have implemented
state-of-the-art deep learning-based methods for table detection to create
several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone
Network achieves the highest performance compared to other methods with a
precision of 79.7%, recall of 89.8%, and f1 score of 84.4% on the TNCR dataset.
We have made TNCR open source in the hope of encouraging more deep learning
approaches to table detection, classification, and structure recognition. The
dataset and trained model checkpoints are available at
https://github.com/abdoelsayed2016/TNCR_Dataset.


Abstract:
  The existence of charged black holes has suggested that wormholes may also be
charged. The purpose of this paper is to construct a general model of a charged
wormhole that proves to be a natural extension of the original Morris-Thorne
wormhole. This goal is achieved by means of the classical embedding theory that
has played a major role in the general theory of relativity.


Abstract:
  Herbarium contains treasures of millions of specimens which have been
preserved for several years for scientific studies. To speed up more scientific
discoveries, a digitization of these specimens is currently on going to
facilitate easy access and sharing of its data to a wider scientific community.
Online digital repositories such as IDigBio and GBIF have already accumulated
millions of specimen images yet to be explored. This presents a perfect time to
automate and speed up more novel discoveries using machine learning and
computer vision. In this study, a thorough analysis and comparison of more than
50 peer-reviewed studies which focus on application of computer vision and
machine learning techniques to digitized herbarium specimen have been examined.
The study categorizes different techniques and applications which have been
commonly used and it also highlights existing challenges together with their
possible solutions. It is our hope that the outcome of this study will serve as
a strong foundation for beginners of the relevant field and will also shed more
light for both computer science and ecology experts.


Abstract:
  We study the contributions to the relativistic Fe $K_{\alpha}$ line profile
from higher order images (HOIs) produced by strongly deflected rays from the
disk which cross the plunging region, located between the innermost stable
circular orbit (ISCO) radius and the event horizon of a Kerr black hole. We
investigate the characteristics features imprinted by the HOIs in the line
profile for different black hole spins, disk emissivity laws and inclinations.
We find that they extend from the red wing of the profile up to energies
slightly lower than those of the blue peak, adding $\sim 0.4 - 1.3$\% to the
total line flux. The contribution to the specific flux is often in the $\sim
1$\% to 7\% range, with the highest values attained for low and negative spin
($a\lesssim 0.3$) black holes surrounded by intermediate inclination angle
($i\sim40^{\circ}$) disks. We simulate future observations of a black hole
X-ray binary system with the Large Area Detector of the planned X-ray astronomy
\emph{enhanced X-ray Timing and Polarimetry Mission} (eXTP) and find that the
\fekal\ of systems accreting at $\lesssim 1 $\% the Eddington rate are affected
by the HOI features for a range of parameters. This would provide evidence of
the extreme gravitational lensing of HOI rays. Our simulations show also that
not accounting for HOI contributions to the Fe $K_{\alpha}$ line profile may
systematically bias measurements of the black hole spin parameter towards
values higher by up to $\sim 0.3$ than the inputted ones.


Abstract:
  Stress-induced martensitic transformations enable metastable alloys to
exhibit enhanced strain hardening capacity, leading to improved formability and
toughness. As is well-known from transformation-induced plasticity (TRIP)
steels, however, the resulting martensite can limit ductility and fatigue life
due to its intrinsic brittleness. In this work, we explore an alloy design
strategy that utilizes stress-induced martensitic transformations but does not
retain the martensite phase. This strategy is based on the introduction of
superelastic nano-precipitates, which exhibit reverse transformation after
initial stress-induced forward transformation. To this end, utilizing ab-initio
simulations and thermodynamic calculations we designed and produced a
V45Ti30Ni25 (at%) alloy. In this alloy, TiNi is present as nano-precipitates
uniformly distributed within a ductile V-rich base-centered cubic (bcc) beta
matrix, as well as being present as a larger matrix phase. We characterized the
microstructure of the produced alloy using various scanning electron microscopy
(SEM) and transmission electron microscopy (TEM) methods. The bulk mechanical
properties of the alloy are demonstrated through tensile tests, and the
reversible transformation in each of the TiNi morphologies were confirmed by
in-situ TEM micro-pillar compression experiments, in-situ high-energy
diffraction synchrotron cyclic tensile tests, indentation experiments, and
differential scanning calorimetry experiments. The observed transformation
pathways and variables impacting phase stability are critically discussed


Abstract:
  In a wide range of applications, the estimate of droplet evaporation time is
based on the classical d2-law, which, assuming a fast mixing and fixed
environmental properties, states that the droplet surface decreases linearly
with time at a determined rate. However, in many cases the predicted
evaporation rate is overestimated. In this Letter, we propose a revision of the
d2-law capable to accurately determine droplet evaporation rate in dilute
conditions by a proper estimate of the asymptotic droplet properties. Besides a
discussion of the main assumptions, we tested the proposed model against data
from direct numerical simulations finding an excellent agreement for predicted
droplet evaporation time in dilute turbulent jet-sprays.


Abstract:
  We prove the Gross order of vanishing conjecture in special cases where the
vanishing order of the character in question can be arbitrarily large. In
almost all previously known cases the vanishing order is zero or one. One major
ingredient of our proofs is the equivalence of this conjecture to the
Gross-Kuz'min conjecture. We present here a direct proof of this equivalence,
using only the known validity of the Iwasawa Main Conjecture over totally real
fields.