  We have analyzed the long pointed observations of the Z sources in the Rossi
X-Ray Timing Explorer (RXTE) public archive to study the high energy emission
in those sources. Our analysis is concentrated on the High Energy X--Ray Timing
Experiment (HEXTE) waveband, since we are primarily interested in studying the
hard X-ray (i.e., E > 20 keV) production in those sources. We give here the
preliminary results of this ongoing study. We have found no hard X-ray tails
(besides Sco X-1) in our database from any of the Z sources, i.e., GX 349+2 (<
7.9 x 10^-5 photons cm^-2 s^-1, 3 sigma, 50-150 keV), Cyg X-2 (< 8.4 x 10^-5
photons cm^-2 s^-1, 3 sigma, 50-150 keV), GX 17+2 (< 4.2 x 10^-5 photons cm^-2
s^-1, 3 sigma, 50-150 keV), GX 5-1 (< 2.1 x 10^-5 photons cm^-2 s^-1, 3 sigma,
50-150 keV), and Gx 340+0 (< 6.0 x 10^-5 photons cm^-2 s^-1, 3 sigma, 50-150
keV). From the point of view of HEXTE/RXTE observations shown here, the
production of hard X-ray tails in Z sources is a process triggered when special
conditions are fulfilled. One of these conditions, as derived from our
analysis, is a threshold of ~ 4 x 10^36 erg s^-1 for the luminosity of the
source's thermal component.


  We discuss implications of a new measurement of $^{14}N(p, \gamma)^{15}O$
concerning solar neutrinos, solar models and globular cluster dating.
Predictions for the gallium and chlorine experiments are reduced by 2 and 0.1
SNU respectively. Predictions for helioseismic observables are unchanged within
uncertainties. The age of globular clusters as deduced from the Turn-Off
luminosity is increased by about 0.7 Gyr.


  Expansive Matching of Experts (EMOE) is a novel method that utilizes
support-expanding, extrapolatory pseudo-labeling to improve prediction and
uncertainty based rejection on out-of-distribution (OOD) points. We propose an
expansive data augmentation technique that generates OOD instances in a latent
space, and an empirical trial based approach to filter out augmented expansive
points for pseudo-labeling. EMOE utilizes a diverse set of multiple base
experts as pseudo-labelers on the augmented data to improve OOD performance
through a shared MLP with multiple heads (one per expert). We demonstrate that
EMOE achieves superior performance compared to state-of-the-art methods on
tabular data.


  We show that every 'conveniently Hoelder' homomorphism between Lie groups in
the sense of convenient differential calculus is smooth (in the convenient
sense). In particular, every Lip^0 homomorphism is smooth.


  We report frictional drag reduction and a complete flow re-laminarization of
elastic turbulence (ET) at vanishing inertia in a viscoelastic channel flow
past an obstacle. We show that intensity of observed elastic waves and
wall-normal vorticity correlate well with the measured drag above the ET onset.
Moreover, we find that the elastic wave frequency grows with Weissenberg
number, and at sufficiently high frequency it causes decay of the elastic
waves, resulting in ET attenuation and drag reduction. Thus, this allows us to
substantiate a physical mechanism, involving interaction of elastic waves with
wall-normal vorticity fluctuations, leading to the drag reduction and
re-laminarization phenomena at low Reynolds number.


  Context: The principle definition of habitability for exoplanets is whether
they can sustain liquid water on their surfaces, i.e. that they orbit within
the habitable zone. However, the planet's magnetosphere should also be
considered, since without it, an exoplanet's atmosphere may be eroded away by
stellar winds. Aims: The aim of this paper is to investigate magnetospheric
protection of a planet from the effects of stellar winds from solar-mass stars.
Methods: We study hypothetical Earth-like exoplanets orbiting in the host
star's habitable zone for a sample of 124 solar-mass stars. These are targets
that have been observed by the Bcool collaboration. Using two wind models, we
calculate the magnetospheric extent of each exoplanet. These wind models are
computationally inexpensive and allow the community to quickly estimate the
magnetospheric size of magnetised Earth-analogues orbiting cool stars. Results:
Most of the simulated planets in our sample can maintain a magnetosphere of ~5
Earth radii or larger. This suggests that magnetised Earth analogues in the
habitable zones of solar analogues are able to protect their atmospheres and is
in contrast to planets around young active M dwarfs. In general, we find that
Earth-analogues around solar-type stars, of age 1.5 Gyr or older, can maintain
at least a Paleoarchean Earth sized magnetosphere. Our results indicate that
planets around 0.6 - 0.8 solar-mass stars on the low activity side of the
Vaughan-Preston gap are the optimum observing targets for habitable Earth
analogues.


  We discuss the nature of the kaon condensation phase transition. We find
several features which, if kaons condense in neutron stars, are not only
remarkable, but must surely effect such properties as superfluidity and
transport properties, which in turn are relevant to the glitch phenomenon and
cooling rates of neutron stars. The mixed phase, because of the extensive
pressure range that it spans, will occupy a broad radial extent in a neutron
star. This region is permeated with microscopic drops (and other
configurations) located at lattice sites of one phase immersed in the
background of the other phase. The electric charge on drops is opposite to that
of the background phase {\sl and} nucleons have a mass approximately a factor
two different depending on whether they are in the drops or the background
phase. A large part of the stellar interior has this highly non-homogeneous
structure.


  This paper studies tabular reinforcement learning (RL) in the hybrid setting,
which assumes access to both an offline dataset and online interactions with
the unknown environment. A central question boils down to how to efficiently
utilize online data collection to strengthen and complement the offline dataset
and enable effective policy fine-tuning. Leveraging recent advances in
reward-agnostic exploration and model-based offline RL, we design a three-stage
hybrid RL algorithm that beats the best of both worlds -- pure offline RL and
pure online RL -- in terms of sample complexities. The proposed algorithm does
not require any reward information during data collection. Our theory is
developed based on a new notion called single-policy partial concentrability,
which captures the trade-off between distribution mismatch and miscoverage and
guides the interplay between offline and online data.


  We used a one-zone chemical evolution model to address the question of how
many masses and metallicities are required in grids of massive stellar models
in order to ensure reliable galactic chemical evolution predictions. We used a
set of yields that includes seven masses between 13 and 30 Msun, 15
metallicities between 0 and 0.03 in mass fraction, and two different remnant
mass prescriptions. We ran several simulations where we sampled subsets of
stellar models to explore the impact of different grid resolutions. Stellar
yields from low- and intermediate-mass stars and from Type Ia supernovae have
been included in our simulations, but with a fixed grid resolution. We compared
our results with the stellar abundances observed in the Milky Way for O, Na,
Mg, Si, Ca, Ti, and Mn. Our results suggest that the range of metallicity
considered is more important than the number of metallicities within that
range, which only affects our numerical predictions by about 0.1 dex. We found
that our predictions at [Fe/H] < -2 are very sensitive to the metallicity range
and the mass sampling used for the lowest metallicity included in the set of
yields. Variations between results can be as high as 0.8 dex, for any remnant
mass prescription. At higher [Fe/H], we found that the required number of
masses depends on the element of interest and on the remnant mass prescription.
With a monotonic remnant mass prescription where every model explodes as a
core-collapse supernova, the mass resolution induces variations of 0.2 dex on
average. But with a remnant mass prescription that includes islands of
non-explodability, the mass resolution can cause variations of about 0.2 to 0.7
dex depending on the choice of metallicity range. With such a prescription,
explosive or non-explosive models can be missed if not enough masses are
selected, resulting in over- or under-estimations of the mass ejected by
massive stars.


  In order to find out if magnetic impurities can mediate interactions between
quasiparticles in metals, we have measured the effect of a magnetic field B on
the energy distribution function f(E) of quasiparticles in two silver wires
driven out-of-equilibrium by a bias voltage U. In a sample showing sharp
distributions at B=0, no magnetic field effect is found, whereas in the other
sample, rounded distributions at low magnetic field get sharper as B is
increased, with a characteristic field proportional to U. Comparison is made
with recent calculations of the effect of magnetic-impurities-mediated
interactions taking into account Kondo physics.


  Let $k$ be a field of characteristic $p > 0$. For $G$ an elementary abelian
$p$-group, there exist collections of permutation module such that if $C^*$ is
any exact bounded complex whose terms are sums of copies of modules from the
collection, then $C^*$ is contractible. A consequence is that if $G$ is any
finite group whose Sylow $p$-subgroups are not cyclic or quaternion, and if
$C^*$ is a bounded exact complex such that each $C^i$ is direct sum of one
dimensional modules and projective modules, then $C^*$ is contractible.


  Third order tracking calculations show that a wavelength shifter on the basis
of a commercially available 12 T split-pair solenoid is compatible with the
ANKA storage ring


  We analyze the structure of noncommutative pure Chern-Simons theory
systematically in the axial gauge. We show that there is no IR/UV mixing in
this theory in this gauge. In fact, we show, using the usual BRST identities as
well as the identities following from vector supersymmetry, that this is a free
theory. As a result, the tree level Chern-Simons coefficient is not
renormalized. It also holds that the Chern-Simons coefficient is not modified
at finite temperature. As a byproduct of our analysis, we prove that the ghosts
completely decouple in the axial gauge in a noncommutative gauge theory.


  We study Chiral Based Electromagnetically Induced Transparency (CBEIT) of a
light pulse and its associated subluminal and superluminal behavior through a
cold and a hot medium of 4-level \textit{double-Lambda type} atomic system. The
dynamical behavior of this chiral based system is temperature dependent. The
magnetic field based chirality and dispersion is always opposite as compared
with the electric field ones. Contrastingly, the response of the chiral effect
along with the incoherence Doppler broadening mechanism enhances the
superluminal behavior as compared with its traditional degrading effect.
Nevertheless, the intensity of a coupled microwave field destroys the coherence
of the medium and degrade superluminality and subluminality of the sysmtem. The
undistorted retrieved pulse from a hot chiral medium delays by $896 ns$ than
from a cold chiral medium under same set of parameters. Nevertheless, it
advances by $-31n s$ in the cold chiral medium when a suitably different
spectroscopic parameters are selected. The corresponding group index of the
medium and the time delay/advance, are studied and analyzed explicitly [Note: A
revise version is under preparation


  Critical Reynolds numbers for the monotone exponential energy stability of
Couette and Poiseuille plane flows were obtained by Orr 1907 \cite{Orr1907} in
a famous paper, and by Joseph 1966 \cite{Joseph1966}, Joseph and Carmi 1969
\cite{JosephCarmi1969} and Busse 1972 \cite{Busse1972}. All these authors
obtained their results applying variational methods to compute the maximum of a
functional ratio derived from the Reynolds-Orr energy identity. Orr and Joseph
obtained different results, for instance in the Couette case Orr computed the
critical Reynolds value of 44.3 (on spanwise perturbations) and Joseph 20.65
(on streamwise perturbations).
  Recently in Falsaperla et al. 2022 \cite{FalsaperlaMulonePerrone2022}, the
authors conjectured that the search of the maximum should be restricted to a
subspace of the space of kinematically admissible perturbations. With this
conjecture, the critical nonlinear energy Reynolds number was found among
spanwise perturbations (a Squire theorem for nonlinear systems).
  With a direct proof and an appropriate and original decomposition of the
dissipation terms in the Reynolds-Orr identity we show the validity of this
conjecture in the space of three dimensional perturbations.


  The usual way to investigate the statistical properties of finitely generated
subgroups of free groups, and of finite presentations of groups, is based on
the so-called word-based distribution: subgroups are generated (finite
presentations are determined) by randomly chosen k-tuples of reduced words,
whose maximal length is allowed to tend to infinity. In this paper we adopt a
different, though equally natural point of view: we investigate the statistical
properties of the same objects, but with respect to the so-called graph-based
distribution, recently introduced by Bassino, Nicaud and Weil. Here, subgroups
(and finite presentations) are determined by randomly chosen Stallings graphs
whose number of vertices tends to infinity. Our results show that these two
distributions behave quite differently from each other, shedding a new light on
which properties of finitely generated subgroups can be considered frequent or
rare. For example, we show that malnormal subgroups of a free group are
negligible in the graph-based distribution, while they are exponentially
generic in the word-based distribution. Quite surprisingly, a random finite
presentation generically presents the trivial group in this new distribution,
while in the classical one it is known to generically present an infinite
hyperbolic group.


  This paper is concerned with the stochastic Hamilton-Jacobi-Bellman equation
with controlled leading coefficients, which is a type of fully nonlinear
backward stochastic partial differential equation (BSPDE for short). In order
to formulate the weak solution for such kind of BSPDEs, the classical potential
theory is generalized in the backward stochastic framework. The existence and
uniqueness of the weak solution is proved, and for the partially non-Markovian
case, we obtain the associated gradient estimate. As a byproduct, the existence
and uniqueness of solution for a class of degenerate reflected BSPDEs is
discussed as well.


  The following comparison rules for the discrete spectrum of the
position-dependent mass (PDM) Schroedinger equation are established. (i) If a
constant mass $m_0$ and a PDM $m(x)$ are ordered everywhere, that is either
$m_0\leq m(x)$ or $m_0\geq m(x)$, then the corresponding eigenvalues of the
constant-mass Hamiltonian and of the PDM Hamiltonian with the same potential
and the BenDaniel-Duke ambiguity parameters are ordered. (ii) The corresponding
eigenvalues of PDM Hamiltonians with the different sets of ambiguity parameters
are ordered if $\nabla^2 (1/m(x))$ has a definite sign. We prove these
statements by using the Hellmann-Feynman theorem and offer examples of their
application.


  We focus on the evidence of a past minor merger discovered in the halo of the
Andromeda galaxy (M31). Previous N-body studies have enjoyed moderate success
in producing the observed giant stellar stream (GSS) and stellar shells in
M31's halo. The observed distribution of stars in the halo of M31 shows an
asymmetric surface brightness profile across the GSS; however, the effect of
the morphology of the progenitor galaxy on the internal structure of the GSS
requires further investigation in theoretical studies. To investigate the
physical connection between the characteristic surface brightness in the GSS
and the morphology of the progenitor dwarf galaxy, we systematically vary the
thickness, rotation velocity and initial inclination of the disc dwarf galaxy
in N-body simulations. The formation of the observed structures appears to be
dominated by the progenitor's rotation. Besides reproducing the observed GSS
and two shells in detail, we predict additional structures for further
observations. We predict the detectability of the progenitor's stellar core in
the phase-space density distribution, azimuthal metallicity gradient of the
western shell-like structure and an additional extended shell in the
north-western direction that may constrain the properties of the progenitor
galaxy.


  Knowledge workers such as healthcare information professionals, legal
researchers, and librarians need to create and execute search strategies that
are comprehensive, transparent, and reproducible. The traditional solution is
to use proprietary query building tools provided by literature database
vendors. In the majority of cases, these query builders are designed using a
form-based paradigm that requires the user to enter keywords and ontology terms
on a line-by-line basis and then combine them using Boolean operators. However,
recent years have witnessed significant changes in human-computer interaction
technologies and users can now engage with online information systems using a
variety of novel data visualisation techniques. In this paper, we evaluate a
new approach to query building in which users express concepts as objects on a
visual canvas and compare this with traditional form-based query building in a
lab-based user study. The results demonstrate the potential of visual
interfaces to mitigate some of the shortcomings associated with form-based
interfaces and encourage more exploratory search behaviour. They also
demonstrate the value of having a temporary 'scratch' space in query
formulation. In addition, the findings highlight an ongoing need for
transparency and reproducibility in professional search and raise further
questions around how these properties may best be supported.


  In this paper we develop a statistical arbitrage trading strategy with two
key elements in hi-frequency trading: stop-loss and leverage. We consider, as
in Bertram (2009), a mean-reverting process for the security price with
proportional transaction costs; we show how to introduce stop-loss and leverage
in an optimal trading strategy.
  We focus on repeated strategies using a self-financing portfolio. For every
given stop-loss level we derive analytically the optimal investment strategy
consisting of optimal leverage and market entry/exit levels.
  First we show that the optimal strategy a' la Bertram depends on the
probabilities to reach entry/exit levels, on expected First-Passage-Times and
on expected First-Exit-Times from an interval. Then, when the underlying
log-price follows an Ornstein-Uhlenbeck process, we deduce analytical
expressions for expected First-Exit-Times and we derive the long-run return of
the strategy as an elementary function of the stop-loss.
  Following industry practice of pairs trading we consider an example of pair
in the energy futures' market, reporting in detail the analysis for a spread on
Heating-Oil and Gas-Oil futures in one year sample of half-an-hour market
prices.


  Using CLEO-c data, we confirm the observation of D0 ->omega eta by BESIII. In
the Dalitz Plot of D0 -> Kshort eta pi0, we find a background in the
Kshort(->pipi)pi0 projection with a m(pipipi0) equal to the omega(782) mass. In
a direct search for D0 -> (omega eta) we find a clear signal and measure BF(D0
->omega eta) = (1.78+/-0.19+/-0.14) x 10**(-3), in good agreement with BESIII.


  This report summarizes a short study of the performance of GPT-4 on the
ETHICS dataset. The ETHICS dataset consists of five sub-datasets covering
different fields of ethics: Justice, Deontology, Virtue Ethics, Utilitarianism,
and Commonsense Ethics. The moral judgments were curated so as to have a high
degree of agreement with the aim of representing shared human values rather
than moral dilemmas. GPT-4's performance is much better than that of previous
models and suggests that learning to work with common human values is not the
hard problem for AI ethics.


  One of Pranab K. Sen's major research areas is sequential nonparametrics and
semiparametrics and their applications to clinical trials, to which he has made
many important contributions. Herein we review a number of these contributions
and related developments. We also describe some recent work on nonparametric
and semiparametric inference and the associated computational methods in
time-sequential clinical trials with survival endpoints.


  One of the major goals in the theory of Toeplitz operators on the Bergman
space over the unit disk $\mathbb{D}$ in the complex place $\mathbb{C}$ is to
completely describe the commutant of a given Toeplitz operator, that is, the
set of all Toeplitz operators that commute with it. Here we shall study the
commutants of a certain class of quasihomogeneous Toeplitz operators.


  We compare two alternative representations of quantum mechanics: Polymer
Quantum Mechanics (PQM), which presents features similar to Loop Quantum
Gravity and Loop Quantum Cosmology, and the Generalized Uncertainty Principle
(GUP) representation, that through a modified Algebra yields an alternative
uncertainty principle similar to those derived in String Theories and Brane
Cosmology. These formalisms can be recast to apparently look similar, but while
the GUP yields an absolute minimal uncertainty on position, PQM implements some
kind of ultraviolet cut-off through a lattice and does not have a minimal
uncertainty. Then we implement them on the anisotropic Bianchi I model in
Misner-like variables on a semiclassical level: PQM always implies a removal of
the singularities, while the GUP fails to do so, highlighting once again how
the two representations are fundamentally incompatible.


  AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is
a research project funded by the Agenzia Spaziale Italiana and by the Istituto
Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per
la Comunit\`a Scientifica Nazionale Sole, Sistema Solare ed Esopianeti''
program. The topic addressed by this project was the development and use of
computational methods for the analysis of remote sensing space data associated
to solar flare emission. This paper overviews the main results obtained by the
project, with specific focus on solar flare forecasting, reconstruction of
morphologies of the flaring sources, and interpretation of acceleration
mechanisms triggered by solar flares.


  We present the stellar kinematics of 48 representative elliptical and
lenticular galaxies obtained with our custom-built integral-field spectrograph
SAURON operating on the William Herschel Telescope. The data were homogeneously
processed through a dedicated reduction and analysis pipeline. All resulting
SAURON datacubes were spatially binned to a constant minimum signal-to-noise.
We have measured the stellar kinematics with an optimized (penalized
pixel-fitting) routine which fits the spectra in pixel space, via the use of
optimal templates, and prevents the presence of emission lines to affect the
measurements. We have thus generated maps of the mean stellar velocity, the
velocity dispersion, and the Gauss-Hermite moments h3 and h4 of the
line-of-sight velocity distributions. The maps extend to approximately one
effective radius. Many objects display kinematic twists, kinematically
decoupled components, central stellar disks, and other peculiarities, the
nature of which will be discussed in future papers of this series.


  We report on the single crystal growth and transport properties of a
topological semimetal CaAgBi which crystallises in the hexagonal $ABC-$type
structure with the non-centrosymmetric space group $\mathit{P6_3mc}$ (No. 186).
The transverse magnetoresistance measurements with current in the basal plane
of the hexagonal crystal structure reveal a value of about 30 % for I //
[10-10] direction and about 50 % for I // [1-210] direction at 10 K in an
applied magnetic field of 14 T. The magnetoresistance shows a cusp-like
behavior in the low magnetic-field region, suggesting the presence of weak
antilocalization effect for temperatures less than 100 K. The Hall measurements
reveal that predominant charge carriers are $p$ type exhibiting a linear
behavior for fields up to 14 T and can be explained based on the single band
model. The magnetoconductance of CaAgBi is analysed based on the modified
Hikami-Larkin-Nagaoka (HLN) model. Our first-principles calculations within a
density-functional theory framework reveal that CaAgBi supports a topological
Dirac semimetal state with Dirac points located on the rotational axis slightly
above the Fermi level and are protected by $C_{6v}$ point-group symmetry. The
Fermi surface consists of both the electron and hole pockets. However, the size
of hole pockets is much larger than electron pockets suggesting the dominant
$p$ type carriers in accord with our experimental results.


  What is the real nature of pulsars? This is essentially a question of the
fundamental strong interaction between quarks at low-energy scale and hence of
the non-perturbative quantum chromo-dynamics, the solution of which would
certainly be meaningful for us to understand one of the seven millennium prize
problems (i.e., "Yang-Mills Theory") named by the Clay Mathematical Institute.
After a historical note, it is argued here that a pulsar is very similar to an
extremely big nucleus, but is a little bit different from the {\em gigantic
nucleus} speculated 80 years ago by L. Landau. The paper demonstrates the
similarity between pulsars and gigantic nuclei from both points of view: the
different manifestations of compact stars and the general behavior of the
strong interaction.


  In this paper we derive the maximum entropy characteristics of a particular
rank order distribution, namely the discrete generalized beta distribution,
which has recently been observed to be extremely useful in modelling many
several rank-size distributions from different context in Arts and Sciences, as
a two-parameter generalization of Zipf's law. Although it has been seen to
provide excellent fits for several real world empirical datasets, the
underlying theory responsible for the success of this particular rank order
distribution is not explored properly. Here we, for the first time, provide its
generating process which describes it as a natural maximum entropy distribution
under an appropriate bivariate utility constraint. Further, considering the
similarity of the proposed utility function with the usual logarithmic utility
function from economic literature, we have also explored its acceptability in
universal modeling of different types of socio-economic factors within a
country as well as across the countries. The values of distributional
parameters estimated through a rigorous statistical estimation method, along
with the $entropy$ values, are used to characterize the distributions of all
these socio-economic factors over the years.


  In this paper, we describe our method for ranking the skills required for a
given job title. Our analysis shows that important/relevant skills appear more
frequently in similar job titles. We train a Language-agnostic BERT Sentence
Encoder (LaBSE) model to predict the importance of the skills using weak
supervision. We show the model can learn the importance of skills and perform
well in other languages. Furthermore, we show how the Inverse Document
Frequency factor of skill boosts the specialised skills.


  Short-term forecasting of residential and commercial building energy
consumption is widely used in power systems and continues to grow in
importance. Data-driven short-term load forecasting (STLF), although promising,
has suffered from a lack of open, large-scale datasets with high building
diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for
STLF. To help address this, we present BuildingsBench, which consists of: 1)
Buildings-900K, a large-scale dataset of 900K simulated buildings representing
the U.S. building stock; and 2) an evaluation platform with over 1,900 real
residential and commercial buildings from 7 open datasets. BuildingsBench
benchmarks two under-explored tasks: zero-shot STLF, where a pretrained model
is evaluated on unseen buildings without fine-tuning, and transfer learning,
where a pretrained model is fine-tuned on a target building. The main finding
of our benchmark analysis is that synthetically pretrained models generalize
surprisingly well to real commercial buildings. An exploration of the effect of
increasing dataset size and diversity on zero-shot commercial building
performance reveals a power-law with diminishing returns. We also show that
fine-tuning pretrained models on real commercial and residential buildings
improves performance for a majority of target buildings. We hope that
BuildingsBench encourages and facilitates future research on generalizable
STLF. All datasets and code can be accessed from
https://github.com/NREL/BuildingsBench.


  A promising option for storing large-scale quantities of green gases (e.g.,
hydrogen) is in subsurface rock salt caverns. The mechanical performance of
salt caverns utilized for long-term subsurface energy storage plays a
significant role in long-term stability and serviceability. However, rock salt
undergoes non-linear creep deformation due to long-term loading caused by
subsurface storage. Salt caverns have complex geometries and the geological
domain surrounding salt caverns has a vast amount of material heterogeneity. To
safely store gases in caverns, a thorough analysis of the geological domain
becomes crucial. To date, few studies have attempted to analyze the influence
of geometrical and material heterogeneity on the state of stress in salt
caverns subjected to long-term loading. In this work, we present a rigorous and
systematic modeling study to quantify the impact of heterogeneity on the
deformation of salt caverns and quantify the state of stress around the
caverns. A 2D finite element simulator was developed to consistently account
for the non-linear creep deformation and also to model tertiary creep. The
computational scheme was benchmarked with the already existing experimental
study. The impact of cyclic loading on the cavern was studied considering
maximum and minimum pressure that depends on lithostatic pressure. The
influence of geometric heterogeneity such as irregularly-shaped caverns and
material heterogeneity, which involves different elastic and creep properties
of the different materials in the geological domain, is rigorously studied and
quantified. Moreover, multi-cavern simulations are conducted to investigate the
influence of a cavern on the adjacent caverns.


  The CLEO III Ring Imaging Cherenkov detector uses LiF radiators to generate
Cherenkov photons which are then detected by proportional wire chambers using a
mixture of CH$_4$ and TEA gases. The first two photon detector modules which
were constructed, were taken to Fermilab and tested in a beam dump that
provided high momentum muons. We report on results using both plane and
"sawtooth" shaped radiators. Specifically, we discuss the number of
photoelectrons observed per ring and the angular resolution. The particle
separation ability is shown to be sufficient for the physics of CLEO III.


  We present new spectroscopic data of the BL Lac RGB 2243+203, and its
surroundings, obtained with the OSIRIS Multi Object Spectrograph (MOS) mounted
in the Gran Telescopio Canarias (GTC). The spectra of neither the BL Lac nor
its host galaxy show any spectral feature, thus hindering direct determination
of its redshift. The spectroscopic redshift distribution of objects in the MOS
field of view shows four galaxies with redshift between 0.5258 and 0.5288. We
make use of a statistical analysis to test the possibility that the targeted BL
Lac may be a member of that group. By using the spectroscopic redshifts
obtained with our GTC observations, we found that this probability is between
86% and 93%.


  If the reported measurements of the time variation of the fine structure
constant from observations of distant QSOs are correct, combined with the Oklo
limit they would strongly constrain the class of the quintessence potential. If
these results prove valid, future satellite experiment (STEP) should measure
the induced violation of the weak equivalence principle. Future cosmological
observations of nearby $(z \siml 0.5)$ absorption systems would make it clear
whether the variation is significant or not.


  We propose a new method for calculating the Chern-Simons orbital
magnetoelectric coupling, conventionally parametrized in terms of a phase angle
$\theta$. According to previous theories, $\theta$ can be expressed as a 3D
Brillouin-zone integral of the Chern-Simons 3-form defined in terms of the
occupied Bloch functions. Such an expression is valid only if a smooth and
periodic gauge has been chosen in the entire Brillouin zone, and even then,
convergence with respect to the $\mathbf{k}$-space mesh density can be
difficult to obtain. In order to solve this problem, we propose to relax the
periodicity condition in one direction (say, the $k_z$ direction) so that a
gauge discontinuity is introduced on a 2D $\mathbf{k}$ plane normal to $k_z$.
The total $\theta$ response then has contributions from both the integral of
the Chern-Simons 3-form over the 3D bulk BZ and the gauge discontinuity
expressed as a 2D integral over the $\mathbf{k}$ plane. Sometimes the boundary
plane may be further divided into subregions by 1D "vortex loops" which make a
third kind of contribution to the total $\theta$, expressed as a combination of
Berry phases around the vortex loops. The total $\theta$ thus consists of three
terms which can be expressed as integrals over 3D, 2D and 1D manifolds. When
time-reversal symmetry is present and the gauge in the bulk BZ is chosen to
respect this symmetry, both the 3D and 2D integrals vanish; the entire
contribution then comes from the vortex-loop integral, which is either 0 or
$\pi$ corresponding to the $\mathbb{Z}_2$ classification of 3D time-reversal
invariant insulators. We demonstrate our method by applying it to the
Fu-Kane-Mele model with an applied staggered Zeeman field.


  Spectroscopic observations of 63 HII regions in six spiral galaxies (NGC 628,
NGC 783, NGC 2336, NGC 6217, NGC 7331, and NGC 7678) were carried out with the
6-meter telescope (BTA) of Russian Special Astrophysical Observatory with the
Spectral Camera attached to the focal reducer SCORPIO in the multislit mode
with a dispersion of 2.1A/pixel and a spectral resolution of 10A. These
observations were used to estimate the oxygen and nitrogen abundances and the
electron temperatures in HII regions through the recent variant of the strong
line method (NS calibration). The parameters of the radial distribution (the
extrapolated central intercept value and the gradient) of the oxygen and
nitrogen abundances in the disks of spiral galaxies NGC 628, NGC 783, NGC 2336,
NGC 7331, and NGC 7678 have been determined. The abundances in the NGC 783, NGC
2336, NGC 6217, and NGC 7678 are measured for the first time. Galaxies from our
sample follow well the general trend in the luminosity - central metallicity
diagram for spiral and irregular galaxies.


  It is predicted that quantum computers will dramatically outperform their
conventional counterparts. However, large-scale universal quantum computers are
yet to be built. Boson sampling is a rudimentary quantum algorithm tailored to
the platform of photons in linear optics, which has sparked interest as a rapid
way to demonstrate this quantum supremacy. Photon statistics are governed by
intractable matrix functions known as permanents, which suggests that sampling
from the distribution obtained by injecting photons into a linear-optical
network could be solved more quickly by a photonic experiment than by a
classical computer. The contrast between the apparently awesome challenge faced
by any classical sampling algorithm and the apparently near-term experimental
resources required for a large boson sampling experiment has raised
expectations that quantum supremacy by boson sampling is on the horizon. Here
we present classical boson sampling algorithms and theoretical analyses of
prospects for scaling boson sampling experiments, showing that near-term
quantum supremacy via boson sampling is unlikely. While the largest boson
sampling experiments reported so far are with 5 photons, our classical
algorithm, based on Metropolised independence sampling (MIS), allowed the boson
sampling problem to be solved for 30 photons with standard computing hardware.
We argue that the impact of experimental photon losses means that demonstrating
quantum supremacy by boson sampling would require a step change in technology.


  We show that a recent proposal for simulating planar hyperbolic lattices with
circuit quantum electrodynamics can be extended to accommodate also higher
dimensional lattices in Euclidean and non-Euclidean spaces if one allows for
circuits with more than three polygons at each vertex. The quantum dynamics of
these circuits, which can be constructed with present-day technology, are
governed by effective tight-binding Hamiltonians corresponding to
higher-dimensional Kagom\'{e}-like structures ($n$-dimensional zeolites), which
are well known to exhibit strong frustration and flat bands. We analyze the
relevant spectra of these systems and derive an exact expression for the
fraction of flat-band states. Our results expand considerably the range of
non-Euclidean geometry realizations with circuit quantum electrodynamics.


  This paper proposes a distributed hybrid millimeter wave (mmWave) scheme to
exploit the structure of a Densely Deployed Distributed (DDD)
small-cell-base-stations (SBSs) system for serving multiple users in a
geographic area. Both the SBSs and the users are equipped with full access
hybrid architectures with multi-antenna arrays and multiple radio frequency
chains. Unlike the conventional cellular networks where users receive data
streams from their nearest BSs, the users in our proposed scheme simultaneously
receive data streams from different SBSs. With appropriate design of analog
beamformers, co-channel multi-data-stream interference can be mitigated and the
extra spatial degrees of freedom induced by the geographic distributed SBSs are
exploited for data multiplexing. Analytical and simulation results show that
the proposed scheme can improve the system sum-rate considerably, especially
when the number of scattering components in millimeter wave channels is
limited.


  The AMR (Abstract Meaning Representation) formalism for representing meaning
of natural language sentences was not designed to deal with scope and
quantifiers. By extending AMR with indices for contexts and formulating
constraints on these contexts, a formalism is derived that makes correct
prediction for inferences involving negation and bound variables. The
attractive core predicate-argument structure of AMR is preserved. The resulting
framework is similar to that of Discourse Representation Theory.


  We present an approach to make molecular optimization more efficient. We
infer a hypergraph replacement grammar from the ChEMBL database, count the
frequencies of particular rules being used to expand particular nonterminals in
other rules, and use these as conditional priors for the policy model.
Simulating random molecules from the resulting probabilistic grammar, we show
that conditional priors result in a molecular distribution closer to the
training set than using equal rule probabilities or unconditional priors. We
then treat molecular optimization as a reinforcement learning problem, using a
novel modification of the policy gradient algorithm - batch-advantage: using
individual rewards minus the batch average reward to weight the log probability
loss. The reinforcement learning agent is tasked with building molecules using
this grammar, with the goal of maximizing benchmark scores available from the
literature. To do so, the agent has policies both to choose the next node in
the graph to expand and to select the next grammar rule to apply. The policies
are implemented using the Transformer architecture with the partially expanded
graph as the input at each step. We show that using the empirical priors as the
starting point for a policy eliminates the need for pre-training, and allows us
to reach optima faster. We achieve competitive performance on common benchmarks
from the literature, such as penalized logP and QED, with only hundreds of
training steps on a budget GPU instance.


  The essence of quantum error-correction is to use redundant Hilbert space to
identify and correct errors, and the channel fidelity of the quantum channel
does not affect which errors can be identified and corrected. Based on this, it
is found that quantum error-correction can be used to classify the independent
quantum channel into 5 types, and 4 of the 5 types can be purified. It is found
in quantum error-correction, the decoherence of quantum state may be related to
the degree of identification for the state under quantum noise, and the results
of this work confirmed that the degree of purity of quantum channel determines
its ability to retain the quantum property of the quantum state, not the
fidelity. In this work, the identification of the independent Pauli channels by
quantum error-correction is demonstrated.


  A semiconductor-based charge qubit, confined in double quantum dots, can be a
platform to implement quantum computing. However, it suffers severely from
charge noises. Here, we provide a theoretical framework to implement universal
geometric quantum gates in this system. We find that, while the detuning noise
can be suppressed by operating near its corresponding sweet spot, the tunneling
noise, on the other hand, is amplified and becomes the dominant source of error
for single-qubit gates, a fact previously insufficiently appreciated. We
demonstrate, through numerical simulation, that the geometric gates outperform
the dynamical gates across a wide range of tunneling noise levels, making them
particularly suitable to be implemented in conjunction with microwave driving.
To obtain a nontrivial two-qubit gate, we introduce a hybrid system with charge
qubits coupled by a superconducting resonator. When each charge qubit is in
resonance with the resonator, it is possible to construct an entangling
geometric gate with fidelity higher than that of the dynamical gate for
experimentally relevant noise levels. Therefore, our results suggest that
geometric quantum gates are powerful tools to achieve high-fidelity
manipulation for the charge qubit.


  We briefly review the main problems related to the computation of the
evolution of intermediate-mass stars: the treatment of turbulent convection and
the occurrence of blue loops during the core He-burning phase. It is shown
that, in order to obtain more accurate and reliable stellar models for this
class of stars, one has to consider all possible theoretical and observational
constraints. This includes observations of low-mass stars to constrain the
treatment of envelope convection, and the analysis of the pulsational behaviour
of Cepheid stars.


  This paper considers the single-server Private Linear Transformation (PLT)
problem with individual privacy guarantees. In this problem, there is a user
that wishes to obtain $L$ independent linear combinations of a $D$-subset of
messages belonging to a dataset of $K$ messages stored on a single server. The
goal is to minimize the download cost while keeping the identity of each
message required for the computation individually private. The individual
privacy requirement ensures that the identity of each individual message
required for the computation is kept private. This is in contrast to the
stricter notion of joint privacy that protects the entire set of identities of
all messages used for the computation, including the correlations between these
identities. The notion of individual privacy captures a broad set of practical
applications. For example, such notion is relevant when the dataset contains
information about individuals, each of them requires privacy guarantees for
their data access patterns. We focus on the setting in which the required
linear transformation is associated with a maximum distance separable (MDS)
matrix. In particular, we require that the matrix of coefficients pertaining to
the required linear combinations is the generator matrix of an MDS code. We
establish lower and upper bounds on the capacity of PLT with individual
privacy, where the capacity is defined as the supremum of all achievable
download rates. We show that our bounds are tight under certain conditions.


  Length generalization (LG) is a challenging problem in learning to reason. It
refers to the phenomenon that when trained on reasoning problems of smaller
lengths or sizes, the resulting model struggles with problems of larger sizes
or lengths. Although LG has been studied by many researchers, the challenge
remains. This paper proposes a theoretical study of LG for problems whose
reasoning processes can be modeled as DAGs (directed acyclic graphs). The paper
first identifies and proves the conditions under which LG can be achieved in
learning to reason. It then designs problem representations based on the theory
to learn to solve challenging reasoning problems like parity, addition, and
multiplication, using a Transformer to achieve perfect LG.


  Many schemes for implementing quantum information processing require that the
atomic states used have a non-zero magnetic moment, however such magnetically
sensitive states of an atom are vulnerable to decoherence due to fluctuating
magnetic fields. Dressing an atom with an external field is a powerful method
of reducing such decoherence [N. Timoney et al., Nature 476, 185], even if the
states being dressed are strongly coupled to the environment. We introduce an
experimentally simpler method of manipulating such a dressed-state qubit, which
allows the implementation of general rotations of the qubit, and demonstrate
this method using a trapped ytterbium ion.


  Object detection in remotely sensed satellite pictures is fundamental in many
fields such as biophysical, and environmental monitoring. While deep learning
algorithms are constantly evolving, they have been mostly implemented and
tested on popular ground-based taken photos. This paper critically evaluates
and compares a suite of advanced object detection algorithms customized for the
task of identifying aircraft within satellite imagery. Using the large
HRPlanesV2 dataset, together with a rigorous validation with the GDIT dataset,
this research encompasses an array of methodologies including YOLO versions 5
and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from
scratch. This exhaustive training and validation study reveal YOLOv5 as the
preeminent model for the specific case of identifying airplanes from remote
sensing data, showcasing high precision and adaptability across diverse imaging
conditions. This research highlight the nuanced performance landscapes of these
algorithms, with YOLOv5 emerging as a robust solution for aerial object
detection, underlining its importance through superior mean average precision,
Recall, and Intersection over Union scores. The findings described here
underscore the fundamental role of algorithm selection aligned with the
specific demands of satellite imagery analysis and extend a comprehensive
framework to evaluate model efficacy. The benchmark toolkit and codes,
available via https://github.com/toelt-llc/FlightScope_Bench, aims to further
exploration and innovation in the realm of remote sensing object detection,
paving the way for improved analytical methodologies in satellite imagery
applications.


  Near Field Communication (NFC) is a perfect example of ubiquitous computing
that is secured, short-ranged, low-powered contactless communication. High
demand is predicted for NFC, especially with wearables, and the variety of
applications may require that this technology be fabricated onto different
materials. In this research, we first designed flexible NFC antennas based on
mathematical models. A simulation of the NFC coil inductance was performed and
verified using the predictive models. Later, two flexible NFC antennas,
including 160x80 mm2 (rectangular) and 80x80 mm2 (square) were fabricated
through a chemical etching process and verified at 13.56 MHz frequency. Two
experiments 1) Read range detection and 2) User experience study on 22
participants validate longer read range and shorter connection time of our
fabricated flexible NFC tags.


  The recently discovered satellite dwarf galaxy Ursa Major III provides a
promising opportunity to explore the signatures resulting from dark matter (DM)
annihilation, due to its proximity and large J-factor. Owing to the absence of
an excess of $\gamma$-ray signatures originating from Ursa Major III,
observations of $\gamma$-rays, such as those from Fermi-LAT, can be utilized to
set constraints on the DM annihilation cross section. In this study, we
determine the DM density profile, and consider the relationship between DM
density and velocity dispersion at different locations within Ursa Major III
through Jeans analysis. We calculate the J-factor of Ursa Major III for s-wave
annihilation, along with the effective J-factors for p-wave and Sommerfeld
enhanced annihilation scenarios. Utilizing these derived J-factors, we set
stringent constraints on DM annihilation cross sections in three scenarios.
Given the substantial impact of member star identification on the J-factor of
Ursa Major III, we further calculate J-factors with the condition of excluding
the largest velocity outlier. Our analysis reveals a notable reduction in the
median value and an increase in the deviation of J-factors, thereby leading to
considerably weaker constraints.


  The dynamic characteristics of multiphase industrial processes present
significant challenges in the field of industrial big data modeling.
Traditional soft sensing models frequently neglect the process dynamics and
have difficulty in capturing transient phenomena like phase transitions. To
address this issue, this article introduces a causality-driven sequence
segmentation (CDSS) model. This model first identifies the local dynamic
properties of the causal relationships between variables, which are also
referred to as causal mechanisms. It then segments the sequence into different
phases based on the sudden shifts in causal mechanisms that occur during phase
transitions. Additionally, a novel metric, similarity distance, is designed to
evaluate the temporal consistency of causal mechanisms, which includes both
causal similarity distance and stable similarity distance. The discovered
causal relationships in each phase are represented as a temporal causal graph
(TCG). Furthermore, a soft sensing model called temporal-causal graph
convolutional network (TC-GCN) is trained for each phase, by using the
time-extended data and the adjacency matrix of TCG. The numerical examples are
utilized to validate the proposed CDSS model, and the segmentation results
demonstrate that CDSS has excellent performance on segmenting both stable and
unstable multiphase series. Especially, it has higher accuracy in separating
non-stationary time series compared to other methods. The effectiveness of the
proposed CDSS model and the TC-GCN model is also verified through a penicillin
fermentation process. Experimental results indicate that the breakpoints
discovered by CDSS align well with the reaction mechanisms and TC-GCN
significantly has excellent predictive accuracy.


  In this paper, an equality between the Hochs-Mathai type index and the
Atiyah-Patodi-Singer type index is established when the manifold and the group
action are both non-compact, which generalizes a result of Ma and Zhang for
compact group actions. As a technical preparation, a problem concerning the
Fredholm property of the global elliptic boundary value problems of the
Atiyah-Patodi-Singer type on a non-compact manifold is studied.


  Radiotherapists require accurate registration of MR/CT images to effectively
use information from both modalities. In a typical registration pipeline, rigid
or affine transformations are applied to roughly align the fixed and moving
images before proceeding with the deformation step. While recent learning-based
methods have shown promising results in the rigid/affine step, these methods
often require images with similar field-of-view (FOV) for successful alignment.
As a result, aligning images with different FOVs remains a challenging task.
Self-supervised landmark detection methods like self-supervised Anatomical
eMbedding (SAM) have emerged as a useful tool for mapping and cropping images
to similar FOVs. However, these methods are currently limited to intra-modality
use only. To address this limitation and enable cross-modality matching, we
propose a new approach called Cross-SAM. Our approach utilizes a novel
iterative process that alternates between embedding learning and CT-MRI
registration. We start by applying aggressive contrast augmentation on both CT
and MRI images to train a SAM model. We then use this SAM to identify
corresponding regions on paired images using robust grid-points matching,
followed by a point-set based affine/rigid registration, and a deformable
fine-tuning step to produce registered paired images. We use these registered
pairs to enhance the matching ability of SAM, which is then processed
iteratively. We use the final model for cross-modality matching tasks. We
evaluated our approach on two CT-MRI affine registration datasets and found
that Cross-SAM achieved robust affine registration on both datasets,
significantly outperforming other methods and achieving state-of-the-art
performance.


  Geosteering, a key component of drilling operations, traditionally involves
manual interpretation of various data sources such as well-log data. This
introduces subjective biases and inconsistent procedures. Academic attempts to
solve geosteering decision optimization with greedy optimization and
Approximate Dynamic Programming (ADP) showed promise but lacked adaptivity to
realistic diverse scenarios. Reinforcement learning (RL) offers a solution to
these challenges, facilitating optimal decision-making through reward-based
iterative learning. State estimation methods, e.g., particle filter (PF),
provide a complementary strategy for geosteering decision-making based on
online information. We integrate an RL-based geosteering with PF to address
realistic geosteering scenarios. Our framework deploys PF to process real-time
well-log data to estimate the location of the well relative to the
stratigraphic layers, which then informs the RL-based decision-making process.
We compare our method's performance with that of using solely either RL or PF.
Our findings indicate a synergy between RL and PF in yielding optimized
geosteering decisions.


  In this paper, we propose a new adaptive cross algorithm for computing a low
tubal rank approximation of third-order tensors, with less memory and lower
computational complexity than the truncated tensor SVD (t-SVD). This makes it
applicable for decomposing large-scale tensors. We conduct numerical
experiments on synthetic and real-world datasets to confirm the efficiency and
feasibility of the proposed algorithm. The simulation results show more than
one order of magnitude acceleration in the computation of low tubal rank
(t-SVD) for large-scale tensors. An application to pedestrian attribute
recognition is also presented.


  We propose a novel way to integrate control techniques with reinforcement
learning (RL) for stability, robustness, and generalization: leveraging
contraction theory to realize modularity in neural control, which ensures that
combining stable subsystems can automatically preserve the stability. We
realize such modularity via signal composition and dynamic decomposition.
Signal composition creates the latent space, within which RL applies to
maximizing rewards. Dynamic decomposition is realized by coordinate
transformation that creates an auxiliary space, within which the latent signals
are coupled in the way that their combination can preserve stability provided
each signal, that is, each subsystem, has stable self-feedbacks. Leveraging
modularity, the nonlinear stability problem is deconstructed into algebraically
solvable ones, the stability of the subsystems in the auxiliary space, yielding
linear constraints on the input gradients of control networks that can be as
simple as switching the signs of network weights. This minimally invasive
method for stability allows arguably easy integration into the modular neural
architectures in machine learning, like hierarchical RL, and improves their
performance. We demonstrate in simulation the necessity and the effectiveness
of our method: the necessity for robustness and generalization, and the
effectiveness in improving hierarchical RL for manipulation learning.


  In this article, we would like to review the main technologies that have been
proposed so far to fight the spread of the virus. Also, we would like to give
an overview of the policy recommendations that some European organisations have
put forward in these regards. Finally, we conclude with some considerations we
would like to present to public attention and discussion.


  The bound state spectra of the doublet states in three-electron atomic
systems are investigated. By using different variational expansions we
determine various bound state properties in these systems. Such properties
include the electron-nucleus and electron-electron delta-functions and cusp
values. The general structure of the bound state spectra in several
three-electron atomic systems (Li, Be+, C3+ and F6+) is investigated with the
use of the Hylleraas-Configuration Interaction and the Configuration
Interaction wave functions. The advantage of our Configuration Interaction
based procedure is that it provides high numerical accuracy for all
rotationally excited states, including the bound states with L >= 7.


  We attack the problem of controlling teams of autonomous units during
skirmishes in real-time strategy games. Earlier work had shown promise in
evolving control algorithm parameters that lead to high performance team
behaviors similar to those favored by good human players in real-time strategy
games like Starcraft. This algorithm specifically encoded parameterized kiting
and fleeing behaviors and the genetic algorithm evolved these parameter values.
In this paper we investigate using influence maps and potential fields alone to
compactly represent and control real-time team behavior for entities that can
maneuver in three dimensions. A two-objective fitness function that maximizes
damage done and minimizes damage taken guides our multi-objective evolutionary
algorithm. Preliminary results indicate that evolving friend and enemy unit
potential field parameters for distance, weapon characteristics, and entity
health suffice to produce complex, high performing, three-dimensional, team
tactics.


  In a generic 'universal' theory of electroweak symmetry breaking, non
fine-tuned heavy new physics affects the low-energy data through four
parameters, which include and properly extend the generally insufficient S and
T. Only by adding the LEP2 data to the global electroweak fit, can all these
four form factors be determined and deviations from the SM be strongly
constrained. Several of the recently proposed models (little Higgs, gauge
bosons in extra dimensions or Higgsless models in 5D) are recognized to be
'universal' in a straightforward way after a proper definition of the effective
vector boson fields. Among various applications, we show that proposed
Higgsless models in 5D, when calculable, do not provide a viable description of
electroweak symmetry breaking in their full range of parameters.


  We propose a real-time decision framework for multimodal freight dispatch
through a system of hierarchical hubs, using a probabilistic model for transit
times. Instead of assigning a fixed time to each transit, we advocate using
historical records to identify characteristics of the probability density
function for each transit time. We formulate a nonlinear optimization problem
that defines dispatch decisions that minimize expected cost, using this
probabilistic information. Finally, we propose an effective homotopy algorithm
that (empirically) outperforms standard optimization algorithms on this problem
by taking advantage of its structure, and we demonstrate its effectiveness on
numerical examples.


  Multimodal knowledge graph completion (MKGC) aims to predict missing entities
in MKGs. Previous works usually share relation representation across
modalities. This results in mutual interference between modalities during
training, since for a pair of entities, the relation from one modality probably
contradicts that from another modality. Furthermore, making a unified
prediction based on the shared relation representation treats the input in
different modalities equally, while their importance to the MKGC task should be
different. In this paper, we propose MoSE, a Modality Split representation
learning and Ensemble inference framework for MKGC. Specifically, in the
training phase, we learn modality-split relation embeddings for each modality
instead of a single modality-shared one, which alleviates the modality
interference. Based on these embeddings, in the inference phase, we first make
modality-split predictions and then exploit various ensemble methods to combine
the predictions with different weights, which models the modality importance
dynamically. Experimental results on three KG datasets show that MoSE
outperforms state-of-the-art MKGC methods. Codes are available at
https://github.com/OreOZhao/MoSE4MKGC.


  This version removed by arXiv administrators because the submitter did not
have the right to agree to the license at the time of submission


  We study star formation histories (SFHs) of $\simeq500$ dwarf galaxies
(stellar mass $M_\ast = 10^5 - 10^9\,M_\odot$) from FIRE-2 cosmological zoom-in
simulations. We compare dwarfs around individual Milky Way (MW)-mass galaxies,
dwarfs in Local Group (LG)-like environments, and true field (i.e. isolated)
dwarf galaxies. We reproduce observed trends wherein higher-mass dwarfs quench
later (if at all), regardless of environment. We also identify differences
between the environments, both in terms of "satellite vs. central" and "LG vs.
individual MWvs. isolated dwarf central." Around the individual MW-mass hosts,
we recover the result expected from environmental quenching: central galaxies
in the "near field" have more extended SFHs than their satellite counterparts,
with the former more closely resemble isolated ("true field") dwarfs (though
near-field centrals are still somewhat earlier forming). However, this
difference is muted in the LG-like environments, where both near-field centrals
and satellites have similar SFHs, which resemble satellites of single MW-mass
hosts. This distinction is strongest for $M_\ast = 10^6 - 10^7\,M_\odot$ but
exists at other masses. Our results suggest that the paired halo nature of the
LG may regulate star formation in dwarf galaxies even beyond the virial radii
of the MW and Andromeda. Caution is needed when comparing zoom-in simulations
targeting isolated dwarf galaxies against observed dwarf galaxies in the LG.


  We examine the problem of adversarial reinforcement learning for multi-agent
domains including a rule-based agent. Rule-based algorithms are required in
safety-critical applications for them to work properly in a wide range of
situations. Hence, every effort is made to find failure scenarios during the
development phase. However, as the software becomes complicated, finding
failure cases becomes difficult. Especially in multi-agent domains, such as
autonomous driving environments, it is much harder to find useful failure
scenarios that help us improve the algorithm. We propose a method for
efficiently finding failure scenarios; this method trains the adversarial
agents using multi-agent reinforcement learning such that the tested rule-based
agent fails. We demonstrate the effectiveness of our proposed method using a
simple environment and autonomous driving simulator.


  What characterizes influential users in online health communities (OHCs)? We
hypothesize that (1) the emotional support received by OHC members can be
assessed from their sentiment ex-pressed in online interactions, and (2) such
assessments can help to identify influential OHC members. Through text mining
and sentiment analysis of users' online interactions, we propose a novel metric
that directly measures a user's ability to affect the sentiment of others.
Using dataset from an OHC, we demonstrate that this metric is highly effective
in identifying influential users. In addition, combining the metric with other
traditional measures further improves the identification of influential users.
This study can facilitate online community management and advance our
understanding of social influence in OHCs.


  Pretraining has been widely explored to augment the adaptability of graph
learning models to transfer knowledge from large datasets to a downstream task,
such as link prediction or classification. However, the gap between training
objectives and the discrepancy between data distributions in pretraining and
downstream tasks hinders the transfer of the pretrained knowledge. Inspired by
instruction-based prompts widely used in pretrained language models, we
introduce instructions into graph pretraining. In this paper, we propose a
novel pretraining framework named Instruction-based Hypergraph Pretraining. To
overcome the discrepancy between pretraining and downstream tasks, text-based
instructions are applied to provide explicit guidance on specific tasks for
representation learning. Compared to learnable prompts, whose effectiveness
depends on the quality and the diversity of training data, text-based
instructions intrinsically encapsulate task information and support the model
to generalize beyond the structure seen during pretraining. To capture
high-order relations with task information in a context-aware manner, a novel
prompting hypergraph convolution layer is devised to integrate instructions
into information propagation in hypergraphs. Extensive experiments conducted on
three public datasets verify the superiority of IHP in various scenarios.


  In this document, we deal with the stabilization problem of slow-fast systems
(or singularly perturbed Ordinary Differential Equations) at a non-hyperbolic
point. The class of systems studied here have the following properties: 1) they
have one fast variable and an arbitrary number of slow variables, 2) they have
a non-hyperbolic singularity of the fold type at the origin. The presence of
the aforementioned singularity complicates the analysis and the controller
design of such systems. In particular, the classical theory of singular
perturbations cannot be used. We show a novel design process based on geometric
desingularization, which allows the stabilization of a fold point of singularly
perturbed control systems. Our results are exemplified on an electric circuit.


  We present iterative Monte Carlo algorithm for which the temperature variable
is attracted by a critical point. The algorithm combines techniques of single
histogram reweighting and linear filtering. The 2d Ising model of ferromagnet
is studied numerically as an illustration. In that case, the iterations
uncovered stationary regime with invariant probability distribution function of
temperature which is peaked nearly the pseudocritical temperature of specific
heat. The sequence of generated temperatures is analyzed in terms of stochastic
autoregressive model. The error of histogram reweighting can be better
understood within the suggested model. The presented model yields a simple
relation, connecting variance of pseudocritical temperature and parameter of
linear filtering.


  We develop a dynamic mean-field theory for polar active particles that
interact through a self-generated field, in particular one generated through
emitting a chemical signal. While being a form of chemotactic response, it is
different from conventional chemotaxis in that particles discontinuously change
their motility when the local concentration surpasses a threshold. The
resulting coupled equations for density and polarization are linear and can be
solved analytically for simple geometries, yielding inhomogeneous density
profiles. Specifically, here we consider a planar and circular interface. Our
theory thus explains the observed coexistence of dense aggregates with an
active gas. There are, however, differences to the more conventional picture of
liquid-gas coexistence based on a free energy, most notably the absence of a
critical point. We corroborate our analytical predictions by numerical
simulations of active particles under confinement and interacting through
volume exclusion. Excellent quantitative agreement is reached through an
effective translational diffusion coefficient. We finally show that an
additional response to the chemical gradient direction is sufficient to induce
vortex clusters. Our results pave the way to engineer motility responses in
order to achieve aggregation and collective behavior even at unfavorable
conditions.


  In this paper, we propose a novel approach to tackle the multiple instance
regression (MIR) problem. This problem arises when the data is a collection of
bags, where each bag is made of multiple instances corresponding to the same
unique real-valued label. Our goal is to train a regression model which maps
the instances of an unseen bag to its unique label. This MIR setting is common
to remote sensing applications where there is high variability in the
measurements and low geographical variability in the quantity being estimated.
Our approach, in contrast to most competing methods, does not make the
assumption that there exists a prime instance responsible for the label in each
bag. Instead, we treat each bag as a set (i.e, an unordered sequence) of
instances and learn to map each bag to its unique label by using all the
instances in each bag. This is done by implementing an order-invariant
operation characterized by a particular type of attention mechanism. This
method is very flexible as it does not require domain knowledge nor does it
make any assumptions about the distribution of the instances within each bag.
We test our algorithm on five real world datasets and outperform previous
state-of-the-art on three of the datasets. In addition, we augment our feature
space by adding the moments of each feature for each bag, as extra features,
and show that while the first moments lead to higher accuracy, there is a
diminishing return.


  Passive quantum key distribution (QKD) has been proposed for discrete
variable protocols to eliminate side channels in the source. Unfortunately, the
key rate of passive DV-QKD protocols suffers from sifting loss and additional
quantum errors. In this work, we propose the general framework of passive
continuous variable quantum key distribution. Rather surprisingly, we find that
the passive source is a perfect candidate for the discrete-modulated continuous
variable quantum key distribution (DMCV QKD) protocol. With the phase space
remapping scheme, we show that passive DMCV QKD offers the same key rate as its
active counterpart. Considering the important advantage of removing side
channels that have plagued the active ones, passive DMCV QKD is a promising
alternative. In addition, our protocol makes the system much simpler by
allowing modulator-free quantum key distribution. Finally, we experimentally
characterize the passive DMCV QKD source, thus showing its practicality.


  This is a double exposure of the probabilistic construction of
Kahler-Einstein metrics on a complex projective algebraic variety X - where the
Kahler-Einstein metric emerges from a canonical random point process on X - and
the variational approach to the Yau-Tian-Donaldson conjecture, highlighting
their connections. The final section is a report on joint work in progress with
S\'ebastien Boucksom and Mattias Jonsson on how the non-Archimedean geometry of
X (with respect to the trivial absolute value) also emerges from the
probabilistic framework.


  We revise critically existing approaches to evaluation of thermodynamic
potentials within the Green's function calculations at finite electronic
temperatures. We focus on the entropy and show that usual technical problems
related to the multivalued nature of the complex logarithm can be overcome.
This results in a simple expression for the electronic entropy, which does not
require any contour integration in the complex energy plane. Properties of the
developed formalism are discussed and its illustrating applications to selected
model systems and to bcc iron with disordered local magnetic moments are
presented as well.


  This paper presents an observer-based event-triggered boundary control
strategy for a class of reaction-diffusion PDEs with Robin actuation. The
observer only requires boundary measurements. The control approach consists of
a backstepping output feedback boundary controller, derived using estimated
states, and a dynamic triggering condition, which determines the time instants
at which the control input needs to be updated. It is shown that under the
proposed observer-based event-triggered boundary control approach, there is a
minimal dwell-time between two triggering instants independent of initial
conditions. Furthermore, the well-posedness and the global exponential
convergence of the closed-loop system to the equilibrium point are established.
A simulation example is provided to validate the theoretical developments.


  We introduce the concept of a deterministic walk in a deterministic
environment on a countable state space (DWDE). For the deterministic walk in a
fixed environment we establish properties analogous to those found in Markov
chain theory, but for systems that do not in general have the Markov property.
In particular, we establish hypotheses ensuring that a DWDE on $\Z$ is either
recurrent or transient. An immediate consequence of this result is that a
symmetric DWDE on $\Z$ is recurrent. Moreover, in the transient case, we show
that the probability that the DWDE diverges to $+ \infty$ is either 0 or 1. In
certain cases we compute the direction of divergence in the transient case.


  We discuss in this paper the phase diagram of a diblock polyampholyte
solution in the limit of high ionic strength as a function of concentration and
charge asymmetry. This system is shown to be very similar to solutions of
so-called charged-neutral diblock copolymers: at zero charge asymmetry the
solution phase separates into a polyelectrolyte complex and almost pure
solvent. Above a charge asymmetry threshold, the copolymers are soluble as
finite size aggregates. Scaling laws of the aggregates radius as a function of
pH of the solution are in qualitative agreement with experiments.


  We present an efficient numerical code and conduct, for the first time, a
null and model-independent CMB test of statistical isotropy using Multipole
Vectors (MVs) at all scales. Because MVs are insensitive to the angular power
spectrum $C_\ell$, our results are independent from the assumed cosmological
model. We avoid a posteriori choices and use pre-defined ranges of scales
$\ell\in[2,30]$, $\ell\in[2,600]$ and $\ell\in[2,1500]$ in our analyses. We
find that all four masked Planck maps, from both 2015 and 2018 releases, are in
agreement with statistical isotropy for $\ell\in[2,30]$, $\ell\in[2,600]$. For
$\ell\in[2,1500]$ we detect anisotropies but this is indicative of simply the
anisotropy in the noise: there is no anisotropy for $\ell < 1300$ and an
increasing level of anisotropy at higher multipoles. Our findings of no
large-scale anisotropies seem to be a consequence of avoiding \emph{a
posteriori} statistics. We also find that the degree of anisotropy in the full
sky (i.e. unmasked) maps vary enormously (between less than 5 and over 1000
standard deviations) among the different mapmaking procedures and data
releases.


  We have detected gamma-ray emission at the 6sigma level at energies greater
than 500GeV from the supernova remnant RX J0852.0-4622 (G266.2-1.2) using the
CANGAROO-II Imaging Atmospheric Cherenkov Telescope (IACT). The flux was 0.12
times of that of Crab at 1TeV. The signal centroid is consistent with the peak
of the X-ray emission in the north-west rim of the remnant.


  The probability distribution of density in isothermal, supersonic, turbulent
gas is approximately lognormal. This behaviour can be traced back to the shock
waves travelling through the medium, which randomly adjust the density by a
random factor of the local sonic Mach number squared. Provided a certain parcel
of gas experiences a large number of shocks, due to the central limit theorem,
the resulting distribution for density is lognormal. We explore a model in
which parcels of gas undergo finite number of shocks before relaxing to the
ambient density, causing the distribution for density to deviate from a
lognormal. We confront this model with numerical simulations with various
r.m.s. Mach numbers ranging from subsonic as low as 0.1 to supersonic at 25. We
find that the fits to the finite formula are an order of magnitude better than
a lognormal. The model naturally extends even to subsonic flows, where no
shocks exist.


  We establish uniform-in-bandwidth consistency for kernel-type estimators of
the differential entropy. We consider two kernel-type estimators of Shannon's
entropy. As a consequence, an asymptotic 100% confidence interval of entropy is
provided.


  Recent study suggests that there are natural connections between quantum
information theory and the Yang--Baxter equation. In this paper, in terms of
the generalized almost-complex structure and with the help of its algebra, we
define the generalized Bell matrix to yield all the GHZ states from the product
base, prove it to form a unitary braid representation and present a new type of
solution of the quantum Yang--Baxter equation. We also study
Yang-Baxterization, Hamiltonian, projectors, diagonalization, noncommutative
geometry, quantum algebra and FRT dual algebra associated with this generalized
Bell matrix.


  We introduce the software toolbox HAZniCS for solving interface-coupled
multiphysics problems. HAZniCS is a suite of modules that combines the
well-known FEniCS framework for finite element discretization with solver and
graph library HAZmath. The focus of the paper is on the design and
implementation of a pool of robust and efficient solver algorithms which tackle
issues related to the complex interfacial coupling of the physical problems
often encountered in applications in brain biomechanics. The robustness and
efficiency of the numerical algorithms and methods is shown in several
numerical examples, namely the Darcy-Stokes equations that model flow of
cerebrospinal fluid in the human brain and the mixed-dimensional model of
electrodiffusion in the brain tissue.


  An exceptional surface wave can propagate in an isolated direction, when
guided by the planar interface of two homogeneous dielectric partnering mediums
of which at least one is anisotropic, provided that the constitutive parameters
of the partnering mediums satisfy certain constraints. Exceptional surface
waves are distinguished from unexceptional surface waves by their localization
characteristics: the fields of an exceptional surface wave in the anisotropic
partnering medium decay as a combined linear-exponential function of distance
from the interface, whereas the decay is purely exponential for an
unexceptional surface wave. If both partnering mediums are anisotropic then a
doubly exceptional surface wave can exist for an isolated propagation
direction. The decay of this wave in both partnering mediums is governed by a
combined linear-exponential function of distance from the interface.


  Accumulation point of period-tripling bifurcations for complexified Henon map
is found. Universal scaling properties of parameter space and Fourier spectrum
intrinsic to this critical point is demonstrated.


  Recent progress in the field of meson production in hadron collisions is
presented. Special emphasis is put new developments in the application of
effective field theory methods to pion production, but also theoretical
concepts for two-pion, eta, and vector-meson production will be presented.


  Let ${\mbox{$\mbox{\boldmath $f$}$}}$ be a square-integrable, zero-mean,
random vector with observable realizations in a Hilbert space $H$, and let
${\mbox{$\mbox{\boldmath $g$}$}}$ be an associated square-integrable,
zero-mean, random vector with realizations, which are not observable, in a
Hilbert space $K$. We seek an optimal filter in the form of a closed linear
operator $X$ acting on the observable realizations of a proximate vector
${\mbox{$\mbox{\boldmath $f$}$}}_{\epsilon} \approx {\mbox{$\mbox{\boldmath
$f$}$}}$ that provides the best estimate $\widehat{{\mbox{$\mbox{\boldmath
$g$}$}}}_{\epsilon} = X {\mbox{$\mbox{\boldmath $f$}$}}_{\epsilon}$ of the
vector ${\mbox{$\mbox{\boldmath $f$}$}}$. We assume the required covariance
operators are known. The results are illustrated with a typical example.


  Let $G$ be a graph, and $g,f:V(G)\rightarrow Z^{+}$ with $g(x)\leq f(x)$ for
each $x\in V(G)$. We say that $G$ admits all fractional $(g,f)$-factors if $G$
contains a fractional $r$-factor for every $r:V(G)\rightarrow Z^{+}$ with
$g(x)\leq r(x)\leq f(x)$ for any $x\in V(G)$. Let $H$ be a subgraph of $G$. We
say that $G$ has all fractional $(g,f)$-factors excluding $H$ if for every
$r:V(G)\rightarrow Z^{+}$ with $g(x)\leq r(x)\leq f(x)$ for all $x\in V(G)$,
$G$ has a fractional $r$-factor $F_h$ such that $E(H)\cap E(F_h)=\emptyset$,
where $h:E(G)\rightarrow [0,1]$ is a function. In this paper, we show a
characterization for the existence of all fractional $(g,f)$-factors excluding
$H$ and obtain two sufficient conditions for a graph to have all fractional
$(g,f)$-factors excluding $H$.


  We examine some consequences of the QCD factorization approach to
non-leptonic B decays into $\pi K$ and $\pi \pi$ final states, including a set
of enhanced power corrections. Among the robust predictions of the approach we
find small strong-interaction phases (with one notable exception) and a pattern
of CP-averaged branching fractions, which in some cases differ significantly
from the current central values reported by the CLEO Collaboration.


  We demonstrate that active carpets of bacteria or self-propelled colloids
generate coherent flows towards the substrate, and propose that these currents
provide efficient pathways to replenish nutrients that feed back into activity.
A full theory is developed in terms of gradients in the active matter density
and velocity, and applied to bacterial turbulence, topological defects and
clustering. Currents with complex spatiotemporal patterns are obtained, which
are tuneable through confinement. Our findings show that diversity in carpet
architecture is essential to maintain biofunctionality.


  We prove a conjecture of Broue about the Jordan decomposition of blocks of
finite reductive groups. We show that a block of a finite connected reductive
group, in non-describing characteristic, is Morita-equivalent to a
quasi-isolated block of a Levi subgroup. This involves showing that some local
system over a Deligne-Lusztig variety has its mod l cohomology concentrated in
one degree. We reduce this question to a question about tamely ramified local
systems by proving that the category of perfect complexes for the group is
generated by the images of the Deligne-Lusztig functors. Then, we describe the
ramification at infinity of local systems associated to characters of tori.


  The minimization of a data-fidelity term and an additive regularization
functional gives rise to a powerful framework for supervised learning. In this
paper, we present a unifying regularization functional that depends on an
operator and on a generic Radon-domain norm. We establish the existence of a
minimizer and give the parametric form of the solution(s) under very mild
assumptions. When the norm is Hilbertian, the proposed formulation yields a
solution that involves radial-basis functions and is compatible with the
classical methods of machine learning. By contrast, for the total-variation
norm, the solution takes the form of a two-layer neural network with an
activation function that is determined by the regularization operator. In
particular, we retrieve the popular ReLU networks by letting the operator be
the Laplacian. We also characterize the solution for the intermediate
regularization norms $\|\cdot\|=\|\cdot\|_{L_p}$ with $p\in(1,2]$. Our
framework offers guarantees of universal approximation for a broad family of
regularization operators or, equivalently, for a wide variety of shallow neural
networks, including the cases (such as ReLU) where the activation function is
increasing polynomially. It also explains the favorable role of bias and skip
connections in neural architectures.


  We investigate traveling solitons of a one-dimensional spin-orbit coupled
Fermi superfluid in both topologically trivial and non-trivial regimes by
solving the static and time-dependent Bogoliubov-de Gennes equations. We find a
critical velocity $v_{h}$ for traveling solitons that is much smaller than the
value predicted using the Landau criterion due to the presence of spin-orbit
coupling, which strongly upshifts the energy level of the soliton-induced
Andreev bound states towards the quasi-particle scattering continuum. Above
$v_{h}$, our time-dependent simulations in harmonic traps indicate that
traveling solitons decay by radiating sound waves. In the topological phase, we
predict the existence of peculiar Majorana solitons, which host two Majorana
fermions and feature a phase jump of $\pi$ across the soliton, irrespective of
the velocity of travel. These unusual properties of Majorana solitons may open
an alternative way to manipulate Majorana fermions for fault-tolerant
topological quantum computations.


  This work presents the foundations of Singular Semi-Riemannian Geometry and
Singular General Relativity, based on the author's research. An extension of
differential geometry and of Einstein's equation to singularities is reported.
Singularities of the form studied here allow a smooth extension of the Einstein
field equations, including matter. This applies to the Big-Bang singularity of
the FLRW solution. It applies to stationary black holes, in appropriate
coordinates (since the standard coordinates are singular at singularity, hiding
the smoothness of the metric). In these coordinates, charged black holes have
the electromagnetic potential regular everywhere. Implications on Penrose's
Weyl curvature hypothesis are presented. In addition, these singularities
exhibit a (geo)metric dimensional reduction, which might act as a regulator for
the quantum fields, including for quantum gravity, in the UV regime. This opens
the perspective of perturbative renormalizability of quantum gravity without
modifying General Relativity.


  For lack of general algorithmic methods that apply to wide classes of logics,
establishing a complexity bound for a given modal logic is often a laborious
task. The present work is a step towards a general theory of the complexity of
modal logics. Our main result is that all rank-1 logics enjoy a shallow model
property and thus are, under mild assumptions on the format of their
axiomatisation, in PSPACE. This leads to a unified derivation of tight
PSPACE-bounds for a number of logics including K, KD, coalition logic, graded
modal logic, majority logic, and probabilistic modal logic. Our generic
algorithm moreover finds tableau proofs that witness pleasant proof-theoretic
properties including a weak subformula property. This generality is made
possible by a coalgebraic semantics, which conveniently abstracts from the
details of a given model class and thus allows covering a broad range of logics
in a uniform way.


  We report the discovery of an ultra-faint Milky Way satellite galaxy in the
constellation of Pegasus. The concentration of stars was detected by applying
our overdensity detection algorithm to the SDSS-DR 10 and confirmed with deeper
photometry from the Dark Energy Camera at the 4-m Blanco telescope. Fitting
model isochrones indicates that this object, Pegasus III, features an old and
metal-poor stellar population ([Fe/H]$\sim-2.1$) at a heliocentric distance of
$205\pm20$ kpc. The new stellar system has an estimated half-light radius of
$r_h=78^{+30}_{-24}$ pc and a total luminosity of $M_{V}\sim-4.1\pm0.5$ that
places it into the domain of dwarf galaxies on the size--luminosity plane.
Pegasus III is spatially close to the MW satellite Pisces II. It is possible
that the two might be physically associated, similar to the Leo IV and Leo V
pair. Pegasus III is also well aligned with the Vast Polar Structure, which
suggests a possible physical association.


  We study the Z boson pair production at LHC in the Randall-Sundrum scenario
with the Goldberger-Wise stabilization mechanism. It is shown that
comprehensive account of the Kaluza-Klein graviton and radion effects is
crucial to probe the model: The KK graviton effects enhance the cross section
of $g g \to Z Z$ on the whole so that the resonance peak of the radion becomes
easy to detect, whereas the RS effects on the $q\bar{q} \to Z Z$ process are
rather insignificant. The $p_T$ and invariant-mass distributions are presented
to study the dependence of the RS model parameters. The production of
longitudinally polarized Z bosons, to which the SM contributions are
suppressed, is mainly due to KK gravitons and the radion, providing one of the
most robust methods to signal the RS effects. The $1 \sigma$ sensitivity bounds
on $(\Lambda_\pi, m_\phi)$ with $k/M_{\rm Pl} =0.1$ are also obtained such that
the effective weak scale $\Lambda_\pi$ of order 5 TeV can be experimentally
probed.


  We examine relationships between the morphology in double radio sources and
the radio-optical position angle offset--the relative orientation of the radio
axis with respect to the major axis of the host galaxy. The study was done for
a representative sample of radio sources: the nearby (redshift z < 0.5) 3CRR
sources, and separately for samples of giant radio sources and X-shaped radio
sources. We find that radio morphological features have a dependence on the
radio-optical position angle offset and on whether the source is a major- or
minor-axis source. The evidence indicates an anisotropic galaxy environment,
related to the ellipticity of the host galaxy, that causes the source linear
size evolution, strength of backflow in the radio lobes, off-axis lobe
distortions and the formation of wings and X-shaped radio sources to depend on
the radio-optical position angle offset. We identify a class of X-shaped radio
sources, which are either edge-darkened or lacking hotspots, and appear to have
inner doubles suggesting a restarting of activity. We suggest a common
formation mechanism, requiring backflows, for these apparently FR-I X-shaped
radio sources as well as the edge-brightened X-shaped sources.


  This paper discusses the lexicographical concept of lexical functions and
their potential exploitation in the development of a machine translation
lexicon designed to handle collocations.


  We discuss effective quantum dynamics obtained by averaging projector with
respect to free dynamics. For unitary dynamics generated by quadratic fermionic
Hamiltonians we obtain effective Heisenberg dynamics. By perturbative
expansions we obtain the correspondent effective time-local Heisenberg
equations. We also discuss a similar problem for bosonic case.


  We study flavour physics and CP violation in the context of low energy
Supersymmetry with non-universal soft mass terms for sfermions. Large
deviations from Standard Model predictions are allowed in B-physics, as shown
in two explicit examples. In particular, the consequences of these models for
a_(J/\psi K) and Delta m_(B_s) are worked out.


  The dust temperature and mass of the supernova remnants (SNRs) in M31 are
estimated by fitting the infrared spectral energy distribution calculated from
the images in the Spitzer/IRAC4 and MIPS24, Herschel/PACS70, 100, 160, and
Herschel/SPIRE250, 350$\mu$m band. Twenty SNRs with relatively reliable
photometry exhibit an average dust temperature of $20.1^{+1.8}_{-1.5}$K, which
is higher than the surrounding and indicating the heating effect of supernova
explosion. The dust mass of these SNRs ranges from about 100 to 800$
M_{\odot}$, much bigger than the SNRs in the Milky Way. On the other hand, this
yields the dust surface density of $0.10^{+0.07}_{-0.04}{ M_{\odot} \rm
pc^{-2}}$, about half of the surrounding area, which implies that about half
dust in the SNRs is destroyed by the supernova explosion. The dust temperature,
the radius, and thus the dust mass all demonstrate that the studied SNRs are
old and very likely in the snowplow or even fade away phase because of the
limitation by the far distance and observation resolution of M31, and the
results can serve as a reference to the final effect of supernova explosion on
the surrounding dust.


  Liquid drops can be kept from touching a plane solid surface by a gas stream
entering from underneath, as it is observed for water drops on a heated plate,
kept aloft by a stream of water vapor. We investigate the limit of small flow
rates, for which the size of the gap between the drop and the substrate becomes
very small. Above a critical drop radius no stationary drops can exist, below
the critical radius two solutions coexist. However, only the solution with the
smaller gap width is stable, the other is unstable. We compare to experimental
data and use boundary integral simulations to show that unstable drops develop
a gas "chimney" which breaks the drop in its middle.


  By evaluating the so-called Bose-ghost propagator, we present the first
numerical evidence of BRST-symmetry breaking for Yang-Mills theory in minimal
Landau gauge, i.e. due to the restriction of the functional integration to the
first Gribov region in the Gribov-Zwanziger approach. Our data are well
described by a simple fitting function, which can be related to a massive gluon
propagator in combination with an infrared-free (Faddeev-Popov) ghost
propagator. As a consequence, the Bose-ghost propagator, which has been
proposed as a carrier of the confining force in minimal Landau gauge, displays
a 1/p^4 singularity in the infrared limit.


  Let $p$ be an odd prime with $2$-adic expansion $\sum_{i=0}^kp_i\cdot2^i$.
For a sequence $\underline{a}=(a(t))_{t\ge 0}$ over $\mathbb{F}_{p}$, each
$a(t)$ belongs to $\{0,1,\ldots, p-1\}$ and has a unique $2$-adic expansion
$$a(t)=a_0(t)+a_1(t)\cdot 2+\cdots+a_{k}(t)\cdot2^k,$$ with $a_i(t)\in\{0,
1\}$. Let $\underline{a_i}$ denote the binary sequence $(a_i(t))_{t\ge 0}$ for
$0\le i\le k$. Assume $i_0$ is the smallest index $i$ such that $p_{i}=0$ and
$\underline{a}$ and $\underline{b}$ are two different m-sequences generated by
a same primitive characteristic polynomial over $\mathbb{F}_p$. We prove that
for $i\neq i_0$ and $0\le i\le k$, $\underline{a_i}=\underline{b_i}$ if and
only if $\underline{a}=\underline{b}$, and for $i=i_0$,
$\underline{a_{i_0}}=\underline{b_{i_0}}$ if and only if
$\underline{a}=\underline{b}$ or $\underline{a}=-\underline{b}$. Then the
period of $\underline{a_i}$ is equal to the period of $\underline{a}$ if $i\ne
i_0$ and half of the period of $\underline{a}$ if $i=i_0$. We also discuss a
possible application of the binary sequences $\underline{a_i}$.


  We consider a mathematical model for wound contraction, which is based on
solving a momentum balance under the assumptions of isotropy, homogeneity,
Hooke's Law, infinitesimal strain theory and point forces exerted by cells.
However, point forces, described by Dirac Delta distributions lead to a
singular solution, which in many cases may cause trouble to finite element
methods due to a low degree of regularity. Hence, we consider several
alternatives to address point forces, that is, whether to treat the region
covered by the cells that exert forces as part of the computational domain or
as 'holes' in the computational domain. The formalisms develop into the
immersed boundary approach and the 'hole approach', respectively. Consistency
between these approaches is verified in a theoretical setting, but also
confirmed computationally. However, the 'hole approach' is much more expensive
and complicated for its need of mesh adaptation in the case of migrating cells
while it increases the numerical accuracy, which makes it hard to adapt to the
multi-cell model. Therefore, for multiple cells, we consider the polygon that
is used to approximate the boundary of cells that exert contractile forces. It
is found that a low degree of polygons, in particular triangular or square
shaped cell boundaries, already give acceptable results in engineering
precision, so that it is suitable for the situation with a large amount of
cells in the computational domain.


  X-ray absorption and its dependence on the polarization of light is a
powerful tool to investigate the orbital and spin moments of magnetic materials
and their orientation relative to crystalline axes. Here, we present a program
for the calculation of dichroic spectra from first principles. We have
implemented the calculation of x-ray absorption spectra for left and right
circularly polarized light into the Wien2k code. In this package, spin-density
functional theory is applied in an all-electron scheme that allows to describe
both core and valence electrons on the same footing. The matrix elements, which
define the dependence of the photo absorption cross section on the polarization
of light and on the sample magnetization, are computed within the dipole
approximation. Results are presented for the L2,3 and M4,5 egdes of CeFe2 and
compared to experiments.