  This article studies Bayesian model averaging (BMA) in the context of
competing expensive computer models in a typical nuclear physics setup. While
it is well known that BMA accounts for the additional uncertainty of the model
itself, we show that it also decreases the posterior variance of the prediction
errors via an explicit decomposition. We extend BMA to the situation where the
competing models are defined on non-identical study regions. Any model's local
forecasting difficulty is offset by predictions obtained from the average
model, thus extending individual models to the full domain. We illustrate our
methodology via pedagogical simulations and applications to forecasting nuclear
observables, which exhibit convincing improvements in both the BMA prediction
error and empirical coverage probabilities.


  Dark energy candidates for which the equation-of-state parameter w is less
than -1 violate the dominant energy condition, and are typically unstable. In
scalar-tensor theories of gravity, however, the expansion of the universe can
mimic the behavior of general relativity with w<-1 dark energy, without
violating any energy conditions. We examine whether this possibility is
phenomenologically viable by studying Brans-Dicke models and characterizing
both the naturalness of the models themselves, and additional observational
constraints from limits on the time-dependence of Newton's constant. We find
that only highly contrived models would lead observers to measure w<-1.


  We study fully supersymmetric $AdS_6$ vacua of half-maxi\-mal $N=(1,1)$
gauged supergravity in six space-time dimensions coupled to $n$ vector
multiplets. We show that the existence of $AdS_6$ backgrounds requires that the
gauge group is of the form $G'\times G"\subset SO(4,n)$ where $G'\subset
SO(3,m)$ and $G"\subset SO(1,n-m)$. In the $AdS_6$ vacua this gauge group is
broken to its maximal compact subgroup $SO(3)\times H'\times H"$ where
$H'\subset SO(m)$ and $H"\subset SO(n-m)$. Furthermore, the $SO(3)$ factor is
the R-symmetry gauged by three of the four graviphotons. We further show that
the $AdS_6$ vacua have no moduli that preserve all supercharges. This is
precisely in agreement with the absence of supersymmetric marginal deformations
in holographically dual five-dimensional superconformal field theories.


  We introduce a model of two coupled reaction-diffusion equations to describe
the dynamics and propagation of flame fronts in random media. The model
incorporates heat diffusion, its dissipation, and its production through
coupling to the background reactant density. We first show analytically and
numerically that there is a finite critical value of the background density,
below which the front associated with the temperature field stops propagating.
The critical exponents associated with this transition are shown to be
consistent with mean field theory of percolation. Second, we study the kinetic
roughening associated with a moving planar flame front above the critical
density. By numerically calculating the time dependent width and equal time
height correlation function of the front, we demonstrate that the roughening
process belongs to the universality class of the Kardar-Parisi-Zhang interface
equation. Finally, we show how this interface equation can be analytically
derived from our model in the limit of almost uniform background density.


  System, design and methodology to load and dispense different articles from
an autonomous aircraft are disclosed. In one embodiment, the design of a unique
detachable dispenser for delivery of articles is described along with an
intelligent methodology of loading and delivering the articles to and from the
dispenser. Design of the dispenser, interaction of the dispenser with the
flight control unit and ground control or base-station, and interaction of the
base station with the sender or recipient of the article, are also described.


  We give sufficient conditions for asymptotic stabilization of equilibrium
points and periodic orbits of a dynamical system when we add a geometric
dissipation of gradient type. We also describe the domain of attraction in the
case of asymptotic stability.


  Drowsy driving represents a major contributor to traffic accidents, and the
implementation of driver drowsy driving detection systems has been proven to
significantly reduce the occurrence of such accidents. Despite the development
of numerous drowsy driving detection algorithms, many of them impose specific
prerequisites such as the availability of complete facial images, optimal
lighting conditions, and the use of RGB images. In our study, we introduce a
novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model
(MAF). MAF is aimed at significantly enhancing classification performance,
especially in scenarios involving partial facial occlusion and low lighting
conditions. It accomplishes this by capitalizing on the local feature
extraction capabilities provided by multi-attention fusion, thereby enhancing
the algorithm's overall robustness. To enhance our dataset, we collected
real-world data that includes both occluded and unoccluded faces captured under
nighttime and daytime lighting conditions. We conducted a comprehensive series
of experiments using both publicly available datasets and our self-built data.
The results of these experiments demonstrate that our proposed model achieves
an impressive driver drowsiness detection accuracy of 96.8%.


  Trust in electoral processes is fundamental for democracies. Further, the
identity management of citizen data is crucial, because final tallies cannot be
guaranteed without the assurance that every final vote was cast by an eligible
voter. In order to establish a basis for a hybrid public verifiability of
voting, this work (1) introduces Proverum, an approach combining a private
environment based on private permissioned Distributed Ledgers with a public
environment based on public Blockchains, (2) describes the application of the
Proverum architecture to the Swiss Remote Postal Voting system, mitigating
threats present in the current system, and (3) addresses successfully the
decentralized identity management in a federalistic state.


  This study showed the structures and the thermal behaviors of Si nanocages
and nanotubes using classical molecular dynamics simulations based on the
Tersoff potential. For hypothetical Si nanotubes based on the Tersoff
potential, Si-Si bond length, cohesive energies per atom, diameters, and
elastic energy to curve the sheet into tube were in good agreement with those
obtained from previous density-functional theory results. Most of the
structures, which were obtained from the SA simulations for several initial
structures with diamond structure, have included encaged, tubular, or
sheet-like structures and have been composed of both sp3 and sp2 bonds. The
cohesive energies per atom for silicon nanotubes were higher than that for the
Si bulk in the diamond structure, and this implies the difficulty in producing
silicon nanotubes or graphitelike sheets. However, since the elastic energy per
atom to curve the sheet into tube for silicon atoms is very low, when
graphitelike sheets of silicon are formed, the extra cost to produce silicon
nanotubes is also very low. When silicon nanotubes are composed of both sp2 and
sp3 bonds and the ratio of sp3 to sp2 is high, since the probability of the
existence of silicon nanotubes increases, silicon nanotubes similar to multi
walled structures are anticipated.


  Recently, generalizations of the classical Three Gap Theorem to higher
dimensions attracted a lot of attention. In particular, upper bounds for the
number of nearest neighbor distances have been established for the Euclidean
and the maximum metric. It was proved that a generic multi-dimensional
Kronecker attains the maximal possible number of different gap lengths for
every sub-exponential subsequence. We mirror this result in dimension $d \in
\left\{ 2, 3 \right\}$ by constructing Kronecker sequences which have a
surprisingly low number of different nearest neighbor distances for infinitely
$N \in \mathbb{N}$. Our proof relies on simple arguments from the theory of
continued fractions.


  We calculate the energies of ground and three low lying excited states of
confined helium atom centered in an impenetrable spherical box. We perform the
calculation by employing variational method with two-parameter variational
forms for the correlated two-particle wave function. With just two variational
parameters we get quite accurate results for both ground and excited state
energies.


  This paper represents a mixed numerical method for the multi-resolution
solution of non-linear partial differential equations based on B-Spline
wavelets. The method is based on a second-order finite difference formula
combined with the collocation method which uses the wavelet basis and applied
to the Burgers equation. Performance and accuracy of the numerical solutions
are studied using three standard test cases with Dirichlet and Neumann boundary
conditions. Comparing the results with other methods such as the fully implicit
finite difference method and mixed finite difference/boundary element method
shows a greater accuracy while using the smaller number of basis functions.


  We study the risk criterion for investments based on the drawdown from the
maximal value of the capital in the past. Depending on investor's risk
attitude, thus his risk exposure, we find that the distribution of these
drawdowns follows a general power law. In particular, if the risk exposure is
Kelly-optimal, the exponent of this power law has the borderline value of 2,
i.e. the average drawdown is just about to diverge


  The local distance ladder estimate of the Hubble constant ($H_0$) is
important in cosmology, given the recent tension with the early universe
inference. We estimate $H_0$ from the Type Ia supernova (SN~Ia) distance
ladder, inferring SN~Ia distances with the hierarchical Bayesian SED model,
BayeSN. This method has a notable advantage of being able to continuously model
the optical and near-infrared (NIR) SN~Ia light curves simultaneously. We use
two independent distance indicators, Cepheids or the tip of the red giant
branch (TRGB), to calibrate a Hubble-flow sample of 67 SNe~Ia with optical and
NIR data. We estimate $H_0 = 74.82 \pm 0.97$ (stat) $\pm\, 0.84$ (sys)
km\,s$^{-1}$\,Mpc$^{-1}$ when using the calibration with Cepheid distances to
37 host galaxies of 41 SNe~Ia, and $70.92 \pm 1.14$ (stat) $\pm\,1.49$ (sys)
km\,s$^{-1}$\,Mpc$^{-1}$ when using the calibration with TRGB distances to 15
host galaxies of 18 SNe~Ia. For both methods, we find a low intrinsic scatter
$\sigma_{\rm int} \lesssim 0.1$ mag. We test various selection criteria and do
not find significant shifts in the estimate of $H_0$. Simultaneous modelling of
the optical and NIR yields up to $\sim$15\% reduction in $H_0$ uncertainty
compared to the equivalent optical-only cases. With improvements expected in
other rungs of the distance ladder, leveraging joint optical-NIR SN~Ia data can
be critical to reducing the $H_0$ error budget.


  The probability distributions, as well as the mean values of stochastic
currents and fluxes, associated with a driven Langevin process, provide a good
and topologically protected measure of how far a stochastic system is driven
out of equilibrium. By viewing a Langevin process on a compact oriented
manifold of arbitrary dimension m as a theory of a random vector field
associated with the environment, we are able to consider stochastic motion of
higher-dimensional objects, which allow new observables, called
higher-dimensional currents, to be introduced. These higher dimensional
currents arise by counting intersections of a k-dimensional trajectory,
produced by a evolving (k-1)-dimensional cycle, with a reference cross section,
represented by a cycle of complimentary dimension (m - k). We further express
the mean fluxes in terms of the solutions of the Supersymmetric Fokker-Planck
(SFP), thus generalizing the corresponding well-known expressions for the
conventional currents.


  Cluster analysis aims at partitioning data into groups or clusters. In
applications, it is common to deal with problems where the number of clusters
is unknown. Bayesian mixture models employed in such applications usually
specify a flexible prior that takes into account the uncertainty with respect
to the number of clusters. However, a major empirical challenge involving the
use of these models is in the characterisation of the induced prior on the
partitions. This work introduces an approach to compute descriptive statistics
of the prior on the partitions for three selected Bayesian mixture models
developed in the areas of Bayesian finite mixtures and Bayesian nonparametrics.
The proposed methodology involves computationally efficient enumeration of the
prior on the number of clusters in-sample (termed as ``data clusters'') and
determining the first two prior moments of symmetric additive statistics
characterising the partitions. The accompanying reference implementation is
made available in the R package 'fipp'. Finally, we illustrate the proposed
methodology through comparisons and also discuss the implications for prior
elicitation in applications.


  In this paper, we study the existence of common fixed points of family of
multivalued mappings satisfying generalized F-contractive conditions in ordered
metric spaces. These results establish some of the general common fixed point
theorems for family of multivalued maps.


  We simulate two-dimensional Josephson junction arrays, including full mutual-
inductance effects, as they are cooled below the transition temperature in a
magnetic field. We show numerical simulations of the array magnetization as a
function of position, as detected by a scanning SQUID which is placed at a
fixed height above the array. The calculated magnetization images show striking
agreement with the experimental images obtained by A. Nielsen et al. The
average array magnetization is found to be paramagnetic for many values of the
applied field, confirming that paramagnetism can arise from magnetic screening
in multiply-connected superconductors without the presence of d-wave
superconductivity.


  Conjunctive table algebras are introduced and axiomatically characterized. A
conjunctive table algebra is a variant of SPJR algebra (a weaker form of
relational algebra), which corresponds to conjunctive queries with equality.
The table operations relate to logical operations (e.g. column deletion
corresponds to existential quantification). This enables a connection between
database theory and algebraic logic, particularly cylindric algebras. A
comparison shows which cylindric algebra axioms hold in conjunctive table
algebras, which ones are modified, and which ones hold in addition.


  We introduce a general method for transforming the equations of motion
following from a Das-Jevicki-Sakita Hamiltonian, with boundary conditions, into
a boundary value problem in one-dimensional quantum mechanics. For the
particular case of a one-dimensional chain of interacting NxN Hermitean
matrices, the corresponding large N boundary value problem is mapped into a
linear Fredholm equation with Hilbert-Schmidt type kernel. The equivalence of
this kernel, in special cases, to a second order differential operator allows
us recover all previously known explicit solutions for the matrix eigenvalues.
In the general case, the distribution of eigenvalues is formally derived
through a series of saddle-point approximations. The critical behaviour of the
system, including a previously observed Kosterlitz-Thouless transition, is
interpreted in terms of the stationary points. In particular we show that a
previously conjectured infinite series of sub-leading critical points are due
to expansion about unstable stationary points and consequently not realized.


  Heterogeneous programming has started becoming the norm in order to achieve
better performance by running portions of code on the most appropriate hardware
resource. Currently, significant engineering efforts are undertaken in order to
enable existing programming languages to perform heterogeneous execution mainly
on GPUs. In this paper we describe Jacc, an experimental framework which allows
developers to program GPGPUs directly from Java. By using the Jacc framework,
developers have the ability to add GPGPU support into their applications with
minimal code refactoring.
  To simplify the development of GPGPU applications we allow developers to
model heterogeneous code using two key abstractions: \textit{tasks}, which
encapsulate all the information needed to execute code on a GPGPU; and
\textit{task graphs}, which capture the inter-task control-flow of the
application. Using this information the Jacc runtime is able to automatically
handle data movement and synchronization between the host and the GPGPU;
eliminating the need for explicitly managing disparate memory spaces.
  In order to generate highly parallel GPGPU code, Jacc provides developers
with the ability to decorate key aspects of their code using annotations. The
compiler, in turn, exploits this information in order to automatically generate
code without requiring additional code refactoring.
  Finally, we demonstrate the advantages of Jacc, both in terms of
programmability and performance, by evaluating it against existing Java
frameworks. Experimental results show an average performance speedup of 32x and
a 4.4x code decrease across eight evaluated benchmarks on a NVIDIA Tesla K20m
GPU.


  Recent measurements of unpolarised and polarised nucleon structure functions
and F_2(gamma) are reviewed. The implications for QCD and the gluon momentum
distribution are discussed. The status of the understanding of the total
virtual-photon proton total cross-section in the transition region between real
photoproduction and deep-inelastic scattering is summarised briefly.


  We present ensmallen, a fast and flexible C++ library for mathematical
optimization of arbitrary user-supplied functions, which can be applied to many
machine learning problems. Several types of optimizations are supported,
including differentiable, separable, constrained, and categorical objective
functions. The library provides many pre-built optimizers (including numerous
variants of SGD and Quasi-Newton optimizers) as well as a flexible framework
for implementing new optimizers and objective functions. Implementation of a
new optimizer requires only one method and a new objective function requires
typically one or two C++ functions. This can aid in the quick implementation
and prototyping of new machine learning algorithms. Due to the use of C++
template metaprogramming, ensmallen is able to support compiler optimizations
that provide fast runtimes. Empirical comparisons show that ensmallen is able
to outperform other optimization frameworks (like Julia and SciPy), sometimes
by large margins. The library is distributed under the BSD license and is ready
for use in production environments.


  The intermediate and late-time behaviour of a massive Dirac field in the
background of static spherically symmetric black hole solutions is
investigated. The intermediate asymptotic behaviour of a massive Dirac field
depends on the mass parameter as well as the wave number of the mode, while the
late time behaviour has a power law decay rate independent of both.


  The multiple-input and multiple-output (MIMO) technique is regarded as a
promising approach to boost the throughput and reliability of optical fiber
communications. However, the fundamental limits of optical fiber MIMO systems
with finite block-length (FBL) are not available in the literature. This paper
studies the fundamental limits of optical fiber multicore/multimode systems in
the FBL regime when the coding rate is a perturbation within
$\mathcal{O}(\frac{1}{\sqrt{ML}})$ of the capacity, where M and L represent the
number of transmit channels and blocklength, respectively. Considering the
Jacobi MIMO channel, which was proposed to model the nearly lossless
propagation and the crosstalks in optical fiber systems, we derive the upper
and lower bounds for the optimal error probability. For that purpose, we first
set up the central limit theorem for the information density in the asymptotic
regime where the number of transmit, receive, available channels and the
blocklength go to infinity at the same pace. The result is then utilized to
derive the upper and lower bounds for the optimal average error probability
with the concerned rate. The derived theoretical results reveal interesting
physical insights for Jacobi MIMO channels with FBL. First, the derived bounds
for Jacobi channels degenerate to those for Rayleigh channels when the number
of available channels approaches infinity. Second, the high signal-to-noise
(SNR) approximation indicates that a larger number of available channels
results in a larger error probability. Numerical results validate the accuracy
of the theoretical results and show that the derived bounds are closer to the
performance of practical LDPC codes than outage probability.


  We study the notion of $\gamma$-negative dependence of random variables. This
notion is a relaxation of the notion of negative orthant dependence (which
corresponds to $1$-negative dependence), but nevertheless it still ensures
concentration of measure and allows to use large deviation bounds of
Chernoff-Hoeffding- or Bernstein-type. We study random variables based on
random points $P$. These random variables appear naturally in the analysis of
the discrepancy of $P$ or, equivalently, of a suitable worst-case integration
error of the quasi-Monte Carlo cubature that uses the points in $P$ as
integration nodes. We introduce the correlation number, which is the smallest
possible value of $\gamma$ that ensures $\gamma$-negative dependence. We prove
that the random variables of interest based on Latin hypercube sampling or on
$(t,m,d)$-nets do, in general, not have a correlation number of $1$, i.e., they
are not negative orthant dependent. But it is known that the random variables
based on Latin hypercube sampling in dimension $d$ are actually
$\gamma$-negatively dependent with $\gamma \le e^d$, and the resulting
probabilistic discrepancy bounds do only mildly depend on the $\gamma$-value.


  Bayesian methods have been very successful in quantifying uncertainty in
physics-based problems in parameter estimation and prediction. In these cases,
physical measurements y are modeled as the best fit of a physics-based model
$\eta(\theta)$ where $\theta$ denotes the uncertain, best input setting. Hence
the statistical model is of the form $y = \eta(\theta) + \epsilon$, where
$\epsilon$ accounts for measurement, and possibly other error sources. When
non-linearity is present in $\eta(\cdot)$, the resulting posterior distribution
for the unknown parameters in the Bayesian formulation is typically complex and
non-standard, requiring computationally demanding computational approaches such
as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the
posterior. While quite generally applicable, MCMC requires thousands, or even
millions of evaluations of the physics model $\eta(\cdot)$. This is problematic
if the model takes hours or days to evaluate. To overcome this computational
bottleneck, we present an approach adapted from Bayesian model calibration.
This approach combines output from an ensemble of computational model runs with
physical measurements, within a statistical formulation, to carry out
inference. A key component of this approach is a statistical response surface,
or emulator, estimated from the ensemble of model runs. We demonstrate this
approach with a case study in estimating parameters for a density functional
theory (DFT) model, using experimental mass/binding energy measurements from a
collection of atomic nuclei. We also demonstrate how this approach produces
uncertainties in predictions for recent mass measurements obtained at Argonne
National Laboratory (ANL).


  We investigate a one-dimensional S=1 antiferromagnetic Heisenberg model
coupled to a lattice distortion by a quantum Monte Carlo method. Investigating
the ground state energy of the static bond-alternating chain, we find that the
instability to a dimerized chain depends on the value of the spin-phonon
coupling, unlike the case of S=1/2. The spin state is the dimer state or the
uniform Haldane state depending on whether the lattice distorts or not,
respectively. At an intermediate value of the spin-phonon coupling, we find the
first-order transition between the two states. We also find the coexistence of
the two states.


  Correlated binary response data with covariates are ubiquitous in
longitudinal or spatial studies. Among the existing statistical models the most
well-known one for this type of data is the multivariate probit model, which
uses a Gaussian link to model dependence at the latent level. However, a
symmetric link may not be appropriate if the data are highly imbalanced. Here,
we propose a multivariate skew-elliptical link model for correlated binary
responses, which includes the multivariate probit model as a special case.
Furthermore, we perform Bayesian inference for this new model and prove that
the regression coefficients have a closed-form unified skew-elliptical
posterior. The new methodology is illustrated by application to COVID-19
pandemic data from three different counties of the state of California, USA. By
jointly modeling extreme spikes in weekly new cases, our results show that the
spatial dependence cannot be neglected. Furthermore, the results also show that
the skewed latent structure of our proposed model improves the flexibility of
the multivariate probit model and provides better fit to our highly imbalanced
dataset.


  We introduce the problem of learning affective correspondence between audio
(music) and visual data (images). For this task, a music clip and an image are
considered similar (having true correspondence) if they have similar emotion
content. In order to estimate this crossmodal, emotion-centric similarity, we
propose a deep neural network architecture that learns to project the data from
the two modalities to a common representation space, and performs a binary
classification task of predicting the affective correspondence (true or false).
To facilitate the current study, we construct a large scale database containing
more than $3,500$ music clips and $85,000$ images with three emotion classes
(positive, neutral, negative). The proposed approach achieves $61.67\%$
accuracy for the affective correspondence prediction task on this database,
outperforming two relevant and competitive baselines. We also demonstrate that
our network learns modality-specific representations of emotion (without
explicitly being trained with emotion labels), which are useful for emotion
recognition in individual modalities.


  We clarify a strong link between general nonlinear Fokker-Planck equations
with gauge fields associated with nonequilibrium dynamics and the Fisher
information of the system. The notion of Abelian gauge theory for the
non-equilibrium Fokker-Planck equation has proposed in the literature, in which
the associated curvature represents internal geometry. We present the
fluctuation of the gauge field can be decomposed into three parts. We further
show that if we define the Fisher information matrix by using a covariant
derivative then it gives correlation of the flux components but it is not gauge
invariant.


  We show that, for Galilean invariant quantum Hall states, the Hall viscosity
appears in the electromagnetic response at finite wave numbers q. In
particular, the leading q dependence of the Hall conductivity at small q
receives a contribution from the Hall viscosity. The coefficient of the q^2
term in the Hall conductivity is universal in the limit of strong magnetic
field.


  We address the issue of hadron deformation within the framework of lattice
QCD. For hadrons with spin greater than 1/2 the deformation can be determined
by evaluating the charge and matter distributions. Deviation of the nucleon
shape from spherical symmetry is determined by evaluating the quadrupole
strength in the transition $\gamma N \to \Delta(1232)$ both in the quenched and
in the unquenched theory.


  Recent progress in semantic scene understanding has primarily been enabled by
the availability of semantically annotated bi-modal (camera and lidar) datasets
in urban environments. However, such annotated datasets are also needed for
natural, unstructured environments to enable semantic perception for
applications, including conservation, search and rescue, environment
monitoring, and agricultural automation. Therefore, we introduce WildScenes, a
bi-modal benchmark dataset consisting of multiple large-scale traversals in
natural environments, including semantic annotations in high-resolution 2D
images and dense 3D lidar point clouds, and accurate 6-DoF pose information.
The data is (1) trajectory-centric with accurate localization and globally
aligned point clouds, (2) calibrated and synchronized to support bi-modal
inference, and (3) containing different natural environments over 6 months to
support research on domain adaptation. Our 3D semantic labels are obtained via
an efficient automated process that transfers the human-annotated 2D labels
from multiple views into 3D point clouds, thus circumventing the need for
expensive and time-consuming human annotation in 3D. We introduce benchmarks on
2D and 3D semantic segmentation and evaluate a variety of recent deep-learning
techniques to demonstrate the challenges in semantic segmentation in natural
environments. We propose train-val-test splits for standard benchmarks as well
as domain adaptation benchmarks and utilize an automated split generation
technique to ensure the balance of class label distributions. The data,
evaluation scripts and pretrained models will be released upon acceptance at
https://csiro-robotics.github.io/WildScenes.


  Disordered network materials abound in both nature and synthetic situations
while rigorous analysis of their nonlinear mechanical behaviors still is very
challenging. The purpose of this paper is to connect the mathematical framework
of sweeping process originally proposed by Moreau to a generic class of Lattice
Spring Models with plasticity phenomenon. We explicitly construct a sweeping
process and provide numerical schemes to find the evolution of stresses in a
Lattice Spring Model with infinitesimal strains and perfect plasticity. In
particular, we develop a highly efficient "leapfrog" computational framework
that allow ones to rigorously track the progression of plastic events in the
system based on the sweeping process theory. The utility of our framework is
demonstrated by analyzing the elastoplastic stresses in a novel class of
disordered network materials exhibiting the property of hyperuniformity, in
which the infinite wave-length density fluctuations associated with the
distribution of network nodes are completely suppressed. We find enhanced
mechanical properties such as increasing stiffness, yield strength and tensile
strength as the degree of hyperuniformity of the material system increases. Our
results have implications for optimal network material design and our
event-based framework can be readily generalized for nonlinear stress analysis
of other heterogeneous material systems. We also include some insights to the
model from the viewpoint of the rigidity theory.


  The design and implementation of large sets of spatially extended baryon
operators for use in lattice simulations are described. The operators are
constructed to maximize overlaps with the low-lying states of interest, while
minimizing the number of sources needed in computing the required quark
propagators.


  While digital libraries offer essential benefits for the digital age such as
constant availability and accessibility, they often fail in providing a
similarly enjoyable browsing experience that benefits engagement and
serendipitous exploration inherent to traditional library experiences. In this
paper, we propose a virtual reality (VR) library environment that emulates a
traditional library promoting an engaging browsing experience that benefits
browsing enjoyment and serendipity. The implemented system uses procedural
content generation to dynamically generate a library-like environment, wherein
each room corresponds to a specific category of the digital library. To assess
the usability of the implemented system, we conducted an A-B study comparing
the VR environment with the web interface of Project Gutenberg. The results of
the study suggest that while the physical demand for using the VR environment
is higher, the system can significantly benefit the browsing experience and
entice users curiosity. Furthermore, the study suggests that the system can
increase user engagement and users generally felt that using the system was
rewarding.


  Viscoelastic subdiffusion governed by a fractional Langevin equation is
studied numerically in a random Gaussian environment modeled by stationary
Gaussian potentials with decaying spatial correlations. This anomalous
diffusion is archetypal for living cells, where cytoplasm is known to be
viscoelastic and a spatial disorder also naturally emerges. We obtain some
first important insights into it within a model one-dimensional study. Two
basic types of potential correlations are studied: short-range exponentially
decaying and algebraically slow decaying with an infinite correlation length,
both for a moderate (several $k_BT$, in the units of thermal energy), and
strong (5-10 $k_BT$) disorder. For a moderate disorder, it is shown that on the
ensemble level viscoelastic subdiffusion can easily overcome the medium's
disorder. Asymptotically, it is not distinguishable from the disorder-free
subdiffusion. However, a strong scatter in single-trajectory averages is
nevertheless seen even for a moderate disorder. It features a weak ergodicity
breaking, which occurs on a very long yet transient time scale. Furthermore,
for a strong disorder, a very long transient regime of logarithmic, Sinai-type
diffusion emerges. It can last longer and be faster in the absolute terms for
weakly decaying correlations as compare with the short-range correlations.
Residence time distributions in a finite spatial domain are of a generalized
log-normal type and are reminiscent also of a stretched exponential
distribution. They can be easily confused for power-law distributions in view
of the observed weak ergodicity breaking. This suggests a revision of some
experimental data and their interpretation.


  This paper contains the most important aspects of computing grids. Grid
computing allows high performance distributed systems to act as a single
computer. An overview of grids structure and techniques is given in order to
understand the way grids work.


  Understanding the strong interactions within baryonic systems beyond the up
and down quark sector is pivotal for a comprehensive description of nuclear
forces. This study explores the interactions involving hyperons, particularly
the $\Lambda$ particle, within the framework of nuclear lattice effective field
theory (NLEFT). By incorporating $\Lambda$ hyperons into the NLEFT framework,
we extend our investigation into the $S = -1$ sector, allowing us to probe the
third dimension of the nuclear chart. We calculate the $\Lambda$ separation
energies ($B_{\Lambda}$) of hypernuclei up to the medium-mass region, providing
valuable insights into hyperon-nucleon ($YN$) and hyperon-nucleon-nucleon
($YNN$) interactions. Our calculations employ high-fidelity chiral interactions
at N${}^3$LO for nucleons and extend it to $\Lambda$ hyperons with
leading-order S-wave $YN$ interactions as well as $YNN$ forces constrained only
by the $A=4,5$ systems. Our results contribute to a deeper understanding of the
SU(3) symmetry breaking and establish a foundation for future improvements in
hypernuclear calculations.


  Planar drawings of graphs tend to be favored over non-planar drawings.
Testing planarity and creating a planar layout of a planar graph can be done in
linear time. However, creating readable drawings of nearly planar graphs
remains a challenge. We therefore seek to answer which edges of nearly planar
graphs create clutter in their drawings generated by mainstream graph drawing
algorithms. We present a heuristic to identify problematic edges in nearly
planar graphs and adjust their weights in order to produce higher quality
layouts with spring-based drawing algorithms. Our experiments show that our
heuristic produces significantly higher quality drawings for augmented grid
graphs, augmented triangulations, and deep triangulations.


  (abridged) We present 8.2m VLT spectroscopic observations of 28 HII regions
in 16 emission-line galaxies and 3.6m ESO telescope spectroscopic observations
of 38 HII regions in 28 emission-line galaxies. These emission-line galaxies
were selected mainly from the Data Release 6 (DR6) of the Sloan Digital Sky
Survey (SDSS) as metal-deficient galaxy candidates. We collect photometric and
high-quality spectroscopic data for a large uniform sample of star forming
galaxies including new observations. Our aim is to study the
luminosity-metallicity (L-Z) relation for nearby galaxies, especially at its
low-metallicity end and compare it with that for higher-redshift galaxies. From
our new observations we find that the oxygen abundance in 61 out of the 66 HII
regions of our sample ranges from 12+logO/H=7.05 to 8.22. Our sample includes
27 new galaxies with 12+logO/H<7.6 which qualify as extremely metal-poor
star-forming galaxies (XBCDs). Among them are 10 HII regions with
12+logO/H<7.3. The new sample is combined with a further 93 low-metallicity
galaxies with accurate oxygen abundance determinations from our previous
studies, yielding in total a high-quality spectroscopic data set of 154 HII
regions. 9000 more galaxies with oxygen abundances, based mainly on the
Te-method, are compiled from the SDSS. Our data set spans a range of 8 mag with
respect to its absolute magnitude in SDSS g (-12>Mg>-20) and nearly 2 dex in
its oxygen abundance (7.0<12+logO/H<8.8), allowing us to probe the L-Z relation
in the nearby universe down to the lowest currently studied metallicity level.
The L-Z relation established on the basis of the present sample is consistent
with previous ones obtained for emission-line galaxies.


  We analyze X-ray spectra of 43 Palomar-Green quasars observed with {\it
XMM-Newton} in order to investigate their mean Fe K line profile and its
dependence on physical properties. The continuum spectra of 39 objects are well
reproduced by a model consisting of a power law and a blackbody modified by
Galactic absorption. The spectra of the remaining four objects require an
additional power-law component absorbed with a column density of $\sim 10^{23}
{\rm cm}^{-2}$. We fit the entire sample simultaneously to derive average Fe
line parameters by assuming a common Fe line shape. The Fe line is relatively
narrow ($\sigma=0.36$ keV), with a center energy of 6.48 keV and a mean
equivalent width (EW) of 248 eV. By combining black hole masses estimated from
the virial method and bolometric luminosities derived from full spectral energy
distributions, we examine the dependence of the Fe K line profile on Eddington
ratio. As the Eddington ratio increases, the line becomes systematically
stronger (EW = 130 to 280 eV), broader ($\sigma=0.1$ to 0.7 keV), and peaks at
higher energies (6.4 to 6.8 keV). This result suggests that the accretion rate
onto the black hole directly influences the geometrical structure and
ionization state of the accretion disk.


  These are notes based on a lecture given at the Cargese summer school 1995. I
describe evidence that the (two-dimensional) integrable chiral Gross-Neveu
model might remain integrable when coupled to gravity. The results presented
here were obtained in collaboration with Ian Kogan.


  In contrast to Hermitian systems, eigenstates of non-Hermitian ones are in
general nonorthogonal. This feature is most pronounced at exceptional points
where several eigenstates are linearly dependent. In this work we show that
near this point a new effect takes place. It exhibits in energy increases in
the system when its parameters change periodically. This effect resembles
parametric resonance in a Hermitian system but there is a fundamental
difference. It comes from the unique properties of the exceptional point that
leads to parametric instability that occurs almost at any change in a
parameter, while in the case of Hermitian systems it is necessary to fulfill
resonance conditions. We illustrate this phenomenon by the case of two coupling
waveguides with gain and loss. This phenomenon opens a wide range of
applications in optics, plasmonics, and optoelectronics, where the loss is an
inevitable problem and plays a crucial role.


  Automated AI classifiers should be able to defer the prediction to a human
decision maker to ensure more accurate predictions. In this work, we jointly
train a classifier with a rejector, which decides on each data point whether
the classifier or the human should predict. We show that prior approaches can
fail to find a human-AI system with low misclassification error even when there
exists a linear classifier and rejector that have zero error (the realizable
setting). We prove that obtaining a linear pair with low error is NP-hard even
when the problem is realizable. To complement this negative result, we give a
mixed-integer-linear-programming (MILP) formulation that can optimally solve
the problem in the linear setting. However, the MILP only scales to
moderately-sized problems. Therefore, we provide a novel surrogate loss
function that is realizable-consistent and performs well empirically. We test
our approaches on a comprehensive set of datasets and compare to a wide range
of baselines.


  We demonstrate how loop effects in gravitational backgrounds lead to a
difference in the propagation of matter and antimatter, and show this is
forbidden in flat space due to CPT and translation invariance. This mechanism,
which is naturally present in beyond the standard model (BSM) theories
exhibiting C and CP violation, generates a curvature-dependent chemical
potential for leptons in the low-energy effective Lagrangian, allowing a
matter-antimatter asymmetry to be generated in thermodynamic equilibrium, below
the BSM scale.


  Large-scale data are often characterized by some degree of inhomogeneity as
data are either recorded in different time regimes or taken from multiple
sources. We look at regression models and the effect of randomly changing
coefficients, where the change is either smoothly in time or some other
dimension or even without any such structure. Fitting varying-coefficient
models or mixture models can be appropriate solutions but are computationally
very demanding and often return more information than necessary. If we just ask
for a model estimator that shows good predictive properties for all regimes of
the data, then we are aiming for a simple linear model that is reliable for all
possible subsets of the data. We propose the concept of "maximin effects" and a
suitable estimator and look at its prediction accuracy from a theoretical point
of view in a mixture model with known or unknown group structure. Under certain
circumstances the estimator can be computed orders of magnitudes faster than
standard penalized regression estimators, making computations on large-scale
data feasible. Empirical examples complement the novel methodology and theory.


  We attack the specific time-dependent Hamiltonian problem H=-{1/2} (t_o/t)^a
\partial_{xx} + (1/2) \omega^2 (t/t_o)^b x^2. This corresponds to a
time-dependent mass (TM) Schr\"odinger equation. We give the specific
transformations to a different time-dependent quadratic Schr\"odinger equations
(TQ) and to a different time-dependent oscillator (TO) equation. For each
Schr\"odinger system, we give the Lie algebra of space-time symmetries, the
number states, the squeezed-state <x> and <p> (with their classical motion),
(\Delta x)^2, (\Delta p)^2, and the uncertainty product.


  We show that a recently conjectured form for perturbative supersymmetric
partition functions on spheres of general dimension $d$ is consistent with the
flat space limit of 6-dimensional $\mathcal{N}=1$ super Yang-Mills. We also
show that the partition functions for $\mathcal{N}=1$ 8- and 9-dimensional
theories are consistent with their known flat space limits.


  Serverless cloud computing handles virtually all the system administration
operations needed to make it easier for programmers to use the cloud. It
provides an interface that greatly simplifies cloud programming, and represents
an evolution that parallels the transition from assembly language to high-level
programming languages. This paper gives a quick history of cloud computing,
including an accounting of the predictions of the 2009 Berkeley View of Cloud
Computing paper, explains the motivation for serverless computing, describes
applications that stretch the current limits of serverless, and then lists
obstacles and research opportunities required for serverless computing to
fulfill its full potential. Just as the 2009 paper identified challenges for
the cloud and predicted they would be addressed and that cloud use would
accelerate, we predict these issues are solvable and that serverless computing
will grow to dominate the future of cloud computing.


  Prediction of events is the challenge in many different disciplines, from
meteorology to finance; the more this task is difficult, the more a system is
{\it complex}. Nevertheless, even according to this restricted definition, a
general consensus on what should be the correct indicator for complexity is
still not reached. In particular, this characterization is still lacking for
systems whose time evolution is influenced by factors which are not under
control and appear as random parameters or random noise. We show in this paper
how to find the correct indicators for complexity in the information theory
context. The crucial point is that the answer is twofold depending on the fact
that the random parameters are measurable or not. The content of this
apparently trivial observation has been often ignored in literature leading to
paradoxical results. Predictability is obviously larger when the random
parameters are measurable, nevertheless, in the contrary case, predictability
improves when the unknown random parameters are time correlated.


  In this report I consider the cosmology of KK gravitinos in models with extra
dimensions, in particular in connection with the known non--standard high
energy regime of expansion which is associated with them. The main result is
that the production of such KK modes, once one considers BBN constraints on the
allowed entropy released in their decays, is not compatible with non--standard
expansion after inflation: there is no five--dimensional Planck mass for which
the produced KK gravitinos are safe with respect to BBN. This conclusion holds
for both flat and warped models in which only gravity propagates in the full
spacetime. This report is based on the work arXiv:0705.4227 [hep-ph].


  In$_2$Se$_3$ is a semiconductor material that can be stabilized in different
crystal structures (at least one 3D and several 2D layered structures have been
reported) with diverse electrical and optical properties. This feature has
plagued its characterization over the years, with reported band gaps varying in
an unacceptable range of 1 eV. Using first-principles calculations based on
density functional theory and the HSE06 hybrid functional, we investigated the
structural and electronic properties of four layered phases of In$_2$Se$_3$,
addressing their relative stability and the nature of their fundamental band
gaps, i.e., direct {\em versus} indirect. Our results show large disparities
between fundamental and optical gaps. The absorption coefficients are found to
be as high as that in direct-gap III-V semiconductors. The band alignment with
respect to conventional semiconductors indicate a tendency to $n$-type
conductivity, explaining recent experimental observations.


  Decoherence effect on quantum entanglement of two optical qubits in a lossy
cavity interacting with a nonlinear medium (Kerr nonlinearity) is analyzed. The
qubits are assumed to be initially in the maximally entangled states (Bell or
Bell-like states) or the maximally entangled mixed states, on the example of
Werner and Werner-like states. Two kinds of measures of the entanglement are
considered: the concurrence to describe a decay of the entanglement of
formation of the qubits, and the negativity to determine a decay of the
entanglement cost under positive-partial-transpose-preserving operations. It is
observed that the Kerr nonlinearity, in the discussed decoherence model, does
not affect the entanglement of the qubits initially in the Bell or Werner
states, although the evolution of the qubits can depend on this nonlinearity
explicitly. However, it is shown that for the initial Bell-like state and the
corresponding Werner-like state, the loss of the entanglement can be
periodically reduced by inserting the Kerr nonlinearity in the lossy cavity.
Moreover, the relativity of the entanglement measures is demonstrated, to our
knowledge for the first time, as a result of a physical process.


  Double neutron star binaries, analogous to the well known Hulse--Taylor
pulsar PSR 1913+16, are guaranteed-to-exist sources of high frequency
gravitational radiation detectable by LIGO. There is considerable uncertainty
in the estimated rate of coalescence of such systems, with conservative
estimates of ~1 per million years per galaxy, and optimistic theoretical
estimates one or more magnitude larger. Formation rates of low-mass black
hole-neutron star binaries may be higher than those of NS-NS binaries, and may
dominate the detectable LIGO signal rate.
  We estimate the enhanced coalescence rate for BH-BH binaries due to weak
asymmetric kicks during the formation of low mass black holes like Nova Sco,
and find they may contribute significantly to the LIGO signal rate, possibly
dominating the phase I detectable signals if the range of BH masses for which
there is significant kick is broad enough. For a standard Salpeter IMF,
assuming mild natal kicks, we project that the R6 merger rate of BH-BH systems
is ~0.5, smaller than that of NS-NS systems. However, the higher chirp mass of
these systems produces a signal nearly four times greater, on average, with a
commensurate increase in search volume.
  The BH-BH coalescence channel considered here also predicts that a
substantial fraction of BH-BH systems should have at least one component with
near-maximal spin (a/M ~ 1).The waveforms produced by the coalescence of such a
system should produce a clear spin signature, so this hypothesis could be
directly tested by LIGO.


  We propose a scheme to produce spin entangled states for two interacting
electrons. One electron is bound in a well in a semiconductor quantum wire and
the second electron is transported along the wire, trapped in a surface
acoustic wave potential (SAW) minimum. We investigate the conditions for which
the Coulomb interaction between the two electrons induces entanglement.
Detailed numerical investigation reveals that the two electrons can be fully
spin entangled depending on the confinement characteristics of the well and the
SAW potential amplitude.


  A combinatorial theory for type $R_I$ orthogonal polynomials is
  given. The ingredients include weighted generalized Motzkin paths, moments,
  continued fractions, determinants, and histories. Several explicit examples
in
  the Askey scheme are given.


  We study the dynamics of Bogoliubov excitations of a Bose-Einstein condensate
in the quasiperiodic kicked rotor. In the weakly interacting regime, the
condensate is stable and both the condensate and the excitations undergo a
phase transition from a quasilocalized to a diffusive regime. The corresponding
critical exponents are identical for the condensate and the excitations, and
compare very well with the value $\nu\approx1.6$ for non-interacting particles.


  In neural networks, the loss function represents the core of the learning
process that leads the optimizer to an approximation of the optimal convergence
error. Convolutional neural networks (CNN) use the loss function as a
supervisory signal to train a deep model and contribute significantly to
achieving the state of the art in some fields of artificial vision.
Cross-entropy and Center loss functions are commonly used to increase the
discriminating power of learned functions and increase the generalization
performance of the model. Center loss minimizes the class intra-class variance
and at the same time penalizes the long distance between the deep features
inside each class. However, the total error of the center loss will be heavily
influenced by the majority of the instances and can lead to a freezing state in
terms of intra-class variance. To address this, we introduce a new loss
function called sigma squared reduction loss ($\sigma^2$R loss), which is
regulated by a sigmoid function to inflate/deflate the error per instance and
then continue to reduce the intra-class variance. Our loss has clear intuition
and geometric interpretation, furthermore, we demonstrate by experiments the
effectiveness of our proposal on several benchmark datasets showing the
intra-class variance reduction and overcoming the results obtained with center
loss and soft nearest neighbour functions.


  We prove that the sequence of eigencones (i.e., cones of nonnegative
eigenvectors) of positive powers A^k of a nonnegative square matrix A is
periodic both in max algebra and in nonnegative linear algebra. Using an
argument of Pullman, we also show that the Minkowski sum of the eigencones of
powers of A is equal to the core of A defined as the intersection of
nonnegative column spans of matrix powers, also in max algebra. Based on this,
we describe the set of extremal rays of the core. The spectral theory of matrix
powers and the theory of matrix core is developed in max algebra and in
nonnegative linear algebra simultaneously wherever possible, in order to unify
and compare both versions of the same theory.


  We study the detailed epidemic spreading process in scale-free networks with
weight that denote familiarity between two people or computers. The result
shows that spreading velocity reaches a peak quickly then decays representing
power-law time behavior, and comparing to non-weighted networks, precise
hierarchical dynamics is not found although the nodes with larger strength is
preferential to be infected.


  The mechanism of superconductivity in ${\rm Sr}_{2}{\rm RuO}_{4}$ is studied
using a degenerate Hubbard model within the weak coupling theory. When the
system approaches the orbital instability which is realized due to increasing
the on-site Coulomb interaction between the electrons in the different
orbitals, it is shown that the triplet superconductivity appears. This
superconducting mechanism is only available in orbitally degenerate systems
with multiple Fermi surfaces.


  We calculate the quantum states corresponding to the drifting and channeled
classical orbits in a two-dimensional electron gas (2DEG) with strong magnetic
and electric modulations along one spatial direction, $x$. The channeled states
carry high, concentrated currents along the $y$ axis, and are confined in an
effective potential well. The quantum and the classical states are compared.


  We present Hubble Space Telescope (HST) images of new planetary nebulae (PNe)
that were discovered in the Reid-Parker AAO/UKST H-alpha survey of the Large
Magellanic Cloud. These serendipitous observations from various HST programs
yield independent confirmations of 6 PNe; one other detected nebula may also be
a PN, and one appears to be a region of diffuse emission. The high resolution
HST archival images enable us to determine the physical sizes, the nebular
morphology, and related features of these new PNe in detail for the first time.
In a few cases we were also able to identify the central star, which was
impossible with the lower resolution, wide-field discovery data. The
confirmation of faint, extended halos surrounding many PNe in the RP catalog
must await the acquisition of new deep, high-resolution, narrow-band imagery.


  Intensity, wavevector, phase, and polarization are the most important
parameters of any light beam. Understanding the wavevector distribution has
emerged as a very important problem in recent days, especially at nanoscale. It
provides unique information about the light-matter interaction. Back focal
plane or Fourier plane imaging and spectroscopy techniques help to measure
wavevector distribution not only from single molecules and single
nanostructures but also from metasurfaces and metamaterials. This review
provides a birds-eye view on the technique of back focal imaging and
spectroscopy, different methodologies used in developing the technique and
applications including angular emission patterns of fluorescence and Raman
signals from molecules, elastic scattering etc. We first discuss on the
information one can obtain at the back focal plane of the objective lens
according to both imaging and spectroscopy viewpoints and then discuss the
possible configurations utilized to project back focal plane of the objective
lens onto the imaging camera or to the spectroscope. We also discuss the
possible sources of error in such measurements and possible ways to overcome it
and then elucidate the possible applications.


  Continuous robot operation in extreme scenarios such as underground mines or
sewers is difficult because exteroceptive sensors may fail due to fog,
darkness, dirt or malfunction. So as to enable autonomous navigation in these
kinds of situations, we have developed a type of proprioceptive localization
which exploits the foot contacts made by a quadruped robot to localize against
a prior map of an environment, without the help of any camera or LIDAR sensor.
The proposed method enables the robot to accurately re-localize itself after
making a sequence of contact events over a terrain feature. The method is based
on Sequential Monte Carlo and can support both 2.5D and 3D prior map
representations. We have tested the approach online and onboard the ANYmal
quadruped robot in two different scenarios: the traversal of a custom built
wooden terrain course and a wall probing and following task. In both scenarios,
the robot is able to effectively achieve a localization match and to execute a
desired pre-planned path. The method keeps the localization error down to 10cm
on feature rich terrain by only using its feet, kinematic and inertial sensing.


  I discuss various aspects of background independence in the context of string
theory, for which so far we have no manifestly background independent
formulation. After reviewing the role of background independence in classical
Einstein gravity, I discuss recent results implying that there is a conflict in
string theory between manifest background independence and manifest duality
invariance when higher-derivative corrections are included. The resolution of
this conflict requires the introduction of new gauge degrees of freedom
together with an enlarged gauge symmetry. This suggests more generally that a
manifestly background independent and duality invariant formulation of string
theory requires significantly enhanced gauge symmetries.


  There is a vast menagerie of plausible candidates for the constituents of
dark matter, both within and beyond extensions of the Standard Model of
particle physics. Each of these candidates may have scattering (and other)
cross section properties that are consistent with the dark matter abundance,
BBN, and the most scales in the matter power spectrum; but which may have
vastly different behavior at sub-galactic "cutoff" scales, below which dark
matter density fluctuations are smoothed out. The only way to quantitatively
measure the power spectrum behavior at sub-galactic scales at distances beyond
the local universe, and indeed over cosmic time, is through probes available in
multiply imaged strong gravitational lenses. Gravitational potential
perturbations by dark matter substructure encode information in the observed
relative magnifications, positions, and time delays in a strong lens. Each of
these is sensitive to a different moment of the substructure mass function and
to different effective mass ranges of the substructure. The time delay
perturbations, in particular, are proving to be largely immune to the
degeneracies and systematic uncertainties that have impacted exploitation of
strong lenses for such studies. There is great potential for a coordinated
theoretical and observational effort to enable a sophisticated exploitation of
strong gravitational lenses as direct probes of dark matter properties. This
opportunity motivates this white paper, and drives the need for: a) strong
support of the theoretical work necessary to understand all astrophysical
consequences for different dark matter candidates; and b) tailored
observational campaigns, and even a fully dedicated mission, to obtain the
requisite data.


  We present a new measurement of the antiproton to proton abundance ratio,
pbar/p, in the cosmic radiation. The HEAT-pbar instrument, a balloon borne
magnet spectrometer with precise rigidity and multiple energy loss measurement
capability, was flown successfully in Spring 2000, at an average atmospheric
depth of 7.2 g/cm^2. A total of 71 antiprotons were identified above the
vertical geomagnetic cut-off rigidity of 4.2 GV. The highest measured proton
energy was 81 GeV. We find that the pbar/p abundance ratio agrees with that
expected from a purely secondary origin of antiprotons produced by primary
protons with a standard soft energy spectrum.


  The equivariant quantum $K$-theory ring of a flag variety is a Frobenius
algebra equipped with a perfect pairing called the quantum $K$-metric. It is
known that in the classical $K$-theory ring for a given flag variety the ideal
sheaf basis is dual to the Schubert basis with regard to the sheaf Euler
characteristic. We define a quantization of the ideal sheaf basis for the
equivariant quantum $K$-theory of cominuscule flag varieties. These quantized
ideal sheaves are then dual to the Schubert basis with regard to the quantum
$K$-metric. We prove explicit type-uniform combinatorial formulae for the
quantized ideal sheaves in terms of the Schubert basis for any cominuscule flag
variety. We also provide an application ultilizing the quantized ideal sheaves
to calculate the Schubert structure constants associated to multiplication by
the top exterior power of the tautological quotient bundle in $QK_T(Gr(k,n))$.


  In this report, we investigate the data transmission model in which a
sequence of data is broadcasted to a number of receivers. The receivers, which
have different channel capacities, wish to decode the data sequentially at
different rates. Our results are applicable to a wide range of scenarios. For
instance, it can be employed in the broadcast streaming of a video clip through
the internet, so that receivers with different bandwidths can play the video at
different speed. Receivers with greater bandwidths can provide a smooth
playback, while receivers with smaller bandwidths can play the video at a
slower speed, or with short pauses or rebuffering.


  Deep learning based visual trackers entail offline pre-training on large
volumes of video datasets with accurate bounding box annotations that are
labor-expensive to achieve. We present a new framework to facilitate bounding
box annotations for video sequences, which investigates a
selection-and-refinement strategy to automatically improve the preliminary
annotations generated by tracking algorithms. A temporal assessment network
(T-Assess Net) is proposed which is able to capture the temporal coherence of
target locations and select reliable tracking results by measuring their
quality. Meanwhile, a visual-geometry refinement network (VG-Refine Net) is
also designed to further enhance the selected tracking results by considering
both target appearance and temporal geometry constraints, allowing inaccurate
tracking results to be corrected. The combination of the above two networks
provides a principled approach to ensure the quality of automatic video
annotation. Experiments on large scale tracking benchmarks demonstrate that our
method can deliver highly accurate bounding box annotations and significantly
reduce human labor by 94.0%, yielding an effective means to further boost
tracking performance with augmented training data.


  The rapid progress of autonomous vehicles~(AVs) has brought the prospect of a
driverless future closer than ever. Recent fatalities, however, have emphasized
the importance of safety validation through large-scale testing. Multiple
approaches achieve this fully automatically using high-fidelity simulators,
i.e., by generating diverse driving scenarios and evaluating autonomous driving
systems~(ADSs) against different test oracles. While effective at finding
violations, these approaches do not identify the decisions and actions that
\emph{caused} them -- information that is critical for improving the safety of
ADSs. To address this challenge, we propose ACAV, an automated framework
designed to conduct causality analysis for AV accident recordings in two
stages. First, we apply feature extraction schemas based on the messages
exchanged between ADS modules, and use a weighted voting method to discard
frames of the recording unrelated to the accident. Second, we use safety
specifications to identify safety-critical frames and deduce causal events by
applying CAT -- our causal analysis tool -- to a station-time graph. We
evaluate ACAV on the Apollo ADS, finding that it can identify five distinct
types of causal events in 93.64% of 110 accident recordings generated by an AV
testing engine. We further evaluated ACAV on 1206 accident recordings collected
from versions of Apollo injected with specific faults, finding that it can
correctly identify causal events in 96.44% of the accidents triggered by
prediction errors, and 85.73% of the accidents triggered by planning errors.


  This contribution to the focus issue covers anisotropic flow in hybrid
approaches. The historical development of hybrid approaches and their impact on
the interpretation of flow measurements is reviewed. The major ingredients of a
hybrid approach and the transition criteria between transport and hydrodynamics
are discussed. The results for anisotropic flow in (event-by-event) hybrid
approaches are presented. Some hybrid approaches rely on hadronic transport for
the late stages for the reaction (so called afterburner) and others employ
transport approaches for the early non equilibrium evolution. In addition,
there are 'full' hybrid calculations where a fluid evolution is dynamically
embedded in a transport simulation. After demonstrating the success of hybrid
approaches at high RHIC and LHC energies, existing hybrid caluclations for
collective flow observables at lower beam energies are discussed and remaining
challenges outlined.


  The initial boundary value problem on a half-line for the KdV equation with
the boundary conditions $u|_{x=0}=a\leq0$, $u_{xx}|_{x=0}=3a^2$ is integrated
by means of the inverse scattering method. In order to find the time evolution
of the scattering matrix it turned out to be sufficient to solve the Riemann
problem on a hyperelliptic curve of genus two, where the conjugation matrices
are effectively defined by initial data.


  When the network is reconstructed, two types of errors can occur: false
positive and false negative errors about the presence or absence of links. In
this paper, the influence of these two errors on the vertex degree distribution
is analytically analysed. Moreover, an analytic formula of the density of the
biased vertex degree distribution is found. In the inverse problem, we find a
reliable procedure to reconstruct analytically the density of the vertex degree
distribution of any network based on the inferred network and estimates for the
false positive and false negative errors based on, e.g., simulation studies.


  We have measured the root-mean-square (RMS) amplitude of intensity
fluctuations, $\Delta I$, in plume and interplume regions of a polar coronal
hole. These intensity fluctuations correspond to density fluctuations. Using
data from the \rev{Sun Watcher using Active Pixel System detector and Image
Processing} (SWAP) on \textit{Project for Onboard Autonomy (Proba2)}, our
results extend up to a height of about 1.35~$R_{\sun}$. One advantage of the
RMS analysis is that it does not rely on a detailed evaluation of the power
spectrum, which is limited by noise levels to low heights in the corona. The
RMS approach can be performed up to larger heights where the noise level is
greater, provided that the noise itself can be quantified. At low heights, both
the absolute $\Delta I$, and the amplitude relative to the mean intensity,
$\Delta I/I$, decrease with height. However, starting at about 1.2~$R_{\sun}$
$\Delta I/I$ increases, reaching 20--40\% by 1.35~$R_{\sun}$. This corresponds
to density fluctuations of $\Delta n_{\mathrm{e}}/n_{\mathrm{e}}
\approx$~10--20\%. The increasing relative amplitude implies that the density
fluctuations are generated in the corona itself. One possibility is that the
density fluctuations are generated by an instability of Alfv\'en waves. This
generation mechanism is consistent with some theoretical models and with
observations of Alfv\'en wave amplitudes in coronal holes. Although we find
that the energy of the observed density fluctuations is small, these
fluctuations are likely to play an important indirect role in coronal heating
by promoting the reflection of Alfv\'en waves and driving turbulence.


  We revisit the Harder-Narasimhan stratification on a minuscule $p$-adic flag
variety, by the theory of modifications of $G$-bundles on the Fargues-Fontaine
curve. We compare the Harder-Narasimhan strata with the Newton strata
introduced by Caraiani-Scholze. As a consequence, we get further equivalent
conditions in terms of $p$-adic Hodge-Tate period domains for fully
Hodge-Newton decomposable pairs. Moreover, we generalize these results to
arbitrary cocharacters case by considering the associated $B_{dR}^+$-affine
Schubert varieties. Applying Hodge-Tate period maps, our constructions give
applications to $p$-adic geometry of Shimura varieties and their local
analogues.


  We establish a large deviation principle for chordal SLE$_\kappa$
parametrized by capacity, as the parameter $\kappa \to 0+$, in the topology
generated by uniform convergence on compact intervals of the positive real
line. The rate function is shown to equal the Loewner energy of the curve. This
strengthens the recent result of E. Peltola and Y. Wang who obtained the
analogous statement using the Hausdorff topology.


  In complex networks, many elements interact with each other in different
ways. A hypergraph is a network in which group interactions occur among more
than two elements. In this study, first, we propose a method to identify
influential subgroups in hypergraphs, named $(k,q)$-core decomposition. The
$(k,q)$-core is defined as the maximal subgraph in which each vertex has at
least $k$ hypergraph degrees \textit{and} each hyperedge contains at least $q$
vertices. The method contains a repeated pruning process until reaching the
$(k,q)$-core, which shares similarities with a widely used $k$-core
decomposition technique in a graph. Second, we analyze the pruning dynamics and
the percolation transition with theoretical and numerical methods in random
hypergraphs. We set up evolution equations for the pruning process, and
self-consistency equations for the percolation properties. Based on our theory,
we find that the pruning process generates a hybrid percolation transition for
either $k\ge 3$ \textit{or} $q\ge 3$. The critical exponents obtained
theoretically are confirmed with finite-size scaling analysis. Next, when
$k=q=2$, we obtain a unconventional degree-dependent critical relaxation
dynamics analytically and numerically. Finally, we apply the $(k,q)$-core
decomposition to a real coauthorship dataset and recognize the leading groups
at an early stage.


Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]


  We investigate selection principles which are motivated by
Arhangel'ski\v{i}'s $\alpha_i$-properties, $i=1,2,3,4$, and their relations
with classical selection principles. It will be shown that they are closely
related to the selection principle $\sone$ and often are equivalent to it.


  There is increasing interest in learning algorithms that involve interaction
between human and machine. Comparison-based queries are among the most natural
ways to get feedback from humans. A challenge in designing comparison-based
interactive learning algorithms is coping with noisy answers. The most common
fix is to submit a query several times, but this is not applicable in many
situations due to its prohibitive cost and due to the unrealistic assumption of
independent noise in different repetitions of the same query.
  In this paper, we introduce a new weak oracle model, where a non-malicious
user responds to a pairwise comparison query only when she is quite sure about
the answer. This model is able to mimic the behavior of a human in noise-prone
regions. We also consider the application of this weak oracle model to the
problem of content search (a variant of the nearest neighbor search problem)
through comparisons. More specifically, we aim at devising efficient algorithms
to locate a target object in a database equipped with a dissimilarity metric
via invocation of the weak comparison oracle. We propose two algorithms termed
WORCS-I and WORCS-II (Weak-Oracle Comparison-based Search), which provably
locate the target object in a number of comparisons close to the entropy of the
target distribution. While WORCS-I provides better theoretical guarantees,
WORCS-II is applicable to more technically challenging scenarios where the
algorithm has limited access to the ranking dissimilarity between objects. A
series of experiments validate the performance of our proposed algorithms.


  Turbulence in classical fluids is characterized by persistent structures that
emerge from the chaotic landscape. We investigate the analogous process in
fully kinetic plasma turbulence by using high-resolution, direct numerical
simulations in two spatial dimensions. We observe the formation of long-living
vortices with a profile typical of macroscopic, magnetically dominated
force-free states. Inspired by the Harris pinch model for inhomogeneous
equilibria, we describe these metastable solutions with a self-consistent
kinetic model in a cylindrical coordinate system centered on a representative
vortex, starting from an explicit form of the particle velocity distribution
function. Such new equilibria can be simplified to a Gold-Hoyle solution of the
modified force-free state. Turbulence is mediated by the long-living
structures, accompanied by transients in which such vortices merge and form
self-similarly new metastable equilibria. This process can be relevant to the
comprehension of various astrophysical phenomena, going from the formation of
plasmoids in the vicinity of massive compact objects to the emergence of
coherent structures in the heliosphere.


  We present two identities (contiguity relation and variation formula)
concerning the volume of a spherically faced simplex in the Euclidean space.
These identities are described in terms of Cayley-Menger determinants and their
differentials involved with hypersphere arrangements. They are derived as a
limit of fundamental identities for hypergeometric integrals.


  It has been shown by several authors that there exists a non-solvable
Bieberbach group of dimension $15$. In this note we show that this is in fact a
minimal dimension for such kind of groups.


  We study the $q$-series $F(q) = \sum q^{m(m+1)/2}/(q)_m^2$ and offer some
commentary on why Ramanujan may have thought $F(q)$ does not "close."


  Change-point detection methods are proposed for the case of temporary
failures, or transient changes, when an unexpected disorder is ultimately
followed by a readjustment and return to the initial state. A base distribution
of the "in-control" state changes to an "out-of-control" distribution for
unknown periods of time. Likelihood based sequential and retrospective tools
are proposed for the detection and estimation of each pair of change-points.
The accuracy of the obtained change-point estimates is assessed. Proposed
methods offer simultaneous control the familywise false alarm and false
readjustment rates at the pre-chosen levels.


  We predict that the collision of two fully dark exciton condensates produces
interference fringes which are not only dark but also bright. So, quite
surprisingly, the collision of coherent states made of dark excitons produces
light. This remarkable effect, which is many-body in essence, comes from the
composite boson nature of excitons, through the fermion exchanges they can have
which transform dark states into bright states. The possibility of optically
detecting quantum coherence in a regime where the system is hidden by its total
darkness, was up to now considered as hopeless.


  In addition to being a widely recognised novelist, Milan Kundera has also
authored three pieces for theatre: The Owners of the Keys (Majitel\'e
kl\'i\v{c}\r{u}, 1961), The Blunder (Pt\'akovina, 1967), and Jacques and his
Master (Jakub a jeho p\'an, 1971). In recent years, however, the hypothesis has
been raised that Kundera is the true author of a fourth play: Juro
J\'ano\v{s}\'ik, first performed in a 1974 production under the name of Karel
Steigerwald, who was Kundera's student at the time. In this study, we make use
of supervised machine learning to settle the question of authorship attribution
in the case of Juro J\'ano\v{s}\'ik, with results strongly supporting the
hypothesis of Kundera's authorship.


  Multiple scattering of wave in strong heterogeneity can cause resonance-like
wave anomaly where the signal exhibits low-frequency, high intensity, and
slowly propagating wave packet velocity. For example, long period event in
volcanic seismology and plasma oscillations in wave-particle interactions.
Collective behaviour in a many-body system is thought to be the source for
generating the anomaly, however the detailed mechanism is not fully understood.
Here I show that the physical mechanism is associated with low-frequency
resonance (LFR) in strong small-scale heterogeneity through seismic wave field
modeling for bubble cloud heterogeneity and 1D heterogeneity. LFR is a kind of
wave coherent scattering enhancement or emergence phenomenon that occurs in
transient regime. Its resonance frequency decreases with increasing
heterogeneous scale, impedance contrast, or random heterogeneous scale and
velocity fluctuations; its intensity diminishes with decreasing impedance
contrast or increasing random heterogeneous scale and velocity fluctuations.
LRF exhibits the characteristics of localized wave in space and the shape of
ocean wave in time and is a ubiquitous wave phenomenon in wave physics. The
concept of LFR can open up new opportunities in many aspects of science and
engineering.


  In this paper, we propose a multi-component system of Camassa-Holm equation,
denoted by CH($N$,$H$) with 2N components and an arbitrary smooth function $H$.
This system is shown to admit Lax pair and infinitely many conservation laws.
We particularly study the case of N=2 and derive the bi-Hamiltonian structures
and peaked soliton (peakon) solutions for some examples.


  We prove some restriction theorems for flat homogeneous surfaces of
codimension greater than one.


  Model-based deep learning solutions to inverse problems have attracted
increasing attention in recent years as they bridge state-of-the-art numerical
performance with interpretability. In addition, the incorporated prior domain
knowledge can make the training more efficient as the smaller number of
parameters allows the training step to be executed with smaller datasets.
Algorithm unrolling schemes stand out among these model-based learning
techniques. Despite their rapid advancement and their close connection to
traditional high-dimensional statistical methods, they lack certainty estimates
and a theory for uncertainty quantification is still elusive. This work
provides a step towards closing this gap proposing a rigorous way to obtain
confidence intervals for the LISTA estimator.


  This is an expository note answering a question posed to us by Richard
Stanley, in which we prove a limit shape theorem for partitions of $n$ which
maximize the number of subpartitions. The limit shape and the growth rate of
the number of subpartitions are explicit. The key ideas are to use large
deviations estimates for random walks, together with convex analysis and the
Hardy-Ramanujan asymptotics. Our limit shape coincides with Vershik's limit
shape for uniform random partitions.


  We have theoretically investigated transport properties of the classical
Heisenberg antiferromagnet on the triangular lattice in which a
binding-unbinding topological transition of $\mathbb{Z}_2$ vortices is
predicted to occur at a finite temperature $T_v$. It is shown by means of the
hybrid Monte-Carlo and spin-dynamics simulations that the longitudinal
spin-current conductivity exhibits a divergence at $T_v$, while the thermal
conductivity only shows a monotonic temperature dependence with no clear
anomaly at $T_v$. The significant enhancement of the spin-current conductivity
is found to be due to the rapid growth of the spin-current-relaxation time
toward $T_v$, which can be understood as a manifestation of the topological
nature of the free $\mathbb{Z}_2$ vortex whose lifetime gets longer toward
$T_v$. The result suggests that the spin-current measurement is a promising
probe to detect the $\mathbb{Z}_2$-vortex topological transition which has
remained elusive in experiments.


  The theory of Raman scattering by the electron--phonon coupled system in
metals and heavily doped semiconductors is developed taking into account the
Coulomb screening and the electron--phonon deformation interaction. The
Boltzmann equation for carriers is applied. Phonon frequencies and optic
coupling constants are renormalized due to interactions with carriers. The
$k-$dependent semiclassical dielectric function is involved instead of the
Lindhard-Mermin expression. The results of calculations are presented for
various values of carrier concentration and electron-phonon coupling constant.


  The structure of the half-integer rectilinear disclination line within the
framework of the Landau-de Gennes effective theory of nematic liquid crystals
is investigated. The consistent perturbative expansion is constructed for the
case of $L_2\neq 0$. It turns out that such expansion can be performed around
only a discrete subset of an infinite set of the degenerate zeroth order
solutions. These solutions correspond to the positive and negative wedge
disclination lines and to four configurations of the twist disclination line.
The first order corrections to both the order parameter field as well as the
free energy of the disclination lines have been found. The results for the free
energy are compared with the ones obtained in the Frank-Oseen-Zocher director
description.


  We effectively conclude the local rigidity program for generic restrictions
of partially hyperbolic Weyl chamber flows. Our methods replace and extend
previous ones by circumventing computations made in Schur multipliers. Instead,
we construct a natural topology on $H_2(G,\mathbb{Z})$, and rely on classical
Lie structure theory for central extensions.


  Gaussian denoising has emerged as a powerful method for constructing
simulation-free continuous normalizing flows for generative modeling. Despite
their empirical successes, theoretical properties of these flows and the
regularizing effect of Gaussian denoising have remained largely unexplored. In
this work, we aim to address this gap by investigating the well-posedness of
simulation-free continuous normalizing flows built on Gaussian denoising.
Through a unified framework termed Gaussian interpolation flow, we establish
the Lipschitz regularity of the flow velocity field, the existence and
uniqueness of the flow, and the Lipschitz continuity of the flow map and the
time-reversed flow map for several rich classes of target distributions. This
analysis also sheds light on the auto-encoding and cycle consistency properties
of Gaussian interpolation flows. Additionally, we study the stability of these
flows in source distributions and perturbations of the velocity field, using
the quadratic Wasserstein distance as a metric. Our findings offer valuable
insights into the learning techniques employed in Gaussian interpolation flows
for generative modeling, providing a solid theoretical foundation for
end-to-end error analyses of learning Gaussian interpolation flows with
empirical observations.


  Discriminative Correlation Filter (DCF) based methods have shown competitive
performance on tracking benchmarks in recent years. Generally, DCF based
trackers learn a rigid appearance model of the target. However, this reliance
on a single rigid appearance model is insufficient in situations where the
target undergoes non-rigid transformations. In this paper, we propose a unified
formulation for learning a deformable convolution filter. In our framework, the
deformable filter is represented as a linear combination of sub-filters. Both
the sub-filter coefficients and their relative locations are inferred jointly
in our formulation. Experiments are performed on three challenging tracking
benchmarks: OTB- 2015, TempleColor and VOT2016. Our approach improves the
baseline method, leading to performance comparable to state-of-the-art.


  Commit messages contain diverse and valuable types of knowledge in all
aspects of software maintenance and evolution. Links are an example of such
knowledge. Previous work on "9.6 million links in source code comments" showed
that links are prone to decay, become outdated, and lack bidirectional
traceability. We conducted a large-scale study of 18,201,165 links from commits
in 23,110 GitHub repositories to investigate whether they suffer the same fate.
Results show that referencing external resources is prevalent and that the most
frequent domains other than github.com are the external domains of Stack
Overflow and Google Code. Similarly, links serve as source code context to
commit messages, with inaccessible links being frequent. Although repeatedly
referencing links is rare (4%), 14% of links that are prone to evolve become
unavailable over time; e.g., tutorials or articles and software homepages
become unavailable over time. Furthermore, we find that 70% of the distinct
links suffer from decay; the domains that occur the most frequently are related
to Subversion repositories. We summarize that links in commits share the same
fate as links in code, opening up avenues for future work.


  We consider two-dimensional electron systems in zero magnetic field at
fractional filling. For such systems a Lieb-Schultz-Mattis theorem applies,
forbidding the existence of a trivial insulator. However, the theorem does not
distinguish between bosonic and fermionic systems. In this work we argue that
in the case of fermionic systems, the topological orders that are compatible
with the microscopic constraints are in general different from the bosonic
case. We find different results in the cases of even and odd denominator
fillings, with even denominator fillings deviating stronger from the bosonic
case. Part of our results also hold in three dimensions.


  We argue that notions in quantum theory should have universal properties in
the sense of category theory. We consider the completely positive trace
preserving (CPTP) maps, the basic notion of quantum channel. Physically,
quantum channels are derived from pure quantum theory by allowing discarding.
We phrase this in category theoretic terms by showing that the category of CPTP
maps is the universal monoidal category with a terminal unit that has a functor
from the category of isometries. In other words, the CPTP maps are the affine
reflection of the isometries.


  This is a white paper submitted to the Stars and Stellar Evolution (SSE)
Science Frontier Panel (SFP) of the NRC's 2010 Astronomy and Astrophysics
Decadal Survey. The white paper is endorsed by the NSF Physics Frontier Center
for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas (CMSO).


  This paper deals with the 2-D Schr\"odinger equation with time-oscillating
exponential nonlinearity $i\partial_t u+\Delta u= \theta(\omega
t)\big(e^{4\pi|u|^2}-1\big)$, where $\theta$ is a periodic $C^1$-function. We
prove that for a class of initial data $u_0 \in H^1(\mathbb{R}^2)$, the
solution $u_{\omega}$ converges, as $|\omega|$ tends to infinity to the
solution $U$ of the limiting equation $i\partial_t U+\Delta U=
I(\theta)\big(e^{4\pi|U|^2}-1\big)$ with the same initial data, where
$I(\theta)$ is the average of $\theta$.


  We use dynamical systems approach and Lagrangian tools to study surface
transport and mixing of water masses in a selected coastal region of the Japan
Sea with moving mesoscale eddies associated with the Primorskoye Current.
Lagrangian trajectories are computed for a large number of particles in an
interpolated velocity field generated by a numerical regional multi-layer
eddy-resolving circulation model. We compute finite-time Lyapunov exponents for
a comparatively long period of time by the method developed and plot the
Lyapunov synoptic map quantifying surface transport and mixing in that region.
This map uncovers the striking flow structures along the coast with a mesoscale
eddy street and repelling material lines. We propose new Lagrangian diagnostic
tools --- the time of exit of particles off a selected box, the number of
changes of the sign of zonal and meridional velocities --- to study transport
and mixing by a pair of strongly interacting eddies often visible at
sea-surface temperature satellite images in that region. We develop a technique
to track evolution of clusters of particles, streaklines and material lines.
The Lagrangian tools used allow us to reveal mesoscale eddies and their
structure, to track different phases of the coastal flow, to find inhomogeneous
character of transport and mixing on mesoscales and submesoscales and to
quantify mixing by the values of exit times and the number of times particles
wind around the eddy's center.


  The focus of this work is on the construction and analysis of optimal-order
multigrid preconditioners to be used in the Newton-Krylov method for a
distributed optimal control problem constrained by the stationary Navier-Stokes
equations. As in our earlier work [7] on the optimal control of the stationary
Stokes equations, the strategy is to eliminate the state and adjoint variables
from the optimality system and solve the reduced nonlinear system in the
control variables. While the construction of the preconditioners extends
naturally the work in [7], the analysis shown in this paper presents a set of
significant challenges that are rooted in the nonlinearity of the constraints.
We also include numerical results that showcase the behavior of the proposed
preconditioners and show that for low to moderate Reynolds numbers they can
lead to significant drops in number of iterations and wall-clock savings.


  We study pulse propagation in one-dimensional chains of spherical granules
decorated with small randomly-sized granules placed between bigger monodisperse
ones. Such "designer chains" are of interest in efforts to control the behavior
of the pulse so as to optimize its propagation or attenuation, depending on the
desired application. We show that a recently proposed effective description of
simple decorated chains can be extended to predict pulse properties in chains
decorated with small granules of randomly chosen radii. Furthermore, we also
show that the binary collision approximation can again be used to provide
analytic results for this system.


  CeRhIn$_{5}$ is a Kondo-lattice prototype in which a magnetic field
B$\bf{^{\ast}\simeq}$ 30 T induces an abrupt Fermi-surface (FS) reconstruction
and pronounced in-plane electrical transport anisotropy all within its
antiferromagnetic state. Though the antiferromagnetic order at zero field is
well-understood, the origin of an emergent state at B$^{\ast}$ remains unknown
due to challenges inherent to probing states microscopically at high fields.
Here, we report low-temperature Nuclear Magnetic Resonance (NMR) measurements
revealing a discontinuous decrease in the $^{115}$In formal Knight shift,
without changes in crystal or magnetic structures, of CeRhIn$_{5}$ at fields
spanning B$^{\ast}$. We show that the emergent state above B$^{\ast}$ results
from a change in Ce's 4f orbitals that arises from field-induced evolution of
crystal-electric field (CEF) energy levels. This change in orbital character
enhances hybridisation between the 4f and the conduction electrons (c.e.) that
leads ultimately to an itinerant quantum-critical point at B$\bf{_{c0} \simeq}$
50 T.


  Surface plasmon polaritons (SPPs) have been widely exploited in various
scientific communities, ranging from physics, chemistry to biology, due to the
strong confinement of light to the metal surface. For many applications it is
important that the free space photon can be coupled to SPPs in a controllable
manner. In this Letter, we apply the concept of interfacial phase discontinuity
for circularly polarizations on a metasurface to the design of a novel type of
polarization dependent SPP unidirectional excitation at normal incidence.
Selective unidirectional excitation of SPPs along opposite directions is
experimentally demonstrated at optical frequencies by simply switching the
helicity of the incident light. This approach, in conjunction with dynamic
polarization modulation techniques, opens gateway towards integrated plasmonic
circuits with electrically reconfigurable functionalities.


  The present thesis deals with some properties of classical and quantum scalar
fields in an inhomogeneous and/or time-dependent background, focusing on models
where the latter can be described as a curved space-time with an event horizon.
While naturally formulated in a gravitational context, such models extend to
many physical systems with an effective Lorentz invariance at low energy. We
shall see how this effective symmetry allows one to relate the behavior of
perturbations in these systems to black-hole physics, what are its limitations,
and in which sense results thus obtained are "analogous" to their general
relativistic counterparts. The first chapter serves as a general introduction.
A few notions from Einstein's theory of gravity are introduced and a derivation
of Hawking radiation is sketched. The correspondence with low-energy systems is
then explained through three important examples. The next four chapters each
details one of the works completed during this thesis, updated and slightly
reorganized to account for new developments which occurred after their
publication. The other articles I contributed to are summarized in the last
chapter, before the general conclusion.


  Recent developments in the techniques of ultrafast pump-probe photoemission
have made possible the search for collective modes in strongly correlated
systems out of equilibrium. Including inelastic scattering processes and a
retarded interaction, we simulate time- and angle- resolved photoemission
spectroscopy (trARPES) to study the amplitude mode of a d-wave superconductor,
a collective mode excited through the nonlinear light-matter coupling to the
pump pulse. We find that the amplitude mode oscillations of the d-wave order
parameter occur in phase at a single frequency that is twice the
quasi-steady-state maximum gap size after pumping. We comment on the necessary
conditions for detecting the amplitude mode in trARPES experiments.


  Batteries are connected in series to meet the voltage requirement in many
applications. A voltage equalizer circuit is necessary to ensure that none of
the batteries is over-charged or over-discharged.
  A novel fast soft-switched cell-to-cell voltage equalizer topology is
proposed in this work. This topology can transfer charge from multiple
over-charged batteries to multiple under-charged batteries simultaneously
avoiding any unnecessary charging or discharging of a battery to achieve fast
voltage equalization. The proposed circuit topology and modulation method
ensure zero voltage switching under all battery conditions. The circuit
operation and soft-switching are analyzed and experimentally verified with a
four battery voltage equalizer prototype. The prototype is tested with a
battery bank and a hybrid ultra-capacitor bank, and a high conversion
efficiency is verified.


  When X is a singular complex projective variety, we prove a Kodaira type
vanishing theorem generalizing results of Navarro Aznar and others. This is
proved by extending the Deligne-Illusie decomposition to the Du Bois complex.
We also give a new definition of Frobenius amplitude (introduced in
math.AG/0202129) using ultraproducts.


  In this paper, we apply discontinuous finite element Galerkin method to the
time-dependent $2D$ incompressible Navier-Stokes model. We derive optimal error
estimates in $L^\infty(\textbf{L}^2)$-norm for the velocity and in
$L^\infty(L^2)$-norm for the pressure with the initial data $\textbf{u}_0\in
\textbf{H}_0^1\cap \textbf{H}^2$ and the source function $\textbf{f}$ in
$L^\infty(\textbf{L}^2)$ space. These estimates are established with the help
of a new $L^2$-projection and modified Stokes operator on appropriate broken
Sobolev space and with standard parabolic or elliptic duality arguments.
Estimates are shown to be uniform under the smallness assumption on data. Then,
a completely discrete scheme based on the backward Euler method is analyzed,
and fully discrete error estimates are derived. We would like to highlight here
that the estiablished semi-discrete error estimates related to the
$L^\infty(\textbf{L}^2)$-norm of velocity and $L^\infty(L^2)$-norm of pressure
are optimal and sharper than those derived in the earlier articles. Finally,
numerical examples validate our theoretical findings.