  The concept of hypergroup is generalization of group, first was introduced by
Marty [9]. This theory had applications to several domains. Marty had applied
them to groups, algebraic functions and rational functions. M. Krasner has
studied the notion of hyperring in [11]. G.G Massouros and C.G Massouros
defined hyperringoids and apply them in generalization of rings in [10]. They
also defined fortified hypergroups as a generalization of divisibility in
algebraic structures and use them in Automata and Language theory. T.
Vougiouklis has defined the representations and fundamental relations in
hyperrings in [14, 15]. R. Ameri, H. Hedayati defined k-hyperideals in
semihyperrings in [2]. B. Davvaz has defined some relations in hyperrings and
prove Isomorphism theorems in [7]. The aim of this article is to initiate the
study of semihyperrings and characterize it with hyperideals. In this article
we defined semihyperrings, hyperideals, prime, semiprime, irreducible, strongly
irreducible hyperideals, m-systems, p-systems, i-systems and regular
semihyperrings. We also shown that the lattice of irreducible hyperideals of
semihyperring R admits the structure of topology, which is called irreducible
spectrum topology.


  The giant elliptical galaxy NGC 1316 (Fornax A) is a well-studied member of
the Fornax Cluster and a prolific producer of Type Ia supernovae, having hosted
four observed events since 1980. Here we present detailed optical and
near-infrared light curves of the spectroscopically normal SN 2006dd. These
data are used, along with previously published photometry of the normal SN
1980N and SN 1981D, and the fast-declining, low-luminosity SN 2006mr, to
compute independent estimates of the host reddening for each supernova, and the
distance to NGC 1316. From the three normal supernovae, we find a distance of
17.8 +/- 0.3 (random) +/- 0.3 (systematic) Mpc for Ho = 72. Distance moduli
derived from the "EBV" and Tripp methods give values that are mutually
consistent to 4 -- 8%. Moreover, the weighted means of the distance moduli for
these three SNe for three methods agree to within 3%. This consistency is
encouraging and supports the premise that Type Ia supernovae are reliable
distance indicators at the 5% precision level or better. On the other hand, the
two methods used to estimate the distance of the fast-declining SN 2006mr both
yield a distance to NGC 1316 which is 25-30% larger. This disparity casts doubt
on the suitability of fast-declining events for estimating extragalactic
distances. Modest-to-negligible host galaxy reddening values are derived for
all four supernovae. Nevertheless, two of them (SN 2006dd and SN 2006mr) show
strong NaID interstellar lines in the host galaxy system. The strength of this
absorption is completely inconsistent with the small reddening values derived
from the supernova light curves if the gas in NGC 1316 is typical of that found
in the interstellar medium of the Milky Way. In addition, the equivalent width
of the NaID lines in SN 2006dd appear to have weakened significantly some
100-150 days after explosion.


  We show, how the classical Berry-Esseen theorem for normal approximation may
be used to derive rates of convergence for random sums of centerd, real-valued
random variables with respect to a certain class of probability metrics,
including the Kolmogorov and the Wasserstein distances. This technique is
applied to several examples, including the approximation by a Laplace
distribution of a geometric sum of centered random variables with finite third
moment, where a concrete Berry-Esseen bound is derived. This bound reduces to a
bound of the supposedly optimal order $\sqrt{p}$ in the i.i.d. case.


  Due to morphological similarities, the differentiation of histologic sections
of cutaneous tumors into individual subtypes can be challenging. Recently, deep
learning-based approaches have proven their potential for supporting
pathologists in this regard. However, many of these supervised algorithms
require a large amount of annotated data for robust development. We present a
publicly available dataset of 350 whole slide images of seven different canine
cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic
classes, including seven cutaneous tumor subtypes. In inter-rater experiments,
we show a high consistency of the provided labels, especially for tumor
annotations. We further validate the dataset by training a deep neural network
for the task of tissue segmentation and tumor subtype classification. We
achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in
particular. For classification, we achieve a slide-level accuracy of 0.9857.
Since canine cutaneous tumors possess various histologic homologies to human
tumors the added value of this dataset is not limited to veterinary pathology
but extends to more general fields of application.


  In this paper, we derive the master equations for gravitational perturbations
of vector and scalar type for static vacuum Lovelock black holes. Together with
our previous work on the tensor type perturbation, we now provide the full set
of master equations that governs all types of gravitational perturbations of
static vacuum Lovelock black holes in any dimensions.


  We search for a five-plet singly charged Higgs $H_5^\pm$ in the
Georgi-Machacek model at the ep colliders. The charged Higgs bosons are
produced via the $ZW^{\pm}$ fusion process $p\, e^- \to j\, e^-\, H_5^\pm$, and
decay as $H_5^\pm \to Z\, W^{\pm} \to (l^+ l^-)\, (jj)$. With a detector-level
simulation at the FCC-eh and LHeC, a multi-variate analysis is performed to
yield limits on the production cross section times branching ratio $\sigma (p\,
e^- \to j e^- H_5^\pm) \times {\rm BR}(H_5^\pm \to Z\, W^\pm)$ and on the model
parameter $\sin\theta_H$ for charged Higgs masses between 200 and 1000 GeV. The
effects of electron beam polarization are also investigated.


  We present the results from a study of the X-ray variability and the near-IR
to X-ray spectral energy distribution of four low-luminosity, Seyfert 1
galaxies. We compared their variability amplitude and broad band spectrum with
those of more luminous AGN in order to investigate whether accretion in
low-luminosity AGN operates as in their luminous counterparts. We used archival
XMM-Newton and, in two cases, ASCA data to estimate their X-ray variability
amplitude and determine their X-ray spectral shape and luminosity. We also used
archival HST data to measure their optical nuclear luminosity, and near-IR
measurements from the literature, in order to construct their near-IR to X-ray
spectra. The X-ray variability amplitude of the four Seyferts is what one would
expect, given their black hole masses. Their near-IR to X-ray spectrum has the
same shape as the spectrum of quasars which are 10^2-10^5 times more luminous.
The objects in our sample are optically classified as Seyfert 1-1.5. This
implies that they host a relatively unobscured AGN-like nucleus. They are also
of low luminosity and accrete at a low rate. They are therefore good candidates
to detect radiation from an inefficient accretion process. However, our results
suggest that they are similar to AGN which are 10^2-10^5 times more luminous.
The combination of a "radiative efficient accretion disc plus an X-ray
producing hot corona" may persist at low accretion rates as well.


  We have performed a detailed dynamical study of the recently identified
Neptunian Trojan 2004 KV18, only the second object to be discovered librating
around Neptune's trailing Lagrange point, L5. We find that 2004 KV18 is moving
on a highly unstable orbit, and was most likely captured from the Centaur
population at some point in the last ~1 Myr, having originated in the Scattered
Disk, beyond the orbit of Neptune. The instability of 2004 KV18 is so great
that many of the test particles studied leave the Neptunian Trojan cloud within
just ~0.1 - 0.3 Myr, and it takes just 37 million years for half of the 91125
test particles created to study its dynamical behaviour to be removed from the
Solar system entirely. Unlike the other Neptunian Trojans previously found to
display dynamical instability on hundred million year timescales (2001 QR322
and 2008 LC18), 2004 KV18 displays such extreme instability that it must be a
temporarily captured Trojan, rather than a primordial member of the Neptunian
Trojan population. As such, it offers a fascinating insight into the processes
through which small bodies are transferred around the outer Solar system, and
represents an exciting addition to the menagerie of the Solar system's small
bodies.


  Calculations of form factors in different forms of relativistic kinematics
are presented. They involve the instant, front and point forms. In the two
first cases, different kinematical conditions are considered while in the
latter case, both a Dirac-inspired approach and a hyperplane-based one are
incorporated in our study. Numerical results are presented for the pion form
factors with emphasis on both the low and high Q**2 range. A new argument is
presented, explaining why some approaches do considerably much better than
other ones whenonly a single-particle current is considered.


  The observation of heavy-ion-like behaviour in pp collisions at the LHC
suggests that more physics mechanisms are at play than traditionally assumed.
The introduction e.g. of quark-gluon plasma or colour rope formation can
describe several of the observations, but as of yet there is no established
paradigm. In this article we study a few possible modifications to the Pythia
event generator, which describes a wealth of data but fails for a number of
recent observations. Firstly, we present a new model for generating the
transverse momentum of hadrons during the string fragmentation process,
inspired by thermodynamics, where heavier hadrons naturally are suppressed in
rate but obtain a higher average transverse momentum. Secondly, close-packing
of strings is taken into account by making the temperature or string tension
environment-dependent. Thirdly, a simple model for hadron rescattering is
added. The effect of these modifications is studied, individually and taken
together, and compared with data mainly from the LHC. While some improvements
can be noted, it turns out to be nontrivial to obtain effects as big as
required, and further work is called for.


  Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.


  The Online Encyclopedia of Integer Sequences (OEIS) is made up of thousands
of numerical sequences considered particularly interesting by some
mathematicians. The graphic representation of the frequency with which a number
n as a function of n appears in that database shows that the underlying
function decreases fast, and that the points are distributed in a cloud,
seemingly split into two by a clear zone that will be referred to here as
"Sloane's Gap". The decrease and general form are explained by mathematics, but
an explanation of the gap requires further considerations.


  Here we examine the evolution of beta-2 microglobulin in terms of its
hydropathic shapes, a theoretical construct that has revealed important trends.
The dynamics of many proteins are largely driven by interactions between the
protein itself and the thin water film that covers it. \b{eta}2m constitutes
the basic building unit of the immunoglobulin superfamily; the evolution of its
amino acid sequences from chickens to mice to humans provides new information
about its multiple functions. Our hydrodynamic method involves concepts of
topological shape evolution towards a critical point for optimized functions.
The results are in excellent agreement with experiment for the details of the
mouse-human evolution, as well as both the dangerous natural amyloid
aggregation mutation D76N, and six other DN test mutations.


  On-orbit spacecraft inspection is an important capability for enabling
servicing and manufacturing missions and extending the life of spacecraft.
However, as space operations become increasingly more common and complex,
autonomous control methods are needed to reduce the burden on operators to
individually monitor each mission. In order for autonomous control methods to
be used in space, they must exhibit safe behavior that demonstrates robustness
to real world disturbances and uncertainty. In this paper, neural network
controllers (NNCs) trained with reinforcement learning are used to solve an
inspection task, which is a foundational capability for servicing missions. Run
time assurance (RTA) is used to assure safety of the NNC in real time,
enforcing several different constraints on position and velocity. The NNC and
RTA are tested in the real world using unmanned aerial vehicles designed to
emulate spacecraft dynamics. The results show this emulation is a useful
demonstration of the capability of the NNC and RTA, and the algorithms
demonstrate robustness to real world disturbances.


  Unsupervised learning aims at the discovery of hidden structure that drives
the observations in the real world. It is essential for success in modern
machine learning. Latent variable models are versatile in unsupervised learning
and have applications in almost every domain. Training latent variable models
is challenging due to the non-convexity of the likelihood objective. An
alternative method is based on the spectral decomposition of low order moment
tensors. This versatile framework is guaranteed to estimate the correct model
consistently. My thesis spans both theoretical analysis of tensor decomposition
framework and practical implementation of various applications. This thesis
presents theoretical results on convergence to globally optimal solution of
tensor decomposition using the stochastic gradient descent, despite
non-convexity of the objective. This is the first work that gives global
convergence guarantees for the stochastic gradient descent on non-convex
functions with exponentially many local minima and saddle points. This thesis
also presents large-scale deployment of spectral methods carried out on various
platforms. Dimensionality reduction techniques such as random projection are
incorporated for a highly parallel and scalable tensor decomposition algorithm.
We obtain a gain in both accuracies and in running times by several orders of
magnitude compared to the state-of-art variational methods. To solve real world
problems, more advanced models and learning algorithms are proposed. This
thesis discusses generalization of LDA model to mixed membership stochastic
block model for learning user communities in social network, convolutional
dictionary model for learning word-sequence embeddings, hierarchical tensor
decomposition and latent tree structure model for learning disease hierarchy,
and spatial point process mixture model for detecting cell types in
neuroscience.


  Audio-visual representation learning aims to develop systems with human-like
perception by utilizing correlation between auditory and visual information.
However, current models often focus on a limited set of tasks, and
generalization abilities of learned representations are unclear. To this end,
we propose the AV-SUPERB benchmark that enables general-purpose evaluation of
unimodal audio/visual and bimodal fusion representations on 7 datasets covering
5 audio-visual tasks in speech and audio processing. We evaluate 5 recent
self-supervised models and show that none of these models generalize to all
tasks, emphasizing the need for future study on improving universal model
performance. In addition, we show that representations may be improved with
intermediate-task fine-tuning and audio event classification with AudioSet
serves as a strong intermediate task. We release our benchmark with evaluation
code and a model submission platform to encourage further research in
audio-visual learning.


  We propose an unsupervised speaker adaptation method inspired by the neural
Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The
proposed model contains a memory block that holds speaker i-vectors extracted
from the training data and reads relevant i-vectors from the memory through an
attention mechanism. The resulting memory vector (M-vector) is concatenated to
the acoustic features or to the hidden layer activations of an E2E neural
network model. The E2E ASR system is based on the joint connectionist temporal
classification and attention-based encoder-decoder architecture. M-vector and
i-vector results are compared for inserting them at different layers of the
encoder neural network using the WSJ and TED-LIUM2 ASR benchmarks. We show that
M-vectors, which do not require an auxiliary speaker embedding extraction
system at test time, achieve similar word error rates (WERs) compared to
i-vectors for single speaker utterances and significantly lower WERs for
utterances in which there are speaker changes.


  Let $G$ be a finite group acting on a closed manifold $M$. We estimate the
size of a minimal triangulation of $M$ for which the action of $G$ is
simplicial and regular. We show that the number of vertices of such
triangulations are bounded bellow by the \emph{$G$-covering type} of $M$,
defined as the minimal cardinality of a $G$-equivariant good cover of a space
that is $G$-homotopy equivalent to $M$. The $G$-covering type is a $G$-homotopy
invariant, so it can be estimated by other $G$-invariants like the equivariant
LS-category, $G$-genus and the multiplicative structure of any equivariant
cohomology theory. Among many examples, we compute the $G$-covering type of
regular $G$-graphs, give a complete description of the number of vertices and
their orbits for orientation-preserving finite group actions on surfaces and
estimate the $G$-covering type of the projectivizations of complex
representations of $G$.


  In this work, we have proposed a system theoretic method to compute
sensitivities of different lines for $N-k$ contingency analysis in power
network. We have formulated the $N-k$ contingency analysis as the stability
problem of power network with uncertain links. We have derived a necessary
condition for stochastic stability of the power network with the link
uncertainty. The necessary condition is then used to rank order the
contingencies. We have shown due to interaction between different uncertainties
the ranking can substantially change. The state of the art $N-k$ contingency
analysis does not consider the possibility of interference between link
uncertainties and rank the links according to the severity of $N-1$
contingencies. We have presented simulation results for New England $39$ bus
system as a support of our claim.


  Loss functions drive the optimization of machine learning algorithms. The
choice of a loss function can have a significant impact on the training of a
model, and how the model learns the data. Binary classification is one of the
major pillars of machine learning problems, used in medical imaging to failure
detection applications. The most commonly used surrogate loss functions for
binary classification include the binary cross-entropy and the hinge loss
functions, which form the focus of our study.
  In this paper, we provide an overview of a novel loss function, the Xtreme
Margin loss function. Unlike the binary cross-entropy and the hinge loss
functions, this loss function provides researchers and practitioners
flexibility with their training process, from maximizing precision and AUC
score to maximizing conditional accuracy for a particular class, through
tunable hyperparameters $\lambda_1$ and $\lambda_2$, i.e., changing their
values will alter the training of a model.


  This is the first paper of a series devoted to the Lambda Orionis
star-forming region, from the X-ray perspective, which will provide a
comprehensive view of this complex region. In this paper we focus in uncovering
the population of the central, young cluster Collinder 69 (C69), and in
particular those diskless members not identified by previous near- and
mid-infrared surveys, and to establish the X-ray luminosity function for the
association. We have combined two exposures taken with the XMM-Newton satellite
with an exhaustive data set of optical, near- and mid-infrared photometry to
assess the membership of the X-ray sources based on color-color and
color-magnitude diagrams, as well as other properties, such as effective
temperatures, masses and bolometric luminosities.
  We detected a total of 164 X-ray sources, of which 66 are probable and
possible cluster members. A total of 16 are newly identified probable members.
The two XMM-Newton pointings east and west of the cluster center have allowed
us to verify the heterogeneous spatial distribution of young stars, probably
related to the large scale structure of the region. The disk fraction of the
X-ray detected cluster sample is very low, close to 10%, in remarkable contrast
to the low-mass stellar and substellar population (mostly undetected in X-rays)
where the disk fraction reaches about 50%. The X-ray luminosity function of C69
provides support for an age of several Myr when compared with other well known
young associations. With our improved cluster census we confirm previous
reports on the untypically low disk fraction compared to other clusters of
several Myr age. The different disk fractions of X-ray detected (essentially
solar-like) and undetected (mostly low-mass stars and brown dwarfs) members can
be understood as a consequence of a mass-dependence of the time-scale for disk
evolution.


  We propose a system for observing the correlated phase dynamics of two
mesoscopic ensembles of atoms through their collective coupling to an optical
cavity. We find a dynamical quantum phase transition induced by pump noise and
cavity output-coupling. The spectral properties of the superradiant light
emitted from the cavity show that at a critical pump rate the system undergoes
a transition from the independent behavior of two disparate oscillators to the
phase-locking that is the signature of quantum synchronization.


  We review recent progress in understanding nearly integrable models within
the framework of generalized hydrodynamics (GHD). Integrable systems have
infinitely many conserved quantities and stable quasiparticle excitations: when
integrability is broken, only a few residual conserved quantities survive,
eventually leading to thermalization, chaotic dynamics and conventional
hydrodynamics. In this review, we summarize recent efforts to take into account
small integrability breaking terms, and describe the transition from GHD to
standard hydrodynamics. We discuss the current state of the art, with emphasis
on weakly inhomogeneous potentials, generalized Boltzmann equations and
collision integrals, as well as bound-state recombination effects. We also
identify important open questions for future works.


  It is demonstrated how the hierarchy between the gauge coupling unification
scale of minimal supersymmetry and the Planck (or string) scale, which
resembles in order of magnitude a loop factor, can actually be explained as
such in supergravity-coupled supersymmetry. A gauge and global singlet field
acquires a linear potential term due to its one-loop supergravity interactions
and slides to the desired scale. The singlet field can then provide the seed
for the breaking of the unified theory at the appropriate scale via its
couplings to fields in the adjoint representation.


  In this paper we analyze in detail a few questions related to the theory of
functions with bounded $p$-Hessian-Schatten total variation, which are relevant
in connection with the theory of inverse problems and machine learning. We
prove an optimal density result, relative to the $p$-Hessian-Schatten total
variation, of continuous piecewise linear (CPWL) functions in any space
dimension $d$, using a construction based on a mesh whose local orientation is
adapted to the function to be approximated. We show that not all extremal
functions with respect to the $p$-Hessian-Schatten total variation are CPWL.
Finally, we prove existence of minimizers of certain relevant functionals
involving the $p$-Hessian-Schatten total variation in the critical dimension
$d=2$.


  As hydrodynamic simulations increase in scale and resolution, identifying
structures with non-trivial geometries or regions of general interest becomes
increasingly challenging. There is a growing need for algorithms that identify
a variety of different features in a simulation without requiring a "by-eye"
search. We present tensor classification as such a technique for smoothed
particle hydrodynamics (SPH). These methods have already been used to great
effect in N-Body cosmological simulations, which require smoothing defined as
an input free parameter. We show that tensor classification successfully
identifies a wide range of structures in SPH density fields using its native
smoothing, removing a free parameter from the analysis and preventing the need
for tesselation of the density field, as required by some classification
algorithms. As examples, we show that tensor classification using the tidal
tensor and the velocity shear tensor successfully identifies filaments, shells
and sheet structures in giant molecular cloud simulations, as well as spiral
arms in discs. The relationship between structures identified using different
tensors illustrates how different forces compete and co-operate to produce the
observed density field. We therefore advocate the use of multiple tensors to
classify structure in SPH simulations, to shed light on the interplay of
multiple physical processes.


  We study the implicit bias of ReLU neural networks trained by a variant of
SGD where at each step, the label is changed with probability $p$ to a random
label (label smoothing being a close variant of this procedure). Our
experiments demonstrate that label noise propels the network to a sparse
solution in the following sense: for a typical input, a small fraction of
neurons are active, and the firing pattern of the hidden layers is sparser. In
fact, for some instances, an appropriate amount of label noise does not only
sparsify the network but further reduces the test error. We then turn to the
theoretical analysis of such sparsification mechanisms, focusing on the
extremal case of $p=1$. We show that in this case, the network withers as
anticipated from experiments, but surprisingly, in different ways that depend
on the learning rate and the presence of bias, with either weights vanishing or
neurons ceasing to fire.


  Generative adversarial networks (GANs) have shown remarkable success in
generating realistic data from some predefined prior distribution (e.g.,
Gaussian noises). However, such prior distribution is often independent of real
data and thus may lose semantic information (e.g., geometric structure or
content in images) of data. In practice, the semantic information might be
represented by some latent distribution learned from data. However, such latent
distribution may incur difficulties in data sampling for GANs. In this paper,
rather than sampling from the predefined prior distribution, we propose an
LCCGAN model with local coordinate coding (LCC) to improve the performance of
generating data. First, we propose an LCC sampling method in LCCGAN to sample
meaningful points from the latent manifold. With the LCC sampling method, we
can exploit the local information on the latent manifold and thus produce new
data with promising quality. Second, we propose an improved version, namely
LCCGAN++, by introducing a higher-order term in the generator approximation.
This term is able to achieve better approximation and thus further improve the
performance. More critically, we derive the generalization bound for both
LCCGAN and LCCGAN++ and prove that a low-dimensional input is sufficient to
achieve good generalization performance. Extensive experiments on four
benchmark datasets demonstrate the superiority of the proposed method over
existing GANs.


  Tangential YORP, or TYORP, has recently been demonstrated to be an important
factor in the evolution of an asteroid's rotation state. It is complementary to
normal YORP, or NYORP, which used to be considered previously. While NYORP is
produced by non-symmetry in the large-scale geometry of an asteroid, TYORP is
due to heat conductivity in stones on the surface of the asteroid. Yet to date
TYORP has been studied only in a simplified 1-dimensional model, substituting
stones by high long walls.
  This article for the first time considers TYORP in a realistic 3-dimensional
model, also including shadowing and self-illumination effects via ray tracing.
TYORP is simulated for spherical stones lying on regolith. The model includes
only 5 free parameters, and the dependence of the TYORP on each of them is
studied. The TYORP torque appears to be smaller than previous estimates from
1-dimensional model, but still comparable to the NYORP torques.
  These results can be used to estimate TYORP of different asteroids, and also
as a basis for more sophisticated models of TYORP.


  In this paper we offer a metric similar to graph edit distance which measures
the distance between two (possibly infinite)weighted graphs with finite norm
(we define the norm of a graph as the sum of absolute values of its edges). The
main result is the completeness of the space. Some other analytical properties
of this space are also investigated. The introduced metric could have some
applications in pattern recognition and face recognition methods.


  In the robotic industry, specular and textureless metallic components are
ubiquitous. The 6D pose estimation of such objects with only a monocular RGB
camera is difficult because of the absence of rich texture features.
Furthermore, the appearance of specularity heavily depends on the camera
viewpoint and environmental light conditions making traditional methods, like
template matching, fail. In the last 30 years, pose estimation of the specular
object has been a consistent challenge, and most related works require massive
knowledge modeling effort for light setups, environment, or the object surface.
On the other hand, recent works exhibit the feasibility of 6D pose estimation
on a monocular camera with convolutional neural networks(CNNs) however they
mostly use opaque objects for evaluation. This paper provides a data-driven
solution to estimate the 6D pose of specular objects for grasping them,
proposes a cost function for handling symmetry, and demonstrates experimental
results showing the system's feasibility.


  If charge carriers accumulate in the charge transport layer of a solar cell,
then the transient response of the electric field that originates from these
accumulated charges results in hysteresis in the current-voltage ($J$-$V$)
characteristics. While this mechanism was previously known, a theoretical model
to explain these $J$-$V$ characteristics has not been considered to date. We
derived an equivalent circuit from the proposed hysteresis mechanism. By
solving the equivalent circuit model, we were able to reproduce some of the
features of hysteresis in perovskite solar cells.


  We study extremal black hole solutions in D dimensions with near horizon
geometry AdS_2\times S^{D-2} in higher derivative gravity coupled to other
scalar, vector and anti-symmetric tensor fields. We define an entropy function
by integrating the Lagrangian density over S^{D-2} for a general AdS_2\times
S^{D-2} background, taking the Legendre transform of the resulting function
with respect to the parameters labelling the electric fields, and multiplying
the result by a factor of 2\pi. We show that the values of the scalar fields at
the horizon as well as the sizes of AdS_2 and S^{D-2} are determined by
extremizing this entropy function with respect to the corresponding parameters,
and the entropy of the black hole is given by the value of the entropy function
at this extremum. Our analysis relies on the analysis of the equations of
motion and does not directly make use of supersymmetry or specific structure of
the higher derivative terms.


  In this paper we attempt to explicate Salmon's idea of a causal process, as
defined in terms of the mark method, in the context of C*-dynamical systems. We
prove two propositions, one establishing mark manifestation infinitely many
times along a given interval of the process, and, a second one, which
establishes continuous manifestation of mark with the exception of a countable
number of isolated points. Furthermore, we discuss how these results can be
implemented in the context of Haag-Araki theories of relativistic quantum
fields in Minkowski spacetime.


  Nuclear spins nearby nitrogen-vacancy (NV) centers in diamond are excellent
quantum memory for quantum computing and quantum sensing, but are difficult to
be initialized due to their weak interactions with the environment. Here we
propose and demonstrate a magnetic-field-independent, deterministic and highly
efficient polarization scheme by introducing chopped laser pulses into the
double-resonance initialization method. With this method, we demonstrate
initialization of single-nuclear-spin approaching $98.1\%$ and a
$^{14}N$-$^{13}C$ double-nuclear-spin system approaching $96.8\%$ at room
temperature. The initialization is limited by a nuclear-spin depolarization
effect due to chopped laser excitation. Our approach could be extended to NV
systems with more nuclear spins and would be a useful tool in future
applications such as nano-MRI and single-cell NMR.


  In many applications of supervised learning, multiple classification or
regression outputs have to be predicted jointly. We consider several extensions
of gradient boosting to address such problems. We first propose a
straightforward adaptation of gradient boosting exploiting multiple output
regression trees as base learners. We then argue that this method is only
expected to be optimal when the outputs are fully correlated, as it forces the
partitioning induced by the tree base learners to be shared by all outputs. We
then propose a novel extension of gradient tree boosting to specifically
address this issue. At each iteration of this new method, a regression tree
structure is grown to fit a single random projection of the current residuals
and the predictions of this tree are fitted linearly to the current residuals
of all the outputs, independently. Because of this linear fit, the method can
adapt automatically to any output correlation structure. Extensive experiments
are conducted with this method, as well as other algorithmic variants, on
several artificial and real problems. Randomly projecting the output space is
shown to provide a better adaptation to different output correlation patterns
and is therefore competitive with the best of the other methods in most
settings. Thanks to model sharing, the convergence speed is also improved,
reducing the computing times (or the complexity of the model) to reach a
specific accuracy.


  Graph neural networks (GNNs) have emerged as a powerful tool for learning
software engineering tasks including code completion, bug finding, and program
repair. They benefit from leveraging program structure like control flow
graphs, but they are not well-suited to tasks like program execution that
require far more sequential reasoning steps than number of GNN propagation
steps. Recurrent neural networks (RNNs), on the other hand, are well-suited to
long sequential chains of reasoning, but they do not naturally incorporate
program structure and generally perform worse on the above tasks. Our aim is to
achieve the best of both worlds, and we do so by introducing a novel GNN
architecture, the Instruction Pointer Attention Graph Neural Networks
(IPA-GNN), which achieves improved systematic generalization on the task of
learning to execute programs using control flow graphs. The model arises by
considering RNNs operating on program traces with branch decisions as latent
variables. The IPA-GNN can be seen either as a continuous relaxation of the RNN
model or as a GNN variant more tailored to execution. To test the models, we
propose evaluating systematic generalization on learning to execute using
control flow graphs, which tests sequential reasoning and use of program
structure. More practically, we evaluate these models on the task of learning
to execute partial programs, as might arise if using the model as a heuristic
function in program synthesis. Results show that the IPA-GNN outperforms a
variety of RNN and GNN baselines on both tasks.


  Classical plasma with any degree degeneration of electronic gas is
considered. In plasma two external electromagnetic field are propagation. It is
required to find the plasma response on these fields. From kinetic Vlasov
equation for collisionless plasmas distribution function in square-law
approximation on sizes of intensivities of two electric fields is received. The
formula for calculation electric current at any temperature (any degree of
degeneration electronic gas) is deduced. This formula contains an
one-dimensional quadrature. It is shown, that the nonlinearity account leads to
occurrence the longitudinal electric current directed along a wave vector. This
longitudinal current is perpendicular to known tranversal classical current,
received at the linear analysis. The case of small values of wave number is
considered. Graphic comparison of dimensionless size of the current depending
on wave number and frequency of oscillation of the electromagnetic fields is
carry out.


  An explicit evaluation of the spin asymmetry of the deuteron and the
associated GDH sum rule is presented which includes photodisintegration, single
and double pion and eta production as well. Photodisintegration is treated with
a realistic retarded potential and a corresponding meson exchange current. For
single pion and eta production the elementary operator from MAID is employed
whereas for double pion production an effective Lagrangean approach is used. A
large cancellation between the disintegration and the meson production channels
yields for the explicit GDH integral a value of 27.31 $\mu$b to be compared to
the sum rule value 0.65 $\mu$b.


  We present new observations of the Kuiper belt analogues around HD 38858 and
HD 20794, hosts of super-Earth mass planets within 1 au. As two of the four
nearby G-type stars (with HD 69830 and 61 Vir) that form the basis of a
possible correlation between low-mass planets and debris disc brightness, these
systems are of particular interest. The disc around HD 38858 is well resolved
with Herschel and we constrain the disc geometry and radial structure. We also
present a probable JCMT sub-mm continuum detection of the disc and a CO J=2-1
upper limit. The disc around HD 20794 is much fainter and appears marginally
resolved with Herschel, and is constrained to be less extended than the discs
around 61 Vir and HD 38858. We also set limits on the radial location of hot
dust recently detected around HD 20794 with near-IR interferometry. We present
HARPS upper limits on unseen planets in these four systems, ruling out
additional super-Earths within a few au, and Saturn-mass planets within 10 au.
We consider the disc structure in the three systems with Kuiper belt analogues
(HD 69830 has only a warm dust detection), concluding that 61 Vir and HD 38858
have greater radial disc extent than HD 20794. We speculate that the greater
width is related to the greater minimum planet masses (10-20 $M_\oplus$ vs. 3-5
$M_\oplus$), arising from an eccentric planetesimal population analogous to the
Solar System's scattered disc. We discuss alternative scenarios and possible
means to distinguish among them.


  We consider the problem of using image queries to retrieve videos from a
database. Our focus is on large-scale applications, where it is infeasible to
index each database video frame independently. Our main contribution is a
framework based on Bloom filters, which can be used to index long video
segments, enabling efficient image-to-video comparisons. Using this framework,
we investigate several retrieval architectures, by considering different types
of aggregation and different functions to encode visual information -- these
play a crucial role in achieving high performance. Extensive experiments show
that the proposed technique improves mean average precision by 24% on a public
dataset, while being 4X faster, compared to the previous state-of-the-art.


  Entanglement distillation is a process via which the strength and purity of
quantum entanglement can be increased probabilistically. It is a key step in
many quantum communication and computation protocols. In particular,
entanglement distillation is a necessary component of the quantum repeater, a
device which counters the degradation of entanglement that inevitably occurs
due to losses in a communication line. Here we report an experiment on
distilling the Einstein-Podolsky-Rosen (EPR) state of light, the workhorse of
continuous-variable entanglement, using the technique of noiseless
amplification. In contrast to previous implementations, the entanglement
enhancement factor achievable by our technique is not fundamentally limited and
permits recovering an EPR state with a macroscopic level of entanglement no
matter how low the initial entanglement or how high the loss may be. In
particular, we recover the original level of entanglement after one of the EPR
modes has passed through a channel with a loss factor of 20. The level of
entanglement in our distilled state is higher than that achievable by direct
transmission of any state through a similar loss channel. This is a key
bench-marking step towards the realization of a practical continuous-variable
quantum repeater and other CV quantum protocols.


  We study interacting systems of linear Brownian motions whose drift vector at
every time point is determined by the relative ranks of the coordinate
processes at that time. Our main objective has been to study the long range
behavior of the spacings between the Brownian motions arranged in increasing
order. For finitely many Brownian motions interacting in this manner, we
characterize drifts for which the family of laws of the vector of spacings is
tight, and show its convergence to a unique stationary joint distribution given
by independent exponential distributions with varying means. We also study one
particular countably infinite system, where only the minimum Brownian particle
gets a constant upward drift, and prove that independent and identically
distributed exponential spacings remain stationary under the dynamics of such a
process. Some related conjectures in this direction have also been discussed.


  The optical response of graphene micro-structures, such as micro-ribbons and
disks, is dominated by the localized plasmon resonance in the far infrared (IR)
spectral range. An ensemble of such structures is usually involved and the
effect of the coupling between the individual structures is expected to play an
important role. In this paper, the plasmonic coupling of graphene
microstructures in different configurations is investigated. While a relatively
weak coupling between graphene disks on the same plane is observed, the
coupling between vertically stacked graphene disks is strong and a drastic
increase of the resonance frequency is demonstrated. The plasmons in a more
complex structure can be treated as the hybridization of plasmons from more
elementary structures. As an example, the plasmon resonances of graphene
micro-rings are presented, in conjunction with their response in a magnetic
field. Finally, the coupling of the plasmon and the surface polar phonons of
SiO2 substrate is demonstrated by the observation of a new hybrid resonance
peak around 500cm-1.


  We present a method to extract the low energy behavior of physical
observables from their high energy expansions, systematically calculable via
the operator product expansion (OPE), in asymptotically free and mass-gapped
theories. By applying the inverse Laplace transform to correlation functions,
their analytic structure is modified such that low-energy information connects
with high energy expansions. Furthermore, this transformation alleviates the
renormalon problem, enabling a more straightforward application of the OPE
compared to the OPE before the transformation. We demonstrate that the low
energy limit of correlation functions can be accurately extracted using the OPE
in the two dimensional $O(N)$ nonlinear $\sigma$ model, serving as a first
testing ground.


  Organic semiconductors are indispensable for today's display technologies in
form of organic light emitting diodes (OLEDs) and further optoelectronic
applications. However, organic materials do not reach the same charge carrier
mobility as inorganic semiconductors, limiting the efficiency of devices. To
find or even design new organic semiconductors with higher charge carrier
mobility, computational approaches, in particular multiscale models, are
becoming increasingly important. However, such models are computationally very
costly, especially when large systems and long time scales are required, which
is the case to compute static and dynamic energy disorder, i.e. dominant factor
to determine charge transport. Here we overcome this drawback by integrating
machine learning models into multiscale simulations. This allows us to obtain
unprecedented insight into relevant microscopic materials properties, in
particular static and dynamic disorder contributions for a series of
application-relevant molecules. We find that static disorder and thus the
distribution of shallow traps is highly asymmetrical for many materials,
impacting widely considered Gaussian disorder models. We furthermore analyse
characteristic energy level fluctuation times and compare them to typical
hopping rates to evaluate the importance of dynamic disorder for charge
transport. We hope that our findings will significantly improve the accuracy of
computational methods used to predict application relevant materials properties
of organic semiconductors, and thus make these methods applicable for virtual
materials design.


  Non-Gaussianity of the cosmological matter density field can be largely
reduced by a local Gaussianization transformation (and its approximations such
as the logrithmic transformation). Such behavior can be recasted as the
Gaussian copula hypothesis, and has been verified to very high accuracy at
two-point level. On the other hand, statistically significant non-Gaussianities
in the Gaussianized field have been detected in simulations. We point out that,
this apparent inconsistency is caused by the very limited degrees of freedom in
the copula function, which make it misleading as a diagnosis of residual
non-Gaussianity in the Gaussianized field. Using the copula density, we
highlight the departure from Gaussianity. We further quantify its impact in the
predicted n-point correlation functions. We explore a remedy of the Gaussian
copula hypothesis, which alleviates but not completely solves the above
problems.


  Decoupled parallel $xyz$ positioning stages with large stroke have been
desired in high-speed and precise positioning fields. However, currently such
stages are either short in stroke or unqualified in parasitic motion and
coupling rate. This paper proposes a novel flexure-based decoupled parallel
$xyz$ positioning stage (FlexDelta) and conducts its conceptual design,
modeling, and experimental study. Firstly, the working principle of FlexDelta
is introduced, followed by its mechanism design with flexure. Secondly, the
stiffness model of flexure is established via matrix-based Castigliano's second
theorem, and the influence of its lateral stiffness on the stiffness model of
FlexDelta is comprehensively investigated and then optimally designed. Finally,
experimental study was carried out based on the prototype fabricated. The
results reveal that the positioning stage features centimeter-stroke in three
axes, with coupling rate less than 0.53%, parasitic motion less than 1.72 mrad
over full range. And its natural frequencies are 20.8 Hz, 20.8 Hz, and 22.4 Hz
for $x$, $y$, and $z$ axis respectively. Multi-axis path tracking tests were
also carried out, which validates its dynamic performance with micrometer
error.


  We report on investigations of the applicability of non-relativistic
constituent quark models to the low-energy nucleon-nucleon (NN) interaction.
The major innovations of a resulting NN potential are the use of the $^3$P$_0$
decay model and quark model wave functions to derive nucleon-nucleon-meson
form-factors, and the use of a colored spin-spin contact hyperfine interaction
to model the repulsive core rather than the phenomenological treatment common
in other NN potentials. We present the results of the model for experimental
free NN scattering phase shifts, S-wave scattering lengths and effective ranges
and deuteron properties. Plans for future study are discussed.


  In this work, we study how to optimally manage the freshness of information
updates sent from a source node to a destination via a channel. A proper metric
for data freshness at the destination is the age-of-information, or simply age,
which is defined as how old the freshest received update is since the moment
that this update was generated at the source node (e.g., a sensor). A
reasonable update policy is the zero-wait policy, i.e., the source node submits
a fresh update once the previous update is delivered and the channel becomes
free, which achieves the maximum throughput and the minimum delay.
Surprisingly, this zero-wait policy does not always minimize the age. This
counter-intuitive phenomenon motivates us to study how to optimally control
information updates to keep the data fresh and to understand when the zero-wait
policy is optimal. We introduce a general age penalty function to characterize
the level of dissatisfaction on data staleness and formulate the average age
penalty minimization problem as a constrained semi-Markov decision problem
(SMDP) with an uncountable state space. We develop efficient algorithms to find
the optimal update policy among all causal policies, and establish sufficient
and necessary conditions for the optimality of the zero-wait policy. Our
investigation shows that the zero-wait policy is far from the optimum if (i)
the age penalty function grows quickly with respect to the age, (ii) the packet
transmission times over the channel are positively correlated over time, or
(iii) the packet transmission times are highly random (e.g., following a
heavy-tail distribution).


  Abstract. In our previous paper arXiv:2210.16008, we show that any prime
$\mathbb{Q}$-Fano 3-folds $X$ with only $1/2(1,1,1)$-singularities in certain 5
classes can be embedded as linear sections into bigger dimensional
$\mathbb{Q}$-Fano varieties called key varieties, where each of the key
varieties is constructed from certain data of the Sarkisov link staring from
the blow-up at one $1/2(1,1,1)$-singularity of $X$. In this paper, we introduce
varieties associated with the key varieties which are dual in a certain sense.
As an application, we interpret a fundamental part of the Sarkisov link for
each $X$ as a linear section of the dual variety. In a natural context
describing the variety dual to the key variety of $X$ of genus 5 with one
$1/2(1,1,1)$-singularity, we also characterize a general canonical curve of
genus 9 with a $g_{7}^{2}$.


  The 1S0 pairing gap associated with the inner crust of a neutron star is
calculated, taking into account the coexistence of the nuclear lattice with the
sea of free neutrons (finite size effects), as well as medium polarization
effects associated with the exchange of density and spin fluctuations. Both
effects are found to be important and to lead to an overall quenching of the
pairing gap. This result, whose quantitative value is dependent on the
effective interaction used to generate the single-particle levels, is a
consequence of the balance between the attractive (repulsive) induced
interaction arising from the exchange of density (spin) modes, balance which in
turn is influenced by the presence of the protons and depends on the
single-particle structure of the system.


  We obtain a limit of a hierarchical Bayes estimator of a finite population
mean when the sample size is large. The limit is in the sense of ordinary
calculus, where the sample observations are treated as fixed quantities. Our
result suggests a simple way to correct the hierarchical Bayes estimator to
achieve design-consistency, a well-known property in the traditional
randomization approach to finite population sampling. We also suggest three
different measures of uncertainty of our proposed estimator.


  Semantic segmentation is a challenging computer vision task demanding a
significant amount of pixel-level annotated data. Producing such data is a
time-consuming and costly process, especially for domains with a scarcity of
experts, such as medicine or forensic anthropology. While numerous
semi-supervised approaches have been developed to make the most from the
limited labeled data and ample amount of unlabeled data, domain-specific
real-world datasets often have characteristics that both reduce the
effectiveness of off-the-shelf state-of-the-art methods and also provide
opportunities to create new methods that exploit these characteristics. We
propose and evaluate a semi-supervised method that reuses available labels for
unlabeled images of a dataset by exploiting existing similarities, while
dynamically weighting the impact of these reused labels in the training
process. We evaluate our method on a large dataset of human decomposition
images and find that our method, while conceptually simple, outperforms
state-of-the-art consistency and pseudo-labeling-based methods for the
segmentation of this dataset. This paper includes graphic content of human
decomposition.


  Recurrent neural networks (RNNs) are instrumental in modelling sequential and
time-series data. Yet, when using RNNs to inform decision-making, predictions
by themselves are not sufficient; we also need estimates of predictive
uncertainty. Existing approaches for uncertainty quantification in RNNs are
based predominantly on Bayesian methods; these are computationally prohibitive,
and require major alterations to the RNN architecture and training.
Capitalizing on ideas from classical jackknife resampling, we develop a
frequentist alternative that: (a) does not interfere with model training or
compromise its accuracy, (b) applies to any RNN architecture, and (c) provides
theoretical coverage guarantees on the estimated uncertainty intervals. Our
method derives predictive uncertainty from the variability of the (jackknife)
sampling distribution of the RNN outputs, which is estimated by repeatedly
deleting blocks of (temporally-correlated) training data, and collecting the
predictions of the RNN re-trained on the remaining data. To avoid exhaustive
re-training, we utilize influence functions to estimate the effect of removing
training data blocks on the learned RNN parameters. Using data from a critical
care setting, we demonstrate the utility of uncertainty quantification in
sequential decision-making.


  We show that vacuum type N Kundt spacetimes in an arbitrary dimension admit a
Kerr-Schild (KS) double copy. This is mostly done in a coordinate-independent
way using the higher-dimensional Newman-Penrose formalism. We also discuss two
kinds of non-uniqueness of an electromagnetic field corresponding to a given KS
metric (i.e., its single copy) - these originate, respectively, from the
rescaling freedom in the KS vector and from the non-uniqueness of the splitting
of the KS metric in the flat part and the KS part. In connection to this, we
show that the subset of KS pp-waves admits both null and non-null
electromagnetic single copies. Since vacuum type N Kundt spacetimes are
universal solutions of virtually any higher-order gravities and null fields in
such backgrounds are immune to higher-order electromagnetic corrections, the
KS-Kundt double copy demonstrated in the present paper also applies to large
classes of modified theories.


  We consider the notion of information distance between two objects x and y
introduced by Bennett, G\'acs, Li, Vitanyi, and Zurek [1] as the minimal length
of a program that computes x from y as well as computing y from x, and study
different versions of this notion. It was claimed by Mahmud [11] that the
prefix version of information distance equals max(K(x|y), K(y|) + O(1) (this
equality with logarithmic precision was one of the main results of the paper by
Bennett, G\'acs, Li, Vitanyi, and Zurek). We show that this claim is false, but
does hold if the information distance is at least super logarithmic.


  We investigate the construction of real analytic Levi-flat hypersurfaces in
K3 surfaces. By taking images of real hyperplanes, one can construct such
hypersurfaces in two-dimensional complex tori. We show that "almost every" K3
surfaces contains infinitely many Levi-flat hypersurfaces of this type. The
proof relies mainly on a recent construction of Koike and Uehara, ideas of
Verbitsky on ergodic complex structures, as well as an argument due to Ghys in
the context of the study of the topology of generic leaves.
  --
  On s'int\'eresse \`a la construction d'hypersurfaces Levi-plates analytiques
r\'elles dans les surfaces K3. On peut en construire dans les tores complexes
de dimension 2 en prenant des images d'hyperplans r\'eels. On montre que
"presque toute" surface K3 contient une infinit\'e d'hypersurfaces Levi-plates
de ce type. La preuve repose principalement sur une construction r\'ecente due
\`a Koike-Uehara, ainsi que sur les id\'ees de Verbitsky sur les structures
complexes ergodiques et une adaptation d'un argument d\^u \`a Ghys dans le
cadre de l'\'etude de la topologie des feuilles g\'en\'eriques.


  We study the critical points of the black hole scalar potential $V_{BH}$ in
N=2, d=4 supergravity coupled to $n_{V}$ vector multiplets, in an
asymptotically flat extremal black hole background described by a
2(n_{V}+1)-dimensional dyonic charge vector and (complex) scalar fields which
are coordinates of a special K\"{a}hler manifold.
  For the case of homogeneous symmetric spaces, we find three general classes
of regular attractor solutions with non-vanishing Bekenstein-Hawking entropy.
They correspond to three (inequivalent) classes of orbits of the charge vector,
which is in a 2(n_{V}+1)-dimensional representation $R_{V}$ of the U-duality
group. Such orbits are non-degenerate, namely they have non-vanishing quartic
invariant (for rank-3 spaces). Other than the 1/2-BPS one, there are two other
distinct non-BPS classes of charge orbits, one of which has vanishing central
charge.
  The three species of solutions to the N=2 extremal black hole attractor
equations give rise to different mass spectra of the scalar fluctuations, whose
pattern can be inferred by using invariance properties of the critical points
of $V_{BH}$ and some group theoretical considerations on homogeneous symmetric
special K\"{a}hler geometry.


  This technical report presents a bibliometric analysis of the top 30 cited
researchers from USA, UK and China. The analysis is based on Google Scholar
data using CIDS. The researchers were identified using their email suffix: edu,
uk and cn. This na\"{i}ve approach was able to produce rankings consistent with
the SCImago country rankings using mininal resources in a fully automated way.


  Scope of this contribution is twofold. First, it describes the potential of
the global astrometry mission Gaia for detecting and measuring planetary
systems based on detailed double-blind mode simulations and on the most recent
predictions of the satellite's astrometric payload performances (launch is
foreseen for late Summer 2012). Then, the identified capabilities are put in
context by highlighting the contribution that the Gaia exoplanet discoveries
will be able to bring to the science of extrasolar planets of the next decade.


  We consider certain elliptic threefolds over the projective plane (more
generally over certain rational surfaces) with a section in Weierstrass normal
form. In particular, over a del Pezzo surface of degree 8, these elliptic
threefolds are Calabi-Yau threefolds. We will discuss especially the generating
functions of Gromov-Witten and Gopakumar-Vafa invariants.


  Experimental identification of three-dimensional (3D) Dirac semimetals in
solid state systems is critical for realizing exotic topological phenomena and
quantum transport such as the Weyl phases, high temperature linear quantum
magnetoresistance and topological magnetic phases. Using high resolution
angle-resolved photoemission spectroscopy, we performed systematic electronic
structure studies on well-known compound Cd3As2. For the first time, we observe
a highly linear bulk Dirac cone located at the Brillouin zone center projected
onto the (001) surface which is consistent with a 3D Dirac semimetal phase in
Cd3As2. Remarkably, an unusually high Dirac Fermion velocity up to 10.2
\textrm{\AA}{\cdot}$eV (1.5 \times 10^{6} ms^-1) is seen in samples where the
mobility far exceeds 40,000 cm^2/V.s suggesting that Cd3As2 can be a promising
candidate as a hypercone analog of graphene in many device-applications which
can also incorporate topological quantum phenomena in a large gap setting. Our
experimental identification of this novel topological 3D Dirac semimetal phase,
distinct from a 3D topological insulator phase discovered previously, paves the
way for exploring higher dimensional relativistic physics in bulk transport and
for realizing novel Fermionic matter such as a Fermi arc nodal metal.


  In the three-dimensional flat space, a classical Hamiltonian, which has five
functionally independent integrals of motion, including the Hamiltonian, is
characterized as superintegrable. Kalnins, Kress and Miller (J. Math. Phys. 48
(2007), 113518, 26 pages) have proved that, in the case of nondegenerate
potentials, i.e. potentials depending linearly on four parameters, with
quadratic symmetries, posses a sixth quadratic integral, which is linearly
independent of the other integrals. The existence of this sixth integral imply
that the integrals of motion form a ternary quadratic Poisson algebra with five
generators. The superintegrability of the generalized Kepler-Coulomb potential
that was investigated by Verrier and Evans (J. Math. Phys. 49 (2008), 022902, 8
pages) is a special case of superintegrable system, having two independent
integrals of motion of fourth order among the remaining quadratic ones. The
corresponding Poisson algebra of integrals is a quadratic one, having the same
special form, characteristic to the nondegenerate case of systems with
quadratic integrals. In this paper, the ternary quadratic associative algebra
corresponding to the quantum Verrier-Evans system is discussed. The subalgebras
structure, the Casimir operators and the the finite-dimensional representation
of this algebra are studied and the energy eigenvalues of the nondegenerate
Kepler-Coulomb are calculated.


  We provide a simple proof of the radial symmetry of any nonnegative minimizer
for a general class of quasi-linear minimization problems.


  We examine the implications of several recently derived conditions [Hillery
and Zubairy, Phys. Rev. Lett. 96, 050503 (2006)] for determining when a
two-mode state is entangled. We first find examples of non-Gaussian states that
satisfy these conditions. We then apply the entanglement conditions to the
study of several linear devices, the beam splitter, the parametric amplifier,
and the linear phase-insensitive amplifier. For the first two, we find
conditions on the input states that guarantee that the output states are
entangled. For the linear amplifier, we determine in the limit of high and no
gain, when an entangled input leads to an entangled output. Finally, we show
how application of two two-mode entanglement conditions to a three-mode state
can serve as a test of genuine three-mode entanglement.


  The field of algorithmic randomness studies what it means for infinite binary
sequences to be random for some given uncertainty model. Classically,
martingale-theoretic notions of such randomness involve precise uncertainty
models, and it is only recently that imprecision has been introduced into this
context. As a consequence, the investigation into how imprecision alters our
view on martingale-theoretic random sequences has only just begun. In this
contribution, where we allow for non-computable uncertainty models, we
establish a close and surprising connection between precise and imprecise
uncertainty models in this randomness context. In particular, we show that
there are stationary imprecise models and non-computable non-stationary precise
models that have the exact same set of random sequences. We also give a
preliminary discussion of the possible implications of our result for a
statistics based on imprecise probabilities, and shed some light on the
practical (ir)relevance of both imprecise and non-computable precise
uncertainty models in that context.


  We study the homology of an explicit finite-index subgroup of the
automorphism group of a partially commutative group, in the case when its
defining graph is a tree. More concretely, we give a lower bound on the first
Betti number of this subgroup, based on the number and degree of a certain type
of vertices, which we call deep. We then use combinatorial methods to analyze
the average value of this Betti number, in terms of the size of the defining
tree.


  Recent experiments of Groeblacher et al. proved the violation of a
Leggett-type inequality that was claimed to be valid for a broad class of
non-local hidden-variable theories. The impossibility of constructing a
non-local and realistic theory, unless it entails highly counterintuitive
features, seems thus to have been experimentally proved. This would bring us
close to a definite refutation of realism. Indeed, realism was proved to be
also incompatible with locality, according to a series of experiments testing
Bell inequalities. The present paper addresses the said experiments of
Groeblacher et al. and presents an explicit, contextual and realistic, model
that reproduces the predictions of quantum mechanics. It thus violates the
Leggett-type inequality that was established with the aim of ruling out a
supposedly broad class of non-local models. We can thus conclude that plausible
contextual, realistic, models are still tenable. This restates the possibility
of a future completion of quantum mechanics by a realistic and contextual
theory which is not in a class containing only highly counterintuitive models.
The class that was ruled out by the experiments of Groeblacher et al. is thus
proved to be a limited one, arbitrarily separating models that physically
belong in the same class.


  We present a study of the dust properties of 12 classical and superbubble HII
regions in the Large Magellanic Cloud. We use infrared photometry from Spitzer
(8, 24, 70, and 160 \mum bands), obtained as part of the Surveying the Agents
of a Galaxy's Evolution (SAGE) program, along with archival spectroscopic
classifications of the ionizing stars to examine the role of stellar sources on
dust heating and processing. Our infrared observations show surprisingly little
correlation between the emission properties of the dust and the effective
temperatures or bolometric magnitudes of stars in the HII regions, suggesting
that the HII region evolutionary timescale is not on the order of the dust
processing timescale. We find that the infrared emission of superbubbles and
classical HII regions shows little differentiation between the two classes,
despite the significant differences in age and morphology. We do detect a
correlation of the 24 \mum emission from hot dust with the ratio of 70 to 160
\mum flux. This correlation can be modeled as a trend in the temperature of a
minority hot dust component, while a majority of the dust remains significantly
cooler.


  Recent results from chemical tagging studies using APOGEE data suggest a
strong link between the chemical abundance patterns of stars found within
globular clusters, and chemically peculiar populations in the Galactic halo
field. In this paper we analyse the chemical compositions of stars within the
cluster body and tidal streams of Palomar 5, a globular cluster that is being
tidally disrupted by interaction with the Galactic gravitational potential. We
report the identification of nitrogen-rich (N-rich) stars both within and
beyond the tidal radius of Palomar 5, with the latter being clearly aligned
with the cluster tidal streams; this acts as confirmation that N-rich stars are
lost to the Galactic halo from globular clusters, and provides support to the
hypothesis that field N-rich stars identified by various groups have a globular
cluster origin.


  Quadruped animal locomotion emerges from the interactions between the spinal
central pattern generator (CPG), sensory feedback, and supraspinal drive
signals from the brain. Computational models of CPGs have been widely used for
investigating the spinal cord contribution to animal locomotion control in
computational neuroscience and in bio-inspired robotics. However, the
contribution of supraspinal drive to anticipatory behavior, i.e. motor behavior
that involves planning ahead of time (e.g. of footstep placements), is not yet
properly understood. In particular, it is not clear whether the brain modulates
CPG activity and/or directly modulates muscle activity (hence bypassing the
CPG) for accurate foot placements. In this paper, we investigate the
interaction of supraspinal drive and a CPG in an anticipatory locomotion
scenario that involves stepping over gaps. By employing deep reinforcement
learning (DRL), we train a neural network policy that replicates the
supraspinal drive behavior. This policy can either modulate the CPG dynamics,
or directly change actuation signals to bypass the CPG dynamics. Our results
indicate that the direct supraspinal contribution to the actuation signal is a
key component for a high gap crossing success rate. However, the CPG dynamics
in the spinal cord are beneficial for gait smoothness and energy efficiency.
Moreover, our investigation shows that sensing the front feet distances to the
gap is the most important and sufficient sensory information for learning gap
crossing. Our results support the biological hypothesis that cats and horses
mainly control the front legs for obstacle avoidance, and that hind limbs
follow an internal memory based on the front limbs' information. Our method
enables the quadruped robot to cross gaps of up to 20 cm (50% of body-length)
without any explicit dynamics modeling or Model Predictive Control (MPC).


  The QED vacuum polarization in external monochromatic plane-wave
electromagnetic fields is calculated with spatial and temporal variations of
the external fields being taken into account. We develop a perturbation theory
to calculate the induced electromagnetic current that appears in the Maxwell
equations, based on Schwinger's proper-time method, and combine it with the
so-called gradient expansion to handle the variation of external fields
perturbatively. The crossed field, i.e., the long wavelength limit of the
electromagnetic wave is first considered. The eigenmodes and the refractive
indices as the eigenvalues associated with the eigenmodes are computed
numerically for the probe photon propagating in some particular directions. In
so doing, no limitation is imposed on the field strength and the photon energy
unlike previous studies. It is shown that the real part of the refractive index
becomes less than unity for strong fields, the phenomenon that has been known
to occur for high-energy probe photons. We then evaluate numerically the
lowest-order corrections to the crossed-field resulting from the field
variations in space and time. It is demonstrated that the corrections occur
mainly in the imaginary part of the refractive index.


  In this work we present a novel approach for single depth map
super-resolution. Modern consumer depth sensors, especially Time-of-Flight
sensors, produce dense depth measurements, but are affected by noise and have a
low lateral resolution. We propose a method that combines the benefits of
recent advances in machine learning based single image super-resolution, i.e.
deep convolutional networks, with a variational method to recover accurate
high-resolution depth maps. In particular, we integrate a variational method
that models the piecewise affine structures apparent in depth data via an
anisotropic total generalized variation regularization term on top of a deep
network. We call our method ATGV-Net and train it end-to-end by unrolling the
optimization procedure of the variational method. To train deep networks, a
large corpus of training data with accurate ground-truth is required. We
demonstrate that it is feasible to train our method solely on synthetic data
that we generate in large quantities for this task. Our evaluations show that
we achieve state-of-the-art results on three different benchmarks, as well as
on a challenging Time-of-Flight dataset, all without utilizing an additional
intensity image as guidance.


  Vertex deletion problems for graphs are studied intensely in classical and
parameterized complexity theory. They ask whether we can delete at most k
vertices from an input graph such that the resulting graph has a certain
property. Regarding k as the parameter, a dichotomy was recently shown based on
the number of quantifier alternations of first-order formulas that describe the
property. In this paper, we refine this classification by moving from
quantifier alternations to individual quantifier patterns and from a dichotomy
to a trichotomy, resulting in a complete classification of the complexity of
vertex deletion problems based on their quantifier pattern. The more
fine-grained approach uncovers new tractable fragments, which we show to not
only lie in FPT, but even in parameterized constant-depth circuit complexity
classes. On the other hand, we show that vertex deletion becomes intractable
already for just one quantifier per alternation, that is, there is a formula of
the form {\forall}x{\exists}y{\forall}z({\psi}), with {\psi} quantifier-free,
for which the vertex deletion problem is W[1]-hard. The fine-grained analysis
also allows us to uncover differences in the complexity landscape when we
consider different kinds of graphs and more general structures: While basic
graphs (undirected graphs without self-loops), undirected graphs, and directed
graphs each have a different frontier of tractability, the frontier for
arbitrary logical structures coincides with that of directed graphs.


  We present a temperature map and a temperature profile of the central part (r
< 20' or 1/4 virial radius) of the Coma cluster. We combined 5 overlapping
pointings made with XMM/EPIC/MOS and extracted spectra in boxes of 3.5' X 3.5'.
The temperature distribution around the two central galaxies is remarkably
homogeneous (r<10'), contrary to previous ASCA results, suggesting that the
core is actually in a relaxed state. At larger distance from the cluster center
we do see evidence for recent matter accretion. We confirm the cool area in the
direction of NGC 4921, probably due to gas stripped from an infalling group. We
find indications of a hot front in the South West, in the direction of NGC4839,
probably due to an adiabatic compression.


  We generalize in this article the classical Sobolev's and Sobolev's trace
inequalities on the Grand Lebesgue Spaces under monomial weight instead the
classical Lebesgue or grand Lebesgue Spaces.
  We will distinguish the classical Sobolev's inequality and the so-called
trace Sobolev's inequality.


  The detection of nanoHertz gravitational waves through pulsar timing arrays
hinges on identifying a common stochastic process affecting all pulsars in a
correlated way across the sky. In the presence of other deterministic and
stochastic processes affecting the time-of-arrival of pulses, a detection claim
must be accompanied by a detailed assessment of the various physical or
phenomenological models used to describe the data. In this study, we propose
posterior predictive checks as a model-checking tool that relies on the
predictive performance of the models with regards to new data. We derive and
study predictive checks based on different components of the models, namely the
Fourier coefficients of the stochastic process, the correlation pattern, and
the timing residuals. We assess the ability of our checks to identify model
misspecification in simulated datasets. We find that they can accurately flag a
stochastic process spectral shape that deviates from the common power-law model
as well as a stochastic process that does not display the expected angular
correlation pattern. Posterior predictive likelihoods derived under different
assumptions about the correlation pattern can further be used to establish
detection significance. In the era of nanoHertz gravitational wave detection
from different pulsar-timing datasets, such tests represent an essential tool
in assessing data consistency and supporting astrophysical inference.


  We consider {\em discretized} Hamiltonian PDEs associated with a Hamiltonian
function that can be split into a linear unbounded operator and a regular
nonlinear part. We consider splitting methods associated with this
decomposition. Using a finite dimensional Birkhoff normal form result, we show
the almost preservation of the {\em actions} of the numerical solution
associated with the splitting method over arbitrary long time, provided the
Sobolev norms of the initial data is small enough, and for asymptotically large
level of space approximation. This result holds under {\em generic} non
resonance conditions on the frequencies of the linear operator and on the step
size. We apply this results to nonlinear Schr\"odinger equations as well as the
nonlinear wave equation.}


  Using a three-dimensional compressible magnetohydrodynamic (MHD) simulation,
we have reproduced the fast solar wind in a direct and self-consistent manner,
based on the wave/turbulence driven scenario. As a natural consequence of
Alfvenic perturbations at its base, highly compressional and turbulent
fluctuations are generated, leading to heating and acceleration of the solar
wind. The analysis of power spectra and structure functions reveals that the
turbulence is characterized by its imbalanced (in the sense of outward Alfvenic
fluctuations) and anisotropic nature. The density fluctuation originates from
the parametric decay instability of outwardly propagating Alfven waves and
plays a significant role in the Alfven wave reflection that triggers
turbulence. Our conclusion is that the fast solar wind is heated and
accelerated by compressible MHD turbulence driven by parametric decay
instability and resultant Alfven wave reflection.


  We consider the problem of graph searching with prediction recently
introduced by Banerjee et al. (2022). In this problem, an agent, starting at
some vertex $r$ has to traverse a (potentially unknown) graph $G$ to find a
hidden goal node $g$ while minimizing the total distance travelled. We study a
setting in which at any node $v$, the agent receives a noisy estimate of the
distance from $v$ to $g$. We design algorithms for this search task on unknown
graphs. We establish the first formal guarantees on unknown weighted graphs and
provide lower bounds showing that the algorithms we propose have optimal or
nearly-optimal dependence on the prediction error. Further, we perform
numerical experiments demonstrating that in addition to being robust to
adversarial error, our algorithms perform well in typical instances in which
the error is stochastic. Finally, we provide alternative simpler performance
bounds on the algorithms of Banerjee et al. (2022) for the case of searching on
a known graph, and establish new lower bounds for this setting.


  The diffraction contrast modalities accessible by X-ray grating
interferometers are not imaged directly but have to be inferred from sine like
signal variations occurring in a series of images acquired at varying relative
positions of the interferometer's gratings. The absolute spatial translations
involved in the acquisition of these phase stepping series usually lie in the
range of only a few hundred nanometers, wherefore positioning errors as small
as 10nm will already translate into signal uncertainties of one to ten percent
in the final images if not accounted for.
  Classically, the relative grating positions in the phase stepping series are
considered input parameters to the analysis and are, for the Fast Fourier
Transform that is typically employed, required to be equidistantly distributed
over multiples of the gratings' period. More general optimization based
approaches with relaxed requirements on the sampling positions are often deemed
too time consuming in contrast.
  In the following, a fast converging optimization scheme is presented
simultaneously determining the phase stepping curves' parameters as well as the
actually performed motions of the stepped grating, including also erroneous
rotational motions which are commonly neglected. While the correction of solely
the translational errors along the stepping direction is found to be sufficient
with regard to the reduction of image artifacts, the possibility to also detect
minute rotations about all axes proves to be a valuable tool for system
calibration and monitoring. The simplicity of the provided algorithm, in
particular when only considering translational errors, makes it well suitable
as a standard evaluation procedure also for large image series.


  We propose a novel framework for crowdsourced images to determine the
likelihood of an image being fake. We use a service-oriented approach to model
and represent crowdsourced images uploaded on social media, as image services.
Trust may, in some circumstances, be determined by using only the
non-functional attributes of an image service, i.e., image metadata. We define
intention of changes as a key parameter to ascertain fake image services. A
novel framework is proposed to estimate the intention of underlying changes
considering change in semantics of an image. Our experiments show high accuracy
using a large real dataset.


  In this note, we give a new proof of the fact that an affine semiprime
algebra R of Gelfand-Kirillov dimension 1 satisfies a polynomial identity. Our
proof uses only the growth properties of the algebra and yields an explicit
upper bound for the pi degree of R.


  A low Schottky barrier height (SBH) at source/drain contact is essential for
achieving high drive current in atomic layer MoS2 channel based field-effect
transistors. Approaches such as choosing metals with appropriate work functions
and chemical doping are employed previously to improve the carrier injection
from the contact electrodes to the channel and to mitigates the SBH between the
MoS2 and metal. Recent experiments demonstrate significant SBH reduction when
graphene layer is inserted between metal slab (Ti and Ni) and MoS2. However,
the physical or chemical origin of this phenomenon is not yet clearly
understood. In this work, density functional theory (DFT) simulations are
performed, employing pseudopotentials with very high basis sets to get insights
of the charge transfer between metal and monolayer MoS2 through the inserted
graphene layer. Our atomistic simulations on 16 different interfaces involving
five different metals (Ti, Ag, Ru, Au and Pt) reveal that: (i) such a decrease
in SBH is not consistent among various metals, rather an increase in SBH is
observed in case of Au and Pt (ii) unlike MoS2-metal interface, the projected
dispersion of MoS2 remains preserved in any MoS2-graphene-metal system with
shift in the bands on the energy axis. (iii) a proper choice of metal (e.g.,
Ru) may exhibit ohmic nature in a graphene inserted MoS2-metal contact. These
understandings would provide a direction in developing high performance
transistors involving hetero atomic layers as contact electrodes.


  While latent diffusion models (LDMs) excel at creating imaginative images,
they often lack precision in semantic fidelity and spatial control over where
objects are generated. To address these deficiencies, we introduce the
Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving
spatial control and semantic accuracy in text-to-image (T2I) diffusion models.
B2B targets three key challenges in T2I: catastrophic neglect, attribute
binding, and layout guidance. The process encompasses two main steps: i) Object
generation, which adjusts the latent encoding to guarantee object generation
and directs it within specified bounding boxes, and ii) attribute binding,
guaranteeing that generated objects adhere to their specified attributes in the
prompt. B2B is designed as a compatible plug-and-play module for existing T2I
models, markedly enhancing model performance in addressing the key challenges.
We evaluate our technique using the established CompBench and TIFA score
benchmarks, demonstrating significant performance improvements compared to
existing methods. The source code will be made publicly available at
https://github.com/nextaistudio/BoxIt2BindIt.


  Glycerol and trehalose-glycerol binary solutions are glass-forming liquids
with remarkable bioprotectant properties. In this paper, we address the effects
of confining of these solutions in straight channels of diameter D=8 nm formed
by porous silicon. Neutron diffraction and incoherent quasielastic neutron
scattering are used to reveal the different effects of nanoconfinement and
addition of trehalose on the intermolecular structure and molecular dynamics of
the liquid and glassy phases, on a nanosecond timescale.


  We provide a simple set of rules for predicting interference effects in
off-resonant transport through single-molecule junctions. These effects fall in
two classes, showing respectively an odd or an even number of nodes in the
linear conductance within a given molecular charge state, and we demonstrate
how to decide the interference class directly from the contacting geometry. For
neutral alternant hydrocarbons, we employ the Coulson-Rushbrooke-McLachlan
pairing theorem to show that the interference class is decided simply by
tunneling on and off the molecule from same, or different sublattices. More
generally, we investigate a range of smaller molecules by means of exact diag-
onalization combined with a perturbative treatment of the molecule-lead tunnel
coupling. While these results generally agree well with GW calculations, they
are shown to be at odds with simpler mean-field treatments. For molecules with
spin-degenerate ground states, we show that for most junctions, interference
causes no transmission nodes, but argue that it may lead to a non-standard
gate-dependence of the zero-bias Kondo resonance.


  The nonlinear dynamics of an obliquely oriented wave packet at sea surface is
studied both analytically and numerically for various initial parameters of the
packet, in connection with the problem of oceanic rogue waves. In the framework
of Gaussian variational ansatz applied to the corresponding (1+2D) hyperbolic
nonlinear Schr\"odinger equation, a simplified Lagrangian system of
differential equations is derived, which determines the evolution of
coefficients of the real and imaginary quadratic forms appearing in the
Gaussian. This model provides a semi-quantitative description for the process
of nonlinear spatio-temporal focusing, which is one of the most probable
mechanisms of rogue wave formation in random wave fields. The system is
integrated in quadratures, which fact allows us to understand qualitative
differences between the linear and nonlinear regimes of the focusing of wave
packet. Comparison of the Gaussian model predictions with results of direct
numerical simulation of fully nonlinear long-crested water waves is carried
out.


  The effectiveness of the BERT model on multiple linguistic tasks has been
well documented. On the other hand, its potentials for narrow and specific
domains such as Legal, have not been fully explored. In this paper, we examine
how BERT can be used in the Arabic legal domain and try customizing this
language model for several downstream tasks using several different
domain-relevant training and testing datasets to train BERT from scratch. We
introduce the AraLegal-BERT, a bidirectional encoder Transformer-based model
that have been thoroughly tested and carefully optimized with the goal to
amplify the impact of NLP-driven solution concerning jurisprudence, legal
documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it
against three BERT variations for Arabic language in three natural languages
understanding (NLU) tasks. The results show that the base version of
AraLegal-BERT achieve better accuracy than the general and original BERT over
the Legal text.


  Boosting is a general method to convert a weak learner (which generates
hypotheses that are just slightly better than random) into a strong learner
(which generates hypotheses that are much better than random). Recently,
Arunachalam and Maity gave the first quantum improvement for boosting, by
combining Freund and Schapire's AdaBoost algorithm with a quantum algorithm for
approximate counting. Their booster is faster than classical boosting as a
function of the VC-dimension of the weak learner's hypothesis class, but worse
as a function of the quality of the weak learner. In this paper we give a
substantially faster and simpler quantum boosting algorithm, based on
Servedio's SmoothBoost algorithm.


  Researchers working with mathematical models are often confronted by the
related problems of parameter estimation, model validation, and model
selection. These are all optimization problems, well-known to be challenging
due to non-linearity, non-convexity and multiple local optima. Furthermore, the
challenges are compounded when only partial data is available. Here, we
consider polynomial models (e.g., mass-action chemical reaction networks at
steady state) and describe a framework for their analysis based on optimization
using numerical algebraic geometry. Specifically, we use probability-one
polynomial homotopy continuation methods to compute all critical points of the
objective function, then filter to recover the global optima. Our approach
exploits the geometric structures relating models and data, and we demonstrate
its utility on examples from cell signaling, synthetic biology, and
epidemiology.


  Infrared-faint radio sources (IFRS) are a class of radio-loud (RL) active
galactic nuclei (AGN) at high redshifts (z > 1.7) that are characterised by
their relative infrared faintness, resulting in enormous radio-to-infrared flux
density ratios of up to several thousand. We aim to test the hypothesis that
IFRS are young AGN, particularly GHz peaked-spectrum (GPS) and compact
steep-spectrum (CSS) sources that have a low frequency turnover. We use the
rich radio data set available for the Australia Telescope Large Area Survey
fields, covering the frequency range between 150 MHz and 34 GHz with up to 19
wavebands from different telescopes, and build radio spectral energy
distributions (SEDs) for 34 IFRS. We then study the radio properties of this
class of object with respect to turnover, spectral index, and behaviour towards
higher frequencies. We also present the highest-frequency radio observations of
an IFRS, observed with the Plateau de Bure Interferometer at 105 GHz, and model
the multi-wavelength and radio-far-infrared SED of this source. We find IFRS
usually follow single power laws down to observed frequencies of around 150
MHz. Mostly, the radio SEDs are steep, but we also find ultra-steep SEDs. In
particular, IFRS show statistically significantly steeper radio SEDs than the
broader RL AGN population. Our analysis reveals that the fractions of GPS and
CSS sources in the population of IFRS are consistent with the fractions in the
broader RL AGN population. We find that at least 18% of IFRS contain young AGN,
although the fraction might be significantly higher as suggested by the steep
SEDs and the compact morphology of IFRS. The detailed multi-wavelength SED
modelling of one IFRS shows that it is different from ordinary AGN, although it
is consistent with a composite starburst-AGN model with a star formation rate
of 170 solar masses per year.


  In this work we consider the multi-image object matching problem, extend a
centralized solution of the problem to a distributed solution, and present an
experimental application of the centralized solution. Multi-image feature
matching is a keystone of many applications, including simultaneous
localization and mapping, homography, object detection, and structure from
motion. We first review the QuickMatch algorithm for multi-image feature
matching. We then present a scheme for distributing sets of features across
computational units (agents) that largely preserves feature match quality and
minimizes communication between agents (avoiding, in particular, the need of
flooding all data to all agents). Finally, we show how QuickMatch performs on
an object matching test with low quality images. The centralized QuickMatch
algorithm is compared to other standard matching algorithms, while the
Distributed QuickMatch algorithm is compared to the centralized algorithm in
terms of preservation of match consistency. The presented experiment shows that
QuickMatch matches features across a large number of images and features in
larger numbers and more accurately than standard techniques.


  Via constructing an explicit Lagrangian for which the perturbation equations
are analogues of a scalar field propagating in a planar black hole space-time,
it is found that all planar black holes conformal to a Painlev\'e-Gullstrand
type line element can be realized as analogue metrics. This is valid for an
arbitrary choice of conformal and blackening factor, thereby vastly extending
the number of known examples of explicitly known analogue metrics.


  We present nine newly observed transits of TrES-3, taken as part of a transit
timing program using the RISE instrument on the Liverpool Telescope. A
Markov-Chain Monte-Carlo analysis was used to determine the planet-star radius
ratio and inclination of the system, which were found to be
Rp/Rstar=0.1664^{+0.0011}_{-0.0018} and i = 81.73^{+0.13}_{-0.04} respectively,
consistent with previous results. The central transit times and uncertainties
were also calculated, using a residual-permutation algorithm as an independent
check on the errors. A re-analysis of eight previously published TrES-3 light
curves was conducted to determine the transit times and uncertainties using
consistent techniques. Whilst the transit times were not found to be in
agreement with a linear ephemeris, giving chi^2 = 35.07 for 15 degrees of
freedom, we interpret this to be the result of systematics in the light curves
rather than a real transit timing variation. This is because the light curves
that show the largest deviation from a constant period either have relatively
little out-of-transit coverage, or have clear systematics. A new ephemeris was
calculated using the transit times, and was found to be T_c(0) = 2454632.62610
+- 0.00006 HJD and P = 1.3061864 +- 0.0000005 days. The transit times were then
used to place upper mass limits as a function of the period ratio of a
potential perturbing planet, showing that our data are sufficiently sensitive
to have probed for sub-Earth mass planets in both interior and exterior 2:1
resonances, assuming the additional planet is in an initially circular orbit.


  Using the (nearly) Ricci-flat Swiss-Cheese metric of [1], in the context of a
mobile space-time filling D3-brane restricted to a nearly special Lagrangian
sub-manifold (in the large volume limit, the pull-back of the Kaehler form
close to zero and the real part of the pull-back of e^{-i theta},theta=pi/2
times the nowhere-vanishing holomorphic three-form providing the volume form on
the three-cycle) of the "big" divisor with (fluxed stacks of) space-time
filling D7-branes also wrapping the "big" divisor (corresponding to a local
minimum), we provide an explicit identification of the electron and the
u-quark, as well as their SU(2)_L-singlet cousins, with fermionic
super-partners of four Wilson line moduli; their superpartners turn out to be
very heavy, the Higgsino-mass parameter turns out to be large, one obtains one
light (with a mass of 125 GeV) and one heavy Higgs and the gluino is long lived
(from a collider point of view) providing a possible realization of "mu-Split
Supersymmetry". By explicitly calculating the lifetimes of decays of the
co-NLSPs - the first generation squark/slepton and a neutralino - to the LSP -
the gravitino - as well as gravitino decays, we verify that BBN constraints
relevant to the former as well as the requirement of the latter to be (more
than) the age of the universe, are satisfied. For the purpose of calculation of
the gravitino relic abundance in terms of the neutralino/slepton relic
abundance, we evaluate the latter by evaluating the netralino/slepton
(co-)annihilation cross sections and hence show that the former satisfies the
requirement for a Dark Matter candidate.