  The COVID-19 pandemic began over two years ago, yet schools, businesses, and
other organizations are still struggling to keep the risk of disease outbreak
low while returning to (near) normal functionality. Observations from these
past years suggest that this goal can be achieved through the right balance of
mitigation strategies, which may include some combination of mask use,
vaccinations, viral testing, and contact tracing. The choice of mitigation
measures will be uniquely based on the needs and available resources of each
organization. This article presents practical guidance for creating these
policies based on an analytical model of disease spread that captures the
combined effects of each of these interventions. The resulting guidance is
tested through simulation across a wide range of parameters and used to discuss
the spread of disease on college campuses.


  Cross-View Geo-Localisation is still a challenging task where additional
modules, specific pre-processing or zooming strategies are necessary to
determine accurate positions of images. Since different views have different
geometries, pre-processing like polar transformation helps to merge them.
However, this results in distorted images which then have to be rectified.
Adding hard negatives to the training batch could improve the overall
performance but with the default loss functions in geo-localisation it is
difficult to include them. In this article, we present a simplified but
effective architecture based on contrastive learning with symmetric InfoNCE
loss that outperforms current state-of-the-art results. Our framework consists
of a narrow training pipeline that eliminates the need of using aggregation
modules, avoids further pre-processing steps and even increases the
generalisation capability of the model to unknown regions. We introduce two
types of sampling strategies for hard negatives. The first explicitly exploits
geographically neighboring locations to provide a good starting point. The
second leverages the visual similarity between the image embeddings in order to
mine hard negative samples. Our work shows excellent performance on common
cross-view datasets like CVUSA, CVACT, University-1652 and VIGOR. A comparison
between cross-area and same-area settings demonstrate the good generalisation
capability of our model.


  New dynamic cloud pricing options are emerging with cloud providers offering
resources as a wide range of CPU frequencies and matching prices that can be
switched at runtime. On the other hand, cloud providers are facing the problem
of growing operational energy costs. This raises a trade-off problem between
energy savings and revenue loss when performing actions such as CPU frequency
scaling. Although existing cloud con- trollers for managing cloud resources
deploy frequency scaling, they only consider fixed virtual machine (VM)
pricing. In this paper we propose a performance-based pricing model adapted for
VMs with different CPU-boundedness properties. We present a cloud controller
that scales CPU frequencies to achieve energy cost savings that exceed service
revenue losses. We evaluate the approach in a simulation based on real VM
workload, electricity price and temperature traces, estimating energy cost
savings up to 32% in certain scenarios.


  We construct a class of theories which extend New Massive Gravity to higher
orders in curvature in any dimension. The lagrangians arise as limits of a new
class of bimetric theories of Lovelock gravity, which are unitary theories free
from the Boulware-Deser ghost. These Lovelock bigravity models represent the
most general non-chiral ghost-free theories of an interacting massless and
massive spin-two field in any dimension. The scaling limit is taken in such a
way that unitarity is explicitly broken, but the Boulware-Deser ghost remains
absent. This automatically implies the existence of a holographic $c$-theorem
for these theories. We also show that the Born-Infeld extension of New Massive
Gravity falls into our class of models demonstrating that this theory is also
free of the Boulware-Deser ghost. These results extend existing connections
between New Massive Gravity, bigravity theories, Galileon theories and
holographic $c$-theorems.


  We study a new process, which we call ASEP$(q,j)$, where particles move
asymmetrically on a one-dimensional integer lattice with a bias determined by
$q\in (0,1)$ and where at most $2j\in\mathbb{N}$ particles per site are
allowed. The process is constructed from a $(2j+1)$-dimensional representation
of a quantum Hamiltonian with $U_q(\mathfrak{sl}_2)$ invariance by applying a
suitable ground-state transformation. After showing basic properties of the
process ASEP$(q,j)$, we prove self-duality with several self-duality functions
constructed from the symmetries of the quantum Hamiltonian. By making use of
the self-duality property we compute the first $q$-exponential moment of the
current for step initial conditions (both a shock or a rarefaction fan) as well
as when the process is started from an homogeneous product measure.


  In the density functional (DF) theory of Kohn and Sham, the kinetic energy of
the ground state of a system of noninteracting electrons in a general external
field is calculated using a set of orbitals. Orbital free methods attempt to
calculate this directly from the electron density by approximating the
universal but unknown kinetic energy density functional. However simple local
approximations are inaccurate and it has proved very difficult to devise
generally accurate nonlocal approximations. We focus instead on the kinetic
potential, the functional derivative of the kinetic energy DF, which appears in
the Euler equation for the electron density. We argue that the kinetic
potential is more local and more amenable to simple physically motivated
approximations in many relevant cases, and describe two pathways by which the
value of the kinetic energy can be efficiently calculated. We propose two
nonlocal orbital free kinetic potentials that reduce to known exact forms for
both slowly varying and rapidly varying perturbations and also reproduce exact
results for the linear response of the density of the homogeneous system to
small perturbations. A simple and systematic approach for generating accurate
and weak ab-initio local pseudopotentials which produce a smooth slowly varying
valence component of the electron density is proposed for use in orbital free
DF calculations of molecules and solids. The use of these local
pseudopotentials further minimizes the possible errors from the kinetic
potentials. Our theory yields results for the total energies and ionization
energies of atoms, and for the shell structure in the atomic radial density
profiles that are in very good agreement with calculations using the full
Kohn-Sham theory.


  Starting from a many-body classical system governed by a trace-form entropy
we derive, in the stochastic quantization picture, a family of non linear and
non-Hermitian Schroedinger equations describing, in the mean filed
approximation, a quantum system of interacting particles. The time evolution of
the main physical observables is analyzed by means of the Ehrenfest equations
showing that, in general, this family of equations takes into account
dissipative and damped effects due to the interaction of the system with the
background. We explore the presence of steady states by means of solitons,
describing conservative solutions. The results are specialized to the case of a
system governed by the Boltzmann-Gibbs entropy.


  In this paper we propose a novel staggered discontinuous Galerkin method for
the Brinkman problem on general quadrilateral and polygonal meshes. The
proposed method is robust in the Stokes and Darcy limits, in addition, hanging
nodes can be automatically incorporated in the construction of the method,
which are desirable features in practical applications. There are three
unknowns involved in our formulation, namely velocity gradient, velocity and
pressure. Unlike the original staggered DG formulation proposed for the Stokes
equations in \cite{KimChung13}, we relax the tangential continuity of velocity
and enforce different staggered continuity properties for the three unknowns,
which is tailored to yield an optimal $L^2$ error estimates for velocity
gradient, velocity and pressure independent of the viscosity coefficient.
Moreover, by choosing suitable projection, superconvergence can be proved for
$L^2$ error of velocity. Finally, several numerical results illustrating the
good performances of the proposed method and confirming the theoretical
findings are presented.


  Recent advances in the study of networked systems have highlighted that our
interconnected world is composed of networks that are coupled to each other
through different "layers" that each represent one of many possible subsystems
or types of interactions. Nevertheless, it is traditional to aggregate
multilayer networks into a single weighted network in order to take advantage
of existing tools. This is admittedly convenient, but it is also extremely
problematic, as important information can be lost as a result. It is therefore
important to develop multilayer generalizations of network concepts. In this
paper, we analyze triadic relations and generalize the idea of transitivity to
multiplex networks. By focusing on triadic relations, which yield the simplest
type of transitivity, we generalize the concept and computation of clustering
coefficients to multiplex networks. We show how the layered structure of such
networks introduces a new degree of freedom that has a fundamental effect on
transitivity. We compute multiplex clustering coefficients for several real
multiplex networks and illustrate why one must take great care when
generalizing standard network concepts to multiplex networks. We also derive
analytical expressions for our clustering coefficients for ensemble averages of
networks in a family of random multiplex networks. Our analysis illustrates
that social networks have a strong tendency to promote redundancy by closing
triads at every layer and that they thereby have a different type of multiplex
transitivity from transportation networks, which do not exhibit such a
tendency. These insights are invisible if one only studies aggregated networks.


  Let $X$ be a real algebraic convex 3-manifold whose real part is equipped
with a $Pin^-$ structure. We show that every irreducible real rational curve
with non-empty real part has a canonical spinor state belonging to $\{\pm 1\}$.
The main result is then that the algebraic count of the number of real
irreducible rational curves in a given numerical equivalence class passing
through the appropriate number of points does not depend on the choice of the
real configuration of points, provided that these curves are counted with
respect to their spinor states. These invariants provide lower bounds for the
total number of such real rational curves independantly of the choice of the
real configuration of points.


  Existing Graph Neural Networks (GNNs) usually assume a balanced situation
where both the class distribution and the node degree distribution are
balanced. However, in real-world situations, we often encounter cases where a
few classes (i.e., head class) dominate other classes (i.e., tail class) as
well as in the node degree perspective, and thus naively applying existing GNNs
eventually fall short of generalizing to the tail cases. Although recent
studies proposed methods to handle long-tail situations on graphs, they only
focus on either the class long-tailedness or the degree long-tailedness. In
this paper, we propose a novel framework for training GNNs, called Long-Tail
Experts for Graphs (LTE4G), which jointly considers the class long-tailedness,
and the degree long-tailedness for node classification. The core idea is to
assign an expert GNN model to each subset of nodes that are split in a balanced
manner considering both the class and degree long-tailedness. After having
trained an expert for each balanced subset, we adopt knowledge distillation to
obtain two class-wise students, i.e., Head class student and Tail class
student, each of which is responsible for classifying nodes in the head classes
and tail classes, respectively. We demonstrate that LTE4G outperforms a wide
range of state-of-the-art methods in node classification evaluated on both
manual and natural imbalanced graphs. The source code of LTE4G can be found at
https://github.com/SukwonYun/LTE4G.


  Loop-level scattering amplitudes for massless particles have singularities in
regions where tree amplitudes are perfectly smooth. For example, a $2\to4$
gluon scattering process has a singularity in which each incoming gluon splits
into a pair of gluons, followed by a pair of $2\to2$ collisions between the
gluon pairs. This singularity mimics double parton scattering because it occurs
when the transverse momentum of a pair of outgoing gluons vanishes. The
singularity is logarithmic at fixed order in perturbation theory. We exploit
the duality between scattering amplitudes and polygonal Wilson loops to study
six-point amplitudes in this limit to high loop order in planar ${\cal N}=4$
super-Yang-Mills theory. The singular configuration corresponds to the limit in
which a hexagonal Wilson loop develops a self-crossing. The singular terms are
governed by an evolution equation, in which the hexagon mixes into a pair of
boxes; the mixing back is suppressed in the planar (large $N_c$) limit. Because
the kinematic dependence of the box Wilson loops is dictated by (dual)
conformal invariance, the complete kinematic dependence of the singular terms
for the self-crossing hexagon on the one nonsingular variable is determined to
all loop orders. The complete logarithmic dependence on the singular variable
can be obtained through nine loops, up to a couple of constants, using a
correspondence with the multi-Regge limit. As a byproduct, we obtain a simple
formula for the leading logs to all loop orders. We also show that, although
the MHV six-gluon amplitude is singular, remarkably, the transcendental
functions entering the non-MHV amplitude are finite in the same limit, at least
through four loops.


  Aims. Our goal is to complete the inventory of S-bearing molecules and their
abundances in the prototypical photodissociation region (PDR) the Horsehead
nebula to gain insight into sulphur chemistry in UV irradiated regions. Based
on the WHISPER millimeter (mm) line survey, our goal is to provide an improved
and more accurate description of sulphur species and their abundances towards
the core and PDR positions in the Horsehead.
  Methods. The Monte Carlo Markov chain (MCMC) methodology and the molecular
excitation and radiative transfer code RADEX were used to explore the parameter
space and determine physical conditions and beam-averaged molecular abundances.
  Results. A total of 13 S-bearing species (CS, SO, SO2, OCS, H2CS - both ortho
and para - HDCS, C2S, HCS+, SO+, H2S, S2H, NS and NS+) have been detected in
the two targeted positions. This is the first detection of SO+ in the Horsehead
and the first detection of NS+ in any PDR. We find a differentiated chemical
behaviour between C-S and O-S bearing species within the nebula. The C-S
bearing species C2S and o-H2CS present fractional abundances a factor grater
than two higher in the core than in the PDR. In contrast, the O-S bearing
molecules SO, SO2, and OCS present similar abundances towards both positions. A
few molecules, SO+, NS, and NS+, are more abundant towards the PDR than towards
the core, and could be considered as PDR tracers.
  Conclusions. This is the first complete study of S-bearing species towards a
PDR. Our study shows that CS, SO, and H2S are the most abundant S-bearing
molecules in the PDR with abundances of a few 1E-9. We recall that SH, SH+, S,
and S+ are not observable at the wavelengths covered by the WHISPER survey. At
the spatial scale of our observations, the total abundance of S atoms locked in
the detected species is < 1E-8, only ~0.1% of the cosmic sulphur abundance.


  Facial Expression Recognition (FER) is a classification task that points to
face variants. Hence, there are certain affinity features between facial
expressions, receiving little attention in the FER literature. Convolution
padding, despite helping capture the edge information, causes erosion of the
feature map simultaneously. After multi-layer filling convolution, the output
feature map named albino feature definitely weakens the representation of the
expression. To tackle these challenges, we propose a novel architecture named
Amending Representation Module (ARM). ARM is a substitute for the pooling
layer. Theoretically, it can be embedded in the back end of any network to deal
with the Padding Erosion. ARM efficiently enhances facial expression
representation from two different directions: 1) reducing the weight of eroded
features to offset the side effect of padding, and 2) decomposing facial
features to simplify representation learning. Experiments on public benchmarks
prove that our ARM boosts the performance of FER remarkably. The validation
accuracies are respectively 90.42% on RAF-DB, 65.2% on Affect-Net, and 58.71%
on SFEW, exceeding current state-of-the-art methods. Our implementation and
trained models are available at
https://github.com/JiaweiShiCV/Amend-Representation-Module.


  Recent measurements suggest that extensive air showers initiated by
ultra-high energy cosmic rays (UHECR) emit signals in the microwave band of the
electromagnetic spectrum caused by the collisions of the free-electrons with
the atmospheric neutral molecules in the plasma produced by the passage of the
shower. Such emission is isotropic and could allow the detection of air showers
with 100% duty cycle and a calorimetric-like energy measurement, a significant
improvement over current detection techniques. We have built MIDAS (MIcrowave
Detection of Air Showers), a prototype of microwave detector, which consists of
a 4.5 m diameter antenna with a cluster of 53 feed-horns in the 4 GHz range.
The details of the prototype and first results will be presented.


  A central problem in quantum information is to determine the minimal physical
resources that are required for quantum computational speedup and, in
particular, for fault-tolerant quantum computation. We establish a remarkable
connection between the potential for quantum speed-up and the onset of negative
values in a distinguished quasi-probability representation, a discrete analog
of the Wigner function for quantum systems of odd dimension. This connection
allows us to resolve an open question on the existence of bound states for
magic-state distillation: we prove that there exist mixed states outside the
convex hull of stabilizer states that cannot be distilled to non-stabilizer
target states using stabilizer operations. We also provide an efficient
simulation protocol for Clifford circuits that extends to a large class of
mixed states, including bound universal states.


  Exogenous heterogeneity, for example, in the form of instrumental variables
can help us learn a system's underlying causal structure and predict the
outcome of unseen intervention experiments. In this paper, we consider linear
models in which the causal effect from covariates $X$ on a response $Y$ is
sparse. We provide conditions under which the causal coefficient becomes
identifiable from the observed distribution. These conditions can be satisfied
even if the number of instruments is as small as the number of causal parents.
We also develop graphical criteria under which identifiability holds with
probability one if the edge coefficients are sampled randomly from a
distribution that is absolutely continuous with respect to Lebesgue measure and
$Y$ is childless. As an estimator, we propose spaceIV and prove that it
consistently estimates the causal effect if the model is identifiable and
evaluate its performance on simulated data. If identifiability does not hold,
we show that it may still be possible to recover a subset of the causal
parents.


  Background: Electromagnetic frequencies up to a few terahertz (THz) can yield
real-time and noninvasive measurements on biological matter. Unfortunately,
strong absorption in aqueous solutions and low spatial resolution return
difficult free-space investigations. A new approach based on integrated THz
circuits was used. The authors designed and fabricated a BioMEMS (Biological
MicroElectro-Mechanical System) compatible with microfluidic circulation and
electromagnetic propagation. It is dedicated to the ex vivo detection of nitric
oxide synthase (NOS) activity, which is involved in neurodegenerative
phenomena.
  Material/Methods: The biological model was a leech's central nervous system.
After its injury, the production of NO was observed and measured in the far-THz
spectral domain. The nerve cord was put inside a BioMEMS realized in
polydimethylsiloxane (PDMS) sealed on a glass wafer. Glass is a good material
for supporting high-frequency integrated waveguides such as coplanar waveguides
(CPWs). Measurements were performed with vectorial network analyser (VNA).
  Results: The transmission parameter in the frequency range of 0.14-0.22 THz
was measured through CPWs located just below the microchannel containing the
injured leech nerve cord. The lesion caused a decreased transmission
coefficient due to the NOS activity. L-NAME was injected inside the
microchannel and it was verified that it inhibits this activity.
  Conclusions: It was demonstrated that THz spectroscopy can detect a
biochemical event, such as NOS activity around an injured leech's nerve cord,
in real time. Future studies will be dedicated to quantitative measurements of
the reaction products using the sophisticated management of several drugs
allowed with microfluidic microsystems.


  There are different definitions of what a troll is. Certainly, a troll can be
somebody who teases people to make them angry, or somebody who offends people,
or somebody who wants to dominate any single discussion, or somebody who tries
to manipulate people's opinion (sometimes for money), etc. The last definition
is the one that dominates the public discourse in Bulgaria and Eastern Europe,
and this is our focus in this paper. In our work, we examine two types of
opinion manipulation trolls: paid trolls that have been revealed from leaked
reputation management contracts and mentioned trolls that have been called such
by several different people. We show that these definitions are sensible: we
build two classifiers that can distinguish a post by such a paid troll from one
by a non-troll with 81-82% accuracy; the same classifier achieves 81-82%
accuracy on so called mentioned troll vs. non-troll posts.


  A new method is presented, that can help a person become aware of his or her
unconscious preferences, and convey them to others in the form of verbal
explanation. The method combines the concepts of reflection, visualization, and
verbalization. The method was tested in an experiment where the unconscious
preferences of the subjects for various artworks were investigated. In the
experiment, two lessons were learned. The first is that it helps the subjects
become aware of their unconscious preferences to verbalize weak preferences as
compared with strong preferences through discussion over preference diagrams.
The second is that it is effective to introduce an adjustable factor into
visualization to adapt to the differences in the subjects and to foster their
mutual understanding.


  Algorithmic discrimination is an important aspect when data is used for
predictive purposes. This paper analyzes the relationships between
discrimination and classification, data set partitioning, and decision models,
as well as correlation. The paper uses real world data sets to demonstrate the
existence of discrimination and the independence between the discrimination of
data sets and the discrimination of classification models.


  We theoretically investigate Raman photoassociation of a degenerate
Bose-Fermi mixture of atoms and the subsequent prospect for anomalous (Cooper)
pairing between atoms and molecules. Stable fermionic molecules are created via
free-bound-bound stimulated Raman adiabatic passage which, in contrast to
purely bosonic systems, can occur in spite of collisions. With the leftover
atomic condensate to enhance intrafermion interactions, the superfluid
transition to atom-molecule Cooper pairs occurs at a temperature that is
roughly an order of magnitude below what is currently feasible.


  The performance of millimeter wave (mmWave) communications critically depends
on the accuracy of beamforming both at base station (BS) and user terminals
(UEs) due to high isotropic path-loss and channel attenuation. In high mobility
environments, accurate beam alignment becomes even more challenging as the
angles of the BS and each UE must be tracked reliably and continuously. In this
work, focusing on the beamforming at the BS, we propose an adaptive method
based on Recurrent Neural Networks (RNN) that tracks and predicts the Angle of
Departure (AoD) of a given UE. Moreover, we propose a modified frame structure
to reduce beam alignment overhead and hence increase the communication rate.
Our numerical experiments in a highly non-linear mobility scenario show that
our proposed method is able to track the AoD accurately and achieve higher
communication rate compared to more traditional methods such as the particle
filter.


  Cubic perovskite oxides are emerging high-mobility transparent conducting
oxides (TCOs), but Ge-based TCOs had not been known until the discovery of
metastable cubic SrGeO$_3$. $0.5 \times 0.4 \times 0.2$-mm$^3$ large single
crystals of the cubic SrGeO$_3$ perovskite were successfully synthesized
employing the high-pressure flux method. The phonon spectrum is determined from
the IR optical reflectance and Raman-scattering analysis to evaluate the
electron transport governed by optical phonon scattering. A calculated
room-temperature mobility on the order of $3.9 \times 10^2$
cm$^2$V$^{-1}$s$^{-1}$ is obtained, identifying cubic SrGeO$_3$ as one of the
most promising TCOs. Employing classical phonon theory and a combined
experimental-theoretical approach, a comprehensive analysis of the intrinsic
electron mobility in the cubic perovskite semiconductors SrGeO$_3$, BaSnO$_3$,
and SrTiO$_3$ is provided based on the magnitude of polarization and
eigenfrequency of optically active phonons.


  We propose a novel point cloud based 3D organ segmentation pipeline utilizing
deep Q-learning. In order to preserve shape properties, the learning process is
guided using a statistical shape model. The trained agent directly predicts
piece-wise linear transformations for all vertices in each iteration. This
mapping between the ideal transformation for an object outline estimation is
learned based on image features. To this end, we introduce aperture features
that extract gray values by sampling the 3D volume within the cone centered
around the associated vertex and its normal vector. Our approach is also
capable of estimating a hierarchical pyramid of non rigid deformations for
multi-resolution meshes. In the application phase, we use a marginal approach
to gradually estimate affine as well as non-rigid transformations. We performed
extensive evaluations to highlight the robust performance of our approach on a
variety of challenge data as well as clinical data. Additionally, our method
has a run time ranging from 0.3 to 2.7 seconds to segment each organ. In
addition, we show that the proposed method can be applied to different organs,
X-ray based modalities, and scanning protocols without the need of transfer
learning. As we learn actions, even unseen reference meshes can be processed as
demonstrated in an example with the Visible Human. From this we conclude that
our method is robust, and we believe that our method can be successfully
applied to many more applications, in particular, in the interventional imaging
space.


  In this paper, we are concerned with a nonlinear optimal control problem of
ordinary differential equations. We consider a discretization of the problem
with the discontinuous Galerkin method with arbitrary order $r \in
\mathbb{N}\cup \{0\}$. Under suitable regularity assumptions on the cost
functional and solutions of the state equations, we provide sharp estimates for
the error of the approximate solutions. Numerical experiments are presented
supporting the theoretical results.


  Within the framework of a Device-to-Device (D2D) data offloading system for
cellular networks, we propose a Content Delivery Management System (CDMS) in
which the instant for transmitting a content to a requesting node, through a
D2D communication, is selected to minimize the energy consumption required for
transmission. The proposed system is particularly fit to highly dynamic
scenarios, such as vehicular networks, where the network topology changes at a
rate which is comparable with the order of magnitude of the delay tolerance. We
present an analytical framework able to predict the system performance, in
terms of energy consumption, using tools from the theory of point processes,
validating it through simulations, and provide a thorough performance
evaluation of the proposed CDMS, in terms of energy consumption and spectrum
use. Our performance analysis compares the energy consumption and spectrum use
obtained with the proposed scheme with the performance of two benchmark
systems. The first one is a plain classic cellular scheme, the second is a D2D
data offloading scheme (that we proposed in previous works) in which the D2D
transmissions are performed as soon as there is a device with the required
content within the maximum D2D transmission range...


  The training phases of Deep neural network~(DNN) consumes enormous processing
time and energy. Compression techniques utilizing the sparsity of DNNs can
effectively accelerate the inference phase of DNNs. However, it is hardly used
in the training phase because the training phase involves dense
matrix-multiplication using General-Purpose Computation on Graphics Processors
(GPGPU), which endorse the regular and structural data layout. In this paper,
we first propose the Approximate Random Dropout that replaces the conventional
random dropout of neurons and synapses with a regular and online generated
row-based or tile-based dropout patterns to eliminate the unnecessary
computation and data access for the multilayer perceptron~(MLP) and long
short-term memory~(LSTM). We then develop a SGD-based Search Algorithm that
produces the distribution of row-based or tile-based dropout patterns to
compensate for the potential accuracy loss. Moreover, aiming at the convolution
neural network~(CNN) training acceleration, we first explore the importance and
sensitivity of input feature maps; and then propose the sensitivity-aware
dropout method to dynamically drop the input feature maps based on their
sensitivity so as to achieve greater forward and backward training acceleration
while reserving better NN accuracy. To facilitate DNN programming, we build a
DNN training computation framework that unifies the proposed techniques in the
software stack. As a result, the GPGPU only needs to support the basic operator
-- matrix multiplication and can achieve significant performance improvement
regardless of DNN model.


  Extensions of the Standard Model that include vector-like quarks commonly
also include additional particles that may mediate new production or decay
modes. Using as example the minimal linear $\sigma$ model, that reduces to the
minimal $SO(5)/SO(4)$ composite Higgs model in a specific limit, we consider
the phenomenology of vector-like quarks when a scalar singlet $\sigma$ is
present. This new particle may be produced in the decays $T \to t \sigma$, $B
\to b \sigma$, where $T$ and $B$ are vector-like quarks of charges $2/3$ and
$-1/3$, respectively, with subsequent decay $\sigma \to W^+ W^-, ZZ, hh$. By
scanning over the allowed parameter space we find that these decays may be
dominant. In addition, we find that the presence of several new particles
allows for single $T$ production cross sections larger than those expected in
minimal models. We discuss the observability of these new signatures in
existing searches.


  Influenza remains a significant burden on health systems. Effective responses
rely on the timely understanding of the magnitude and the evolution of an
outbreak. For monitoring purposes, data on severe cases of influenza in England
are reported weekly to Public Health England. These data are both readily
available and have the potential to provide valuable information to estimate
and predict the key transmission features of seasonal and pandemic influenza.
We propose an epidemic model that links the underlying unobserved influenza
transmission process to data on severe influenza cases. Within a Bayesian
framework, we infer retrospectively the parameters of the epidemic model for
each seasonal outbreak from 2012 to 2015, including: the effective reproduction
number; the initial susceptibility; the probability of admission to intensive
care given infection; and the effect of school closure on transmission. The
model is also implemented in real time to assess whether early forecasting of
the number of admission to intensive care is possible. Our model of admissions
data allows reconstruction of the underlying transmission dynamics revealing:
increased transmission during the season 2013/14 and a noticeable effect of
Christmas school holiday on disease spread during season 2012/13 and 2014/15.
When information on the initial immunity of the population is available,
forecasts of the number of admissions to intensive care can be substantially
improved. Readily available severe case data can be effectively used to
estimate epidemiological characteristics and to predict the evolution of an
epidemic, crucially allowing real-time monitoring of the transmission and
severity of the outbreak.


  We report a detailed analysis of the energy spectra, second- and high-order
structure functions of velocity differences in superfluid $^4$He counterflow
turbulence, measured in a wide range of temperatures and heat fluxes. We show
that the one-dimensional energy spectrum $E_{xz} (k_y)$ (averaged over the
$xz$-plane, parallel to the channel wall), directly measured as a function of
the wall-normal wave-vector $k_y$, gives more detailed information on the
energy distribution over scales than the corresponding second-order structure
function $S_{2}(\delta_y)$. In particular, we discover two intervals of $k_y$
with different apparent exponents: $E_{xz} (k_y)\propto k_y^{-m_C}$ for
$k\lesssim k_\times$ and $E_{xz} (k_y)\propto k_y^{-m_F}$ for $k\gtrsim
k_\times$. Here $k_\times$ denotes wavenumber that separate scales with
relatively strong (for $k\lesssim k_\times$) and relatively weak (for $k\gtrsim
k_\times$) coupling between the normal-fluid and superfluid velocity
components. We interpret these $k$-ranges as cascade-dominated and mutual
friction-dominated intervals, respectively. General behavior of the
experimental spectra $E_{xz}(k_y)$ agree well with the predicted spectra [Phys.
Rev. B 97, 214513 (2018)]. Analysis of the $n$-th order structure functions
statistics shows that in the energy-containing interval the statistics of
counterflow turbulence is close to Gaussian, similar to the classical
hydrodynamic turbulence. In the cascade- and mutual friction-dominated
intervals we found some modest enhancement of intermittency with respect of its
level in classical turbulence. However, at small scales, the intermittency
becomes much stronger than in the classical turbulence.


  We extend a theorem of Herman from the case of unicritical polynomials to the
case of polynomials with two finite critical values. This theorem states that
Siegel disks of such polynomials, under a diophantine condition (called
Herman's condition) on the rotation number, must have a critical point on their
boundaries.


  In this article, dedicated with admiration and gratitude to guru Neil Sloane
on his 75-th birthday, we observe that the generating functions for multi-set
permutations that do not contain an increasing subsequence of length d, and
where every letter appears the same number of times, say r, are always
D-finite, (for every d and every r), and we actually crank out the first few
terms of quite a few of them, many of whom are not yet in the OEIS. We also
state a conjectured asymptotic formula for these sequences, that reduces to
Amitai Regev's famous formula when r=1, and pledge a 100 dollar donation to the
OEIS in honor of the first one to prove our conjecture. We pledge another 100
dollars for extending Ira Gessel's spectacular Bessel determinant, from the r=1
case to general r.


  W. C. Lang determined wavelets on Cantor dyadic group by using
Multiresolution analysis method. In this paper we have given characterization
of wavelet sets on Cantor dyadic group providing another method for the
construction of wavelets. All the wavelets originating from wavelet sets are
not necessarily associated with a multiresolution analysis. Relation between
multiresolution analysis and, wavelets determined from wavelet sets is
established along with relevant examples.


  Detailed 57Fe Mossbauer spectroscopy measurements on superconducting NaFeAs
powder samples have been performed in the temperature range 13 K < T < 300 K.
The 57Fe spectra recorded in the paramagnetic range (T > TN ~ 46 K) were
discussed supposing that most of the Fe2+ ions are located in distorted (FeAs4)
tetrahedral of NaFeAs phase, while additional minor (< 10%) component of the
spectra corresponds to impurity or intergrowth NaFe2As2 phase with a nominal
composition near "NaFe2As2". Our results reveal that the structural transition
(TS ~ 55K) has a weak effect on the electronic structure of iron ions, while at
T < TN the spectra show a continuous distribution of hyperfine fields HFe .
Shape of these spectra was analyzed in term of two models: (i) an
incommensurate spin density wave modulation of iron magnetic structure, (ii) a
formation of a microdomain structure or phase separation. It was shown that the
hyperfine parameters obtained using these two methods have very similar values
over the whole temperature range. The analysis of the temperature dependence
HFe(T) with the Bean-Rodbell model leads to ksi = 1.16 +/- 0.05, suggesting
that the magnetic phase transition is first-order in nature. A sharp evolution
of the VZZ(T) and etha(T) parameters of the full Hamiltonian of hyperfine
interactions near T ~ (TN, TS), were interpreted as a manifestation of the
anisotropic electron redistribution between the dxz, dyz- and dxy-orbitals of
the iron ions.


  We use an analytic criterion for vanishing of exponential damping of
correlations developed previously (Piasecki et al, J. Chem. Phys., 133, 164507,
2010) to determine the threshold volume fractions for structural transitions in
hard sphere systems in dimensions D=3,4,5 and 6, proceeding from the YBG
hierarchy and using the Kirkwood superposition approximation. We conclude that
the theory does predict phase transitions in qualitative agreement with
numerical studies. We also derive, within the superposition approximation, the
asymptotic form of the analytic condition for occurence of a structural
transition in the D->Infinity limit .


  Let $(X, T^{1,0}X)$ be a compact connected orientable strongly pseudoconvex
CR manifold of dimension $2n+1$, $n\geq1$. Assume that $X$ admits a connected
compact Lie group $G$ action and a transversal CR $S^1$ action, we compute the
coefficients of the first two lower order terms of the equivariant Szeg\H{o}
kernel asymptotic expansions with respect to the $S^1$ action.


  Super-resolution (SR) has been widely used to convert low-resolution legacy
videos to high-resolution (HR) ones, to suit the increasing resolution of
displays (e.g. UHD TVs). However, it becomes easier for humans to notice motion
artifacts (e.g. motion judder) in HR videos being rendered on larger-sized
display devices. Thus, broadcasting standards support higher frame rates for
UHD (Ultra High Definition) videos (4K@60 fps, 8K@120 fps), meaning that
applying SR only is insufficient to produce genuine high quality videos. Hence,
to up-convert legacy videos for realistic applications, not only SR but also
video frame interpolation (VFI) is necessitated. In this paper, we first
propose a joint VFI-SR framework for up-scaling the spatio-temporal resolution
of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training
scheme with a multi-scale temporal loss that imposes temporal regularization on
the input video sequence, which can be applied to any general video-related
task. The proposed structure is analyzed in depth with extensive experiments.


  In this paper we consider the implications that the effect gravitational
memory would have on primordial black holes, within the theoretical context of
$F(R)$ related scalar-tensor theories. As we will demonstrate, under the
assumption that the initial mass of the primordial black hole is such so that
it evaporates today, this can potentially constrain the $F(R)$ related theories
of gravity. We study two scalar-tensor models and discuss the evolution of
primordial black holes created at some initial time $t_f$ in the early
universe. The results between the two models vary significantly which shows us
that, if the effect of gravitational memory is considered valid, some of the
scalar-tensor models and their corresponding $F(R)$ theories must be further
constrained.


  Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10
minutes after the injection of a contrast agent (LGE-MRI) is a necessary step
in the processing allowing the identification and diagnosis of cardiac diseases
such as myocardial infarction. However, this segmentation is challenging due to
high variability across subjects and the potential lack of contrast between
structures. Then, the main objective of this work is to develop an accurate
automatic segmentation method based on deep learning models for the myocardial
borders on LGE-MRI. To this end, 2.5 D residual neural network integrated with
a squeeze and excitation blocks in encoder side with specialized convolutional
has been proposed. Late fusion has been used to merge the output of the best
trained proposed models from a different set of hyperparameters. A total number
of 320 exams (with a mean number of 6 slices per exam) were used for training
and 28 exams used for testing. The performance analysis of the proposed
ensemble model in the basal and middle slices was similar as compared to
intra-observer study and slightly lower at apical slices. The overall Dice
score was 82.01% by our proposed method as compared to Dice score of 83.22%
obtained from the intra observer study. The proposed model could be used for
the automatic segmentation of myocardial border that is a very important step
for accurate quantification of no-reflow, myocardial infarction, myocarditis,
and hypertrophic cardiomyopathy, among others.


  We present a model for third-family quark-lepton unification at the TeV scale
featuring a composite Higgs sector. The model is based on a variant of the
Pati-Salam model, the so-called 4321 model, consisting of the gauge group
$SU(4)\times SU(3)^\prime\times SU(2)_L\times U(1)_X$. The spontaneous symmetry
breaking to the SM gauge group is triggered dynamically by a QCD-like confining
sector. The same strong dynamics also produces the Higgs as a pseudo
Nambu-Goldstone boson, connecting the energy scales of both sectors. The model
predicts a massive $U_1$ vector leptoquark coupled dominantly to the third
generation, recently put forward as a possible solution to the $B$-meson
anomalies.


  We present the first lattice QCD study of coupled isoscalar
$\pi\pi,K\overline{K},\eta\eta$ $S$- and $D$-wave scattering extracted from
discrete finite-volume spectra computed on lattices which have a value of the
quark mass corresponding to $m_\pi\sim391$ MeV. In the $J^P=0^+$ sector we find
analogues of the experimental $\sigma$ and $f_0(980)$ states, where the
$\sigma$ appears as a stable bound-state below $\pi\pi$ threshold, and, similar
to what is seen in experiment, the $f_0(980)$ manifests itself as a dip in the
$\pi\pi$ cross section in the vicinity of the $K\overline{K}$ threshold. For
$J^P=2^+$ we find two states resembling the $f_2(1270)$ and $f_2'(1525)$,
observed as narrow peaks, with the lighter state dominantly decaying to
$\pi\pi$ and the heavier state to $K\overline{K}$. The presence of all these
states is determined rigorously by finding the pole singularity content of
scattering amplitudes, and their couplings to decay channels are established
using the residues of the poles.


  In this work we derive the evolution equation for the density contrast
considering open system cosmology, where the influence of adiabatic particle
production process on the dynamic of a homogeneous and isotropic is
investigated within a manifestly covariant formulation. As application we
derive the solution for two sources, one of them is a generalization of
Prigogine's model. Then we establish the condition for the reach of the
non-linear regime for the density contrast which turns out to be a necessary
condition for the structure formation.


  Ring polymers remain a major challenge to our current understanding of
polymer dynamics. Experimental results are difficult to interpret because of
the uncertainty in the purity and dispersity of the sample. Using both
equilibrium and non-equilibrium molecular dynamics simulations we have
systematically investigated the structure, dynamics and rheology of perfectly
controlled ring/linear polymer blends with chains of such length and
flexibility that the number of entanglements is up to about 14 per chain, which
is comparable to experimental systems examined in the literature. The smallest
concentration at which linear contaminants increase the zero-shear viscosity of
a ring polymer melt of these chain lengths by 10% is approximately one-fifth of
their overlap concentration. When the two architectures are present in equal
amounts the viscosity of the blend is approximately twice as large as that of
the pure linear melt. At this concentration the diffusion coefficient of the
rings is found to decrease dramatically, while the static and dynamic
properties of the linear polymers are mostly unaffected. Our results are
supported by a primitive path analysis.


  We propose and discuss a new experimental approach to measure the centroid
shift induced by gravitational microlensing in the images of lensed quasars
(astrometric microlensing). Our strategy is based on taking the photocenter of
a region in the quasar large enough as to be insensitive to microlensing as
reference to measure the centroid displacement of the continuum. In this way,
single-epoch measurements of astrometric microlensing can be performed. Using
numerical simulations, we show that, indeed, the centroid shift monotonically
decreases as the size of the emitting region increases, and only for relatively
large regions, like the broad line region (BLR), does the centroid shift become
negligible. This opens interesting possibilities to study the stratification of
the different emitters in the accretion disk and the BLR. We estimate the
amplitude of the centroid shifts for 79 gravitationally lensed images and study
more thoroughly the special cases Q2237+030 A, RXJ1131-1231 A, PG1115+080 A2
and SDSS J1004+4112 A. We propose to use spectro-astrometry to simultaneously
obtain the photocenters of the continuum and of different emission line regions
since, with the precision of forthcoming instruments, astrometric microlensing
by $\sim 1 M_\odot$ mass microlenses may be detected in many quasar lensed
images. When we consider more massive micro/millilenses, $M\gtrsim 10 M_\odot$,
often proposed as the constituents of dark matter, the BLR becomes sensitive to
microlensing and can no longer be used as a positional reference to measure
centroid shifts. Differential microlensing between the images of a lensed
quasar along several epochs should be used instead.


  Satellites enable widespread, regional or global surveillance of volcanoes
and can provide the first indication of volcanic unrest or eruption. Here we
consider Interferometric Synthetic Aperture Radar (InSAR), which can be
employed to detect surface deformation with a strong statistical link to
eruption. The ability of machine learning to automatically identify signals of
interest in these large InSAR datasets has already been demonstrated, but
data-driven techniques, such as convolutional neutral networks (CNN) require
balanced training datasets of positive and negative signals to effectively
differentiate between real deformation and noise. As only a small proportion of
volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine
learning for detecting volcanic unrest is more challenging. In this paper, we
address this problem using synthetic interferograms to train the AlexNet. The
synthetic interferograms are composed of 3 parts: 1) deformation patterns based
on a Monte Carlo selection of parameters for analytic forward models, 2)
stratified atmospheric effects derived from weather models and 3) turbulent
atmospheric effects based on statistical simulations of correlated noise. The
AlexNet architecture trained with synthetic data outperforms that trained using
real interferograms alone, based on classification accuracy and positive
predictive value (PPV). However, the models used to generate the synthetic
signals are a simplification of the natural processes, so we retrain the CNN
with a combined dataset consisting of synthetic models and selected real
examples, achieving a final PPV of 82%. Although applying atmospheric
corrections to the entire dataset is computationally expensive, it is
relatively simple to apply them to the small subset of positive results. This
further improves the detection performance without a significant increase in
computational burden.


  Motivated by topological Tverberg-type problems and by classical results
about embeddings (maps without double points), we study the question whether a
finite simplicial complex K can be mapped into R^d without triple, quadruple,
or, more generally, r-fold points. Specifically, we are interested in maps f
from K to R^d that have no r-Tverberg points, i.e., no r-fold points with
preimages in r pairwise disjoint simplices of K, and we seek necessary and
sufficient conditions for the existence of such maps.
  We present a higher-multiplicity analogue of the completeness of the Van
Kampen obstruction for embeddability in twice the dimension. Specifically, we
show that under suitable restrictions on the dimensions, a well-known Deleted
Product Criterion (DPC) is not only necessary but also sufficient for the
existence of maps without r-Tverberg points. Our main technical tool is a
higher-multiplicity version of the classical Whitney trick.
  An important guiding idea for our work was that sufficiency of the DPC,
together with an old result of Ozaydin on the existence of equivariant maps,
might yield an approach to disproving the remaining open cases of the
long-standing topological Tverberg conjecture. Unfortunately, our proof of the
sufficiency of the DPC requires a "codimension 3" proviso, which is not
satisfied for when K is the N-simplex.
  Recently, Frick found an extremely elegant way to overcome this last
"codimension 3" obstacle and to construct counterexamples to the topological
Tverberg conjecture for d at least 3r+1 (r not a prime power). Here, we present
a different construction that yields counterexamples for d at least 3r (r not a
prime power).


  We prove that the reduced Kurosh rank of the intersection of two subgroups
$H$ and $K$ of a free product of right-orderable groups is bounded above by the
product of the reduced Kurosh ranks of $H$ and $K$.
  In particular, taking the fundamental group of a graph of groups with trivial
vertex and edge groups, and its Bass-Serre tree, our Theorem becomes the
desired inequality of the usual Strengthened Hanna Neumann conjecture for free
groups.


  Let $K$ be a local non-Archimedean field of positive characteristic and let
$L$ be the degree-$n$ unramified extension of $K$. Via the local Langlands and
Jacquet-Langlands correspondences, to each sufficiently generic multiplicative
character of $L$, one can associate an irreducible representation of the
multiplicative group of the central division algebra $D$ of invariant $1/n$
over $K$.
  In 1979, Lusztig proposed a cohomological construction of supercuspidal
representations of reductive $p$-adic groups analogous to Deligne-Lusztig
theory for finite reductive groups. In this paper we prove that when $n=2$, the
$p$-adic Deligne-Lusztig (ind-)scheme $X$ induces a correspondence between
smooth one-dimensional representations of $L^\times$ and representations of
$D^\times$ that matches the correspondence given by the LLC and JLC.


  We give a functional equation for the refined Herglotz-Zagier function. It is
analogous to a result in the theory of modular forms.


  Static (not stationary) solutions of the Einstein-Klein-Gordon (EKG)
equations including matter are obtained for real scalar fields. The scalar
field interaction with matter is considered. The introduced coupling allows the
existence of static solutions in contraposition with the case of the simpler
EKG equations for real scalar fields and gravity. Surprisingly, when the
considered matter is a photon-like gas, it turns out that the gravitational
field intensity at large radial distances becomes nearly a constant, exerting
an approximately fixed force to small bodies at any distance. The effect is
clearly related with the massless character of the photon-like field. It is
also argued that the gravitational field can generate a bounding attraction,
that could avoid the unlimited increase in mass with the radius of the obtained
here solution. This phenomenon, if verified, may furnish a possible mechanism
for explaining how the increasing gravitational potential associated to dark
matter, finally decays at large distances from the galaxies. A method for
evaluating these photon bounding effects is just formulated in order to be
further investigated.


  We restrict the possibilities for the character degrees of $p$-groups $G$
satisfying $|G:G'| = p^2$. E.g. if $G$ is of maximal class and has an
irreducible character of degree $> p$, then it has such a character of degree
at most $p^{\frac{p+1}{2}}$.


  As we read this text, our eyes dynamically adjust the focal length to keep
the line image in focus on the retina. Similarly, in many optics applications
the focal length must be dynamically tunable. In the quest for compactness and
tunability, flat lenses based on metasurfaces were introduced. However, their
dynamic tunability is still limited because their functionality mostly relies
upon fixed geometry. In contrast, we put forward an original concept of a
tunable Optical Magnetic Lens (OML) that focuses photon beams using a
subwavelength-thin layer of a magneto-optical material in a non-uniform
magnetic field. We applied the OML concept to a wide range of materials and
found out that the effect of OML is present in a broad frequency range from
microwaves to visible light. For terahertz light, OML can allow 50% relative
tunability of the focal length on the picosecond time scale, which is of
practical interest for ultrafast shaping of electron beams in microscopy. The
OML based on magneto-optical natural bulk and 2D materials may find broad use
in technologies such as 3D optical microscopy and acceleration of charged
particle beams by THz beams.


  We compute fully relativistic disc response functions in the case of the
"lamp-post" geometry using the full observed reflection spectrum for various
X-ray source heights, disc inclination, and spin values of the central black
hole. Since the observed PSD is equal to the product of the intrinsic power
spectrum with the "transfer function" (i.e. the Fourier transform of the disc
response function), we are able to predict the observed PSDs in the case of
X-ray illumination of the inner disc. The observed PSD should show a prominent
dip at high frequencies and an oscillatory behaviour, with a decreasing
amplitude, at higher frequencies. The reverberation "echo" features should be
more prominent in energy bands where the reflection component is more
pronounced. The frequency of the dip is independent of energy, and it is mainly
determined by the black hole mass and the X-ray source height. The amplitude of
the dip increases with increasing black hole spin and inclination angle, as
long as the height of the "lamp" is smaller than ~10 gravitational radii. The
detection of the X-ray reverberation signals in the PSDs can provide further
evidence for X-ray illumination of the inner disc in AGN. Our results are
largely independent of the assumed geometry of the disc-corona system, as long
as it does not change with time, and the disc response function is
characterized by a sharp rise, a "plateau", and a decline at longer times.
Irrespective of the geometry, the frequency of the main dip should decrease
with increasing "mean time" of the response function, and the amplitude of the
dip should increase with increasing reflection fraction.


  A recent scanning tunneling microscopy (STM) experiment reports the
observation of charge density wave (CDW) with period of approximately 8a in the
halo region surrounding the vortex core, in striking contrast to the
approximately period 4a CDW that are commonly observed in the cuprates.
Inspired by this work, we study a model where a bi-directional pair density
wave (PDW) with period 8 is at play. This further divides into two classes, (1)
where the PDW is a competing state of the d wave superconductor and can exist
only near the vortex core where the d wave order is suppressed, and (2) where
the PDW is the primary order, the so called mother state that persists with
strong phase fluctuations to high temperature and high magnetic field and lies
behind the pseudogap phenomenology. We study the charge density wave structures
near the vortex core in these models. We emphasize the importance of the phase
winding of the d-wave order parameter. The PDW can be pinned by the vortex core
due to this winding and become static. Furthermore, the period 8 CDW inherits
the properties of this winding, which gives rise to a special feature of the
Fourier transform peak, namely, it is split in certain directions. There are
also a line of zeros in the inverse Fourier transform of filtered data. We
propose that these are key experimental signatures that can distinguish between
the PDW-driven scenario from the more mundane option that the period 8 CDW is
primary. We discuss the pros and cons of the options considered above. Finally
we attempt to place the STM experiment in the broader context of pseudogap
physics of underdoped cuprates and relate this observation to the unusual
properties of X ray scattering data on CDW carried out to very high magnetic
field.


  We study the variety of actions of a fixed (Chevalley) group on arbitrary
geodesic, Gromov hyperbolic spaces. In high rank we obtain a complete
classification. In rank one, we obtain some partial results and give a
conjectural picture.


  Sports activities are an integral part of our day to day life. Introducing
autonomous decision making and predictive models to recognize and analyze
different sports events and activities has become an emerging trend in computer
vision arena. Albeit the advances and vivid applications of artificial
intelligence and computer vision in recognizing different popular western
games, there remains a very minimal amount of efforts in the application of
computer vision in recognizing traditional Bangladeshi games. We, in this
paper, have described a novel Deep Learning based approach for recognizing
traditional Bengali games. We have retrained the final layer of the renowned
Inception V3 architecture developed by Google for our classification approach.
Our approach shows promising results with an average accuracy of 80%
approximately in correctly recognizing among 5 traditional Bangladeshi sports
events.


  In this paper, we present a novel Gabor wavelet based Kernel Entropy
Component Analysis (KECA) method by integrating the Gabor wavelet
transformation (GWT) of facial images with the KECA method for enhanced face
recognition performance. Firstly, from the Gabor wavelet transformed images the
most important discriminative desirable facial features characterized by
spatial frequency, spatial locality and orientation selectivity to cope with
the variations due to illumination and facial expression changes were derived.
After that KECA, relating to the Renyi entropy is extended to include cosine
kernel function. The KECA with the cosine kernels is then applied on the
extracted most important discriminating feature vectors of facial images to
obtain only those real kernel ECA eigenvectors that are associated with
eigenvalues having positive entropy contribution. Finally, these real KECA
features are used for image classification using the L1, L2 distance measures;
the Mahalanobis distance measure and the cosine similarity measure. The
feasibility of the Gabor based KECA method with the cosine kernel has been
successfully tested on both frontal and pose-angled face recognition, using
datasets from the ORL, FRAV2D and the FERET database.


  Smart cities stand as pivotal components in the ongoing pursuit of elevating
urban living standards, facilitating the rapid expansion of urban areas while
efficiently managing resources through sustainable and scalable innovations. In
this regard, as emerging technologies like Artificial Intelligence (AI), the
Internet of Things (IoT), big data analytics, and fog and edge computing have
become increasingly prevalent, smart city applications grapple with various
challenges, including the potential for unauthorized disclosure of confidential
and sensitive data. The seamless integration of emerging technologies has
played a vital role in sustaining the dynamic pace of their development. This
paper explores the substantial potential and applications of Deep Learning
(DL), Federated Learning (FL), IoT, Blockchain, Natural Language Processing
(NLP), and large language models (LLMs) in optimizing ICT processes within
smart cities. We aim to spotlight the vast potential of these technologies as
foundational elements that technically strengthen the realization and
advancement of smart cities, underscoring their significance in driving
innovation within this transformative urban milieu. Our discourse culminates
with an exploration of the formidable challenges that DL, FL, IoT, Blockchain,
NLP, and LLMs face within these contexts, and we offer insights into potential
future directions.


  We use results for the structure functions $F_L$ for a gluon target having
nonzero transverse momentum square at order $\alpha_s$, obtained in our
previous paper, to compare with recent H1 experimental data for $F_L$ at fixwd
W values and with collinear GRV predictions at LO and NLO approximation.


  After reviewing how M-theory subsumes string theory, I report on some new and
interesting developments, focusing on the ``brane-world'': circumventing no-go
theorems for supersymmetric brane-worlds, complementarity of the Maldacena and
Randall-Sundrum pictures; self-tuning of the cosmological constant. I conclude
with the top ten unsolved problems.


  Multi-modal representation learning by pretraining has become an increasing
interest due to its easy-to-use and potential benefit for various
Visual-and-Language~(V-L) tasks. However its requirement of large volume and
high-quality vision-language pairs highly hinders its values in practice. In
this paper, we proposed a novel label-augmented V-L pretraining model, named
LAMP, to address this problem. Specifically, we leveraged auto-generated labels
of visual objects to enrich vision-language pairs with fine-grained alignment
and correspondingly designed a novel pretraining task. Besides, we also found
such label augmentation in second-stage pretraining would further universally
benefit various downstream tasks. To evaluate LAMP, we compared it with some
state-of-the-art models on four downstream tasks. The quantitative results and
analysis have well proven the value of labels in V-L pretraining and the
effectiveness of LAMP.


  We consider slant normal magnetic curves in $(2n+1)$-dimensional
$S$-manifolds. We prove that $\gamma $ is a slant normal magnetic curve in an
$% S $-manifold $(M^{2m+s},\varphi ,\xi _{\alpha },\eta ^{\alpha },g)$ if and
only if it belongs to a list of slant $\varphi $-curves satisfying some special
curvature equations. This list consists of some specific geodesics, slant
circles, Legendre and slant helices of order $3$. We construct slant normal
magnetic curves in $\mathbb{R}^{2n+s}(-3s)$ and give the parametric equations
of these curves.


  While edge computing is envisioned to superbly serve latency sensitive
applications, the implementation-based studies benchmarking its performance are
few and far between. To address this gap, we engineer a modular edge cloud
computing system architecture that is built on latest advances in
containerization techniques, including Kafka, for data streaming, Docker, as
application platform, and Firebase Cloud, as realtime database system. We
benchmark the performance of the system in terms of scalability, resource
utilization and latency by comparing three scenarios: cloud-only, edge-only and
combined edge-cloud. The measurements show that edge-only solution outperforms
other scenarios only when deployed with data located at one edge only, i.e.,
without edge computing wide data synchronization. In case of applications
requiring data synchronization through the cloud, edge-cloud scales around a
factor 10 times better than cloud-only, until certain number of concurrent
users in the system, and above this point, cloud-only scales better. In terms
of resource utilization, we observe that whereas the mean utilization increases
linearly with the number of user requests, the maximum values for the memory
and the network I/O heavily increase when with an increasing amount of data.


  We study a well-known technique of using absoluteness for giving choice-free
proofs to some statements which are known to be provable with the axiom of
choice. The idea is to reduce the problem to an inner model where the axiom of
choice holds and use absoluteness. We examine the complexity of the sentences
that this technique can be applied to, and show that many of the theorems in
basic partition calculus have the adequate complexity for this technique to
apply.


  I present a simple baby-steps reconstruction of quantum mechanics as a fully
classical theory. The most radical conceptual leap required is that there are
many coexisting classical worlds, but even this is justified by the necessity
of objective probabilities. These baby steps lead to a version of the
many-worlds interpretation of quantum mechanics with built-in probabilities,
built-in classicality at the macroscopic level, and an explanation of the
complex numbers in quantum mechanics. Despite its simplicity and minimalism of
radical concepts, this is not a toy model, being equivalent with quantum field
theory.


  We present a tunable, hybrid waveguide-fiber optical parametric oscillator
(OPO) synchronously pumped by an ultra-fast fiber laser exploiting four-wave
mixing (FWM) generated in silicon nitride waveguides. Parametric oscillation
results in a 35 dB enhancement of the idler spectral power density in
comparison to spontaneous FWM, with the ability of wide wavelength tuning over
86 nm in the O-band. Measurements of the oscillation threshold and the
efficiency of the feedback loop reveal how an integration of the OPO on a
single silicon nitride chip can be accomplished at standard repetition rates of
pump lasers in the order of 100 MHz.


  We study the creation of a black hole (BH) pair separated by a domain wall,
in the presence of a cosmological constant. We construct the solution
representing a BH pair with a domain wall and compute the Euclidean action to
evaluate the probability of the pair creation in the background with a
preexisting domain wall. The BHs can be either neutral or magnetically charged
ones. We compare the results of the charged case with those of the neutral case
with the same cosmological constant. We find that the production rate of a
charged BH pair is always suppressed in comparison with that of the neutral one
in both four and five dimensions, irrespective of the sign of the cosmological
constant. The Euclidean action is equal to the minus of the entropy. Since the
horizon area of a BH is decreased as the magnitude of its charge is increased
in general, the decreasing creation rate can be understood in terms of the
increasing charge. We obtain the explicit confirmation on the relation between
the pair creation rate of the charged BHs and the area of horizons in both the
four- and five-dimensional cases in the presence of a cosmological constant.
The singularity of the domain wall universe with charged BHs, as distinct from
that with neutral BHs, can be avoided.


  Robotic grasping is a fundamental skill required for object manipulation in
robotics. Multi-fingered robotic hands, which mimic the structure of the human
hand, can potentially perform complex object manipulation. Nevertheless,
current techniques for multi-fingered robotic grasping frequently predict only
a single grasp for each inference time, limiting computational efficiency and
their versatility, i.e. unimodal grasp distribution. This paper proposes a
differentiable multi-fingered grasp generation network (DMFC-GraspNet) with
three main contributions to address this challenge. Firstly, a novel neural
grasp planner is proposed, which predicts a new grasp representation to enable
versatile and dense grasp predictions. Secondly, a scene creation and label
mapping method is developed for dense labeling of multi-fingered robotic hands,
which allows a dense association of ground truth grasps. Thirdly, we propose to
train DMFC-GraspNet end-to-end using using a forward-backward automatic
differentiation approach with both a supervised loss and a differentiable
collision loss and a generalized Q 1 grasp metric loss. The proposed approach
is evaluated using the Shadow Dexterous Hand on Mujoco simulation and ablated
by different choices of loss functions. The results demonstrate the
effectiveness of the proposed approach in predicting versatile and dense
grasps, and in advancing the field of multi-fingered robotic grasping.


  The main theorem of this paper is a result of estimated transversality with
respect to stratifications of jet spaces in the approximately holomorphic
category over an almost-complex manifold. The notion of asymptotic ampleness of
complex vector bundles over an almost-complex manifold is also discussed, as
well as applications to the construction of maps with generic singularities
from compact symplectic manifolds to projective spaces.


  The Hercules Thick Disk Cloud (Larsen et al. 2008) was initially discovered
as an excess in the number of faint blue stars between quadrants 1 and 4 of the
Galaxy. The origin of the Cloud could be an interaction with the disk bar, a
triaxial thick disk or a merger remnant or stream. To better map the spatial
extent of the Cloud along the line of sight, we have obtained multi-color UBVR
photometry for 1.2 million stars in 63 fields approximately 1 square degree
each. Our analysis of the fields beyond the apparent boundaries of the excess
have already ruled out a triaxial thick disk as a likely explanation (Larsen,
Humphreys and Cabanela 2010) In this paper we present our results for the star
counts over all of our fields, determine the spatial extent of the over density
across and along the line of sight, and estimate the size and mass of the
Cloud. Using photometric parallaxes, the stars responsible for the excess are
between 1 and 6 kiloparsecs from the Sun, 0.5 -- 4 kpc above the Galactic
plane, and extends approximately 3-4 kiloparsecs across our line of sight. It
is thus a major substructure in the Galaxy. The distribution of the excess
along our sight lines corresponds with the density contours of the bar in the
Disk, and its most distant stars are directly over the bar. We also see through
the Cloud to its far side. Over the entire 500 square degrees of sky containing
the Cloud, we estimate more than 5.6 million stars and 1.9 million solar masses
of material. If the over density is associated with the bar, it would exceed
1.4 billion stars and more than than 50 million solar masses. Finally, we argue
that the Hercules-Aquila Cloud (Belokurov et al. 2007) is actually the Hercules
Thick Disk Cloud.


  The organic chemistry occurring in interstellar environments may lead to the
production of complex molecules that are relevant to the emergence of life.
Therefore, in order to understand the origins of life itself, it is necessary
to probe the chemistry of carbon-bearing molecules under conditions that
simulate interstellar space. Several of these regions, such as dense molecular
cores, are exposed to ionizing radiation in the form of galactic cosmic rays,
which may act as an important driver of molecular destruction and synthesis. In
this paper, we report the results of a comparative and systematic study of the
irradiation of CH4:H2O ice mixtures by 1 MeV protons and 2 keV electrons at 20
K.We demonstrate that our irradiations result in the formation of a number of
new products, including both simple and complex daughter molecules such as
C2H6, C3H8, C2H2, CH3OH, CO, CO2, and probably also H2CO. A comparison of the
different irradiation regimes has also revealed that proton irradiation
resulted in a greater abundance of radiolytic daughter molecules compared to
electron irradiation, despite a lower radiation dose having been administered.
These results are important in the context of the radiation astrochemistry
occurring within the molecular cores of dense interstellar clouds, as well as
on outer Solar System objects.


  We study the defocusing energy-critical nonlinear wave equation in four
dimensions. Our main result proves the stability of the scattering mechanism
under random pertubations of the initial data. The random pertubation is
defined through a microlocal randomization, which is based on a unit-scale
decomposition in physical and frequency space. In contrast to the previous
literature, we do not require the spherical symmetry of the pertubation. The
main novelty lies in a wave packet decomposition of the random linear
evolution. Through this decomposition, we can adaptively estimate the
interaction between the rough and regular components of the solution. Our
argument relies on techniques from restriction theory, such as Bourgain's bush
argument and Wolff's induction on scales.


  We consider the development of high order space and time numerical methods
based on Implicit-Explicit (IMEX) multistep time integrators for hyperbolic
systems with relaxation. More specifically, we consider hyperbolic balance laws
in which the convection and the source term may have very different time and
space scales. As a consequence the nature of the asymptotic limit changes
completely, passing from a hyperbolic to a parabolic system. From the
computational point of view, standard numerical methods designed for the
fluid-dynamic scaling of hyperbolic systems with relaxation present several
drawbacks and typically lose efficiency in describing the parabolic limit
regime. In this work, in the context of Implicit-Explicit linear multistep
methods we construct high order space-time discretizations which are able to
handle all the different scales and to capture the correct asymptotic behavior,
independently from its nature, without time step restrictions imposed by the
fast scales. Several numerical examples confirm the theoretical analysis.


  A major uncertainty in models for photoionised outflows in AGN is the
distance of the gas to the central black hole. We present the results of a
massive multiwavelength monitoring campaign on the bright Seyfert 1 galaxy Mrk
509 to constrain the location of the outflow components dominating the soft
X-ray band.
  Mrk 509 was monitored by XMM-Newton, Integral, Chandra, HST/COS and Swift in
2009. We have studied the response of the photoionised gas to the changes in
the ionising flux produced by the central regions. We were able to put tight
constraints on the variability of the absorbers from day to year time scales.
This allowed us to develop a model for the time-dependent photoionisation in
this source.
  We find that the more highly ionised gas producing most X-ray line opacity is
at least 5 pc away from the core; upper limits to the distance of various
absorbing components range between 20 pc up to a few kpc. The more lowly
ionised gas producing most UV line opacity is at least 100 pc away from the
nucleus.
  These results point to an origin of the dominant, slow (v<1000 km/s) outflow
components in the NLR or torus-region of Mrk 509. We find that while the
kinetic luminosity of the outflow is small, the mass carried away is likely
larger than the 0.5 Solar mass per year accreting onto the black hole.
  We also determined the chemical composition of the outflow as well as
valuable constraints on the different emission regions. We find for instance
that the resolved component of the Fe-K line originates from a region 40-1000
gravitational radii from the black hole, and that the soft excess is produced
by Comptonisation in a warm (0.2-1 keV), optically thick (tau~10-20) corona
near the inner part of the disk.


  Using a fermionic renormalization group approach we analyse a model where the
electrons diffusing on a quantum dot interact via Fermi-liquid interactions.
Describing the single-particle states by Random Matrix Theory, we find that
interactions can induce phase transitions (or crossovers for finite systems) to
regimes where fluctuations and collective effects dominate at low energies.
Implications for experiments and numerical work on quantum dots are discussed.


  We study numerically time evolution of a system which consists of two
attractors connected by Fermi-Pasta-Ulam (FPU) chain. It is found that after
sufficiently long time there exits self-consistent large scale structure in the
system. The wavelet transform is used to separate the modes in different
scales. We found that the nonlinear long wavelength mode propagating along the
chain assists the energy transport. Further, all points in the system are found
to be correlated. Our results explain satisfactorily why the chaotic behaviour
is not enough to ensure the Fourier heat law and the thermal conductivity
diverges as it is found recently by Lepri et al (PRL 78, 1897(1997).).


  Many composite Higgs models predict the existence of vector-like quarks with
masses outside the reach of the LHC, e.g. $m_Q \gtrsim 2$ TeV, in particular if
these models contain a dark matter candidate. In such models the mass of the
new resonances is bounded from above to satisfy the constraint from the
observed relic density. We therefore develop new strategies to search for
vector-like quarks at a future $100$ TeV collider and evaluate what masses and
interactions can be probed. We find that masses as large as $\sim 6.4$ ($\sim
9$) TeV can be tested if the fermionic resonances decay into Standard Model
(dark matter) particles. We also discuss the complementarity of dark matter
searches, showing that most of the parameter space can be closed. On balance,
this study motivates further the consideration of a higher-energy hadron
collider for a next generation of facilities.


  A linear transmitter with correlated Gaussian white additive and
multiplicative noises and a periodic signal coupled either additively or
multiplicatively is considered. The correlations have a weak destructive effect
in case of the additive signal and a strong constructive effect in case of the
multiplicative one. Analytical results for the linear transmitter with the
additive signal agree with those obtained previously by a different approach.
We also show how to analytically calculate certain expectation values involving
exponentials of Gaussian processes.


  Numerous deep learning applications benefit from multi-task learning with
multiple regression and classification objectives. In this paper we make the
observation that the performance of such systems is strongly dependent on the
relative weighting between each task's loss. Tuning these weights by hand is a
difficult and expensive process, making multi-task learning prohibitive in
practice. We propose a principled approach to multi-task deep learning which
weighs multiple loss functions by considering the homoscedastic uncertainty of
each task. This allows us to simultaneously learn various quantities with
different units or scales in both classification and regression settings. We
demonstrate our model learning per-pixel depth regression, semantic and
instance segmentation from a monocular input image. Perhaps surprisingly, we
show our model can learn multi-task weightings and outperform separate models
trained individually on each task.


  We consider a scalar dark matter model, the SM4+D, consisting of the standard
model with four generations (SM4) and a real gauge-singlet scalar called
darkon, D, as the weakly interacting massive particle (WIMP) dark-matter (DM)
candidate. We explore constraints on the darkon sector of the SM4+D from WIMP
DM direct-search experiments, including CDMS II and CoGeNT, and from the decay
of a B meson into a kaon plus missing energy. We find that a sizable portion of
the darkon parameter space is still compatible with the experimental data.
Since the darkon-Higgs interaction may give rise to considerable enhancement of
the Higgs invisible decay mode, the existence of the darkon could lead to the
weakening or evasion of some of the restrictions on the Higgs mass in the
presence of fourth-generation quarks. In addition, it can affect the
flavor-changing decays of these new heavy quarks into a lighter quark and the
Higgs boson, as the Higgs may subsequently decay invisibly. Therefore we also
study these flavor-changing neutral transitions involving the darkon, as well
as the corresponding top-quark decay t -> cDD, some of which may be observable
at the Tevatron or LHC and thus provide additional tests for the SM4+D.


  Compact neutron imagers using double-scatter kinematic reconstruction are
being designed for localization and characterization of special nuclear
material. These neutron imaging systems rely on scintillators with a rapid
prompt temporal response as the detection medium. As n-p elastic scattering is
the primary mechanism for light generation by fast neutron interactions in
organic scintillators, proton light yield data are needed for accurate
assessment of scintillator performance. The proton light yield of a series of
commercial fast plastic organic scintillators---EJ-200, EJ-204, and
EJ-208---was measured via a double time-of-flight technique at the 88-Inch
Cyclotron at Lawrence Berkeley National Laboratory. Using a tunable deuteron
breakup neutron source, target scintillators housed in a dual photomultiplier
tube configuration, and an array of pulse-shape-discriminating observation
scintillators, the fast plastic scintillator light yield was measured over a
broad and continuous energy range down to proton recoil energies of
approximately 50 keV. This work provides key input to event reconstruction
algorithms required for utilization of these materials in emerging neutron
imaging modalities.


The method in this paper is wrong.


  In this article, the solution of a statistical inverse problem $M =
AU+\mathcal{E}$ by the Bayesian approach is studied where $U$ is a function on
the unit circle $\mathbb{T}$, i.e., a periodic signal. The mapping $A$ is a
smoothing linear operator and $\mathcal{E}$ a Gaussian noise. The connection to
the solution of a finite-dimensional computational model $M_{kn} = A_k U_n +
\mathcal{E}_k$ is discussed. Furthermore, a novel hierarchical prior model for
obtaining edge-preserving conditional mean estimates is introduced. The
convergence of the method with respect to finer discretization is studied and
the posterior distribution is shown to converge weakly. Finally, theoretical
findings are illustrated by a numerical example with simulated data.


  Evolutionary structure searches are used to predict stable phases with unique
stoichiometries in the hydrogen-rich region of the magnesium/hydrogen phase
diagram under pressure. MgH$_4$, MgH$_{12}$ and MgH$_{16}$ are found to be
thermodynamically stable with respect to decomposition into MgH$_2$ and H$_2$
near 100 GPa, and all lie on the convex hull by 200 GPa. MgH$_4$ contains two
H$^-$ anions and one H$_2$ molecule per Mg$^{2+}$ cation, whereas the
hydrogenic sublattices of MgH$_{12}$ and MgH$_{16}$ are composed solely of
H$_2^{\delta -}$ molecules. The high-hydrogen content stoichiometries have a
large density of states at the Fermi level, and the $T_c$ of MgH$_{12}$ at 140
GPa is calculated to be nearly three times greater than that of the classic
hydride, MgH$_2$, at 180 GPa.


  The mass shifts for two-fermion bound and scattering P-wave states subject to
the long-range interactions due to QED in the non-relativistic regime are
derived. Introducing a short range force coupling the spinless fermions to one
unit of angular momentum in the framework of pionless EFT, we first calculate
both perturbatively and non-perturbatively the Coulomb corrections to
fermion-fermion scattering in the continuum and infinite volume context.
Motivated by the research on particle-antiparticle bound states, we extend the
results to fermions of identical mass and opposite charge. Second, we transpose
the system onto a cubic lattice with periodic boundary conditions and we
calculate the finite volume corrections to the energy of the lowest bound and
unbound $T_1^{\pm}$ eigenstates. In particular, power law corrections
proportional to the fine structure constant and resembling the recent results
for S-wave states are found. Higher order contributions in $\alpha$ are
neglected, since the gapped nature of the momentum operator in the lattice
environnement allows for a perturbative treatment of the QED interactions.


  During the last few decades, solid-core photonic crystal fibers (PCFs) have
been extensively explored to generate broadband, high-coherence supercontinua
(SC). Limited by the material absorption and relatively low nonlinearity of
fused silica, spectral broadening in silica PCF-based SCs is usually restricted
to the blue to near-infrared spectral regions, even in developed commercial
sources. The output spectra of these sources are missing short wavelengths of
the full range. Many efforts have been spent to break the limitation. Among
them, dispersive-wave (DW) generation has been investigated for triggering new
frequencies in short wavelengths. With satisfied phase-matching conditions,
excessive energy can be directly transferred from solitons of the anomalous
dispersion region to DWs of the short wavelengths. However, a systematical
study of factors, including phase-matched DWs, strongly related to the
dispersion tailoring of higher-order modes (HOMs), has rarely been shown. This
study reports the experimental observations of HOM-enhanced phase-matchings for
the DW generation in the deep-blue and ultraviolet regions. A solid-core
PCF-based, UV-extended SC source spanning a 2.8-octave-wide (350 nm to 2500 nm)
is demonstrated. Meanwhile, we carefully verify our findings via numerical
calculations.


  We consider a model of a dynamical Lorentz gaz : a single particle is moving
in $\mathbb{R}^d$ through an array of fixed an soft scatterers each possessing
an internal degree of freedom coupled to the particle. Assuming the initial
velocity is sufficiently high and modelling the parameters of the scatterers as
random variables, we describe the evolution of the kinetic energy of the
particle by a Markov chain for which each step corresponds to a collision. We
show that the momentum distribution of the particle approaches a
Maxwell-Boltzmann distribution with effective temperature $T$ such that $k_BT$
corresponds to an average of the scatterers' kinetic energy.


  A model of growth of icosahedral viral capsids is proposed. It takes into
account the diversity of hexamers' compositions, leading to definite capsid
size. We show that the observed yield of capsid production implies a very high
level of self-organization of elementary building blocks. The exact number of
different protein dimers composing hexamers is related to the size of a given
capsid, labeled by its T-number. Simple rules determining these numbers for
each value of T are deduced and certain consequences are discussed.


  This article is concerned with the study of existence and properties of
stationary solutions for the dynamics of $N$ point vortices in an idealised
fluid constrained to a bounded two--dimen\-sional domain $\Omega$, which is
governed by a Hamiltonian system \[ \left\{\begin{aligned} \Gamma_i\frac{d
x_i}{d t}
  &=\frac{\partial H_\Omega}{\partial y_i}(z_1,\dots,z_N)\\ \Gamma_i\frac{d
y_i}{d t}
  &=-\frac{\partial H_\Omega}{\partial x_i}(z_1,\dots,z_N) \end{aligned}
\hspace{2cm}\text{where}\ z_i=(x_i,y_i),\ i=1,\dots,N, \right. \] where
$H_\Omega(z):=\sum_{j=1}^N\Gamma_j^2h(z_j)+\sum_{i,j=1,
i\not=j}^N\Gamma_i\Gamma_jG(z_i,z_j)$ is the so--called Kirchhoff--Routh--path
function under various conditions on the "vorticities" $\Gamma_i$ and various
topological and geometrical assumptions on $\Omega$.
  In particular, we will prove that (under an additional technical assumption)
if it is possible to align the vortices along a line, such that the signs of
the $\Gamma_i$ are alternating and $|\Gamma_i|$ is increasing, $H_\Omega$ has a
critical point.
  If $\Omega$ is not simply connected, we are able to derive a critical point
of $H_\Omega$, if $\sum_{j\in J}\Gamma_j^2>\sum_{\substack{i,j\in J\\
i\not=j}}|\Gamma_i\Gamma_j|$ for all $J\subset\{1,\dots,N\}$, $|J|\ge 2$.


  This work proposes a solution for the longitudinal and lateral control
problem of urban autonomous vehicles using a gain scheduling LPV control
approach. Using the kinematic and dynamic vehicle models, a linear parameter
varying (LPV) representation is adopted and a cascade control methodology is
proposed for controlling both vehicle behaviours. In particular, for the
control design, the use of both models separately lead to solve two LPV LMI-LQR
problems. Furthermore, to achieve the desired levels of performance, an
approach based on cascade design of the the kinematic and dynamic controllers
has been proposed. This cascade control scheme is based on the idea that the
dynamic closed loop behaviour is designed to be faster than the kinematic
closed loop one. The obtained gain scheduling LPV control approach, jointly
with a trajectory generation module, has presented suitable results in a
simulated city driving scenario.


  The superintegrability of the non-periodic Toda lattice is explained in the
framework of systems written in action-angles coordinates. Moreover, a simpler
form of the first integrals is given.


  We study distribution functions (DF) of mesoscopic hopping conductance
numerically by searching for the shortest path. We have found that the
distributions obtained by choosing randomly the chemical potentials (for a
fixed impurity configuration), which corresponds to a typical experimental
situation, coincide with those obtained when both impurity configuration and
chemical potential is chosen randomly, in agreement with the ergodicity
hypothesis. The DFs obtained for one-dimensional systems were found to be quite
close to the independent predictions of V.I. Mel'nikov, A.A. Abrikosov and P.
Lee et al. For D=2, the DFs both for narrow system and thin film look similar
(and close to the 1D case).The distribution function for the conductance of the
square sample is nearly Gaussian as predicted by both Altshuler et al and
Serota et al.


  Pure {\it compact} $U(1)$ lattice gauge theory exhibits a phase transition at
gauge coupling $g \sim {\cal{O}}(1)$ separating a familiar weak coupling
Coulomb phase, having free massless photons, from a strong coupling phase.
However, the phase transition was found to be of first order, ruling out any
nontrivial theory resulting from a continuum limit from the strong coupling
side. In this work, a compact $U(1)$ lattice gauge theory is studied with
addition of a dimension-two mass counterterm and a higher derivative (HD) term
that ensures a unique vacuum and produces a covariant gauge-fixing term in the
naive continuum limit. For a reasonably large coefficient of the HD term, now
there exists a continuous transition from a regular ordered phase to a
spatially modulated ordered phase. For weak gauge couplings, a continuum limit
from the regular ordered phase results in a familiar theory consisting of free
massless photons. For strong gauge couplings with $g\ge {\cal{O}}(1)$, this
transition changes from first order to continuous as the coefficient of the HD
term is increased, resulting in tricritical points which appear to be a
candidate in this theory for a possible nontrivial continuum limit.


  The diffusion and flow of amorphous materials, such as glasses and granular
materials, has resisted a simple microscopic description, analogous to defect
theories for crystals. Early models were based on either gas-like inelastic
collisions or crystal-like vacancy diffusion, but here we propose a cooperative
mechanism for dense random-packing dynamics, based on diffusing ``spots'' of
interstitial free volume. Simulations with the Spot Model can efficiently
generate realistic flowing packings, and yet the model is simple enough for
mathematical analysis. Starting from a non-local stochastic differential
equation, we derive continuum equations for tracer diffusion, given the
dynamics of free volume (spots). Throughout the paper, we apply the model to
granular drainage in a silo, and we also briefly discuss glassy relaxation. We
conclude by discussing the prospects of spot-based multiscale modeling and
simulation of amorphous materials.


  In a large sample of ULIRGs imaged with HST, we have identified a significant
subsample that shows evidence for multiple mergers. The evidence is seen among
two classes of ULIRGs: (1) those with multiple remnant nuclei in their core,
sometimes accompanied by a complex system of tidal tails; and (2) those that
are in fact dense groupings of interacting (soon-to-merge) galaxies. We
conservatively estimate that, in the redshift range 0.05<z<0.20, at least 20
(out of 99) ULIRGs satisfy one or both of these criteria. We present several
cases and discuss the possibility that the progenitors of ULIRGs may be the
more classical weakly interacting compact groups of galaxies (Hickson 1997). An
evolutionary progression is consistent with the results: from compact groups to
pairs to ULIRGs to ellipticals. The last step follows the blowout of gas and
dust from the ULIRG.


  Tensor accelerators have gained popularity because they provide a cheap and
efficient solution for speeding up computational-expensive tasks in Deep
Learning and, more recently, in other Scientific Computing applications.
However, since their features are specifically designed for tensor algebra
(typically dense matrix-product), it is commonly assumed that they are not
suitable for applications with sparse data. To challenge this viewpoint, we
discuss methods and present solutions for accelerating sparse matrix
multiplication on such architectures. In particular, we present a 1-dimensional
blocking algorithm with theoretical guarantees on the density, which builds
dense blocks from arbitrary sparse matrices. Experimental results show that,
even for unstructured and highly-sparse matrices, our block-based solution
which exploits Nvidia Tensor Cores is faster than its sparse counterpart. We
observed significant speed-ups of up to two orders of magnitude on real-world
sparse matrices.


  It is shown that the Monge equation is equivalent to the ordinary
differential equation $\ddot X=0$ of free motion. Equations of Monge type (with
their general solutions) are connected with each ordinary differential equation
of second order $\ddot X=F(\dot X,X; t)$, integrable by quadratures. The result
is generalised to a system of equations of the second order, which is in one to
one correspondence with the multidimensional Monge-Bateman system.


  In this book there will be found an introduction to transcendental number
theory, starting at the beginning and ending at the frontiers. The emphasis is
on the conceptual aspects of the subject, thus the effective theory has been
more or less completely ignored, as has been the theory of $E$-functions and
$G$-functions. Still, a fair amount of ground is covered and while I take
certain results without proof, this is done primarily so as not to get bogged
down in technicalities, otherwise the exposition is detailed and little is left
to the reader.


  We present a method to determine the critical rotational frequencies for
superfluidity of both uniform and trapped Fermi gases across wide Feshbach
resonance. It is found that as one approaches the resonance from the BCS side,
beyond a critical scattering length, pairing is so robust that superfluidity
cannot be destroyed by rotation. Moreover, the critical frequency has a
sequence of jumps revealing the appearance of Landau levels, which are
particularly prominent for systems up to a few thousand particles. For
rotational frequency below an "ultimate" critical frequency, defined to be the
lowest frequency at which the center of the cloud goes normal, a trapped gas
has a superfluid core surrounded by a normal gas, as seen in recent
experiments.


  The focus of this article are issues related to the assessment of the
teacher, working in small schools. The author analyzes the educational
activities ungraded school teacher, as one of the factors increasing the
quality of teaching in a rural school. We have tried to consider other
resources to successfully solve the problem of increasing the quality of
education in rural schools. Teacher in a rural school is a key figure in the
training, development and education of children. From his profession,
qualification, ability to work with a small but different age student
population is entirely dependent and learning outcomes of students. Therefore,
the educational activities of teachers of small schools should be analyzed and
monitored not only for assessing his work, but with the aim to provide all
possible assistance in the specific conditions of ungraded schools.