  Let $k$ be a field, $n \geqslant 5$ be an integer, $x_1, \dots, x_n$ be
independent variables and $L_n = k(x_1, \dots, x_n)$. The symmetric group $S_n$
acts on $L_n$ by permuting the variables, and the projective linear group ${\rm
PGL}_2$ acts by applying (the same) fractional linear transformation to each
varaible. The fixed field $L_n^{{\rm PGL}_2}$ is called "the field of
cross-ratios". Let $S \subset S_n$ be a subgroup. The Noether Problem asks
whether the field extension $L_n^S/k$ is rational, and the Noether Problem for
cross-ratios asks whether $K_n^S/k$ is rational. In an effort to relate these
two problems, H. Tsunogai posed the following question: Is $L_n^S$ rational
over $K_n^S$? He answered this question in several situations, in particular,
in the case where $S = S_n$. In this paper we extend his results by recasting
the problem in terms of Galois cohomology. Our main theorem asserts that the
following conditions on a subgroup $S \subset S_n$ are equivalent: (a) $L_n^S$
is rational over $K_n^S$, (b) $L_n^S$ is unirational over $K_n^S$, (c) $S$ has
an orbit of odd order in $\{1, \dots, n \}$.


  We formulate the WT identity for proper vertices in a simple and compact form
$\partial \Gamma / {\partial \theta } =0 $ in a superspace formulation of gauge
theories proposed earlier. We show this WT identity (together with a subsidiary
constraint) lead, in transparent way, the superfield superspace multiplet
renormalizations formulated earlier (and shown to explain symmetries of
Yang-Mills theory renormalization).


  The theoretical understanding of plasmon behavior is crucial for an accurate
interpretation of inelastic scattering diagnostics in many experiments. We
highlight the utility of linear-response time-dependent density functional
theory (LR-TDDFT) as a first-principles framework for consistently modeling
plasmon properties. We provide a comprehensive analysis of plasmons in aluminum
from ambient to warm dense matter conditions and assess typical properties such
as the dynamical structure factor, the plasmon dispersion, and the plasmon
lifetime. We compare our results with scattering measurements and with other
TDDFT results as well as models such as the random phase approximation, the
Mermin approach, and the dielectric function obtained using static local field
corrections of the uniform electron gas parametrized from path integral Monte
Carlo simulations. We conclude that results for the plasmon dispersion and
lifetime are inconsistent between experiment and theories and that the common
practice of extracting and studying plasmon dispersion relations is an
insufficient procedure to capture the complicated physics contained in the
dynamic structure factor in its full breadth.


  We carry out a rigorous error analysis of the first-order semi-discrete (in
time) consistent splitting scheme coupled with a generalized scalar auxiliary
variable (GSAV) approach for the Navier-Stokes equations with no-slip boundary
conditions. The scheme is linear, unconditionally stable, and only requires
solving a sequence of Poisson type equations at each time step. By using the
build-in unconditional stability of the GSAV approach, we derive optimal global
(resp. local) in time error estimates in the two (resp. three) dimensional case
for the velocity and pressure approximations.


  We study, by numerical simulations on a lattice, the behaviour of the
gauge-invariant quark-antiquark nonlocal condensates in the QCD vacuum with
dynamical fermions. A determination is also done in the quenched approximation
and the results are compared with the full-QCD case. The fermionic correlation
length is extracted and compared with the analogous gluonic quantity.


  In this paper, we design, analyze and implement efficient time parallel
method for a class of fourth order time-dependent partial differential
equations (PDEs), namely biharmonic heat equation, linearized Cahn-Hilliard
(CH) equation and the nonlinear CH equation. We use diagonalization technique
on all-at-once system to develop efficient iterative time parallel methods for
investigating the solution behaviour of said equations. We present the
convergence analysis of Parallel-in-Time (PinT) algorithms. We verify our
findings by presenting numerical results.


  Wave shoaling of water waves over mild bottom slopes is well described by
linearized theories. However, the analytical treatment of nonlinear wave
shoaling subject to rapidly varying bottoms has proven to be elusive in the
past decades. As the spatial evolution of the exceedance probability of
irregular waves is affected by second-order effects in steepness, the nonlinear
shoaling coefficient throughout a symmetrical and steep breakwater is
investigated through a stochastic framework. By inverting the effect of slope
on normalized wave height distribution, it is possible to obtain a closed-form
slope dependence of the nonlinear shoaling coefficient compatible with
experiments over steep breakwaters.


  We construct a poset from a simple acyclic digraph together with a valuation
on its vertices, and we compute the values of its M\"obius function. We show
that the weak order on Coxeter groups of type A, B, affine A, and the flag weak
order on the wreath product $\mathbb{Z} \_r \wr S\_n$ introduced by Adin,
Brenti and Roichman, are special instances of our construction. We conclude by
associating a quasi-symmetric function to each element of these posets. In the
$A$ and $\widetilde{A}$ cases, this function coincides respectively with the
classical Stanley symmetric function, and with Lam's affine generalization.


  The power spectrum of local field potentials (LFPs) has been reported to
scale as the inverse of the frequency, but the origin of this "1/f noise" is at
present unclear. Macroscopic measurements in cortical tissue demonstrated that
electric conductivity (as well as permittivity) is frequency dependent, while
other measurements failed to evidence any dependence on frequency. In the
present paper, we propose a model of the genesis of LFPs which accounts for the
above data and contradictions. Starting from first principles (Maxwell
equations), we introduce a macroscopic formalism in which macroscopic
measurements are naturally incorporated, and also examine different physical
causes for the frequency dependence. We suggest that ionic diffusion primes
over electric field effects, and is responsible for the frequency dependence.
This explains the contradictory observations, and also reproduces the 1/f power
spectral structure of LFPs, as well as more complex frequency scaling. Finally,
we suggest a measurement method to reveal the frequency dependence of current
propagation in biological tissue, and which could be used to directly test the
predictions of the present formalism.


  presented. The 2.38 -- 4.08 micron wavelength-range of band 1 of ISO-SWS
(Short-Wavelength Spectrometers on board of the Infrared Space Observatory) in
which many different molecules -- with their own dependence on each of the
stellar parameters -- are absorbing, enables us to estimate the effective
temperature, the gravity, the microturbulence, the metallicity, the
CNO-abundances, the 12C/13C-ratio and the angular diameter from the ISO-SWS
data. Using the Hipparcos' parallax, the radius, luminosity and
gravity-inferred mass are derived. The stellar parameters obtained are in good
agreement with other published values, though also some discrepancies with
values deduced by other authors are noted. For a few stars (Delta Dra, Xi Dra,
Alpha Tuc, H Sco and Alpha Cet) some parameters -- e.g. the CNO-abundances --
are derived for the first time. By examining the correspondence between
different ISO-SWS observations of the same object and between the ISO-SWS data
and the corresponding synthetic spectrum, it is shown that the relative
accuracy of ISO-SWS in band 1 (2.38 -- 4.0 micron) is better than 2% for these
high-flux sources. The high level of correspondence between observations and
theoretical predictions, together with a confrontation of the estimated
Teff(ISO) value with Teff-values derived from colours -- which demonstrates the
consistency between V-K, BC(K), Teff and the angular diameter derived from
optical or IR data -- proves that both the used MARCS models to derive the
stellar quantities and the flux calibration of the ISO-SWS detectors have
reached a high level of reliability.


  Synthetic diamond production is key to the development of quantum metrology
and quantum information applications of diamond. The major quantum sensor and
qubit candidate in diamond is the nitrogen-vacancy (NV) color center. This
lattice defect comes in four different crystallographic orientations leading to
an intrinsic inhomogeneity among NV centers that is undesirable in some
applications. Here, we report a microwave plasma-assisted chemical vapor
decomposition (MPCVD) diamond growth technique on (111)-oriented substrates
that yields perfect alignment ($94\pm2%$) of as-grown NV centers along a single
crystallographic direction. In addition, clear evidence is found that the
majority ($74\pm4%$) of the aligned NV centers were formed by the nitrogen
being first included in the (111) growth surface and then followed by the
formation of a neighboring vacancy on top. The achieved homogeneity of the
grown NV centers will tremendously benefit quantum information and metrology
applications.


  The theory of free Majorana-Weyl spinors is the prototype of conformal field
theory in two dimensions in which the gravitational anomaly and the Weyl
anomaly obstruct extending the flat spacetime results to curved backgrounds. In
this paper, we investigate a quantization scheme in which the short distance
singularity in the two-point function of chiral fermions on a two dimensional
curved spacetime is given by the Green's function corresponding to the
classical field equation. We compute the singular term in the Green's function
explicitly and observe that the short distance limit is not well-defined in
general. We identify constraints on the geometry which are necessary to resolve
this problem. On such special backgrounds the theory has locally
$c=\frac{1}{2}$ conformal symmetry.


  We demonstrate that giant Faraday rotation in graphene in the terahertz range
due to the cyclotron resonance is further increased by constructive Fabry-Perot
interference in the supporting substrate. Simultaneously, an enhanced total
transmission is achieved, making this effect doubly advantageous for
graphene-based magneto-optical applications. As an example, we present
far-infrared spectra of epitaxial multilayer graphene grown on the C-face of
6H-SiC, where the interference fringes are spectrally resolved and a Faraday
rotation up to 0.15 radians (9{\deg}) is attained. Further, we discuss and
compare other ways to increase the Faraday rotation using the principle of an
optical cavity.


  An asymmetric Bose-Einstein condensation (BEC) dome was observed in a recent
experiment on the quantum dimer magnet $\rm Yb_2Si_2O_7$, which is modeled by a
"breathing" honeycomb lattice Heisenberg model with possible anisotropies. We
report a remarkable agreement between key experimental features and predictions
from numerical simulations of the magnetic model. Both critical fields, as well
as critical temperatures of the BEC dome, can be accurately captured, as well
as the occurrence of two regimes inside the BEC phase. Furthermore, we
investigate the role of anisotropies in the exchange coupling and the
$g$-tensor. While we confirm a previous proposal that anisotropy can induce a
zero temperature phase transition at magnetic fields smaller than the fully
polarizing field strength, we find that this effect becomes negligible at
temperatures above the anisotropy scale. Instead, the two regimes inside the
BEC dome are found to be due to a non-linear magnetization behavior of the
isotropic breathing honeycomb Heisenberg antiferromagnet. Our analysis is
performed by combining the density matrix renormalization group (DMRG) method
with the finite-temperature techniques of minimally entangled typical thermal
states (METTS) and quantum Monte Carlo (QMC).


  We numerically study the behaviour of entanglement entropy for a free scalar
field on the noncommutative ("fuzzy") sphere after a mass quench. It is known
that the entanglement entropy before a quench violates the usual area law due
to the non-local nature of the theory. By comparing our results to the ordinary
sphere, we find results that, despite this non-locality, are compatible with
entanglement being spread by ballistic propagation of entangled quasi-particles
at a speed no greater than the speed of light. However, we also find that, when
the pre-quench mass is much larger than the inverse of the short-distance
cutoff of the fuzzy sphere (a regime with no commutative analogue), the
entanglement entropy spreads faster than allowed by a local model.


  We give an algebro-geometric first-order axiomatization of DCF$_{0,m}$ (the
theory of differentially closed fields of characteristic zero with m commuting
derivations) in the spirit of the classical geometric axioms of DCF$_0$.


  Dynamics of a particle is formulated from classical principles that are
amended by the uncertainty principle. Two best known quantum effects:
interference and tunneling are discussed from these principles. It is shown
that identical to quantum results are obtained by solving only classical
equations of motion. Within the context of interference Aharonov-Bohm effect is
solved as a local action of magnetic force on the particle. On the example of
tunneling it is demonstrated how uncertainty principle amends traditional
classical mechanics: it allows the momentum of the particle to change without
the force being the cause of it.


  Latest deep learning methods for object detection provide remarkable
performance, but have limits when used in robotic applications. One of the most
relevant issues is the long training time, which is due to the large size and
imbalance of the associated training sets, characterized by few positive and a
large number of negative examples (i.e. background). Proposed approaches are
based on end-to-end learning by back-propagation [22] or kernel methods trained
with Hard Negatives Mining on top of deep features [8]. These solutions are
effective, but prohibitively slow for on-line applications. In this paper we
propose a novel pipeline for object detection that overcomes this problem and
provides comparable performance, with a 60x training speedup. Our pipeline
combines (i) the Region Proposal Network and the deep feature extractor from
[22] to efficiently select candidate RoIs and encode them into powerful
representations, with (ii) the FALKON [23] algorithm, a novel kernel-based
method that allows fast training on large scale problems (millions of points).
We address the size and imbalance of training data by exploiting the stochastic
subsampling intrinsic into the method and a novel, fast, bootstrapping
approach. We assess the effectiveness of the approach on a standard Computer
Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a
real robotic scenario with the iCubWorld Transformations [18] dataset.


  We investigate within a self-consistent theory the molecular instabilities
arising in the normal state of a homogeneous degenerate Fermi gas, covering the
whole BEC-BCS crossover. These are the standard instability for molecular
formation, the BCS instability which corresponds to the formation of Cooper
pairs and the related Bose-Einstein instability. These instabilities manifest
themselves in the properties of the particle-particle vertex, which we
calculate in a ladder approximation. To find the critical temperatures
corresponding to these various instabilities, we handle the properties of the
interacting Fermi gas on the same footing as the instabilities by making use of
the same vertex. This approximate treatment is shown to be quite satisfactory
in a number of limiting situations where it agrees with known exact results.
The results for the BCS critical temperature and for the BE condensation are
found to be in fair agreement with earlier results. The threshold for formation
of molecules at rest undergoes a sizeable shift toward the BEC side, due to
quantum effects arising from the presence of the degenerate Fermi gas. This
should make its experimental observation fairly easy. This shift remains
important at least up to temperatures comparable to the Fermi energy of the
gas.


  Quantum cryptography could well be the first application of quantum mechanics
at the individual quanta level. The very fast progress in both theory and
experiments over the recent years are reviewed, with emphasis on open questions
and technological issues.


  The case of a coupling between dark energy and matter (Coupled Quintessence)
or gravity (Extended Quintessence) has recently attracted a deep interest and
has been widely investigated both in the Einstein and in the Jordan frames (EF,
JF), within scalar tensor theories. Focusing on the simplest models proposed so
far, in this paper we study the relation existing between the two scenarios,
isolating the Weyl scaling which allows to express them in the EF and JF.
Moreover, we perform a comparative study of the behavior of linear
perturbations in both scenarios, which turn out to behave in a markedly
different way. In particular, while the clustering is enhanced in the
considered CQ models with respect to the corresponding Quintessence ones where
the coupling is absent and to the ordinary cosmologies with a Cosmological
Constant and Cold Dark Matter (LCDM), structures in EQ models may grow slower.
This is likely to have direct consequences on the inner properties of
non-linear structures, like cluster concentration, as well as on the weak
lensing shear on large scales. Finally, we specialize our study for interfacing
linear dynamics and N-body simulations in these cosmologies, giving a recipe
for the corrections to be included in N-body codes in order to take into
account the modifications to the expansion rate, growth of structures, and
strength of gravity.


  Most people who have tried to learn a foreign language would have experienced
difficulties understanding or speaking with a native speaker's accent. For
native speakers, understanding or speaking a new accent is likewise a difficult
task. An accent conversion system that changes a speaker's accent but preserves
that speaker's voice identity, such as timbre and pitch, has the potential for
a range of applications, such as communication, language learning, and
entertainment. Existing accent conversion models tend to change the speaker
identity and accent at the same time. Here, we use adversarial learning to
disentangle accent dependent features while retaining other acoustic
characteristics. What sets our work apart from existing accent conversion
models is the capability to convert an unseen speaker's utterance to multiple
accents while preserving its original voice identity. Subjective evaluations
show that our model generates audio that sound closer to the target accent and
like the original speaker.


  We construct a Hamiltonian whose ground state encodes a time-independent
emulation of quan- tum teleportation. We calculate properties of the
Hamiltonian, using exact diagonalization and a mean-field theory, and argue
that it has a gap. The system exhibits an illuminating relationship to the
well-known AKLT (Affleck, Lieb, Kennedy and Tasaki) model.


  Random motions on the line and on the plane with space-varying velocities are
considered and analyzed in this paper.
  On the line we investigate symmetric and asymmetric telegraph processes with
space-dependent velocities and we are able to present the explicit distribution
of the position $\mathcal{T}(t)$, $t>0$, of the moving particle.
  Also the case of a non-homogeneous Poisson process (with rate $\lambda =
\lambda(t)$) governing the changes of direction is analyzed in three specific
cases. For the special case $\lambda(t)= \alpha/t$ we obtain a random motion
related to the Euler-Poisson-Darboux (EPD) equation which generalizes the
well-known case treated e.g. in Foong and Van Kolck (1992), Garra and Orsingher
(2016) and Rosencrans (1973).
  A EPD--type fractional equation is also considered and a parabolic solution
(which in dimension $d=1$ has the structure of a probability density) is
obtained.
  Planar random motions with space--varying velocities and infinite directions
are finally analyzed in Section 5. We are able to present their explicit
distributions and for polynomial-type velocity structures we obtain the hyper
and hypo-elliptic form of their support (of which we provide a picture).


  Chimera states -- named after the mythical beast with a lion's head, a goat's
body, and a dragon's tail -- correspond to spatiotemporal patterns
characterised by the coexistence of coherent and incoherent domains in coupled
systems. They were first identified in 2002 in theoretical studies of spatially
extended networks of Stuart-Landau oscillators, and have been subject to
extensive theoretical and experimental research ever since. While initially
considered peculiar to networks with weak nonlocal coupling, recent theoretical
studies have predicted that chimera-like states can emerge even in systems with
purely local coupling. Here we report on the first experimental observations of
chimera-like states in a system with local coupling -- a coherently-driven Kerr
nonlinear optical resonator. We show that artificially engineered discreteness
-- realised by suitably modulating the coherent driving field -- allows for the
nonlinear localisation of spatiotemporal complexity, and we demonstrate
unprecedented control over the existence, characteristics, and dynamics of the
resulting chimera-like states. Moreover, using ultrafast time lens imaging, we
resolve the chimeras' picosecond-scale internal structure in real time.


  In the realm of deep learning-based recommendation systems, the increasing
computational demands, driven by the growing number of users and items, pose a
significant challenge to practical deployment. This challenge is primarily
twofold: reducing the model size while effectively learning user and item
representations for efficient recommendations. Despite considerable
advancements in model compression and architecture search, prevalent approaches
face notable constraints. These include substantial additional computational
costs from pre-training/re-training in model compression and an extensive
search space in architecture design. Additionally, managing complexity and
adhering to memory constraints is problematic, especially in scenarios with
strict time or space limitations. Addressing these issues, this paper
introduces a novel learning paradigm, Dynamic Sparse Learning (DSL), tailored
for recommendation models. DSL innovatively trains a lightweight sparse model
from scratch, periodically evaluating and dynamically adjusting each weight's
significance and the model's sparsity distribution during the training. This
approach ensures a consistent and minimal parameter budget throughout the full
learning lifecycle, paving the way for "end-to-end" efficiency from training to
inference. Our extensive experimental results underline DSL's effectiveness,
significantly reducing training and inference costs while delivering comparable
recommendation performance.


  This paper is concerned with testing and dating structural breaks in the
dependence structure of multivariate time series. We consider a cumulative sum
(CUSUM) type test for constant copula-based dependence measures, such as
Spearman's rank correlation and quantile dependencies. The asymptotic null
distribution is not known in closed form and critical values are estimated by
an i.i.d. bootstrap procedure. We analyze size and power properties in a
simulation study under different dependence measure settings, such as skewed
and fat-tailed distributions. To date break points and to decide whether two
estimated break locations belong to the same break event, we propose a pivot
confidence interval procedure. Finally, we apply the test to the historical
data of ten large financial firms during the last financial crisis from 2002 to
mid-2013.


  The estimation of the direction of electromagnetic (EM) waves from a radio
source using electrically short antennas is one of the challenging problems in
the field of radio astronomy. In this paper we have developed an algorithm
which performs better in direction and polarization estimations than the
existing algorithms. Our proposed algorithm Snapshot Averaged Matrix Pencil
Method (SAM) is a modification to the existing Matrix Pencil Method (MPM) based
Direction of Arrival (DoA) algorithm. In general, MPM estimates DoA of the
incoherent EM waves in the spectra using unitary transformations and least
square method (LSM). Our proposed SAM modification is made in context to the
proposed Space Electric and Magnetic Sensor (SEAMS) mission to study the radio
universe below 16 MHz. SAM introduces a snapshot averaging method to improve
the incoherent frequency estimation improving the accuracy of estimation. It
can also detect polarization to differentiate between Right Hand Circular
Polarlization (RHCP), Right Hand Elliptical Polarlization (RHEP), Left Hand
Circular Polarlization (LHCP), Left Hand Elliptical Polarlization (LHEP) and
Linear Polarlization (LP). This paper discusses the formalism of SAM and shows
the initial results of a scaled version of a DoA experiment at a resonant
frequency of ~72 MHz.


  Cosmological $N$-body simulations are the standard tool to study the
emergence of the observed large-scale structure of the Universe. Such
simulations usually solve for the gravitational dynamics of matter within the
Newtonian approximation, thus discarding general relativistic effects such as
the coupling between matter and radiation ($\equiv$ photons and neutrinos). In
this paper we investigate novel hybrid simulations which incorporate
interactions between radiation and matter to the leading order in General
Relativity, whilst evolving the matter dynamics in full non-linearity according
to Newtonian theory. Our hybrid simulations come with a relativistic space-time
and make it possible to investigate structure formation in a unified framework.
In the present work we focus on simulations initialized at $z=99$, and show
that the extracted matter power spectrum receives up to $3\%$ corrections on
very large scales through radiation. Our numerical findings compare favourably
with linear analytical results from Fidler et al. (2016), from which we deduce
that there cannot be any significant non-linear mode-coupling induced through
linear radiation corrections.


  Live video streaming has become a mainstay as a standard communication
solution for several enterprises worldwide. To efficiently stream high-quality
live video content to a large amount of offices, companies employ distributed
video streaming solutions which rely on prior knowledge of the underlying
evolving enterprise network. However, such networks are highly complex and
dynamic. Hence, to optimally coordinate the live video distribution, the
available network capacity between viewers has to be accurately predicted. In
this paper we propose a graph representation learning technique on weighted and
dynamic graphs to predict the network capacity, that is the weights of
connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural
network architecture with a self-attention mechanism to capture the evolution
of the graph structure of live video streaming events. VStreamDRLS employs the
graph convolutional network (GCN) model over the duration of a live video
streaming event and introduces a self-attention mechanism to evolve the GCN
parameters. In doing so, our model focuses on the GCN weights that are relevant
to the evolution of the graph and generate the node representation,
accordingly. We evaluate our proposed approach on the link prediction task on
two real-world datasets, generated by enterprise live video streaming events.
The duration of each event lasted an hour. The experimental results demonstrate
the effectiveness of VStreamDRLS when compared with state-of-the-art
strategies. Our evaluation datasets and implementation are publicly available
at https://github.com/stefanosantaris/vstreamdrls


  Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
  We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction.


  The field of 2D materials has gained a lot of attention for vast range of
applications. Amongst others, the sensing ability towards harmful gases is the
application, which we explored in the present work using quantum-mechanical
simulations for the SnP3 material. Its electronic properties, namely 1 and 2
layers being semiconducting, while multilayers being metallic, offer a
possibility to build a single-material logical junction. In addition, the
harmful gases studied here show physical adsorption with charge transfer from
the substrate to the gas molecules. Calculated recovery times show promise of a
good sensing material. The I-V characteristics calculated for all cases
indicates that SnP3 could be a viable sensing material towards NO gas via
negative differential resistance.


  Recent experiments on single crystals of YNi$_2$B$_2$C have revealed the
presence of point nodes in the superconducting energy gap Delta(k} at k =
(1,0,0), (0,1,0), (-1,0,0), and (0,-1,0). In this paper we investigate the
effects of impurity scattering on the quasiparticle spectrum in the vortex
state of s+g-wave superconductors, which is found to be strongly modified in
the presence of disorder. In particular, a gap in the quasiparticle energy
spectrum is found to open even for infinitesimal impurity scattering, giving
rise to exponentially activated thermodynamic response functions, such as the
specific heat, the spin susceptibility, the superfluid density, and the nuclear
spin lattice relaxation. Predictions derived from this study can be verified by
measurements of the angular dependent magnetospecific heat and the
magnetothermal conductivity.


  This thesis describes the development of some basic mathematical tools of
wide relevance to mathematical physics. Transmission and reflection
coefficients are associated with quantum tunneling phenomena, while Bogoliubov
coefficients are associated with the mathematically related problem of
excitations of a parametric oscillator. While many approximation techniques for
these quantities are known, very little is known about rigorous upper and lower
bounds. In this thesis four separate problems relating to rigorous bounds on
transmission, reflection and Bogoliubov coefficients are considered, divided
into four separate themes:
  1) Bounding the Bogoliubov coefficients;
  2) Bounding the greybody factors for Schwarzschild black holes;
  3) Transformation probabilities and the Miller--Good transformation;
  4) Analytic bounds on transmission probabilities.


  This note suggests that near earth objects and Central Force Optimization
have something in common, that NEO theory may hold the key to solving some
vexing problems in deterministic optimization: local trapping and proof of
convergence. CFO analogizes Newton's laws to locate the global maxima of a
function. The NEO-CFO nexus is the striking similarity between CFO's Davg and
an NEO's Delta-V curves. Both exhibit oscillatory plateau-like regions
connected by jumps, suggesting that CFO's metaphorical "gravity" indeed behaves
like real gravity, thereby connecting NEOs and CFO and being the basis for
speculating that NEO theory may address difficult issues in optimization.


  Artificial releases of Wolbachia-infected Aedes mosquitoes have been under
study in the past years for fighting vector-borne diseases such as dengue,
chikungunya and zika. Several strains of this bacterium cause cytoplasmic
incompatibility (CI) and can also affect their host's fecundity or lifespan,
while highly reducing vector competence for the main arboviruses .We consider
and answer the following questions: 1) what should be the initial condition
(i.e. size of the initial mosquito population) to have invasion with one
mosquito release source? We note that it is hard to have an invasion in such
case. 2) How many release points does one need to have sufficiently high
probability of invasion? 3) What happens if one accounts for uncertainty in the
release protocol (e.g. unequal spacing among release points)? We build a
framework based on existing reaction-diffusion models for the uncertainty
quantification in this context, obtain both theoretical and numerical lower
bounds for the probability of release success and give new quantitative results
on the one dimensional case.


  In this work we present for the first time an exact solution of Maxwell
equations in vacuum, having non trivial topology, in which there is an exchange
of helicity between the electric and magnetic part of such field. We calculate
the temporal evolution of the magnetic and electric helicities, and explain the
exchange of helicity making use of the Chern-Simon form. We also have found and
explained that, as time goes to infinity, both helicities reach the same value
and the exchange between the magnetic and electric part of the field stops.


  The effects of the existence of exotic nuclear shapes at the bottom of the
neutron star inner crust - nuclear `pasta' - on observational phenomena are
estimated by comparing the limiting cases that those phases have a vanishing
shear modulus and that they have the shear modulus of a crystalline solid . We
estimate the effect on torsional crustal vibrations and on the maximum
quadrupole ellipticity sustainable by the crust. The crust composition and
transition densities are calculated consistently with the global properties,
using a liquid drop model with a bulk nuclear equation of state (EoS) which
allows a systematic variation of the nuclear symmetry energy. The symmetry
energy J and its density dependence L at nuclear saturation density are the
dominant nuclear inputs which determine the thickness of the crust, the range
of densities at which pasta might appear, as well as global properties such as
the radius and moment of inertia. We show the importance of calculating the
global neutron star properties on the same footing as the crust EoS, and
demonstrate that in the range of experimentally acceptable values of L, the
pasta phase can alter the crust frequencies by up to a factor of three,
exceeding the effects of superfluidity on the crust modes, and decrease the
maximum quadrupole ellipticity sustainable by the crust by up to an order of
magnitude. The signature of the pasta phases and the density dependence of the
symmetry energy on the potential observables highlights the possibility of
constraining the EoS of dense, neutron-rich matter and the properties of the
pasta phases using astrophysical observations.


  We study the physical and photometric properties of galaxies at z=4 in
cosmological hydrodynamic simulations of a lambda-CDM universe. We focus on
galaxies satisfying the GOODS "B-dropout" criteria. Our goals are: (1) to study
the nature of high-redshift galaxies; (2) to test the simulations against
published measurements of high-redshift galaxies; (3) to find relations between
photometric measurements by HST/ACS (0.4 -- 1 micron) and Spitzer/IRAC (3.6 --
8 micron) and the intrinsic physical properties of GOODS "B-dropouts" such as
stellar mass, stellar age, dust reddening, and star-formation rate; and (4) to
assess how representative the GOODS survey is at this epoch. Our simulations
predict that high-redshift galaxies show strong correlations in star formation
rate versus stellar mass, and weaker correlations versus environment and age,
such that GOODS galaxies are predicted to be the most massive, most rapidly
star-forming galaxies at that epoch, living preferentially in dense regions.
The simulated rest-frame UV luminosity function (LF) and integrated luminosity
density are in broad agreement with observations at z~4. The predicted
rest-frame optical (observed 3.6 micron) LF is similar to the rest-frame UV LF,
shifted roughly one magnitude brighter. We predict that GOODS detects less than
50% of the total stellar mass density formed in galaxies more massive than
10^8.7 M_sun by z=4, mainly because of brightness limits in the HST/ACS bands.
The most rapidly star forming galaxies in our simulations have rates exceeding
1000 M_sun yr^-1, similar to observed sub-mm galaxies. The star formation rates
of these galaxies show at most a mild excess (2--3x) over the rates that would
be expected for their stellar mass. Whether these bright galaxies would be
observable as LBGs depends on the uncertain effects of dust reddening.


  We consider the semigroup crossed product of the additive natural numbers by
the multiplicative natural numbers. We study its Toeplitz C*-algebra generated
by the right-regular representation, which we call the right Toeplitz algebra.
We analyse its structure by studying three distinguished quotients. We show
that the multiplicative boundary quotient is isomorphic to a crossed product of
the Toeplitz algebra of the additive rationals by an action of the
multiplicative rationals, and study its ideal structure. We identify the
Crisp-Laca boundary quotient as the C*-algebra of the corresponding group built
from rational numbers. There is a natural dynamics on the right Toeplitz
algebra and all its KMS states factor through the additive boundary quotient.
We describe the KMS simplex for inverse temperatures greater than one.


  A covariant non-local extention if the stationary Schr\"odinger equation is
presented and it's solution in terms of Heisenbergs's matrix quantum mechanics
is proposed. For the special case of the Riesz fractional derivative, the
calculation of corresponding matrix elements for the non-local kinetic energy
term is performed fully analytically in the harmonic oscillator basis and leads
to a new interpretation of non local operators in terms of generalized Glauber
states.
  As a first application, for the fractional harmonic oscillator the potential
energy matrix elements are calculated and the and the corresponding
Schr\"odinger equation is diagonalized. For the special case of invariance of
the non-local wave equation under Fourier-transforms a new symmetry is deduced,
which may be interpreted as an extension of the standard parity-symmetry.


  Snap-fits are versatile mechanical designs in industrial products, which
enable the repeated assembling and disassembling of two solid parts. This
important property is attributed to a fine balance between geometry, friction,
and bending elasticity. In the present study, we combine theory, simulation,
and experiment to reveal the fundamental physical principles of snap-fit
functions in the simplest possible setup consisting of a rigid cylinder and a
thin elastic shell. We construct a phase diagram using geometric parameters and
identify four distinct mechanical phases. We develop analytical predictions
based on the linear elasticity theory combined with the law of static friction
and rationalize the numerical and experimental results. The study reveals how
an operational asymmetry of snap-fits (i.e., easy to assemble but difficult to
disassemble) emerges from an exquisite combination of geometry, elasticity, and
friction and suggests optimization of the tunable functionalities for a range
of mechanical designs.


  Dialogue systems and large language models (LLMs) have gained considerable
attention. However, the direct utilization of LLMs as task-oriented dialogue
(TOD) models has been found to underperform compared to smaller task-specific
models. Nonetheless, it is crucial to acknowledge the significant potential of
LLMs and explore improved approaches for leveraging their impressive abilities.
Motivated by the goal of leveraging LLMs, we propose an alternative approach
called User-Guided Response Optimization (UGRO) to combine it with a smaller
TOD model. This approach uses LLM as annotation-free user simulator to assess
dialogue responses, combining them with smaller fine-tuned end-to-end TOD
models. By utilizing the satisfaction feedback generated by LLMs, UGRO further
optimizes the supervised fine-tuned TOD model. Specifically, the TOD model
takes the dialogue history as input and, with the assistance of the user
simulator's feedback, generates high-satisfaction responses that meet the
user's requirements. Through empirical experiments on two TOD benchmarks, we
validate the effectiveness of our method. The results demonstrate that our
approach outperforms previous state-of-the-art (SOTA) results.


  The famous concyclicity theorem stated by John H. Conway is here reconsidered
by means of a parametrisation of the associated triangular configuration with
arbitrary triplets of real numbers ($\alpha$;$\beta$;$\gamma$). This theorem,
thus corresponding to the case ($\alpha$;$\beta$;$\gamma$)=(1;1;1), is
generalized while demonstrating that there always exist an infinite family of
such triplets which keeps unchanged the conclusion. The "anti-Conway"
configuration corresponding to the case ($\alpha$;$\beta$;$\gamma$)=(-1;-1;-1)
is also investigated : Xavier Dussau's theorem of concurrent lines is
redemonstrated and completed by another concyclicity theorem. It is also proved
that there exist in general a unique triplet
($\alpha$;$\beta$;$\gamma$)$\ne$(-1;-1;-1) which is a function of the sides of
the considered triangle and which keeps unchanged the conclusion of Dussau's
theorem.


  Connected vehicles (CVs), because of the external connectivity with other CVs
and connected infrastructure, are vulnerable to cyberattacks that can instantly
compromise the safety of the vehicle itself and other connected vehicles and
roadway infrastructure. One such cyberattack is the false information attack,
where an external attacker injects inaccurate information into the connected
vehicles and eventually can cause catastrophic consequences by compromising
safety-critical applications like the forward collision warning. The occurrence
and target of such attack events can be very dynamic, making real-time and
near-real-time detection challenging. Change point models, can be used for
real-time anomaly detection caused by the false information attack. In this
paper, we have evaluated three change point-based statistical models;
Expectation Maximization, Cumulative Summation, and Bayesian Online Change
Point Algorithms for cyberattack detection in the CV data. Also, data-driven
artificial intelligence (AI) models, which can be used to detect known and
unknown underlying patterns in the dataset, have the potential of detecting a
real-time anomaly in the CV data. We have used six AI models to detect false
information attacks and compared the performance for detecting the attacks with
our developed change point models. Our study shows that change points models
performed better in real-time false information attack detection compared to
the performance of the AI models. Change point models having the advantage of
no training requirements can be a feasible and computationally efficient
alternative to AI models for false information attack detection in connected
vehicles.


  To compute the persistent homology of a grayscale digital image one needs to
build a simplicial or cubical complex from it. For cubical complexes, the two
commonly used constructions (corresponding to direct and indirect digital
adjacencies) can give different results for the same image. The two
constructions are almost dual to each other, and we use this relationship to
extend and modify the cubical complexes to become dual filtered cell complexes.
We derive a general relationship between the persistent homology of two dual
filtered cell complexes, and also establish how various modifications to a
filtered complex change the persistence diagram. Applying these results to
images, we derive a method to transform the persistence diagram computed using
one type of cubical complex into a persistence diagram for the other
construction. This means software for computing persistent homology from images
can now be easily adapted to produce results for either of the two cubical
complex constructions without additional low-level code implementation.


  The complete details of our calculation of the NLO QCD corrections to heavy
flavor photo- and hadroproduction with longitudinally polarized initial states
are presented. The main motivation for investigating these processes is the
determination of the polarized gluon density at the COMPASS and RHIC
experiments, respectively, in the near future. All methods used in the
computation are extensively documented, providing a self-contained introduction
to this type of calculations. Some employed tools also may be of general
interest, e.g., the series expansion of hypergeometric functions. The relevant
parton level results are collected and plotted in the form of scaling
functions. However, the simplification of the obtained gluon-gluon virtual
contributions has not been completed yet. Thus NLO phenomenological predictions
are only given in the case of photoproduction. The theoretical uncertainties of
these predictions, in particular with respect to the heavy quark mass, are
carefully considered. Also it is shown that transverse momentum cuts can
considerably enhance the measured production asymmetries. Finally unpolarized
heavy quark production is reviewed in order to derive conditions for a
successful interpretation of future spin-dependent experimental data.


  Point cloud, as a 3D representation, is widely used in autonomous driving,
virtual reality (VR), and augmented reality (AR). However, traditional
communication systems think that the point cloud's semantic information is
irrelevant to communication, which hinders the efficient transmission of point
clouds in the era of artificial intelligence (AI). This paper proposes a point
cloud based semantic communication system (PCSC), which uses AI-based encoding
techniques to extract the semantic information of the point cloud and joint
source-channel coding (JSCC) technology to overcome the distortion caused by
noise channels and solve the "cliff effect" in traditional communication. In
addition, the system realizes the controllable coding rate without fine-tuning
the network. The method analyzes the coded semantic vector's importance and
discards semantically-unimportant information, thereby improving the
transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal
model division multiple access (MDMA) technology are combined to design a point
cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant
experimental results show that the proposed method outperforms the traditional
method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2
metrics. In terms of transmission, the proposed method can effectively solve
the "cliff effect" in the traditional methods.


  Educators and policy-makers have advocated for reform of undergraduate
biology education, calling for greater integration of mathematics and physics
in the biology curriculum. While these calls reflect the increasingly
interdisciplinary nature of biology research, crossing disciplinary boundaries
in the classroom carries epistemological challenges for both instructors and
students. In this paper we expand on the construct of authenticity to better
describe and understand disciplinary practices, in particular to examine those
used in physics and biology courses. We then apply these ideas to examine an
introductory biology course that incorporates physics and mathematics. We
characterize the uses of interdisciplinary tools in this biology course and
contrast them with the typical uses of these tools in physics courses. Finally,
we examine student responses to the use of mathematics and physics in this
course, to better understand the challenges and consequences of using
interdisciplinary tools in introductory courses. We link these results to the
reform initiatives of introductory physics courses for life-science students.


  We discuss the Ward identity and the low-energy theorem for the divergence of
the axial-vector current in the massless Thirring model with fermion fields
quantized in the chirally broken phase (Eur. Phys. J. C20, 723 (2001)). The
Ward identity and the low-energy theorem are analysed in connection with
Witten's criterion for Goldstone bosons (Nucl. Phys. B145, 110 (1978)). We show
that the free massless (pseudo)scalar field bosonizing the massless Thirring
model in the chirally broken phase satisfies Witten's criterion to interpret
quanta of this field as Goldstone bosons. As has been shown in hep-th/0210104
and hep-th/0212226, Goldstone's criterion, the non-invariance of the wave
function of the ground state, is also fulfilled.


  Yes-no questions expect a yes or no for an answer, but people often skip
polar keywords. Instead, they answer with long explanations that must be
interpreted. In this paper, we focus on this challenging problem and release
new benchmarks in eight languages. We present a distant supervision approach to
collect training data. We also demonstrate that direct answers (i.e., with
polar keywords) are useful to train models to interpret indirect answers (i.e.,
without polar keywords). Experimental results demonstrate that monolingual
fine-tuning is beneficial if training data can be obtained via distant
supervision for the language of interest (5 languages). Additionally, we show
that cross-lingual fine-tuning is always beneficial (8 languages).


  In recent years, the Natural Language Inference (NLI) task has garnered
significant attention, with new datasets and models achieving near human-level
performance on it. However, the full promise of NLI -- particularly that it
learns knowledge that should be generalizable to other downstream NLP tasks --
has not been realized. In this paper, we study this unfulfilled promise from
the lens of two downstream tasks: question answering (QA), and text
summarization. We conjecture that a key difference between the NLI datasets and
these downstream tasks concerns the length of the premise; and that creating
new long premise NLI datasets out of existing QA datasets is a promising avenue
for training a truly generalizable NLI model. We validate our conjecture by
showing competitive results on the task of QA and obtaining the best reported
results on the task of Checking Factual Correctness of Summaries.


  We derive the leading contribution to the four-nucleon force within the
framework of chiral effective field theory. It is governed by the exchange of
pions and the lowest-order nucleon-nucleon contact interaction and includes
effects due to the nonlinear pion-nucleon couplings and the pion self
interactions constrained by the chiral symmetry of QCD. The resulting
four-nucleon force does not contain any unknown parameters and can be tested in
few- and many-nucleon studies.


  Given any $\epsilon >0$ and any planar region $\Omega$ bounded by a simple
n-gon $P$ we construct a ($1 + \epsilon)$-quasiconformal map between $\Omega$
and the unit disk in time $C(\epsilon)n$. One can take $ C(\epsilon) = C + C
\log (1/\epsilon) \log \log (1/\epsilon)$.


  We study the comoving relative velocities, v12, of model isolated galaxy
pairs at z=0.5. For this purpose, we use the predictions from the GALFORM
semi-analytical model of galaxy formation and evolution based on a Lambda cold
dark matter cosmology consistent with the results from WMAP7. In real space, we
find that isolated pairs of galaxies are predicted to form an angle t with the
line-of-sight that is uniformily distributed as expected if the Universe is
homogeneous and isotropic. We also find that isolated pairs of galaxies
separated by a comoving distance between 1 and 3 Mpc/h are predicted to have
<v12>=0. For galaxies in this regime, the distribution of the angle t is
predicted to change minimally from real to redshift space, with a change
smaller than 5% in <sin^2 t>. However, the distances defining the comoving
regime strongly depends on the applied isolation criteria.


  The barrel calorimeter is part of the new spectrometer installed in Hall D at
Jefferson Lab for the GlueX experiment. The calorimeter was installed in 2013,
commissioned in 2014 and has been operating routinely since early 2015. The
detector configuration, associated Monte Carlo simulations, calibration and
operational performance are described herein. The calorimeter records the time
and energy deposited by charged and neutral particles created by a multi-GeV
photon beam. It is constructed as a lead and scintillating-fiber calorimeter
and read out with 3840 large-area silicon photomultiplier arrays. Particles
impinge on the detector over a wide range of angles, from normal incidence at
90 degrees down to 11.5 degrees, which defines a geometry that is fairly unique
among calorimeters. The response of the calorimeter has been measured during a
running experiment and performs as expected for electromagnetic showers below
2.5 GeV. We characterize the performance of the BCAL using the energy
resolution integrated over typical angular distributions for $\pi^0$ and $\eta$
production of $\sigma_E/E$=5.2\%/$\sqrt{E(\rm{GeV})} \oplus$ 3.6\% and a timing
resolution of $\sigma$\,=\,150\,ps at 1\,GeV.


  We revisit the method of Kirschenhofer, Prodinger and Tichy to calculate the
moments of comparisons used by the quick sort algorithm. We reemphasize that
this approach helps in calculating these quantities with less computation. We
also point out that as observed by Knuth this method also gives moments for
total path length of a binary search tree built over a random set of n keys.


  The meteorology of hot Jupiters has been characterized primarily with thermal
measurements, but recent observations suggest the possibility of directly
detecting the winds by observing the Doppler shift of spectral lines seen
during transit. Motivated by these observations, we show how Doppler
measurements can place powerful constraints on the meteorology. We show that
the atmospheric circulation--and Doppler signature--of hot Jupiters splits into
two regimes. Under weak stellar insolation, the day-night thermal forcing
generates fast zonal jet streams from the interaction of atmospheric waves with
the mean flow. In this regime, air along the terminator (as seen during
transit) flows toward Earth in some regions and away from Earth in others,
leading to a Doppler signature exhibiting superposed blueshifted and redshifted
components. Under intense stellar insolation, however, the strong thermal
forcing damps these planetary-scale waves, inhibiting their ability to generate
jets. Strong frictional drag likewise damps these waves and inhibits jet
formation. As a result, this second regime exhibits a circulation dominated by
high-altitude, day-to-night airflow, leading to a predominantly blueshifted
Doppler signature during transit. We present state-of-the-art circulation
models including nongray radiative transfer to quantify this regime shift and
the resulting Doppler signatures; these models suggest that cool planets like
GJ 436b lie in the first regime, HD 189733b is transitional, while planets
hotter than HD 209458b lie in the second regime. Moreover, we show how the
amplitude of the Doppler shifts constrains the strength of frictional drag in
the upper atmospheres of hot Jupiters. If due to winds, the ~2-km/sec blueshift
inferred on HD 209458b may require drag time constants as short as 10^4-10^6
seconds, possibly the result of Lorentz-force braking on this planet's hot
dayside.


  In this paper we consider the supercritical generalized Korteweg-de Vries
equation $\partial_t\psi + \partial_{xxx}\psi + \partial_x(|\psi|^{p-1}\psi) =
0$, where $5\leq p\in\R$. We prove a local well-posedness result in the
homogeneous Besov space $\dot B^{s_p,2}_{\infty}(\mathbb{R})$, where
$s_p=\frac12-\frac{2}{p-1}$ is the scaling critical index. In particular local
well-posedness in the smaller inhomogeneous Sobolev space $H^{s_p}(\mathbb{R})$
can be proved similarly. As a byproduct a global well-posedness result for
small initial data is also obtained.


  Ensuring the security of transactions is currently one of the major
challenges that banking systems deal with. The usage of face for biometric
authentication of users is attracting large investments from banks worldwide
due to its convenience and acceptability by people, especially in cross-domain
scenarios, in which facial images from ID documents are compared with digital
self-portraits (selfies) for the automated opening of new checking accounts,
e.g, or financial transactions authorization. Actually, the comparison of
selfies and IDs has also been applied in another wide variety of tasks
nowadays, such as automated immigration control. The major difficulty in such
process consists in attenuating the differences between the facial images
compared given their different domains. In this work, in addition to collecting
a large cross-domain face dataset, with 27,002 real facial images of selfies
and ID documents (13,501 subjects) captured from the databases of the major
public Brazilian bank, we propose a novel architecture for such cross-domain
matching problem based on deep features extracted by two well-referenced
Convolutional Neural Networks (CNN). Results obtained on the dataset collected,
called FaceBank, with accuracy rates higher than 93%, demonstrate the
robustness of the proposed approach to the cross-domain face matching problem
and its feasible application in real banking security systems.


  Multi-object astronomical adaptive-optics (MOAO) is now a mature wide-field
observation mode to enlarge the adaptive-optics-corrected field in a few
specific locations over tens of arc-minutes.
  The work-scope provided by open-loop tomography and pupil conjugation is
amenable to a spatio-angular Linear-Quadratic Gaussian (SA-LQG) formulation
aiming to provide enhanced correction across the field with improved
performance over static reconstruction methods and less stringent computational
complexity scaling laws.
  Starting from our previous work [1], we use stochastic time-progression
models coupled to approximate sparse measurement operators to outline a
suitable SA-LQG formulation capable of delivering near optimal correction.
Under the spatio-angular framework the wave-fronts are never explicitly
estimated in the volume,providing considerable computational savings on
10m-class telescopes and beyond.
  We find that for Raven, a 10m-class MOAO system with two science channels,
the SA-LQG improves the limiting magnitude by two stellar magnitudes when both
Strehl-ratio and Ensquared-energy are used as figures of merit. The
sky-coverage is therefore improved by a factor of 5.


  We present results from a detailed dynamical analysis of five high surface
brightness, late type spirals, studied with the aim to quantify the
luminous-to-dark matter ratio inside their optical radii. The galaxies' stellar
light distribution and gas kinematics have been observed and compared to
hydrodynamic gas simulations, which predict the 2D gas dynamics arising in
response to empirical gravitational potentials, which are combinations of
differing stellar disk and dark halo contributions. The gravitational potential
of the stellar disk was derived from near-infrared photometry, color-corrected
to constant (M/L); the dark halo was modelled by an isothermal sphere with a
core. Hydrodynamic gas simulations were performed for each galaxy for a
sequence of five different mass fractions of the stellar disk and for a wide
range of spiral pattern speeds. These two parameters mainly determine the
modelled gas distribution and kinematics. The agreement between the
non-axisymmetric part of the simulated and observed gas kinematics permitted us
to conclude that the galaxies with the highest rotation velocities tend to
possess near-maximal stellar disks. In less massive galaxies, with v_max<200
km/s, the mass of the dark halo at least equals the stellar mass within 2-3
R_disk. The simulated gas morphology provides a powerful tool to determine the
dominant spiral pattern speed. The corotation radius for all galaxies was found
to be constant at R_corotation ~ 3 R_disk and encloses the strong part of the
stellar spiral in all cases.


  We calculate the amplitude for the forward electroproduction of two light
vector mesons in next-to-leading order BFKL. This amplitude is written as a
convolution of two impact factors for the virtual photon to light vector meson
transition with the BFKL Green's function. It represents the first
next-to-leading order amplitude ever calculated for a collision process between
strongly interacting colorless particles.


  We study the geometric phase for the ground state of a generalized
one-dimensional non-Hermitian quantum XY model, which has
transverse-field-dependent intrinsic rotation-time reversal symmetry. Based on
the exact solution, this model is shown to have full real spectrum in multiple
regions for the finite size system. The result indicates that the phase diagram
or exceptional boundary, which separates the unbroken and broken symmetry
regions corresponds to the divergence of the Berry curvature. The scaling
behaviors of the groundstate energy and Berry curvature are obtained in an
analytical manner for a concrete system.


  New properties are derived of renormalized volume functionals, which arise as
coefficients in the asymptotic expansion of the volume of an asymptotically
hyperbolic Einstein (AHE) manifold. A formula is given for the renormalized
volume of an even-dimensional AHE manifold in terms of an arbitrary totally
geodesic compactification. The second variation of renormalized volume
functionals under conformal change is identified, and is used to show that
Einstein metrics of nonzero scalar curvature are local extrema.


  The notion of quasi-unit has been introduced by Yosida in unital Riesz
spaces. Later on, a fruitful potential theoretic generalization was obtained by
Arsove and Leutwiler. Due to the work of Eriksson and Leutwiler, this notion
also turned out to be an effective tool by investigating the extreme structure
of operator segments. This paper has multiple purposes which are interwoven,
and are intended to be equally important. On the one hand, we identify
quasi-units as orthogonal projections acting on an appropriate auxiliary
Hilbert space. As projections form a lattice and are extremal points of the
effect algebra, we conclude the same properties for quasi-units. Our second aim
is to apply these results for nonnegative sesquilinear forms. Constructing an
order preserving bijection between operator- and form segments, we provide a
characterization of being extremal in the convexity sense, and we give a
necessary and sufficient condition for the existence of the greatest lower
bound of two forms. Closing the paper we revisit some statements by using the
machinery developed by Hassi, Sebesty\'en, and de Snoo. It will turn out that
quasi-units are exactly the closed elements with respect to the antitone Galois
connection induced by parallel addition and subtraction.


  Hybrid atom-ion systems are a rich and powerful platform for studying
chemical reactions, as they feature both excellent control over the electronic
state preparation and readout as well as a versatile tunability over the
scattering energy, ranging from the few-partial wave regime to the quantum
regime. In this work, we make use of these excellent control knobs, and present
a joint experimental and theoretical study of the collisions of a single
$^{138}$Ba$^+$ ion prepared in the $5d\,^2D_{3/2,5/2}$ metastable states with a
ground state $^6$Li gas near quantum degeneracy. We show that in contrast to
previously reported atom-ion mixtures, several non-radiative processes,
including charge exchange, excitation exchange and quenching, compete with each
other due to the inherent complexity of the ion-atom molecular structure. We
present a full quantum model based on high-level electronic structure
calculations involving spin-orbit couplings. Results are in excellent agreement
with observations, highlighting the strong coupling between the internal
angular momenta and the mechanical rotation of the colliding pair, which is
relevant in any other hybrid system composed of an alkali-metal atom and an
alkaline-earth ion.


  The present paper considers a stochastic optimal control problem, in which
the cost function is defined through a backward stochastic differential
equation with infinite horizon driven by G-Brownian motion. Then we study the
regularities of the value function and establish the dynamic programming
principle. Moreover, we prove that the value function is the uniqueness
viscosity solution of the related HJBI equation.


  We consider an alternative approach to non-linear special-relativistic
theories. The point of departure is not $\kappa$-deformed algebra (or even
group-theoretical considerations) but rather 3 physical postulates defining
particle's velocity, mass, and the upper bound on its energy in terms of the
respective classical quantities. For a specific definition of particle's
velocity we obtain Magueijo-Smolin (MS) version of the double
special-relativistic theory. It is shown that this version follows from the
$\kappa$-Poincare algebra by the appropriate choice of on the shell mass, such
that it is always less or equal Planck's mass. The $\kappa$-deformed
Hamiltonian is found which invalidates some arguments about unphysical
predictions of the MS transformation.


  Calibration error is commonly adopted for evaluating the quality of
uncertainty estimators in deep neural networks. In this paper, we argue that
such a metric is highly beneficial for training predictive models, even when we
do not explicitly measure the uncertainties. This is conceptually similar to
heteroscedastic neural networks that produce variance estimates for each
prediction, with the key difference that we do not place a Gaussian prior on
the predictions. We propose a novel algorithm that performs simultaneous
interval estimation for different calibration levels and effectively leverages
the intervals to refine the mean estimates. Our results show that, our approach
is consistently superior to existing regularization strategies in deep
regression models. Finally, we propose to augment partial dependence plots, a
model-agnostic interpretability tool, with expected prediction intervals to
reveal interesting dependencies between data and the target.


  The Influence Maximization (IM) problem aims at finding k seed vertices in a
network, starting from which influence can be spread in the network to the
maximum extent. In this paper, we propose QuickIM, the first versatile IM
algorithm that attains all the desirable properties of a practically applicable
IM algorithm at the same time, namely high time efficiency, good result
quality, low memory footprint, and high robustness. On real-world social
networks, QuickIM achieves the $\Omega(n + m)$ lower bound on time complexity
and $\Omega(n)$ space complexity, where $n$ and $m$ are the number of vertices
and edges in the network, respectively. Our experimental evaluation verifies
the superiority of QuickIM. Firstly, QuickIM runs 1-3 orders of magnitude
faster than the state-of-the-art IM algorithms. Secondly, except EasyIM,
QuickIM requires 1-2 orders of magnitude less memory than the state-of-the-art
algorithms. Thirdly, QuickIM always produces as good quality results as the
state-of-the-art algorithms. Lastly, the time and the memory performance of
QuickIM is independent of influence probabilities. On the largest network used
in the experiments that contains more than 3.6 billion edges, QuickIM is able
to find hundreds of influential seeds in less than 4 minutes, while all the
state-of-the-art algorithms fail to terminate in an hour.


  Self-attention mechanisms are commonly included in a convolutional neural
networks to achieve an improved efficiency performance balance. However, adding
self-attention mechanisms adds additional hyperparameters to tune for the
application at hand. In this work we propose a novel type of DNN analysis
called Multi-Scale Class Representational Response Similarity Analysis
(ClassRepSim) which can be used to identify specific design interventions that
lead to more efficient self-attention convolutional neural network
architectures. Using insights grained from ClassRepSim we propose the Spatial
Transformed Attention Condenser (STAC) module, a novel attention-condenser
based self-attention module. We show that adding STAC modules to ResNet style
architectures can result in up to a 1.6% increase in top-1 accuracy compared to
vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to
SENet models on the ImageNet64x64 dataset, at the cost of up to 1.7% increase
in FLOPs and 2x the number of parameters. In addition, we demonstrate that
results from ClassRepSim analysis can be used to select an effective
parameterization of the STAC module resulting in competitive performance
compared to an extensive parameter search.


  A few fast radio bursts' (FRBs) light-curves have exhibited large intrinsic
modulations of their flux on extremely short ($t_{\rm r}\sim 10\mu$s) time
scales, compared to pulse durations ($t_{\rm FRB}\sim1$ms). Light-curve
variability timescales, the small ratio of rise time of the flux to pulse
duration, and the spectro-temporal correlations in the data constrain the
compactness of the source and the mechanism responsible for the powerful radio
emission. The constraints are strongest when radiation is produced far
($\gtrsim 10^{10}$cm) from the compact object. We describe different physical
set-ups that can account for the observed $t_{\rm r}/t_{\rm FRB}\ll 1$ despite
having large emission radii. The result is either a significant reduction in
the radio production efficiency or distinct light-curves features that could be
searched for in observed data. For the same class of models, we also show that
due to high-latitude emission, if a flux $f_1(\nu_1)$ is observed at $t_1$ then
at a lower frequency $\nu_2<\nu_1$ the flux should be at least
$(\nu_2/\nu_1)^2f_1$ at a slightly later time ($t_2=t_1\nu_1/\nu_2$)
independent of the duration and spectrum of the emission in the comoving frame.
These features can be tested, once light-curve modulations due to scintillation
are accounted for. We provide the timescales and coherence bandwidths of the
latter for a range of possibilities regarding the physical screens and the
scintillation regime. Finally, if future highly resolved FRB light-curves are
shown to have intrinsic variability extending down to $\sim \mu$s timescales,
this will provide strong evidence in favor of magnetospheric models.


  The amorphous aluminium silicate (Al2O3)2(SiO2) [AS2] is investigated by
means of large scale molecular dynamics computer simulations. We consider fully
equilibrated melts in the temperature range 6100K >= T >= 2300K as well as
glass configurations that were obtained from cooling runs from T=2300K to 300K
with a cooling rate of about 10^12K/s. Already at temperatures as high as
4000K, most of the Al and Si atoms are four-fold coordinated by oxygen atoms.
Thus, the structure of AS2 is that of a disordered tetrahedral network. The
packing of AlO4 tetrahedra is very different from that of SiO4 tetrahedra in
that Al is involved with a relatively high probability in small-membered rings
and in triclusters in which an O atom is surrounded by four cations. We find as
typical configurations two-membered rings with two Al atoms in which the shared
O atoms form a tricluster. On larger length scales, the system shows a
microphase separation in which the Al-rich network structure percolates through
the SiO2 network. The latter structure gives rise to a prepeak in the static
structure factor at a wavenumber q=0.5\AA^{-1}. The comparison of experimental
X-ray data with the results from the simulation shows a good agreement for the
structure function. The diffusion dynamics in AS2 is found to be much faster
than in SiO2. We show that the self-diffusion constants for O and Al are very
similar and that they are by a factor of 2-3 larger than the one for Si.


  This preliminary investigation addresses the discovery of an unidentified
crater located south of the Sahara Desert between Qaret Had El Bahr and Qaret
El Allafa, Egypt. The unidentified crater (hereafter tentatively named El Bahr
Crater) was discovered during a terrain analysis of the Sahara Desert. El Bahr
Crater is located Southwest Al-Jiza Giza and is approximately 327 meters
across, has a rim with a circumference of approximately 1,027 meters, and
occupies a surface area of approximately 83,981 square meters. Preliminary
spectral and topographic analysis reveal features characteristic of an impact
crater produced by a hypervelocity event of extraterrestrial origin, including
a bowl-shaped rim and a crater wall. No proximal and/or distal ejecta, however,
are visible from Landsat imagery. Moreover, the geomorphic features, along with
the fact that the El Bahr basalts are known to be rich in orthopyroxene while
the surrounding basalts are not, imply an impact as the most plausible
explanation. The El Bahr Crater is not indexed in the Earth Impact Database,
and an analysis of impact structures in Africa did not identify it as either a
confirmed, proposed or disproved impact crater. In collaboration with the
University of Cairo, therefore, an expedition has been organized to conduct an
in-situ investigation of El Bahr Crater, to ascertain if planar formations,
shatter cones, and shock metamorphic and or other meteoritic properties are
present.


  We introduce a new class of primitive building blocks for realizing quantum
logic elements based on nanoscale magnetization textures called skyrmions. In a
skyrmion qubit, information is stored in the quantum degree of helicity, and
the logical states can be adjusted by electric and magnetic fields, offering a
rich operation regime with high anharmonicity. By exploring a large parameter
space, we propose two skyrmion qubit variants depending on their quantized
state. We discuss appropriate microwave pulses required to generate
single-qubit gates for quantum computing, and skyrmion multiqubit schemes for a
scalable architecture with tailored couplings. Scalability, controllability by
microwave fields, operation time scales, and readout by nonvolatile techniques
converge to make the skyrmion qubit highly attractive as a logical element of a
quantum processor.


  We analyze the time-dependent energy and heat flows in a resonant level
coupled to a fermionic continuum. The level is periodically forced with an
external power source that supplies energy into the system. Based on the
tunneling Hamiltonian approach and scattering theory, we discuss the different
contributions to the total energy flux. We then derive the appropriate
expression for the dynamical dissipation, in accordance with the fundamental
principles of thermodynamics. Remarkably, we find that the dissipated heat can
be expressed as a Joule law with a universal resistance that is constant at all
times.


  We develop statistical methods for identifying star streams in the halo of
the Milky Way galaxy that exploit observed spatial and radial velocity
distributions. Within a great circle, departures of the observed spatial
distribution from random provide a measure of the likelihood of a potential
star stream. Comparisons between the radial velocity distribution within a
great circle and the radial velocity distribution of the entire sample also
measure the statistical significance of potential streams. The radial
velocities enable construction of a more powerful joint statistical test for
identifying star streams in the Milky Way halo. Applying our method to halo
stars in the Hypervelocity Star (HVS) survey, we detect the Sagittarius stream
at high significance. Great circle counts and comparisons with theoretical
models suggest that the Sagittarius stream comprises 10% to 17% of the halo
stars in the HVS sample. The population of blue stragglers and blue horizontal
branch stars varies along the stream and is a potential probe of the
distribution of stellar populations in the Sagittarius dwarf galaxy prior to
disruption.


  This note examines the question of randomness in a sequence based on the
continued fraction (CF) representation of its corresponding representation as a
number, or as D sequence. We propose a randomness measure that is directly
equal to the number of components of the CF representation. This provides a
means of quantifying the randomness of the popular PN sequences as well. A
comparison is made of representation as a fraction and as a continued fraction.


  Power partial isometries are not always hyperreflexive neither reflexive. In
the present paper it will be shown that power partial isometries are always
hyporeflexive and $2$--hyperreflexive.


  We consider the spatial Lambda-Fleming-Viot process model for frequencies of
genetic types in a population living in R^d, with two types of individuals (0
and 1) and natural selection favouring individuals of type 1. We first prove
that the model is well-defined and provide a measure-valued dual process
encoding the locations of the `potential ancestors' of a sample taken from such
a population. We then consider two cases, one in which the dynamics of the
process are driven by events of bounded radii and one incorporating large-scale
events whose radii have a polynomial tail distribution. In both cases, we
consider a sequence of spatial Lambda-Fleming-Viot processes indexed by n, and
we assume that the fraction of individuals replaced during a reproduction event
and the relative frequency of events during which natural selection acts tend
to 0 as n tends to infinity. We choose the decay of these parameters in such a
way that when reproduction is only local, the measure-valued process describing
the local frequencies of the less favoured type converges in distribution to a
(measure-valued) solution to the stochastic Fisher-KPP equation in one
dimension, and to a (measure-valued) solution to the deterministic Fisher-KPP
equation in more than one dimension. When large-scale extinction-recolonisation
events occur, the sequence of processes converges instead to the solution to
the analogous equation in which the Laplacian is replaced by a fractional
Laplacian. We also consider the process of `potential ancestors' of a sample of
individuals taken from these populations, which we see as a system of branching
and coalescing symmetric jump processes. We show their convergence in
distribution towards a system of Brownian or stable motions which branch at
some finite rate. In one dimension, in the limit, pairs of particles also
coalesce at a rate proportional to their collision local time.


  The existence of CEF states and the magnetism in conducting compounds NdAl2,
UPd2Al3 and YbRh2Si2 is discussed. We point out that in metallic compounds
localized crystal-field states coexist with conduction electrons. These
compounds are physical realization of an anisotropic spin liquid, which in case
of the atomic-like configuration with an odd number of f electrons, is unstable
with respect to spin fluctuations for T->0K. YbRh2Si2 is discussed as a
compound with the Kramers doublet ground state with a very low magnetic
ordering temperature being a reason for the quantum critical point phenomena at
low T.


  Boosting combines weak learners into a predictor with low empirical risk. Its
dual constructs a high entropy distribution upon which weak learners and
training labels are uncorrelated. This manuscript studies this primal-dual
relationship under a broad family of losses, including the exponential loss of
AdaBoost and the logistic loss, revealing:
  - Weak learnability aids the whole loss family: for any {\epsilon}>0,
O(ln(1/{\epsilon})) iterations suffice to produce a predictor with empirical
risk {\epsilon}-close to the infimum;
  - The circumstances granting the existence of an empirical risk minimizer may
be characterized in terms of the primal and dual problems, yielding a new proof
of the known rate O(ln(1/{\epsilon}));
  - Arbitrary instances may be decomposed into the above two, granting rate
O(1/{\epsilon}), with a matching lower bound provided for the logistic loss.


  Recent advancements in quantum computing suggest the potential to
revolutionize computational algorithms across various scientific domains
including oceanography and atmospheric science. The field is still relatively
young and quantum computation is so different from classical computation that
suitable frameworks to represent oceanic and atmospheric dynamics are yet to be
explored. Quantum annealing, one of the major paradigms, focuses on
combinatorial optimization tasks. In this paper, we solve the classical Stommel
problem by quantum annealing (QA) and simulated annealing (SA), a classical
counterpart of quantum annealing. We cast the linear partial differential
equation into an optimization problem by the least-squares method and
discretize the cost function in two ways: finite difference and truncated basis
expansion. In either case, SA successfully reproduces the expected solution
when appropriate parameters are chosen, demonstrating that annealing has the
potential. In contrast, QA using the D-Wave quantum annealing machine fails to
obtain good solutions for some cases owing to hardware limitations; in
particular, the highly limited connectivity graph of the machine limits the
size of the solvable problems, at least under currently available algorithms.
Either expanding the connectivity graph or improving the graph embedding
algorithms would probably be necessary for quantum annealing machines to be
usable for oceanic and atmospheric dynamics problems. While this finding
emphasizes the need for hardware improvements and enhancements in graph
embedding algorithms for practical applications of quantum annealers, the
results from simulated annealing suggest its potential to address practical
geophysical dynamics problems. As quantum calculation continues to evolve,
addressing these challenges may lead to transformative advancements in ocean
and atmosphere modeling.


  Image super-resolution is a common task on mobile and IoT devices, where one
often needs to upscale and enhance low-resolution images and video frames.
While numerous solutions have been proposed for this problem in the past, they
are usually not compatible with low-power mobile NPUs having many computational
and memory constraints. In this Mobile AI challenge, we address this problem
and propose the participants to design an efficient quantized image
super-resolution solution that can demonstrate a real-time performance on
mobile NPUs. The participants were provided with the DIV2K dataset and trained
INT8 models to do a high-quality 3X image upscaling. The runtime of all models
was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU
capable of accelerating quantized neural networks. All proposed solutions are
fully compatible with the above NPU, demonstrating an up to 60 FPS rate when
reconstructing Full HD resolution images. A detailed description of all models
developed in the challenge is provided in this paper.


  Li et al. found that the critical current density JcJ across atomically clean
c-axis twist junctions of Bi2Sr2CaCu2O(8+x) is the same as that of the
constituent single crystal, JcS, independent of the twist angle phi0, even at
Tc. We investigated theoretically if a dx2-y2-wave order parameter might twist
by mixing in dxy components, but find that such twisting cannot possibly
explain the data near to Tc. Hence, the order parameter contains an s-wave
component, but not any dx2-y2-wave component. In addition, the c-axis Josephson
tunneling is completely incoherent. We also propose a c-axis junction
tricrystal experiment which does not rely upon expensive substrates.


  Little groups are enumerated for the irreps and their components in any basis
of O(3) and SO(3) up to rank 9, and for all irreps of C$_{\infty}$, C$_{\infty
h}$, C$_{\infty v}$, D$_{\infty}$ and D$_{\infty h}$. The results are obtained
by a new chain criterion, which distinguishes massive (rotationally
inequivalent) irrep basis functions and allows for multiple branching paths,
and are verified by inspection. These results are relevant to the determination
of the symmetry of a material from its linear and nonlinear optical properties
and to the choices of order parameters for symmetry breaking in liquid
crystals.


  In this paper, we introduce concepts of separable functions in balls and in
the whole space, and develop a new method to investigate the qualitative
properties of separable functions. We first study the axial symmetry and
monotonicity of separable functions in unit circles by geometry analysis, and
we prove the uniqueness of the symmetry axis for nontrivial separable
functions. Then by using reduction dimension and convex analysis, we get the
axial symmetry and monotonicity of separable functions in high dimensional
spheres. Based on the above results on unit circles and spheres, we deduce the
axial symmetry and monotonicity of separable functions in balls and the radial
symmetry and monotonicity of separable functions in the whole space.
Conversely, the function with axial symmetry and monotonicity in the ball
domain is separable function, and the function with radial symmetry and
monotonicity in the whole space is also separable function. These enable us to
provide easily some examples that separable functions in balls may be just
axially symmetric not radially symmetric. Finally, as applications, we obtain
the axial symmetry and monotonicity of all the positive ground states to the
Choquard equation in a ball as well as the radial symmetry and monotonicity of
all the positive ground states in the whole space.


  This paper describes a deep reinforcement learning (DRL) approach that won
Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to
a more difficult manipulation task. The RRC consisted of using a TriFinger
robot to manipulate a cube along a specified positional trajectory, but with no
requirement for the cube to have any specific orientation. We used a relatively
simple reward function, a combination of goal-based sparse reward and distance
reward, in conjunction with Hindsight Experience Replay (HER) to guide the
learning of the DRL agent (Deep Deterministic Policy Gradient (DDPG)). Our
approach allowed our agents to acquire dexterous robotic manipulation
strategies in simulation. These strategies were then applied to the real robot
and outperformed all other competition submissions, including those using more
traditional robotic control techniques, in the final evaluation stage of the
RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to
require the robot to maintain the cube in a particular orientation, while the
cube is moved along the required positional trajectory. The requirement to also
orient the cube makes the agent unable to learn the task through blind
exploration due to increased problem complexity. To circumvent this issue, we
make novel use of a Knowledge Transfer (KT) technique that allows the
strategies learned by the agent in the original task (which was agnostic to
cube orientation) to be transferred to this task (where orientation matters).
KT allowed the agent to learn and perform the extended task in the simulator,
which improved the average positional deviation from 0.134 m to 0.02 m, and
average orientation deviation from 142{\deg} to 76{\deg} during evaluation.
This KT concept shows good generalisation properties and could be applied to
any actor-critic learning algorithm.


  As part of our continuing study of light variability in proto-planetary
nebulae (PPNe), we present the results from a long-term study of nine southern
hemisphere objects. We have monitored their light variations over a nine-year
interval from 2010-2018. These were supplemented by data from the ASAS-SN and
ASAS-3 surveys, leading to combined light curves from 2000 to 2020. Pulsation
periods were found in seven of the objects, although the three shortest must be
regarded as tentative. The periods range from 24 to 73 days. When compared with
the results of previous studies of the light variations in PPNe, we find that
they show the same trends of shorter period and smaller light variations with
higher temperatures. Luminosities were calculated based on the spectral energy
distributions, reddening, and Gaia distances, and these confirm the
identification of all but one as post-AGB objects. Three of the stars possess
long-period variations of 5 to 19 years. These are most likely due to the
periodic obscuration of the star by a disk, suggesting the presence of a binary
companion and a circumbinary disk.


  We have determined Li abundances for eleven metal-poor turn-off stars, among
which eight have [Fe/H]<-3, based on LTE analyses of high-resolution spectra
obtained with the HDS on the Subaru telescope. The Li abundances for four of
these eight stars are determined for the first time by this study. Effective
temperatures are determined by a profile analysis of H-alpha and H-beta. While
seven stars have Li abundances as high as the Spite Plateau value, the
remaining four objects with [Fe/H] <-3 have A(Li)=log(Li/H)+12 ~< 2.0,
confirming the existence of extremely metal-poor turn-off stars having low Li
abundances, as reported by previous work. The average of the Li abundances for
stars with [Fe/H]<-3 is lower by 0.2 dex than that of the stars with higher
metallicity. No clear constraint on the metallicity dependence or scatter of
the Li abundances is derived from our measurements for the stars with
[Fe/H]<-3. Correlations of the Li abundance with effective temperatures, with
abundances of Na, Mg and Sr, and with the kinematical properties are
investigated, but no clear correlation is seen in the extremely metal-poor star
sample.


  In this work, we modify the affine wealth model of wealth distributions to
examine the effects of nonconstant redistribution on the very wealthy. Previous
studies of this model, restricted to flat redistribution schemes, have
demonstrated the presence of a phase transition to a partially wealth-condensed
state, or "partial oligarchy," at the critical value of an order parameter.
These studies have also indicated the presence of an exponential tail in wealth
distribution precisely at criticality. Away from criticality, the tail was
observed to be Gaussian. In this work, we generalize the flat redistribution
within the affine wealth model to allow for an essentially arbitrary
redistribution policy. We show that the exponential tail observed near
criticality in prior work is, in fact, a special case of a much broader class
of critical, slower-than-Gaussian decays that depend sensitively on the
corresponding asymptotic behavior of the progressive redistribution model used.
We thereby demonstrate that the functional form of the tail of the wealth
distribution in a near-critical society is not universal in nature but rather
entirely determined by the specifics of public policy decisions. This is
significant because most major economies today are observed to be
near-critical.


  We report the results of combined analyses of X-ray and optical data of two
galaxy clusters, CL 0024$+$1654 and RX J0152.7$-$1357 at redshift $z = 0.395$
and $z = 0.830$, respectively, which offer a holistic physical description of
the two clusters. Our X-ray analysis yields temperature and density profile of
the gas in the intra-cluster medium (ICM). Using optical photometric and
spectroscopic data, complemented with mass distribution from gravitational
lensing study, we investigate any possible correlation between the physical
properties of the galaxy members, i.e., their color, morphology, and star
formation rate (SFR) with their environments. We quantify the properties of the
environment of each galaxy by galaxy number density, ICM temperature, and mass
density. Although our result shows that the two clusters exhibit a weaker
correlation compared to relaxed clusters, it still confirms the significant
effect of the ICM on the SFRs in the galaxies. The closer relation between
galaxy physical property and the condition of its immediate environment found
in this work indicates the locality of galaxy evolution, even within a larger
bound system such as cluster. Various physical mechanisms are suggested to
explain the relation between the properties of galaxies and their environment.


  Object affordance is an important concept in hand-object interaction,
providing information on action possibilities based on human motor capacity and
objects' physical property thus benefiting tasks such as action anticipation
and robot imitation learning. However, the definition of affordance in existing
datasets often: 1) mix up affordance with object functionality; 2) confuse
affordance with goal-related action; and 3) ignore human motor capacity. This
paper proposes an efficient annotation scheme to address these issues by
combining goal-irrelevant motor actions and grasp types as affordance labels
and introducing the concept of mechanical action to represent the action
possibilities between two objects. We provide new annotations by applying this
scheme to the EPIC-KITCHENS dataset and test our annotation with tasks such as
affordance recognition, hand-object interaction hotspots prediction, and
cross-domain evaluation of affordance. The results show that models trained
with our annotation can distinguish affordance from other concepts, predict
fine-grained interaction possibilities on objects, and generalize through
different domains.


  We study and review disk protoplanet interactions using local shearing box
simulations. These suffer the disadvantage of having potential artefacts
arising from periodic boundary conditions but the advantage, when compared to
global simulations, of being able to capture much of the dynamics close to the
protoplanet at high resolution for low computational cost. Cases with and
without self sustained MHD turbulence are considered. The conditions for gap
formation and the transition from type I migration are investigated and found
to depend on whether the single parameter M_p R^3/(M_* H^3), with M_p, M_*, R
and H being the protoplanet mass, the central mass, the orbital radius and the
disk semi-thickness respectively exceeds a number of order unity. We also
investigate the coorbital torques experienced by a moving protoplanet in an
inviscid disk. This is done by demonstrating the equivalence of the problem for
a moving protoplanet to one where the protoplanet is in a fixed orbit which the
disk material flows through radially as a result of the action of an
appropriate external torque. For sustainable coorbital torques to be realized a
quasi steady state must be realized in which the planet migrates through the
disk without accreting significant mass. In that case although there is
sensitivity to computational parameters, in agreement with earlier work by
Masset & Papaloizou (2003) based on global simulations, the coorbital torques
are proportional to the migration speed and result in a positive feedback on
the migration, enhancing it and potentially leading to a runaway. This could
lead to a fast migration for protoplanets in the Saturn mass range in massive
disks and may be relevant to the mass period correlation for extrasolar planets
which gives a preponderance of sub Jovian masses at short orbital period.


  This paper presents a new generator of chaotic bit sequences with mixed-mode
(continuous and discrete) inputs. The generator has an improved level of
chaotic properties in comparison with the existing single source (input)
digital chaotic bit generators. The 0-1 test is used to show the improved
chaotic behavior of our generator having a chaotic continuous input (Chua,
R\"{o}ssler or Lorenz system) intermingled with a discrete input (logistic,
Tinkerbell or Henon map) with various parameters. The obtained sequences of
chaotic bits show some features of random processes with increased entropy
levels, even in the cases of small numbers of bit representations. The
properties of the new generator and its binary sequences compare well with
those obtained from a truly random binary reference quantum generator, as
evidenced by the results of the $ent$ tests.


  Low Latency, Low Loss Scalable throughput (L4S) is being proposed as the new
default Internet service. L4S can be considered as an `incrementally deployable
clean-slate' for new Internet flow-rate control mechanisms. Because, for a
brief period, researchers are free to develop host and network mechanisms in
tandem, somewhat unconstrained by any pre-existing legacy.
  Scaling requirements represent the main constraints on a clean-slate design
space. This document confines its scope to the steady state. It aims to resolve
the tensions between a number of apparently conflicting scalability
requirements for L4S congestion controllers. It has been produced to inform and
provide structure to the debate as researchers work towards pre-standardization
consensus on this issue.
  This work is important, because clean-slate opportunities like this arise
only rarely and will only be available briefly---for roughly one year. The
decisions we make now will tend to dirty the slate again, probably for many
decades.