  In this paper, we present an open source software ($\texttt{SAMUS}$) which
simulates constant-density, constant-viscosity liquid bodies subject to tidal
forces for a range of assumed viscosites and sizes. This software solves the
Navier-Stokes equations on a finite-element mesh, incorporating the
centrifugal, Coriolis, self-gravitational, and tidal forces. The primary
functionality is to simulate the deformation of minor bodies under the
influence of tidal forces. It may therefore be used to constrain the
composition and physical structure of bodies experiencing significant tidal
forces, such as 99942 Apophis and 1I/`Oumuamua. We demonstrate that
$\texttt{SAMUS}$ will be useful to constrain the material properties of Apophis
during its near-Earth flyby in 2029. Depending on the material properties,
Apophis may experience an area change of up to 0.5\%, with similar effects on
the photometric brightness. We also apply $\texttt{SAMUS}$ to constrain the
material dynamic viscosity of 1I/`Oumuamua, the first interstellar object
discovered traversing the inner Solar System. `Oumuamua experienced a close
approach to the Sun at perihelion ($q\simeq 0.25$ au) during which there were
significant tidal forces that may have caused deformation of the body. This
deformation could have lead to observable changes in the photometric light
curve based on the material properties. The application of $\texttt{SAMUS}$ to
produce synthetic observations which incorporate tidal deformation effects
demonstrate that no deformation -- an infinite dynamic viscosity -- best
reproduces the photometric data. While these results indicate that `Oumuamua
did not experience significant tidal deformation, a sophisticated model
incorporating non-principal axis rotation is necessary to conclusively analyze
both `Oumuamua and Apophis.


  In this paper, we have proposed the spiron equation of free and non-free
photon, and give the spin operator and spin wave function of photon. We
calculate the helicity of photon and prove there are left-handed and
right-handed photon. By the spiron equation of non-free photon, we can study
the quantum property of photon in medium, which can be used in quantum optics,
photonic crystals and so on.


  We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net),
a learning-based technique for disparity computation in mono-camera structured
light systems. In our hardware setting, a static pattern is projected onto a
dynamic scene and captured by a monocular camera. Different from most former
disparity estimation methods that operate in a frame-wise manner, our network
acquires disparity maps in a temporally incremental way. Specifically, We
exploit the deformation of projected patterns (named pattern flow ) on captured
image sequences, to model the temporal information. Notably, this newly
proposed pattern flow formulation reflects the disparity changes along the
epipolar line, which is a special form of optical flow. Tailored for pattern
flow, the TIDE-Net, a recurrent architecture, is proposed and implemented. For
each incoming frame, our model fuses correlation volumes (from current frame)
and disparity (from former frame) warped by pattern flow. From fused features,
the final stage of TIDE-Net estimates the residual disparity rather than the
full disparity, as conducted by many previous methods. Interestingly, this
design brings clear empirical advantages in terms of efficiency and
generalization ability. Using only synthetic data for training, our extensitve
evaluation results (w.r.t. both accuracy and efficienty metrics) show superior
performance than several SOTA models on unseen real data. The code is available
on https://github.com/CodePointer/TIDENet.


  Recent experimental results on Bose-Einstein correlations are presented.
Emphasis will be put on the measurement of between-W correlations in WW events
at LEP 2.


  We used the Advanced Camera for Surveys on board the Hubble Space Telescope
to obtain high resolution i-band images of the centers of 23 single galaxies,
which were selected because they have SDSS velocity dispersions larger than 350
km/s. The surface brightness profiles of the most luminous of these objects
(M_i<-24) have well-resolved `cores' on scales of 150-1000 pc, and share
similar properties to BCGs. The total luminosity of the galaxy is a better
predictor of the core size than is the velocity dispersion. The correlations of
luminosity and velocity dispersion with core size agree with those seen in
previous studies of galaxy cores. Because of high velocity dispersions, our
sample of galaxies can be expected to harbor the most massive black holes, and
thus have large cores with large amounts of mass ejection. The mass-deficits
inferred from core-Sersic fits to the surface-brightness profiles are
approximately double the black-hole masses inferred from the M_bh-sigma
relation and the same as those inferred from the M_bh-L relation. The less
luminous galaxies (M_i>-23) tend to have steeper `power-law' inner profiles,
higher-ellipticity, diskier isophotes, and bulge-to-total ratios of order 0.5
-- all of which suggest that they are `fast-rotators' and rotational motions
could have contaminated the velocity dispersion estimate. There are obvious
dust features within about 300 pc of the center in about 35% of the sample,
predominantly in power-law rather than core galaxies.


  We investigate the ultraslow structural relaxation of ageing foams with
rheologically-tunable continuous phases. We probe the bubble dynamics
associated with pressure-driven foam coarsening using differential dynamic
microscopy, which allows characterizing the sample dynamics in the reciprocal
space with imaging experiments. Similar to other out-of-equilibrium jammed soft
systems, these foams exhibit compressed exponential relaxations, with a
ballistic-like linear dependency of the relaxation rate on the scattering
wavevector. By tuning the rheology of the continuous phase we observe changes
in the relaxation shape, where stiffer matrices yield larger compressing
exponents. Our results corroborate recent real-space observations obtained with
bubble tracking, providing a comprehensive overview of structural relaxation in
these complex systems, both in direct and reciprocal space.


  Network models are useful tools for modelling complex associations. If a
Gaussian graphical model is assumed, conditional independence is determined by
the non-zero entries of the inverse covariance (precision) matrix of the data.
The Bayesian graphical horseshoe estimator provides a robust and flexible
framework for precision matrix inference, as it introduces local, edge-specific
parameters which prevent over-shrinkage of non-zero off-diagonal elements.
However, for many applications such as statistical omics, the current
implementation based on Gibbs sampling becomes computationally inefficient or
even unfeasible in high dimensions. Moreover, the graphical horseshoe has only
been formulated for a single network, whereas interest has grown in the network
analysis of multiple data sets that might share common structures. We propose
(i) a scalable expectation conditional maximisation (ECM) algorithm for
obtaining the posterior mode of the precision matrix in the graphical
horseshoe, and (ii) a novel joint graphical horseshoe estimator, which borrows
information across multiple related networks to improve estimation. We show, on
both simulated and real omics data, that our single-network ECM approach is
more scalable than the existing graphical horseshoe Gibbs implementation, while
achieving the same level of accuracy. We also show that our joint-network
proposal successfully leverages shared edge-specific information between
networks while still retaining differences, outperforming state-of-the-art
methods at any level of network similarity.


  Let $\mathcal G$ be the Cayley graph of a finitely generated, infinite group
$\Gamma$. We show that $\Gamma$ has the Haagerup property if and only if for
every $\alpha<1$, there is a $\Gamma$-invariant bond percolation $\mathbb P$ on
$\mathcal G$ with $\mathbb
E[\mathrm{deg}_{\omega}(g)]>\alpha\mathrm{deg}_{\mathcal G}(g)$ for every
vertex $g$ and with the two-point function $\tau(g,h)=\mathbb
P\big[g\leftrightarrow h\big]$ vanishing as $d(g,h)\to\infty$. On the other
hand, we show that $\Gamma$ has Kazhdan's property (T) if and only if there
exists a threshold $\alpha^*<1$ such that for every $\Gamma$-invariant bond
percolation $\mathbb P$ on $\mathcal G$, $\mathbb
E[\mathrm{deg}_\omega(o)]>\alpha^*\mathrm{deg}(o)$ implies that the two-point
function is uniformly bounded away from zero. These results in particular
answer questions raised by Lyons (J. Math. Phys. 41. 1099-1126 (2000)) about
characterizations of properties of groups beyond amenability through
group-invariant percolations.
  The method of proof is new and is based on a construction of percolations
with suitable dependence structures built from invariant point processes on
spaces with measured walls. This construction furthermore leads to quantitative
bounds on the two-point functions, exhibiting in particular exponential decay
of the two-point function in several prominent examples of Haagerup groups,
including co-compact Fuchsian groups, co-compact discrete subgroups of
$\mathrm{Isom}(\mathbb H^n)$ and lamplighters over free groups. This method
also allows us to extend the aforementioned characterization of property (T) to
the setting of relative property (T) and provide an application to Bernoulli
percolation at the uniqueness threshold.


  The large-N limit of asymptotically flat two-dimensional dilaton gravity
coupled to N free matter fields provides a useful toy model for semiclassical
black holes and the information paradox. Analyses of the asymptotic information
flux as given by the entanglement entropy show that it follows the Hawking
curve, indicating that information is destroyed in these models. Recently,
motivated by developments in AdS/CFT, a semiclassical island rule for entropy
has been proposed. We define and compute the island rule entropy for black hole
formation and evaporation in the large-N RST model of dilaton gravity and show
that, in contrast, it follows the unitary Page curve. The relation of these two
observations, and interesting properties of the dilaton gravity island rule,
are discussed.


  We consider the porous medium equation with power-type reaction terms $u^p$
on negatively curved Riemannian manifolds, and solutions corresponding to
bounded, nonnegative and compactly supported data. If $p>m$, small data give
rise to global-in-time solutions while solutions associated to large data blow
up in finite time. If $p<m$, large data blow up at worst in infinite time, and
under the stronger restriction $p\in(1,(1+m)/2]$ all data give rise to
solutions existing globally in time, whereas solutions corresponding to large
data blow up in infinite time. The results are in several aspects significantly
different from the Euclidean ones, as has to be expected since negative
curvature is known to give rise to faster diffusion properties of the porous
medium equation.


  The WWgamma triple gauge boson coupling parameters are studied using p-pbar
-> l nu gamma + X (l = e,mu) events at sqrt(s) = 1.96 TeV. The data were
collected with the DO detector from an integrated luminosity of 162 pb^{-1}
delivered by the Fermilab Tevatron Collider. The cross section times branching
fraction for p-pbar -> W(gamma) + X -> l nu gamma + X with E_T^{gamma} > 8 GeV
and Delta R_{l gamma} > 0.7 is 14.8 +/- 1.6 (stat) +/- 1.0 (syst) +/- 1.0 (lum)
pb. The one-dimensional 95% confidence level limits on anomalous couplings are
-0.88 < Delta kappa_{gamma} < 0.96 and -0.20 < lambda_{gamma} < 0.20.


  A fundamental question in QCD is the non-perturbative structure of hadrons at
the amplitude level--not just the single-particle flavor, momentum, and
helicity distributions of the quark constituents, but also the multi-quark,
gluonic, and hidden-color correlations intrinsic to hadronic and nuclear
wavefunctions. The light-cone Fock-state representation of QCD encodes the
properties of a hadrons in terms of frame-independent wavefunctions. A number
of applications are discussed, including semileptonic B decays, deeply virtual
Compton scattering, and dynamical higher twist effects in inclusive reactions.
A new type of jet production reaction, "self-resolving diffractive
interactions" can provide direct information on the light-cone wavefunctions of
hadrons in terms of their quark and gluon degrees of freedom as well as the
composition of nuclei in terms of their nucleon and mesonic degrees of freedom.
The relation of the intrinsic sea to the light-cone wavefunctions is discussed.
The physics of light-cone wavefunctions is illustrated for the quantum
fluctuations of an electron.


  We introduce quantum sensing protocol for detection spatially varying fields
by using two coupled harmonic oscillators as a quantum probe. We discuss a
physical implementation of the sensing technique with two trapped ions coupled
via Coulomb mediated phonon hopping. Our method relies on using the coupling
between the localized ion oscillations and the internal states of the trapped
ions which allows to measure spatially varying electric and magnetic fields.
First we discuss an adiabatic sensing technique which is capable to detect a
very small force difference simply by measuring the ion spin population. We
also show that the adiabatic method can be used for detection magnetic field
gradient which is independent of the magnetic offset. Second, we show that the
strong spin phonon coupling could leads to improve sensitivity to force as well
as to phase estimations. We quantify the sensitivity in terms of quantum Fisher
information and show that it diverges by approaching the critical coupling.


  This paper deals with the consistency and a rate of convergence for a
Nadaraya-Watson estimator of the drift function of a stochastic differential
equation driven by an additive fractional noise. The results of this paper are
obtained via both some long-time behavior properties of Hairer and some
properties of the Skorokhod integral with respect to the fractional Brownian
motion. These results are illustrated on the fractional Ornstein-Uhlenbeck
process.


  Nichols algebras of group type with many cubic relations are classified under
a technical assumption on the structure of Hurwitz orbits of the third power of
the underlying indecomposable rack. All such Nichols algebras are
finite-dimensional and their Hilbert series have a factorization into quantum
integers. Also, all known finite-dimensional elementary Nichols algebras of
group type turn out to have many cubic relations. The technical assumption of
our theorem can be removed if a conjecture in the theory of cellular automata
can be proven.


  HATS-18b is a transiting planet with a large mass and a short orbital period,
and is one of the best candidates for the detection of orbital decay induced by
tidal effects. We present extensive photometry of HATS-18 from which we measure
27 times of mid-transit. Two further transit times were measured from data from
the Transiting Exoplanet Survey Satellite (TESS) and three more taken from the
literature. The transit timings were fitted with linear and quadratic
ephemerides and an upper limit on orbital decay was determined. This
corresponds to a lower limit on the modified stellar tidal quality factor of
$Q_\star^{\,\prime} > 10^{5.11 \pm 0.04}$. This is at the cusp of constraining
the presence of enhanced tidal dissipation due to internal gravity waves. We
also refine the measured physical properties of the HATS-18 system, place upper
limits on the masses of third bodies, and compare the relative performance of
TESS and the 1.54-m Danish Telescope in measuring transit times for this
system.


  Results are presented on the performance of Adaptive Neuro-Fuzzy Inference
system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region
in the state of Oaxaca, Mexico. The data bank was provided by the
meteorological station located at the University of Isthmus, Tehuantepec
campus, and this data bank covers the period from 2008 to 2011. Three data
models were constructed to carry out 16, 24 and 48 hours forecasts using the
following variables: wind velocity, temperature, barometric pressure, and date.
The performance measure for the three models is the mean standard error (MSE).
In this work, performance analysis in short-term prediction is presented,
because it is essential in order to define an adequate wind speed model for
eolian parks, where a right planning provide economic benefits.


  We construct a discrete subset of Narain CFTs from quantum stabilizer codes
with qudit (including qubit) systems whose dimension is a prime number. Our
construction exploits three important relations. The first relation is between
qudit stabilizer codes and classical codes. The second is between classical
codes and Lorentzian lattices. The third is between Lorentzian lattices and
Narain CFTs. In particular, we study qudit Calderbank-Shor-Steane (CSS) codes
as a special class of qudit stabilizer codes and the ensembles of the Narain
code CFTs constructed from CSS codes. We obtain exact results for the averaged
partition functions over the ensembles and discuss their implications for
holographic duality.


  The problem of large order behaviour of perturbation theory for quantum
mechanical systems is considered. A new approach to it is developed. An
explicit mechanism showing the connection between large order recursive
relations and classical euclidean equations of motion is found. Large order
asymptotics of the solution to the recursive relations is constructed. The
developed method is applicable to the excited states, as well as to the ground
state. Singular points of the obtained asymptotics of the perturbation series
for eigenfunctions and density matrices are investigated and formulas being
valid near such points are obtained.


  The CW RF test of 1.3 GHz 9-cell cavity in liquid helium bath at 2 K is a
very important key point in the cavity procurement. Some problems can be found
through the test, according which to optimized and improve the process of
cavity. Recently, Medium temperature (mid-T) furnace bake of 1.3 GHz 9-cell
cavities have been carried out at IHEP. Through the proceed of mid-T bake, the
quality factor of cavity has been greatly improved. While the excitation of the
parasitic modes in the high Q cavities CW cold test has been encountered, which
implies an error source for the cavity gradient and quality factor
determination. In order to ensure the testing accuracy of superconducting
cavity, we have improved the testing system. Finally, the parasitic mode is
completely suppressed and the CW RF cold test of high Q cavity is guaranteed.


  We propose a simple SIR model in order to investigate the impact of various
confinement strategies on a most virulent epidemic. Our approach is motivated
by the current COVID-19 pandemic. The main hypothesis is the existence of two
populations of susceptible persons, one which obeys confinement and for which
the infection rate does not exceed 1, and a population which, being non
confined for various imperatives, can be substantially more infective. The
model, initially formulated as a differential system, is discretised following
a specific procedure, the discrete system serving as an integrator for the
differential one. Our model is calibrated so as to correspond to what is
observed in the COVID-19 epidemic.
  Several conclusions can be reached, despite the very simple structure of our
model. First, it is not possible to pinpoint the genesis of the epidemic by
just analysing data from when the epidemic is in full swing. It may well turn
out that the epidemic has reached a sizeable part of the world months before it
became noticeable. Concerning the confinement scenarios, a universal feature of
all our simulations is that relaxing the lockdown constraints leads to a
rekindling of the epidemic. Thus we sought the conditions for the second
epidemic peak to be lower than the first one. This is possible in all the
scenarios considered (abrupt, progressive or stepwise exit) but typically a
progressive exit can start earlier than an abrupt one. However, by the time the
progressive exit is complete, the overall confinement times are not too
different. From our results, the most promising strategy is that of a stepwise
exit. And in fact its implementation could be quite feasible, with the major
part of the population (minus the fragile groups) exiting simultaneously but
obeying rigorous distancing constraints.


  Some of the most important compartmental systems, such as irreversible
catenary, mamillary and circular systems are symbolically simplified by the
method of exact linear lumping. A few symbolically unmanageable systems are
numerically lumped. Transformation of the qualitative properties under lumping
are also traced.


  We present the results obtained from timing and spectral studies of high mass
X-ray binary pulsar OAO 1657-415 using a Suzaku observations in 2011 September.
X-ray pulsations were detected in the light curves up to $\sim$70 keV. The
continuum spectra during the high- and low-flux regions in light curves were
well described by high energy cutoff power-law model along with a blackbody
component and iron fluorescent lines at 6.4 keV and 7.06 keV. Time resolved
spectroscopy was carried out by dividing the entire observations into 18 narrow
segments. Presence of additional dense matter at various orbital phases was
confirmed as the cause of low-flux regions in the observations. Presence of
additional matter at several orbital phases of the pulsar was interpreted as
due to the inhomogeneously distributed clumps of matter around the neutron
star. Using clumpy wind hypothesis, the physical parameters of the clumps
causing the high- and low-flux episodes in the pulsar light curve were
estimated. The equivalent width of iron emission lines was found to be
significantly large at certain orbital phases of low-flux segments. We
investigated the iron line emitting regions and suggest the existence of
neutral and ionized iron atoms in emission sites that are located within the
accretion radius.


  In the presented paper exclusive decays $B_{c}\to J/\psi KK_{S}$ and
$B_{c}\to\psi(2S)KK_{S}$ are analyzed. It is shown that contributions of the
excited $\rho$ mesons should be taken into account to describe these decays. It
is also shown that, unlike the corresponding $\tau$ lepton decays, peaks in
$m_{KK_{S}}$ distributions caused by these resonances are clearly seen and can
be easily separated. Theoretical predictions for the branching fractions of the
reactions and $m_{\psi K}$ distributions are also presented.


  Several sets of quaternionic functions are described and studied with respect
to hyperholomorphy, addition and (non commutative) multiplication, on open sets
of $\mathbb H$. The aim is to get a local function theory.


  In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.


  We investigate the optimal performance of dense sensor networks by studying
the joint source-channel coding problem. The overall goal of the sensor network
is to take measurements from an underlying random process, code and transmit
those measurement samples to a collector node in a cooperative multiple access
channel with imperfect feedback, and reconstruct the entire random process at
the collector node. We provide lower and upper bounds for the minimum
achievable expected distortion when the underlying random process is Gaussian.
In the case where the random process satisfies some general conditions, we
evaluate the lower and upper bounds explicitly and show that they are of the
same order for a wide range of sum power constraints. Thus, for these random
processes, under these sum power constraints, we determine the achievability
scheme that is order-optimal, and express the minimum achievable expected
distortion as a function of the sum power constraint.


  RRT* is one of the most widely used sampling-based algorithms for
asymptotically-optimal motion planning. This algorithm laid the foundations for
optimality in motion planning as a whole, and inspired the development of
numerous new algorithms in the field, many of which build upon RRT* itself. In
this paper, we first identify a logical gap in the optimality proof of RRT*,
which was developed in Karaman and Frazzoli (2011). Then, we present an
alternative and mathematically-rigorous proof for asymptotic optimality. Our
proof suggests that the connection radius used by RRT* should be increased from
$\gamma \left(\frac{\log n}{n}\right)^{1/d}$ to $\gamma' \left(\frac{\log
n}{n}\right)^{1/(d+1)}$ in order to account for the additional dimension of
time that dictates the samples' ordering. Here $\gamma$, $\gamma'$, are
constants, and $n$, $d$, are the number of samples and the dimension of the
problem, respectively.


  In this talk, we report our recent progresses on the Lambda(1520)
photoproduction using the effective Lagrangian approach. In addition to the
tree-level Born diagrams, we take into account the Regge-trajectories for the
possible strange-meson exchanges in the t channel. We compute the angular and
energy dependences of the production process, including polarization
observables, such as the photon-beam asymmetry and the polarization-transfer
coefficients, resulting in good qualitative agreement with current experimental
data. We also compute the K^- angle distribution function in the
Gottfried-Jackson frame, using the polarization-transfer coefficients in the z
direction.


  We investigate the impact of resonant gravitational waves on quadrupole
acoustic modes of Sun-like stars located nearby stellar black hole binary
systems (such as GW150914 and GW151226). We find that the stimulation of the
low-overtone modes by gravitational radiation can lead to sizeable photometric
amplitude variations, much larger than the predictions for amplitudes driven by
turbulent convection, which in turn are consistent with the photometric
amplitudes observed in most Sun-like stars. For accurate stellar evolution
models, using up-to-date stellar physics, we predict photometric amplitude
variations of $1$ -- $10^3$ ppm for a solar mass star located at a distance
between 1 au and 10 au from the black hole binary, and belonging to the same
multi-star system. The observation of such a phenomenon will be within the
reach of the Plato mission because telescope will observe several portions of
the Milky Way, many of which are regions of high stellar density with a
substantial mixed population of Sun-like stars and black hole binaries.


  The operating conditions defining the current data taking campaign at the
Large Hadron Collider, known as Run 3, present unparalleled challenges for the
real-time data acquisition workflow of the LHCb experiment at CERN. To address
the anticipated surge in luminosity and consequent event rate, the LHCb
experiment is transitioning to a fully software-based trigger system. This
evolution necessitated innovations in hardware configurations, software
paradigms, and algorithmic design. A significant advancement is the integration
of monotonic Lipschitz neural networks into the LHCb trigger system. These deep
learning models offer certified robustness against detector instabilities, and
the ability to encode domain-specific inductive biases. Such properties are
crucial for the inclusive heavy-flavour triggers and, most notably, for the
topological triggers designed to inclusively select $b$-hadron candidates by
exploiting the unique kinematic and decay topologies of beauty decays. This
paper describes the recent progress in integrating Lipschitz neural networks
into the topological triggers, highlighting the resulting enhanced sensitivity
to highly displaced multi-body candidates produced within the LHCb acceptance.


  In this article, we propose a resolution to the paradox of apparent
superluminal velocities for tunneling particles, by a careful treatment of
temporal observables in quantum theory and through a precise application of the
duality between particles and waves. To this end, we employ a new method for
constructing probabilities associated to quantum time measurements that
provides an explicit link between the tunneling time of particles and the
associated quantum fields. We demonstrate that the idea of faster-than-light
speeds in tunneling follows from an inadmissible use of classical reasoning in
the description of quantum systems. Our results suggest that direct
measurements of the transit time in tunneling could provide a new testing
ground for the predictions of quantum theory versus local hidden-variables
theories.


  We derive an analytical theory of the PDF of density fluctuations in
supersonic turbulence in the presence of gravity in star-forming clouds. The
theory is based on a rigorous derivation of a combination of the Navier-Stokes
continuity equations for the fluid motions and the Poisson equation for the
gravity. It extends upon previous approaches first by including gravity, second
by considering the PDF as a dynamical system, not a stationary one. We derive
the transport equations of the density PDF, characterize its evolution and
determine the density threshold above which gravity strongly affects and
eventually dominates the dynamics of turbulence. We demonstrate the occurence
of {\it two} power law tails in the PDF, with two characteristic exponents,
corresponding to two different stages in the balance between turbulence and
gravity. Another important result of this study is to provide a procedure to
relate the observed {\it column density} PDFs to the corresponding {\it volume
density} PDFs. This allows to infer, from the observation of column-densities,
various physical parameters characterizing molecular clouds, notably the virial
parameter. Furthermore, the theory offers the possibility to date the clouds in
units of ${t}_{\rm coll}$, the time since a statistically significant fraction
of the cloud started to collapse. The theoretical results and diagnostics
reproduce very well numerical simulations and observations of star-forming
clouds. The theory provides a sound theoretical foundation and quantitative
diagnostics to analyze observations or numerical simulations of star-forming
regions and to characterize the evolution of the density PDF in various regions
of molecular clouds.


  In this paper we describe our work on spin polarization in high-energy
electron storage rings which we base on the Bloch equation for the polarization
density and which aims towards the e-/e+ option of the proposed Future Circular
Collider (FCC-ee) and the proposed Circular Electron Positron Collider (CEPC).
The Bloch equation takes into account non spin-flip and spin-flip effects due
to synchrotron radiation including the spin-diffusion effects and the
Sokolov-Ternov effect with its Baier-Katkov generalization as well as the
kinetic-polarization effect. This mathematical model is an alternative to the
standard mathematical model based on the Derbenev-Kondratenko formulas. For our
numerical and analytical studies of the Bloch equation we develop an
approximation to the latter to obtain an effective Bloch equation. This is
accomplished by finding a third mathematical model based on a system of
stochastic differential equations underlying the Bloch equation and by
approximating that system via the method of averaging from perturbative ODE
theory. We also give an overview of our algorithm for numerically integrating
the effective Bloch equation. This discretizes the phase space using spectral
methods and discretizes time via the additive Runge-Kutta method which is a
high-order semi-implicit method. We also discuss the relevance of the third
mathematical model for spin tracking.


  We study the ground state and magnetic hysteresis properties of 2$d$ arrays
($L^{}_x\times L^{}_y$) of dipolar interacting magnetic nanoparticles (MNPs) by
performing micromagnetic simulations. Our primary interest is to understand the
effect of sample shape, $\Theta$- the ratio of the dipolar strength to the
anisotropy strength, and the direction of the applied field $\vec{H} =
H_{o}\hat{e}^{}_H$ on the ground state and the magnetic hysteresis in an array
of MNPs. To study the effect of shape of the sample, we have varied the aspect
ratio $A^{}_r=L^{}_y/L^{}_x$ which in turn, is found to induce shape anisotropy
in the system. Our main observations are: (a) When the dipolar interaction is
strong $(\Theta>1)$, the ground state morphology has in-plane ordering of
magnetic moments. (b) The ground state morphology has randomly oriented
magnetic moments which is robust with respect to system sizes and $A^{}_r$ for
weakly interacting MNPs ($\Theta<1$). (c) Micromagnetic simulations suggests
that the dipolar interaction decreases the coercive field $H^{}_c$. (d) The
remanence magnetization $M^{}_r$ is found to be strongly dependent not only on
the strength of dipolar interaction but also on the shape of the sample. (e)
Due to anisotropic nature of dipolar interaction, a strong effect of shape
anisotropy is observed when the field is applied along longer axis of the
sample. The dipolar interaction in such a case induces an effective
ferromagnetic coupling when the aspect ratio is very large. These results are
of vital importance in high-density recording systems, magneto-impedance
sensors, etc.


  The effects of nonmagnetic impurities on the Q=(pi,pi) spin-fluctuation
spectral weight Im chi(Q,omega) are studied within the framework of the
two-dimensional Hubbard model using the random phase approximation. In the
first part of the paper, the effects of the nonmagnetic impurities on the
magnetic susceptibility of the noninteracting (U=0) system, chi_0(Q,omega), are
calculated with the self-energy and the vertex corrections using various forms
of the effective electron-impurity interaction. Here, the range and the
strength of the impurity potential are varied as well as the concentration of
the impurities. It is shown that the main effect of dilute impurities on
chi_0(Q,omega) is to cause a weak smearing. In the second part, Im chi(Q,omega)
is calculated for the interacting system. Here, the calculations are
concentrated on the processes which involve the impurity scattering of the spin
fluctuations with finite momentum transfers. In order to make comparisons with
the experimental data on the frequency dependence of Im chi(Q,omega) in Zn
substituted YBa2Cu3O7, results are given for various values of the model
parameters.


  We discuss the analysis of data from piN elastic scattering and single pion
photo- and electroproduction. The main focus is a study of low-lying
non-strange baryon resonances. Here we concentrate on some difficulties
associated with resonance identification, in particular the Roper and higher
P11 states.


  We study the Be star $\delta$ Cen circumstellar disk using long-baseline
interferometry which is the only observing technique capable of resolving
spatially and spectroscopically objects smaller than 5 mas in the H and K b
and. We used the VLTI/AMBER instrument on January 6, 8, and 9, 2008, in the H
and K bands to complete low (35) and medium (150 0) spectral resolution
observations. We detected an oscillation in the visibility curve plotted as a
function of the spatial frequency which is a clear signat ure of a companion
around $\delta$ Cen. Our best-fit soltution infers a binary separation of 68.7
mas, a companion flux co ntribution in the K band of about 7% of the total
flux, a PA of 117.5 $\degr$, and an envelope flux around the Be primary that
contributes up to about 50 % of the total flux, in agreement with our Spectral
Energy Distribution (SED) fit. The e nvelope size is estimated to be 1.6 mas in
K but no departure from spherical symmetry is detected.


  The Infrared Spectrograph (IRS) is one of three science instruments on the
Spitzer Space Telescope. The IRS comprises four separate spectrograph modules
covering the wavelength range from 5.3 to 38micron with spectral resolutions, R
\~90 and 600, and it was optimized to take full advantage of the very low
background in the space environment. The IRS is performing at or better than
the pre-launch predictions. An autonomous target acquisition capability enables
the IRS to locate the mid-infrared centroid of a source, providing the
information so that the spacecraft can accurately offset that centroid to a
selected slit. This feature is particularly useful when taking spectra of
sources with poorly known coordinates. An automated data reduction pipeline has
been developed at the Spitzer Science Center.


  A sigma-phase Fe65.9V34.1 alloy was investigated with the AC magnetic
susceptibility as a function of temperature, frequency and external magnetic
field. An evidence was found that its magnetism shows features characteristic
of a reentrant behavior viz. two transitions: first at TC ca.312K from the
paramagnetic state into the collinear ferromagnetic one, and second at Tf
ca.302K to a mixed state (sometimes termed as a ferromagnetic re-entrant spin
glass) which, finally, at a lower temperature (TRSG ca.60K) transforms to a
state where replica symmetry is broken. The frequency dependence of Tf is lower
than that of canonical spin glasses, a feature, that in the light of a high
concentration of magnetic carriers, can be understood in terms of a weak
coupling between magnetic clusters.


  We study dynamic fluctuation effects of $YBa_2Cu_3O_{7-\delta}$ thin films in
zero field around $T_c$ by doing frequency-dependent microwave conductivity
measurements at different powers. The length scales probed in the experiments
are varied systematically allowing us to analyze data which are not affected by
the finite thickness of the films, and to observe single-parameter scaling. DC
current-voltage characteristics have also been measured to independently probe
fluctuations in the same samples. The combination of DC and microwave
measurements allows us to precisely determine critical parameters. Our results
give a dynamical scaling exponent $z=1.55\pm0.15$, which is consistent with
model E-dynamics.


  The problem of learning tree-structured Gaussian graphical models from
independent and identically distributed (i.i.d.) samples is considered. The
influence of the tree structure and the parameters of the Gaussian distribution
on the learning rate as the number of samples increases is discussed.
Specifically, the error exponent corresponding to the event that the estimated
tree structure differs from the actual unknown tree structure of the
distribution is analyzed. Finding the error exponent reduces to a least-squares
problem in the very noisy learning regime. In this regime, it is shown that the
extremal tree structure that minimizes the error exponent is the star for any
fixed set of correlation coefficients on the edges of the tree. If the
magnitudes of all the correlation coefficients are less than 0.63, it is also
shown that the tree structure that maximizes the error exponent is the Markov
chain. In other words, the star and the chain graphs represent the hardest and
the easiest structures to learn in the class of tree-structured Gaussian
graphical models. This result can also be intuitively explained by correlation
decay: pairs of nodes which are far apart, in terms of graph distance, are
unlikely to be mistaken as edges by the maximum-likelihood estimator in the
asymptotic regime.


  We summarize our current understanding of the connection between the QCD
phase line and the chemical freeze-out curve as deduced from thermal analyses
of yields of particles produced in central collisions between relativistic
nuclei.


  The general theory of motion in the vicinity of a moving quantum nodal point
(vortex) is studied in the framework of the de Broglie - Bohm trajectory method
of quantum mechanics. Using an adiabatic approximation, we find that near any
nodal point of an arbitrary wavefunction $\psi$ there is an unstable point
(called X-point) in a frame of reference moving with the nodal point. We find
general formulae for the nodal point - X-point complex as well as necessary and
sufficient conditions of validity of the adiabatic approximation. Chaos emerges
from the consecutive scattering events of the orbits with nodal point - X-point
complexes. A theoretical model is constructed yielding the local value of the
Lyapunov characteristic number in a scattering event, which scales as an
inverse power of the speed of the nodal point in the rest frame, or
proportionally to the size of the nodal point X- point complex. The results of
detailed numerical experiments with different wavefunctions possessing multiple
moving nodal points are reported. The statistics of the Lyapunov characteristic
numbers of the orbits are found and compared to the number of encounter events
of each orbit with the nodal point X-point complexes. Various phenomena
appearing at first as counter-intuitive find a straightforward explanation.


  In this paper, we consider the Markov-Chain Monte Carlo (MCMC) approach for
random sampling of combinatorial objects. The running time of such an algorithm
depends on the total mixing time of the underlying Markov chain and is unknown
in general. For some Markov chains, upper bounds on this total mixing time
exist but are too large to be applicable in practice. We try to answer the
question, whether the total mixing time is close to its upper bounds, or if
there is a significant gap between them. In doing so, we present the software
library marathon which is designed to support the analysis of MCMC based
sampling algorithms. The main application of this library is to compute
properties of so-called state graphs which represent the structure of Markov
chains. We use marathon to investigate the quality of several bounding methods
on four well-known Markov chains for sampling perfect matchings and bipartite
graph realizations. In a set of experiments, we compute the total mixing time
and several of its bounds for a large number of input instances. We find that
the upper bound gained by the famous canonical path method is several
magnitudes larger than the total mixing time and deteriorates with growing
input size. In contrast, the spectral bound is found to be a precise
approximation of the total mixing time.


  We present the methodology for, and the first results from, a new imaging
program aimed at identifying and characterizing the host galaxies of damped
Lyman-alpha absorbers (DLAs) at z>2. We target quasar sightlines with multiple
optically-thick HI absorbers and use the higher-redshift system as a "blocking
filter" (via its Lyman-limit absorption) to eliminate all far-ultraviolet (FUV)
emission from the quasar. This allows us to directly image the rest-frame FUV
continuum emission of the lower-redshift DLA, without any quasar contamination
and with no bias towards large impact parameters. We introduce a formalism
based on galaxy number counts and Bayesian statistics with which we quantify
the probability that a candidate is the DLA host galaxy. This method will allow
the identification of a bona fide sample of DLAs that are too faint to be
spectroscopically confirmed. The same formalism can be adopted to the study of
other quasar absorption line systems (e.g. MgII absorbers). We have applied
this imaging technique to two QSO sightlines. For the z~2.69 DLA towards
J073149+285449, a galaxy with impact parameter b=1.54"=11.89 kpc and implied
star formation rate (SFR) of ~5 M/yr is identified as the most reliable
candidate. In the case of the z~2.92 DLA towards J211444-005533, no likely host
is found down to a 3-sigma SFR limit of 1.4 M/yr. Studying the HI column
density as a function of the impact parameter, including 6 DLAs with known
hosts from the literature, we find evidence that the observed HI distribution
is more extended than what is generally predicted from numerical simulation.


  Understanding the power of depth in feed-forward neural networks is an
ongoing challenge in the field of deep learning theory. While current works
account for the importance of depth for the expressive power of
neural-networks, it remains an open question whether these benefits are
exploited during a gradient-based optimization process. In this work we explore
the relation between expressivity properties of deep networks and the ability
to train them efficiently using gradient-based algorithms. We give a depth
separation argument for distributions with fractal structure, showing that they
can be expressed efficiently by deep networks, but not with shallow ones. These
distributions have a natural coarse-to-fine structure, and we show that the
balance between the coarse and fine details has a crucial effect on whether the
optimization process is likely to succeed. We prove that when the distribution
is concentrated on the fine details, gradient-based algorithms are likely to
fail. Using this result we prove that, at least in some distributions, the
success of learning deep networks depends on whether the distribution can be
well approximated by shallower networks, and we conjecture that this property
holds in general.


  We investigate the impact of modified theories of gravity on the kinetic
Sunyaev-Zeldovich (kSZ) effect of the cosmic microwave background. We focus on
a specific class of $f(R)$ models of gravity and compare their predictions for
the kSZ power spectrum to that of the $\Lambda$CDM model. We use a publicly
available modified version of Halofit to properly include the nonlinear matter
power spectrum of $f(R)$ in the modeling of the kSZ signal. We find that the
well-known modifications of the growth rate of structure in $f(R)$ can indeed
induce sizable changes in the kSZ signal, which are more significant than the
changes induced by modifications of the expansion history. We discuss prospects
of using the kSZ signal as a complementary probe of modified gravity, giving an
overview of assumptions and possible caveats in the modeling.


  Event classification is a common task in gamma-ray astrophysics. It can be
treated with rapidly-advancing machine learning algorithms, which have the
potential to outperform traditional analysis methods. However, a major
challenge for machine learning models is extracting reliably labelled training
examples from real data. Citizen science offers a promising approach to tackle
this challenge.
  We present "Muon Hunter", a citizen science project hosted on the Zooniverse
platform, where VERITAS data are classified multiple times by individual users
in order to select and parameterize muon events, a product from cosmic ray
induced showers. We use this dataset to train and validate a convolutional
neural-network model to identify muon events for use in monitoring and
calibration. The results of this work and our experience of using the
Zooniverse are presented.


  The Einstein equivalence principle is certainly a key element in the
development of new enhanced theories of gravity. Although being an important
building block in Einstein's general relativity, theoretically predicted
violations of its validity are a main feature in alternative, nonmetric
gravitation theories if they are able to incorporate quantum mechanical
principles.
  We have investigated the metric-affine gauge theory of gravity which predict
that a gravitational field singles out an orthogonal pair of polarization
states of light that propagate with different phase velocities. This
gravity-induced birefringence implies that propagation through a gravitational
field can alter the polarization of light and, so, violates the Einstein
equivalence principle. We use solar and white dwarf polarimetric data to
constrain birefringence induced by the gravitational field of these objects and
set limits on coupling constants required by such theories.


  Eikonal tomography has become a popular methodology for deriving phase
velocity maps from surface wave phase delay measurements. Its high efficiency
makes it popular for handling datasets deriving from large-N arrays, in
particular in the ambient-noise tomography setting. However, the results of
eikonal tomography are crucially dependent on the way in which phase delay
measurements are interpolated, a point which has not been thoroughly
investigated. In this work, I provide a rigorous formulation for eikonal
tomography using Gaussian processes (GPs) to interpolate phase delay
measurements, including uncertainties. GPs allow the posterior phase delay
gradient to be analytically derived. From the phase delay gradient, an
excellent approximate solution for phase velocities can be obtained using the
saddlepoint method. The result is a fully Bayesian result for phase velocities
of surface waves, incorporating the nonlinear wavefront bending inherent in
eikonal tomography, with no sampling required. The results of this analysis
imply that the uncertainties reported for eikonal tomography are often
underestimated.


  We show that if a closed atoroidal 3-manifold M contains a genuine
lamination, then it is group negatively curved in the sense of Gromov.
Specifically, we exploit the structure of the non-product complementary regions
of the genuine lamination and then apply the first author's Ubiquity Theorem to
show that M satisfies a linear isoperimetric inequality.


  The Variogram Analysis of Response Surfaces (VARS) has been proposed by
Razavi and Gupta as a new comprehensive framework in sensitivity analysis.
According to these authors, VARS provides a more intuitive notion of
sensitivity and it is much more computationally efficient than Sobol' indices.
Here we review these arguments and critically compare the performance of
VARS-TO, for total-order index, against the total-order Jansen estimator. We
argue that, unlike classic variance-based methods, VARS lacks a clear
definition of what an "important" factor is, and show that the alleged
computational superiority of VARS does not withstand scrutiny. We conclude that
while VARS enriches the spectrum of existing methods for sensitivity analysis,
especially for a diagnostic use of mathematical models, it complements rather
than substitutes classic estimators used in variance-based sensitivity
analysis.


  We study magnetic and charge order in the electron-doped high-$T_c$ cuprates
based on the one-band Hubbard model with onsite ($U$) and nearest-neighbor
$(V)$ interactions. To investigate the interplay between the orders, we employ
the Kotliar-Ruckenstein slave-boson method and analyze fluctuations descending
from an antiferromagnetic parent state. Our analysis reveals incommensurate
charge order whose ordering vector matches the doping-dependence of resonant
inelastic x-ray scattering (RIXS) measurements in Nd$_{2-x}$Ce$_x$CuO$_4$
(NCCO). From our calculations of paramagnon dispersion as well as dynamical
charge and spin structure factors, we reproduce all qualitative features of the
RIXS signal.


  This paper establishes the precise asymptotic behavior, as time $t$ tends to
infinity, for nontrivial, decaying solutions of genuinely nonlinear systems of
ordinary differential equations. The lowest order term in these systems, when
the spatial variables are small, is not linear, but rather positively
homogeneous of a degree greater than one. We prove that the solution behaves
like $\xi t^{-p}$, as $t\to\infty$, for a nonzero vector $\xi$ and an explicit
number $p>0$.


  Gesture recognition based on surface electromyography (sEMG) has been gaining
importance in many 3D Interactive Scenes. However, sEMG is easily influenced by
various forms of noise in real-world environments, leading to challenges in
providing long-term stable interactions through sEMG. Existing methods often
struggle to enhance model noise resilience through various predefined data
augmentation techniques. In this work, we revisit the problem from a short term
enhancement perspective to improve precision and robustness against various
common noisy scenarios with learnable denoise using sEMG intrinsic pattern
information and sliding-window attention. We propose a Short Term Enhancement
Module(STEM) which can be easily integrated with various models. STEM offers
several benefits: 1) Learnable denoise, enabling noise reduction without manual
data augmentation; 2) Scalability, adaptable to various models; and 3)
Cost-effectiveness, achieving short-term enhancement through minimal
weight-sharing in an efficient attention mechanism. In particular, we
incorporate STEM into a transformer, creating the Short Term Enhanced
Transformer (STET). Compared with best-competing approaches, the impact of
noise on STET is reduced by more than 20%. We also report promising results on
both classification and regression datasets and demonstrate that STEM
generalizes across different gesture recognition tasks.


  It has been recently reported that elastic waves induced by nanosecond light
pulses can be used to drive nano-motion of micro-objects on frictional solid
interfaces, a challenging task for traditional techniques using tiny optical
force. In this technique, the main physical quantities/parameters involved are:
temporal width and energy of light pulses, thermal heating and cooling time,
friction force and elastic waves. Despite a few experimental observations based
on micro-fiber systems, a microscopic theory, which reveals how these
quantities collaboratively enable motion of the micro-objects and derives what
the underlying manipulation principles emerge, is absent. In this article, a
comprehensive theoretical analysis--centralized around the above listed
physical quantities, and illuminated by a single-friction-point model in
conjunction with numerical simulations--is established to pedagogically clarify
the physics. Our results reveal the two essential factors in this technique:
(1) the use of short light pulses for rapid thermal expansion overwhelming
friction resistance and (2) the timescale asymmetry in thermal heating and
cooling for accumulating a net sliding distance. Moreover, we examine the
effects of spatially distributed friction beyond the single-friction-point
consideration, and show "tug-of-war"-like friction stretching in the driving
process. Given these insights, we positively predict that this
elastic-wave-based manipulation principle could be directly translated to
micro/nano-scale optical waveguides on optical chips, and propose a practical
design. We wish that these results offer theoretical guidelines for ongoing
efforts of optical manipulation on solid interfaces with light-induced elastic
waves.


  We consider positive solutions of the problem \begin{equation}
\left\{\begin{array}{l}-\mbox{div}(x_{n}^{a}\nabla u)=0\qquad
\mbox{in}\;\;\mathbb{R}_+^n,\\ \frac{\partial u}{\partial \nu^a}=u^{q} \qquad
\mbox{on}\;\;\partial \mathbb{R}_+^n,\\ \end{array} \right. \end{equation}
where $a\in (-1,0)\cup(0,1)$, $q>1$ and $\frac{\partial u}{\partial
\nu^a}:=-\lim_{x_{n}\rightarrow 0^+}x_{n}^{a}\frac{\partial u}{\partial
x_{n}}$.
  We obtain some qualitative properties of positive axially symmetric solutions
in
  $n\geq3$ for the case $a\in (-1,0)$ under the condition
$q\geq\frac{n-a}{n+a-2}$. In particular, we establish the asymptotic expansion
of positive axially symmetric solutions.


  Coronary angioplasty with stent implantation is the most frequently used
interventional treatment for coronary artery disease. However, reocclusion
within the stent, referred to as in-stent restenosis, occurs in up to 10% of
lesions. It is widely accepted that mechanical loads on the vessel wall
strongly affect adaptive and maladaptive mechanisms. Yet, the role of
procedural and lesion-specific influence on restenosis risk remains
understudied. Computational modeling of the stenting procedure can provide new
mechanistic insights, such as local stresses, that play a significant role in
tissue growth and remodeling. Previous simulation studies often featured
simplified artery and stent geometries and cannot be applied to real-world
examples. Realistic simulations were computationally expensive since they
featured fully resolved stenting device models. The aim of this work is to
develop and present a mixed-dimensional formulation to simulate the
patient-specific stenting procedure with a reduced-dimensional beam model for
the stent and 3D models for the artery. In addition to presenting the numerical
approach, we apply it to realistic cases to study the intervention's mechanical
effect on the artery and correlate the findings with potential high-risk
locations for in-stent restenosis. We found that high artery wall stresses
develop during the coronary intervention in severely stenosed areas and at the
stent boundaries. Herewith, we lay the groundwork for further studies towards
preventing in-stent restenosis after coronary angioplasty.


  The two-body decay rate of a weakly decaying particle (such as the kaon) is
shown to be proportional to the square of a well-defined transition matrix
element in finite volume. Contrary to the physical amplitude, the latter can be
extracted from finite-volume correlation functions in euclidean space without
analytic continuation. The K -> pi pi transitions and other non-leptonic decays
thus become accessible to established numerical techniques in lattice QCD.


  In this paper, we perform an experimental study of optimal recombination
operator for makespan minimization problem on single machine with
sequence-dependent setup times ($1|s_{vu}|C_{\max}$). The computational
experiment on benchmark problems from TSPLIB library indicates practical
applicability of optimal recombination in crossover operator of genetic
algorithm for $1|s_{vu}|C_{\max}$.


  In this paper we apply Kraichnan's direct interaction approximation, which is
a one loop perturbation expansion, to magnetohydrodynamic turbulence. By
substituting the energy spectra both from kolmogorov-like MHD turbulence
phenomenology and a generalization of Dobrowolny et al.'s model we obtain
Kolmogorov's and Kraichnan's constant for MHD turbulence. We find that the
constants depend of the Alfv\'en ratio and normalized cross helicity; the
dependence has been studied here. We also demonstrate the inverse cascade of
magnetic energy for Kolmogorov-like models. Our results are in general
agreement with the earlier simulation results except for large normalized cross
helicity.


  We investigate the effect of the cosmological constant on the angular size of
a black hole shadow. It is known that the accelerated expansion which is
created by the cosmological constant changes the angular size of the black hole
shadow for static observers. However, the shadow size must be calculated for
the appropriate cosmological observes. We calculate the angular size of the
shadow measured by cosmological comoving observers by projecting the shadow
angle to this observer rest frame. We show that the shadow size tends to zero
as the observer approaches the cosmological horizon. We estimate the angular
size of the shadow for a typical supermassive black hole, e.g M87. It is found
that the angular size of the shadow for cosmological observers and static
observers is approximately the same at these scales of mass and distance. We
present a catalog of supermassive black holes and calculate the effect of the
cosmological constant on their shadow size and find that the effect could be
$3\; precent$ for distant known sources like the Phoenix Cluster supermassive
black hole.


  In this thesis, we introduce a new cohomology theory associated to a Lie
2-algebras and a new cohomology theory associated to a Lie 2-group. These
cohomology theories are shown to extend the classical cohomology theories of
Lie algebras and Lie groups in that their second groups classify extensions. We
use this fact together with an adapted van Est map to prove the integrability
of Lie 2-algebras anew.


  In quantum electrodynamics with fermions f = e,\mu..., knowledge of the
vacuum polarization spectral function determined from the tree level e^+e^- ->
f^+f^- cross sections, together with a single low energy measurement of the
fine structure constant \alpha, enables the construction of the one-loop
effective charge \alpha_eff(q^2) for all q^2. It is shown how an identical
procedure can be followed in the electroweak sector of the Standard Model to
construct three gauge-, scale- and scheme-independent one-loop electroweak
effective charges and an effective weak mixing angle from the tree level e^+e^-
-> W^+W^-, ZH and e^+\nu_e -> W^+Z, W^+\gamma, W^+H differential cross
sections, together with three low energy measurements, which may be chosen to
be \alpha and the masses of the W and Z bosons. It is found that the
corresponding proper self-energy-like functions thus constructed are identical
to those obtained in the pinch technique framework. In this way, it is shown
how the concept of effective charges in the electroweak Standard Model is as
well-defined and unique as in quantum electrodynamics.


  The production of runaway massive binaries offers key insights into the
evolution of close binary stars and open clusters. The stars HD 14633 and HD
15137 are rare examples of such runaway systems, and in this work we
investigate the mechanism by which they were ejected from their parent open
cluster, NGC 654. We discuss observational characteristics that can be used to
distinguish supernova ejected systems from those ejected by dynamical
interactions, and we present the results of a new radio pulsar search of these
systems as well as estimates of their predicted X-ray flux assuming that each
binary contains a compact object. Since neither pulsars nor X-ray emission are
observed in these systems, we cannot conclude that these binaries contain
compact companions. We also consider whether they may have been ejected by
dynamical interactions in the dense environment where they formed, and our
simulations of four-body interactions suggest that a dynamical origin is
possible but unlikely. We recommend further X-ray observations that will
conclusively identify whether HD 14633 or HD 15137 contain neutron stars.


  The bacterial transcription factor LacI loops DNA by binding to two separate
locations on the DNA simultaneously. Despite being one of the best-studied
model systems for transcriptional regulation, the number and conformations of
loop structures accessible to LacI remain unclear, though the importance of
multiple co-existing loops has been implicated in interactions between LacI and
other cellular regulators of gene expression. To probe this issue, we have
developed a new analysis method for tethered particle motion, a versatile and
commonly-used in vitro single-molecule technique. Our method, vbTPM, performs
variational Bayesian inference in hidden Markov models. It learns the number of
distinct states (i.e., DNA-protein conformations) directly from tethered
particle motion data with better resolution than existing methods, while easily
correcting for common experimental artifacts. Studying short (roughly 100 bp)
LacI-mediated loops, we provide evidence for three distinct loop structures,
more than previously reported in single-molecule studies. Moreover, our results
confirm that changes in LacI conformation and DNA binding topology both
contribute to the repertoire of LacI-mediated loops formed in vitro, and
provide qualitatively new input for models of looping and transcriptional
regulation. We expect vbTPM to be broadly useful for probing complex
protein-nucleic acid interactions.


  In this article we study the geodesic motion of test particles and light in
the five-dimensional Myers-Perry-anti de Sitter spacetime. We derive the
equations of motion and present their solutions in terms of the Weierstra{\ss}
$\wp$-, $\sigma$- and $\zeta$-functions. With the help of parametric diagrams
and effective potentials we analyze the geodesic motion and give a list of all
possible orbit types for timelike, null and spacelike geodesics. We plot
examples of the orbits and take a look at the photon region in five dimensions.
Furthermore we study spacelike geodesics and their relation to the AdS/CFT
correspondence.


  Using an effective potential method, a replica formulism is set up for
describing supercooled liquids near their glass transition. The resulting
potential is equivalent to that for an Ising spin glass in a magnetic field.
Results taken from the droplet picture of spin glasses are then used to provide
an explanation of the main features of fragile glasses.


  The various non-linear transformations incurred by the rays in an optical
system can be modelled by matrix products up to any desired order of
approximation. Mathematica software has been used to find the appropriate
matrix coefficients for the straight path transformation and for the
transformations induced by conical surfaces, both direction change and position
offset. The same software package was programmed to model optical systems in
seventh-order. A Petzval lens was used to exemplify the modelling power of the
program.


  For the first time, we obtained the high-resolution (R=15000 and 60000)
optical spectra for the extremely luminous star No.12, associated with the
IR-source IRAS20308+4104, a member of the CygOB2 association. We have found
about 200 spectral features in range 4552-7939AA, including the interstellar
NaI, KI lines and numerous DIBs, which are the strongest absorption lines in
the spectrum, along with the HeI, CII, and SiII lines. A two-dimensional
spectral classification indicates that the spectral type is B5+/-0.5 Ia+. Our
analysis of the Vr data shows the presence of a Vr gradient in the stellar
atmosphere, caused by the infall of matter onto the star. The strong Halpha
emission displays broad Thompson wings and time-variable core absorption,
providing evidence that the stellar wind is inhomogeneous, and a slightly
blue-shifted PCyg type absorption profile. We concluded that the wind is
variable in time.


  We construct explicit approximating nets for crossed products of C*-algebras
by actions of discrete quantum groups. This implies that C*-algebraic
approximation properties such as nuclearity, exactness or completely bounded
approximation property are preserved by taking crossed products by actions of
amenable discrete quantum groups. We also show that the noncommutative
topological entropy of a transformation commuting with the quantum group action
does not change when we pass to the canonical extension to the crossed product.
Both these results are extended to twisted crossed products via a stabilisation
trick.


  The non-rigid registration between CT data and ultrasonic images of liver can
facilitate the diagnosis and treatment, which has been widely studied in recent
years. To improve the registration accuracy of the Demons model on the
non-rigid registration between 3D CT liver data and 2D ultrasonic images, a
novel boundary extraction and enhancement method based on radial directional
local intuitionistic fuzzy entropy in the polar coordinates has been put
forward, and a new registration workflow has been provided. Experiments show
that our method can acquire high-accuracy registration results. Experiments
also show that the accuracy of the results of our method is higher than that of
the original Demons method and the Demons method using simulated ultrasonic
image by Field II. The operation time of our registration workflow is about 30
seconds, and it can be used in the surgery.


  The kinetics of two (2D) and three (3D) dimensional diffusion-assisted
escaping of Brownian particles from a potential well in the presence of an
external force is analyzed in detail. The kinetics is studied within the
two-state model (TSM) proposed for processes in the absence of external force.
The generalized variant of this model, taking into account the force effect, is
proposed which is shown to be quite accurate for some shapes of the well both
for 2D and 3D processes. Within the generalized TSM simple expressions for the
well depopulation kinetics and, in particular, for the escape rate are
obtained. The effect of the force ($F$) is shown to manifest itself in the
escape rate dependence on the only parameter $\varphi = Fa/(2k_b T)$, where $a$
is the Onsager radius of the attractive part of the well $U(r)$, defined by the
relation $|U(a)| \approx k_b T$. The limiting behavior of this dependence in
the cases of weak and strong force is studied in detail both in 2D and 3D
processes. Some applications of obtained results to the analysis of experiments
are briefly discussed.


  The variation of the dimensionless fundamental physical constant mu = m_p/m_e
(the proton to electron mass ratio) can be constrained via observation of Lyman
and Werner lines of molecular hydrogen in the spectra of damped Lyman alpha
systems (DLAs) in the line of sight to distant QSOs. Drawing on VLT-UVES high
resolution data sets of QSO 0347-383 and its DLA obtained in 2009 our analysis
yields dmu/mu = (4.3 +/- 7.2) * 10^-6 at z_abs =3.025. We apply corrections for
the observed offsets between discrete spectra and for the first time we find
indications for inter-order distortions. Current analyses tend to underestimate
the impact of systematic errors. Based on the scatter of the measured redshifts
and the corresponding low significance of the redshift-sensitivity correlation
we estimate the limit of accuracy of line position measurements to about 220
m/s, consisting of roughly 150 m/s due to the uncertainty of the absorption
line fit and about 150 m/s allocated to systematics related to instrumentation
and calibration.


  The widespread adoption of deep neural networks in machine learning calls for
an objective quantification of esoteric trust. In this paper we propose
GradTrust, a classification trust measure for large-scale neural networks at
inference. The proposed method utilizes variance of counterfactual gradients,
i.e. the required changes in the network parameters if the label were
different. We show that GradTrust is superior to existing techniques for
detecting misprediction rates on $50000$ images from ImageNet validation
dataset. Depending on the network, GradTrust detects images where either the
ground truth is incorrect or ambiguous, or the classes are co-occurring. We
extend GradTrust to Video Action Recognition on Kinetics-400 dataset. We
showcase results on $14$ architectures pretrained on ImageNet and $5$
architectures pretrained on Kinetics-400. We observe the following: (i) simple
methodologies like negative log likelihood and margin classifiers outperform
state-of-the-art uncertainty and out-of-distribution detection techniques for
misprediction rates, and (ii) the proposed GradTrust is in the Top-2 performing
methods on $37$ of the considered $38$ experimental modalities. The code is
available at: https://github.com/olivesgatech/GradTrust


  We investigate the specific heat $C_V$ at constant volume and effects of
uniaxial anisotropy of a $p$-wave attractive interaction in the normal state of
an ultracold Fermi gas. Within the framework of the strong-coupling theory
developed by Nozi\`eres and Schmitt-Rink, we evaluate this thermodynamic
quantity as a function of temperature, in the whole interaction regime. While
the uniaxial anisotropy is not crucial for $C_V$ in the weak-coupling regime,
$C_V$ is found to be sensitive to the uniaxial anisotropy in the
strong-coupling regime. This originates from the population imbalance among
$p_i$-wave molecules ($i=x,y,z$), indicating that the specific heat is a useful
observable to see which kinds of $p$-wave molecules dominantly exist in the
strong-coupling regime when the $p$-wave interaction has uniaxial anisotropy.
Using this strong point, we classify the strong-coupling regime into some
characteristic regions. Since a $p$-wave pairing interaction with uniaxial
anisotropy has been discovered in a $^{40}$K Fermi gas, our results would be
useful in considering strong-coupling properties of a $p$-wave interacting
Fermi gas, when the interaction is uniaxially anisotropic.


  We do not have a final answer to the question of why galaxies choose a
particular internal mass distribution. Here we examine whether the distribution
is set by thermodynamic equilibrium (TE). Traditionally, TE is discarded for a
number of reasons including the inefficiency of two-body collisions to
thermalize the mass distribution in a Hubble time, and the fact that the mass
distribution maximizing the classical Boltzmann-Gibbs entropy is unphysical.
These arguments are questionable. In particular, when the Tsallis entropy that
describes self-gravitating systems is used to define TE, the mass distributions
that result (i.e., the polytropes) are physically sensible. This work spells
out this and other arguments for TE, and presents the polytropes and their
properties. It puts forward empirical evidence for the mass distribution
observed in galaxies to be consistent with polytropes. It compares polytropes
with Sersic functions and it shows how the DM halos resulting from cosmological
numerical simulations become polytropes when efficient collisions are allowed.
It also discusses pathways to thermalization bypassing two-body collisions. It
finally outlines future developments including deciphering whether or not DM
particles collide efficiently.


  The training of autonomous agents often requires expensive and unsafe
trial-and-error interactions with the environment. Nowadays several data sets
containing recorded experiences of intelligent agents performing various tasks,
spanning from the control of unmanned vehicles to human-robot interaction and
medical applications are accessible on the internet. With the intention of
limiting the costs of the learning procedure it is convenient to exploit the
information that is already available rather than collecting new data.
Nevertheless, the incapability to augment the batch can lead the autonomous
agents to develop far from optimal behaviours when the sampled experiences do
not allow for a good estimate of the true distribution of the environment.
Offline learning is the area of machine learning concerned with efficiently
obtaining an optimal policy with a batch of previously collected experiences
without further interaction with the environment. In this paper we adumbrate
the ideas motivating the development of the state-of-the-art offline learning
baselines. The listed methods consist in the introduction of epistemic
uncertainty dependent constraints during the classical resolution of a Markov
Decision Process, with and without function approximators, that aims to
alleviate the bad effects of the distributional mismatch between the available
samples and real world. We provide comments on the practical utility of the
theoretical bounds that justify the application of these algorithms and suggest
the utilization of Generative Adversarial Networks to estimate the
distributional shift that affects all of the proposed model-free and
model-based approaches.


  Significant advances were made in recent years on the global evolution
problem for self-gravitating massive matter in the small-perturbative regime
close to Minkowski spacetime. To study the coupling between a Klein-Gordon
equation and Einstein's field equations, we introduced the
``Euclidean-hyperboloidal foliation method'', which is based on the
construction of a spacetime foliation adapted to the derivation of sharp decay
estimates for wave and Klein-Gordon equations in a curved spacetime. We give
here an outline of this method, together with a full proof for a
wave-Klein-Gordon model which retains some main challenges arising with the
Einstein-matter system.


  We study effects of viscous fluid coupled with dark matter in our universe.
We consider bulk viscosity in the cosmic fluid and we suppose the existence of
a coupling between fluid and dark matter, in order to reproduce a stable de
Sitter universe protected against future-time singularities. More general
inhomogeneous fluids are studied related to future singularities.


  Organic compounds are always promising candidates of superconductors with
high transition temperatures. We examine this proposal by choosing
2,2$'$-bipyridine solely composed by C, H, and N atoms. The presence of
Meissner effect with a transition temperature of 7.2 K in this material upon
potassium doping is demonstrated by the $dc$ magnetic susceptibility
measurements. The real part of the $ac$ susceptibility exhibits the same
transition temperature as that in $dc$ magnetization, and a sharp peak appeared
in the imaginary part indicates the formation of the weakly linked
superconducting vortex current. The occurence of superconductivity is further
supported by the resistance drop at the transition together with its
suppression by the applied magnetic fields. The superconducting phase is
identified to be K$_3$-2,2$'$-bipyridine from the analysis of Raman scattering
spectra. This work not only opens an encouraging window for finding
superconductivity after optoelectronics in 2,2$'$-bipyridine-based materials
but also offers an example to realize superconductivity from conducting
polymers and their derivatives.


  Object detection, for the most part, has been formulated in the euclidean
space, where euclidean or spherical geodesic distances measure the similarity
of an image region to an object class prototype. In this work, we study whether
a hyperbolic geometry better matches the underlying structure of the object
classification space. We incorporate a hyperbolic classifier in two-stage,
keypoint-based, and transformer-based object detection architectures and
evaluate them on large-scale, long-tailed, and zero-shot object detection
benchmarks. In our extensive experimental evaluations, we observe categorical
class hierarchies emerging in the structure of the classification space,
resulting in lower classification errors and boosting the overall object
detection performance.


  Gauge-invariant phase difference and critical currents of cross-type
Josephson junctions with thin and narrow superconducting strips exposed to
three-dimensional magnetic fields are theoretically investigated. When a
sandwich-type Josephson junction in the xy plane is exposed to parallel
magnetic fields Hx and Hy, the phase difference linearly depends on the spatial
coordinates, x and y, and the critical currents exhibit the standard
Fraunhofer-type magnetic interference. The perpendicular field Hz, on the other
hand, nonlinearly modulates the distribution of the phase difference and the
critical currents as the functions of the oblique field exhibit anomalous
magnetic interference. We obtain simple analytical expressions for critical
currents of small cross-type junctions by neglecting the effects of self-field
and trapped vortices. The resulting dc critical currents show anomalous and
diverse interference patterns depending on the parallel and perpendicular
magnetic fields


  We study $A_{\infty}$-structures extending the natural algebra structure on
the cohomology of $\oplus_n L^n$, where $L$ is a very ample line bundle on a
projective $d$-dimensional variety $X$ such that $H^i(X,L^n)=0$ for $0<i<d$ and
all $n$. We prove that there exists a unique such nontrivial
$A_{\infty}$-structure up to homotopy and rescaling. In the case when $X$ is a
curve we also compute the group of self-homotopies of this
$A_{\infty}$-structure.


  Tourism industry has grown tremendously in the previous several decades.
Despite its global impact, there still remain a number of open questions
related to better understanding of tourists and their habits. In this work we
analyze the largest data set of travel receipts considered thus far, and focus
on exploring and modeling booking behavior of online customers. We extract
useful, actionable insights into the booking behavior, and tackle the task of
predicting the booking time. The presented results can be directly used to
improve booking experience of customers and optimize targeting campaigns of
travel operators.


  In the evolving landscape of clinical informatics, the integration and
utilization of software tools developed through governmental funding represent
a pivotal advancement in research and application. However, the dispersion of
these tools across various repositories, with no centralized knowledge base,
poses significant challenges to leveraging their full potential. This study
introduces an automated methodology to bridge this gap by systematically
extracting GitHub repository URLs from academic papers indexed in arXiv,
focusing on the field of clinical informatics. Our approach encompasses
querying the arXiv API for relevant papers, cleaning extracted GitHub URLs,
fetching comprehensive repository information via the GitHub API, and analyzing
repository maturity based on defined metrics such as stars, forks, open issues,
and contributors. The process is designed to be robust, incorporating error
handling and rate limiting to ensure compliance with API constraints.
Preliminary findings demonstrate the efficacy of this methodology in compiling
a centralized knowledge base of NIH-funded software tools, laying the
groundwork for an enriched understanding and utilization of these resources
within the clinical informatics community. We propose the future integration of
Large Language Models (LLMs) to generate concise summaries and evaluations of
the tools. This approach facilitates the discovery and assessment of clinical
informatics tools and also enables ongoing monitoring of new and actively
updated repositories, revolutionizing how researchers access and leverage
federally funded software. The implications of this study extend beyond
simplification of access to valuable resources; it proposes a scalable model
for the dynamic aggregation and evaluation of scientific software, encouraging
more collaborative, transparent, and efficient research practices in clinical
informatics and beyond.


  Recent numerical simulations of magnetic reconnection in two dimensions have
shown that, when the resistivity is strongly localized, the reconnection region
develops a Petschek-like structure, with the width of the inner diffusion
region being of the order of the resistivity localization scale. In this paper,
we combine this fact with a realistic model for locally-enhanced anomalous
resistivity generated by current-driven microturbulence. The result is a
qualitative model of the reconnection layer where the size of Petschek's
diffusion region and, therefore, the final reconnection rate are determined
self-consistently in terms of the main parameters of the functional dependence
of anomalous resistivity on the electric current density. We then consider
anomalous resistivity due to ion-acoustic turbulence as a particular case. This
enables us to express the reconnection region's parameters directly in terms of
the basic parameters of the plasma. Finally, we apply this reconnection model
to solar flares and obtain specific predictions for typical reconnection times,
which are very consistent with observations.


  Single slepton production in association with a top quark at the CERN Large
Hadron Collider (LHC) is one of the important processes in probing the R-parity
violation couplings. We calculate the QCD next-to-leading order (NLO)
corrections to the $pp \to t\slep^{-}(\bar{t}\slep^{+})+X$ process at the LHC
and discuss the impacts of the QCD corrections on kinematic distributions. We
investigate the dependence of the leading order (LO) and the NLO QCD corrected
integrated cross section on the factorization/renormalization energy scale,
slepton, stop-quark and gluino masses. We find that the uncertainty of the LO
cross section due to the energy scale is obviously improved by the NLO QCD
corrections, and the exclusive jet event selection scheme keeps the convergence
of the perturbative series better than the inclusive scheme. The results show
that the polarization asymmetry of the top-quark will be reduced by the NLO QCD
corrections, and the QCD corrections generally increase with the increment of
the $\tilde{t}_1$ or $\tilde{g}$ mass value.


  A decomposition of identity is given as a complex integral over the coherent
states associated with a class of shape-invariant self-similar potentials.
There is a remarkable connection between these coherent states and Ramanujan's
integral extension of the beta function.


  The paper addresses the challenge of constructing conforming finite element
spaces for high-order differential operators in high dimensions, with a focus
on the curl div operator in three dimensions. Tangential-normal continuity is
introduced in order to develop distributional finite element curl div
complexes. The spaces constructed are applied to discretize a quad curl
problem, demonstrating optimal order of convergence. Furthermore, a
hybridization technique is proposed, demonstrating its equivalence to
nonconforming finite elements and weak Galerkin methods.


  Electromagnetic (EM) composites have stimulated tremendous fundamental and
practical interests owing to their flexible electromagnetic properties and
extensive potential engineering applications. Hence, it is necessary to
systematically understand the physical mechanisms and design principles
controlling EM composites. In this tutorial, we first provide an overview of
the basic theory of electromagnetism about electromagnetic constitutive
parameters that can represent the electromagnetic properties of materials. We
show how this corpus allows a consistent construction of effective medium
theories and allows for numerical simulation of EM composites to deal with
structure-property relationships. We then discuss the influence of spatial
dispersion of shaped inclusions in the material medium on the EM properties of
composites, which has not been systematically illustrated in the context of
this interdisciplinary topic. Next, artificial composites or metamaterials with
peculiar properties not readily available in nature are highlighted with
particular emphasis on the control of the EM interaction with composites. We
conclude by discussing appropriate methods of electromagnetic measurement and
practical aspects for implementing composites for specific applications are
described. Overall, this tutorial will serve the purpose of introducing the
basics and applications of electromagnetic composites to newcomers in this
field. It is also anticipated that researchers from different backgrounds
including materials science, optics, and electrical engineering can communicate
to each other with the same language when dealing with this interdisciplinary
subject and further push forward this advancement from fundamental science to
technological applications.


  In machine learning approach to image denoising a network is trained to
recover a clean image from a noisy one. In this paper a novel structure is
proposed based on training multiple specialized networks as opposed to existing
structures that are base on a single network. The proposed model is an
alternative for training a very deep network to avoid issues like vanishing or
exploding gradient. By dividing a very deep network into two smaller networks
the same number of learnable parameters will be available, but two smaller
networks should be trained which are easier to train. Over smoothing and waxy
artifacts are major problems with existing methods; because the network tries
to keep the Mean Square Error (MSE) low for general structures and details,
which leads to overlooking of details. This problem is more severe in the
presence of strong noise. To reduce this problem, in the proposed structure,
the image is decomposed into its low and high frequency components and each
component is used to train a separate denoising convolutional neural network.
One network is specialized to reconstruct the general structure of the image
and the other one is specialized to reconstruct the details. Results of the
proposed method show higher peak signal to noise ratio (PSNR), and structural
similarity index (SSIM) compared to a popular state of the art denoising method
in the presence of strong noises.


  We have investigated the non-quasi-static (NQS) effects in graphene
field-effect transistors (GFETs), which are relevant for GFET operation at high
frequencies as a result of significant carrier inertia. A small-signal NQS
model is derived from the analytical solution of drift-diffusion equation
coupled with the continuity equation, which can be expressed in terms of
modified Bessel functions of the first kind. The NQS model can be conveniently
simplified to provide an equivalent circuit of lumped elements ready to be used
in standard circuit simulators. Taking into account only first-order NQS
effects, accurate GFET based circuit simulations up to several times the
cut-off frequency (fT) can be performed. Notably, it reduces to the
quasi-static (QS) approach when the operation frequency is below ~fT/4. To
validate the NQS model, we have compared its outcome against simulations based
on a multisegment approach consisting of breaking down the channel length in N
equal segments described by the QS model each one.


  In recent years, the issue of energy consumption in high performance
computing (HPC) systems has attracted a great deal of attention. In response to
this, many energy-aware algorithms have been developed in different layers of
HPC systems, including the hardware layer, service layer and system layer.
These algorithms are of two types: first, algorithms which directly try to
improve the energy by tweaking frequency operation or scheduling algorithms;
and second, algorithms which focus on improving the performance of the system,
with the assumption that efficient running of a system may indirectly save more
energy.
  In this thesis, we develop algorithms in both layers. First, we introduce
three algorithms to directly improve the energy of scheduled tasks at the
hardware level by using Dynamic Voltage Frequency Scaling (DVFS). Second, we
propose two algorithms for modelling and resource provisioning of MapReduce
applications (a well-known parametric distributed framework currently used by
Google, Yahoo, Facebook and LinkedIn) based on its configuration parameters.
Certainly, estimating the performance (e.g., execution time or CPU clock ticks)
of a MapReduce application can be later used for smart scheduling of such
applications in clouds or clusters.
  To evaluate the algorithms, we have conducted extensive simulation and real
experiments on a 5-node physical cluster with up to 25 virtual nodes, using
both synthetic and real world applications. Also, the proposed new algorithms
are compared with existing algorithms by experimentation, and the experimental
results reveal new information on the performance of these algorithms, as well
as on the properties of MapReduce and DVFS. In the end, three open problems are
revealed by the experimental observations, and their importance is explained.


  Determining the number G of components in a finite mixture distribution is an
important and difficult inference issue. This is a most important question,
because statistical inference about the resulting model is highly sensitive to
the value of G. Selecting an erroneous value of G may produce a poor density
estimate. This is also a most difficult question from a theoretical perspective
as it relates to unidentifiability issues of the mixture model. This is further
a most relevant question from a practical viewpoint since the meaning of the
number of components G is strongly related to the modelling purpose of a
mixture distribution. We distinguish in this chapter between selecting G as a
density estimation problem in Section 2 and selecting G in a model-based
clustering framework in Section 3. Both sections discuss frequentist as well as
Bayesian approaches. We present here some of the Bayesian solutions to the
different interpretations of picking the "right" number of components in a
mixture, before concluding on the ill-posed nature of the question.


  We present the status of the automation of NLO processes within the event
generator WHIZARD. The program provides an automated FKS subtraction and phase
space integration over the FKS regions, while the (QCD) NLO matrix element is
accessed via the Binoth Les Houches Interface from an externally linked
one-loop program. Massless and massive test cases and validation are shown for
several e+e- processes. Furthermore, we discuss work in progress and future
plans. The second part covers the matching of the NRQCD prediction with NLL
threshold resummation to the NLO continuum top pair production at lepton
colliders. Both the S-wave and P-wave production of the top pair are taken into
account in the resummation. The inclusion in WHIZARD allows to study more
exclusive observables than just the total cross section and automatically
accounts for important electroweak and relativistic corrections in the
threshold region.


  In this paper, the proposed methodology for conducting experiments on a small
number of devices for evaluating systems with a large number of devices. A
software package for assessing the performance of LPWAN (Long Range Wide Area
Networks, LoRaWAN) networks is presented. An example of the operation of this
software system as applied to the LoRaWAN network for estimating the
probability of message delivery from a number of devices to a base station when
implementing this technique is shown. This technique can be used to evaluate
the performance of other LPWAN networks.


  In this paper, we consider the estimation of generalized linear models with
covariates that are missing completely at random. We propose a model averaging
estimation method and prove that the corresponding model averaging estimator is
asymptotically optimal under certain assumptions. Simulaiton results illustrate
that this method has better performance than other alternatives under most
situations.


  The VC-dimension plays an important role for the algorithmic problem of
guarding art galleries efficiently. We prove that inside a simple polygon at
most $5$ points can be shattered by $L_1$-visibility polygons and give an
example where 5 points are shattered. The VC-dimension is exactly $5$. The
proof idea for the upper bound is different from previous approaches.
  Keywords: Art gallery, VC-dimension, $L_1$-visibility, polygons


  Managing cloud services is a fundamental challenge in todays virtualized
environments. These challenges equally face both providers and consumers of
cloud services. The issue becomes even more challenging in virtualized
environments that support mobile clouds. Cloud computing platforms such as
Amazon EC2 provide customers with flexible, on demand resources at low cost.
However, they fail to provide seamless infrastructure management and monitoring
capabilities that many customers may need. For instance, Amazon EC2 doesn't
fully support cloud services automated discovery and it requires a private set
of authentication credentials. Salesforce.com, on the other hand, do not
provide monitoring access to their underlying systems. Moreover, these systems
fail to provide infrastructure monitoring of heterogenous and legacy systems
that don't support agents. In this work, we explore how to build a cloud
management system that combines heterogeneous management of virtual resources
with comprehensive management of physical devices. We propose an initial
prototype for automated cloud management and monitoring framework. Our ultimate
goal is to develop a framework that have the capability of automatically
tracking configuration and relationships while providing full event management,
measuring performance and testing thresholds, and measuring availability
consistently. Armed with such a framework, operators can make better decisions
quickly and more efficiently.


  Facial expression recognition is a major problem in the domain of artificial
intelligence. One of the best ways to solve this problem is the use of
convolutional neural networks (CNNs). However, a large amount of data is
required to train properly these networks but most of the datasets available
for facial expression recognition are relatively small. A common way to
circumvent the lack of data is to use CNNs trained on large datasets of
different domains and fine-tuning the layers of such networks to the target
domain. However, the fine-tuning process does not preserve the memory integrity
as CNNs have the tendency to forget patterns they have learned. In this paper,
we evaluate different strategies of fine-tuning a CNN with the aim of assessing
the memory integrity of such strategies in a cross-dataset scenario. A CNN
pre-trained on a source dataset is used as the baseline and four adaptation
strategies have been evaluated: fine-tuning its fully connected layers;
fine-tuning its last convolutional layer and its fully connected layers;
retraining the CNN on a target dataset; and the fusion of the source and target
datasets and retraining the CNN. Experimental results on four datasets have
shown that the fusion of the source and the target datasets provides the best
trade-off between accuracy and memory integrity.


  In fractionally filled Landau levels there is only a small energy difference
between broken translational symmetry electron-crystal states and exotic
correlated quantum fluid states. We show that the spatially periodic substrate
interaction associated with the long period moir\'{e} patterns present in
graphene on nearly aligned hexagonal boron nitride (hBN) tilts this close
competition in favor of the former, explaining surprising recent experimental
findings.


  We study the compact Hermitian spin surfaces with positive conformal scalar
curvature on which the first eigenvalue of the Dolbeault operator of the spin
structure is the smallest possible. We prove that such a surface is either a
ruled surface or a Hopf surface. We give a complete classification of the ruled
surfaces with this property. For the Hopf surfaces we obtain a partial
classification and some examples.