  Defying the conventional wisdom regarding first--order transitions, {\it
solid--solid displacive transformations} are often accompanied by pronounced
pretransitional phenomena. Generally, these phenomena are indicative of some
mesoscopic lattice deformation that ``anticipates'' the upcoming phase
transition. Among these precursive effects is the observation of the so-called
``tweed'' pattern in transmission electron microscopy in a wide variety of
materials. We have investigated the tweed deformation in a two dimensional
model system, and found that it arises because the compositional disorder
intrinsic to any alloy conspires with the natural geometric constraints of the
lattice to produce a frustrated, glassy phase. The predicted phase diagram and
glassy behavior have been verified by numerical simulations, and diffraction
patterns of simulated systems are found to compare well with experimental data.
Analytically comparing to alternative models of strain-disorder coupling, we
show that the present model best accounts for experimental observations.


  The evolution of the interface propagation in a slowly rotating half-filled
horizontal cylinder is studied using MRI. Initially, the cylinder contains two
axially segregated bands of small and large particles with a sharp interface.
The process of the formation of the radial core is clearly captured, and the
shape and the velocity of the propagating front are calculated by assuming a
one-dimensional diffusion process along the rotation axis of the cylinder and a
separation of time scales associated with segregation in the radial and axial
directions. We found that the interfacial dynamics are best described when a
concentration dependent diffusion process is assumed.


  Stacks of intrinsic Josephson junctions (IJJs) in high-temperature
superconductors enable the fabrication of compact sources of coherent terahertz
radiation. Here we demonstrate that multiple stacks patterned on the same
Bi2Sr2CaCu2O8+delta crystal can - under optimized conditions - be synchronized
to emit high-power THz-radiation. For three synchronized stacks we achieved 610
microwatts of continuous-wave coherent radiation power at 0.51 THz. We suggest
that synchronization is promoted by THz-waves in the base crystal. We note
though that synchronization cannot be achieved in all samples. However, even in
these cases powers on the 100-microwatt scale can be generated.


  We describe collaboration networks consisting of research projects funded by
the European Union and the organizations involved in those projects. The
networks are of substantial size and complexity, but are important to
understand due to the significant impact they could have on research policies
and national economies in the EU. In empirical determinations of the network
properties, we observe characteristics similar to other collaboration networks,
including scale-free degree distributions, small diameter, and high clustering.
We present some plausible models for the formation and structure of networks
with the observed properties.


  We prove that if T is a tree on n vertices wih maximum degree D and the edge
probability p(n) satisfies: np>c*max{D*logn,n^{\epsilon}} for some constant
\epsilon>0, then with high probability the random graph G(n,p) contains a copy
of T. The obtained bound on the edge probability is shown to be essentially
tight for D=n^{\Theta(1)}.


  We measure the Tully-Fisher relations of 14 lenticular galaxies (S0s) and 14
spirals. We use two measures of rotational velocity. One is derived directly
from observed spatially-resolved stellar kinematics and the other from the
circular velocities of mass models that include a dark halo and whose
parameters are constrained by detailed kinematic modelling. Contrary to the
naive expectations of theories of S0 formation, we find no significant
difference between the Tully-Fisher relations of the two samples when plotted
as functions of both brightness and stellar mass.


  There are conflicting requirements on the value of the momentum compaction
factor during energy ramping in a synchrotron: at low energies it should be
positive and sufficiently large to make the slippage factor small so that it is
possible to work closer to the RF voltage crest and ensure sufficient RF bucket
area, whereas at higher energies it should be small or negative to avoid
transition crossing. In the present report we propose a lattice with a variable
momentum compaction factor and consider the possibility of using it in a high
repetition rate proton driver for a muon collider and neutrino factory.


  In this paper we investigate the induced inflation with two flat regions: one
Starobinsky-like plateau in big field regime and one shorter plateau around the
saddle point of the Einstein frame potential. This multi-phase inflationary
scenario can be used to solve the problem of classical cosmology. The inflation
at the saddle-point plateau is consistent with the data and can have
arbitrarily low scale. The results can be useful in the context of the
Higgs-Axion relaxation and in a certain limit they are equivalent to the
$\alpha$-attractors.


  We extend the work of Fouvry, Kowalski and Michel on correlation between
Hecke eigenvalues of modular forms and algebraic trace functions in order to
establish an asymptotic formula for a generalized cubic moment of modular
L-functions at the central point s = 1/2 and for prime moduli q. As an
application, we exploit our recent result on the mollification of the fourth
moment of Dirichlet L-functions to derive that for any pair
$(\omega_1,\omega_2)$ of multiplicative characters modulo q, there is a
positive proportion of $\chi$ (mod q) such that $L(\chi, 1/2 ), L(\chi\omega_1,
1/2 )$ and $L(\chi\omega_2, 1/2)$ are simultaneously not too small.


  In this paper we study the relationship between query and search engine by
exploring the adaptive properties for doubleton as a space of event based on a
simple search engine. We employ set theory for defining doubleton and generate
some properties.


  We investigate the restoration of spontaneously broken gauge symmetry in
collisions of gauge boson wave packets in the SU(2) Higgs model.


  Geant4 is a toolkit for the simulation of the passage of particles through
matter. It provides a comprehensive set of tools for geometry, tracking,
detector response, run, event and track management, visualization and user
interfaces. Geant4 also has an abundant set of physics models that handle the
diverse interactions of particles with matter across a wide energy range.
However, there are also many well-established reaction codes currently used in
the same fields where Geant4 is applied. In order to take advantage of these
codes, we began to investigate their use from within the framework of Geant4.
The first codes chosen for this investigation were the Jaeri Quantum Molecular
Dynamics (JQMD) and Jet AA Microscopic Transportation Model (JAM) codes. JQMD
is a QMD model code which is widely used to analyze various aspects of heavy
ion reactions. JAM is a hadronic cascade model code which explicitly treats all
established hadronic states, including resonances with explicit spin and
isospin, as well as their anti-particles. We successfully developed interfaces
between these codes and Geant4. These allow a user to construct a detector
using the powerful material and geometrical capabilities of Geant4, while at
the same time implementing nuclear reactions handled by the JQMD and JAM models
and the Hadronic framework of Geant4 proved its flexibility and expandability.


  In this article, we study the asymptotics of Dirichlet eigenvalues and
eigenfunctions of the fractional Laplacian $(-\Delta)^s$ in bounded open
Lipschitz sets in the small order limit $s \to 0^+$. While it is easy to see
that all eigenvalues converge to $1$ as $s \to 0^+$, we show that the first
order correction in these asymptotics is given by the eigenvalues of the
logarithmic Laplacian operator, i.e., the singular integral operator with
symbol $2\log|\xi|$. By this we generalize a result of Chen and the third
author which was restricted to the principal eigenvalue. Moreover, we show that
$L^2$-normalized Dirichlet eigenfunctions of $(-\Delta)^s$ corresponding to the
$k$-th eigenvalue are uniformly bounded and converge to the set of
$L^2$-normalized eigenvalues of the logarithmic Laplacian. In order to derive
these spectral asymptotics, we need to establish new uniform regularity and
boundary decay estimates for Dirichlet eigenfunctions for the fractional
Laplacian. As a byproduct, we also obtain corresponding regularity properties
of eigenfunctions of the logarithmic Laplacian.


  This paper discusses microphysical simulation of interactions in liquid
xenon, the active detector medium in many leading rare-event physics searches,
and describes experimental observables useful to understanding detector
performance. The scintillation and ionization yield distributions for signal
and background are presented using the Noble Element Simulation Technique, or
NEST, which is a toolkit based upon experimental data and simple, empirical
formulae. NEST models of light and of charge production as a function of
particle type, energy, and electric field are reviewed, as well as of energy
resolution and final pulse areas. After vetting of NEST against raw data, with
several specific examples pulled from XENON, ZEPLIN, LUX / LZ, and PandaX, we
interpolate and extrapolate its models to draw new conclusions on the
properties of future detectors (e.g., XLZD), in terms of the best possible
discrimination of electronic recoil backgrounds from the potential nuclear
recoil signal due to WIMP dark matter. We find that the oft-quoted value of a
99.5% discrimination is likely too conservative. NEST shows that another order
of magnitude improvement (99.95% discrimination) may be achievable with a high
photon detection efficiency (g1 about 15-20%) and reasonably achievable drift
field of approximately 300 V/cm.


  We consider a kinematic model of a controlled car with two trailers by
assuming that each trailer is attached at some distance from the preceding axle
("off-hooked trailers"). For this model, we derive the transformation towards
privileged coordinates and present the corresponding nilpotent quasihomogeneous
approximate system. The components of this nilpotent approximation are written
explicitly in terms of mechanical parameters of the original system. The
constructed system does not satisfy the Brockett necessary stabilizability
condition, and the design of time-varying feedback controllers with oscillating
components is proposed. It is proved that these controllers ensure the
exponential convergence of solutions to the trivial equilibrium, and simulation
results are presented to illustrate the behavior of the closed-loop system.


  The assumption that vector mortality remains constant with age is used widely
to assess malaria transmission risk and predict the public health consequences
of vector control strategies. However, laboratory studies commonly demonstrate
clear evidence of senescence, or a decrease in physiological function and
increase in vector mortality rate with age. We developed methods to integrate
available field data to understand mortality in wild Anopheles gambiae, the
most import vector of malaria in sub-Saharan Africa. We found evidence for an
increase in rates of mortality with age, a component of senescence. As
expected, we also found that overall mortality is far greater in wild cohorts
than commonly observed under protected laboratory conditions. The magnitude of
senescence increases with An. gambiae lifespan, implying that wild mosquitoes
die long before cohorts can exhibit strong senescence. We reviewed available
published mortality studies of Anopheles spp. to confirm this fundamental
prediction of aging in wild populations. Senescence becomes most apparent in
long-living mosquito cohorts, and cohorts with low extrinsic mortality, such as
those raised under protected laboratory conditions, suffer a relatively high
proportion of senescent deaths. Imprecision in estimates of vector mortality
and changes in mortality with age will severely bias models of vector borne
disease transmission risk, such as malaria, and the sensitivity of transmission
to bias increases as the extrinsic incubation period of the parasite decreases.
While we focus here on malaria, we caution that future models for
anti-vectorial interventions must therefore incorporate both realistic
mortality rates and age-dependent changes in vector mortality.


  We investigate a decoy-state quantum key distribution (QKD) scheme with a
sub-Poissonian single-photon source, which is generated on demand by scattering
a coherent state off a two-level system in a one-dimensional waveguide. We show
that, compared to coherent state decoy-state QKD, there is a two-fold increase
of the key generation rate. Furthermore, the performance is shown to be robust
against both parameter variations and loss effects of the system.


  Given an exact category $\mathcal{C}$, it is well known that the connected
component reflector $\pi_0\colon\mathsf{Gpd}(\mathcal{C})\to\mathcal{C}$ from
the category $\mathsf{Gpd}(\mathcal{C})$ of internal groupoids in $\mathcal{C}$
to the base category $\mathcal{C}$ is semi-left-exact. In this article we
investigate the existence of a monotone-light factorisation system associated
with this reflector. We show that, in general, there is no monotone-light
factorisation system $(\mathcal{E}',\mathcal{M}^*)$ in
$\mathsf{Gpd}(\mathcal{C})$, where $\mathcal{M}^*$ is the class of coverings in
the sense of the corresponding Galois theory. However, when restricting to the
case where $\mathcal{C}$ is an exact Mal'tsev category, we show that the
so-called comprehensive factorization of regular epimorphisms in
$\mathsf{Gpd}(\mathcal{C})$ is the relative monotone-light factorisation system
(in the sense of Chikhladze) in the category $\mathsf{Gpd}(\mathcal{C})$
corresponding to the connected component reflector, where $\mathcal{E}'$ is the
class of final functors and $\mathcal{M}^*$ the class of regular epimorphic
discrete fibrations.


  Radiolarians are significant contributors to the oceanic primary productivity
and the global silica cycle in the last 500 Myr. Their diversity throughout the
Phanerozoic shows periodic fluctuations. We identify a possible abiotic
candidate for driving these patterns which seems to potentially influence
radiolarian diversity changes during this period at a significance level of
$\sim 2.2 \sigma$. Our finding suggests a significant correlation between the
origination of new radiolaria species and maximum excursions of the Solar
system from the Galactic plane, where the magnetic shielding of cosmic rays is
expected to be weaker. We connect the particularly strong radiolaria blooming
during the Middle Triassic to the so-called Mesozoic dipole-low of the
geomagnetic field, which was in its deepest state when radiolarias were
blooming. According to the scenario, high-energy cosmic rays presumably implied
particular damage to the DNA during the maximum excursions which may trigger
large chromosomal abnormalities leading to the appearance of a large number of
new genera and species during these periods.


  Lattices and periodic point sets are well known objects from discrete
geometry. They are also used in crystallography as one of the models of atomic
structure of periodic crystals. In this paper we study the embedding properties
of spaces of lattices and periodic point sets equipped with optimal bijection
metrics (i.e., bottleneck and Euclidean bottleneck metrics). We focus our
treatment on embeddings into Hilbert space. On one hand this is motivated by
modern data analysis, which is mostly based on statistical approaches developed
on Euclidean on Hilbert spaces, hence such embeddings play a major role in
applied pipelines. On the other hand there is a well-established methodology
related to such questions in coarse geometry, arising from the work on the
Novikov conjecture. The main results of this paper provide different
conditions, under which the spaces of lattices or periodic point sets are
Lipschitz or coarsely (non)embeddable into Hilbert space. The various
conditions are phrased in terms of density, packing radius, covering radius,
the cardinality of the motif, and the diameter of the unit cell.


  Anomaly detection is a challenging task that frequently arises in practically
all areas of industry and science, from fraud detection and data quality
monitoring to finding rare cases of diseases and searching for new physics.
Most of the conventional approaches to anomaly detection, such as one-class SVM
and Robust Auto-Encoder, are one-class classification methods, i.e. focus on
separating normal data from the rest of the space. Such methods are based on
the assumption of separability of normal and anomalous classes, and
subsequently do not take into account any available samples of anomalies.
Nonetheless, in practical settings, some anomalous samples are often available;
however, usually in amounts far lower than required for a balanced
classification task, and the separability assumption might not always hold.
This leads to an important task -- incorporating known anomalous samples into
training procedures of anomaly detection models.
  In this work, we propose a novel model-agnostic training procedure to address
this task. We reformulate one-class classification as a binary classification
problem with normal data being distinguished from pseudo-anomalous samples. The
pseudo-anomalous samples are drawn from low-density regions of a normalizing
flow model by feeding tails of the latent distribution into the model. Such an
approach allows to easily include known anomalies into the training process of
an arbitrary classifier. We demonstrate that our approach shows comparable
performance on one-class problems, and, most importantly, achieves comparable
or superior results on tasks with variable amounts of known anomalies.


  Imitation learning (IL) is a frequently used approach for data-efficient
policy learning. Many IL methods, such as Dataset Aggregation (DAgger), combat
challenges like distributional shift by interacting with oracular experts.
Unfortunately, assuming access to oracular experts is often unrealistic in
practice; data used in IL frequently comes from offline processes such as
lead-through or teleoperation. In this paper, we present a novel imitation
learning technique called Collocation for Demonstration Encoding (CoDE) that
operates on only a fixed set of trajectory demonstrations. We circumvent
challenges with methods like back-propagation-through-time by introducing an
auxiliary trajectory network, which takes inspiration from collocation
techniques in optimal control. Our method generalizes well and more accurately
reproduces the demonstrated behavior with fewer guiding trajectories when
compared to standard behavioral cloning methods. We present simulation results
on a 7-degree-of-freedom (DoF) robotic manipulator that learns to exhibit
lifting, target-reaching, and obstacle avoidance behaviors.


  The effect of entanglement on off-diagonal geometric phases is investigated
in the paper. Two spin-1/2 particles in magnetic fields along the $y$ direction
are taken as an example. Three parameters (the purity of state $r$, the mixing
angle $\theta$ and the relative phase $\beta$) are chosen to characterize the
initial states. The nodal points at which the usual geometric phases disappear
are calculated and illustrated as a function of the three parameters.


  Cooling and heating functions of cosmic gas are a crucial ingredient for any
study of gas dynamics and thermodynamics in the interstellar and intergalactic
medium. As such, they have been studied extensively in the past under the
assumption of collisional ionization equilibrium. However, for a wide range of
applications, the local radiation field introduces a non-negligible, often
dominant, modification to the cooling and heating functions. In the most
general case, these modifications cannot be described in simple terms, and
would require a detailed calculation with a large set of chemical species using
a radiative transfer code (the well-known code Cloudy, for example). We show,
however, that for a sufficiently general variation in the spectral shape and
intensity of the incident radiation field, the cooling and heating functions
can be approximated as depending only on several photoionization rates, which
can be thought of as representative samples of the overall radiation field.
This dependence is easy to tabulate and implement in cosmological or
galactic-scale simulations, thus economically accounting for an important but
rarely-included factor in the evolution of cosmic gas. We also show a few
examples where the radiation environment has a large effect, the most
spectacular of which is a quasar that suppresses gas cooling in its host halo
without any mechanical or non-radiative thermal feedback.


  The ability to predict and plan into the future is fundamental for agents
acting in the world. To reach a faraway goal, we predict trajectories at
multiple timescales, first devising a coarse plan towards the goal and then
gradually filling in details. In contrast, current learning approaches for
visual prediction and planning fail on long-horizon tasks as they generate
predictions (1) without considering goal information, and (2) at the finest
temporal resolution, one step at a time. In this work we propose a framework
for visual prediction and planning that is able to overcome both of these
limitations. First, we formulate the problem of predicting towards a goal and
propose the corresponding class of latent space goal-conditioned predictors
(GCPs). GCPs significantly improve planning efficiency by constraining the
search space to only those trajectories that reach the goal. Further, we show
how GCPs can be naturally formulated as hierarchical models that, given two
observations, predict an observation between them, and by recursively
subdividing each part of the trajectory generate complete sequences. This
divide-and-conquer strategy is effective at long-term prediction, and enables
us to design an effective hierarchical planning algorithm that optimizes
trajectories in a coarse-to-fine manner. We show that by using both
goal-conditioning and hierarchical prediction, GCPs enable us to solve visual
planning tasks with much longer horizon than previously possible.


  Seven distinct families of superconductors with critical temperatures at
ambient pressure that equal or surpass the historic 23 K limit for Nb3Ge have
been discovered in the last 25 years. Each family is reviewed briefly and their
common chemical features are discussed. High temperature superconductors are
distinguished by having a high (\geq 50%) content of nonmetallic elements and
fall into two broad classes. 'Metal-nonmetal' superconductors require a
specific combination of elements such as Cu-O and Fe-As which give rise to the
highest known Tc's, probably through a magnetic pairing mechanism.
'Nonmetal-bonded' materials contain covalently-bonded nonmetal anion networks
and are BCS-like superconductors. Fitting an extreme value function to the
distribution of Tc values for the known high-Tc families suggests that the
probability of a newly discovered superconductor family having maximum Tc > 100
K is ~0.1-1%, decreasing to ~0.02-0.2% for room temperature superconductivity.


  We study the numerical bounds obtained using a conformal-bootstrap method -
advocated in ref. [1] but never implemented so far - where different points in
the plane of conformal cross ratios $z$ and $\bar z$ are sampled. In contrast
to the most used method based on derivatives evaluated at the symmetric point
$z=\bar z =1/2$, we can consistently "integrate out" higher-dimensional
operators and get a reduced simpler, and faster to solve, set of bootstrap
equations. We test this "effective" bootstrap by studying the 3D Ising and
$O(n)$ vector models and bounds on generic 4D CFTs, for which extensive results
are already available in the literature. We also determine the scaling
dimensions of certain scalar operators in the $O(n)$ vector models, with
$n=2,3,4$, which have not yet been computed using bootstrap techniques.


  The extremely rapid burst of TeV photons from Mkn 421 (15 May 1996) can be
reconciled with the standard properties of a relativistic gamma-ray emitting
jet (bulk Lorentz factor $\approx$ 10; size $\10^{17}$ cm) if one assumes that
the electrons are accelerated in conical shocks with both opening and viewing
angles $\approx 1/\Gamma$. If the injection time and the cooling time are much
less than the photon crossing time, an emission ring moves along the jet and
leads to the appearance of a very rapid flare, in satisfactory agreement with
the observations.


  The InSight mission has operated on the surface of Mars for nearly two Earth
years, returning detections of the first Marsquakes. The lander also deployed a
meteorological instrument package and cameras to monitor local surface
activity. These instruments have detected boundary layer phenomena, including
small-scale vortices. These vortices register as short-lived, negative pressure
excursions and closely resemble those that could generate dust devils. Although
our analysis shows InSight encountered more than 900 vortices and collected
more than 1000 images of the martian surface, no active dust devils were
imaged. In spite of the lack of dust devil detections, we can leverage the
vortex detections and InSight's daily wind speed measurements to learn about
the boundary layer processes that create dust devils. We discuss our analysis
of InSight's meteorological data to assess the statistics of vortex and dust
devil activity. We also infer encounter distances for the vortices and,
therefrom, the maximum vortex wind speeds. Surveying the available imagery, we
place upper limits on what fraction of vortices carry dust (i.e., how many are
bonafide dust devils) and estimate threshold wind speeds for dust lifting.
Comparing our results to detections of dust devil tracks seen in space-based
observations of the InSight landing site, we can also infer thresholds and
frequency of track formation by vortices. Comparing vortex encounters and
parameters with advective wind speeds, we find evidence that high wind speeds
at InSight may have suppressed the formation of dust devils, explaining the
lack of imaged dust devils.


  Imaginary time is often used in quantum tunnelling calculations. This article
advocates a conceptually sounder alternative: complex lapse. In the ``3+1''
action for the Einstein gravitational field minimally coupled to a Klein-Gordon
field, allowing the lapse function to be complex yields a complex action which
generates both the usual Lorentzian theory and its Riemannian analogue, and in
particular allows a change of signature between the two. The action and
variational equations are manifestly well defined in the Hamiltonian
representation, with the momentum fields consequently being complex. The
complex action interpolates between the Lorentzian and Riemannian actions as
they appear formally in the respective path integrals. Thus the complex-lapse
theory provides a unified basis for a path-integral quantum theory of gravity
involving both Lorentzian and Riemannian aspects. A major motivation is the
quantum-tunnelling scenario for the origin of the universe. Taken as an
explanation for the observed quantum tunnelling of particles, the complex-lapse
theory determines that the argument of the lapse for the universe now is
extremely small but negative.


  If pairs of new colored particles are produced at the Large Hadron Collider,
determining their quantum numbers, and even discovering them, can be
non-trivial. We suggest that valuable information can be obtained by measuring
the resonant signals of their near-threshold QCD bound states. If the particles
are charged, the resulting signatures include photons and leptons and are
sufficiently rich for unambiguously determining their various quantum numbers,
including the charge, color representation and spin, and obtaining a precise
mass measurement. These signals provide well-motivated benchmark models for
resonance searches in the dijet, photon+jet, diphoton and dilepton channels.
While these measurements require that the lifetime of the new particles be not
too short, the resulting limits, unlike those from direct searches for pair
production above threshold, do not depend on the particles' decay modes. These
limits may be competitive with more direct searches if the particles decay in
an obscure way.


  In order to determine the role of non-thermal transport of hot carriers which
is decisive for the dissipation of energy in condensed matter we performed
time-resolved broadband femtosecond transient reflectivity measurements on
$7-197 \mathrm{nm}$ thick Bi(111) films epitaxially grown on Si(111). We
monitored the behavior of the Fourier amplitude and the central frequency of
the coherent $A_\mathrm{1g}$ phonon mode as function of the incident fluence,
film thickness, and probe wavelength in the range of $580 -700 \mathrm{nm}$.
The frequency redshift that follows photoexcitation was used as a robust
quantity to determine the effective distribution of excited carriers that
governs the displacive excitation mechanism of coherent $A_\mathrm{1g}$ phonons
in Bi. For Bi films up to $50 \mathrm{nm}$ thickness a homogeneous excitation
due to the ultrafast transport of hot charge carriers is observed, limited by a
carrier penetration depth of $60 \mathrm{nm}$ independent of the totally
deposited laser energy.


  Magnetic skyrmions are topological spin structures having immense potential
for energy efficient spintronic devices. However, observations of skyrmions at
room temperature are limited to patterned nanostructures. Here, we report the
observation of stable skyrmions in unpatterned Ta/Co2FeAl(CFA)/MgO thin film
heterostructures at room temperature and in zero external magnetic field
employing magnetic force microscopy. The skyrmions are observed in a trilayer
structure comprised of heavy metal (HM)/ferromagnet (FM)/Oxide interfaces which
result in strong interfacial Dzyaloshinskii-Moriya interaction (i-DMI) as
evidenced by Brillouin light scattering measurements, in agreement with the
results of micromagnetic simulations. We also emphasize on room temperature
observation of multiple skyrmions which can be stabilized for suitable choices
of CFA layer thickness, perpendicular magnetic anisotropy, and i-DMI. These
results open up a new paradigm for designing room temperature spintronic
devices based on skyrmions in FM continuous thin films.


  The rate and corresponding gluon jet energy distribution for the process
$e^+e^- \to t\bar tg$ are sensitive to the presence of anomalous dipole-like
couplings of the top to the photon and $Z$ at the production vertex. For
sizeable anomalous couplings of this type substantial deviations from the
expectations of the Standard Model are likely. We explore the capability of the
NLC to discover or place bounds on these types of top quark couplings. The
resulting constraints are found to be quite complementary to those which arise
from direct probes of the top quark production vertex. [To appear in {\it
Physics and Technology of the Next Linear Collider}, eds. D.\ Burke and M.\
Peskin, reports submitted to Snowmass 1996.]


  Using pole decompositions as starting points, the one parameter (-1 =< c < 1)
nonlocal and nonlinear Zhdanov-Trubnikov (ZT) equation for the steady shapes of
premixed gaseous flames is studied in the large-wrinkle limit. The singular
integral equations for pole densities are closely related to those satisfied by
the spectral density in the O(n) matrix model, with n = -2(1 + c)/(1 - c). They
can be solved via the introduction of complex resolvents and the use of complex
analysis. We retrieve results obtained recently for -1 =< c =< 0, and we
explain and cure their pathologies when they are continued naively to 0 < c <
1. Moreover, for any -1 =< c < 1, we derive closed-form expressions for the
shapes of steady isolated flame crests, and then bicoalesced periodic fronts.
These theoretical results fully agree with numerical resolutions. Open problems
are evoked.


  Non-parallel many-to-many voice conversion is recently attract-ing huge
research efforts in the speech processing community. A voice conversion system
transforms an utterance of a source speaker to another utterance of a target
speaker by keeping the content in the original utterance and replacing by the
vocal features from the target speaker. Existing solutions, e.g., StarGAN-VC2,
present promising results, only when speech corpus of the engaged speakers is
available during model training. AUTOVCis able to perform voice conversion on
unseen speakers, but it needs an external pretrained speaker verification
model. In this paper, we present our new GAN-based zero-shot voice conversion
solution, called GAZEV, which targets to support unseen speakers on both source
and target utterances. Our key technical contribution is the adoption of
speaker embedding loss on top of the GAN framework, as well as adaptive
instance normalization strategy, in order to address the limitations of speaker
identity transfer in existing solutions. Our empirical evaluations demonstrate
significant performance improvement on output speech quality and comparable
speaker similarity to AUTOVC.


  Based on well known photometric rules for the number of galaxies as function
of the distance in Mpc we model the so called "Great Wall" which is visible on
the Cosmicflows-2 catalog. The gravitational field is evaluated at the light of
the shell theorem and a finite value for the gravitational field is numerically
derived.


  A family of boundary conditions corresponding to exclusion processes is
introduced. This family is a generalization of the boundary conditions
corresponding to the simple exclusion process, the drop-push model, and the
one-parameter solvable family of pushing processes with certain rates on the
continuum [1-3]. The conditional probabilities are calculated using the Bethe
ansatz, and it is shown that at large times they behave like the corresponding
conditional probabilities of the family of diffusion-pushing processes
introduced in [1-3].


  We show that the diffusion approximation breaks down for particle
acceleration at oblique shocks with velocities typical of young supernova
remnants. Higher order anisotropies flatten the spectral index at
quasi-parallel shocks and steepen the spectral index at quasi-perpendicular
shocks. We compare the theory with observed spectral indices.


  In peripheral collisions of relativistic heavy ions highly excited spectators
containing Lambda-hyperons can be produced. Such strange spectator matter may
undergo a break-up into many fragments (multifragmentation) as it is well
established for ordinary nuclear systems. We generalize the statistical
multifragmentation model, previously successfully used for the description of
experimental data, for the case of hypernuclear systems. We predict relative
yields of hypernuclei and the main characteristics of such a break-up. We point
at a connection of this phenomenon with a liquid-gas phase transition in
hypermatter.


  We consider streaming, one-pass principal component analysis (PCA), in the
high-dimensional regime, with limited memory. Here, $p$-dimensional samples are
presented sequentially, and the goal is to produce the $k$-dimensional subspace
that best approximates these points. Standard algorithms require $O(p^2)$
memory; meanwhile no algorithm can do better than $O(kp)$ memory, since this is
what the output itself requires. Memory (or storage) complexity is most
meaningful when understood in the context of computational and sample
complexity. Sample complexity for high-dimensional PCA is typically studied in
the setting of the {\em spiked covariance model}, where $p$-dimensional points
are generated from a population covariance equal to the identity (white noise)
plus a low-dimensional perturbation (the spike) which is the signal to be
recovered. It is now well-understood that the spike can be recovered when the
number of samples, $n$, scales proportionally with the dimension, $p$. Yet, all
algorithms that provably achieve this, have memory complexity $O(p^2)$.
Meanwhile, algorithms with memory-complexity $O(kp)$ do not have provable
bounds on sample complexity comparable to $p$. We present an algorithm that
achieves both: it uses $O(kp)$ memory (meaning storage of any kind) and is able
to compute the $k$-dimensional spike with $O(p \log p)$ sample-complexity --
the first algorithm of its kind. While our theoretical analysis focuses on the
spiked covariance model, our simulations show that our algorithm is successful
on much more general models for the data.


  With the recent technological feasibility of electronic commerce over the
Internet, much attention has been given to the design of electronic markets for
various types of electronically-tradable goods. Such markets, however, will
normally need to function in some relationship with markets for other related
goods, usually those downstream or upstream in the supply chain. Thus, for
example, an electronic market for rubber tires for trucks will likely need to
be strongly influenced by the rubber market as well as by the truck market. In
this paper we design protocols for exchange of information between a sequence
of markets along a single supply chain. These protocols allow each of these
markets to function separately, while the information exchanged ensures
efficient global behavior across the supply chain. Each market that forms a
link in the supply chain operates as a double auction, where the bids on one
side of the double auction come from bidders in the corresponding segment of
the industry, and the bids on the other side are synthetically generated by the
protocol to express the combined information from all other links in the chain.
The double auctions in each of the markets can be of several types, and we
study several variants of incentive compatible double auctions, comparing them
in terms of their efficiency and of the market revenue.


  We have performed high-resolution angle-resolved photoemission spectroscopy
on the optimally-doped Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$ compound and determined
the accurate momentum dependence of the superconducting (SC) gap in four
Fermi-surface sheets including a newly discovered outer electron pocket at the
M point. The SC gap on this pocket is nearly isotropic and its magnitude is
comparable ($\Delta$ $\sim$ 11 meV) to that of the inner electron and hole
pockets ($\sim$12 meV), although it is substantially larger than that of the
outer hole pocket ($\sim$6 meV). The Fermi-surface dependence of the SC gap
value is basically consistent with $\Delta$($k$) = $\Delta$$_0$cos$k_x$cos$k_y$
formula expected for the extended s-wave symmetry. The observed finite
deviation from the simple formula suggests the importance of multi-orbital
effects.


  This paper investigates a background charge one Skyrme field chirally coupled
to light fermions on the 3-sphere. The Dirac equation for the system commutes
with a generalised angular momentum or grand spin. It can be solved explicitly
for a Skyrme configuration given by the hedgehog form. The energy spectrum and
degeneracies are derived for all values of the grand spin. Solutions for
non-zero grand spin are each characterised by a set of four polynomials. The
paper also discusses the energy of the Dirac sea using zeta function
regularization.


  Gaussian belief propagation (BP) has been widely used for distributed
estimation in large-scale networks such as the smart grid, communication
networks, and social networks, where local measurements/observations are
scattered over a wide geographical area. However, the convergence of Gaus- sian
BP is still an open issue. In this paper, we consider the convergence of
Gaussian BP, focusing in particular on the convergence of the information
matrix. We show analytically that the exchanged message information matrix
converges for arbitrary positive semidefinite initial value, and its dis- tance
to the unique positive definite limit matrix decreases exponentially fast.


  This paper investigates the frequency control of multi-machine power systems
subject to uncertain and dynamic net loads. We propose distributed internal
model controllers that coordinate synchronous generators and demand response to
tackle the unpredictable nature of net loads. Frequency stability is formally
guaranteed via Lyapunov analysis. Numerical simulations on the IEEE 68-bus test
system demonstrate the effectiveness of the controllers.


  Strong light-matter coupling has recently been demonstrated in sub-wavelength
volumes by coupling engineered optical transitions in semiconductor
heterostructures (e.g., quantum wells) to metasurface resonances via near
fields. It has also been shown that different resonator shapes may lead to
different Rabi splittings, though this has not yet been well explained. In this
paper, our aim is to understand the correlation between resonator shape and
Rabi splitting, and in particular determine and quantify the physical
parameters that affect strong coupling by developing an equivalent circuit
network model whose elements describe energy and dissipation. Because of the
subwavelength dimension of each metasurface element, we resort to the
quasi-static (electrostatic) description of the near-field and hence define an
equivalent capacitance associated to each dipolar element of a flat
metasurface, and we show that this is also able to accurately model the
phenomenology involved in strong coupling between the metasurface and the
intersubband transitions in quantum wells. We show that the spectral properties
and stored energy of a metasurface/quantum-well system obtained using our model
are in good agreement with both full-wave simulation and experimental results.
We then analyze metasurfaces made of three different resonator geometries and
observe that the magnitude of the Rabi splitting increases with the resonator
capacitance in agreement with our theory, providing a phenomenological
explanation for the resonator shape dependence of the strong coupling process.


  The effect of the interaction of mesons in the final state is additionally
considered within the description of $\tau \to \pi \eta(\eta') \nu_{\tau}$
decays. This interaction is taken into account at the level of production of
intermediate pions. One of them, in turn, might be transited into $\eta$ or
$\eta'$ mesons. Our results do not exceed the experimentally established
branching fractions, and they are in agreement with the results of other
theoretical studies.


  We introduce an explicit, adaptive time-stepping scheme for the simulation of
SPDEs with one-sided Lipschitz drift coefficients. Strong convergence rates are
proven for the full space-time discretisation with multiplicative trace-class
noise by considering the space and time discretisation separately. Adapting the
time-step size to ensure strong convergence is shown numerically to produce
more accurate solutions when compared to alternative fixed time-stepping
strategies for the same computational effort.


  In this paper, we investigate the performance of a reconfigurable intelligent
surface (RIS)-assisted dual-hop mixed radio-frequency underwater wireless
optical communication (RF-UWOC) system. An RIS is an emerging and low-cost
technology that aims to enhance the strength of the received signal, thus
improving the system performance. In the considered system setup, a ground
source does not have a reliable direct link to a given marine buoy and
communicates with it through an RIS installed on a building. In particular, the
buoy acts as a relay that sends the signal to an underwater destination. In
this context, analytical expressions for the outage probability (OP), average
bit error rate (ABER), and average channel capacity (ACC) are derived assuming
fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relaying
protocols at the marine buoy. Moreover, asymptotic analyses of the OP and ABER
are carried out in order to gain further insights from the analytical
frameworks. In particular, the system diversity order is derived and it is
shown to depend on the RF link parameters and on the detection schemes of the
UWOC link. Finally, it is demonstrated that RIS-assisted systems can
effectively improve the performance of mixed dual-hop RF-UWOC systems.


  An {\it inversion} of a tournament $T$ is obtained by reversing the direction
of all edges with both endpoints in some set of vertices. Let ${\rm inv}_k(T)$
be the minimum length of a sequence of inversions using sets of size at most
$k$ that result in the transitive tournament. Let ${\rm inv}_k(n)$ be the
maximum of ${\rm inv}_k(T)$ taken over $n$-vertex tournaments. It is well-known
that ${\rm inv}_2(n)=(1+o(1))n^2/4$ and it was recently proved by Alon et al.
that ${\rm inv}(n):={\rm inv}_{n}(n)=n(1+o(1))$. In these two extreme cases
($k=2$ and $k=n$), random tournaments are asymptotically extremal objects. It
is proved that the random tournament {\em does not} asymptotically attain ${\rm
inv}_k(n)$ when $k \ge k_0$ and conjectured that ${\rm inv}_3(n)$ is (only)
attained by (quasi) random tournaments. It is further proved that $(1+o(1)){\rm
inv}_3(n)/n^2 \in [\frac{1}{12}, 0.0992)$ and $(1+o(1)){\rm inv}_k(n)/n^2 \in
[\frac{1}{2k(k-1)}+\delta_k, \frac{1}{2 \lfloor k^2/2 \rfloor}-\epsilon_k]$
where $\epsilon_k > 0$ for all $k \ge 3$ and $\delta_k > 0$ for all $k \ge
k_0$.


  In this study, we determined the effect of doping with the noble metal Ag on
Ir$_2$Te$_4$ superconductors. Based on the resistivity, magnetization, and heat
capacity, we explored the changes in the superconductivity and charge density
wave for Cu$_{1-x}$Ag$_x$Ir$_2$Te$_4$ as a function of isoelectric
substitution. We assessed a complete set of competing states from suppressed
charge density wave in the low doping region to superconductor in the middle
doping region and re-entrant charge density wave in the high doping region,
thereby obtaining an electronic phase diagram, where the superconducting dome
was near bipartite charge density wave regions with a maximum superconducting
temperature Tc of about 2.93 K at an Ag doping level of 12%. The lower H$_{c1}$
and upper H$_{c2}$ critical magnetic fields were determined for some
representative samples in the Cu$_{1-x}$Ag$_x$Ir$_2$Te$_4$ series based on
magnetization and resistivity measurements, respectively. We showed that
H$_{c1}$ decreased whereas H$_{c2}$ increased as the doping content increased.
The specific heat anomalies at the superconducting transitions $\Delta
C_{el}/\gamma T_c$ for representative samples comprising
Cu$_{0.92}$Ag$_{0.08}$Ir$_2$Te$_4$, Cu$_{0.88}$Ag$_{0.12}$Ir$_2$Te$_4$, and
Cu$_{0.85}$Ag$_{0.15}$Ir$_2$Te$_4$ were approximately 1.40, 1.44, and 1.42,
respectively, which are all near the Bardeen-Cooper-Schrieffer value of 1.43
and they indicate bulk superconductivity in these compounds.


  The splitting of $\e^{h(A+B)}$ into a single product of $\e^{h A}$ and
$\e^{hB}$ results in symplectic integrators when $A$ and $B$ are classical Lie
operators. However, at high orders, a single product splitting, with
exponentially growing number of operators, is very difficult to derive. This
work shows that, if the splitting is generalized to a sum of products, then a
simple choice of the basis product reduces the problem to that of
extrapolation, with analytically known coefficients and only quadratically
growing number of operators. When a multi-product splitting is applied to
classical Hamiltonian systems, the resulting algorithm is no longer symplectic
but is of the Runge-Kutta-Nystr\"om (RKN) type. Multi-product splitting, in
conjunction with a special force-reduction process,explains why at orders $p=4$
and 6, RKN integrators only need $p-1$ force evaluations.


  Existing methods in the semantic computer vision community seem unable to
deal with the explosion and richness of modern, open-source and social video
content. Although sophisticated methods such as object detection or
bag-of-words models have been well studied, they typically operate on low level
features and ultimately suffer from either scalability issues or a lack of
semantic meaning. On the other hand, video supervoxel segmentation has recently
been established and applied to large scale data processing, which potentially
serves as an intermediate representation to high level video semantic
extraction. The supervoxels are rich decompositions of the video content: they
capture object shape and motion well. However, it is not yet known if the
supervoxel segmentation retains the semantics of the underlying video content.
In this paper, we conduct a systematic study of how well the actor and action
semantics are retained in video supervoxel segmentation. Our study has human
observers watching supervoxel segmentation videos and trying to discriminate
both actor (human or animal) and action (one of eight everyday actions). We
gather and analyze a large set of 640 human perceptions over 96 videos in 3
different supervoxel scales. Furthermore, we conduct machine recognition
experiments on a feature defined on supervoxel segmentation, called supervoxel
shape context, which is inspired by the higher order processes in human
perception. Our ultimate findings suggest that a significant amount of
semantics have been well retained in the video supervoxel segmentation and can
be used for further video analysis.


  We axiomatize and study the matrices of type $H\in M_N(A)$, having unitary
entries, $H_{ij}\in U(A)$, and whose rows and columns are subject to
orthogonality type conditions. Here $A$ can be any $C^*$-algebra, for instance
$A=\mathbb C$, where we obtain the usual complex Hadamard matrices, or
$A=C(X)$, where we obtain the continuous families of complex Hadamard matrices.
Our formalism allows the construction of a quantum permutation group $G\subset
S_N^+$, whose structure and computation is discussed here.


  Metastable vacua in supersymmetric QCD in the presence of single and
multitrace deformations of the superpotential are explored, with the aim of
obtaining an acceptable phenomenology. The metastable vacua appear at one loop,
have a broken R-symmetry, and a magnetic gauge group that is completely
Higgsed. With only a single trace deformation, the adjoint fermions from the
meson superfield are approximately massless at one loop, even though they are
massive at tree level and R-symmetry is broken. Consequently, if charged under
the standard model, they are unacceptably light. A multitrace quadratic
deformation generates fermion masses proportional to the deformation parameter.
Phenomenologically viable models of direct gauge mediation can then be
obtained, and some of their features are discussed.


  Let $k$ be a field of characteristic $p>0$. We discuss the automorphisms of
the polynomial ring $k[x_1,\ldots ,x_n]$ of order $p$, or equivalently the
${\bf Z}/p{\bf Z}$-actions on the affine space ${\bf A}_k^n$. When $n=2$, such
an automorphism is know to be a conjugate of an automorphism fixing a variable.
It is an open question whether the same holds when $n\ge 3$.
  In this paper,
  (1) we give the first counterexample to this question when $n=3$. In fact, we
show that every ${\bf G}_a$-action on ${\bf A}_k^3$ of rank three yields
counterexamples for $n=3$. We give a family of counterexamples by constructing
a family of rank three ${\bf G}_a$-actions on ${\bf A}_k^3$.
  (2) For the automorphisms induced by this family of ${\bf G}_a$-actions, we
show that the invariant ring is isomorphic to $k[x_1,x_2,x_3]$ if and only if
the plinth ideal is principal, under some mild assumptions.
  (3) We study the Nagata type automorphisms of $R[x_1,x_2]$, where $R$ is a
UFD of characteristic $p>0$. This type of automorphisms are of order $p$. We
give a necessary and sufficient condition for the invariant ring to be
isomorphic to $R[x_1,x_2]$. This condition is equivalent to the condition that
the plinth ideal is principal.


  There doesn't exists a finite planar map with all edges having the same
length, and each vertex on exactly 5 edges.


  It is well known that perturbative expansions of path integrals are
divergent. These expansions are to be understood as asymptotic expansions,
which encode the limiting behaviour of the path integral for positive small
coupling. Conventionally, the method of Borel summation assigns a finite answer
to the divergent expansion. Still, the Borel sum might not encode the full
information of a function, because it misses exponentially small corrections.
In the present work, we consider a slight variation of the conventional Borel
summation, in which a generalised Borel transform (an inverse Laplace
transform) is followed by a directional Laplace transform. These new tools will
allow us to give perhaps better answers to typical problems in Borel summation:
missing exponential corrections and ambiguities in the Borel summation. In
addition, we will define resurgence as a connection between the discontinuity
of a function and the coefficients of its asymptotic expansion. From this
definition, we will be able to reduce resurgence to the problem of missing
exponential corrections in asymptotic expansions and understand, within a
unified framework, different approaches to resurgence found in the literature.


  We provide the first mathematically complete derivation of the Nystr\"om
method for low-rank approximation of indefinite kernels and propose an
efficient method for finding an approximate eigendecomposition of such kernel
matrices. Building on this result, we devise highly scalable methods for
learning in reproducing kernel Kre\u{\i}n spaces. The devised approaches
provide a principled and theoretically well-founded means to tackle large scale
learning problems with indefinite kernels. The main motivation for our work
comes from problems with structured representations (e.g., graphs, strings,
time-series), where it is relatively easy to devise a pairwise (dis)similarity
function based on intuition and/or knowledge of domain experts. Such functions
are typically not positive definite and it is often well beyond the expertise
of practitioners to verify this condition. The effectiveness of the devised
approaches is evaluated empirically using indefinite kernels defined on
structured and vectorial data representations.


  This paper proposes and tests the first-ever reduced basis warm-start
iterative method for the parametrized linear systems, exemplified by those
discretizing the parametric partial differential equations. Traditional
iterative methods are usually used to obtain the high-fidelity solutions of
these linear systems. However, they typically come with a significant
computational cost which becomes challenging if not entirely untenable when the
parametrized systems need to be solved a large number of times (e.g.
corresponding to different parameter values or time steps). Classical
techniques for mitigating this cost mainly include acceleration approaches such
as preconditioning. This paper advocates for the generation of an initial
prediction with controllable fidelity as an alternative approach to achieve the
same goal. The proposed reduced basis warm-start iterative method leverages the
mathematically rigorous and efficient reduced basis method to generate a
high-quality initial guess thereby decreasing the number of iterative steps.
Via comparison with the iterative method initialized with a zero solution and
the RBM preconditioned and initialized iterative method tested on two 3D
steady-state diffusion equations, we establish the efficacy of the proposed
reduced basis warm-start approach.


  In this paper, we provide a throughput analysis of the IEEE 802.11 protocol
at the data link layer in non-saturated traffic conditions taking into account
the impact of both transmission channel and capture effects in Rayleigh fading
environment. Impacts of both non-ideal channel and capture become important in
terms of the actual observed throughput in typical network conditions whereby
traffic is mainly unsaturated, specially in an environment of high
interference.
  We extend the multi-dimensional Markovian state transition model
characterizing the behavior at the MAC layer by including transmission states
that account for packet transmission failures due to errors caused by
propagation through the channel, along with a state characterizing the system
when there are no packets to be transmitted in the buffer of a station.


  Deep neural networks have achieved remarkable performance in various
applications but are extremely vulnerable to adversarial perturbation. The most
representative and promising methods that can enhance model robustness, such as
adversarial training and its variants, substantially degrade model accuracy on
benign samples, limiting practical utility. Although incorporating extra
training data can alleviate the trade-off to a certain extent, it remains
unsolved to achieve both robustness and accuracy under limited training data.
Here, we demonstrate the feasibility of overcoming the trade-off, by developing
an adversarial feature stacking (AFS) model, which combines multiple
independent feature extractors with varied levels of robustness and accuracy.
Theoretical analysis is further conducted, and general principles for the
selection of basic feature extractors are provided. We evaluate the AFS model
on CIFAR-10 and CIFAR-100 datasets with strong adaptive attack methods,
significantly advancing the state-of-the-art in terms of the trade-off. The AFS
model achieves a benign accuracy improvement of ~6% on CIFAR-10 and ~10% on
CIFAR-100 with comparable or even stronger robustness than the state-of-the-art
adversarial training methods.


  The classical random walk isomorphism theorems relate the local times of a
continuous-time random walk to the square of a Gaussian free field. A Gaussian
free field is a spin system that takes values in Euclidean space, and this
article generalises the classical isomorphism theorems to spin systems taking
values in hyperbolic and spherical geometries. The corresponding random walks
are no longer Markovian: they are the vertex-reinforced and vertex-diminished
jump processes. We also investigate supersymmetric versions of these formulas.
  Our proofs are based on exploiting the continuous symmetries of the
corresponding spin systems. The classical isomorphism theorems use the
translation symmetry of Euclidean space, while in hyperbolic and spherical
geometries the relevant symmetries are Lorentz boosts and rotations,
respectively. These very short proofs are new even in the Euclidean case.
  Isomorphism theorems are useful tools, and to illustrate this we present
several applications. These include simple proofs of exponential decay for spin
system correlations, exact formulas for the resolvents of the joint processes
of random walks together with their local times, and a new derivation of the
Sabot--Tarr\`es formula for the limiting local time of the vertex-reinforced
jump process.


  A cooling scheme for trapped atoms is proposed, which combines
cavity-enhanced scattering and electromagnetically induced transparency. The
cooling dynamics exploits a three-photon resonance, which combines laser and
cavity excitations. It is shown that relatively fast ground-state cooling can
be achieved in the Lamb-Dicke regime and for large cooperativity. Efficient
ground-state cooling is found for parameters of ongoing experiments.


  The pull vector is a jet observable sensitive to the distribution of soft
radiation controlled by the color flow in a collider event. We present
calculations to leading order in the soft and collinear limits for the pull
vector measured between pairs of jets that do not form a color-singlet dipole.
Our calculations are presented within the context of $e^+e^-\to$ three jets
events, on which pull is measured between the two subleading jets. A subset of
these calculations can be re-interpreted as a bottom--anti-bottom quark jet
pair in a color octet configuration, which can be a background to Higgs
production at large boost. We also present a universal expression for the pull
distribution in the high-boost and small jet radius limit. This distribution is
controlled by color SU(3) quadratic Casimirs that arise from product
representations of pairs of QCD jets.


  We present the result of our accurate measurements of the a- and b-axis
resistivity (\rho_a and \rho_b), magnetoconductivity (\Delta\sigma / \sigma),
Hall coefficient R_H, and the a-axis thermopower S_a in untwinned
YBa_{2}Cu_{3}O_{y} single crystals in a wide range of doping (6.45 < y < 7.0).
The systematics of our data reveals a number of novel 60-K-phase anomalies in
the charge transport: (i) Temperature dependences of \rho_a show anomalous
overlap below ~130 K for 6.65 < y < 6.80, (ii) Hall mobility \mu_H shows an
enhancement near y ~ 6.65, which is reflected in an anomalous y dependence of
\sigma_{xy}, (iii) With decreasing temperature R_H shows a marked drop upon
approaching T_c only in samples with 6.70 < y < 6.85, (iv) Superconducting
fluctuation magnetoconductivity is anomalously enhanced near y ~ 6.7, and (v)
H_{c2} is anomalously reduced near y ~ 6.70. We discuss that the fluctuating
charge stripes might be responsible for these anomalies in the charge
transport.


  This paper presents a novel reinforcement learning (RL) approach called
HAAM-RL (Heuristic Algorithm-based Action Masking Reinforcement Learning) for
optimizing the color batching re-sequencing problem in automobile painting
processes. The existing heuristic algorithms have limitations in adequately
reflecting real-world constraints and accurately predicting logistics
performance. Our methodology incorporates several key techniques including a
tailored Markov Decision Process (MDP) formulation, reward setting including
Potential-Based Reward Shaping, action masking using heuristic algorithms
(HAAM-RL), and an ensemble inference method that combines multiple RL models.
The RL agent is trained and evaluated using FlexSim, a commercial 3D simulation
software, integrated with our RL MLOps platform BakingSoDA. Experimental
results across 30 scenarios demonstrate that HAAM-RL with an ensemble inference
method achieves a 16.25% performance improvement over the conventional
heuristic algorithm, with stable and consistent results. The proposed approach
exhibits superior performance and generalization capability, indicating its
effectiveness in optimizing complex manufacturing processes. The study also
discusses future research directions, including alternative state
representations, incorporating model-based RL methods, and integrating
additional real-world constraints.


  Image Fusion is the process in which core information from a set of component
images is merged to form a single image, which is more informative and complete
than the component input images in quality and appearance. This paper presents
a fast and effective image fusion method for creating high quality fused images
by merging component images. In the proposed method, the input image is broken
down to a two-scale image representation with a base layer having large scale
variations in intensity, and a detail layer containing small scale details.
Here fusion of the base and detail layers is implemented by means of a Local
Edge preserving filtering based technique. The proposed method is an efficient
image fusion technique in which the noise component is very low and quality of
the resultant image is high so that it can be used for applications like
medical image processing, requiring very accurate edge preserved images.
Performance is tested by calculating PSNR and SSIM of images. The benefit of
the proposed method is that it removes noise without altering the underlying
structures of the image. This paper also presents an image zooming technique
using bilinear interpolation in which a portion of the input image is cropped
and bilinear interpolation is applied. Experimental results showed that the
when PSNR value is calculated, the noise is found to be very low for the
resultant image portion.


  We use wrapped D-brane probes to measure position dependent perturbations of
compactification moduli. Due to the backreaction of the D-branes on the local
geometry, we suspect that measuring the fluctuations of one modulus to high
precision will generically affect the others. These considerations lead us to
conjecture a novel uncertainty principle on the Calabi-Yau moduli space. We
begin our investigation with a gedanken experiment on a torus.


  We investigate the dual of the kappa=0 Gonihedric Ising model on a 3D cubic
lattice, which may be written as an anisotropically coupled Ashkin-Teller
model. The original kappa=0 Gonihedric model has a purely plaquette
interaction, displays a first order transition and possesses a highly
degenerate ground state.
  We find that the dual model admits a similar large ground state degeneracy as
a result of the anisotropic couplings and investigate the coupled mean field
equations for the model on a single cube. We also carry out Monte Carlo
simulations which confirm a first order phase transition in the model and
suggest that the ground state degeneracy persists throughout the low
temperature phase. Some exploratory cooling simulations also hint at
non-trivial dynamical behaviour.


  The problem of finding an optimal sensing schedule for a mobile device that
encounters an intermittent WiFi access opportunity is considered. At any given
time, the WiFi is in any of the two modes, ON or OFF, and the mobile's
incentive is to connect to the WiFi in the ON mode as soon as possible, while
spending as little sensing energy. We introduce a dynamic programming framework
which enables the characterization of an explicit solution for several models,
particularly when the OFF periods are exponentially distributed. While the
problem for non-exponential OFF periods is ill-posed in general, a usual
workaround in literature is to make the mobile device aware if one ON period is
completely missed. In this restricted setting, using the DP framework, the
deterministic nature of the optimal sensing policy is established, and value
iterations are shown to converge to the optimal solution. Finally, we address
the blind situation where the distributions of ON and OFF periods are unknown.
A continuous bandit based learning algorithm that has vanishing regret (loss
compared to the optimal strategy with the knowledge of distributions) is
presented, and comparisons with the optimal schemes are provided for
exponential ON and OFF times.


  Genuine multimode entanglement in continuous variable systems can be
quantified by exploring the geometry of the state-space, namely via the
generalized geometric measure (GGM) which is defined as the shortest distance
of a given multimode state from a nongenunely multimode entangled state. For
multimode Gaussian states, we derive a closed form expression of GGM in terms
of the symplectic invariants of the reduced states. Following that
prescription, the characteristics of GGM for typical three- and four-mode
Gaussian states are investigated. In the non-Gaussian paradigm, we compute GGM
for photon-added as well as -subtracted states having three- and four-modes and
find that both addition and subtraction of photons lead to enhancement of the
genuine multimode entanglement content of the state compared to its Gaussian
counterpart. Our analysis reveals that when an initial three-mode vacuum state
is evolved according to an interacting Hamiltonian, photon addition is more
beneficial in increasing GGM compared to photon subtraction while the picture
is opposite for four-mode case. Specifically, subtracting photons from
four-mode squeezed vacuum states almost always results in higher multimode
entanglement content than that of photon addition with both single as well as
multimode and constrained as well as unconstrained operations. Furthermore, we
observe a novel freezing feature of GGM during some specific cases of photon
subtraction with respect to the number of photons subtracted.


  A representation of the Pad\'e approximation of the $Z$-transform of a signal
as a resolvent of a tridiagonal matrix $J$ is given. Several formulas for the
poles, zeros and residues of the Pad\'e approximation in terms of the matrix
$J$ are proposed. Their numerical stability is tested and compared. Methods for
computing forward and backward errors are presented.


  The thalamus is the major gate to the cortex and its contribution to cortical
receptive field properties is well established. Cortical feedback to the
thalamus is, in turn, the anatomically dominant input to relay cells, yet its
influence on thalamic processing has been difficult to interpret. For an
understanding of complex sensory processing, detailed concepts of the
corticothalamic interplay need yet to be established. To study
corticogeniculate processing in a model, we draw on various physiological and
anatomical data concerning the intrinsic dynamics of geniculate relay neurons,
the cortical influence on relay modes, lagged and nonlagged neurons, and the
structure of visual cortical receptive fields. In extensive computer
simulations we elaborate the novel hypothesis that the visual cortex controls
via feedback the temporal response properties of geniculate relay cells in a
way that alters the tuning of cortical cells for speed.


  We propose an optimistic model-based algorithm, dubbed SMRL, for
finite-horizon episodic reinforcement learning (RL) when the transition model
is specified by exponential family distributions with $d$ parameters and the
reward is bounded and known. SMRL uses score matching, an unnormalized density
estimation technique that enables efficient estimation of the model parameter
by ridge regression. Under standard regularity assumptions, SMRL achieves
$\tilde O(d\sqrt{H^3T})$ online regret, where $H$ is the length of each episode
and $T$ is the total number of interactions (ignoring polynomial dependence on
structural scale parameters).


  Exact evaluation of $<{\rm Tr} S^p>$ is here performed for real symmetric
matrices $S$ of arbitrary order $n$, up to some integer $p$, where the matrix
entries are independent identically distributed random variables, with an
arbitrary probability distribution.
  These expectations are polynomials in the moments of the matrix entries ;
they provide useful information on the spectral density of the ensemble in the
large $n$ limit. They also are a straightforward tool to examine a variety of
rescalings of the entries in the large $n$ limit.


  This paper introduces an innovative approach to road network generation
through the utilization of a multi-modal Large Language Model (LLM). Our model
is specifically designed to process aerial images of road layouts and produce
detailed, navigable road networks within the input images. The core innovation
of our system lies in the unique training methodology employed for the large
language model to generate road networks as its output. This approach draws
inspiration from the BLIP-2 architecture arXiv:2301.12597, leveraging
pre-trained frozen image encoders and large language models to create a
versatile multi-modal LLM.
  Our work also offers an alternative to the reasoning segmentation method
proposed in the LISA paper arXiv:2308.00692. By training the large language
model with our approach, the necessity for generating binary segmentation
masks, as suggested in the LISA paper arXiv:2308.00692, is effectively
eliminated. Experimental results underscore the efficacy of our multi-modal LLM
in providing precise and valuable navigational guidance. This research
represents a significant stride in bolstering autonomous navigation systems,
especially in road network scenarios, where accurate guidance is of paramount
importance.


  This document gives an algebraic and two polygraphic translations of Petri
nets, all three providing an easier way to describe reductions and to identify
some of them. The first one sees places as generators of a commutative monoid
and transitions as rewriting rules on it: this setting is totally equivalent to
Petri nets, but lacks any graphical intuition. The second one considers places
as 1-dimensional cells and transitions as 2-dimensional ones: this translation
recovers a graphical meaning but raises many difficulties since it uses
explicit permutations. Finally, the third translation sees places as
degenerated 2-dimensional cells and transitions as 3-dimensional ones: this is
a setting equivalent to Petri nets, equipped with a graphical interpretation.


  We report the observation of nuclear magnetic resonance (NMR) in a
ferromagnet-semiconductor heterostructure in the presence of a spin-polarized
current. Spin-polarized electrons injected from a metallic ferromagnet generate
a large nuclear spin population in a GaAs quantum well by dynamic polarization.
The characteristic time for the polarization process is approximately 20 sec,
and the nuclear polarization can persist for several minutes after the current
is turned off. Resonant depolarization is observed in the presence of an AC
magnetic field or when the injection current is modulated at the NMR frequency.


  In the present article, we review a continual effort on generalization of the
Trotter formula to higher-order exponential product formulas. The exponential
product formula is a good and useful approximant, particularly because it
conserves important symmetries of the system dynamics. We focuse on two
algorithms of constructing higher-order exponential product formulas. The first
is the fractal decomposition, where we construct higher-order formulas
recursively. The second is to make use of the quantum analysis, where we
compute higher-order correction terms directly. As interludes, we also have
described the decomposition of symplectic integrators, the approximation of
time-ordered exponentials, and the perturbational composition.


  An expanding literature articulates the view that Taylor rules are helpful in
predicting exchange rates. In a changing world however, Taylor rule parameters
may be subject to structural instabilities, for example during the Global
Financial Crisis. This paper forecasts exchange rates using such Taylor rules
with Time Varying Parameters (TVP) estimated by Bayesian methods. In core
out-of-sample results, we improve upon a random walk benchmark for at least
half, and for as many as eight out of ten, of the currencies considered. This
contrasts with a constant parameter Taylor rule model that yields a more
limited improvement upon the benchmark. In further results, Purchasing Power
Parity and Uncovered Interest Rate Parity TVP models beat a random walk
benchmark, implying our methods have some generality in exchange rate
prediction.


  This paper proposes a new trapezoidal ordered fuzzy number representation of
windowed time series based on the idea of a Japanese candlestick to those
previously proposed in the literature. We define and illustrate several
descriptive statistics based on the information contained in the ordered fuzzy
numbers. We utilize financial trading data from three automotive companies as a
case study. Further expansion can be applied to any other forms of time series
data to offer further insight into many other data driven situations.


  We present the results of short baseline interferometry observations at
submillimetre wavelengths, using a two-element interferometer comprising the
JCMT and CSO, of circumstellar discs around young YSOs. We model data for the
Class 0 protostar IRAS03282 and the Class I protostar L1709B with discs and
compare them with previously published results. We find evidence for the ratio
of the disc mass to envelope mass to increase as the objects evolve.


  By moving from massive antennas to antenna surfaces for software-defined
wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays
of unit cells to control the scattering and reflection profiles of signals,
mitigating the propagation loss and multipath attenuation, and thereby
improving the coverage and spectral efficiency. In this paper, covert
communication is considered in the presence of the RIS. While there is an
ongoing transmission boosted by the RIS, both the intended receiver and an
eavesdropper individually try to detect this transmission using their own deep
neural network (DNN) classifiers. The RIS interaction vector is designed by
balancing two (potentially conflicting) objectives of focusing the transmitted
signal to the receiver and keeping the transmitted signal away from the
eavesdropper. To boost covert communications, adversarial perturbations are
added to signals at the transmitter to fool the eavesdropper's classifier while
keeping the effect on the receiver low. Results from different network
topologies show that adversarial perturbation and RIS interaction vector can be
jointly designed to effectively increase the signal detection accuracy at the
receiver while reducing the detection accuracy at the eavesdropper to enable
covert communications.


  We present theoretical and experimental analysis of an interferometric
measurement of the {\it in-situ} phase drop across and current flow through a
rotating barrier in a toroidal Bose-Einstein condensate (BEC). This experiment
is the atomic analog of the rf-superconducting quantum interference device
(SQUID). The phase drop is extracted from a spiral-shaped density profile
created by the spatial interference of the expanding toroidal BEC and a
reference BEC after release from all trapping potentials. We characterize the
interferometer when it contains a single particle, which is initially in a
coherent superposition of a torus and reference state, as well as when it
contains a many-body state in the mean-field approximation. The single-particle
picture is sufficient to explain the origin of the spirals, to relate the
phase-drop across the barrier to the geometry of a spiral, and to bound the
expansion times for which the {\it in-situ} phase can be accurately determined.
Mean-field estimates and numerical simulations show that the inter-atomic
interactions shorten the expansion time scales compared to the single-particle
case. Finally, we compare the mean-field simulations with our experimental data
and confirm that the interferometer indeed accurately measures the {\it
in-situ} phase drop.


  We study exceptional collections of line bundles on surfaces. We prove that
any full cyclic strong exceptional collection of line bundles on a rational
surface is an augmentation in the sense of L.Hille and M.Perling. We find
simple geometric criteria of exceptionality (strong exceptionality, cyclic
strong exceptionality) for collections of line bundles on weak del Pezzo
surfaces. As a result, we classify smooth projective surfaces admitting a full
cyclic strong exceptional collection of line bundles. Also, we provide an
example of a weak del Pezzo surface of degree 2 and a full strong exceptional
collection of line bundles on it which does not come from augmentations, thus
answering a question by Hille and Perling.


  We construct a holographic multi-band superconductor model with each complex
scalar field in the bulk minimally coupled to a same gauge field. Taking into
account the back reaction of matter fields on the background geometry and
focusing on the two band case with two scalar order parameters, we find that
depending on the strength of the back reaction and the charge ratio of the two
bulk scalars, five different superconducting phases exist, and three of five
phases exhibit some region where both orders coexist and are thermodynamically
favored. The other two superconducting phases have only one scalar order. The
model exhibits rich phase structure and we construct the full diagram for the
five superconducting phases. Our analysis indicates that the equivalent
attractive interaction mediated by gravity between the two order parameters
tends to make the coexistence of two orders much more easy rather than more
difficult.


  We present a design for a piezoelectric-driven uniaxial stress cell suitable
for use at ambient and cryogenic temperatures, and that incorporates both a
displacement and a force sensor. The cell has a diameter of 46 mm and a height
of 13 mm. It can apply a zero-load displacement of up to ~45 $\mu$m, and a
zero-displacement force of up to ~245 N. With combined knowledge of the
displacement and force applied to the sample, it can quickly be determined
whether the sample and its mounts remain within their elastic limits. In tests
on the oxide metal Sr$_2$RuO$_4$, we found that at room temperature serious
plastic deformation of the sample onset at a uniaxial stress of ~0.2 GPa, while
at 5 K the sample deformation remained elastic up to almost 2 GPa. This result
highlights the usefulness of in situ tuning, in which the force can be applied
after cooling samples to cryogenic temperatures.


  The results of this paper can be divided into two parts, geometric and
arithmetic. Let $X$ be a smooth projective curve over $\mathbb{C}$, and
$e,\infty\in X(\mathbb{C})$ be distinct points. Let $L_n$ be the mixed Hodge
structure of functions on $\pi_1(X-\{\infty\},e)$ given by iterated integrals
of length $\leq n$ (as defined by Hain). In the geometric part, inspired by a
work of Darmon, Rotger, and Sols, we express the mixed Hodge extension
$\mathbb{E}^\infty_{n,e}$ given by the weight filtration on
$\frac{L_n}{L_{n-2}}$ in terms of certain null-homologous algebraic cycles on
$X^{2n-1}$. As a corollary, we show that the extension
$\mathbb{E}^\infty_{n,e}$ determines the point $\infty\in X-\{e\}$. The
arithmetic part of the paper gives some number-theoretic applications of the
geometric part. We assume that $X=X_0\otimes_K\mathbb{C}$ and $e,\infty\in
X_0(K)$, where $K$ is a subfield of $\mathbb{C}$ and $X_0$ is a projective
curve over $K$. Let $Jac$ be the Jacobian of $X_0$. We use the extension
$\mathbb{E}^\infty_{n,e}$ to associate to each $Z\in CH_{n-1}(X_0^{2n-2})$ a
point $P_Z\in Jac(K)$, which can be described analytically in terms of iterated
integrals. The proof of $K$-rationality of $P_Z$ uses that the algebraic cycles
constructed in the geometric part of the paper are defined over $K$. Assuming a
certain plausible hypothesis on the Hodge filtration on $L_n(X-\{\infty\},e)$
holds, we show that an algebraic cycle $Z$ for which $P_Z$ is torsion, gives
rise to relations between periods of $L_2(X-\{\infty\},e)$. Interestingly,
these relations are non-trivial even when one takes $Z$ to be the diagonal of
$X_0$. The geometric result of the paper in $n=2$ case, and the fact that one
can associate to $\mathbb{E}^\infty_{2,e}$ a family of points in $Jac(K)$, are
due to Darmon, Rotger, and Sols. Our contribution is in generalizing the
picture to higher weights.


  Systematic study of the optical and magnetic excitations of twelve MSi$_{12}$
and four MSi$_{10}$ transition metal encapsulating Si cages has been carried
out by employing real time time-dependent density functional theory. Criteria
for the choice of transition metals (M) are clusters' stability,
synthesizability, and diversity. It was found that both the optical absorption
and the spin-susceptibility spectra are mainly determined by, in decreasing
order of importance: 1) the cage shape, 2) the group in the Periodic Table M
belongs to, and 3) the period of M in the Periodic Table. Cages with similar
structures and metal species that are close to each other in the Periodic Table
possess spectra sharing many similarities, e. g., the optical absorption
spectra of the MSi$_{12}$ (M = V, Nb, Ta, Cr, Mo, and W), which are highly
symmetric and belong to groups 4 and 5 of the Periodic Table, all share a very
distinctive peak at around 4 eV. In all cases, although some of the observed
transitions are located at the Si skeleton of the cages, the transition metal
specie is always significant for the optical absorption and the
spin-susceptibility spectra. Our results provide finger-print data for
identification of gas-phase MSi$_{12}$ and MSi$_{10}$ by optical absorption
spectroscopy.


  We define a discrete-time, coined quantum walk on weighted graphs that is
inspired by Szegedy's quantum walk. Using this, we prove that many
lackadaisical quantum walks, where each vertex has $l$ integer self-loops, can
be generalized to a quantum walk where each vertex has a single self-loop of
real-valued weight $l$. We apply this real-valued lackadaisical quantum walk to
two problems. First, we analyze it on the line or one-dimensional lattice,
showing that it is exactly equivalent to a continuous deformation of the
three-state Grover walk with faster ballistic dispersion. Second, we generalize
Grover's algorithm, or search on the complete graph, to have a weighted
self-loop at each vertex, yielding an improved success probability when $l < 3
+ 2\sqrt{2} \approx 5.828$.


  The box-ball system is studied from the viewpoint of combinatorics of words
and tableaux. Each state of the box-ball system can be transformed into a pair
of tableaux $(P,Q)$ by the Robinson-Schensted-Knuth correspondence. In the
language of tableaux, the $P$-symbol gives rise to a conserved quantity of the
box-ball system, and the $Q$-symbol evolves independently of the $P$-symbol.
The time evolution of the $Q$-symbol is described explicitly in terms of the
box-labels.


  The thesis is dedicated to studying methods to improve the efficiency of
random access schemes and to facilitate their deployment in machine-type
communications (MTC). First, a joint user activity identification and channel
estimation scheme is designed for grant-free random access systems. We propose
a decentralized transmission control and design a compressed sensing (CS)-based
user identification and channel estimation scheme. We analyze the packet delay
and throughput of the proposed scheme. We also optimize the transmission
control scheme to maximize the system throughput. Second, a random access
scheme, i.e., the coded slotted ALOHA (CSA) scheme, is designed for erasure
channels to improve the system throughput. By deriving the extrinsic
information transfer (EXIT) functions and optimizing their convergence
behavior, we design the code probability distributions for CSA schemes with
repetition codes and maximum distance separable (MDS) codes to maximize the
expected traffic load, under packet erasure and slot erasure channels. We
derive the asymptotic throughput of CSA schemes over the erasure channels for
an infinite frame length, which is verified to well approximate the throughput
for a practical frame length. Third, an efficient data decoding algorithm for
the CSA scheme is proposed to further improve the system efficiency. We present
a low-complexity physical-layer network coding (PNC) method to obtain linear
combinations of collided packets and design an enhanced message-level
successive interference cancellation (SIC) algorithm to exploit the linear
combinations of collided packets. We propose an analytical framework and derive
the system throughput for the proposed scheme. The CSA scheme is further
optimized to maximize the system throughput and energy efficiency.


  We analyze the effect of the simultaneous presence of correlated additive and
multiplicative noises on the stochastic resonance response of a modulated
bistable system. We find that when the correlation parameter is also modulated,
the system's response, measured through the output signal-to-noise ratio,
becomes largely independent of the additive noise intensity.


  Standard probabilistic linear discriminant analysis (PLDA) for speaker
recognition assumes that the sample's features (usually, i-vectors) are given
by a sum of three terms: a term that depends on the speaker identity, a term
that models the within-speaker variability and is assumed independent across
samples, and a final term that models any remaining variability and is also
independent across samples. In this work, we propose a generalization of this
model where the within-speaker variability is not necessarily assumed
independent across samples but dependent on another discrete variable. This
variable, which we call the channel variable as in the standard PLDA approach,
could be, for example, a discrete category for the channel characteristics, the
language spoken by the speaker, the type of speech in the sample
(conversational, monologue, read), etc. The value of this variable is assumed
to be known during training but not during testing. Scoring is performed, as in
standard PLDA, by computing a likelihood ratio between the null hypothesis that
the two sides of a trial belong to the same speaker versus the alternative
hypothesis that the two sides belong to different speakers. The two likelihoods
are computed by marginalizing over two hypothesis about the channels in both
sides of a trial: that they are the same and that they are different. This way,
we expect that the new model will be better at coping with same-channel versus
different-channel trials than standard PLDA, since knowledge about the channel
(or language, or speech style) is used during training and implicitly
considered during scoring.


  Science and engineering problems subject to uncertainty are frequently both
computationally expensive and feature nonsmooth parameter dependence, making
standard Monte Carlo too slow, and excluding efficient use of accelerated
uncertainty quantification methods relying on strict smoothness assumptions. To
remedy these challenges, we propose an adaptive stratification method suitable
for nonsmooth problems and with significantly reduced variance compared to
Monte Carlo sampling. The stratification is iteratively refined and samples are
added sequentially to satisfy an allocation criterion combining the benefits of
proportional and optimal sampling. Theoretical estimates are provided for the
expected performance and probability of failure to correctly estimate essential
statistics. We devise a practical adaptive stratification method with strata of
the same kind of geometrical shapes, cost-effective refinement satisfying a
greedy variance reduction criterion. Numerical experiments corroborate the
theoretical findings and exhibit speedups of up to three orders of magnitude
compared to standard Monte Carlo sampling.


  The aim of this work was to use a multi-approach technique to derive the most
accurate values possible of the physical parameters of the \delta Sct star
HD174966. In addition, we searched for a periodic pattern in the frequency
spectra with the goal of using it to determine the mean density of the star.
First, we extracted the frequency content from the CoRoT light curve. Then, we
derived the physical parameters of HD174966 and carried a mode identification
out from the spectroscopic and photometric observations. We used this
information to look for the models fulfilling all the conditions and discussed
the inaccuracies of the method because of the rotation effects. In a final
step, we searched for patterns in the frequency set using a Fourier transform,
discussed its origin and studied the possibility of using the periodicity to
obtain information about the physical parameters of the star. A total of 185
peaks were obtained from the Fourier analysis of the CoRoT light curve, being
almost all reliable pulsating frequencies. From the spectroscopic observations,
18 oscillation modes were detected and identified, and the inclination angle
($62.5^{\circ}$$^{+7.5}_{-17.5}$) and the rotational velocity of the star (142
km/s) were estimated. From the multi-colour photometric observations, 3
frequencies were detected, which correspond to the main ones in the CoRoT light
curve. We looked for periodicities within the 185 frequencies and found a
periodic pattern ~64 \mu Hz. Using the inclination angle, the rotational
velocity and an Echelle diagram, showing a double comb outside the asymptotic
regime, we concluded that the periodicity corresponds to a large separation
structure. The periodic pattern allowed us to discriminate models from a grid,
finding that the value of the mean density is achieved with a 6% uncertainty.
So, the pattern could be used as a new observable for A-F type stars.


  In a recent paper, a "distance" function, \cal D, was defined which measures
the distance between pure classical and quantum systems. In this work, we
present a new definition of a "distance", D, which measures the distance
between either pure or impure classical and quantum states. We also compare the
new distance formula with the previous formula, when the latter is applicable.
To illustrate these distances, we have used 2 \times 2 matrix examples and
2-dimensional vectors for simplicity and clarity. Several specific examples are
calculated.


  In most real scenarios the construction of a risk-neutral portfolio must be
performed in discrete time and with transaction costs. Two human imposed
constraints are the risk-aversion and the profit maximization, which together
define a nonlinear optimization problem with a model-dependent solution. In
this context, an optimal fixed frequency hedging strategy can be determined a
posteriori by maximizing a sharpe ratio simil path dependent reward function.
Sampling from Heston processes, a convolutional neural network was trained to
infer which period is optimal using partial information, thus leading to a
dynamic hedging strategy in which the portfolio is hedged at various
frequencies, each weighted by the probability estimate of that frequency being
optimal.


  The International Workshop on Locational Analysis and Related Problems will
take place during January 23-24, 2020 in Seville (Spain). It is organized by
the Spanish Location Network and the Location Group GELOCA from the Spanish
Society of Statistics and Operations Research(SEIO). The Spanish Location
Network is a group of more than 140 researchers from several Spanish
universities organized into 7 thematic groups. The Network has been funded by
the Spanish Government since 2003.
  One of the main activities of the Network is a yearly meeting aimed at
promoting the communication among its members and between them and other
researchers, and to contribute to the development of the location field and
related problems. The last meetings have taken place in C\'adiz (January
20-February 1, 2019), Segovia (September 27-29, 2017), M\'alaga (September
14-16, 2016), Barcelona (November 25-28, 2015), Sevilla (October 1-3, 2014),
Torremolinos (M\'alaga, June 19-21, 2013), Granada (May 10-12, 2012), Las
Palmas de Gran Canaria (February 2-5, 2011) and Sevilla (February 1-3, 2010).
  The topics of interest are location analysis and related problems. This
includes location models, networks, transportation, logistics, exact and
heuristic solution methods, and computational geometry, among others.


  In this paper, we study a proposal put forward recently by Bodendorfer, Mele
and M\"unch and Garc\'\i{}a-Quismondo and Marug\'an, in which the two
polymerization parameters of spherically symmetric black hole spacetimes are
the Dirac observables of the four-dimensional Ashtekar's variables. In this
model, black and white hole horizons in general exist and naturally divide the
spacetime into the external and internal regions. In the external region, the
spacetime can be made asymptotically flat by properly choosing the dependence
of the two polymerization parameters on the Ashtekar variables. Then, we find
that the asymptotical behavior of the spacetime is universal, and, to the
leading order, the curvature invariants are independent of the mass parameter
$m$. For example, the Kretschmann scalar approaches zero as $K \simeq
A_0r^{-4}$ asymptotically, where $A_0$ is generally a non-zero constant and
independent of $m$, and $r$ the geometric radius of the two-spheres. In the
internal region, all the physical quantities are finite, and the Schwarzschild
black hole singularity is replaced by a transition surface whose radius is
always finite and non-zero. The quantum gravitational effects are negligible
near the black hole horizon for very massive black holes. However, the behavior
of the spacetime across the transition surface is significantly different from
all loop quantum black holes studied so far. In particular, the location of the
maximum amplitude of the curvature scalars is displaced from the transition
surface and depends on $m$, so does the maximum amplitude. In addition, the
radius of the white hole is much smaller than that of the black hole, and its
exact value sensitively depends on $m$, too.


  We study packings of hard spheres on lattices. The partition function, and
therefore the pressure, may be written solely in terms of the accessible free
volume, i.e. the volume of space that a sphere can explore without touching
another sphere. We compute these free volumes using a leaky cell model, in
which the accessible space accounts for the possibility that spheres may escape
from the local cage of lattice neighbors. We describe how elementary geometry
may be used to calculate the free volume exactly for this leaky cell model in
two- and three-dimensional lattice packings and compare the results to the
well-known Carnahan-Starling and Percus-Yevick liquid models. We provide
formulas for the free volumes of various lattices and use the common tangent
construction to identify several phase transitions between them in the leaky
cell regime, indicating the possibility of coexistence in crystalline
materials.


  Data inconsistency leads to a slow training process when deep neural networks
are used for the inverse design of photonic devices, an issue that arises from
the fundamental property of non-uniqueness in all inverse scattering problems.
Here we show that by combining forward modeling and inverse design in a tandem
architecture, one can overcome this fundamental issue, allowing deep neural
networks to be effectively trained by data sets that contain non-unique
electromagnetic scattering instances. This paves the way for using deep neural
networks to design complex photonic structures that requires large training
sets.


  We outline the key elements of a recent calculation aimed at determining the
equation of state of deconfined (but unpaired) quark matter at zero temperature
and high density, using finite quark masses. The computation is performed in
perturbation theory up to three loops, and necessitates the development and
application of some novel computational tools. In this talk, we introduce the
basic features of these new techniques and review the main sources of
motivation for considering finite quark mass effects in perturbation theory.