  We describe the asymptotic behavior of the mapping function at an analytic
cusp compared with Kaiser's results for cusps with small perturbation of angles
and the known explicit formulae for cusps with circular boundary curves. We
propose a boundary curve parametrization by generalized power series which
allows us to give explicit representations for locally univalent mapping
functions with given asymptotic properties and for cusp boundary curves having
an arbitrary order of tangency.


  Precise knowledge of the cross sections for neutrino interactions with nuclei
is important not only for existing experiments but also for development of
future detectors. When momentum transferred to the nucleus by the probe is high
enough, the impulse approximation is valid, i.e. the nucleus can be described
as composed of independent nucleons and the interaction happens between
neutrino and a single bound nucleon. The cross section of the nucleus is then
expressed as an integral of the free cross section (modified by the off-shell
effect) folded with the so-called spectral function, describing the
distribution of nucleon momenta and energies.
  I present results obtained for the approximate spectral functions of argon
and calcium. The accuracy of the approach is verified by comparison with a
broad spectrum of precise electron-scattering data for the calcium target. Some
of these data lie in the kinematical region corresponding to neutrino
interactions what indirectly shows that a similar level of accuracy is achieved
for neutrinos. The calculated Ar(nu_mu,mu) cross section is significantly lower
than the one predicted within the Fermi gas model.


  The influence of a curved spacetime $M$ on the physical behavior of an ideal
gas of $N$ particles is analyzed by considering the phase space of the system
as a region of the cotangent bundle $T^{*}M^{N}$ and using Souriau's Lie group
{thermodynamics} to define the corresponding probability distribution function.
While the construction of the phase space respects the separability of the
system, by forcing each particle to satisfy the so-called mass-shell
constraint, the probability distribution is constructed by mixing the natural
symplectic structure of the cotangent bundle with a Hamiltonian description of
the system. In this way, the spacetime is introduced into the statistics and
its isometries turn out to be of special interest because the distributions are
parameterized by the elements of the Lie algebra of the isometry group, through
the momentum map of the action of the isometries in $T^{*}M^{N}$. We find the
Gibbs distribution that, in the simplest case of a flat spacetime, reduces to
the so-called modified J\"{u}ttner distribution, used to describe ideal gases
in the regime of special relativity. We also define a temperature-like function
using the norm of a Killing vector, which allows us to recover the so-called
Tolman-Ehrenfest effect. As a particular example, we study the outer region of
a Schwarzschild black hole, for which a power series expansion of the
Schwarzschild radius allows us to represent the partition function and the
Gibbs distribution in terms of the corresponding quantities of the Minkowski
spacetime.


  The Froggatt-Nielsen (FN) mechanism, a prominent framework for explaining the
observed flavor hierarchies, generically predicts the existence of an
axion-like particle (ALP). This work examines a class of FN models based on
$\mathbb{Z}_N$ discrete symmetries. We chart the allowed parameter space from a
set of theoretical considerations and construct explicit renormalizable
completions with minimal field content necessary to generate consistent
textures. We then conduct comprehensive phenomenological analyses of two
particularly elegant $\mathbb{Z}_4$ and $\mathbb{Z}_8$ models, highlighting the
interplay between the effects of the ALP and the associated UV fields. We find
that the FN scale can be as low as a few TeV.


  In this research, we propose the first approach for integrating the
Kolmogorov-Arnold Network (KAN) with various pre-trained Convolutional Neural
Network (CNN) models for remote sensing (RS) scene classification tasks using
the EuroSAT dataset. Our novel methodology, named KCN, aims to replace
traditional Multi-Layer Perceptrons (MLPs) with KAN to enhance classification
performance. We employed multiple CNN-based models, including VGG16,
MobileNetV2, EfficientNet, ConvNeXt, ResNet101, and Vision Transformer (ViT),
and evaluated their performance when paired with KAN. Our experiments
demonstrated that KAN achieved high accuracy with fewer training epochs and
parameters. Specifically, ConvNeXt paired with KAN showed the best performance,
achieving 94% accuracy in the first epoch, which increased to 96% and remained
consistent across subsequent epochs. The results indicated that KAN and MLP
both achieved similar accuracy, with KAN performing slightly better in later
epochs. By utilizing the EuroSAT dataset, we provided a robust testbed to
investigate whether KAN is suitable for remote sensing classification tasks.
Given that KAN is a novel algorithm, there is substantial capacity for further
development and optimization, suggesting that KCN offers a promising
alternative for efficient image analysis in the RS field.


  Multidimensional modulation was widely studied in the past decades due to the
explosive development of modern wireless communication. Here, we propose a
spirally reconfigurable slotted antenna inspired by the multidimensional
modulation. The amplitude, phase, and frequency-shift modulation are analog by
integrating three-dimensional mechanical switching to a spiral slotted antenna.
The maximum gain of the reconfigurable antenna can be adjusted in the type of 2
encoding bits at 9.5 GHz. Our work may pave the way to a high-performance
reconfigurable antenna for 6G communication.


  Explaining the behavior of AI systems is an important problem that, in
practice, is generally avoided. While the XAI community has been developing an
abundance of techniques, most incur a set of costs that the wider deep learning
community has been unwilling to pay in most situations. We take a pragmatic
view of the issue, and define a set of desiderata that capture both the
ambitions of XAI and the practical constraints of deep learning. We describe an
effective way to satisfy all the desiderata: train the AI system to build a
causal model of itself. We develop an instance of this solution for Deep RL
agents: Causal Self-Talk. CST operates by training the agent to communicate
with itself across time. We implement this method in a simulated 3D
environment, and show how it enables agents to generate faithful and
semantically-meaningful explanations of their own behavior. Beyond
explanations, we also demonstrate that these learned models provide new ways of
building semantic control interfaces to AI systems.


  We establish pointwise growth estimates for the spherical derivative of
solutions of the first order algebraic differential equations. A generalization
of this result to higher order equations is also given. We discuss the related
question of when for a given class $X$ of meromorphic functions in the unit
disc, defined by means of the spherical derivative, and $m \in \mathbb{N}
\setminus \{1\}$, $f^m\in X$ implies $f\in X$. An affirmative answer to this is
given for example in the case of $\mathord{\rm UBC}$, the $\alpha$-normal
functions with $\alpha\ge1$ and certain (sufficiently large) Dirichlet type
classes.


  Crystalline materials have atomic-scale fluctuations in their chemical
composition that modulate various mesoscale properties. Establishing
chemistry-microstructure relationships in such materials requires proper
characterization of these chemical fluctuations. Yet, current characterization
approaches (e.g., Warren-Cowley parameters) make only partial use of the
complete chemical and structural information contained in local chemical
motifs. Here we introduce a framework based on E(3)-equivariant graph neural
networks that is capable of completely identifying chemical motifs in arbitrary
crystalline structures with any number of chemical elements. This approach
naturally leads to a proper information-theoretic measure for quantifying
chemical short-range order (SRO) in chemically complex materials, and a reduced
- but complete - representation of the chemical space. Our framework enables
the correlation of any per-atom property with their corresponding local
chemical motif, thereby offering novel avenues to explore structure-property
relationships in chemically-complex materials. Using the MoTaNbTi high-entropy
alloy as a test system, we demonstrate the versatility of this approach by
evaluating the lattice strain associated with each chemical motif, and
computing the temperature dependence of chemical-fluctuations length scale.


  Based on a generalization of Bohr's equivalence relation for general
Dirichlet series, in this paper we study the sets of values taken by certain
classes of equivalent almost periodic functions in their strips of almost
periodicity. In fact, the main result of this paper consists of a result like
Bohr's equivalence theorem extended to the case of these functions.


  Focusing on the transient regime, we explore atomic two-photon spectroscopy
with self-aligned homodyne interferometry in a $\Lambda$-system with large
optical depth. The two light sources at the origin of the interference are the
single-photon transient transmission of the probe, and the slow light of the
electromagnetically induced transparency. By switching off the probe laser
abruptly (flash effect), the transient transmission signal is reinforced by
cooperativity, showing enhanced sensitivity to the two-photon frequency
detuning. If the probe laser is periodically switched on and off, the amplitude
of the transmission signal varies and remains large even for high modulation
frequency. This technique has potential applications in sensing, such as
magnetometry and velocimetry, and in coherent population trapping clocks.


  We consider the phase diagram of a ferromagnetic system driven to a quantum
phase transition with a tuning parameter $p$. Before being suppressed, the
transition becomes of the first order at a tricritical point, from which wings
emerge under application of the magnetic field $H$ in the $T$-$p$-$H$ phase
diagram. We show that the edge of the wings merge with tangent slopes at the
tricritical point.


  We give the complete Bott-Chern-Aeppli cohomology for compact complex 3-folds
in terms of Dolbeault, Frolicher, a bi-degree DeRham-like type of cohomology,
$K^{p,q}$, defined as $$ K^{p,q}=\frac{ker( \partial ) \cap ker(
{\bar{\partial}}) }{im( \partial )\cap ker( {\bar{\partial}} )+im(
{\bar{\partial}})\cap ker( \partial )}$$ and ${\check{H}}^1({\mathcal{PH}})$.
(Here $\mathcal{PH}$ is the sheaf of phuri-harmonic functions.) We then work
out the complete Bott-Chern-Aeppli cohomology in some examples. We give the
Bott-Chern-Aeppli cohomology for a hypothetical complex structure on $S^6$ in
terms of Dolbeault and Frolicher. We also give the Bott-Chern-Aeppli cohomology
on a Calabi-Eckman 3-fold concurring with the calculations of Angella and
Tomassini\cite{AngellaAndTomassini}. Finally, we show agreement of our results
with the calculation by Angella\cite{Angella} of the Bott-Chern-Aeppli
cohomology for small Kuranishi deformations of the Iwasawa manifold.


  The conceptual design of a Variable-Phase Asynchronous Cyclotron (VPAC) is
describe, which provides longitudinal bunch compression of accelerated proton
or ion beams, and thus, permits high current acceleration at higher accelerator
efficiency, where the possible beam losses are minimized and the accelerator's
mechanical tolerances are relaxed. Beam control is assured by the ability to
independently set and vary the acceleration phase and rf voltage amplitude, the
inter-cavity harmonic number and the transverse focusing strength, which
considerably overcome the space charge effects in each sector and turn of the
proposed cyclotron. The new accelerator concept is especially suitable to
accelerate intense proton beams up to 800 MeV in energy and average beam
current in the 100-mA class. All accelerator elements are based on currently
available and feasible technologies. To demonstrate feasibility of design, the
detailed calculations and modeling of a 10-turn VPAC prototype for the
production of 25.6 MeV, 100 mA proton beam are presented and the key features
of the new accelerator concept are discussed.


  Hard spheres interacting through a square-well potential were simulated using
two different methods: Brownian Cluster Dynamics (BCD) and Event Driven
Brownian Dynamics (EDBD). The structure of the equilibrium states obtained by
both methods were compared and found to be almost the identical. Self diffusion
coefficients ($D$) were determined as a function of the interaction strength.
The same values were found using BCD or EDBD. Contrary the EDBD, BCD allows one
to study the effect of bond rigidity and hydrodynamic interaction within the
clusters. When the bonds are flexible the effect of attraction on $D$ is
relatively weak compared to systems with rigid bonds. $D$ increases first with
increasing attraction strength, and then decreases for stronger interaction.
Introducing intra-cluster hydrodynamic interaction weakly increases $D$ for a
given interaction strength. Introducing bond rigidity causes a strong decrease
of $D$ which no longer shows a maximum as function of the attraction strength.


  Learning algorithms for implicit generative models can optimize a variety of
criteria that measure how the data distribution differs from the implicit model
distribution, including the Wasserstein distance, the Energy distance, and the
Maximum Mean Discrepancy criterion. A careful look at the geometries induced by
these distances on the space of probability measures reveals interesting
differences. In particular, we can establish surprising approximate global
convergence guarantees for the $1$-Wasserstein distance,even when the
parametric generator has a nonconvex parametrization.


  Baez-Duarte reformulated the Riemann hypothesis as a statement about a
Hilbert space distance, involving the integer dilations of the "fractional
part" function. Under the assumption of the Riemann hypothesis, we improve on
the currently known estimate for this distance.


  Topological phase imprinting is a well-established technique for
deterministic vortex creation in spinor Bose-Einstein condensates of alkali
metal atoms. It was recently shown that counter-diabatic quantum control may
accelerate vortex creation in comparison to the standard adiabatic protocol and
suppress the atom loss due to nonadiabatic transitions. Here we apply this
technique, assisted by an optical plug, for vortex pumping to theoretically
show that sequential phase imprinting up to 20 cycles generates a vortex with a
very large winding number. Our method significantly increases the fidelity of
the pump for rapid pumping compared to the case without the counter-diabatic
control, leading to the highest angular momentum per particle reported to date
for the vortex pump. Our studies are based on numerical integration of the
three-dimensional multi-component Gross-Pitaevskii equation which conveniently
yields the density profiles, phase profiles, angular momentum, and other
physically important quantities of the spin-1 system. Our results motivate the
experimental realization of the vortex pump and studies of the rich physics it
involves.


  This paper is based on a talk presented by the first author at the Short
Program on Riemannian Geometry that took place at the Centre de Recherche
Math\'ematiques, Universit\'e de Montr\'eal, during the period June 28-July 16,
2004. It is a report on our joint work with J\'anos Koll\'ar concerning the
existence of an abundance of Einstein metrics on odd dimensional spheres,
including exotic spheres.


  Magnetosphere at ion kinetic scales, or mini-magnetosphere, possesses unusual
features as predicted by numerical simulations. However, there are practically
no data on the subject from space observations and the data which are available
are far too incomplete. In the present work we describe results of laboratory
experiment on interaction of plasma flow with magnetic dipole with parameters
such that ion inertia length is smaller than a size of observed magnetosphere.
A detailed structure of non-coplanar or out-of-plane component of magnetic
field has been obtained in meridian plane. Independence of this component on
dipole moment reversal, as was reported in previous work, has been verified. In
the tail distinct lobes and central current sheet have been observed. It was
found that lobe regions adjacent to boundary layer are dominated by
non-coplanar component of magnetic field. Tail-ward oriented electric current
in plasma associated with that component appears to be equal to ion current in
the frontal part of magnetosphere and in the tail current sheet implying that
electrons are stationary in those regions while ions flow by. Obtained data
strongly support the proposed model of mini-magnetosphere based on two-fluid
effects as described by the Hall term.


  Little is known about the physical properties of cometary nuclei. Apart from
space mission targets, measuring the thermal emission of a nucleus is one of
the few means to derive its size, independently of its albedo, and to constrain
some of its thermal properties. This emission is difficult to detect from Earth
but space telescopes (Infrared Space Observatory, Spitzer Space Telescope,
Herschel Space Observatory) allow reliable measurements in the infrared and the
sub-millimetre domains. We aim at better characterizing the thermal properties
of the nucleus of comet 8P/Tuttle using multi-wavelentgh space- and
ground-based observations, in the visible, infrared, and millimetre range. We
used the Plateau de Bure Interferometer to measure the millimetre thermal
emission of comet 8P/Tuttle at 240 GHz (1.25 mm) and analysed the observations
with the shape model derived from Hubble Space Telescope observations and the
nucleus size derived from Spitzer Space Telescope observations. We report on
the first detection of the millimetre thermal emission of a cometary nucleus
since comet C/1995 O1 Hale-Bopp in 1997. Using the two contact spheres shape
model derived from Hubble Space Telescope observations, we constrained the
thermal properties of the nucleus. Our millimetre observations are best match
with: i) a thermal inertia lower than ~10 J K-1 m-2 s-1/2, ii) an emissivity
lower than 0.8, indicating a non-negligible contribution of the colder
sub-surface layers to the outcoming millimetre flux.


  We address the problem of sampling double-ended diffusive paths. The ensemble
of paths is expressed using a symmetric version of the Onsager-Machlup formula,
which only requires evaluation of the force field and which, upon direct time
discretization, gives rise to a symmetric integrator that is accurate to second
order. Efficiently sampling this ensemble requires avoiding the well-known
stiffness problem associated with sampling infinitesimal Brownian increments of
the path, as well as a different type of stiffness associated with sampling the
coarse features of long paths. The fine-feature sampling stiffness is
eliminated with the use of the fast sampling algorithm (FSA), and the
coarse-feature sampling stiffness is avoided by introducing the sliding and
sampling (S&S) algorithm. A key feature of the S&S algorithm is that it enables
massively parallel computers to sample diffusive trajectories that are long in
time. We use the algorithm to sample the transition path ensemble for the
structural interconversion of the 38-atom Lennard-Jones cluster at low
temperature.


  Developing agents that can perform complex control tasks from high
dimensional observations such as pixels is challenging due to difficulties in
learning dynamics efficiently. In this work, we propose to learn forward and
inverse dynamics in a fully unsupervised manner via contrastive estimation.
Specifically, we train a forward dynamics model and an inverse dynamics model
in the feature space of states and actions with data collected from random
exploration. Unlike most existing deterministic models, our energy-based model
takes into account the stochastic nature of agent-environment interactions. We
demonstrate the efficacy of our approach across a variety of tasks including
goal-directed planning and imitation from observations. Project videos and code
are at https://jianrenw.github.io/cloud/.


  Current and future experiments observing the cosmic microwave background
require a detailed understanding of optical performance at cryogenic
temperatures. Pre-deployment analysis of optics can be performed in
custom-engineered cryogenic test beds, such as Mod-Cam, a first light camera
for the CCAT project. This work presents studies of the mechanical and thermal
performance of CryoSim, a model of a generic cylindrical 4-K cryostat cooled
with a commercial pulse tube cryocooler that can be used to characterise
optical components and full reimaging optical systems. CryoSim is extensively
parametrised, allowing the joint analysis and optimisation of mechanical and
thermal performance via finite element methods. Results from this model are
validated against measured cooldown data of the Mod-Cam cryostat. Due to the
extensive parametrisation of the model, significant modifications of the
cryostat geometry may be implemented to be representative of any system the
scientific community may desire, and validation of thermal and mechanical
performance can be carried out rapidly.


  We report on the discovery of a very distant galaxy cluster serendipitously
detected in the archive of the XMM-Newton mission, within the scope of the
XMM-Newton Distant Cluster Project (XDCP). XMMUJ0044.0-2033 was detected at a
high significance level (5sigma) as a compact, but significantly extended
source in the X-ray data, with a soft-band flux f(r<40")=(1.5+-0.3)x10^(-14)
erg/s/cm2. Optical/NIR follow-up observations confirmed the presence of an
overdensity of red galaxies matching the X-ray emission. The cluster was
spectroscopically confirmed to be at z=1.579 using ground-based VLT/FORS2
spectroscopy. The analysis of the I-H colour-magnitude diagram shows a sequence
of red galaxies with a colour range [3.7 < I-H < 4.6] within 1' from the
cluster X-ray emission peak. However, the three spectroscopic members (all with
complex morphology) have significantly bluer colours relative to the observed
red-sequence. In addition, two of the three cluster members have [OII]
emission, indicative of on-going star formation. Using the spectroscopic
redshift we estimated the X-ray bolometric luminosity, Lbol = 5.8x10^44 erg/s,
implying a massive galaxy cluster. This places XMMU J0044.0-2033 at the
forefront of massive distant clusters, closing the gap between lower redshift
systems and recently discovered proto- and low-mass clusters at z >1.6.


  I construct solutions to Einstein's equations in 6 dimensions with bulk
cosmological constant and intersecting 4-branes. Solutions exist for a
continuous range of 4-brane tension, with long distance gravity localized to a
3+1 dimensional Minkowski intersection, provided that the additional tension of
the intersection satisfies one condition.


  Effective close-proximity human-robot interaction (CP-HRI) requires robots to
be able to both efficiently perform tasks as well as adapt to human behavior
and preferences. However, this ability is mediated by many, sometimes
competing, aspects of interaction. We propose a real-time motion-planning
framework for robotic manipulators that can simultaneously optimize a set of
both task- and human-centric cost functions. To this end, we formulate a
Nonlinear Model-Predictive Control (NMPC) problem with kino-dynamic constraints
and efficiently solve it by leveraging recent advances in nonlinear trajectory
optimization. We employ stochastic predictions of the human partner's
trajectories in order to adapt the robot's nominal behavior in anticipation of
its human partner. Our framework explicitly models and allows balancing of
different task- and human-centric cost functions. While previous approaches to
trajectory optimization for CP-HRI take anywhere from several seconds to a full
minute to compute a trajectory, our approach is capable of computing one in 318
ms on average, enabling real-time implementation. We illustrate the
effectiveness of our framework by simultaneously optimizing for separation
distance, end-effector visibility, legibility, smoothness, and deviation from
nominal behavior. We also demonstrate that our approach performs comparably to
prior work in terms of the chosen cost functions, while significantly improving
computational efficiency.


  Mobile applications and on-body devices are becoming increasingly ubiquitous
tools for physical activity tracking. We propose utilizing a self-tracker's
habits to support continuous prediction of whether they will reach their daily
step goal, thus enabling a variety of potential persuasive interventions. Our
aim is to improve the prediction by leveraging historical data and other
qualitative (motivation for using the systems, location, gender) and,
quantitative (age) features. We have collected datasets from two activity
tracking platforms (Moves and Fitbit) and aim to check if the model we derive
from one is generalizable over the other. In the following paper we establish a
pipeline for extracting the data and formatting it for modeling. We discuss the
approach we took and our findings while selecting the features and
classification models for the dataset. We further discuss the notion of
generalizability of the model across different types of dataset and the
probable inclusion of non standard features to further improve the model's
accuracy.


  We study the spin-boson model (SBM) with two spins in staggered biases by a
numerically exact method based on variational matrix product states. Several
observables such as the magnetization, the entanglement entropy between the two
spins and the bosonic environment, the ground-state energy, as well as the
correlation function for two spins are calculated exactly. The characteristics
of these observables suggest that the staggered biases can drive the 2nd-order
quantum phase transition (QPT) to the 1st-order QPT in the sub-Ohmic SBM, while
the Kosterlitz-Thouless QPT in the Ohmic SBM goes directly to the 1st-order
one. A quantum tricritical point, where the continuous QPT meets the 1st-order
one, can then be detected. It is found that the staggered biases would not
change the universality of { the phase transition in this model} below the
quantum tricritical point.


  The behaviour of neurons under the influence of periodic external input has
been modelled very successfully by circle maps. The aim of this note is to
extend certain aspects of this analysis to a much more general class of forcing
processes. We apply results on the fibred rotation number of randomly forced
circle maps to show the uniqueness of the asymptotic firing frequency of
ergodically forced pacemaker neurons. The details of the analysis are carried
out for the forced leaky integrate-and-fire model, but the results should also
remain valid for a large class of further models.


  The problem of robustly reconstructing an integer vector from its erroneous
remainders appears in many applications in the field of multidimensional (MD)
signal processing. To address this problem, a robust MD Chinese remainder
theorem (CRT) was recently proposed for a special class of moduli, where the
remaining integer matrices left-divided by a greatest common left divisor
(gcld) of all the moduli are pairwise commutative and coprime. The strict
constraint on the moduli limits the usefulness of the robust MD-CRT in
practice. In this paper, we investigate the robust MD-CRT for a general set of
moduli. We first introduce a necessary and sufficient condition on the
difference between paired remainder errors, followed by a simple sufficient
condition on the remainder error bound, for the robust MD-CRT for general
moduli, where the conditions are associated with (the minimum distances of)
these lattices generated by gcld's of paired moduli, and a closed-form
reconstruction algorithm is presented. We then generalize the above results of
the robust MD-CRT from integer vectors/matrices to real ones. Finally, we
validate the robust MD-CRT for general moduli by employing numerical
simulations, and apply it to MD sinusoidal frequency estimation based on
multiple sub-Nyquist samplers.


  The increasing amount of data on the Web, in particular of Linked Data, has
led to a diverse landscape of datasets, which make entity retrieval a
challenging task. Explicit cross-dataset links, for instance to indicate
co-references or related entities can significantly improve entity retrieval.
However, only a small fraction of entities are interlinked through explicit
statements. In this paper, we propose a two-fold entity retrieval approach. In
a first, offline preprocessing step, we cluster entities based on the
\emph{x--means} and \emph{spectral} clustering algorithms. In the second step,
we propose an optimized retrieval model which takes advantage of our
precomputed clusters. For a given set of entities retrieved by the BM25F
retrieval approach and a given user query, we further expand the result set
with relevant entities by considering features of the queries, entities and the
precomputed clusters. Finally, we re-rank the expanded result set with respect
to the relevance to the query. We perform a thorough experimental evaluation on
the Billions Triple Challenge (BTC12) dataset. The proposed approach shows
significant improvements compared to the baseline and state of the art
approaches.


  Self-supervised learning (SSL) has helped extend speech technologies to more
languages by reducing the need for labeled data. However, models are still far
from supporting the world's 7000+ languages. We propose XEUS, a Cross-lingual
Encoder for Universal Speech, trained on over 1 million hours of data across
4057 languages, extending the language coverage of SSL models 4-fold. We
combine 1 million hours of speech from existing publicly accessible corpora
with a newly created corpus of 7400+ hours from 4057 languages, which will be
publicly released. To handle the diverse conditions of multilingual speech
data, we augment the typical SSL masked prediction approach with a novel
dereverberation objective, increasing robustness. We evaluate XEUS on several
benchmarks, and show that it consistently outperforms or achieves comparable
results to state-of-the-art (SOTA) SSL models across a variety of tasks. XEUS
sets a new SOTA on the ML-SUPERB benchmark: it outperforms MMS 1B and w2v-BERT
2.0 v2 by 0.8% and 4.4% respectively, despite having less parameters or
pre-training data. Checkpoints, code, and data are found in
https://www.wavlab.org/activities/2024/xeus/.


  Let $\mathsf{Ord} $ be the category of (pre)ordered sets. Unlike
$\mathsf{Ord}/X$, whose behaviour is well-known, not much can be found in the
literature about the lax comma 2-category $\mathsf{Ord} //X$. In this paper we
show that the forgetful functor $\mathsf{Ord} //X\to \mathsf{Ord} $ is
topological if and only if $X$ is complete. Moreover, under suitable
hypothesis, $\mathsf{Ord} // X$ is complete and cartesian closed if and only if
$X$ is. We end by analysing descent in this category. Namely, when $X$ is
complete and cartesian closed, we show that, for a morphism in $\mathsf{Ord}
//X$, being pointwise effective for descent in $\mathsf{Ord} $ is sufficient,
while being effective for descent in $\mathsf{Ord} $ is necessary, to be
effective for descent in $\mathsf{Ord} //X$.


  The conformational behavior of a giant DNA mediated by condensing agents in
the bulk solution has been investigated through experimental and theoretical
approaches. Experimentally, a pronounced conformational hysteresis is observed
for folding and unfolding processes, by increasing and decreasing the
concentration of condensing agent PEG (Polyethylene glycol), respectively. To
elucidate the observed hysteresis, a semiflexible chain model is studied by
using Monte Carlo simulations for the coil-globule transition. In the
simulations, the hysteresis loop emerges for stiff enough chains, indicating
distinct pathways for folding and unfolding processes. Also, our results show
that globular state is thermodynamically more stable than coiled state in the
hysteresis loop. Our findings suggest that increasing chain stiffness may
reduce the chain conformations relevant to the folding pathway, which impedes
the folding process.


  In this paper we improve the best known to date result of
Dress-Iwaniec-Tenenbaum, getting (log x)^2 instead of (log x)^(5/2). We use a
weighted form of Vaughan's identity, allowing a smooth truncation inside the
procedure, and an estimate due to Barban-Vehov and Graham related to Selberg's
sieve. We give effective and non-effective versions of the result.


  We study cosmology on a BPS D3-brane evolving in the 10D SUGRA background
describing a non-BPS brane. Initially the BPS brane is taken to be a probe
whose dynamics we determine in the non-compact non-BPS background. The
cosmology observed on the brane is of the FRW type with a scale factor
$S(\tau)$. In this mirage cosmology approach, there is no self-gravity on the
brane which cannot inflate. Self-gravity is then included by compactifying the
background space-time. The low energy effective theory below the
compactification scale is shown to be bi-metric, with matter coupling to a
different metric than the geometrically induced metric on the brane. The
geometrical scale factor on the brane is now $S(\tau) a(\tau)$ where $a(\tau)$
arises from brane self-gravity. In this non-BPS scenario the brane generically
inflates. We study the resulting inflationary scenario taking into account the
fact that the non-BPS brane eventually decays on a time-scale much larger than
the typical inflationary time-scale. After the decay, the theory ceases to be
bi-metric and COBE normalization is used to estimate the string scale which is
found to be of order $10^{14}$ GeV.


  We present the leading-order prediction of baryon chiral perturbation theory
for the proton polarizability contribution to the 2S hyperfine splitting in
muonic hydrogen, and compare with the results of dispersive calculations.


  The main result is Theorem MAT 11 which states that every finite closure
operator is the ground set of a matroid. Its base sets consist of nonredundant
covers of of the closure. These are minimal subsets that determine the closure
operator using a closure algorithm from the theory of relational databases. For
each hereditary collection there is one flat closure that define a matroid or
simplicial complexes, but every closure defines a hereditary collection, its
keys in database theory. The normalization algorithm by Maier is the basis of
this paper. This follows his development, and his relevant results are cited to
aid reading his original work. The main purpose of this paper is to introduce
the importance of database theory into the analysis of all finite closure
including the flat closures of matroids.


  Large language models (LLMs) are powerful dialogue agents, but specializing
them towards fulfilling a specific function can be challenging. Instructing
tuning, i.e. tuning models on instruction and sample responses generated by
humans (Ouyang et al., 2022), has proven as an effective method to do so, yet
requires a number of data samples that a) might not be available or b) costly
to generate. Furthermore, this cost increases when the goal is to make the LLM
follow a specific workflow within a dialogue instead of single instructions.
Inspired by the self-play technique in reinforcement learning and the use of
LLMs to simulate human agents, we propose a more effective method for data
collection through LLMs engaging in a conversation in various roles. This
approach generates a training data via "self-talk" of LLMs that can be refined
and utilized for supervised fine-tuning. We introduce an automated way to
measure the (partial) success of a dialogue. This metric is used to filter the
generated conversational data that is fed back in LLM for training. Based on
our automated and human evaluations of conversation quality, we demonstrate
that such self-talk data improves results. In addition, we examine the various
characteristics that showcase the quality of generated dialogues and how they
can be connected to their potential utility as training data.


  The method of the optical parameter estimations of the nonisothermal giant
planet atmospheres by using intensity data of Raman scattering features was
used. We applied this method to the observational data of Uranus' geometric
albedo spectra from 1981, 1993 and 1995 and obtained the spectral values of the
optical depth, namely, $\tau_a/\tau_R$ and $\tau_\kappa/\tau_S$ (where
$\tau_a$, $\tau_R$ are aerosol and gas components, $\tau_S=\tau_a+\tau_R$ and
$\tau_\kappa$ is absorption component of the effective optical depth of the
intensity of diffuse - reflected irradiation forming). We showed that these
ratios are different for the three years. The conclusion is that this effect
can be due to the horizontal inhomogeneity of aerosol component of optical
depth over the Uranus' disk.


  A nanofocusing optical system referred to as $\textit{100 exa}$ for an X-ray
free-electron laser (XFEL) was developed to generate an extremely high
intensity of 100 EW/cm$^2$ (10$^2$$^0$ W/cm$^2$) using total reflection
mirrors. The system is based on Kirkpatrick-Baez geometry, with 250 mm long
elliptically figured mirrors optimized for the SPring-8 Angstrom Compact
Free-Electron Laser (SACLA) XFEL facility. The nano-precision surface employed
is coated with rhodium and offers a high reflectivity of 80%, with a photon
energy of up to 12 keV, under total reflection conditions. Incident X-rays on
the optics are reflected with a large spatial acceptance of over 900 $\mu$m.
The focused beam is 210 nm $\times$ 120 nm (full width at half maximum) and was
evaluated at a photon energy of 10 keV. The optics developed for $\textit{100
exa}$ efficiently achieved an intensity of 1 $\times$ 10$^2$$^0$ W/cm$^2$ with
a pulse duration of 7 fs and a pulse energy of 150 $\mu$J (25% of the pulse
energy generated at the light source). The experimental chamber, which can
provide varied stage arrangements and sample conditions, including vacuum
environments and atmospheric pressure helium, was set up with the focusing
optics to meet the experimental requirements.


  In the field of small angle x-ray scattering (SAXS), the task of estimating
the size of particles in solution is usually synonymous with the Guinier plot.
The approximation behind this plot, developed by Guinier in 1939 provides a
simple yet accurate characterization of the scattering behavior of particles at
low scattering angle $q$, together with a computationally efficient way of
inferring their radii of gyration $R_G$. Moreover, this approximation is valid
beyond spherical scatterers, making its use ubiquitous in the SAXS world.
However, when it is important to estimate further particle characteristics,
such as the anisotropy of the scatterer's shape, no similar or extended
approximations are available. Existing tools to characterize the shape of
scatterers rely either on prior knowledge of the scatterers' geometry or on
iterative procedures to infer the particle shape \textit{ab initio}.\\
  In this work we develop a low angle approximation of the scattering intensity
$I(q)$ for ellipsoids of revolution and show how to extract size and anisotropy
information from the parameters of that approximation. Beyond ideal ellipsoids
of revolution, we show that this approximation can be used to infer the size
and shape of molecules in solution, both in computational and experimental
scenarios. We discuss the limits of our approach and study the impact of a
particle's anisotropy in the Guinier estimate of $R_G$.


  We study the kinematics of the barred spiral galaxy NGC 6946 by investigating
the velocity field from H-alpha Fabry-Perot observations, determined the
pattern speed of the bar by using the Tremaine-Weinberg method, and find a main
pattern speed of 21.7 (+4.0,-0.8) km/s/kpc. Our data clearly suggest the
presence of an additional pattern with a pattern speed more than twice that of
the large pattern in this galaxy. We use the epicycle approximation to deduce
the location of the resonance radii and subsequently determine the pattern
speed between the radii, and find that inside the inner Inner Lindblad
Resonance radius, a bar-like system has evolved.


  Out-of-distribution (OOD) detection is essential for the reliable and safe
deployment of machine learning systems in the real world. Great progress has
been made over the past years. This paper presents the first review of recent
advances in OOD detection with a particular focus on natural language
processing approaches. First, we provide a formal definition of OOD detection
and discuss several related fields. We then categorize recent algorithms into
three classes according to the data they used: (1) OOD data available, (2) OOD
data unavailable + in-distribution (ID) label available, and (3) OOD data
unavailable + ID label unavailable. Third, we introduce datasets, applications,
and metrics. Finally, we summarize existing work and present potential future
research topics.


  The extensive use of smartphones and wearable devices has facilitated many
useful applications. For example, with Global Positioning System (GPS)-equipped
smart and wearable devices, many applications can gather, process, and share
rich metadata, such as geolocation, trajectories, elevation, and time. For
example, fitness applications, such as Strava and Runkeeper, utilize
information for activity tracking, and have recently witnessed a boom in
popularity. Those trackers have their own web platforms, and allow users to
share activities on such platforms, or even with other social network
platforms. To preserve privacy of users while allowing sharing, those platforms
allow users to disclose partial information, such as the elevation profile for
an activity, which supposedly will not leak the location trajectory.
  In this work we examine the extent to which publicly available elevation
profiles can be used to predict the location trajectory of users. To tackle
this problem, we devise three threat settings under which the city, borough, or
even a route can be predicted. Those threat settings define the amount of
information available to the adversary to launch the prediction attacks.
Establishing that simple features of elevation profiles, e.g., spectral
features, are insufficient, we devise both natural language processing
(NLP)-inspired text-like representation and computer vision-inspired image-like
representation of elevation profiles, and we convert the problem at hand into
text and image classification problem. We use both traditional machine
learning- and deep learning-based techniques, and achieve a prediction success
rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight
that sharing information such as elevation profile may have significant privacy
risks.


  Marine cables under low tension and torsion on the sea floor can form highly
contorted three-dimensional geometries that include loops (e.g. hockles) and
tangles. These geometries arise from the conversion of torsional strain energy
to bending strain energy or, kinematically, a conversion of twist to writhe. A
dynamic form of Kirchhoff rod theory is reviewed herein that captures these
nonlinear dynamic processes. The resulting theory is discretized using the
generalized-alpha method for finite differencing in both space and time.
Numerical solutions are presented for an example system of a cable subjected to
increasing twist at one end. The solutions show the dynamic evolution of the
cable from an initially straight element, through a buckled element in the
approximate form of a helix, through the dynamic collapse of this helix into a
loop, and subsequent intertwining of the loop with multiple sites of
self-contact.


  We use the Bogoliubov-de Gennes formalism to analyze the effects of rotation
on the ground state phases of harmonically trapped Fermi gases, under the
assumption that quantized vortices are not excited. We find that the rotation
breaks Cooper pairs that are located near the trap edge, and that this leads to
a phase separation between the nonrotating superfluid (fully paired) atoms
located around the trap center and the rigidly rotating normal (nonpaired)
atoms located towards the trap edge, with a coexistence (partially paired)
region in between. Furthermore, we show that the superfluid phase that occurs
in the coexistence region is characterized by a gapless excitation spectrum,
and that it is distinct from the gapped phase that occurs near the trap center.


  In this paper, we present a foreground analysis of the WMAP 5-year data using
the FASTICA algorithm, improving on the treatment of the WMAP 3-year data in
Bottino et al 2008. We revisit the nature of the free-free spectrum with the
emphasis on attempting to confirm or otherwise the spectral feature claimed in
Dobbler et al 2008b and explained in terms of spinning dust emission in the
warm ionised medium. With the application of different Galactic cuts, the index
is always flatter than the canonical value of 2.14 except for the Kp0 mask
which is steeper. Irrespective of this, we can not confirm the presence of any
feature in the free-free spectrum. We experiment with a more extensive approach
to the cleaning of the data, introduced in connection with the iterative
application of FASTICA. We confirm the presence of a residual foreground whose
spatial distribution is concentrated along the Galactic plane, with pronounced
emission near the Galactic center. This is consistent with the WMAP haze
detected in Finkbeiner 2004. Finally, we attempted to perform the same analysis
on full-sky maps. The code returns good results even for those regions where
the cross-talk among the components is high. However, slightly better results
in terms of the possibility of reconstructing a full-sky CMB map, are achieved
with a simultaneous analysis of both the five WMAP maps and foreground
templates. Nonetheless, some residuals are still present and detected in terms
of an excess in the CMB power spectrum, on small angular scales. Therefore, a
minimal mask for the brightest regions of the plane is necessary, and has been
defined.


  This paper has a two-folded purpose. First, we attempt to outline the
development of the turnpike theorems in the the last several decades. Second,
we study turnpike theorems in finite-horizon two-person zero-sum Markov games
on a general Borel state space. Utilising the Bellman (or Shapley) operator
defined for this game, we prove the stochastic versions of the early turnpike
theorem on the set of optimal strategies and the middle turnpike theorem on the
distribution of the state space.


  Modern NLP tasks increasingly rely on dense retrieval methods to access
up-to-date and relevant contextual information. We are motivated by the premise
that retrieval benefits from segments that can vary in size such that a
content's semantic independence is better captured. We propose LumberChunker, a
method leveraging an LLM to dynamically segment documents, which iteratively
prompts the LLM to identify the point within a group of sequential passages
where the content begins to shift. To evaluate our method, we introduce
GutenQA, a benchmark with 3000 "needle in a haystack" type of question-answer
pairs derived from 100 public domain narrative books available on Project
Gutenberg. Our experiments show that LumberChunker not only outperforms the
most competitive baseline by 7.37% in retrieval performance (DCG@20) but also
that, when integrated into a RAG pipeline, LumberChunker proves to be more
effective than other chunking methods and competitive baselines, such as the
Gemini 1.5M Pro. Our Code and Data are available at
https://github.com/joaodsmarques/LumberChunker


  A variety of pulsar studies, ranging from high precision astrometry to tests
for theories of gravity, require high time resolution data. Few such
observations at more than two frequencies below 1 GHz are available. Giant
Meterwave Radio Telescope (GMRT) has the unique capability to provide such
multi-frequency pulsar data at low observation frequencies, but the quality and
time resolution of pulsar radio signals is degraded due to dispersion in the
inter-stellar medium at these frequencies. Such degradation is usually taken
care of by employing specialized digital hardware, which implement coherent
dedispersion algorithm. In recent years, a new alternative is provided by the
availability of cheap computer hardware. In this approach, the required signal
processing is implemented in software using commercially off-the-shelf
available computing hardware. This makes such a receiver flexible and
upgradeable unlike a hardware implementation. The salient features and the
modes of operation of a high time resolution pulsar instrument for GMRT based
on this approach is described in this paper. The capability of the instrument
is demonstrated by illustrations of test observations. We have obtained the
average profile of PSR B1937+21 at 235 MHz for the first time and this profile
indicates a scattering timescale of about 300 us. Lastly, the possible future
extensions of this concept are discussed.


  We show that memristive systems can be used very efficiently to generate
passively both double and higher frequency harmonics. A technique for
maximizing the power conversion efficiency into any given harmonic is developed
and applied to a single memristive system and memristive bridge circuits. We
find much higher rates of power conversion compared to the standard diode
bridge, with the memristive bridge more efficient for second and higher
harmonics generation compared to the single memristive system. The memristive
bridge circuit optimized for second harmonic generation behaves as a
two-quarter-wave rectifier.


  Numbers, both very large and very small, are crucially important for
understanding the modern world. This paper assesses trials of a mathematics and
physics module called Powers of the Universe in which arithmetic with extreme
numbers (large and small) is developed through early learning of the powers of
ten notation. We trialled a 6-hour progression of lessons based on activities
and group learning with students aged 7-13 years. We measured students' ability
to estimate, compare, and calculate extreme numbers using pre and post-tests to
evaluate the program. Results demonstrated students' strong enthusiasm and
positive learning outcomes in areas normally assumed to be beyond the
capability of students in this age group. We discuss the age dependence of some
results and suggest an optimum strategy for enhancing primary school
mathematics. The module has been delivered, as part of a broader five-module
program called Maths for Einstein's Universe, that aims to reduce maths anxiety
through programs with direct relevance to the modern world and reduced emphasis
on exactness.


  It is shown that the only functionals, within a natural class, which are
monotonic in time for all solutions of the vacuum Einstein equations admitting
a smooth ``piece'' of conformal null infinity Scri, are those depending on the
metric only through a specific combination of the Bondi `mass aspect' and other
next--to--leading order terms in the metric. Under the extra condition of
passive BMS invariance, the unique such functional (up to a multiplicative
factor) is the Trautman--Bondi energy. It is also shown that this energy
remains well-defined for a wide class of `polyhomogeneous' metrics.


  The high-contrast coronagraph for direct imaging earth-like exoplanet at the
visible needs a contrast of 10^(-10) at a small angular separation of 4
lambda/D or less. Here we report our recent laboratory experiment that is close
to the limits. The test of the high-contrast imaging coronagraph is based on
our step-transmission apodized filter. To achieve the goal, we use a liquid
crystal array (LCA) as a phase corrector to create a dark hole based on our
dedicated focal dark algorithm. We have suppressed the diffracted and speckle
noise near the star point image to a level of 1.68 x 10^(-9) at 4 lambda/D,
which can be immediately used for the direct imaging of Jupiter like
exoplanets. This demonstrates that high-contrast coronagraph telescope in space
has the potentiality to detect and characterize earth-like planets.


  The quaternionic Cauchy-Szeg\"{o} kernel of the Hardy space
$\mathcal{H}^2(\mathcal{U}_n)$ on the quaternionic Siegel half space
$\mathcal{U}_n$ is derived and the Hardy spaces on the octonionic Siegel half
space is investigated.


  We give a lower bound for the value at q=1 of a Kazhdan-Lustig polynomial in
a Weyl group W in terms of "patterns''. This is expressed by a "pattern map"
from W to W' for any parabloic subgroup W'. This notion generalizes the concept
of patterns and pattern avoidance for permutations to all Weyl groups. The main
tool of the proof is a "hyperbolic localization" on intersection cohomology;
see the related paper http://front.math.ucdavis.edu/math.AG/0202251


  Landauer's principle bridges information theory and thermodynamics by linking
the entropy change of a system during a process to the average energy
dissipated to its environment. Although typically discussed in the context of
erasing a single bit of information, Landauer's principle can be generalised to
characterise irreversibility in out-of-equilibrium processes, such as those
involving complex quantum many-body systems. Specifically, the relationship
between the entropy change of the system and the energy dissipated to its
environment can be decomposed into changes in quantum mutual information and a
difference in relative entropies of the environment. Here we experimentally
probe Landauer's principle in the quantum many-body regime using a quantum
field simulator of ultracold Bose gases. Employing a dynamical tomographic
reconstruction scheme, we track the temporal evolution of the quantum field
following a global mass quench from a Klein-Gordon to a Tomonaga-Luttinger
liquid model and analyse the information-theoretic contributions to Landauer's
principle for various system-environment partitions of the composite system.
Our results agree with theoretical predictions, interpreted using a
semi-classical quasiparticle picture. Our work demonstrates the potential of
ultracold atom-based quantum field simulators to experimentally investigate
quantum thermodynamics.


  In this work, we study the thermodynamic functions of quantum gases confined
to spaces of various shapes, namely, a sphere, a cylinder, and an ellipsoid. We
start with the simplest situation, namely, a spinless gas treated within the
canonical ensemble framework. As a next step, we consider
\textit{noninteracting} gases (fermions and bosons) with the usage of the grand
canonical ensemble description. For this case, the calculations are performed
numerically. We also observe that our results may possibly be applied to
\textit{Bose-Einstein condensate} and to \textit{helium dimer}. Moreover, the
bosonic sector, independently of the geometry, acquires entropy and internal
energy greater than for the fermionic case. Finally, we also devise a model
allowing us to perform analytically the calculations in the case of
\textit{interacting} quantum gases, and, afterwards, we apply it to a cubical
box.


  We construct the Fermi gas formalism for the partition function of
supersymmetric Chern-Simons theories with affine $D$-type quiver diagrams with
non-uniform ranks of the gauge groups and Fayet-Illiopoulos parameters by two
different approaches: the open string formalism and the closed string
formalism. In the closed string formalism approach, we find a novel connection
between the partition function of this theory and the partition function of a
four-nodes circular quiver supersymmetric Chern-Simons theory. We also studied
a symmetry of a density matrix appeared in the closed string formalism. We
further calculate the exact values of the partition function for finite $N$,
with which we identified the exponent of the leading non-perturbative effect in
$1/N$ corresponding to the worldsheet instantons in the circular quiver
supersymmetric Chern-Simons theories.


  Generation and detection of structured light have recently been the subject
of intense study, aiming to realize high-capacity optical storage and
continuous-variable quantum technologies. Here, we present a scheme to extract
the orbital angular momentum content of Laguerre-Gaussian light beams in a
double-$\Lambda$ four level system of GaAs/AlGaAs multiple quantum wells.
Arising from a quantum interference term, absorption of a non-vortex probe
field depends upon the azimuthal phase of vortex fields so that both magnitude
and sign of the azimuthal index/indices can be mapped into the absorption
profile.


  We calculate radio light curves produced by the bow shock that is likely to
form in front of the G2 cloud when it penetrates the accretion disk of Sgr A*.
The shock acceleration of the radio-emitting electrons is captured
self-consistently by means of first-principles particle-in-cell simulations. We
show that the radio luminosity is expected to reach maximum in early 2013,
roughly a month after the bow shock crosses the orbit pericenter. We estimate
the peak radio flux at 1.4 GHz to be 1.4 - 22 Jy depending on the assumed orbit
orientation and parameters. We show that the most promising frequencies for
radio observations are in the 0.1<nu<1 GHz range, for which the bow shock
emission will be much stronger than the intrinsic radio flux for all the models
considered.


  The 1D flow of a continuous beam of Bose-Einstein condensed atoms in the
presence of an obstacle is studied as a function of the beam velocity and of
the type of perturbing potential (representing the interaction of the obstacle
with the atoms of the beam). We identify the relevant regimes:
stationary/time-dependent and superfluid/dissipative; the absence of drag is
used as a criterion for superfluidity. There exists a critical velocity below
which the flow is superfluid. For attractive obstacles, we show that this
critical velocity can reach the value predicted by Landau's approach. For
penetrable obstacles, it is shown that superfluidity is recovered at large beam
velocity. Finally, enormous differences in drag occur when switching from
repulsive to attractive potential.


  After an eleven year observing campaign, we present the combined
visual{spectroscopic orbit of the formerly unremarkable bright star HR 7345 (HD
181655, HIP 94981, GJ 754.2). Using the Separated Fringe Packet (SFP) method
with the CHARA Array, we were able to determine a difficult to complete orbital
period of 331.609 +/- 0.004 days. The 11 month period causes the system to be
hidden from interferometric view behind the Sun for 3 years at a time. Due to
the high eccentricity orbit of about 90% of a year, after 2018 January the
periastron phase will not be observable again until late 2021. Hindered by its
extremely high eccentricity of 0.9322 +/- 0.0001, the double-lined
spectroscopic phase of HR 7345 is observable for 15 days. Such a high
eccentricity for HR 7345 places it among the most eccentric systems in catalogs
of both visual and spectroscopic orbits. For this system we determine nearly
identical component masses of 0.941 +/- 0.076 Msun and 0.926 +/- 0.075 Msun as
well as an orbital parallax of 41.08 +/- 0.77 mas.


  We consider type IIB supergravity backgrounds which describe marginal
deformations of the Coulomb branch of N=4 super Yang-Mills theory with SO(4) x
SO(2) global symmetry. Wilson loop calculations indicate that certain
deformations enhance the Coulombic attraction between quarks and anti-quarks at
the UV conformal fixed-point. In the IR region, these deformations can induce a
transition to linear confinement.


  Dynamic modulation of electromagnetic responses is theoretically examined in
dielectric antiferromagnets. While both magneto-electric and magneto-elastic
coupling can achieve robust electrical control of magnetic anisotropy, the
latter is considered in a bilayer structure with a piezoelectric material.
Numerical calculations based on the frequency-dependent permeability tensor
clearly illustrate that the anisotropy profile in the typical uniaxial or
biaxial antiferromagnets such as NiO and Cr2O3 can be modified sufficiently to
induce a shift in the resonance frequency by as much as tens of percent in the
sub-mm wavelength range (thus, an electrically tunable bandwidth over 10's of
GHz). The polarization of the electromagnetic response is also affected due to
the anisotropic nature of the effect, offering a possibility to encode the
signal. The intrinsic delay in switching may be minimized to the ns level by
using a sufficiently thin antiferromagnets. Application to specific devices
such as a band-pass filter further illustrates the validity of the concept.


  Superconductivity allows to construct and operate magnets at field values
beyond 2 Tesla, the practical limitation of normal-conducting magnets
exploiting ferro-magnetism. The field of superconducting magnets is dominated
by the field generated in the coil. The stored energy and the electromagnetic
forces generated by the coil are the main challenges to be overcome in the
design of these magnets. For further reading you may consult the following
books: [1], [2], [3], [4] or the proceedings of two specialized CAS courses:
[5] and [6].


  Using multiple robots for exploring and mapping environments can provide
improved robustness and performance, but it can be difficult to implement. In
particular, limited communication bandwidth is a considerable constraint when a
robot needs to determine if it has visited a location that was previously
explored by another robot, as it requires for robots to share descriptions of
places they have visited. One way to compress this description is to use
constellations, groups of 3D points that correspond to the estimate of a set of
relative object positions. Constellations maintain the same pattern from
different viewpoints and can be robust to illumination changes or dynamic
elements. We present a method to extract from these constellations compact
spatial and semantic descriptors of the objects in a scene. We use this
representation in a 2-step decentralized loop closure verification: first, we
distribute the compact semantic descriptors to determine which other robots
might have seen scenes with similar objects; then we query matching robots with
the full constellation to validate the match using geometric information. The
proposed method requires less memory, is more interpretable than global image
descriptors, and could be useful for other tasks and interactions with the
environment. We validate our system's performance on a TUM RGB-D SLAM sequence
and show its benefits in terms of bandwidth requirements.


  We present a review of the follow-up observations carried out from
observatories located in Spain; Calar-Alto, Izanha and Roque de Los Muchachos.
It summarizes the observations carried out by our group for 27 GRBs occurred in
the period 1999-2000, spanning from GRB 990123 to GRB 001007.


  We consider fermions on a 2D square lattice with a finite-range pairing
interaction, and obtain signatures for unconventional pair-symmetry states,
$d_{x^2-y^2}$ and extended-s ($s^*$), in the BCS-BEC crossover region. We find
that the fermion momentum distribution function, $v_k^2$, the ratio of the
Bogoliubov coefficients, $v_k/u_k$, and the Fourier transform of $v_k^2$ are
strikingly different for $d$- and $s^*$ symmetries in the crossover region. The
chemical potential and the gaps functions for both pairing symmetries show
several interesting features as a function of interaction. Fermionic atoms in
2D optical lattices may provide a way to test these signatures. We discuss
current generation cold atom experiments that may be utilized.


  In the holographic Ricci dark energy (RDE) model, the parameter $\alpha$
plays an important role in determining the evolutionary behavior of the dark
energy. When $\alpha<1/2$, the RDE will exhibit a quintom feature, i.e., the
equation of state of dark energy will evolve across the cosmological constant
boundary $w=-1$. Observations show that the parameter $\alpha$ is indeed
smaller than 1/2, so the late-time evolution of RDE will be really like a
phantom energy. Therefore, it seems that the big rip is inevitable in this
model. On the other hand, the big rip is actually inconsistent with the
theoretical framework of the holographic model of dark energy. To avoid the big
rip, we appeal to the extra dimension physics. In this Letter, we investigate
the cosmological evolution of the RDE in the braneworld cosmology. It is of
interest to find that for the far future evolution of RDE in a Randall-Sundrum
braneworld, there is an attractor solution where the steady state (de Sitter)
finale occurs, in stead of the big rip.


  The reliable facility location problem (RFLP) is an important research topic
of operational research and plays a vital role in the decision-making and
management of modern supply chain and logistics. Through solving RFLP, the
decision-maker can obtain reliable location decisions under the risk of
facilities' disruptions or failures. In this paper, we propose a novel model
for the RFLP. Instead of assuming allocating a fixed number of facilities to
each customer as in the existing works, we set the number of allocated
facilities as an independent variable in our proposed model, which makes our
model closer to the scenarios in real life but more difficult to be solved by
traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary
algorithm, which combines a memorable local search (MLS) method and an
evolutionary algorithm (EA). Additionally, a novel metric called l3-value is
proposed to assist the analysis of the algorithm's convergence speed and exam
the process of evolution. The experimental results show the effectiveness and
superior performance of our EAMLS, compared to a CPLEX solver and a Genetic
Algorithm (GA), on large-scale problems.


  A pattern class is a set of permutations closed under the formation of
subpermutations. Such classes can be characterised as those permutations not
involving a particular set of forbidden permutations. A simple collection of
necessary and sufficient conditions on sets of forbidden permutations which
ensure that the associated pattern class is of polynomial growth is determined.
A catalogue of all such sets of forbidden permutations having three or fewer
elements is provided together with bounds on the degrees of the associated
enumerating polynomials.


  In this paper we show how prescritive type checking and constraint solving
can be combined to increase automation during software verification. We do so
by defining a type system and implementing a typechecker for {log} (read
`setlog'), a Constraint Logic Programming (CLP) language and satisfiability
solver based on set theory. Hence, we proceed as follows: a) a type system for
{log} is defined; b) the constraint solver is proved to be safe w.r.t. the type
system; c) the implementation of a concrete typechecker is presented; d) the
integration of type checking and set constraint solving to increase automation
during software verification is discussed; and f) two industrial-strength case
studies are presented where this combination is used with very good results.
  Under consideration in Theory and Practice of Logic Programming (TPLP)


  Let $H$ be a fixed graph. A {\em fractional $H$-decomposition} of a graph $G$
is an assignment of nonnegative real weights to the copies of $H$ in $G$ such
that for each $e \in E(G)$, the sum of the weights of copies of $H$ containing
$e$ in precisely one. An {\em $H$-packing} of a graph $G$ is a set of edge
disjoint copies of $H$ in $G$. The following results are proved. For every
fixed $k > 2$, every graph with $n$ vertices and minimum degree at least
$n(1-1/9k^{10})+o(n)$ has a fractional $K_k$-decomposition and has a
$K_k$-packing which covers all but $o(n^2)$ edges.


  We study chiral symmetry breaking in quenched QED$_4$, using a vertex Ansatz
recently proposed by Curtis and Pennington. Bifurcation analysis is employed to
establish the existence of a critical coupling and to estimate its value. The
main results are in qualitative agreement with the ladder approximation, the
numerical changes being minor.


  We propose a model of interdependent scheduling games in which each player
controls a set of services that they schedule independently. A player is free
to schedule his own services at any time; however, each of these services only
begins to accrue reward for the player when all predecessor services, which may
or may not be controlled by the same player, have been activated. This model,
where players have interdependent services, is motivated by the problems faced
in planning and coordinating large-scale infrastructures, e.g., restoring
electricity and gas to residents after a natural disaster or providing medical
care in a crisis when different agencies are responsible for the delivery of
staff, equipment, and medicine. We undertake a game-theoretic analysis of this
setting and in particular consider the issues of welfare maximization,
computing best responses, Nash dynamics, and existence and computation of Nash
equilibria.


  Flare-prolific active region NOAA 12673 produced consecutive X2.2 and X9.3
flares on 06/09/2017. To scrutinize the morphological, magnetic, and horizontal
flow properties associated with these flares, a 7-hour time-series was used
consisting of continuum images, line-of-sight/vector magnetograms, and 1600
{\AA} UV images. These data were acquired with the SDO HMI and AIA. The
white-light flare emission differed for both flares, while the X2.2 flare
displayed localized, confined flare kernels, the X9.3 flare exhibited a
two-ribbon structure. In contrast, the excess UV emission exhibited a similar
structure for both flares, but with larger areal extent for the X9.3 flare.
These two flares represented a scenario, where the first confined flare acted
as precursor, setting up the stage for the more extended flare. Difference maps
for continuum and magnetograms revealed locations of significant changes, i.e.,
penumbral decay and umbral strengthening. The curved magnetic polarity
inversion line in the {\delta}-spot was the fulcrum of most changes. Horizontal
proper motions were computed using the DAVE4VM. Persistent flow features
included (1) strong shear flows along the polarity inversion line, where the
negative, parasitic polarity tried to bypass the majority, positive-polarity
part of the {\delta}-spot in the north, (2) a group of positive-polarity spots,
which moved around the {\delta}-spot in the south, moving away from the
{\delta}-spot with significant horizontal flow speeds, and (3) intense moat
flows partially surrounding the penumbra of several sunspots, which became
weaker in regions with penumbral decay. The enhanced flare activity has its
origin in the head-on collision of newly emerging flux with an already existing
regular, {\alpha}-spot.


  We investigate Majorana modes in a quantum spin chain with bond-dependent
exchange interactions by studying its dynamics. Specifically, we consider
two-time correlations for the Kitaev-Heisenberg (KH) Hamiltonian close to the
so-called Kitaev critical point. Here, the model coincides with a phase
boundary of two uncoupled instances of Kitaev's model for p-wave
superconductors, together supporting a degenerate ground state characterized by
multiple Majorana modes. In this regime, the real-time dynamics of local spins
reveal a set of strong zero modes, corresponding to a set of protruding
frequencies in the two-time correlation function. We derive perturbative
interactions that map the KH spin chain onto the topological regime of Kitaev's
fermionic model, thus opening up a bulk gap whilst retaining almost degenerate
modes in the mesoscopic regime, i.e., for finite system sizes. This showcases
the emergence of Majorana modes in a chain of effective dimers. Here, the
binding energy within each unit cell competes with the inter-dimer coupling to
generate a finite size energy gap, in analogy with local energy terms in the
transverse-field Ising model. These modes give rise to long coherence times of
local spins located at the system edges. By breaking the local symmetry in each
dimer, one can also observe a second class of Majorana modes in terms of a
beating frequency in the two-time correlations function of the edge spin.
Furthermore, we develop a scenario for realizing these model predictions in
ion-trap quantum simulators with collective addressing of the ions.


  Under the framework of V. Yakhot [Phys.Rev.E, {\bf57}, 1737 (1998)] modelling
of intermittent structure functions in fully developed turbulence and based on
the experimentally supported Markovian nature of turbulence cascades
[R.Friedrich and J.Peinke, Phys.Rev.Lett, {\bf 78}, 863 (1997)], we calculate
the multiscaling correlation functions. Fusion rules [V.S.L'vov and I.
Procaccia, Phys.Rev.Lett. {\bf 76}, 2898 (1996)] which are experimentally
tested [R. Benzi, L. Biferale, and F. Toschi, Phys.Rev.Lett. {\bf 80}, 3244
(1998)] to be compatible with almost uncorrelated multiplicative process can
analyticaly be supported by direct calculations.


  A closed and predictive particle cloud tracer method is presented. The tracer
builds upon the Subgrid Particle Averaged Reynolds Stress Equivalent (SPARSE)
formulation first introduced in [Davis et al., Proceedings of the Royal Society
A, 473(2199), 2017] for the tracing of particle clouds. It was later extended
to a Cloud-In-Cell (CIC) formulation in [Taverniers et al., Journal of
Computational Physics, 390, 2019] using a Gaussian distribution of a cloud's
influence over a mesh-based, velocity field solution. SPARSE corrects the
cloud's trace to second order by combining a Taylor series expansion of the
drag coefficient and Nusselt number correction factors around the mean relative
velocity of a cloud of particles with a Reynolds decomposition of the particle
equations to obtain a governing system for the first two statistical moments of
the cloud's position, velocity and temperature. Here, we close the thus far
unclosed SPARSE formulation by determining the velocity field in the vicinity
of the mean cloud location using a truncated Taylor series velocity
representation and by combining that with averaging. The resulting tracer is
predictive. It enables the tracing of a cloud of particles through a single
point and so reduces the required degrees of freedom in the accurate tracing of
groups of particles. We demonstrate the accuracy and convergence of the method
in several one-, two- and three-dimensional test cases.


  A crowd density forecasting task aims to predict how the crowd density map
will change in the future from observed past crowd density maps. However, the
past crowd density maps are often incomplete due to the miss-detection of
pedestrians, and it is crucial to develop a robust crowd density forecasting
model against the miss-detection. This paper presents a MAsked crowd density
Completion framework for crowd density forecasting (CrowdMAC), which is
simultaneously trained to forecast future crowd density maps from partially
masked past crowd density maps (i.e., forecasting maps from past maps with
miss-detection) while reconstructing the masked observation maps (i.e.,
imputing past maps with miss-detection). Additionally, we propose
Temporal-Density-aware Masking (TDM), which non-uniformly masks tokens in the
observed crowd density map, considering the sparsity of the crowd density maps
and the informativeness of the subsequent frames for the forecasting task.
Moreover, we introduce multi-task masking to enhance training efficiency. In
the experiments, CrowdMAC achieves state-of-the-art performance on seven
large-scale datasets, including SDD, ETH-UCY, inD, JRDB, VSCrowd, FDST, and
croHD. We also demonstrate the robustness of the proposed method against both
synthetic and realistic miss-detections.


  We perform a parton analysis within $\texttt{xFitter}$ to determine the gluon
parton distribution function (PDF) at next-to-leading order (NLO) at
moderate-to-low $x$ using the measurements of exclusive $J/\psi$ production in
$ep$ and $pp$ collisions from HERA and LHC. We further study the constraints
from the corresponding $\Upsilon$ production process and finish by discussing
the possible effects at next-to-next-to-leading order (NNLO) through
incorporation of a $K$ factor for the exclusive heavy vector-meson coefficient
function at NLO.


  Many text generation applications require the generated text to be factually
consistent with input information. Automatic evaluation of factual consistency
is challenging. Previous work has developed various metrics that often depend
on specific functions, such as natural language inference (NLI) or question
answering (QA), trained on limited data. Those metrics thus can hardly assess
diverse factual inconsistencies (e.g., contradictions, hallucinations) that
occur in varying inputs/outputs (e.g., sentences, documents) from different
tasks. In this paper, we propose AlignScore, a new holistic metric that applies
to a variety of factual inconsistency scenarios as above. AlignScore is based
on a general function of information alignment between two arbitrary text
pieces. Crucially, we develop a unified training framework of the alignment
function by integrating a large diversity of data sources, resulting in 4.7M
training examples from 7 well-established tasks (NLI, QA, paraphrasing, fact
verification, information retrieval, semantic similarity, and summarization).
We conduct extensive experiments on large-scale benchmarks including 22
evaluation datasets, where 19 of the datasets were never seen in the alignment
training. AlignScore achieves substantial improvement over a wide range of
previous metrics. Moreover, AlignScore (355M parameters) matches or even
outperforms metrics based on ChatGPT and GPT-4 that are orders of magnitude
larger.


  We study the isospin-singlet neutron-proton pairing in bulk nuclear matter as
a function of density and isospin asymmetry within the BCS formalism. In the
high-density, weak-coupling regime the neutron-proton paired state is strongly
suppressed by a minor neutron excess. As the system is diluted, the BCS state
with large, overlapping Cooper pairs evolves smoothly into a Bose-Einstein
condensate of tightly bound neutron-proton pairs (deuterons). In the resulting
low-density system a neutron excess is ineffective in quenching the pair
correlations because of the large spatial separation of the deuterons and
neutrons. As a result, the Bose-Einstein condensation of deuterons is weakly
affected by an additional gas of free neutrons even at very large asymmetries.


  We study a novel regime of the Rydberg excitation blockade using highly
Stark-shifted, yet long-living, states of Rb atoms subject to electric fields
above the classical ionization limit. Such states allow tuning the
dipole-dipole interaction strength while their ionization rate can be changed
over two orders of magnitude by small variations of the electric field. We
demonstrate laser excitation of the interacting Rydberg states followed by
their detection using controlled ionization and magnified imaging with high
spatial and temporal resolution. Our work reveals the hitherto unexplored
possibilities to control the interaction strength and dynamically tune the
ionization and detection of Rydberg atoms, which can be useful for realizing
and assessing quantum simulators that vary in space and time.


  We consider solutions of the 2D Toda lattice hierarchy which are elliptic
functions of the zeroth time t_0=x. It is known that their poles as functions
of t_1 move as particles of the elliptic Ruijsenaars-Schneider model. The goal
of this paper is to extend this correspondence to the level of hierarchies. We
show that the Hamiltonians which govern the dynamics of poles with respect to
the m-th hierarchical times t_m and \bar t_m of the 2D Toda lattice hierarchy
are obtained from expansion of the spectral curve for the Lax matrix of the
Ruijsenaars-Schneider model at the marked points.


  It has been a long challenge to analytically construct the quantitative
temperature-dependent multi-well free-energy landscape over the space of order
parameters describing phase transitions and associated critical phenomena. Here
we propose a simple analytical model for the free energy landscape based on a
priori concept of Boltzmann thermal mixing among multiple parabolic potentials
representing different energetically degenerated ground states in contrast to
the popular Landau theory using the high-temperature disordered state as a
reference. The model recovers both the Weiss molecular field theory and the
temperature dependent behaviors of the second order Landau coefficient. It is
rather remarkable that such a simple analytical expression can describe a wide
variety of properties across the ferroelectric phase transition in PbTiO3,
including the temperature dependences of the spontaneous polarization, the heat
capacities, the permittivity tensor, and the lattice parameters. The approach
also allows parametrization of the free energy function across phase
transitions based directly on the 0 K thermodynamics data that can be obtained
from DFT calculations. We anticipate that the approach can be equally applied
to other types of critical phenomena, e.g., the superconducting phase
transition, the metal-insulator transition, and the magnetic transitions.


  For reaction-diffusion processes without exclusion, in which the particles
can exist in the same site of a one-dimensional lattice, we study all the
integrable models which can be obtained by imposing a boundary condition on the
master equation of the asymmetric diffusion process. The annihilation process
is also added. The Bethe ansatz solution and the exact N-particle conditional
probabilities are obtained.


  In the light of recent experimental observation of the radiation amplitude
zero (RAZ) in W\gamma production by CDF at Fermilab, we consider its
consequences. is the RAZ unique to the standard model? Although it is not for
neutrino-electron scattering, in the case of d-ubar scattering which is the
case of experimental interest, observation of the RAZ implies that the SM must
be correct.


  Human annotations are vital to supervised learning, yet annotators often
disagree on the correct label, especially as annotation tasks increase in
complexity. A strategy to improve label quality is to ask multiple annotators
to label the same item and aggregate their labels. Many aggregation models have
been proposed for categorical or numerical annotation tasks, but far less work
has considered more complex annotation tasks involving open-ended,
multivariate, or structured responses. While a variety of bespoke models have
been proposed for specific tasks, our work is the first to introduce
aggregation methods that generalize across many diverse complex tasks,
including sequence labeling, translation, syntactic parsing, ranking, bounding
boxes, and keypoints. This generality is achieved by devising a task-agnostic
method to model distances between labels rather than the labels themselves.
  This article extends our prior work with investigation of three new research
questions. First, how do complex annotation properties impact aggregation
accuracy? Second, how should a task owner navigate the many modeling choices to
maximize aggregation accuracy? Finally, what diagnoses can verify that
aggregation models are specified correctly for the given data? To understand
how various factors impact accuracy and to inform model selection, we conduct
simulation studies and experiments on real, complex datasets. Regarding
testing, we introduce unit tests for aggregation models and present a suite of
such tests to ensure that a given model is not mis-specified and exhibits
expected behavior.
  Beyond investigating these research questions above, we discuss the
foundational concept of annotation complexity, present a new aggregation model
as a bridge between traditional models and our own, and contribute a new
semi-supervised learning method for complex label aggregation that outperforms
prior work.


  In this work, we outline the set of problems, which any Object Detection CNN
faces when its development comes to the deployment stage and propose methods to
deal with such difficulties. We show that these practices allow one to get
Object Detection network, which can recognize two classes: vehicles and
pedestrians and achieves more than 60 frames per second inference speed on
Core$^{TM}$ i5-6500 CPU. The proposed model is built on top of the popular
Single Shot MultiBox Object Detection framework but with substantial
improvements, which were inspired by the discovered problems. The network has
just 1.96 GMAC complexity and less than 7 MB model size. It is publicly
available as a part of Intel$\circledR$ OpenVINO$^{TM}$ Toolkit.


  A new probabilistic network construction system, DYNASTY, is proposed for
diagnostic reasoning given variables whose probabilities change over time.
Diagnostic reasoning is formulated as a sequential stochastic process, and is
modeled using influence diagrams. Given a set O of observations, DYNASTY
creates an influence diagram in order to devise the best action given O.
Sensitivity analyses are conducted to determine if the best network has been
created, given the uncertainty in network parameters and topology. DYNASTY uses
an equivalence class approach to provide decision thresholds for the
sensitivity analysis. This equivalence-class approach to diagnostic reasoning
differentiates diagnoses only if the required actions are different. A set of
network-topology updating algorithms are proposed for dynamically updating the
network when necessary.


  Probing the structure of complex astrophysical objects requires effective
three-dimensional (3D) numerical simulation of the relevant radiative transfer
(RT) processes. As with any numerical simulation code, the choice of an
appropriate discretization is crucial. Adaptive grids with cuboidal cells such
as octrees have proven very popular, however several recently introduced
hydrodynamical and RT codes are based on a Voronoi tessellation of the spatial
domain. Such an unstructured grid poses new challenges in laying down the rays
(straight paths) needed in RT codes. We show that it is straightforward to
implement accurate and efficient RT on 3D Voronoi grids. We present a method
for computing straight paths between two arbitrary points through a 3D Voronoi
grid in the context of a RT code. We implement such a grid in our RT code
SKIRT, using the open source library Voro++ to obtain the relevant properties
of the Voronoi grid cells based solely on the generating points. We compare the
results obtained through the Voronoi grid with those generated by an octree
grid for two synthetic models, and we perform the well-known Pascucci RT
benchmark using the Voronoi grid. The presented algorithm produces correct
results for our test models. Shooting photon packages through the geometrically
much more complex 3D Voronoi grid is only about three times slower than the
equivalent process in an octree grid with the same number of cells, while in
fact the total number of Voronoi grid cells may be lower for an equally good
representation of the density field. We conclude that the benefits of using a
Voronoi grid in RT simulation codes will often outweigh the somewhat slower
performance.


  The basic tenets of the alternative interpretation to be presented here are
that the spectral profiles of the star light polarization peaks observed in the
visible and near IR are a result of the optical properties of silicate grains
in the same spectral range, not of the grain size, provided it remains within
the range of Rayleigh's approximation. The silicate properties are those
obtained experimentally by Scott and Duley \cite{sco} for the non-iron bearing
\bf amorphous \rm forsterite and enstatite. The whole range of observed
Serkowski polarization profiles can be simulated with mixtures made of
forsterite plus an increasing fraction (0 to 0.5) of enstatite as the spectral
peak shifts from 0.8 to 0.3 $\mu$m. Fits to individual observed polarization
spectra are also demonstrated. The optical extinction of silicates in the
vis/IR (the "transparency range") can be understood by analogy with the
thoroughly studied amorphous hydrogenated carbons and amorphous silica. It is
due to structural disorder (dangling bonds and coordination defects) and
impurities, which give rise to electronic states in the forbidden gap of
semi-conductors. Because they are partially localized, their extinction power
is dramatically reduced and has been ignored or simply described by a low, flat
plateau. As their number density depends on the environment, one expects
variations in the ratio of optical extinction coefficients in the visible and
mid-IR.


  Quantum operations arise naturally in many fields of quantum information
theory and quantum computing. One of the simplest example of quantum operation
is the von Neumann-Lueders measurement. Destruction of states in quantum
mechanics can be treated as a supplement to the von Neumann-Lueders measurement
[P. Caban, J. Rembielinski, K. A. Smolinski and Z. Walczak, J. Phys. A 35, 3265
(2002)]. We show that destruction of states in one-qudit system is a quantum
operation by finding its Kraus representation.


  We investigate the physics of quantum reference frames. Specifically, we
study several simple scenarios involving a small number of quantum particles,
whereby we promote one of these particles to the role of a quantum observer and
ask what is the description of the rest of the system, as seen by this
observer? We highlight the interesting aspects of such questions by presenting
a number of apparent paradoxes. By unravelling these paradoxes we get a better
understanding of the physics of quantum reference frames.


  In 1999 the LEP experiments collected data at centre of mass energies between
192 and 202 GeV for about 900 pb-1 integrated luminosity. Combined results are
presented for the search for the Standard Model Higgs boson. No statistically
significant excess has been observed when compared to Standard Model background
expectation which can be translated into a lower bound on the mass of the Higgs
boson at 107.9 GeV/c^2 at 95 % confidence level.


  In the present paper we consider a class of extended
scalar-tensor-Gauss-Bonnet (ESTGB) theories for which the scalar degree of
freedom is excited only in the extreme curvature regime. We show that in the
mentioned class of ESTGB theories there exist new black hole solutions which
are formed by spontaneous scalarization of the Schwarzaschild balck holes in
the extreme curvature regime. In this regime, below certain mass, the
Schwarzschild solution becomes unstable and new branch of solutions with
nontrivial scalar field bifurcate from the Schwarzschild one. As a matter of
fact, more than one branches with nontrivial scalar field can bifurcate at
different masses but only the first one is supposed to be stable. This effect
is quite similar to the spontaneous scalarization of neutron stars. In contrast
with the standard spontaneous scalarization of neutron stars which is induced
by the presence of matter, in our case the scalarization is induced by the
curvature of the spacetime.


  We measure the complex scattering amplitudes of a flat microwave cavity (a
"chaotic billiard"). Time-reversal T-invariance is partially broken by a
magnetized ferrite placed within the cavity. We extend the random-matrix
approach to T-violation in scattering, fit some of the properties of the
scattering amplitudes, and then successfully predict others. Our work
constitutes the most precise test of the theoretical approach to T-violation
within the framework of random-matrix theory so far available.


  Discrete symmetries, parity, time reversal, antipodal, and charge conjugation
transformations for spinor field in de Sitter space, are presented in the
ambient space notation, i.e. in a coordinate independent way. The PT and PCT
transformations are also discussed in this notation. The five-current density
is studied and their transformation under the discrete symmetries is discussed.


  We present an analysis of charm quark fragmentation at 10.6 GeV, based on a
data sample of 103 fb collected by the Belle detector at the KEKB accelerator.
We consider fragmentation into the main charmed hadron ground states, namely
\DZ, \DP, \Ds and \LC, as well as the excited states \DSZ and \DSP. The
fragmentation functions are important to measure as they describe processes at
a low energy scale, where calculations in perturbation theory lead to large
uncertainties. Fragmentation functions can also be used as input distributions
for Monte Carlo generators. Additionally, we determine the average number of
these charmed hadrons produced per B decay at the \Ys resonance and measure the
distribution of their production angle in \epem annihilation events and in B
decays.


  The development of medical science greatly depends on the increased
utilization of machine learning algorithms. By incorporating machine learning,
the medical imaging field can significantly improve in terms of the speed and
accuracy of the diagnostic process. Computed tomography (CT), magnetic
resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron
emission tomography (PET) are the most commonly used types of imaging data in
the diagnosis process, and machine learning can aid in detecting diseases at an
early stage. However, training machine learning models with limited annotated
medical image data poses a challenge. The majority of medical image datasets
have limited data, which can impede the pattern-learning process of
machine-learning algorithms. Additionally, the lack of labeled data is another
critical issue for machine learning. In this context, active learning
techniques can be employed to address the challenge of limited annotated
medical image data. Active learning involves iteratively selecting the most
informative samples from a large pool of unlabeled data for annotation by
experts. By actively selecting the most relevant and informative samples,
active learning reduces the reliance on large amounts of labeled data and
maximizes the model's learning capacity with minimal human labeling effort. By
incorporating active learning into the training process, medical imaging
machine learning models can make more efficient use of the available labeled
data, improving their accuracy and performance. This approach allows medical
professionals to focus their efforts on annotating the most critical cases,
while the machine learning model actively learns from these annotated samples
to improve its diagnostic capabilities.


  An analysis of 7.5 years of data in the high-energy starting event sample has
been recently published by the IceCube collaboration. The hottest spot in a
search for neutrino sources was found far above the Galactic plane and is thus,
at first sight, difficult to reconcile with a Galactic origin. In this work, we
calculate the cosmic ray (CR) density around nearby, young supernova remnants
assuming anisotropic diffusion. Combining the obtained CR densities with the
matter distribution deduced from extinction maps, we find two prominent hot
spots: The one close to the most significant point in the IceCube search for
point sources is created by CRs from the Cygnus loop and has an intensity
corresponding to two to four neutrino events. Another, more extended one may be
caused by CRs from Vela if CR trajectories are sufficiently disturbed by the
magnetic field in the shell around the superbubble Loop I.


  The influenza pandemic of 1918-1919 killed at least 50 million people. The
reasons why this pandemic was so deadly remain largely unknown. However, It has
been shown that the 1918 viral hemagglutinin allows to reproduce the hallmarks
of the illness observed during the original pandemic. Thanks to the wealth of
hemagglutinin sequences accumulated over the last decades, amino-acid
substitutions that are found in the 1918-1919 sequences but rare otherwise can
be identified with high confidence. Such an analysis reveals that Gly188, which
is located within a key motif of the receptor binding site, is so rarely found
in hemagglutinin sequences that, taken alone, it is likely to be deleterious.
Monitoring this singular mutation in viral sequences may help prevent another
dramatic pandemic.


  We study the renormalization group (RG) equations of a pair of spin-boson
systems coupled in the z-direction with each other. Each spin is coupled to a
different bath of harmonic oscillators. We introduce a systematic adiabatic RG,
which generalizes the first-order adiabatic renormalization previously used for
the single spin-boson model, and we obtain the flow equations for the tunneling
constant, the dissipation strength and the inter-spin coupling up to third
order in the tunneling. If one of the two spins is treated as a constant
magnetization the other spin is described by a biased spin-boson Hamiltonian.
In this case the RG equations we find coincide with the ones obtained via a
mapping to a long-range Ising chain. If the whole Ohmic two-spin system is
considered the Kosterlitz-Thouless phase transition is replaced by a
second-order phase transition. In the case of a sub-Ohmic bath our approach
predicts that the two-spin system is always localized.


  We propose a framework to distributed diagnos- ability analysis of concurrent
systems modeled with Petri nets as a collection of components synchronizing on
common observable transitions, where faults can occur in several components.
The diagnosability analysis of the entire system is done in parallel by
verifying the interaction of each component with the fault free versions of the
other components. Furthermore, we use existing efficient methods and tools, in
particular parallel LTL-X model checking based on unfoldings, for
diagnosability verification.