Abstract:
  This paper establishes new connections between many-body quantum systems,
One-body Reduced Density Matrices Functional Theory (1RDMFT) and Optimal
Transport (OT), by interpreting the problem of computing the ground-state
energy of a finite dimensional composite quantum system at positive temperature
as a non-commutative entropy regularized Optimal Transport problem. We develop
a new approach to fully characterize the dual-primal solutions in such
non-commutative setting. The mathematical formalism is particularly relevant in
quantum chemistry: numerical realizations of the many-electron ground state
energy can be computed via a non-commutative version of Sinkhorn algorithm. Our
approach allows to prove convergence and robustness of this algorithm, which,
to our best knowledge, were unknown even in the two marginal case. Our methods
are based on careful a priori estimates in the dual problem, which we believe
to be of independent interest. Finally, the above results are extended in
1RDMFT setting, where bosonic or fermionic symmetry conditions are enforced on
the problem.


Abstract:
  Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of
algorithms for scalable Bayesian inference. However, these algorithms include
hyperparameters such as step size or batch size that influence the accuracy of
estimators based on the obtained posterior samples. As a result, these
hyperparameters must be tuned by the practitioner and currently no principled
and automated way to tune them exists. Standard MCMC tuning methods based on
acceptance rates cannot be used for SGMCMC, thus requiring alternative tools
and diagnostics. We propose a novel bandit-based algorithm that tunes the
SGMCMC hyperparameters by minimizing the Stein discrepancy between the true
posterior and its Monte Carlo approximation. We provide theoretical results
supporting this approach and assess various Stein-based discrepancies. We
support our results with experiments on both simulated and real datasets, and
find that this method is practical for a wide range of applications.


Abstract:
  Observations of the redshifted 21-cm line of neutral hydrogen (HI) are a new
and powerful window of observation that offers us the possibility to map the
spatial distribution of cosmic HI and learn about cosmology. BINGO (Baryon
Acoustic Oscillations [BAO] from Integrated Neutral Gas Observations) is a new
unique radio telescope designed to be one of the first to probe BAO at radio
frequencies. BINGO has two science goals: cosmology and astrophysics. Cosmology
is the main science goal and the driver for BINGO's design and strategy. The
key of BINGO is to detect the low redshift BAO to put strong constraints in the
dark sector models. Given the versatility of the BINGO telescope, a secondary
goal is astrophysics, where BINGO can help discover and study Fast Radio Bursts
(FRB) and other transients, Galactic and extragalactic science. In this paper,
we introduce the latest progress of the BINGO project, its science goals,
describing the scientific potential of the project in each science and the new
developments obtained by the collaboration. We introduce the BINGO project and
its science goals and give a general summary of recent developments in
construction, science potential and pipeline development obtained by the BINGO
collaboration in the past few years. We show that BINGO will be able to obtain
competitive constraints for the dark sector, and also that will allow for the
discovery of several FRBs in the southern hemisphere. The capacity of BINGO in
obtaining information from 21-cm is also tested in the pipeline introduced
here. There is still no measurement of the BAO in radio, and studying cosmology
in this new window of observations is one of the most promising advances in the
field. The BINGO project is a radio telescope that has the goal to be one of
the first to perform this measurement and it is currently being built in the
northeast of Brazil. (Abridged)


Abstract:
  We explore cross-lingual transfer of register classification for web
documents. Registers, that is, text varieties such as blogs or news are one of
the primary predictors of linguistic variation and thus affect the automatic
processing of language. We introduce two new register annotated corpora,
FreCORE and SweCORE, for French and Swedish. We demonstrate that deep
pre-trained language models perform strongly in these languages and outperform
previous state-of-the-art in English and Finnish. Specifically, we show 1) that
zero-shot cross-lingual transfer from the large English CORE corpus can match
or surpass previously published monolingual models, and 2) that lightweight
monolingual classification requiring very little training data can reach or
surpass our zero-shot performance. We further analyse classification results
finding that certain registers continue to pose challenges in particular for
cross-lingual transfer.


Abstract:
  Compact binary systems emit gravitational radiation which is potentially
detectable by current Earth bound detectors. Extracting these signals from the
instruments' background noise is a complex problem and the computational cost
of most current searches depends on the complexity of the source model. Deep
learning may be capable of finding signals where current algorithms hit
computational limits. Here we restrict our analysis to signals from
non-spinning binary black holes and systematically test different strategies by
which training data is presented to the networks. To assess the impact of the
training strategies, we re-analyze the first published networks and directly
compare them to an equivalent matched-filter search. We find that the deep
learning algorithms can generalize low signal-to-noise ratio (SNR) signals to
high SNR ones but not vice versa. As such, it is not beneficial to provide high
SNR signals during training, and fastest convergence is achieved when low SNR
samples are provided early on. During testing we found that the networks are
sometimes unable to recover any signals when a false alarm probability
$<10^{-3}$ is required. We resolve this restriction by applying a modification
we call unbounded Softmax replacement (USR) after training. With this
alteration we find that the machine learning search retains $\geq 97.5\%$ of
the sensitivity of the matched-filter search down to a false-alarm rate of 1
per month.


Abstract:
  The position of the Sun inside the Milky Way's disc hampers the study of the
spiral arm structure. We aim to analyse the spiral arms along the line-of-sight
towards the Galactic centre (GC) to determine their distance, extinction, and
stellar population. We use the GALACTICNUCLEUS survey, a JHKs high angular
resolution photometric catalogue (0.2") for the innermost regions of the
Galaxy. We fitted simple synthetic colour-magnitude models to our data via
$\chi^2$ minimisation. We computed the distance and extinction to the detected
spiral arms. We also analysed the extinction curve and the relative extinction
between the detected features. Finally, we built extinction-corrected Ks
luminosity functions (KLFs) to study the stellar populations present in the
second and third spiral arm features. We determined the mean distances to the
spiral arms: $d1=1.6\pm0.2$, $d2=2.6\pm0.2$, $d3=3.9\pm0.3$, and $d4=4.5\pm0.2$
kpc, and the mean extinctions: $A_{H1}=0.35\pm0.08$, $A_{H2}=0.77\pm0.08$,
$A_{H3}=1.68\pm0.08$, and $A_{H4}=2.30\pm0.08$ mag. We analysed the extinction
curve in the near infrared for the stars in the spiral arms and found mean
values of $A_J/A_{H}=1.89\pm0.11$ and $A_H/A_{K_s}=1.86\pm0.11$, in agreement
with the results obtained for the GC. This implies that the shape of the
extinction curve does not depend on distance or absolute extinction. We also
built extinction maps for each spiral arm and obtained that they are
homogeneous and might correspond to independent extinction layers. Finally,
analysing the KLFs from the second and the third spiral arms, we found that
they have similar stellar populations. We obtained two main episodes of star
formation: $>6$ Gyr ($\sim60-70\%$ of the stellar mass), and $1.5-4$ Gyr
($\sim20-30\%$ of the stellar mass), compatible with previous work. We also
detected recent star formation at a lower level ($\sim10\%$) for the third
spiral arm.


Abstract:
  We present a comprehensive comparison of spin and energy dynamics in quantum
and classical spin models on different geometries, ranging from one-dimensional
chains, over quasi-one-dimensional ladders, to two-dimensional square lattices.
Focusing on dynamics at formally infinite temperature, we particularly consider
the autocorrelation functions of local densities, where the time evolution is
governed either by the linear Schr\"odinger equation in the quantum case, or
the nonlinear Hamiltonian equations of motion in the case of classical
mechanics. While, in full generality, a quantitative agreement between quantum
and classical dynamics can therefore not be expected, our large-scale numerical
results for spin-$1/2$ systems with up to $N = 36$ lattice sites in fact defy
this expectation. Specifically, we observe a remarkably good agreement for all
geometries, which is best for the nonintegrable quantum models in quasi-one or
two dimensions, but still satisfactory in the case of integrable chains, at
least if transport properties are not dominated by the extensive number of
conservation laws. Our findings indicate that classical or semi-classical
simulations provide a meaningful strategy to analyze the dynamics of quantum
many-body models, even in cases where the spin quantum number $S = 1/2$ is
small and far away from the classical limit $S \to \infty$.


Abstract:
  Event coreference continues to be a challenging problem in information
extraction. With the absence of any external knowledge bases for events,
coreference becomes a clustering task that relies on effective representations
of the context in which event mentions appear. Recent advances in
contextualized language representations have proven successful in many tasks,
however, their use in event linking been limited. Here we present a three part
approach that (1) uses representations derived from a pretrained BERT model to
(2) train a neural classifier to (3) drive a simple clustering algorithm to
create coreference chains. We achieve state of the art results with this model
on two standard datasets for within-document event coreference task and
establish a new standard on a third newer dataset.


Abstract:
  Safety is a fundamental requirement in any human-robot collaboration
scenario. To ensure the safety of users for such scenarios, we propose a novel
Virtual Barrier system facilitated by an augmented reality interface. Our
system provides two kinds of Virtual Barriers to ensure safety: 1) a Virtual
Person Barrier which encapsulates and follows the user to protect them from
colliding with the robot, and 2) Virtual Obstacle Barriers which users can
spawn to protect objects or regions that the robot should not enter. To enable
effective human-robot collaboration, our system includes an intuitive robot
programming interface utilizing speech commands and hand gestures, and features
the capability of automatic path re-planning when potential collisions are
detected as a result of a barrier intersecting the robot's planned path. We
compared our novel system with a standard 2D display interface through a user
study, where participants performed a task mimicking an industrial
manufacturing procedure. Results show that our system increases the user's
sense of safety and task efficiency, and makes the interaction more intuitive.


Abstract:
  We show that the change of basis matrices of a set of $m$ bases of a finite
vector space is a connected groupoid of order $m^2$. We define a general method
to express the elements of change of basis matrices as algebraic expressions
using optimizations of evaluations of vector dot products. Examples are given
with orthogonal polynomials.


Abstract:
  We experimentally observe the dipole scattering of a nanoparticle using a
high numerical aperture (NA) imaging system. The optically levitated
nanoparticle provides an environment free of particle-substrate interaction. We
illuminate the silica nanoparticle in vacuum with a 532 nm laser beam
orthogonally to the propagation direction of the 1064 nm trapping laser beam
strongly focused by the same high NA objective used to collect the scattering,
which results in a dark background and high signal-noise ratio. The dipole
orientations of the nanoparticle induced by the linear polarization of the
incident laser are studied by measuring the scattering light distribution in
the image and the Fourier space (k-space) as we rotate the illuminating light
polarization. The polarization vortex (vector beam) is observed for the special
case, when the dipole orientation of the nanoparticle is aligned along the
optical axis of the microscope objective. Our work offers an important platform
for studying the scattering anisotropy with Kerker conditions.


Abstract:
  We prove that the Feynman Path Integral is equivalent to a novel stringy
description of elementary particles characterized by a single compact (cyclic)
world-line parameter playing the role of the particle internal clock. Such a
possible description of elementary particles as characterized by intrinsic
periodicity in time has been indirectly confirmed, even experimentally, by
recent developments of Time Crystals. We clearly obtain an exact unified
formulation of quantum and relativistic physics, potentially deterministic,
fully falsifiable having no fine-tunable parameters, also proven in previous
papers to be completely consistent with all known physics, from theoretical
physics to condensed matter. New physics will be discovered by probing quantum
phenomena with experimental time accuracy of the order of $10^{-21}$ sec.


Abstract:
  Recent papers on the theory of representation learning has shown the
importance of a quantity called diversity when generalizing from a set of
source tasks to a target task. Most of these papers assume that the function
mapping shared representations to predictions is linear, for both source and
target tasks. In practice, researchers in deep learning use different numbers
of extra layers following the pretrained model based on the difficulty of the
new task. This motivates us to ask whether diversity can be achieved when
source tasks and the target task use different prediction function spaces
beyond linear functions. We show that diversity holds even if the target task
uses a neural network with multiple layers, as long as source tasks use linear
functions. If source tasks use nonlinear prediction functions, we provide a
negative result by showing that depth-1 neural networks with ReLu activation
function need exponentially many source tasks to achieve diversity. For a
general function class, we find that eluder dimension gives a lower bound on
the number of tasks required for diversity. Our theoretical results imply that
simpler tasks generalize better. Though our theoretical results are shown for
the global minimizer of empirical risks, their qualitative predictions still
hold true for gradient-based optimization algorithms as verified by our
simulations on deep neural networks.


Abstract:
  In [Kim05], Kim gave a new proof of Siegel's Theorem that there are only
finitely many $S$-integral points on $\mathbb P^1_{\mathbb
Z}\setminus\{0,1,\infty\}$. One advantage of Kim's method is that it in
principle allows one to actually find these points, but the calculations grow
vastly more complicated as the size of $S$ increases. In this paper, we
implement a refinement of Kim's method to explicitly compute various examples
where $S$ has size $2$ which has been introduced in [BD19]. In so doing, we
exhibit new examples of a natural generalisation of a conjecture of Kim.


Abstract:
  This tool paper presents the High-Assurance ROS (HAROS) framework. HAROS is a
framework for the analysis and quality improvement of robotics software
developed using the popular Robot Operating System (ROS). It builds on a static
analysis foundation to automatically extract models from the source code. Such
models are later used to enable other sorts of analyses, such as Model
Checking, Runtime Verification, and Property-based Testing. It has been applied
to multiple real-world examples, helping developers find and correct various
issues.


Abstract:
  The latest conjunction of Jupiter and Saturn occurred at an optical distance
of 6 arc minutes on 21 December 2020. We re-analysed all encounters of these
two planets between -1000 and +3000 CE, as the extraordinary ones
(<10$^{\prime}$) take place near the line of nodes every 400 years. An
occultation of their discs did not and will not happen within the historical
time span of $\pm$5,000 years around now. When viewed from Neptune though,
there will be an occultation in 2046.


Abstract:
  Filters (such as Bloom Filters) are data structures that speed up network
routing and measurement operations by storing a compressed representation of a
set. Filters are space efficient, but can make bounded one-sided errors: with
tunable probability epsilon, they may report that a query element is stored in
the filter when it is not. This is called a false positive. Recent research has
focused on designing methods for dynamically adapting filters to false
positives, reducing the number of false positives when some elements are
queried repeatedly.
  Ideally, an adaptive filter would incur a false positive with bounded
probability epsilon for each new query element, and would incur o(epsilon)
total false positives over all repeated queries to that element. We call such a
filter support optimal.
  In this paper we design a new Adaptive Cuckoo Filter and show that it is
support optimal (up to additive logarithmic terms) over any n queries when
storing a set of size n. Our filter is simple: fixing previous false positives
requires a simple cuckoo operation, and the filter does not need to store any
additional metadata. This data structure is the first practical data structure
that is support optimal, and the first filter that does not require additional
space to fix false positives.
  We complement these bounds with experiments showing that our data structure
is effective at fixing false positives on network traces, outperforming
previous Adaptive Cuckoo Filters.
  Finally, we investigate adversarial adaptivity, a stronger notion of
adaptivity in which an adaptive adversary repeatedly queries the filter, using
the result of previous queries to drive the false positive rate as high as
possible. We prove a lower bound showing that a broad family of filters,
including all known Adaptive Cuckoo Filters, can be forced by such an adversary
to incur a large number of false positives.


Abstract:
  Let $(G,K)$ be a Gelfand pair, with $G$ a Lie group of polynomial growth, and
let $\Sigma\subset{\mathbb R}^\ell$ be a homeomorphic image of the Gelfand
spectrum, obtained by choosing a generating system $D_1,\dots,D_\ell$ of
$G$-invariant differential operators on $G/K$ and associating to a bounded
spherical function $\varphi$ the $\ell$-tuple of its eigenvalues under the
action of the $D_j$'s.
  We say that property (S) holds for $(G,K)$ if the spherical transform maps
the bi-$K$-invariant Schwartz space ${\mathcal S}(K\backslash G/K)$
isomorphically onto ${\mathcal S}(\Sigma)$, the space of restrictions to
$\Sigma$ of the Schwartz functions on ${\mathbb R}^\ell$. This property is
known to hold for many nilpotent pairs, i.e., Gelfand pairs where $G=K\ltimes
N$, with $N$ nilpotent.
  In this paper we enlarge the scope of this analysis outside the range of
nilpotent pairs, stating the basic setting for general pairs of polynomial
growth and then focussing on strong Gelfand pairs.


Abstract:
  Recent photometric surveys of Trans-Neptunian Objects (TNOs) have revealed
that the cold classical TNOs have distinct z-band color characteristics, and
occupy their own distinct surface class. This suggested the presence of an
absorption band in the reflectance spectra of cold classicals at wavelengths
above 0.8 micron. Here we present reflectance spectra spanning 0.55-1.0 micron
for six TNOs occupying dynamically cold orbits at semimajor axes close to 44
au. Five of our spectra show a clear and broadly consistent reduction in
spectral gradient above 0.8 micron that diverges from their linear red optical
continuum and agrees with their reported photometric colour data. Despite
predictions, we find no evidence that the spectral flattening is caused by an
absorption band centered near 1.0 micron. We predict that the overall
consistent shape of these five spectra is related to the presence of similar
refractory organics on each of their surfaces, and/or their similar physical
surface properties such as porosity or grain size distribution. The observed
consistency of the reflectance spectra of these five targets aligns with
predictions that the cold classicals share a common history in terms of
formation and surface evolution. Our sixth target, which has been ambiguously
classified as either a hot or cold classical at various points in the past, has
a spectrum which remains nearly linear across the full range observed. This
suggests that this TNO is a hot classical interloper in the cold classical
dynamical range, and supports the idea that other such interlopers may be
identifiable by their linear reflectance spectra in the range 0.8-1.0 micron.


Abstract:
  We study the relations of the positive frequency mode functions of Dirac
field in 4-dimensional Minkowski spacetime covered with Rindler and Kasner
coordinates, and describe the explicit form of the Minkowski vacuum state with
the quantum states in Kasner and Rindler regions, and analytically continue the
solutions. As a result, we obtain the correspondence of the positive frequency
mode functions in Kasner region and Rindler region in a unified manner which
derives vacuum entanglement.


Abstract:
  Based on a progressively type-II censored sample from the exponential
distribution with unknown location and scale parameter, confidence bands are
proposed for the underlying distribution function by using confidence regions
for the parameters and Kolmogorov-Smirnov type statistics. Simple explicit
representations for the boundaries and for the coverage probabilities of the
confidence bands are analytically derived, and the performance of the bands is
compared in terms of band width and area by means of a data example. As a
by-product, a novel confidence region for the location-scale parameter is
obtained. Extensions of the results to related models for ordered data, such as
sequential order statistics, as well as to other underlying location-scale
families of distributions are discussed.


Abstract:
  The famous Yang-Yau inequality provides an upper bound for the first
eigenvalue of the Laplacian on an orientable Riemannian surface solely in terms
of its genus $\gamma$ and the area. Its proof relies on the existence of
holomorhic maps to $\mathbb{CP}^1$ of low degree. Very recently, A.~Ros was
able to use certain holomorphic maps to $\mathbb{CP}^2$ in order to give a
quantitative improvement of the Yang-Yau inequality for $\gamma=3$. In the
present paper, we generalize Ros' argument to make use of holomorphic maps to
$\mathbb{CP}^n$ for any $n>0$. As an application, we obtain a quantitative
improvement of the Yang-Yau inequality for all genera $\gamma>3$ except for
$\gamma = 4,6,8,10,14$.


Abstract:
  All yield criteria that determine the onset of plastic deformation in
crystalline materials must be invariant under the inversion symmetry associated
with a simultaneous change of sign of the slip direction and the slip plane
normal. We demonstrate the consequences of this symmetry on the functional form
of the effective stress, where only the lowest order terms that obey this
symmetry are retained. A particular form of yield criterion is obtained for
materials that do not obey the Schmid law, hereafter called non-Schmid
materials. Application of this model to body-centered cubic and hexagonal
close-packed metals shows under which conditions the non-Schmid stress terms
become significant in predicting the onset of yielding. In the special case,
where the contributions of all non-Schmid stresses vanish, this model reduces
to the maximum shear stress theory of Tresca.


Abstract:
  We explore recent progress and open questions concerning local minima and
saddle points of the Cahn--Hilliard energy in $d\geq 2$ and the critical
parameter regime of large system size and mean value close to $-1$. We employ
the String Method of E, Ren, and Vanden-Eijnden -- a numerical algorithm for
computing transition pathways in complex systems -- in $d=2$ to gain additional
insight into the properties of the minima and saddle point. Motivated by the
numerical observations, we adapt a method of Caffarelli and Spruck to study
convexity of level sets in $d\geq 2$.


Abstract:
  Federated Learning is an emerging privacy-preserving distributed machine
learning approach to building a shared model by performing distributed training
locally on participating devices (clients) and aggregating the local models
into a global one. As this approach prevents data collection and aggregation,
it helps in reducing associated privacy risks to a great extent. However, the
data samples across all participating clients are usually not independent and
identically distributed (non-iid), and Out of Distribution(OOD) generalization
for the learned models can be poor. Besides this challenge, federated learning
also remains vulnerable to various attacks on security wherein a few malicious
participating entities work towards inserting backdoors, degrading the
generated aggregated model as well as inferring the data owned by participating
entities. In this paper, we propose an approach for learning invariant (causal)
features common to all participating clients in a federated learning setup and
analyze empirically how it enhances the Out of Distribution (OOD) accuracy as
well as the privacy of the final learned model.


Abstract:
  Ostrovsky's equation with time- and space- dependent forcing is studied. This
equation is model for long waves in a rotating fluid with a non-constant depth
(topography). A classification of Lie point symmetries and low-order
conservation laws is presented. Generalized travelling wave solutions are
obtained through symmetry reduction. These solutions exhibit a wave profile
that is stationary in a moving reference frame whose speed can be constant,
accelerating, or decelerating.


Abstract:
  A subalgebra $\mathcal{A}$ of a $C^*$-algebra $\mathcal{M}$ is logmodular
(resp. has factorization) if the set $\{a^*a; a\text{ is invertible with
}a,a^{-1}\in\mathcal{A}\}$ is dense in (resp. equal to) the set of all positive
and invertible elements of $\mathcal{M}$. There are large classes of well
studied algebras, both in commutative and non-commutative settings, which are
known to be logmodular. In this paper, we show that the lattice of projections
in a von Neumann algebra $\mathcal{M}$ whose ranges are invariant under a
logmodular algebra in $\mathcal{M}$, is a commutative subspace lattice.
Further, if $\mathcal{M}$ is a factor then this lattice is a nest. As a special
case, it follows that all reflexive (in particular, completely distributive
CSL) logmodular subalgebras of type I factors are nest algebras, thus answering
a question of Paulsen and Raghupathi [Trans. Amer. Math. Soc., 363 (2011)
2627-2640]. We also discuss some sufficient criteria under which an algebra
having factorization is automatically reflexive and is a nest algebra.


Abstract:
  Quantum computing, an innovative computing system carrying prominent
processing rate, is meant to be the solutions to problems in many fields. Among
these realms, the most intuitive application is to help chemical researchers
correctly de-scribe strong correlation and complex systems, which are the great
challenge in current chemistry simulation. In this paper, we will present a
standalone quantum simulation tool for chemistry, ChemiQ, which is designed to
assist people carry out chemical research or molecular calculation on real or
virtual quantum computers. Under the idea of modular programming in C++
language, the software is designed as a full-stack tool without third-party
physics or chemistry application packages. It provides services as follow:
visually construct molecular structure, quickly simulate ground-state energy,
scan molecular potential energy curve by distance or angle, study chemical
reaction, and return calculation results graphically after analysis.


Abstract:
  Microwave circulators play an important role in quantum technology based on
superconducting circuits. The conventional circulator design, which employs
ferrite materials, is bulky and involves strong magnetic fields, rendering it
unsuitable for integration on superconducting chips. One promising design for
an on-chip superconducting circulator is based on a passive Josephson-junction
ring. In this paper, we consider two operational issues for such a device:
circuit tuning and the effects of quasiparticle tunneling. We compute the
scattering matrix using adiabatic elimination and derive the parameter
constraints to achieve optimal circulation. We then numerically optimize the
circulator performance over the full set of external control parameters,
including gate voltages and flux bias, to demonstrate that this
multi-dimensional optimization converges quickly to find optimal working
points. We also consider the possibility of quasiparticle tunneling in the
circulator ring and how it affects signal circulation. Our results form the
basis for practical operation of a passive on-chip superconducting circulator
made from a ring of Josephson junctions.


Abstract:
  A Robinson similarity matrix is a symmetric matrix where the entry values on
all rows and columns increase toward the diagonal. Decompose the Robinson
matrix into the sum of k {0, 1}-matrices, then these k {0, 1}-matrices are the
adjacency matrices of a set of nested unit interval graphs. Previous studies
show that unit interval graphs coincide with indifference graphs. An
indifference graph has an embedding that maps each vertex to a real number,
where two vertices are adjacent if their embedding is within a fixed threshold
distance. In this thesis, consider k different threshold distances, we study
the problem of finding an embedding that, simultaneously and with respect to
each threshold distance, embeds the k indifference graphs corresponding to the
k adjacency matrices. This is called a uniform embedding of a Robinson matrix
with respect to the k threshold distances. We give a sufficient and necessary
condition on Robinson matrices that have a uniform embedding, which is derived
from paths in an associated graph. We also give an efficient combinatorial
algorithm to find a uniform embedding or give proof that it does not exist, for
the case where k = 2.


Abstract:
  Stationary memoryless sources produce two correlated random sequences $X^n$
and $Y^n$. A guesser seeks to recover $X^n$ in two stages, by first guessing
$Y^n$ and then $X^n$. The contributions of this work are twofold: (1) We
characterize the least achievable exponential growth rate (in $n$) of any
positive $\rho$-th moment of the total number of guesses when $Y^n$ is obtained
by applying a deterministic function $f$ component-wise to $X^n$. We prove
that, depending on $f$, the least exponential growth rate in the two-stage
setup is lower than when guessing $X^n$ directly. We further propose a simple
Huffman code-based construction of a function $f$ that is a viable candidate
for the minimization of the least exponential growth rate in the two-stage
guessing setup. (2) We characterize the least achievable exponential growth
rate of the $\rho$-th moment of the total number of guesses required to recover
$X^n$ when Stage 1 need not end with a correct guess of $Y^n$ and without
assumptions on the stationary memoryless sources producing $X^n$ and $Y^n$.


Abstract:
  Network Traffic Classification (NTC) has become an important feature in
various network management operations, e.g., Quality of Service (QoS)
provisioning and security services. Machine Learning (ML) algorithms as a
popular approach for NTC can promise reasonable accuracy in classification and
deal with encrypted traffic. However, ML-based NTC techniques suffer from the
shortage of labeled traffic data which is the case in many real-world
applications. This study investigates the applicability of an active form of
ML, called Active Learning (AL), in NTC. AL reduces the need for a large number
of labeled examples by actively choosing the instances that should be labeled.
The study first provides an overview of NTC and its fundamental challenges
along with surveying the literature on ML-based NTC methods. Then, it
introduces the concepts of AL, discusses it in the context of NTC, and review
the literature in this field. Further, challenges and open issues in AL-based
classification of network traffic are discussed. Moreover, as a technical
survey, some experiments are conducted to show the broad applicability of AL in
NTC. The simulation results show that AL can achieve high accuracy with a small
amount of data.


Abstract:
  On Titan, methane (CH4) and ethane (C2H6) are the dominant species found in
the lakes and seas. In this study, we have combined laboratory work and
modeling to refine the methane-ethane binary phase diagram at low temperatures
and probe how the molecules interact at these conditions. We used visual
inspection for the liquidus and Raman spectroscopy for the solidus. Through
these methods we determined a eutectic point of 71.15$\pm$0.5 K at a
composition of 0.644$\pm$0.018 methane - 0.356$\pm$0.018 ethane mole fraction
from the liquidus data. Using the solidus data, we found a eutectic isotherm
temperature of 72.2 K with a standard deviation of 0.4 K. In addition to
mapping the binary system, we looked at the solid-solid transitions of pure
ethane and found that, when cooling, the transition of solid I-III occurred at
89.45$\pm$0.2 K. The warming sequence showed transitions of solid III-II
occurring at 89.85$\pm$0.2 K and solid II-I at 89.65$\pm$0.2 K. Ideal
predictions were compared to molecular dynamics simulations to reveal that the
methane-ethane system behaves almost ideally, and the largest deviations occur
as the mixing ratio approaches the eutectic composition.


Abstract:
  Heavy-ion collisions at the LHC provide the conditions to investigate regions
of quark-gluon plasma that reach higher temperatures and that persist for
longer periods of time compared to collisions at the Relativistic Heavy Ion
Collider. This extended duration allows correlations from charge conservation
to better separate during the quark-gluon plasma phase, and thus be better
distinguished from correlations that develop during the hadron phase or during
hadronization. In this study charge balance functions binned by relative
rapidity and azimuthal angle and indexed by species are considered. A detailed
theoretical model that evolves charge correlations throughout the entirety of
an event is compared to preliminary results from the ALICE Collaboration. The
comparison with experiment provides insight into the evolution of the chemistry
and diffusivity during the collision. A ratio of balance functions is proposed
to better isolate the effects of diffusion and thus better constrain the
diffusivity.


Abstract:
  A new scaling is derived that yields a Reynolds number independent profile
for all components of the Reynolds stress in the near-wall region of wall
bounded flows, including channel, pipe and boundary layer flows. The scaling
demonstrates the important role played by the wall shear stress fluctuations
and how the large eddies determine the Reynolds number dependence of the
near-wall turbulence behavior.


Abstract:
  We train convolutional neural networks to predict whether or not a set of
measurements is informationally complete to uniquely reconstruct any given
quantum state with no prior information. In addition, we perform fidelity
benchmarking based on this measurement set without explicitly carrying out
state tomography. The networks are trained to recognize the fidelity and a
reliable measure for informational completeness. By gradually accumulating
measurements and data, these trained convolutional networks can efficiently
establish a compressive quantum-state characterization scheme by accelerating
runtime computation and greatly reducing systematic drifts in experiments. We
confirm the potential of this machine-learning approach by presenting
experimental results for both spatial-mode and multiphoton systems of large
dimensions. These predictions are further shown to improve when the networks
are trained with additional bootstrapped training sets from real experimental
data. Using a realistic beam-profile displacement error model for
Hermite-Gaussian sources, we further demonstrate numerically that the
orders-of-magnitude reduction in certification time with trained networks
greatly increases the computation yield of a large-scale quantum processor
using these sources, before state fidelity deteriorates significantly.


Abstract:
  When an approximant is accurate on the interval, it is only natural to try to
extend it to several-dimensional domains. In the present article, we make use
of the fact that linear rational barycentric interpolants converge rapidly
toward analytic and several times differentiable functions to interpolate on
two-dimensional starlike domains parametrized in polar coordinates. In radial
direction, we engage interpolants at conformally shifted Chebyshev nodes, which
converge exponentially toward analytic functions. In circular direction, we
deploy linear rational trigonometric barycentric interpolants, which converge
similarly rapidly for periodic functions, but now for conformally shifted
equispaced nodes. We introduce a variant of a tensor-product interpolant of the
above two schemes and prove that it converges exponentially for two-dimensional
analytic functions up to a logarithmic factor and with an order limited only by
the order of differentiability for real functions, if the boundary is as
smooth. Numerical examples confirm that the shifts permit to reach a much
higher accuracy with significantly less nodes, a property which is especially
important in several dimensions.


Abstract:
  A novel approach to reduced-order modeling of high-dimensional time varying
systems is proposed. It leverages the formalism of the Dynamic Mode
Decomposition technique together with the concept of balanced realization. It
is assumed that the only information available on the system comes from input,
state, and output trajectories generated by numerical simulations or recorded
and estimated during experiments, thus the approach is fully data-driven. The
goal is to obtain an input-output low dimensional linear model which
approximates the system across its operating range. Since the dynamics of
aeroservoelastic systems markedly changes in operation (e.g. due to change in
flight speed or altitude), time-varying features are retained in the
constructed models. This is achieved by generating a Linear Parameter-Varying
representation made of a collection of state-consistent linear time-invariant
reduced-order models. The algorithm formulation hinges on the idea of replacing
the orthogonal projection onto the Proper Orthogonal Decomposition modes, used
in Dynamic Mode Decomposition-based approaches, with a balancing oblique
projection constructed entirely from data. As a consequence, the input-output
information captured in the lower-dimensional representation is increased
compared to other projections onto subspaces of same or lower size. Moreover, a
parameter-varying projection is possible while also achieving
state-consistency. The validity of the proposed approach is demonstrated on a
morphing wing for airborne wind energy applications by comparing the
performance against two algorithms recently proposed in the literature.
Comparisons cover both prediction accuracy and performance in model predictive
control applications.


Abstract:
  We consider a machine learning algorithm to detect and identify strong
gravitational lenses on sky images. First, we simulate different artificial but
very close to reality images of galaxies, stars and strong lenses, using six
different methods, i.e. two for each class. Then we deploy a convolutional
neural network architecture to classify these simulated images. We show that
after neural network training process one achieves about 93 percent accuracy.
As a simple test for the efficiency of the convolutional neural network, we
apply it on an real Einstein cross image. Deployed neural network classifies it
as gravitational lens, thus opening a way for variety of lens search
applications of the deployed machine learning scheme.


Abstract:
  This article describes the regularization of the generally relativistic gauge
field representation of gravity on a piecewise linear lattice. It is a part of
the program concerning the classical relativistic theory of fundamental
interactions, represented by minimally coupled gauge vector field densities and
half-densities. The correspondence between the local Darboux coordinates on
phase space and the local structure of the links of the lattice, embedded in
the spatial manifold, is demonstrated. Thus, the canonical coordinates are
replaceable by links-related quantities. This idea and the significant part of
formalism are directly based on the model of canonical loop quantum gravity
(CLQG).
  The first stage of this program is formulated regarding the gauge field,
which dynamics is independent of other fundamental fields, but contributes to
their dynamics. This gauge field, which determines systems equivalence in the
actions defining all fundamental interactions, represents Einsteinian gravity.
The related links-defined quantities depend on holonomies of gravitational
connections and fluxes of densitized dreibeins. This article demonstrates how
to determine these quantities, which lead to a nonpertubative formalism that
preserves the general postulate of relativity. From this perspective, the
formalism presented in this article is analogous to the Ashtekar-Barbero-Holst
formulation on which CLQG is based. However, in this project, it is
additionally required that the fields' coordinates are quantizable in the
standard canonical procedure for a gauge theory and that any approximation in
the construction of the model is at least as precisely demonstrated as the
gauge invariance. These requirements lead to new relations between holonomies
and connections, and the representation of the densitized deibein determinant
that is more precise than the volume representation in CLQG.


Abstract:
  We present the Stromlo Stellar Tracks, a set of stellar evolutionary tracks,
computed by modifying the Modules for Experiments in Stellar Astrophysics
(MESA) 1D stellar evolution package, to fit the Galactic Concordance abundances
for hot ($T > 8000$ K) massive ($\geq 10M_\odot$) Main-Sequence (MS) stars.
Until now, all stellar evolution tracks are computed at solar, scaled-solar, or
alpha-element enhanced abundances, and none of these models correctly represent
the Galactic Concordance abundances at different metallicities. This paper is
the first implementation of Galactic Concordance abundances to the stellar
evolution models. The Stromlo tracks cover massive stars ($10\leq M/M_\odot
\leq 300$) with varying rotations ($v/v_{\rm crit} = 0.0, 0.2, 0.4$) and a
finely sampled grid of metallicities ($-2.0 \leq {\rm [Z/H]} \leq +0.5$;
$\Delta {\rm [Z/H]} = 0.1$) evolved from the pre-main sequence to the end of
$^{12}$Carbon burning. We find that the implementation of Galactic Concordance
abundances is critical for the evolution of main-sequence, massive hot stars in
order to estimate accurate stellar outputs (L, T$_{\rm eff}$, $g$), which, in
turn, have a significant impact on determining the ionizing photon luminosity
budgets. We additionally support prior findings of the importance that rotation
plays on the evolution of massive stars and their ionizing budget. The
evolutionary tracks for our Galactic Concordance abundance scaling provide a
more empirically motivated approach than simple uniform abundance scaling with
metallicity for the analysis of HII regions and have considerable implications
in determining nebular emission lines and metallicity. Therefore, it is
important to refine the existing stellar evolutionary models for comprehensive
high-redshift extragalactic studies. The Stromlo tracks are publicly available
to the astronomical community online.


Abstract:
  Let $G=\operatorname{O}(1,n+1)$ with maximal compact subgroup $K$ and let
$\Pi$ be a unitary irreducible representation of $G$ with non-trivial
$(\mathfrak{g},K)$-cohomology. Then $\Pi$ occurs inside a principal series
representation of $G$, induced from the $\operatorname{O}(n)$-representation
$\bigwedge\nolimits^p(\mathbb{C}^n)$ and characters of a minimal parabolic
subgroup of $G$ at the limit of the complementary series. Considering the
subgroup $G'=\operatorname{O}(1,n)$ of $G$ with maximal compact subgroup $K'$,
we prove branching laws and explicit Plancherel formulas for the restrictions
to $G'$ of all unitary representations occurring in such principal series,
including the complementary series, all unitary $G$-representations with
non-trivial $(\mathfrak{g},K)$-cohomology and further relative discrete series
representations in the cases $p=0,n$. Discrete spectra are constructed
explicitly as residues of $G'$-intertwining operators which resemble the
Fourier transforms on vector bundles over the Riemannian symmetric space
$G'/K'$.


Abstract:
  Given coprime positive integers $d',d''$, B\'ezout's Lemma tells us that
there are integers $u,v$ so that $d'u-d''v=1$. We show that, interchanging $d'$
and $d''$ if necessary, we may choose $u$ and $v$ to be Loeschian numbers,
i.e., of the form $|\alpha|^2$, where $\alpha\in\mathbb{Z}[j]$, the ring of
integers of the number field $\mathbb{Q}(j)$, where $j^2+j+1=0$. We do this by
using Atkin-Lehner elements in some quaternion algebras $\mathcal{H}$. We use
this fact to count the number of conjugacy classes of elements of order 3 in an
order $\mathcal{O}\subset\mathcal{H}$.


Abstract:
  Ear recognition can be described as a revived scientific field. Ear
biometrics were long believed to not be accurate enough and held a secondary
place in scientific research, being seen as only complementary to other types
of biometrics, due to difficulties in measuring correctly the ear
characteristics and the potential occlusion of the ear by hair, clothes and ear
jewellery. However, recent research has reinstated them as a vivid research
field, after having addressed these problems and proven that ear biometrics can
provide really accurate identification and verification results. Several 2D and
3D imaging techniques, as well as acoustical techniques using sound emission
and reflection, have been developed and studied for ear recognition, while
there have also been significant advances towards a fully automated recognition
of the ear. Furthermore, ear biometrics have been proven to be mostly
non-invasive, adequately permanent and accurate, and hard to spoof and
counterfeit. Moreover, different ear recognition techniques have proven to be
as effective as face recognition ones, thus providing the opportunity for ear
recognition to be used in identification and verification applications.
Finally, even though some issues still remain open and require further
research, the scientific field of ear biometrics has proven to be not only
viable, but really thriving.


Abstract:
  A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect
images in different polarization states, making it a rich source of information
for target characterization. PolSAR images are inherently affected by speckle.
Therefore, before deriving ad hoc products from the data, the polarimetric
covariance matrix needs to be estimated by reducing speckle. In recent years,
deep learning based despeckling methods have started to evolve from single
channel SAR images to PolSAR images. To this aim, deep learning based
approaches separate the real and imaginary components of the complex-valued
covariance matrix and use them as independent channels in a standard
convolutional neural networks. However, this approach neglects the mathematical
relationship that exists between the real and imaginary components, resulting
in sub-optimal output. Here, we propose a multi-stream complex-valued fully
convolutional network to reduce speckle and effectively estimate the PolSAR
covariance matrix. To evaluate the performance of CV-deSpeckNet, we used
Sentinel-1 dual polarimetric SAR images to compare against its real-valued
counterpart, that separates the real and imaginary parts of the complex
covariance matrix. CV-deSpeckNet was also compared against the state of the art
PolSAR despeckling methods. The results show CV-deSpeckNet was able to be
trained with a fewer number of samples, has a higher generalization capability
and resulted in a higher accuracy than its real-valued counterpart and
state-of-the-art PolSAR despeckling methods. These results showcase the
potential of complex-valued deep learning for PolSAR despeckling.


Abstract:
  Urban areas are not only one of the biggest contributors to climate change,
but also they are one of the most vulnerable areas with high populations who
would together experience the negative impacts. In this paper, I address some
of the opportunities brought by satellite remote sensing imaging and artificial
intelligence (AI) in order to measure climate adaptation of cities
automatically. I propose an AI-based framework which might be useful for
extracting indicators from remote sensing images and might help with predictive
estimation of future states of these climate adaptation related indicators.
When such models become more robust and used in real-life applications, they
might help decision makers and early responders to choose the best actions to
sustain the wellbeing of society, natural resources and biodiversity. I
underline that this is an open field and an ongoing research for many
scientists, therefore I offer an in depth discussion on the challenges and
limitations of AI-based methods and the predictive estimation models in
general.


Abstract:
  Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm
become the dominant way to learn on graphic data. Models in this paradigm have
to spend extra space to look up adjacent nodes with adjacency matrices and
extra time to aggregate multiple messages from adjacent nodes. To address this
issue, we develop a method called LinkDist that distils self-knowledge from
connected node pairs into a Multi-Layer Perceptron (MLP) without the need to
aggregate messages. Experiment with 8 real-world datasets shows the MLP derived
from LinkDist can predict the label of a node without knowing its adjacencies
but achieve comparable accuracy against GNNs in the contexts of semi- and
full-supervised node classification. Moreover, LinkDist benefits from its
Non-Message Passing paradigm that we can also distil self-knowledge from
arbitrarily sampled node pairs in a contrastive way to further boost the
performance of LinkDist.


Abstract:
  This work addresses whether a human-in-the-loop cyber-physical system (HCPS)
can be effective in improving the longitudinal control of an individual vehicle
in a traffic flow. We introduce the CAN Coach, which is a system that gives
feedback to the human-in-the-loop using radar data (relative speed and position
information to objects ahead) that is available on the controller area network
(CAN). Using a cohort of six human subjects driving an instrumented vehicle, we
compare the ability of the human-in-the-loop driver to achieve a constant
time-gap control policy using only human-based visual perception to the car
ahead, and by augmenting human perception with audible feedback from CAN sensor
data. The addition of CAN-based feedback reduces the mean time-gap error by an
average of 73%, and also improves the consistency of the human by reducing the
standard deviation of the time-gap error by 53%. We remove human perception
from the loop using a ghost mode in which the human-in-the-loop is coached to
track a virtual vehicle on the road, rather than a physical one. The loss of
visual perception of the vehicle ahead degrades the performance for most
drivers, but by varying amounts. We show that human subjects can match the
velocity of the lead vehicle ahead with and without CAN-based feedback, but
velocity matching does not offer regulation of vehicle spacing. The viability
of dynamic time-gap control is also demonstrated. We conclude that (1) it is
possible to coach drivers to improve performance on driving tasks using CAN
data, and (2) it is a true HCPS, since removing human perception from the
control loop reduces performance at the given control objective.


Abstract:
  Quantitative phase imaging (QPI) is a valuable label-free modality that has
gained significant interest due to its wide potentials, from basic biology to
clinical applications. Most existing QPI systems measure microscopic objects
via interferometry or nonlinear iterative phase reconstructions from intensity
measurements. However, all imaging systems compromise spatial resolution for
field of view and vice versa, i.e., suffer from a limited space bandwidth
product. Current solutions to this problem involve computational phase
retrieval algorithms, which are time-consuming and often suffer from
convergence problems. In this article, we presented synthetic aperture
interference light (SAIL) microscopy as a novel modality for high-resolution,
wide field of view QPI. The proposed approach employs low-coherence
interferometry to directly measure the optical phase delay under different
illumination angles and produces large space-bandwidth product (SBP) label-free
imaging. We validate the performance of SAIL on standard samples and illustrate
the biomedical applications on various specimens: pathology slides, entire
insects, and dynamic live cells in large cultures. The reconstructed images
have a synthetic numeric aperture of 0.45, and a field of view of 2.6 x 2.6
mm2. Due to its direct measurement of the phase information, SAIL microscopy
does not require long computational time, eliminates data redundancy, and
always converges.


Abstract:
  We train neural models for morphological analysis, generation and
lemmatization for morphologically rich languages. We present a method for
automatically extracting substantially large amount of training data from FSTs
for 22 languages, out of which 17 are endangered. The neural models follow the
same tagset as the FSTs in order to make it possible to use them as fallback
systems together with the FSTs. The source code, models and datasets have been
released on Zenodo.


Abstract:
  Quantized nano-objects offer a myriad of exciting possibilities for
manipulating electrons and light that impact photonics, nanoelectronics, and
quantum information. In this context, ultrashort laser pulses combined with
nanotips and field emission have permitted renewing nano-characterization and
control electron dynamics with unprecedented space and time resolution reaching
femtosecond and even attosecond regimes. A crucial missing step in these
experiments is that no signature of quantized energy levels has yet been
observed. We combine in situ nanostructuration of nanotips and ultrashort laser
pulse excitation to induce multiphoton excitation and electron emission from a
single quantized nano-object attached at the apex of a metal nanotip.
Femtosecond induced tunneling through well-defined localized confinement states
that are tunable in energy is demonstrated. This paves the way for the
development of ultrafast manipulation of electron emission from isolated
nano-objects including stereographically fixed individual molecules and high
brightness, ultrafast, coherent single electron sources for quantum optics
experiments.


Abstract:
  We present a fast and feature-complete differentiable physics engine, Nimble
(nimblephysics.org), that supports Lagrangian dynamics and hard contact
constraints for articulated rigid body simulation. Our differentiable physics
engine offers a complete set of features that are typically only available in
non-differentiable physics simulators commonly used by robotics applications.
We solve contact constraints precisely using linear complementarity problems
(LCPs). We present efficient and novel analytical gradients through the LCP
formulation of inelastic contact that exploit the sparsity of the LCP solution.
We support complex contact geometry, and gradients approximating
continuous-time elastic collision. We also introduce a novel method to compute
complementarity-aware gradients that help downstream optimization tasks avoid
stalling in saddle points. We show that an implementation of this combination
in an existing physics engine (DART) is capable of a 87x single-core speedup
over finite-differencing in computing analytical Jacobians for a single
timestep, while preserving all the expressiveness of original DART.


Abstract:
  We study the Gram determinant and construct bases of hom spaces for the
one-dimensional topological theory of decorated unoriented one-dimensional
cobordisms, as recently defined by Khovanov, when the pair of generating
functions is linear.


Abstract:
  Existing near-eye display designs struggle to balance between multiple
trade-offs such as form factor, weight, computational requirements, and battery
life. These design trade-offs are major obstacles on the path towards an
all-day usable near-eye display. In this work, we address these trade-offs by,
paradoxically, \textit{removing the display} from near-eye displays. We present
the beaming displays, a new type of near-eye display system that uses a
projector and an all passive wearable headset. We modify an off-the-shelf
projector with additional lenses. We install such a projector to the
environment to beam images from a distance to a passive wearable headset. The
beaming projection system tracks the current position of a wearable headset to
project distortion-free images with correct perspectives. In our system, a
wearable headset guides the beamed images to a user's retina, which are then
perceived as an augmented scene within a user's field of view. In addition to
providing the system design of the beaming display, we provide a physical
prototype and show that the beaming display can provide resolutions as high as
consumer-level near-eye displays. We also discuss the different aspects of the
design space for our proposal.


Abstract:
  Models of front propagation like the famous FKPP equation have extensive
applications across scientific disciplines e.g., in the spread of infectious
diseases. A common feature of such models is the existence of a static state
into which to propagate, e.g., the uninfected host population. Here, we instead
model an infectious front propagating into a growing host population. The
infectious agent spreads via self-similar waves whereas the amplitude of the
wave of infected organisms increases exponentially. Depending on the population
under consideration, wave speeds are either advanced or retarded compared to
the non-growing case. We identify a novel selection mechanism in which the
shape of the infectious wave controls the speeds of the various waves and we
propose experiments with bacteria and bacterial viruses to test our
predictions. Our work reveals the complex interplay between population growth
and front propagation.


Abstract:
  The spin Hall effect and its inverse are important spin-charge conversion
mechanisms. The direct spin Hall effect induces a surface spin accumulation
from a transverse charge current due to spin orbit coupling even in
non-magnetic conductors. However, most detection schemes involve additional
interfaces, leading to large scattering in reported data. Here we perform
interface free x-ray spectroscopy measurements at the Cu L_{3,2} absorption
edges of highly Bi-doped Cu (Cu_{95}Bi_{5}). The detected X-ray magnetic
circular dichroism (XMCD) signal corresponds to an induced magnetic moment of
(2.7 +/- 0.5) x 10-12 {\mu}_{B} A^{-1} cm^{2} per Cu atom averaged over the
probing depth, which is of the same order as for Pt measured by magneto-optics.
The results highlight the importance of interface free measurements to assess
material parameters and the potential of CuBi for spin-charge conversion
applications.


Abstract:
  Ensuring that a predictor is not biased against a sensible feature is the key
of Fairness learning. Conversely, Global Sensitivity Analysis is used in
numerous contexts to monitor the influence of any feature on an output
variable. We reconcile these two domains by showing how Fairness can be seen as
a special framework of Global Sensitivity Analysis and how various usual
indicators are common between these two fields. We also present new Global
Sensitivity Analysis indices, as well as rates of convergence, that are useful
as fairness proxies.


Abstract:
  Virtualization of distributed real-time systems enables the consolidation of
mixed-criticality functions on a shared hardware platform thus easing system
integration. Time-triggered communication and computation can act as an enabler
of safe hard real-time systems. A time-triggered hypervisor that activates
virtual CPUs according to a global schedule can provide the means to allow for
a resource efficient implementation of the time-triggered paradigm in
virtualized distributed real-time systems. A prerequisite of time-triggered
virtualization for hard real-time systems is providing access to a global time
base to VMs as well as to the hypervisor. A global time base is the result of
clock synchronization with an upper bound on the clock synchronization
precision.
  We present a formalization of the notion of time in virtualized real-time
systems. We use this formalization to propose a virtual clock condition that
enables us to test the suitability of a virtual clock for the design of
virtualized time-triggered real-time systems. We discuss and model how
virtualization, in particular resource consolidation versus resource
partitioning, degrades clock synchronization precision. Finally, we apply our
insights to model the IEEE~802.1AS clock synchronization protocol and derive an
upper bound on the clock synchronization precision of IEEE 802.1AS. We present
our implementation of a dependent clock for ACRN that can be synchronized to a
grandmaster clock. The results of our experiments illustrate that a type-1
hypervisor implementing a dependent clock yields native clock synchronization
precision. Furthermore, we show that the upper bound derived from our model
holds for a series of experiments featuring native as well as virtualized
setups.


Abstract:
  We study several parameters of a random Bienaym\'e-Galton-Watson tree $T_n$
of size $n$ defined in terms of an offspring distribution $\xi$ with mean $1$
and nonzero finite variance $\sigma^2$. Let $f(s)={\bf E}\{s^\xi\}$ be the
generating function of the random variable $\xi$. We show that the independence
number is in probability asymptotic to $qn$, where $q$ is the unique solution
to $q = f(1-q)$. One of the many algorithms for finding the largest independent
set of nodes uses a notion of repeated peeling away of all leaves and their
parents. The number of rounds of peeling is shown to be in probability
asymptotic to $\log n / \log\bigl(1/f'(1-q)\bigr)$. Finally, we study a related
parameter which we call the leaf-height. Also sometimes called the protection
number, this is the maximal shortest path length between any node and a leaf in
its subtree. If $p_1 = {\bf P}\{\xi=1\}>0$, then we show that the maximum
leaf-height over all nodes in $T_n$ is in probability asymptotic to $\log
n/\log(1/p_1)$. If $p_1 = 0$ and $\kappa$ is the first integer $i>1$ with ${\bf
P}\{\xi=i\}>0$, then the leaf-height is in probability asymptotic to
$\log_\kappa\log n$.


Abstract:
  In this work we ask how an Unruh-DeWitt (UD) detector with harmonic
oscillator internal degrees of freedom $Q$ measuring an evolving quantum matter
field $\Phi(\bm{x}, t)$ in an expanding universe with scale factor $a(t)$
responds. We investigate the detector's response which contains non-Markovian
information about the quantum field squeezed by the dynamical spacetime. The
challenge is in the memory effects accumulated over the evolutionary history.
We first consider a detector $W$, the `\textsl{Witness}', which co-existed and
evolved with the quantum field from the beginning. We derive a nonMarkovian
quantum Langevin equation for the detector's $Q$ by integrating over the
squeezed quantum field. The solution of this integro-differential equation
would answer our question, in principle, but very challenging, in practice.
Striking a compromise, we then ask, to what extent can a detector $D$
introduced at late times, called the `\textsl{Detective}', decipher past
memories. This situation corresponds to many cosmological experiments today
probing specific stages in the past, such as COBE targeting activities at the
surface of last scattering. Somewhat surprisingly we show that it is possible
to retrieve to some degree certain global physical quantities, such as the
resultant squeezing, particles created, quantum coherence and correlations. The
reason is because the quantum field has all the fine-grained information from
the beginning in how it was driven by the cosmic dynamics $a(t)$. How long the
details of past history can persist in the quantum field depends on the memory
time. The fact that a squeezed field cannot come to complete equilibrium under
constant driving, as in an evolving spacetime, actually helps to retain the
memory. We discuss interesting features and potentials of this
`\textit{archaeological}' perspective toward cosmological issues.


Abstract:
  We present updated measurements of the Crab pulsar glitch of 2019 July 23
using a dataset of pulse arrival times spanning $\sim$5 months. On MJD 58687,
the pulsar underwent its seventh largest glitch observed to date, characterised
by an instantaneous spin-up of $\sim$1 $\mu$Hz. Following the glitch the
pulsar's rotation frequency relaxed exponentially towards pre-glitch values
over a timescale of approximately one week, resulting in a permanent frequency
increment of $\sim$0.5 $\mu$Hz. Due to our semi-continuous monitoring of the
Crab pulsar, we were able to partially resolve a fraction of the total spin-up.
This delayed spin-up occurred exponentially over a timescale of $\sim$18 hours.
This is the sixth Crab pulsar glitch for which part of the initial rise was
resolved in time and this phenomenon has not been observed in any other
glitching pulsars, offering a unique opportunity to study the microphysical
processes governing interactions between the neutron star interior and the
crust.


Abstract:
  In this work we shall study $k$-inflation theories with non-minimal coupling
of the scalar field to gravity, in the presence of only a higher order kinetic
term of the form $\sim \mathrm{const}\times X^{\mu}$, with
$X=\frac{1}{2}\partial_{\mu}\phi\partial^{\mu}\phi$. The study will be focused
in the cases where a scalar potential is included or is absent, and the
evolution of the scalar field will be assumed to satisfy the slow-roll or the
constant-roll condition. In the case of the slow-roll models with scalar
potential, we shall calculate the slow-roll indices, and the corresponding
observational indices of the theory, and we demonstrate that the resulting
theory is compatible with the latest Planck data. The same results are obtained
in the constant-roll case, at least in the presence of a scalar potential. In
the case that models without potential are considered, the results are less
appealing since these are strongly model dependent, and at least for a
power-law choice of the non-minimal coupling, the theory is non-viable.
Finally, due to the fact that the scalar and tensor power spectra are conformal
invariant quantities, we argue that the Einstein frame counterpart of the
non-minimal $k$-inflation models with scalar potential, can be a viable theory,
due to the conformal invariance of the observational indices. The Einstein
frame theory is more involved and thus more difficult to work with it
analytically, so one implication of our work is that we provide evidence for
the viability of another class of $k$-inflation models.


Abstract:
  Some known fixed point theorems for nonexpansive mappings in metric spaces
are extended here to the case of primitive uniform spaces. The reasoning
presented in the proofs seems to be a natural way to obtain other general
results.


Abstract:
  Carroll's group is presented as a group of transformations in a 5-dimensional
space ($\mathcal{C}$) obtained by embedding the Euclidean space into a (4;
1)-de Sitter space. Three of the five dimensions of $\mathcal{C}$ are related
to $\mathcal{R}^3$, and the other two to mass and time. A covariant formulation
of Caroll's group, analogous as introduced by Takahashi to Galilei's group, is
deduced. Unit representations are studied.


Abstract:
  In this paper, we consider the spectral dependences of transverse
electromagnetic waves generated in solar plasma at coalescence of Langmuir
waves. It is shown that different spectra of Langmuir waves lead to
characteristic types of transversal electromagnetic wave spectra, what makes it
possible to diagnose the features of the spectra of Langmuir waves generated in
solar plasma.


Abstract:
  A duality between an electrostatic problem in a three dimensional world and a
quantum mechanical problem in a one dimensional world which allows one to
obtain the ground state solution of the Schr\"odinger equation by using
electrostatic results is generalized to three dimensions. Here, it is
demonstrated that the same transformation technique is also applicable to the
s-wave Schr\"odinger equation in three dimensions for central potentials. This
approach leads to a general relationship between the electrostatic potential
and the s-wave function and the electric energy density to the quantum
mechanical energy.


Abstract:
  The past year has witnessed the rapid development of applying the Transformer
module to vision problems. While some researchers have demonstrated that
Transformer-based models enjoy a favorable ability of fitting data, there are
still growing number of evidences showing that these models suffer over-fitting
especially when the training data is limited. This paper offers an empirical
study by performing step-by-step operations to gradually transit a
Transformer-based model to a convolution-based model. The results we obtain
during the transition process deliver useful messages for improving visual
recognition. Based on these observations, we propose a new architecture named
Visformer, which is abbreviated from the `Vision-friendly Transformer'. With
the same computational complexity, Visformer outperforms both the
Transformer-based and convolution-based models in terms of ImageNet
classification accuracy, and the advantage becomes more significant when the
model complexity is lower or the training set is smaller. The code is available
at https://github.com/danczs/Visformer.


Abstract:
  We employ various quantum-mechanical approaches for studying the impact of
electric fields on both nonretarded and retarded noncovalent interactions
between atoms or molecules. To this end, we apply perturbative and
non-perturbative methods within the frameworks of quantum mechanics (QM) as
well as quantum electrodynamics (QED). In addition, to provide a transparent
physical picture of the different types of resulting interactions, we employ a
stochastic electrodynamic approach based on the zero-point fluctuating field.
Atomic response properties are described via harmonic Drude oscillators - an
efficient model system that permits an analytical solution and has been
convincingly shown to yield accurate results when modeling non-retarded
intermolecular interactions. The obtained intermolecular energy contributions
are classified as field-induced (FI) electrostatics, FI polarization, and
dispersion interactions. The interplay between these three types of
interactions enables the manipulation of molecular dimer conformations by
applying transversal or longitudinal electric fields along the intermolecular
axis. Our framework combining four complementary theoretical approaches paves
the way toward a systematic description and improved understanding of molecular
interactions when molecules are subject to both external and vacuum fields.


Abstract:
  In this short paper we recall the (Garfield) Impact Factor of a Journal, we
improve and extend it, and eventually we present the Total Impact Factor that
reflects the most accurate impact factor.


Abstract:
  This Letter capitalizes on a unique set of total solar eclipse observations,
acquired between 2006 and 2020, in white light, Fe XI 789.2 nm ($\rm T_{fexi}$
= $1.2 \pm 0.1$ MK) and Fe XIV 530.3 nm ($\rm T_{fexiv}$ = $ 1.8 \pm 0.1$ MK)
emission, complemented by in situ Fe charge state and proton speed measurements
from ACE/SWEPAM-SWICS, to identify the source regions of different solar wind
streams. The eclipse observations reveal the ubiquity of open structures,
invariably associated with Fe XI emission from $\rm Fe^{10+}$, hence a constant
electron temperature, $\rm T_{c}$ = $\rm T_{fexi}$, in the expanding corona.
The in situ Fe charge states are found to cluster around $\rm Fe^{10+}$,
independently of the 300 to 700 km $\rm s^{-1}$ stream speeds, referred to as
the continual solar wind. $\rm Fe^{10+}$ thus yields the fiducial link between
the continual solar wind and its $\rm T_{fexi}$ sources at the Sun. While the
spatial distribution of Fe XIV emission, from $\rm Fe^{13+}$, associated with
streamers, changes throughout the solar cycle, the sporadic appearance of
charge states $> \rm Fe^{11+}$, in situ, exhibits no cycle dependence
regardless of speed. These latter streams are conjectured to be released from
hot coronal plasmas at temperatures $\ge \rm T_{fexiv}$ within the bulge of
streamers and from active regions, driven by the dynamic behavior of
prominences magnetically linked to them. The discovery of continual streams of
slow, intermediate and fast solar wind, characterized by the same $\rm
T_{fexi}$ in the expanding corona, places new constraints on the physical
processes shaping the solar wind.


Abstract:
  Our goal is to estimate the star formation main sequence (SFMS) and the star
formation rate density (SFRD) at z <= 0.017 (d < 75 Mpc) using the Javalambre
Photometric Local Universe Survey (J-PLUS) first data release, that probes
897.4 deg2 with twelve optical bands. We extract the Halpha emission flux of
805 local galaxies from the J-PLUS filter J0660, being the continuum level
estimated with the other eleven J-PLUS bands, and the dust attenuation and
nitrogen contamination corrected with empirical relations. Stellar masses (M),
Halpha luminosities (L), and star formation rates (SFRs) were estimated by
accounting for parameters covariances. Our sample comprises 689 blue galaxies
and 67 red galaxies, classified in the (u-g) vs (g-z) color-color diagram, plus
49 AGN. The SFMS is explored at log M > 8 and it is clearly defined by the blue
galaxies, with the red galaxies located below them. The SFMS is described as
log SFR = 0.83 log M - 8.44. We find a good agreement with previous estimations
of the SFMS, especially those based on integral field spectroscopy. The Halpha
luminosity function of the AGN-free sample is well described by a Schechter
function with log L* = 41.34, log phi* = -2.43, and alpha = -1.25. Our
measurements provide a lower characteristic luminosity than several previous
studies in the literature. The derived star formation rate density at d < 75
Mpc is log rho_SFR = -2.10 +- 0.11, with red galaxies accounting for 15% of the
SFRD. Our value is lower than previous estimations at similar redshift, and
provides a local reference for evolutionary studies regarding the star
formation history of the Universe.


Abstract:
  Capacitated vehicle routing problem (CVRP) is being one of the most common
optimization problems in our days, considering the wide usage of routing
algorithms in multiple fields such as transportation domain, food delivery,
network routing, ... Capacitated vehicle routing problem is classified as an
NP-Hard problem, hence normal optimization algorithm can't solve it. In our
paper, we discuss a new way to solve the mentioned problem, using a recursive
approach of the most known clustering algorithm "K-Means", one of the known
shortest path algorithm "Dijkstra", and some mathematical operations. In this
paper, we will show how to implement those methods together in order to get the
nearest solution of the optimal route, since research and development are still
on go, this research paper may be extended with another one, that will involve
the implementational results of this thoric side.


Abstract:
  This paper explores Google's Edge TPU for implementing a practical network
intrusion detection system (NIDS) at the edge of IoT, based on a deep learning
approach. While there are a significant number of related works that explore
machine learning based NIDS for the IoT edge, they generally do not consider
the issue of the required computational and energy resources. The focus of this
paper is the exploration of deep learning-based NIDS at the edge of IoT, and in
particular the computational and energy efficiency. In particular, the paper
studies Google's Edge TPU as a hardware platform, and considers the following
three key metrics: computation (inference) time, energy efficiency and the
traffic classification performance. Various scaled model sizes of two major
deep neural network architectures are used to investigate these three metrics.
The performance of the Edge TPU-based implementation is compared with that of
an energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation
shows some unexpected results, such as the fact that the CPU significantly
outperforms the Edge TPU for small model sizes.


Abstract:
  We study the nonlinear stability of plane Couette and Poiseuille flows with
the Lyapunov second method by using the classical L2-energy. We prove that the
streamwise perturbations are L2-energy stable for any Reynolds number. This
contradicts the results of Joseph [10], Joseph and Carmi [12] and Busse [4],
and allows us to prove that the critical nonlinear Reynolds numbers are
obtained along two-dimensional perturbations, the spanwise perturbations, as
Orr [16] had supposed. This conclusion combined with recent results by
Falsaperla et al. [8] on the stability with respect to tilted rolls, provides a
possible solution to the Couette-Sommerfeld paradox.


Abstract:
  We investigate the production of photons from coherently oscillating,
spatially localized clumps of axionic fields (oscillons and axion stars) in the
presence of external electromagnetic fields. We delineate different qualitative
behaviour of the photon luminosity in terms of an effective dimensionless
coupling parameter constructed out of the axion-photon coupling, and field
amplitude, oscillation frequency and radius of the axion star. For small values
of this dimensionless coupling, we provide a general analytic formula for the
dipole radiation field and the photon luminosity per solid angle, including a
strong dependence on the radius of the configuration. For moderate to large
coupling, we report on a non-monotonic behavior of the luminosity with the
coupling strength in the presence of external magnetic fields. After an initial
rise in luminosity with the coupling strength, we see a suppression (by an
order of magnitude or more compared to the dipole radiation approximation) at
moderately large coupling. At sufficiently large coupling, we find a transition
to a regime of exponential growth of the luminosity due to parametric
resonance. We carry out 3+1 dimensional lattice simulations of axion
electrodynamics, at small and large coupling, including non-perturbative
effects of parametric resonance as well as backreaction effects when necessary.
We also discuss medium (plasma) effects that lead to resonant axion to photon
conversion, relevance of the coherence of the soliton, and implications of our
results in astrophysical and cosmological settings.


Abstract:
  Recent studies in three dimensional spintronics propose that the \OE rsted
field plays a significant role in cylindrical nanowires. However, there is no
direct report of its impact on magnetic textures. Here, we use time-resolved
scanning transmission X-ray microscopy to image the dynamic response of
magnetization in cylindrical Co$_{30}$Ni$_{70}$ nanowires subjected to
nanosecond \OE rsted field pulses. We observe the tilting of longitudinally
magnetized domains towards the azimuthal \OE rsted field direction and create a
robust model to reproduce the differential magnetic contrasts and extract the
angle of tilt. Further, we report the compression and expansion, or breathing,
of a Bloch-point domain wall that occurs when weak pulses with opposite sign
are applied. We expect that this work lays the foundation for and provides an
incentive to further studying complex and fascinating magnetization dynamics in
nanowires, especially the predicted ultra-fast domain wall motion and
associated spin wave emissions.


Abstract:
  By an unquenched quark model, we predict a charmed-strange baryon state,
namely, the $\Omega_{c0}^d(1P,1/2^-)$. Its mass is predicted to be 2945 MeV,
which is below the $\Xi_c\bar{K}$ threshold due to the nontrivial
coupled-channel effect. So the $\Omega_{c0}^d(1P,1/2^-)$ state could be
regraded as the analog of the charmed-strange meson $D_{s0}^*(2317)$. It is a
good opportunity for the running Belle II experiment to search for this state
in the $\Omega_c^{(*)}\gamma$ mass spectrum experiment in the future.


Abstract:
  We present a flexible discretization technique for computational models of
thin tubular networks embedded in a bulk domain, for example a porous medium.
These systems occur in the simulation of fluid flow in vascularized biological
tissue, root water and nutrient uptake in soil, hydrological or petroleum wells
in rock formations, or heat transport in micro-cooling devices. The key
processes, such as heat and mass transfer, are usually dominated by the
exchange between the network system and the embedding domain. By explicitly
resolving the interface between these domains with the computational mesh, we
can accurately describe these processes. The network is efficiently described
by a network of line segments. Coupling terms are evaluated by projection of
the interface variables. The new method is naturally applicable for nonlinear
and time-dependent problems and can therefore be used as a reference method in
the development of novel implicit interface 1D-3D methods and in the design of
verification benchmarks for embedded tubular network methods. Implicit
interface, not resolving the bulk-network interface explicitly have proven to
be very efficient but have only been mathematically analyzed for linear
elliptic problems so far. Using two application scenarios, fluid perfusion of
vascularized tissue and root water uptake from soil, we investigate the effect
of some common modeling assumptions of implicit interface methods numerically.


Abstract:
  In this paper a family of non-autonomous scalar parabolic PDEs over a general
compact and connected flow is considered. The existence or not of a
neighbourhood of zero where the problems are linear has an influence on the
methods used and on the dynamics of the induced skew-product semiflow. That is
why two cases are distinguished: linear-dissipative and purely dissipative
problems. In both cases, the structure of the global and pullback attractors is
studied using principal spectral theory. Besides, in the purely dissipative
setting, a simple condition is given, involving both the underlying linear
dynamics and some properties of the nonlinear term, to determine the nontrivial
sections of the attractor.


Abstract:
  In this paper we study the variety of one dimensional representations of a
finite $W$-algebra attached to a classical Lie algebra, giving a precise
description of the dimensions of the irreducible components. We apply this to
prove a conjecture of Losev describing the image of his orbit method map. In
order to do so we first establish new Yangian-type presentations of
semiclassical limits of the $W$-algebras attached to distinguished nilpotent
elements in classical Lie algebras, using Dirac reduction.


Abstract:
  We examine the possibility that dark matter (DM) consists of a gapped
continuum, rather than ordinary particles. A Weakly-Interacting Continuum (WIC)
model, coupled to the Standard Model via a Z-portal, provides an explicit
realization of this idea. The thermal DM relic density in this model is
naturally consistent with observations, providing a continuum counterpart of
the "WIMP miracle". Direct detection cross sections are strongly suppressed
compared to ordinary Z-portal WIMP, thanks to a unique effect of the continuum
kinematics. Continuum DM states decay throughout the history of the universe,
and observations of cosmic microwave background place constraints on potential
late decays. Production of WICs at colliders can provide a striking
cascade-decay signature. We show that a simple Z-portal WIC model with the gap
scale between 40 and 110 GeV provides a fully viable DM candidate consistent
with all current experimental constraints.


Abstract:
  We propose a new method with $\mathcal{L}_2$ distance that maps one
$N$-dimensional distribution to another, taking into account available
information about correspondences. We solve the high-dimensional problem in 1D
space using an iterative projection approach. To show the potentials of this
mapping, we apply it to colour transfer between two images that exhibit
overlapped scenes. Experiments show quantitative and qualitative competitive
results as compared with the state of the art colour transfer methods.


Abstract:
  We propose an optimization scheme for ground-state cooling of a mechanical
mode by coupling to a general three-level system. We formulate the optimization
scheme, using the master equation approach, over a broad range of system
parameters including detunings, decay rates, coupling strengths, and pumping
rate. We implement the optimization scheme on three physical systems: a
colloidal quantum dot coupled to its confined phonon mode, a polariton coupled
to a mechanical resonator mode, and a coupled-cavity system coupled to a
mechanical resonator mode. These three physical systems span a broad range of
mechanical mode frequencies, coupling rates, and decay rates. Our optimization
scheme lowers the stead-state phonon number in all three cases by orders of
magnitude. We also calculate the net cooling rate by estimating the phonon
decay rate and show that the optimized system parameters also result in
efficient cooling. The proposed optimization scheme can be readily extended to
any generic driven three-level system coupled to a mechanical mode.


Abstract:
  Experimental studies of high-purity kagome-lattice antiferromagnets (KAFM)
are of great importance in attempting to better understand the predicted
enigmatic quantum spin-liquid ground state of the KAFM model. However,
realizations of this model can rarely evade magnetic ordering at low
temperatures due to various perturbations to its dominant isotropic exchange
interactions. Such a situation is for example encountered due to sizable
Dzyaloshinskii-Moriya magnetic anisotropy in YCu$_3$(OH)$_6$Cl$_3$, which
stands out from other KAFM materials by its perfect crystal structure. We find
evidence of magnetic ordering also in the distorted sibling compound
Y$_3$Cu$_9$(OH)$_{18}$[Cl$_8$(OH)], which has recently been proposed to feature
a spin-liquid ground state arising from a spatially anisotropic kagome lattice.
Our findings are based on a combination of bulk susceptibility, specific heat,
and magnetic torque measurements that disclose a N\'eel transition temperature
of $T_N=11$~K in this material, which might feature a coexistence of magnetic
order and persistent spin dynamics as previously found in
YCu$_3$(OH)$_6$Cl$_3$. Contrary to previous studies of single crystals and
powders containing impurity inclusions, we use high-purity single crystals of
Y$_3$Cu$_9$(OH)$_{18}$[Cl$_8$(OH)] grown via an optimized hydrothermal
synthesis route that minimizes such inclusions. This study thus demonstrates
that the lack of magnetic ordering in less pure samples of the investigated
compound does not originate from the reduced symmetry of spin lattice but is
instead of extrinsic origin.


Abstract:
  In the present work, we explore analytically and numerically the co-existence
and interactions of ring dark solitons (RDSs) with other RDSs, as well as with
vortices. The azimuthal instabilities of the rings are explored via the
so-called filament method. As a result of their nonlinear interaction, the
vortices are found to play a stabilizing role on the rings, yet their effect is
not sufficient to offer complete stabilization of RDSs. Nevertheless, complete
stabilization of the relevant configuration can be achieved by the presence of
external ring-shaped barrier potentials. Interactions of multiple rings are
also explored, and their equilibrium positions (as a result of their own
curvature and their tail-tail interactions) are identified. In this case too,
stabilization is achieved via multi-ring external barrier potentials.


Abstract:
  To explain X-ray spectra of active galactic nuclei (AGN), non-thermal
activity in AGN coronae such as pair cascade models has been extensively
discussed in the past literature. Although X-ray and gamma-ray observations in
the 1990s disfavored such pair cascade models, recent millimeter-wave
observations of nearby Seyferts establish the existence of weak non-thermal
coronal activity. Besides, the IceCube collaboration reported NGC 1068, a
nearby Seyfert, as the hottest spot in their 10-yr survey. These pieces of
evidence are enough to investigate the non-thermal perspective of AGN coronae
in depth again. This article summarizes our current observational
understandings of AGN coronae and describes how AGN coronae generate
high-energy particles. We also provide ways to test the AGN corona model with
radio, X-ray, MeV gamma-ray, and high-energy neutrino observations.


Abstract:
  We present a method for contraction-based feedback motion planning of locally
incrementally exponentially stabilizable systems with unknown dynamics that
provides probabilistic safety and reachability guarantees. Given a dynamics
dataset, our method learns a deep control-affine approximation of the dynamics.
To find a trusted domain where this model can be used for planning, we obtain
an estimate of the Lipschitz constant of the model error, which is valid with a
given probability, in a region around the training data, providing a local,
spatially-varying model error bound. We derive a trajectory tracking error
bound for a contraction-based controller that is subjected to this model error,
and then learn a controller that optimizes this tracking bound. With a given
probability, we verify the correctness of the controller and tracking error
bound in the trusted domain. We then use the trajectory error bound together
with the trusted domain to guide a sampling-based planner to return
trajectories that can be robustly tracked in execution. We show results on a 4D
car, a 6D quadrotor, and a 22D deformable object manipulation task, showing our
method plans safely with learned models of high-dimensional underactuated
systems, while baselines that plan without considering the tracking error bound
or the trusted domain can fail to stabilize the system and become unsafe.


Abstract:
  Fairness-aware machine learning for multiple protected at-tributes (referred
to as multi-fairness hereafter) is receiving increasing attention as
traditional single-protected attribute approaches cannot en-sure fairness
w.r.t. other protected attributes. Existing methods, how-ever, still ignore the
fact that datasets in this domain are often imbalanced, leading to unfair
decisions towards the minority class. Thus, solutions are needed that achieve
multi-fairness,accurate predictive performance in overall, and balanced
performance across the different classes.To this end, we introduce a new
fairness notion,Multi-Max Mistreatment(MMM), which measures unfairness while
considering both (multi-attribute) protected group and class membership of
instances. To learn an MMM-fair classifier, we propose a multi-objective
problem formulation. We solve the problem using a boosting approach that
in-training,incorporates multi-fairness treatment in the distribution update
and post-training, finds multiple Pareto-optimal solutions; then uses
pseudo-weight based decision making to select optimal solution(s) among
accurate, balanced, and multi-attribute fair solutions


Abstract:
  This paper proposes a set of techniques to investigate eye gaze and fixation
patterns while users interact with electronic user interfaces. In particular,
two case studies are presented - one on analysing eye gaze while interacting
with deceptive materials in web pages and another on analysing graphs in
standard computer monitor and virtual reality displays. We analysed spatial and
temporal distributions of eye gaze fixations and sequence of eye gaze
movements. We used this information to propose new design guidelines to avoid
deceptive materials in web and user-friendly representation of data in 2D
graphs. In 2D graph study we identified that area graph has lowest number of
clusters for user's gaze fixations and lowest average response time. The
results of 2D graph study were implemented in virtual and mixed reality
environment. Along with this, it was ob-served that the duration while
interacting with deceptive materials in web pages is independent of the number
of fixations. Furthermore, web-based data visualization tool for analysing eye
tracking data from single and multiple users was developed.


Abstract:
  We analyze the top Lyapunov exponent of the product of sequences of two by
two matrices that appears in the analysis of several statistical mechanics
models with disorder: for example these matrices are the transfer matrices for
the nearest neighbor Ising chain with random external field, and the free
energy density of this Ising chain is the Lyapunov exponent we consider. We
obtain the sharp behavior of this exponent in the large interaction limit when
the external field is centered: this balanced case turns out to be critical in
many respects. From a mathematical standpoint we precisely identify the
behavior of the top Lyapunov exponent of a product of two dimensional random
matrices close to a diagonal random matrix for which top and bottom Lyapunov
exponents coincide. In particular, the Lyapunov exponent is only
$\log$-H\"older continuous.


Abstract:
  The favourable properties of tungsten borides for shielding the central High
Temperature Superconductor (HTS) core of a spherical tokamak fusion power plant
are modelled using the MCNP code. The objectives are to minimize the power
deposition into the cooled HTS core, and to keep HTS radiation damage to
acceptable levels by limiting the neutron and gamma fluxes. The shield
materials compared are W2B, WB, W2B5 and WB4 along with a reactively sintered
boride B0.329C0.074Cr0.024Fe0.274W0.299, monolithic W and WC. Of all these W2B5
gave the most favourable results with a factor of ~10 or greater reduction in
neutron flux and gamma energy deposition as compared to monolithic W. These
results are compared with layered water-cooled shields, giving the result that
the monolithic shields, with moderating boron, gave comparable neutron flux and
power deposition, and (in the case of W2B5) even better performance. Good
performance without water-coolant has advantages from a reactor safety
perspective due to the risks associated with radio-activation of oxygen. 10B
isotope concentrations between 0 and 100% are considered for the boride
shields. The naturally occurring 20% fraction gave much lower energy
depositions than the 0% fraction, but the improvement largely saturated beyond
40%. Thermophysical properties of the candidate materials are discussed, in
particular the thermal strain. To our knowledge, the performance of W2B5 is
unrivalled by other monolithic shielding materials. This is partly as its
trigonal crystal structure gives it higher atomic density compared with other
borides. It is also suggested that its high performance depends on it having
just high enough 10B content to maintain a constant neutron energy spectrum
across the shield.


Abstract:
  We show that the action of the Kauffman bracket skein algebra of a surface
$\Sigma$ on the skein module of the handlebody bounded by $\Sigma$ is faithful
if and only if the quantum parameter is not a root of 1.


Abstract:
  Shared Memory is a mechanism that allows several processes to communicate
with each other by accessing -- writing or reading -- a set of variables that
they have in common. A Consistency Model defines how each process observes the
state of the Memory, according to the accesses performed by it and by the rest
of the processes in the system. Therefore, it determines what value a read
returns when a given process issues it. This implies that there must be an
agreement among all, or among processes in different subsets, on the order in
which all or a subset of the accesses happened. It is clear that a higher
quantity of accesses or proceses taking part in the agreement makes it possibly
harder or slower to be achieved. This is the main reason for which a number of
Consistency Models for Shared Memory have been introduced. This paper is a
handy summary of [2] and [3] where consistency models (Sequential, Causal,
PRAM, Cache, Processors, Slow), including synchronized ones (Weak, Release,
Entry), were formally defined. This provides a better understanding of those
models and a way to reason and compare them through a concise notation. There
are many papers on this subject in the literature such as [11] with which this
work shares some concepts.