  Despite spin (SAM) and orbital (OAM) angular momentum dynamics are
well-studied in demagnetization processes, their components receive less focus.
Here, we utilize the non-collinear spin version of real-time time-dependent
density functional theory (rt-TDDFT) to unveil significant x and y components
of SAM and OAM induced by circularly left and right polarized laser pulse in
Ferromagnetic Fe, Co and Ni. Our results show that the magnitude of OAM is an
order of magnitude larger than that of SAM, highlighting a stronger optical
response from the orbital degrees of freedom of electrons compared to spin.
Additionally, we observe a marked dependency of the oscillations of the x and y
components on the helicity of the pulse. Intriguingly, left and right polarized
pulses induce chirality in the precession of SAM and OAM, respectively, with
clear associations with laser frequency and duration. Finally, we demonstrate
that the time scale of OAM and SAM precession occurs even earlier than that of
the demagnetization process and OISTR effect. Our investigation provides
detailed insight into the dynamics of SAM and OAM of electrons during and
shortly after a circularly polarized laser excitation.


  In 4-dimensional General Relativity, black holes are described by the Kerr
solution and are completely specified by their mass $M$ and by their spin
angular momentum $J$. A fundamental limit for a black hole in General
Relativity is the Kerr bound $|a_*| \le 1$, where $a_* = J/M^2$ is the spin
parameter. Future experiments will be able to probe the geometry around these
objects and test the Kerr black hole hypothesis. Interestingly, if these
objects are not black holes, the accretion process may spin them up to $a_* >
1$.


  Joyce structures are a class of geometric structures which first arose in
relation to holomorphic generating functions for Donaldson-Thomas invariants.
They can be thought of as non-linear analogues of Frobenius structures, or as
special classes of complex hyperkahler manifolds. We give a detailed
introduction to Joyce structures, with particular focus on the geometry of the
associated twistor space. We also prove several new results.


  We introduce a family of varieties $Y_{n,\lambda,s}$, which we call the
\emph{$\Delta$-Springer varieties}, that generalize the type A Springer fibers.
We give an explicit presentation of the cohomology ring $H^*(Y_{n,\lambda,s})$
and show that there is a symmetric group action on this ring generalizing the
Springer action on the cohomology of a Springer fiber. In particular, the top
cohomology groups are induction products of Specht modules with trivial
modules. The $\lambda=(1^k)$ case of this construction gives a compact
geometric realization for the expression in the Delta Conjecture at $t=0$.
Finally, we generalize results of De Concini and Procesi on the scheme of
diagonal nilpotent matrices by constructing an ind-variety $Y_{n,\lambda}$
whose cohomology ring is isomorphic to the coordinate ring of the
scheme-theoretic intersection of an Eisenbud--Saltman rank variety and diagonal
matrices.


  Monte Carlo simulations have been used to investigate the dynamic phase
behavior of a classical Heisenberg spin system with a bilinear exchange
anisotropy in a planar thin film geometry. Studies of the field amplitude,
frequency and temperature dependence show dynamic phase transitions in films
subject to a pulsed oscillatory external field. Thin films with competing
surface fields show separate and distinct dynamic phase transitions for the
bulk and surface layers of the film. Between the two transitions, a mixed state
with coexisting dynamically ordered and dynamically disordered phases is
observed in the film. In contrast, the free film with no surface fields shows a
single dynamic phase transition as in a bulk system.


  At the present scenario of the internet, there exist many optimization
techniques to improve the Web speed but almost expensive in terms of bandwidth.
So after a long investigation on different techniques to compress the data
without any loss, a new algorithm is proposed based on L Z 77 family which
selectively models the references with backward movement and encodes the
longest matches through greedy parsing with the shortest path technique to
compresses the data with high density. This idea seems to be useful since the
single Web Page contains many repetitive words which create havoc in consuming
space, so let it removes such unnecessary redundancies with 70% efficiency and
compress the pages with 23.75 - 35% compression ratio.


  We develop statistical enumeration methods for self-avoiding walks using a
powerful sampling technique called the multicanonical Monte Carlo method. Using
these methods, we estimate the numbers of the two dimensional N-step
self-avoiding walks up to N=256 with statistical errors. The developed methods
are based on statistical mechanical models of paths which include self-avoiding
walks. The criterion for selecting a suitable model for enumerating
self-avoiding walks is whether or not the configuration space of the model
includes a set for which the number of the elements can be exactly counted. We
call this set a scale fixing set. We selected the following two models which
satisfy the criterion: the Go model for lattice proteins and the Domb-Joyce
model for generalized random walks. There is a contrast between these two
models in the structures of the configuration space. The configuration space of
the Go model is defined as the universal set of self-avoiding walks, and the
set of the ground state conformation provides a scale fixing set. On the other
hand, the configuration space of the Domb-Joyce model is defined as the
universal set of random walks which can be used as a scale fixing set, and the
set of the ground state conformation is the same as the universal set of
self-avoiding walks. From the perspective of enumeration performance, we
conclude that the Domb-Joyce model is the better of the two. The reason for the
performance difference is partly explained by the existence of the first-order
phase transition of the Go model.


  Heavy quarks, namely, the top and bottom quarks, may show great sensitiveness
to new physics effects. In particular, they might have unusually large electric
dipole moments. This possibility is analyzed via the corresponding one-loop
correction to the neutron electric dipole moment, $d_n$. The current
experimental limit on $d_n$ is used then to derive the uppers bounds
$|d_t|<3.06\times 10^{-15}$ e-cm, $|d_b|<1.22\times 10^{-13}$ e-cm.


  The boundary element method (BEM) is an efficient numerical method for
simulating harmonic wave propagation. It uses boundary integral formulations of
the Helmholtz equation at the interfaces of piecewise homogeneous domains. The
discretisation of its weak formulation leads to a dense system of linear
equations, which is typically solved with an iterative linear method such as
GMRES. The application of BEM to simulating wave propagation through
large-scale geometries is only feasible when compression and preconditioning
techniques reduce the computational footprint. Furthermore, many different
boundary integral equations exist that solve the same boundary value problem.
The choice of preconditioner and boundary integral formulation is often
optimised for a specific configuration, depending on the geometry, material
characteristics, and driving frequency. On the one hand, the design flexibility
for the BEM can lead to fast and accurate schemes. On the other hand, efficient
and robust algorithms are difficult to achieve without expert knowledge of the
BEM intricacies. This study surveys the design of boundary integral
formulations for acoustics and their acceleration with operator
preconditioners. Extensive benchmarks provide valuable information on the
computational characteristics of several hundred different models for multiple
reflection and transmission of acoustic waves.


  Despite the huge success of deep neural networks (NNs), finding good
mechanisms for quantifying their prediction uncertainty is still an open
problem. Bayesian neural networks are one of the most popular approaches to
uncertainty quantification. On the other hand, it was recently shown that
ensembles of NNs, which belong to the class of mixture models, can be used to
quantify prediction uncertainty. In this paper, we build upon these two
approaches. First, we increase the mixture model's flexibility by replacing the
fixed mixing weights by an adaptive, input-dependent distribution (specifying
the probability of each component) represented by NNs, and by considering
uncountably many mixture components. The resulting class of models can be seen
as the continuous counterpart to mixture density networks and is therefore
referred to as compound density networks (CDNs). We employ both maximum
likelihood and variational Bayesian inference to train CDNs, and empirically
show that they yield better uncertainty estimates on out-of-distribution data
and are more robust to adversarial examples than the previous approaches.


  The possibility of excitations with fractional spin and statististics in
$1+1$ dimensions is explored. The configuration space of a two-particle system
is the half-line. This makes the Hamiltonian self-adjoint for a family of
boundary conditions parametrized by one real number $\gamma$. The limit $\gamma
\rightarrow 0, (\infty$) reproduces the propagator of non-relativistic
particles whose wavefunctions are even (odd) under particle exchange. A
relativistic ansatz is also proposed which reproduces the correct Polyakov spin
factor for the spinning particle in $1+1$ dimensions. These checks support
validity of the interpretation of $\gamma$ as a parameter related to the
``spin'' that interpolates continuously between bosons ($\gamma =0$) and
fermions ($\gamma =\infty$). Our approach can thus be useful for obtaining the
propagator for one-dimensional anyons.


  Time global wellposedness in L^p for the Chern-Simons-Dirac equation in 1+1
dimension is discussed.


  We study by numerical simulations the scattering of $s$-polarized light from
a rough dielectric film deposited on the planar surface of a semi-infinite
perfect conductor. The dielectric film is allowed to be either active or
passive, situations that we model by assigning negative and positive values,
respectively, to the imaginary part $\epsilon_2$ of the dielectric constant of
the film. We study the reflectance ${\cal R}$ and the total scattered energy
${\cal U}$ for the system as functions of both $\epsilon_2$ and the angle of
incidence of the light. Furthermore, the positions and widths of the enhanced
backscattering and satellite peaks are discussed. It is found that these peaks
become narrower and higher when the amplification of the system is increased,
and that their widths scale linearly with $\epsilon_2$. The positions of the
backscattering peaks are found to be independent of $\epsilon_2$, while we find
a weak dependence on this quantity in the positions of the satellite peaks.


  We define a natural class of graphs by generalizing prior notions of
visibility, allowing the representing regions and sightlines to be arbitrary.
We consider mainly the case of compact connected representing regions, proving
two results giving necessary properties of visibility graphs, and giving some
examples of classes of graphs that can be so represented. Finally, we give some
applications of the concept, and we provide potential avenues for future
research in the area.


  Video analytics requires operating with large amounts of data. Compressive
sensing allows to reduce the number of measurements required to represent the
video using the prior knowledge of sparsity of the original signal, but it
imposes certain conditions on the design matrix. The Bayesian compressive
sensing approach relaxes the limitations of the conventional approach using the
probabilistic reasoning and allows to include different prior knowledge about
the signal structure. This paper presents two Bayesian compressive sensing
methods for autonomous object detection in a video sequence from a static
camera. Their performance is compared on the real datasets with the
non-Bayesian greedy algorithm. It is shown that the Bayesian methods can
provide the same accuracy as the greedy algorithm but much faster; or if the
computational time is not critical they can provide more accurate results.


  The split feasibility problem is to find an element in the intersection of a
closed set $C$ and the linear preimage of another closed set $D$, assuming the
projections onto $C$ and $D$ are easy to compute. This class of problems arises
naturally in many contemporary applications such as compressed sensing. While
the sets $C$ and $D$ are typically assumed to be convex in the literature, in
this paper, we allow both sets to be possibly nonconvex. We observe that, in
this setting, the split feasibility problem can be formulated as an
optimization problem with a difference-of-convex objective so that standard
majorization-minimization type algorithms can be applied. Here we focus on the
nonmonotone proximal gradient algorithm with majorization studied in [15,
Appendix A]. We show that, when this algorithm is applied to a split
feasibility problem, the sequence generated clusters at a stationary point of
the problem under mild assumptions. We also study local convergence property of
the sequence under suitable assumptions on the closed sets involved. Finally,
we perform numerical experiments to illustrate the efficiency of our approach
on solving split feasibility problems that arise in completely positive matrix
factorization, (uniformly) sparse matrix factorization, and outlier detection.


  We use a 3D general circulation model to compare the primitive Martian
hydrological cycle in "warm and wet" and "cold and icy" scenarios. In the warm
and wet scenario, an anomalously high solar flux or intense greenhouse warming
artificially added to the climate model are required to maintain warm
conditions and an ice-free northern ocean. Precipitation shows strong surface
variations, with high rates around Hellas basin and west of Tharsis but low
rates around Margaritifer Sinus (where the observed valley network drainage
density is nonetheless high). In the cold and icy scenario, snow migration is a
function of both obliquity and surface pressure, and limited episodic melting
is possible through combinations of seasonal, volcanic and impact forcing. At
surface pressures above those required to avoid atmospheric collapse (~0.5 bar)
and moderate to high obliquity, snow is transported to the equatorial highland
regions where the concentration of valley networks is highest. Snow
accumulation in the Aeolis quadrangle is high, indicating an ice-free northern
ocean is not required to supply water to Gale crater. At lower surface
pressures and obliquities, both H2O and CO2 are trapped as ice at the poles and
the equatorial regions become extremely dry. The valley network distribution is
positively correlated with snow accumulation produced by the cold and icy
simulation at 41.8 degrees obliquity but uncorrelated with precipitation
produced by the warm and wet simulation. Because our simulations make specific
predictions for precipitation patterns under different climate scenarios, they
motivate future targeted geological studies.


  Magnetic toroidal dipole (MTD) is one of a fundamental constituent to induce
magneto-electric effects in the absence of both spatial inversion and
time-reversal symmetries. We report on a microscopic investigation of the
atomic-scale MTD in solids by taking into account the orbital degree of freedom
with a different parity. We construct an effective two-orbital $d$-$f$
tight-binding model on a polar tetragonal system for describing the
atomic-scale MTD, which are obtained by incorporating the atomic spin-orbit
coupling and odd-parity hybridization. The effective model exhibits two types
of the MTDs: in-plane $x, y$ components activated through spontaneous
ferromagnetic ordering or external magnetic field and an out-of-plane $z$
component by a spontaneous odd-parity hybridization without spin moments. We
show that the intra-orbital (inter-orbital) Coulomb interaction in
multi-orbital systems plays an important role in stabilizing the in-plane
(out-of-plane) MTD orderings. We also examine the magneto-electric effect under
each MTD ordering by calculating a linear response tensor. We show that the
odd-parity hybridization enhances the magneto-electric effect for the in-plane
MTDs, while it suppresses that for the out-of-plane MTD.


  We present the study of bound surface modes sustained at the termination of
truncated bulk dielectric photonic crystals and isolated metasurfaces of
dielectric meta-atoms. We discuss the origins of bound modes in the two systems
and their relation. For both systems, we theoretically study and experimentally
demonstrate wavefront manipulation, in particular directional emission,
frequency splitting and beam collimation achieved by coupling of the bound
states to radiation modes through leaky wave radiation mechanism using properly
designed scattering gratings.


  We present the description and early results of the mJy Imaging VLBA
Exploration at 20 cm (mJIVE-20). mJIVE-20 is a large project on the Very Long
Baseline Array which uses "filler" time to systematically inspect a large
sample of mJy radio sources, pre-selected from the FIRST survey made with the
Very Large Array, to identify any compact emission which may be present. The
newly available multifield capability of the VLBA makes it possible for us to
inspect of order 100 sources per hour of observing time with a 6.75 sigma
detection sensitivity of approximately 1 mJy/beam. The results of the mJIVE-20
survey are made publicly available as soon as the data are calibrated. After 18
months of observing, over 20,000 FIRST sources have been inspected, with 4,336
VLBI detections. These initial results suggest that within the range 1 - 200
mJy, fainter sources are somewhat more likely to be dominated by a very compact
component than brighter sources. Over half of all arcsecond-scale mJy radio
sources contain a compact component, although the fraction of sources which are
dominated by milliarcsecond scale structure is smaller at 30-35%, increasing
towards lower flux densities. Significant differences are seen depending on the
optical classification of the source. Radio sources with a stellar/point-like
counterpart in the Sloan Digital Sky Survey (SDSS) are more likely to be
detected overall, but this detection likelihood appears to be independent of
the arcsecond-scale radio flux density; the trend towards higher radio
compactness for fainter sources is confined to SDSS galaxies and
non-detections. These results are consistent with a unification model of AGN in
which less luminous sources have on average slower radio jets, with lower
Doppler suppression of compact core emission over a wider range of viewing
angles. (abridged)


  We have studied the Zero Bias Conductance Peak (ZBCP) of the tunneling
conductance measured on (1,1,0) oriented Y(1-y)Ca(y)Ba(2)Cu(3)O(7-x) thin films
as a function of doping and of magnetic field. A spontaneous (zero field) split
of the ZBCP was observed only in overdoped samples (either by O or by Ca). The
magnitude of this split was found to be linear in doping. All samples exhibited
a magnetic field splitting, also strongly doping dependent. The field
susceptibility chi=d(delta)/dH diverges at the point at which spontaneous ZBCP
splitting occurs, its inverse value, chi^(-1), following a linear doping
dependence on both the underdoped and overdoped sides. We discuss these results
in terms of recent theoretical models of Time Reversal Symmetry Breaking
(TRSB).


  Late on July 23, 2012, the STEREO-A spacecraft encountered a fast forward
shock driven by a coronal mass ejection launched from the Sun earlier that same
day. The estimated travel time of the disturbance ($\sim 20$ hrs), together
with the massive magnetic field strengths measured within the ejecta ($>
100$nT), made it one of the most extreme events observed during the space era.
In this study, we examine the properties of the shock wave. Because of an
instrument malfunction, plasma measurements during the interval surrounding the
CME were limited, and our approach has been modified to capitalize on the
available measurements and suitable proxies, where possible. We were able to
infer the following properties. First, the shock normal was pointing
predominantly in the radial direction (${\bf n} = 0.97 {\bf e}_r -0.09 {\bf
e}_t -0.23 {\bf e}_n$). Second, the angle between ${\bf n}$ and the upstream
magnetic field, $\theta_{Bn}$, was estimated to be $\approx 34^{\circ}$, making
the shock "quasi-parallel," and supporting the idea of an earlier
"preconditioning" ICME. Third, the shock speed was estimated to be $\approx
3300$ km s$^{-1}$. Fourth, the sonic Mach number, $M_s$, for this shock was
$\sim 28$. We support these results with an idealized numerical simulation of
the ICME. Finally, we estimated the change in ram pressure upstream of the
shock to be $\sim 5$ times larger than the pressure from the energetic
particles, suggesting that this was not a standard "steady-state" cosmic-ray
modified shock (CRMS). Instead it might represent an early, transient phase in
the evolution of the CRMS.


  This paper presents a method for jointly estimating the state, input, and
parameters of linear systems in an online fashion. The method is specially
designed for measurements that are corrupted with non-Gaussian noise or
outliers, which are commonly found in engineering applications. In particular,
it combines recursive, alternating, and iteratively-reweighted least squares
into a single, one-step algorithm, which solves the estimation problem online
and benefits from the robustness of least-deviation regression methods. The
convergence of the iterative method is formally guaranteed. Numerical
experiments show the good performance of the estimation algorithm in presence
of outliers and in comparison to state-of-the-art methods.


  This is the second of a series of papers that focuses on searching large sets
of photometric light curves for evidence of close binaries with a dormant black
hole, and, in some cases, a dormant neutron star. The detection of such a
binary is based on identifying a star that displays a large ellipsoidal
periodic modulation, induced by tidal interaction with its companion. Based on
the observed ellipsoidal amplitude and the primary mass and radius, one can
derive a minimum mass ratio of the binary. A binary with a minimum mass ratio
significantly larger than unity might be a candidate for having a dormant
compact-object companion. Unfortunately, the photometric search is hampered by
the fact that in many cases the primary mass and radius are not well known. In
this paper we present a simple approach that circumvents this problem by
suggesting a robust modified minimum mass ratio, assuming the primary fills its
Roche lobe. The newly defined modified minimum mass ratio is always smaller
than the minimum mass ratio, which is, in its turn, smaller than the actual
mass ratio. Therefore, binaries with a modified minimum mass ratio larger than
unity are candidates for having a compact-object secondary.


  We have classified special solutions around the origin for the
two-dimensional degenerate Garnier system G(1112) with generic values of
complex parameters, whose linear monodromy can be calculated explicitly.


  We demonstrate efficient spin-polarized tunneling between a ferromagnetic
metal and a ferromagnetic semiconductor with highly mismatched conductivities.
This is indicated by a large tunneling magnetoresistance (up to 30%) at low
temperatures in epitaxial magnetic tunnel junctions composed of a ferromagnetic
metal (MnAs) and a ferromagnetic semiconductor (GaMnAs) separated by a
nonmagnetic semiconductor (AlAs). Analysis of the current-voltage
characteristics yields detailed information about the asymmetric tunnel
barrier. The low temperature conductance-voltage characteristics show a zero
bias anomaly and a V^1/2 dependence of the conductance, indicating a
correlation gap in the density of states of GaMnAs. These experiments suggest
that MnAs/AlAs heterostructures offer well characterized tunnel junctions for
high efficiency spin injection into GaAs.


  Pure, highly nuclear-spin-polarized molecules have only been produced with
molecular beam-separation methods, with production rates up to
${\sim}3{\times}10^{12}$ s$^{-1}$. Here, we propose the production of
spin-polarized molecular photofragments from the IR-excitation and
photodissociation of molecular beams, with production rates approaching the
tabletop-IR-laser photon fluxes of $10^{21}$ s$^{-1}$. We give details on the
production of spin-polarized molecular hydrogen and water isotopes, from
formaldehyde and formic acid beams, respectively. Macroscopic quantities of
these molecules are important for NMR signal enhancement, and for the needs of
a nuclear fusion reactor, to increase the D-T or D-$^{3}$He unpolarized nuclear
fusion cross section by ${\sim}50{\%}$.


  The effect of three-body interatomic contributions in the equation of state
of 4He are investigated. A recent two-body potential together with the Cohen
and Murrell (Chem. Phys. Lett. 260, 371 (1996)) three-body potential are
applied to describe bulk helium. The triple-dipole dispersion and exchange
energies are evaluated subjected only to statistical uncertainties. An
extension of the diffusion Monte Carlo method is applied in order to compute
very small energies differences. The results show how the three-body
contributions affects the ground-state energy, the equilibrium, melting and
freezing densities.


  We study sequences of scaled edge-corrected empirical (generalized)
K-functions (modifying Ripley's K-function) each of them constructed from a
single observation of a $d$-dimensional fourth-order stationary point process
in a sampling window W_n which grows together with some scaling rate
unboundedly as n --> infty. Under some natural assumptions it is shown that the
normalized difference between scaled empirical and scaled theoretical
K-function converges weakly to a mean zero Gaussian process with simple
covariance function. This result suggests discrepancy measures between
empirical and theoretical K-function with known limit distribution which allow
to perform goodness-of-fit tests for checking a hypothesized point process
based only on its intensity and (generalized) K-function. Similar test
statistics are derived for testing the hypothesis that two independent point
processes in W_n have the same distribution without explicit knowledge of their
intensities and K-functions.


  Information distances like the Hellinger distance and the Jensen-Shannon
divergence have deep roots in information theory and machine learning. They are
used extensively in data analysis especially when the objects being compared
are high dimensional empirical probability distributions built from data.
However, we lack common tools needed to actually use information distances in
applications efficiently and at scale with any kind of provable guarantees. We
can't sketch these distances easily, or embed them in better behaved spaces, or
even reduce the dimensionality of the space while maintaining the probability
structure of the data.
  In this paper, we build these tools for information distances---both for the
Hellinger distance and Jensen--Shannon divergence, as well as related measures,
like the $\chi^2$ divergence. We first show that they can be sketched
efficiently (i.e. up to multiplicative error in sublinear space) in the
aggregate streaming model. This result is exponentially stronger than known
upper bounds for sketching these distances in the strict turnstile streaming
model. Second, we show a finite dimensionality embedding result for the
Jensen-Shannon and $\chi^2$ divergences that preserves pair wise distances.
Finally we prove a dimensionality reduction result for the Hellinger,
Jensen--Shannon, and $\chi^2$ divergences that preserves the information
geometry of the distributions (specifically, by retaining the simplex structure
of the space). While our second result above already implies that these
divergences can be explicitly embedded in Euclidean space, retaining the
simplex structure is important because it allows us to continue doing inference
in the reduced space. In essence, we preserve not just the distance structure
but the underlying geometry of the space.


  The use of smart devices as media for digital learning constitutes a
new-generation digital learning paradigm. Therefore, context-aware game-based
learning has attracted considerable attention. Location-based games have not
only positive effects on learning but also pronounced effects on culture and
history. Accordingly, focusing on railway cultural heritages, we attempted to
assess interdependent relationships between key factors crucial for the design
of a location-based mobile game for cultural heritages. We adopted the analytic
network process (ANP) for our assessment. We initially performed a literature
review to generalize relevant criteria and elements and developed a
questionnaire based on the fuzzy delphi method (FDM); thus, key factors, namely
3 criteria and 15 elements, were selected. We also applied an online ANP-based
questionnaire; on the basis of the experts opinions, we established a network
model and determined the priority order of the key factors. The results
revealed that experts considered culture learning to be of the highest
importance, with the most important three elements being prior knowledge,
challenge levels, and cultural narrative. In addition, culture learning
exhibited a strong interaction with content design. In each criterion, one
element had a considerable influence on the remaining elements, as determined
by an analysis of matrices.


  We obtain a series improvement to higher-order $L^p$-Rellich inequalities on
a Riemannian manifold $M$. The improvement is shown to be sharp as each new
term of the series is added.


  Recent papers in neural machine translation have proposed the strict use of
attention mechanisms over previous standards such as recurrent and
convolutional neural networks (RNNs and CNNs). We propose that by running
traditionally stacked encoding branches from encoder-decoder attention- focused
architectures in parallel, that even more sequential operations can be removed
from the model and thereby decrease training time. In particular, we modify the
recently published attention-based architecture called Transformer by Google,
by replacing sequential attention modules with parallel ones, reducing the
amount of training time and substantially improving BLEU scores at the same
time. Experiments over the English to German and English to French translation
tasks show that our model establishes a new state of the art.


  Many open-world applications require the detection of novel objects, yet
state-of-the-art object detection and instance segmentation networks do not
excel at this task. The key issue lies in their assumption that regions without
any annotations should be suppressed as negatives, which teaches the model to
treat the unannotated objects as background. To address this issue, we propose
a simple yet surprisingly powerful data augmentation and training scheme we
call Learning to Detect Every Thing (LDET). To avoid suppressing hidden
objects, background objects that are visible but unlabeled, we paste annotated
objects on a background image sampled from a small region of the original
image. Since training solely on such synthetically-augmented images suffers
from domain shift, we decouple the training into two parts: 1) training the
region classification and regression head on augmented images, and 2)~training
the mask heads on original images. In this way, a model does not learn to
classify hidden objects as background while generalizing well to real images.
LDET leads to significant improvements on many datasets in the open-world
instance segmentation task, outperforming baselines on cross-category
generalization on COCO, as well as cross-dataset evaluation on UVO and
Cityscapes.


  Independence testing plays a central role in statistical and causal inference
from observational data. Standard independence tests assume that the data
samples are independent and identically distributed (i.i.d.) but that
assumption is violated in many real-world datasets and applications centered on
relational systems. This work examines the problem of estimating independence
in data drawn from relational systems by defining sufficient representations
for the sets of observations influencing individual instances. Specifically, we
define marginal and conditional independence tests for relational data by
considering the kernel mean embedding as a flexible aggregation function for
relational variables. We propose a consistent, non-parametric, scalable kernel
test to operationalize the relational independence test for non-i.i.d.
observational data under a set of structural assumptions. We empirically
evaluate our proposed method on a variety of synthetic and semi-synthetic
networks and demonstrate its effectiveness compared to state-of-the-art
kernel-based independence tests.


  The long-time behavior of stochastic Hamilton-Jacobi equations is analyzed,
including the stochastic mean curvature flow as a special case. In a variety of
settings, new and sharpened results are obtained. Among them are (i) a
regularization by noise phenomenon for the mean curvature flow with homogeneous
noise which establishes that the inclusion of noise speeds up the decay of
solutions, and (ii) the long-time convergence of solutions to spatially
inhomogeneous stochastic Hamilton-Jacobi equations. A number of motivating
examples about nonlinear stochastic partial differential equations are
presented in the appendix.


  Despite tremendous interest in the topic and decades of research, the origins
of the major losses of biodiversity in the history of life on Earth remain
elusive. A variety of possible causes for these mass-extinction events have
been investigated, including impacts of asteroids or comets, large-scale
volcanic eruptions, effects from changes in the distribution of continents
caused by plate tectonics, and biological factors, to name but a few. Many of
these suggested drivers involve or indeed require changes of the Earth's
climate, which then affect the biosphere of our planet causing a global
reduction in the diversity of biological species. It can be argued, therefore,
that a detailed understanding of these climatic variations and their effects on
ecosystems are prerequisites for a solution to the enigma of biological
extinctions. Apart from investigations of paleoclimate data of the time periods
of mass extinctions, climate-modelling experiments should be able to shed some
light on these dramatic events. Somewhat surprisingly, however, only few
comprehensive modelling studies of the climate changes associated with
extinction events have been undertaken. These studies will be reviewed in this
paper. Furthermore, the role of modelling in extinction research in general and
suggestions for future research are discussed.


  This work presents a gradient index device for insulating from vibrations a
circular area of a thin plate. The gradient of the refractive index is achieved
by locally changing the thickness of the plate, exploiting in this way the
thickness-dependence of the dispersion relation of flexural waves in thin
plates. A well-like thickness profile in an annular region of the plate is used
to mimic the combination of an attractive and repulsive potentials, focusing
waves at its bottom and dissipating them by means of a properly designed
absorptive layer placed on top of the plate. The central area is therefore
isolated from vibrations while they are dissipated at the bottom of the well.
Simulations have been done using the multilayer multiple scattering method and
the results prove their broadband efficiency and omnidirectional properties.


  Pollutant emissions have been a topic of interest in the last decades. Not
only environmentalists but also governments are taking rapid action to reduce
emissions. As one of the main contributors, the transport sector is being
subjected to strict scrutiny to ensure it complies with the short and long-term
regulations. The measures imposed by the governments clearly involve, all the
stakeholders in the logistics sector, from road authorities and logistic
operators to truck manufacturers. Improvement of traffic conditions is one of
the perspectives in which the reduction of emissions is being addressed.
Optimization of traffic flow, avoidance of unnecessary stops, control of the
cruise speed, and coordination of trips in an energy-efficient way are
necessary steps to remain compliant with the upcoming regulations. In this
study, we have measured the $CO_2$ and $NO_x$ emissions in heavy-duty vehicles
while traversing signalized intersections and we examined the differences
between various scenarios. We found that avoiding a stop can reduce $CO_2$ and
$NO_x$ emissions on 0.32 kg and 1.8 g, respectively. These results put traffic
control in the main scene as a yet unexplored dimension to control pollutant
emissions, enabling the authorities to more accurately estimate cost-benefit
plans for traffic control system investments.


  The advancement of artificial intelligence demands flexible multimodal data
processing with high throughput and energy efficiency. Photonic integrated
circuits (PIC) has demonstrated promising potentials in terms of low latency
and low power consumption per operation for linear operations such as
matrix-vector multiplication. However, the existing schemes face challenges in
their scalability due to the use of photonic circuits that expand with the
scale of the operants, despite efforts of exploiting the multiple optical
parameter dimensions such as time, wavelength and spatial parallelism. They
also lacked flexibility and efficiency in switching between different types of
operations or tasks and adapting to multimodal data. In this article, we
introduce an optical matrix processor (MP) with a minimalistic recursive
structure for both multiplications and accumulations. The MP consists of an
eletro-optic ring-modulator implemented as a thin-film lithium niobate PIC that
allows flexible configurability and time-division multiplexed scheduling. The
MP supports not only versatile linear operations including vector/matrix-vector
multiplication and single/multi-kernel convolution but also ultrafast task
switching and adaptability to data of different sizes, by simply adjusting the
data baud rate relative to the ring delay without structural modifications. We
demonstrate its capabilities in a optic-electronic convolutional neural network
with a computing throughput up to 73.4 billion operations per second. The MP
further supports high scalability through appropriate allocation of wavelength
and space resources,extending computing parallelism to handle higher data
volumes with higher energy efficiency. This novel scheme paves the way for a
new class of photonic processorscapable of managing escalating data workloads
with unprecedented flexibility, efficiency and scalability.


  We analyse the design and optimisation of quantum logic circuits suitable for
the experimental demonstration of a three-qubit quantum computation prototype
based on optically-controlled, solid-state quantum logic gates. In these gates,
the interaction between two qubits carried by the electron-spin of donors is
mediated by the optical excitation of a control particle placed in their
proximity. First, we use a geometrical approach for analysing the entangling
characteristics of these quantum gates. Then, using a genetic programming
algorithm, we develop circuits for the refined Deutsch-Jozsa algorithm
investigating different strategies for obtaining short total computational
times. We test two separate approaches based on using different sets of
entangling gates with the shortest possible gate computation time which,
however, does not introduce leakage of quantum information to the control
particles. The first set exploits fast approximations of controlled-phase gates
as entangling gates, while the other one arbitrary entangling gates with a
shorter gate computation time compared to the first set. We have identified
circuits with consistently shorter total computation times when using
controlled-phase gates.


  Searches for $\Delta L=2$ decays are crucial to disentangle the Dirac vs
Majorana nature of neutrinos. While neutrinoless nuclear double beta decays are
the most promising tool to look for di-electron signals of $\Delta L=2$,
searches of di-lepton channels with other flavors are far less limited by
statistics. We revisit the calculation of $\Delta L=2$ decays of hyperons
$B^-_i\to B^+_f\ell^-\ell'^-$. We compute the rates of these decays by
including the hyperon form factors, which yields finite results in the one-loop
model mechanism involving Majorana neutrinos (long-range contributions). In
addition, we study the short-range contributions to these kinds of processes in
two appealing scenarios. First, we consider the effects of heavy Majorana
neutrinos in low-scale seesaw models that involve a minimal parametrization
with only two heavy Majorana states. Second, we study an alternative model
where $\Delta L=2$ decays are induced by a scalar boson coupled to di-leptons
to provide additional predictions.


  We discuss certain specific features of the calculation of the critical
temperature of a dilute repulsive Bose gas. Interactions modify the critical
temperature in two different ways. First, for gases in traps, temperature
shifts are introduced by a change of the density profile, arising itself from a
modification of the equation of state of the gas (reduced compressibility);
these shifts can be calculated simply within mean field theory. Second, even in
the absence of a trapping potential (homogeneous gas in a box), temperature
shifts are introduced by the interactions; they arise from the correlations
introduced in the gas, and thus lie inherently beyond mean field theory - in
fact, their evaluation requires more elaborate, non-perturbative, calculations.
One illustration of this non-perturbative character is provided by the solution
of self-consistent equations, which relate together non-linearly the various
energy shifts of the single particle levels k. These equations predict that
repulsive interactions shift the critical temperature (at constant density) by
an amount which is positive, and simply proportional to the scattering length
a; nevertheless, the numerical coefficient is difficult to compute. Physically,
the increase of the temperature can be interpreted in terms of the reduced
density fluctuations introduced by the repulsive interactions, which facilitate
the propagation of large exchange cycles across the sample.


  We study the case in which the five dimensional theory is the transformed
Weyl$-$Yang$-$Kaluza$-$Klein gravity. The dimensionally reduced equations of
motion are derived by considering an alternative form of the main equation of
the theory in the coordinate basis. The conformal transformation rules are
applied to the invariants. We also discuss the possible specific cases and the
new Lorentz force density term, in detail.


  Monte Carlo renormalization group methods were designed to study the phase
structure and critical behavior of statistical systems. They are well suited to
determine the running coupling and to investigate the properties of fixed
points of gauge-fermion models, including the existence of conformal infrared
fixed points in many-fermion systems. I discuss the implementation of the
2-lattice matching method and present results for SU(3) gauge theories with
N_f=0,12 and 16 fundamental fermion flavors.


  We analyze the evolution of cosmological perturbations in the cyclic model,
paying particular attention to their behavior and interplay over multiple
cycles. Our key results are: (1) galaxies and large scale structure present in
one cycle are generated by the quantum fluctuations in the preceding cycle
without interference from perturbations or structure generated in earlier
cycles and without interfering with structure generated in later cycles; (2)
the ekpyrotic phase, an epoch of gentle contraction with equation of state
$w\gg 1$ preceding the hot big bang, makes the universe homogeneous, isotropic
and flat within any given observer's horizon; and, (3) although the universe is
uniform within each observer's horizon, the global structure of the cyclic
universe is more complex, owing to the effects of superhorizon length
perturbations, and cannot be described in a uniform Friedmann-Robertson-Walker
picture. In particular, we show that the ekpyrotic phase is so effective in
smoothing, flattening and isotropizing the universe within the horizon that
this phase alone suffices to solve the horizon and flatness problems even
without an extended period of dark energy domination (a kind of low energy
inflation). Instead, the cyclic model rests on a genuinely novel,
non-inflationary mechanism (ekpyrotic contraction) for resolving the classic
cosmological conundrums.


  We compute the shear viscosity in the supersymmetric Yang-Mills theory dual
to the STU background. This is a thermal gauge theory with a chemical
potential. The quotient of the shear viscosity over the entropy density
exhibits no deviation from the well known result 1/4\pi.


  Correlating BASS DR3 catalogue with ALLWISE database, the data from optical
and infrared information are obtained. The quasars from SDSS are taken as
training and test samples while those from LAMOST are considered as external
test sample. We propose two schemes to construct the redshift estimation models
with XGBoost, CatBoost and Random forest. One scheme (namely one-step model) is
to predict photometric redshifts directly based on the optimal models created
by these three algorithms; the other scheme (namely two-step model) is to
firstly classify the data into low- and high- redshift datasets, and then
predict photometric redshifts of these two datasets separately. For one-step
model, the performance of these three algorithms on photometric redshift
estimation is compared with different training samples, and CatBoost is
superior to XGBoost and Random forest. For two-step model, the performance of
these three algorithms on the classification of low- and high-redshift
subsamples are compared, and CatBoost still shows the best performance.
Therefore CatBoost is regard as the core algorithm of classification and
regression in two-step model. By contrast with one-step model, two-step model
is optimal when predicting photometric redshift of quasars, especially for high
redshift quasars. Finally the two models are applied to predict photometric
redshifts of all quasar candidates of BASS DR3. The number of high redshift
quasar candidates is 3938 (redshift $\ge 3.5$) and 121 (redshift $\ge 4.5$) by
two-step model. The predicted result will be helpful for quasar research and
follow up observation of high redshift quasars.


  Quantum detector properties of the quantum point contact (QPC) are analyzed
for arbitrary electron transparency and coupling strength to the measured
system and are shown to be determined by the electron counting statistics.
Conditions of the quantum-limited operation of the QPC detector which prevent
information loss through the scattering time and scattering phases are found
for arbitrary coupling. We show that the phase information can be restored and
used for the quantum-limited detection by inclusion of the QPC detector in the
electronic Mach-Zehnder interferometer.


  In this paper we show how to compute cup products in the anchored
configuration space of the circle with two anchored points using discrete Morse
theory. Knowing how to compute cup products allows us to obtain bounds for the
(higher) topological complexity $TC_s$, which are sharp for a sufficiently
large value of $s$.


  We consider open symplectic manifolds which admit dilations (in the sense
previously introduced by Solomon and the author). We obtain restrictions on
collections of Lagrangian submanifolds which are pairwise disjoint (or pairwise
disjoinable by Hamiltonian isotopies) inside such manifolds. This includes the
Milnor fibres of isolated hypersurface singularities which have been stabilized
(by adding quadratic terms) sufficiently often.


  Atmosphere escape is one key process controlling the evolution of planets.
However, estimating the escape rate in any detail is difficult because there
are many physical processes contributing to the total escape rate. Here we show
that as a result of energy conservation the total escape rate from hydrodynamic
planetary atmospheres where the outflow remains subsonic is nearly constant
under the same stellar XUV photon flux when increasing the escape efficiency
from the exobase level, consistent with the energy limited escape
approximation. Thus the estimate of atmospheric escape in a planet's evolution
history can be greatly simplified.


  The complexity class NP is quintessential and ubiquitous in theoretical
computer science. Two different approaches have been made to define "Quantum
NP," the quantum analogue of NP: NQP by Adleman, DeMarrais, and Huang, and QMA
by Knill, Kitaev, and Watrous. From an operator point of view, NP can be viewed
as the result of the exists-operator applied to P. Recently, Green, Homer,
Moore, and Pollett proposed its quantum version, called the N-operator, which
is an abstraction of NQP. This paper introduces the exists^{Q}-operator, which
is an abstraction of QMA, and its complement, the forall^{Q}-operator. These
operators not only define Quantum NP but also build a quantum hierarchy,
similar to the Meyer-Stockmeyer polynomial hierarchy, based on two-sided
bounded-error quantum computation.


  Hardware specialization is becoming a key enabler of energyefficient
performance. Future systems will be increasingly heterogeneous, integrating
multiple specialized and programmable accelerators, each with different memory
demands. Traditionally, communication between accelerators has been
inefficient, typically orchestrated through explicit DMA transfers between
different address spaces. More recently, industry has proposed unified coherent
memory which enables implicit data movement and more data reuse, but often
these interfaces limit the coherence flexibility available to heterogeneous
systems. This paper demonstrates the benefits of fine-grained coherence
specialization for heterogeneous systems. We propose an architecture that
enables low-complexity independent specialization of each individual coherence
request in heterogeneous workloads by building upon a simple and flexible
baseline coherence interface, Spandex. We then describe how to optimize
individual memory requests to improve cache reuse and performance-critical
memory latency in emerging heterogeneous workloads. Collectively, our
techniques enable significant gains, reducing execution time by up to 61% or
network traffic by up to 99% while adding minimal complexity to the Spandex
protocol.


  By increase of culture and knowledge of the people, request for visiting
museums has increased and made the management of these places more complex.
Valuable things in a museum or ancient place must be maintained well and also
it need to managing visitors. To maintain things we should prevent them from
theft, as well as environmental factors such as temperature, humidity, PH,
chemical factors and mechanical events should be monitored. And if the
conditions are damaging, appropriate alerts or reports to managers and experts
should be announced. Visitors should also be monitored, as well as visitors
need to be guided and getting information in the environment. By utilizing RFID
technology and short-distance network tools, technical solutions for more
efficient management and more effective retention in museums can be
implemented.


  Astrophysical sources of high frequency gravitational radiation are
considered in association with a new interest to very sensitive HFGW receivers
required for the laboratory GW Hertz experiment. A special attention is paid to
the phenomenon of primordial black holes evaporation. They act like black body
to all kinds of radiation, including gravitons, and, therefore, emit an
equilibrium spectrum of gravitons during its evaporation. Limit on the density
of high frequency gravitons in the Universe is obtained, and possibilities of
their detection are briefly discussed.


  We generalize the coupled braces {x}{y} of Gerstenhaber and {x}{y,...,z} of
Getzler depicting compositions of multilinear maps in the Hochschild complex
C(A)=Hom(TA;A) of a graded vector space A to expressions of the form
{x,...,y}...{z,...,w} on the extended space Hom(TA;TA), and clarify many of the
existing sign conventions that show up in the algebra of mathematical physics
(namely in associative and Lie algebras, Batalin-Vilkovisky algebras, homotopy
associative and homotopy Lie algebras). As a result, we introduce a new variant
of the master identity for homotopy Lie algebras. We also comment on the
bialgebra cohomology differential of Gerstenhaber and Schack, and define
multilinear higher order differential operators with respect to multilinear
maps using the new language. The continuation of this work will be on the
various homotopy structures on a topological vertex operator algebra, as
introduced by Kimura, Voronov, and Zuckerman.


  PDS365 is a newly detected rapidly rotating (vsin i = 20 km/s) single low
mass giant star which with HD233517 and HD219025 forms a remarkable ensemble of
single K giants with the unique properties of rapid rotation, very strong Li
lines, an asymmetrical Halpha profile, and a large far infrared excess. Their
vsin i values are between 18 and 23 km/s, and LTE Li abundances, log e(Li), are
between 2.9 and 3.9. Detailed analysis of PDS365 reveals it to be near one
solar mass giant with a value of 12C/13C approximately equal to 12. A clear
relation between high rotational velocities and very high Li abundances for K
giant stars is found only when asymmetrical Halpha profiles and large
far-infrared excesses are present. If we consider single K giants, we find that
among rapid (vsin i >= 8 km/s) rotators, a very large proportion (~ 50%) are
Li-rich giants. This proportion is in contrast with a very low proportion (~
2%) of Li-rich stars among the much more common slowly rotating K giants. This
striking difference is discussed in terms of proposed mechanisms for Li
enrichment.


  This paper discusses a class of uncertain optimization problems, in which
unknown parameters are modeled by fuzzy intervals. The membership functions of
the fuzzy intervals are interpreted as possibility distributions for the values
of the uncertain parameters. It is shown how the known concepts of robustness
and light robustness, for the interval uncertainty representation of the
parameters, can be generalized to choose solutions under the assumed model of
uncertainty in the possibilistic setting. Furthermore, these solutions can be
computed efficiently for a wide class of problems, in particular for linear
programming problems with fuzzy parameters in constraints and objective
function. In this paper a theoretical framework is presented and results of
some computational tests are shown.


  Density ratio estimation is a vital tool in both machine learning and
statistical community. However, due to the unbounded nature of density ratio,
the estimation procedure can be vulnerable to corrupted data points, which
often pushes the estimated ratio toward infinity. In this paper, we present a
robust estimator which automatically identifies and trims outliers. The
proposed estimator has a convex formulation, and the global optimum can be
obtained via subgradient descent. We analyze the parameter estimation error of
this estimator under high-dimensional settings. Experiments are conducted to
verify the effectiveness of the estimator.


  The observation of neutrino oscillations establishes that neutrinos
($\nu_{\ell}$) have non-zero mass and provides one of the more compelling
arguments for physics beyond the standard model (SM) of particle physics. We
present a feasibility study to search for hypothetical Majorana neutrinos
($N_{\ell}$) with TeV scale masses, predicted by extensions of the SM to
explain the small but non-zero $\nu_{\ell}$ mass, using vector boson fusion
(VBF) processes at the 13 TeV LHC. In the context of the minimal Type-I seesaw
mechanism (mTISM), the VBF $\ell N_{\ell}$ production cross-section surpasses
that of the Drell-Yan process at approximately $m_{N_{\ell}} = 1.4$ TeV. We
consider $\mu N_{\mu}$ and $\tau N_{\tau}$ production through VBF processes
(e.g. $qq'\to \tau N_{\tau}qq'$), with subsequent $N_{\mu}$ and $N_{\tau}$
decays to $\mu jj$ and $\tau jj$, as benchmark cases to show the effectiveness
of the VBF topology for $N_{\ell}$ seaches at the 13 TeV LHC. The requirement
of a dilepton pair combined with four jets, two of which are identified as VBF
jets with large separation in pseudorapidity and a TeV scale dijet mass, is
effective at reducing the SM background. This criteria may provide expected
exclusion bounds, at 95\% confidence level, of $m_{N_{\ell}} < 1.7$ ($2.4$)
TeV, assuming $100$ (1000) fb$^{-1}$ of 13 TeV data from the LHC and mixing
$|V_{\ell N_{\ell}}|^{2} = 1$. The use of the VBF topology to search for
$m_{N_{\ell}}$ increases the discovery reach at the LHC, with expected
significances greater than 5$\sigma$ (3$\sigma$) for $N_{\ell}$ masses up to
1.7 (2.05) TeV using 1000 fb$^{-1}$ of 13 TeV data from the LHC.


  We are concerned with demonstrating productivity of specifications of
infinite streams of data, based on orthogonal rewrite rules. In general, this
property is undecidable, but for restricted formats computable sufficient
conditions can be obtained. The usual analysis disregards the identity of data,
thus leading to approaches that we call data-oblivious. We present a method
that is provably optimal among all such data-oblivious approaches. This means
that in order to improve on the algorithm in this paper one has to proceed in a
data-aware fashion.


  In this paper, we study warped products of contact skew-CR submanifolds,
called contact skew CR-warped products. We establish an inequality for the
squared norm of the second fundamental form in terms of the warping function
and the slant angle. The equality case in the statement of the inequality is
investigated and some applications of derived inequality are given.
Furthermore, we provide a non-trivial example of such submanifolds.


  It is not always possible to recognise objects and infer material properties
for a scene from visual cues alone, since objects can look visually similar
whilst being made of very different materials. In this paper, we therefore
present an approach that augments the available dense visual cues with sparse
auditory cues in order to estimate dense object and material labels. Since
estimates of object class and material properties are mutually informative, we
optimise our multi-output labelling jointly using a random-field framework. We
evaluate our system on a new dataset with paired visual and auditory data that
we make publicly available. We demonstrate that this joint estimation of object
and material labels significantly outperforms the estimation of either category
in isolation.


  We study self-excited oscillation (SEO) in an on-fiber optomechanical cavity.
While the phase of SEO randomly diffuses in time when the laser power that is
injected into the cavity is kept constant, phase locking may occur when the
laser power is periodically modulated in time. We investigate the dependence of
phase locking on the amplitude and frequency of the laser power modulation. We
find that phase locking can be induced with a relatively low modulation
amplitude provided that the ratio between the modulation frequency and the
frequency of SEO is tuned close to a rational number of relatively low
hierarchy in the Farey tree. To account for the experimental results a one
dimensional map, which allows evaluating the time evolution of the phase of
SEO, is theoretically derived. By calculating the winding number of the one
dimensional map the regions of phase locking can be mapped in the plane of
modulation amplitude and modulation frequency. Comparison between the
theoretical predictions and the experimental findings yields a partial
agreement.


  A Monte Carlo code based on Geant 3.21 has been used for simulations of
energy losses and angular scattering in a time-of-flight Suprathermal Ion
Telescope (SIT) on the Solar-Terrestrial Relations Observatory (STEREO). A
hemispherical isotropic particle distribution, a monoenergetic or power law in
energy is used in these simulations. The impact of scattering, energy losses
and system noise on the instrument mass resolution is discussed.


  This paper considers a key agreement problem in which two parties aim to
agree on a key by exchanging messages in the presence of adversarial tampering.
The aim of the adversary is to disrupt the key agreement process, but there are
no secrecy constraints (i.e. we do not insist that the key is kept secret from
the adversary). The main results of the paper are coding schemes and bounds on
maximum key generation rates for this problem.


  We have developed a thorough and accurate method of determining anharmonic
free energies, the temperature dependent effective potential technique (TDEP).
It is based on \emph{ab initio} molecular dynamics followed by a mapping onto a
model Hamiltonian that describes the lattice dynamics. The formalism and the
numerical aspects of the technique are described in details. A number of
practical examples are given, and results are presented, which confirm the
usefulness of TDEP within \emph{ab initio} and classical molecular dynamics
frameworks. In particular, we examine from first-principles the behavior of
force constants upon the dynamical stabilization of body centered phase of Zr,
and show that they become more localized. We also calculate phase diagram for
$^4$He modeled with the Aziz \emph{et al.} potential and obtain results which
are in favorable agreement both with respect to experiment and established
techniques.


  A parabolic integro differential operator operator L suitable to describe
many phenomena in various physical fields,is considered. By means of
equivalence between L and the third order equation which describe the evolution
inside an exponentially shaped Josephson junction (ESJJ), an asymptotic
analysis for (ESJJ) is achieved, evaluating explicitly boundary contributions
related to the Dirichlet problem.


  Today, one of the major challenges that autonomous vehicles are facing is the
ability to drive in urban environments. Such a task requires communication
between autonomous vehicles and other road users in order to resolve various
traffic ambiguities. The interaction between road users is a form of
negotiation in which the parties involved have to share their attention
regarding a common objective or a goal (e.g. crossing an intersection), and
coordinate their actions in order to accomplish it. In this literature review
we aim to address the interaction problem between pedestrians and drivers (or
vehicles) from joint attention point of view. More specifically, we will
discuss the theoretical background behind joint attention, its application to
traffic interaction and practical approaches to implementing joint attention
for autonomous vehicles.


  The nonlinear force-free field (NLFFF) modeling has been extensively used as
a tool to infer three-dimensional (3D) magnetic field structure. In this study,
the dependency of the NLFFF calculation with respect to the initial guess of
the 3D magnetic field is investigated. While major part of the previous studies
used potential field as the initial guess in the NLFFF modeling, we adopt the
linear force-free fields with different constant force-free alpha as the
initial guesses. This method enables us to investigate how unique the magnetic
field obtained by the NLFFF extrapolation with respect to the initial guess is.
The dependence of the initial condition of the NLFFF extrapolation is smaller
in the strong magnetic field region. Therefore, the magnetic field at the lower
height ($< 10$ Mm) tends to be less affected by the initial condition
(correlation coefficient C>0.9 with different initial condition), although the
Lorentz force is concentrated at the lower height.


  In this contribution, the one-gluon radiation spectrum off a massive
quark-antiquark ($q {\bar q}$) antenna traversing a QCD medium is calculated in
the eikonal approximation. The interference between emissions from the quark
and the antiquark is considered. The gluon spectrum computed at first order in
the opacity expansion is collinear finite but infrared divergent, which is in
contrast with the result obtained from an independent emitter which is both
infrared and collinear finite. Phenomenological consequences on the broadening
of the emitted gluon are investigated. In the soft gluon emission limit, the
interference between emitters causes a different broadening style from the
typical {\bf k}-broadening in the case of an independent emitter.


  This paper reports the first application of a new technique to measure the
beta-decay half -lives of exotic nuclei in complex background conditions. Since
standard tools were not adapted to extract the relevant information, a new
analysis method was developed. The time distribution of background events is
established by recording time correlations in backward time. The beta half
lives of the nuclides and the detection efficiency of the set-up are determined
simultaneously from a least-squares fit of the ratio of the time-correlation
spectra recorded in forward and in backward time, using numerical functions.
The necessary numerical functions are calculated in a Monte-Carlo code using
the known operation parameters of the experiment and different values for the
two free parameters, half-life and detection efficiency, as input parameters.


  LUX was the first dark matter experiment to use a $^{83\textrm{m}}$Kr
calibration source. In this paper we describe the source preparation and
injection. We also present several $^{83\textrm{m}}$Kr calibration applications
in the context of the 2013 LUX exposure, including the measurement of temporal
and spatial variation in scintillation and charge signal amplitudes, and
several methods to understand the electric field within the time projection
chamber.


  Spatially extended chaotic systems with power-law decaying interactions are
considered. Two coupled replicas of such systems synchronize to a common
spatio-temporal chaotic state above a certain coupling strength. The
synchronization transition is studied as a nonequilibrium phase transition and
its critical properties are analyzed at varying the interaction range. The
transition is found to be always continuous, while the critical indexes vary
with continuity with the power law exponent characterizing the interaction.
Strong numerical evidences indicate that the transition belongs to the {\it
anomalous directed percolation} family of universality classes found for
L{\'e}vy-flight spreading of epidemic processes.


  Heterogeneous Information Networks (HINs), which consist of various types of
nodes and edges, have recently demonstrated excellent performance in graph
mining. However, most existing heterogeneous graph neural networks (HGNNs)
ignore the problems of missing attributes, inaccurate attributes and scarce
labels for nodes, which limits their expressiveness. In this paper, we propose
a generative self-supervised model SHAVA to address these issues
simultaneously. Specifically, SHAVA first initializes all the nodes in the
graph with a low-dimensional representation matrix. After that, based on the
variational graph autoencoder framework, SHAVA learns both node-level and
attribute-level embeddings in the encoder, which can provide fine-grained
semantic information to construct node attributes. In the decoder, SHAVA
reconstructs both links and attributes. Instead of directly reconstructing raw
features for attributed nodes, SHAVA generates the initial low-dimensional
representation matrix for all the nodes, based on which raw features of
attributed nodes are further reconstructed to leverage accurate attributes. In
this way, SHAVA can not only complete informative features for non-attributed
nodes, but rectify inaccurate ones for attributed nodes. Finally, we conduct
extensive experiments to show the superiority of SHAVA in tackling HINs with
missing and inaccurate attributes.


  We show that layered quenched randomness in planar magnets leads to an
unusual intermediate phase between the conventional ferromagnetic
low-temperature and paramagnetic high-temperature phases. In this intermediate
phase, which is part of the Griffiths region, the spin-wave stiffness
perpendicular to the random layers displays anomalous scaling behavior, with a
continuously variable anomalous exponent, while the magnetization and the
stiffness parallel to the layers both remain finite. Analogous results hold for
superfluids and superconductors. We study the two phase transitions into the
anomalous elastic phase, and we discuss the universality of these results, and
implications of finite sample size as well as possible experiments.


  We have obtained an analytical solution for the dispersion relation of the
light field modes in the nanowaveguide structure. The solution has been
analyzed for the planar waveguide with metamaterial claddings and dielectric
core. The analytical solution is valid within the broadband spectral range and
is confirmed by existing numerical calculations. The developed theoretical
approach opens vast possibilities for the analytical investigations of the
light fields in the various waveguides.


  Contrary to Google Search's mission of delivering information from "many
angles so you can form your own understanding of the world," we find that
Google and its most prominent returned results - Wikipedia and YouTube - simply
reflect a narrow set of culturally dominant views tied to the search language
for complex topics like "Buddhism," "Liberalism," "colonization," "Iran" and
"America." Simply stated, they present, to varying degrees, distinct
information across the same search in different languages, a phenomenon we call
language bias. This paper presents evidence and analysis of language bias and
discusses its larger social implications. We find that our online searches and
emerging tools like ChatGPT turn us into the proverbial blind person touching a
small portion of an elephant, ignorant of the existence of other cultural
perspectives. Language bias sets a strong yet invisible cultural barrier
online, where each language group thinks they can see other groups through
searches, but in fact, what they see is their own reflection.


  We consider a model of the double perovskite Ba2FeReO6, a room temperature
ferrimagnet with correlated and spin-orbit coupled Re t2g electrons moving in
the background of Fe moments stabilized by Hund's coupling. We show that for
such 3d/5d double perovskites, strong correlations on the 5d-element (Re) are
essential in driving a half-metallic ground state. Incorporating both strong
spin-orbit coupling and the Hubbard repulsion on Re leads to a band structure
consistent with ab initio calculations. Using our model, we find a large spin
polarization at the Fermi level, and obtain a semi-quantitative understanding
of the saturation magnetization of Ba2FeReO6, as well as X-ray magnetic
circular dichroism data indicating a significant orbital magnetization. Based
on the orbital populations obtained in our theory, we predict a specific doping
dependence to the tetragonal distortion accompanying ferrimagnetic order.
Finally, the combination of a net magnetization and spin-orbit interactions is
shown to induce Weyl nodes in the band structure, and we predict a significant
intrinsic anomalous Hall effect in hole-doped Ba2FeReO6. The uncovered
interplay of strong correlations and spin-orbit coupling lends partial support
to our previous work, which used a local moment description to capture the spin
wave dispersion found in neutron scattering measurements. Our work is of broad
interest for understanding metallic 4d-based and 5d-based double perovskites
which are of fundamental interest and of possible relevance to spintronic
applications.


  Based on statistical thermal approaches, the transverse momentum distribution
of the well-identified produced particles, $\pi^+$, $\pi^-$, $K^+$, $K^-$, $p$,
$\bar{p}$, is studied. From the partition function of grand-canonical ensemble,
we propose a generic expression for the dependence of the full chemical
potential $\mu$ on rapidity $y$. Then, by fitting this expression with the
experimental results of most central $p_{\perp}$ and $d^2 N/2 \pi p_{\bot}
dp_{\bot} dy$, at $7.7$, $11.5$, $19.6$, $27$, $39$, $130$, $200~$GeV, we
introduce a generic expression for the rapidity dependence of $\mu$, at
different energies and particle types, $\mu=a+b y^2$. The resulting energy
dependence reads $\sqrt{s_{\mathtt{NN}}}=c[(\mu-a)/b]^{d/2}$. As a validation
check, the proposed approach reproduces, excellently, the rapidity spectra
measured at different energies.


  Among the challenges in discriminating between theoretical approaches to the
glass transition is obtaining suitable data. In particular, particle--resolved
data in liquids supercooled past the mode--coupling crossover has until
recently been hard to obtain. Here we combine nano-particle resolved
experiments and GPU simulation data which addresses this and investigate the
predictions of differing theoretical approaches. We find support for both
dynamic facilitation and thermodynamic approaches. In particular, excitations,
the elementary units of relaxation in dynamic facilitation theory follow the
predicted scaling behaviour and the properties of cooperatively rearranging
regions (CRRs) are consistent with RFOT theory. At weak supercooling there is
limited correlation between particles identified in excitations and CRRs, but
this increases very substantially at deep supercooling. We identify a timescale
related to the CRRs which is coupled to the structural relaxation time and thus
decoupled from the excitation timescale, which remains microscopic.


  Linear upper bounds are provided for the size of the torsion homology of
negatively curved manifolds of finite volume in all dimensions $d\ne 3$. This
extends a classical theorem by Gromov. In dimension $3$, as opposed to the
Betti numbers, the size of torsion homology is unbounded in terms of the
volume. Moreover, there is a sequence of $3$-dimensional hyperbolic manifolds
that converges to $\mathbb{H}^3$ in the Benjamini--Schramm topology while its
normalized torsion in the first homology is dense in $[0,\infty]$. In dimension
$d\geq 4$ a somewhat precise estimate is given for the number of negatively
curved manifolds of finite volume, up to homotopy, and in dimension $d\ge 5$ up
to homeomorphism. These results are based on an effective simplicial thick-thin
decomposition which is of independent interest.


  Survival prediction is crucial for cancer patients as it provides early
prognostic information for treatment planning. Recently, deep survival models
based on deep learning and medical images have shown promising performance for
survival prediction. However, existing deep survival models are not well
developed in utilizing multi-modality images (e.g., PET-CT) and in extracting
region-specific information (e.g., the prognostic information in Primary Tumor
(PT) and Metastatic Lymph Node (MLN) regions). In view of this, we propose a
merging-diverging learning framework for survival prediction from
multi-modality images. This framework has a merging encoder to fuse
multi-modality information and a diverging decoder to extract region-specific
information. In the merging encoder, we propose a Hybrid Parallel
Cross-Attention (HPCA) block to effectively fuse multi-modality features via
parallel convolutional layers and cross-attention transformers. In the
diverging decoder, we propose a Region-specific Attention Gate (RAG) block to
screen out the features related to lesion regions. Our framework is
demonstrated on survival prediction from PET-CT images in Head and Neck (H&N)
cancer, by designing an X-shape merging-diverging hybrid transformer network
(named XSurv). Our XSurv combines the complementary information in PET and CT
images and extracts the region-specific prognostic information in PT and MLN
regions. Extensive experiments on the public dataset of HEad and neCK TumOR
segmentation and outcome prediction challenge (HECKTOR 2022) demonstrate that
our XSurv outperforms state-of-the-art survival prediction methods.


  The methods of approximation, regularization and smoothing of trigonometric
interpolation splines are considered in the paper. It is shown that
trigonometric splines can be considered from two points of view - as a
trigonometric Fourier series and as discrete trigonometric Fourier series
according to certain systems of functions that are smoothness carriers. It is
argued that with approximation and smoothing of trigonometric splines it is
expedient to consider as discrete rows, since their differential properties are
stored.


  Coupled aggregation and sedimentation processes were studied by means of
three dimensional computer simulations. For this purpose, a large prism with no
periodic boundary conditions for the sedimentation direction was considered.
Furthermore, three equally sized and mutually excluded regions were defined
inside the prism, a top, a middle and a bottom region. This allows to study the
time evolution of the cluster size distribution and the cluster structure
separately for each region. The mass distribution profile and the center of
mass position were also accessed as a function of time. For the bottom region,
the effects of the sediment formation on the kinetics of growth and on the
cluster structure were clearly observed. The obtained results not only agree
with the experimental data obtained by Allain et. al. and with the simulations
made by Gonzalez but also allow to gain further insight in the details.


  We argue that in models with several high scales; e.g. in composite Higgs
models or in gauge extensions of the Standard Model (SM), vector-like leptons
can be likely produced in a relatively large $\sqrt{s}$ region of the phase
space. Likewise, they can easily decay into final states not containing SM
gauge bosons. This contrasts with the topology in which these new particles are
being searched for at the LHC. Adopting an effective field theory approach, we
show that searches for excited leptons must be used instead to test this
scenario. We derive bounds on all the relevant interactions of dimension six;
the most constrained ones being of about $0.05$ TeV$^{-2}$. We build new
observables to improve current analyses and study the impact on all
single-field UV completions of the SM extended with a vector-like lepton that
can be captured by the effective field theory at tree level, in the current and
in the high-luminosity phase of the LHC.


  In this paper, I investigated the Doppler magnification effect in the
flux-limited galaxy number counts with finite redshift bin width. In
contradiction with our intuition, the number counts correction formula for
different redshift bin are different. For the redshift window as delta function
and constant function, the correction formulas have been derived analytically
in this paper. These two windows correspond to two extreme cases(i.e. extremely
narrow bin and extremely broad bin). An simulation have been implemented to
test these formulas. The simulation results indicate the perturbation changes
smoothly from one extreme case to another while extending the redshift bin
width from half bin width $0.05$ to $0.4$. As a result, Doppler magnification
caused number counts perturbation of finite redshift bin width can NOT be
compute via redshift integration of the perturbation formula derived with delta
function redshift window. It is only a good approximation when the bin width is
small. These results are important for galaxy redshift survey kinematic dipole
estimation, and meaningful for the relativistic galaxy number counts angular
power spectrum estimation, such as CLASSgal and CAMB source.


  We define a new type of Hardy-Orlicz spaces of conformal mappings on the unit
disk where in place of the value |f(x)| we consider the intrinsic path distance
between f(x) and f(0) in the image domain. We show that if the Orlicz function
is doubling then these two spaces are actually the same, and we give an example
when the intrinsic Hardy-Orlicz space is strictly smaller.


  Through an interaction of theory and optimization-guided design, we have
developed a novel bunch compression scheme based on an asymmetric double
emittance exchange (DEEX) with greatly reduced coherent synchrotron radiation
(CSR) effects. First, we discuss the benefits of the phase space exchange-based
bunch compressor (BC) in comparison to the chicane-based counterpart in the
approximation of linear single particle dynamics and with respect to
microbunching instabilities driven by longitudinal space charge forces. We
present simulation results for nonlinear dynamics in a symmetric DEEX BC which
accounts for the CSR effects of the scheme with direct and mirrored
longitudinal phase space at the exit of the beamline. The later scheme
demonstrates self-compensation of CSR effects on the longitudinal dynamics.
Next, we optimize the parameters of the beam elements with an adaptive
feedback, resulting in an asymmetric configuration of the scheme with
compensated CSR and nonlinear effects on the transverse dynamics, which we
further improve by incorporating several sextupoles relying on an
eigen-emittance analysis. Finally, we discuss how this method can be
generalized for optimizing nonlinear beam dynamics with collective effects.


  Anisotropic distributions of charged particles including two-stream
distributions give rise to generation of either stochastic electric fields (in
the form of Langmuir waves, Buneman instability) or random quasi-static
magnetic fields (Weibel and filamentation instabilities) or both. These
two-stream instabilities are known to play a key role in collisionless shock
formation, shock-shock interactions, and shock-induced electromagnetic
emission. This paper applies the general non-perturbative stochastic theory of
radiation to study electromagnetic emission produced by relativistic particles,
which random walk in the stochastic electric fields of the Langmuir waves. This
analysis takes into account the cumulative effect of uncorrelated Langmuir
waves on the radiating particle trajectory giving rise to angular diffusion of
the particle, which eventually modifies the corresponding radiation spectra. We
demonstrate that the radiative process considered is probably relevant for
emission produced in various kinds of astrophysical jets, in particular, prompt
gamma-ray burst spectra, including X-ray excesses and prompt optical flashes.


  A Robinson similarity matrix is a symmetric matrix where the entry values on
all rows and columns increase toward the diagonal. Decompose the Robinson
matrix into the sum of k {0, 1}-matrices, then these k {0, 1}-matrices are the
adjacency matrices of a set of nested unit interval graphs. Previous studies
show that unit interval graphs coincide with indifference graphs. An
indifference graph has an embedding that maps each vertex to a real number,
where two vertices are adjacent if their embedding is within a fixed threshold
distance. In this thesis, consider k different threshold distances, we study
the problem of finding an embedding that, simultaneously and with respect to
each threshold distance, embeds the k indifference graphs corresponding to the
k adjacency matrices. This is called a uniform embedding of a Robinson matrix
with respect to the k threshold distances. We give a sufficient and necessary
condition on Robinson matrices that have a uniform embedding, which is derived
from paths in an associated graph. We also give an efficient combinatorial
algorithm to find a uniform embedding or give proof that it does not exist, for
the case where k = 2.


  In this work we propose a different surgical modified model for the
construction of counterfactual variables under non parametric structural
equation models. This approach allows the simultaneous representation of
counterfactual responses and observed treatment assignment, at least when the
intervention is done in one node. Using the new proposal, the d-separation
criterion is used verify conditions related with ignorability or conditional
ignorability and a new proof of the back door theorem is provided under this
framework.


  Lattice actions and amplitudes that perfectly mirror continuum physics are
known as perfect discretizations. Such perfect discretizations naturally
preserve the symmetries of the continuum. This is a key concern for general
relativity, where diffeomorphism symmetry and dynamics are deeply connected,
and diffeomorphisms play a crucial role in quantization. In this work we
construct for the first time a perfect discretizations for four-dimensional
linearized gravity. We show how the perfect discretizations do lead to a
straightforward construction of the one-loop quantum corrections for manifolds
with boundary. This will also illustrate, that for manifolds with boundaries,
gauge modes that affect the boundary, need to be taken into account for the
computation of the one-loop correction. This work provides therefore an
evaluation of the boundary action for the diffeomorphism modes for a general
class of backgrounds.


  We show that any pivotal Hopf monoid $H$ in a symmetric monoidal category
$\mathcal{C}$ gives rise to actions of mapping class groups of oriented
surfaces of genus $g \geq 1$ with $n \geq 1$ boundary components. These mapping
class group actions are given by group homomorphisms into the group of
automorphisms of certain Yetter-Drinfeld modules over $H$. They are associated
with edge slides in embedded ribbon graphs that generalise chord slides in
chord diagrams. We give a concrete description of these mapping class group
actions in terms of generating Dehn twists and defining relations. For the case
where $\mathcal{C}$ is finitely complete and cocomplete, we also obtain actions
of mapping class groups of closed surfaces by imposing invariance and
coinvariance under the Yetter-Drinfeld module structure.


  We present a catalog of $\sim$52,000 extragalactic HII regions and their
spectroscopic properties obtained using Integral Field Spectroscopy (IFS) from
MUSE observations. The sample analyzed in this study contains 678 galaxies
within the nearby Universe (0.004 < z < 0.06) covering different morphological
types and a wide range of stellar masses (6 < log(M$_{*}$/M$_{\odot}$) < 13).
Each galaxy was analyzed using the Pipe3D and pyHIIextractor codes to obtain
information of the ionized gas and underlying stellar populations.
Specifically, the fluxes, equivalent widths, velocities and velocity
dispersions of 30 emission lines covering the wavelength range between
$\lambda$4750A to $\lambda$9300A, were extracted and were used to estimate
luminosity weighted ages and metallicities of the underlying stellar
populations from each HII region (of the original sample we detect HII regions
in 539 galaxies). In addition, we introduce and apply a novel method and
independent of any intrinsic physical property to estimate and decontaminate
the contribution of the diffuse ionized gas. Using the final catalog, we
explore the dependence of properties of the HII regions on different local and
global galaxy parameters: (i) Hubble type, (ii) stellar mass, (iii)
galactocentric distance, and (iv) the age and metallicity of the
underlying/neighbour stellar populations. We confirm known relations between
properties of the HII regions and the underlying stellar populations (in
particular with the age) uncovered using data of lower spatial and spectral
resolution. Furthermore, we describe the existence of two main families of
diffuse ionized gas different for galaxies host or not of HII region


  For a nonempty convex subset $C$ of a Hadamard space $X$, it is proved that
$u=P_Cx$ if and only if $\langle\overrightarrow{xu},\overrightarrow{uy}\rangle
\geqslant0$ for all $y\in C$. As an application of this characterization, we
prove strong convergence of two iterative algorithms with perturbations for
nonexpansive mappings.


  We present a general method to obtain static anisotropic spherically
symmetric solutions, satisfying a nonlocal equation of state, from known
density profiles. This equation of state describes, at a given point, the
components of the corresponding energy-momentum tensor not only as a function
at that point, but as a functional throughout the enclosed configuration. In
order to establish the physical aceptability of the proposed static family of
solutions satisfying nonlocal equation of state,\textit{}we study the
consequences imposed by the junction and energy conditions for anisotropic
fluids in bounded matter distribution. It is shown that a general relativistic
spherically symmetric bounded distributions of matter, at least for certain
regions, could satisfy a nonlocal equation of state.


  Purpose: Identify and examine the associations between health behaviors and
increased risk of adolescent suicide attempts, while controlling for
socioeconomic and demographic differences. Design: A data-driven analysis using
cross-sectional data. Setting: Communities in the state of Montana from 1999 to
2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted
suicide at least once. Measures: Overall 29 variables (predictors) accounting
for psychological behaviors, illegal substances consumption, daily activities
at schools and demographic backgrounds, were considered. Analysis: A library of
machine learning algorithms along with the traditionally-used logistic
regression were used to model and predict suicide attempt risk. Model
performances (goodness-of-fit and predictive accuracy) were measured using
accuracy, precision, recall and F-score metrics. Results: The non-parametric
Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy
in goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy
(F-score:0.785). Key health-behaviors identified include: being sad/hopeless,
followed by safety concerns at school, physical fighting, inhalant usage,
illegal drugs consumption at school, current cigarette usage, and having first
sex at an early age (below 15 years of age). Additionally, the minority groups
(American Indian/Alaska Natives, Hispanics/Latinos), and females are also found
to be highly vulnerable to attempting suicides. Conclusion: Significant
contribution of this work is understanding the key health-behaviors and health
disparities that lead to higher frequency of suicide attempts among
adolescents, while accounting for the non-linearity and complex interactions
among the outcome and the exposure variables.


  We discuss the development and evaluation of quantum interactive learning
tutorials (QuILTs) which are suitable for undergraduate courses in quantum
mechanics. QuILTs are based on the investigation of student difficulties in
learning quantum physics. They exploit computer-based visualization tools and
help students build links between the formal and conceptual aspects of quantum
physics without compromising the technical content. They can be used both as
supplements to lectures or as a self-study tool.


  The third realization of the International Celestial Reference Frame (ICRF3)
was adopted in August 2018 and includes positions of extragalactic objects at
three frequencies: 8.4 GHz, 24 GHz, and 32 GHz. In this paper, we present
celestial reference frames estimated from Very Long Baseline Interferometry
measurements at K-band (24 GHz) including data until June 2022. The data set
starts in May 2002 and currently consists of more than 120 24h observing
sessions performed over the past 20 years. Since the publication of ICRF3, the
additional observations of the sources during the last four years allow
maintenance of the celestial reference frame and more than 200 additional radio
sources ensure an expansion of the frame. A study of the presented solutions is
carried out helping us to understand systematic differences between the
astrometric catalogs and moving us towards a better next ICRF solution. We
compare K-band solutions (VIE-K-2022b and USNO-K-2022July05) computed by two
analysts with two independent software packages (VieVS and Calc/Solve) and
describe the differences in the solution strategy. We assess the systematic
differences using vector spherical harmonics and describe the reasons for the
most prominent ones.


  We report a joint theoretical and experimental study focused on understanding
the optical and magneto-optical properties of Co-based full-Heusler compounds.
We show that magneto-optical spectra calculated within ab-initio density
functional theory are able to uniquely identify the features of the
experimental spectra in terms of spin resolved electronic transitions. As
expected for 3d-based magnets, we find that the largest Kerr rotation for these
alloys is of the order of 0.3o in polar geometry. In addition, we demonstrate
that (i) multilayered structures have to be carefully handled in the
theoretical calculations in order to improve the agreement with experiments,
and (ii) combined theoretical and experimental investigations constitute a
powerful approach to designing new materials for magneto-optical and
spin-related applications


  This Letter presents the results of a direct search with the ATLAS detector
at the LHC for a Standard Model Higgs boson of mass 110 < m(H) < 130 GeV
produced in association with a W or Z boson and decaying to bb. Three decay
channels are considered: ZH->llbb, WH->lvbb, and ZH->vvbb, where l corresponds
to an electron or a muon. No evidence for Higgs boson production is observed in
a dataset of 7 TeV pp collisions corresponding to 4.7/fb of integrated
luminosity collected by ATLAS in 2011. Exclusion limits on Higgs boson
production, at the 95% confidence level, of 2.5 to 5.5 times the Standard Model
cross section are obtained in the mass range 110 - 130 GeV. The expected
exclusion limits range between 2.5 and 4.9 for the same mass interval.


  We construct examples of principal groupoids that have weak containment but
are not amenable, thus answering questions by Claire Anantharaman-Delaroche and
Rufus Willett.


  The aim of this paper is to describe the group of endo-trivial modules for a
$p$-group $P$, in terms of the obstruction group for the gluing problem of
Borel-Smith functions.


  Recently, there has been a trend to combine independent component analysis
and canonical polyadic decomposition (ICA-CPD) for an enhanced robustness for
the computation of CPD, and ICA-CPD could be further converted into CPD of a
5th-order partially symmetric tensor, by calculating the eigenmatrices of the
4th-order cumulant slices of a trilinear mixture. In this study, we propose a
new 5th-order CPD algorithm constrained with partial symmetry based on joint
diagonalization. As the main steps involved in the proposed algorithm undergo
no updating iterations for the loading matrices, it is much faster than the
existing algorithm based on alternating least squares and enhanced line search,
with competent performances. Simulation results are provided to demonstrate the
performance of the proposed algorithm.


  We overview the existing OCT work, especially the practical aspects of it. We
create a novel algorithm for 3D OCT segmentation with the goals of speed and/or
accuracy while remaining flexible in the design and implementation for future
extensions and improvements. The document at this point is a running draft
being iteratively "developed" as a progress report as the work and survey
advance. It contains the review and summarization of select OCT works, the
design and implementation of the OCTMARF experimentation application and some
results.


  Magnetic energy around astrophysical compact objects can strongly dominate
over plasma rest mass. Emission observed from these systems may be fed by
dissipation of Alfv\'en wave turbulence, which cascades to small damping
scales, energizing the plasma. We use 3D kinetic simulations to investigate
this process. When the cascade is excited naturally, by colliding large-scale
Alfv\'en waves, we observe quasithermal heating with no nonthermal particle
acceleration. We also find that the particles are energized along the magnetic
field lines and so are poor producers of synchrotron radiation. At low plasma
densities, our simulations show the transition to "charge-starved" cascades,
with a distinct damping mechanism.


  Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and
DRAM, have been proposed to address the growing memory demand of applications.
Emerging NVM technologies, such as phase-change memories (PCM), memristor, and
3D XPoint, have higher capacity density, minimal static power consumption and
lower cost per GB. However, NVM has longer access latency and limited write
endurance as opposed to DRAM. The different characteristics of two memory
classes point towards the design of hybrid memory systems containing multiple
classes of main memory.
  In the iterative and incremental development of new architectures, the
timeliness of simulation completion is critical to project progression. Hence,
a highly efficient simulation method is needed to evaluate the performance of
different hybrid memory system designs. Design exploration for hybrid memory
systems is challenging, because it requires emulation of the full system stack,
including the OS, memory controller, and interconnect. Moreover, benchmark
applications for memory performance test typically have much larger working
sets, thus taking even longer simulation warm-up period.
  In this paper, we propose a FPGA-based hybrid memory system emulation
platform. We target at the mobile computing system, which is sensitive to
energy consumption and is likely to adopt NVM for its power efficiency. Here,
because the focus of our platform is on the design of the hybrid memory system,
we leverage the on-board hard IP ARM processors to both improve simulation
performance while improving accuracy of the results. Thus, users can implement
their data placement/migration policies with the FPGA logic elements and
evaluate new designs quickly and effectively. Results show that our emulation
platform provides a speedup of 9280x in simulation time compared to the
software counterpart Gem5.