  We investigate an instability toward a square-lattice formation of magnetic
skyrmions in centrosymmetric layered systems. By focusing on a bilayer
square-lattice structure with the inversion center at the interlayer bond
instead of the atomic site, we numerically examine the stability of the square
skyrmion crystal based on an effective spin model with the momentum-resolved
interaction in the ground state through the simulated annealing. As a result,
we find that a layer-dependent staggered Dzyaloshinskii-Moriya interaction
built in the lattice structure becomes the origin of the square skyrmion
crystal in an external magnetic field irrespective of the sign of the
interlayer exchange interaction. The obtained square skyrmion crystal is
constituted of the skyrmion crystals with different helicities in each layer
due to the staggered Dzyaloshinskii-Moriya interaction. Furthermore, we show
that the interplay between the staggered Dzyaloshinskii-Moriya interaction and
the interlayer exchange interaction gives rise to a double-$Q$ state with a
uniform component of the scalar chirality in the low-field region. The present
results provide another way of stabilizing the square skyrmion crystal in
centrosymmetric magnets, which will be useful to explore further exotic
topological spin textures.


  The four-year Kepler mission collected long cadence images of the open
clusters NGC 6791 and NGC 6819, known as "superstamps." Each superstamp region
is a 200-pixel square that captures thousands of cluster members, plus
foreground and background stars, of which only the brightest were targeted for
long or short cadence photometry during the Kepler mission. Using image
subtraction photometry, we have produced light curves for every object in the
Kepler Input Catalog that falls on the superstamps. The IRIS catalog includes
light curves for 9,150 stars, and contains a wealth of new data: 8,427 of these
stars were not targeted at all by Kepler, and we have increased the number of
available quarters of long cadence data for 382 stars. The catalog is available
as a high-level science product on MAST, with both raw photometric data for
each quarter and corrected light curves for all available quarters for each
star. We also present an introduction to our implementation of image
subtraction photometry and the open source IRIS pipeline, alongside an overview
of the data products, systematics, and catalog statistics.


  To date, optical orientation of free-carrier spins and spin currents have
been achieved by circularly polarized light, while the linearly polarized light
has been used for optical alignment of electron momenta. Here we show that, in
low-dimensional structures, absorption of the linearly polarized light also
leads to the spin polarization and spin photocurrent, and, thus, the electron
and hole spins can be manipulated by light of zero helicity. The microscopic
description of the both effects is developed for interband optical transitions
in undoped quantum wells (QWs) as well as for direct intersubband and indirect
intrasubband (Drude-like) transitions in $n$-doped QW structures.


  We study models in which supersymmetry breaking appears at an intermediate
scale, M_{in}, below the GUT scale. We assume that the soft
supersymmetry-breaking parameters of the MSSM are universal at M_{in}, and
analyze the morphology of the constraints from cosmology and collider
experiments on the allowed regions of parameter space as M_{in} is reduced from
the GUT scale. We present separate analyses of the (m_{1/2},m_0) planes for
tan(beta)=10 and tan(beta)=50, as well as a discussion of non-zero trilinear
couplings, A_0. Specific scenarios where the gaugino and scalar masses appear
to be universal below the GUT scale have been found in mirage-mediation models,
which we also address here. We demand that the lightest neutralino be the LSP,
and that the relic neutralino density not conflict with measurements by WMAP
and other observations. At moderate values of M_{in}, we find that the allowed
regions of the (m_{1/2},m_0) plane are squeezed by the requirements of
electroweak symmetry breaking and that the lightest neutralino be the LSP,
whereas the constraint on the relic density is less severe. At very low M_{in},
the electroweak vacuum conditions become the dominant constraint, and a
secondary source of astrophysical cold dark matter would be necessary to
explain the measured relic density for nearly all values of the soft
SUSY-breaking parameters and tan(beta). We calculate the neutralino-nucleon
cross sections for viable scenarios and compare them with the present and
projected limits from direct dark matter searches.


  It has been recently demonstrated experimentally that graphene, or
single-layer carbon, is a gapless semiconductor with massless Dirac energy
spectrum. A finite conductivity per channel of order of $e^{2}/h$ in the limit
of zero temperature and zero charge carrier density is one of the striking
features of this system. Here we analyze this peculiarity based on the Kubo and
Landauer formulas. The appearance of a finite conductivity without scattering
is shown to be a characteristic property of Dirac chiral fermions in two
dimensions.


  We give elementary applications of quasi-homomorphisms to growth problems in
groups. A particular case concerns the number of torsion elements required to
factorise a given element in the mapping class group of a surface.


  Localized wrinkles of thin premixed flames subject to hydrodynamic
instability and geometrical stretch of uniform intensity (S) are studied. A
stretch-affected nonlinear and nonlocal equation, derived from an inhomogeneous
Michelson-Sivashinsky equation, is used as a starting point, and pole
decompositions are used as a tool. Analytical and numerical descriptions of
isolated (centered or multicrested) wrinkles with steady shapes (in a frame)
and various amplitudes are provided; their number increases rapidly with 1/S >
0. A large constantS > 0 weakens or suppresses all localized wrinkles (the
larger the wrinkles, the easier the suppression), whereasS < 0 strengthens
them; oscillations of S further restrict their existence domain. Self-similar
evolutions of unstable many-crested patterns are obtained. A link between
stretch, nonlinearity, and instability with the cutoff size of the wrinkles in
turbulent flames is suggested. Open problems are evoked.


  We construct a CCCZ gate using six T gates, assisted by stabilizer operations
and classical feedback. More generally, we reduce the T cost of a $C^{n}Z$ gate
from $4n-4$ to $4n-6$, for $n > 2$.


  In this paper, we study the class of free hyperplane arrangements.
Specifically, we investigate the relations between freeness over a field of
finite characteristic and freeness over $\mathbb{Q}$.


  For nearly a century the universal logarithmic behaviour of the mean velocity
profile in a parallel flow was a mainstay of turbulent fluid mechanics and its
teaching. Yet many experiments and numerical simulations are not fit
exceedingly well by it, and the question whether the logarithmic law is indeed
universal keeps turning up in discussion and in writing. Large experiments have
been set up in different parts of the world to confirm or deny the logarithmic
law and accurately estimate von K\'arm\'an's constant, the coefficient that
governs it. We show that the discrepancy among flows in different (circular or
plane) geometries, and between these and the logarithmic law, can be ascribed
to the effect of the pressure gradient. When this effect is accounted for in
the form of a higher-order perturbation, universal agreement and a
satisfactorily simple formulation are recovered.


  The currently favored method for estimating radii and other parameters of
transiting-planet host stars is to match theoretical models to observations of
the stellar mean density rho_*, the effective temperature T_eff, and the
composition parameter [Z]. This explicitly model-dependent approach is based on
readily-available observations, and results in small formal errors. Here I use
two calibration samples of stars (eclipsing binaries and stars for which
asteroseismic analyses are available) having well-determined masses and radii
to estimate the accuracy and systematic errors inherent in the rho_* method.
When matching to the Yonsei-Yale stellar evolution models, I find the most
important systematic error results from selection bias favoring
rapidly-rotating (hence probably magnetically active) stars among the eclipsing
binary sample. If unaccounted for, this bias leads to a mass-dependent
underestimate of stellar radii by as much as 4% for stars of 0.4 M_sun,
decreasing to zero for masses above about 1.4 M_sun. The asteroseismic sample
suggests (albeit with significant uncertainty) that systematic errors are small
for slowly-rotating, inactive stars. Systematic errors arising from failings of
the Yonsei-Yale models of inactive stars probably exist, but are difficult to
assess because of the small number of well-characterized comparison stars
having low mass and slow rotation. Poor information about [Z] is an important
source of random error, and may be a minor source of systematic error as well.
With suitable corrections for rotation, it is likely that systematic errors in
the rho_* method can be comparable to or smaller than the random errors,
yielding radii that are accurate to about 2% for most stars.


  In his classical work, Kuramoto analytically described the onset of
synchronization in all-to-all coupled networks of phase oscillators with random
intrinsic frequencies. Specifically, he identified a critical value of the
coupling strength, at which the incoherent state loses stability and a gradual
build-up of coherence begins. Recently, Kuramoto's scenario was shown to hold
for a large class of coupled systems on convergent families of deterministic
and random graphs. Guided by these results, in the present work, we study
several model problems illustrating the link between network topology and
synchronization in coupled dynamical systems.
  First, we identify several families of graphs, for which the transition to
synchronization in the Kuramoto model starts at the same critical value of the
coupling strength and proceeds in practically the same way. These examples
include Erd\H{o}s-R\'enyi random graphs, Paley graphs, complete bipartite
graphs, and certain stochastic block graphs. These examples illustrate that
some rather simple structural properties such as the volume of the graph may
determine the onset of synchronization, while finer structural features may
affect only higher order statistics of the transition to synchronization.
Further, we study the transition to synchronization in the Kuramoto model on
power law and small-world random graphs. The former family of graphs endows the
Kuramoto model with very good synchronizability: the synchronization threshold
can be made arbitrarily low by varying the parameter of the power law degree
distribution. For the Kuramoto model on small-world graphs, in addition to the
transition to synchronization, we identify a new bifurcation leading to stable
random twisted states. The examples analyzed in this work complement the
results in [Chiba, Medvedev, The mean field analysis for the Kuramoto model on
graphs (parts I and II), arxiv].


  We report inelastic neutron scattering measurements of the phonon spectrum of
the pressure-induced ferromagnetic superconductor UGe$_{2}$. No changes of the
spectrum were found on cooling down to low temperature. The phonon contribution
to the specific heat was estimated from a fit to our data. The excess specific
heat previously noted at around $T_{x} \approx$ 30 K is not due to phonons but
is well described by the temperature dependence of the magnetic order parameter
at the molecular field level.


  We discuss possible options for interpreting the newly observed state Y(4140)
by the CDF collaboration in $B^+\to J/\psi\phi K^+$ decay above the
$J/\psi\phi$ threshold, and argue that it is more likely to be a
$D_s^*$-$D_s^*$ molecular state or an exotic ($J^{PC}=1^{-+}$) hybrid
charmonium. We have discussed decay modes which would allow unambiguous
identification of the hybrid charmonium option.


  The symmetries that govern the laws of nature can be spontaneously broken,
enabling the occurrence of ordered states. Crystals arise from the breaking of
translation symmetry, magnets from broken spin rotation symmetry and massive
particles break a phase rotation symmetry. Time translation symmetry can be
spontaneously broken in exactly the same way. The order associated with this
form of spontaneous symmetry breaking is characterised by the emergence of
quantum state reduction: systems which spontaneously break time translation
symmetry act as ideal measurement machines. In this review the breaking of time
translation symmetry is first compared to that of other symmetries such as
spatial translations and rotations. It is then discussed how broken time
translation symmetry gives rise to the process of quantum state reduction and
how it generates a pointer basis, Born's rule, etc. After a comparison between
this model and alternative approaches to the problem of quantum state
reduction, the experimental implications and possible tests of broken time
translation symmetry in realistic experimental settings are discussed.


  We report the development of a superconducting acousto-optic phase modulator
fabricated on a lithium niobate substrate. A titanium-diffused optical
waveguide is placed in a surface acoustic wave resonator, where the electrodes
for mirrors and an interdigitated transducer are made of a superconducting
niobium titanium nitride thin film. The device performance is evaluated as a
substitute for the current electro-optic modulators, with the same fiber
coupling scheme and comparable device size. Operating the device at a cryogenic
temperature (T=8K), we observe the length-half-wave-voltage (length-$V_\pi$)
product of 1.78 V$\cdot$cm. Numerical simulation is conducted to reproduce and
extrapolate the performance of the device. An optical cavity with mirror
coating on the input/output facets of the optical waveguide is tested for
further enhancement of the modulation efficiency. A simple extension of the
current device is estimated to achieve an efficient modulation with $V_\pi=$
0.27 V.


  We investigate the near infrared interband absorption of semiconductor
quantum wells driven by intense terahertz radiation in the regime of
ultrastrong coupling, where the Rabi frequency is a significant fraction of the
frequency of the strongly driven transition. With the driving frequency tuned
just below the lowest frequency transition between valence subbands, a
particularly interesting phenomenon is observed. As the THz power increases, a
new peak emerges above the frequency of the undriven exciton peak which grows
and eventually becomes the larger of the two. This reversal of relative peak
intensity is inconsistent with the Autler-Townes effect in a three-state system
while within the rotating wave approximation (RWA). In the samples
investigated, the Bloch-Siegert shift (associated with abandoning the RWA),
exciton binding energy, the Rabi energy, and non-resonant AC Stark effects are
all of comparable magnitude. Solution of a semiconductor Bloch model with one
conduction and multiple valence subbands indicates that the AC Stark effect is
predominantly responsible for the observed phenomenon.


  Exotic nuclear structures such as bubbles and tori are analyzed through
semiclassical extended Thomas-Fermi calculations with the Skyrme force SkM$^*$.
The variational equations for neutron and proton densities are solved fully
self-consistently in spherical (bubbles) and cylindrical (tori) symmetries. The
possible existence of bubble configurations in some astrophysical scenarios is
discussed. The stability of toroidal structures against change of quadrupole
moment is studied. A global minimum of the energy is found in heavy


  An accurate multiclass classification strategy is crucial to interpreting
antibody tests. However, traditional methods based on confidence intervals or
receiver operating characteristics lack clear extensions to settings with more
than two classes. We address this problem by developing a multiclass
classification based on probabilistic modeling and optimal decision theory that
minimizes the convex combination of false classification rates. The
classification process is challenging when the relative fraction of the
population in each class, or generalized prevalence, is unknown. Thus, we also
develop a method for estimating the generalized prevalence of test data that is
independent of classification. We validate our approach on serological data
with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) na\"ive,
previously infected, and vaccinated classes. Synthetic data are used to
demonstrate that (i) prevalence estimates are unbiased and converge to true
values and (ii) our procedure applies to arbitrary measurement dimensions. In
contrast to the binary problem, the multiclass setting offers wide-reaching
utility as the most general framework and provides new insight into prevalence
estimation best practices.


  Significant progress has been made in developing identification and
estimation techniques for missing data problems where modeling assumptions can
be described via a directed acyclic graph. The validity of results using such
techniques rely on the assumptions encoded by the graph holding true; however,
verification of these assumptions has not received sufficient attention in
prior work. In this paper, we provide new insights on the testable implications
of three broad classes of missing data graphical models, and design
goodness-of-fit tests for them. The classes of models explored are: sequential
missing-at-random and missing-not-at-random models which can be used for
modeling longitudinal studies with dropout/censoring, and a no self-censoring
model which can be applied to cross-sectional studies and surveys.


  This paper is devoted to the numerical resolution of McKean-Vlasov control
problems via the class of mean-field neural networks introduced in our
companion paper [25] in order to learn the solution on the Wasserstein space.
We propose several algorithms either based on dynamic programming with control
learning by policy or value iteration, or backward SDE from stochastic maximum
principle with global or local loss functions. Extensive numerical results on
different examples are presented to illustrate the accuracy of each of our
eight algorithms. We discuss and compare the pros and cons of all the tested
methods.


  The Burst and Transient Source Experiment (BATSE) aboard CGRO monitored the
whole sky through 8 NaI(Tl) crystal scintillators sensitive in the 20 keV - 2
MeV energy band continuously from April 1991 until June 2000. Results are
presented on the long term variabilty observed in the brightest Active Galactic
Nuclei (AGN) present in the BATSE data archive. This was achieved through the
application of the Earth Occultation technique to data flat-fielded using the
BATSE Mass Model. Removal of the temporal background variations from the data
should allow a more sensitive extraction of source parameters. Analysis of the
general trends of the 9-year light curves are also presented.


  We employ supersymmetric localization to determine the exact partition
function of 3d $\mathcal{N}=2$ gauge theories on a background given by a round
$S^2$ fibered over a circle and certain complexified background fields. The
Coulomb branch localization locus includes monopole configurations, and the
partition function reduces to a matrix model. We consider the partition
function of the ABJM theory on this background as an explicit case. We verify
that the large-$N$ limit of the ABJM theory partition function produces, in the
Cardy limit, the entropy function of the dual rotating, electrically charged
asymptotically AdS$_4$ supersymmetric black holes and thus provides a
microscopic explanation for the Bekenstein-Hawking entropy.


  This paper contributes to the understanding of strongly coupled
spatio-temporal processes by describing a generic method based on Granger
causality. The method is validated by the robust identification of causality
regimes and of their phase diagram for an urban morphogenesis model that
couples network growth with density. The application to the real case study of
Greater Paris transportation projects shows a link between territorial
dynamics, more particularly of real estate and socio-economic, and the
anticipated network growth. We finally discuss potential extensions to other
temporal and spatial scales.


  Let $\{x_1, x_2, ..., x_n\}$ be a vector of real numbers. An integer relation
algorithm is a computational scheme to find the $n$ integers $a_k$, if they
exist, such that $a_1 x_1 + a_2 x_2 + ... + a_n x_n= 0$. In the past few years,
integer relation algorithms have been utilized to discover new results in
mathematics and physics. Existing programs for this purpose require very large
amounts of computer time, due in part to the requirement for multiprecision
arithmetic, yet are poorly suited for parallel processing. This paper presents
a new integer relation algorithm designed for parallel computer systems, but as
a bonus it also gives superior results on single processor systems. Single- and
multi-level implementations of this algorithm are described, together with
performance results on a parallel computer system. Several applications of
these programs are discussed, including some new results in number theory,
quantum field theory and chaos theory.


  We study possible Scott sentence complexities of linear orderings using two
approaches. First, we investigate the effect of the Friedman-Stanley embedding
on Scott sentence complexity and show that it only preserves
$\Pi^{\mathrm{in}}_{\alpha}$ complexities. We then take a more direct approach
and exhibit linear orderings of all Scott complexities except
$\Sigma^{\mathrm{in}}_{3}$ and $\Sigma^{\mathrm{in}}_{\lambda+1}$ for $\lambda$
a limit ordinal. We show that the former can not be the Scott sentence
complexity of a linear ordering. In the process we develop new techniques which
appear to be helpful to calculate the Scott sentence complexities of
structures.


  We introduce Instruction Document Visual Question Answering (iDocVQA) dataset
and Large Language Document (LLaDoc) model, for training Language-Vision (LV)
models for document analysis and predictions on document images, respectively.
Usually, deep neural networks for the DocVQA task are trained on datasets
lacking instructions. We show that using instruction-following datasets
improves performance. We compare performance across document-related datasets
using the recent state-of-the-art (SotA) Large Language and Vision Assistant
(LLaVA)1.5 as the base model. We also evaluate the performance of the derived
models for object hallucination using the Polling-based Object Probing
Evaluation (POPE) dataset. The results show that instruction-tuning performance
ranges from 11X to 32X of zero-shot performance and from 0.1% to 4.2% over
non-instruction (traditional task) finetuning. Despite the gains, these still
fall short of human performance (94.36%), implying there's much room for
improvement.


  Online portfolio selection research has so far focused mainly on minimizing
regret defined in terms of wealth growth. Practical financial decision making,
however, is deeply concerned with both wealth and risk. We consider online
learning of portfolios of stocks whose prices are governed by arbitrary
(unknown) stationary and ergodic processes, where the goal is to maximize
wealth while keeping the conditional value at risk (CVaR) below a desired
threshold. We characterize the asymptomatically optimal risk-adjusted
performance and present an investment strategy whose portfolios are guaranteed
to achieve the asymptotic optimal solution while fulfilling the desired risk
constraint. We also numerically demonstrate and validate the viability of our
method on standard datasets.


  We present an application of the thermal Comptonization/disk reprocessing
model recently proposed by Zdziarski, Lubi\'nski, & Smith. We show that the
absence of strong optical variations in the presence of strong concurrent X-ray
variations, similar to those found by HST/RXTE monitoring observations of NGC
3516, can be explained by changing the geometry of the Comptonizing plasma
rather than the accretion disk itself. The total X-ray luminosity of the
Comptonizing plasma must decrease as its spatial extent increases. In contrast,
the disk inner radius must be roughly fixed in order not to produce
optical/ultraviolet color variations stronger than observed. By including
emission due to internal viscous dissipation in the disk, we can roughly match
the optical and X-ray flux levels and variability amplitudes seen from NGC 3516
during the HST/RXTE campaign.


  The global existence of weak solutions for the three-dimensional axisymmetric
Euler-$\alpha$ (also known as Lagrangian-averaged Euler-$\alpha$) equations,
without swirl, is established, whenever the initial unfiltered velocity $v_0$
satisfies $\frac{\nabla \times v_0}{r}$ is a finite Randon measure with compact
support. Furthermore, the global existence and uniqueness, is also established
in this case provided $\frac{\nabla \times v_0}{r} \in L^p_c(\mathbb{R}^3)$
with $p>{3/2}$. It is worth mention that no such results are known to be
available, so far, for the three-dimensional Euler equations of ideal
incompressible flows.


  We propose an information-theoretic quantifier for the advantage gained from
cooperation that captures the degree of dependency between subsystems of a
global system. The quantifier is distinct from measures of multipartite
correlations despite sharing many properties with them. It is directly
computable for classical as well as quantum systems and reduces to comparing
the respective conditional mutual information between any two subsystems.
Exemplarily we show the benefits of using the new quantifier for symmetric
quantum secret sharing. We also prove an inequality characterizing the lack of
monotonicity of conditional mutual information under local operations and
provide intuitive understanding for it. This underlines the distinction between
the multipartite dependence measure introduced here and multipartite
correlations.


  The turnaround epoch of gravitational collapse is examined by means of
relativistic Lagrangian perturbation theory. Averaged, scalar equations applied
to the fluid's evolution reveal some scale-independent universality of
parameters for a wide variety of initial conditions. In particular, the density
contrast of the collapsing domain at the turnaround is shown to be
significantly smaller than the value provided by Eulerian perturbative
(homogeneous and spherical) model. Combined curvature and kinematical
backreaction are shown to be of the order of the energy density. Possible
improvements of our treatment are put into perspective.


  Our traditional notion of a cell is changing dramatically given the
increasing degree of heterogeneity in 4G and emerging 5G systems. Rather than
belonging to a specific cell, a device would choose the most suitable
connection from the plethora of connections available. In such a setting, given
the transmission powers differ significantly between downlink (DL) and uplink
(UL), a wireless device that sees multiple Base Stations (BSs) may access the
infrastructure in a way that it receives the downlink (DL) traffic from one BS
and sends uplink (UL) traffic through another BS. This situation is referred to
as Downlink and Uplink Decoupling (DUDe). In this paper, the capacity and
throughput gains brought by decoupling are rigorously derived using stochastic
geometry. Theoretical findings are then corroborated by means of simulation
results. A further constituent of this paper is the verification of the
theoretically derived results by means of a real-world system simulation
platform. Despite theoretical assumptions differing from the very complete
system simulator, the trends in the association probabilities and capacity
gains are similar. Based on the promising results, we then outline
architectural changes needed to facilitate the decoupling of DL and UL.


  Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown
great potential in novel view synthesis of a scene. However, current NeRF-based
methods cannot enable users to perform user-controlled shape deformation in the
scene. While existing works have proposed some approaches to modify the
radiance field according to the user's constraints, the modification is limited
to color editing or object translation and rotation. In this paper, we propose
a method that allows users to perform controllable shape deformation on the
implicit representation of the scene, and synthesizes the novel view images of
the edited scene without re-training the network. Specifically, we establish a
correspondence between the extracted explicit mesh representation and the
implicit neural representation of the target scene. Users can first utilize
well-developed mesh-based deformation methods to deform the mesh representation
of the scene. Our method then utilizes user edits from the mesh representation
to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining
the rendering results of the edited scene. Extensive experiments demonstrate
that our framework can achieve ideal editing results not only on synthetic
data, but also on real scenes captured by users.


  The Milky Way Galaxy contains an unknown number, $N$, of civilizations that
emit electromagnetic radiation (of unknown wavelengths) over a finite lifetime,
$L$. Here we are assuming that the radiation is not produced indefinitely, but
within $L$ as a result of some unknown limiting event. When a civilization
stops emitting, the radiation continues traveling outward at the speed of
light, $c$, but is confined within a shell wall having constant thickness,
$cL$. We develop a simple model of the Galaxy that includes both the birthrate
and detectable lifetime of civilizations to compute the possibility of a SETI
detection at the Earth. Two cases emerge for radiation shells that are (1)
thinner than or (2) thicker than the size of the Galaxy, corresponding to
detectable lifetimes, $L$, less than or greater than the light-travel time,
$\sim 100,000$ years, across the Milky Way, respectively. For case (1), each
shell wall has a thickness smaller than the size of the Galaxy and intersects
the galactic plane in a donut shape (annulus) that fills only a fraction of the
Galaxy's volume, inhibiting SETI detection. But the ensemble of such shell
walls may still fill our Galaxy, and indeed may overlap locally, given a
sufficiently high birthrate of detectable civilizations. In the second case,
each radiation shell is thicker than the size of our Galaxy. Yet, the ensemble
of walls may or may not yield a SETI detection depending on the civilization
birthrate. We compare the number of different electromagnetic transmissions
arriving at Earth to Drake's $N$, the number of currently emitting
civilizations, showing that they are equal to each other for both cases (1) and
(2). However, for $L < 100,000$ years, the transmissions arriving at Earth may
come from distant civilizations long extinct, while civilizations still alive
are sending signals yet to arrive.


  Because of new telescopes that will dramatically improve our knowledge of the
interstellar medium, chemical models will have to be used to simulate the
chemistry of many regions with diverse properties. To make these models more
robust, it is important to understand their sensitivity to a variety of
parameters. In this article, we report a study of the sensitivity of a chemical
model of a cold dense core, with homogeneous and time-independent physical
conditions, to variations in the following parameters: initial chemical
inventory, gas temperature and density, cosmic-ray ionization rate, chemical
reaction rate coefficients, and elemental abundances. From the results of the
parameter variations, we can quantify the sensitivity of the model to each
parameter as a function of time. Our results can be used in principle with
observations to constrain some parameters for different cold clouds. We also
attempted to use the Monte Carlo approach with all parameters varied
collectively. Within the parameter ranges studied, the most critical parameters
turn out to be the reaction rate coefficients at times up to 4e5 yr and
elemental abundances at later times. At typical times of best agreement with
observation, models are sensitive to both of these parameters. The models are
less sensitive to other parameters such as the gas density and temperature. The
improvement of models will require that the uncertainties in rate coefficients
of important reactions be reduced. As the chemistry becomes better understood
and more robust, it should be possible to use model sensitivities concerning
other parameters, such as the elemental abundances and the cosmic ray
ionization rate, to yield detailed information on cloud properties and history.
Nevertheless, at the current stage, we cannot determine the best values of all
the parameters simultaneously based on purely observational constraints.


  This chapter is going to deal with enhancing the efficiency of Biometric by
integrating it with Salt Value (randomly generated value of varying length).
Normally at an enterprise level or data centres, the servers are maintained
with complex passwords and they are known only to the system administrators.
Even after applying lot of securities at an expert level, the hackers are able
to penetrate through the network and break the passwords easily. Here how the
biometric can play a vital role and that too with the inclusion of Salt value
can prevent the hacker from stealing the confidential data's of an
organization.


  We study the problem of corralling stochastic bandit algorithms, that is
combining multiple bandit algorithms designed for a stochastic environment,
with the goal of devising a corralling algorithm that performs almost as well
as the best base algorithm. We give two general algorithms for this setting,
which we show benefit from favorable regret guarantees. We show that the regret
of the corralling algorithms is no worse than that of the best algorithm
containing the arm with the highest reward, and depends on the gap between the
highest reward and other rewards.


  Redshift measurements, primarily obtained from host galaxies, are essential
for inferring cosmological parameters from type Ia supernovae (SNe Ia).
Matching SNe to host galaxies using images is non-trivial, resulting in a
subset of SNe with mismatched hosts and thus incorrect redshifts. We evaluate
the host galaxy mismatch rate and resulting biases on cosmological parameters
from simulations modeled after the Dark Energy Survey 5-Year (DES-SN5YR)
photometric sample. For both DES-SN5YR data and simulations, we employ the
directional light radius method for host galaxy matching. In our SN Ia
simulations, we find that 1.7% of SNe are matched to the wrong host galaxy,
with redshift difference between the true and matched host of up to 0.6. Using
our analysis pipeline, we determine the shift in the dark energy equation of
state parameter (Dw) due to including SNe with incorrect host galaxy matches.
For SN Ia-only simulations, we find Dw = 0.0013 +/- 0.0026 with constraints
from the cosmic microwave background (CMB). Including core-collapse SNe and
peculiar SNe Ia in the simulation, we find that Dw ranges from 0.0009 to 0.0032
depending on the photometric classifier used. This bias is an order of
magnitude smaller than the expected total uncertainty on w from the DES-SN5YR
sample of around 0.03. We conclude that the bias on w from host galaxy mismatch
is much smaller than the uncertainties expected from the DES-SN5YR sample, but
we encourage further studies to reduce this bias through better host-matching
algorithms or selection cuts.


  We study the photoinduced magnetic transition in an itinerant magnet
described by the double-exchange model, in which conduction electrons couple
with localized spins through the ferromagnetic (FM) Hund coupling. It is shown
that intense light applied to the FM ground state induces an antiferromagnetic
(AFM) order, in contrast to the AFM-to-FM transition due to the photocarrier
injection. In particular, we focus on the mechanism for instability of the FM
structure by the light irradiation. The magnon spectrum in the Floquet state is
formulated on the basis of the pertrubative expansion of the Floquet Green
function. The magnon dispersion shows softening at momentum $(\pi,\pi)$ in the
square lattice with increasing the light amplitude, implying photoinduced AFM
instability. This result is mainly attributed to a nonequilibrium electron
distribution, which promotes low-energy Stoner excitations. The transient
optical conductivity spectra characterized by interband excitations and Floquet
sidepeaks are available to identify the photoinduced AFM state.


  We study a contract design problem between a principal and multiple agents.
Each agent participates in an independent task with binary outcomes (success or
failure), in which it may exert costly effort towards improving its probability
of success, and the principal has a fixed budget which it can use to provide
outcome-dependent rewards to the agents. Crucially, we assume the principal
cares only about maximizing the agents' probabilities of success, not how much
of the budget it expends. We first show that a contract is optimal for some
objective if and only if it is a successful-get-everything contract. An
immediate consequence of this result is that piece-rate contracts and
bonus-pool contracts are never optimal in this setting. We then show that for
any objective, there is an optimal priority-based weighted contract, which
assigns positive weights and priority levels to the agents, and splits the
budget among the highest-priority successful agents, with each such agent
receiving a fraction of the budget proportional to her weight. This result
provides a significant reduction in the dimensionality of the principal's
optimal contract design problem and gives an interpretable and easily
implementable optimal contract. Finally, we discuss an application of our
results to the design of optimal contracts with two agents and quadratic costs.
In this context, we find that the optimal contract assigns a higher weight to
the agent whose success it values more, irrespective of the heterogeneity in
the agents' cost parameters. This suggests that the structure of the optimal
contract depends primarily on the bias in the principal's objective and is, to
some extent, robust to the heterogeneity in the agents' cost functions.


  Measurements of neutron star masses, radii, and tidal deformability have
direct connections to nuclear physics via the equation of state (EoS), which
for the cold, catalyzed matter in neutron star cores is commonly represented as
the pressure as a function of energy density. Microscopic models with exotic
degrees of freedom display nontrivial structure in the speed of sound ($c_s$)
in the form of first-order phase transitions and bumps, oscillations, and
plateaus in the case of crossovers and higher-order phase transitions. We
present a procedure based on Gaussian processes to generate an ensemble of EoSs
that include nontrivial features. Using a Bayesian analysis incorporating
measurements from X-ray sources, gravitational wave observations, and
perturbative QCD results, we show that these features are compatible with
current constraints. We investigate the possibility of a global maximum in
$c_s$ that occurs within the densities realized in neutron stars -- implying a
softening of the EoS and possibly an exotic phase in the core of massive stars
-- and find that such a global maximum is consistent with, but not required by,
current constraints.


  The (co)associative, partially (co)associative and totally (co)associative
$3$-ary (co) algebras and infinitesimal bialgebras are constructed and
discussed. Their trimodules and matched pairs are defined and completely
characterized. The main structural properties and relations are also deduced
and analyzed.


  There are constructed linear Hamiltonian (dynamical) systems such that their
no nonzero trajectory has usual asymptotical dichotomy property. In particular
there is solved (in the negative) one of the so-called M. G. Krein problem. In
fact Definition: Let J be period-2 unitary operator and U be linear operator.
  If U^*JU = UJU^* = J then U is said to be J-unitary.
  KREIN Problem: given a J-unitary operator U, does there exist an U-invariant
subspace L, say, with r(U|L)\leq1 ?
  In the special case that the operator U^*U-I is compact this problem was
solved in the positive by M.G.Krein in 1964. We shall show that, by contrast,
in the general case such a subspace L needs not exist. Moreover, there asserts
Theorem: For every real c>0 there exists some J-unitary operator U such that
(i) if L is some nonzero U-invariant subspace, then r(U|L)>c; (ii) if L' is
some nonzero U^{-1}-invariant subspace, then r(U^{-1}|L')>c;
  This result applies both to the real space case and to the complex space
case. In addition, one can assume that U is linear symplectic automorphism. A
similar result is obtained for the case of continuous 'dynamic' and for the
question: does there exist a nonzero quasistable manifold?


  Betelgeuse is the nearest Red Supergiant star and it underwent an unusually
deep minimum at optical wavelengths during its most recent pulsation cycle. We
present submillimetre observations taken by the James Clerk Maxwell Telescope
and Atacama Pathfinder Experiment over a time span of 13 years including the
optical dimming. We find that Betelgeuse has also dimmed by \sim20\% at these
longer wavelengths during this optical minimum. Using radiative-transfer
models, we show that this is likely due to changes in the photosphere
(luminosity) of the star as opposed to the surrounding dust as was previously
suggested in the literature.


  With the growth of computing power neural machine translation (NMT) models
also grow accordingly and become better. However, they also become harder to
deploy on edge devices due to memory constraints. To cope with this problem, a
common practice is to distill knowledge from a large and accurately-trained
teacher network (T) into a compact student network (S). Although knowledge
distillation (KD) is useful in most cases, our study shows that existing KD
techniques might not be suitable enough for deep NMT engines, so we propose a
novel alternative. In our model, besides matching T and S predictions we have a
combinatorial mechanism to inject layer-level supervision from T to S. In this
paper, we target low-resource settings and evaluate our translation engines for
Portuguese--English, Turkish--English, and English--German directions. Students
trained using our technique have 50% fewer parameters and can still deliver
comparable results to those of 12-layer teachers.


  We propose a new law for the deceleration parameter that varies linearly with
time and covers Berman's law where it is constant. Our law not only allows one
to generalize many exact solutions that were obtained assuming constant
deceleration parameter, but also gives a better fit with data (from SNIa, BAO
and CMB), particularly concerning the late time behavior of the universe.
According to our law only the spatially closed and flat universes are allowed;
in both cases the cosmological fluid we obtain exhibits quintom like behavior
and the universe ends with a big-rip. This is a result consistent with recent
cosmological observations.


  Young protoplanetary discs and the outer radii of active galactic nucleii may
be subject to gravitational instability and, as a consequence, fall into a
`gravitoturbulent' state. While in this state, appreciable angular momentum can
be transported. Alternatively, the gas may collapse into bound clumps, the
progenitors of planets or stars. In this paper, we numerically characterize the
properties of 3D gravitoturbulence, focussing especially on its dependence on
numerical parameters (resolution, domain size) and its excitation of
small-scale dynamics. Via a survey of vertically stratified shearing box
simulations with PLUTO and RODEO, we find (a) evidence that certain
gravitoturbulent properties are independent of horizontal box size only when
the box is larger than $\simeq 40 H_0$, where $H_0$ is the height scale, (b) at
high resolution, small-scale isotropic turbulence appears off the midplane
around $z\simeq 0.5 -1 H_0$, and (c) this small-scale dynamics results from a
parametric instability, involving the coupling of inertial waves with a
large-scale axisymmetric epicyclic mode. This mode oscillates at a frequency
close to $\Omega$ and is naturally excited by gravito-turbulence, via a
nonlinear process to be determined. The small-scale turbulence we uncover has
potential implications for a wide range of disc physics, e.g. turbulent
saturation levels, fragmentation, turbulent mixing, and dust settling.


  Many problems in machine learning can be solved by rounding the solution of
an appropriate linear program (LP). This paper shows that we can recover
solutions of comparable quality by rounding an approximate LP solution instead
of the ex- act one. These approximate LP solutions can be computed efficiently
by applying a parallel stochastic-coordinate-descent method to a
quadratic-penalty formulation of the LP. We derive worst-case runtime and
solution quality guarantees of this scheme using novel perturbation and
convergence analysis. Our experiments demonstrate that on such combinatorial
problems as vertex cover, independent set and multiway-cut, our approximate
rounding scheme is up to an order of magnitude faster than Cplex (a commercial
LP solver) while producing solutions of similar quality.


  We review a number of experimental techniques that are beginning to reveal
fine details of the bosonic spectrum \alpha^2F(\Omega) that dominates the
interaction between the quasiparticles in high temperature superconductors.
Angle-resolved photo emission (ARPES) shows kinks in electronic dispersion
curves at characteristic energies that agree with similar structures in the
optical conductivity and tunnelling spectra. Each technique has its advantages.
ARPES is momentum resolved and offers independent measurements of the real and
imaginary part of the contribution of the bosons to the self energy of the
quasiparticles. The optical conductivity can be used on a larger variety of
materials and with the use of maximum entropy techniques reveals rich details
of the spectra including their evolution with temperature and doping. Scanning
tunnelling spectroscopy offers spacial resolution on the unit cell level. We
find that together the various spectroscopies, including recent Raman results,
are pointing to a unified picture of a broad spectrum of bosonic excitations at
high temperature which evolves, as the temperature is lowered into a peak in
the 30 to 60 meV region and a featureless high frequency background in most of
the materials studied. This behaviour is consistent with the spectrum of spin
fluctuations as measured by magnetic neutron scattering. However, there is
evidence for a phonon contribution to the bosonic spectrum as well.


  We propose a distributed bundle adjustment (DBA) method using the exact
Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the
existing methods partition the global map to small ones and conduct bundle
adjustment in the submaps. In order to fit the parallel framework, they use
approximate solutions instead of the LM algorithm. However, those methods often
give sub-optimal results. Different from them, we utilize the exact LM
algorithm to conduct global bundle adjustment where the formation of the
reduced camera system (RCS) is actually parallelized and executed in a
distributed way. To store the large RCS, we compress it with a block-based
sparse matrix compression format (BSMC), which fully exploits its block
feature. The BSMC format also enables the distributed storage and updating of
the global RCS. The proposed method is extensively evaluated and compared with
the state-of-the-art pipelines using both synthetic and real datasets.
Preliminary results demonstrate the efficient memory usage and vast scalability
of the proposed method compared with the baselines. For the first time, we
conducted parallel bundle adjustment using LM algorithm on a real datasets with
1.18 million images and a synthetic dataset with 10 million images (about 500
times that of the state-of-the-art LM-based BA) on a distributed computing
system.


  Recent results from short--baseline neutrino oscillation experiments and
Cosmic Microwave Background anisotropy measurements suggest the presence of
additional sterile neutrinos. In this paper we properly combine these data sets
to derive bounds on the sterile neutrino masses in the 3+1 and 3+2 frameworks,
finding a potentially good agreement between the two datasets. However, when
galaxy clustering is included in the analysis a tension between the oscillation
and cosmological data is clearly present.


  It is believed that there are extra fundamental gauge symmetries beyond these
described by the Standard Model of particle physics. The scale of these new
gauge symmetries are usually too high to be reachable by particle colliders.
Considering that the phase transition (PT) relating to the spontaneous breaking
of new gauge symmetries to the electroweak symmetry might be strongly first
order, we propose in this paper taking the stochastic gravitational waves (GW)
arising from this phase transition as an indirect way of detecting these new
fundamental gauge symmetries. As an illustration, we explore the possibility of
detecting the stochastic GW generated from the PT of $\mathbf{ B-L}$ in the
space-based interferometer detectors. Out study shows that the GW energy
spectrum is reachable by the LISA, BBO, Taiji and DECIGO experiments only for
the case where the spontaneous breaking of $\mathbf{B-L}$ is triggered by at
least two electroweak singlet scalars.


  The paper presents the kinematic modelling for the coupled motion of a 6-DOF
surgical parallel robot PARA-SILSROB which guides a mobile platform carrying
the surgical instruments, and the actuators of the sub-modules which hold these
tools. To increase the surgical procedure safety, a closed form solution for
the kinematic model is derived and then, the forward and inverse kinematic
models for the mobile orientation platform are obtained. The kinematic models
are used in numerical simulations for the reorientation of the endoscopic
camera, which imposes an automated compensatory motion from the active
instruments' mod-ules.


  Stroking a path is one of the two basic rendering operations in vector
graphics standards (e.g., PostScript, PDF, SVG). We survey path stroking
rendering results from real-world software implementations of path stroking for
anecdotal evidence that such implementations are prone to rendering variances.
While our survey is limited and informal, the rendering results we gathered
indicate widespread rendering variations for simple-but-problematic stroked
paths first identified decades ago. We conclude that creators of vector
graphics content would benefit from a mathematically grounded standardization
for how a stroked path should be rasterized.


  We apply the matching functions technique in the setting of contact Anosov
flows which satisfy a bunching assumption. This allows us to generalize the
3-dimensional rigidity result of Feldman-Ornstein~\cite{FO}. Namely, we show
that if two such Anosov flows are $C^0$ conjugate then they are $C^{r}$,
conjugate for some $r\in[1,2)$ or even $C^\infty$ conjugate under some
additional assumptions. This, for example, applies to $1/4$-pinched geodesic
flows on compact Riemannian manifolds of negative sectional curvature. We can
also use our result to recover Hamendst\"adt's marked length spectrum rigidity
result for real hyperbolic manifolds.


  An efficient classification of light quark meson states is discussed based on
the dominance of angular and radial quark excitation. A synthesis of Regge and
quark dynamics allows a natural unification of light quark-antiquark
spectroscopies and indicates the states that fall outside this category such as
molecules, hybrids, and glueballs.


  In the field of representation learning on knowledge graphs (KGs), a
hyper-relational fact consists of a main triple and several auxiliary
attribute-value descriptions, which is considered more comprehensive and
specific than a triple-based fact. However, currently available
hyper-relational KG embedding methods in a single view are limited in
application because they weaken the hierarchical structure that represents the
affiliation between entities. To overcome this limitation, we propose a
dual-view hyper-relational KG structure (DH-KG) that contains a
hyper-relational instance view for entities and a hyper-relational ontology
view for concepts that are abstracted hierarchically from the entities. This
paper defines link prediction and entity typing tasks on DH-KG for the first
time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and
HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding
model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms
baseline models on DH-KG, according to experimental results. Finally, we
provide an example of how this technology can be used to treat hypertension.
Our model and new datasets are publicly available.


  Convergence results are stated for the variational iteration method applied
to solve an initial value problem for a system of ordinary differential
equations.


  Grating couplers are widely used to couple waveguide modes with the far
field. Their usefulness is determined not only by energy efficiency but also by
additional supported functionality. In this paper, we demonstrate a plasmonic
grating on a silicon nitride slab waveguide that couples both TE and TM
waveguide modes with circularly polarized light in the far field. Specifically,
we experimentally confirmed that circularly polarized light excites TE and TM
modes propagating in opposite directions, and the direction is controlled by
the handedness. The routing efficiency for normally incident light reaches up
to 95%. The same structure operates in the outcoupling regime as well,
demonstrating up to 97% degree of circular polarization, where the handedness
is determined by the polarization and propagation direction of outcoupled
modes. Our results pave the way for the realization of polarization-division
multiplexers and demultiplexers, integrated circular polarization emitters, as
well as detectors of the polarization state of the incident optical field.


  We consider a nonlinear reaction diffusion system of parabolic type known as
the monodomain equations, which model the interaction of the electric current
in a cell. Together with the FitzHugh-Nagumo model for the nonlinearity they
represent defibrillation processes of the human heart. We study a fairly
general type with co-located inputs and outputs describing both boundary and
distributed control and observation. The control objective is output trajectory
tracking with prescribed performance. To achieve this we employ the funnel
controller, which is model-free and of low complexity. The controller
introduces a nonlinear and time-varying term in the closed-loop system, for
which we prove existence and uniqueness of solutions. Additionally, exploiting
the parabolic nature of the problem, we obtain H\"older continuity of the
state, inputs and outputs. We illustrate our results by a simulation of a
standard test example for the termination of reentry waves.


  We investigate the effects of quenched disorder on a non-interacting tilted
Dirac semimetal in two dimensions. Depending on the magnitude of the tilting
parameter, the system can have either Fermi points (type-I) or Fermi lines
(type-II). In general, there are three different types of disorders for Dirac
fermions in two dimensions, namely, the random scalar potential, the random
vector potentials along and perpendicular to the tilting direction, and the
random mass. We study the effects of weak disorder in terms of the
renormalization group, which is performed by integrating out the modes with
large energies, instead of large momenta. Since the parametrization of the
low-energy degrees of freedom depends on the structure of the Fermi surface,
the resulting one-loop renormalization-group equations depend on the type of
tilted Dirac fermions. Whenever the disorder is a marginal perturbation, we
examine its role on low-energy physics by a mean-field approximation of the
replica field theory or the first-order Born approximation. Based on our
analysis, we suggest the phase diagrams of a two-dimensional tilted Dirac
fermion in the presence of different types of disorder.


  Traceability to the International System of Units (SI) is fundamental to
measurement accuracy and reliability. In this study, we demonstrate
subnanometer traceability of localization microscopy, establishing a
metrological foundation for the maturation and application of super-resolution
methods. To do so, we create a master standard by measuring the positions of
submicrometer apertures in an array by traceable atomic-force microscopy. We
perform correlative measurements of this master standard by optical microscopy,
calibrating scale factor and correcting aberration effects. We introduce the
concept of a localization uncertainty field due to optical localization errors
and scale factor uncertainty, with regions of position traceability to within a
68 % coverage interval of +/- 1 nm. These results enable localization metrology
with high throughput, which we apply to measure working standards that we
fabricate by electron-beam lithography, validating the accuracy of mean pitch
and closing the loop for disseminating and integrating reference arrays. We
then apply our novel methods to calibrate an optical microscope with a sample
cryostat, accounting for thermal contraction by use of a submicrometer pillar
array in silicon as a reference material and elucidating complex distortion.
This new calibration enables the accurate integration of quantum emitters and
photonic structures with high yield, as we demonstrate theoretically through
simulations of the dependence of the Purcell factor of radiative enhancement on
registration errors across a wide field. Our study illuminates the challenges
and opportunities of achieving traceable localization for comparison of
position data across lithography and microscopy systems, from ambient to
cryogenic temperatures.


  Consider a bounded strongly pseudo-convex domain $\Omega $ with a smooth
boundary in $\mathbb{C}^n$. Let $\mathcal{T}$ be the Toeplitz algebra on the
Bergman space $L^2_a(\Omega )$. That is, $\mathcal{T}$ is the $C^\ast $-algebra
generated by the Toeplitz operators $\{T_f : f \in L^\infty (\Omega )\}$.
Extending previous work in the special case of the unit ball, we show that on
any such $\Omega $, $\mathcal{T}$ and $\{T_f : f \in {\text{VO}}_{\text{bdd}}\}
+ \mathcal{K}$ are essential commutants of each other. On a general $\Omega $
considered in this paper, the proofs require many new ideas and techniques.
These same techniques also enable us to show that for $A \in \mathcal{T}$, if
$\langle Ak_z,k_z\rangle \rightarrow 0$ as $z \rightarrow \partial \Omega $,
then $A$ is a compact operator.


  In this work we aim to predict the driver's focus of attention. The goal is
to estimate what a person would pay attention to while driving, and which part
of the scene around the vehicle is more critical for the task. To this end we
propose a new computer vision model based on a multi-branch deep architecture
that integrates three sources of information: raw video, motion and scene
semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes
for which eye-tracking annotations are available. This dataset features more
than 500,000 registered frames, matching ego-centric views (from glasses worn
by drivers) and car-centric views (from roof-mounted camera), further enriched
by other sensors measurements. Results highlight that several attention
patterns are shared across drivers and can be reproduced to some extent. The
indication of which elements in the scene are likely to capture the driver's
attention may benefit several applications in the context of human-vehicle
interaction and driver attention analysis.


  I summarize the main characteristics of AGNs in nearby galaxies and present a
physical picture of their central engines.


  Recent research has found that knowledge distillation can be effective in
reducing the size of a network and in increasing generalization. A pre-trained,
large teacher network, for example, was shown to be able to bootstrap a student
model that eventually outperforms the teacher in a limited label environment.
Despite these advances, it still is relatively unclear \emph{why} this method
works, that is, what the resulting student model does 'better'. To address this
issue, here, we utilize two non-linear, low-dimensional embedding methods
(t-SNE and IVIS) to visualize representation spaces of different layers in a
network. We perform a set of extensive experiments with different architecture
parameters and distillation methods. The resulting visualizations and metrics
clearly show that distillation guides the network to find a more compact
representation space for higher accuracy already in earlier layers compared to
its non-distilled version.


  In this paper, we extend Deligne's functorial Riemann-Roch isomorphism for
hermitian holomorphic line bundles on Riemann surfaces to the case of flat, not
necessarily unitary connections. The Quillen metric and star-product of
Gillet-Soule are replaced with complex valued logarithms. On the determinant of
cohomology side, the idea goes back to Fay's holomorphic extension of
determinants of Dolbeault laplacians, and it is shown here to be equivalent to
the holomorphic Cappell-Miller torsion. On the Deligne pairing side, the
logarithm is a refinement of the intersection connections considered in
previous work. The construction naturally leads to an Arakelov theory for flat
line bundles on arithmetic surfaces and produces arithmetic intersection
numbers valued in ${\mathbb C}/\pi i {\mathbb Z}$. In this context we prove an
arithmetic Riemann-Roch theorem. This realizes a program proposed by
Cappell-Miller to show that the holomorphic torsion exhibits properties similar
to those of the Quillen metric proved by Bismut, Gillet and Soule. Finally, we
give examples that clarify the kind of invariants that the formalism captures;
namely, periods of differential forms.


  Traditional supervised learning with deep neural networks requires a
tremendous amount of labelled data to converge to a good solution. For 3D
medical images, it is often impractical to build a large homogeneous annotated
dataset for a specific pathology. Self-supervised methods offer a new way to
learn a representation of the images in an unsupervised manner with a neural
network. In particular, contrastive learning has shown great promises by
(almost) matching the performance of fully-supervised CNN on vision tasks.
Nonetheless, this method does not take advantage of available meta-data, such
as participant's age, viewed as prior knowledge. Here, we propose to leverage
continuous proxy metadata, in the contrastive learning framework, by
introducing a new loss called y-Aware InfoNCE loss. Specifically, we improve
the positive sampling during pre-training by adding more positive examples with
similar proxy meta-data with the anchor, assuming they share similar
discriminative semantic features.With our method, a 3D CNN model pre-trained on
$10^4$ multi-site healthy brain MRI scans can extract relevant features for
three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer's
detection. When fine-tuned, it also outperforms 3D CNN trained from scratch on
these tasks, as well as state-of-the-art self-supervised methods. Our code is
made publicly available here.


  Through an uncertainty quantification (UQ) perspective, we show that
score-based generative models (SGMs) are provably robust to the multiple
sources of error in practical implementation. Our primary tool is the
Wasserstein uncertainty propagation (WUP) theorem, a model-form UQ bound that
describes how the $L^2$ error from learning the score function propagates to a
Wasserstein-1 ($\mathbf{d}_1$) ball around the true data distribution under the
evolution of the Fokker-Planck equation. We show how errors due to (a) finite
sample approximation, (b) early stopping, (c) score-matching objective choice,
(d) score function parametrization expressiveness, and (e) reference
distribution choice, impact the quality of the generative model in terms of a
$\mathbf{d}_1$ bound of computable quantities. The WUP theorem relies on
Bernstein estimates for Hamilton-Jacobi-Bellman partial differential equations
(PDE) and the regularizing properties of diffusion processes. Specifically, PDE
regularity theory shows that stochasticity is the key mechanism ensuring SGM
algorithms are provably robust. The WUP theorem applies to integral probability
metrics beyond $\mathbf{d}_1$, such as the total variation distance and the
maximum mean discrepancy. Sample complexity and generalization bounds in
$\mathbf{d}_1$ follow directly from the WUP theorem. Our approach requires
minimal assumptions, is agnostic to the manifold hypothesis and avoids absolute
continuity assumptions for the target distribution. Additionally, our results
clarify the trade-offs among multiple error sources in SGMs.


  Despite the millimeter wave technology fulfills the low-latency and high data
transmission, it will cause severe Doppler Frequency Shift (DFS) for high-speed
vehicular network, which tremendously damages the communication performance. In
this paper, we propose an Integrated Sensing and Communication (ISAC) enabled
DFS estimation and compensation algorithm. Firstly, the DFS is coarsely
estimated and compensated using radar detection. Then, the designed preamble
sequence is used to accurately estimate and compensate DFS. In addition, an
adaptive DFS estimator is designed to reduce the computational complexity.
Compared with the traditional DFS estimation algorithm, the improvement of the
proposed algorithm is verified in bit error rate and mean square error
performance by simulation results.


  The irregular domain and lack of ordering make it challenging to design deep
neural networks for point cloud processing. This paper presents a novel
framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is
based on Transformer, which achieves huge success in natural language
processing and displays great potential in image processing. It is inherently
permutation invariant for processing a sequence of points, making it
well-suited for point cloud learning. To better capture local context within
the point cloud, we enhance input embedding with the support of farthest point
sampling and nearest neighbor search. Extensive experiments demonstrate that
the PCT achieves the state-of-the-art performance on shape classification, part
segmentation and normal estimation tasks.


  Learning to predict multi-label outputs is challenging, but in many problems
there is a natural metric on the outputs that can be used to improve
predictions. In this paper we develop a loss function for multi-label learning,
based on the Wasserstein distance. The Wasserstein distance provides a natural
notion of dissimilarity for probability measures. Although optimizing with
respect to the exact Wasserstein distance is costly, recent work has described
a regularized approximation that is efficiently computed. We describe an
efficient learning algorithm based on this regularization, as well as a novel
extension of the Wasserstein distance from probability measures to unnormalized
measures. We also describe a statistical learning bound for the loss. The
Wasserstein loss can encourage smoothness of the predictions with respect to a
chosen metric on the output space. We demonstrate this property on a real-data
tag prediction problem, using the Yahoo Flickr Creative Commons dataset,
outperforming a baseline that doesn't use the metric.


  The mixed states are important in quantum optics since they frequently appear
in the decoherence problems. When one of the components of the system is
prepared in the mixed state and the evolution operator of this system is not
available, one cannot deduce the density matrix. We present analytical approach
to accurately solve this problem. The approach can be applied on the condition
that the Schr\"odinger's equation of the system is solvable with any arbitrary
initial state. In deriving the solution we exploit the fact that any mixed
state can be expressed in terms of a phase state. The approach is illustrated
by deriving the density matrix of a single-mode heat environment interacting
asymmetrically with two qubits. Our results are in good agreement with the
available results in the literature. This approach opens new perspectives for
treating complicated systems and may impact other applications in the quantum
theory.


  Nested sampling is an efficient algorithm for the calculation of the Bayesian
evidence and posterior parameter probability distributions. It is based on the
step-by-step exploration of the parameter space by Monte Carlo sampling with a
series of values sets called live points that evolve towards the region of
interest, i.e. where the likelihood function is maximal. In presence of several
local likelihood maxima, the algorithm converges with difficulty. Some
systematic errors can also be introduced by unexplored parameter volume
regions. In order to avoid this, different methods are proposed in the
literature for an efficient search of new live points, even in presence of
local maxima. Here we present a new solution based on the mean shift cluster
recognition method implemented in a random walk search algorithm. The
clustering recognition is integrated within the Bayesian analysis program
NestedFit. It is tested with the analysis of some difficult cases. Compared to
the analysis results without cluster recognition, the computation time is
considerably reduced. At the same time, the entire parameter space is
efficiently explored, which translates into a smaller uncertainty of the
extracted value of the Bayesian evidence.


  Scheduling the power exchange between a population of heterogeneous
distributed energy resources and the corresponding upper-level system is an
important control problem in power systems. A key challenge is the large number
of (partially uncertain) parameters and decision variables that increase the
computational burden and that complicate the structured consideration of
uncertainties. Reducing the number of decision variables by means of
aggregation can alleviate these issues. However, despite the frequent use of
aggregation for storage, few works in the literature provide formal
justification. In the present paper, we investigate aggregation of
heterogeneous (storage) devices with time-varying power and energy constraints.
In particular, we propose mild conditions on the constraints of each device
guaranteeing the applicability of an aggregated model in scheduling without any
loss of optimality in comparison to the complete problem. We conclude with a
discussion of limitations and possible extensions of our findings.


  Metal mixed polymers are a cheap and effective way to produce flexible metals
and superconductors. As part of an on-going effort to learn how to tune the
properties of these systems with ion implantation, we present a study of the
electrical properties of these systems prior to metal-mixing. We show that the
electrical properties of tin-antimony thin films are remarkably robust to
variations in the substrate morphology. We demonstrate that the optical
absorbance of the films at a fixed wavelength provides a reliable and
reproducible characterization of the relative film thickness. We find that as
the film thickness is reduced, the superconducting transition in the
unimplanted thin films is broadened, but the onset of the transition remains at
~3.7 K, the transition temperature of bulk Sn. This is in marked contrast to
the behavior of metal mixed films, which suggests that the metal mixing process
has a significant effect of the physics of the superconducting state beyond
that achieved by reducing the film thickness alone.


  This paper proposes to deploy multiple reconfigurable intelligent surfaces
(RISs) in device-to-device (D2D)-underlaid cellular systems. The uplink
sum-rate of the system is maximized by jointly optimizing the transmit powers
of the users, the pairing of the cellular users (CUs) and D2D links, the
receive beamforming of the base station (BS), and the configuration of the
RISs, subject to the power limits and quality-of-service (QoS) of the users. To
address the non-convexity of this problem, we develop a new block coordinate
descent (BCD) framework which decouples the D2D-CU pairing, power allocation
and receive beamforming, from the configuration of the RISs. Specifically, we
derive closed-form expressions for the power allocation and receive beamforming
under any D2D-CU pairing, which facilitates interpreting the D2D-CU pairing as
a bipartite graph matching solved using the Hungarian algorithm. We transform
the configuration of the RISs into a quadratically constrained quadratic
program (QCQP) with multiple quadratic constraints. A low-complexity algorithm,
named Riemannian manifold-based alternating direction method of multipliers
(RM-ADMM), is developed to decompose the QCQP into simpler QCQPs with a single
constraint each, and solve them efficiently in a decentralized manner.
Simulations show that the proposed algorithm can significantly improve the
sum-rate of the D2D-underlaid system with a reduced complexity, as compared to
its alternative based on semidefinite relaxation (SDR).


  In this paper we prove that every quasi-projective base space $V$ of smooth
family of minimal projective manifolds with maximal variation is pseudo
Kobayashi hyperbolic, i.e. $V$ is Kobayashi hyperbolic modulo a proper
subvariety $Z\subsetneq V$. In particular, $V$ is algebraically degenerate,
that is, every nonconstant entire curve $f:\mathbb{C}\to V$ has image
$f(\mathbb{C})$ which lies in that proper subvariety $Z\subsetneq V$. As a
direct consequence, we prove the Brody hyperbolicity of moduli spaces of
minimal projective manifolds, which answers a question by Viehweg-Zuo in 2003.


  A collection of algorithms in object-oriented MATLAB is described for
numerically computing with smooth functions defined on the unit ball in the
Chebfun software. Functions are numerically and adaptively resolved to
essentially machine precision by using a three-dimensional analogue of the
double Fourier sphere method to form "ballfun" objects. Operations such as
function evaluation, differentiation, integration, fast rotation by an Euler
angle, and a Helmholtz solver are designed. Our algorithms are particularly
efficient for vector calculus operations, and we describe how to compute the
poloidal-toroidal and Helmholtz--Hodge decomposition of a vector field defined
on the ball.


  We study the $\kappa$-color cyclic particle system on the one-dimensional
integer lattice $\mathbb{Z}$, first introduced by Bramson and Griffeath in
\cite{bramson1989flux}. In that paper they show that almost surely, every site
changes its color infinitely often if $\kappa\in \{3,4\}$ and only finitely
many times if $\kappa\ge 5$. In addition, they conjecture that for $\kappa\in
\{3,4\}$ the system clusters, that is, for any pair of sites $x,y$, with
probability tending to 1 as $t\to\infty$, $x$ and $y$ have the same color at
time $t$. Here we prove that conjecture.


  The pharmacovigilance databases consist of several case reports involving
drugs and adverse events (AEs). Some methods are applied consistently to
highlight all signals, i.e. all statistically significant associations between
a drug and an AE. These methods are appropriate for verification of more
complex relationships involving one or several drug(s) and AE(s) (e.g;
syndromes or interactions) but do not address the identification of them. We
propose a method for the extraction of these relationships based on Formal
Concept Analysis (FCA) associated with disproportionality measures. This method
identifies all sets of drugs and AEs which are potential signals, syndromes or
interactions. Compared to a previous experience of disproportionality analysis
without FCA, the addition of FCA was more efficient for identifying false
positives related to concomitant drugs.


  We consider a time-harmonic wave problem, appearing for example in
water-waves and in acoustics, in a setting such that the analysis reduces to
the study of a 2D waveguide problem with a Neumann boundary condition. The
geometry is symmetric with respect to an axis orthogonal to the direction of
propagation of waves. Moreover, the waveguide contains one branch of finite
length. We analyse the behaviour of the complex scattering coefficients
$\mathcal{R}$, $\mathcal{T}$ as the length of the branch increases and we show
how to design geometries where non reflectivity ($\mathcal{R}=0$,
$|\mathcal{T}|=1$), perfect reflectivity ($|\mathcal{R}|=1$, $\mathcal{T}=0$)
or perfect invisibility ($\mathcal{R}=0$, $\mathcal{T}=1$) hold. Numerical
experiments illustrate the different results.


  We derive the Feynman rules for the graviton in the presence of a flat
Robertson-Walker background and give explicit expressions for the propagator in
the physically interesting cases of inflation, radiation domination, and matter
domination. The aforementioned background is generated by a scalar field source
which should be taken to be dynamical. As an elementary application, we compute
the corrections to the Newtonian gravitational force in the present matter
dominated era and conclude -- as expected -- that they are negligible except
for the largest scales.


  This survey studies the potential data link candidates for unmanned aircraft
vehicles (UAVs). There has been tremendous growth in different applications of
UAVs such as lifesaving and rescue missions, commercial use, recreations, etc.
Unlike the traditional wireless communications, the data links for these
systems do not have any general standardized framework yet to ensure safe
co-existence of UAVs with other flying vehicles. This motivated us to provide a
comprehensive survey of potential data link technologies available for UAVs.
Our goal is to study the current trends and available candidates and carry out
a comprehensive comparison among them. The contribution of this survey is to
highlight the strength and weakness of the current data link options and their
suitability to satisfy the UAVs communication requirements. Satellite links,
cellular technologies, Wi-Fi and several similar wireless technologies are
studied thoroughly in this paper. We also focus on several available promising
standards that can be modified for these data links. Then, we discuss
standard-related organizations that are working actively in the area of
civilian unmanned systems. Finally, we bring up some future challenges in this
area with several potential solutions to motivate further research work.


  We address the problem of suppressing facial expressions in videos because
expressions can hinder the retrieval of important information in applications
such as face recognition. To achieve this, we present an optical strain
suppression method that removes any facial expression without requiring
training for a specific expression. For each frame in a video, an optical
strain map that provides the strain magnitude value at each pixel is generated;
this strain map is then utilized to neutralize the expression by replacing
pixels of high strain values with pixels from a reference face frame.
Experimental results of testing the method on various expressions namely
happiness, sadness, and anger for two publicly available data sets (i.e.,
BU-4DFE and AM-FED) show the ability of our method in suppressing facial
expressions.


  Necessary optimality conditions and numerical methods for solving an optimal
control problem for a linear continuous-time dynanical system with controlled
coefficients and quadratic goal functional are discussed.


  The phonon Boltzmann transport equation (BTE) is a powerful tool for studying
non-diffusive thermal transport. Here, we develop a new universal variational
approach to solving the BTE that enables extraction of phonon mean free path
(MFP) distributions from experiments exploring non-diffusive transport. By
utilizing the known Fourier solution as a trial function, we present a direct
approach to calculating the effective thermal conductivity from the BTE. We
demonstrate this technique on the transient thermal grating (TTG) experiment,
which is a useful tool for studying non-diffusive thermal transport and probing
the mean free path (MFP) distribution of materials. We obtain a closed form
expression for a suppression function that is materials dependent, successfully
addressing the non-universality of the suppression function used in the past,
while providing a general approach to studying thermal properties in the
non-diffusive regime.


  Applying the method of integral estimates to the analysis of three-wave
processes we derive the sufficient criteria for the hard loss of stability of
the charged plane surface of liquids with different physical properties. The
influence of higher-order wave interactions on the instability dynamics is also
discussed.


  Given a Sobolev homeomorphism $f\in W^{2,1}$ in the plane we find a piecewise
quadratic homeomorphism that approximates it up to a set of $\epsilon$ measure.
We show that this piecewise quadratic map can be approximated by
diffeomorphisms in the $W^{2,1}$ norm on this set.


  We study the effect of shear flow on the entropic Helfrich interaction in
lyotropic surfactant smectic fluids. Arguing that flow induces an effective
anisotropic surface tension in bilayers due to a combination of intermonolayer
friction, bilayer collisions and convection, we calculate the reduction in
fluctuations and hence the renormalised change in effective compression modulus
and steady-state layer spacing. We demonstrate that non-permeable or slowly
permeating membranes can be susceptible to a undulatory instability of the
Helfrich-Hurault type, and speculate that such an instability could be one
source of a transition to multilamellar vesicles.


  On the basis of analysis on the adele ring of any algebraic numbers field
(Tate's formula) a regularization for divergent adelic products of gamma- and
beta-functions for all completions of this field are proposed, and
corresponding regularized adelic formulas are obtained.


  Ettore Majorana (1906-1938) disappeared while traveling by ship from Palermo
to Naples in 1938. His fate has never been fully resolved and several articles
have been written that explore the mystery itself. His demise intrigues us
still today because of his seminal work, published the previous year, that
established symmetric solutions to the Dirac equation that describe a fermionic
particle that is its own anti-particle. This work has long had a significant
impact in neutrino physics, where this fundamental question regarding the
particle remains unanswered. But the formalism he developed has found many uses
as there are now a number of candidate spin-1/2 neutral particles that may be
truly neutral with no quantum number to distinguish them from their
anti-particles. If such particles exist, they will influence many areas of
nuclear and particle physics. Most notably the process of neutrinoless double
beta decay can only exist if neutrinos are massive Majorana particles. Hence,
many efforts to search for this process are underway. Majorana's influence
doesn't stop with particle physics, however, even though that was his original
consideration. The equations he derived also arise in solid state physics where
they describe electronic states in materials with superconducting order. Of
special interest here is the class of solutions of the Majorana equation in one
and two spatial dimensions at exactly zero energy. These Majorana zero modes
are endowed with some remarkable physical properties that may lead to advances
in quantum computing and, in fact, there is evidence that they have been
experimentally observed. This review first summarizes the basics of Majorana's
theory and its implications. It then provides an overview of the rich
experimental programs trying to find a fermion that is its own anti-particle in
nuclear, particle, and solid state physics.


  One of the most striking properties of Kinetic Alfv\'en Waves (KAW) is that,
unlike the also Alfv\'enic Electromagnetic Ion Cyclotron (EMIC) waves, these
waves are right-hand polarized in the plasma frame. In particular, this
signature property is key for the identification of KAW from in situ
measurements of plasma waves. From the theoretical point of view, both the
dispersion relation and the polarization of KAW has been mostly studied in
proton-electron plasmas. However, most astrophysical and space plasmas are
multi-species, and therefore in these systems the dispersion properties of the
KAW may not depend only on the macroscopic parameters of proton and electron
distributions, but also on the parameters of heavier ions. Here, using Vlasov
linear theory we study the dispersion properties of Alfv\'enic modes in
multi-species plasmas composed by electrons, protons, and O$^+$ ions, with
macroscopic plasma parameters relevant to the inner magnetosphere. In
consistency with recent observations, our numerical results show that the
presence of O$^+$ ions allows the existence of KAW in a wider wave-number range
and at smaller wave-normal angles compared to the electron-proton case, but at
the same time isotropic O$^+$ ions tend to reduce (or even inhibiting) the
growth rates of unstable KAW triggered by anisotropic protons. These results
suggest that magnetospheric ions may play an important role on the energy
transfer from large macroscopic scales to sub-ionic and electronic scales,
especially during intense geomagnetic storms in which O$^+$ ions can dominate
the plasma composition in the inner magnetosphere.


  In the framework of the perturbation theory an expression suitable for
calculation of the effective conductivity of 3-D inhomogeneous metals is
derived. Formally, the final expression is an exact result, however, a function
written as a perturbation series enters the answer. More accurately, when
statistical properties of the given inhomogeneous medium are known, our result
provides the regular algorithm for calculation of the effective conductivity up
to an arbitrary term of the perturbation series. As examples, we examine (i) an
isotropic metal whose local conductivity is a Gaussianly distributed random
function, (ii) the effective conductivity of polycrystalline metals.


  In a recent work, we demonstrated that a modified gravity model in which a
scalar "darkon" field is coupled to both the standard Riemannian metric and to
another non-Riemannian volume form is compatible with observational data from
Supernovae Type Ia. Here, we investigate a more complicated model with an
additional "inflaton" scalar field. We demonstrate numerically that the model
can qualitatively reproduce the Universe inflation epoch, matter dominated
epoch, and present accelerating expansion in a seamless way. We show that such
solutions occur only when the model parameters are within a very particular
range. The main numerical problem we are faced with is reproducing the
extremely small time of the inflation epoch. Here, we present how the variation
of some parameters affects this time.


  We propose a radiative seesaw model at the three-loop level, in which quarks,
leptons, leptoquark bosons, and a Majorana fermion of dark matter candidate are
involved in the neutrino loop. Analyzing neutrino oscillation data including
all possible constraints such as flavor changing neutral currents, lepton
flavor violations, upper/lower bound on the mass of leptoquark from the
collider physics, and the measured relic density of the dark matter, we show
the allowed region to satisfy all the data/constraints.


  The optimum subspace decomposition of the infinite-dimensional compressible
random processes in the locally convex Hausdorff space has been propose and its
dimension has been measured. We conduct topological analysis of finite- and
infinite-dimensional compressible vector spaces. We prove that if there are a
sufficient number of separating points in compressible topological vector
space, the Banach Limit coincides the minimal linear functional. Then, optimum
orthogonal decomposition of the compressible topological vector space can be
formulated as a limit for which minimal linear functional occurs. It has been
shown that the separable space, also referred to as an optimum subspace, is the
subset of the compressible vector space that contains its extreme points. The
inseparable subspace is characterized using a novel absorbing null space
property through Minkowski functional and Lebesgue measure. We prove that the
absorbing null space is a connected subspace of the locally convex space. We
purpose reflexive homomorphism that establishes a relation between signal space
and double dual space. It has been shown that the Banach limit can be
determined by the compact convergence of the Cauchy nets on the left and right
hands sides of a reflexive homomorphism. Finally, we propose to measure the
optimum subspace dimension using the Frechet distance metric. To apply the
Frechet distance to the compressible topological vector space, the Kothe
sequence sampled from a continuous function has been proposed. Briefly, the
proposed approach measures the sufficient number of separating points of the
finite- and infinite-dimensional compressible vector space for the given
undersampled operator with respect to Hahn-Banach and Daniell-Kolmogorov
theorems. The numerical analysis have been presented for finite- and
infinite-dimensional signals.


  The phenomenon of peripheral coronal loop contraction during solar flares and
eruptions, recently discovered in observations, gradually intrigues solar
physicists. However, its underlying physical mechanism is still uncertain. One
is Hudson (2000)'s implosion conjecture which attributes it to magnetic
pressure reduction in the magnetic energy liberation core, while other
researchers proposed alternative explanations. In previous observational
studies we also note the disappearance of peripheral shrinking loops in the
late phase, of which there is a lack of investigation and interpretation. In
this paper, we exploit a full MHD simulation of solar eruption to study the
causes of the two phenomena. It is found that the loop motion in the periphery
is well correlated with magnetic energy accumulation and dissipation in the
core, and the loop shrinkage is caused by a more significant reduction in
magnetic pressure gradient force than in magnetic tension force, consistent
with the implosion conjecture. The peripheral contracting loops in the late
phase act as inflow to reconnect with central erupting structures, which
destroys their identities and naturally explains their disappearance. We also
propose a positive feedback between the peripheral magnetic reconnection and
the central eruption.


  Various methods for lifted probabilistic inference have been proposed, but
our understanding of these methods and the relationships between them is still
limited, compared to their propositional counterparts. The only existing
theoretical characterization of lifting is for weighted first-order model
counting (WFOMC), which was shown to be complete domain-lifted for the class of
2-logvar models. This paper makes two contributions to lifted variable
elimination (LVE). First, we introduce a novel inference operator called group
inversion. Second, we prove that LVE augmented with this operator is complete
in the same sense as WFOMC.


  We exactly calculate the momentum-dependent critical exponents for spectral
functions in the one-dimensional anisotropic t-J models with a gap either in
the spin or charge excitation spectrum. Our approach is based on the Bethe
ansatz technique combined with finite-size scaling techniques in conformal
field theory. It is found that the spectral functions show a power-law
singularity, which occurs at frequencies determined by the dispersion of a
massive spin (or charge) excitation.We discuss how the nontrivial contribution
of a massive excitation controls the singular behavior in optical response
functions.


  We show that there exist infinitely many families of Sasaki-Einstein metrics
on every odd-dimensional standard sphere of dimension at least $5$. We also
show that the same result is true for all odd-dimensional exotic spheres that
bound parallelizable manifolds.


  In this paper, we show the existence of uniform rational lc polytopes for
foliations with functional boundaries in dimension $\leq 3$. As an application,
we prove the global ACC for foliated threefolds with arbitrary DCC
coefficients. We also provide applications on the accumulation points of lc
thresholds of foliations in dimension $\leq 3$.


  Almost disjoint families of true cardinality $\mathfrak{c}$ are used to
produce an example of a mildly-normal not partly-normal $\Psi$-space and a
quasi-normal not almost-normal $\Psi$-space. This is related with a problem
posed by Lufti Kalantan where he asks whether there exists a mad family so that
the related Mr\'owka-Isbell space is partly-normal. In addition, a consistent
example of a Luzin mad family such that its associated $\Psi$-space is
quasi-normal is provided.


  We present a Markov-chain analysis of blockwise-stochastic algorithms for
solving partially block-separable optimization problems. Our main contributions
to the extensive literature on these methods are statements about the Markov
operators and distributions behind the iterates of stochastic algorithms, and
in particular the regularity of Markov operators and rates of convergence of
the distributions of the corresponding Markov chains. This provides a detailed
characterization of the moments of the sequences beyond just the expected
behavior. This also serves as a case study of how randomization restores
favorable properties to algorithms that iterations of only partial information
destroys. We demonstrate this on stochastic blockwise implementations of the
forward-backward and Douglas-Rachford algorithms for nonconvex (and, as a
special case, convex), nonsmooth optimization.


  We establish a relation between the Schwarz inequality and the generalized
concurrence of an arbitrary, pure, bipartite or tripartite state. This relation
places concurrence in a geometrical and functional-analytical setting.


  The unidentified VHE (E>100 GeV) gamma-ray source HESS J1507-622 seems to not
fit into standard models for sources related to young supernova remnants,
pulsar wind nebulae, or young stellar populations in general. This is due to
its intrinsically extended, but yet compact morphology, coupled with a relative
large offset (~3.5 deg) from the Galactic plane. Therefore, it has been
suggested that this object may be the first representative of a new distinct
class of extended off-plane gamma-ray sources. The distance to HESS J1507-622
is the key parameter to constrain the source's most important properties, such
as age and energetics of the relativistic particle population.
  In this article we report on results of follow-up observations of the
potential X-ray counterpart with Suzaku. We present detailed measurements of
its spectral parameters and find a high absorbing hydrogen column density,
compatible with the total amount of Galactic gas in this direction. In
comparisons to measurements and models of the Galactic three-dimensional gas
distribution we show that the potential X-ray counterpart of HESS J1507-622 may
be located at the far end of the Galaxy. If the gamma-ray source is indeed
physically connected to this extended X-ray source, this in turn would place
the object outside of the usual distribution of Galactic VHE gamma-ray
emitters.


  String theory on ${\rm AdS}_3 \times {\rm S}^3 \times {\rm S}^3 \times {\rm
S}^1$ with pure NS-NS flux and minimal flux through one of the two ${\rm
S}^3$'s is studied from a world-sheet perspective. It is shown that the
spacetime spectrum, as well as the algebra of spectrum generating operators,
matches precisely that of the symmetric orbifold of ${\rm S}^3\times
\mathrm{S}^1$ in the large $N$ limit. This gives strong support for the
proposal that these two descriptions are exactly dual to one another.


  In below freezing winter conditions, road surface friction can greatly vary
based on the mixture of snow, ice, and water on the road. Friction between the
road and vehicle tyres is a critical parameter defining vehicle dynamics, and
therefore road surface friction information is essential to acquire for several
intelligent transportation applications, such as safe control of automated
vehicles or alerting drivers of slippery road conditions. This paper explores
computer vision-based evaluation of road surface friction from roadside
cameras. Previous studies have extensively investigated the application of
convolutional neural networks for the task of evaluating the road surface
condition from images. Here, we propose a hybrid deep learning architecture,
WCamNet, consisting of a pretrained visual transformer model and convolutional
blocks. The motivation of the architecture is to combine general visual
features provided by the transformer model, as well as finetuned feature
extraction properties of the convolutional blocks. To benchmark the approach,
an extensive dataset was gathered from national Finnish road infrastructure
network of roadside cameras and optical road surface friction sensors. Acquired
results highlight that the proposed WCamNet outperforms previous approaches in
the task of predicting the road surface friction from the roadside camera
images.


  Every Riordan array has what we call a horizontal half and a vertical half.
These halves of a Riordan array have been studied separately before. Here, we
place them in a common context, showing that one may be obtained from the
other. We also ask and answer the question: given a Riordan array, when is it
the half (either horizontal of vertical) of a Riordan array?