  Context: Geometrically thin, detached shells of gas have been found around a
handful of carbon stars. --Aims: Previous observations of scattered stellar
light in the circumstellar medium around the carbon star U Ant were taken
through filters centred on the resonance lines of K and Na. These observations
could not separate the scattering by dust and atoms. The aim of this paper is
to remedy this situation. --Methods: We have obtained polarization data on
stellar light scattered in the circumstellar medium around U Ant through
filters which contain no strong lines, making it possible to differentiate
between the two scattering agents. Kinematic, as well as spatial, information
on the gas shells were obtained through high-resolution echelle spectrograph
observations of the KI and NaD lines. --Results: We confirm the existence of
two detached shells around U Ant. The inner shell (at a radius of approx 43"
and a width of approx 2") consists mainly of gas, while the outer shell (at a
radius of approx 50" and a width of approx 7") appears to consist exclusively
of dust. Both shells appear to have an over-all spherical geometry. The gas
shell mass is estimated to be 2x10^-3 M(Sun), while the mass of the dust shell
is estimated to be 5x10^-5 M(Sun). The derived expansion velocity, from the KI
and NaD lines, of the gas shell, 19.5 km/s, agrees with that obtained from CO
radio line data. The inferred shell age is 2700 years. There is structure, e.g.
in the form of arcs, inside the gas shell, but it is not clear whether these
are due to additional shells. --Conclusions: Our results support the hypothesis
that the observed geometrically thin, detached shells around carbon stars are
the results of brief periods of intense mass loss, probably associated with
thermal pulses, and subsequent wind-wind interactions.


  In this paper, we present three estimators of the ROC curve when missing
observations arise among the biomarkers. Two of the procedures assume that we
have covariates that allow to estimate the propensity and the estimators are
obtained using an inverse probability weighting method or a smoothed version of
it. The other one assumes that the covariates are related to the biomarkers
through a regression model which enables us to construct convolution--based
estimators of the distribution and quantile functions. Consistency results are
obtained under mild conditions. Through a numerical study we evaluate the
finite sample performance of the different proposals. A real data set is also
analysed.


  In this article, we study integrals on the unitary group with respect to the
Haar measure.We give a combinatorial interpretation in terms of maps of the
asymptotic topological expan-sion, established previously by Guionnet and
Novak. The maps we introduce -- the maps ofunitary type -- satisfy Tutte-like
equations. It allows us to show that in the perturbative regimethey describe
the different orders of the asymptotic topological expansion. Furthermore,
theygeneralize the monotone Hurwitz numbers.


  We show that there is a Borel graph on a standard Borel space of Borel
chromatic number three that admits a Borel homomorphism to every analytic graph
on a standard Borel space of Borel chromatic number at least three. Moreover,
we characterize the Borel graphs on standard Borel spaces of vertex-degree at
most two with this property, and show that the analogous result for digraphs
fails.


  Atomic sensors employing cold-atom technology enable unprecedented accuracy
and resolution for next generation atomic clocks, magnetometers, gravimeters,
and gyroscopes. To date, however, the size and complexity of cold atom systems
have prevented their deployment in practical applications outside of large
research laboratories. Here we demonstrate a low power, palm-top, and fully
integrated cold atom system that functions as an atomic clock with a stability
of 2 parts in 10^11 at 1s. This work demonstrates the feasibility of developing
compact, robust, and portable devices based on laser cooled atoms.


  A split graph is a graph whose vertex set can be partitioned into a clique
and an independent set. A connected graph $G$ is said to be $t$-admissible if
admits a special spanning tree in which the distance between any two adjacent
vertices is at most $t$. Given a graph $G$, determining the smallest $t$ for
which $G$ is $t$-admissible, i.e. the stretch index of $G$ denoted by
$\sigma(G)$, is the goal of the $t$-admissibility problem. Split graphs are
$3$-admissible and can be partitioned into three subclasses: split graphs with
$\sigma=1, 2 $ or $3$. In this work we consider such a partition while dealing
with the problem of coloring a split graph. Vizing proved that any graph can
have its edges colored with $\Delta$ or $\Delta+1$ colors, and thus can be
classified as Class 1 or Class 2, respectively. When both, edges and vertices,
are simultaneously colored, i.e., a total coloring of $G$, it is conjectured
that any graph can be total colored with $\Delta+1$ or $\Delta+2$ colors, and
thus can be classified as Type 1 or Type 2. These both variants are still open
for split graphs. In this paper, using the partition of split graphs presented
above, we consider the edge coloring problem and the total coloring problem for
split graphs with $\sigma=2$. For this class, we characterize Class 2 and Type
2 graphs and we provide polynomial-time algorithms to color any Class 1 or Type
1 graph.


  We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
is applied in the news translation task at WMT 2017, where it ranked first in
both the human and automatic evaluations for English--Finnish. We discuss the
success of English--Finnish translations and the overall advantage of NMT over
a strong SMT baseline. We also discuss our submissions for English--Latvian,
English--Chinese and Chinese--English.


  Conversational Recommender System (CRS) leverages real-time feedback from
users to dynamically model their preferences, thereby enhancing the system's
ability to provide personalized recommendations and improving the overall user
experience. CRS has demonstrated significant promise, prompting researchers to
concentrate their efforts on developing user simulators that are both more
realistic and trustworthy. The emergence of Large Language Models (LLMs) has
marked the onset of a new epoch in computational capabilities, exhibiting
human-level intelligence in various tasks. Research efforts have been made to
utilize LLMs for building user simulators to evaluate the performance of CRS.
Although these efforts showcase innovation, they are accompanied by certain
limitations. In this work, we introduce a Controllable, Scalable, and
Human-Involved (CSHI) simulator framework that manages the behavior of user
simulators across various stages via a plugin manager. CSHI customizes the
simulation of user behavior and interactions to provide a more lifelike and
convincing user interaction experience. Through experiments and case studies in
two conversational recommendation scenarios, we show that our framework can
adapt to a variety of conversational recommendation settings and effectively
simulate users' personalized preferences. Consequently, our simulator is able
to generate feedback that closely mirrors that of real users. This facilitates
a reliable assessment of existing CRS studies and promotes the creation of
high-quality conversational recommendation datasets.


  In this article, we use concepts and methods from the theory of simplicial
sets to study discrete Morse theory. We focus on the discrete flow category
introduced by Vidit Nanda, and investigate its properties in the case where it
is defined from a discrete Morse function on a regular CW complex. We design an
algorithm to efficiently compute the Hom posets of the discrete flow category
in this case. Furthermore, we show that in the special case where the discrete
Morse function is defined on a simplicial complex, then each Hom poset has the
structure of a face poset of a regular CW complex. Finally, we prove that the
spectral sequence associated to the double nerve of the discrete flow category
collapses on page 2.


  In this work, we discuss a non-Hermitian system described via a
one-dimensional single-particle tight-binding model, where the non-Hermiticity
is governed by random nearest-neighbour tunnellings, such that the
left-to-right and right-to-left hopping strengths are unequal. A physical
situation of completely real eigenspectrum arises owing to the Hamiltonian's
tridiagonal matrix structure under a simple sign conservation of the product of
the conjugate nearest-neighbour tunnelling terms. The off-diagonal disorder
leads the non-Hermitian system to a delocalization-localization crossover in
finite systems. The emergent nature of the crossover is recognized through a
finite-size spectral analysis. The system enters into a localized phase for
infinitesimal disorder strength in the thermodynamic limit. We perform a
careful scaling analysis of localization length, inverse participation ratio
(IPR), and energy splitting and report the corresponding scaling exponents.
Noticeably, in contrast to the diagonal disorder, the density of states (DOS)
has a singularity at E=0 in the presence of the off-diagonal disorder and the
corresponding wavefunction remains delocalized for any given disorder strength.


  Metaverse, the core of the next-generation Internet, is a computer-generated
holographic digital environment that simultaneously combines spatio-temporal,
immersive, real-time, sustainable, interoperable, and data-sensitive
characteristics. It cleverly blends the virtual and real worlds, allowing users
to create, communicate, and transact in virtual form. With the rapid
development of emerging technologies including augmented reality, virtual
reality and blockchain, the metaverse system is becoming more and more
sophisticated and widely used in various fields such as social, tourism,
industry and economy. However, the high level of interaction with the real
world also means a huge risk of privacy leakage both for individuals and
enterprises, which has hindered the wide deployment of metaverse. Then, it is
inevitable to apply privacy computing techniques in the framework of metaverse,
which is a current research hotspot. In this paper, we conduct comprehensive
research on the necessity, taxonomy and challenges when privacy computing meets
metaverse. Specifically, we first introduce the underlying technologies and
various applications of metaverse, on which we analyze the challenges of data
usage in metaverse, especially data privacy. Next, we review and summarize
state-of-the-art solutions based on federated learning, differential privacy,
homomorphic encryption, and zero-knowledge proofs for different privacy
problems in metaverse. Finally, we show the current security and privacy
challenges in the development of metaverse and provide open directions for
building a well-established privacy-preserving metaverse system. For easy
access and reference, we integrate the related publications and their codes
into a GitHub repository:
https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse.git.


  This paper examines the role of community animators in technology adoption.
Community animators are individuals that actively build social networks and
broker ties between nodes in those networks. The present study observes
technology adoption patterns through data collected from a mobile application
at a local arts festival. A social network was constructed through
photo-sharing and interaction within the app. Given this data, we propose the
use of key player analysis to identify community animators. In addition, we use
a graph invariant (i.e., fragmentation in the network) to describe the role and
impact of key players on the full network of interactions. Our results
contribute to literature on technology adoption in usability studies by
proposing a method to quantify and identify the theoretical concept of
community animators. We further analyze the types of community animators to be
found in early adoption of technology: the early adopters themselves, and the
initiating developers.


  The relativistic $D=4$ Snyder model is formulated in terms of $D=4$ $dS$
algebra $o(4,1)$ generators, with noncommutative Lorentz-invariant Snyder
quantum space-time provided by $\frac{O(4,1)}{O(3,1)}$ coset generators.
Analogously, in relativistic $D=4$ Yang models the quantum-deformed
relativistic phase space is described by the algebras of coset generators
$\frac{O(5,1)}{O(3,1)}$ or $\frac{O(4,2)}{O(3,1)}$. We extend these algebraic
considerations by using respective $dS$ superalgebras, which provide
Lorentz-covariant quantum superspaces (SUSY Snyder model) as well as
relativistic quantum phase super spaces (SUSY Yang model).


  Monte Carlo simulation provides a powerful tool for understanding and
exploring thermodynamic phase equilibria in many-particle interacting systems.
Among the most physically intuitive simulation methods is Gibbs ensemble Monte
Carlo (GEMC), which allows direct computation of phase coexistence curves of
model fluids by assigning each phase to its own simulation cell. When one or
both of the phases can be modeled virtually via an analytic free energy
function [M. Mehta and D. A. Kofke, Molecular Physics 79, 39 (1993)], the GEMC
method takes on new pedagogical significance as an efficient means of analyzing
fluctuations and illuminating the statistical foundation of phase behavior in
finite systems. Here we extend this virtual GEMC method to binary fluid
mixtures and demonstrate its implementation and instructional value with two
applications: (1) a lattice model of simple mixtures and polymer blends and (2)
a free-volume model of a complex mixture of colloids and polymers. We present
algorithms for performing Monte Carlo trial moves in the virtual Gibbs
ensemble, validate the method by computing fluid demixing phase diagrams, and
analyze the dependence of fluctuations on system size. Our open-source
simulation programs, coded in the platform-independent Java language, are
suitable for use in classroom, tutorial, or computational laboratory settings.


  With its vast expanse, exceeding that of Western Europe by twice, the Amazon
rainforest stands as the largest forest of the Earth, holding immense
importance in global climate regulation. Yet, deforestation detection from
remote sensing data in this region poses a critical challenge, often hindered
by the persistent cloud cover that obscures optical satellite data for much of
the year. Addressing this need, this paper proposes three deep-learning models
tailored for deforestation monitoring, utilizing SAR (Synthetic Aperture Radar)
multitemporal data moved by its independence on atmospheric conditions.
Specifically, the study proposes three novel recurrent fully convolutional
network architectures-namely, RRCNN-1, RRCNN-2, and RRCNN-3, crafted to enhance
the accuracy of deforestation detection. Additionally, this research explores
replacing a bitemporal with multitemporal SAR sequences, motivated by the
hypothesis that deforestation signs quickly fade in SAR images over time. A
comprehensive assessment of the proposed approaches was conducted using a
Sentinel-1 multitemporal sequence from a sample site in the Brazilian
rainforest. The experimental analysis confirmed that analyzing a sequence of
SAR images over an observation period can reveal deforestation spots
undetectable in a pair of images. Notably, experimental results underscored the
superiority of the multitemporal approach, yielding approximately a five
percent enhancement in F1-Score across all tested network architectures.
Particularly the RRCNN-1 achieved the highest accuracy and also boasted half
the processing time of its closest counterpart.


  Iron line spectroscopy has been one of the leading methods not only for
measuring the spins of accreting black holes but also for testing fundamental
physics. Basing on such a method, we present an analysis of a dataset observed
simultaneously by NuSTAR and NICER for the black hole binary candidate MAXI
J1803-298, which shows prominent relativistic reflection features. Various
relxill_nk flavors are utilized to test the Kerr black hole hypothesis. The
results obtained from our analysis provide stringent constraints on Johannsen
deformation parameter $\alpha_{13}$ with the highest precise to date, namely
$\alpha_{13}=0.023^{+0.071}_{-0.038}$ from relxillD_nk and
$\alpha_{13}=0.006^{+0.045}_{-0.022}$ from relxillion_nk respectively in
3-$\sigma$ credible lever, where Johannsen metric reduces to Kerr metric when
$\alpha_{13}$ vanishes. Furthermore, we investigate the best model-fit results
using Akaike Information Criterion and assess its systematic uncertainties.


  We study the growth of an N-component (including N=1) order parameter when a
system with a Lifshitz point is quenched from the homogeneous disordered state
to the ordered states. We study the scaling behaviours of the structure factors
for both the non-conserved and conserved order parameters in the long time
limit after a quench through the Lifshitz point by using the large-N and
renormalisation group (RG) methods. We construct the analogues of Allen-Cahn
and Lifshitz-Slyzov growth laws for nonconserved and conserved order parameters
which agree with our RG results for N=1. By extending our large-N methods to
the anisotropic Lifshitz point we show that the anisotropy is relevant for the
growth of the nonconserved order parameter, but irrelevant for the conserved
case in the large-N limit. We discuss the effects of mode coupling terms on
scaling of the structure factor for the conserved order parameter case. We also
consider the ordering dynamics after a quench through an off-Lifshitz point. In
a large-N set up we calculate the form of the structure factors of a
non-conserved order parameter when a system is quenched from the homogeneous
paramegnetic to the modulated phase. We show that after a quench from the
paramegnetic to the modulated phase the form of the structure factor violates
the standard form of dynamical scaling. Our results compare favourably with the
available numerical results.


  With a large-area field electron emitter, when an individual post-like
emitter is sufficiently resistive, and current through it sufficiently large,
then voltage loss occurs along it. This Letter provides a simple analytical and
conceptual demonstration that this voltage loss is directly and inextricably
linked to a reduction in the field enhancement factor (FEF) at the post apex. A
formula relating apex-FEF reduction to this voltage loss was obtained in the
paper by E. Minoux, O. Groening, K. B. K. Teo, S. H. Dalal, L. Gangloff, J.-P.
Schnell, L. Hudanski, I. Y. Y. Bu., P. Vincent, P. Legagneux, G. A. J.
Amaratunga, and W. I. Milne [Nano Lett. 5, 2135 (2005)], by fitting to
numerical results from a Laplace solver. This Letter derives the same formula
analytically, by using a "floating sphere" model. The analytical proof brings
out the underlying physics more clearly, and shows that the effect is a general
phenomenon, related to reduction in the magnitude of the surface charge in the
most protruding parts of an emitter. Voltage-dependent FEF-reduction is one
cause of "saturation" in Fowler-Nordheim plots. Another is a voltage-divider
effect, due to measurement-circuit resistance. An integrated theory of both
effects is presented. Both together, or either by itself, can cause saturation.
Experimentally, if saturation occurs but voltage loss is small (< 20 V, say),
then saturation is more probably due to FEF-reduction than voltage division. In
this case, existing treatments of electrostatic interaction ("shielding")
between closely spaced emitters may need modification. Other putative causes of
saturation exist, so the present theory is a partial story. Its extension seems
possible, and could lead to a more general physical understanding of the causes
of FN-plot saturation.


  Spectra of density perturbations produced during chaotic inflation are
calculated, taking both adiabatic and isocurvature modes into account in a
class of scalar-tensor theories of gravity in which the dilaton is metrically
coupled. Comparing the predicted spectrum of the cosmic microwave background
radiation anisotropies with the one observed by the COBE-DMR we calculate
constraints on the parameters of these theories, which turn out to be stronger
by an order-of-magnitude than those obtained from post-Newtonian experiments.


  The water abundance in a planetary atmosphere provides a key constraint on
the planet's primordial origins because water ice is expected to play an
important role in the core accretion model of planet formation. However, the
water content of the Solar System giant planets is not well known because water
is sequestered in clouds deep in their atmospheres. By contrast, short-period
exoplanets have such high temperatures that their atmospheres have water in the
gas phase, making it possible to measure the water abundance for these objects.
We present a precise determination of the water abundance in the atmosphere of
the 2 $M_\mathrm{Jup}$ short-period exoplanet WASP-43b based on thermal
emission and transmission spectroscopy measurements obtained with the Hubble
Space Telescope. We find the water content is consistent with the value
expected in a solar composition gas at planetary temperatures (0.4-3.5x solar
at 1 $\sigma$ confidence). The metallicity of WASP-43b's atmosphere suggested
by this result extends the trend observed in the Solar System of lower metal
enrichment for higher planet masses.


  We calculate the model-independent radiative corrections to the Dalitz plot
of K_{l3}^\pm decays to order (\alpha/\pi)(q/M_1), where q is the momentum
transfer and M_1 is the mass of the kaon. The final results are presented,
first, with the triple integration over the variables of the bremsstrahlung
photon ready to be performed numerically and, second, in an analytical form.
These two forms are useful to crosscheck on one another and with other
calculations. This paper is organized to make it accessible and reliable in the
analysis of the Dalitz plot of precision experiments and is not compromised to
fixing the form factors at predetermined values. It is assumed that the real
photons are kinematically discriminated. Otherwise, our results have a general
model-independent applicability.


  Based on the numerical solution non-stationary Ginzburg-Landau equations, we
investigated the evolution of the order parameter of superconducting channels
with different lengths under applied voltage (so-called voltage-driven regime).
We calculated current-voltage characteristics for channels of different lengths
and found out the origin of theirs characteristic disorder oscillations. For
very long channels in the certain voltage interval we reveal the chaotic
dynamic of the order parameter. Collected data allowed us to plot the most
complete and detailed phase diagram of the resistive state of the
superconducting channel in the voltage-driven regime.


  Affect recognition aims to detect a person's affective state based on
observables, with the goal to e.g. provide reasoning for decision making or
support mental wellbeing. Recently, besides approaches based on audio, visual
or text information, solutions relying on wearable sensors as observables
(recording mainly physiological and inertial parameters) have received
increasing attention. Wearable systems offer an ideal platform for long-term
affect recognition applications due to their rich functionality and form
factor. However, existing literature lacks a comprehensive overview of
state-of-the-art research in wearable-based affect recognition. Therefore, the
aim of this paper is to provide a broad overview and in-depth understanding of
the theoretical background, methods, and best practices of wearable affect and
stress recognition. We summarise psychological models, and detail
affect-related physiological changes and their measurement with wearables. We
outline lab protocols eliciting affective states, and provide guidelines for
ground truth generation in field studies. We also describe the standard data
processing chain, and review common approaches to preprocessing, feature
extraction, and classification. By providing a comprehensive summary of the
state-of-the-art and guidelines to various aspects, we would like to enable
other researchers in the field of affect recognition to conduct and evaluate
user studies and develop wearable systems.


  We have measured the cross section and single spin asymmetries from forward
$W^{\pm}\rightarrow\mu^{\pm}\nu$ production in longitudinally polarized
$p$$+$$p$ collisions at $\sqrt{s}=510$ GeV using the PHENIX detector at the
Relativistic Heavy Ion Collider. The cross sections are consistent with
previous measurements at this collision energy, while the most forward and
backward longitudinal single spin asymmetries provide new insights into the sea
quark helicities in the proton. The charge of the W bosons provides a natural
flavor separation of the participating partons.


  In this work, we explore the phenomenon of cosmic evolution using curved FLRW
space-time bounded by apparent horizon with a specific holographic cut-off. To
this end, we use the framework of Rastall gravity (RG) and universe is assumed
to be consists of interacting/ non-interacting dark energy (DE) and dark matter
(DM). In both scenarios, we evaluate exact solutions of the dynamical equations
and constraint the holographic parameter $c^{2}(z)$ assuming a slowly varying
function of red-shift during non-interacting model. For interacting model, we
consider $c^{2}$ as a constant function of red-shift. Moreover, we analyze
nature of the obtained results via deceleration parameter $(q)$, statefinder
pair $(j,s)$ and $Om(z)$-diagnostic by constraining the involved model
parameters using latest observational data. The graphical analysis showed that
interacting model is very close to $\Lambda$ cold DM ($\Lambda$CDM) model as
compared to non-interacting case. We conclude that this holographic proposal is
enough to describe the cosmic evolution at an accelerating rate.


  A general definition of a black hole is given, and general `laws of
black-hole dynamics' derived. The definition involves something similar to an
apparent horizon, a trapping horizon, defined as a hypersurface foliated by
marginal surfaces of one of four non-degenerate types, described as future or
past, and outer or inner. If the boundary of an inextendible trapped region is
suitably regular, then it is a (possibly degenerate) trapping horizon. The
future outer trapping horizon provides the definition of a black hole. Outer
marginal surfaces have spherical or planar topology. Trapping horizons are null
only in the instantaneously stationary case, and otherwise outer trapping
horizons are spatial and inner trapping horizons are Lorentzian. Future outer
trapping horizons have non-decreasing area form, constant only in the null
case---the `second law'. A definition of the trapping gravity of an outer
trapping horizon is given, generalizing surface gravity. The total trapping
gravity of a compact outer marginal surface has an upper bound, attained if and
only if the trapping gravity is constant---the `zeroth law'. The variation of
the area form along an outer trapping horizon is determined by the trapping
gravity and an energy flux---the `first law'.


  A novel optimization strategy, Info-Evo, is described, in which natural
gradient search using nonparametric Fisher information is used to provide
ongoing guidance to an evolutionary learning algorithm, so that the
evolutionary process preferentially moves in the directions identified as
"shortest paths" according to the natural gradient. Some specifics regarding
the application of this approach to automated program learning are reviewed,
including a strategy for integrating Info-Evo into the MOSES program learning
framework.


  In the article we develop Euler-Lagrange method and calculate all the roots
of an arbitrary complex polynomial $P(z)$ on the base of calculation (similar
to the Bernoulli-Aitken-Nikiporets methods) of the limits of ratios of Hadamard
determinants built by means of coefficients of expansions into Taylor and
Laurent series of the function~$\frac{P'(z)}{P(z)}$.


  In this paper we study the prediction of heart rate from acceleration using a
wrist worn wearable. Although existing photoplethysmography (PPG) heart rate
sensors provide reliable measurements, they use considerably more energy than
accelerometers and have a major impact on battery life of wearable devices. By
using energy-efficient accelerometers to predict heart rate, significant energy
savings can be made. Further, we are interested in understanding patient
recovery after a heart rate intervention, where we expect a variation in heart
rate over time. Therefore, we propose an online approach to tackle the concept
as time passes. We evaluate the methods on approximately 4 weeks of free living
data from three patients over a number of months. We show that our approach can
achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while
using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).


  In this paper, we propose MAGICSTYLEGAN and MAGICSTYLEGAN-ADA - both
incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to
experiment with their capacity of transfer learning into a rather different
domain: creating new illustrations for the vast universe of the game "Magic:
The Gathering" cards. This is a challenging task especially due to the variety
of elements present in these illustrations, such as humans, creatures,
artifacts, and landscapes - not to mention the plethora of art styles of the
images made by various artists throughout the years. To solve the task at hand,
we introduced a novel dataset, named MTG, with thousands of illustration from
diverse card types and rich in metadata. The resulting set is a dataset
composed by a myriad of both realistic and fantasy-like illustrations.
Although, to investigate effects of diversity we also introduced subsets that
contain specific types of concepts, such as forests, islands, faces, and
humans. We show that simpler models, such as DCGANs, are not able to learn to
generate proper illustrations in any setting. On the other side, we train
instances of MAGICSTYLEGAN using all proposed subsets, being able to generate
high quality illustrations. We perform experiments to understand how well
pre-trained features from StyleGan2 can be transferred towards the target
domain. We show that in well trained models we can find particular instances of
noise vector that realistically represent real images from the dataset.
Moreover, we provide both quantitative and qualitative studies to support our
claims, and that demonstrate that MAGICSTYLEGAN is the state-of-the-art
approach for generating Magic illustrations. Finally, this paper highlights
some emerging properties regarding transfer learning in GANs, which is still a
somehow under-explored field in generative learning research.


  100% inverter-based renewable units are becoming more prevalent, introducing
new challenges in the protection of microgrids that incorporate these
resources. This is particularly due to low fault currents and bidirectional
flows. Previous work has studied the protection of microgrids with high
penetration of inverter-interfaced distributed generators; however, very few
have studied the protection of a 100% inverter-based microgrid. This work
proposes machine learning (ML)-based protection solutions using local
electrical measurements that consider implementation challenges and effectively
combine short-circuit fault detection and type identification. A decision tree
method is used to analyze a wide range of fault scenarios. PSCAD/EMTDC
simulation environment is used to create a dataset for training and testing the
proposed method. The effectiveness of the proposed methods is examined under
seven distinct fault types, each featuring varying fault resistance, in a 100%
inverter-based microgrid consisting of four inverters.


  An extension of time-dependent covariant density functional theory that
includes particle-vibration coupling is applied to the charge-exchange channel.
Spin-dipole excitation spectra are calculated an compared to available data for
$^{90}$Zr and $^{208}$Pb. A significant fragmentation is found for all three
angular-momentum components of the spin-dipole strength as a result of
particle-vibration coupling, as well as a shift of a portion of the strength to
higher energy. A high-energy tail is formed in the strength distribution that
linearly decreases with energy. Using a model-independent sum rule,the
corresponding neutron skin thickness is estimated and shown to be consistent
with values obtained at the mean-field level.


  This article focuses on a combinatorial structure specific to triangulated
plane graphs with quadrangular outer face and no separating triangle, which are
called irreducible triangulations. The structure has been introduced by Xin He
under the name of regular edge-labelling and consists of two bipolar
orientations that are transversal. For this reason, the terminology used here
is that of transversal structures. The main results obtained in the article are
a bijection between irreducible triangulations and ternary trees, and a
straight-line drawing algorithm for irreducible triangulations. For a random
irreducible triangulation with $n$ vertices, the grid size of the drawing is
asymptotically with high probability $11n/27\times 11n/27$ up to an additive
error of $\cO(\sqrt{n})$. In contrast, the best previously known algorithm for
these triangulations only guarantees a grid size $(\lceil n/2\rceil -1)\times
\lfloor n/2\rfloor$.


  We compute the high-temperature limit of the free energy for four-dimensional
N=4 supersymmetric SU(N_c) Yang-Mills theory. At weak coupling we do so for a
general ultrastatic background spacetime, and in the presence of slowly-varying
background gauge fields. Using Maldacena's conjectured duality, we calculate
the strong-coupling large-N_c expression for the special case that the
three-space has constant curvature. We compare the two results paying
particular attention to curvature corrections to the leading order expressions.


  Using Ba(Fe$_{0.975}$Co$_{0.025}$)$_2$As$_{2}$ as an exemplar material
exhibiting second order electronic-nematic and antiferromagnetic transitions,
we present measurements that reveal anomalies in the elastoresistance
$\left(\frac{\partial \rho_{ij}}{\partial\varepsilon_{kl}}\right)$ and
elastocaloric effect $\left(\frac{\partial T}{\partial\varepsilon_{kl}}\right)$
at both phase transitions. Both effects are understood to arise from the effect
of strain on the transition temperatures; in the region close to the phase
transitions this leads to (1) similarity between the strain and temperature
derivatives of the resistivity and (2) similarity between the elastocaloric
effect and the singular part of the specific heat. These mechanisms for
elastoresistance and elastocaloric effect should be anticipated for any
material in which mechanical deformation changes the transition temperature.
Furthermore, these measurements provide evidence that the Fisher-Langer
relation $\rho^{(c)} \propto U^{(c)}$ between the scattering from critical
degrees of freedom and their energy-density, respectively, holds near each of
the Ne\'{e}l and electronic nematic transitions in the material studied.


  It is commonly assumed that the cosmological and astrophysical bounds on the
mixings of sterile with active neutrinos are much more stringent than those
obtained from laboratory measurements. We point out that in scenarios with a
very low reheating temperature T_RH << 100 MeV at the end of (the last episode
of) inflation or entropy creation, the abundance of sterile neutrinos becomes
largely suppressed with respect to that obtained within the standard framework.
Thus, in this case cosmological bounds become much less stringent than usually
assumed, allowing sterile neutrinos to be ``visible'' in future experiments.
Here, we concentrate on massive (mostly sterile) neutrinos heavier than 1 MeV.


  We present a canonical derivation of an influence superoperator which
generates the reduced dynamics of a Fermionic quantum system linearly coupled
to a Fermionic environment initially at thermal equilibrium. We use this
formalism to derive a generalized-Lindblad master equation (in the Markovian
limit) and a generalized version of the hierarchical equations of motion valid
in arbitrary parity-symmetry conditions, important for the correct evaluation
of system correlation functions and spectra.


  We theoretically study an extension of the Dicke model, where the
single-particle Hamiltonian has three energy levels in Lambda-configuration,
i.e. the excited state is coupled to two non-degenerate ground states via two
independent quantized light fields. The corresponding many-body Hamiltonian can
be diagonalized in the thermodynamic limit with the help of a generalized
Holstein--Primakoff transformation. Analyzing the ground-state energy and the
excitation energies, we identify one normal and two superradiant phases,
separated by phase transitions of both first and second order. A phase with
both superradiant states coexisting is not stable. In addition, in the limit of
two degenerate ground states a dark state emerges, which seems to be analogous
to the dark state appearing in the well known stimulated Raman adiabatic
passage scheme.


  The planned HL-LHC (High Luminosity LHC) in 2025 is being designed to
maximise the physics potential through a sizable increase in the luminosity up
to 6*10^34 cm^-2 s^-1. A consequence of this increased luminosity is the
expected radiation damage at 3000 fb^-1 after ten years of operation, requiring
the tracking detectors to withstand fluences to over 1*10^16 1 MeV n_eq/cm^2 .
In order to cope with the consequent increased readout rates, a complete
re-design of the current ATLAS Inner Detector (ID) is being developed as the
Inner Tracker (ITk). Two proposed detectors for the ATLAS strip tracker region
of the ITk were characterized at the Diamond Light Source with a 3 um FWHM 15
keV micro focused X-ray beam. The devices under test were a 320 Um thick
silicon stereo (Barrel) ATLAS12 strip mini sensor wire bonded to a 130 nm CMOS
binary readout chip (ABC130) and a 320 Um thick full size radial (end-cap)
strip sensor - utilizing bi-metal readout layers - wire bonded to 250 nm CMOS
binary readout chips (ABCN-25). A resolution better than the inter strip pitch
of the 74.5 um strips was achieved for both detectors. The effect of the p-stop
diffusion layers between strips was investigated in detail for the wire bond
pad regions. Inter strip charge collection measurements indicate that the
effective width of the strip on the silicon sensors is determined by p-stop
regions between the strips rather than the strip pitch.


  Ontology Matching (OM), is a critical task in knowledge integration, where
aligning heterogeneous ontologies facilitates data interoperability and
knowledge sharing. Traditional OM systems often rely on expert knowledge or
predictive models, with limited exploration of the potential of Large Language
Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate
the effectiveness of LLMs in OM tasks. This framework utilizes two modules for
retrieval and matching, respectively, enhanced by zero-shot prompting across
three ontology representations: concept, concept-parent, and concept-children.
Through comprehensive evaluations using 20 OM datasets from various domains, we
demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass
the performance of traditional OM systems, particularly in complex matching
scenarios. Our results highlight the potential of LLMs to significantly
contribute to the field of OM.


  Density functional theory (DFT) and many body perturbation theory at the
G$_0$W$_0$ level are employed to study the electronic properties of
polythiophene (PT) adsorbed on graphene surface. Analysis of charge density
difference shows the substrate-adsorbate interaction leading to a strong
physisorption and interfacial electric dipole moment formation. The
electrostatic potential displays a -0.19 eV shift in the graphene work function
from its initial value of 4.53 eV, as the result of the interaction. The LDA
band gap of the polymer does not show any change, however the energy level
lineshapes are modified by the orbital hybridization. The interfacial
polarization effects on the band gap and levels alignment are investigated
within G$_0$W$_0$ level and shows notable reduction of PT band gap compared to
that of the isolated chain.


  For a large class of expanding maps of the interval, we prove that partial
sums of Lipschitz observables satisfy an almost sure central limit theorem
(ASCLT). In fact, we provide a speed of convergence in the Kantorovich metric.
Maxima of partial sums are also shown to obey an ASCLT. The key-tool is an
exponential inequality recently obtained. Then we derive almost-sure
convergence rates for the supremum of moving averages of Lipschitz observables
(Erdos-Renyi type law). We end up with an application to entropy estimation
ASCLT's that refi ne Shannon-McMillan-Breiman and Ornstein-Weiss theorems.


  We report an investigation of quantum Hall induced currents by simultaneous
measurements of their magnetic moment and their effect on the conductance of a
quantum point contact (QPC). Features in the magnetic moment and QPC resistance
are correlated at Landau-level filling factors nu=1, 2 and 4, which
demonstrates the common origin of the effects. Temperature and non-linear sweep
rate dependences are observed to be similar for the two effects. Furthermore,
features in the noise of the induced currents, caused by breakdown of the
quantum Hall effect, are observed to have clear correlations between the two
measurements. In contrast, there is a distinct difference in the way that the
induced currents decay with time when the sweeping field halts at integer
filling factor. We attribute this difference to the fact that, while both
effects are sensitive to the magnitude of the induced current, the QPC
resistance is also sensitive to the proximity of the current to the QPC
split-gate. Although it is clearly demonstrated that induced currents affect
the electrostatics of a QPC, the reverse effect, the QPC influencing the
induced current, was not observed.


  The projected systematic uncertainties of single trapped Ytterbium-ion
optical frequency standards are estimated for the quadrupole and octupole
transitions which are at wavelengths 435.5 nm and 467 nm, respectively. Finite
temperature of the ion and its interaction with the external fields introduce
drift in the measured frequency compared to its absolute value. Frequency
shifts due to electric quadrupole moment, induced polarization and excess
micromotion of the ion depend on electric fields, which are estimated in this
article. Geometry of the trap electrodes also result in unwanted electric
fields which have been considered in our calculation. Magnetic field induced
shift and Stark shifts due to electro-magnetic radiation at a surrounding
temperature are also estimated. At CSIR-NPL, we are developing a frequency
standard based on the octupole transition for which the systematic
uncertainties are an order of magnitude smaller than that using the quadrupole
transition, as described here.


  Triggered by a recent criterion, due to A.~Petrunin [17], to check if a
complete, non-compact, Riemannian manifold admits an isometric embedding into a
Euclidean space with positive reach, we extend to manifolds with such property
the singular extension results of B.~Bulanyi and J.~Van~Schaftingen [5] for
maps in the critical, nonlinear Sobolev space
$W^{m/(m+1),m+1}\left(X^m,\mathcal{N}\right)$, where $m \in \mathbb{N}
\setminus \{0\}$, $\mathcal{N}$ is a compact Riemannian manifold, and $X^m$ is
either the sphere $\mathbb{S}^m = \partial \mathbb{B}^{m+1}_+$, the plane
$\mathbb{R}^m$, or again $\mathbb{S}^m$ but seen as the boundary sphere of the
Poincar\'{e} ball model of the hyperbolic space $\mathbb{H}^{m+1}$. As in [5],
we obtain that the extended maps satisfy an exponential weak-type
Sobolev-Marcinkiewicz estimate. Finally, we provide some illustrative examples.


  We analyzed all published frequencies, nu_1 and nu_2, of the twin kilohertz
quasi-periodic oscillations (kHz QPOs) in bright neutron star low-mass X-ray
binaries. The two frequencies are well correlated but, contrary to recent
suggestions, the frequency-frequency correlation is significantly different
from a nu_2 = (3/2) nu_1 relation. To check whether, although not following the
the 3/2 relation, the QPO frequencies cluster around a region where nu_2/nu_1 ~
3/2, we re-analyzed the Sco X-1 data that were used to report that ratio and
show that, because the distribution of ratios of linearly correlated
measurements is intrinsically biased, although the significance of the
clustering around nu_2/nu_1 ~ 3/2 previously reported in the case of Sco X-1 is
formally correct, it does not provide any useful information about a possible
underlying resonance mechanism in this source. Using the same data, we then
show that the (unbiased) distribution of QPO frequencies is consistent with a
uniform distribution at a 2.4 sigma level. To investigate this further, we
analyzed a larger data set of Sco X-1 and four other sources, 4U 1608-52, 4U
1636-53, 4U 1728-34 and 4U 1820-30. We find that for all five sources the
distribution of the kHz QPO frequencies is not uniform and has multiple peaks,
which have no analogy in the distribution of points in the spectral color-color
diagrams of these sources. Finally, we demonstrate that a simple random walk of
the QPO frequencies can reproduce qualitatively the observed distributions in
frequency and frequency ratio. This result weakens the support for resonance
models of kHz QPOs in neutron stars.


  We extend results of Videla and Fukuzaki to define algebraic integers in
large classes of infinite algebraic extensions of Q and use these definitions
for some of the fields to show the first-order undecidability. We also obtain a
structural sufficient condition for definability of the ring of integers over
its field of fractions. In particular, we show that the following propositions
hold. (1) For any rational prime $q$ and any positive rational integer $m$,
algebraic integers are definable in any Galois extension of Q where the degree
of any finite subextension is not divisible by $q^{m}$. (2) Given a prime $q$,
and an integer $m>0$, algebraic integers are definable in a cyclotomic
extension (and any of its subfields) generated by any set $\{\xi_{p^{\ell}}|
\ell \in \Z_{>0}, p \not=q {is any prime such that} q^{m +1}\not | (p-1)\}$.
(3) The first-order theory of any abelian extension of Q with finitely many
ramified rational primes is undecidable. We also show that under a condition on
the splitting of one rational prime in an infinite algebraic extension of Q,
the existence of a finitely generated elliptic curve over the field in question
is enough to have a definition of Z and to show that the field is indecidable.


  We propose a general framework to effectively `open' a high-Q resonator, that
is, to release the quantum state initially prepared in it in the form of a
traveling electromagnetic wave. This is achieved by employing a mediating mode
that scatters coherently the radiation from the resonator into a
one-dimensional continuum of modes such as a waveguide. The same mechanism may
be used to `feed' a desired quantum field to an initially empty cavity.
Switching between an `open' and `closed' resonator may then be obtained by
controlling either the detuning of the scatterer or the amount of time it
spends in the resonator. First, we introduce the model in its general form,
identifying (i) the traveling mode that optimally retains the full quantum
information of the resonator field and (ii) a suitable figure of merit that we
study analytically in terms of the system parameters. Then, we discuss two
feasible implementations based on ensembles of two-level atoms interacting with
cavity fields. In addition, we discuss how to integrate traditional cavity QED
in our proposal using three-level atoms.


  Existing polarimetric synthetic aperture radar (PolSAR) image classification
methods cannot achieve satisfactory performance on complex scenes characterized
by several types of land cover with significant levels of noise or similar
scattering properties across land cover types. Hence, we propose a supervised
classification method aimed at constructing a classifier based on self-paced
learning (SPL). SPL has been demonstrated to be effective at dealing with
complex data while providing classifier. In this paper, a novel Support Vector
Machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC)
is proposed. The proposed method leverages the easiest samples first to obtain
an initial parameter vector. Then, more complex samples are gradually
incorporated to update the parameter vector iteratively. Moreover, neighborhood
constraints are introduced during the training process to further improve
performance. Experimental results on three real PolSAR images show that the
proposed method performs well on complex scenes.


  Deep F555W and F814W Hubble Space Telescope ACS images are the basis for a
study of the present day mass function (PDMF) of NGC346, the largest active
star forming region in the Small Magellanic Cloud (SMC). We find a PDMF slope
of Gamma=-1.43+/-0.18 in the mass range 0.8-60 Mo, in excellent agreement with
the Salpeter Initial Mass Function (IMF) in the solar neighborhood. Caveats on
the conversion of the PDMF to the IMF are discussed. The PDMF slope changes, as
a function of the radial distance from the center of the NGC 346 star cluster,
indicating a segregation of the most massive stars. This segregation is likely
primordial considering the young age (~3 Myr) of NGC346, and its clumpy
structure which suggests that the cluster has likely not had sufficient time to
relax. Comparing our results for NGC346 with those derived for other star
clusters in the SMC and the Milky Way (MW), we conclude that, while the star
formation process might depend on the local cloud conditions, the IMF does not
seem to be affected by general environmental effects such as galaxy type,
metallicity, and dust content.


  From the work of Phong and Sturm in 2007, for a polarised projective manifold
and an ample test configuration, one can associate the geodesic ray of
plurisubharmonic metrics on the polarising line bundle using the solution of
the Monge-Amp\`ere equation on an equivariant resolution of singularities of
the test configuration. We prove that the Mabuchi chordal distance between the
geodesic rays associated with two ample test configurations coincides with the
spectral distance between the associated filtrations on the section ring.
  This gives an algebraic description of the boundary at the infinity of the
space of positive metrics, viewed - as it is usually done for spaces of
negative curvature - through geodesic rays.


  We introduce a new method of calculating intersections on \bar{M}_{g,n},
using localization of equivariant cohomology. As an application, we give a
proof of Mirzakhani's recursion relation for calculating intersections of mixed
psi and kappa_1 classes.


  Solar irradiance is considered one of the main natural factors affecting
terrestrial climate, and its variations are included in most numerical models
estimating the effects of natural versus anthropogenic factors for climate
change. Solar wind causing geomagnetic disturbances is another solar activity
agent whose role in climate change is not yet fully estimated but is a subject
of intense research. For the purposes of climate modeling, it is essential to
evaluate both the past and the future variations of solar irradiance and
geomagnetic activity which are ultimately due to the variations of solar
magnetic fields. Direct measurements of solar magnetic fields are available for
a limited period, but can be reconstructed from geomagnetic activity records.
Here we present a reconstruction of total solar irradiance based on geomagnetic
data, and a forecast of the future irradiance and geomagnetic activity relevant
for the expected climate change.


  We consider fixed-budget best-arm identification in two-armed Gaussian bandit
problems. One of the longstanding open questions is the existence of an optimal
strategy under which the probability of misidentification matches a lower
bound. We show that a strategy following the Neyman allocation rule (Neyman,
1934) is asymptotically optimal when the gap between the expected rewards is
small. First, we review a lower bound derived by Kaufmann et al. (2016). Then,
we propose the "Neyman Allocation (NA)-Augmented Inverse Probability weighting
(AIPW)" strategy, which consists of the sampling rule using the Neyman
allocation with an estimated standard deviation and the recommendation rule
using an AIPW estimator. Our proposed strategy is optimal because the upper
bound matches the lower bound when the budget goes to infinity and the gap goes
to zero.


  Data augmentation techniques are widely used in low-resource automatic
morphological inflection to overcome data sparsity. However, the full
implications of these techniques remain poorly understood. In this study, we
aim to shed light on the theoretical aspects of the prominent data augmentation
strategy StemCorrupt (Silfverberg et al., 2017; Anastasopoulos and Neubig,
2019), a method that generates synthetic examples by randomly substituting stem
characters in gold standard training examples. To begin, we conduct an
information-theoretic analysis, arguing that StemCorrupt improves compositional
generalization by eliminating spurious correlations between morphemes,
specifically between the stem and the affixes. Our theoretical analysis further
leads us to study the sample efficiency with which StemCorrupt reduces these
spurious correlations. Through evaluation across seven typologically distinct
languages, we demonstrate that selecting a subset of datapoints with both high
diversity and high predictive uncertainty significantly enhances the
data-efficiency of StemCorrupt. However, we also explore the impact of
typological features on the choice of the data selection strategy and find that
languages incorporating a high degree of allomorphy and phonological
alternations derive less benefit from synthetic examples with high uncertainty.
We attribute this effect to phonotactic violations induced by StemCorrupt,
emphasizing the need for further research to ensure optimal performance across
the entire spectrum of natural language morphology.


  Li and Wu proposed Rule 2, a localized approximation algorithm that attempts
to find a small connected dominating set in a graph. Here we study the
asymptotic performance of Rule 2 on random unit disk graphs formed from n
random points in an s_n by s_n square region of the plane. If s_n is below the
threshold for connectivity, then Rule 2 produces a dominating set whose
expected size is O(n/(loglog n)^{3/2}). We conjecture that this bound is not
optimal.


  The hard X-ray emission from Cygnus X-1 has been monitored continually by
BATSE since the launch of CGRO in April 1991. We present the hard X-ray
intensity and spectral history of the source covering a period of more than
five years. Power spectral analysis shows a significant peak at the binary
orbital period. The 20-100 keV orbital light curve is roughly sinusoidal with a
minimum near superior conjunction of the X-ray source and an rms modulation
fraction of approximately 1.7%. No longer-term periodicities are evident in the
power spectrum. We compare our results with other observations and discuss the
implications for models of the source geometry.


  For a compact set $E \subset \mathbb R^d$ and a connected graph $G$ on $k+1$
vertices, we define a $G$-framework to be a collection of $k+1$ points in $E$
such that the distance between a pair of points is specified if the
corresponding vertices of $G$ are connected by an edge. We regard two such
frameworks as equivalent if the specified distances are the same. We show that
in a suitable sense the set of equivalences of such frameworks naturally embeds
in ${\mathbb R}^m$ where $m$ is the number of "essential" edges of $G$. We
prove that there exists a threshold $s_k<d$ such that if the Hausdorff
dimension of $E$ is greater than $s_k$, then the $m$-dimensional Hausdorff
measure of the set of equivalences of $G$-frameworks is positive. The proof
relies on combinatorial, topological and analytic considerations.


  Recent measurements of solar wind turbulence report the presence of
intermittent, exponentially distributed angular discontinuities in the magnetic
field. In this Letter, we study whether such discontinuities can be produced by
magnetohydrodynamic (MHD) turbulence. We detect the discontinuities by
measuring the fluctuations of the magnetic field direction, Delta theta, across
fixed spatial increments Delta x in direct numerical simulations of MHD
turbulence with an imposed uniform guide field B_0. A large region of the
probability density function (pdf) for Delta theta is found to follow an
exponential decay, proportional to exp(-Delta theta/theta_*), with
characteristic angle theta_* ~ (14 deg) (b_rms/B_0)^0.65 for a broad range of
guide-field strengths. We find that discontinuities observed in the solar wind
can be reproduced by MHD turbulence with reasonable ratios of b_rms/B_0. We
also observe an excess of small angular discontinuities when Delta x becomes
small, possibly indicating an increasing statistical significance of
dissipation-scale structures. The structure of the pdf in this case closely
resembles the two-population pdf seen in the solar wind. We thus propose that
strong discontinuities are associated with inertial-range MHD turbulence, while
weak discontinuities emerge from near-dissipation-range turbulence. In
addition, we find that the structure functions of the magnetic field direction
exhibit anomalous scaling exponents, which indicates the existence of
intermittent structures.


  TODO comments are very widely used by software developers to describe their
pending tasks during software development. However, after performing the task
developers sometimes neglect or simply forget to remove the TODO comment,
resulting in obsolete TODO comments. These obsolete TODO comments can confuse
development teams and may cause the introduction of bugs in the future,
decreasing the software's quality and maintainability. In this work, we propose
a novel model, named TDCleaner (TODO comment Cleaner), to identify obsolete
TODO comments in software projects. TDCleaner can assist developers in
just-in-time checking of TODO comments status and avoid leaving obsolete TODO
comments. Our approach has two main stages: offline learning and online
prediction. During offline learning, we first automatically establish
<code_change, todo_comment, commit_msg> training samples and leverage three
neural encoders to capture the semantic features of TODO comment, code change
and commit message respectively. TDCleaner then automatically learns the
correlations and interactions between different encoders to estimate the final
status of the TODO comment. For online prediction, we check a TODO comment's
status by leveraging the offline trained model to judge the TODO comment's
likelihood of being obsolete. We built our dataset by collecting TODO comments
from the top-10,000 Python and Java Github repositories and evaluated TDCleaner
on them. Extensive experimental results show the promising performance of our
model over a set of benchmarks. We also performed an in-the-wild evaluation
with real-world software projects, we reported 18 obsolete TODO comments
identified by TDCleaner to Github developers and 9 of them have already been
confirmed and removed by the developers, demonstrating the practical usage of
our approach.


  Since the electromagnetic current for a pion system coincides with the third
component of the isovector current, the isospin angular oscillation of a
coherent field can be a significant source for the electromagnetic emissions.
We study the characteristic dilepton and photon emissions from the classical
pion field oscillation in the QCD vacuum. The general analytical solution
obtained in the nonlinear sigma model is used to calculate the electromagnetic
current density, which exhibits a light-front singularity and decreases rapidly
as inverse square of the proper time due to a longitudinal expansion. The
momentum and invariant mass spectra of the direct photon and dilepton are found
to be a sensitive probe of the space-time evolution of the chiral condensate
field.


  Existing few-shot segmentation methods are based on the meta-learning
strategy and extract instance knowledge from a support set and then apply the
knowledge to segment target objects in a query set. However, the extracted
knowledge is insufficient to cope with the variable intra-class differences
since the knowledge is obtained from a few samples in the support set. To
address the problem, we propose a multi-information aggregation network
(MIANet) that effectively leverages the general knowledge, i.e., semantic word
embeddings, and instance information for accurate segmentation. Specifically,
in MIANet, a general information module (GIM) is proposed to extract a general
class prototype from word embeddings as a supplement to instance information.
To this end, we design a triplet loss that treats the general class prototype
as an anchor and samples positive-negative pairs from local features in the
support set. The calculated triplet loss can transfer semantic similarities
among language identities from a word embedding space to a visual
representation space. To alleviate the model biasing towards the seen training
classes and to obtain multi-scale information, we then introduce a
non-parametric hierarchical prior module (HPM) to generate unbiased
instance-level information via calculating the pixel-level similarity between
the support and query image features. Finally, an information fusion module
(IFM) combines the general and instance information to make predictions for the
query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet
yields superior performance and set a new state-of-the-art. Code is available
at https://github.com/Aldrich2y/MIANet.


  The received view in foundations and philosophy of physics holds that if we
reject supplementing quantum theory (QT) with certain hidden variables and
consider that unitary QT is correct and universal, we should adopt a
relationalist interpretation of QT. Relationalist interpretations relativize
measurement outcomes to, for example, worlds, systems, agents, or reference
frames. It includes the Many-Worlds Interpretation, Relational Quantum
Mechanics, and QBism. These interpretations have potential costs connected with
their relationalism that make them unattractive. Thus, if there exists a
non-relational non-hidden variable universal intepretations of QT, it should be
taken seriously. I will present an interpretation of this kind called
Environmental Determinacy-based or EnD Quantum Theory (EnDQT), which goes
beyond relationalism and the received view. EnDQT circumvents relationalism by
constructing an account of indeterminate and determinate values and underlying
quantum properties that is not relational while maintaining unitary non-hidden
variable universal QT. In situations where a relationalist assumes that
measurement outcomes are relativized, such as in the extended Wigner's friend
scenarios, according to EnDQT there aren't determinate outcomes but systems
with non-relational indeterminate values. In this approach, determinate values
arose at some point in time through certain systems and persist due to them via
certain interactions represented by certain networks. When there is isolation
from the rest of the systems that belong to these networks, such as inside the
friend's lab in the extended Wigner's friend scenarios, indeterminate values
non-relationally arise inside. I will discuss other independent good reasons
for adopting EnDQT, including providing a local causal explanation of Bell
correlations and novel empirical posits represented by these networks.


  Fronts and near-inertial waves are energetic motions in the upper ocean that
can interact and provide a route for kinetic energy (KE) dissipation of
balanced oceanic flows. A quasilinear model is developed to study the KE
exchanges between a two-dimensional geostrophically-balanced front undergoing
strain-induced semigeostrophic frontogenesis and internal wave (IW) vertical
modes. The quasilinear model is solved numerically for variable imposed strain
magnitudes, initial IW vertical modes, and for both minimum frequency
(near-inertial, NI) and high-frequency IWs. The front-IW KE exchanges are
quantified separately during two frontogenetic stages -- an exponential
sharpening stage that is characterized by a low Rossby number and is driven by
the imposed geostrophic strain, followed by a superexponential sharpening stage
that is characterized by an order-one Rossby number and is driven by the
convergence of the ageostrophic secondary circulation. It is demonstrated that
high-frequency IWs quickly escape the frontal zone and are very efficient at
extracting KE from the imposed geostrophic strain field through the deformation
shear production (DSP) mechanism. Part of the extracted KE is then converted to
wave potential energy. Minimum frequency IWs remain locked to the frontal zone
and therefore exchange energy with the ageostrophic frontal circulation. During
the exponential stage, IWs extract KE from the geostrophic strain through DSP
and transfer it to the frontal secondary circulation via the ageostrophic shear
production (AGSP) mechanism. During the superexponential stage a newly
identified mechanism, convergence production (CP), which is directly linked to
the convergent secondary circulation, plays an important role in the NIW KE
budget.


  The HERAFitter project provides a framework for the determination of parton
distribution functions (PDFs), and tools for assessing the impact of new data
on PDFs. In this contribution, HERAFitter is used for a QCD analysis of the
legacy measurements of the $W$-boson charge asymmetry and of the $Z$-boson
production cross sections, performed at the Tevatron collider in Run II by the
D0 and CDF collaborations. The Tevatron measurements are included in a PDF fit
performed at next-to-leading order, and compared to the predictions obtained
using other PDF sets from different groups. The measurements are in good
agreement with NLO QCD theoretical predictions. The Tevatron data provide
significant constraints on the $d$-valence quark distribution.


  This paper deals with inference in a class of stable but nearly-unstable
processes. Autoregressive processes are considered, in which the bridge between
stability and instability is expressed by a time-varying companion matrix
$A_{n}$ with spectral radius $\rho(A_{n}) < 1$ satisfying $\rho(A_{n})
\rightarrow 1$. This framework is particularly suitable to understand unit root
issues by focusing on the inner boundary of the unit circle. Consistency is
established for the empirical covariance and the OLS estimation together with
asymptotic normality under appropriate hypotheses when $A$, the limit of $A_n$,
has a real spectrum, and a particular case is deduced when $A$ also contains
complex eigenvalues. The asymptotic process is integrated with either one unit
root (located at 1 or $-1$), or even two unit roots located at 1 and $-1$.
Finally, a set of simulations illustrate the asymptotic behavior of the OLS.
The results are essentially proved by $L^2$ computations and the limit theory
of triangular arrays of martingales.


  3D complete renal structures(CRS) segmentation targets on segmenting the
kidneys, tumors, renal arteries and veins in one inference. Once successful, it
will provide preoperative plans and intraoperative guidance for laparoscopic
partial nephrectomy(LPN), playing a key role in the renal cancer treatment.
However, no success has been reported in 3D CRS segmentation due to the complex
shapes of renal structures, low contrast and large anatomical variation. In
this study, we utilize the adversarial ensemble learning and propose Ensemble
Multi-condition GAN(EnMcGAN) for 3D CRS segmentation for the first time. Its
contribution is three-fold. 1)Inspired by windowing, we propose the
multi-windowing committee which divides CTA image into multiple narrow windows
with different window centers and widths enhancing the contrast for salient
boundaries and soft tissues. And then, it builds an ensemble segmentation model
on these narrow windows to fuse the segmentation superiorities and improve
whole segmentation quality. 2)We propose the multi-condition GAN which equips
the segmentation model with multiple discriminators to encourage the segmented
structures meeting their real shape conditions, thus improving the shape
feature extraction ability. 3)We propose the adversarial weighted ensemble
module which uses the trained discriminators to evaluate the quality of
segmented structures, and normalizes these evaluation scores for the ensemble
weights directed at the input image, thus enhancing the ensemble results. 122
patients are enrolled in this study and the mean Dice coefficient of the renal
structures achieves 84.6%. Extensive experiments with promising results on
renal structures reveal powerful segmentation accuracy and great clinical
significance in renal cancer treatment.


  We study the electric double layer by combining the effects of ion finite
size and dielectric decrement. At high surface potential, both mechanisms can
cause saturation of the counter-ion concentration near a charged surface. The
modified Grahame equation and differential capacitance are derived analytically
for a general expression of a permittivity epsilon(n) that depends on the local
ion concentration, n, and under the assumption that the co-ions are fully
depleted from the surface. The concentration at counter-ion saturation is found
for any epsilon(n), and a criterion predicting which of the two mechanisms
(steric vs. dielectric decrement) is the dominant one is obtained. At low
salinity, the differential capacitance as function of surface potential has two
peaks (so-called camel-shape). Each of these two peaks is connected to a
saturation of counter-ion concentration caused either by dielectric decrement
or by their finite size. Because these effects depend mainly on the counter-ion
concentration at the surface proximity, for opposite surface-potential polarity
either the cations or anions play the role of counter-ions, resulting in an
asymmetric camel-shape. At high salinity, we obtain and analyze the crossover
in the differential capacitance from a double-peak shape to a uni-modal one.
Finally, several nonlinear models of the permittivity decrement are considered,
and we predict that the concentration at dielectrophoretic saturation shifts to
higher concentration than those obtained by the linear decrement model.


  Efficient and faithful implementation of quantum information tasks, e.g.,
quantum computing, quantum communication and quantum metrology, requires robust
and state-independent decoherence-suppressing measures to protect quantum
information carriers. Here we present an experimental demonstration of a robust
distribution scheme in which one photon of an entangled photon pair is
successfully encoded into and decoded from a decoherence-free subspace (DFS) by
a state-independent scheme. We achieved a high-fidelity distribution of the
entangled state over fibre communication channel, and also demonstrated that
the scheme is robust against fragility of the reference frame. The scheme,
thanks to its state-independence, is also applicable to multipartite case where
the photon to be distributed is entangled with many other photons. Such a
universal scheme opens the possibility of robust distribution of quantum
information among quantum communication and computing networks.


  In randomly created structures (be they natural or artificial) very often
there exist ordered substructures. In this Hungarian language scientific essay
we will present some of such structures in graph theory. E.g. R\'edei's
theorem, Ramsey theory, Tur\'an's problem, Hamiltonian cycles in De Bruijn
graphs.


  We investigate the correlation-induced Mott, magnetic, and topological phase
transitions in artificial (111) bilayers of perovskite transition-metal oxides
LaAuO$_3$ and SrIrO$_3$ for which the previous density-functional theory
calculations predicted topological insulating states. Using the
dynamical-mean-field theory with realistic band structures and Coulomb
interactions, LaAuO$_3$ bilayer is shown to be far away from a Mott insulating
regime, and a topological-insulating state is robust. On the other hand,
SrIrO$_3$ bilayer is on the verge of an orbital-selective topological Mott
transition and turns to a trivial insulator by an antiferromagnetic ordering.
Oxide bilayers thus provide a novel class of topological materials for which
the interplay between the spin-orbit coupling and electron-electron
interactions is a fundamental ingredient.


  $R_{D^{(*)}}$ is the ratio of branching ratio $\overline{B} \rightarrow
D^{(*)}\tau\overline{\nu}_{\tau}$ to $\overline{B} \rightarrow
D^{(*)}l\overline{\nu}_{l}~(l=e,~\mu)$. There is a gap of 2$\sigma_{exp}$ or
more between its experimental value and the prediction under the standard
model(SM). People extend the MSSM with the local gauge group $U(1)_X$ to obtain
the $U(1)_X$SSM. Compared with MSSM, $U(1)_X$SSM has more superfields and
effects. In $U(1)_X$SSM, we research the semileptonic decays $\overline{B}
\rightarrow D^{(*)}l\overline{\nu}_{l}$ and calculate $R_{D^{(*)}}$. The
numerical results of $R_{D^{(*)}}$ are further corrected under $U(1)_X$SSM,
which are much better than the SM predictions. After correction, the
theoretical value of $R_{D^{(*)}}$ can reach one $\sigma_{exp}$ range of the
averaged experiment central value.


  The state-of-the-art theoretical formalism for a covariant description of
non-Gaussian fluctuation dynamics in relativistic fluids is discussed.


  Time evolution of wave packets built from the eigenstates of the Dirac
equation for a hydrogenic system is considered. We investigate the space and
spin motion of wave packets which, in the non-relativistic limit, are
stationary states with a probability density distributed uniformly along the
classical, elliptical orbit (elliptic WP). We show that the precession of such
a WP, due to relativistic corrections to the energy eigenvalues, is strongly
correlated with the spin motion. We show also that the motion is universal for
all hydrogenic systems with an arbitrary value of the atomic number Z.


  The previous fine-grained datasets mainly focus on classification and are
often captured in a controlled setup, with the camera focusing on the objects.
We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the
wild, captured from a moving camera mounted on a car. It contains 5502 scene
images with 210 unique fine-grained labels of multiple vehicle types organized
in a three-level hierarchy. While previous classification datasets also include
makes for different kinds of cars, the FGVD dataset introduces new class labels
for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is
challenging as it has vehicles in complex traffic scenarios with intra-class
and inter-class variations in types, scale, pose, occlusion, and lighting
conditions. The current object detectors like yolov5 and faster RCNN perform
poorly on our dataset due to a lack of hierarchical modeling. Along with
providing baseline results for existing object detectors on FGVD Dataset, we
also present the results of a combination of an existing detector and the
recent Hierarchical Residual Network (HRN) classifier for the FGVD task.
Finally, we show that FGVD vehicle images are the most challenging to classify
among the fine-grained datasets.


  The Boltzmann equation for inelastic Maxwell models is considered to
determine the rheological properties in a granular binary mixture in the simple
shear flow state. The transport coefficients (shear viscosity and viscometric
functions) are {\em exactly} evaluated in terms of the coefficients of
restitution, the (reduced) shear rate and the parameters of the mixture
(particle masses, diameters and concentration). The results show that in
general, for a given value of the coefficients of restitution, the above
transport properties decrease with increasing shear rate.


  The external-Q factor of a resonant mode of a cavity represents the strength
of the electromagnetic coupling between this resonant mode and the transmission
mode of a waveguide coupled to the cavity. In this paper, we derive formulas to
explicitly give the external Q of the cavities coupled with the waveguide
through capacitive and inductive coupling. The derivation is based on the
classical method that uses the reflection coefficient to estimate the coupling
strength. Although the external Q is evaluated via three-dimensional computer
simulations currently, these formulas may be useful for making speculations in
the initial stages of cavity design.


  We point out that non-leptonic B decays driven by b-->ccbar s should provide
a favourable environment for the production of hidden charm diquark-antidiquark
bound states that have been suggested to explain the resonances with masses
around 4 GeV recently observed by BaBar and BELLE. Studying their relative
abundances in non-leptonic B decays can teach us novel lessons about their
structure and the strong interactions. Through their decay into psi they can
provide a natural explanation of the excess of B-->psi X observed for p_psi < 1
GeV. Other phenomenological consequences are mentioned as well.


  Layered transition metal dichalcogenides (TMDs) are attracting increasing
attention because they exhibit unconventional magnetic properties due to
crystal imperfections in their usually non-magnetic 2D structure. This work
aims to investigate the magnetic response of self-engineered Se deficient
TiSe$_2$ thin films, synthesized using chemical vapour deposition. We
demonstrate tunability of the ferromagnetic order with the introduction of Au
atoms using low energy Au ion implantation, which works as a controlling knob
to vary the stoichiometry of Se in TiSe$_{2-x}$. The corresponding isothermal
field-magnetization curves fit well with a modified Brillouin J function with J
value of 1.5 for Ti$^{3+}$, and 4 for Au$^{3+}$, accounting for the
diamagnetism that arises from Au implantation. We propose a qualitative model
for the experimentally observed magnetization as a function of ion fluence,
corroborated with high-resolution transmission electron microscopy. Depending
on the Au nanoparticle size in the implanted samples, magnetization saturates
faster at a much lower applied magnetic field than the pristine sample. Our
findings hold potential to expand the range of 2D ferromagnetic materials for
spintronics and magnetic sensing applications.


  There have been significant developments in the period estimation tools and
methods for analysing high energy pulsars in the past few decades. However,
these tools lack well-standardised methods for calculating uncertainties in
period estimation and other recovered parameters for Poisson--dominated data.
Error estimation is important for assigning confidence intervals to the models
we study, but due to their high computational cost, errors in the pulsar
periods were largely ignored in the past. Furthermore, existing literature has
often employed semi-analytical techniques that lack rigorous mathematical
foundations or exhibit a predominant emphasis on the analysis of white noise
and time series data. We present results from our numerical and analytical
study of the error distribution of the recovered parameters of high energy
pulsar data using the $Z_n^2$ method. We comprehensively formalise the measure
of error for the generic pulsar period with much higher reliability than some
common methods. Our error estimation method becomes more reliable and robust
when observing pulsars for few kilo-seconds, especially for typical pulsars
with periods ranging from a few milliseconds to a few seconds. We have verified
our results with observations of the \emph{Crab} pulsar, as well as a large set
of simulated pulsars. Our codes are publicly available for use.


  We present a theory of viscoelasticity of amorphous media, which takes into
account the effects of confinement along one of three spatial dimensions. The
framework is based on the nonaffine extension of lattice dynamics to amorphous
systems, or nonaffine response theory. The size effects due to the confinement
are taken into account via the nonaffine part of the shear storage modulus
$G'$. The nonaffine contribution is written as a sum over modes in $k$-space.
With a rigorous argument based on the analysis of the $k$-space integral over
modes, it is shown that the confinement size $L$ in one spatial dimension, e.g.
the $z$ axis, leads to a infrared cut-off for the modes contributing to the
nonaffine (softening) correction to the modulus that scales as $L^{-3}$.
Corrections for finite sample size $D$ in the two perpendicular dimensions
scale as $\sim (L/D)^4$, and are negligible for $L \ll D$. For liquids it is
predicted that $G'\sim L^{-3}$ in agreement with a previous more approximate
analysis, whereas for amorphous materials $G' \sim G'_{bulk} + \beta L^{-3}$.
For the case of liquids, four different experimental systems are shown to be
very well described by the $L^{-3}$ law.


  Past research has identified a rich set of handcrafted linguistic features
that can potentially assist various tasks. However, their extensive number
makes it difficult to effectively select and utilize existing handcrafted
features. Coupled with the problem of inconsistent implementation across
research works, there has been no categorization scheme or generally-accepted
feature names. This creates unwanted confusion. Also, most existing handcrafted
feature extraction libraries are not open-source or not actively maintained. As
a result, a researcher often has to build such an extraction system from the
ground up.
  We collect and categorize more than 220 popular handcrafted features grounded
on past literature. Then, we conduct a correlation analysis study on several
task-specific datasets and report the potential use cases of each feature.
Lastly, we devise a multilingual handcrafted linguistic feature extraction
system in a systematically expandable manner. We open-source our system for
public access to a rich set of pre-implemented handcrafted features. Our system
is coined LFTK and is the largest of its kind. Find it at
github.com/brucewlee/lftk.


  Colorectal cancer is the fourth leading cause of cancer deaths worldwide and
the second leading cause in the United States. The risk of colorectal cancer
can be mitigated by the identification and removal of premalignant lesions
through optical colonoscopy. Unfortunately, conventional colonoscopy misses
more than 20% of the polyps that should be removed, due in part to poor
contrast of lesion topography. Imaging tissue topography during a colonoscopy
is difficult because of the size constraints of the endoscope and the deforming
mucosa. Most existing methods make geometric assumptions or incorporate a
priori information, which limits accuracy and sensitivity. In this paper, we
present a method that avoids these restrictions, using a joint deep
convolutional neural network-conditional random field (CNN-CRF) framework.
Estimated depth is used to reconstruct the topography of the surface of the
colon from a single image. We train the unary and pairwise potential functions
of a CRF in a CNN on synthetic data, generated by developing an endoscope
camera model and rendering over 100,000 images of an anatomically-realistic
colon. We validate our approach with real endoscopy images from a porcine
colon, transferred to a synthetic-like domain, with ground truth from
registered computed tomography measurements. The CNN-CRF approach estimates
depths with a relative error of 0.152 for synthetic endoscopy images and 0.242
for real endoscopy images. We show that the estimated depth maps can be used
for reconstructing the topography of the mucosa from conventional colonoscopy
images. This approach can easily be integrated into existing endoscopy systems
and provides a foundation for improving computer-aided detection algorithms for
detection, segmentation and classification of lesions.


  Incorporating encoding-decoding nets with adversarial nets has been widely
adopted in image generation tasks. We observe that the state-of-the-art
achievements were obtained by carefully balancing the reconstruction loss and
adversarial loss, and such balance shifts with different network structures,
datasets, and training strategies. Empirical studies have demonstrated that an
inappropriate weight between the two losses may cause instability, and it is
tricky to search for the optimal setting, especially when lacking prior
knowledge on the data and network.
  This paper gives the first attempt to relax the need of manual balancing by
proposing the concept of \textit{decoupled learning}, where a novel network
structure is designed that explicitly disentangles the backpropagation paths of
the two losses.
  Experimental results demonstrate the effectiveness, robustness, and
generality of the proposed method. The other contribution of the paper is the
design of a new evaluation metric to measure the image quality of generative
models. We propose the so-called \textit{normalized relative discriminative
score} (NRDS), which introduces the idea of relative comparison, rather than
providing absolute estimates like existing metrics.


  In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.


  The study of the non-linear anomalous Hall effect (NLAHE) in
$\mathcal{P}\mathcal{T}$-symmetric systems has focussed on intrinsic
mechanisms. Here we show that disorder contributes substantially to NLAHE and
often overwhelms intrinsic terms. We identify terms to zeroth order in the
disorder strength involving the Berry curvature dipole, skew scattering and
side-jump, all exhibiting a strong peak as a function of the Fermi energy, a
signature of interband coherence. Our results suggest NLAHE at experimentally
relevant transport densities in $\mathcal{P}\mathcal{T}$-symmetric systems is
likely to be extrinsic.


  This document is a short and informal tutorial on some aspects of calculating
phase diagrams with the ATAT-tools emc2 and phb and on creating cluster
expansions with maps. It is neither complete, nor in any way an official
document, but mainly a set of collected notes I took during experimentation
with ATAT.


  We demonstrate that the idempotent barycenter map, associated with a t-norm
$\ast$, is open if and only if the map of max-$\ast$ convex combination is
open. As a corollary, we deduce that the idempotent barycenter map is open for
spaces of idempotent measures associated with any t-norm $\ast$. Nevertheless,
we illustrate that the characteristics of the idempotent barycenter map, in
general, depend on the specific t-norm being employed.


  In this letter, we propose a new model that explains the Raman peak downshift
observed in nanoparticles with respect to bulk materials. The proposed model
takes into account discreteness of the vibrational spectra of nanoparticles.
For crystals with a cubic lattice (Diamond, Silicon, Germanium) we give a
relation between the displacement of Raman peak position and the size of
nanoparticles. The proposed model does not include any uncertain parameters,
unlike the conventionally used phonon confinement model (PCM), and can be
employed for unambiguous nanoparticles size estimation.


  We consider a combinatorial problem occurring naturally in a group
theoretical setting and provide a constructive solution in a special case. More
precisely, in 1999 the author established a logarithmic bound for the derived
length of the quotient of a finite solvable group modulo the second Fitting
subgroup in terms of the number of irreducible character degrees of the group.
Along the way, in two key lemmas an inductive process was used which at its
core required a solution of some weak form of the combinatorial problem studied
in this paper. This problem can be stated and studied without any group
theoretical background, and in this paper we present the problem, discuss what
is known and what the main conjecture is, and solve the conjecture in the
smallest open case.


  Marginal models involve restrictions on the conditional and marginal
association structure of a set of categorical variables. They generalize
log-linear models for contingency tables, which are the fundamental tools for
modelling the conditional association structure. This chapter gives an overview
of the development of marginal models during the past 20 years. After providing
some motivating examples, the first few sections focus on the definition and
characteristics of marginal models. Specifically, we show how their fundamental
properties can be understood from the properties of marginal log-linear
parameterizations. Algorithms for estimating marginal models are discussed,
focussing on the maximum likelihood and the generalized estimating equations
approaches. It is shown how marginal models can help to understand directed
graphical and path models, and a description is given of marginal models with
latent variables.


  Delay-tolerant networking (DTN) offers a novel architecture that can be used
to enhance store-carry-forward routing in satellite networks. Since these
networks can take advantage of scheduled contact plans, distributed algorithms
like the Contact Graph Routing (CGR) can be utilized to optimize data delivery
performance. However, despite the numerous improvements made to CGR, there is a
lack of proposals to prioritize traffic with distinct quality of service (QoS)
requirements. This study presents adaptations to CGR to improve QoS-compliant
delivery ratio when transmitting traffic with different latency constraints,
along with an integer linear programming optimization model that serves as a
performance upper bound. The extensive results obtained by simulating different
scenarios show that the proposed algorithms can effectively improve the
delivery ratio and energy efficiency while meeting latency constraints.


  We present a concept for a high-precision optical atomic clock (OAC)
operating on an Earth-orbiting space station. This pathfinder science mission
will compare the space-based OAC with one or more ultra-stable terrestrial OACs
to search for space-time-dependent signatures of dark scalar fields that
manifest as anomalies in the relative frequencies of station-based and
ground-based clocks. This opens the possibility of probing models of new
physics that are inaccessible to purely ground-based OAC experiments where a
dark scalar field may potentially be strongly screened near Earth's surface.
This unique enhancement of sensitivity to potential dark matter candidates
harnesses the potential of space-based OACs.


  The next generation of lepton flavor violation experiments need high
intensity and high quality muon beams. Production of such beams requires
sending a short, high intensity proton pulse to the pion production target,
capturing pions and collecting the resulting muons in the large acceptance
transport system. The substantial increase of beam quality can be obtained by
applying the RF phase rotation on the muon beam in the dedicated FFAG ring,
which was proposed for the PRISM project.This allows to reduce the momentum
spread of the beam and to purify from the unwanted components like pions or
secondary protons. A PRISM Task Force is addressing the accelerator and
detector issues that need to be solved in order to realize the PRISM
experiment. The parameters of the required proton beam, the principles of the
PRISM experiment and the baseline FFAG design are introduced. The spectrum of
alternative designs for the PRISM FFAG ring are shown. Progress on ring main
systems like injection and RF are presented. The current status of the study
and its future directions are discussed.


  The combined presence of a Rashba and a Zeeman effect in a ballistic
one-dimensional conductor generates a spin pseudogap and the possibility to
propagate a beam with well defined spin orientation. Without interactions
transmission through a barrier gives a relatively well polarized beam. Using
renormalization group arguments, we examine how electron-electron interactions
may affect the transmission coefficient and the polarization of the outgoing
beam.


  Offline reinforcement learning algorithms still lack trust in practice due to
the risk that the learned policy performs worse than the original policy that
generated the dataset or behaves in an unexpected way that is unfamiliar to the
user. At the same time, offline RL algorithms are not able to tune their most
important hyperparameter - the proximity of the learned policy to the original
policy. We propose an algorithm that allows the user to tune this
hyperparameter at runtime, thereby addressing both of the above mentioned
issues simultaneously. This allows users to start with the original behavior
and grant successively greater deviation, as well as stopping at any time when
the policy deteriorates or the behavior is too far from the familiar one.


  Ion beam irradiation of semiconductors is a method to produce regular
periodic nanoscale patterns self-organized on wafer scale. At low temperatures,
the surface of semiconductors is typically amorphized by the ion beam. Above a
material dependent dynamic recrystallization temperature however, the surface
remains crystalline and ion beam irradiation produces regular arrays of faceted
ripples on III-V semiconductors. This provides a powerful single-step technique
for the production of nanostructures on a large area. On $(001)$ surfaces these
ripples are parallel to the $[1\bar{1}0]$ direction without any external
anisotropy. The origin of this self-alignment was not fully understood until
now. A simple experiment exposing the front side and back side of a GaAs$(001)$
wafer to the ion beam clarifies its origin and proves that the ripples align to
the Ga dimer rows. As the direction of Ga dimer rows rotates by 90{\deg} on the
back side, the orientation of the ripples also rotates by 90{\deg} to $[110]$.
We discuss the experimental results in view of a model where the pattern
formation is driven by creation of vacancies and ad-atoms by the ion beam and
their diffusion, which is linked to the direction of dimers on the surface.


  The smoothing procedure known as the gradient flow that suppresses
ultraviolet fluctuations of gauge fields plays an important role in lattice
gauge theory calculations. In particular, this procedure is often used for
high-precision scale setting and renormalization of operators. The gradient
flow equation is defined on the SU(3) manifold and therefore requires
geometric, or structure-preserving, integration methods to obtain its numerical
solutions. We examine the properties and origins of the three-stage third-order
explicit Runge-Kutta Lie group integrator commonly used in the lattice gauge
theory community, demonstrate its relation to 2N-storage classical Runge-Kutta
methods and explore how its coefficients can be tuned for optimal performance
in integrating the gradient flow. We also compare the performance of the tuned
method with two third-order variable step size methods. Next, based on the
recently established connection between low-storage Lie group integrators and
classical 2N-storage Runge-Kutta methods, we study two fourth-order low-storage
methods that provide a computationally efficient alternative to the commonly
used third-order method while retaining the convenient iterative property of
the latter. Finally, we demonstrate that almost no coding effort is needed to
implement the low-storage Lie group methods into existing gradient flow codes.


  We consider the task of performing Jaccard similarity queries over a large
collection of items that are dynamically updated according to a streaming input
model. An item here is a subset of a large universe $U$ of elements. A
well-studied approach to address this important problem in data mining is to
design fast-similarity data sketches. In this paper, we focus on global
solutions for this problem, i.e., a single data structure which is able to
answer both Similarity Estimation and All-Candidate Pairs queries, while also
dynamically managing an arbitrary, online sequence of element insertions and
deletions received in input.
  We introduce and provide an in-depth analysis of a dynamic, buffered version
of the well-known $k$-MinHash sketch. This buffered version better manages
critical update operations thus significantly reducing the number of times the
sketch needs to be rebuilt from scratch using expensive recovery queries. We
prove that the buffered $k$-MinHash uses $O(k \log |U|)$ memory words per
subset and that its amortized update time per insertion/deletion is $O(k \log
|U|)$ with high probability. Moreover, our data structure can return the
$k$-MinHash signature of any subset in $O(k)$ time, and this signature is
exactly the same signature that would be computed from scratch (and thus the
quality of the signature is the same as the one guaranteed by the static
$k$-MinHash).
  Analytical and experimental comparisons with the other, state-of-the-art
global solutions for this problem given in [Bury et al.,WSDM'18] show that the
buffered $k$-MinHash turns out to be competitive in a wide and relevant range
of the online input parameters.