  Pressure and temperature dependence of the negative thermal expansion in
Zn(CN)$_2$ is fully investigated using molecular dynamics simulations with a
built potential model. The advantage of this study allows us to reproduce all
the exotic behaviours of the material, including the negative thermal expansion
(NTE), the reduction of NTE with elevated temperature, the pressure enhancement
of NTE and the pressure-induced softening. Results of the study provide us
detailed data to link the properties in the energy space and the real space,
giving us insights to understand the properties and the connections between
them.


  Li, Chen, Li, Ma, and Vit\'anyi (2004) introduced a similarity metric based
on Kolmogorov complexity. It followed work by Shannon in the 1950s on a metric
based on entropy. We define two computable similarity metrics, analogous to the
Jaccard distance and Normalized Information Distance, based on conditional
automatic complexity and show that they satisfy all axioms of metric spaces.


  Recent advances in the development of commercial quantum annealers such as
the D-Wave 2X allow solving NP-hard optimization problems that can be expressed
as quadratic unconstrained binary programs. However, the relatively small
number of available qubits (around 1000 for the D-Wave 2X quantum annealer)
poses a severe limitation to the range of problems that can be solved. This
paper explores the suitability of preprocessing methods for reducing the sizes
of the input programs and thereby the number of qubits required for their
solution on quantum computers. Such methods allow us to determine the value of
certain variables that hold in either any optimal solution (called strong
persistencies) or in at least one optimal solution (weak persistencies). We
investigate preprocessing methods for two important NP-hard graph problems, the
computation of a maximum clique and a maximum cut in a graph. We show that the
identification of strong and weak persistencies for those two optimization
problems is very instance-specific, but can lead to substantial reductions in
the number of variables.


  We work out the orthogonality relations for the set of Carniglia-Mandel
triple modes which provide a set of normal modes for the source-free
electromagnetic field in a background consisting of a passive dielectric
half-space and the vacuum, respectively. Due to the inherent computational
complexity of the problem, an efficient strategy to accomplish this task is
desirable, which is presented in the paper. Furthermore, we provide all main
steps for the various proofs pertaining to different combinations of triple
modes in the orthogonality integral.


  Agriculture is one of the most critical activities developed today by
humankind and is in constant technical evolution to supply food and other
essential products to everlasting and increasing demand. New machines, seeds,
and fertilizers were developed to increase the productivity of cultivated
areas. It is estimated that by 2050 we will have a population of 9 billion
people and the production of food to meet this demand must occur sustainably.
To achieve this goal, it is paramount the adoption of sustainable management
techniques for agroecosystems. However, this is a complex task due to a large
number of variables involved. One of the solutions for the handling and
treatment of such diverse data is the use of Green IS. In this work, we adopt a
methodology called Indicators of Sustainability in Agroecosystems (Indicadores
de Sustentabilidade em Agroecossistemas -- ISA), implement an information
system based on it and apply Data Science techniques over the gathered data -
from 100 real rural properties - to compute which are the most relevant ISA
Indicators for the final ISA Sustainability Index Score. As a result, we have
developed a set of tools for data collection, processing, visualization, and
analysis of the sustainability of a rural property or region, following the ISA
methodology. We also have that with only 7 of the 21 Indicators present in ISA
we can identify the level of sustainability in more than 90% of cases, allowing
for a new discussion about shrinking the amount of data needed for the
computation of ISA, or remodelling the final computation of the Sustainability
Index so other Indicators can be more expressive. Users of the solutions
developed in this work can identify best practices for sustainability in
participating agroecosystems.


  Massive stars are important for the evolution of the interstellar medium. The
detailed study of their properties (such as mass loss, rotation, magnetic
fields) is enormously facilitated by samples of these objects in young massive
galactic star clusters. Using 2MASS we have searched for so far unknown
candidates of red supergiant clusters along the Galactic Plane. Utilising deep
high resolution UKIDSS GPS and VISTA VVV data to study colour-magnitude
diagrams, we uncover six new massive cluster candidates in the inner Galaxy. If
spectroscopically confirmed as real clusters, two of them could be part of the
Scutum-Complex. One cluster candidate has a number of potential red supergiant
members comparable to RSGC1 and 3.
  Our investigation of UKIDSS data reveals for the first time the main sequence
of the massive cluster RSGC2. The stars of the sequence show an increased
projected density at the same position as the known red supergiants in the
cluster and have E(J-K)=1.6mag. This either indicates an unusual extinction law
along the line of sight or a much lower near infrared extinction to the cluster
than previously estimated in the literature. We suggest that psf-photometry in
UKIDSS images might be able to uncover the main sequence of other RSGC
clusters.


  For a symmetric $\alpha$-stable process $X$ on $\RR^n$ with $0<\alpha <2$,
$n\geq 2$ and a domain $D \subset \RR^n$, let $L^D$ be the infinitesimal
generator of the subprocess of $X$ killed upon leaving $D$. For a Kato class
function $q$, it is shown that $L^D+q$ is intrinsic ultracontractive on a
H\"older domain $D$ of order 0. This is then used to establish the conditional
gauge theorem for $X$ on bounded Lipschitz domains in $\RR^n$. It is also shown
that the conditional lifetimes for symmetric stable process in a H\"older
domain of order 0 are uniformly bounded.


  Understanding the relationship between the magnetic field and coronal heating
is one of the central problems of solar physics. However, studies of the
magnetic properties of impulsively heated loops have been rare. We present
results from a study of 34 evolving coronal loops observed in the Fe XVIII line
component of AIA/SDO 94 A filter images from three active regions with
different magnetic conditions. We show that the peak intensity per unit
cross-section of the loops depends on their individual magnetic and geometric
properties. The intensity scales proportionally to the average field strength
along the loop ($B_{avg}$) and inversely with the loop length ($L$) for a
combined dependence of $(B_{avg}/L)^{0.52\pm0.13}$. These loop properties are
inferred from magnetic extrapolations of the photospheric HMI/SDO line-of-sight
and vector magnetic field in three approximations: potential and two Non Linear
Force-Free (NLFF) methods. Through hydrodynamic modeling (EBTEL model) we show
that this behavior is compatible with impulsively heated loops with a
volumetric heating rate that scales as $\epsilon_H\sim
B_{avg}^{0.3\pm0.2}/L^{0.2\pm^{0.2}_{0.1}}$.


  Kac--Moody groups $G$ over $\mathbb{R}$ have been conjectured to occur as
symmetry groups of supergravity theories dimensionally reduced to dimensions
less than 3, and their integral forms $G(\mathbb{Z})$ conjecturally encode
quantized symmetries. In this review paper, we briefly introduce the
conjectural symmetries of Kac--Moody groups in supergravity as well as the
known evidence for these conjectures. We describe constructions of Kac--Moody
groups over $\R$ and $\Z$ using certain choices of fundamental modules that are
considered to have physical relevance. Eisenstein series on certain finite
dimensional algebraic groups are known to encode quantum corrections in the low
energy limit of superstring theories. We describe briefly how the construction
of Eisenstein series extends to Kac--Moody groups. The constant terms of
Eisenstein series on $E_9$, $E_{10}$ and $E_{11}$ are predicted to encode
perturbative string theory corrections.


  The capacity of symmetric instance of the multiple unicast index coding
problem with neighboring antidotes (side-information) with number of messages
equal to the number of receivers was given by Maleki \textit{et al.} In this
paper, we construct matrices of size $ m \times n (m \geq n)$ over $F_q$ such
that any $n$ adjacent rows of the matrix are linearly independent. By using
such matrices, we give an optimal scalar linear index codes over $F_q$ for the
symmetric one-sided antidote problems considered by Maleki \textit{et al.} for
any given number of messages and one-sided antidotes. The constructed codes are
independent of field size and hence works over every field.


  We present the first images of the 28SiO v=1, J=2-1 maser emission around the
closest known massive young stellar object Orion Source I observed at 86 GHz
(3mm) with the VLBA. These images have high spatial (~0.3 mas) and spectral
(~0.054 km/s) resolutions. We find that the 3mm masers lie in an X-shaped locus
consisting of four arms, with blue-shifted emission in the south and east arms
and red-shifted emission in the north and west arms. Comparisons with previous
images of the 28SiO v=1,2, J=1-0 transitions at 7mm (observed in 2001-2002)
show that the bulk of the J=2-1 transition emission follows the streamlines of
the J=1-0 emission and exhibits an overall velocity gradient consistent with
the gradient at 7mm. While there is spatial overlap between the 3mm and 7mm
transitions, the 3mm emission, on average, lies at larger projected distances
from Source I (~44 AU compared with ~35 AU for 7mm). The spatial overlap
between the v=1, J=1-0 and J=2-1 transitions is suggestive of a range of
temperatures and densities where physical conditions are favorable for both
transitions of a same vibrational state. However, the observed spatial offset
between the bulk of emission at 3mm and 7mm possibly indicates different ranges
of temperatures and densities for optimal excitation of the masers. We discuss
different maser pumping models that may explain the observed offset. We
interpret the 3mm and 7mm masers as being part of a single wide-angle outflow
arising from the surface of an edge-on disk rotating about a
northeast-southwest axis, with a continuous velocity gradient indicative of
differential rotation consistent with a Keplerian profile in a high-mass
proto-binary.


  Sequential numerical methods for integrating initial value problems (IVPs)
can be prohibitively expensive when high numerical accuracy is required over
the entire interval of integration. One remedy is to integrate in a parallel
fashion, "predicting" the solution serially using a cheap (coarse) solver and
"correcting" these values using an expensive (fine) solver that runs in
parallel on a number of temporal subintervals. In this work, we propose a
time-parallel algorithm (GParareal) that solves IVPs by modelling the
correction term, i.e. the difference between fine and coarse solutions, using a
Gaussian process emulator. This approach compares favourably with the classic
parareal algorithm and we demonstrate, on a number of IVPs, that GParareal can
converge in fewer iterations than parareal, leading to an increase in parallel
speed-up. GParareal also manages to locate solutions to certain IVPs where
parareal fails and has the additional advantage of being able to use archives
of legacy solutions, e.g. solutions from prior runs of the IVP for different
initial conditions, to further accelerate convergence of the method --
something that existing time-parallel methods do not do.


  In this work, we use the first-principle density-functional approach to study
the electronic structure of a H atom adsorbed on the ideal Pt(111) and vicinal
Pt(211) and Pt(331) surfaces. Full three-dimensional potential-energy surfaces
(3D PES's) as well as local electronic density of states on various adsorption
sites are obtained. The results show that the steps modify the PES
considerably. The effect is nonlocal and extends into the region of the (111)
terraces. We also find that different type of steps have different kind of
influence on the PES when compared to the one of the ideal Pt(111) surface. The
full 3D PES's calculated in this work provide a starting point for the
theoretical study of vibrational and diffusive dynamics of H adatoms adsorbed
on these vicinal surfaces.


  It is proved that a classical (respec. quantum) system consisting of a
particle in a constant magnetic field is canonically (respec. unitarily)
equivalent to a 2-dimensional harmonic oscillator plus a free particle. It is
also shown that the eigenvectors of the discrete spectrum are entangled states
of the 2-dimensional harmonic oscillator.


  We present multi-wavelength high-spatial resolution (~0.1'', 70 pc)
observations of UGC 4211 at z=0.03474, a late-stage major galaxy merger at the
closest nuclear separation yet found in near-IR imaging (0.32'', ~230 pc
projected separation). Using Hubble Space Telescope/STIS, VLT/MUSE+AO,
Keck/OSIRIS+AO spectroscopy, and ALMA observations, we show that the spatial
distribution, optical and NIR emission lines, and millimeter continuum emission
are all consistent with both nuclei being powered by accreting supermassive
black holes (SMBHs). Our data, combined with common black hole mass
prescriptions, suggests that both SMBHs have similar masses, log MBH~8.1
(south) and log MBH~8.3 (north), respectively. The projected separation of 230
pc (~6X the black hole sphere of influence) represents the closest-separation
dual AGN studied to date with multi-wavelength resolved spectroscopy and shows
the potential of nuclear (<50 pc) continuum observations with ALMA to discover
hidden growing SMBH pairs. While the exact occurrence rate of close-separation
dual AGN is not yet known, it may be surprisingly high, given that UGC 4211 was
found within a small, volume-limited sample of nearby hard X-ray detected AGN.
Observations of dual SMBH binaries in the sub-kpc regime at the final stages of
dynamical friction provide important constraints for future gravitational wave
observatories.


  Due to the heterogeneity of the global distribution of ecological and
hydrological ground-truth observations, machine learning models can have
limited adaptability when applied to unknown locations, which is referred to as
weak extrapolability. Domain adaptation techniques have been widely used in
machine learning domains such as image classification, which can improve the
model generalization ability by adjusting the difference or inconsistency of
the domain distribution between the training and test sets. However, this
approach has rarely been used explicitly in machine learning models in ecology
and hydrology at the global scale, although these models have often been
questioned due to geographic extrapolability issues. This paper briefly
describes the shortcomings of current machine learning models of ecology and
hydrology in terms of the global representativeness of the distribution of
observations and the resulting limitations of the lack of extrapolability and
suggests that future related modelling efforts should consider the use of
domain adaptation techniques to improve extrapolability.


  We report on the results of two new approaches to considering how many
principal components to retain from an analysis of a multivariate time series.
The first is by using a "heat map" based approach. A heat map in this context
refers to a series of principal component coefficients created by applying a
sliding window to a multivariate time series. Furthermore the heat maps can
provide detailed insights into the evolution of the structure of each principal
component over time. The second is by examining the change of the angle of the
principal component over time within the high-dimensional data space. We
provide evidence that both are useful in studying structure and evolution of a
multivariate time series.


  Recent research has shown Deep Neural Networks (DNNs) to be vulnerable to
adversarial examples that induce desired misclassifications in the models. Such
risks impede the application of machine learning in security-sensitive domains.
Several defense methods have been proposed against adversarial attacks to
detect adversarial examples at test time or to make machine learning models
more robust. However, while existing methods are quite effective under blackbox
threat model, where the attacker is not aware of the defense, they are
relatively ineffective under whitebox threat model, where the attacker has full
knowledge of the defense.
  In this paper, we propose ExAD, a framework to detect adversarial examples
using an ensemble of explanation techniques. Each explanation technique in ExAD
produces an explanation map identifying the relevance of input variables for
the model's classification. For every class in a dataset, the system includes a
detector network, corresponding to each explanation technique, which is trained
to distinguish between normal and abnormal explanation maps. At test time, if
the explanation map of an input is detected as abnormal by any detector model
of the classified class, then we consider the input to be an adversarial
example. We evaluate our approach using six state-of-the-art adversarial
attacks on three image datasets. Our extensive evaluation shows that our
mechanism can effectively detect these attacks under blackbox threat model with
limited false-positives. Furthermore, we find that our approach achieves
promising results in limiting the success rate of whitebox attacks.


  Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has
attracted unprecedented attention for its impressive zero-shot recognition
ability and excellent transferability to downstream tasks. However, CLIP is
quite data-hungry and requires 400M image-text pairs for pre-training, thereby
restricting its adoption. This work proposes a novel training paradigm, Data
efficient CLIP (DeCLIP), to alleviate this limitation. We demonstrate that by
carefully utilizing the widespread supervision among the image-text pairs, our
De-CLIP can learn generic visual features more efficiently. Instead of using
the single image-text contrastive supervision, we fully exploit data potential
through the use of (1) self-supervision within each modality; (2) multi-view
supervision across modalities; (3) nearest-neighbor supervision from other
similar pairs. Benefiting from intrinsic supervision, our DeCLIP-ResNet50 can
achieve 60.4% zero-shot top1 accuracy on ImageNet, which is 0.8% above the
CLIP-ResNet50 while using 7.1 x fewer data. Our DeCLIP-ResNet50 outperforms its
counterpart in 8 out of 11 visual datasets when transferred to downstream
tasks. Moreover, Scaling up the model and computing also works well in our
framework.Our code, dataset and models are released at:
https://github.com/Sense-GVT/DeCLIP


  Many theories of modified gravity, including the well studied Horndeski
models, are characterized by a screening mechanism that ensures that standard
gravity is recovered near astrophysical bodies. In a recently introduced class
of gravitational theories that goes beyond Horndeski, it has been found that
new derivative interactions lead to a partial breaking of the Vainshtein
screening mechanism inside any gravitational source, although not outside. We
study the impact of this new type of deviation from standard gravity on the
density profile of a spherically symmetric matter distribution, in the
nonrelativistic limit. For simplicity, we consider a polytropic equation of
state and derive the modifications to the standard Lane-Emden equations. We
also show the existence of a universal upper bound on the amplitude of this
type of modified gravity, independently of the details of the equation of
state.


  The main objective of this article is to derive a new set of gravitational
field equations and to establish a new unified theory for dark energy and dark
matter. The new gravitational field equations with scalar potential $\varphi$
are derived using the Einstein-Hilbert functional, and the scalar potential
$\varphi$ is a natural outcome of the divergence-free constraint of the
variational elements. Gravitation is now described by the Riemannian metric
$g_{ij}$, the scalar potential $\varphi$ and their interactions, unified by the
new gravitational field equations. Associated with the scalar potential
$\varphi$ is the scalar potential energy density $\frac{c^4}{8\pi G}
\Phi=\frac{c^4}{8\pi G} g^{ij}D_iD_j \varphi$, which represents a new type of
energy caused by the non-uniform distribution of matter in the universe. The
negative part of this potential energy density produces attraction, and the
positive part produces repelling force. This potential energy density is
conserved with mean zero: $\int_M \Phi dM=0$. The sum of this new potential
energy density $\frac{c^4}{8\pi G} \Phi$ and the coupling energy between the
energy-momentum tensor $T_{ij}$ and the scalar potential field $\varphi$ gives
rise to a new unified theory for dark matter and dark energy: The negative part
of this sum represents the dark matter, which produces attraction, and the
positive part represents the dark energy, which drives the acceleration of
expanding galaxies. In addition, the scalar curvature of space-time obeys
$R=\frac{8\pi G}{c^4} T + \Phi$. Furthermore, the new field equations resolve a
few difficulties encountered by the classical Einstein field equations.


  A new class of statistical deformable models is introduced to study
high-dimensional curves or images. In addition to the standard measurement
error term, these deformable models include an extra error term modeling the
individual variations in intensity around a mean pattern. It is shown that an
appropriate tool for statistical inference in such models is the notion of
sample Fr\'echet means, which leads to estimators of the deformation parameters
and the mean pattern. The main contribution of this paper is to study how the
behavior of these estimators depends on the number n of design points and the
number J of observed curves (or images). Numerical experiments are given to
illustrate the finite sample performances of the procedure.


  Massive vector particles are minimal dark matter candidates that motivate a
wide range of laboratory searches, primarily exploiting a postulated kinetic
mixing with the photon. However, depending on the high energy field content,
the dominant vector dark matter (VDM) coupling to visible particles may arise
at higher operator dimension, motivating efforts to predict direct detection
rates for more general interactions. Here we present the first calculation of
VDM absorption through its coupling to electron electric (EDM) or magnetic
(MDM) dipole moments, which can be realized in minimal extensions to the
Standard Model and yield the observed abundance through a variety of mechanisms
across the eV\,-\,MeV mass range. We compute the absorption rate of the MDM and
EDM models for a general target, and then derive direct detection constraints
from targets currently in use: Si and Ge crystals and Xe and Ar atoms. We find
that current experiments are already sensitive to VDM parameter space
corresponding to a cosmological freeze-in scenario, and future experiments will
be able to completely exclude MDM and EDM freeze-in models with reheat
temperatures below the electroweak scale. Additionally, we find that while
constraints on the MDM interaction can be related to constraints on axion-like
particles, the same is not true for the EDM model, so the latter absorption
rate must be computed from first principles. To achieve this, we update the
publicly available program EXCEED-DM to perform these new calculations.


  In this paper we present the Wheeler-DeWitt equation of $f(R,L_m)$ gravity in
flat FRW universe, which is the first step of the study of quantum $f(R,L_m)$
cosmology. In the minisuperspace spanned by the FRW scale factor $a$ and the
Ricci scalar $R$, we explicitly examine and verify the equivalence of the
reduced action. We then canonical quantize the $f(R,L_m)$ model and derive the
corresponding Wheeler-DeWitt equation.


  In this paper, we address the dataset scarcity issue with the hyperspectral
image classification. As only a few thousands of pixels are available for
training, it is difficult to effectively learn high-capacity Convolutional
Neural Networks (CNNs). To cope with this problem, we propose a novel
cross-domain CNN containing the shared parameters which can co-learn across
multiple hyperspectral datasets. The network also contains the non-shared
portions designed to handle the dataset specific spectral characteristics and
the associated classification tasks. Our approach is the first attempt to learn
a CNN for multiple hyperspectral datasets, in an end-to-end fashion. Moreover,
we have experimentally shown that the proposed network trained on three of the
widely used datasets outperform all the baseline networks which are trained on
single dataset.


  We investigate the interaction of leading jet partons within a strongly
interacting quark-gluon plasma (sQGP) medium, using the effective dynamical
quasiparticle model (DQPM). The DQPM offers a description of the sQGP's
non-perturbative nature at finite temperature $T$ and baryon chemical potential
$\mu_B$ through a propagator representation of massive off-shell partons
(quarks and gluons). These partons are characterized by spectral functions with
$T,\mu_B$ dependent masses and widths, adjusted to reproduce the lattice
Quantum Chromodynamics (lQCD) equation-of-state (EoS) for the QGP in
thermodynamic equilibrium. Our focus lies on examining the jet transport
coefficients by elastic scattering in sQGP, specifically the transverse
momentum transfer squared per unit length denoted as $\hat{q}$, within the QGP.
Furthermore, we investigate the dependence of these coefficients on both the
medium temperature $T$ and the jet parton energy. By studying the jet transport
coefficients and their relationship to temperature and parton energy, we aim to
gain insights into the dynamics of jet propagation in the strongly interacting
quark-gluon plasma medium.


  We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent
quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in
the sense that they depend on individual-specific covariates. The
individualized DAG structure of the proposed approach can be uniquely
identified at any given quantile, based on purely observational data without
strong assumptions such as a known topological ordering. To scale the proposed
method to a large number of variables and covariates, we propose for the model
parameters a novel parameter expanded horseshoe prior that affords a number of
attractive theoretical and computational benefits to our approach. By modeling
the conditional quantiles, qDAGx overcomes the common limitations of mean
regression for DAGs, which can be sensitive to the choice of likelihood, e.g.,
an assumption of multivariate normality, as well as to the choice of priors. We
demonstrate the performance of qDAGx through extensive numerical simulations
and via an application in precision medicine, which infers patient-specific
protein--protein interaction networks in lung cancer.


  Let $G$ be an infinite locally compact group and $\aleph$ a cardinal
satisfying $\aleph_0\le\aleph\le w(G)$ for the weight $w(G)$ of $G$. It is
shown that there is a closed subgroup $N$ of $G$ with $w(N)=\aleph$. Sample
consequences are:
  (1) Every infinite compact group contains an infinite closed metric subgroup.
  (2) For a locally compact group $G$ and $\aleph$ a cardinal satisfying
$\aleph_0\le\aleph\le \lw(G)$, where $\lw(G)$ is the local weight of $G$, there
are either no infinite compact subgroups at all or there is a compact subgroup
$N$ of $G$ with $w(N)=\aleph$.
  (3) For an infinite abelian group $G$ there exists a properly ascending
family of locally quasiconvex group topologies on $G$, say,
$(\tau_\aleph)_{\aleph_0\le \aleph\le \card(G)}$, such that
$(G,\tau_\aleph)\hat{\phantom{m}}\cong\hat G$.
  Items (2) and (3) are shown in Section 5.


  Magnetars are neutron stars with very strong magnetic fields on the order of
$10^{13}$ to $10^{15}$ G. Young magnetars with oppositely-oriented magnetic
fields and spin moments may emit high-energy (HE) neutrinos from their polar
caps as they may be able to accelerate cosmic rays to above the photomeson
threshold (Zhang et al. 2003). Giant flares of soft gamma-ray repeaters (a
subclass of magnetars) may also produce HE neutrinos and therefore a HE
neutrino flux from this class is potentially detectable (Ioka et al. 2005).
Here we present plans to search for neutrino emission from magnetars listed in
the McGill Online Magnetar Catalog using 10 years of well-reconstructed IceCube
muon-neutrino events looking for significant clustering around magnetars'
direction. IceCube is a cubic kilometer neutrino observatory at the South Pole
and has been fully operational for the past ten years.


  The impact of developmental and aging processes on brain connectivity and the
connectome has been widely studied. Network theoretical measures and certain
topological principles are computed from the entire brain, however there is a
need to separate and understand the underlying subnetworks which contribute
towards these observed holistic connectomic alterations. One organizational
principle is the rich-club - a core subnetwork of brain regions that are
strongly connected, forming a high-cost, high-capacity backbone that is
critical for effective communication in the network. Investigations primarily
focus on its alterations with disease and age. Here, we present a systematic
analysis of not only the rich-club, but also other subnetworks derived from
this backbone - namely feeder and seeder subnetworks. Our analysis is applied
to structural connectomes in a normal cohort from a large, publicly available
lifespan study. We demonstrate changes in rich-club membership with age
alongside a shift in importance from 'peripheral' seeder to feeder subnetworks.
Our results show a refinement within the rich-club structure (increase in
transitivity and betweenness centrality), as well as increased efficiency in
the feeder subnetwork and decreased measures of network integration and
segregation in the seeder subnetwork. These results demonstrate the different
developmental patterns when analyzing the connectome stratified according to
its rich-club and the potential of utilizing this subnetwork analysis to reveal
the evolution of brain architectural alterations across the life-span.


  A key agreement problem is considered that has a biometric or physical
identifier, a terminal for key enrollment, and a terminal for reconstruction. A
nested convolutional code design is proposed that performs vector quantization
during enrollment and error control during reconstruction. Physical identifiers
with small bit error probability illustrate the gains of the design. One
variant of the nested convolutional codes improves on the best known key vs.
storage rate ratio but it has high complexity. A second variant with lower
complexity performs similar to nested polar codes. The results suggest that the
choice of code for key agreement with identifiers depends primarily on the
complexity constraint.


  We consider $U(1)_H$ extensions of Type-I 2HDM plus a singlet scalar
$\phi_H$, introducing a new Higgs doublet $H_2$ and a singlet $\phi_H$ charged
under $U(1)_H$. The SM Higgs doublet $H_1$ as well as all the SM fermions and
three right-handed singlet neutrinos, introduced to generate nonzero neutrino
masses and mixings, are neutral under $U(1)_H$. We also introduce a SM singlet
Dirac fermion, charged under $U(1)_H$ and utilize it as sterile neutrinos
relevant to the MiniBooNE experiment. The $U(1)_H$ symmetry breaks due to the
vacuum expectation values of $H_2$ and $\phi_H$, leading to the emergence of a
light vector boson with a mass of approximately 17 MeV. This vector boson
interacts with fermions through mass mixing and kinetic mixing process
involving other neutral gauge bosons. Furthermore, alongside the light vector
boson, another light scalar particle with a mass around 10--100 MeV may arise
from scalar sector mixing. By utilizing the gauge couplings of the light vector
boson and the Yukawa couplings of the light scalar, this model can
simultaneously provide an explanation for the Beryllium anomaly observed in the
ATOMKI experiment, the anomalous magnetic moment of charged leptons and the
excess of electron-like events detected at the MiniBooNE experiment.


  Statistical physics models ranging from simple lattice to complex quantum
Hamiltonians are one of the mainstays of modern physics, that have allowed both
decades of scientific discovery and provided a universal framework to
understand a broad range of phenomena from alloying to frustrated and
phase-separated materials to quantum systems. Traditionally, exploration of the
phase diagrams corresponding to multidimensional parameter spaces of
Hamiltonians was performed using a combination of basic physical principles,
analytical approximations, and extensive numerical modeling. However,
exploration of complex multidimensional parameter spaces is subject to the
classic dimensionality problem, and the behaviors of interest concentrated on
low dimensional manifolds can remain undiscovered. Here, we demonstrate that a
combination of exploration and exploration-exploitation with Gaussian process
modeling and Bayesian optimization allows effective exploration of the
parameter space for lattice Hamiltonians, and effectively maps the regions at
which specific macroscopic functionalities or local structures are maximized.
We argue that this approach is general and can be further extended well beyond
the lattice Hamiltonians to effectively explore parameter space of more complex
off-lattice and dynamic models.


  Nature presents multiple intriguing examples of processes which proceed at
high precision and regularity. This remarkable stability is frequently counter
to modelers' experience with the inherent stochasticity of chemical reactions
in the regime of low copy numbers. Moreover, the effects of noise and
nonlinearities can lead to "counter-intuitive" behavior, as demonstrated for a
basic enzymatic reaction scheme that can display stochastic focusing (SF).
Under the assumption of rapid signal fluctuations, SF has been shown to convert
a graded response into a threshold mechanism, thus attenuating the detrimental
effects of signal noise. However, when the rapid fluctuation assumption is
violated, this gain in sensitivity is generally obtained at the cost of very
large product variance, and this unpredictable behavior may be one possible
explanation of why, more than a decade after its introduction, SF has still not
been observed in real biochemical systems.
  In this work we explore the noise properties of a simple enzymatic reaction
mechanism with a small and fluctuating number of active enzymes that behaves as
a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We
then show that the inclusion of a plausible negative feedback mechanism turns
the system from a noisy signal detector to a strong homeostatic mechanism by
exchanging high gain with strong attenuation in output noise and robustness to
parameter variations. Moreover, we observe that the discrepancy between
deterministic and stochastic descriptions of stochastically focused systems in
the evolution of the means almost completely disappears, despite very low
molecule counts and the additional nonlinearity due to feedback.
  The reaction mechanism considered here can provide a possible resolution to
the apparent conflict between intrinsic noise and high precision in critical
intracellular processes.


  Diversification is nested, and early models suggested this could lead to a
great deal of evolutionary redundancy in the Tree of Life. This result is based
on a particular set of branch lengths produced by the common coalescent, where
pendant branches leading to tips can be very short compared to branches deeper
in the tree. Here, we analyze alternative and more realistic Yule and
birth-death models. We show how censoring at the present both makes average
branches one half what we might expect and makes pendant and interior branches
roughly equal in length. Although dependent on whether we condition on the size
of the tree, its age, or both, these results hold both for the Yule model and
for birth-death models with moderate extinction. Importantly, the rough
equivalency in interior and exterior branch lengths means the loss of
evolutionary history with loss of species can be roughly linear. Under these
models, the Tree of Life may offer limited redundancy in the face of ongoing
species loss.


  Stereo dense image matching can be categorized to low-level feature based
matching and deep feature based matching according to their matching cost
metrics. Census has been proofed to be one of the most efficient low-level
feature based matching methods, while fast Convolutional Neural Network
(fst-CNN), as a deep feature based method, has small computing time and is
robust for satellite images. Thus, a comparison between fst-CNN and census is
critical for further studies in stereo dense image matching. This paper used
cost function of fst-CNN and census to do stereo matching, then utilized
semi-global matching method to obtain optimized disparity images. Those images
are used to produce digital surface model to compare with ground truth points.
It addresses that fstCNN performs better than census in the aspect of absolute
matching accuracy, histogram of error distribution and matching completeness,
but these two algorithms still performs in the same order of magnitude.


  We show that a complete $m$-dimensional immersed submanifold $M$ of
$\mathbb{R}^{n}$ with $a(M)<1$ is properly immersed and have finite topology,
where $a(M)\in [0,\infty]$ is an scaling invariant number that gives the rate
that the norm of the second fundamental form decays to zero at infinity. The
class of submanifolds $M$ with $a(M)<1$ contains all complete minimal surfaces
in $\mathbb{R}^{n}$ with finite total curvature, all $m$-dimensional minimal
submanifolds $M $ of $ \mathbb{R}^{n}$ with finite total scalar curvature
$\smallint_{M}| \alpha |^{m} dV<\infty $ and all complete 2-dimensional
complete surfaces with $\smallint_{M}| \alpha |^{2} dV<\infty $ and nonpositive
curvature with respect to every normal direction, since $a(M)=0$ for them.


  If Dark Matter is composed of hidden-sector photons that kinetically mix with
photons of the visible sector, then Dark Matter has a tiny oscillating electric
field component. Its presence would lead to a small amount of visible radiation
being emitted from a conducting surface, with the photon frequency given
approximately by the mass of the hidden photon. Here, we report on experimental
efforts that have started recently to search for such hidden photon Dark Matter
in the (sub-)eV regime with a prototype mirror for the Auger fluorescence
detector at the Karlsruhe Institute for Technology.


  In this paper, we present our dzFinNlp team's contribution for intent
detection in financial conversational agents, as part of the AraFinNLP shared
task. We experimented with various models and feature configurations, including
traditional machine learning methods like LinearSVC with TF-IDF, as well as
deep learning models like Long Short-Term Memory (LSTM). Additionally, we
explored the use of transformer-based models for this task. Our experiments
show promising results, with our best model achieving a micro F1-score of
93.02% and 67.21% on the ArBanking77 dataset, in the development and test sets,
respectively.


  We present images of NGC4696, the central galaxy in the Centaurus cluster,
showing the large extent of cool filaments which are bright in H-alpha line
emission. These filaments share the detailed structure of both the central dust
lane and the inner regions of the arc-like plumes seen in soft X-ray emission.
The X-ray gas is at its coolest, and most absorbed in this same region. The
smoothness of the features implies that the local environment is not strongly
turbulent. We suggest that these filaments are either shaped by confinement due
to a strong magnetic field, or by bulk flows within the intracluster medium. We
propose that like similar filamentary systems in the core of clusters, these
cooler components have been drawn out of the central galaxy behind buoyant gas
bubbles from previous episodes of radio activity. We find a spur of
low-frequency radio emission leading to a region of low X-ray pressure within
the intracluster medium supporting this interpretation.


  Recently, the BaBar collaboration reported first evidence for new physics in
B->D\tau\nu and B->D*\tau\nu. Combining both processes the significance is
3.4\sigma. This result cannot be explained in a two Higgs doublet model of type
II. Furthermore, the CKMfitter group finds a 2.9\sigma discrepancy between the
Standard Model prediction for Br[B->\tau\nu] (using Vub from a global fit to
the unitary triangle) and the measurements of the B factories. Altogether,
these measurements are strong indications for physics beyond the SM in B meson
decays to taus.
  We show that in a two Higgs doublet model of type III it is possible to
simultaneously explain \bdtau and \bdstau using a single free parameter
\epsilon^u_32. Also Br[B->\tau\nu] can be brought into agreement with
experiment using \epsilon^u_31. Furthermore, for Higgs (A^0, H^0, H^+) masses
around 500 GeV, as suggested by recent CMS results, all bounds from FCNC
processes are satisfied and B->D\tau\nu, B->D*\tau\nu and B->\tau\nu can be
explained without a significant degree of fine-tuning.


  The stellar stream connected to the Sagittarius (Sgr) dwarf galaxy is the
most massive tidal stream that has been mapped in the Galaxy, and is the
dominant contributor to the outer stellar halo of the Milky Way. We present
metallicity maps of the Sgr stream, using 34,240 red giant branch stars with
inferred metallicities from Gaia BP/RP spectra. This sample is larger than
previous samples of Sgr stream members with chemical abundances by an order of
magnitude. We measure metallicity gradients with respect to Sgr stream
coordinates $(\Lambda, B)$, and highlight the gradient in metallicity with
respect to stream latitude coordinate $B$, which has not been observed before.
We find $\nabla \mathrm{[M/H]} = -2.48 \pm 0.08 \times 10^{-2}$ dex/deg above
the stream track ($B>B_0$ where $B_0=1.5$ deg is the latitude of the Sgr
remnant) and $\nabla \mathrm{[M/H]} =- 2.02 \pm 0.08 \times 10^{-2}$ dex/deg
below the stream track ($B<B_0$). By painting metallicity gradients onto a
tailored N-body simulation of the Sgr stream, we find that the observed
metallicities in the stream are consistent with an initial radial metallicity
gradient in the Sgr dwarf galaxy of $\sim -0.1$ to $-0.2$ dex/kpc, well within
the range of observed metallicity gradients in Local Group dwarf galaxies. Our
results provide novel observational constraints for the internal structure of
the dwarf galaxy progenitor of the Sgr stream. Leveraging new large datasets in
conjunction with tailored simulations, we can connect the present day
properties of disrupted dwarfs in the Milky Way to their initial conditions.


  Artificial intelligence (AI) will pave the way to a new era in medicine.
However, currently available AI systems do not interact with a patient, e.g.,
for anamnesis, and thus are only used by the physicians for predictions in
diagnosis or prognosis. However, these systems are widely used, e.g., in
diabetes or cancer prediction. In the current study, we developed an AI that is
able to interact with a patient (virtual doctor) by using a speech recognition
and speech synthesis system and thus can autonomously interact with the
patient, which is particularly important for, e.g., rural areas, where the
availability of primary medical care is strongly limited by low population
densities. As a proof-of-concept, the system is able to predict type 2 diabetes
mellitus (T2DM) based on non-invasive sensors and deep neural networks.
Moreover, the system provides an easy-to-interpret probability estimation for
T2DM for a given patient. Besides the development of the AI, we further
analyzed the acceptance of young people for AI in healthcare to estimate the
impact of such system in the future.


  We discuss energy dependence of mean $p_{\rm T}$ correlation with $N_{\rm
ch}$ basing on general features of high energy collisions such as saturation
and geometrical scaling. We use Color Glass Condensate calculation of an
effective interaction radius that scales as a third root of multiplicity, and
then saturates. With this model input we construct scaling variable for $<
p_{\rm T} > (N_{\rm ch})$ at different energies both for pp and pPb collisions,
and show that recent ALICE data indeed does exhibit this scaling property. We
discuss energy dependence of the interaction radius and argue that since the
radius cannot grow too large, a universal behavior of $< p_{\rm T} >$ for large
multiplicities is expected.


  The algebra $\Psi(M)$ of order zero pseudodifferential operators on a compact
manifold $M$ defines a well-known $C^*$-extension of the algebra $C(S^*M)$ of
continuous functions on the cospherical bundle $S^*M\subset T^*M$ by the
algebra $\K$ of compact operators. In his proof of the index theorem, Higson
defined and used an asymptotic homomorphism $T$ from $C_0(T^*M)$ to $\K$, which
plays the role of a deformation for the commutative algebra $C_0(T^*M)$.
Similar constructions exist also for operators and symbols with coefficients in
a $C^*$-algebra. We show that the image of the above extension under the
Connes--Higson construction is $T$ and that this extension can be reconstructed
out of $T$. This explains, why the classical approach to the index theory
coincides with the one based on asymptotic homomorphisms.


  Nowadays, data analysis has become a problem as the amount of data is
constantly increasing. In order to overcome this problem in textual data, many
models and methods are used in natural language processing. The topic modeling
field is one of these methods. Topic modeling allows determining the semantic
structure of a text document. Latent Dirichlet Allocation (LDA) is the most
common method among topic modeling methods. In this article, the proposed
n-stage LDA method, which can enable the LDA method to be used more
effectively, is explained in detail. The positive effect of the method has been
demonstrated by the applied English and Turkish studies. Since the method
focuses on reducing the word count in the dictionary, it can be used
language-independently. You can access the open-source code of the method and
the example: https://github.com/anil1055/n-stage_LDA


  We examine for representative gaugino-higgsino mixing scenarios
slepton-neutralino and slepton-chargino production in deep inelastic
ep-scattering at $\sqrt{s}=1.8$ TeV. We find sneutrino-chargino production to
be the dominant process with cross sections more than one order of magnitude
bigger than those for slepton-squark production. Also for associated production
of sneutrinos and zino-like neutralinos the cross sections are at least
comparable to those for $\tilde l \tilde q$-production, whereas
selectron-neutralino/chargino production is with cross sections significantly
smaller than those for selectron-squark production less favorable. Typical
signatures include events with up to four charged leptons, hadronic jets and,
in some cases, gauge bosons.


  Code-mixing, the blending of multiple languages within a single conversation,
introduces a distinctive challenge, particularly in the context of response
generation. Capturing the intricacies of code-mixing proves to be a formidable
task, given the wide-ranging variations influenced by individual speaking
styles and cultural backgrounds. In this study, we explore response generation
within code-mixed conversations. We introduce a novel approach centered on
harnessing the Big Five personality traits acquired in an unsupervised manner
from the conversations to bolster the performance of response generation. These
inferred personality attributes are seamlessly woven into the fabric of the
dialogue context, using a novel fusion mechanism, PA3. It uses an effective
two-step attention formulation to fuse the dialogue and personality
information. This fusion not only enhances the contextual relevance of
generated responses but also elevates the overall performance of the model. Our
experimental results, grounded in a dataset comprising of multi-party
Hindi-English code-mix conversations, highlight the substantial advantages
offered by personality-infused models over their conventional counterparts.
This is evident in the increase observed in ROUGE and BLUE scores for the
response generation task when the identified personality is seamlessly
integrated into the dialogue context. Qualitative assessment for personality
identification and response generation aligns well with our quantitative
results.


  We argue that our recent success in using our resummed quantum gravity
approach to Einstein's general theory of relativity, in the context of the
Planck scale cosmology formulation of Bonanno and Reuter, to estimate the value
of the cosmological constant \Lambda supports the use of quantum mechanical
consistency requirements to constrain the main uncertainty in that very
promising result. This main uncertainty, which is due to the uncertainty in the
value of the time t_{\text{tr}} at which the transition from the Planck scale
cosmology to the FRW model occurs, is shown to be reduced, by requiring
consistency between the Heisenberg uncertainty principle and the known
properties of the solutions of Einstein's equations, from four orders of
magnitude to the level of a factor of {\cal O}(10). This lends more credibility
to the over-all resummed quantum gravity approach itself, in general, and to
our estimate of $\Lambda$ in particular.


  We present observations of the (field of the) Seyfert galaxy Mark290, which
probes the high-velocity cloud (HVC) complex C, one of the largest HVCs. We
find that this object has a metallicity of 0.094pm0.020(+0.022-0.019) times
solar. We determine a lower limit to its distance of 5 kpc (z>3.5 kpc). If the
gas is in thermal equilibrium with a hot halo, then it is also likely that D<30
kpc, putting the HVC in the Galactic Halo, as was the case for HVC complex~A
(for which z=2.5--7 kpc). We find that, on this sightline, H+ represents
23pm10% of the hydrogen in the HVC. The total gaseous mass is 2x10^6 (D/5
kpc)^2 Msun and, depending on whether the space velocity is completely radial
or vertical, the HVC represents a downward mass flux of ~0.036-0.083 (D/5 kpc)
Msun/yr, or 2.9--6.7x10^{-3} (D/5 kpc)^{-1} Msun/yr/kpc^2. The low metallicity
and large mass suggest that complex C is unlikely to be part of a Galactic
Fountain, but rather represents accreting low-metallicity material. It may be a
present-day analogue of the damped Ly-alpha absorbers seen in QSO spectra. Our
abundance result provides the first direct observational evidence for the
infall of low-metallicity gas on the Milky Way, required in models of galactic
chemical evolution. It remains to be seen whether ultimately complex C is a
remnant of the formation of the Milky Way (Oort 1970), a gas cloud orbiting the
Galaxy (Kerr & Sullivan 1969), a Local Group object (Verschuur 1969, Blitz et
al. 1996), the result of tidal interactions between the Magellanic System and
the Galaxy >3 Gyr ago (an "Old Magellanic Stream"; Toomre, quoted in Kerr &
Sullivan 1969), or was formed when hot, ionized intergalactic gas was
compressed by the motion of the Milky Way (Silk et al. 1987).


  The electroweak corrections at one-loop level to the process $H\rightarrow
t{\bar t}$ are calculated, especially the fresh top mass value announced
recently by CDF is concerned. For $M_H < 1. TeV$ where a perturbative
calculation is valid, the corrections themselves would gain a few to 20 percent
increment in the decay width as the Higgs mass $M_H$ is increasing within the
region, but they are in opposite direction to the QCD ones. If the electroweak
and QCD corrections are concerned in the meantime, the resultant decay width of
the mode yields a reduction about a few percent of the tree level one.


  This paper reports on a simulated evolution project, which had the goal of
simulating the refractive components of the PM eye of Australian netting spider
diopis subrufus on a desktop computer. The model for the simulation is the
anatomy of the eye described by Blest and Land [Ble & Lan 1977]. The evolution
simulation was able to produce hundreds of eyes with equivalent optical
qualities to the measured eyes for the phenotype of the netting spider. These
artificially evolved eyes began to occur in the computer simulation between
8X106 and 35X106 cycles after Demonstration of Darwinian Theory of Evolution;
arXiv 1006.0480 simulated the evolution for the ctenid spider PM eye,
cupiennius sale. This paper follows the previous paper, but the netting spider
eye is more complex than the ctenid PM eye so the simulated evolution equations
are more complex.


  We establish the MF property of the reduced group $ C^* $-algebra of an
amalgamated free product of countable Abelian discrete groups. This result is
then used to give a characterization of the amalgamated free products of
Abelian groups for which the BDF semigroup of the reduced group $ C^* $-algebra
is a group. Along the way we get a tensor product factorization of the
corresponding group von Neumann algebra. We end the exposition by applying the
ideas from the first part to give a few more examples of groups with a reduced
group $ C^* $-algebra which is MF.


  In this paper we study a bandwidth-controlled direct, continuous, phase
transition from a Mott insulator, with easy plane Neel order, to a fully gapped
$d_{x^2-y^2}$ superconductor with a doubled unit cell on the square lattice, a
transition that is forbidden according to the Landau paradigm. This transition
is made possible because the vortices of the antiferromagnet are charged and
the vortices of the superconductor carry spins. These nontrivial vortex quantum
numbers arise because the ordered phases are intimately related to a
topological band insulator. We describe the lattice model as well as the
effective field theory.


  Non-M\=aori-speaking New Zealanders (NMS)are able to segment M\=aori words in
a highlysimilar way to fluent speakers (Panther et al.,2024). This ability is
assumed to derive through the identification and extraction of statistically
recurrent forms. We examine this assumption by asking how NMS segmentations
compare to those produced by Morfessor, an unsupervised machine learning model
that operates based on statistical recurrence, across words formed by a variety
of morphological processes. Both NMS and Morfessor succeed in segmenting words
formed by concatenative processes (compounding and affixation without
allomorphy), but NMS also succeed for words that invoke templates
(reduplication and allomorphy) and other cues to morphological structure,
implying that their learning process is sensitive to more than just statistical
recurrence.


  The theorem referred to in the title is a technical result that is needed for
the classification of elliptic and K3 fibrations birational to Fano 3-fold
hypersurfaces in weighted projective space. We present a complete proof of the
Curve Exclusion Theorem, which first appeared in the author's unpublished PhD
thesis [Ry02] and has since been relied upon in solutions to many cases of the
fibration classification problem. We give examples of these solutions and
discuss them briefly.


  A model subspace configuration interaction method is developed to obtain
chemically accurate electron correlations by diagonalising a very compact
effective Hamiltonian of realistic molecule. The construction of the effective
Hamiltonian is deterministic and implemented by iteratively building a
sufficiently small model subspace comprising local clusters of a small number
of determinants. Through the low-rank reciprocal of interaction Hamiltonian,
important determinants in the outer subspace are identified to couple with the
subspace of pairwise clusters. This method avoids direct ordering and selection
of the configurations in the entire space. We demonstrate the efficiency and
accuracy of this theory for several molecules of the multireference nature
relevant to chemistry.


  In autonomic computing, self-adaptation has been proposed as a fundamental
paradigm to manage the complexity of multiagent systems (MASs). This achieved
by extending a system with support to monitor and adapt itself to achieve
specific concerns of interest. Communication in these systems is key given that
in scenarios involving agent interaction, it enhances cooperation and reduces
coordination challenges by enabling direct, clear information exchange.
However, improving the expressiveness of the interaction communication with
MASs is not without challenges. In this sense, the interplay between
self-adaptive systems and effective communication is crucial for future MAS
advancements. In this paper, we propose the integration of large language
models (LLMs) such as GPT-based technologies into multiagent systems. We anchor
our methodology on the MAPE-K model, which is renowned for its robust support
in monitoring, analyzing, planning, and executing system adaptations in
response to dynamic environments. We also present a practical illustration of
the proposed approach, in which we implement and assess a basic MAS-based
application. The approach significantly advances the state-of-the-art of
self-adaptive systems by proposing a new paradigm for MAS self-adaptation of
autonomous systems based on LLM capabilities.


  The concept of "reality" is often raised in the context of philosophical
foundations of physics or interpretations of quantum mechanics. When this term
is so raised, it is a warning to me that I am about to be led down a rabbit
hole. Such diversions usually lead nowhere unless you consider endless
discussions of Schrodinger's cat, wave function collapse, quantum non-locality,
and parallel universes to be useful. A prime example is the famous Einstein,
Podolsky, and Rosen paper wherein they concluded that the quantum wave function
cannot provide a complete description of physical reality. In this essay I
suggest that, in physics discourse, the term "reality" should be avoided at all
costs.


  We present a one-parameter deformation of the AdS_{4} x S^{7} vacuum, which
is a regular solution in M-theory, invariant under SO(2,2) x SO(4) x SO(4), and
which preserves 16 supersymmetries. The solution corresponds to a holographic
realization of a Janus-like interface/defect theory, despite the absence of a
dilaton in M-theory. The 2+1-dimensional CFT dual results from the maximally
symmetric CFT through the insertion of a dimension 2 operator which is
localized along a 1+1-dimensional linear interface/defect, thereby partially
breaking the superconformal symmetry. The solution admits a regular ABJM
reduction to a quotient solution which is invariant under SO(2,2) x SO(4) x
U(1)^2, preserves 12 supersymmetries, and provides a Janus-like
interface/defect solution in ABJM theory.


  We investigate the nonequilibrium dynamical properties of a
weakly-interacting Bose gas at zero temperature under the multi-pulsed quantum
quench in interaction by calculating one-body, two-body correlation functions
and Tan's contact of the model system. The multi-pulsed quench is represented
as follows: first suddenly quenching the interatomic interaction from $g_{i}$
to $g_{f}$ at time $t=0$, holding time $t$, and then suddenly quenching
interaction from $g_{f}$ back to $g_{i}$, holding the time $t$ sequence $n$
times. In particular, two typical kinds of quenching parameters are chosen,
corresponding to $(g_{i}/g_{f}>1)$ and $(g_{i}/g_{f}<1)$ respectively. We find
that the more the quenching times of $n$ are, the more the excitations are
excited, which suggests that the multi-pulsed QQ is more powerful way of
studying the non-equilibrium dynamics of many-body quantum system than the
`one-off' quantum quench. Finally, we discuss the ultra-short-range properties
of the two-body correlation function after the $n$th quenching, which can be
used to probe the `Tan'scontact' in experiments. All our calculations can be
tested in current cold atom experiments.


  Customizing voice and speaking style in a speech synthesis system with
intuitive and fine-grained controls is challenging, given that little data with
appropriate labels is available. Furthermore, editing an existing human's voice
also comes with ethical concerns. In this paper, we propose a method to
generate artificial speaker embeddings that cannot be linked to a real human
while offering intuitive and fine-grained control over the voice and speaking
style of the embeddings, without requiring any labels for speaker or style. The
artificial and controllable embeddings can be fed to a speech synthesis system,
conditioned on embeddings of real humans during training, without sacrificing
privacy during inference.


  In this thesis a connection between the worlds of discrete and continuous
conformal geometry is explored. Specifically, a disk pattern production theroem
is proved using an energy which measures how ``uniform'' the angle data of a
triangulation is, see also math.DG/0002150. Then this energy is averaged over
all the Delaunay triangulation of a Riemannian surface to form an energy
measuring how ``uniform'' a metric is, see also math.DG/0010316.


  We show that a 1-parameter family Ricci flow ancient solutions arises from
the natural collapsings of the twistor space of positive quaternion K\"ahler
manifolds. We use these ancient solutions to show that a positive quaternion
K\"ahler manifold is isometric to one of the Wolf spaces.


  Magnetic properties of the pyrovanadate CaCoV$_2$O$_7$ have been studied by
means of the bulk magnetization and neutron powder diffraction measurements.
Magnetic susceptibility in the paramagnetic phase shows Curie-Weiss behavior
with negative Weiss temperature $\Theta \simeq -22.5$ K, indicating dominant
antiferromagnetic interactions. At $T_{\rm N} = 3.44$ K, CaCoV$_2$O$_7$ shows
antiferromagnetic order, accompanied by a weak net ferromagnetic moment of
$\sim 0.05$~$\mu_{\rm B}/{\rm Co}^{2+}$. Neutron powder diffraction confirms
the formation of antiferromagnetic order below $T_{\rm N}$. It was further
confirmed from the magnetic structure determination that the two Co$^{2+}$ ions
in the adjacent edge-sharing octahedra have almost parallel (ferromagnetic)
spin arrangement, indicative of a formation of a ferromagnetic spin dimer. The
antiferromagnetic order is, in turn, stabilized by sizable inter-dimer
antiferromagnetic interactions.


  Most state-of-the-art information extraction approaches rely on token-level
labels to find the areas of interest in text. Unfortunately, these labels are
time-consuming and costly to create, and consequently, not available for many
real-life IE tasks. To make matters worse, token-level labels are usually not
the desired output, but just an intermediary step. End-to-end (E2E) models,
which take raw text as input and produce the desired output directly, need not
depend on token-level labels. We propose an E2E model based on pointer
networks, which can be trained directly on pairs of raw input and output text.
We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT
movie corpus and compare to neural baselines that do use token-level labels. We
achieve competitive results, within a few percentage points of the baselines,
showing the feasibility of E2E information extraction without the need for
token-level labels. This opens up new possibilities, as for many tasks
currently addressed by human extractors, raw input and output data are
available, but not token-level labels.


  We prove the first nontrivial reconstruction theorem for modular tensor
categories: the category associated to any twisted Drinfeld double of any
finite group, can be realised as the representation category of a completely
rational conformal net. We also show that any twisted double of a solvable
group is the category of modules of a completely rational vertex operator
algebra. In the process of doing this, we identify the 3-cocycle twist for
permutation orbifolds of holomorphic conformal nets: unexpectedly, it can be
nontrivial, and depends on the value of the central charge modulo 24. In
addition, we determine the branching coefficients of all possible local
(conformal) extensions of any finite group orbifold of holomorphic conformal
nets, and identify their modular tensor categories. All statements also apply
to vertex operator algebras, provided the conjecture holds that finite group
orbifolds of holomorphic VOAs are rational, with a category of modules given by
a twisted group double.


  Variation of fragility (m) of specially homogenized GexSe100-x melts are
established from complex specific heat measurements, and show m(x) has a global
minimum at an extremely low value (m=14.8(0.5)) in the 21.5%<x<23% range of Ge.
Outside of that compositional range, m(x) then increases at first rapidly then
slowly to about m=25-30. By directly mapping melt stoichiometry as a function
of reaction time at a fixed temperature T>Tg, we observe a slowdown of
melt-homogenization by the super-strong melt compositions, 21.5%<x<23%. This
range furthermore appears to be correlated to the one observed between the
flexible and stressed rigid phase in network glasses. These spectacular
features underscore the crucial role played by topology and rigidity in the
properties of network-forming liquids and glasses which are highlighted when
fragility is represented as a function of variables tracking the effect of
rigidity. Finally, we investigate the fragility-glass transition temperature
relationship, and find that reported scaling laws do not apply in the flexible
phase, while being valid for intermediate and stressed rigid compositions.


  One of the inherent limitations of current AI systems, stemming from the
passive learning mechanisms (e.g., supervised learning), is that they perform
well on labeled datasets but cannot deduce knowledge on their own. To tackle
this problem, we derive inspiration from a highly intentional learning system
via action: the toddler. Inspired by the toddler's learning procedure, we
design an interactive agent that can learn and store task-agnostic visual
representation while exploring and interacting with objects in the virtual
environment. Experimental results show that such obtained representation was
expandable to various vision tasks such as image classification, object
localization, and distance estimation tasks. In specific, the proposed model
achieved 100%, 75.1% accuracy and 1.62% relative error, respectively, which is
noticeably better than autoencoder-based model (99.7%, 66.1%, 1.95%), and also
comparable with those of supervised models (100%, 87.3%, 0.71%).


  Vajda and Buttyan (VB) proposed a set of five lightweight RFID authentication
protocols. Defend, Fu, and Juels (DFJ) did cryptanalysis on two of them - XOR
and SUBSET. To the XOR protocol, DFJ proposed repeated keys attack and nibble
attack. In this paper, we identify the vulnerability existed in the original
VB's successive session key permutation algorithm. We propose three
enhancements to prevent DFJ's attacks and make XOR protocol stronger without
introducing extra resource cost.


  Superconductivity was recently observed in boron-doped
($\sqrt{3}\times\sqrt{3}$)Sn/Si(111). The material can be described by an
extended Hubbard model on a triangular lattice. Here, we use the random-phase
approximation to investigate the charge and spin fluctuations as well as the
superconducting properties of the system with respect to filling and the
relative strength of the extended versus the on-site Hubbard interactions. Our
calculations reveal that near half-filling and weak extended Hubbard
interactions, the superconducting ground state exhibits chiral $d$-wave
pairing. Far from half-filling and for stronger nearest-neighbor Coulomb
interactions, the system shows chiral $p$-wave (hole-doping) and $f$-wave
(electron-doping) pairings. The dependence of the pairing symmetry on the
extended Hubbard interactions suggests that charge fluctuations play an
important role in the formation of Cooper pairs. Finally, the temperature
dependence of the Knight shift is calculated for all observed superconducting
textures and put forward as an experimental method to examine the symmetry of
the superconducting gap function.


  Two-dimensional SU($N$) gauge theory is accurately analyzed with the
light-front Tamm-Dancoff approximation, both numerically and analytically. The
light-front Einstein-Schr\"odinger equation for mesonic mass reduces to the 't
Hooft equation in the large $N$ limit, $g^2N$ fixed, where $g$ is the coupling
constant. Hadronic masses are numerically obtained in the region of $m^2 \ll
g^2N$, where $m$ is the bare quark (q) mass. The lightest mesonic and baryonic
states are almost in valence. The second lightest mesonic state is highly
relativistic in the sense that it has a large 4-body ($ {\rm qq} \bar{\rm q}
\bar{\rm q} $) component in addition to the valence (${\rm q} \bar{\rm q}$)
one. In the strong coupling limit our results are consistent with the
prediction of the bosonization for ratios of the lightest and second lightest
mesonic masses to the lightest baryonic one. Analytic solutions to the lightest
hadronic masses are obtained, with a reasonable approximation, as
$\sqrt{2Cm}(1-1/N^2)^{1/4}$ in the mesonic case and
$\sqrt{CmN(N-1)}(1-1/N^2)^{1/4}$ in the baryonic case, where
$C=(g^2N\pi/6)^{1/2}$. The solutions well reproduce the numerical ones. The
$N$- and $m$-dependences of the hadronic masses are explicitly shown by the
analytical solutions.


  We propose a scheme to distinguish the orbital angular momentum state of the
Laguerre-Gaussian (LG) beam based on the electromagnetically induced
transparency modulated by a microwave field in atomic ensembles. We show that
the transverse phase variation of a probe beam with the LG mode can be mapped
into the spatial intensity distribution due to the change of atomic coherence
caused by the microwave. The proposal may provide a useful tool for studying
higher-dimensional quantum information based on atomic ensembles.


  Given an unconditionally stable algorithm for solving the Cahn-Hilliard
equation, we present a general calculation for an analytic time step $\d \tau$
in terms of an algorithmic time step $\dt$. By studying the accumulative
multi-step error in Fourier space and controlling the error with arbitrary
accuracy, we determine an improved driving scheme $\dt=At^{2/3}$ and confirm
the numerical results observed in a previous study \cite{Cheng1}.


  We overview a number of precise notions for a holomorphic automorphism group
to be big together with their implications, in particular we give an exposition
of the notions of flexibility and of density property.
  These studies have their origin in the famous result of Anders\'en and
Lempert from 1992 proving that the overshears generate a dense subgroup in the
holomorphic automorphism group of $\C^n, n\ge 2$. There are many applications
to natural geometric questions in complex geometry, several of which we mention
here.
  Also the Linearization Problem, well known since the 1950 s and considered by
many authors, has had a strong influence on those studies. It asks whether a
compact subgroup in the holomorphic automorphism group of $\C^n$ is necessarily
conjugate to a group of linear automorphisms. Despite many positive results,
the answer in this generality is negative as shown by Derksen and the author.
We describe various developments around that problem.


  We obtain new results on Fourier multipliers for Dirichlet-Hardy spaces. As a
consequence, we establish a Littlewood-Paley type inequality which yields a
simple proof that the Dirichlet monomials form a Schauder basis for p>1.


  In this paper we use the planar circular restricted three-body problem where
one of the primary bodies is an oblate spheroid or an emitter of radiation in
order to determine the basins of attraction associated with the equilibrium
points. The evolution of the position of the five Lagrange points is monitored
when the values of the mass ratio $\mu$, the oblateness coefficient $A_1$, and
the radiation pressure factor $q$ vary in predefined intervals. The regions on
the configuration $(x,y)$ plane occupied by the basins of attraction are
revealed using the multivariate version of the Newton-Raphson method. The
correlations between the basins of convergence of the equilibrium points and
the corresponding number of iterations needed in order to obtain the desired
accuracy are also illustrated. We conduct a thorough and systematic numerical
investigation demonstrating how the dynamical quantities $\mu$, $A_1$, and $q$
influence the basins of attractions. Our results suggest that the mass ratio
and the radiation pressure factor are the most influential parameters, while on
the other hand the structure of the basins of convergence are much less
affected by the oblateness coefficient.


  We extend to n-dimensions a characterization of the Marcinkiewicz
$L(p,\infty)$ spaces first obtained by Garsia-Rodemich in the one dimensional
case. This leads to a new proof of the John-Nirenberg self-improving
inequalities. We also show a related result that provides a still a new
characterization of the $L(p,\infty)$ spaces in terms of distribution
functions, reflects the self-improving inequalities directly, and also
characterizes $L(\infty,\infty),$ the rearrangement invariant hull of $BMO.$ We
show an application to the study of tensor products with $L(\infty,\infty)$
spaces, which complements the classical work of O'Neil \cite{oneil} and the
more recent work of Astashkin \cite{astashkin}.


  We investigate the phase portrait of the (1+1)-dimensional massless
two-flavored NJL$_2$ model containing a quark number chemical potential $\mu$
and an isospin chemical potential $\mu_I$ in the limit of a large number of
colors $N_c\to\infty$. Particular attention is paid to the question to what
extent the inclusion of an isospin asymmetry affects chiral condensates to have
a spatial inhomogeneity in the form of the so-called chiral density waves (CDW)
(chiral spirals). It is shown that at zero temperature and comparatively small
values of $\mu$, i.e. at $\mu<\mu_c\approx 0.68M_0$ ($M_0$ is the dynamical
quark mass in the vacuum) only the homogeneous charged pion condensation phase
is realized for arbitrary nonzero values of $\mu_I$. Contrary to this, for
large values of $\mu>\mu_c$, two CDW phases appear in the $(\mu_I,\mu)$-phase
diagram of the model. In the first phase, CDWs are clockwise twisted chiral
spirals and in the second phase they are counterclockwise. The influence of
nonzero temperature on the formation of the CDW phases is also investigated.


  Let $E$ be a locally convex Hausdorff space satisfying the convex compact
property and let $(T_x)_{x \in \mathbb{R}^d}$ be a locally equicontinuous
$C_0$-group of linear continuous operators on $E$. In this article, we show
that if $E$ is quasinormable, then the space of smooth vectors in $E$
associated to $(T_x)_{x \in \mathbb{R}^d}$ is also quasinormable. In
particular, we obtain that the space of smooth vectors associated to a
$C_0$-group on a Banach space is always quasinormable. As an application, we
show that the translation-invariant Fr\'echet spaces of smooth functions of
type $\mathcal{D}_E$ [8] are quasinormable, thereby settling the question posed
in [8, Remark 7]. Furthermore, we show that $\mathcal{D}_E$ is not Montel if
$E$ is a solid translation-invariant Banach space of distributions [10]. This
answers the question posed in [8, Remark 6] for the class of solid
translation-invariant Banach spaces of distributions.


  In this report we present results of the ICDAR 2021 edition of the Document
Visual Question Challenges. This edition complements the previous tasks on
Single Document VQA and Document Collection VQA with a newly introduced on
Infographics VQA. Infographics VQA is based on a new dataset of more than 5,000
infographics images and 30,000 question-answer pairs. The winner methods have
scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document
Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a
summary of the datasets used for each task, description of each of the
submitted methods and the results and analysis of their performance. A summary
of the progress made on Single Document VQA since the first edition of the
DocVQA 2020 challenge is also presented.


  Training reinforcement learning (RL) agents using scalar reward signals is
often infeasible when an environment has sparse and non-Markovian rewards.
Moreover, handcrafting these reward functions before training is prone to
misspecification, especially when the environment's dynamics are only partially
known. This paper proposes a novel pipeline for learning non-Markovian task
specifications as succinct finite-state `task automata' from episodes of agent
experience within unknown environments. We leverage two key algorithmic
insights. First, we learn a product MDP, a model composed of the
specification's automaton and the environment's MDP (both initially unknown),
by treating the product MDP as a partially observable MDP and using the
well-known Baum-Welch algorithm for learning hidden Markov models. Second, we
propose a novel method for distilling the task automaton (assumed to be a
deterministic finite automaton) from the learnt product MDP. Our learnt task
automaton enables the decomposition of a task into its constituent sub-tasks,
which improves the rate at which an RL agent can later synthesise an optimal
policy. It also provides an interpretable encoding of high-level environmental
and task features, so a human can readily verify that the agent has learnt
coherent tasks with no misspecifications. In addition, we take steps towards
ensuring that the learnt automaton is environment-agnostic, making it
well-suited for use in transfer learning. Finally, we provide experimental
results compared with two baselines to illustrate our algorithm's performance
in different environments and tasks.


  We study cosmic string formation within supersymmetric grand unified
theories. We consider gauge groups having a rank between 4 and 8. We examine
all possible spontaneous symmetry breaking patterns from the GUT down to the
standard model gauge group. Assuming standard hybrid inflation, we select all
the models which can solve the GUT monopole problem, lead to baryogenesis after
inflation and are consistent with proton lifetime measurements. We conclude
that in all acceptable spontaneous symmetry breaking schemes, cosmic string
formation is unavoidable. The strings which form at the end of inflation have a
mass which is proportional to the inflationary scale. Sometimes, a second
network of strings form at a lower scale. Models based on gauge groups which
have rank greater than 6 can lead to more than one inflationary era; they all
end by cosmic string formation.


  Gaussian processes are powerful non-parametric probabilistic models for
stochastic functions. However, the direct implementation entails a complexity
that is computationally intractable when the number of observations is large,
especially when estimated with fully Bayesian methods such as Markov chain
Monte Carlo. In this paper, we focus on a low-rank approximate Bayesian
Gaussian processes, based on a basis function approximation via Laplace
eigenfunctions for stationary covariance functions. The main contribution of
this paper is a detailed analysis of the performance, and practical
recommendations for how to select the number of basis functions and the
boundary factor. Intuitive visualizations and recommendations, make it easier
for users to improve approximation accuracy and computational performance. We
also propose diagnostics for checking that the number of basis functions and
the boundary factor are adequate given the data. The approach is simple and
exhibits an attractive computational complexity due to its linear structure,
and it is easy to implement in probabilistic programming frameworks. Several
illustrative examples of the performance and applicability of the method in the
probabilistic programming language Stan are presented together with the
underlying Stan model code.


  CONTEXT - Transient low-mass X-ray binaries (LMXBs) often show outbursts
lasting typically a few-weeks and characterized by a high X-ray luminosity
($L_{x} \approx 10^{36}-10^{38}$ erg/sec), while for most of the time they are
found in X-ray quiescence ($L_X\approx10^{31} -10^{33}$ erg/sec). EXO 1745-248
is one of them. AIMS - The broad-band coverage, and the sensitivity of
instrument on board of {\xmm} and {\igr}, offers the opportunity to
characterize the hard X-ray spectrum during {\exo} outburst. METHODS - In this
paper we report on quasi-simultaneous {\xmm} and {\igr} observations of the
X-ray transient {\exo} located in the globular cluster Terzan 5, performed ten
days after the beginning of the outburst (on 2015 March 16th) shown by the
source between March and June 2015. The source was caught in a hard state,
emitting a 0.8-100 keV luminosity of $\simeq10^{37}$~{\lumcgs}. RESULTS - The
spectral continuum was dominated by thermal Comptonization of seed photons with
temperature $kT_{in}\simeq1.3$ keV, by a cloud with moderate optical depth
$\tau\simeq2$ and electron temperature $kT_e\simeq 40$ keV. A weaker soft
thermal component at temperature $kT_{th}\simeq0.6$--0.7 keV and compatible
with a fraction of the neutron star radius was also detected. A rich emission
line spectrum was observed by the EPIC-pn on-board {\xmm}; features at energies
compatible with K-$\alpha$ transitions of ionized sulfur, argon, calcium and
iron were detected, with a broadness compatible with either thermal Compton
broadening or Doppler broadening in the inner parts of an accretion disk
truncated at $20\pm6$ gravitational radii from the neutron star. Strikingly, at
least one narrow emission line ascribed to neutral or mildly ionized iron is
needed to model the prominent emission complex detected between 5.5 and 7.5
keV. (Abridged)


  A key-value cache is a key component of many services to provide low-latency
and high-throughput data accesses to a huge amount of data. To improve the
end-to-end performance of such services, a key-value cache must achieve a high
cache hit ratio with high throughput. In this paper, we propose a new cache
replacement algorithm, multi-step LRU, which achieves high throughput by
efficiently exploiting SIMD instructions without using per-item additional
memory (LRU metadata) to record information such as the last access timestamp.
For a small set of items that can fit within a vector register, SIMD-based LRU
management without LRU metadata is known (in-vector LRU). It remembers the
access history by reordering items in one vector using vector shuffle
instruction. In-vector LRU alone cannot be used for a caching system since it
can manage only few items. Set-associative cache is a straightforward way to
build a large cache using in-vector LRU as a building block. However, a naive
set-associative cache based on in-vector LRU has a poorer cache hit ratio than
the original LRU although it can achieve a high throughput. Our multi-step LRU
enhances naive set-associative cache based on in-vector LRU for improving cache
accuracy by taking both access frequency and access recency of items into
account while keeping the efficiency by SIMD instructions. Our results indicate
that multi-step LRU outperforms the original LRU and GCLOCK algorithms in terms
of both execution speed and cache hit ratio. Multi-step LRU improves the cache
hit ratios over the original LRU by implicitly taking access frequency of items
as well as access recency into account. The cache hit ratios of multi-step LRU
are similar to those of ARC, which achieves a higher a cache hit ratio in a
tradeoff for using more LRU metadata.


  In this paper, we explore the connection between secret key agreement and
secure omniscience within the setting of the multiterminal source model with a
wiretapper who has side information. While the secret key agreement problem
considers the generation of a maximum-rate secret key through public
discussion, the secure omniscience problem is concerned with communication
protocols for omniscience that minimize the rate of information leakage to the
wiretapper. The starting point of our work is a lower bound on the minimum
leakage rate for omniscience, $R_{\mathop{\mathrm{L}}}$, in terms of the
wiretap secret key capacity, $C_{\mathop{\mathrm{W}}}$. Our interest is in
identifying broad classes of sources for which this lower bound is met with
equality, in which case we say that there is a duality between secure
omniscience and secret key agreement. We show that this duality holds in the
case of certain finite linear source (FLS) models, such as two-terminal FLS
models and pairwise independent network models on trees with a linear
wiretapper. Duality also holds for any FLS model in which
$C_{\mathop{\mathrm{W}}}$ is achieved by a perfect linear secret key agreement
scheme. We conjecture that the duality in fact holds unconditionally for any
FLS model. On the negative side, we give an example of a (non-FLS) source model
for which duality does not hold if we limit ourselves to
communication-for-omniscience protocols with at most two (interactive)
communications. We also address the secure function computation problem and
explore the connection between the minimum leakage rate for computing a
function and the wiretap secret key capacity.


  For a non-endoscopic cohomological cuspidal automorphic representation of
$\mathrm{GSp}_4 \times \mathrm{GL}_2$, assumed to be $p$-ordinary, we construct
an Euler system for the Galois representation associated to it. Both the
construction and the verification of tame norm relations are based on
Novodvorsky's integral formula for the $L$-function of $\mathrm{GSp}_4 \times
\mathrm{GL}_2$.


  Hidden symmetries of non-relativistic $\mathfrak{so} (2,1)\cong
\mathfrak{sl}(2, {\mathbb R})$ invariant systems in a cosmic string background
are studied using the conformal bridge transformation. Geometric properties of
this background are analogous to those of a conical surface with a
deficiency/excess angle encoded in the "geometrical parameter" $\alpha$,
determined by the linear positive/negative mass density of the string. The free
particle and the harmonic oscillator on this background are shown to be related
by the conformal bridge transformation. To identify the integrals of the free
system, we employ a local canonical transformation that relates the model with
its planar version. The conformal bridge transformation is then used to map the
obtained integrals to those of the harmonic oscillator on the cone.
Well-defined classical integrals in both models exist only at $\alpha=q/k$ with
$q,k=1,2,\ldots,$ which for $q>1$ are higher-order generators of finite
nonlinear algebras. The systems are quantized for arbitrary values of $\alpha$;
however, the well-defined hidden symmetry operators associated with spectral
degeneracies only exist when $\alpha$ is an integer, that reveals a quantum
anomaly.


  Aims. Measuring dynamical masses of substellar companions is a powerful tool
to test models of mass-luminosity-age relations, as well as determining
observational features that constrain the boundary between stellar and
substellar companions. In order to dynamically constrain the mass of such
companions, we use multiple exoplanet measurement techniques to remove
degeneracies in the orbital fits of these objects and place tight constraints
on their model-independent masses. Methods. We combine long-period
radial-velocity data from the CORALIE survey with relative astrometry from
direct imaging with VLT/SPHERE, along with astrometric accelerations from
Hipparcos-Gaia eDR3 to perform a combined orbital fit and measure precise
dynamical masses of two newly discovered benchmark brown dwarfs. Results. We
report the discovery of HD112863B and HD206505B, which are two new benchmark
likely brown dwarfs that sit at the substellar-stellar boundary, with precise
dynamical masses. We perform an orbital fit which yields dynamical masses for
HD112863B and HD206505B to be $77.1^{+2.9}_{-2.8}~M_{\rm{Jup}}$ and
$79.8\pm1.8~M_{\rm{Jup}}$ respectively. The orbital period for HD112863B is
determined to be $21.59\pm0.05$ years and the orbital period of HD206505B is
determined to be ${50.9}_{-1.5}^{+1.7}$ years. From the $H$ and $K$ band
photometry from IRDIS data taken with VLT/SPHERE, we estimate the spectral
types of both HD112863B and HD206505B to be early-mid L-types.


  In this paper, we study the non-linear matter power spectrum in a specific
family of $f(R)$ models that can reproduce the $\Lambda$CDM background
expansion history, using high resolution $N$-body simulations based on the {\sc
ecosmog} code. We measure the matter power spectrum in the range of $0.05h{\rm
Mpc}^{-1}<k<10h{\rm Mpc}^{-1}$ from simulations for our $f(R)$ models and give
theoretical explanations to their behaviour and evolution patterns. We also
examine the chameleon mechanism for our models and find that it works
throughout the cosmic history in dense regions, for our $f(R)$ models with
$|f_{R0}|<10^{-4}$. On the other hand, for models with $|f_{R0}|>10^{-3}$, we
find no chameleon screening in dense regions at late times ($z<3$), which means
that those models could be ruled out due to the factor-of-$1/3$ enhancement to
the strength of Newtonian gravity. We also give the best-fit parameters for a
generalised PPF fitting formula which works well for the models studied here.


  Thermal Sunyaev-Zel'dovich (tSZ) effect and X-ray emission from galaxy
clusters have been extensively used to constrain cosmological parameters. These
constraints are highly sensitive to the relations between cluster masses and
observables (tSZ and X-ray fluxes). The cross-correlation of tSZ and X-ray data
is thus a powerful tool, in addition of tSZ and X-ray based analysis, to test
our modeling of both tSZ and X-ray emission from galaxy clusters. We chose to
explore this cross correlation as both emissions trace the hot gas in galaxy
clusters and thus constitute one the easiest correlation that can be studied.
We present a complete modeling of the cross correlation between tSZ effect and
X-ray emission from galaxy clusters, and focuses on the dependencies with
clusters scaling laws and cosmological parameters. We show that the present
knowledge of cosmological parameters and scaling laws parameters leads to an
uncertainties of 47\% on the overall normalization of the tSZ-X cross
correlation power spectrum. We present the expected signal-to-noise ratio for
the tSZ-X cross-correlation angular power spectrum considering the sensitivity
of actual tSZ and X-ray surveys from {\it Planck}-like data and ROSAT. We
demonstrate that this signal-to-noise can reach 31.5 in realistic situation,
leading to a constraint on the amplitude of tSZ-X cross correlation up to
3.2\%, fifteen times better than actual modeling limitations. Consequently,
used in addition to other probes of cosmological parameters and scaling
relations, we show that the tSZ-X is a powerful probe to constrain scaling
relations and cosmological parameters.


  We report the design, fabrication and characterization of a periodic grating
of shallow rectangular grooves in a metallic film with the goal of maximizing
the coupling efficiency of an extended plane wave (PW) of visible or
near-infrared light into a single surface plasmon polariton (SPP) mode on a
flat metal surface. A PW-to-SPP power conversion factor > 45 % is demonstrated
at a wavelength of 780 nm, which exceeds by an order of magnitude the
experimental performance of SPP grating couplers reported to date at any
wavelength. Conversion efficiency is maximized by matching the dissipative SPP
losses along the grating surface to the local coupling strength. This critical
coupling condition is experimentally achieved by tailoring the groove depth and
width using a focused ion beam.


  The $n$-vehicle exploration problem (NVEP) is a combinatorial search problem.
Assuming the $n$ vehicles travel together, with some vehicles being used to
refuel others, the NVEP tries to find a permutation of $n$ vehicles to make one
of the vehicles travel the maximal distance, and at last all the vehicles
return to the start point. NVEP has a fractional form of objective function,
and its computational complexity of general case remains open. Given a directed
graph $G$, it can be reduced in polynomial time to an instance of NVEP. We
prove that $G$ has a hamiltonian path of total weight of $n$ if and only if the
reduced NVEP instance has a feasible sequence of length at least $n$. Therefore
we show that Hamiltonian path $\leq_P$ NVEP, and consequently prove that NVEP
is NP-complete.


  The recently introduced Theory of the Adjacent Possible (TAP) is a model of
combinatorial innovation aiming to explain the "hockey-stick" upward trend of
human technological evolution, where an explosion in the number of produced
items with increasing complexity suddenly occurs. In addition, the TAP model
was also used to explain the rapidly emerging biological complexity. Inspired
by TAP here we propose a reaction-diffusion system aiming to extend the model
in both space and time. We show that the new model exhibits similar
characteristics to the TAP model, like the sudden increase in the production of
items, after a longer period of slow growth. The new model also exhibits wave
propagation of "innovation", resulting in self-sustained complex interference
patterns.


  NASA's Kepler mission observed background regions across its field of view
for more than three consecutive years using custom designed super apertures
(EXBA masks). Since these apertures were designed to capture a region of the
sky rather than single targets, the Kepler Science Data Processing pipeline
produced Target Pixel Files, but did not produce light curves for the sources
within these background regions. In this work we produce light curves for
$9,327$ sources observed in the EXBA masks. These light curves are generated
using aperture photometry estimated from the instrument's Pixel Response
Function (PRF) profile computed from Kepler's full-frame images. The PRF models
enable the creation of apertures that follow the characteristic shapes of the
PSF in the image and the computation of flux completeness and contamination
metrics. The light curves are available at MAST as a High Level Science Product
(kbonus-apexba). Alongside this dataset, we present kepler-apertures, a Python
library to compute PRF models and use them to perform aperture photometry on
Kepler-like data. Using light curves from the EXBA masks we found an exoplanet
candidate around Gaia EDR3 2077240046296834304 consistent with a large planet
companion with a $0.81 R_J$ radius. Additionally, we report a catalog of 69
eclipsing binaries. We encourage the community to exploit this new dataset to
perform in depth time domain analysis, such as eclipsing binaries demographic
and others.


  In this paper, we present a novel pseudospectral (PS) method for solving a
new class of initial-value problems (IVPs) of time-dependent one-dimensional
fractional partial differential equations (FPDEs) with variable coefficients
and periodic solutions. A main ingredient of our work is the use of the
recently developed periodic RL/Caputo fractional derivative (FD) operators with
sliding positive fixed memory length of Bourafa et al. [1] or their reduced
forms obtained by Elgindy [2] as the natural FD operators to accurately model
FPDEs with periodic solutions. The proposed method converts the IVP into a
well-conditioned linear system of equations using the PS method based on
Fourier collocations and Gegenbauer quadratures. The reduced linear system has
a simple special structure and can be solved accurately and rapidly by using
standard linear system solvers. A rigorous study of the computational storage
requirements as well as the error and convergence of the proposed method is
presented. The idea and results presented in this paper are expected to be
useful in the future to address more general problems involving FPDEs with
periodic solutions.


  Let $R$ be a commutative noetherian ring, $I,J$ be two ideals of $R$, $M$ be
an $R$-module, and $\mathcal{S}$ be a Serre class of $R$-modules. A positive
answer to the Huneke$^,$s conjecture is given for a noetherian ring $R$ and
minimax $R$-module $M$ of krull dimension less than 3, with respect to
$\mathcal{S}$. There are some results on cofiniteness and artinianness of local
cohomology modules with respect to a pair of ideals. For a ZD-module $M$ of
finite krull dimension and an integer $n\in\mathbb{N}$, if
$\lc^{i}_{I,J}(M)\in\mathcal{S}$ for all $i>n$, then
$\lc^{i}_{I,J}(M)/\fa^{j}\lc^{i}_{I,J}(M)\in\mathcal{S}$ for any
$\fa\in\tilde{W}(I,J)$, all $i\geq n$, and all $j\geq0$. By introducing the
concept of Seree cohomological dimension of $M$ with respect to $(I,J)$, for an
integer $r\in\mathbb{N}_0$, $\lc^{j}_{I,J}(R)\in\mathcal{S}$ for all $j>r$ iff
$\lc^{j}_{I,J}(M)\in\mathcal{S}$ for all $j>r$ and any finite $R$-module $M$.


  This paper is based on a formulation of the Navier-Stokes equations developed
by Iyer and Constantin \cite{Cont} , where the velocity field of a viscous
incompressible fluid is written as the expected value of a stochastic process.
Our contribution is to establish this probabilistic representation formula for
mild solutions of the Navier-Stokes equations on $\mathbb{R}^{d} $.