Abstract:
  In a recent work, two of the authors have formulated the non-linear
space-time Hasegawa-Mima plasma equation as a coupled system of two linear
PDEs, a solution of which is a pair $(u,w)$, with $w=(I-\Delta)u$. The first
equation is of hyperbolic type and the second of elliptic type. Variational
frames for obtaining weak solutions to the initial value Hasegawa-Mima problem
with periodic boundary conditions were also derived. Using the Fourier basis in
the space variables, existence of solutions were obtained. Implementation of
algorithms based on Fourier series leads to systems of dense matrices. In this
paper, we use a finite element space-domain approach to semi-discretize the
coupled variational Hasegawa-Mima model, obtaining global existence of
solutions in $H^2$ on any time interval $[0,T]$ for all T. In the sequel,
full-discretization using an implicit time scheme on the semi-discretized
system leads to a nonlinear full space-time discrete system with a
nonrestrictive condition on the time step. Tests on a semi-linear version of
the implicit nonlinear full-discrete system are conducted for several initial
data, assessing the efficiency of our approach.


Abstract:
  It is widely assumed in the literature that the specularity parameters for
phonon transmission (forward scattering) and reflection (backward scattering)
at a boundary are identical, i.e., the statistical distributions of the
transition probabilities between an incident phonon and the range of outgoing
phonon modes are the same for both transmission and reflection. However, it is
hypothesized by Li and McGaughey that separate specularity parameters are
needed to describe the behavior of transmitted and reflected phonons in
superlattices and polycrystalline materials correctly. We test this hypothesis
by analyzing the mode-resolved specularity parameters computed separately for
transmission and reflection processes at a graphene grain boundary. Our results
show that backward scattering is considerably more diffuse than forward
scattering at most frequencies and polarizations, providing strong evidence for
Li and McGaughey's hypothesis, and shed new light on how surfaces and
interfaces modify phonon transport within and between domains in nanostructured
materials.


Abstract:
  We prove that the moduli space of double covers ramified at two points
$\mathcal{R}_{g,2}$ is uniruled for $3\leq g\leq 6$ and of general type for
$g\geq 16$. Furthermore, we consider Prym-canonical divisorial strata in the
moduli space $\overline{\mathcal{C}^n\mathcal{R}}_g$ parametrizing $n$-pointed
Prym curves, and we compute their classes in
$\mathrm{Pic}_\mathbb{Q}(\overline{\mathcal{C}^n\mathcal{R}}_g)$.


Abstract:
  The advancement in distributed generation technologies in modern power
systems has led to a widespread integration of renewable power generation at
customer side. However, the intermittent nature of renewable energy pose new
challenges to the network operational planning with underlying uncertainties.
This paper proposes a novel Bayesian probabilistic technique for forecasting
renewable power generation by addressing data and model uncertainties by
integrating bidirectional long short-term memory (BiLSTM) neural networks while
compressing the weight parameters using variational autoencoder (VAE). Existing
Bayesian deep learning methods suffer from high computational complexities as
they require to draw a large number of samples from weight parameters expressed
in the form of probability distributions. The proposed method can deal with
uncertainty present in model and data in a more computationally efficient
manner by reducing the dimensionality of model parameters. The proposed method
is evaluated using pinball loss, reconstruction error, and other forecasting
evaluation metrics. It is inferred from the numerical results that VAE-Bayesian
BiLSTM outperforms other probabilistic deep learning methods in terms of
forecasting accuracy and computational efficiency for different sizes of the
dataset.


Abstract:
  We present methods to strictly calculate the finite-key effects in quantum
key distribution (QKD) with error rejection through two-way classical
communication (TWCC) for the sending-or-not-sending twin-field protocol. Unlike
the normal QKD without TWCC, here the probability of tagging or untagging for
each two-bit random group is not independent. We rigorously solve this problem
by imagining a virtual set of bits where every bit is independent and
identical. We show the relationship between the outcome starting from this
imagined set containing independent and identical bits and the outcome starting
with the real set of non-independent bits. With explicit formulas, we show that
simply applying Chernoff bound in the calculation gives correct key rate, but
the failure probability changes a little bit.


Abstract:
  After reading such a question, any mathematician will say that, according to
a well known result of L.P. Eisenhart found in 1926, the answer is " One " of
course, namely the only constant allowing to describe the so-called " {\it
constant riemannian curvature} " condition. The purpose of this paper is to
prove the contrary by studying the case of two dimensional riemannian geometry
in the light of an old work of E. Vessiot published in 1903 but {\it still
totally unknown today} after more than a century. In fact, we shall compute
locally the {\it Vessiot structure equations} and prove that there are indeed "
Two " {\it Vessiot structure constants} satisfying a single {\it linear Jacobi
condition} showing that one of them must vanish while the other one must be
equal to the known one. This result depends on deep mathematical reasons in the
formal theory of Lie pseudogroups, which are involving both the Spencer
$\delta$-cohomology and diagram chasing in homological algebra. Another similar
example will illustrate and justify this comment out of the classical tensorial
framework of the famous " {\it equivalence problem} ". The case of contact
transformations will also be studied. Though it is quite unexpected, we shall
reach the conclusion that the mathematical foundations of both classical and
conformal riemannian geometry must be revisited. We have treated the case of
conformal geometry in a recent arXiv preprint.


Abstract:
  We propose a new nonparametric modeling framework for causal inference when
outcomes depend on how agents are linked in a social or economic network. Such
network interference describes a large literature on treatment spillovers,
social interactions, social learning, information diffusion, disease and
financial contagion, social capital formation, and more. Our approach works by
first characterizing how an agent is linked in the network using the
configuration of other agents and connections nearby as measured by path
distance. The impact of a policy or treatment assignment is then learned by
pooling outcome data across similarly configured agents. We demonstrate the
approach by proposing an asymptotically valid test for the hypothesis of policy
irrelevance/no treatment effects and bounding the mean-squared error of a
k-nearest-neighbor estimator for the average or distributional policy
effect/treatment response.


Abstract:
  Software defect prediction heavily relies on the metrics collected from
software projects. Earlier studies often used machine learning techniques to
build, validate, and improve bug prediction models using either a set of
metrics collected within a project or across different projects. However,
techniques applied and conclusions derived by those models are restricted by
how identical those metrics are. Knowledge coming from those models will not be
extensible to a target project if no sufficient overlapping metrics have been
collected in the source projects. To explore the feasibility of transferring
knowledge across projects without common labeled metrics, we systematically
integrated Heterogeneous Defect Prediction (HDP) by replicating and validating
the obtained results. Our main goal is to extend prior research and explore the
feasibility of HDP and finally to compare its performance with that of its
predecessor, Cross-Project Defect Prediction. We construct an HDP model on
different publicly available datasets. Moreover, we propose a new ensemble
voting approach in the HDP context to utilize the predictive power of multiple
available datasets. The result of our experiment is comparable to that of the
original study. However, we also explored the feasibility of HDP in real cases.
Our results shed light on the infeasibility of many cases for the HDP algorithm
due to its sensitivity to the parameter selection. In general, our analysis
gives a deep insight into why and how to perform transfer learning from one
domain to another, and in particular, provides a set of guidelines to help
researchers and practitioners to disseminate knowledge to the defect prediction
domain.


Abstract:
  Measuring the interaction between cities is an important research topic in
many disciplines, such as sociology, geography, economics and transportation
science. The traditional and most widely used spatial interaction model is the
gravity model, but it requires the parameters to be artificially set. In this
paper, we propose a parameter-free interactive city choice (ICC) model that
measures intercity interaction from the perspective of individual choice
behavior. The ICC model assumes that the probability of an individual choosing
to interact with a city is proportional to the number of opportunities in the
destination city and inversely proportional to the number of intervening
opportunities between the origin city and the destination city, calculated
using the travel time in the transportation network. The intercity interaction
intensity can be obtained by calculating the product of this probability and
the origin city's population. We apply the ICC model to measure the interaction
intensity among 339 cities in China and analyze the impact of changes in the
Chinese land transportation network from 2005 to 2018 on the intercity and city
interaction intensity. Compared with the previously widely used spatial
interaction models, the measurement results of the ICC model are more
consistent with the actual situation.


Abstract:
  In this paper, we establish a theory of adelic line bundles over
quasi-projective varieties over finitely generated fields. Besides definitions
of adelic line bundles, we consider their intersection theory, volume theory,
and height theory, and apply these to study heights of algebraic points of
quasi-projective varieties.


Abstract:
  It is known that data rates in standard cellular networks are limited due to
inter-cell interference. An effective solution of this problem is to use the
multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a
candidate solution in 5G and future communication networks, cooperation is
applied by means of central processors (CPs) connected to simple remote radio
heads with finite capacity fronthaul links. In this study, we consider a
downlink C-RAN with a wireless fronthaul and aim to minimize total power spent
by jointly designing beamformers for fronthaul and access links. We consider
the case where perfect channel state information is not available in the CP. We
first derive a novel theoretical performance bound for the problem defined.
Then we propose four algorithms with different complexities to show the
tightness of the bound. The first two algorithms apply successive convex
optimizations with semi-definite relaxation idea where other two are adapted
from well-known beamforming design methods. The detailed simulations under
realistic channel conditions show that as the complexity of the algorithm
increases, the corresponding performance becomes closer to the bound.


Abstract:
  In this work, using a detailed dataset furnished by National Health
Authorities concerning the Province of Pavia (Lombardy, Italy), we propose to
determine the essential features of the ongoing COVID-19 pandemic in term of
contact dynamics. Our contribution is devoted to provide a possible planning of
the needs of medical infrastructures in the Pavia Province and to suggest
different scenarios about the vaccination campaign which possibly help in
reducing the fatalities and/or reducing the number of infected in the
population. The proposed research combines a new mathematical description of
the spread of an infectious diseases which takes into account both age and
average daily social contacts with a detailed analysis of the dataset of all
traced infected individuals in the Province of Pavia. These information are
used to develop a data-driven model in which calibration and feeding of the
model are extensively used. The epidemiological evolution is obtained by
relying on an approach based on statistical mechanics. This leads to study the
evolution over time of a system of probability distributions characterizing the
age and social contacts of the population. One of the main outcomes shows that,
as expected, the spread of the disease is closely related to the mean number of
contacts of individuals. The model permits to forecast thanks to an uncertainty
quantification approach and in the short time horizon, the average number and
the confidence bands of expected hospitalized classified by age and to test
different options for an effective vaccination campaign with age-decreasing
priority.


Abstract:
  There is a growing interest in the estimation of the number of unseen
features, mostly driven by applications in biological sciences. A recent work
brought out the upside and the downside of the popular stable-Beta process
prior, and generalizations thereof, in Bayesian nonparametric inference for the
unseen-features problem: i) the downside lies in the limited use of the
sampling information in the posterior distributions, which depend on the
observable sample only through the sample size; ii) the upside lies in the
analytical tractability and interpretability of the posterior distributions,
which are simple Poisson distributions whose parameters are simple to compute,
and depend on the sample size and the prior's parameter. In this paper, we
introduce and investigate an alternative nonparametric prior, referred to as
the stable-Beta scaled process prior, which is the first prior that allows to
enrich the posterior distribution of the number of unseen features, through the
inclusion of the sampling information on the number of distinct features in the
observable sample, while maintaining the same analytical tractability and
interpretability as the stable-Beta process prior. Our prior leads to a
negative Binomial posterior distribution, whose parameters depends on the
sample size, the observed number of distinct features and the prior's
parameter, providing estimates that are simple, linear in the sampling
information and computationally efficient. We apply our approach to synthetic
and real genetic data, showing that it outperforms parametric and nonparametric
competitors in terms of estimation accuracy.


Abstract:
  We propose a method to stabilize Laughlin states of a large number of
strongly interacting photons by combining a frequency-selective incoherent pump
with a step-like potential in the angular momentum basis. Analytical
expressions for the preparation efficiency and for the principal error sources
are obtained. Direct extension of the preparation scheme to states containing
single or multiple quasiholes is discussed.


Abstract:
  The highest-energy known gamma-ray sources are all located within 0.5 degrees
of extremely powerful pulsars. This raises the question of whether
ultra-high-energy (UHE; $>$ 56 TeV) gamma-ray emission is a universal feature
expected near pulsars with a high spin-down power. Using four years of data
from the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory, we present
a joint-likelihood analysis of ten extremely powerful pulsars to search for UHE
gamma-ray emission correlated with these locations. We report a significant
detection ($>$ 3$\sigma$), indicating that UHE gamma-ray emission is a generic
feature of powerful pulsars. We discuss the emission mechanisms of the gamma
rays and the implications of this result. The individual environment that each
pulsar is found in appears to play a role in the amount of emission.


Abstract:
  We study the motion of an impurity in a two-leg ladder interacting with two
fermionic baths along each leg, a simple model bridging cold atom quantum
simulators with an idealised description of the basic transport processes in a
layered heterostructure. Using the linked-cluster expansion we obtain exact
analytical results for the single-particle Green's function and find that the
long-time behaviour is dominated by an intrinsic orthogonality catastrophe
associated to the motion of the impurity in each one-dimensional chain. We
explore both the case of two identical legs as well as the case where the legs
are characterised by different interaction strengths: in the latter case we
observe a subleading correction which can be relevant for intermediate-time
transport at an interface between different materials. In all the cases we do
not find significant differences between the intra- and inter-leg Green's
functions in the long-time limit.


Abstract:
  In 1974, Robert Caro published The Power Broker, a critical biography of
Robert Moses's dictatorial tenure as the "master builder" of mid-century New
York. Moses profoundly transformed New York's urban fabric and transportation
system, producing the Brooklyn Battery Tunnel, the Verrazano Narrows Bridge,
the Westside Highway, the Cross-Bronx Expressway, the Lincoln Center, the UN
headquarters, Shea Stadium, Jones Beach State Park and many other projects.
However, The Power Broker did lasting damage to his public image and today he
remains one of the most controversial figures in city planning history. On
August 26, 1974, Moses issued a turgid 23-page statement denouncing Caro's work
as "full of mistakes, unsupported charges, nasty baseless personalities, and
random haymakers." Moses's original typewritten statement survives today as a
grainy photocopy in the New York City Parks Department archive. To better
preserve and disseminate it, I have extracted and transcribed its text using
optical character recognition and edited the result to correct errors. Here I
compile my transcription of Moses's statement, alongside Caro's reply to it.


Abstract:
  Our objective in this work is video-text retrieval - in particular a joint
embedding that enables efficient text-to-video retrieval. The challenges in
this area include the design of the visual architecture and the nature of the
training data, in that the available large scale video-text training datasets,
such as HowTo100M, are noisy and hence competitive performance is achieved only
at scale through large amounts of compute. We address both these challenges in
this paper. We propose an end-to-end trainable model that is designed to take
advantage of both large-scale image and video captioning datasets. Our model is
an adaptation and extension of the recent ViT and Timesformer architectures,
and consists of attention in both space and time. The model is flexible and can
be trained on both image and video text datasets, either independently or in
conjunction. It is trained with a curriculum learning schedule that begins by
treating images as 'frozen' snapshots of video, and then gradually learns to
attend to increasing temporal context when trained on video datasets. We also
provide a new video-text pretraining dataset WebVid-2M, comprised of over two
million videos with weak captions scraped from the internet. Despite training
on datasets that are an order of magnitude smaller, we show that this approach
yields state-of-the-art results on standard downstream video-retrieval
benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC.


Abstract:
  Owing to the remarkable capability of extracting effective graph embeddings,
graph convolutional network (GCN) and its variants have been successfully
applied to a broad range of tasks, such as node classification, link
prediction, and graph classification. Traditional GCN models suffer from the
issues of overfitting and oversmoothing, while some recent techniques like
DropEdge could alleviate these issues and thus enable the development of deep
GCN. However, training GCN models is non-trivial, as it is sensitive to the
choice of hyperparameters such as dropout rate and learning weight decay,
especially for deep GCN models. In this paper, we aim to automate the training
of GCN models through hyperparameter optimization. To be specific, we propose a
self-tuning GCN approach with an alternate training algorithm, and further
extend our approach by incorporating the population based training scheme.
Experimental results on three benchmark datasets demonstrate the effectiveness
of our approaches on optimizing multi-layer GCN, compared with several
representative baselines.


Abstract:
  We present the first X-ray dedicated study of the galaxy cluster A795 and of
the Fanaroff-Riley Type 0 hosted in its brightest cluster galaxy. Using an
archival 30 ks \textit{Chandra} observation we study the dynamical state and
cooling properties of the intracluster medium, and we investigate whether the
growth of the radio galaxy is prevented by the surrounding environment. We
discover that A795 is a weakly cool core cluster, with an observed mass
deposition rate $\lessapprox 14\,$ M$_{\odot}$yr$^{-1}$ in the cooling region
(central $\sim$66 kpc). In the inner $\sim$ 30 kpc we identify two putative
X-ray cavities, and we unveil the presence of two prominent cold fronts at
$\sim$60 kpc and $\sim$178 kpc from the center, located along a cold ICM spiral
feature. The central galaxy, which is offset by 17.7 kpc from the X-ray peak,
is surrounded by a multi-temperature gas with an average density of
$n_{\text{e}} = 2.14 \times 10^{-2}$ cm$^{-3}$. We find extended radio emission
at 74-227 MHz centered on the cluster, exceeding the expected flux from the
radio galaxy extrapolated at low frequency. We propose that sloshing is
responsible for the spiral morphology of the gas and the formation of the cold
fronts, and that the environment alone cannot explain the compactness of the
radio galaxy. We argue that the power of the two cavities and the sloshing
kinetic energy can reduce and offset cooling. Considering the spectral and
morphological properties of the extended radio emission, we classify it as a
candidate radio mini-halo.


Abstract:
  A quantum interface for two-way entanglement transfer between orbital angular
momentum degree of freedom in free space and time-energy degree of freedom in
optical fibers, provides a novel way toward establishing entanglement between
remote heterogeneous quantum nodes. Here, we experimentally demonstrate this
kind of transfer interface by using two interferometric cyclic gates. By using
this quantum interface, we perform two-way entanglement transfer for the two
degrees of freedom. The results show that the quantum entangled state can be
switched back and forth between orbital angular momentum and time-energy
degrees of freedom, and the fidelity of the state before and after switching is
higher than 90%. Our work demonstrates the feasibility and high performance of
our proposed transfer interface, and paves a route toward building a
large-scale quantum communication network.


Abstract:
  In this paper, we investigate the photon sphere, shadow radius and
quasinormal modes of a charged black hole in presence of quintessence. The
result shows that the shadow radius decreases with the increase of the electric
charge. The quasinormal modes are derived by the sixth WKB approximation method
and shadow radius, respectively. For the fixed electric charge and multipole
number, the values of the real and imaginary parts of the quasinormal modes
decrease with the increase of the quintessence charge. When the value of the
multipole number is large, the quasinormal modes derived by the two methods are
consistent, which shows the correspondence between the quasinormal modes in the
eikonal limit and shadow. When the value of the multipole number is small, the
quasinormal modes obtained by the two methods are also in good agreement.


Abstract:
  Fuzzy systems have good modeling capabilities in several data science
scenarios, and can provide human-explainable intelligence models with
explainability and interpretability. In contrast to transaction data, which
have been extensively studied, sequence data are more common in real-life
applications. To obtain a human-explainable data intelligence model for
decision making, in this study, we investigate explainable fuzzy-theoretic
utility mining on multi-sequences. Meanwhile, a more normative formulation of
the problem of fuzzy utility mining on sequences is formulated. By exploring
fuzzy set theory for utility mining, we propose a novel method termed pattern
growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences
with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy
quantity and utility regions of sequences. To improve the efficiency and
feasibility of PGFUM, we develop two compressed data structures with
explainable fuzziness. Furthermore, one existing and two new upper bounds on
the explainable fuzzy utility of candidates are adopted in three proposed
pruning strategies to substantially reduce the search space and thus expedite
the mining process. Finally, the proposed PGFUM algorithm is compared with
PFUS, which is the only currently available method for the same task, through
extensive experimental evaluation. It is demonstrated that PGFUM achieves not
only human-explainable mining results that contain the original nature of
revealable intelligibility, but also high efficiency in terms of runtime and
memory cost.


Abstract:
  Let $R$ be a ring and $S$ a multiplicative subset of $R$. An $R$-module $P$
is called $S$-projective provided that the induced sequence $0\rightarrow {\rm
Hom}_R(P,A)\rightarrow {\rm Hom}_R(P,B)\rightarrow {\rm Hom}_R(P,C)\rightarrow
0$ is $S$-exact for any $S$-short exact sequence $0\rightarrow A\rightarrow
B\rightarrow C\rightarrow 0$. Some characterizations and properties of
$S$-projective modules are obtained. The notion of $S$-semisimple modules is
also introduced. A ring $R$ is called an $S$-semisimple ring provided that
every free $R$-module is $S$-semisimple. Several characterizations of
$S$-semisimple rings are provided by using $S$-semisimple modules,
$S$-projective modules, $S$-injective modules and $S$-split $S$-exact
sequences.


Abstract:
  Satellite imagery is widely used in many application sectors, including
agriculture, navigation, and urban planning. Frequently, satellite imagery
involves both large numbers of images as well as high pixel counts, making
satellite datasets computationally expensive to analyze. Recent approaches to
satellite image analysis have largely emphasized deep learning methods. Though
extremely powerful, deep learning has some drawbacks, including the requirement
of specialized computing hardware and a high reliance on training data. When
dealing with large satellite datasets, the cost of both computational resources
and training data annotation may be prohibitive.


Abstract:
  We apply the coded caching scheme proposed by Maddah-Ali and Niesen to a
multipoint multicasting video paradigm. Partially caching the video files on
the wireless devices provides an opportunity to decrease data traffic load in
peak hours via sending multicast coded messages to users. In this paper, we
propose a two-hop wireless network for video multicasting, where the common
coded multicast message is transmitted through different single antenna Edge
Nodes (ENs) to multiple antenna users. Each user can decide to decode any EN by
using a zero forcing receiver. Motivated by Scalable Video Coding (SVC), we
consider successive refinement source coding in order to provide a ``softer''
tradeoff between the number of decoded ENs and the source distortion at each
user receiver. The resulting coding scheme can be seen as the concatenation of
Maddah-Ali and Niesen coded caching for each source-coded layer, and multiple
description coding. Using stochastic geometry, we investigate the tradeoff
between delivery time and per-user average source distortion. The proposed
system is spatially scalable in the sense that, for given users' and ENs'
spatial density, the achieved distortion-delivery time performance is
independent of the coverage area (for in the limit of large area).


Abstract:
  After the prediction of many sub- and super-Chandrasekhar (at least a dozen
for the latter) limiting mass white dwarfs, hence apparently peculiar class of
white dwarfs, from the observations of luminosity of type Ia supernovae,
researchers have proposed various models to explain these two classes of white
dwarfs separately. We earlier showed that these two peculiar classes of white
dwarfs, along with the regular white dwarfs, can be explained by a single form
of the f(R) gravity, whose effect is significant only in the high-density
regime, and it almost vanishes in the low-density regime. However, since there
is no direct detection of such white dwarfs, it is difficult to single out one
specific theory from the zoo of modified theories of gravity. We discuss the
possibility of direct detection of such white dwarfs in gravitational wave
astronomy. It is well-known that in f(R) gravity, more than two polarization
modes are present. We estimate the amplitudes of all the relevant modes for the
peculiar as well as the regular white dwarfs. We further discuss the
possibility of their detections through future-based gravitational wave
detectors, such as LISA, ALIA, DECIGO, BBO, or Einstein Telescope, and thereby
put constraints or rule out various modified theories of gravity. This
exploration links the theory with possible observations through gravitational
wave in f(R) gravity.


Abstract:
  In this paper, we study the mixed problem for new class of nonlinear
reaction-diffusion PDEs with the nonlocal nonlinearity with variable exponents.
Here we obtain results on solvability and behavior of solutions both when these
are yet dissipative and when these get become non-dissipative problems. These
problems are possess special properties: these can be to remain dissipative all
time or can get become non dissipative after finite time. It is shown that if
the studied problems get become non-dissipative can have an infinite number of
different unstable solutions with varying speeds and also an infinite number of
different states of spatio-temporal (diffusion) chaos that are generated by
cascades of bifurcations governed by the corresponding steady states. The
behavior of these solutions is analyzed in detail and it is explained how
space-time chaos can arise. Since these problems desctibe the dynamics of
cancer, we explain the behavior of the dynamics of cancer using obtained here
results.


Abstract:
  Even a small fraction of nanoparticles in fluids affects the splashing
behavior of a droplet upon impact on a smooth surface. Nanofluid drop impact
onto a smooth sapphire substrate is experimentally investigated over wide
ranges of Reynolds ($10^2<\mathrm{Re}<10^4$) and Weber ($50<\mathrm{We}<500$)
numbers for three nanofluid mass concentrations (0.01%, 0.1%, 1%) using
high-speed photography. Nanofluids are prepared by diluting a commercial
Al$_2$O$_3$-water nanofluid in aqueous glycerol solutions without dispersants.
In total, 30 samples are prepared and 1799 data points are acquired. Every
sample is experimentally characterized prior to droplet impact measurements in
terms of stability, density, viscosity, and surface tension to demonstrate the
observed outcomes on the We-Re maps. Each droplet impact condition is repeated
at least 3 times to ensure good repeatability. The non-monotonic behavior of
the spreading-to-splashing transition remains the same for nanofluids. However,
nanofluids influence this boundary by promoting splashing at low Reynolds
numbers. We explain this behavior by increased lamella spreading speed and lift
during the lamella spreading stage. Finally, we develop an empirical
correlation which describes the splashing threshold dependency on nanoparticle
concentration for the first time.


Abstract:
  We analyze composition methods with complex coefficients exhibiting the
so-called ``symmetry-conjugate'' pattern in their distribution. In particular,
we study their behavior with respect to preservation of qualitative properties
when projected on the real axis and we compare them with the usual left-right
palindromic compositions. New schemes within this family up to order 8 are
proposed and their efficiency is tested on several examples. Our analysis shows
that higher-order schemes are more efficient even when time step sizes are
relatively large.


Abstract:
  We study the emergence of the traveling chimera state in a two-dimensional
network of Hindmarsh-Rose burst neurons with the mutual presence of local and
non-local couplings. We show that in the unique presence of the non-local
chemical coupling modeled by a nonlinear function, the traveling chimera
phenomenon occurs with a displacement in both directions of the plane of the
grid. The introduction of local electrical coupling shows that the mutual
influence of the two types of coupling can, for certain values, generate
traveling chimera, imperfect-traveling, traveling multi-clusters, and
alternating traveling chimera, ie the presence in the network under study, of
patterns of coherent elements interspersed by other incoherent elements in
movement and alternately changing their position over time. The confirmation of
the states of coherence is done by introducing the parameter of instantaneous
local order parameter in two dimensions. We extend our analysis through
mathematical tools such as the Hamilton energy function to determine the
direction of propagation of patterns in two dimensions.


Abstract:
  This paper focuses on the so-called Weighted Inertia-Dissipation-Energy
(WIDE) variational approach for the approximation of unsteady Leray-Hopf
solutions of the incompressible Navier-Stokes system. Initiated in [56], this
variational method is here extended to the case of non-Newtonian fluids with
power-law index $r\geq11/5$ in three space dimensions and large nonhomogeneous
data. Moreover, boundary conditions are not imposed on some parts of
boundaries, representing, e.g., outflows. Correspondingly, natural boundary
conditions arise from the minimization. In particular, at walls we recover
boundary conditions of Navier-slip type. At outflows and inflows, we obtain the
condition
$-\frac12|\boldsymbol{v}|^2\boldsymbol{n}+\mathbb{T}\boldsymbol{n}=0$. This
provides the first theoretical explanation for the onset of such boundary
conditions.


Abstract:
  The stabilizer rank of a quantum state $\psi$ is the minimal $r$ such that
$\left| \psi \right \rangle = \sum_{j=1}^r c_j \left|\varphi_j \right\rangle$
for $c_j \in \mathbb{C}$ and stabilizer states $\varphi_j$. The running time of
several classical simulation methods for quantum circuits is determined by the
stabilizer rank of the $n$-th tensor power of single-qubit magic states.
  We prove a lower bound of $\Omega(n)$ on the stabilizer rank of such states,
improving a previous lower bound of $\Omega(\sqrt{n})$ of Bravyi, Smith and
Smolin (arXiv:1506.01396). Further, we prove that for a sufficiently small
constant $\delta$, the stabilizer rank of any state which is $\delta$-close to
those states is $\Omega(\sqrt{n}/\log n)$. This is the first non-trivial lower
bound for approximate stabilizer rank.
  Our techniques rely on the representation of stabilizer states as quadratic
functions over affine subspaces of $\mathbb{F}_2^n$, and we use tools from
analysis of boolean functions and complexity theory. The proof of the first
result involves a careful analysis of directional derivatives of quadratic
polynomials, whereas the proof of the second result uses Razborov-Smolensky low
degree polynomial approximations and correlation bounds against the majority
function.


Abstract:
  Inspired by the capability of structured illumination microscopy in
subwavelength imaging, many researchers devoted themselves to investigating
this methodology. However, due to the free propagating feature of the
traditional structured illumination fields, the resolution can be only improved
up to double times compared with the diffractied limited microscopy. Besides,
most of the previous studies, relying on incoherent illumination sources, are
restricted to fluorescent samples. In this work, a subwavelength nonfluorescent
imaging method is proposed based on the terahertz traveling wave and plasmonics
illumination. Excited along with a metal grating, the spoof surface plasmons
are employed as the plasmonics illumination. When the scattering waves with the
SSPs illumination are captured, the high order spatial frequency components of
the sample are already encoded into the obtainable low order ones. Then, an
algorithm is summarized to shift the modulated SF components to their actual
positions in the Fourier domain. In this manner, high order SF components
carrying the fine information are introduced to reconstruct the desired
imaging, leading to an improvement of the resolution up to 0.12 lambda.
Encouragingly, the resolution can be further enhanced by tuning the working
frequency of the SSPs. This method holds promise for some important
applications in terahertz nonfluorescent microscopy and sample detection with
weak scattering.


Abstract:
  Browsers often include security features to detect phishing web pages. In the
past, some browsers evaluated an unknown URL for inclusion in a list of known
phishing pages. However, as the number of URLs and known phishing pages
continued to increase at a rapid pace, browsers started to include one or more
machine learning classifiers as part of their security services that aim to
better protect end users from harm. While additional information could be used,
browsers typically evaluate every unknown URL using some classifier in order to
quickly detect these phishing pages. Early phishing detection used standard
machine learning classifiers, but recent research has instead proposed the use
of deep learning models for the phishing URL detection task. Concurrently, text
embedding research using transformers has led to state-of-the-art results in
many natural language processing tasks. In this work, we perform a
comprehensive analysis of transformer models on the phishing URL detection
task. We consider standard masked language model and additional domain-specific
pre-training tasks, and compare these models to fine-tuned BERT and RoBERTa
models. Combining the insights from these experiments, we propose URLTran which
uses transformers to significantly improve the performance of phishing URL
detection over a wide range of very low false positive rates (FPRs) compared to
other deep learning-based methods. For example, URLTran yields a true positive
rate (TPR) of 86.80% compared to 71.20% for the next best baseline at an FPR of
0.01%, resulting in a relative improvement of over 21.9%. Further, we consider
some classical adversarial black-box phishing attacks such as those based on
homoglyphs and compound word splits to improve the robustness of URLTran. We
consider additional fine tuning with these adversarial samples and demonstrate
that URLTran can maintain low FPRs under these scenarios.


Abstract:
  In this work, the results of Ultra-Wideband air-to-ground measurements
carried out in a real-world factory environment are presented and discussed.
With intelligent in-dustrial deployments in mind, we envision a scenario where
the Unmanned Aerial Vehicle can be used as a supplementary tool for factory
operation, optimization and control. Measurements address narrow band and wide
band characterization of the wireless radio channel, and can be used for link
budget calculation, interference studies and time dispersion assessment in real
factories, without the usual limitation for both radio terminals to be close to
ground. The measurements are performed at different locations and different
heights over the 3.1-5.3 GHz band. Some fundamental propagation parameters
values are determined vs. distance, height and propagation conditions. The
measurements are complemented with, and compared to, conventional
ground-to-ground measurements with the same setup. The conducted measurement
campaign gives an insight for realizing wireless applications in smart
connected factories, including UAV-assisted applications.


Abstract:
  The optimization of low-thrust, multi-revolution orbit transfer trajectories
is often regarded as a difficult problem in modern astrodynamics. In this
paper, a flexible and computationally efficient approach is presented for the
optimization of low-thrust orbit transfers under eclipse constraints. The
proposed approach leverages a new dynamic model of the orbital motion and a
Lyapunov-based initial guess generation scheme that is very easy to tune. A
multi-objective, single-phase formulation of the optimal control problem is
devised, which provides a convenient way to trade off fuel consumption and time
of flight. A distinctive feature of such a formulation is that it requires no
prior information about the structure of the optimal solution. Simulation
results for two benchmark orbit transfer scenarios indicate that minimum-time,
minimum-fuel and mixed time/fuel-optimal instances of the control problem can
be readily solved via direct collocation, while incurring a significantly lower
computational demand with respect to existing techniques.


Abstract:
  It is a well-established fact in the scientific literature that Picard
iterations of backward stochastic differential equations with globally
Lipschitz continuous nonlinearity converge at least exponentially fast to the
solution. In this paper we prove that this convergence is in fact at least
square-root factorially fast. We show for one example that no higher
convergence speed is possible in general. Moreover, if the nonlinearity is
$z$-independent, then the convergence is even factorially fast. Thus we reveal
a phase transition in the speed of convergence of Picard iterations of backward
stochastic differential equations.


Abstract:
  Automated commonsense reasoning is essential for building human-like AI
systems featuring, for example, explainable AI. Event Calculus (EC) is a family
of formalisms that model commonsense reasoning with a sound, logical basis.
Previous attempts to mechanize reasoning using EC faced difficulties in the
treatment of the continuous change in dense domains (e.g., time and other
physical quantities), constraints among variables, default negation, and the
uniform application of different inference methods, among others. We propose
the use of s(CASP), a query-driven, top-down execution model for Predicate
Answer Set Programming with Constraints, to model and reason using EC. We show
how EC scenarios can be naturally and directly encoded in s(CASP) and how it
enables deductive and abductive reasoning tasks in domains featuring
constraints involving both dense time and dense fluents.


Abstract:
  This paper aims to construct optimal Z-complementary code set (ZCCS) with
non-power-of-two (NPT) lengths to enable interference-free multicarrier
code-division multiple access (MC-CDMA) systems. The existing ZCCSs with NPT
lengths, which are constructed from generalized Boolean functions (GBFs), are
sub-optimal only with respect to the set size upper bound. For the first time
in the literature, we advocate the use of pseudo-Boolean functions (PBFs) (each
of which transforms a number of binary variables to a real number as a natural
generalization of GBF) for direct constructions of optimal ZCCSs with NPT
lengths.


Abstract:
  Let $A$ be an unital alternative $*$-algebra. Assume that $A$ contains a
nontrivial symmetric idempotent element $e$ which satisfies $xA \cdot e = 0$
implies $x = 0$ and $xA \cdot (1_A - e) = 0$ implies $x = 0$. In this paper, it
is shown that $\Phi$ is a nonlinear $*$-Jordan-type derivation on A if and only
if $\Phi$ is an additive $*$-derivation. As application, we get a result on
alternative $W^{*}$-algebras.


Abstract:
  Multi-stage learning is an effective technique to invoke multiple
deep-learning modules sequentially. This paper applies multi-stage learning to
speech enhancement by using a multi-stage structure, where each stage comprises
a self-attention (SA) block followed by stacks of temporal convolutional
network (TCN) blocks with doubling dilation factors. Each stage generates a
prediction that is refined in a subsequent stage. A fusion block is inserted at
the input of later stages to re-inject original information. The resulting
multi-stage speech enhancement system, in short, multi-stage SA-TCN, is
compared with state-of-the-art deep-learning speech enhancement methods using
the LibriSpeech and VCTK data sets. The multi-stage SA-TCN system's
hyper-parameters are fine-tuned, and the impact of the SA block, the fusion
block and the number of stages are determined. The use of a multi-stage SA-TCN
system as a front-end for automatic speech recognition systems is investigated
as well. It is shown that the multi-stage SA-TCN systems perform well relative
to other state-of-the-art systems in terms of speech enhancement and speech
recognition scores.


Abstract:
  We calculate transition amplitudes and probabilities between the coherent and
Fock states of a quantum harmonic oscillator with a moving center for an
arbitrary law of motion. These quantities are determined by the Fourier
transform of the moving center acceleration. In the case of a constant
acceleration, the probabilities oscillate with the oscillator frequency, so
that no excitation occurs after every period. Examples of oscillating and
rotating motion of the harmonic trap center are considered too. Estimations
show that the effect of excitation of vibration states due to the motion of the
harmonic trap center can be observed in available atomic traps.


Abstract:
  Reciprocal transformations associated with admitted conservation laws were
originally used to derive invariance properties in non-relativistic gasdynamics
and applied to obtain reduction to tractable canonical forms. They have
subsequently been shown to have diverse physical applications to nonlinear
systems, notably in the analytic treatment of Stefan-type moving boundary
problem and in linking inverse scattering systems and integrable hierarchies in
soliton theory. Here,invariance under classes of reciprocal transformations in
relativistic gasdynamics is shown to be linked to a Lie group procedure.


Abstract:
  The number of connected smart devices has been increasing exponentially for
different Internet-of-Things (IoT) applications. Security has been a long run
challenge in the IoT systems which has many attack vectors, security flaws and
vulnerabilities. Securing billions of B connected devices in IoT is a must task
to realize the full potential of IoT applications. Recently, researchers have
proposed many security solutions for IoT. Machine learning has been proposed as
one of the emerging solutions for IoT security and Reinforcement learning is
gaining more popularity for securing IoT systems. Reinforcement learning,
unlike other machine learning techniques, can learn the environment by having
minimum information about the parameters to be learned. It solves the
optimization problem by interacting with the environment adapting the
parameters on the fly. In this paper, we present an comprehensive survey of
different types of cyber-attacks against different IoT systems and then we
present reinforcement learning and deep reinforcement learning based security
solutions to combat those different types of attacks in different IoT systems.
Furthermore, we present the Reinforcement learning for securing CPS systems
(i.e., IoT with feedback and control) such as smart grid and smart
transportation system. The recent important attacks and countermeasures using
reinforcement learning B in IoT are also summarized in the form of tables. With
this paper, readers can have a more thorough understanding of IoT security
attacks and countermeasures using Reinforcement Learning, as well as research
trends in this area.


Abstract:
  This paper combines the graph-theoretical ideas behind Moravcsik's theorem
with a completely analytic derivation of discrete phase-ambiguities, recently
published by Nakayama. The result is a new graphical procedure for the
derivation of certain types of complete sets of observables for an
amplitude-extraction problem with $N$ helicity-amplitudes. The procedure is
applied to pseudoscalar meson photoproduction ($N = 4$ amplitudes) and
electroproduction ($N = 6$ amplitudes), yielding complete sets with minimal
length of $2N$ observables. For the case of electroproduction, this is the
first time an extensive list of minimal complete sets is published.
Furthermore, the generalization of the proposed procedure to processes with a
larger number of amplitudes, i.e. $N > 6$ amplitudes, is sketched. The
generalized procedure is outlined for the next more complicated example of
two-meson photoproduction ($N = 8$ amplitudes).


Abstract:
  A constrained multivariate linear model is a multivariate linear model with
the columns of its coefficient matrix constrained to lie in a known subspace.
This class of models includes those typically used to study growth curves and
longitudinal data. Envelope methods have been proposed to improve estimation
efficiency in the class of unconstrained multivariate linear models, but have
not yet been developed for constrained models that we develop in this article.
We first compare the standard envelope estimator based on an unconstrained
multivariate model with the standard estimator arising from a constrained
multivariate model in terms of bias and efficiency. Then, to further improve
efficiency, we propose a novel envelope estimator based on a constrained
multivariate model. Novel envelope-based testing methods are also proposed. We
provide support for our proposals by simulations and by studying the classical
dental data and data from the China Health and Nutrition Survey and a study of
probiotic capacity to reduced Salmonella infection.


Abstract:
  We present a new dynamical proof of the Thouless-Anderson-Palmer (TAP)
equations for the classical Sherrington-Kirkpatrick spin glass at sufficiently
high temperature. In our derivation, the TAP equations are a simple consequence
of the decay of the two point correlation functions. The methods can also be
used to establish the decay of higher order correlation functions. We
illustrate this by proving a suitable decay bound on the three point functions
from which we derive an analogue of the TAP equations for the two point
functions.


Abstract:
  We establish a quantitative relationship between photometric and
spectroscopic detections of solar-like oscillations using ab initio,
three-dimensional (3D), hydrodynamical numerical simulations of stellar
atmospheres. We present a theoretical derivation as proof of concept for our
method. We perform realistic spectral line formation calculations to quantify
the ratio between luminosity and radial velocity amplitude for two case
studies: the Sun and the red giant $\epsilon$ Tau. Luminosity amplitudes are
computed based on the bolometric flux predicted by 3D simulations with
granulation background modelled the same way as asteroseismic observations.
Radial velocity amplitudes are determined from the wavelength shift of
synthesized spectral lines with methods closely resembling those used in BiSON
and SONG observations. Consequently, the theoretical luminosity to radial
velocity amplitude ratios are directly comparable with corresponding
observations. For the Sun, we predict theoretical ratios of 21.0 and 23.7
ppm/[m/s] from BiSON and SONG respectively, in good agreement with observations
19.1 and 21.6 ppm/[m/s]. For $\epsilon$ Tau, we predict K2 and SONG ratios of
48.4 ppm/[m/s], again in good agreement with observations 42.2 ppm/[m/s], and
much improved over the result from conventional empirical scaling relations
which gives 23.2 ppm/[m/s]. This study thus opens the path towards a
quantitative understanding of solar-like oscillations, via detailed modelling
of 3D stellar atmospheres.


Abstract:
  This work intends to analyze the nonlinear stochastic dynamics of
drillstrings in horizontal configuration. For this purpose, it considers a beam
theory, with effects of rotatory inertia and shear deformation, which is
capable of reproducing the large displacements that the beam undergoes. The
friction and shock effects, due to beam/borehole wall transversal impacts, as
well as the force and torque induced by bit-rock interaction, are also
considered in the model. Uncertainties of bit-rock interaction model are taken
into account using a parametric probabilistic approach. Numerical simulations
have shown that the mechanical system of interest has a very rich nonlinear
stochastic dynamics, which generate phenomena such as bit-bounce, stick-slip,
and transverse impacts. A study aiming to maximize the drilling process
efficiency, varying drillstring velocities of translation and rotation is
presented. Also, the work presents the definition and solution of two
optimizations problems, one deterministic and one robust, where the objective
is to maximize drillstring rate of penetration into the soil respecting its
structural limits.


Abstract:
  Future long-baseline neutrino experiments will measure neutrino oscillation
properties with unprecedented precision and will search for clear signatures of
CP violation in the leptonic sector. Near detectors can measure the
neutrino-electron scattering with high statistics, giving the chance for its
precise measurement. We study, in this work, the expectations for the
measurement of radiative corrections in this process. We focus on the
determination of contributions that are exclusive to the neutrino channels,
particularly on the neutrino charge radius. We illustrate how the perspectives
in a first clear measurement of this effective quantity are encouraging.


Abstract:
  Pre-trained multilingual language encoders, such as multilingual BERT and
XLM-R, show great potential for zero-shot cross-lingual transfer. However,
these multilingual encoders do not precisely align words and phrases across
languages. Especially, learning alignments in the multilingual embedding space
usually requires sentence-level or word-level parallel corpora, which are
expensive to be obtained for low-resource languages. An alternative is to make
the multilingual encoders more robust; when fine-tuning the encoder using
downstream task, we train the encoder to tolerate noise in the contextual
embedding spaces such that even if the representations of different languages
are not aligned well, the model can still achieve good performance on zero-shot
cross-lingual transfer. In this work, we propose a learning strategy for
training robust models by drawing connections between adversarial examples and
the failure cases of zero-shot cross-lingual transfer. We adopt two widely used
robust training methods, adversarial training and randomized smoothing, to
train the desired robust model. The experimental results demonstrate that
robust training improves zero-shot cross-lingual transfer on text
classification tasks. The improvement is more significant in the generalized
cross-lingual transfer setting, where the pair of input sentences belong to two
different languages.


Abstract:
  Temporal abstractions in the form of options have been shown to help
reinforcement learning (RL) agents learn faster. However, despite prior work on
this topic, the problem of discovering options through interaction with an
environment remains a challenge. In this paper, we introduce a novel
meta-gradient approach for discovering useful options in multi-task RL
environments. Our approach is based on a manager-worker decomposition of the RL
agent, in which a manager maximises rewards from the environment by learning a
task-dependent policy over both a set of task-independent discovered-options
and primitive actions. The option-reward and termination functions that define
a subgoal for each option are parameterised as neural networks and trained via
meta-gradients to maximise their usefulness. Empirical analysis on gridworld
and DeepMind Lab tasks show that: (1) our approach can discover meaningful and
diverse temporally-extended options in multi-task RL domains, (2) the
discovered options are frequently used by the agent while learning to solve the
training tasks, and (3) that the discovered options help a randomly initialised
manager learn faster in completely new tasks.


Abstract:
  The conceptualization of a claim lies at the core of argument mining. The
segregation of claims is complex, owing to the divergence in textual syntax and
context across different distributions. Another pressing issue is the
unavailability of labeled unstructured text for experimentation. In this paper,
we propose LESA, a framework which aims at advancing headfirst into expunging
the former issue by assembling a source-independent generalized model that
captures syntactic features through part-of-speech and dependency embeddings,
as well as contextual features through a fine-tuned language model. We resolve
the latter issue by annotating a Twitter dataset which aims at providing a
testing ground on a large unstructured dataset. Experimental results show that
LESA improves upon the state-of-the-art performance across six benchmark claim
datasets by an average of 3 claim-F1 points for in-domain experiments and by 2
claim-F1 points for general-domain experiments. On our dataset too, LESA
outperforms existing baselines by 1 claim-F1 point on the in-domain experiments
and 2 claim-F1 points on the general-domain experiments. We also release
comprehensive data annotation guidelines compiled during the annotation phase
(which was missing in the current literature).


Abstract:
  A star edge coloring of a graph $G$ is a proper edge coloring of $G$ such
that every path and cycle of length four in $G$ uses at least three different
colors. The star chromatic index of $G$, is the smallest integer $k$ for which
$G$ admits a star edge coloring with $k$ colors.
  In this paper, we obtain tight upper bound
$\left\lfloor\frac{3\Delta}{2}\right\rfloor+2$ for the star chromatic index of
every Halin graph, that proves the conjecture of Dvo{\v{r}}{\'a}k et al. (J
Graph Theory, 72 (2013), 313--326) for cubic Halin graphs.


Abstract:
  Having in mind extensions of 2D holography beyond the Jackiw-Teitelboim model
we propose holographic counterterms and asymptotic conditions for a family of
asymptotically AdS$_2$ dilaton gravity models leading to a consistent
variational problem and a finite on-shell action. We show the presence of
asymptotic Virasoro symmetries in all these models. The Schwarzian action
generates (a part) of the equations of motion governing the asymptotic degrees
of freedom. We also analyse the applicability of various entropy formulae. By a
dilaton-dependent conformal transformation our results are extended to an even
larger class of models having exotic asymptotic behavior. We also analyse
asymptotic symmetries for some other classes of dilaton gravities without,
however, constructing holographic counterterms.


Abstract:
  In part I of the foundation of the hyperunified field theory, we have shown
the presence of entangled hyperqubit-spinor field as fundamental building block
with appearance of Minkowski hyper-spacetime as free-motion spacetime and
emergence of inhomogeneous hyperspin symmetry as fundamental symmetry. In this
paper as part II, we demonstrate by following along gauge invariance principle
and scaling invariance hypothesis that the inhomogeneous hyperspin gauge
symmetry and scaling gauge symmetry govern fundamental interactions and reveal
the nature of gravity and spacetime. With the fiber bundle structure of biframe
hyper-spacetime and emergence of non-commutative geometry, we explore
methodically the gauge-geometry duality and genesis of gravitational
interaction in locally flat gravigauge hyper-spacetime. A whole hyperunified
field theory in 19-dimensional hyper-spacetime is established to unify not only
all discovered leptons and quarks into hyperunified qubit-spinor field but also
all known basic forces into hyperunified interaction governed by inhomogeneous
hyperspin gauge symmetry. We present a systematic investigate on the
hyperunified field theory with deriving the dynamics of fundamental fields as
basic laws of nature and conservation laws relative to basic symmetries and
showing Higgs-like bosons and three families of lepton-quark states. We provide
a detailed analysis on inflationary early universe with evolving graviscalar
field and a discussion on scaling gauge field as dark matter candidate and
$\cQ_c$-spin scalar field as source of dark energy.


Abstract:
  A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme
is proposed in this paper, by embedding a recently introduced adaptive denoiser
using the Schroedinger equation's solutions of quantum physics. The potential
of the proposed model is studied for Poisson image deconvolution, which is a
common problem occurring in number of imaging applications, such as limited
photon acquisition or X-ray computed tomography. Numerical results show the
efficiency and good adaptability of the proposed scheme compared to recent
state-of-the-art techniques, for both high and low signal-to-noise ratio
scenarios. This performance gain regardless of the amount of noise affecting
the observations is explained by the flexibility of the embedded quantum
denoiser constructed without anticipating any prior statistics about the noise,
which is one of the main advantages of this method. The main novelty of this
work resided in the integration of a modified quantum denoiser into the
PnP-ADMM framework and the numerical proof of convergence of the resulting
algorithm.


Abstract:
  Automatic recognition of an online series of unsegmented actions requires a
method for segmentation that determines when an action starts and when it ends.
In this paper, a novel approach for recognizing unsegmented actions in online
test experiments is proposed. The method uses self-organizing neural networks
to build a three-layer cognitive architecture. The unique features of an action
sequence are represented as a series of elicited key activations by the
first-layer self-organizing map. An average length of a key activation vector
is calculated for all action sequences in a training set and adjusted in
learning trials to generate input patterns to the second-layer self-organizing
map. The pattern vectors are clustered in the second layer, and the clusters
are then labeled by an action identity in the third layer neural network. The
experiment results show that although the performance drops slightly in online
experiments compared to the offline tests, the ability of the proposed
architecture to deal with the unsegmented action sequences as well as the
online performance makes the system more plausible and practical in real-case
scenarios.


Abstract:
  The vertex connectivity of an $m$-edge $n$-vertex undirected graph is the
smallest number of vertices whose removal disconnects the graph, or leaves only
a singleton vertex. In this paper, we give a reduction from the vertex
connectivity problem to a set of maxflow instances. Using this reduction, we
can solve vertex connectivity in $\tilde O(m^{\alpha})$ time for any $\alpha
\ge 1$, if there is a $m^{\alpha}$-time maxflow algorithm. Using the current
best maxflow algorithm that runs in $m^{4/3+o(1)}$ time (Kathuria, Liu and
Sidford, FOCS 2020), this yields a $m^{4/3+o(1)}$-time vertex connectivity
algorithm. This is the first improvement in the running time of the vertex
connectivity problem in over 20 years, the previous best being an $\tilde
O(mn)$-time algorithm due to Henzinger, Rao, and Gabow (FOCS 1996). Indeed, no
algorithm with an $o(mn)$ running time was known before our work, even if we
assume an $\tilde O(m)$-time maxflow algorithm. Our new technique is robust
enough to also improve the best $\tilde O(mn)$-time bound for directed vertex
connectivity to $mn^{1-1/12+o(1)}$ time


Abstract:
  Users of OCR systems, from different institutions and scientific disciplines,
prefer and produce different transcription styles. This presents a problem for
training of consistent text recognition neural networks on real-world data. We
propose to extend existing text recognition networks with a Transcription Style
Block (TSB) which can learn from data to switch between multiple transcription
styles without any explicit knowledge of transcription rules. TSB is an
adaptive instance normalization conditioned by identifiers representing
consistently transcribed documents (e.g. single document, documents by a single
transcriber, or an institution). We show that TSB is able to learn completely
different transcription styles in controlled experiments on artificial data, it
improves text recognition accuracy on large-scale real-world data, and it
learns semantically meaningful transcription style embedding. We also show how
TSB can efficiently adapt to transcription styles of new documents from
transcriptions of only a few text lines.


Abstract:
  We present an extremely simple Ultra-Resolution Style Transfer framework,
termed URST, to flexibly process arbitrary high-resolution images (e.g.,
10000x10000 pixels) style transfer for the first time. Most of the existing
state-of-the-art methods would fall short due to massive memory cost and small
stroke size when processing ultra-high resolution images. URST completely
avoids the memory problem caused by ultra-high resolution images by (1)
dividing the image into small patches and (2) performing patch-wise style
transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN
can extract thumbnail features' normalization statistics and apply them to
small patches, ensuring the style consistency among different patches.
  Overall, the URST framework has three merits compared to prior arts. (1) We
divide input image into small patches and adopt TIN, successfully transferring
image style with arbitrary high-resolution. (2) Experiments show that our URST
surpasses existing SOTA methods on ultra-high resolution images benefiting from
the effectiveness of the proposed stroke perceptual loss in enlarging the
stroke size. (3) Our URST can be easily plugged into most existing style
transfer methods and directly improve their performance even without training.
Code is available at https://git.io/URST.


Abstract:
  This paper considers the problem of real-time control and learning in dynamic
systems subjected to uncertainties. Adaptive approaches are proposed to address
the problem, which are combined to with methods and tools in Reinforcement
Learning (RL) and Machine Learning (ML). Algorithms are proposed in
continuous-time that combine adaptive approaches with RL leading to online
control policies that guarantee stable behavior in the presence of parametric
uncertainties that occur in real-time. Algorithms are proposed in discrete-time
that combine adaptive approaches proposed for parameter and output estimation
and ML approaches proposed for accelerated performance that guarantee stable
estimation even in the presence of time-varying regressors, and for accelerated
learning of the parameters with persistent excitation. Numerical validations of
all algorithms are carried out using a quadrotor landing task on a moving
platform and benchmark problems in ML. All results clearly point out the
advantage of adaptive approaches for real-time control and learning.


Abstract:
  Neural Architecture Search (NAS) has recently gained increased attention, as
a class of approaches that automatically searches in an input space of network
architectures. A crucial part of the NAS pipeline is the encoding of the
architecture that consists of the applied computational blocks, namely the
operations and the links between them. Most of the existing approaches either
fail to capture the structural properties of the architectures or use
hand-engineered vector to encode the operator information. In this paper, we
propose the replacement of fixed operator encoding with learnable
representations in the optimization process. This approach, which effectively
captures the relations of different operations, leads to smoother and more
accurate representations of the architectures and consequently to improved
performance of the end task. Our extensive evaluation in ENAS benchmark
demonstrates the effectiveness of the proposed operation embeddings to the
generation of highly accurate models, achieving state-of-the-art performance.
Finally, our method produces top-performing architectures that share similar
operation and graph patterns, highlighting a strong correlation between the
structural properties of the architecture and its performance.


Abstract:
  Learned iterative shrinkage thresholding algorithm (LISTA), which adopts deep
learning techniques to learn optimal algorithm parameters from labeled training
data, can be successfully applied to small-scale multidimensional harmonic
retrieval (MHR) problems. However, LISTA computationally demanding for
large-scale MHR problems because the matrix size of the learned mutual
inhibition matrix exhibits quadratic growth with the signal length. These large
matrices consume costly memory/computation resources and require a huge amount
of labeled data for training, restricting the applicability of the LISTA
method. In this paper, we show that the mutual inhibition matrix of a MHR
problem naturally has a Toeplitz structure, which means that the degrees of
freedom (DoF) of the matrix can be reduced from a quadratic order to a linear
order. By exploiting this characteristic, we propose a structured
LISTA-Toeplitz network, which imposes a Toeplitz structure restriction on the
mutual inhibition matrices and applies linear convolution instead of the
matrix-vector multiplication involved in the traditional LISTA network. Both
simulation and field test for air target detection with radar are carried out
to validate the performance of the proposed network. For small-scale MHR
problems, LISTAToeplitz exhibits close or even better recovery accuracy than
traditional LISTA, while the former significantly reduces the network
complexity and requires much less training data. For large-scale MHR problems,
where LISTA is difficult to implement due to the huge size of the mutual
inhibition matrices, our proposed LISTA-Toeplitz still enjoys desirable
recovery performance.


Abstract:
  Describing networks geometrically through low-dimensional latent metric
spaces has helped design efficient learning algorithms, unveil network
symmetries and study dynamical network processes. However, latent space
embeddings are limited to specific classes of networks because incompatible
metric spaces generally result in information loss. Here, we study arbitrary
networks geometrically by defining a dynamic edge curvature measuring the
similarity between pairs of dynamical network processes seeded at nearby nodes.
We show that the evolution of the curvature distribution exhibits gaps at
characteristic timescales indicating bottleneck-edges that limit information
spreading. Importantly, curvature gaps are robust to large fluctuations in node
degrees, encoding communities until the phase transition of detectability,
where spectral and node-clustering methods fail. Using this insight, we derive
geometric modularity to find multiscale communities based on deviations from
constant network curvature in generative and real-world networks, significantly
outperforming most previous methods. Our work suggests using network geometry
for studying and controlling the structure of and information spreading on
networks.


Abstract:
  During the last two decades, we have progressively turned to the Internet and
social media to find news, entertain conversations and share opinion. Recently,
OpenAI has developed a ma-chine learning system called GPT-2 for Generative
Pre-trained Transformer-2, which can pro-duce deepfake texts. It can generate
blocks of text based on brief writing prompts that look like they were written
by humans, facilitating the spread false or auto-generated text. In line with
this progress, and in order to counteract potential dangers, several methods
have been pro-posed for detecting text written by these language models. In
this paper, we propose a transfer learning based model that will be able to
detect if an Arabic sentence is written by humans or automatically generated by
bots. Our dataset is based on tweets from a previous work, which we have
crawled and extended using the Twitter API. We used GPT2-Small-Arabic to
generate fake Arabic Sentences. For evaluation, we compared different recurrent
neural network (RNN) word embeddings based baseline models, namely: LSTM,
BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new
transfer-learning model has obtained an accuracy up to 98%. To the best of our
knowledge, this work is the first study where ARABERT and GPT2 were combined to
detect and classify the Arabic auto-generated texts.


Abstract:
  For $s<r$, let $B_{r,s}$ be the graph consisting of two copies of $K_r$,
which share exactly $s$ vertices. Denote by $ex(n, K_r, B_{r,s})$ the maximum
number of copies of $K_r$ in a $B_{r,s}$-free graph on $n$ vertices. In 1976,
Erd\H{o}s and S\'{o}s determined $ex(n,K_3,B_{3,1})$. Recently, Gowers and
Janzer showed that $ex(n,K_r,B_{r,r-1})=n^{r-1-o(1)}$. It is a natural question
to ask for $ex(n,K_r,B_{r,s})$ for general $r$ and $s$. In this paper, we
mainly consider the problem for $s=1$. Utilizing the Zykov's symmetrization, we
show that $ex(n,K_4, B_{4,1})=\lfloor (n-2)^2/4\rfloor$ for $n\geq 45$. For
$r\geq 5$ and $n$ sufficiently large, by the F\"{u}redi's structure theorem we
show that $ex(n,K_r,B_{r,1}) =\mathcal{N}(K_{r-2},T_{r-2}(n-2))$, where
$\mathcal{N}(K_{r-2},T_{r-2}(n-2))$ represents the number of copies of
$K_{r-2}$ in the $(r-2)$-partite Tur\'{a}n graph on $n-2$ vertices.


Abstract:
  With concurrency being integral to most software systems, developers combine
high-level concurrency models in the same application to tackle each problem
with appropriate abstractions. While languages and libraries offer a wide range
of concurrency models, debugging support for applications that combine them has
not yet gained much attention. Record & replay aids debugging by
deterministically reproducing recorded bugs, but is typically designed for a
single concurrency model only. This paper proposes a practical
concurrency-model-agnostic record & replay approach for multi-paradigm
concurrent programs, i.e. applications that combine concurrency models. Our
approach traces high-level nondeterministic events by using a uniform
model-agnostic trace format and infrastructure. This enables orderingbased
record & replay support for a wide range of concurrency models, and thereby
enables debugging of applications that combine them. In addition, it allows
language implementors to add new concurrency models and reuse the
model-agnostic record & replay support. We argue that a
concurrency-model-agnostic record & replay is practical and enables advanced
debugging support for a wide range of concurrency models. The evaluation shows
that our approach is expressive and flexible enough to support record & replay
of applications using threads & locks, communicating event loops, communicating
sequential processes, software transactional memory and combinations of those
concurrency models. For the actor model, we reach recording performance
competitive with an optimized special-purpose record & replay solution. The
average recording overhead on the Savina actor benchmark suite is 10% (min. 0%,
max. 23%). The performance for other concurrency models and combinations
thereof is at a similar level. We believe our concurrency-model-agnostic
approach helps developers of applications that mix and match concurrency
models. We hope that this substrate inspires new tools and languages making
building and maintaining of multi-paradigm concurrent applications simpler and
safer.


Abstract:
  In comparison to the interpretation of classification models, the explanation
of sequence generation models is also an important problem, however it has seen
little attention. In this work, we study model-agnostic explanations of a
representative text generation task -- dialogue response generation. Dialog
response generation is challenging with its open-ended sentences and multiple
acceptable responses. To gain insights into the reasoning process of a
generation model, we propose anew method, local explanation of response
generation (LERG) that regards the explanations as the mutual interaction of
segments in input and output sentences. LERG views the sequence prediction as
uncertainty estimation of a human response and then creates explanations by
perturbing the input and calculating the certainty change over the human
response. We show that LERG adheres to desired properties of explanations for
text generation including unbiased approximation, consistency and cause
identification. Empirically, our results show that our method consistently
improves other widely used methods on proposed automatic- and human- evaluation
metrics for this new task by 4.4-12.8%. Our analysis demonstrates that LERG can
extract both explicit and implicit relations between input and output segments.


Abstract:
  Signal estimation in the presence of background noise is a common problem in
several scientific disciplines. An 'On/Off' measurement is performed when the
background itself is not known, being estimated from a background control
sample. The 'frequentist' and Bayesian approaches for signal estimation in
On/Off measurements are reviewed and compared, focusing on the weakness of the
former and on the advantages of the latter in correctly addressing the
Poissonian nature of the problem. In this work, we devise a novel
reconstruction method, dubbed BASiL (Bayesian Analysis including Single-event
Likelihoods), for estimating the signal rate based on the Bayesian formalism.
It uses information on event-by-event individual parameters and their
distribution for the signal and background population. Events are thereby
weighted according to their likelihood of being a signal or a background event
and background suppression can be achieved without performing fixed fiducial
cuts. Throughout the work, we maintain a general notation, that allows to apply
the method generically, and provide a performance test using real data and
simulations of observations with the MAGIC telescopes, as demonstration of the
performance for Cherenkov telescopes. BASiL allows to estimate the signal more
precisely, avoiding loss of exposure due to signal extraction cuts. We expect
its applicability to be straightforward in similar cases.


Abstract:
  We extend the Schoenberg correspondence for universal independences by
Sch\"urmann and Vo{\ss} to the multivariate setting of Manzel and Sch\"urmann,
covering, e.g., Voiculescu's bifreeness as well as Bo\.zejko and Speicher's
c-free independence. At the same time, we free the proof in the univariate
situation from its dependence on Muraki's classification theorem (which states
that there are only five univariate independences: tensor, free, Boolean,
monotone, and antimonotone independence). In contrast to the univariate case,
the class of multivariate independences considered here is infinite and not yet
fully understood. Furthermore, helpful tools such quantum stochastic calculi
and Fock space constructions, which are used by Sch\"urmann and Vo{\ss} to
prove Schoenberg correspondence in the univariate case, are not available in
general. This problem is overcome by relating convolution exponentials with
respect to different independences to each other, which ultimately allows to
reduce all Schoenberg correspondences for multivariate independences to the
case of convolution semigroups on *-bialgebras.


Abstract:
  In this paper we give two characterizations of the $p \times q$-grid graphs
as co-edge-regular graphs with four distinct eigenvalues.


Abstract:
  Developments in statistical learning have fueled the analysis of
high-dimensional time series. However, even in low-dimensional contexts the
issues arising from ill-conditioned regression problems are well-known. Because
linear time series modeling is naturally prone to such issues, I propose to
apply ridge regression to the estimation of dense VAR models. Theoretical
non-asymptotic results concerning the addition of a ridge-type penalty to the
least squares estimator are discussed, while standard asymptotic and inference
techniques are proven to be valid under mild conditions on the regularizer. The
proposed estimator is then applied to the problem of sieve approximation of
VAR($\infty$) processes under moderately harsh sample sizes. Simulation
evidence is used to discuss the small sample properties of the ridge estimator
(RLS) when compared to least squares and local projection approaches: I use a
Monte Carlo exercise to argue that RLS with a lag-adapted cross-validated
regularizer achieve meaningfully better performance in recovering impulse
response functions and asymptotic confidence intervals than other common
approaches.


Abstract:
  This submission has been removed by arXiv administrators as the submitter did
not have the right to agree to the license at the time of submission.


Abstract:
  Dwarf carbon (dC) stars, main sequence stars showing carbon molecular bands,
are enriched by mass transfer from a previous asymptotic-giant-branch (AGB)
companion, which has since evolved to a white dwarf. While previous studies
have found radial-velocity variations for large samples of dCs, there are still
relatively few dC orbital periods in the literature and no dC eclipsing
binaries have yet been found. Here, we analyze photometric light curves from
DR5 of the Zwicky Transient Facility for a sample of 944 dC stars. From these
light curves, we identify 34 periodically variable dC stars. Remarkably, of the
periodic dCs, 82\% have periods less than two days. We also provide
spectroscopic follow-up for four of these periodic systems, measuring radial
velocity variations in three of them. Short-period dCs are almost certainly
post-common-envelope binary systems, since the periodicity is most likely
related to the orbital period, with tidally locked rotation and photometric
modulation on the dC either from spots or from ellipsoidal variations. We
discuss evolutionary scenarios that these binaries may have taken to accrete
sufficient C-rich material while avoiding truncation of the thermally pulsing
AGB phase needed to provide such material in the first place. We compare these
dCs to common-envelope models to show that dC stars probably cannot accrete
enough C-rich material during the common-envelope phase, suggesting another
mechanism like wind-Roche lobe overflow is necessary. The periodic dCs in this
paper represent a prime sample for spectroscopic follow-up and for comparison
to future models of wind-Roche lobe overflow mass transfer.


Abstract:
  It is of paramount importance to achieve efficient data collection in the
Internet of Things (IoT). Due to the inherent structural properties (e.g.,
sparsity) existing in many signals of interest, compressive sensing (CS)
technology has been extensively used for data collection in IoT to improve both
accuracy and energy efficiency. Apart from the existing works which leverage CS
as a channel coding scheme to deal with data loss during transmission, some
recent results have started to employ CS as a source coding strategy. The
frequently used projection matrices in these CS-based source coding schemes
include dense random matrices (e.g., Gaussian matrices or Bernoulli matrices)
and structured matrices (e.g., Toeplitz matrices). However, these matrices are
either difficult to be implemented on resource-constrained IoT sensor nodes or
have limited applicability. To address these issues, in this paper, we design a
novel simple and efficient projection matrix, named sparse Gaussian matrix,
which is easy and resource-saving to be implemented in practical IoT
applications. We conduct both theoretical analysis and experimental evaluation
of the designed sparse Gaussian matrix. The results demonstrate that employing
the designed projection matrix to perform CS-based source coding could
significantly save time and memory cost while ensuring satisfactory signal
recovery performance.


Abstract:
  This paper introduces an agent-centric approach to handle novelty in the
visual recognition domain of handwriting recognition (HWR). An ideal
transcription agent would rival or surpass human perception, being able to
recognize known and new characters in an image, and detect any stylistic
changes that may occur within or across documents. A key confound is the
presence of novelty, which has continued to stymie even the best machine
learning-based algorithms for these tasks. In handwritten documents, novelty
can be a change in writer, character attributes, writing attributes, or overall
document appearance, among other things. Instead of looking at each aspect
independently, we suggest that an integrated agent that can process known
characters and novelties simultaneously is a better strategy. This paper
formalizes the domain of handwriting recognition with novelty, describes a
baseline agent, introduces an evaluation protocol with benchmark data, and
provides experimentation to set the state-of-the-art. Results show feasibility
for the agent-centric approach, but more work is needed to approach
human-levels of reading ability, giving the HWR community a formal basis to
build upon as they solve this challenging problem.


Abstract:
  Change detection, which aims to distinguish surface changes based on
bi-temporal images, plays a vital role in ecological protection and urban
planning. Since high resolution (HR) images cannot be typically acquired
continuously over time, bi-temporal images with different resolutions are often
adopted for change detection in practical applications. Traditional
subpixel-based methods for change detection using images with different
resolutions may lead to substantial error accumulation when HR images are
employed; this is because of intraclass heterogeneity and interclass
similarity. Therefore, it is necessary to develop a novel method for change
detection using images with different resolutions, that is more suitable for HR
images. To this end, we propose a super-resolution-based change detection
network (SRCDNet) with a stacked attention module. The SRCDNet employs a super
resolution (SR) module containing a generator and a discriminator to directly
learn SR images through adversarial learning and overcome the resolution
difference between bi-temporal images. To enhance the useful information in
multi-scale features, a stacked attention module consisting of five
convolutional block attention modules (CBAMs) is integrated to the feature
extractor. The final change map is obtained through a metric learning-based
change decision module, wherein a distance map between bi-temporal features is
calculated. The experimental results demonstrate the superiority of the
proposed method, which not only outperforms all baselines -with the highest F1
scores of 87.40% on the building change detection dataset and 92.94% on the
change detection dataset -but also obtains the best accuracies on experiments
performed with images having a 4x and 8x resolution difference. The source code
of SRCDNet will be available at https://github.com/liumency/SRCDNet.


Abstract:
  In the proofs of most cases of the Andr\'e-Oort conjecture, there are two
different steps whose effectivity is unclear: the use of generalizations of
Brauer-Siegel and the use of Pila-Wilkie. Only the case of curves in ${\bf
C}^2$ is currently known effectively (by other methods).
  We give an effective proof of Andr\'e-Oort for non-compact curves in every
Hilbert modular surface and every Hilbert modular variety of odd genus (under a
minor generic simplicity condition). In particular we show that in these cases
the first step may be replaced by the endomorphism estimates of W\"ustholz and
the second author together with the specialization method of Andr\'e via
G-functions, and the second step may be effectivized using the Q-functions of
Novikov, Yakovenko and the first author.


Abstract:
  The COVID-19 crisis has shown that we can only prevent the risk of mass
contagion through timely, large-scale, coordinated, and decisive actions.
However, frequently the models used by experts [from whom decision-makers get
their main advice] focus on a single perspective [for example, the
epidemiological one] and do not consider many of the multiple forces that
affect the COVID-19 outbreak patterns. The epidemiological, socioeconomic, and
human mobility context of COVID-19 can be considered as a complex adaptive
system. So, these interventions (for example, lock-downs) could have many
and/or unexpected ramifications. This situation makes it difficult to
understand the overall effect produced by any public policy measure and,
therefore, to assess its real effectiveness and convenience. By using mobile
phone data, socioeconomic data, and COVID-19 cases data recorded throughout the
pandemic development, we aim to understand and explain [make sense of] the
observed heterogeneous regional patterns of contagion across time and space. We
will also consider the causal effects produced by confinement policies by
developing data-based models to explore, simulate, and estimate these policies'
effectiveness. We intend to develop a methodology to assess and improve public
policies' effectiveness associated with the fight against the pandemic,
emphasizing its convenience, the precise time of its application, and
extension. The contributions of this work can be used regardless of the region.
The only likely impediment is the availability of the appropriate data.


Abstract:
  We establish an analog of the Hardy-Ramanujan inequality for counting members
of sifted sets with a given number of distinct prime factors. In particular, we
establish a bound for the number of shifted primes p+a below x with k distinct
prime factors, uniformly for all positive integers k.


Abstract:
  Measuring time lags between time-series or lighcurves at different
wavelengths from a variable or transient source in astronomy is an essential
probe of physical mechanisms causing multiwavelength variability. Time-lags are
typically quantified using discrete correlation functions (DCF) which are
appropriate for linear relationships. However, in variable sources like X-ray
binaries, active galactic nuclei (AGN) and other accreting systems, the
radiative processes and the resulting multiwavelength lightcurves often have
non-linear relationships. For such systems it is more appropriate to use
non-linear information-theoretic measures of causation like mutual information,
routinely used in other disciplines. We demonstrate with toy models loopholes
of using the standard DCF & show improvements when using the mutual information
correlation function (MICF). For non-linear correlations, the latter accurately
& sharply identifies the lag components as opposed to the DCF which can be
erroneous. Following that we apply the MICF to the multiwavelength lightcurves
of AGN NGC 4593. We find that X-ray fluxes lead UVW2 fluxes by ~0.2 days,
closer to model predictions from reprocessing by the accretion disk than the
DCF estimate. The uncertainties with the current lightcurves are too large
though to rule out -ve lags. Additionally, we find another delay component at
~-1 day i.e. UVW2 leading X-rays consistent with inward propagating
fluctuations in the accretion disk scenario. This is not detected by the DCF.
Keeping in mind the non-linear relation between X-ray & UVW2, this is worthy of
further theoretical investigation. From both toy models & real observations, it
is clear that the mutual information based estimator is highly sensitive to
complex non-linear correlations. With sufficiently high temporal resolution, we
will precisely detect each of the lag features corresponding to these
correlations.


Abstract:
  We now turn to understanding the impact that COVID-19 had on the personal
productivity and well-being of information workers as their work practices were
impacted by remote work. This chapter overviews people's productivity,
satisfaction, and work patterns, and shows that the challenges and benefits of
remote work are closely linked. Looking forward, the infrastructure surrounding
work will need to evolve to help people adapt to the challenges of remote and
hybrid work.


Abstract:
  We present a method for automatically labelling all faces in video archives,
such as TV broadcasts, by combining multiple evidence sources and multiple
modalities (visual and audio). We target the problem of ever-growing online
video archives, where an effective, scalable indexing solution cannot require a
user to provide manual annotation or supervision. To this end, we make three
key contributions: (1) We provide a novel, simple, method for determining if a
person is famous or not using image-search engines. In turn this enables a
face-identity model to be built reliably and robustly, and used for high
precision automatic labelling; (2) We show that even for less-famous people,
image-search engines can then be used for corroborative evidence to accurately
label faces that are named in the scene or the speech; (3) Finally, we
quantitatively demonstrate the benefits of our approach on different video
domains and test settings, such as TV shows and news broadcasts. Our method
works across three disparate datasets without any explicit domain adaptation,
and sets new state-of-the-art results on all the public benchmarks.


Abstract:
  The main aim of this paper is to investigate Darboux rectifying curves on a
smooth surface immersed in the Euclidean space. First, we discuss the component
of the position vector of a Darboux rectifying curve on a smooth immersed
surface under the isometry of surfaces. Next we find a sufficient condition for
the conformal invariance of Darboux rectifying curve.


Abstract:
  An Euler tour in a hypergraph (also called a rank-2 universal cycle or
1-overlap cycle in the context of designs) is a closed walk that traverses
every edge exactly once. In this paper, we define a covering $k$-hypergraph to
be a non-empty $k$-uniform hypergraph in which every $(k-1)$-subset of vertices
appear together in at least one edge. We then show that every covering
$k$-hypergraph, for $k\geq 3$, admits an Euler tour if and only if it has at
least two edges.


Abstract:
  The quasi-chemical organization of the potential distribution theorem --
molecular quasi-chemical theory (QCT) -- enables practical calculations and
also provides a conceptual framework for molecular hydration phenomena. QCT can
be viewed from multiple perspectives: (a) As a way to regularize an
ill-conditioned statistical thermodynamic problem; (b) As an introduction of
and emphasis on the neighborship characteristics of a solute of interest; (c)
Or as a way to include accurate electronic structure descriptions of
near-neighbor interactions in defensible statistical thermodynamics by clearly
defining neighborship clusters. The theory has been applied to solutes of a
wide range of chemical complexity, ranging from ions that interact with water
with both long-ranged and chemically intricate short-ranged interactions, to
solutes that interact with water solely through traditional van~der~Waals
interations, and including water itself. The solutes range in variety from
monoatomic ions to chemically heterogeneous macromolecules. A notable feature
of QCT is that in applying the theory to this range of solutes, the theory
itself provides guidance on the necessary approximations and simplifications
that can facilitate the calculations. In this Perspective, we develop these
ideas and document them with examples that reveal the insights that can be
extracted using the QCT formulation.


Abstract:
  In optical communications, space-division multiplexing is a promising
strategy to augment the fiber network capacity. It relies on modern fiber
designs that support the propagation of multiple spatial modes. One of these
fibers, the ring-core fiber (RCF), is able to propagate modes that carry
orbital angular momentum (OAM), and has been shown to enhance not only
classical, but also quantum communication systems. Typically, the RCF spatial
modes are used as orthogonal transmission channels for data streams that are
coupled into the fiber using different Laguerre-Gaussian (LG) beams. Here, we
study the optimal conditions to multiplex information into ring-core fibers in
this scheme. We determine which are the most relevant LG beams to be
considered, and how their coupling efficiency can be maximized by properly
adjusting the beam width with respect to the fiber parameters. Our results show
that the coupling efficiency depends upon the OAM value, and that this can
limit the achievable transmission rates. In this regard, we show that LG beams
are not the optimal choice to couple information into RCF. Rather, another
class of OAM-carrying beam, the perfect vortex beam, allows for nearly perfect
coupling efficiencies for all spatial modes supported by these fibers.


Abstract:
  We study the clustering of HI intensity maps produced from simulations with a
focus on baryonic acoustic oscillations (BAO) and the effects induced by
telescope beam smoothing and foreground cleaning. We start by creating a HI
catalogue at $z=1.321$ based on the Semi-Analytic Galaxy Evolution (SAGE) model
applied to the UNIT simulations. With this catalogue we investigate the
relation between model HI and the dark matter haloes and we also study the
abundance of HI, $\Omega_{\rm HI}$, predicted by this model. We then create
synthetic HI intensity maps with a Nearest-Grid-Point approach. In order to
simulate the telescope beam effect, a Gaussian smoothing is applied on the
plane perpendicular to the line of sight. The effect of foreground removal
methods is simulated by exponentially damping the largest wavelength Fourier
modes on the radial direction. We study the anisotropic 2-point correlation
function (2PCF) $\xi(r_\perp,r_\parallel)$ and how it is affected by the
aforementioned observational effects. In order to better isolate the BAO
signal, we study several 2PCF $\mu$-wedges (with a restricted range of
orientations $\mu$) tailored to address the systematics effects and we compare
them with different definitions of radial 2PCFs. Finally, we discuss our
findings in the context of an SKA-like survey, finding a clear BAO signal in
most of the estimators here proposed.