Abstract:
  Several methods are available in the literature to stochastically compare
random variables and random vectors. We introduce the notion of asymptotic
stochastic order for random processes and define four such orders. Various
properties and interrelations of the orders are discussed. Sufficient
conditions for these orders to hold for certain stochastic processes, evolving
from some statistical entities of interest, are derived.


Abstract:
  We reduce the problem of quantization of the Yang-Mills field Hamiltonian to
a problem for defining a probability measure on an infinite-dimensional space
of gauge equivalence classes of connections on $\mathbb{R}^3$. We suggest a
formally self-adjoint expression for the quantized Yang-Mills Hamiltonian as an
operator on the corresponding Lebesgue $L^2$-space. In the case when the
Yang-Mills field is associated to the Abelian group $U(1)$ we define the
probability measure which depends on two real parameters $m>0$ and $c\neq 0$.
This yields a non-standard quantization of the Hamiltonian of the
electromagnetic field, and the associated probability measure is Gaussian. The
corresponding quantized Hamiltonian is a self-adjoint operator in a Fock space
the spectrum of which is $\{0\}\cup[\frac12m, \infty)$, i.e. it has a gap.


Abstract:
  The vast majority of stars in galaxy groups are contained within their
constituent galaxies. Some small fraction of stars is expected, however, to
follow the global dark matter potential of the group. In compact groups,
interactions between the galaxies should be frequent. This leads to a more
intensive material stripping from the group members, which finally forms an
intra-group light component (IGL). Therefore, the distribution of the IGL
should be related to the distribution of the total mass in the compact group
and its dynamical status. In this study we consider the distribution and
fraction of the IGL in a sample of 36 Hickson compact groups (HCGs). We use
deep observations of these compact groups (down to surface brightness $\sim 28$
mag\,arcsec$^{-2}$ in the $r$ band) obtained with the WISE $28$-inch telescope.
For five HCGs with a bright symmetric IGL component, we carry out
multicomponent photometric decomposition to simultaneously fit the galaxy
profiles and the IGL. For the remaining groups, we only fit the profiles of
their constituent galaxies. We find that the mean surface brightness of the IGL
correlates with the mean morphology of the group: it becomes brighter in the
groups with a larger fraction of early-type galaxies. On the other hand, the
IGL brightness depends on the total luminosity of the group. The IGL profile
tends to have a S\'ersic index $n\sim0.5-1$, which is generally consistent with
the mass density profile of dark matter haloes in compact groups obtained from
cosmological simulations.


Abstract:
  A scheme for the enhanced generation of higher photon-number states is
realized, using an optical time-multiplexing setting that exploits a parametric
down-conversion source for an iterative state generation. We use a quantum
feedback mechanism for already generated photons to induce self-seeding of the
consecutive nonlinear process, enabling us to coherently add photons to the
light that propagates in the feedback loop. The addition can be carried out for
any chosen number of round trips, resulting in a successive buildup of
multiphoton states. Our system is only limited by loop losses. The looped
design is rendered possible by a carefully engineered waveguide source that is
compatible with and preserves the shape of the propagating mode. We compare the
fidelities and success probabilities of our protocol with the common direct
heralding of photon-number states. This comparison reveals that, for same the
fidelity, our feedback-based setup significantly enhances success
probabilities, being vital for an efficient utilization in quantum
technologies. Moreover, quantum characteristics of the produced states are
analyzed, and the flexibility of producing higher photon-number states with our
setup beyond the common direct heralding is demonstrated.


Abstract:
  Attribute-Based Encryption (ABE) is an emerging cryptographic technique that
allows one to embed a fine-grained access control mechanism into encrypted
data. In this paper we propose a novel ABE scheme called SEA-BREW (Scalable and
Efficient Abe with Broadcast REvocation for Wireless networks), which is suited
for Internet of Things (IoT) and Industrial IoT (IIoT) applications. In
contrast to state-of-the-art ABE schemes, ours is capable of securely
performing key revocations with a single short broadcast message, instead of a
number of unicast messages that is linear with the number of nodes. This is
desirable for low-bitrate Wireless Sensor and Actuator Networks (WSANs) which
often are the heart of (I)IoT systems. In SEA-BREW, sensors, actuators, and
users can exchange encrypted data via a cloud server, or directly via wireless
if they belong to the same WSAN. We formally prove that our scheme is secure
also in case of an untrusted cloud server that colludes with a set of users,
under the generic bilinear group model. We show by simulations that our scheme
requires a constant computational overhead on the cloud server with respect to
the complexity of the access control policies. This is in contrast to
state-of-the-art solutions, which require instead a linear computational
overhead.


Abstract:
  Stock trend forecasting, aiming at predicting the stock future trends, is
crucial for investors to seek maximized profits from the stock market. Many
event-driven methods utilized the events extracted from news, social media, and
discussion board to forecast the stock trend in recent years. However, existing
event-driven methods have two main shortcomings: 1) overlooking the influence
of event information differentiated by the stock-dependent properties; 2)
neglecting the effect of event information from other related stocks. In this
paper, we propose a relational event-driven stock trend forecasting (REST)
framework, which can address the shortcoming of existing methods. To remedy the
first shortcoming, we propose to model the stock context and learn the effect
of event information on the stocks under different contexts. To address the
second shortcoming, we construct a stock graph and design a new propagation
layer to propagate the effect of event information from related stocks. The
experimental studies on the real-world data demonstrate the efficiency of our
REST framework. The results of investment simulation show that our framework
can achieve a higher return of investment than baselines.


Abstract:
  The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, recent recommendation methods based on graph
embeddings have shown state-of-the-art performance. In general, these methods
encode latent rating patterns and content features. Differently from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Additionally, our method has the advantage
of providing explanations that involve the coverage of aspect-based opinions
given by users about recommended items.


Abstract:
  We address the question of transport of heat, in out-of-equilibrium systems.
The experimental set-up consists in two coupled granular gas Non-Equilibrium
Steady State (NESS) heat baths, in which Brownian-like rotors are imbedded.
These rotors are electro-mechanically coupled thanks to DC micro-motors,
through a resistor $R$, such that energy flows between them. The average flux
depends linearly in the difference of the baths' temperature. Varying $R$
allows to extrapolate in the non-dissipative coupling limit ($R\rightarrow0$).
We show that, in this limit, the heat flux obeys the Fluctuation Theorem, in a
form proposed by Jarzynski and W\'ojcik in $2004$ for the fluctuations of the
flux between finite size equilibrium heat baths.


Abstract:
  A small-cell network with multiple transmitters and unreliable wireless
backhaul is considered for secrecy enhancement. The small-cell network is
operating under a spectrum sharing agreement with a primary network in a
cognitive radio system. A constraint on the desired outage probability at the
primary receiver is assumed as a part of the spectrum sharing agreement. The
reliability of the wireless backhaul links are modeled by a set of independent
and identically distributed Bernoulli random variables. A sub-optimal and an
optimal small-cell transmitter selection (TS) scheme is proposed to improve the
performance of the system, depending on the availability of channel state
information. Selection schemes are designed for the scenario where knowledge is
available regarding which backhaul links are active. The corresponding secrecy
outage probabilities along with their asymptotic expressions are derived. It is
shown that the secrecy performance is significantly improved compared to the
case where knowledge of the active backhaul links is unavailable.


Abstract:
  This work presents a system identification procedure based on Convolutional
Neural Networks (CNN) for human posture control using the DEC (Disturbance
Estimation and Compensation) parametric model. The modular structure of the
proposed control model inspired the design of a modular identification
procedure, in the sense that the same neural network is used to identify the
parameters of the modules controlling different degrees of freedom. In this way
the presented examples of body sway induced by external stimuli provide several
training samples at once.


Abstract:
  Coronal holes are the observational manifestation of the solar magnetic field
open to the heliosphere and are of pivotal importance for our understanding of
the origin and acceleration of the solar wind. Observations from space missions
such as the Solar Dynamics Observatory now allow us to study coronal holes in
unprecedented detail. Instrumental effects and other factors, however, pose a
challenge to automatically detect coronal holes in solar imagery. The science
community addresses these challenges with different detection schemes. Until
now, little attention has been paid to assessing the disagreement between these
schemes. In this COSPAR ISWAT initiative, we present a comparison of nine
automated detection schemes widely-applied in solar and space science. We
study, specifically, a prevailing coronal hole observed by the Atmospheric
Imaging Assembly instrument on 2018 May 30. Our results indicate that the
choice of detection scheme has a significant effect on the location of the
coronal hole boundary. Physical properties in coronal holes such as the area,
mean intensity, and mean magnetic field strength vary by a factor of up to 4.5
between the maximum and minimum values. We conclude that our findings are
relevant for coronal hole research from the past decade, and are therefore of
interest to the solar and space research community.


Abstract:
  Variance-based global sensitivity analysis, in particular Sobol' analysis, is
widely used for determining the importance of input variables to a
computational model. Sobol' indices can be computed cheaply based on spectral
methods like polynomial chaos expansions (PCE). Another choice are the recently
developed Poincar\'e chaos expansions (PoinCE), whose orthonormal
tensor-product basis is generated from the eigenfunctions of one-dimensional
Poincar\'e differential operators. In this paper, we show that the Poincar\'e
basis is the unique orthonormal basis with the property that partial
derivatives of the basis form again an orthogonal basis with respect to the
same measure as the original basis. This special property makes PoinCE ideally
suited for incorporating derivative information into the surrogate modelling
process. Assuming that partial derivative evaluations of the computational
model are available, we compute spectral expansions in terms of Poincar\'e
basis functions or basis partial derivatives, respectively, by sparse
regression. We show on two numerical examples that the derivative-based
expansions provide accurate estimates for Sobol' indices, even outperforming
PCE in terms of bias and variance. In addition, we derive an analytical
expression based on the PoinCE coefficients for a second popular sensitivity
index, the derivative-based sensitivity measure (DGSM), and explore its
performance as upper bound to the corresponding total Sobol' indices.


Abstract:
  The Simon tensor gives rise to a local characterization of the Kerr-NUT
family in the stationary class of vacuum spacetimes. We find that a symmetric
and traceless tensor in the quotient space of the stationary Killing trajectory
offers a useful alternative to the Simon tensor. Our tensor is distinct from
the spatial dual of the Simon tensor and illustrates the geometric property of
the three dimensional quotient space more manifest. The reconstruction
procedure of the metric for which the generalized Simon tensor vanishes is
spelled out in detail. We give a four dimensional description of this tensor in
terms of the Coulomb part of the imaginary selfdual Weyl tensor, which
corresponds to the generalization of the three-index tensor defined by Mars.
This allows us to establish a new and simple criterion for the Kerr-NUT family:
the gradient of the Ernst potential becomes the non-null eigenvector of the
Coulomb part of the imaginary selfdual Weyl tensor. We also discuss the ${\rm
SU}(1,2)$ covariant extension of the obstruction tensor into the
Einstein-Maxwell system as an intrinsic characterization of the Kerr-Newman-NUT
family.


Abstract:
  We study the use of the Euler characteristic for multiparameter topological
data analysis. Euler characteristic is a classical, well-understood topological
invariant that has appeared in numerous applications, including in the context
of random fields. The goal of this paper is to present the extension of using
the Euler characteristic in higher-dimensional parameter spaces. While
topological data analysis of higher-dimensional parameter spaces using stronger
invariants such as homology continues to be the subject of intense research,
Euler characteristic is more manageable theoretically and computationally, and
this analysis can be seen as an important intermediary step in multi-parameter
topological data analysis. We show the usefulness of the techniques using
artificially generated examples, and a real-world application of detecting
diabetic retinopathy in retinal images.


Abstract:
  Photometric observations of the double-mode pulsator VX Hya are presented.
They are analyzed with a stroboscopic method, completed by Fourier analysis.


Abstract:
  Charged excitons (trions) are essential for the optical spectra in low
dimensional doped monolayers (ML) of transitional metal dichalcogenides (TMDC).
Using a direct diagonalization of the three-body Hamiltonian, we explore the
low-lying trion states in four types of TMDC MLs. We show that the trions fine
structure results from the interplay between the spin-valley fine structure of
the single-particle bands and the exchange interaction between the composing
particles. We demonstrate that by variations of the doping and dielectric
environment, trion energy fine structure can be tuned, leading to anti-crossing
of the bright and dark states with substantial implications for the optical
spectra of TMDC MLs.


Abstract:
  Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet
despite its prevalence, the majority of affected people lack access to the
specialized ophthalmologists and equipment required for assessing their
condition. This can lead to delays in the start of treatment, thereby lowering
their chances for a successful outcome. Machine learning systems that
automatically detect the disease in eye fundus images have been proposed as a
means of facilitating access to DR severity estimates for patients in remote
regions or even for complementing the human expert's diagnosis. In this paper,
we propose a machine learning system for the detection of referable DR in
fundus images that is based on the paradigm of multiple-instance learning. By
extracting local information from image patches and combining it efficiently
through an attention mechanism, our system is able to achieve high
classification accuracy. Moreover, it can highlight potential image regions
where DR manifests through its characteristic lesions. We evaluate our approach
on publicly available retinal image datasets, in which it exhibits near
state-of-the-art performance, while also producing interpretable visualizations
of its predictions.


Abstract:
  We consider Bayesian optimization in settings where observations can be
adversarially biased, for example by an uncontrolled hidden confounder. Our
first contribution is a reduction of the confounded setting to the dueling
bandit model. Then we propose a novel approach for dueling bandits based on
information-directed sampling (IDS). Thereby, we obtain the first efficient
kernelized algorithm for dueling bandits that comes with cumulative regret
guarantees. Our analysis further generalizes a previously proposed
semi-parametric linear bandit model to non-linear reward functions, and
uncovers interesting links to doubly-robust estimation.


Abstract:
  This paper studies inference in linear models whose parameter of interest is
a high-dimensional matrix. We focus on the case where the high-dimensional
matrix parameter is well-approximated by a ``spiked low-rank matrix'' whose
rank grows slowly compared to its dimensions and whose nonzero singular values
diverge to infinity. We show that this framework covers a broad class of models
of latent-variables which can accommodate matrix completion problems, factor
models, varying coefficient models, principal components analysis with missing
data, and heterogeneous treatment effects. For inference, we propose a new
``rotation-debiasing" method for product parameters initially estimated using
nuclear norm penalization. We present general high-level results under which
our procedure provides asymptotically normal estimators. We then present
low-level conditions under which we verify the high-level conditions in a
treatment effects example.


Abstract:
  Let $D\ne \mathbb{C}$ be a simply connected domain and $f$ be the Riemann
mapping from $\mathbb{D}$ onto $D$. The Hardy number of $D$ is the supremum of
all $p$ for which $f$ belongs in the Hardy space ${H^p}\left( \mathbb{D}
\right)$. A comb domain is the entire plane minus an infinite number of
vertical rays symmetric with respect to the real axis. In this paper we prove
that for any $p\in [1,+\infty]$, there is a comb domain with Hardy number equal
to $p$ and this result is sharp. It is known that the Hardy number is related
with the moments of the exit time of Brownian motion from the domain. In
particular, our result implies that given $ p < q$ there exists a comb domain
with finite $p$-th moment but infinite $q$-th moment if and only if $q\geq
1/2$. This answers a question posed by Boudabra and Markowsky.


Abstract:
  In this paper, we introduce a new technique that combines two popular methods
to estimate uncertainty in object detection. Quantifying uncertainty is
critical in real-world robotic applications. Traditional detection models can
be ambiguous even when they provide a high-probability output. Robot actions
based on high-confidence, yet unreliable predictions, may result in serious
repercussions. Our framework employs deep ensembles and Monte Carlo dropout for
approximating predictive uncertainty, and it improves upon the uncertainty
estimation quality of the baseline method. The proposed approach is evaluated
on publicly available synthetic image datasets captured from sequences of
video.


Abstract:
  Domain generalization is challenging due to the domain shift and the
uncertainty caused by the inaccessibility of target domain data. In this paper,
we address both challenges with a probabilistic framework based on variational
Bayesian inference, by incorporating uncertainty into neural network weights.
We couple domain invariance in a probabilistic formula with the variational
Bayesian inference. This enables us to explore domain-invariant learning in a
principled way. Specifically, we derive domain-invariant representations and
classifiers, which are jointly established in a two-layer Bayesian neural
network. We empirically demonstrate the effectiveness of our proposal on four
widely used cross-domain visual recognition benchmarks. Ablation studies
validate the synergistic benefits of our Bayesian treatment when jointly
learning domain-invariant representations and classifiers for domain
generalization. Further, our method consistently delivers state-of-the-art mean
accuracy on all benchmarks.


Abstract:
  Photoexcitation is well-known to trigger electronic metastable states and
lead to phenomena like long-lived photoluminescence and photoconductivity. In
contrast, persistent photo-response due to ionic metastable states is rare. In
this work, we report persistent structural and ferroelectric photo-responses
due to proton metastable states via a nuclear quantum mechanism in
ferroelectric croconic acid, in which the proton-transfer origin of
ferroelectricity is important for the ionic metastable states. We show that,
after photoexcitation, the changes of structural and ferroelectric properties
relax in about 1000 s, while the photoconductivity decays within 1 s,
indicating the dominant ionic origin of the responses. The photogenerated
internal bias field that survives polarization switching process suggests
another proton transfer route and metastable state, in addition to the
metastable states resulting from proton transfer along the hydrogen bonds
proposed previously. Analysis based on Frank Condon principle reveals the
quantum mechanical nature of the proton-transfer process both within the
hydrogen bonds and out of the hydrogen bonds, where small mass of proton and
significant change of potential landscape due to the excited electronic states
are the key. The demonstration of persistent photo-responses due to the proton
metastable states unveils a nuclear quantum mechanism for photo-tunability of
materials, which is expected to impact many material properties sensitive to
ionic positions.


Abstract:
  Developing an agent in reinforcement learning (RL) that is capable of
performing complex control tasks directly from high-dimensional observation
such as raw pixels is yet a challenge as efforts are made towards improving
sample efficiency and generalization. This paper considers a learning framework
for Curiosity Contrastive Forward Dynamics Model (CCFDM) in achieving a more
sample-efficient RL based directly on raw pixels. CCFDM incorporates a forward
dynamics model (FDM) and performs contrastive learning to train its deep
convolutional neural network-based image encoder (IE) to extract conducive
spatial and temporal information for achieving a more sample efficiency for RL.
In addition, during training, CCFDM provides intrinsic rewards, produced based
on FDM prediction error, encourages the curiosity of the RL agent to improve
exploration. The diverge and less-repetitive observations provide by both our
exploration strategy and data augmentation available in contrastive learning
improve not only the sample efficiency but also the generalization. Performance
of existing model-free RL methods such as Soft Actor-Critic built on top of
CCFDM outperforms prior state-of-the-art pixel-based RL methods on the DeepMind
Control Suite benchmark.


Abstract:
  In a decentralized system with $m$ machines, we study the selfish scheduling
problem where each user strategically chooses which machine to use. Each
machine incurs a cost, which is a function of the total load assigned to it,
and some cost-sharing mechanism distributes this cost among the machine's
users. The users choose a machine aiming to minimize their own share of the
cost, so the cost-sharing mechanism induces a game among them. We approach this
problem from the perspective of a designer who can select which cost-sharing
mechanism to use, aiming to minimize the price of anarchy (PoA) of the induced
games.
  Recent work introduced the class of \emph{resource-aware} cost-sharing
mechanisms, whose decisions can depend on the set of machines in the system,
but are oblivious to the total number of users. These mechanisms can guarantee
low PoA bounds for instances where the cost functions of the machines are all
convex or concave, but can suffer from very high PoA for cost functions that
deviate from these families.
  In this paper we show that if we enhance the class of resource-aware
mechanisms with some prior information regarding the users, then they can
achieve low PoA for a much more general family of cost functions. We first show
that, as long as the mechanism knows just two of the participating users, then
it can assign special roles to them and ensure a constant PoA. We then extend
this idea to settings where the mechanism has access to the probability with
which each user is present in the system. For all these instances, we provide a
mechanism that achieves an expected PoA that is logarithmic in the expected
number of users.


Abstract:
  This work is devoted to the structure of the time-discrete Green-Naghdi
equations including bathymetry. We use the projection structure of the
equations to characterize homogeneous and inhomogeneous boundary conditions for
which the semi-discrete equations are well-posed. This structure allows us to
propose efficient and robust numerical treatment of the boundary conditions
that ensures entropy stability of the scheme by construction. Numerical
evidence is provided to illustrate that our approach is suitable for situations
of practical interest that are not covered by existing theory.


Abstract:
  We describe a new phenomenon in models of coalescence and fragmentation, that
of gel-shatter cycles. These are dynamical, unforced, stochastic cycles in
which slow, approximately deterministic coalescence up to and beyond gelation
is followed by abrupt random shattering. We describe their appearance in
simulations of stochastic models with multiplicative kernels for coalescence
and spontaneous fragmentation into monomers (`shattering'). The regime in which
such cycles occur is characterized by a cyclicity order parameter, and we
provide a simple scaling argument which describes both this regime and those
which border it.


Abstract:
  We show that every graded ideal of a Leavitt path algebra is graded
isomorphic to a Leavitt path algebra. It is known that a graded ideal $I$ of a
Leavitt path algebra is isomorphic to the Leavitt path algebra of a graph,
known as the generalized hedgehog graph, which is defined based on certain sets
of vertices uniquely determined by $I$. However, this isomorphism may not be
graded. We show that replacing the short "spines" of the generalized hedgehog
graph with possibly fewer, but then necessarily longer spines, we obtain a
graph (which we call the porcupine graph) such that its Leavitt path algebra is
graded isomorphic to $I$. Our proof adapts to show that for every closed
gauge-invariant ideal $J$ of a graph $C^*$-algebra, there is a gauge-invariant
$*$-isomorphism mapping the graph $C^*$-algebra of the porcupine graph of $J$
onto $J.$


Abstract:
  We introduce methods for deriving analytic solutions from
differential-algebraic systems of equations (DAEs), as well as methods for
deriving governing equations for analytic characterization which is currently
limited to very small systems as it is carried out by hand. Analytic solutions
to the system and analytic characterization through governing equations provide
insights into the behaviors of DAEs as well as the parametric regions of
operation for each potential behavior. For each system (DAEs), and choice of
dependent variable, there is a corresponding governing equation which is
univariate ODE or PDE that is typically higher order than the constitutive
equations of the system. We first introduce a direct formulation for
representing systems of linear DAEs. Unlike state space formulations, our
formulation follows very directly from the system of constitutive equations
without the need for introducing state variables or singular matrices. Using
this formulation for the system of constitutive equations (DAEs), we develop
methods for deriving analytic expressions for the full solution (complementary
and particular) for all dependent variables of systems that consist of constant
coefficient ordinary-DAEs and special cases of partial-DAEs. We also develop
methods for deriving the governing equation for a chosen dependent variable for
the constant coefficient ordinary-DAEs and partial-DAEs as well as special
cases of variable coefficient DAEs. The methods can be automated with symbolic
coding environments thereby allowing for dealing with systems of any size while
retaining analytic nature. This is relevant for interpretable modeling,
analytic characterization and estimation, and engineering design in which the
objective is to tune parameter values to achieve specific behavior. Such
insights cannot directly be obtained using numerical simulations.


Abstract:
  The next decade will be an exciting period for solar astrophysics, as new
ground- and space-based instrumentation will provide unprecedented observations
of the solar atmosphere and heliosphere. The synergy between modeling effort
and comprehensive analysis of observations is crucial for the understanding of
the physical processes behind the observed phenomena. However, the
unprecedented wealth of data on one hand, and the complexity of the physical
phenomena on the other, require the development of new approaches in both data
analysis and numerical modeling. In this white paper, we summarize recent
numerical achievements to reproduce structure, dynamics, and observed phenomena
from the photosphere to the low corona and outline challenges we expect to face
for the interpretation of future observations.


Abstract:
  We study the algebraic properties of binary relations whose underlying
digraph is smooth, that is has no source or sink. Such objects have been
studied as surjective hyper-operations (shops) on the corresponding vertex set,
and as binary relations that are defined everywhere and whose inverse is also
defined everywhere. In the latter formulation, they have been called
multipermutations. We study the lattice structure of sets (monoids) of
multipermutations over an n-element domain. Through a Galois connection, these
monoids form the algebraic counterparts to sets of relations closed under
definability in positive first-order logic without equality. The first side of
this Galois connection has been elaborated previously, we show the other side
here. We study the property of inverse on multipermutations and how it connects
our monoids to groups. We use our results to give a simple dichotomy theorem
for the evaluation problem of positive first-order logic without equality on
the class of structures whose preserving multipermutations form a monoid closed
under inverse. These problems turn out either to be in Logspace or to be
Pspace-complete. We go on to study the monoid of all multipermutations on an
n-element domain, under usual composition of relations. We characterise its
Green relations, regular elements and show that it does not admit a generating
set that is polynomial on n.


Abstract:
  Portfolio optimization methods suffer from a catalogue of known problems,
mainly due to the facts that pair correlations of asset returns are unstable,
and that extremal risk measures such as maximum drawdown are difficult to
predict due to the non-Gaussianity of portfolio returns. \\ In order to look at
optimal portfolios for arbitrary risk penalty functions, we construct portfolio
shapes where the penalty is proportional to a moment of the returns of
arbitrary order $p>2$. \\ The resulting component weight in the portfolio
scales sub-linearly with its return, with the power-law $w \propto
\mu^{1/(p-1)}$. This leads to significantly improved diversification when
compared to Kelly portfolios, due to the dilution of the winner-takes-all
effect.\\ In the limit of penalty order $p\rightarrow\infty$, we recover the
simple trading heuristic whereby assets are allocated a fixed positive weight
when their return exceeds the hurdle rate, and zero otherwise. Infinite order
power-law portfolios thus fall into the class of perfectly diversified
portfolios.


Abstract:
  Optical wireless communication (OWC) meets the demands of the future
six-generation mobile network (6G) as it operates at several hundreds of
Terahertz and has the potential to enable data rate in the order of Tbps.
However, most beam steering OWC technologies require high-accuracy positioning
and high-speed control. Resonant beam communication (RBCom), as one kind of
non-positioning OWC technologies, has been proposed for high-rate mobile
communications. The mobility of RBCom relies on its self-alignment
characteristic where no positioning is required. In a previous study, an
external-cavity second-harmonic-generation (SHG) RBCom system has been proposed
for eliminating the echo interference inside the resonator. However, its energy
conversion efficiency and complexity are of concern. In this paper, we propose
an intra-cavity SHG RBCom system to simplify the system design and improve the
energy conversion efficiency. We elaborate the system structure and establish
an analytical model. Numerical results show that the energy consumption of the
proposed intra-cavity design is reduced to reach the same level of channel
capacity at the receiver compared with the external-cavity one.


Abstract:
  A foundational question in the theory of linear compartmental models is how
to assess whether a model is identifiable -- that is, whether parameter values
can be inferred from noiseless data -- directly from the combinatorics of the
model. We completely answer this question for those models (with one input and
one output) in which the underlying graph is a bidirectional tree. Such models
include two families of models appearing often in biological applications:
catenary and mammillary models. Our proofs are enabled by two supporting
results, which are interesting in their own right. First, we give the first
general formula for the coefficients of input-output equations (certain
equations that can be used to determine identifiability). Second, we prove that
identifiability is preserved when a model is enlarged in specific ways
involving adding a new compartment with a bidirected edge to an existing
compartment.


Abstract:
  Biometric technologies, especially face recognition, have become an essential
part of identity management systems worldwide. In deployments of biometrics,
secure storage of biometric information is necessary in order to protect the
users' privacy. In this context, biometric cryptosystems are designed to meet
key requirements of biometric information protection enabling a
privacy-preserving storage and comparison of biometric data.
  This work investigates the application of a well-known biometric
cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors
extracted through deep convolutional neural networks. To this end, a feature
transformation method is introduced which maps fixed-length real-valued deep
feature vectors to integer-valued feature sets. As part of said feature
transformation, a detailed analysis of different feature quantisation and
binarisation techniques is conducted. At key binding, obtained feature sets are
locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency
of different polynomial reconstruction techniques is investigated. The proposed
feature transformation method and template protection scheme are agnostic of
the biometric characteristic. In experiments, an unlinkable improved deep face
fuzzy vault-based template protection scheme is constructed employing features
extracted with a state-of-the-art deep convolutional neural network trained
with the additive angular margin loss (ArcFace). For the best configuration, a
false non-match rate below 1% at a false match rate of 0.01%, is achieved in
cross-database experiments on the FERET and FRGCv2 face databases. On average,
a security level of up to approximately 28 bits is obtained. This work presents
an effective face-based fuzzy vault scheme providing privacy protection of
facial reference data as well as digital key derivation from face.


Abstract:
  A mapping from Fock space boson states to qubits is given and an underlying
digital quantum simulation algorithm of bosons is derived. We realize the
algorithm in MPS (matrix product state) which simulating real time dynamics of
Yukawa coupling in varies initial states and coupling constants. This proposal
may be achieved in superconductivity NISQ (noisy intermediate-scale quantum)
computer not far future.


Abstract:
  Over the past few years, graph neural networks (GNN) and label
propagation-based methods have made significant progress in addressing node
classification tasks on graphs. However, in addition to their reliance on
elaborate architectures and algorithms, there are several key technical details
that are frequently overlooked, and yet nonetheless can play a vital role in
achieving satisfactory performance. In this paper, we first summarize a series
of existing tricks-of-the-trade, and then propose several new ones related to
label usage, loss function formulation, and model design that can significantly
improve various GNN architectures. We empirically evaluate their impact on
final node classification accuracy by conducting ablation studies and
demonstrate consistently-improved performance, often to an extent that
outweighs the gains from more dramatic changes in the underlying GNN
architecture. Notably, many of the top-ranked models on the Open Graph
Benchmark (OGB) leaderboard and KDDCUP 2021 Large-Scale Challenge MAG240M-LSC
benefit from these techniques we initiated.


Abstract:
  Hypothesis. Isoniazid is one of the primary drugs used in tuberculosis
treatment. Isoniazid encapsulation in liposomal vesicles can improve drug
therapeutic index and minimize toxic and side effects. In this work, we
consider mixtures of hydrogenated soy phosphatidylcholine/phosphatidylglycerol
(HSPC/DPPG) to get novel biocompatible liposomes for isoniazid pulmonary
delivery. Our goal is to understand if the entrapped drug affects bilayer
structure.
  Experiments. HSPC-DPPG unilamellar liposomes are prepared and characterized
by dynamic light scattering, $\zeta$-potential, fluorescence anisotropy and
Transmission Electron Microscopy. Isoniazid encapsulation is determined by UV
and Laser Transmission Spectroscopy. Calorimetry, light scattering and Surface
Pressure measurements are used to get insight on adsorption and thermodynamic
properties of lipid bilayers in the presence of the drug.
  Findings. We find that INH-lipid interaction can increase the entrapment
capability of the carrier due to isoniazid adsorption. The preferential
INH-HSPC dipole-dipole interaction promotes modification of lipid packing and
ordering and favors the condensation of a HSPC-richer phase in molar excess of
DPPG. Our findings highlight the importance of fundamental investigations of
drug-lipid interactions for the optimal design of liposomal nanocarriers.


Abstract:
  Studies of disordered spin chains have recently experienced a renewed
interest, inspired by the question to which extent the exact numerical
calculations comply with the existence of a many-body localization phase
transition. For the paradigmatic random field Heisenberg spin chains, many
intriguing features were observed when the disorder is considerable compared to
the spin interaction strength. Here, we introduce a phenomenological theory
that may explain some of those features. The theory is based on the proximity
to the noninteracting limit, in which the system is an Anderson insulator.
Taking the spin imbalance as an exemplary observable, we demonstrate that the
proximity to the local integrals of motion of the Anderson insulator determines
the dynamics of the observable at infinite temperature. In finite interacting
systems our theory quantitatively describes its integrated spectral function
for a wide range of disorders.


Abstract:
  We investigated the adsorption of iodine on silver (111) in ultra-high
vacuum. Using low-temperature scanning tunneling microscopy (LT-STM)
measurements we catalog the complex surface structures on the local scale. We
identified three distinct phases with increasing iodine coverage which we
tentatively associate with three phases previously reported in LEED experiments
(sqrt(3)x sqrt(3)R30, "triangular", "hexagonal"). We used Fourier space and
real space analysis to fully characterize each phase. While Fourier analysis
most easily connects our measurements to previous LEED studies, the real space
inspection reveals local variations in the superstructures of the "hexagonal"
and "triangular" phase. The latter, observed here for the first time by LT-STM,
stabilized by one or two adatoms sitting at the center of a rosette-like iodine
reconstruction. The most stunning discovery is that variation in the adatom
separation of the "triangular" phase reconstruct the Ag (111) surface lattice.


Abstract:
  We investigate the impact of different assumptions in the modeling of
one-loop galaxy bias on the recovery of cosmological parameters, as a follow up
of the analysis done in the first paper of the series at fixed cosmology. We
use three different synthetic galaxy samples whose clustering properties match
the ones of the CMASS and LOWZ catalogues of BOSS and the SDSS Main Galaxy
Sample. We investigate the relevance of allowing for either short range
non-locality or scale-dependent stochasticity by fitting the real-space galaxy
auto power spectrum or the combination of galaxy-galaxy and galaxy-matter power
spectrum. From a comparison among the goodness-of-fit ($\chi^2$), unbiasedness
of cosmological parameters (FoB), and figure-of-merit (FoM), we find that a
four-parameter model (linear, quadratic, cubic non-local bias, and constant
shot-noise) with fixed quadratic tidal bias provides a robust modelling choice
for the auto power spectrum of the three samples, up to $k_{\rm
max}=0.3\,h\,\mathrm{Mpc}^{-1}$ and for an effective volume of
$6\,h^{-3}\,\mathrm{Gpc}^3$. Instead, a joint analysis of the two observables
fails at larger scales, and a model extension with either higher derivatives or
scale-dependent shot-noise is necessary to reach a similar $k_{\rm max}$, with
the latter providing the most stable results. These findings are obtained with
three, either hybrid or perturbative, prescriptions for the matter power
spectrum, \texttt{RESPRESSO}, gRPT and EFT. In all cases, the inclusion of
scale-dependent shot-noise increases the range of validity of the model in
terms of FoB and $\chi^2$. Interestingly, these model extensions with
additional free parameters do not necessarily lead to an increase in the
maximally achievable FoM for the cosmological parameters
$\left(h,\,\Omega_ch^2,\,A_s\right)$, which are generally consistent to those
of the simpler model at smaller $k_{\rm max}$.


Abstract:
  We consider the two-body problem in a periodic potential, and study the
bound-state dispersion of a spin-$\uparrow$ fermion that is interacting with a
spin-$\downarrow$ fermion through a short-range attractive interaction. Based
on a variational approach, we obtain the exact solution of the dispersion in
the form of a set of self-consistency equations, and apply it to tight-binding
Hamiltonians with onsite interactions. We pay special attention to the
bipartite lattices with a two-point basis that exhibit time-reversal symmetry,
and show that the lowest-energy bound states disperse quadratically with
momentum, whose effective-mass tensor is partially controlled by the quantum
metric tensor of the underlying Bloch states. In particular, we apply our
theory to the Mielke checkerboard lattice, and study the special role played by
the interband processes in producing a finite effective mass for the bound
states in a non-isolated flat band.


Abstract:
  The final aim of this paper is to expand the sparse group of X-ray binaries
with gamma-ray counterparts as laboratories to study high-energy processes
under physical conditions that periodically repeat. A follow-up of a candidate
system has been carried out. We applied both photometric and spectroscopic
techniques in the optical domain together with a period analysis using the
phase dispersion minimization and CLEAN methods. A tentative period search was
also conducted in the gamma-ray domain. Our main result is having established
the binary nature of the optical star and X-ray emitter HD 3191 towards the
Fermi gamma-ray source 4FGL J0035.8+6131, the last one proposed to be
associated with a blazar of an unknown type. An orbital period close to 16 days
is reported for HD 3191 together with a likely rotation, or pulsation, period
of about 0.6 d. Although no convincing evidence for the orbital cycle has been
found in the Fermi light curve up to now, the confirmed presence of a high-mass
X-ray binary towards 4FGL J0035.8+6131 now strengthens the need for caution
about its true nature.


Abstract:
  This study addresses the question whether visually grounded speech
recognition (VGS) models learn to capture sentence semantics without access to
any prior linguistic knowledge. We produce synthetic and natural spoken
versions of a well known semantic textual similarity database and show that our
VGS model produces embeddings that correlate well with human semantic
similarity judgements. Our results show that a model trained on a small
image-caption database outperforms two models trained on much larger databases,
indicating that database size is not all that matters. We also investigate the
importance of having multiple captions per image and find that this is indeed
helpful even if the total number of images is lower, suggesting that
paraphrasing is a valuable learning signal. While the general trend in the
field is to create ever larger datasets to train models on, our findings
indicate other characteristics of the database can just as important important.


Abstract:
  Commonly used automatic speech recognition (ASR) systems can be classified
into frame-synchronous and label-synchronous categories, based on whether the
speech is decoded on a per-frame or per-label basis. Frame-synchronous systems,
such as traditional hidden Markov model systems, can easily incorporate
existing knowledge and can support streaming ASR applications.
Label-synchronous systems, based on attention-based encoder-decoder models, can
jointly learn the acoustic and language information with a single model, which
can be regarded as audio-grounded language models. In this paper, we propose
rescoring the N-best hypotheses or lattices produced by a first-pass
frame-synchronous system with a label-synchronous system in a second-pass. By
exploiting the complementary modelling of the different approaches, the
combined two-pass systems achieve competitive performance without using any
extra speech or text data on two standard ASR tasks. For the 80-hour AMI IHM
dataset, the combined system has a 13.7% word error rate (WER) on the
evaluation set, which is up to a 29% relative WER reduction over the individual
systems. For the 300-hour Switchboard dataset, the WERs of the combined system
are 5.7% and 12.1% on Switchboard and CallHome subsets of Hub5'00, and 13.2%
and 7.6% on Switchboard Cellular and Fisher subsets of RT03, up to a 33%
relative reduction in WER over the individual systems.


Abstract:
  In this paper, we study the general form of three-point functions of
conserved current multiplets $S_{\alpha(k)}= S_{(\alpha_1 \dots \alpha_k)}$ of
arbitrary rank in four-dimensional ${\mathcal N}=1$ superconformal theory. We
find that the correlation function of three such operators $\langle
\bar{S}_{\dot{\alpha}(k)} (z_1) S_{\beta(k+l)} (z_2) \bar{S}_{\dot{\gamma}(l)}
(z_3) \rangle$ is fixed by the superconformal symmetry up to a single complex
coefficient though the precise form of the correlator depends on the values of
$k$ and $l$. In addition, we present the general structure of mixed correlators
of the form $\langle \bar{S}_{\dot{\alpha}(k)} (z_1) S_{\alpha(k)} (z_2) L(z_3)
\rangle$ and $\langle \bar{S}_{\dot{\alpha}(k)} (z_1) S_{\alpha(k)} (z_2)
J_{\gamma \dot{\gamma}} (z_3) \rangle$, where $L$ is the flavour current
multiplet and $J_{\gamma \dot{\gamma}}$ is the supercurrent.


Abstract:
  Machine Learning (ML) techniques are becoming an invaluable support for
network intrusion detection, especially in revealing anomalous flows, which
often hide cyber-threats. Typically, ML algorithms are exploited to
classify/recognize data traffic on the basis of statistical features such as
inter-arrival times, packets length distribution, mean number of flows, etc.
Dealing with the vast diversity and number of features that typically
characterize data traffic is a hard problem. This results in the following
issues: i) the presence of so many features leads to lengthy training processes
(particularly when features are highly correlated), while prediction accuracy
does not proportionally improve; ii) some of the features may introduce bias
during the classification process, particularly those that have scarce relation
with the data traffic to be classified. To this end, by reducing the feature
space and retaining only the most significant features, Feature Selection (FS)
becomes a crucial pre-processing step in network management and, specifically,
for the purposes of network intrusion detection. In this review paper, we
complement other surveys in multiple ways: i) evaluating more recent datasets
(updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch
Python-based procedure; ii) providing a synopsis of most credited FS approaches
in the field of intrusion detection, including Multi-Objective Evolutionary
techniques; iii) assessing various experimental analyses such as feature
correlation, time complexity, and performance. Our comparisons offer useful
guidelines to network/security managers who are considering the incorporation
of ML concepts into network intrusion detection, where trade-offs between
performance and resource consumption are crucial.


Abstract:
  Finding a reduction of complex, high-dimensional dynamics to its essential,
low-dimensional "heart" remains a challenging yet necessary prerequisite for
designing efficient numerical approaches. Machine learning methods have the
potential to provide a general framework to automatically discover such
representations. In this paper, we consider multiscale stochastic systems with
local slow-fast time scale separation and propose a new method to encode in an
artificial neural network a map that extracts the slow representation from the
system. The architecture of the network consists of an encoder-decoder pair
that we train in a supervised manner to learn the appropriate low-dimensional
embedding in the bottleneck layer. We test the method on a number of examples
that illustrate the ability to discover a correct slow representation.
Moreover, we provide an error measure to assess the quality of the embedding
and demonstrate that pruning the network can pinpoint an essential coordinates
of the system to build the slow representation.


Abstract:
  In this paper, we establish optimal Berry--Esseen bounds for the generalized
$U$-statistics. The proof is based on a new Berry--Esseen theorem for
exchangeable pair approach by Stein's method under a general linearity
condition setting. As applications, an optimal convergence rate of the normal
approximation for subgraph counts in Erd\"os--R\'enyi graphs and graphon-random
graph is obtained.


Abstract:
  High-rate generation of hybrid photon-matter entanglement remains a
fundamental building block of quantum network architectures enabling protocols
such as quantum secure communication or quantum distributed computing. While a
tremendous effort has been made to overcome technological constraints limiting
the efficiency and coherence times of current systems, an important
complementary approach is to employ parallel and multiplexed architectures.
Here we follow this approach experimentally demonstrating the generation of
bipartite polarization-entangled photonic states across more than 500 modes,
with a programmable delay for the second photon enabled by qubit storage in a
wavevector multiplexed cold-atomic quantum memory. We demonstrate Clauser,
Horne, Shimony, Holt inequality violation by over 3 standard deviations,
lasting for at least 45 {\mu}s storage time for half of the modes. The ability
to shape hybrid entanglement between the polarization and wavevector degrees of
freedom provides not only multiplexing capabilities but also brings prospects
for novel protocols.


Abstract:
  A cohomology theory, associated to a $n$-Lie algebra and a representation
space of it, is introduced. It is observed that this cohomology theory is
qualified to encode the generalized derivation extensions, and that it
coincides, for $n=3$, with the known cohomology of $n$-Lie algebras. The
abelian extensions and infinitesimal deformations of $n$-Lie algebras, on the
other hand, are shown to be characterized by the usual cohomology of $n$-Lie
algebras. Furthermore, the Hochschild-Serre spectral sequence of the Lie
algebra cohomology is upgraded to the level of $n$-Lie algebras, and is applied
to the cohomology of generalized derivation extensions.


Abstract:
  The scaled relative graph (SRG) of an operator is a subset of the complex
plane. It captures several salient features of an operator, such as
contractiveness, and can be used to reveal the geometric nature of many of the
inequality based arguments used in the convergence analyses of fixed point
iterations. In this paper we show that the SRG of a linear operator can be
determined from the numerical range of a closely related linear operator.
Furthermore we demonstrate that the SRG of a linear operator has a range of
spectral and convexity properties, and satisfies an analogue of Hildebrant's
theorem.


Abstract:
  Several approaches to the formulation of a fractional theory of calculus of
"variable order" have appeared in the literature over the years. Unfortunately,
most of these proposals lack a rigorous mathematical framework. We consider an
alternative view on the problem, originally proposed by G. Scarpi in the early
seventies, based on a naive modification of the representation in the Laplace
domain of standard kernels functions involved in (constant-order) fractional
calculus. We frame Scarpi's ideas within recent theory of General Fractional
Derivatives and Integrals, that mostly rely on the Sonine condition, and
investigate the main properties of the emerging variable-order operators. Then,
taking advantage of powerful and easy-to-use numerical methods for the
inversion of Laplace transforms of functions defined in the Laplace domain, we
discuss some practical applications of the variable-order Scarpi integral and
derivative.


Abstract:
  Time-dependent dynamical properties of a fluid can not be estimated from a
single configuration without performing a simulation. Here we show, however,
that the scaling properties of both structure and dynamics can be predicted
from a single configuration. The new method is demonstrated to work very well
for equilibrium dynamics of the Kob-Andersen Binary Lennard-Jones mixture.
Furthermore, the method is applied to isobaric cooling where the liquid falls
out of equilibrium and forms a glass, demonstrating that the method requires
neither equilibrium nor constant volume conditions to work, in contrast to
existing methods.


Abstract:
  Given an undirected graph, $G$, and vertices, $s$ and $t$ in $G$, the
tracking paths problem is that of finding the smallest subset of vertices in
$G$ whose intersection with any $s$-$t$ path results in a unique sequence. This
problem is known to be NP-complete and has applications to animal migration
tracking and detecting marathon course-cutting, but its approximability is
largely unknown. In this paper, we address this latter issue, giving novel
algorithms having approximation ratios of $(1+\epsilon)$, $O(\lg OPT)$ and
$O(\lg n)$, for $H$-minor-free, general, and weighted graphs, respectively. We
also give a linear kernel for $H$-minor-free graphs and make improvements to
the quadratic kernel for general graphs.


Abstract:
  We analyze the well-known equivalence between the quadratic Kontsevich-Penner
and Hermitian matrix models from the point of view of superintegrability
relations, i.e. explicit formulas for character averages. This is not that
trivial on the Kontsevich side and seems important for further studies of
various deformations of Kontsevich models. In particular, the Brezin-Hikami
extension of the above equivalence becomes straightforward.


Abstract:
  Cytology is a low-cost and non-invasive diagnostic procedure employed to
support the diagnosis of a broad range of pathologies. Computer Vision
technologies, by automatically generating quantitative and objective
descriptions of examinations' contents, can help minimize the chances of
misdiagnoses and shorten the time required for analysis. To identify the
state-of-art of computer vision techniques currently applied to cytology, we
conducted a Systematic Literature Review. We analyzed papers published in the
last 5 years. The initial search was executed in September 2020 and resulted in
431 articles. After applying the inclusion/exclusion criteria, 157 papers
remained, which we analyzed to build a picture of the tendencies and problems
present in this research area, highlighting the computer vision methods,
staining techniques, evaluation metrics, and the availability of the used
datasets and computer code. As a result, we identified that the most used
methods in the analyzed works are deep learning-based (70 papers), while fewer
works employ classic computer vision only (101 papers). The most recurrent
metric used for classification and object detection was the accuracy (33 papers
and 5 papers), while for segmentation it was the Dice Similarity Coefficient
(38 papers). Regarding staining techniques, Papanicolaou was the most employed
one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of
the datasets used in the papers are publicly available, with the DTU/Herlev
dataset being the most used one. We conclude that there still is a lack of
high-quality datasets for many types of stains and most of the works are not
mature enough to be applied in a daily clinical diagnostic routine. We also
identified a growing tendency towards adopting deep learning-based approaches
as the methods of choice.


Abstract:
  We demonstrate coherent control of photoemission from a gold needle tip using
a two-color laser field. The relative phase between a fundamental field and its
second harmonic imprints a strong modulation on the emitted photocurrent with
up to 96.5 % contrast. The contrast as a function of the second harmonic
intensity can be described by three interfering quantum pathways. Increasing
the bias voltage applied to the tip reduces the maximum achievable contrast and
modifies the weights of the involved pathways. Simulations based on the
time-dependent Schr\"odinger equation reproduce the characteristic cooperative
signal and its dependence on the second harmonic intensity, which further
confirms the involvement of three emission pathways.


Abstract:
  In Guo and Peng's article [Spherically convex sets and spherically convex
functions, J. Convex Anal. 28 (2021), 103--122], one defines the notions of
spherical convex sets and functions on "general curved surfaces" in
$\mathbb{R}^{n}$ $(n\ge2)$, one studies several properties of these classes of
sets and functions, and one establishes analogues of Radon, Helly,
Carath\'eodory and Minkowski theorems for spherical convex sets, as well as
some properties of spherical convex functions which are analogous to those of
usual convex functions. In obtaining such results, the authors use an analytic
approach based on their definitions. Our aim in this note is to provide simpler
proofs for the results on spherical convex sets; our proofs are based on some
characterizations/representations of spherical convex sets by usual convex sets
in $\mathbb{R}^{n}$.


Abstract:
  New categories can be discovered by transforming semantic features into
synthesized visual features without corresponding training samples in zero-shot
image classification. Although significant progress has been made in generating
high-quality synthesized visual features using generative adversarial networks,
guaranteeing semantic consistency between the semantic features and visual
features remains very challenging. In this paper, we propose a novel zero-shot
learning approach, GAN-CST, based on class knowledge to visual feature learning
to tackle the problem. The approach consists of three parts, class knowledge
overlay, semi-supervised learning and triplet loss. It applies class knowledge
overlay (CKO) to obtain knowledge not only from the corresponding class but
also from other classes that have the knowledge overlay. It ensures that the
knowledge-to-visual learning process has adequate information to generate
synthesized visual features. The approach also applies a semi-supervised
learning process to re-train knowledge-to-visual model. It contributes to
reinforcing synthesized visual features generation as well as new category
prediction. We tabulate results on a number of benchmark datasets demonstrating
that the proposed model delivers superior performance over state-of-the-art
approaches.


Abstract:
  Almost flat finitely generated projective Hilbert C*-module bundles were
successfully used by Hanke and Schick to prove special cases of the Strong
Novikov Conjecture. Dadarlat later showed that it is possible to calculate the
index of a K-homology class $\eta\in K_*(M)$ twisted with an almost flat bundle
in terms of the image of $\eta$ under Lafforgue's assembly map and the almost
representation associated to the bundle. Mishchenko used flat
infinite-dimensional bundles equipped with a Fredholm operator in order to
prove special cases of the Novikov higher signature conjecture.
  We show how to generalize Dadarlat's theorem to the case of an
infinite-dimensional bundle equipped with a continuous family of Fredholm
operators on the fibers. Along the way, we show that special cases of the
Strong Novikov Conjecture can be proven if there exist sufficiently many almost
flat bundles with Fredholm operator.
  To this end, we introduce the concept of an asymptotically flat Fredholm
bundle and its associated asymptotic Fredholm representation, and prove an
index theorem which relates the index of the asymptotic Fredholm bundle with
the so-called asymptotic index of the associated asymptotic Fredholm
representation.


Abstract:
  We report the first mode-locked fiber laser to operate in the femtosecond
regime well beyond 3 {\mu}m. The laser uses dual-wavelength pumping and
non-linear polarisation rotation to produce 3.5 {\mu}m wavelength pulses with
minimum duration of 580 fs at a repetition rate of 68 MHz. The pulse energy is
3.2 nJ, corresponding to a peak power of 5.5 kW.


Abstract:
  Interferometry can completely redirect light, providing the potential for
strong and controllable optical forces. However, small particles do not
naturally act like interferometric beamsplitters, and the optical scattering
from them is not generally thought to allow efficient interference. Instead,
optical trapping is typically achieved via deflection of the incident field.
Here we show that a suitably structured incident field can achieve
beamsplitter-like interactions with scattering particles. The resulting trap
offers order-of-magnitude higher stiffness than the usual Gaussian trap in one
axis, even when constrained to phase-only structuring. We demonstrate trapping
of 3.5 to 10.0~$\mu$m silica spheres, achieving stiffness up to 27.5$\pm$4.1
times higher than is possible using Gaussian traps, and two orders of magnitude
higher measurement signal-to-noise ratio. These results are highly relevant to
many applications, including cellular manipulation, fluid dynamics,
micro-robotics, and tests of fundamental physics.


Abstract:
  We report the direct observation of intervalley exciton between the Q
conduction valley and $\Gamma$ valence valley in bilayer WSe$_2$ by
photoluminescence. The Q$\Gamma$ exciton lies at ~18 meV below the QK exciton
and dominates the luminescence of bilayer WSe$_2$. By measuring the exciton
spectra at gate-tunable electric field, we reveal different interlayer electric
dipole moments and Stark shifts between Q$\Gamma$ and QK excitons. Notably, we
can use the electric field to switch the energy order and dominant luminescence
between Q$\Gamma$ and QK excitons. Both Q$\Gamma$ and QK excitons exhibit
pronounced phonon replicas, in which two-phonon replicas outshine the
one-phonon replicas due to the existence of (nearly) resonant exciton-phonon
scatterings and numerous two-phonon scattering paths. We can simulate the
replica spectra by comprehensive theoretical modeling and calculations. The
good agreement between theory and experiment for the Stark shifts and phonon
replicas strongly supports our assignment of Q$\Gamma$ and QK excitons.


Abstract:
  Cosmological phase transitions proceed via the nucleation of bubbles that
subsequently expand and collide. The resulting gravitational wave spectrum
depends crucially on the bubble wall velocity. Microscopic calculations of this
velocity are challenging even in weakly coupled theories. We use holography to
compute the wall velocity from first principles in a strongly coupled,
non-Abelian, four-dimensional gauge theory. The wall velocity is determined
dynamically in terms of the nucleation temperature. We find an approximately
linear relation between the velocity and the ratio $\Delta
\mathcal{P}/\mathcal{E}$, with $\Delta \mathcal{P}$ the pressure difference
between the inside and the outside of the bubble and $\mathcal{E}$ the energy
density outside the bubble. Up to a rescaling, the wall profile is well
approximated by that of an equilibrium, phase-separated configuration at the
critical temperature. We verify that ideal hydrodynamics provides a good
description of the system everywhere except near the wall.


Abstract:
  Nanocontact properties of two-dimensional (2D) materials are closely
dependent on their unique nanomechanical systems, such as the number of atomic
layers and the supporting substrate. Here, we report a direct observation of
toplayer-dependent crystallographic orientation imaging of 2D materials with
the transverse shear microscopy (TSM). Three typical nanomechanical systems,
MoS2 on the amorphous SiO2/Si, graphene on the amorphous SiO2/Si, and MoS2 on
the crystallized Al2O3, have been investigated in detail. This experimental
observation reveals that puckering behaviour mainly occurs on the top layer of
2D materials, which is attributed to its direct contact adhesion with the AFM
tip. Furthermore, the result of crystallographic orientation imaging of
MoS2/SiO2/Si and MoS2/Al2O3 indicated that the underlying crystalline
substrates almost do not contribute to the puckering effect of 2D materials.
Our work directly revealed the top layer dependent puckering properties of 2D
material, and demonstrate the general applications of TSM in the bilayer 2D
systems.


Abstract:
  We present a stochastic modeling framework for atomistic propagation of a
Mode I surface crack, with atoms interacting according to the Lennard-Jones
interatomic potential at zero temperature. Specifically, we invoke the
Cauchy-Born rule and the maximum entropy principle to infer probability
distributions for the parameters of the interatomic potential. We then study
how uncertainties in the parameters propagate to the quantities of interest
relevant to crack propagation, namely, the critical stress intensity factor and
the lattice trapping range. For our numerical investigation, we rely on an
automated version of the so-called numerical-continuation enhanced flexible
boundary (NCFlex) algorithm.


Abstract:
  The count-min sketch (CMS) is a randomized data structure that provides
estimates of tokens' frequencies in a large data stream using a compressed
representation of the data by random hashing. In this paper, we rely on a
recent Bayesian nonparametric (BNP) view on the CMS to develop a novel
learning-augmented CMS under power-law data streams. We assume that tokens in
the stream are drawn from an unknown discrete distribution, which is endowed
with a normalized inverse Gaussian process (NIGP) prior. Then, using
distributional properties of the NIGP, we compute the posterior distribution of
a token's frequency in the stream, given the hashed data, and in turn
corresponding BNP estimates. Applications to synthetic and real data show that
our approach achieves a remarkable performance in the estimation of
low-frequency tokens. This is known to be a desirable feature in the context of
natural language processing, where it is indeed common in the context of the
power-law behaviour of the data.


Abstract:
  The collision dynamics of hard spheres and cylindrical pores is solved
exactly, which is the minimal model for a regularly porous membrane.
  Nonequilibrium event-driven molecular dynamics simulations are used to show
that the permeability $P$ of hard spheres of size $\sigma$ through cylinderical
pores of size $d$ follow the hindered diffusion mechanism due to size exclusion
as $P \propto (1-\sigma/d)^2$. Under this law, the separation of binary
mixtures of large and small particles exhibits a linear relationship between
$\alpha^{-1/2}$ and $P^{-1/2}$, where $\alpha$ and $P$ are the selectivity and
permeability of the smaller particle, respectively. The mean permeability
through polydisperse pores is the sum of permeabilities of individual pores,
weighted by the fraction of the single pore area over the total pore area.


Abstract:
  We want in this article to show the usefulness of Quantum Turing Machine
(QTM) in a high-level didactic context as well as in theoretical studies. We
use QTM to show its equivalence with quantum circuit model for Deutsch and
Deutsch-Jozsa algorithms. Further we introduce a strategy of translation from
Quantum Circuit to Quantum Turing models by these examples. Moreover we
illustrate some features of Quantum Computing such as superposition from a QTM
point of view and starting with few simple examples very known in Quantum
Circuit form.


Abstract:
  Purpose: This article develops theoretical, algorithmic, perceptual, and
interaction aspects of script legibility enhancement in the visible light
spectrum for the purpose of scholarly editing of papyri texts. - Methods: Novel
legibility enhancement algorithms based on color processing and visual
illusions are compared to classic methods in a user experience experiment. -
Results: (1) The proposed methods outperformed the comparison methods. (2)
Users exhibited a broad behavioral spectrum, under the influence of factors
such as personality and social conditioning, tasks and application domains,
expertise level and image quality, and affordances of software, hardware, and
interfaces. No single enhancement method satisfied all factor configurations.
Therefore, it is suggested to offer users a broad choice of methods to
facilitate personalization, contextualization, and complementarity. (3) A
distinction is made between casual and critical vision on the basis of signal
ambiguity and error consequences. The criteria of a paradigm for enhancing
images for critical applications comprise: interpreting images skeptically;
approaching enhancement as a system problem; considering all image structures
as potential information; and making uncertainty and alternative
interpretations explicit, both visually and numerically.


Abstract:
  We present Omnidirectional Neural Radiance Fields (OmniNeRF), the first
method to the application of parallax-enabled novel panoramic view synthesis.
Recent works for novel view synthesis focus on perspective images with limited
field-of-view and require sufficient pictures captured in a specific condition.
Conversely, OmniNeRF can generate panorama images for unknown viewpoints given
a single equirectangular image as training data. To this end, we propose to
augment the single RGB-D panorama by projecting back and forth between a 3D
world and different 2D panoramic coordinates at different virtual camera
positions. By doing so, we are able to optimize an Omnidirectional Neural
Radiance Field with visible pixels collecting from omnidirectional viewing
angles at a fixed center for the estimation of new viewing angles from varying
camera positions. As a result, the proposed OmniNeRF achieves convincing
renderings of novel panoramic views that exhibit the parallax effect. We
showcase the effectiveness of each of our proposals on both synthetic and
real-world datasets.


Abstract:
  Let $X$ be an integrable discrete random variable over $\{0, 1, 2, \ldots\}$
with $\mathbb{P}(X = i + 1) \leq \mathbb{P}(X = i)$ for all $i$. Then for any
integer $a \geq 1$, $\mathbb{P}(X \leq a) \leq \mathbb{E}[X] / (2a - 1)$. Let
$W$ be an discrete random variable over $\{\ldots, -2, -1, 0, 1, 2, \ldots\}$
with finite second moment where the $\mathbb{P}(W = i)$ values are unimodal.
Then $\mathbb{P}(|W - \mathbb{E}[W]| \geq a) \leq (\mathbb{V}(W) + 1 / 12) /
(2(a - 1 / 2)^2)$.


Abstract:
  We point out that light gauge boson mediators could induce new interference
effects in neutrino-electron scattering that can be used to enhance the
sensitivity of neutrino-flavor-selective high-intensity neutrino experiments,
such as DUNE. We particularly emphasize a destructive interference effect,
leading to a deficit between the Standard Model expectation and the
experimental measurement of the differential cross-sections, which is prominent
only in either the neutrino or the antineutrino mode, depending on the mediator
couplings. Therefore, the individual neutrino (or antineutrino) mode could
allow for sensitivity reaches superior to the combined analysis, and moreover,
could distinguish between different types of gauge boson mediators.


Abstract:
  Modeling and simulation of disease spreading in pedestrian crowds has been
recently become a topic of increasing relevance. In this paper, we consider the
influence of the crowd motion in a complex dynamical environment on the course
of infection of the pedestrians. To model the pedestrian dynamics we consider a
kinetic equation for multi-group pedestrian flow based on a social force model
coupled with an Eikonal equation. This model is coupled with a non-local SEIS
contagion model for disease spread, where besides the description of local
contacts also the influence of contact times has been modelled. Hydrodynamic
approximations of the coupled system are derived. Finally, simulations of the
hydrodynamic model are carried out using a mesh-free particle method. Different
numerical test cases are investigated including uni- and bi-directional flow in
a passage with and without obstacles.


Abstract:
  We describe the formalization of the existence and uniqueness of Haar measure
in the Lean theorem prover. The Haar measure is an invariant regular measure on
locally compact groups, and it has not been formalized in a proof assistant
before. We will also discuss the measure theory library in Lean's mathematical
library \textsf{mathlib}, and discuss the construction of product measures and
the proof of Fubini's theorem for the Bochner integral.


Abstract:
  This paper is concerned with linear parameter-dependent systems and considers
the notion uniform ensemble reachability. The focus of this work is on
constructive methods to compute suitable parameter-independent open-loop inputs
for such systems. In contrast to necessary and sufficient conditions for
ensemble reachability, computational methods have to distinguish between
continuous-time and discrete-time systems. Based on recently derived sufficient
conditions and techniques from complex approximation we present two algorithms
for discrete-time singe-input linear systems. Moreover, we illustrate that one
method can also be applied to certain continuous-time single-input systems.


Abstract:
  The orchestra performance is full of sublime rich sounds. In particular, the
unison of violins sounds different from the solo violin. We try to clarify this
difference and similarity of unison and solo numerically analyzing the beat of
`violins` with timbre, vibrato, melody, and resonance. Characteristic
properties appear in the very low-frequency part in the power spectrum of the
wave amplitude squared. This ultra-buss richness (UBR) can be a new
characteristic of sound on top of the well-known pitch, loudness, and timbre,
although being inaudible directly. We find this UBR is always characterized by
a power-law at low-frequency with the index around -1 and appears everywhere in
music and thus being universal. Furthermore, we explore this power-law property
towards much smaller frequency regions and suggest possible relation to the 1/f
noise often found in music and many other fields in nature.


Abstract:
  Tackling online hatred using informed textual responses - called counter
narratives - has been brought under the spotlight recently. Accordingly, a
research line has emerged to automatically generate counter narratives in order
to facilitate the direct intervention in the hate discussion and to prevent
hate content from further spreading. Still, current neural approaches tend to
produce generic/repetitive responses and lack grounded and up-to-date evidence
such as facts, statistics, or examples. Moreover, these models can create
plausible but not necessarily true arguments. In this paper we present the
first complete knowledge-bound counter narrative generation pipeline, grounded
in an external knowledge repository that can provide more informative content
to fight online hatred. Together with our approach, we present a series of
experiments that show its feasibility to produce suitable and informative
counter narratives in in-domain and cross-domain settings.


Abstract:
  Pretraining Bidirectional Encoder Representations from Transformers (BERT)
for downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that
differ in the size of their training sets, mixture of formal and informal
Arabic, and linguistic preprocessing. All are intended to support Arabic
dialects and social media. The experiments highlight the centrality of data
diversity and the efficacy of linguistically aware segmentation. They also
highlight that more data or more training step do not necessitate better
models. Our new models achieve new state-of-the-art results on several
downstream tasks. The resulting models are released to the community under the
name QARiB.


Abstract:
  We show that any weakly separated Bessel system of model spaces in the Hardy
space on the unit disc is a Riesz system and we highlight some applications to
interpolating sequences of matrices. This will be done without using the recent
solution of the Feichtinger conjecture, whose natural generalization to
multi-dimensional model sub-spaces of $\mathrm{H}^2$ turns out to be false.


Abstract:
  In quantum metrology, entanglement represents a valuable resource that can be
used to overcome the Standard Quantum Limit (SQL) that bounds the precision of
sensors that operate with independent particles. Measurements beyond the SQL
are typically enabled by relatively simple entangled states (squeezed states
with Gaussian probability distributions), where quantum noise is redistributed
between different quadratures. However, due to both fundamental limitations and
the finite measurement resolution achieved in practice, sensors based on
squeezed states typically operate far from the true fundamental limit of
quantum metrology, the Heisenberg Limit. Here, by implementing an effective
time-reversal protocol through a controlled sign change in an optically
engineered many-body Hamiltonian, we demonstrate atomic-sensor performance with
non-Gaussian states beyond the limitations of spin squeezing, and without the
requirement of extreme measurement resolution. Using a system of 350 neutral
$^{171}$Yb atoms, this signal amplification through time-reversed interaction
(SATIN) protocol achieves the largest sensitivity improvement beyond the SQL
($11.8 \pm 0.5$~dB) demonstrated in any interferometer to date. Furthermore, we
demonstrate a precision improving in proportion to the particle number
(Heisenberg scaling), at fixed distance of 12.6~dB from the Heisenberg Limit.
These results pave the way for quantum metrology using complex entangled
states, with potential broad impact in science and technology. Potential
applications include searches for dark matter and for physics beyond the
standard model, tests of the fundamental laws of physics, timekeeping, and
geodesy.


Abstract:
  We prove that many of the recently-constructed algebras and categories which
appear in categorification can be equipped with an action of the Lie algebra
sl_2 by derivations. The representations which appear are filtered by tensor
products of coverma modules. In a future paper, we will address the
implications of this structure for categorification.


Abstract:
  Modern engineering education tends to focus on mathematics and fundamentals,
eschewing critical reflections on technology and the field of engineering. In
this paper, I present an elective engineering course and a 3-lecture module in
an introductory course that emphasize engaging with the social impacts of
technology.


Abstract:
  We study how the presence of committed volunteers influences the collective
helping behavior in emergency evacuation scenarios. In this study, committed
volunteers do not change their decision to help injured persons, implying that
other evacuees may adapt their helping behavior through strategic interactions.
An evolutionary game theoretic model is developed which is then coupled to a
pedestrian movement model to examine the collective helping behavior in
evacuations. By systematically controlling the number of committed volunteers
and payoff parameters, we have characterized and summarized various collective
helping behaviors in phase diagrams. From our numerical simulations, we observe
that the existence of committed volunteers can promote cooperation but adding
additional committed volunteers is effective only above a minimum number of
committed volunteers. This study also highlights that the evolution of
collective helping behavior is strongly affected by the evacuation process.


Abstract:
  Let $(M,g)$ be a smooth Anosov Riemannian manifold and $\mathcal{C}^\sharp$
the set of its primitive closed geodesics. Given a Hermitian vector bundle
$\mathcal{E}$ equipped with a unitary connection $\nabla^{\mathcal{E}}$, we
define $\mathcal{T}^\sharp(\mathcal{E}, \nabla^{\mathcal{E}})$ as the sequence
of traces of holonomies of $\nabla^{\mathcal{E}}$ along elements of
$\mathcal{C}^\sharp$. This descends to a homomorphism on the additive moduli
space $\mathbb{A}$ of connections up to gauge $\mathcal{T}^\sharp: (\mathbb{A},
\oplus) \to \ell^\infty(\mathcal{C}^\sharp)$, which we call the
$\textit{primitive trace map}$. It is the restriction of the well-known
$\textit{Wilson loop}$ operator to primitive closed geodesics.
  The main theorem of this paper shows that the primitive trace map
$\mathcal{T}^\sharp$ is locally injective near generic points of $\mathbb{A}$
when $\dim(M) \geq 3$. We obtain global results in some particular cases: flat
bundles, direct sums of line bundles, and general bundles in negative curvature
under a spectral assumption which is satisfied in particular for connections
with small curvature. As a consequence of the main theorem, we also derive a
spectral rigidity result for the connection Laplacian.
  The proofs are based on two new ingredients: a Liv\v{s}ic-type theorem in
hyperbolic dynamical systems showing that the cohomology class of a unitary
cocycle is determined by its trace along closed primitive orbits, and a theorem
relating the local geometry of $\mathbb{A}$ with the Pollicott-Ruelle resonance
near zero of a certain natural transport operator.


Abstract:
  We present a detailed investigation of millimeter-wave line emitters ALMA
J010748.3-173028 (ALMA-J0107a) and ALMA J010747.0-173010 (ALMA-J0107b), which
were serendipitously uncovered in the background of the nearby galaxy VV114
with spectral scan observations at $\lambda$ = 2 - 3 mm. Via Atacama Large
Millimeter/submillimeter Array (ALMA) detection of CO(4-3), CO(3-2), and
[CI](1-0) lines for both sources, their spectroscopic redshifts are
unambiguously determined to be $z= 2.4666\pm0.0002$ and $z=2.3100\pm0.0002$,
respectively. We obtain the apparent molecular gas masses $M_{\rm gas}$ of
these two line emitters from [CI] line fluxes as $(11.2 \pm 3.1) \times 10^{10}
M_\odot$ and $(4.2 \pm 1.2) \times 10^{10} M_\odot$, respectively. The observed
CO(4-3) velocity field of ALMA-J0107a exhibits a clear velocity gradient across
the CO disk, and we find that ALMA-J0107a is characterized by an inclined
rotating disk with a significant turbulence, that is, a deprojected maximum
rotation velocity to velocity dispersion ratio $v_{\rm max}/\sigma_{v}$ of $1.3
\pm 0.3$. We find that the dynamical mass of ALMA-J0107a within the CO-emitting
disk computed from the derived kinetic parameters, $(1.1 \pm 0.2) \times
10^{10}\ M_\odot$, is an order of magnitude smaller than the molecular gas mass
derived from dust continuum emission, $(3.2\pm1.6)\times10^{11}\ M_{\odot}$. We
suggest this source is magnified by a gravitational lens with a magnification
of $\mu \gtrsim10$, which is consistent with the measured offset from the
empirical correlation between CO-line luminosity and width.


Abstract:
  Biosignals are nowadays important subjects for scientific researches from
both theory and applications especially with the appearance of new pandemics
threatening humanity such as the new Coronavirus. One aim in the present work
is to prove that Wavelets may be successful machinery to understand such
phenomena by applying a step forward extension of wavelets to multiwavelets. We
proposed in a first step to improve the multiwavelet notion by constructing
more general families using independent components for multi-scaling and
multiwavelet mother functions. A special multiwavelet is then introduced,
continuous and discrete multiwavelet transforms are associated, as well as new
filters and algorithms of decomposition and reconstruction. The constructed
multiwavelet framework is applied for some experimentations showing fast
algorithms, ECG signal, and a strain of Coronavirus processing.


Abstract:
  We study the dynamics of the group of holomorphic automorphisms of the affine
cubic surfaces \begin{align*} S_{A,B,C,D} = \{(x,y,z) \in \mathbb{C}^3 \, : \,
x^2 + y^2 + z^2 +xyz = Ax + By+Cz+D\}, \end{align*} where $A,B,C,$ and $D$ are
complex parameters. We focus on a finite index subgroup $\Gamma_{A,B,C,D} <
{\rm Aut}(S_{A,B,C,D})$ whose action not only describes the dynamics of
Painlev\'e 6 differential equations but also arises naturally in the context of
character varieties. We define the Julia and Fatou sets of this group action
and prove that there is a dense orbit in the Julia set. In order to show that
the Julia set is ``large'' we consider a second dichotomy, between locally
discrete and locally non-discrete dynamics. For an open set in parameter space,
$\mathcal{N} \subset \mathbb{C}^4$, we show that there simultaneously exists an
open set in $S_{A,B,C,D}$ on which $\Gamma_{A,B,C,D}$ acts locally discretely
and a second open set in $S_{A,B,C,D}$ on which $\Gamma_{A,B,C,D}$ acts locally
non-discretely. After removing a countable union of real-algebraic
hypersurfaces from $\mathcal{N}$ we show that $\Gamma_{A,B,C,D}$ simultaneously
exhibits a non-empty Fatou set and also a Julia set having non-trivial
interior. The open set $\mathcal{N}$ contains a natural family of parameters
previously studied by Dubrovin-Mazzocco.
  The interplay between the Fatou/Julia dichotomy and the locally
discrete/non-discrete dichotomy plays a major theme in this paper and seems
bound to play an important role in further dynamical studies of holomorphic
automorphism groups.


Abstract:
  Smart power grids are one of the most complex cyber-physical systems,
delivering electricity from power generation stations to consumers. It is
critically important to know exactly the current state of the system as well as
its state variation tendency; consequently, state estimation and state
forecasting are widely used in smart power grids. Given that state forecasting
predicts the system state ahead of time, it can enhance state estimation
because state estimation is highly sensitive to measurement corruption due to
the bad data or communication failures. In this paper, a hybrid deep
learningbased method is proposed for power system state forecasting. The
proposed method leverages Convolutional Neural Network (CNN) for predicting
voltage magnitudes and a Deep Recurrent Neural Network (RNN) for predicting
phase angels. The proposed CNN-RNN model is evaluated on the IEEE 118-bus
benchmark. The results demonstrate that the proposed CNNRNN model achieves
better results than the existing techniques in the literature by reducing the
normalized Root Mean Squared Error (RMSE) of predicted voltages by 10%. The
results also show a 65% and 35% decrease in the average and maximum absolute
error of voltage magnitude forecasting.


Abstract:
  In several previous studies, quasars exhibiting broad emission lines with
>1000 km/s velocity offsets with respect to the host galaxy rest frame have
been discovered. One leading hypothesis for the origin of these velocity-offset
broad lines is the dynamics of a binary supermassive black hole (SMBH). We
present high-resolution radio imaging of 34 quasars showing these
velocity-offset broad lines with the Very Long Baseline Array (VLBA), aimed at
finding evidence for the putative binary SMBHs (such as dual radio cores), and
testing the competing physical models. We detect exactly half of the target
sample from our VLBA imaging, after implementing a 5 detection limit. While we
do not resolve double radio sources in any of the targets, we obtain limits on
the instantaneous projected separations of a radio-emitting binary for all of
the detected sources under the assumption that a binary still exists within our
VLBA angular resolution limits. We also assess the likelihood that a
radio-emitting companion SMBH exists outside of our angular resolution limits,
but its radio luminosity is too weak to produce a detectable signal in the VLBA
data. Additionally, we compare the precise sky positions afforded by these data
to optical positions from both the SDSS and Gaia DR2 source catalogs. We find
projected radio/optical separations on the order of 10 pc for three quasars.
Finally, we explore how future multi-wavelength campaigns with optical, radio,
and X-ray observatories can help discriminate further between the competing
physical models.


Abstract:
  We compute holographic complexity for the non-supersymmetric Janus
deformation of AdS$_5$ according to the volume conjecture. The result is
characterized by a power-law ultraviolet divergence. When a ball-shaped region
located around the interface is considered, a sub-leading logarithmic divergent
term and a finite part appear in the corresponding subregion volume complexity.
Using two different prescriptions to regularize the divergences, we find that
the coefficient of the logarithmic term is universal.


Abstract:
  We consider networks of small, autonomous devices that communicate with each
other wirelessly. Minimizing energy usage is an important consideration in
designing algorithms for such networks, as battery life is a crucial and
limited resource. Working in a model where both sending and listening for
messages deplete energy, we consider the problem of finding a maximal matching
of the nodes in a radio network of arbitrary and unknown topology.
  We present a distributed randomized algorithm that produces, with high
probability, a maximal matching. The maximum energy cost per node is $O(\log^2
n)$, where $n$ is the size of the network. The total latency of our algorithm
is $O(n \log n)$ time steps. We observe that there exist families of network
topologies for which both of these bounds are simultaneously optimal up to
polylog factors, so any significant improvement will require additional
assumptions about the network topology.
  We also consider the related problem of assigning, for each node in the
network, a neighbor to back up its data in case of node failure. Here, a key
goal is to minimize the maximum load, defined as the number of nodes assigned
to a single node. We present a decentralized low-energy algorithm that finds a
neighbor assignment whose maximum load is at most a polylog($n$) factor bigger
that the optimum.


Abstract:
  Classical approaches for OLAP assume that the data of all tables is complete.
However, in case of incomplete tables with missing tuples, classical approaches
fail since the result of a SQL aggregate query might significantly differ from
the results computed on the full dataset. Today, the only way to deal with
missing data is to manually complete the dataset which causes not only high
efforts but also requires good statistical skills to determine when a dataset
is actually complete. In this paper, we propose an automated approach for
relational data completion called ReStore using a new class of (neural)
schema-structured completion models that are able to synthesize data which
resembles the missing tuples. As we show in our evaluation, this efficiently
helps to reduce the relative error of aggregate queries by up to 390% on
real-world data compared to using the incomplete data directly for query
answering.


Abstract:
  Cultural diversity encoded within languages of the world is at risk, as many
languages have become endangered in the last decades in a context of growing
globalization. To preserve this diversity, it is first necessary to understand
what drives language extinction, and which mechanisms might enable coexistence.
Here, we study language shift mechanisms using theoretical and data-driven
perspectives. A large-scale empirical analysis of multilingual societies using
Twitter and census data yields a wide diversity of spatial patterns of language
coexistence. It ranges from a mixing of language speakers to segregation with
multilinguals on the boundaries of disjoint linguistic domains. To understand
how these different states can emerge and, especially, become stable, we
propose a model in which language coexistence is reached when learning the
other language is facilitated and when bilinguals favor the use of the
endangered language. Simulations carried out in a metapopulation framework
highlight the importance of spatial interactions arising from people mobility
to explain the stability of a mixed state or the presence of a boundary between
two linguistic regions. Further, we find that the history of languages is
critical to understand their present state.


Abstract:
  In this paper we develop a new approach to the study of uncountable
fundamental groups by using Hurewicz fibrations with the unique path-lifting
property (lifting spaces for short) as a replacement for covering spaces. In
particular, we consider the inverse limit of a sequence of covering spaces of
$X$. It is known that the path-connectivity of the inverse limit can be
expressed by means of the derived inverse limit functor $\varprojlim^1$, which
is, however, notoriously difficult to compute when the $\pi_1(X)$ is
uncountable.To circumvent this difficulty, we express the set of
path-components of the inverse limit, $\widehat X$, in terms of the functors
$\varprojlim$ and $\varprojlim^1$ applied to sequences of countable groups
arising from polyhedral approximations of $X$.
  A consequence of our computation is that path-connectedness of lifting space
implies that $\pi_1(\tilde X)$ supplements $\pi_1(X)$ in $\check\pi_1(X)$ where
$\check\pi_1(X)$ is the inverse limit of fundamental groups of polyhedral
approximations of $X$. As an application we show that $\mathcal G\cdot
\ker_{\mathbb Z}(\widehat F)= \widehat F\ne\mathcal G\cdot
\ker_{B(1,n)}(\widehat F)$, where $\widehat F$ is the canonical inverse limit
of finite rank free groups, $\mathcal G$ is the fundamental group of the
Hawaiian Earring, and $\ker_A(\widehat F)$ is the intersection of kernels of
homomorphisms from $\widehat{F}$ to $A$.


Abstract:
  Singular beams have attracted great attention due to their optical properties
and broad applications from light manipulation to optical communications.
However, there has been a lack of practical schemes with which to achieve
switchable singular beams with sub-wavelength resolution using ultrathin and
flat optical devices. In this work, we demonstrate the generation of switchable
vector and vortex beams utilizing dynamic metasurfaces at visible frequencies.
The dynamic functionality of the metasurface pixels is enabled by the
utilization of magnesium nanorods, which possess plasmonic reconfigurability
upon hydrogenation and dehydrogenation. We show that switchable vector beams of
different polarization states and switchable vortex beams of different
topological charges can be implemented through simple hydrogenation and
dehydrogenation of the same metasurfaces. Furthermore, we demonstrate a
two-cascade metasurface scheme for holographic pattern switching, taking
inspiration from orbital angular momentum-shift keying. Our work provides an
additional degree of freedom to develop high-security optical elements for
anti-counterfeiting applications.


Abstract:
  In this paper, we considered the gravitational collapse of a symmetric
radiating star consisting of perfect fluid (baryonic) in the background of dark
energy (DE) with general equation of state. The effect of DE on the singularity
formation has been discussed first separately (only DE present) and then
combination of both baryonic and DE interaction. We have also showed that DE
components play important role in the formation of Black-Hole(BH). In some
cases the collapse of radiating star leads to black hole formation and in other
cases it forms Naked-Singularity(or, eternally collapse). The present work is
in itself remarkable to describe the effect of dark energy on singularity
formation in radiating star.