  We establish an operational characterization of general convex resource
theories -- describing the resource content of not only states, but also
measurements and channels, both within quantum mechanics and in general
probabilistic theories (GPTs) -- in the context of state and channel
discrimination. We find that discrimination tasks provide a unified operational
description for quantification and manipulation of resources by showing that
the family of robustness measures can be understood as the maximum advantage
provided by any physical resource in several different discrimination tasks, as
well as establishing that such discrimination problems can fully characterize
the allowed transformations within the given resource theory.
  Specifically, we introduce quantifiers of resourcefulness of states,
measurements, and channels in any GPT based on the generalized robustness, and
show that they exactly characterize the maximum advantage that a given resource
provides over all free states, measurements, or channels in a class of state or
channel discrimination tasks. In quantum mechanics, we show that the robustness
of measurement can be alternatively understood as the maximal increase in
one-shot accessible information when compared to free measurements. We
furthermore endow the standard robustness of a state with an operational
meaning as the quantifier of the maximum advantage in binary channel
discrimination tasks. Finally, we show that several classes of channel and
state discrimination tasks can form complete families of monotones fully
characterizing the transformations of states and measurements under any chosen
class of free operations. Our results establish a fundamental connection
between operational tasks of discrimination and core concepts of resource
theories, valid for all physical theories with no additional assumptions about
the structure of the GPT required.


  The paper analyzes traffic data of the Dutch freeway A9 with respect to
certain aspects which are relevant for traffic flow modeling as well as the
calibration of model parameters and functions. Apart from the dynamic velocity
distribution, the density-dependence and the temporal evolution of various,
partly lane-specific quantities is investigated. The results are well
compatible with recent macroscopic traffic flow models which have been derived
from the dynamics of driver-vehicle units. These have also solved the
inconsistencies, which previous models have been criticized for.


  Detecting and Counting people in a human crowd from a moving drone present
challenging problems that arisefrom the constant changing in the image
perspective andcamera angle. In this paper, we test two different
state-of-the-art approaches, density map generation with VGG19 trainedwith the
Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as
backbone, in order to comparetheir precision for counting and detecting people
in differentreal scenarios taken from a drone flight. We show empiricallythat
both proposed methodologies perform especially well fordetecting and counting
people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19
provides betterprecision on both tasks while also being lighter than
FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense
crowds, proving to be more robust toscale variations and strong occlusions,
being more suitable forsurveillance applications using drones


  Recently, Gutman defined a new vertex-degree-based graph invariant, named the
Sombor index $SO$ of a graph $G$, and is defined by $$SO(G)=\sum_{uv\in
E(G)}\sqrt{d_G(u)^2+d_G(v)^2},$$ where $d_G(v)$ is the degree of the vertex $v$
of $G$. In this paper, we obtain the sharp lower and upper bounds on $SO(G)$ of
a connected graph, and characterize graphs for which these bounds are attained.


  The properties of a system of n = 3 coupled oscillators with linear terms in
the velocities (magnetic terms) depending in two parameters are studied. We
proved the existence of a bi-Hamiltonian structure arising from a
non-symplectic symmetry, as well the existence of master symmetries and
additional integrals of motion (weak superintegrability) for certain particular
values of the parameters.


  Mitochondria instance segmentation from electron microscopy (EM) images has
seen notable progress since the introduction of deep learning methods. In this
paper, we propose two advanced deep networks, named Res-UNet-R and Res-UNet-H,
for 3D mitochondria instance segmentation from Rat and Human samples.
Specifically, we design a simple yet effective anisotropic convolution block
and deploy a multi-scale training strategy, which together boost the
segmentation performance. Moreover, we enhance the generalizability of the
trained models on the test set by adding a denoising operation as
pre-processing. In the Large-scale 3D Mitochondria Instance Segmentation
Challenge at ISBI 2021, our method ranks the 1st place. Code is available at
https://github.com/Limingxing00/MitoEM2021-Challenge.


  We report the synthesis, magnetic susceptibility and crystal structure
analysis for NbB2+x (x = 0.0 to 1.0) samples. The study facilitates in finding
a correlation among the lattice parameters, chemical composition and the
superconducting transition temperature Tc. Rietveld analysis is done on the X-
ray diffraction patterns of all synthesized samples to determine the lattice
parameters. The a parameter decreases slightly and has a random variation with
increasing x, while c parameter increases from 3.26 for pure NbB2 to 3.32 for
x=0.4 i.e. NbB2.4. With higher Boron content (x>0.4) the c parameter decreases
slightly. The stretching of lattice in c direction induces superconductivity in
the non- stoichiometric niobium boride. Pure NbB2 is non-superconductor while
the other NbB2+x (x>0.0) samples show diamagnetic signal in the temperature
range 8.9-11K. Magnetization measurements (M-H) at a fixed temperature of 5K
are also carried out in both increasing and decreasing directions of field. The
estimated lower and upper critical fields (Hc1 & Hc2) as viewed from M-H plots
are around 590 and 2000Oe respectively for NbB2.6 samples. In our case,
superconductivity is achieved in NbB2 by varying the Nb/B ratios, rather than
changing the processing conditions as reported by others.


  The quantum correction of the anisotropy parameter, $\eta$, is calculated for
$\xi=2$ and 3 in the $\beta$ region where numerical simulations such as hadron
spectroscopy are currently carried out, for the improved actions composed of
plaquette and rectangular 6-link loops. The $\beta$ dependences of $\eta$ for
the renormalization group improved actions are quite different from those of
the standard and Symanzik actions. In Iwasaki and DBW2 actions, $\eta$ stays
almost constant in a wide range of $\beta$, which also differs from the
one-loop perturbative result, while in the case of Symanzik action, it
increases as $\beta$ decreases, which is qualitatively similar to the
perturbative result, but the slope is steeper. In the calculation of the $\eta$
parameter close to and in the confined phase, we have applied the link
integration method to suppress the fluctuation of the gauge fields. Some
technical details are summarized.


  We revisit the non-linear BPS equation: the Dirac monopole of the Born-Infeld
theory in the B-field background. The rotation used in our previous papers to
discuss the scalar field by transforming the BPS equation into a linear one is
extended to the case of gauge field. We also find that this transformation is a
symmetry of the action. Moreover using the Legendre-dual formalism we present a
simple expression of the BPS equation.


  Accreting neutron stars differ from black holes by the presence of the star's
own magnetic field, whose interaction with the accretion flow is a central
component in understanding these systems' disk structure, outflows, jets, and
spin evolution. It also introduces an additional degree of freedom, as the
stellar dipole can have any orientation relative to the inner disk's magnetic
field. We present a suite of 3D general-relativistic magnetohydrodynamic
(GRMHD) simulations in which we investigate the two extreme polarities, with
the dipole field being either parallel or antiparallel to the initial disk
field, in both the accreting and propeller states. When the magnetosphere
truncates the disk near or beyond the corotation radius, most of the system's
properties, including the relativistic jet power, are independent of the
star-disk relative polarity. However, when the disk extends well inside the
corotation radius, in the parallel orientation the jet power is suppressed and
the inner disk is less dense and more strongly magnetized. We suggest a
physical mechanism that may account for this behavior - the interchange
slingshot - and discuss its astrophysical implications.


  In this paper, we first give a brief review of the theoretical framework for
microscopic shell-model calculations starting from the free nucleon-nucleon
potential. In this context, we discuss the use of the low-momentum
nucleon-nucleon interaction V-low-k in the derivation of the shell-model
effective interaction and emphasize its practical value as an alternative to
the Brueckner G-matrix method. Then, we present some results of our current
study of exotic nuclei around doubly magic 132Sn, which have been obtained
starting from the CD-Bonn potential renormalized by use of the V-low-k
approach. Attention is focused on the nuclei 134Te, 134Sn, and 136Te, in
particular on the latter which is a direct source of information on the
proton-neutron effective interaction in the 132Sn region. Comparison shows that
our results are in very good agreement with the available experimental data.


  It is a long-standing question whether an arbitrary variety is desingularized
by finitely many normalized Nash blow-ups. We consider this question in the
case of a toric variety. We interpret the normalized Nash blow-up in polyhedral
terms, show how continued fractions can be used to give an affirmative answer
for a toric surface, and report on a computer investigation in which over a
thousand 3- and 4-dimensional toric varieties were successfully resolved.


  In the last few decades, the size of spatial and spatio-temporal datasets in
many research areas has rapidly increased with the development of data
collection technologies. As a result, classical statistical methods in spatial
statistics are facing computational challenges. For example, the kriging
predictor in geostatistics becomes prohibitive on traditional hardware
architectures for large datasets as it requires high computing power and memory
footprint when dealing with large dense matrix operations. Over the years,
various approximation methods have been proposed to address such computational
issues, however, the community lacks a holistic process to assess their
approximation efficiency. To provide a fair assessment, in 2021, we organized
the first competition on spatial statistics for large datasets, generated by
our {\em ExaGeoStat} software, and asked participants to report the results of
estimation and prediction. Thanks to its widely acknowledged success and at the
request of many participants, we organized the second competition in 2022
focusing on predictions for more complex spatial and spatio-temporal processes,
including univariate nonstationary spatial processes, univariate stationary
space-time processes, and bivariate stationary spatial processes. In this
paper, we describe in detail the data generation procedure and make the
valuable datasets publicly available for a wider adoption. Then, we review the
submitted methods from fourteen teams worldwide, analyze the competition
outcomes, and assess the performance of each team.


  We consider a simple extension of the Standard Model Higgs inflation with one
new real scalar field which preserves unitarity up to the Planck scale. The new
scalar field (called sigma) completes in the ultraviolet the theory of Higgs
inflation by linearizing the Higgs kinetic term in the Einstein frame, just as
the non-linear sigma model is unitarized into its linear version. The unitarity
cutoff of the effective theory, obtained by integrating out the sigma field,
varies with the background value of the Higgs field. In our setup, both the
Higgs field and the sigma field participate in the inflationary dynamics,
following the flat direction of the potential. We obtain the same slow-roll
parameters and spectral index as in the original Higgs inflation but we find
that the Hubble rate during inflation depends not only on the Higgs
self-coupling, but also on the unknown couplings of the sigma field.


  We study the conformational properties of complex polymer macromolecules,
consisting in general of $n$ subsequently connected chains (blocks) of
different lengths and distinct chemical structure. Depending on the solvent
conditions, the inter- or intrachain interactions of some blocks may vanish,
causing the rich conformational behavior. Our main attention is focused on the
universal conformational properties of such molecules. Applying the direct
polymer renormalization group approach, we derive the analytical expressions
for the scaling exponent $\gamma(n)$, governing the number of possible
conformations of $n$-block copolymer, and analyze the effective linear size
measures of individual blocks. In particular, it is quantitatively estimated
the degree of extension of the block sizes as functions of $n$ and position of
blocks in sequence. The numerical simulations of the simplest $n=2$-block
copolymer chain are performed as well for better illustration of the
conformational behavior of such molecules.


  We demonstrate that every non-tubular channel linear Weingarten surface in
Euclidean space is a surface of revolution, hence parallel to a catenoid or a
rotational surface of non-zero constant Gauss curvature. We provide explicit
parametrizations and deduce existence of complete hyperbolic linear Weingarten
surfaces.


  Helium nanodroplets ("HNDs") are widely used for forming tailor-made clusters
and molecular complexes in a cold, transparent, and weakly-interacting matrix.
Characterization of embedded species by mass spectrometry is often complicated
by fragmentation and trapping of ions in the HNDs. Here, we systematically
study fragment ion mass spectra of HND-aggregated water and oxygen clusters
following their ionization by charge transfer ionization ("CTI") and Penning
ionization ("PEI"). While the efficiency of PEI of embedded clusters is lower
than for CTI by about factor 10, both the mean sizes of detected water clusters
and the relative yields of unprotonated cluster ions are significantly larger,
making PEI a ``soft ionization'' scheme. However, the tendency of ions to
remain bound to HNDs leads to a reduced detection efficiency for large HNDs
containing $>10^4$ helium atoms. These results are instrumental for determining
optimal conditions for mass spectrometry and photoionization spectroscopy of
molecular complexes and clusters aggregated in HNDs.


  Two-dimensional (2D) materials, such as graphene and hexagonal boron nitride,
are new kind of materials that can serve as substrates for surface enhanced
Raman spectroscopy (SERS). When combined with traditional metallic plasmonic
structures, the hybrid 2D materials/metal SERS platform brings extra benefits,
including higher SERS enhancement factors, oxidation protection of metal
surface, and protection of molecules from photo-induced damage. This
perspective gives an overview of recent progress in 2D materials coated
plasmonic structure in SERS application. This paper focuses on the fabrication
of the hybrid 2D materials/metal SERS platform and their applications for Raman
enhancement.


  Improving healthcare quality and access remains a critical concern for
countries worldwide. Consequently, the rise of large language models (LLMs) has
erupted a wealth of discussion around healthcare applications among researchers
and consumers alike. While the ability of these models to pass medical exams
has been used to argue in favour of their use in medical training and
diagnosis, the impact of their inevitable use as a self-diagnostic tool and
their role in spreading healthcare misinformation has not been evaluated. In
this work, we critically evaluate LLMs' capabilities from the lens of a general
user self-diagnosing, as well as the means through which LLMs may aid in the
spread of medical misinformation. To accomplish this, we develop a testing
methodology which can be used to evaluate responses to open-ended questions
mimicking real-world use cases. In doing so, we reveal that a) these models
perform worse than previously known, and b) they exhibit peculiar behaviours,
including overconfidence when stating incorrect recommendations, which
increases the risk of spreading medical misinformation.


  We investigate the decay of metastable de Sitter, Minkowski and anti-de
Sitter vacua catalyzed by a black hole and a cloud of strings. We apply the
method to the creation of the four dimensional bubble universe in the five
dimensional anti-de Sitter spacetime recently proposed by Banerjee, Danielsson,
Dibitetto, Giri and Schillo. We study the bounce action for the creation and
find that the bubble with very small cosmological constant, of order
$\Lambda^{(4)}/M^2_4 \sim 10^{-120}$, is favored by the catalysis by assuming
appropriate mass scales of the black hole and the cloud of strings to reproduce
the present energy densities of matter and radiation in the bubble universe.


  The issue intensively claimed in the literature on the generation of a
CPT-odd and Lorentz violating Chern-Simons-like term by radiative corrections
owing to a CPT violating interaction -- the axial coupling of fermions with a
constant vector field $b_\m$ -- is mistaken. The presence of massless gauge
field triggers IR divergences that might show up from the UV subtractions,
therefore, so as to deal with the (actual physical) IR divergences, the
Lowenstein-Zimmermann subtraction scheme, in the framework of BPHZL
renormalization method, has to be adopted. The proof on the non generation of
such a Chern-Simons-like term is done, independent of any kind of
regularization scheme, at all orders in perturbation theory.


  Predictive power in theoretical nuclear physics has been a major concern in
the study of nuclear structure and reactions. The Effective Field Theory (EFT)
based on chiral expansions provides a model independent hierarchy for many body
forces at long distances but their predictive power may be undermined by the
regularization scheme de- pendence induced by the counterterms and encoding the
short distances dynamics which seem to dominate the uncertainties. We analyze
several examples including zero energy NN scattering or perturbative
counterterm-free peripheral scattering where one would ex- pect these methods
to work best and unveil relevant systematic discrepancies when a fair
comparison to the Granada-2013 NN-database and partial wave analysis is
undertaken.


  Beam driven ionisation injection is characterised for a variety of high-Z
dopant. We discuss the region of extraction and why the position where
electrons are captured influences the final quality of the internally-injected
bunch. The beam driven ionisation injection relies on the capability to produce
a high gradient fields at the bubble closure, with magnitudes high enough to
ionise by tunnelling effect the still bounded electrons (of a high-Z dopant).
The ionised electrons are captured by the nonlinear plasma wave at the
accelerating and focusing wake phase leading to high-brightness trailing
bunches. The high transformer ratio guarantees that the ionisation only occurs
at the bubble closure. The quality of the ionisation-injected trailing bunches
strongly and non-linearly depends on the properties of the dopant gas (density
and initial ionisation state). We use the full 3D PIC code ${\tt ALaDyn}$ to
consider the highly three-dimensional nature of the effect. By means of a
systematic approach we have investigated the emittance and energy spread
formation and the evolution for different dopant gases and configurations.


  The sparse representation problem of recovering an N dimensional sparse
vector x from M < N linear observations y = Dx given dictionary D is
considered. The standard approach is to let the elements of the dictionary be
independent and identically distributed (IID) zero-mean Gaussian and minimize
the l1-norm of x under the constraint y = Dx. In this paper, the performance of
l1-reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O1
O2], where O1,O2 are independent and drawn uniformly according to the Haar
measure on the group of orthogonal M x M matrices. By an application of the
replica method, we obtain the critical conditions under which perfect
l1-recovery is possible with bi-orthogonal dictionaries.


  Experiments performed at a temperature of a few millikelvin require effective
thermalization schemes, low-pass filtering of the measurement lines and
low-noise electronics. Here, we report on the modifications to a commercial
dilution refrigerator with a base temperature of 3.5 mK that enable us to lower
the electron temperature to 6.7 mK measured from the Coulomb peak width of a
quantum dot gate-defined in an [Al]GaAs heteostructure. We present the design
and implementation of a liquid $^4$He immersion cell tight against superleaks,
implement an innovative wiring technology and develop optimized transport
measurement procedures.


  The analysis of time variability, whether fast variations on time scales well
below the second or slow changes over years, is becoming more and more
important in high-energy astronomy. Many sophisticated tools are available for
data analysis and complex practical aspects are described in technical papers.
Here, we present the basic concepts upon which all these techniques are based.
It is intended as a condensed primer of Fourier analysis, dealing with
fundamental aspects that can be examined in detailed elsewhere. It is not
intended to be a presentation of detailed Fourier tools for data analysis, but
the reader will find the theoretical basis to understand available analysis
techniques.


  Gaussian distributions are plentiful in applications dealing in uncertainty
quantification and diffusivity. They furthermore stand as important special
cases for frameworks providing geometries for probability measures, as the
resulting geometry on Gaussians is often expressible in closed-form under the
frameworks. In this work, we study the Gaussian geometry under the
entropy-regularized 2-Wasserstein distance, by providing closed-form solutions
for the distance and interpolations between elements. Furthermore, we provide a
fixed-point characterization of a population barycenter when restricted to the
manifold of Gaussians, which allows computations through the fixed-point
iteration algorithm. As a consequence, the results yield closed-form
expressions for the 2-Sinkhorn divergence. As the geometries change by varying
the regularization magnitude, we study the limiting cases of vanishing and
infinite magnitudes, reconfirming well-known results on the limits of the
Sinkhorn divergence. Finally, we illustrate the resulting geometries with a
numerical study.


  We adapt the approach of Rudnev, Shakan, and Shkredov to prove that in an
arbitrary field $\mathbb{F}$, for all $A \subset \mathbb{F}$ finite with $|A| <
p^{1/4}$ if $p:= Char(\mathbb{F})$ is positive, we have $$|A(A+1)| \gtrsim
|A|^{11/9}, \qquad |AA| + |(A+1)(A+1)| \gtrsim |A|^{11/9}.$$ This improves upon
the exponent of $6/5$ given by an incidence theorem of Stevens and de Zeeuw.


  Simulation-driven shape optimisation (SDSO) of marine propellers is often
obstructed by high-dimensional design spaces stemming from its complex geometry
and baseline parameterisation, which leads to the notorious curse of
dimensionality. In this study, we propose using the shape-supervised dimension
reduction (SSDR) approach to expedite the SDSO of marine propellers by
extracting latent features for a lower-dimensional subspace. SSDR is different
from other dimension reduction approaches as it utilises a shape-signature
vector function, which consists of a shape modification function and geometric
moments, maximising the retained geometric and physical information in the
subspace. The resulting shape-supervised subspace from SSDR enables us to
efficiently and effectively find an optimal design in appropriate areas of the
design space. The feasibility of the proposed method is tested for the E779A
propeller parameterised with 40 design parameters with the objective to
maximise efficiency while reducing suction side cavitation. The results
demonstrate that the shape-supervised subspace achieved an 87.5% reduction in
the original design space's dimensionality, resulting in faster optimisation
convergence.


  A first-principles electronic structure study of orthorhombic, monoclinic and
rhombohedral LiMnO$_{2}$ has been carried out using the full-potential
linearized augmented plane-wave method. The exchange and correlations have been
treated within the local spin-density approximation (LSDA) and the LSDA+U
methods. In the LSDA, the stable ground state is antiferromagnetic insulator
for the orthorhombic and monoclinic structures but is ferromagnetic metal for
the rhombohedral structure. The LSDA+U, on the other hand, predicts the ground
state to be an antiferromagnetic insulator for all structures. We find that
strong correlations change the density of states dramatically around the Fermi
level. The LSDA+U predicts the nature of band gap to be a mixture of charge
transfer and Mn $d \leftrightarrow d$ like excitations for orthorhombic and
monoclinic LiMnO$_{2}$ and Mott-insulator for rhombohedral LiMnO$_{2}$ in
agreement with the available experimental results. The inclusion of U increases
the magnetic moment on Mn and gives a value in better agreement with
experiment. However, Mn valency is not affected by the inclusion of U. We have
also calculated X-ray emission photoelectron spectra for the orthorhombic and
monoclinic LiMnO$_{2}$ by the LSDA and the LSDA+U methods. We find that LSDA+U
gives better agreement with the available experimental results.


  The Abuaf-Ueda flop is a 7-dimensional flop related to $G_2$ homogeneous
spaces. The derived equivalence for this flop was first proved by Ueda using
mutations of semi-orthogonal decompositions. In this article, we give an
alternative proof for the derived equivalence using tilting bundles. Our proof
also shows the existence of a non-commutative crepant resolution of the
singularity appearing in the flopping contraction. We also give some results on
moduli spaces of finite-length modules over this non-commutative crepant
resolution.


  Bidirectional teleportation is a fundamental protocol for exchanging quantum
information between two parties by means of a shared resource state and local
operations and classical communication (LOCC). Here we develop two seemingly
different ways of quantifying the simulation error of unideal bidirectional
teleportation by means of the normalized diamond distance and the channel
infidelity, and we prove that they are equivalent. By relaxing the set of
operations allowed from LOCC to those that completely preserve the positivity
of the partial transpose, we obtain semi-definite programming lower bounds on
the simulation error of unideal bidirectional teleportation. We evaluate these
bounds for several key examples: when there is no resource state at all and for
isotropic and Werner states, in each case finding an analytical solution. The
first aforementioned example establishes a benchmark for classical versus
quantum bidirectional teleportation. Another example consists of a resource
state resulting from the action of a generalized amplitude damping channel on
two Bell states, for which we find an analytical expression for the simulation
error. We then evaluate the performance of some schemes for bidirectional
teleportation due to Kiktenko et al. and find that they are suboptimal and do
not go beyond the aforementioned classical limit. We offer a scheme alternative
to theirs that is provably optimal. Finally, we generalize the whole
development to the setting of bidirectional controlled teleportation, in which
there is an additional assisting party who helps with the exchange of quantum
information, and we establish semi-definite programming lower bounds on the
simulation error for this task. More generally, we provide semi-definite
programming lower bounds on the performance of bipartite and multipartite
channel simulation using a shared resource state and LOCC.


  We study effects of the medium polarization on superfluidity in symmetric
nuclear matter in a relativistic formalism. An effect of the liquid-gas
instability is emphasized. We examine two types of decomposition of the nucleon
propagator; the standard Feynman-density and the particle-hole-antiparticle
ones. In both cases, the medium polarization effect is determined by a
characteristic cancellation among the \sigma, the longitudinal \omega, and the
\sigma-\omega mixed polarizations. The instability leads to increase of pairing
gap. Around the saturation density that is free from the instability the medium
polarization enhances pairing gap in the former case whereas reduces in the
latter. At the lowest density that is also free from the instability the gap
increases in both cases.


  The differential cross section for scattering of a Dirac particle in a black
hole background is found. The result is the gravitational analog of the Mott
formula for scattering in a Coulomb background. The equivalence principle is
neatly embodied in the cross section, which depends only on the incident
velocity, and not the particle mass. The low angle limit agrees with classical
calculations based on the geodesic equation. The calculation employs a
well-defined iterative scheme which can be extended to higher orders. Repeating
the calculation in different gauges shows that our result for the cross section
is gauge-invariant and highlights the issues involved in setting up a sensible
iterative scheme.


  OBJECTIVES: Estimates of the basic reproduction number (R0) of COVID-19 vary
across countries. This paper aims to characterise the spatial variability in R0
across the first six months of the global COVID-19 outbreak, and to explore
social factors that impact R0 estimates at national and regional level.
  METHODS: We searched PubMed, LitCOVID and the WHO COVID-19 database from
January to June 2020. Peer-reviewed English-language papers were included that
provided R0 estimates. For each study, the value of the estimate, country under
study and publication month were extracted. The median R0 value was calculated
per country, and the median and variance were calculated per region. For each
country with an R0 estimate, the Human Development Index (HDI), Sustainable
Mobility Index (SMI), median age, population density and development status
were obtained from external sources.
  RESULTS: A total of 81 studies were included in the analysis. These studies
provided at least one estimate of R0, along with sufficient methodology to
explain how the value was calculated. Values of R0 ranged between 0.48 and
14.8, and between 0.48 and 6.7 when excluding outliers.
  CONCLUSIONS: This systematic review provides a comprehensive overview of the
estimates of the basic reproduction number of COVID-19 globally and highlights
the spatial heterogeneity in R0. Higher values were recorded in more developed
countries, and countries with an older population or more sustainable mobility.
Countries with higher population density had lower R0 estimates. For most
regions, variability in R0 spiked initially before reducing and stabilising as
more estimates became available.


  Deep learning has been used in a wide range of areas and made a huge
breakthrough. With the ever-increasing model size and train-ing data volume,
distributed deep learning emerges which utilizes a cluster to train a model in
parallel. Unfortunately, the performance is often far from linear speedup due
to the communication overhead between cluster nodes. To address this challenge,
this paper designs and implements Libra, a network aggregator, that utilizes
in-network computation to optimize the communication for distributed DL
training in two aspects: 1) reduce active connections and 2) aggregate
exchanged network packets. We implemented our Libra on Intel Tofino switches,
customized a lightweight host stack and integrated it into an open-source
training framework PS-lite. The experimental result shows that our Libra can
achieve 1.5~4 times speedup.


  With each resonance of the Laplacian acting on the compactly supported
sections of a homogeneous vector bundle over a Riemannian symmetric space of
the non-compact type, One can associate a residue representation. The purpose
of this paper is to study them. The symmetric space is assumed to have rank-one
but the irreducible representation {\tau} of K defining the vector bundle is
arbitrary. We give an algorithm that aims at determining if these
representations are irreducible, finding their Langlands parameters, their
Gelfand-Kirillov dimensions and wave front sets. As an example, we apply this
algorithm to the Laplacian of the p-forms in the cases of all the classical
real rank-one Lie groups.


  We evaluate the short-range asymptotic behavior of Green's function for a
Hamiltonian when its potential energy part has an inverse power singularity at
the origin. The analytically solvable case of sharply screened Coulomb
potential is considered firstly. For this potential the additional logarithmic
singular term has been found in the short-range asymptote of the Green function
as in the case of the pure Coulomb potential. The case of a short-range
potential of an arbitrary form with inverse power singularity is treated on the
basis of the integral Lippmann-Schwinger equation. It is shown that, if the
singularity is weaker than the Coulomb one, the Green function has only
standard singularity. For the case of $r^{-\rho}$ singularity of the potential
with $1\le\rho<2$ the additional singularity in the asymptotic behavior of the
Green function appears. In the case of $\rho=1$ the additional logarithmic
singularity has the same form as in the case of the pure Coulomb potential. In
the case of $1<\rho<2$ the additional singularity of the Green function has the
form of the polar singularity $r^{-\rho+1}$. These results are applied for
extending the zero-range potential formalism on Hamiltonians with singular
potentials.


  The experimental limitations in the signal enhancement and spatial resolution
in spectroscopic imaging have been always a challenging task in the application
of near-field spectroscopy for nanostructured materials in the sub-diffraction
limit. In addition, the scattering efficiency also plays an important role in
improving signal enhancement and contrast of the spectroscopic imaging of
nanostructures by scattering of light. We report the effect of scattering
efficiency in the Raman intensity enhancement, and contrast generation in
near-field tip-enhanced Raman spectroscopic (TERS) imaging of one dimensional
inorganic crystalline nanostructures of Si and AlN having a large variation in
polarizability change. The Raman enhancement of pure covalently bonded Si
nanowire (NW) is found to be two orders of higher in magnitude for the TERS
imaging, as compared to that of AlN nanotip (NT) having a higher degree of
ionic bonding, suggesting the importance of scattering efficiency of the
materials in TERS imaging. The strong contrast generation due to higher signal
enhancement in TERS imaging of Si NW also helped in achieving the better
resolved spectroscopic images than that of the AlN NT. The study provides an
insight into the role of scattering efficiency in the resolution of near-field
spectroscopic images.


  We prove the full equivalence between Assembly Theory (AT) and Shannon
Entropy via a method based upon the principles of statistical compression
renamed `assembly index' that belongs to the LZ family of popular compression
algorithms (ZIP, GZIP, JPEG). Such popular algorithms have been shown to
empirically reproduce the results of AT, results that have also been reported
before in successful applications to separating organic from non-organic
molecules and in the context of the study of selection and evolution. We show
that the assembly index value is equivalent to the size of a minimal
context-free grammar. The statistical compressibility of such a method is
bounded by Shannon Entropy and other equivalent traditional LZ compression
schemes, such as LZ77, LZ78, or LZW. In addition, we demonstrate that AT, and
the algorithms supporting its pathway complexity, assembly index, and assembly
number, define compression schemes and methods that are subsumed into the
theory of algorithmic (Kolmogorov-Solomonoff-Chaitin) complexity. Due to AT's
current lack of logical consistency in defining causality for non-stochastic
processes and the lack of empirical evidence that it outperforms other
complexity measures found in the literature capable of explaining the same
phenomena, we conclude that the assembly index and the assembly number do not
lead to an explanation or quantification of biases in generative (physical or
biological) processes, including those brought about by (abiotic or Darwinian)
selection and evolution, that could not have been arrived at using Shannon
Entropy or that have not been reported before using classical information
theory or algorithmic complexity.


  From the present limits on the masses and mixings of fourth family quarks,
they are expected to have mass larger than the top quark and allow a large
range of mixing of the third family. They could also have different dynamics
than the quarks of three families of the Standard Model. The single production
of the fourth family up type quark t' has been studied via anomalous production
process pp-> t'VX (where V=g,Z,\gamma) at the LHC with the center of mass
energy of 7 and 14 TeV. The signatures of such process are discussed within
both the SM decay modes and anomalous decay modes of t' quarks. The sensitivity
to anomalous coupling kappa/Lambda=0.004 TeV^(-1) can be reached at sqrt(s)=14
TeV and L_(int)=100 pb^(-1).


  We consider the problem of linear estimation, and establish an extension of
the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero
but bounded with respect to a matrix norm of Schatten type. We derive simple
and explicit formulas for the optimal estimator in the cases of Nuclear and
Spectral norms (with the Frobenius case recovering ridge regression).
Additionally, we analytically derive the generalization error in multiple
random matrix ensembles, and compare with Ridge regression. Finally, we conduct
an extensive simulation study, in which we show that the cross-validated
Nuclear and Spectral regressors can outperform Ridge in several circumstances.


  We review many body calculations of the equation of state of dilute neutron
matter in the context of effective field theories of the nucleon-nucleon
interaction.


  Mediation analysis allows one to use observational data to estimate the
importance of each potential mediating pathway involved in the causal effect of
an exposure on an outcome. However, current approaches to mediation analysis
with multiple mediators either involve assumptions not verifiable by
experiments, or estimate the effect when mediators are manipulated jointly
which precludes the practical design of experiments due to curse of
dimensionality, or are difficult to interpret when arbitrary causal
dependencies are present. We propose a method for mediation analysis for
multiple manipulable mediators with arbitrary causal dependencies. The proposed
method is clinically relevant because the decomposition of the total effect
does not involve effects under cross-world assumptions and focuses on the
effects after manipulating (i.e. treating) one single mediator, which is more
relevant in a clinical scenario. We illustrate the approach using simulated
data, the "framing" dataset from political science, and the HIV-Brain Age
dataset from a clinical retrospective cohort study. Our results provide
potential guidance for clinical practitioners to make justified choices to
manipulate one of the mediators to optimize the outcome.


  The kinematical parameters, spatial shape and structure of the open cluster
IC 2391 and the associated stellar stream are studied here using Gaia-DR2
(GDR2) astrometry data. The apex positions are determined for the open cluster
IC 2391 (data taken from Cantat-Gaudin et al.) and for the kinematical streams
stars mentioned in Montes et al. using both convergent point and AD-diagram
methods. The values of apex coordinates identified. The results are in good
agreement with the previously calculated values. The positions of the stars in
the disk and the spatial dispersion velocities are determined. The paths of
cluster and associated stream are traced in the disk by orbit calculation back
in time to their places of formation. A possible genetic relationship between
the cluster and the stream has been detected. The approximation of the spatial
and kinematical shape of the stream and the cluster is made. According to this
study, even though currently the cluster and the stream seem to have spatial
difference in their locations but they appear to have formed in the same region
of the Galactic disk.


  GaSb/InAs/GaSb layer stacks have been grown on InAs metamorphic substrates
(pseudosubstrates) by MOVPE, using nitrogen as major carrier gas. We
demonstrate that GaSb growth by nitrogen MOVPE on InAs metamorphic substrates
(and on InAs wafers) is possible in a very narrow range of growth parameters.
As demonstrators, GaSb/InAs/GaSb structures were grown for electron mobility
tests, obtaining (unintentional) 2D electron gas densities in the order of
9/5x10e12 cm(e-2) and mobilities up to 1.2/1.8x10e4 cm(e2)/Vs at room and
liquid nitrogen temperature respectively. We show that it is beneficial to have
some hydrogen in the carrier gas mixture for GaSb growth to achieve good
crystal quality, morphology and electrical properties. Furthermore, an
unexpected and previously unreported decomposition process of GaSb is observed
at relatively low growth temperatures under the supply of the precursors for
InAs epitaxial overgrowth. This nevertheless gets suppressed at even lower
growth temperatures.


  After a suitable gauge fixing, the local gravitational degrees of freedom of
the Gowdy $S^1\times S^2$ and $S^3$ cosmologies are encoded in an axisymmetric
field on the sphere $S^2$. Recently, it has been shown that a standard field
parametrization of these reduced models admits no Fock quantization with a
unitary dynamics. This lack of unitarity is surpassed by a convenient
redefinition of the field and the choice of an adequate complex structure. The
result is a Fock quantization where both the dynamics and the SO(3)-symmetries
of the field equations are unitarily implemented. The present work proves that
this Fock representation is in fact unique inasmuch as, up to equivalence,
there exists no other possible choice of SO(3)-invariant complex structure
leading to a unitary implementation of the time evolution.


  In this contribution, we present some predictions for the production of D and
B mesons in ultrarelativistic heavy ion collisions at RHIC and LHC energies and
confront them with experimental results obtained so far by the STAR, PHENIX,
ALICE and CMS collaborations. We next discuss some preliminary results obtained
with an improved description of the medium based on EPOS initial conditions,
and its possible implications on the nuclear modification factor and on the
elliptic flow of heavy quarks.


  In the vicinity of the deconfinement transition the behaviour of the
interquark potential can be precisely predicted using the Effective String
Theory (EST). If the transition is continuous we can combine EST results with a
conformal perturbation analysis and reach the degree of precision needed to
detect the corrections beyond the Nambu-Goto approximation in the EST. We
discuss in detail this issue in the case of the deconfinement transition of the
SU(2) gauge theory in $(2+1)$ dimensions (which belongs to the same
universality class of the 2d Ising model) by means of an extensive set of high
precision simulations. We show that the Polyakov loops correlator of the SU(2)
model is precisely described by the spin-spin correlator of the 2d Ising model
not only at the critical point, but also down to temperatures of the order of
$0.8 T_c$. Thanks to the exact integrability of the Ising model we can extend
the comparison in the whole range of Polyakov loop separations, even beyond the
conformal perturbation regime. We use these results to quantify the first EST
correction beyond Nambu-Goto and show that it is compatible with the bounds
imposed by a bootstrap analysis of EST. This correction encodes important
physical information and may shed light on the nature of the flux tube and of
its EST description.


  The Area Under Curve measure (AUC) seems apt to evaluate and compare diverse
models, possibly without calibration. An important example of AUC application
is the evaluation and benchmarking of models that predict faithfulness of
generated text. But we show that the AUC yields an academic and optimistic
notion of accuracy that can misalign with the actual accuracy observed in
application, yielding significant changes in benchmark rankings. To paint a
more realistic picture of downstream model performance (and prepare a model for
actual application), we explore different calibration modes, testing
calibration data and method.


  Cops and robbers is a vertex-pursuit game played on graphs. In the classical
cops-and-robbers game, a set of cops and a robber occupy the vertices of the
graph and move alternately along the graph's edges with perfect information
about each other's positions. If a cop eventually occupies the same vertex as
the robber, then the cops win; the robber wins if she can indefinitely evade
capture. Aigner and Frommer established that in every connected planar graph,
three cops are sufficient to capture a single robber. In this paper, we
consider a recently studied variant of the cops-and-robbers game, alternately
called the one-active-cop game, one-cop-moves game or the lazy-cops-and-robbers
game, where at most one cop can move during any round. We show that Aigner and
Frommer's result does not generalise to this game variant by constructing a
connected planar graph on which a robber can indefinitely evade three cops in
the one-cop-moves game. This answers a question recently raised by Sullivan,
Townsend and Werzanski.


  We consider the stochastic Stokes drift of a flexible dumbbell. The dumbbell
consists of two isotropic Brownian particles connected by a linear spring with
zero natural length, and is advected by a sinusoidal wave. We find an
asymptotic approximation for the Stokes drift in the limit of a weak wave, and
find good agreement with the results of a Monte Carlo simulation. We show that
it is possible to use this effect to sort particles by their flexibility even
when all the particles have the same diffusivity.


  This paper presents a rich knowledge representation language aimed at
formalizing causal knowledge. This language is used for accurately and directly
formalizing common benchmark examples from the literature of actual causality.
A definition of cause is presented and used to analyze the actual causes of
changes with respect to sequences of actions representing those examples.


  A polar code extension method which supports incremental redundancy hybrid
ARQ(IR-HARQ) is proposed in this paper. By copying information bits to proper
positions of the extend part, the extended polar code can give additional
protection for the bits weakly protected by the first transmission. A
comparison between the proposed algorithm with directly generated polar code,
LTE turbo code, and some other IR-HARQ supporting polar code is given.
Simulation results show that the proposed algorithm has nearly the same
performance as directly generated polar code, which is chosen as base line for
comparison.


  The mass of an AdS black hole represents its enthalpy, which in addition to
internal energy, generally includes the energy required to assemble the system
in its environment. In this paper, we consider black holes immersed in a more
complex environment, generated by a scalar field with an exponential potential.
In the analogue of the AdS vacuum, which we call dilaton-AdS, the scalar field
has non-trivial behavior, breaking the hyperscaling symmetry of AdS and
modifying the asymptotic form of the spacetime. We find that the scalar field
falloff at infinity makes novel contributions to the ADM mass and spatial
tensions of dilaton-AdS black holes. We derive a first law and Smarr formula
for planar dilaton-AdS black holes. We study the analogue of thermodynamic
volume in this system and show that the mass of a black hole again represents
its enthalpy.


  We prove a conjecture of Medvedev and Scanlon in the case of regular
morphisms of semiabelian varieties. That is, if $G$ is a semiabelian variety
defined over an algebraically closed field $K$ of characteristic $0$, and
$\varphi\colon G\to G$ is a dominant regular self-map of $G$ which is not
necessarily a group homomorphism, we prove that one of the following holds:
either there exists a non-constant rational fibration preserved by $\varphi$,
or there exists a point $x\in G(K)$ whose $\varphi$-orbit is Zariski dense in
$G$.


  Exponential Puiseux semirings are additive submonoids of $\qq_{\geq 0}$
generated by almost all of the nonnegative powers of a positive rational
number, and they are natural generalizations of rational cyclic semirings. In
this paper, we investigate some of the factorization invariants of exponential
Puiseux semirings and briefly explore the connections of these properties with
semigroup-theoretical invariants. Specifically, we prove that sets of lengths
of atomic exponential Puiseux semirings are almost arithmetic progressions with
a common bound, while unions of sets of lengths are arithmetic progressions.
Additionally, we provide exact formulas to compute the catenary degrees of
these monoids and show that minima and maxima of their sets of distances are
always attained at Betti elements. We conclude by providing various
characterizations of the atomic exponential Puiseux semirings with finite omega
functions; in particular, we completely describe them in terms of their
presentations.


  Purpose: To investigate the feasibility of myelin water content
quantification using fast dual-echo steady-state (DESS) scans and machine
learning with kernels.
  Methods: We optimized combinations of steady-state (SS) scans for precisely
estimating the fast-relaxing signal fraction ff of a two-compartment signal
model, subject to a scan time constraint. We estimated ff from the optimized
DESS acquisition using a recently developed method for rapid parameter
estimation via regression with kernels (PERK). We compared DESS PERK ff
estimates to conventional myelin water fraction (MWF) estimates from a longer
multi-echo spin-echo (MESE) acquisition in simulation, in vivo, and ex vivo
studies.
  Results: Simulations demonstrate that DESS PERK ff estimators and MESE MWF
estimators achieve comparable error levels. In vivo and ex vivo experiments
demonstrate that MESE MWF and DESS PERK ff estimates are quantitatively
comparable measures of WM myelin water content. To our knowledge, these
experiments are the first to demonstrate myelin water images from a SS
acquisition that are quantitatively similar to conventional MESE MWF images.
  Conclusion: Combinations of fast DESS scans can be designed to enable precise
ff estimation. PERK is well-suited for ff estimation. DESS PERK ff and MESE MWF
estimates are quantitatively similar measures of WM myelin water content.


  Many discriminative natural language understanding (NLU) tasks have large
label spaces. Learning such a process of large-space decision making is
particularly challenging due to the lack of training instances per label and
the difficulty of selection among many fine-grained labels. Inspired by dense
retrieval methods for passage finding in open-domain QA, we propose a
reformulation of large-space discriminative NLU tasks as a learning-to-retrieve
task, leading to a novel solution named Dense Decision Retrieval (DDR ).
Instead of predicting fine-grained decisions as logits, DDR adopts a
dual-encoder architecture that learns to predict by retrieving from a decision
thesaurus. This approach not only leverages rich indirect supervision signals
from easy-to-consume learning resources for dense retrieval, it also leads to
enhanced prediction generalizability with a semantically meaningful
representation of the large decision space. When evaluated on tasks with
decision spaces ranging from hundreds to hundred-thousand scales, DDR
outperforms strong baselines greatly by 27.54% in P@1 on two extreme
multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing,
and 1.26% in accuracy on three few-shot intent classification tasks on average.
Code and resources are available at https://github.com/luka-group/DDR


  We study nonresonantly pumped exciton-polariton system in the vicinity of the
dynamical instability threshold. We find that the system exhibits unique and
rich dynamics, which leads to spatiotemporal pattern formation. The patterns
have a tree-like structure and are reminiscent of structures that appear in a
variety of soft matter systems. Within the approximation of slow and fast time
scales, we show that the polariton model exhibits self-replication point in
analogy to reaction-diffusion systems.


  We prove some criteria for the convergence of weak solutions of the 2D
incompressible Navier-Stokes equations with Navier slip boundary conditions to
a strong solution of incompressible Euler. The slip rate depends on a power of
the Reynolds number, and it is increasingly apparent that the power 1 may be
critical for L^2 convergence, as hinted at in [hal-01093331].


  Intelligent reflecting surface (IRS) is a new and promising paradigm to
substantially improve the spectral and energy efficiency of wireless networks,
by constructing favorable communication channels via tuning massive low-cost
passive reflecting elements. Despite recent advances in the link-level
performance optimization for various IRS-aided wireless systems, it still
remains an open problem whether the large-scale deployment of IRSs in wireless
networks can be a cost-effective solution to achieve their sustainable capacity
growth in the future. To address this problem, we study in this paper a new
hybrid wireless network comprising both active base stations (BSs) and passive
IRSs, and characterize its achievable spatial throughput in the downlink as
well as other pertinent key performance metrics averaged over both channel
fading and random locations of the deployed BSs/IRSs therein based on
stochastic geometry. Compared to prior works on characterizing the performance
of wireless networks with active BSs only, our analysis needs to derive the
power distributions of both the signal and interference reflected by
distributed IRSs in the network under spatially correlated channels, which
exhibit channel hardening effects when the number of IRS elements becomes
large. Extensive numerical results are presented to validate our analysis and
demonstrate the effectiveness of deploying distributed IRSs in enhancing the
hybrid network throughput against the conventional network without IRS, which
significantly boosts the signal power but results in only marginally increased
interference in the network. Moreover, it is unveiled that there exists an
optimal IRS/BS density ratio that maximizes the hybrid network throughput
subject to a total deployment cost given their individual costs, while the
conventional network without IRS is generally suboptimal in terms of throughput
per unit cost.


  We obtain necessary and sufficient conditions on a function in order that it
be the Laplace transform of an absolutely monotonic function. Several closely
related results are also given.


  In this paper we present the publicly available open-source spectral energy
distribution (SED) fitting code SMART (Spectral energy distributions Markov
chain Analysis with Radiative Transfer models). Implementing a Bayesian Markov
chain Monte Carlo (MCMC) method, SMART fits the ultraviolet to millimetre SEDs
of galaxies exclusively with radiative transfer models that currently
constitute four types of pre-computed libraries, which describe the starburst,
active galactic nucleus (AGN) torus, host galaxy and polar dust components. An
important novelty of SMART is that, although it fits SEDs exclusively with
radiative transfer models, it takes comparable time to popular energy balance
methods to run. Here we describe the key features of SMART and test it by
fitting the multi-wavelength SEDs of the 42 local ultraluminous infrared
galaxies (ULIRGs) that constitute the HERschel Ultraluminous Infrared Galaxy
Survey (HERUS) sample. The Spitzer spectroscopy data of the HERUS ULIRGs are
included in the fitting at a spectral resolution, which is matched to that of
the radiative transfer models. We also present other results that highlight the
performance and versatility of SMART. SMART promises to be a useful tool for
studying galaxy evolution in the JWST era. SMART is developed in PYTHON and is
available at https://github.com/ch-var/SMART.git.


  We prove that every lens space contains a genus one homologically fibered
knot, which is contrast to the fact that some lens spaces contain no genus one
fibered knot. In the proof, the Chebotarev density theorem and binary quadratic
forms in number theory play a key role. We also discuss the Alexander
polynomial of homologically fibered knots.


  In this paper, we consider the 1D compressible Euler equation with the
damping coefficient $\lambda/(1+t)^{\mu}$. Under the assumption that $0\leq \mu
<1$ and $\lambda >0$ or $\mu=1$ and $\lambda > 2$, we prove that solutions
exist globally in time, if initial data are small $C^1$ perturbation near
constant states. In particular, we remove the conditions on the limit
$\lim_{|x| \rightarrow \infty} (u (0,x), v (0,x))$, assumed in previous
results.


  Blind super-resolution methods based on stable diffusion showcase formidable
generative capabilities in reconstructing clear high-resolution images with
intricate details from low-resolution inputs. However, their practical
applicability is often hampered by poor efficiency, stemming from the
requirement of thousands or hundreds of sampling steps. Inspired by the
efficient adversarial diffusion distillation (ADD), we design~\name~to address
this issue by incorporating the ideas of both distillation and ControlNet.
Specifically, we first propose a prediction-based self-refinement strategy to
provide high-frequency information in the student model output with marginal
additional time cost. Furthermore, we refine the training process by employing
HR images, rather than LR images, to regulate the teacher model, providing a
more robust constraint for distillation. Second, we introduce a
timestep-adaptive ADD to address the perception-distortion imbalance problem
introduced by original ADD. Extensive experiments demonstrate
our~\name~generates better restoration results, while achieving faster speed
than previous SD-based state-of-the-art models (e.g., $7$$\times$ faster than
SeeSR).


  Nearest neighbor search is a basic computational tool used extensively in
almost research domains of computer science specially when dealing with large
amount of data. However, the use of nearest neighbor search is restricted for
the purpose of algorithmic development by the existence of the notion of
nearness among the data points. The recent trend of research is on large,
complex networks and their structural analysis, where nodes represent entities
and edges represent any kind of relation between entities. Community detection
in complex network is an important problem of much interest. In general, a
community detection algorithm represents an objective function and captures the
communities by optimizing it to extract the interesting communities for the
user. In this article, we have studied the nearest neighbor search problem in
complex network via the development of a suitable notion of nearness.
Initially, we have studied and analyzed the exact nearest neighbor search using
metric tree on proposed metric space constructed from complex network. After,
the approximate nearest neighbor search problem is studied using locality
sensitive hashing. For evaluation of the proposed nearest neighbor search on
complex network we applied it in community detection problem. The results
obtained using our methods are very competitive with most of the well known
algorithms exists in the literature and this is verified on collection of real
networks. On the other-hand, it can be observed that time taken by our
algorithm is quite less compared to popular methods.


  Although several methods of environmental sound synthesis have been proposed,
there has been no discussion on how synthesized environmental sounds should be
evaluated. Only either subjective or objective evaluations have been conducted
in conventional evaluations, and it is not clear what type of evaluation should
be carried out. In this paper, we investigate how to evaluate synthesized
environmental sounds. We also propose a subjective evaluation methodology to
evaluate whether the synthesized sound appropriately represents the information
input to the environmental sound synthesis system. In our experiments, we
compare the proposed and conventional evaluation methods and show that the
results of subjective evaluations tended to differ from those of objective
evaluations. From these results, we conclude that it is necessary to conduct
not only objective evaluation but also subjective evaluation.


  When compact manifolds $X$ and $Y$ are both even dimensional, their Euler
characteristics obey the K\"unneth formula $\chi(X\times Y)=\chi(X) \chi(Y)$.
In terms of the Betti numbers $b_p(X)$, $\chi(X)=\sum_{p}(-1)^p b_p(X)$,
implying that $\chi(X)=0$ when $X$ is odd dimensional. We seek a linear
combination of Betti numbers, called $\rho$, that obeys an analogous formula
$\rho(X\times Y)=\chi(X) \rho(Y)$ when $Y$ is odd dimensional. The unique
solution is $\rho(Y)=-\sum_{p}(-1)^p p b_p(Y)$. Physical applications include:
(1) $\rho \rightarrow (-1)^m \rho $ under a generalized mirror map in $d=2m+1$
dimensions, in analogy with $\chi \rightarrow (-1)^m \chi $ in $d=2m$; (2)
$\rho$ appears naturally in compactifications of M-theory. For example, the
4-dimensional Weyl anomaly for M-theory on $X^4 \times Y^7$ is given by
$\chi(X^4)\rho(Y^7)=\rho(X^4 \times Y^7) $ and hence vanishes when $Y^7$ is
self-mirror. Since, in particular, $\rho(Y\times S^1)=\chi(Y)$, this is
consistent with the corresponding anomaly for Type IIA on $X^4 \times Y^6$,
given by $\chi(X^4)\chi(Y^6)=\chi(X^4 \times Y^6)$, which vanishes when $Y^6$
is self-mirror; (3) In the partition function of $p$-form gauge fields, $\rho$
appears in odd dimensions as $\chi$ does in even.


  Derivative-Free optimization (DFO) focuses on designing methods to solve
optimization problems without the analytical knowledge of gradients of the
objective function. There are two main families of DFO methods: model-based
methods and direct search methods. In model-based DFO methods, a model of the
objective function is constructed using only objective function values, and the
model is used to guide the computation of the next iterate. Natural questions
in this class of algorithms include how many function evaluations should be
used to construct the model? And, should this number be fixed, or adaptively
selected by the algorithm? In this paper, we numerically examine these
questions, using Hare and Lucet's Derivative-Free Proximal Point (DFPP)
algorithm [Hare, Lucet, 2014] as a case study. Results suggest that the number
of function evaluations used to construct the model has a huge impact on
algorithm performance, and adaptive strategies can both improve and hinder
algorithm performance.


  Manipulating deformable objects remains a challenge within robotics due to
the difficulties of state estimation, long-horizon planning, and predicting how
the object will deform given an interaction. These challenges are the most
pronounced with 3D deformable objects. We propose SculptDiff, a
goal-conditioned diffusion-based imitation learning framework that works with
point cloud state observations to directly learn clay sculpting policies for a
variety of target shapes. To the best of our knowledge this is the first
real-world method that successfully learns manipulation policies for 3D
deformable objects. For sculpting videos and access to our dataset and hardware
CAD models, see the project website:
https://sites.google.com/andrew.cmu.edu/imitation-sculpting/home


  We investigate the fundamental problem of the nonlinear wavefield scattering
data corrections in response to a perturbation of initial condition using
inverse scattering transform theory. We present a complete theoretical linear
perturbation framework to evaluate first-order corrections of the full set of
the scattering data within the integrable one-dimensional focusing nonlinear
Schr\"odinger (NLSE) equation. The general scattering data portrait reveals
nonlinear coherent structures - solitons - playing the key role in the
wavefield evolution. Applying the developed theory to a classic box-shaped
wavefield we solve the derived equations analytically for a single Fourier mode
acting as a perturbation to the initial condition, thus, leading to the
sensitivity closed-form expressions for basic soliton characteristics, i.e. the
amplitude, velocity, phase and its position. With the appropriate statistical
averaging we model the soliton noise-induced effects resulting in compact
relations for standard deviations of soliton parameters. Relying on a concept
of a virtual soliton eigenvalue we derive the probability of a soliton
emergence or the opposite due to noise and illustrate these theoretical
predictions with direct numerical simulations of the NLSE evolution. The
presented framework can be generalised to other integrable systems and
wavefield patterns.


  The conditions under which ferromagnetism may occur in transition metal
oxides with partially filled $t_{2g}$ shells such as vanadium-based perovskites
are studied using a combination of density functional and single-site dynamical
mean field methods. For reasonable values of the correlation strength,
rotations of the VO$_6$ octahedra play an important role in enabling
ferromagnetism, with ferromagnetism typically occurring for rotations larger
than a nonzero critical value. Ferromagnetism is suppressed near the Mott
insulating phase but the phase boundary is otherwise only weakly dependent on
carrier concentration. Design rules are suggested for new oxide systems
exhibiting ferromagnetism.


  Cool giant and supergiant star atmospheres are characterized by complex
velocity fields originating from convection and pulsation processes which are
not fully understood yet. The velocity fields impact the formation of spectral
lines, which thus contain information on the dynamics of stellar atmospheres.
The tomographic method allows to recover the distribution of the component of
the velocity field projected on the line of sight at different optical depths
in the stellar atmosphere. The computation of the contribution function to the
line depression aims at correctly identifying the depth of formation of
spectral lines in order to construct numerical masks probing spectral lines
forming at different optical depths. The tomographic method is applied to 1D
model atmospheres and to a realistic 3D radiative hydrodynamics simulation
performed with CO5BOLD in order to compare their spectral line formation depths
and velocity fields. In 1D model atmospheres, each spectral line forms in a
restricted range of optical depths. On the other hand, in 3D simulations, the
line formation depths are spread in the atmosphere mainly because of
temperature and density inhomogeneities. Comparison of CCF profiles obtained
from 3D synthetic spectra with velocities from the 3D simulation shows that the
tomographic method correctly recovers the distribution of the velocity
component projected on the line of sight in the atmosphere.


  The hierarchy in scale between atmospheric and solar neutrino mass splittings
is investigated through two distinct neutrino mass mechanisms, from tree- and
one-loop-level contributions. We demonstrate that the minimal discrete dark
matter mechanism contains the ingredients for explaining this hierarchy. This
scenario is characterized by adding new RH neutrinos and scalar
$SU(2)$-doublets to the Standard Model as triplet representations of an $A_4$
flavor symmetry. The $A_4$ symmetry breaking, which occurs at the electroweak
scale, leads to a residual $\mathbb{Z}_2$ symmetry responsible for the dark
matter stability and dictates the neutrino phenomenology. Finally, we show that
to reproduce the neutrino mixing angles correctly it is necessary to violate CP
in the scalar potential.


  A Heegaard splitting of a closed, orientable three-manifold satisfies the
disjoint curve property if the splitting surface contains an essential simple
closed curve and each handlebody contains an essential disk disjoint from this
curve [Thompson, 1999]. A splitting is full if it does not have the disjoint
curve property. This paper shows that in a closed, orientable three-manifold
all splittings of sufficiently large genus have the disjoint curve property.
  From this and a solution to the generalized Waldhausen conjecture it would
follow that any closed, orientable three manifold contains only finitely many
full splittings.


  Sufficient condition for the symmetric moment problem to be determinate is
given using standards methods of $q$-Fourier analysis. This condition it cannot
be a particular case of Carleman's criterion.


  Deep learning is one of the new and important branches in machine learning.
Deep learning refers to a set of algorithms that solve various problems such as
images and texts by using various machine learning algorithms in multi-layer
neural networks. Deep learning can be classified as a neural network from the
general category, but there are many changes in the concrete realization. At
the core of deep learning is feature learning, which is designed to obtain
hierarchical information through hierarchical networks, so as to solve the
important problems that previously required artificial design features. Deep
Learning is a framework that contains several important algorithms. For
different applications (images, voice, text), you need to use different network
models to achieve better results. With the development of deep learning and the
introduction of deep convolutional neural networks, the accuracy and speed of
face recognition have made great strides. However, as we said above, the
results from different networks and models are very different. In this paper,
facial features are extracted by merging and comparing multiple models, and
then a deep neural network is constructed to train and construct the combined
features. In this way, the advantages of multiple models can be combined to
mention the recognition accuracy. After getting a model with high accuracy, we
build a product model. This article compares the pure-client model with the
server-client model, analyzes the pros and cons of the two models, and analyzes
the various commercial products that are required for the server-client model.


  The solar activity cycle is successfully modeled by the flux transport
dynamo, in which the meridional circulation of the Sun plays an important role.
Most of the kinematic dynamo simulations assume a one-cell structure of the
meridional circulation within the convection zone, with the equatorward return
flow at its bottom. In view of the recent claims that the return flow occurs at
a much shallower depth, we explore whether a meridional circulation with such a
shallow return flow can still retain the attractive features of the flux
transport dynamo (such as a proper butterfly diagram, the proper phase relation
between the toroidal and poloidal fields). We consider additional cells of the
meridional circulation below the shallow return flow---both the case of
multiple cells radially stacked above one another and the case of more
complicated cell patterns. As long as there is an equatorward flow in low
latitudes at the bottom of the convection zone, we find that the solar behavior
is approximately reproduced. However, if there is either no flow or a poleward
flow at the bottom of the convection zone, then we cannot reproduce solar
behavior. On making the turbulent diffusivity low, we still find periodic
behavior, although the period of the cycle becomes unrealistically large. Also,
with a low diffusivity, we do not get the observed correlation between the
polar field at the sunspot minimum and the strength of the next cycle, which is
reproduced when diffusivity is high. On introducing radially downward pumping,
we get a more reasonable period and more solar-like behavior even with low
diffusivity.


  We solved a stylized fact on a long memory process of volatility cluster
phenomena by using Minkowski metric for GARCH(1,1) under assumption that price
and time can not be separated. We provide a Yang-Mills equation in financial
market and anomaly on superspace of time series data as a consequence of the
proof from the general relativity theory. We used an original idea in Minkowski
spacetime embedded in Kolmogorov space in time series data with behavior of
traders.The result of this work is equivalent to the dark volatility or the
hidden risk fear field induced by the interaction of the behavior of the trader
in the financial market panic when the market crashed.


  Existing image/video datasets for cattle behavior recognition are mostly
small, lack well-defined labels, or are collected in unrealistic controlled
environments. This limits the utility of machine learning (ML) models learned
from them. Therefore, we introduce a new dataset, called Cattle Visual
Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long,
captured in natural lighting conditions, and annotated with eleven visually
perceptible behaviors of grazing cattle. We use the Computer Vision Annotation
Tool (CVAT) to collect our annotations. To make the procedure more efficient,
we perform an initial detection and tracking of cattle in the videos using
appropriate pre-trained models. The results are corrected by domain experts
along with cattle behavior labeling in CVAT. The pre-hoc detection and tracking
step significantly reduces the manual annotation time and effort. Moreover, we
convert CVB to the atomic visual action (AVA) format and train and evaluate the
popular SlowFast action recognition model on it. The associated preliminary
results confirm that we can localize the cattle and recognize their frequently
occurring behaviors with confidence. By creating and sharing CVB, our aim is to
develop improved models capable of recognizing all important behaviors
accurately and to assist other researchers and practitioners in developing and
evaluating new ML models for cattle behavior classification using video data.


  We are looking at quasar environment in the context of large-scale structure
(LSS) - a new approach, giving a more informed interpretation of quasar-galaxy
correlations. This paper presents our first results for a sample of z ~ 0.3
quasars. We are using Voronoi tessellation applied in colour (B_J - R) slices
for the detection of galaxy clusters and the minimal spanning tree (MST) to
delineate the large-scale structure. This new cluster-detection method allows
us to find reliably galaxy clusters at z < 0.3 from SuperCOSMOS measurements of
UK Schmidt plates. By reconstructing the large-scale structure in a relatively
narrow redshift band (0.2 < z < 0.3), we show that quasars follow the
large-scale structure traced by galaxy clusters. None of the quasars in our
radio-quiet sample is located in the central area of a galaxy cluster. Two
quasars, found in a very rich environment, are actually located between two
very close galaxy clusters, consistent with results on z ~ 1 quasars suggesting
that cluster merg ers may be involved in one of the quasar formation
mechanisms.


  This is a survey paper on algebraic surfaces in positive characteristic based
on a series of lectures that the author gave at the University of Edinburgh in
March 2023. It is focused on certain positive characteristic phenomena like
infinitesimal group schemes and their actions on algebraic surfaces as well as
the failure in positive characteristic of certain fundamental characteristic
zero results like the Kodaira vanishing theorem. Many explicit examples are
presented.


  Using limits on photon flux from Dwarf Spheroidal galaxies, we place bounds
on the parameter space of models in which Dark Matter annihilates into multiple
final state particle pair channels. We derive constraints on effective operator
models with Dark Matter couplings to third generation fermions and to pairs of
Standard Model vector bosons. We present limits in various slices of model
parameter space along with estimations of the region of maximal validity of the
effective operator approach for indirect detection. We visualize our bounds for
models with multiple final state annihilations by projecting parameter space
constraints onto triangles, a technique familiar from collider physics; and we
compare our bounds to collider limits on equivalent models.


  We study D-branes on a three complex dimensional nonabelian orbifold ${\bf
C}^3/\Gamma$ with $\Gamma$ a finite subgroup of SU(3). We present general
formulae necessary to obtain quiver diagrams which represent the gauge group
and the spectrum of the D-brane worldvolume theory for dihedral-like subgroups
$\Delta(3n^2)$ and $\Delta(6n^2)$. It is found that the quiver diagrams have a
similar structure to webs of branes.


  In this paper, we consider a leader-following consensus problem for networks
of continuous-time integrator agents with a time-varying leader under
measurement noises. We propose a neighbor-based state-estimation protocol for
every agent to track the leader, and time-varying consensus gains are
introduced to attenuate the noises. By combining the tools of stochastic
analysis and algebraic graph theory, we study mean square convergence of this
multi-agent system under directed fixed as well as switching interconnection
topologies. Sufficient conditions are given for mean square consensus in both
cases. Finally, a numerical example is given to illustrate our theoretical
results.


  We introduce a nominal actor-based language and study its expressive power.
We have identified the presence/absence of fields as a crucial feature: the
dynamic creation of names in combination with fields gives rise to Turing
completeness. On the other hand, restricting to stateless actors gives rise to
systems for which properties such as termination are decidable. This
decidability result still holds for actors with states when the number of
actors is bounded and the state is read-only.


  We examine avalanche statistics of rain- and vibration-driven granular slides
in miniature sand mounds. A crossover from power-law to non power-law
avalanche-size statistics is demonstrated as a generic driving rate $\nu$ is
increased. For slowly-driven mounds, the tail of the avalanche-size
distribution is a power-law with exponent $-1.97\pm 0.31$, reasonably close to
the value previously reported for landslide volumes. The interevent occurrence
times are also analyzed for slowly-driven mounds; its distribution exhibits a
power-law with exponent $-2.670\pm 0.001$.


  We predict twisted double bilayer graphene to be a versatile platform for the
realization of fractional Chern insulators readily targeted by tuning the gate
potential and the twist angle. Remarkably, these topologically ordered states
of matter, including spin singlet Halperin states and spin polarized states in
Chern number $\mathcal C=1$ and $\mathcal{C}= 2$ bands, occur at high
temperatures and without the need for an external magnetic field.


  We present a compelling finite element framework to model hydrogen assisted
fatigue by means of a hydrogen- and cycle-dependent cohesive zone formulation.
The model builds upon: (i) appropriate environmental boundary conditions, (ii)
a coupled mechanical and hydrogen diffusion response, driven by chemical
potential gradients, (iii) a mechanical behavior characterized by finite
deformation J2 plasticity, (iv) a phenomenological trapping model, (v) an
irreversible cohesive zone formulation for fatigue, grounded on continuum
damage mechanics, and (vi) a traction-separation law dependent on hydrogen
coverage calculated from first principles. The computations show that the
present scheme appropriately captures the main experimental trends; namely, the
sensitivity of fatigue crack growth rates to the loading frequency and the
environment. The role of yield strength, work hardening, and constraint
conditions in enhancing crack growth rates as a function of the frequency is
thoroughly investigated. The results reveal the need to incorporate additional
sources of stress elevation, such as gradient-enhanced dislocation hardening,
to attain a quantitative agreement with the experiments.


  Phase retardation is a cornerstone of modern optics, yet, at mid-infrared
(mid-IR) frequencies, it remains a major challenge due to the scarcity of
simultaneously transparent and birefringent crystals. Most materials resonantly
absorb due to lattice vibrations occurring at mid-IR frequencies, and natural
birefringence is weak, calling for hundreds of microns to millimeters-thick
phase retarders for sufficient polarization rotation. We demonstrate mid-IR
phase retardation with flakes of $\alpha$-molybdenum trioxide
($\alpha$-MoO$_3$) that are more than ten times thinner than the operational
wavelength, achieving 90 degrees polarization rotation within one micrometer of
material. We report conversion ratios above 50% in reflection and transmission
mode, and wavelength tunability by several micrometers. Our results showcase
that exfoliated flakes of low-dimensional crystals can serve as a platform for
mid-IR miniaturized integrated polarization control.


  We revisit the question of what mechanism is responsible for the spins of
halos of dark matter. The answer to this question is of high importance for
modeling galaxy intrinsic alignment, which can potentially contaminate current
and future lensing data. In particular, we show that when the dark matter halos
pass nearly by each other in dense environments-- namely halo assemblies-- they
swing and spin each other via exerting mutual tidal torques. We show that this
has a significant contribution to the spin of dark matter halos comparable to
that of calculated by the so-called tidal torque theory (TTT). We use the
results of state-of-the-art simulation of Illutris to check the prediction of
this theory against the simulation data.


  Empirical data, on which deep learning relies, has substantial internal
structure, yet prevailing theories often disregard this aspect. Recent research
has led to the definition of structured data ensembles, aimed at equipping
established theoretical frameworks with interpretable structural elements, a
pursuit that aligns with the broader objectives of spin glass theory. We
consider a one-parameter structured ensemble where data consists of correlated
pairs of patterns, and a simplified model of unsupervised learning, whereby the
internal representation of the training set is fixed at each layer. A mean
field solution of the model identifies a set of layer-wise recurrence equations
for the overlaps between the internal representations of an unseen input and of
the training set. The bifurcation diagram of this discrete-time dynamics is
topologically inequivalent to the unstructured one, and displays transitions
between different phases, selected by varying the load (the number of training
pairs divided by the width of the network). The network's ability to resolve
different patterns undergoes a discontinuous transition to a phase where signal
processing along the layers dissipates differential information about an
input's proximity to the different patterns in a pair. A critical value of the
parameter tuning the correlations separates regimes where data structure
improves or hampers the identification of a given pair of patterns.


  Needle-like metallic particles have been suggested to explain a wide variety
of astrophysical phenomena, ranging from the mid-infrared interstellar
extinction to the thermalization of starlight to generate the cosmic microwave
background. These suggestions rely on the amplitude and the wavelength
dependence of the absorption cross sections of metallic needles. On the absence
of an exact solution to the absorption properties of metallic needles, their
absorption cross sections are often derived from the antenna approximation.
However, it is shown here that the antenna approximation is not an appropriate
representation since it violates the Kramers-Kronig relation. Stimulated by the
recent discovery of iron whiskers in asteroid Itokawa and graphite whiskers in
carbonaceous chondrites, we call for rigorous calculations of the absorption
cross sections of metallic needle-like particles, presumably with the discrete
dipole approximation. We also call for experimental studies of the formation
and growth mechanisms of metallic needle-like particles as well as experimental
measurements of the absorption cross sections of metallic needles of various
aspect ratios over a wide wavelength range to bound theoretical calculations.


  minimal-lagrangians is a Python program which allows one to specify the field
content of an extension of the Standard Model of particle physics and, using
this information, to generate the most general renormalizable Lagrangian that
describes such a model. As the program was originally created for the study of
minimal dark matter models with radiative neutrino masses, it can handle
additional scalar or Weyl fermion fields which are
$\mathrm{SU}(3)_{\mathrm{C}}$ singlets, $\mathrm{SU}(2)_{\mathrm{L}}$ singlets,
doublets or triplets, and can have arbitrary $\mathrm{U}(1)_{\mathrm{Y}}$
hypercharge. It is also possible to enforce an arbitrary number of global
$\mathrm{U}(1)$ symmetries (with $\mathbb{Z}_2$ as a special case) so that the
new fields can additionally carry such global charges. In addition to
human-readable and $\mathrm{\LaTeX}$ output, the program can generate SARAH
model files containing the computed Lagrangian, as well as information about
the fields after electroweak symmetry breaking (EWSB), such as vacuum
expectation values (VEVs) and mixing matrices. This capability allows further
detailed investigation of the model in question, with minimal-lagrangians as
the first component in a tool chain for rapid phenomenological studies of
"minimal" dark matter models requiring little effort and no unnecessary input
from the user.


  Fermionic systems differ from bosonic ones in several ways, in particular
that the time-reversal operator $T$ is odd, $T^2=-1$. For $PT$-symmetric
bosonic systems, the no-signaling principle and the quantum brachistochrone
problem have been studied to some degree, both of them controversially. In this
paper, we apply the basic methods proposed for bosonic systems to {\it
fermionic} two- and four-dimensional $PT$-symmetric Hamiltonians, and obtain
several surprising results: We find - in contrast to the bosonic case - that
the no-signaling principle is upheld for two-dimensional fermionic
Hamiltonians, however, the $PT$ symmetry is broken. In addition, we find that
the time required for the evolution from a given initial state, the spin-up, to
a given final state, the spin-down, is a constant, independent of the
parameters of the Hamiltonian, under the eigenvalue constraint. That is, it
cannot - as in the bosonic case - be optimized. We do, however, also find a
dimensional dependence: four-dimensional $PT$-symmetric fermionic Hamiltonians
considered here again uphold the no-signaling principle, but it is not
essential that the $PT$ symmetry be broken. The symmetry is, however, broken if
the measure of entanglement is conserved. In the four-dimensional systems, the
evolution time between orthogonal states is dependent on the parameters of the
Hamiltonian, with the conclusion that it again can be optimized, and approach
zero under certain circumstances. However, if we require the conservation of
entanglement, the transformation time between these two states becomes the same
constant as found in the two-dimensional case, which coincides with the minimum
time for such a transformation to take place in the Hermitian case.


  Using the Ehlers transformation along with the gravitoelectromagnetic
approach to stationary spacetimes we start from the Morgan-Morgan disk
spacetime (without radial pressure) as the seed metric and find its
corresponding stationary spacetime. As expected from the Ehlers transformation
the stationary spacetime obtained suffers from a NUT-type singularity and the
new parameter introduced in the stationary case could be interpreted as the
gravitomagnetic monopole charge (or the NUT factor). As a consequence of this
singularity there are closed timelike curves (CTCs) in the singular region of
the spacetime. Some of the properties of this spacetime including its particle
velocity distribution, gravitational redshift, stability and energy conditions
are discussed.


  We discuss the role of fixed scalars($\nu,\lambda$) in scattering off a
five-dimensional balck hole. The issue is to explain the disagreement of the
greybody factor for $\lambda$ between the semiclassical and effective string
calculations. In the effective string approach, this is related to the
operators with dimension (3,1) and (1,3). On the semiclassical calculation,
this originates from a complicated mixing between $\lambda$ and other fields.
Hence it may depend on the decoupling procedure. It is shown that $\lambda$
depends on the gauge choices such as the harmonic, dilaton gauges, and the
Krasnitz-Klebanov setting for $h_{\mu\nu}$. It turns out that $\nu$ plays a
role of test field well, while the role of $\lambda$ is obscure.


  We present a connection establishment protocol with integrated
authentication, suited for Massive Machine-Type Communications (mMTC). The
protocol is contention-based and its main feature is that a device contends
with a unique signature that also enables the authentication of the device
towards the network. The signatures are inspired by Bloom filters and are
created based on the output of the MILENAGE authentication and encryption
algorithm set, which is used in the authentication and security procedures in
the LTE protocol family. We show that our method utilizes the system resources
more efficiently, achieves lower latency of connection establishment for
Poisson arrivals and allows a $87\%$ signalling overhead reduction. An
important conclusion is that the mMTC traffic benefits profoundly from
integration of security features into the connection establishment/access
protocols, instead of addressing them post-hoc, which has been a common
practice.


  It is argued that the factorization of compound probability over subsystems
is a consequence of the existence of thermodynamic equilibrium in the composite
system having Tsallis entropy. So it should be respected by all exact
calculations concerning interacting nonextensive subsystems. Using nonextensive
energy satisfying the factorization, we propose an additive formalism of
nonextensive statistical mechanics with additive q-deformed physical quantities
and exponential distributions. This formalism leads to exact quantum gas
distributions different from those given by factorization approximation. The
fermion distribution of present work shows similar behaviors to that of
strongly correlated electrons.


  We investigate mode amplitudes in the active and quiet Sun in both maximum
and minimum phases of the solar activity cycle. We confirm previous studies
showing that p-mode amplitudes at solar minimum are higher than at solar
maximum. We mask active regions of a certain magnetic field strength and
compare the masked and unmasked acoustic power. After applying the masks, the
preliminary analysis indicates that the amplitude decreases over all degrees
during solar minimum, compared to the unmasked case, while at solar maximum the
amplitude first decreases up to l ~ 300 and then increases at higher degrees.


  We present an efficient way to calculate the effect of soft QCD radiation at
one loop, which is needed for predictions at next-to-next-to-leading
logarithmic accuracy. We use rapidity coordinates and isolate the divergences
in the integrand. By performing manipulations with cumulative variables, we
avoid complications from plus distributions. We address rapidity divergences,
divergences with an azimuthal dependence, complicated jet boundaries and
multi-differential measurements. The process and measurements can be easily
adjusted, as we demonstrate by reproducing many existing soft functions. The
results for a general LHC process with multiple Wilson lines are obtained by
treating Wilson lines that are not back-to-back using a boost. We also obtain,
for the first time, the N-jettiness soft function for generic jet angularities,
and the collinear-soft function for the measurement of two angularities.


  The intracluster medium (ICM) of galaxy clusters encodes the impact of the
physical processes that shape these massive halos, including feedback from
central supermassive black holes (SMBHs). In this study, we examine the gas
thermodynamics, kinematics, and the effects of SMBH feedback on the core of
Perseus-like galaxy clusters with a new simulation suite: TNG-Cluster. We first
make a selection of simulated clusters similar to Perseus based on the total
mass and inner ICM properties, such as their cool-core nature. We identify 30
Perseus-like systems among the 352 TNG-Cluster halos at $z=0$. Many exhibit
thermodynamical profiles and X-ray morphologies with disturbed features such as
ripples, bubbles, and shock fronts that are qualitatively similar to X-ray
observations of Perseus. To study observable gas motions, we generate XRISM
mock X-ray observations and conduct a spectral analysis of the synthetic data.
In agreement with existing Hitomi measurements, TNG-Cluster predicts subsonic
gas turbulence in the central regions of Perseus-like clusters, with a typical
line-of-sight velocity dispersion of 200 km/s. This implies that turbulent
pressure contributes $< 10\%$ to the dominant thermal pressure. In TNG-Cluster,
such low (inferred) values of ICM velocity dispersion coexist with
high-velocity outflows and bulk motions of relatively small amounts of
super-virial hot gas, moving up to thousands of km/s. However, detecting these
outflows in observations may prove challenging due to their anisotropic nature
and projection effects. Driven by SMBH feedback, such outflows are responsible
for many morphological disturbances in the X-ray maps of cluster cores. They
also increase both the inferred and intrinsic ICM velocity dispersion. This
effect is somewhat stronger when velocity dispersion is measured from
higher-energy lines.


  The one dimensional Bose-Hubbard model at a unit filling factor is studied by
means of a very high order symbolic perturbative expansion. Analytical
expressions are derived for the ground state quantities such as energy per
site, variance of on-site occupation, and different correlation functions.
These findings are compared to numerics and good agreement is found in the Mott
insulator phase. Our results provide analytical approximations to important
observables in the Mott phase, and are also of direct relevance to future
experiments with ultra cold atomic gases placed in optical lattices. We also
discuss the symmetry of the Bose-Hubbard model associated with the sign change
of the tunneling coupling.


  We consider group-valued cocycles over dynamical systems with hyperbolic
behavior. The base system is either a hyperbolic diffeomorphism or a mixing
subshift of finite type. The cocycle $A$ takes values in the group of
invertible bounded linear operators on a Banach space and is H\"older
continuous. We consider the periodic data of $A$, i.e. the set of its return
values along the periodic orbits in the base. We show that if the periodic data
of $A$ is uniformly quasiconformal or bounded or contained in a compact set,
then so is the cocycle. Moreover, in the latter case the cocycle is isometric
with respect to a H\"older continuous family of norms. We also obtain a general
result on existence of a measurable family of norms invariant under a cocycle.


  Despite advances in neural machine translation (NMT) quality, rare words
continue to be problematic. For humans, the solution to the rare-word problem
has long been dictionaries, but dictionaries cannot be straightforwardly
incorporated into NMT. In this paper, we describe a new method for "attaching"
dictionary definitions to rare words so that the network can learn the best way
to use them. We demonstrate improvements of up to 1.8 BLEU using bilingual
dictionaries.


  We introduce a family of mixed finite element pairs for use on geodesic grids
and with adaptive mesh refinement for numerical weather prediction and ocean
modelling. We prove that when these finite element pairs are applied to the
linear rotating shallow water equations, the geostrophically balanced states
are exactly steady, which means that the numerical schemes do not introduce any
spurious inertia-gravity waves; this makes these finite element pairs in some
sense optimal for numerical weather prediction and ocean modelling
applications. We further prove that these finite element pairs satisfy an
inf-sup condition which means that they are free of spurious pressure modes
which would pollute the numerical solution over the timescales required for
large-scale geophysical applications. We then discuss the extension to
incompressible Euler-Boussinesq equations with rotation, and show that for the
linearised equations the balanced states are again exactly steady on arbitrary
unstructured meshes. We also show that the discrete pressure Poisson equation
resulting from these discretisations satisfies an optimal stencil property. All
these properties make the discretisations in this family excellent candidates
for numerical weather prediction and large-scale ocean modelling applications
when unstructured grids are required.