  We explore a mechanism for generating the Yukawa hierarchies by displacing
the left- and right-handed components of the Standard Model fermions in a
higher-dimensional space. We find a unique configuration of displacements which
fits all quark and lepton masses and mixing angles, with a prediction for the
strange quark mass m_s = 120 MeV.


  Models of new physics induce K_0-K_0bar mixing through operators having Dirac
structures other than the "left-left" form of the Standard Model. To carry out
the chiral-continuum extrapolation of results from numerical simulations, one
needs to know the quark mass and lattice spacing dependence of the
corresponding B-parameters in the partially quenched theory at least at
next-to-leading order. For simulations using staggered fermions (such as that
we are doing with HYP-smeared valence fermions on the MILC asqtad lattices) one
must determine this dependence using staggered chiral perturbation theory
(SChPT). We have calculated the required dependence in both SU(3) and SU(2)
SChPT, working at next-to-leading order, and we give here an overview of the
methodology and results. The SU(3) SChPT result turns out to be much simpler
than that for the Standard Model B_K operator, due to the absence of chiral
suppression for the new operators. The SU(2) SChPT result turns out to be
closely related to that for B_K: the chiral logarithms are identical, up to an
operator-dependent sign. Our results are also useful for fermions with chiral
symmetry as they provide, in the continuum limit, the partially quenched
generalization of existing continuum results.


  We use the recently developed massive spinor-helicity formalism [1] of
Arkani- Hamed et al. to propose a new class of recursion relations for
tree-level amplitudes in gauge theories. These relations are based on a
combined complex deformation of massless as well as massive external momenta.
We use these relations to study tree-level amplitudes in scalar QCD as well as
amplitudes involving massive vector bosons in the Higgsed phase of Yang-Mills
theory. We prove the validity of our proposal by showing that in the limit of
infinite momenta of two of the external particles, the amplitude once again is
controlled by an enhanced Spin-Lorentz symmetry paralleling the proof of BCFW
shift for massless gauge theories. Simple examples illustrate that the proposed
shift may lead to an efficient computation of tree-level amplitudes.


  We present Spitzer photometric data for a complete sample of 19 low redshift
(z<0.1) 3CRR radio galaxies as part of our efforts to understand the origin of
the prodigious mid- to far-infrared (MFIR) emission from radio-loud AGN. Our
results show a correlation between AGN power (indicated by [OIII] 5007 emission
line luminosity) and 24 micron luminosity. This result is consistent with the
24 micron thermal emission originating from warm dust heated directly by AGN
illumination. Applying the same correlation test for 70 micron luminosity
against [OIII] luminosity we find this relation to suffer from increased
scatter compared to that of 24 micron. In line with our results for the
higher-radio-frequency-selected 2Jy sample, we are able to show that much of
this increased scatter is due to heating by starbursts which boost the
far-infrared emission at 70 micron in a minority of objects (17-35%). Overall
this study supports previous work indicating AGN illumination as the dominant
heating mechanism for MFIR emitting dust in the majority of low to intermediate
redshift radio galaxies (0.03<z<0.7), with the advantage of strong statistical
evidence. However, we find evidence that the low redshift broad-line objects
(z<0.1) are distinct in terms of their positions on the MFIR vs. [OIII]
correlations.


  In previous studies the pressure dependence of the magnetic ordering
temperature $T_{\text{o}}$ of Dy was found to exhibit a sharp increase above
its volume collapse pressure of 73 GPa, appearing to reach temperatures well
above ambient at 157 GPa. In a search for a second such lanthanide, electrical
resistivity measurements were carried out on neighboring Tb to 141 GPa over the
temperature range 3.8 - 295 K. Below Tb's volume collapse pressure of 53 GPa,
the pressure dependence $T_{\text{o}}(P)$ mirrors that of both Dy and Gd.
However, at higher pressures $T_{\text{o}}(P)$ for Tb becomes highly anomalous.
This result, together with the very strong suppression of superconductivity by
dilute Tb ions in Y, suggests that extreme pressure transports Tb into an
unconventional magnetic state with an anomalously high magnetic ordering
temperature.


  We give a new proof of the simultaneous embedded local uniformization Theorem
in zero characteristic for essentially of finite type rings and for quasi
excellent rings. The results are a consequence of the simultaneaous
monomialization presented here. The methods develop the key elements theory
that is a more subtle notion than the notion of key polynomials.


  The angular-dependent magnetoresistance (AMR) of the ab plane is measured on
the single crystals of FeSe1-xSx (x = 0, 0.07, 0.13 and 1) and FeSe1-yTey (y =
0.06, 0.61 and 1) at various temperatures under fields up to 9 T. A pronounced
twofold-anisotropic carrier-scattering effect is identified by AMR, and
attributed to a magnetic-field-induced spin nematicity that emerges from the
tetragonal normal-state regime below a characteristic temperature Tsn. This
magnetically polarized spin nematicity is found to be ubiquitous in the
isoelectronic FeSe1-xSx and FeSe1-yTey systems, no matter whether the sample
shows an electronic nematic order at Ts < Tsn, or an antiferromagnetic order at
TN < Tsn, or neither order. Importantly, we find that the isoelectronic
substitution with sulfur does not suppress but even enhances the characteristic
Tsn of the induced spin nematicity in FeSe1-xSx samples. This contrasts sharply
with their rapidly suppressed Ts, the transition temperature of the spontaneous
electronic nematicity. Furthermore, we find that the superconductivity is
significantly suppressed with the enhancement of the induced spin nematicity in
both FeSe1-xSx and FeSe1-yTey samples.


  In this paper we study the $J/\Psi p$ spectroscopy in the process of
$\Lambda_b\to J/\Psi p K^-$. The final state interactions of coupled channel
$J/\Psi p$ ~-~ $\bar{D} \Sigma_c$~-~$\bar{D}^{*} \Sigma_c$ are constructed
based on K-matrix with the Chew-Mandelstam function. We build the $\Lambda_b\to
J/\Psi p K^-$ amplitude according to the Au-Morgan-Pennington method. The event
shape is fitted and the decay width of $\Lambda_b\to J/\Psi p K^-$ is used to
constrain the parameters, too. With the amplitudes we extract out the poles and
their residues. Our amplitude and pole analysis suggest that the $P_c(4312)$
should be $\bar{D}\Sigma_c$ molecule, the $P_c(4440)$ could be an S-wave
compact pentaquark state, and the structure around $P_c(4457)$ is caused by the
cusp effect. The future experimental measurement of the decays of $\Lambda_b\to
\bar{D}\Sigma_c K^-$ and $\Lambda_b\to \bar{D}^*\Sigma_c K^-$ would further
help to study the nature of these resonances.


  Thought experiments involving gases and pistons, such as Maxwell's demon and
Gibbs' mixing, are central to our understanding of thermodynamics. Here we
present a quantum thermodynamic thought experiment in which the energy transfer
from two photonic gases to a piston membrane grows quadratically with the
number of photons for indistinguishable gases, while linearly for
distinguishable gases. This signature of Bosonic bunching may be observed in
optomechanical experiments, highlighting the potential of these systems for the
realisation of thermodynamic thought experiments in the quantum realm.


  Knowledge is a network of interconnected concepts. Yet, precisely how the
topological structure of knowledge constrains its acquisition remains unknown,
hampering the development of learning enhancement strategies. Here we study the
topological structure of semantic networks reflecting mathematical concepts and
their relations in college-level linear algebra texts. We hypothesize that
these networks will exhibit structural order, reflecting the logical sequence
of topics that ensures accessibility. We find that the networks exhibit strong
core-periphery architecture, where a dense core of concepts presented early is
complemented with a sparse periphery presented evenly throughout the
exposition; the latter is composed of many small modules each reflecting more
narrow domains. Using tools from applied topology, we find that the
expositional evolution of the semantic networks produces and subsequently fills
knowledge gaps, and that the density of these gaps tracks negatively with
community ratings of each textbook. Broadly, our study lays the groundwork for
future efforts developing optimal design principles for textbook exposition and
teaching in a classroom setting.


  The ability of linear stochastic response analysis to estimate coherent
motions is investigated in turbulent channel flow at friction Reynolds number
Re$_\tau$ = 1007. The analysis is performed for spatial scales characteristic
of buffer-layer and large-scale motions by separating the contributions of
different temporal frequencies. Good agreement between the measured
spatio-temporal power spectral densities and those estimated by means of the
resolvent is found when the effect of turbulent Reynolds stresses, modelled
with an eddy-viscosity associated to the turbulent mean flow, is included in
the resolvent operator. The agreement is further improved when the flat forcing
power spectrum (white noise) is replaced with a power spectrum matching the
measures. Such a good agreement is not observed when the eddy-viscosity terms
are not included in the resolvent operator. In this case, the estimation based
on the resolvent is unable to select the right peak frequency and wall-normal
location of buffer-layer motions. Similar results are found when comparing
truncated expansions of measured streamwise velocity power spectral densities
based on a spectral proper orthogonal decomposition to those obtained with
optimal resolvent modes.


  This paper presents our proposal for the evolution of the metamodel for the
Task Algebra in the Task Flow model for the Discovery Method. The original Task
Algebra is based on simple and compound tasks structured using operators such
as sequence, selection, and parallel composition. Recursion and encapsulation
were also considered. We propose additional characteristics to improve the
capabilities of the metamodel to represent accurately the Task Flow Model.


  GZKFast is a low-cost astrophysical event generator designed to simulate
photohadron processes resulting from ultra high energy cosmic ray fluxes.
GZKFast is an easy to use event generator which specifically addresses issues
relevant to cosmogenic neutrinos and the ultra high energy (UHE) neutrino
spectrum. GZKFast injects UHE particles into a simulated cosmic microwave
background (CMB) using a Monte Carlo approach. The interaction of each particle
is simulated and the resulting detectable events are spooled to log files.
Although GZKFast uses a simplified particle physics model and limited
accelerator data it generates event distributions which are comparable with
more thorough simulations such as the event generator SOPHIA


  Distances between data points are widely used in machine learning
applications. Yet, when corrupted by noise, these distances -- and thus the
models based upon them -- may lose their usefulness in high dimensions. Indeed,
the small marginal effects of the noise may then accumulate quickly, shifting
empirical closest and furthest neighbors away from the ground truth. In this
paper, we exactly characterize such effects in noisy high-dimensional data
using an asymptotic probabilistic expression. Previously, it has been argued
that neighborhood queries become meaningless and unstable when distance
concentration occurs, which means that there is a poor relative discrimination
between the furthest and closest neighbors in the data. However, we conclude
that this is not necessarily the case when we decompose the data in a ground
truth -- which we aim to recover -- and noise component. More specifically, we
derive that under particular conditions, empirical neighborhood relations
affected by noise are still likely to be truthful even when distance
concentration occurs. We also include thorough empirical verification of our
results, as well as interesting experiments in which our derived 'phase shift'
where neighbors become random or not turns out to be identical to the phase
shift where common dimensionality reduction methods perform poorly or well for
recovering low-dimensional reconstructions of high-dimensional data with dense
noise.


  Visual question answering (VQA) is challenging because it requires a
simultaneous understanding of both the visual content of images and the textual
content of questions. The approaches used to represent the images and questions
in a fine-grained manner and questions and to fuse these multi-modal features
play key roles in performance. Bilinear pooling based models have been shown to
outperform traditional linear models for VQA, but their high-dimensional
representations and high computational complexity may seriously limit their
applicability in practice. For multi-modal feature fusion, here we develop a
Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and
effectively combine multi-modal features, which results in superior performance
for VQA compared with other bilinear pooling approaches. For fine-grained image
and question representation, we develop a co-attention mechanism using an
end-to-end deep network architecture to jointly learn both the image and
question attentions. Combining the proposed MFB approach with co-attention
learning in a new network architecture provides a unified model for VQA. Our
experimental results demonstrate that the single MFB with co-attention model
achieves new state-of-the-art performance on the real-world VQA dataset. Code
available at https://github.com/yuzcccc/mfb.


  An important question is if the gap in the high temperature cuprates has
d_{x^2-y^2} symmetry, what does that tell us about the underlying interaction
responsible for pairing. Here we explore this by determining how three
different types of electron-phonon interactions affect the d_{x^2-y^2} pairing
found within an RPA treatment of the 2D Hubbard model. These results imply that
interactions which become more positive as the momentum transfer increases
favor d_{x^2-y^2} pairing in a nearly half-filled band.


  Accurate distances to local molecular clouds are critical for understanding
the star and planet formation process, yet distance measurements are often
obtained inhomogeneously on a cloud-by-cloud basis. We have recently developed
a method which combines stellar photometric data with Gaia DR2 parallax
measurements in a Bayesian framework to infer the distances of nearby dust
clouds to a typical accuracy of $\sim5\%$. After refining the technique to
target lower latitudes and incorporating deep optical data from DECam in the
southern Galactic plane, we have derived a catalog of distances to molecular
clouds in Reipurth (2008, Star Formation Handbook, vols I and II) which
contains a large fraction of the molecular material in the solar neighborhood.
Comparison with distances derived from maser parallax measurements towards the
same clouds shows our method produces consistent distances with $\lesssim10\%$
scatter for clouds across our entire distance spectrum (150 pc $-$ 2.5 kpc). We
hope this catalog of homogeneous distances will serve as a baseline for future
work.


  Affine phase retrieval is the problem of recovering signals from the
magnitude-only measurements with a priori information. In this paper, we use
the $\ell_1$ minimization to exploit the sparsity of signals for affine phase
retrieval, showing that $O(k\log(en/k))$ Gaussian random measurements are
sufficient to recover all $k$-sparse signals by solving a natural $\ell_1$
minimization program, where $n$ is the dimension of signals. For the case where
measurements are corrupted by noises, the reconstruction error bounds are given
for both real-valued and complex-valued signals. Our results demonstrate that
the natural $\ell_1$ minimization program for affine phase retrieval is stable.


  The objective of this paper is to deepen the understanding of the connection
between the continuous and smooth dependence of solutions on initial conditions
and the regularity of the history functionals for retarded functional
differential equations. We consider some differential equation with a single
constant delay with the history space of $L^p$-type and obtain the above
dependence result by assuming the growth rate of the nonlinearity and its
derivative. The corresponding history functional is discontinuous, and it
becomes clear that there are the continuity and the smoothness of the
composition operators (also called the superposition operators or the Nemytskij
operators) between $L^p$-spaces behind the dependence results.


  We study the statistical properties of the gravitational field generated by
galaxy distribution observed bythe Sloan Digital Sky Survey (DR7). We
characterize the probability density function of gravitational force
fluctuations and relate its limiting behaviors to the correlation properties of
the underlying density field. In addition, we study whether the PDF converges
to an asymptotic shape within sample volumes. We consider several
volume-limited samples of the Sloan Digital Sky Survey and we compute the
gravitational force probability density function (PDF). The gravitational force
is computed in spheres of varying radius as is its PDF. We find that (i) the
PDF of the force displays features that can be understood in terms of galaxy
two-point correlations and (ii) density fluctuations on the largest scales
probed, i.e. r~100 Mpc/h, still contribute significantly to the amplitude of
the gravitational force. Our main conclusion is that fluctuations in the
gravitational force field generated by galaxy structures are also relevant on
scales ~ 100 Mpc/h. By assuming that the gravitational fluctuations in the
galaxy distribution reflect those in the whole matter distribution, and that
peculiar velocities and accelerations are simply correlated, we may conclude
that large-scale fluctuations in the galaxy density field may be the source of
the large-scale flows recently observed.


  The 182Hf-182W chronology of iron meteorites provides crucial information on
the timescales of accretion and differentiation of some of the oldest
planetesimals of the Solar System. Determining accurate Hf-W model ages of iron
meteorites requires correction for cosmic ray expo-sure (CRE) induced
modifications of W isotope compositions, which can be achieved using in-situ
neutron dosimeters such as Pt isotopes. Until now it has been assumed that all
Pt isotope variations in meteorites reflect CRE, but here we show that some
ungrouped iron meteorites display small nucleosynthetic Pt isotope anomalies.
These provide the most appropriate starting composition for the correction of
CRE-induced W isotope variations in iron meteorites from all major chemical
groups, which leads to a ~1 Ma upward revision of previously reported Hf-W
model ages. The revised ages indicate that core formation in non-carbonaceous
(NC) iron meteorite parent bodies occurred at ~1-2 Ma after CAI formation,
whereas most carbonaceous (CC) iron meteorite parent bodies underwent core
formation ~2 Ma later. We show that the younger CC cores have lower Fe/Ni
ratios than the earlier-formed NC cores, indicating that core formation under
more oxidizing conditions occurred over a more protracted timescale. Thermal
modeling of planetesimals heated by 26Al-decay reveals that this protracted
core formation timescale is consistent with a higher fraction of water ice in
CC compared to NC planetesimals, implying that in spite of distinct core
formation timescales, NC and CC iron meteorite parent bodies accreted about
contemporaneously within ~1 Ma after CAI formation, but at different radial
locations in the disk.


  We investigate the variation of black hole masses (Mbh) as a function of
their host galaxy stellar mass (Mstar) and half-light radius (Re). We confirm
that the scatter in Mbh within this plane is essentially the same as that in
the Mbh - sigma relation, as expected from the negligible scatter reported in
the virial mass estimator sigma_v^2=GxMstar/(5xRe). All variation in Mbh
happens along lines of constant sigma_v on the (Mstar, Re) plane, or Mstar
$\propto$ Re for Mstar <2x10^11 Msun. This trend is qualitatively the same as
those previously reported for galaxy properties related to stellar populations,
like age, metallicity, alpha enhancement, mass-to-light ratio and gas content.
We find evidence for a change in the Mbh variation above the critical mass of
Mcrit ~ 2x10^11 Msun. This behaviour can be explained assuming that Mbh in
galaxies less massive than Mcrit can be predicted by the Mbh - sigma relation,
while Mbh in more massive galaxies follow a modified relation which is also
dependent on Mstar once Mstar >Mcrit. This is consistent with the scenario
where the majority of galaxies grow through star formation, while the most
massive galaxies undergo a sequence of dissipation-less mergers. In both
channels black holes and galaxies grow synchronously, giving rise to the black
hole - host galaxy scaling relations, but there is no underlying single
relation that is universal across the full range of galaxy masses.


  This paper focuses on $\alpha$-divergence minimisation methods for
Variational Inference. More precisely, we are interested in algorithms
optimising the mixture weights of any given mixture model, without any
information on the underlying distribution of its mixture components
parameters. The Power Descent, defined for all $\alpha \neq 1$, is one such
algorithm and we establish in our work the full proof of its convergence
towards the optimal mixture weights when $\alpha <1$. Since the
$\alpha$-divergence recovers the widely-used forward Kullback-Leibler when
$\alpha \to 1$, we then extend the Power Descent to the case $\alpha = 1$ and
show that we obtain an Entropic Mirror Descent. This leads us to investigate
the link between Power Descent and Entropic Mirror Descent: first-order
approximations allow us to introduce the Renyi Descent, a novel algorithm for
which we prove an $O(1/N)$ convergence rate. Lastly, we compare numerically the
behavior of the unbiased Power Descent and of the biased Renyi Descent and we
discuss the potential advantages of one algorithm over the other.


  This work focuses on the validation of the dynamic wake meandering (DWM)
model against large eddy simulation (LES). The wake deficit, mean deflection,
and meandering under different wind turbine misalignment angles in yaw and
tilt, for the IEA 15MW wind turbine, for two turbulent inflows with different
shear and turbulence intensities are compared. Simulation results indicate that
the DWM model as implemented in FAST.Farm shows very good agreement with the
LES (VFS-Wind) data when predicting the time-averaged horizontal and vertical
wake, especially at x > 6D and for cases with positive tilt angles (> 6deg).
The wake dynamics captured by the DWM model include the large-eddy-induced wake
meandering at low Strouhal number (St < 0.1). Additionally, the wake
oscillation induced by the shear layer at St approx. 0.27 is captured only by
LES. The mean and standard deviation of the wake deflection, as computed by the
DWM, are sensitive to the size of the polar grid used to calculate the
spatial-averaged velocity with which the wake planes meander. The power output
of a turbine in the wake of a wind turbine in free-wind deflected by a yaw
angle gamma = 30deg is almost doubled compared to the fully-waked condition.


  An Electromagnetic (EM) pulse falling on a plasma medium from vacuum can
either reflect or propagate inside the plasma depending on whether it is
overdense or underdense. In a magnetised plasma, however, there are usually
several pass and stop bands for the EM wave depending on the orientation of the
magnetic field with respect to the propagation direction. The EM wave while
propagating in a plasma can excite electrostatic disturbances in the plasma [1,
2]. In this work Particle - In - Cell simulations have been carried out to
illustrate the complete transparency of the EM wave propagation inside a
strongly magnetised plasma. The external magnetic field is chosen to be
perpendicular to both the wave propagation direction and the electric field of
the EM wave, which is the X mode configuration. Despite the presence of charged
electron and ion species the plasma medium behaves like a vacuum. The
observation is understood with the help of particle drifts. It is shown that
though the two particle species move under the influence of EM fields their
motion does not lead to any charge or current source to alter the dispersion
relation of the EM wave propagating in the medium. Furthermore, it is also
shown that the stop band for EM wave in this regime shrinks to a zero width as
both the resonance and cut-off points approach each other. Thus transparency to
the EM radiation in such a strongly magnetised case appears to be a norm. This
may have important implications in astrophysical scenarios. For instance, the
plasma surrounding objects like pulsars and magnetars is often threaded with
strong magnetic fields.


  In the Boehm theorem workshop on Crete island, Zoran Petric called Statman's
``Typical Ambiguity theorem'' typed Boehm theorem. Moreover, he gave a new
proof of the theorem based on set-theoretical models of the simply typed lambda
calculus. In this paper, we study the linear version of the typed Boehm theorem
on a fragment of Intuitionistic Linear Logic. We show that in the
multiplicative fragment of intuitionistic linear logic without the
multiplicative unit 1 (for short IMLL) weak typed Boehm theorem holds. The
system IMLL exactly corresponds to the linear lambda calculus without
exponentials, additives and logical constants. The system IMLL also exactly
corresponds to the free symmetric monoidal closed category without the unit
object. As far as we know, our separation result is the first one with regard
to these systems in a purely syntactical manner.


  This is an overview of the idea of a crossed module. For a group, the triple
that consists of the group, its group of automorphisms, and the canonical
homomorphism from the group to its group of automorphisms constitutes a crossed
module. Crossed modules arise from the identities among the relations of the
presentation of a group, from the extension problem for groups and, more
generally, in low dimensional topology. Also, the (successful) attempt to
extend the idea of a normal extension of commutative fields to the realm of
non-commutative algebras leads to crossed modules. Crossed modules appear
implicitly in a forgotten paper by A. Turing which in principle settles the
extension problem for groups. Crossed modules make perfect sense for Lie
algebras.


  A modified cathodic arc technique has been used to deposit carbon nitride
thin films directly on n+ Si substrates. Transmission Electron Microscopy
showed that clusters of fullerene-like nanoparticles are embedded in the
deposited material. Field emission in vacuum from as-grown films starts at an
electric field strength of 3.8 V/micron. When the films were etched in an
HF:NH4F solution for ten minutes, the threshold field decreased to 2.6
V/micron. The role of the carbon nanoparticles in the field emission process
and the influence of the chemical etching treatment are discussed.


  Paris (PArallel, Robust, Interface Simulator) is a finite volume code for
simulations of immiscible multifluid or multiphase flows. It is based on the
"one-fluid" formulation of the Navier-Stokes equations where different fluids
are treated as one material with variable properties, and surface tension is
added as a singular interface force. The fluid equations are solved on a
regular structured staggered grid using an explicit projection method with a
first-order or second-order time integration scheme. The interface separating
the different fluids is tracked by a Front-Tracking (FT) method, where the
interface is represented by connected marker points, or by a Volume-of-Fluid
(VOF) method, where the marker function is advected directly on the fixed grid.
Paris is written in Fortran95/2002 and parallelized using MPI and domain
decomposition. It is based on several earlier FT or VOF codes such as Ftc3D,
Surfer or Gerris. These codes and similar ones, as well as Paris, have been
used to simulate a wide range of multifluid and multiphase flows.


  In the 1950-ies P. F. Strawson proposed the Logic of Presuppositions. In this
system sentences with empty subject are considered neither true nor false.
Strawson considered only simple sentences with two predicate letters. But the
concept can be extended to arbitrary monadic, even polyadic sentences utilizing
truth-relevant logic developed by Richard Diaz. It can be shown that
self-referential G\"odel's sentence in fact has an empty subject, and can thus
be classified by the Logic of Presuppositions as neither true nor false.


  The equitable distribution of resources in a network is a complex process,
considering that not all nodes have the same requirements, and the In-Band
On-Channel (IBOC) hybrid transmission system is no exception. The IBOC system
utilizes a hybrid in-band transmission to simultaneously broadcast analog and
digital audio over the FM band. This article proposes the use of a Max-Min
Fairness (MMF) algorithm, with a strategy to optimize resource allocation for
IBOC FM transmission in a multiservice scenario. Additionally, the MMF
algorithm offers low computational complexity for implementation in low-cost
embedded systems, aiming to achieve fair resource distribution and provide
adequate Quality of Service (QoS) levels for each node in the RF network,
considering channel conditions and traffic types. The article explores a
scenario under saturated traffic conditions to assess the optimization
capabilities of the MMF algorithm under well-defined traffic and channel
conditions. The evaluation process yielded highly favorable results, indicating
that theMMF algorithm can be considered a viable alternative for bandwidth
optimization in digital broadcasting over IBOC on FM with 95% confidence, and
it holds potential for implementation in other digital broadcasting system.


  After more than three decades the fractional quantum Hall effect still poses
challenges to contemporary physics. Recent experiments point toward a fractal
scenario for the Hall resistivity as a function of the magnetic field. Here, we
consider the so-called thin-torus limit of the Hamiltonian describing
interacting electrons in a strong magnetic field, restricted to the lowest
Landau level, and we show that it can be mapped onto a one-dimensional lattice
gas with repulsive interactions, with the magnetic field playing the role of a
chemical potential. The statistical mechanics of such models leads to interpret
the sequence of Hall plateaux as a fractal phase diagram, whose landscape shows
a qualitative agreement with experiments.


  In a companion paper, we presented BayesCal, a mathematical formalism for
mitigating sky-model incompleteness in interferometric calibration. In this
paper, we demonstrate the use of BayesCal to calibrate the degenerate gain
parameters of full-Stokes simulated observations with a HERA-like hexagonal
close-packed redundant array, for three assumed levels of completeness of the a
priori known component of the calibration sky model. We compare the BayesCal
calibration solutions to those recovered by calibrating the degenerate gain
parameters with only the a priori known component of the calibration sky model
both with and without imposing physically motivated priors on the gain
amplitude solutions and for two choices of baseline length range over which to
calibrate. We find that BayesCal provides calibration solutions with up to four
orders of magnitude lower power in spurious gain amplitude fluctuations than
the calibration solutions derived for the same data set with the alternate
approaches, and between $\sim10^7$ and $\sim10^{10}$ times smaller than in the
mean degenerate gain amplitude on the full range of spectral scales accessible
in the data. Additionally, we find that in the scenarios modelled only BayesCal
has sufficiently high fidelity calibration solutions for unbiased recovery of
the 21 cm power spectrum on large spectral scales ($k_\parallel \lesssim
0.15~h\mathrm{Mpc}^{-1}$). In all other cases, in the completeness regimes
studied, those scales are contaminated.


  Metal-organic frameworks (MOFs) are of immense interest in applications such
as gas storage and carbon capture due to their exceptional porosity and tunable
chemistry. Their modular nature has enabled the use of template-based methods
to generate hypothetical MOFs by combining molecular building blocks in
accordance with known network topologies. However, the ability of these methods
to identify top-performing MOFs is often hindered by the limited diversity of
the resulting chemical space. In this work, we propose MOFDiff: a
coarse-grained (CG) diffusion model that generates CG MOF structures through a
denoising diffusion process over the coordinates and identities of the building
blocks. The all-atom MOF structure is then determined through a novel assembly
algorithm. Equivariant graph neural networks are used for the diffusion model
to respect the permutational and roto-translational symmetries. We
comprehensively evaluate our model's capability to generate valid and novel MOF
structures and its effectiveness in designing outstanding MOF materials for
carbon capture applications with molecular simulations.


  The non-crossing rule for the energy levels of a parameter-dependent
Hamiltonian is revisited and a flaw in a commonly accepted proof is revealed.
Some aspects of avoided crossings are illustrated by means of simple models.
One of them shows the close relationship between avoided crossings and
exceptional points.


  By decoupling substrate resources, network virtualization (NV) is a promising
solution for meeting diverse demands and ensuring differentiated quality of
service (QoS). In particular, virtual network embedding (VNE) is a critical
enabling technology that enhances the flexibility and scalability of network
deployment by addressing the coupling of Internet processes and services.
However, in the existing deep neural networks (DNNs)-based works, the black-box
nature DNNs limits the analysis, development, and improvement of systems. For
example, in the industrial Internet of Things (IIoT), there is a conflict
between decision interpretability and the opacity of DNN-based methods. In
recent times, interpretable deep learning (DL) represented by deep neuro fuzzy
systems (DNFS) combined with fuzzy inference has shown promising
interpretability to further exploit the hidden value in the data. Motivated by
this, we propose a DNFS-based VNE algorithm that aims to provide an
interpretable NV scheme. Specifically, data-driven convolutional neural
networks (CNNs) are used as fuzzy implication operators to compute the
embedding probabilities of candidate substrate nodes through entailment
operations. And, the identified fuzzy rule patterns are cached into the weights
by forward computation and gradient back-propagation (BP). Moreover, the fuzzy
rule base is constructed based on Mamdani-type linguistic rules using
linguistic labels. In addition, the DNFS-driven five-block structure-based
policy network serves as the agent for deep reinforcement learning (DRL), which
optimizes VNE decision-making through interaction with the environment.
Finally, the effectiveness of evaluation indicators and fuzzy rules is verified
by simulation experiments.


  We explore phenomenological signatures of light quark jet quenching within
the AdS/CFT correspondence. Numerical studies of the instantaneous energy loss
of light quarks, modeled as falling strings, suggest a linear path dependence.
We propose a phenomenological model for the energy loss and use it to compute
the nuclear modification factor $R_{AA}$ for light quarks in an expanding
plasma with Glauber initial conditions. The results are compared to the light
hadron $R_{AA}$ data at the LHC and, although they show qualitative agreement,
the quantitative disagreement we found motivated the exploration of effects
from higher order derivative corrections to $AdS_5$ on this observable.


  Neural mechanisms of extraclassical receptive field phenomena in V1 are
commonly assumed to result from long-range lateral connections and/or
extrastriate feedback. We address two such phenomena: surround suppression and
contrast dependent receptive field size. We present rigorous computational
support for the hypothesis that the phenomena largely result from local
short-range (< 0.5 mm) cortical connections and LGN input. Surround suppression
in our simulations results from (A) direct cortical inhibition or (B)
suppression of recurrent cortical excitation, or (C) action of both these
mechanisms simultaneously. Mechanisms B and C are substantially more prevalent
than A. We observe an average growth in the range of spatial summation of
excitatory and inhibitory synaptic inputs for low contrast. However, we find
this is neither sufficient nor necessary to explain contrast dependent
receptive field size, which usually involves additional changes in the relative
gain of these inputs.


  We show that the recent Comment by Liu and Wilczek (cond-mat/0304632) on our
work (PRA 67, 053603) is incorrect.


  Although a source star is fainter than the detection limit imposed by
crowding, it is still possible to detect an event if the star is located in the
seeing disk of a bright star is and gravitationally amplified: amplification
bias. Using a well-constrained luminosity function, I show that $\sim 40\%$ of
events detected toward the Galactic bulge are affected by amplification bias
and the optical depth might be overestimated by a factor $\sim 1.7$. In
addition, I show that if one takes amplification bias into consideration, the
observed time scale distribution matches significantly better, especially in
the short time-scale region, with the distribution expected from a
mass-spectrum model in which lenses are composed of the known stellar
population plus an additional population of brown dwarfs than it is without the
effect of the amplification bias.


  The experimental search for solar hadronic axions is started at the Baksan
Neutrino Observatory of the Institute for Nuclear Researches Russian Academy of
Science. It is assumed that axions are created in the Sun during M1-transition
between the first thermally excited level at 9.4 keV and the ground state in
Kr-83. The experiment is based on axion detection via resonant absorption
process by the same nucleus in the detector. The big copper proportional
counter filled with krypton is used to detect signals from axions. The
experimental setup is situated in the deep underground low background
laboratory. No evidence of axion detection were found after the 26.5 days data
collection. Resulting new upper limit on axion mass is m_{A} < 130 eV at 95%
C.L.


  We analysed the ground state energy of some dimerized spin-1/2 transverse XX
and Heisenberg chains with Dzyaloshinskii-Moriya (DM) interaction to study the
influence of the latter interaction on the spin-Peierls instability. We found
that DM interaction may act either in favour of the dimerization or against it.
The actual result depends on the dependence of DM interaction on the distortion
amplitude in comparison with such dependence for the isotropic exchange
interaction.


  Spatial distributions of heavy particles suspended in an incompressible
isotropic and homogeneous turbulent flow are investigated by means of high
resolution direct numerical simulations. In the dissipative range, it is shown
that particles form fractal clusters with properties independent of the
Reynolds number. Clustering is there optimal when the particle response time is
of the order of the Kolmogorov time scale $\tau_\eta$. In the inertial range,
the particle distribution is no longer scale-invariant. It is however shown
that deviations from uniformity depend on a rescaled contraction rate, which is
different from the local Stokes number given by dimensional analysis. Particle
distribution is characterized by voids spanning all scales of the turbulent
flow; their signature in the coarse-grained mass probability distribution is an
algebraic behavior at small densities.


  We summarize semiclassical modeling methods, including drift-diffusion,
kinetic transport equation and Monte Carlo simulation approaches, utilized in
studies of spin dynamics and transport in semiconductor structures. As a review
of the work by our group, several examples of applications of these modeling
techniques are presented.


  A self-training scheme geared at inducing students to improve their skills
through independent homework is presented. The motivation is to identify an
inexpensive, yet effective tool for raising the competence level of students in
the Fundamental Sciences (in particular Physics). Since globally existing
financial restrictions do not allow for extensive supervised work, a scheme is
devised where the additional personal training is rewarded through bonuses in
the grade, while safeguarding against the danger of cheating. Overburdening the
instructors is avoided through the use of computer-based grading of homework,
while a carefully chosen bonus plan, weighted by the grades obtained in
supervised tests, counters the effects of potential cheating.


  We observe that, within the effective generating function formalism for the
implementation of canonical transformations within wave mechanics, non-trivial
canonical transformations which leave invariant the form of the Hamilton
function of the classical analogue of a quantum system manifest themselves in
an integral equation for its stationary state eigenfunctions. We restrict
ourselves to that subclass of these dynamical symmetries for which the
corresponding effective generating functions are necessaarily free of quantum
corrections. We demonstrate that infinite families of such transformations
exist for a variety of familiar conservative systems of one degree of freedom.
We show how the geometry of the canonical transformations and the symmetry of
the effective generating function can be exploited to pin down the precise form
of the integral equations for stationary state eigenfunctions. We recover
several integral equations found in the literature on standard special
functions of mathematical physics. We end with a brief discussion (relevant to
string theory) of the generalization to scalar field theories in 1+1
dimensions.


  I review lattice quantum chromodynamics as relevant to the kaon system.
Topics covered include the pseudoscalar masses and decay constants, the chiral
effective lagrangian, $f_K/f_\pi$, semi-leptonic kaon decay form factors, and
the neutral kaon oscillation parameter $B_K$.


  A hierarchy of multimode uncertainty relations on the second moments of n
pairs of canonical operators is derived in terms of quantities invariant under
linear canonical (i.e. symplectic) transformations. Conditions for the
separability of multimode continuous variable states are derived from the
uncertainty relations, generalizing the inequalities obtained in [Phys. Rev.
Lett. 96, 110402 (2006)] to states with some transposed symplectic eigenvalues
equal to 1. Finally, to illustrate the methodology proposed for the detection
of continuous variable entanglement, the separability of multimode noisy
GHZ-like states is analysed in detail with the presented techniques, deriving a
necessary and sufficient condition for the separability of such states under an
`even' bipartition of the modes.


  The evolution of complex transport networks is investigated under three
strategies of link removal: random, intentional attack and "Pseudo-Darwinian"
strategy. At each evolution step and regarding the selected strategy, one
removes either a randomly chosen link, or the link carrying the strongest flux,
or the link with the weakest flux, respectively. We study how the network
structure and the total flux between randomly chosen source and drain nodes
evolve. We discover a universal power-law decrease of the total flux, followed
by an abrupt transport collapse. The time of collapse is shown to be determined
by the average number of links per node in the initial network, highlighting
the importance of this network property for ensuring safe and robust transport
against random failures, intentional attacks and maintenance cost
optimizations.


  We study three-hadron azimuthal angular correlations in high energy
proton-proton and central nucleus-nucleus collisions at RHIC and LHC at
mid-rapidity. We use the LO parton matrix elements for 2 \rightarrow 3
processes and include the effect of parton energy loss in the Quark-Gluon
Plasma using the modified fragmentation function approach. For the case when
the produced hadrons have either same or not too different momenta, we observe
two away side peaks at 2pi/3 and 4pi/3. We consider the dependence of the
angular correlations on energy loss parameters that have been used in studies
of single inclusive hadron production at RHIC. Our results on the angular
dependence of the cross section agree well with preliminary data by the PHENIX
collaboration. We comment on the possible contribution of 2 \rightarrow 3
processes to di-hadron angular correlations and how a comparison of the two
processes may help characterize the plasma further.


  Hard diffractive photo- and electroproduction of heavy vector mesons
($J/\psi$ and $\Upsilon$) is evaluated within the leading $\alpha_s\ln{Q^2
\over\Lambda_{QCD}^2}$ approximation of QCD. In difference from our earlier
work on that subject, also the production of transversely polarized vector
mesons is calculated. Special emphasis is placed on the role of the vector
meson's $q\bar q$ light-cone wave function. In that context, conventional
non-relativistic quarkonium models and a light-front QCD bound state
calculation are critically examined and confronted with QCD expectations. Our
numerical analysis finds a significant high momentum tail in the latter wave
functions and a deviation from the expected asymptotic behavior of
$\phi_V(z,b=0)\propto z(1-z)$. We then design an interpolation to match the
quarkonium models at large inter-quark separations with QCD expectations at
small distances. We use these results to compare our predictions for the
forward differential cross section of $J/\psi$ photo- and electroproduction
with recent experimental results from HERA. In addition, our earlier discussion
of $\rho^o$ electroproduction is updated in light of recent experimental and
theoretical enhancements.


  We present a finite-dimensional version of the quantum model for the stock
market proposed in [C. Zhang and L. Huang, A quantum model for the stock
market, Physica A 389(2010) 5769]. Our approach is an attempt to make this
model consistent with the discrete nature of the stock price and is based on
the mathematical formalism used in the case of the quantum systems with
finite-dimensional Hilbert space. The rate of return is a discrete variable
corresponding to the coordinate in the case of quantum systems, and the
operator of the conjugate variable describing the trend of the stock return is
defined in terms of the finite Fourier transform. The stock return in
equilibrium is described by a finite Gaussian function, and the time evolution
of the stock price, directly related to the rate of return, is obtained by
numerically solving a Schrodinger type equation.


  Bayesian optimization (BO) is a flexible and powerful framework that is
suitable for computationally expensive simulation-based applications and
guarantees statistical convergence to the global optimum. While remaining as
one of the most popular optimization methods, its capability is hindered by the
size of data, the dimensionality of the considered problem, and the nature of
sequential optimization. These scalability issues are intertwined with each
other and must be tackled simultaneously. In this work, we propose the
Scalable$^3$-BO framework, which employs sparse GP as the underlying surrogate
model to scope with Big Data and is equipped with a random embedding to
efficiently optimize high-dimensional problems with low effective
dimensionality. The Scalable$^3$-BO framework is further leveraged with
asynchronous parallelization feature, which fully exploits the computational
resource on HPC within a computational budget. As a result, the proposed
Scalable$^3$-BO framework is scalable in three independent perspectives: with
respect to data size, dimensionality, and computational resource on HPC. The
goal of this work is to push the frontiers of BO beyond its well-known
scalability issues and minimize the wall-clock waiting time for optimizing
high-dimensional computationally expensive applications. We demonstrate the
capability of Scalable$^3$-BO with 1 million data points, 10,000-dimensional
problems, with 20 concurrent workers in an HPC environment.


  We have obtained a set of coupled differential equations from the continuous
limit of the transfer matrix method. Decoupling such a set of equations yields
an extension to the Wentzel-Kramers-Brillouin (WKB) approximation for the
Schr\"{o}dinger equation with the position-dependent effective mass (PDEM). In
the classically allowed region, the decoupling is to ignore the reflection
resulting from the variations of both the potential and effective mass. By
considering an infinite-well example with the PDEM, it is shown that the
extended WKB approximation can provide not only an estimation to eigenenergies,
but also an analytic form to approximate wavefunctions.


  Four-graviton couplings in the low energy effective action of type II string
vacua compactified on tori are strongly constrained by supersymmetry and
U-duality. While the $R^4$ and $D^4 R^4$ couplings are known exactly in terms
of Langlands-Eisenstein series of the U-duality group, the $D^6 R^4$ couplings
are not nearly as well understood. Exploiting the coincidence of the U-duality
group in $D=6$ with the T-duality group in $D=5$, we propose an exact formula
for the $D^6 R^4$ couplings in type II string theory compactified on $T^4$, in
terms of a genus-two modular integral plus a suitable Eisenstein series. The
same modular integral computes the two-loop correction to $D^6 R^4$ in 5
dimensions, but here provides the non-perturbative completion of the known
perturbative terms in $D=6$. This proposal hinges on a systematic re-analysis
of the weak coupling and large radius of the $D^6 R^4$ in all dimensions $D\geq
3$, which fills in some gaps and resolves some inconsistencies in earlier
studies.


  The power prior and its variations have been proven to be a useful class of
informative priors in Bayesian inference due to their flexibility in
incorporating the historical information by raising the likelihood of the
historical data to a fractional power {\delta}. The derivation of the marginal
likelihood based on the original power prior,and its variation, the normalized
power prior, introduces a scaling factor C({\delta}) in the form of a prior
predictive distribution with powered likelihood. In this paper, we show that
the scaling factor might be infinite for some positive {\delta} with
conventionally used initial priors, which would change the admissible set of
the power parameter. This result seems to have been almost completely ignored
in the literature. We then illustrate that such a phenomenon may jeopardize the
posterior inference under the power priors when the initial prior of the model
parameters is improper. The main findings of this paper suggest that special
attention should be paid when the suggested level of borrowing is close to 0,
while the actual optimum might be below the suggested value. We use a normal
linear model as an example for illustrative purposes.


  The branching ratio, forward-backward asymmetry and CP violating asymmetry
for (B -> X_d l^+ l^-) decay, in framework of the general two Higgs doublet
model, with extra phase angle in the charged-Higgs-fermion coupling, are
calculated. It is shown that studying the CP violating asymmetry is an
efficient tool for establishing new physics.


  In this paper we introduce the shifted twisted Yangian of type {\sf AI},
following the work of Lu--Wang--Zhang, and we study their semiclassical limits,
a class of Poisson algebras. We demonstrate that they coincide with the Dirac
reductions of the semiclassical shifted Yangian for $\gl_n$. We deduce that
these shifted twisted Yangians admit truncations which are isomorphic to
Slodowy slices for many non-rectangular nilpotent elements in types {\sf B},
{\sf C}, {\sf D}. As a direct consequence we obtain parabolic presentations of
the semiclassical shifted twisted Yangian, analogous to those introduced by
Brundan--Kleshchev for the Yangian of type {\sf A}. Finally we give Poisson
presentations of Slodowy slices for all even nilpotent elements in types {\sf
B}, {\sf C}, {\sf D}, generalising the recent work of the second author.


  Weak gravitational lensing measurements are traditionally made at optical
wavelengths where many highly resolved galaxy images are readily available.
However, the Square Kilometre Array (SKA) holds great promise for this type of
measurement at radio wavelengths owing to its greatly increased sensitivity and
resolution over typical radio surveys. The key to successful weak lensing
experiments is in measuring the shapes of detected sources to high accuracy. In
this document we describe a simulation pipeline designed to simulate radio
images of the quality required for weak lensing, and will be typical of SKA
observations. We provide as input, images with realistic galaxy shapes which
are then simulated to produce images as they would have been observed with a
given radio interferometer. We exploit this pipeline to investigate various
stages of a weak lensing experiment in order to better understand the effects
that may impact shape measurement. We first show how the proposed SKA1-Mid
array configurations perform when we compare the (known) input and output
ellipticities. We then investigate how making small changes to these array
configurations impact on this input-outut ellipticity comparison. We also
demonstrate how alternative configurations for SKA1-Mid that are smaller in
extent, and with a faster survey speeds produce similar performance to those
originally proposed. We then show how a notional SKA configuration performs in
the same shape measurement challenge. Finally, we describe ongoing efforts to
utilise our simulation pipeline to address questions relating to how applicable
current (mostly originating from optical data) shape measurement techniques are
to future radio surveys. As an alternative to such image plane techniques, we
lastly discuss a shape measurement technique based on the shapelets formalism
that reconstructs the source shapes directly from the visibility data.


  In this paper, we study the problem of utility maximization in the uplink of
vehicle-to-infrastructure communication systems. The studied scenarios consider
four practical aspects of mobile radio communication links: i) Interference
between adjacent channels, ii) interference between roadside units along the
way, iii) fast and slow channel fadings, and iv) Doppler shift effects. We
present first the system model for the IEEE 802.11p standard, which considers a
communication network between vehicles and roadside infrastructure. Next, we
formulate the problem of utility maximization in the network, and propose a
distributed optimization scheme. This distributed scheme is based on a two-loop
feedback configuration, where an outer-loop establishes the optimal signal to
interference-noise ratio (SINR) that maximizes the utility function per vehicle
and defines a quality-of-service objective. Meanwhile, inner-control loops
adjust the transmission power to achieve this optimal SINR reference in each
vehicle node regardless of interference, time-varying channel profiles and
network latency. The computation complexity of the distributed utility
maximization scheme is analyzed for each feedback loop. Simulation results
indicate that the proposed scheme reaches the objective SINRs that maximize
utility and improve energy efficiency in the network with a low time cost. The
results also show that the maximum utility is consistently achieved for
different propagation scenarios inside the vehicular communication network.


  The density of the Bose-Einstein condensate for non-interacting pions in a
cubic box, at given temperature and (average) total pion density, is
calculaated for three sets of boundary conditions. The densities are much
larger than predicted from thermodynamics and depend significantly on the
choice of the boundary conditions, even for volumes as large as $6.4\cdot
10$fm$^3$.


  We investigate the limit of sequences of vertex algebras. We discuss under
what condition the vector space direct limit of such a sequence is again a
vertex algebra. We then apply this framework to permutation orbifolds of vertex
operator algebras and their large N limit. We establish that for any nested
oligomorphic permutation orbifold such a large N limit exists, and we give a
necessary and sufficient condition for that limit to factorize. This helps
clarify the question of what VOAs are candidates for holographic conformal
field theories in physics.


  We report magnetotransport measurements on magnetically doped
(Bi,Sb)$_2$Te$_3$ films grown by molecular beam epitaxy. In Hallbar devices,
logarithmic dependence on temperature and bias voltage are obseved in both the
longitudinal and anomalous Hall resistance. The interplay of disorder and
electron-electron interactions is found to explain quantitatively the observed
logarithmic singularities and is a dominant scattering mechanism in these
samples. Submicron scale devices exhibit intriguing quantum oscillations at
high magnetic fields with dependence on bias voltage. The observed quantum
oscillations can be attributed to bulk and surface transport.


  In this paper, Al-Cu-Fe quasicrystal alloy was used as anode material of
lithium-ion batteries. The first specific discharge capacity of quasicrystal
was 204mAh/g. Cyclic voltammetry showed that oxidation peak of Al-Cu-Fe
quasicrystal was at about 1.4V. The reduction peak was at 0.3V. Al-Cu-Fe
quasicrystal have higher Li-ion diffusion impedance and Warburg in the first
cycle. X-ray diffraction analysis demonstrate that Li atom enter into
quasicrystal structure can not fully leave quasicrytal during first
charge/discharge cycle which induce the irreversible capacity.


  Globally, cardiovascular diseases (CVDs) are the leading cause of mortality,
accounting for an estimated 17.9 million deaths annually. One critical clinical
objective is the early detection of CVDs using electrocardiogram (ECG) data, an
area that has received significant attention from the research community.
Recent advancements based on machine learning and deep learning have achieved
great progress in this domain. However, existing methodologies exhibit inherent
limitations, including inappropriate model evaluations and instances of data
leakage. In this study, we present a streamlined workflow paradigm for
preprocessing ECG signals into consistent 10-second durations, eliminating the
need for manual feature extraction/beat detection. We also propose a hybrid
model of Long Short-Term Memory (LSTM) with Support Vector Machine (SVM) for
fraud detection. This architecture consists of two LSTM layers and an SVM
classifier, which achieves a SOTA results with an Average precision score of
0.9402 on the MIT-BIH arrhythmia dataset and 0.9563 on the MIT-BIH atrial
fibrillation dataset. Based on the results, we believe our method can
significantly benefit the early detection and management of CVDs.


  We study the variational problem as described by Balaban in his
renormalization group method for Yang-Mills theories in $d= 3, 4$ and adapt it
to a class of Non-Linear Sigma Models in $d=2$. The result of the variational
problem is a minimal configuration which can serve as a classical background
field in the renormalization group analysis.


  For the continuous Wigner function and for certain discrete Wigner functions,
permuting the values of the Wigner function in accordance with a symplectic
linear transformation is equivalent to performing a certain unitary
transformation on the state. That is, performing this unitary transformation is
simply a matter of moving Wigner-function values around in phase space. This
result holds in particular for the simplest discrete Wigner function defined on
a $d \times d$ phase space when the Hilbert-space dimension $d$ is odd. It does
not hold for a $d \times d$ phase space if the dimension is even. Here we show,
though, that a generalized version of this correspondence does apply in the
case of a two-qubit phase space. In this case, a symplectic linear permutation
of the points of the phase space, together with a certain reinterpretation of
the Wigner function, is equivalent to a unitary transformation.


  Shortest paths play an important role in mathematical modeling and image
processing. Usually, shortest path problems are formulated on planar graphs
that consist of vertices and weighted arcs. In this context, one is interested
in finding a path of minimum weight from a start vertex to an end vertex. The
concept of minimum-weight surfaces extends shortest paths to 3d. The
minimum-weight surface problem is formulated on a cellular complex with
weighted facets. A cycle on the arcs of the complex serves as input and one is
interested in finding a surface of minimum weight bounded by that cycle. In
practice, minimum-weight surfaces can be used to segment 3d images. Vice versa,
it is possible to use them as a modeling tool for geometric structures such as
cracks. In this work, we present an approach for using minimum-weight surfaces
in bounded Voronoi diagrams to generate synthetic 3d images of cracks.


  Investigating lateral electrical transport in p-type thin film chalcogenides
is important to evaluate their potential for field-effect transistors (FETs)
and phase-change memory applications. For instance, p-type FETs with sputtered
materials at low temperature (<= 250 C) could play a role in flexible
electronics or back-end-of-line (BEOL) silicon-compatible processes. Here, we
explore lateral transport in chalcogenide films (Sb2Te3, Ge2Sb2Te5, Ge4Sb6Te7)
and multilayers, with Hall measurements (in <= 50 nm thin films) and with
p-type transistors (in <= 5 nm ultrathin films). The highest Hall mobilities
are measured for Sb2Te3/GeTe superlattices (~18 cm2/V/s at room temperature),
over 2-3x higher than the other films. In ultrathin p-type FETs with Ge2Sb2Te5,
we achieve field-effect mobility up to ~5.5 cm2/V/s with current on/off ratio
~10000, the highest for Ge2Sb2Te5 transistors to date. We also explore process
optimizations (e.g., AlOx capping layer, type of developer for lithography) and
uncover their trade-offs towards the realization of p-type transistors with
acceptable mobility and on/off current ratio. Our study provides essential
insights into the optimization of electronic devices based on p-type
chalcogenides.


  We present a new model which represents data as a mixture of simplices.
Simplices are geometric structures that generalize triangles. We give a simple
geometric understanding that allows us to learn a simplicial structure
efficiently. Our method requires that the data are unit normalized (and thus
lie on the unit sphere). We show that under this restriction, building a model
with simplices amounts to constructing a convex hull inside the sphere whose
boundary facets is close to the data. We call the boundary facets of the convex
hull that are close to the data Activated Simplices. While the total number of
bases used to build the simplices is a parameter of the model, the dimensions
of the individual activated simplices are learned from the data. Simplices can
have different dimensions, which facilitates modeling of inhomogeneous data
sources. The simplicial structure is bounded --- this is appropriate for
modeling data with constraints, such as human elbows can not bend more than 180
degrees. The simplices are easy to interpret and extremes within the data can
be discovered among the vertices. The method provides good reconstruction and
regularization. It supports good nearest neighbor classification and it allows
realistic generative models to be constructed. It achieves state-of-the-art
results on benchmark datasets, including 3D poses and digits.


  Adsorption of 4He on atomically flat substrates such as graphene provides a
route towards the engineering of low dimensional quantum phases including
superfluids and strongly interacting insulators. In this study, we explore the
effects of graphene corrugation on the helium adsorption process via quantum
Monte Carlo simulations in the grand canonical ensemble. We utilize an
empirical adsorption potential based on the superposition of individual
helium-carbon interactions and systematically control corrugation, from a
smooth membrane to the fully rough potential, via the implementation of a
cutoff in reciprocal space. The results highlight the importance of using a
fully corrugated potential to understand the plethora of commensurate and
incommensurate phases in the first adsorbed layer. Surprisingly, some residual
effects of corrugation are still present before and during the promotion and
onset of a second adsorbed layer, where a smooth adsorption can lead to
enhanced particle fluctuations in a helium-interaction dominated regime.


  This work studies microlensing effects in strongly lensed gravitational wave
(GW) signals corresponding to global minima images in galaxy-scale lenses. We
find that stellar microlenses alone are unable to introduce noticeable wave
effects in the global minima GW signals at strong lensing magnification
$(\mu)<50$ with match value between unlensed and lensed GW signals being above
${\sim}99.5\%$ in ${\sim}90\%$ of systems implying that GW signals
corresponding to global minima can be treated as reference signal to determine
the amount of microlensing in other strongly lensed counterparts. Since the
stellar microlenses introduce negligible wave effects in global minima, they
can be used to probe the intermediate-mass black hole (IMBH) lenses in the
galaxy lens. We show that the presence of an IMBH lens with mass in the range
$[50,10^3] {\rm M_\odot}$ such that the global minima lies within five Einstein
radius of it, the microlensing effects at $f<10^2$Hz are mainly determined by
the IMBH lens for $\mu<50$. Assuming that a typical strong lensing
magnification of 3.8 and high enough signal-to-noise ratio (in the range
${\simeq}[10, 30]$) to detect the microlensing effect in GW signals
corresponding to global minima, with non-detection of IMBH-led microlensing
effects in ${\simeq}15\:({\simeq}150)$ lensed GW signals, we can rule out dark
matter fraction $>10\%\:(>1\%)$ made of IMBH population inside galaxy lenses
with mass values $>150 {\rm M_\odot}$ with ${\sim}90\%$ confidence. Although we
have specifically used IMBHs as an example, the same analysis applies to any
subhalo (or compact objects) with lensing masses (i.e., the total mass inside
Einstein radius) satisfying the above criterion.


  We consider the approximate U(2)^3 flavour symmetry exhibited by the quark
sector of the Standard Model and all its possible breaking terms appearing in
the quark Yukawa couplings. Taking an Effective Field Theory point of view, we
determine the current bounds on these parameters, assumed to control the
breaking of flavour in a generic extension of the Standard Model at a reference
scale Lambda. In particular, a significant bound from epsilon'/epsilon is
derived, which is relevant to Minimal Flavour Violation as well. In the
up-quark sector, the recently observed CP violation in D -> pi+ pi-, K+ K-
decays might be accounted for in this generic framework, consistently with any
other constraint.


  This paper studies the joint design of optimal convolutional codes (CCs) and
CRC codes when serial list Viterbi algorithm (S-LVA) is employed in order to
achieve the target frame error rate (FER). We first analyze the S-LVA
performance with respect to SNR and list size, repsectively, and prove the
convergence of the expected number of decoding attempts when SNR goes to the
extreme. We then propose the coded channel capacity as the criterion to jointly
design optimal CC-CRC pair and optimal list size and show that the optimal list
size of S-LVA is always the cardinality of all possible CCs. With the maximum
list size, we choose the design metric of optimal CC-CRC pair as the SNR gap to
random coding union (RCU) bound and the optimal CC-CRC pair is the one that
achieves a target SNR gap with the least complexity. Finally, we show that a
weaker CC with a strong optimal CRC code could be as powerful as a strong CC
with no CRC code.


  We introduce a notion of index for shrinkers of the mean curvature flow. We
then prove a gap theorem for the index of rotationally symmetric immersed
shrinkers in R^3, namely, that such shrinkers have index at least 3, unless
they are one of the stable ones: the sphere, the cylinder, or the plane. We
also provide a generalization of the result to higher dimensions.


  Multi-wavelength observations are mandatory to understand the physical
properties of astrophysical sources. In this paper we use observations in the
far infrared to radio range to derive the spectral energy distribution (SED) of
the Carina nebula. To do this, we carefully subtract the irregularly varying
diffuse emission from the Galactic plane, which can be of the order of 10 of
the nebula flux at these wavelengths. We find that the far infrared SED can be
modeled as emission from a dust population with a single temperature
T_d=(34.5^{+2.0}_{-1.8}), and with a spectral index of emissivity
alpha=-1.37^{+0.09}_{-0.08}. We also find a total infrared luminosity of the
Nebula of (7.4^{+2.5}_{-1.4})x10^6 L_{\odot} and, assuming a single temperature
of the dust, a mass of the dust of (9500^{+4600}_{-3500})M_\odot.


  The formation of inorganic dust in circumstellar environments of evolved
stars is poorly understood. Spectra of molecules thought to be most important
for the nucleation, i.e. AlO, TiO, and TiO2, have been recently detected in the
red supergiant VY CMa. These molecules are effectively formed in VY CMa and the
observations suggest that non-equilibrium chemistry must be involved in their
formation and nucleation into dust. In addition to exploring the recent
observations of VY CMa, we briefly discuss the possibility of detecting these
molecules in the dust-poor circumstellar environment of Betelgeuse.


  Let $D$ be an integral domain and $X$ an indeterminate over $D$. It is well
known that (a) $D$ is quasi-Pr\"ufer (i.e, its integral closure is a Pr\"ufer
domain) if and only if each upper to zero $Q$ in $D[X] $ contains a polynomial
$g \in D[X]$ with content $\co_D(g) = D$; (b) an upper to zero $Q$ in $D[X]$ is
a maximal $t$-ideal if and only if $Q$ contains a nonzero polynomial $g \in
D[X]$ with $\co_D(g)^v = D$. Using these facts, the notions of UM$t$-domain
(i.e., an integral domain such that each upper to zero is a maximal $t$-ideal)
and quasi-Pr\"ufer domain can be naturally extended to the semistar operation
setting and studied in a unified frame. In this paper, given a semistar
operation $\star$ in the sense of Okabe-Matsuda, we introduce the
$\star$-quasi-Pr\"ufer domains. We give several characterizations of these
domains and we investigate their relations with the UM$t$-domains and the
Pr\"ufer $v$-multiplication domains.


  This paper proposes a new logic optimization paradigm based on circuit
simulation, which reduces the need for Boolean computations such as SAT-solving
or constructing BDDs. The paper develops a Boolean resubstitution framework to
demonstrate the effectiveness of the proposed approach. Methods to generate
highly expressive simulation patterns are developed, and the generated patterns
are used in resubstitution for efficient filtering of potential resubstitution
candidates to reduce the need for SAT validation. Experimental results show
that improvements in circuit size reduction were achieved by up to 74%,
compared to a state-of-the-art resubstitution algorithm.


  Big Data are growing at an exponential rate and it becomes necessary the use
of tools and technologies to manage, process and visualize them in order to
extract value. In this paper a micro-service based platform is presented for
the composition, deployment and execution of Big Data Analytics (BDA)
application workflows in several domains and scenarios is presented. ALIDA is a
result coming from previous research activities by ENGINEERING. It aims to
achieve a unified platform that allows both BDA application developers and data
analysts to interact with it. Developers will be able to register new BDA
applications through the exposed API and/or through the web user interface.
Data analysts will be able to use the BDA applications provided to create
batch/stream workflows through a dashboard user interface to manipulate and
subsequently visualize results from one or more sources. The platform also
supports the auto-tuning of Big Data frameworks deployment properties to
improve metrics for analytics application. ALIDA has been properly extended and
integrated into a software solution for the analysis of large amounts of data
from the avionic industries. A use case within this context is then presented.


  Anisotropic Bianchi-III cosmological model is investigated with variable
gravitational and cosmological constants in the framework of Einstein's general
relativity. The shear scalar is considered to be proportional to the expansion
scalar. The dynamics of the anisotropic universe with variable G and Lambda are
discussed. Without assuming any specific forms for Lambda and the metric
potentials, we have tried to extract the time variation of G and Lambda from
the anisotropic model. The extracted G and Lambda are in conformity with the
present day observation. Basing upon the observational limits, the behaviour
and range of the effective equation of state parameter are discussed.


  We show a precise formula, in the form of a monomial, for certain families of
parabolic Kazhdan-Lusztig polynomials of the symmetric group.
  The proof stems from results of Lapid-Minguez on irreducibility of products
in the Bernstein-Zelevinski ring. By quantizing those results into a statement
on quantum groups and their canonical bases, we obtain identities of
coefficients of certain transition matrices that relate Kazhdan-Lusztig
polynomials to their parabolic analogues.
  This affirms some basic cases of conjectures raised recently by Lapid.


  Recently, CLIP-based approaches have exhibited remarkable performance on
generalization and few-shot learning tasks, fueled by the power of contrastive
language-vision pre-training. In particular, prompt tuning has emerged as an
effective strategy to adapt the pre-trained language-vision models to
downstream tasks by employing task-related textual tokens. Motivated by this
progress, in this work we question whether other fundamental problems, such as
weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning.
Our findings reveal two interesting observations that shed light on the impact
of prompt tuning on WSSS. First, modifying only the class token of the text
prompt results in a greater impact on the Class Activation Map (CAM), compared
to arguably more complex strategies that optimize the context. And second, the
class token associated with the image ground truth does not necessarily
correspond to the category that yields the best CAM. Motivated by these
observations, we introduce a novel approach based on a PrOmpt cLass lEarning
(POLE) strategy. Through extensive experiments we demonstrate that our simple,
yet efficient approach achieves SOTA performance in a well-known WSSS
benchmark. These results highlight not only the benefits of language-vision
models in WSSS but also the potential of prompt learning for this problem. The
code is available at https://github.com/rB080/WSS_POLE.


  Since the launch of the Rossi X-ray Timing Explorer in 1995 our understanding
of jetted outflows has significantly improved. Indeed, relativistic jets are
now believed to be a fairly ubiquitous property of accreting compact objects,
that are intimately coupled with the accretion history. In this review, we
summarize the observational connections in X-ray binaries between accretion
flows and relativistic outflows (especially the relation with the X-ray
states). We emphasize those aspects that have significantly benefited from the
RXTE experiment, including the role that jets could play at high energies. We
also review recent observations of large scale relativistic jets that could
point to their long term effects on the interstellar medium.


  We report on spectroscopic and photometric observations through transits of
the exoplanets WASP-22b and WASP-26b, intended to determine the systems'
spin-orbit angles. We combine these data with existing data to refine the
system parameters. We measure a sky-projected spin-orbit angle of 22 +/- 16 deg
for WASP-22b, showing the planet's orbit to be prograde and, perhaps, slightly
misaligned. We do not detect the Rossiter-McLaughlin effect of WASP-26b due to
its low amplitude and observation noise. We place 3-sigma upper limits on
orbital eccentricity of 0.063 for WASP-22b and 0.050 for WASP-26b. After
refining the drift in the systemic velocity of WASP-22 found by Maxted et al.
(2010, AJ, 140, 2007) we find the third body in the system to have a
minimum-mass of 5.3 +/- 0.3 Mjup (a3/5 AU)^2, where a3 is the orbital distance
of the third body.


  Epilepsy is a neurological disorder with recurrent seizures of complexity and
randomness. Until now, the mechanism of epileptic randomness has not been fully
elucidated. Inspired by the recent finding that astrocyte GTPase-activating
protein (G-protein)-coupled receptors could be involved in stochastic epileptic
seizures, we proposed a neuron-astrocyte network model, incorporating the noise
of the astrocytic second messager, inositol triphosphate (IP3) which is
modulated by the G-protein)-coupled receptor activation. Based on this model,
we have statistically analysed the transitions of epileptic seizures by
performing tens of simulation trials. Our simulation results show that the
increase of the IP3 noise intensity induces the depolarization-block epileptic
seizures together with an increase in neuronal firing frequency. Meanwhile, a
bistable state of neuronal firing emerges under certain noise intensity, during
which the neuronal firing pattern switches between regular sparse spiking and
epileptic seizure states. This random presence of epileptic seizures is absent
when the noise intensity continues to increase, accompanying with an increase
in the epileptic depolarization block duration. The simulation results also
shed light on the fact that calcium signals in astrocytes play significant
roles in the pattern formations of the epileptic seizure. Our results provide a
potential pathway for understanding the epileptic randomness.


  (abridged) The interaction of a fast wind with a spherical Asymptotic Giant
Branch (AGB) wind is thought to be the basic mechanism for shaping
Pre-Planetary Nebulae (PPN) and later Planetary Nebulae (PN). Due to the large
speed of the fast wind, one expects extended X-ray emission from these objects,
but X-ray emission has only been detected in a small fraction of PNs and only
in one PPN. Using numerical simulations we investigate the constraints that can
be set on the physical properties of the fast wind (speed, mass-flux, opening
angle) in order to produce the observed X-ray emission properties of PPNs and
PNs. We combine numerical hydrodynamical simulations including radiative
cooling using the code FLASH with calculations of the X-ray properties of the
resulting expanding hot bubble using the atomic database ATOMDB. In this first
study, we compute X-ray fluxes and spectra using one-dimensional models.
Comparing our results with analytical solutions, we find some agreements and
many disagreements. In particular, we test the effect of different time
histories of the fast wind on the X-ray emission and find that it is determined
by the final stage of the time history during which the fast wind velocity has
its largest value. The disagreements which are both qualitative and
quantitative in nature argue for the necessity of using numerical simulations
for understanding the X-ray properties of PNs.


  We study the prompt production of the $\chi_c(1^+)$ and $\chi_b(1^+)$ mesons
at high energies. Unlike $\chi(0^+,2^+)$ production, $\chi (1^+)$ mesons cannot
be created at LO via the fusion of two on-mass-shell gluons, that is
$gg\to\chi_{c,b}(1^+)$ are not allowed. However, the available experimental
data show that the cross sections for $\chi_c(1^+)$ and $\chi_c(2^+)$ are
comparable. We therefore investigate four other $\chi(1^+)$ production
mechanisms: namely, (i) the standard NLO process $gg\to\chi_{c,b}(1^+)+g$, (ii)
via gluon virtuality, (iii) via gluon reggeization and, finally, (iv) the
possibility to form $\chi_{c,b}(1^+)$ by the fusion of three gluons, where one
extra gluon comes from another parton cascade, as in the Double Parton
Scattering processes.


  Purpose: Echo modulation curve (EMC) modeling can provide accurate and
reproducible quantification of T2 relaxation times. The standard EMC-T2 mapping
framework, however, requires sufficient echoes and cumbersome pixel-wise
dictionary-matching steps. This work proposes a deep learning version of EMC-T2
mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps
from fewer echoes without a dictionary.
  Methods: DeepEMC-T2 mapping was developed using a modified U-Net to estimate
both T2 and Proton Density (PD) maps directly from multi-echo spin-echo (MESE)
images. The modified U-Net employs several new features to improve the accuracy
of T2/PD estimation. MESE datasets from 68 subjects were used for training and
evaluation of the DeepEMC-T2 mapping technique. Multiple experiments were
conducted to evaluate the impact of the proposed new features on DeepEMC-T2
mapping.
  Results: DeepEMC-T2 mapping achieved T2 estimation errors ranging from 3%-12%
in different T2 ranges and 0.8%-1.7% for PD estimation with 10/7/5/3 echoes,
which yielded more accurate parameter estimation than standard EMC-T2 mapping.
The new features proposed in DeepEMC-T2 mapping enabled improved parameter
estimation. The use of a larger echo spacing with fewer echoes can maintain the
accuracy of T2 and PD estimations while reducing the number of 180-degree
refocusing pulses.
  Conclusions: DeepEMC-T2 mapping enables simplified, efficient, and accurate
T2 quantification directly from MESE images without a time-consuming
dictionary-matching step and requires fewer echoes. This allows for increased
volumetric coverage and/or decreased SAR by reducing the number of 180-degree
refocusing pulses.


  Gaussian processes provide a compact representation for modeling and
estimating an unknown function, that can be updated as new measurements of the
function are obtained. This paper extends this powerful framework to the case
where the unknown function dynamically changes over time. Specifically, we
assume that the function evolves according to an integro-difference equation
and that the measurements are obtained locally in a spatial sense. In this
setting, we will provide the expressions for the conditional mean and
covariance of the process given the measurements, which results in a
generalized estimation framework, for which we coined the term Dynamic Gaussian
Process (DGP) estimation. This new framework generalizes both Gaussian process
regression and Kalman filtering. For a broad class of kernels, described by a
set of basis functions, fast implementations are provided. We illustrate the
results on a numerical example, demonstrating that the method can accurately
estimate an evolving continuous function, even in the presence of noisy
measurements and disturbances.


  We present the gauged N=4 (half-maximal) supergravities in four and five
spacetime dimensions coupled to an arbitrary number of vector multiplets. The
gaugings are parameterized by a set of appropriately constrained constant
tensors, which transform covariantly under the global symmetry groups SL(2) x
SO(6,n) and SO(1,1) x SO(5,n), respectively. In terms of these tensors the
universal Lagrangian and the Killing Spinor equations are given. The known
gaugings, in particular those originating from flux compactifications, are
incorporated in the formulation, but also new classes of gaugings are found.
Finally, we present the embedding chain of the five dimensional into the four
dimensional into the three dimensional gaugings, thereby showing how the
deformation parameters organize under the respectively larger duality groups.


  We present a spectroscopic analysis of the central disc regions of barred
spiral galaxies, concentrating on the region that is swept by the bar but not
including the bar itself (the `Star Formation Desert' or SFD region). New
spectroscopy is presented for 34 galaxies, and the full sample analysed
comprises 48 SBa - SBcd galaxies. These data confirm the full suppression of
star formation within the SFD regions of all but the latest type (SBcd)
galaxies. However, diffuse [NII] and H alpha line emission is detected in all
galaxies. The ubiquity and homogeneous properties of this emission from SBa -
SBc galaxies favour post-Asymptotic Giant Branch (p-AGB) stars as the source of
this line excitation, rather than extreme Blue Horizontal Branch stars. The
emission-line ratios strongly exclude any contribution from recent star
formation, but are fully consistent with recent population synthesis modelling
of p-AGB emission by other authors, and favour excitation dominated by ambient
gas of approximately solar abundance, rather than ejecta from the AGB stars
themselves. The line equivalent widths are also larger than those observed in
many fully passive (e.g. elliptical) galaxies, which may also be a consequence
of a greater ambient gas density in the SFD regions.


  We provide the cosmological solutions for Randall-Sundrum models with the
bulk energy-momentum $\hat T^5_5$ incorporated. It alters the Friedmann
equation for the brane scale factor. We make a specific choice of $\hat T^5_5$
which is adjusted to stabilize the extra dimension. This makes it possible to
compactify the extra dimension with a single positive tension brane, and this
model provides a RS-type solution to the cosmological constant problem. When
the same idea is applied to the RS model with two branes, the wrong sign of
Friedmann equation for the negative tension brane can be resolved and usual FRW
cosmology is reproduced for the brane.


  Deep imitation learning requires many expert demonstrations, which can be
hard to obtain, especially when many tasks are involved. However, different
tasks often share similarities, so learning them jointly can greatly benefit
them and alleviate the need for many demonstrations. But, joint multi-task
learning often suffers from negative transfer, sharing information that should
be task-specific. In this work, we introduce a method to perform multi-task
imitation while allowing for task-specific features. This is done by using
proto-policies as modules to divide the tasks into simple sub-behaviours that
can be shared. The proto-policies operate in parallel and are adaptively chosen
by a selector mechanism that is jointly trained with the modules. Experiments
on different sets of tasks show that our method improves upon the accuracy of
single agents, task-conditioned and multi-headed multi-task agents, as well as
state-of-the-art meta learning agents. We also demonstrate its ability to
autonomously divide the tasks into both shared and task-specific
sub-behaviours.


  The recently observed by CLEO cascade decays Upsilon(3S) -> gamma
chi_{bJ}^\prime -> gamma omega Upsilon are discussed. It is shown that within
the nonrelativistic description of bottomonium in the heavy b quark limit, the
ratio of the rates of the transitions chi_{bJ}^\prime -> omega Upsilon with J=1
and J=2 should be given by the ratio of the corresponding S wave phase space
factors. As a result, the rate of the observed cascade transitions through the
chi_{b2}^\prime resonance should be close to that through the chi_{b1}^\prime.
It is suggested that the ratio of the discussed cascade rates can also be
tested by measuring a simple angular correlation.


  Supervised learning is an active research area, with numerous applications in
diverse fields such as data analytics, computer vision, speech and audio
processing, and image understanding. In most cases, the loss functions used in
machine learning assume symmetric noise models, and seek to estimate the
unknown function parameters. However, loss functions such as quantile and
quantile Huber generalize the symmetric $\ell_1$ and Huber losses to the
asymmetric setting, for a fixed quantile parameter. In this paper, we propose
to jointly infer the quantile parameter and the unknown function parameters,
for the asymmetric quantile Huber and quantile losses. We explore various
properties of the quantile Huber loss and implement a convexity certificate
that can be used to check convexity in the quantile parameter. When the loss if
convex with respect to the parameter of the function, we prove that it is
biconvex in both the function and the quantile parameters, and propose an
algorithm to jointly estimate these. Results with synthetic and real data
demonstrate that the proposed approach can automatically recover the quantile
parameter corresponding to the noise and also provide an improved recovery of
function parameters. To illustrate the potential of the framework, we extend
the gradient boosting machines with quantile losses to automatically estimate
the quantile parameter at each iteration.


  We present a new approach to investigate the coexistence of
antiferromagnetism and d-wave superconductivity in the two dimensional extended
Hubbard model within a numerically exact cluster dynamical mean-field
approximation. Self-consistent solutions with two non-zero order parameters
exists in the wide range of doping level and temperatures. A linearized
equation for energy spectrum near the Fermi level have been solved. The
resulting d-wave gap has the correct magnitude and k-dependence but some
distortion compare to the pure d_{x^2-y^2} superconducting order parameter due
to the presence of underlying antiferromagnetic ordering.


  Efficient and accurate object detection in video and image analysis is one of
the major beneficiaries of the advancement in computer vision systems with the
help of deep learning. With the aid of deep learning, more powerful tools
evolved, which are capable to learn high-level and deeper features and thus can
overcome the existing problems in traditional architectures of object detection
algorithms. The work in this thesis aims to achieve high accuracy in object
detection with good real-time performance.
  In the area of computer vision, a lot of research is going into the area of
detection and processing of visual information, by improving the existing
algorithms. The binarized neural network has shown high performance in various
vision tasks such as image classification, object detection, and semantic
segmentation. The Modified National Institute of Standards and Technology
database (MNIST), Canadian Institute for Advanced Research (CIFAR), and Street
View House Numbers (SVHN) datasets are used which is implemented using a
pre-trained convolutional neural network (CNN) that is 22 layers deep.
Supervised learning is used in the work, which classifies the particular
dataset with the proper structure of the model. In still images, to improve
accuracy, Googlenet is used. The final layer of the Googlenet is replaced with
the transfer learning to improve the accuracy of the Googlenet. At the same
time, the accuracy in moving images can be maintained by transfer learning
techniques. Hardware is the main backbone for any model to obtain faster
results with a large number of datasets. Here, Nvidia Jetson Nano is used which
is a graphics processing unit (GPU), that can handle a large number of
computations in the process of object detection. Results show that the accuracy
of objects detected by the transfer learning method is more when compared to
the existing methods.


  In this paper we develop the classical multiplier technique to prove a virial
identity and smoothing estimates (in a perturbative setting) for the
electromagnetic variable coefficients Schroedinger equation.


  It is shown that crystals of molecular nanomagnets exhibit enhanced magnetic
relaxation when placed inside a resonant cavity. Strong dependence of the
magnetization curve on the geometry of the cavity has been observed, providing
evidence of the coherent microwave radiation by the crystals. A similar
dependence has been found for a crystal placed between Fabry-Perot
superconducting mirrors. These observations open the possibility of building a
nanomagnetic microwave laser pumped by the magnetic field.


  Differential privacy offers formal quantitative guarantees for algorithms
over datasets, but it assumes attackers that know and can influence all but one
record in the database. This assumption often vastly overapproximates the
attackers' actual strength, resulting in unnecessarily poor utility.
  Recent work has made significant steps towards privacy in the presence of
partial background knowledge, which can model a realistic attacker's
uncertainty. Prior work, however, has definitional problems for correlated data
and does not precisely characterize the underlying attacker model. We propose a
practical criterion to prevent problems due to correlations, and we show how to
characterize attackers with limited influence or only partial background
knowledge over the dataset. We use these foundations to analyze practical
scenarios: we significantly improve known results about the privacy of counting
queries under partial knowledge, and we show that thresholding can provide
formal guarantees against such weak attackers, even with little entropy in the
data. These results allow us to draw novel links between k-anonymity and
differential privacy under partial knowledge. Finally, we prove composition
results on differential privacy with partial knowledge, which quantifies the
privacy leakage of complex mechanisms.
  Our work provides a basis for formally quantifying the privacy of many
widely-used mechanisms, e.g. publishing the result of surveys, elections or
referendums, and releasing usage statistics of online services.


  Recently, intensive studies have revealed fascinating physics, such as charge
density wave and superconducting states, in the newly synthesized
kagome-lattice materials $A$V$_3$Sb$_5$ ($A$=K, Rb, Cs). Despite the rapid
progress, fundamental aspects like the magnetic properties and electronic
correlations in these materials have not been clearly understood yet. Here,
based on the density functional theory plus the single-site dynamical
mean-field theory calculations, we investigate the correlated electronic
structure and the magnetic properties of the KV$_3$Sb$_5$ family materials in
the normal state. We show that these materials are good metals with weak local
correlations. The obtained Pauli-like paramagnetism and the absence of local
moments are consistent with recent experiment. We reveal that the band
crossings around the Fermi level form three groups of nodal lines protected by
the spacetime inversion symmetry, each carrying a quantized $\pi$ Berry phase.
Our result suggests that the local correlation strength in these materials
appears to be too weak to generate unconventional superconductivity, and
non-local electronic correlation might be crucial in this kagome system.


  The recent debate on hyper-computation has raised new questions both on the
computational abilities of quantum systems and the Church-Turing Thesis role in
Physics. We propose here the idea of geometry of effective physical process as
the essentially physical notion of computation. In Quantum mechanics we cannot
use the traditional Euclidean geometry but we introduce more sophisticate non
Euclidean geometry which include a new kind of information diffuse in the
entire universe and that we can represent as Fisher information or active
information. We remark that from the Fisher information we can obtain the Bohm
and Hiley quantum potential and the classical Schrodinger equation. We can see
the quantum phenomena do not affect a limited region of the space but is
reflected in a change of the geometry of all the universe. In conclusion any
local physical change or physical process is reflected in all the universe by
the change of its geometry, This is the deepest meaning of the entanglement in
Quantum mechanics and quantum computing. We stress the connection between
metric and information as measure of change. Because computation is not
restricted to calculus but is the environment changing via physical processes,
super-Turing potentialities derive from an incomputable information source
embedded into the geometry of the universe in accordance with Bell's
constraints. In the general relativity we define the geometry of the space
time. In our approach quantum phenomena define the geometry of the parameters
of the probability distribution that include also the space time parameters. To
study this new approach to the computation we use the new theory of Morphogenic
systems.


  We prove that all finite graphs of groups with cyclic vertex and edge groups
act freely and isometrically on a complete, nonpositively curved geodesic
metric space.