  Based on the formulation of Yang-Baxter sigma models developed by Klimcik and
Delduc-Magro-Vicedo, we explain that various deformations of type IIB
superstring on AdS$_5\times$S$^5$ can be characterized by classical
$r$-matrices satisfying the classical Yang-Baxter equation (CYBE). The relation
may be referred to as `the gravity/CYBE correspondence.' We present non-trivial
examples of the correspondence including Lunin-Maldacena backgrounds for
$\beta$-deformations of the ${\cal N} = 4$ super Yang-Mills theory and the
gravity duals for non-commutative gauge theories. We also discuss
non-integrable backgrounds such as AdS$_5\times T^{1,1}$ as a generalization.


  This paper proposes a new methodology to study sequential corpora by
implementing a two-stage algorithm that learns time-based topics with respect
to a scale of document positions and introduces the concept of Topic Scaling
which ranks learned topics within the same document scale. The first stage
ranks documents using Wordfish, a Poisson-based document scaling method, to
estimate document positions that serve, in the second stage, as a dependent
variable to learn relevant topics via a supervised Latent Dirichlet Allocation.
This novelty brings two innovations in text mining as it explains document
positions, whose scale is a latent variable, and ranks the inferred topics on
the document scale to match their occurrences within the corpus and track their
evolution. Tested on the U.S. State Of The Union two-party addresses, this
inductive approach reveals that each party dominates one end of the learned
scale with interchangeable transitions that follow the parties' term of office.
Besides a demonstrated high accuracy in predicting in-sample documents'
positions from topic scores, this method reveals further hidden topics that
differentiate similar documents by increasing the number of learned topics to
unfold potential nested hierarchical topic structures. Compared to other
popular topic models, Topic Scaling learns topics with respect to document
similarities without specifying a time frequency to learn topic evolution, thus
capturing broader topic patterns than dynamic topic models and yielding more
interpretable outputs than a plain latent Dirichlet allocation.


  In high-temperature cuprate superconductors, the anti-ferromagnetic spin
fluctuations are thought to have a very important role in naturally producing
an attractive interaction between the electrons in the $d$-wave channel. The
connection between superconductivity and spin fluctuations is expected to be
especially consequential at the overdoped end point of the superconducting
dome. In some materials, that point seems to coincide with a Lifshitz
transition, where the Fermi surface changes from the hole-like centered at
($\pi, \pi$) to the electron-like, centered at the $\Gamma$ point causing a
loss of large momentum anti-ferromagnetic fluctuations. Here, we study the
doping dependence of the electronic structure of
Bi$_{1.8}$Pb$_{0.4}$Sr$_2$CuO$_{6+\delta}$ in angle-resolved photoemission and
find that the superconductivity vanishes at lower doping than at which the
Lifshitz transition occurs. This requires a more detailed re-examination of a
spin-fluctuation scenario.


  We present radial velocities for 144 globular clusters around the Virgo gE
NGC 4472 (M49), and ages and metallicities from co-added spectra of 131
clusters. We confirm our earlier finding that the metal-poor clusters have a
significantly higher velocity dispersion than the metal-rich clusters, and we
find little or no rotation in the metal-rich clusters. The velocity dispersion
profile is consistent with isotropic orbits for the clusters and the NGC 4472
mass distribution inferred from X-ray data. Our sample of clusters spans a
metallicity range of -1.6 < [Fe/H] < 0 dex. The metal-poor and metal-rich
cluster populations are coeval within the errors, and all clusters are older
than 6 Gyr at 95% confidence. Our findings are consistent with a merger origin
for NGC 4472, but other elliptical formation models cannot be ruled out.


  We propose a unified framework for likelihood-based regression modeling when
the response variable has finite support. Our work is motivated by the fact
that, in practice, observed data are discrete and bounded. The proposed methods
assume a model which includes models previously considered for
interval-censored variables with log-concave distributions as special cases.
The resulting log-likelihood is concave, which we use to establish asymptotic
normality of its maximizer as the number of observations $n$ tends to infinity
with the number of parameters $d$ fixed, and rates of convergence of
$L_1$-regularized estimators when the true parameter vector is sparse and $d$
and $n$ both tend to infinity with $\log(d) / n \to 0$. We consider an inexact
proximal Newton algorithm for computing estimates and give theoretical
guarantees for its convergence. The range of possible applications is wide,
including but not limited to survival analysis in discrete time, the modeling
of outcomes on scored surveys and questionnaires, and, more generally,
interval-censored regression. The applicability and usefulness of the proposed
methods are illustrated in simulations and data examples.


  In oncology, phase II studies are crucial for clinical development plans as
such studies identify potent agents with sufficient activity to continue
development in the subsequent phase III trials. Traditionally, phase II studies
are single-arm studies, with the primary endpoint being short-term treatment
efficacy. However, drug safety is also an important consideration. In the
context of such multiple-outcome designs, predictive probability-based Bayesian
monitoring strategies have been developed to assess whether a clinical trial
will provide enough evidence to continue with a phase III study at the
scheduled end of the trial. Herein, we propose a new simple index vector for
summarizing results that cannot be captured by existing strategies.
Specifically, for each interim monitoring time point, we calculate the Bayesian
predictive probability using our new index and use it to assign a go/no-go
decision. Finally, simulation studies are performed to evaluate the operating
characteristics of the design. The obtained results demonstrate that the
proposed method makes appropriate interim go/no-go decisions.


  Many tasks in machine learning and data mining, such as data diversification,
non-parametric learning, kernel machines, clustering etc., require extracting a
small but representative summary from a massive dataset. Often, such problems
can be posed as maximizing a submodular set function subject to a cardinality
constraint. We consider this question in the streaming setting, where elements
arrive over time at a fast pace and thus we need to design an efficient,
low-memory algorithm. One such method, proposed by Badanidiyuru et al. (2014),
always finds a $0.5$-approximate solution. Can this approximation factor be
improved? We answer this question affirmatively by designing a new algorithm
SALSA for streaming submodular maximization. It is the first low-memory,
single-pass algorithm that improves the factor $0.5$, under the natural
assumption that elements arrive in a random order. We also show that this
assumption is necessary, i.e., that there is no such algorithm with better than
$0.5$-approximation when elements arrive in arbitrary order. Our experiments
demonstrate that SALSA significantly outperforms the state of the art in
applications related to exemplar-based clustering, social graph analysis, and
recommender systems.


  Despite many efforts to rationalize the strongly correlated electronic ground
states in doped Mott insulators, the nature of the doping induced insulator to
metal transition is still a subject under intensive investigation. Here we
probe the nanoscale electronic structure of the Mott insulator
Sr$_2$IrO$_{4-\delta}$ with low-temperature scanning tunneling microscopy and
find enhanced local density of states (LDOS) inside the Mott gap at the
location of individual apical oxygen site defects. We visualize paths of
enhanced conductance arising from the overlapping of defect states which
induces finite LDOS at the Fermi level. By combining these findings with the
typical spatial extension of isolated defects of about 2~nm, we show that the
insulator to metal transition in Sr$_2$IrO$_{4-\delta}$ is of percolative
nature.


  Leveraging their eBird crowdsourcing project, the Cornell Lab of Ornithology
generates sophisticated Spatio-Temporal Exploratory Model (STEM) maps of bird
migrations. Such maps are highly relevant for both scientific and educational
purposes, but creating them requires advanced modeling techniques that rely on
long and potentially expensive computations.
  In this paper, we share our experience porting the eBird STEM data pipeline
from a physical cluster to the cloud, providing a seamless deployment at a
lower cost. Using open source tools and cloud "marketplaces", we managed to
divide the operating costs by a factor of 6, making it possible to scale our
pipeline on a research budget.


  In private federated learning (FL), a server aggregates differentially
private updates from a large number of clients in order to train a machine
learning model. The main challenge in this setting is balancing privacy with
both classification accuracy of the learnt model as well as the number of bits
communicated between the clients and server. Prior work has achieved a good
trade-off by designing a privacy-aware compression mechanism, called the
minimum variance unbiased (MVU) mechanism, that numerically solves an
optimization problem to determine the parameters of the mechanism. This paper
builds upon it by introducing a new interpolation procedure in the numerical
design process that allows for a far more efficient privacy analysis. The
result is the new Interpolated MVU mechanism that is more scalable, has a
better privacy-utility trade-off, and provides SOTA results on
communication-efficient private FL on a variety of datasets.


  Volume of moduli space of non-Abelian BPS domain-walls is exactly obtained in
U(N_c) gauge theory with N_f matters. The volume of the moduli space is
formulated, without an explicit metric, by a path integral under constraints on
BPS equations. The path integral over fields reduces to a finite dimensional
contour integral by a localization mechanism. Our volume formula satisfies a
Seiberg like duality between moduli spaces of the U(N_c) and U(N_f-N_c)
non-Abelian BPS domain-walls in a strong coupling region. We also find a
T-duality between domain-walls and vortices on a cylinder. The moduli space
volume of non-Abelian local (N_c=N_f) vortices on the cylinder agrees exactly
with that on a sphere. The volume formula reveals various geometrical
properties of the moduli space.


  Let $f_r(n,v,e)$ denote the maximum number of edges in an $r$-uniform
hypergraph on $n$ vertices, in which the union of any $e$ distinct edges
contains at least $v+1$ vertices. The study of $f_r(n,v,e)$ was initiated by
Brown, Erd{\H{o}}s and S{\'o}s more than forty years ago. In the literature,
the following conjecture is well known.
  Conjecture: $n^{k-o(1)}<f_r(n,er-(e-1)k+1,e)=o(n^k)$ holds for all fixed
integers $r>k\ge 2$ and $e\ge 3$ as $n\rightarrow\infty$.
  For $r=3, e=3, k=2$, the bound $n^{2-o(1)}<f_3(n,6,3)=o(n^2)$ was proved by
the celebrated (6,3)-theorem of Ruzsa and Szemer{\'e}di. In this paper, we add
more evidence for the validity of the conjecture. On one hand, using the
hypergraph removal lemma we show that the upper bound part of the conjecture is
true for all fixed integers $r\ge k+1\ge e\ge3$.
  On the other hand, using tools from additive number theory we present several
constructions showing that the lower bound part of the conjecture is true for
$r\ge3$, $k=2$ and $e=4,5,7,8$.
  Prior to our results, all known constructions that match the conjectured
lower bound satisfy either $r=3$ or $e=3$. Our constructions are the first ones
in the literature that break this barrier.


  We present an adaptive finite element method for the incompressible
Navier--Stokes equations based on a standard splitting scheme (the incremental
pressure correction scheme). The presented method combines the efficiency and
simplicity of a splitting method with the powerful framework offered by the
finite element method for error analysis and adaptivity. An a posteriori error
estimate is derived which expresses the error in a goal functional of interest
as a sum of contributions from spatial discretization, time discretization and
a term that measures the deviation of the splitting scheme from a pure Galerkin
scheme (the computational error). Numerical examples are presented which
demonstrate the performance of the adaptive algorithm and high quality
efficiency indices. It is further demonstrated that the computational error of
the Navier--Stokes momentum equation is linear in the size of the time step
while the computational error of the continuity equation is quadratic in the
size of the time step.


  In this paper, we analyze the three-level explicit time-split MacCormack
procedure in the numerical solutions of two-dimensional viscous coupled
Burgers' equations subject to initial and boundary conditions. The differential
operators split the two-dimensional problem into two pieces so that the
two-step explicit MacCormack scheme can be easily applied to each subproblem.
This reduces the computational cost of the algorithm. For low Reynolds numbers,
the proposed method is second order accurate in time and fourth convergent in
space, while it is second order convergent in both time and space for high
Reynolds numbers problems. This shows the efficiency and effectiveness of the
considered method compared to a large set of numerical schemes widely studied
in the literature for solving the two-dimensional time dependent nonlinear
coupled Burgers' equations. Numerical examples which confirm the theoretical
results are presented and discussed.


  In many modern applications, including analysis of gene expression and text
documents, the data are noisy, high-dimensional, and unordered--with no
particular meaning to the given order of the variables. Yet, successful
learning is often possible due to sparsity: the fact that the data are
typically redundant with underlying structures that can be represented by only
a few features. In this paper we present treelets--a novel construction of
multi-scale bases that extends wavelets to nonsmooth signals. The method is
fully adaptive, as it returns a hierarchical tree and an orthonormal basis
which both reflect the internal structure of the data. Treelets are especially
well-suited as a dimensionality reduction and feature selection tool prior to
regression and classification, in situations where sample sizes are small and
the data are sparse with unknown groupings of correlated or collinear
variables. The method is also simple to implement and analyze theoretically.
Here we describe a variety of situations where treelets perform better than
principal component analysis, as well as some common variable selection and
cluster averaging schemes. We illustrate treelets on a blocked covariance model
and on several data sets (hyperspectral image data, DNA microarray data, and
internet advertisements) with highly complex dependencies between variables.


  We investigate the properties and the environment of radio sources with
optical counterpart from the combined VLA-COSMOS and zCOSMOS samples. The
advantage of this sample is the availability of optical spectroscopic
information, high quality redshifts, and accurate density determination.
  By comparing the star formation rates estimated from the optical spectral
energy distribution with those based on the radio luminosity, we divide the
radio sources in three families, passive AGN, non-passive AGN and star forming
galaxies. These families occupy specific regions of the 8.0-4.5 $\mu$m infrared
color--specific star formation plane, from which we extract the corresponding
control samples.
  Only the passive AGN have a significantly different environment distribution
from their control sample. The fraction of radio-loud passive AGN increases
from ~2% in underdense regions to ~15% for overdensities (1+delta) greater than
10. This trend is also present as a function of richness of the groups hosting
the radio sources. Passive AGN in overdensities tend to have higher radio
luminosities than those in lower density environments. Since the black hole
mass distribution is similar in both environments, we speculate that, for low
radio luminosities, the radio emission is controlled (through fuel
disponibility or confinement of radio jet by local gas pressure) by the
interstellar medium of the host galaxy, while in other cases it is determined
by the structure (group or cluster) in which the galaxy resides.


  The extension of photonic technologies such as lidar and free-space optical
communications from the traditional visible and near-infrared wavelengths to
longer wavelengths can improve performance in adverse environments such as
haze, fog, smoke, or strong solar background. Non-mechanical beam steerers will
be a critical component of the low size, weight, and power modules needed for
the portable or unmanned systems deployed in these environments. In this work,
we demonstrate the first 2D optical phased array for non-mechanical beam
steering in the mid-infrared spectral region. We combine a total
internal-reflection mirror emitter with a non-redundant array of 30 elements to
carry out 2D beam steering at a single wavelength of 4.6 $\mu$m. The experiment
yielded 600 resolvable far-field points, with 2400 points over a $28^\circ
\times 28^\circ$ field of view calculated theoretically. Moreover, the device
was fabricated in a passive InGaAs/InP platform, contributing another advance
in the ongoing development of quantum cascade laser-based photonic integration.


  We study the effect of surface texture on an overlying turbulent flow for
textures made of an alternating slip/no-slip pattern, a common model for
superhydrophobic surfaces, but also a particularly simple form of texture. For
texture sizes $L^+ \gtrsim 25$, the texture effectively imposes homogeneous
slip boundary conditions on the overlying, background turbulence, but this is
not its sole effect. The effective conditions only produce an origin offset on
the background turbulence, which remains otherwise smooth-wall-like. For actual
textures, however, for $L^+ \gtrsim 25$ the flow progressively departs from
this smooth-wall-like regime, resulting in additional shear Reynolds stress and
increased drag, in a non-homogeneous fashion not reproduced by the effective
boundary conditions. We focus on the underlying physical mechanism of this
phenomenon. We argue that it is caused by the non-linear interaction of the
texture-coherent flow, directly induced by the surface topology, and the
background turbulence, as it acts directly on the latter and alters it, not at
the boundary where effective conditions are imposed, but within the flow
itself. The interaction acts as a forcing on the governing equations of the
background turbulence, and takes the form of cross-advective terms between the
latter and the texture-coherent flow. We show this with simulations with the
texture removed and additional, forcing terms in the Navier-Stokes equations,
in addition to the effective boundary conditions. The forcing captures the
effect of the non-linear interaction on the background turbulence without the
need to resolve the texture. When the forcing is derived accounting for the
background turbulence amplitude-modulating the texture-coherent flow, it
captures the changes in the flow up to $L^+ \approx 70$--$100$, including the
roughness function and also the changes in the flow statistics and structure.


  Garber, Kahrobaei, and Lam studied polycyclic groups generated by number
field as platform for the AAG key-exchange protocol. In this paper, we discuss
the use of a different kind of polycyclic groups, Heisenberg groups, as a
platform group for AAG by submitting Heisenberg groups to one of AAG's major
attacks, the length-based attack.


  Face reenactment is challenging due to the need to establish dense
correspondence between various face representations for motion transfer. Recent
studies have utilized Neural Radiance Field (NeRF) as fundamental
representation, which further enhanced the performance of multi-view face
reenactment in photo-realism and 3D consistency. However, establishing dense
correspondence between different face NeRFs is non-trivial, because implicit
representations lack ground-truth correspondence annotations like mesh-based 3D
parametric models (e.g., 3DMM) with index-aligned vertexes. Although aligning
3DMM space with NeRF-based face representations can realize motion control, it
is sub-optimal for their limited face-only modeling and low identity fidelity.
Therefore, we are inspired to ask: Can we learn the dense correspondence
between different NeRF-based face representations without a 3D parametric model
prior? To address this challenge, we propose a novel framework, which adopts
tri-planes as fundamental NeRF representation and decomposes face tri-planes
into three components: canonical tri-planes, identity deformations, and motion.
In terms of motion control, our key contribution is proposing a Plane
Dictionary (PlaneDict) module, which efficiently maps the motion conditions to
a linear weighted addition of learnable orthogonal plane bases. To the best of
our knowledge, our framework is the first method that achieves one-shot
multi-view face reenactment without a 3D parametric model prior. Extensive
experiments demonstrate that we produce better results in fine-grained motion
control and identity preservation than previous methods.


  Seismology of the gas giants holds the potential to resolve long-standing
questions about their internal structure and rotation state. We construct a
family of Saturn interior models constrained by the gravity field and compute
their adiabatic mode eigenfrequencies and corresponding Lindblad and vertical
resonances in Saturn's C ring, where more than twenty waves with pattern speeds
faster than the ring mean motion have been detected and characterized using
high-resolution Cassini Visual and Infrared Mapping Spectrometer (VIMS) stellar
occultation data. We present identifications of the fundamental modes of Saturn
that appear to be the origin of these observed ring waves, and use their
observed pattern speeds and azimuthal wavenumbers to estimate the bulk rotation
period of Saturn's interior to be $10{\rm h}\, 33{\rm m}\, 38{\rm s}^{+1{\rm
m}\, 52{\rm s}}_{-1{\rm m}\, 19{\rm s}}$ (median and 5%/95% quantiles),
significantly faster than Voyager and Cassini measurements of periods in
Saturn's kilometric radiation, the traditional proxy for Saturn's bulk rotation
period. The global fit does not exhibit any clear systematics indicating strong
differential rotation in Saturn's outer envelope.


  We formulate a conjecture which describes the Fukaya category of an exact
Lefschetz fibration defined by a Laurent polynomial in two variables in terms
of a pair consisting of a consistent dimer model and a perfect matching on it.
We prove this conjecture in some cases, and obtain homological mirror symmetry
for quotient stacks of toric del Pezzo surfaces by finite subgroups of the
torus as a corollary.


  We analyze new and published optical photometric data of FU Orionis, an
eruptive pre-main sequence star. The outburst consists of a 5.5 mag rise at B
with an e-folding timescale of roughly 50 days. The rates of decline at B and V
are identical, 0.015 +- 0.001 mag per yr. Random fluctuations superimposed on
this decline have an amplitude of 0.035 +- 0.005 mag at V and occur on
timescales of 1 day or less. Correlations between V and the color indices U-B,
B-V, and V-R indicate that the variable source has the optical colors of a G0
supergiant. We associate this behavior with small amplitude flickering of the
inner accretion disk.


  Along a single strand of micro-capillary optical waveguide, we achieved an
efficient transfer of the light momentum onto the liquid contained there
within, successfully atomizing it into nanoscale droplets. A hollow optical
fiber (HOF), with a ring core and central air hole, was used to optically drive
jetting of non-conducting transparent liquid of sub-pico liter volume, out of a
surface-treated facet orifice, producing droplets ranging from nano to
micrometer scale. These droplets were carried over the propagating light field
forming a spherical cone, which were then deposited on a silica substrate in a
Gaussian spatial distribution. The deposited patterns and sizes of individual
droplets were characterized as a function of the laser power, irradiation time,
and distance between the HOF and a substrate. This HOF based laser driven
atomization technique obviates imperative electrode or aerial pressure
requirements in prior methods, opening a new pathway to drastically scale down
the form-factor of liquid jetting devices, and has a high potential to in-situ
atomization and delivery of bio-medical non-conducting liquids in a microscopic
environment, which was not possible in prior arts.


  We investigate some characteristic properties of specific Weingarten surfaces
in the three-dimensional Euclidean space using the nets of the lines of
curvature resp. the asymptotic lines on both central surfaces of them.


  We investigate the strong lensing properties of the Sersic profile as an
alternative to the NFW profile, focusing on applications to lens modelling of
clusters. Given an underlying Sersic dark matter profile, we study whether an
NFW profile can provide an acceptable fit to strong lensing constraints in the
form of single or multiple measured Einstein radii. We conclude that although
an NFW profile that fits the lensing constraints can be found in many cases,
the derived parameters may be biased. In particular, we find that for n~2,
which corresponds to massive clusters, the mass at r_200 of the best fit NFW is
overestimated (by a factor of ~2) and the concentration is very low (c~2). The
differences are important enough to warrant the inclusion of Sersic profile for
future analysis of strong lensing clusters.


  We report the pressure and temperature dependence of the phonon
density-of-states in superconducting Ca0.6Na0.4Fe2As2 (Tc=21 K) and the parent
compound CaFe2As2, using inelastic neutron scattering. We observe no
significant change in the phonon spectrum for Ca0.6Na0.4Fe2As2 at 295 K up to
pressures of 5 kbar. The phonon spectrum for CaFe2As2 shows softening of the
low-energy modes by about 1 meV when decreasing the temperature from 300 K to
180 K. There is no appreciable change in the phonon density of states across
the structural and anti-ferromagnetic phase transition at 172 K. These results,
combined with our earlier temperature dependent phonon density of states
measurements for Ca0.6Na0.4Fe2As2, indicate that the softening of low-energy
phonon modes in these compounds may be due to the interaction of phonons with
electron or short-range spin fluctuations in the normal state of the
superconducting compound as well as in the parent compound. The phonon spectra
are analyzed with ab-initio and empirical model calculations giving partial
densities of states and dispersion relations.


  In this work we investigate magneto-optical properties of two-dimensional
semiconductor quantum-ring excitons with Rashba and Dresselhaus spin-orbit
interactions threaded by a magnetic flux perpendicular to the plane of the
ring. By calculating the excitonic Aharonov-Bohm spectrum, we study the Coulomb
and spin-orbit effects on the Aharonov-Bohm features. From the light-matter
interactions of the excitons, we find that for scalar excitons, there are open
channels for spontaneous recombination resulting in a bright photoluminescence
spectrum, whereas the forbidden recombination of dipolar excitons results in a
dark photoluminescence spectrum. We investigate the generation of persistent
charge and spin currents. The exploration of spin orientations manifests that
by adjusting the strength of the spin-orbit interactions, the exciton can be
constructed as a squeezed complex with specific spin polarization. Moreover, a
coherently moving dipolar exciton acquires a nontrivial dual Aharonov-Casher
phase, creating the possibility to generate persistent dipole currents and spin
dipole currents. Our study reveals that a manipulation of the spin-orbit
interactions provides a potential application for quantum-ring spinor excitons
to be utilized in nano-scaled magneto-optical switches.


  In this paper, we show that although the minimizers of cross-entropy and
related classification losses are off at infinity, network weights learned by
gradient flow converge in direction, with an immediate corollary that network
predictions, training errors, and the margin distribution also converge. This
proof holds for deep homogeneous networks -- a broad class of networks allowing
for ReLU, max-pooling, linear, and convolutional layers -- and we additionally
provide empirical support not just close to the theory (e.g., the AlexNet), but
also on non-homogeneous networks (e.g., the DenseNet). If the network further
has locally Lipschitz gradients, we show that these gradients also converge in
direction, and asymptotically align with the gradient flow path, with
consequences on margin maximization, convergence of saliency maps, and a few
other settings. Our analysis complements and is distinct from the well-known
neural tangent and mean-field theories, and in particular makes no requirements
on network width and initialization, instead merely requiring perfect
classification accuracy. The proof proceeds by developing a theory of unbounded
nonsmooth Kurdyka-{\L}ojasiewicz inequalities for functions definable in an
o-minimal structure, and is also applicable outside deep learning.


  In this work, we obtain a Lyapunov-type and a Hartman-Wintner-type
inequalities for a linear and a nonlinear fractional differential equation with
generalized Hilfer operator subject to Dirichlet-type boundary conditions. We
prove existence of positive solutions to a nonlinear fractional boundary value
problem. As an application, we obtain a lower bound for the eigenvalues of
corresponding equations.


  Making use of 36 pb^-1 of proton-proton collision data at sqrt{s} = 7 TeV,
the ATLAS Collaboration has performed a search for diphoton events with large
missing transverse energy. Observing no excess of events above the Standard
Model prediction, a 95% Confidence Level (CL) upper limit is set on the cross
section for new physics of sigma < 0.38 - 0.65 pb in the context of a
generalised model of gauge mediated supersymmetry breaking (GGM) with a
bino-like lightest neutralino, and of sigma < 0.18 - 0.23 pb in the context of
a specific model with one universal extra dimension (UED). A 95 % CL lower
limit of 560 GeV, for bino masses above 50 GeV, is set on the GGM gluino mass,
while a lower limit of 1/R > 961 GeV is set on the UED compactification radius
R. These limits provide the most stringent tests of these models to date.


  In this paper we introduce a $\theta$-family of spacelike surfaces in the
Lorentz-Minkowski space R^4_1 based in two complex valued functions $a(w),
\mu(w)$, which when they are holomorphic we will be dealing with a family of
spacelike minimal surfaces. The $\theta$-family is such that it connects
spacelike minimal surfaces in R^3_1 to spacelike minimal surfaces in R^3. We
study the family through of the curvature and we prove that the family
preserves planar points and moreover, that the existence of planar points
corresponds to the existence of solutions of equation |a_w(w)|^2 =0. We also
show that if a pair of surfaces are associated through of a $\theta$-family
then they can not be complete surfaces. As applications we focus to one type of
graph surfaces in R^4_1 and we prove that if the imaginary part of $a(w)$ is
zero at least in a point then the surface cannot assume local representations
of that type of graph. Several explicit examples are given.


  If $A$, $B$, $C$ are subsets in a finite simple group of Lie type $G$ at
least two of which are normal with $|A||B||C|$ relatively large, then we
establish a stronger conclusion than $ABC = G$. This is related to a theorem of
Gowers and is a generalization of a theorem of Larsen, Shalev, Tiep and the
second author and Pyber.


  In this paper, we introduce a threshold free approach, motivated from Chinese
Restaurant Process, for the purpose of cognate clustering. We show that our
approach yields similar results to a linguistically motivated cognate
clustering system known as LexStat. Our Chinese Restaurant Process system is
fast and does not require any threshold and can be applied to any language
family of the world.


  We introduce a method for calculating the extent to which chain non-crossing
is important in the most efficient, optimal trajectories or pathways for a
protein to fold. This involves recording all unphysical crossing events of a
ghost chain, and calculating the minimal uncrossing cost that would have been
required to avoid such events. A depth-first tree search algorithm is applied
to find minimal transformations to fold $\alpha$, $\beta$, $\alpha/\beta$, and
knotted proteins. In all cases, the extra uncrossing/non-crossing distance is a
small fraction of the total distance travelled by a ghost chain. Different
structural classes may be distinguished by the amount of extra uncrossing
distance, and the effectiveness of such discrimination is compared with other
order parameters. It was seen that non-crossing distance over chain length
provided the best discrimination between structural and kinetic classes. The
scaling of non-crossing distance with chain length implies an inevitable
crossover to entanglement-dominated folding mechanisms for sufficiently long
chains. We further quantify the minimal folding pathways by collecting the
sequence of uncrossing moves, which generally involve leg, loop, and elbow-like
uncrossing moves, and rendering the collection of these moves over the unfolded
ensemble as a multiple-transformation "alignment". The consensus minimal
pathway is constructed and shown schematically for representative cases of an
$\alpha$, $\beta$, and knotted protein. An overlap parameter is defined between
pathways; we find that $\alpha$ proteins have minimal overlap indicating
diverse folding pathways, knotted proteins are highly constrained to follow a
dominant pathway, and $\beta$ proteins are somewhere in between. Thus we have
shown how topological chain constraints can induce dominant pathway mechanisms
in protein folding.


  NaCaNi$_2$F$_7$ is an insulating, frustrated A$_2$B$_2$F$_7$ pyrochlore with
magnetic S = 1 Ni$^{2+}$ on the pyrochlore B site. Non-magnetic Na and Ca are
disordered on the A-site. Magnetic susceptibility measurements made on an
oriented single crystal, grown in a floating zone furnace, show isotropic
behavior at temperatures between 5 and 300 K, with an effective moment of 3.7
$\mu_B$/Ni. Despite displaying a large Curie-Weiss theta (-129 K),
spin-ordering-related features are not seen in the susceptibility or specific
heat until a spin glass transition at 3.6 K. This yields an empirical
frustration index of f = -${\theta}_{CW}$/T$_f$ $\approx$ 36. The spin glass
behavior is substantiated by a shift of the freezing temperature with frequency
in the AC susceptibility, bifurcation in the DC susceptibility, and by a broad
maximum in the magnetic specific heat. The observations as made on large single
crystals suggest that NaCaNi$_2$F$_7$ is likely a realization of a frustrated
spin 1 pyrochlore antiferromagnet with weak bond disorder.


  The formation of a finite density multipolaronic state is analyzed in the
context of the Holstein model using the Dynamical Mean-Field Theory. The
spinless and spinful fermion cases are compared to disentangle the polaron
crossover from the bipolaron formation. The exact solution of Dynamical
Mean-Field Theory is compared with weak-coupling perturbation theory,
non-crossing (Migdal), and vertex correction approximations. We show that
polaron formation is not associated to a metal-insulator transition, which is
instead due to bipolaron formation.


  From the metric and one Killing-Yano tensor of rank D-2 in any D-dimensional
spacetime with such a principal Killing-Yano tensor, we show how to generate
k=[(D+1)/2] Killing-Yano tensors, of rank D-2j for all j=0,...,k-1, and k
rank-2 Killing tensors, giving k constants of geodesic motion that are in
involution. For the example of the Kerr-NUT-AdS spacetime (hep-th/0604125) with
its principal Killing-Yano tensor (gr-qc/0610144), these constants and the
constants from the k Killing vectors give D independent constants in
involution, making the geodesic motion completely integrable (hep-th/0611083).
The constants of motion are also related to the constants recently obtained in
the separation of the Hamilton-Jacobi and Klein-Gordon equations
(hep-th/0611245).


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


  Magnetic properties of quark matter are discussed. The possibility of
ferromagnetic transition is studied by using the one-gluon-exchange
interaction. Magnetic susceptibility is evaluated within Landau Fermi liquid
theory, and the important roles of the screening for the gluon propagation are
elucidated. Static screening for the longitudinal gluons improves the infrared
singularities, while the transverse gluons receive only dynamic screening. The
latter property gives rise to a novel non-Fermi-liquid behaviour in the
magnetic susceptibility. The critical density is estimated to be around the
nuclear density and the Curie temperature several tens MeV. The spin density
wave is also discussed at moderate densities, where chiral transition becomes
important. Pseudoscalar condensate as well as scalar one takes a spatially
non-uniform form in a chirally invariant way. Accordingly magnetization
oscillates like spin density wave. These results should have some implications
on compact star phenomena.


  We compute the masses of even spin glueball states $ J^{PC} $, with $ P=C=+1
$, using a twist two operator from an AdS/QCD model known as the hardwall
model, using Dirichlet and Neumann boundary conditions. Within this approach,
we found that the glueball masses are comparable with those in literature. From
these masses, we obtained the Pomeron Regge trajectories for both boundary
conditions in agreement with experimental data available and other holographic
models.


  The principle of maximum entropy is applied to the spectral analysis of a
data signal with general variance matrix and containing gaps in the record. The
role of the entropic regularizer is to prevent one from overestimating
structure in the spectrum when faced with imperfect data. Several arguments are
presented suggesting that the arbitrary prefactor should not be introduced to
the entropy term. The introduction of that factor is not required when a
continuous Poisson distribution is used for the amplitude coefficients. We
compare the formalism for when the variance of the data is known explicitly to
that for when the variance is known only to lie in some finite range. The
result of including the entropic measure factor is to suggest a spectrum
consistent with the variance of the data which has less structure than that
given by the forward transform. An application of the methodology to example
data is demonstrated.


  In the present paper, we have studied the Matsumoto change
$\overline{L}(x,y)= \frac{L^{2}(x,y)}{L(x,y) - \beta(x,y)} $ with an
\textsl{h-}vector $b_{i}(x,y)$. We have derived some fundamental tensors for
this transformation. We have also obtained the necessary and sufficient
condition for which the Cartan connection coefficients for both the spaces
$F^{n}=(M^n,L)$ and $\overline{F}^{\,n}=(M^{n},\overline{L})$ are same.


  Interfaces of van der Waals (vdW) materials such as graphite and hexagonal
boron nitride (hBN) exhibit low-friction sliding due to their atomically-flat
surfaces and weak vdW bonding. We demonstrate that microfabricated gold also
slides with low friction on hBN. This enables the arbitrary post-fabrication
repositioning of device features both at ambient conditions as well as in-situ
to a measurement cryostat. We demonstrate mechanically-reconfigurable vdW
devices where device geometry and position are continuously-tunable parameters.
By fabricating slidable top gates on a graphene-hBN device, we produce a
mechanically-tunable quantum point contact where electron confinement and
edge-state coupling can be continuously modified. Moreover, we combine in-situ
sliding with simultaneous electronic measurements to create new types of
scanning probe experiments, where gate electrodes and even entire vdW
heterostructures devices can be spatially scanned by sliding across a target.


  We present results of an investigation of clustering evolution of field
galaxies between a redshift of z\~1 and the present epoch. The current analysis
relies on a sample of ~14000 galaxies in two fields of the COMBO-17 survey. The
redshift distribution extends to z~1.1. The amplitude of the three-dimensional
correlation function can be estimated by means of the projected correlation
function w(r_p). The validity of the deprojection was tested on the Las
Campanas Redshift Survey (LCRS). In a flat cosmology with non-zero cosmological
constant for bright galaxies (M_B<-18) the clustering growth is proportional to
(1+z)^{-2}. However, the measured clustering evolution clearly depends on
Hubble type. While locally the clustering strength of early type galaxies is
equal to that of the bright galaxies, at high redshifts they are much stronger
clustered, and thus the clustering has to evolve much more slowly.


  We investigated the microwave-assisted DC voltages of ferromagnetic
resonances and exchangedominated standing spin wave excitations in two
different in-plane magnetized permalloy thin films via homodyne detection. The
line shapes of ferromagnetic resonance spectra and the dispersion curves of
ferromagnetic resonance and standing spin wave are in agreement of previous
studies, while further investigations of DC voltage spectra for these two
excitations reveal that 1. unlike ferromagnetic resonance signals, the
anti-symmetrical line shapes of standing spin wave excitations are not depend
on the electromagnetic relative phase of assisted microwave, and 2. linewidths
of their DC voltage spectra are distinct. The complicated spin dynamics of
standing spin wave is consequently discussed by applying
Landau-Lifshitz-Gilbert equation in term of exchange interaction.


  In this work, we propose a novel microaneurysm (MA) detection for early
diabetic retinopathy screening using color fundus images. Since MA usually the
first lesions to appear as an indicator of diabetic retinopathy, accurate
detection of MA is necessary for treatment. Each pixel of the image is
classified as either MA or non-MA using a deep neural network with dropout
training procedure using maxout activation function. No preprocessing step or
manual feature extraction is required. Substantial improvements over standard
MA detection method based on the pipeline of preprocessing, feature extraction,
classification followed by post processing is achieved. The presented method is
evaluated in publicly available Retinopathy Online Challenge (ROC) and
Diaretdb1v2 database and achieved state-of-the-art accuracy.


  A Gabor system in $L^2(\mathbb{R})$, generated by a window $g\in
L^2(\mathbb{R})$ and associated with a sequence of times and frequencies
$\Gamma\subset\mathbb{R}^2$, is a set formed by translations in time and
modulations of $g$. In this paper we consider the case when $g$ is the Gaussian
function and $\Gamma$ is a sequence whose associated Gabor system
$\mathcal{G}_\Gamma$ is complete and minimal in $L^2(\mathbb{R})$. We consider
two main cases: that of the lattice without one point and that of the sequence
constructed by Ascensi, Lyubarskii and Seip lying on the union of the
coordinate axes of the time-frequency space. We study the stability problem for
these two systems. More precisely, we describe the perturbations of $\Gamma$
such that the associated Gabor systems remain to be complete and minimal. Our
method of proof is based essentially on estimates of some infinite products.


  Sound can move particles. A good example of this phenomenon is the Chladni
plate, in which an acoustic wave is induced in a metallic plate and particles
migrate to the nodes of the acoustic wave. For several years, acoustophoresis
has been used to manipulate microparticles in microscopic scales. In this fluid
dynamics video, submitted to the 30th Annual Gallery of Fluid Motion, we show
the basic mechanism of the technique and a simple way of visualize it. Since
acoustophoretic phenomena is essentially a three-dimensional effect, we employ
a simple technique to visualize the particles in 3D. The technique is called
Astigmatism Particle Tracking Velocimetry and it consists in the use of
cylindrical lenses to induce a deformation in the particle shape, which will be
then correlated with its distance from the observer. With this method we are
able to dive with the particles and observe in detail particle motion that
would otherwise be missed. The technique not only permits visualization but
also precise quantitative measurements that can be compared with theory and
simulations.


  Much effort has been made to improve the famous step up test of Benjamini and
Hochberg given by linear critical values $\frac{i\alpha}{n}$. It is pointed out
by Gavrilov, Benjamini and Sarkar that step down multiple tests based on the
critical values $\beta_i=\frac{i\alpha}{n+1-i(1-\alpha)}$ still control the
false discovery rate (FDR) at the upper bound $\alpha$ under basic independence
assumptions. Since that result in not longer true for step up tests or
dependent single tests, a big discussion about the corresponding FDR starts in
the literature. The present paper establishes finite sample formulas and bounds
for the FDR and the expected number of false rejections for multiple tests
using critical values $\beta_i$ under martingale and reverse martingale
dependence models. It is pointed out that martingale methods are natural tools
for the treatment of local FDR estimators which are closely connected to the
present coefficients $\beta_i.$ The martingale approach also yields new results
and further inside for the special basic independence model.


  Energy system models are increasingly being used to explore scenarios with
large shares of variable renewables. This requires input data of high spatial
and temporal resolution and places a considerable preprocessing burden on the
modeling team. Here we present a new code set with an open source license for
automatic generation of input data for large-scale energy system models for
arbitrary regions of the world, including sub-national regions, along with an
associated generic capacity expansion model of the electricity system. We use
ECMWF ERA5 global reanalysis data along with other public geospatial datasets
to generate detailed supply curves and hourly capacity factors for solar
photovoltaic power, concentrated solar power, onshore and offshore wind power,
and existing and future hydropower. Further, we use a machine learning approach
to generate synthetic hourly electricity demand series that describe current
demand, which we extend to future years using regional SSP scenarios. Finally,
our code set automatically generates costs and losses for HVDC interconnections
between neighboring regions. The usefulness of our approach is demonstrated by
several different case studies based on input data generated by our code. We
show that our model runs of a future European electricity system with high
share of renewables are in line with results from more detailed models, despite
our use of global datasets and synthetic demand.


  Traditional color images only depict color intensities in red, green and blue
channels, often making object trackers fail in challenging scenarios, e.g.,
background clutter and rapid changes of target appearance. Alternatively,
material information of targets contained in a large amount of bands of
hyperspectral images (HSI) is more robust to these difficult conditions. In
this paper, we conduct a comprehensive study on how material information can be
utilized to boost object tracking from three aspects: benchmark dataset,
material feature representation and material based tracking. In terms of
benchmark, we construct a dataset of fully-annotated videos, which contain both
hyperspectral and color sequences of the same scene. Material information is
represented by spectral-spatial histogram of multidimensional gradient, which
describes the 3D local spectral-spatial structure in an HSI, and fractional
abundances of constituted material components which encode the underlying
material distribution. These two types of features are embedded into
correlation filters, yielding material based tracking. Experimental results on
the collected benchmark dataset show the potentials and advantages of material
based object tracking.


  We point out that algebraically special Einstein fields with twisting rays
exhibit the basic properties of conformal Universes considered recently by
Roger Penrose.


  Although model-based fault tolerant control (FTC) has become prevalent in
various engineering fields, its application to air-conditioning systems is
limited due to the lack of control-oriented models to characterize the phase
change of refrigerant in the vapor compression cycle. The emergence of moving
boundary method (MBM) illuminates a promising way for FTC design. In this
paper, we exploit a control-oriented nonlinear model comparable to MBM to
design an FTC framework with a generalized internal model control (GIMC)
approach. A fault detector and isolator (FDI) is developed to identify
potential actuator and sensor faults. A fault compensator is employed to
compensate these faults if detected. Comprehensive simulations are carried out
to evaluate the developed FTC framework with promising results. Plant
variations are explicitly considered to enhance the gain-scheduled FTC
developments.


  Graph signals are signals with an irregular structure that can be described
by a graph. Graph neural networks (GNNs) are information processing
architectures tailored to these graph signals and made of stacked layers that
compose graph convolutional filters with nonlinear activation functions. Graph
convolutions endow GNNs with invariance to permutations of the graph nodes'
labels. In this paper, we consider the design of trainable nonlinear activation
functions that take into consideration the structure of the graph. This is
accomplished by using graph median filters and graph max filters, which mimic
linear graph convolutions and are shown to retain the permutation invariance of
GNNs. We also discuss modifications to the backpropagation algorithm necessary
to train local activation functions. The advantages of localized activation
function architectures are demonstrated in four numerical experiments: source
localization on synthetic graphs, authorship attribution of 19th century
novels, movie recommender systems and scientific article classification. In all
cases, localized activation functions are shown to improve model capacity.


  We construct biorthogonal polynomials for a measure over the complex plane
which consists in the exponential of a potential V(z,z*) and in a set of
external sources at the numerator and at the denominator. We use the pseudonorm
of these polynomials to calculate the resolvent integral for correlation
functions of traces of powers of complex matrices (under certain conditions).


  We report a photoluminescence observation of robust excitonic polarons due to
strong coupling of exciton and longitudinal optical (LO) phonon as well as
Fano-type interference in high quality ZnO crystal. At low enough temperatures,
the strong coupling of excitons and LO phonons leads to not only traditional
Stokes lines (SLs) but also up to second-order anti-Stokes lines (ASLs) besides
the zero-phonon line (ZPL). The SLs and ASLs are found to be not mirror
symmetric with respect to the ZPL, strongly suggesting that they are from
different coupling states of exciton and phonons. It is more interesting that a
new group of peaks, including a ZPL and several SLs, are observed. The
observations can be explained with a newly developed theory in which this group
is attributed to the ground excitonic polaron state and the other group is from
the excited polaron states with LO phonon components partially decaying into
environal phonon modes. Besides these spectral features showing the
quasiparticle properties of exciton-phonon coupling system, the first-order SL
is found to exhibit characteristic Fano lineshape, caused by quantum
interference between the LO components of excitonic polarons and the environal
phonons. These findings lead to a new insight into fundamental effects of
exciton-phonon interaction.


  Using optical eigenmodes defined by the interaction between the
electromagnetic fields and photonic structures it is possible to determine the
optimal illumination of these structures with respect to a specific measurable
quantity. One such quantity considered here is the electric field intensity in
the hotspot regions of an array of nano-antennas. This paper presents two
possible methods, both based on optical eigenmodes, to determine the optimal
and most efficient illumination that couples to a single hotspot on top of a
single nano-antenna taken from an array of nano-antennas. The two methods are
compared in terms of cross-talk and overall coupling efficiency.


  We find that the set of local quantum operations and classical communication
for multiparty quantum states can be considered as analogous to online meetings
between members of a population. Moreover, monotonicity properties of quantum
and classical correlations of quantum states of shared systems also carry over
to relations between members of the population, giving rise to what may be
termed as a second law of anthropology.


  We presented background information about various entropies in the
literature. The pathway idea of Mathai (2005) is shown to be inferable from the
maximization of a certain generalized entropy measure and established
connections to outstanding problems in astronomy and physics. In this paper we
proved that the generalized entropy of Mathai associated with diffusive
processes grows linearly with the logarithm of time, and the rate of growth is
indepen- dent of the generalized entropy parameter. We also proposed some
results concerning images of generalized Bessel function under the pathway
operator and its special cases including some trigonometric functions.
Situations are listed where a generalized entropy of order ? leads to pathway
models, exponential and power law behavior and related differential equations.


  We present the first results of the search for nonlinear memory from subsolar
mass binary black hole (BBH) mergers during the second observing run (O2) of
the LIGO and Virgo detectors. The oscillatory chirp signal from the inspiral
and merger of low mass BBHs ($M_\mathrm{Tot} \leq 0.4 M_\odot$) are at very
high frequencies and fall outside the sensitivity band of the current
ground-based detectors. However, the non-oscillatory memory signal during the
merger saturates towards the lower frequencies and can be detected for those
proposed BBHs. We show in this work that the morphology of the memory signal
depends minimally upon the source parameters of the binary, thus only the
overall amplitude of the signal changes and hence the result can be
interpolated for extremely low mass BBH mergers. We did not find any signal
which can be interpreted as a memory signal and we place upper limits on the
rate of BBH mergers with $M_\mathrm{Tot} \leq 0.4 M_\odot$ for the first time.


  Dynamic mode decomposition (DMD) is a versatile approach that enables the
construction of low-order models from data. Controller design tasks based on
such models require estimates and guarantees on predictive accuracy. In this
work, we provide a theoretical analysis of DMD model errors that reveals impact
of model order and data availability. The analysis also establishes conditions
under which DMD models can be made asymptotically exact. We verify our results
using a 2D diffusion system.


  Approximate inference techniques are the cornerstone of probabilistic methods
based on Gaussian process priors. Despite this, most work approximately
optimizes standard divergence measures such as the Kullback-Leibler (KL)
divergence, which lack the basic desiderata for the task at hand, while chiefly
offering merely technical convenience. We develop a new approximate inference
method for Gaussian process models which overcomes the technical challenges
arising from abandoning these convenient divergences. Our method---dubbed
Quantile Propagation (QP)---is similar to expectation propagation (EP) but
minimizes the $L_2$ Wasserstein distance (WD) instead of the KL divergence. The
WD exhibits all the required properties of a distance metric, while respecting
the geometry of the underlying sample space. We show that QP matches quantile
functions rather than moments as in EP and has the same mean update but a
smaller variance update than EP, thereby alleviating EP's tendency to
over-estimate posterior variances. Crucially, despite the significant
complexity of dealing with the WD, QP has the same favorable locality property
as EP, and thereby admits an efficient algorithm. Experiments on classification
and Poisson regression show that QP outperforms both EP and variational Bayes.


  We count permutations avoiding a nonconsecutive instance of a two- or
three-letter pattern, that is, the pattern may occur but only as consecutive
entries in the permutation. Two-letter patterns give rise to the Fibonacci
numbers. The counting sequences for the two representative three-letter
patterns, 321 and 132, have respective generating functions
(1+x^2)(C(x)-1)/(1+x+x^2-x C(x)) and C(x+x^3) where C(x) is the generating
function for the Catalan numbers.


  The O I 1355{\AA} spectral line is one of the only optically thin lines that
are both routinely observed and thought to be formed in the chromosphere. We
present analysis of a variety of observations of this line with the Interface
Region Imaging Spectrograph (IRIS), and compare it with other IRIS diagnostics
as well as diagnostics of the photospheric magnetic field. We utilize special
deep exposure modes on IRIS and provide an overview of the statistical
properties of this spectral line for several different regions on the Sun. We
analyze the spatio-temporal variations of the line intensity, and find that it
is often significantly enhanced when and where magnetic flux of opposite
polarities cancel. Significant emission occurs in association with
chromospheric spicules. Because of the optically thin nature of the O I line,
the non-thermal broadening can provide insight into unresolved small-scale
motions. We find that the non-thermal broadening is modest, with typical values
of 5-10 km/s, and shows some center-to-limb variation, with a modest increase
towards the limb. The dependence with height of the intensity and line
broadening off-limb is compatible with the line broadening being dominated by
the superposition of Alfv\'en waves on different structures. The non-thermal
broadening shows a modest but significant enhancement above locations that are
in between photospheric magnetic flux concentrations in plage, i.e., where the
magnetic field is likely to be more inclined with respect to the line-of-sight.
Our measurements provide strict constraints on future theoretical models of the
chromosphere.


  We give a new, and hopefully more easily understandable, structural proof of
the decomposition of a $k$-valued transducer into $k$ unambiguous functional
ones, a result established by A. Weber in 1996. Our construction is based on a
lexicographic ordering of computations of automata and on two coverings that
can be build by means of this ordering. The complexity of the construction,
measured as the number of states of the transducers involved in the
decomposition, improves the original one by one exponential. Moreover, this
method allows further generalisation that solves the problem of decomposition
of rational relations with bounded length-degree, which was left open in
Weber's paper.


  Knowledge of the human visual system helps to develop better computational
models of visual attention. State-of-the-art models have been developed to
mimic the visual attention system of young adults that, however, largely ignore
the variations that occur with age. In this paper, we investigated how visual
scene processing changes with age and we propose an age-adapted framework that
helps to develop a computational model that can predict saliency across
different age groups. Our analysis uncovers how the explorativeness of an
observer varies with age, how well saliency maps of an age group agree with
fixation points of observers from the same or different age groups, and how age
influences the center bias. We analyzed the eye movement behavior of 82
observers belonging to four age groups while they explored visual scenes.
Explorativeness was quantified in terms of the entropy of a saliency map, and
area under the curve (AUC) metrics was used to quantify the agreement analysis
and the center bias. These results were used to develop age adapted saliency
models. Our results suggest that the proposed age-adapted saliency model
outperforms existing saliency models in predicting the regions of interest
across age groups.


  Owing to their remarkable learning (and relearning) capabilities, deep neural
networks (DNNs) find use in numerous real-world applications. However, the
learning of these data-driven machine learning models is generally as good as
the data available to them for training. Hence, training datasets with
long-tail distribution pose a challenge for DNNs, since the DNNs trained on
them may provide a varying degree of classification performance across
different output classes. While the overall bias of such networks is already
highlighted in existing works, this work identifies the node bias that leads to
a varying sensitivity of the nodes for different output classes. To the best of
our knowledge, this is the first work highlighting this unique challenge in
DNNs, discussing its probable causes, and providing open challenges for this
new research direction. We support our reasoning using an empirical case study
of the networks trained on a real-world dataset.


  Using, as main tool, the convergence theorem for discrete martingales and the
mean value property of harmonic functions we solve, a particular case of,
Dirichlet problem.


  To cope with the increasing complexity of developing and maintaining modern
(software) systems, multiple abstractions (models) of the same system can be
established and used to allow different domain experts to collaborate and
contribute their respective expertise. This divide-and-conquer, model-based
approach requires, however, support for a concurrent engineering process, i.e.,
providing a means of checking, restoring, and ensuring the consistency of all
involved and concurrently maintained models. The task of providing such support
is often referred to as consistency management.
  Although there exist various approaches to consistency management and
numerous (industrial) case studies described in the literature on bidirectional
transformations (bx), there is currently no uniform description of diverse but
related industrial applications of model synchronisation and other forms of
consistency management. This makes it challenging to detect similarities and
differences related to requirements, constraints, applied techniques and tools.
It is thus difficult to compare and transfer knowledge gained from (successful)
projects to other bx approaches or even other bx tools for the same general
approach.
  In this paper, therefore, we propose a description language for envisioned
scenarios in the problem domain of consistency management, as well as a
complementary description language for solution strategies in terms of method
fragments and method patterns in the solution domain of Model-Driven
Engineering (MDE). Our work is inspired by previous research in the bx and MDE
communities, and is also based on our collective experience from over ten years
of investigating a series of application scenarios in the industry automation
section together with Siemens AG as an industrial partner. [Abridged due to
arXiv]


  Direct evidence of an inertial-range turbulent energy cascade has been
provided by spacecraft observations in heliospheric plasmas. In the solar wind,
the average value of the derived heating rate near 1 au is $\sim 10^{3}\,
\mathrm{J\,kg^{-1}\,s^{-1}}$, an amount sufficient to account for observed
departures from adiabatic expansion. Parker Solar Probe (PSP), even during its
first solar encounter, offers the first opportunity to compute, in a similar
fashion, a fluid-scale energy decay rate, much closer to the solar corona than
any prior in-situ observations. Using the Politano-Pouquet third-order law and
the von K\'arm\'an decay law, we estimate the fluid-range energy transfer rate
in the inner heliosphere, at heliocentric distance $R$ ranging from
$54\,R_{\odot}$ (0.25 au) to $36\,R_{\odot}$ (0.17 au). The energy transfer
rate obtained near the first perihelion is about 100 times higher than the
average value at 1 au. This dramatic increase in the heating rate is
unprecedented in previous solar wind observations, including those from Helios,
and the values are close to those obtained in the shocked plasma inside the
terrestrial magnetosheath.


  We show that for a Baire generic rotation number $\alpha \in \mathbb{R} /
\mathbb{Z}$, the set of area preserving $C^\infty$-pseudo-rotations of the
annulus $\mathbb{A}$ with rotation number $\alpha$ equals the closure of the
set of area preserving $C^\infty$-pseudo-rotations which are smoothly conjugate
to the rotation $R_{\alpha}$. As a corollary, a $C^\infty$-generic area
preserving pseudo-rotation of the annulus with a Baire generic rotation number
$\alpha$ is weakly mixing.


  I review recent results on conformal field theories in four dimensions and
quantum field theories interpolating between conformal fixed points,
supersymmetric and non-supersymmetric. The talk is structured in three parts:
i) central charges, ii) anomalous dimensions and iii) quantum irreversibility.


  We describe an experimental concept at the National Ignition Facility for
specifically tailored spherical implosions to compress hydrogen to extreme
densities (up to $\sim$800$\times$ solid density, electron number density
n$_e$$\sim$4$\times$10$^{25}$ cm$^{-3}$ ) at moderate temperatures (T$\sim$200
eV), i.e., to conditions, which are relevant to the interiors of red dwarf
stars. The dense plasma will be probed by laser-generated x-ray radiation of
different photon energy to determine the plasma opacity due to collisional
(free-free) absorption and Thomson scattering. The obtained results will
benchmark radiation transport models, which in the case for free-free
absorption show strong deviations at conditions relevant to red dwarfs. This
very first experimental test of free-free opacity models at these extreme
states will help to constrain where inside those celestial objects energy
transport is dominated by radiation or convection. Moreover, our study will
inform models for other important processes in dense plasmas, which are based
on electron-ion collisions, e.g., stopping of swift ions or electron-ion
temperature relaxation.


  Rooted tree maps assign to an element of the Connes-Kreimer Hopf algebra of
rooted trees a linear map on the noncommutative polynomial algebra in two
letters. Evaluated at any admissible word these maps induce linear relations
between multiple zeta values. In this note we show that the derivation
relations for multiple zeta values are contained in this class of linear
relations.


  A new generalization of the Calogero's rational ($A_N$) many-body quantum
model is proposed and studied. The key innovation lies in an asymmetrization of
the Calogero's two-body interaction. In the generalized model the exact
solvability is preserved while the Calogeero's single coupling constant $C$ is
replaced by a certain multiplet of freely variable parameters. The resulting
Calogero-like Hamiltonian ceases to be re-arrangement-invariant. As a
consequence, the degeneracy of the bound-state spectrum gets partially
unfolded.


  We consider an inverse problem for the nonlinear Boltzmann equation with a
time-dependent kernel in dimensions $n\ge 2$. We establish a logarithm-type
stability result for the collision kernel from measurements under certain
additional conditions. A uniqueness result is derived as an immediate
consequence of the stability result. Our approach relies on second-order
linearization, multivariate finite differences, as well as the stability of the
light-ray transform.


  Increased distributed Photo-Voltaic (PV) generation leads to an increase in
voltages and unwarranted backflows into the grid. This paper investigates
Demand Response (DR) with Electric Water Heaters (EWHs) as a way to increase
the PV hosting capacity of a low-voltage feeder. A control strategy relying
only on power measurements at the transformer is proposed. Flexible loads are
optimally dispatched considering energy acquisition costs, a PV shedding
penalty, and power and energy constraints. Furthermore, grouping of loads and
PV plants is investigated, and switching penalties are used to reduce the
unnecessary switching of loads. It is shown that this strategy can
substantially increase the PV hosting capacity of a Low-Voltage (LV) feeder,
even when only basic controllability is available.


  We have adapted a set of classification algorithms, also known as Machine
Learning, to the identification of fluid and gel domains close to the main
transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic
molecular dynamics conformations in the low and high temperature phases as
learning sets, the algorithm was trained to categorize individual lipid
configurations as fluid or gel, in relation with the usual two-states
phenomenological description of the lipid melting transition. We demonstrate
that our machine can learn and sort lipids according to their most likely state
without prior assumption regarding the nature of the order parameter of the
transition. Results from our machine learning approach provides strong support
in favor of a two-states model approach of membrane fluidity.


  Using the Dirichlet integrals, which are employed in the theory of Fourier
series, this paper develops a useful method for the summation of series and the
evaluation of integrals.


  Shared memory programming models usually provide worksharing and task
constructs. The former relies on the efficient fork-join execution model to
exploit structured parallelism; while the latter relies on fine-grained
synchronization among tasks and a flexible data-flow execution model to exploit
dynamic, irregular, and nested parallelism. On applications that show both
structured and unstructured parallelism, both worksharing and task constructs
can be combined. However, it is difficult to mix both execution models without
penalizing the data-flow execution model. Hence, on many applications
structured parallelism is also exploited using tasks to leverage the full
benefits of a pure data-flow execution model. However, task creation and
management might introduce a non-negligible overhead that prevents the
efficient exploitation of fine-grained structured parallelism, especially on
many-core processors. In this work, we propose worksharing tasks. These are
tasks that internally leverage worksharing techniques to exploit fine-grained
structured loop-based parallelism. The evaluation shows promising results on
several benchmarks and platforms.


  Timing guarantees are crucial to cyber-physical applications that must bound
the end-to-end delay between sensing, processing and actuation. For example, in
a flight controller for a multirotor drone, the data from a gyro or inertial
sensor must be gathered and processed to determine the attitude of the
aircraft. Sensor data fusion is followed by control decisions that adjust the
flight of a drone by altering motor speeds. If the processing pipeline between
sensor input and actuation is not bounded, the drone will lose control and
possibly fail to maintain flight.
  Motivated by the implementation of a multithreaded drone flight controller on
the Quest RTOS, we develop a composable pipe model based on the system's task,
scheduling and communication abstractions. This pipe model is used to analyze
two semantics of end-to-end time: reaction time and freshness time. We also
argue that end-to-end timing properties should be factored in at the early
stage of application design. Thus, we provide a mathematical framework to
derive feasible task periods that satisfy both a given set of end-to-end timing
constraints and the schedulability requirement. We demonstrate the
applicability of our design approach by using it to port the Cleanflight flight
controller firmware to Quest on the Intel Aero board. Experiments show that
Cleanflight ported to Quest is able to achieve end-to-end latencies within the
predicted time bounds derived by analysis.


  In this article we present a study of embeddings of complex supermanifolds.
We are broadly guided by the question: when will a submanifold of a split
supermanifold itself be split? As an application of our study, we will address
this question for certain superspace embeddings over rational normal curves.


  The electrical properties of a two-dimensional packing of metallic beads are
studied. Small mechanical perturbations of the packing leads to giant
electrical fluctuations. Fluctuations are found to be non-gaussian and seem to
belong to Levy stable distributions. Anticorrelations have been also found for
the sign of these fluctuations.


  We study the constraints imposed by the existence of a single higher spin
conserved current on a three dimensional conformal field theory. A single
higher spin conserved current implies the existence of an infinite number of
higher spin conserved currents. The correlation functions of the stress tensor
and the conserved currents are then shown to be equal to those of a free field
theory. Namely a theory of N free bosons or free fermions. This is an extension
of the Coleman-Mandula theorem to CFT's, which do not have a conventional S
matrix. We also briefly discuss the case where the higher spin symmetries are
"slightly" broken.


  Multi-agent reinforcement learning (MARL) becomes more challenging in the
presence of more agents, as the capacity of the joint state and action spaces
grows exponentially in the number of agents. To address such a challenge of
scale, we identify a class of cooperative MARL problems with permutation
invariance, and formulate it as a mean-field Markov decision processes (MDP).
To exploit the permutation invariance therein, we propose the mean-field
proximal policy optimization (MF-PPO) algorithm, at the core of which is a
permutation-invariant actor-critic neural architecture. We prove that MF-PPO
attains the globally optimal policy at a sublinear rate of convergence.
Moreover, its sample complexity is independent of the number of agents. We
validate the theoretical advantages of MF-PPO with numerical experiments in the
multi-agent particle environment (MPE). In particular, we show that the
inductive bias introduced by the permutation-invariant neural architecture
enables MF-PPO to outperform existing competitors with a smaller number of
model parameters, which is the key to its generalization performance.


  Building on recently constructed inflationary vector dark matter production
mechanisms as well as studies of magnetogenesis, we show that a dark Schwinger
mechanism can generate the observed dark matter relic abundance. This mechanism
can take place during inflation and/or after reheating and leads to a feebly
interacting dark sector which is comprised of dark fermions and/or scalars
which can be as light as ~ 10 MeV and as heavy as ~ 10^10 GeV depending on how
the (unknown) Schwinger current redshifts after inflation. The background dark
electric field is generated during inflation and can continue sourcing
Schwinger pair production after reheating until the dark charged particles
become non-relativistic. The dark matter can interact very weakly via the
exchange of ultralight dark photons and has a power spectrum which is peaked at
very small scales, thus evading potential isocurvature constraints. We
emphasize that this mechanism is viable even near the conformal limit where
(purely) gravitational particle production is negligible. Thus dark matter can
be produced solely via the Schwinger effect even with a very weak electric
force.


  The concept of communicability is introduced for complex socio-economic
networks. The communicability function expresses how an impact propagates from
one place to another in the network. This function is used to define
unambiguously the concept of socio-economic community. The concept of
temperature in complex socio-economic networks is also introduced as a way of
accounting for the external stresses to which such systems are submitted. This
external stress can change dramatically the structure of the communities in a
network. We analyze here a trade network of countries exporting 'miscellaneous
manufactures of metal.' We determine the community structure of this network
showing that there are 27 communities with diverse degree of overlapping. When
only communities with less than 80% of overlap are considered we found 5
communities which are well characterized in terms of geopolitical
relationships. The analysis of external stress on these communities reveals
that several countries are very much influenced by these critical situations,
i.e., economical crisis. These weakest links are clearly identified and
represent countries that are isolated from the main trade as soon as the
external "temperature" of the system is increased. The current approach adds an
important tool for the analysis of socio-economic networks in the real-world.


  Based on the direct method of molecular dynamics, the temperature dependence
of energy, partition function, entropy and free energy, describing the
first-order phase transition, is calculated for the classical
Peyrard-Bishop-Dauxois model of the DNA molecule. It is shown that without
taking into account the quantum freezing of the degrees of freedom at low
temperatures, the relationship between these quantities is not determined. An
estimation was given for the number of bubbles in homogeneous DNA chains:
PolyA/PolyT and PolyG/PolyC.


  The double differential dilepton spectrum $dN/ (dM_\perp^2 dM^2)$ at fixed
transverse mass $M_\perp$ allows a direct access to the vector meson spectral
functions. Within a fireball model the sensitivity of $dN/ (dM_\perp^2 dM^2)$
against variations of both the in-medium properties of mesons and the dynamics
of the fireball is investigated. In contrast to the integrated invariant-mass
spectrum $dN/dM^2$, in the spectrum $dN/dM^2 dM_\perp^2$ with fixed $M_\perp$
the $\omega$ signal is clearly seen as bump riding on the $\rho$ background
even in case of strong in-medium modifications.


  Derivations play a fundamental role in the definition of vertex (operator)
algebras, sometimes regarded as a generalization of differential commutative
algebras. This paper studies the role played by the integral counterpart of the
derivations, namely Rota-Baxter operators, in vertex (operator) algebras. The
closely related notion of dendriform algebras is also defined for vertex
operator algebras. It is shown that the classical relations among dendriform
algebras, associative algebras, and Rota-Baxter algebras are preserved for
their vertex algebra analogs.


  Tax manipulation comes in a variety of forms with different motivations and
of varying complexities. In this paper, we deal with a specific technique used
by tax-evaders known as circular trading. In particular, we define algorithms
for the detection and analysis of circular trade. To achieve this, we have
modelled the whole system as a directed graph with the actors being vertices
and the transactions among them as directed edges. We illustrate the results
obtained after running the proposed algorithm on the commercial tax dataset of
the government of Telangana, India, which contains the transaction details of a
set of participants involved in a known circular trade.


  The infrared-radio correlation (IRRC) offers a way to assess star formation
from radio emission. Multiple studies found the IRRC to decrease with
increasing redshift. This may in part be due to the lack of knowledge about the
possible radio spectral energy distributions (SEDs) of star-forming galaxies.
We constrain the radio SED of a complete sample of highly star-forming galaxies
($SFR>100\,\mathrm{M_{\odot}}/\,\mathrm{yr}$) based on the VLA-COSMOS
$1.4\,\mathrm{GHz}$ Joint and $3\,\mathrm{GHz}$ Large Project catalogs. We
reduce archival GMRT $325\,\mathrm{MHz}$ and $610\,\mathrm{MHz}$ observations,
broadening the rest-frame frequency range to $0.3-15\,\mathrm{GHz}$. Employing
survival analysis and fitting a double power law SED, we find that the slope
steepens from a spectral index of $\alpha_1=0.51\pm 0.04$ below
$4.5\,\mathrm{GHz}$ to $\alpha_2=0.98\pm0.07$ above $4.5\,\mathrm{GHz}.$ Our
results suggest that the use of a K-correction assuming a single power-law
radio SED for star forming galaxies is likely not the root cause of the IRRC
trend.


  There is currently a great need for solid state lasers that emit in the
infrared. Whether or not conjugated polymers that emit in the IR can be
synthesized is an interesting theoretical challenge. We show that emission in
the IR can be achieved in designer polymers in which the effective Coulomb
correlation is smaller than that in existing systems. We also show that the
structural requirement for having small effective Coulomb correlations is that
there exist transverse $\pi$--conjugation over a few bonds in addition to
longitudinal conjugation with large conjugation lengths.


  State-of-the-art (SOTA) hierarchical localisation pipelines (HLoc) rely on
image retrieval (IR) techniques to establish 2D-3D correspondences by selecting
the $k$ most similar images from a reference image database for a given query
image. Although higher values of $k$ enhance localisation robustness, the
computational cost for feature matching increases linearly with $k$. In this
paper, we observe that queries that are the most similar to images in the
database result in a higher proportion of feature matches and, thus, more
accurate positioning. Thus, a small number of images is sufficient for queries
very similar to images in the reference database. We then propose a novel
approach, AIR-HLoc, which divides query images into different localisation
difficulty levels based on their similarity to the reference image database. We
consider an image with high similarity to the reference image as an easy query
and an image with low similarity as a hard query. Easy queries show a limited
improvement in accuracy when increasing $k$. Conversely, higher values of $k$
significantly improve accuracy for hard queries. Given the limited improvement
in accuracy when increasing $k$ for easy queries and the significant
improvement for hard queries, we adapt the value of $k$ to the query's
difficulty level. Therefore, AIR-HLoc optimizes processing time by adaptively
assigning different values of $k$ based on the similarity between the query and
reference images without losing accuracy. Our extensive experiments on the
Cambridge Landmarks, 7Scenes, and Aachen Day-Night-v1.1 datasets demonstrate
our algorithm's efficacy, reducing 30\%, 26\%, and 11\% in computational
overhead while maintaining SOTA accuracy compared to HLoc with fixed image
retrieval.


  Condensed matter systems are potential candidates to realize the integration
of quantum information circuits. Surface phonon polariton (SPhP) is a special
propagation mode in condensed matter systems. We present an investigation on
the entanglement of SPhP modes. The entangled pairs are generated from
entangled photons injected to the system. Quantum performances of entangled
SPhPs are investigated by using the interaction Hamiltonian and the
perturbation theory. The wave mechanics approach is taken to describe the
coupling process as a comparison. Finally, the correlation of system is
examined. A whole set of descriptions of SPhP entanglement thus are presented.


  In this paper, we first propose and analyze a novel mixed-type DG method for
the coupled Stokes-Darcy problem on simplicial meshes. The proposed formulation
is locally conservative. A mixed-type DG method in conjunction with the
stress-velocity formulation is employed for the Stokes equations, where the
symmetry of stress is strongly imposed. The staggered DG method is exploited to
discretize the Darcy equations. As such, the discrete formulation can be easily
adapted to account for the Beavers-Joseph-Saffman interface conditions without
introducing additional variables. Importantly, the continuity of normal
velocity is satisfied exactly at the discrete level. A rigorous convergence
analysis is performed for all the variables. Then we devise and analyze a
domain decomposition method via the use of Robin-type interface boundary
conditions, which allows us to solve the Stokes subproblem and the Darcy
subproblem sequentially with low computational costs. The convergence of the
proposed iterative method is analyzed rigorously. In particular, the proposed
iterative method also works for very small viscosity coefficient. Finally,
several numerical experiments are carried out to demonstrate the capabilities
and accuracy of the novel mixed-type scheme, and the convergence of the domain
decomposition method.


  We present observations of a solar quiet region obtained by the ground-based
Dutch Open Telescope (DOT), and by instruments on the spacecraft SOHO and
TRACE. The observations were obtained during a coordinated observing campaign
on October 2005. The aim of this work is to present the rich diversity of
fine-scale structures that are found at the network boundaries and their
appearance in different instruments and different spectral lines that span the
photosphere to the corona. Detailed studies of these structures are crucial to
understanding their dynamics in different solar layers, as well as the role
such structures play in the mass balance and heating of the solar atmosphere.


  One of the general strategies for realizing a wide class of interacting QFTs
is via junctions and intersections of higher-dimensional bulk theories. In the
context of string/M-theory, this includes many $D > 4$ superconformal field
theories (SCFTs) coupled to an IR free bulk. Gauging the flavor symmetries of
these theories and allowing position dependent gauge couplings provides a
general strategy for realizing novel higher-dimensional junctions of theories
coupled to localized edge modes. Here, we show that M-theory on singular,
asymptotically conical $G_2$-holonomy orbifolds provides a general template for
realizing strongly coupled 5D bulk theories with 4D $\mathcal{N} = 1$ edge
modes. This geometric approach also shows how bulk generalized symmetries are
inherited in the boundary system.


  Consider a memoryless degraded broadcast channel (DBC) in which the channel
output is a single-letter function of the channel input and the channel noise.
As examples, for the Gaussian broadcast channel (BC) this single-letter
function is regular Euclidian addition and for the binary-symmetric BC this
single-letter function is Galois-Field-two addition. This paper identifies
several classes of discrete memoryless DBCs for which a relatively simple
encoding scheme, which we call natural encoding, achieves capacity. Natural
Encoding (NE) combines symbols from independent codebooks (one for each
receiver) using the same single-letter function that adds distortion to the
channel. The alphabet size of each NE codebook is bounded by that of the
channel input.
  Inspired by Witsenhausen and Wyner, this paper defines the conditional
entropy bound function $F^*$, studies its properties, and applies them to show
that NE achieves the boundary of the capacity region for the multi-receiver
broadcast Z channel. Then, this paper defines the input-symmetric DBC,
introduces permutation encoding for the input-symmetric DBC, and proves its
optimality. Because it is a special case of permutation encoding, NE is
capacity achieving for the two-receiver group-operation DBC. Combining the
broadcast Z channel and group-operation DBC results yields a proof that NE is
also optimal for the discrete multiplication DBC. Along the way, the paper also
provides explicit parametric expressions for the two-receiver binary-symmetric
DBC and broadcast Z channel.


  We present a structural representation of the Herbrand content of LK-proofs
with cuts of complexity prenex Sigma-2/Pi-2. The representation takes the form
of a typed non-deterministic tree grammar of order 2 which generates a finite
language of first-order terms that appear in the Herbrand expansions obtained
through cut-elimination. In particular, for every Gentzen-style reduction
between LK-proofs we study the induced grammars and classify the cases in which
language equality and inclusion hold.


  Since early 2020, the world has been dealing with a raging pandemic outbreak:
COVID-19. A year later, vaccines have become accessible, but in limited
quantities, so that governments needed to devise a strategy to decide which
part of the population to prioritize when assigning the available doses, and
how to manage the interval between doses for multi-dose vaccines. In this
paper, we present an optimization framework to address the dynamic double-dose
vaccine allocation problem whereby the available vaccine doses must be
administered to different age-groups to minimize specific societal objectives.
In particular, we first identify an age-dependent
Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model including an
extension capturing partially and fully vaccinated people, whereby we account
for age-dependent immunity and infectiousness levels together with disease
severity. Second, we leverage our model to frame the dynamic age-dependent
vaccine allocation problem for different societal objectives, such as the
minimization of infections or fatalities, and solve it with nonlinear
programming techniques. Finally, we carry out a numerical case study with
real-world data from The Netherlands. Our results show how different societal
objectives can significantly alter the optimal vaccine allocation strategy. For
instance, we find that minimizing the overall number of infections results in
delaying second doses, whilst to minimize fatalities it is important to fully
vaccinate the elderly first.


  We construct a linear sigma model for open-strings ending on special
Lagrangian cycles of a Calabi-Yau manifold. We illustrate the construction for
the cases considered by Aganagic and Vafa in hep-th/0012041. This leads
naturally to concrete models for the moduli space of open-string instantons.
These instanton moduli spaces can be seen to be intimately related to certain
auxiliary boundary toric varieties. By considering the relevant
Gelfand-Kapranov-Zelevinsky (GKZ) differential equations of the boundary toric
variety, we obtain the contributions to the worldvolume superpotential on the
A-branes from open-string instantons. By using an ansatz due to Aganagic, Klemm
and Vafa in hep-th/0105045, we obtain the relevant change of variables from the
linear sigma model to the non-linear sigma model variables - the open-string
mirror map. Using this mirror map, we obtain results in agreement with those of
AV and AKV for the counting of holomorphic disc instantons.


  We deal with unweighted and weighted enumerations of lozenge tilings of a
hexagon with side lengths $a,b+m,c,a+m,b,c+m$, where an equilateral triangle of
side length $m$ has been removed from the center. We give closed formulas for
the plain enumeration and for a certain $(-1)$-enumeration of these lozenge
tilings. In the case that $a=b=c$, we also provide closed formulas for certain
weighted enumerations of those lozenge tilings that are cyclically symmetric.
For $m=0$, the latter formulas specialize to statements about weighted
enumerations of cyclically symmetric plane partitions. One such specialization
gives a proof of a conjecture of Stembridge on a certain weighted count of
cyclically symmetric plane partitions. The tools employed in our proofs are
nonstandard applications of the theory of nonintersecting lattice paths and
determinant evaluations. In particular, we evaluate the determinants
$\det_{0\le i,j\le n-1}\big(\om \delta_{ij}+\binom {m+i+j}j\big)$, where $\om$
is any 6th root of unity. These determinant evaluations are variations of a
famous result due to Andrews (Invent. Math. 53 (1979), 193--225), which
corresponds to $\om=1$.


  It is commonly assumed that the properties and geometry of the accretion flow
in transient low-mass X-ray binaries (LMXBs) significantly change when the
X-ray luminosity decays below $\sim 10^{-2}$ of the Eddington limit ($L_{\rm
Edd}$). However, there are few observational cases where the evolution of the
accretion flow is tracked in a single X-ray binary over a wide dynamic range.
In this work, we use NuSTAR and NICER observations obtained during the 2018
accretion outburst of the neutron star LMXB 4U 1608-52, to study changes in the
reflection spectrum. We find that the broad Fe-K$\alpha$ line and Compton hump,
clearly seen during the peak of the outburst when the X-ray luminosity is $\sim
10^{37}$ erg/s ($\sim 0.05$ $L_{\rm Edd}$), disappear during the decay of the
outburst when the source luminosity drops to $\sim 4.5 \times 10^{35}$ erg/s
($\sim 0.002$ $L_{\rm Edd}$). We show that this non-detection of the reflection
features cannot be explained by the lower signal-to-noise at lower flux, but is
instead caused by physical changes in the accretion flow. Simulating synthetic
NuSTAR observations on a grid of inner disk radius, disk ionisation, and
reflection fraction, we find that the disappearance of the reflection features
can be explained by either increased disk ionisation ($\log \xi \geq 4.1$) or a
much decreased reflection fraction. A changing disk truncation alone, however,
cannot account for the lack of reprocessed Fe-K$\alpha$ emission. The required
increase in ionisation parameter could occur if the inner accretion flow
evaporates from a thin disk into a geometrically thicker flow, such as the
commonly assumed formation of an radiatively inefficient accretion flow at
lower mass accretion rates.


  We present a new test for a possible Mach-Sciama dependence of the
Gravitational constant G. According to Ernst Mach (1838-1916), the
gravitational interaction depends on the distribution of masses in the
universe. A corresponding hypothesis of Sciama (1953) on the gravitational
constant, $c^2/G = \sum m_i/r_i$, can be tested since the elliptic earth orbit
should then cause minute annual variations in G. The test is performed by
analyzing the gravity signals of a network of superconducting gravimeters (SG)
which reach a precision of $10^{-10} m/s^2$. After reducing the signal by
modelling tidal, meteorologic and geophysical effects, no significant evidence
for the above dependence is found.


  We have studied Markov processes on denumerable state space and continuous
time. We found that all these processes are connected via gauge
transformations. We have used this result before as a method for resolution of
equations, included the case where the sample space is time dependent in a
previous work \textit{Phys. Rev. E} \textbf{90}, 022125 (2014). We found a
general solution through a dilation of the state space, although the prior
probability distribution of the states defined in this new space takes smaller
values with respect to the one in the initial problem. The gauge (local) group
of dilations modifies the distribution on the dilated space to restore the
original process. In this work we show how Markov process in general could be
linked via gauge (local) transformations and we present some illustrative
examples for this results.