  We study the response of giant stars to mass loss. One-dimensional
simulations of red and asymptotic giant branch stars with mass loss rates from
$10^{-3}$ up to a few \msun/yr show in no case any significant radius increase.
The largest radius increase of 0.2% was found in the case with the lowest mass
loss rate. For dynamical-timescale mass loss rates, that may be encountered
during a common envelope phase, the evolution is not adiabatic. The
superadiabatic outer layer of the giant's envelope has a local thermal
timescale comparable to the dynamical timescale. Therefore, this layer has
enough time to readjust thermally. Moreover, the giant star is driven out of
hydrostatic equilibrium and evolves dynamically. In these cases no increase of
the stellar radius with respect to its initial value is found. If the mass loss
rate is high enough, the superadiabaticity of the outer layer is lost
progressively and a radiative zone forms due to a combination of thermal and
dynamical readjustment. Conditions for unstable mass transfer based on
adiabatic mass loss models that predict a significant radius increase, may need
to be re-evaluated.


  These lectures give an introduction to a probabilistic approach to Liouville
Quantum Field Theory developed in a joint work with F. David, R. Rhodes and V.
Vargas.


  New technologies allow to store vast amount of data about users interaction.
From those data the social network can be created. Additionally, because
usually also time and dates of this activities are stored, the dynamic of such
network can be analysed by splitting it into many timeframes representing the
state of the network during specific period of time. One of the most
interesting issue is group evolution over time. To track group evolution the
GED method can be used. However, choice of the timeframe type and length might
have great influence on the method results. Therefore, in this paper, the
influence of timeframe type as well as timeframe length on the GED method
results is extensively analysed.


  We analyze the (computational) complexity distribution of sphere-decoding
(SD) for random infinite lattices. In particular, we show that under fairly
general assumptions on the statistics of the lattice basis matrix, the tail
behavior of the SD complexity distribution is solely determined by the inverse
volume of a fundamental region of the underlying lattice. Particularizing this
result to NxM, N>=M, i.i.d. Gaussian lattice basis matrices, we find that the
corresponding complexity distribution is of Pareto-type with tail exponent
given by N-M+1. We furthermore show that this tail exponent is not improved by
lattice-reduction, which includes layer-sorting as a special case.


  In recent years, probabilistic forecasting is an emerging topic, which is why
there is a growing need of suitable methods for the evaluation of multivariate
predictions. We analyze the sensitivity of the most common scoring rules,
especially regarding quality of the forecasted dependency structures.
Additionally, we propose scoring rules based on the copula, which uniquely
describes the dependency structure for every probability distribution with
continuous marginal distributions. Efficient estimation of the considered
scoring rules and evaluation methods such as the Diebold-Mariano test are
discussed. In detailed simulation studies, we compare the performance of the
renowned scoring rules and the ones we propose. Besides extended synthetic
studies based on recently published results we also consider a real data
example. We find that the energy score, which is probably the most widely used
multivariate scoring rule, performs comparably well in detecting forecast
errors, also regarding dependencies. This contradicts other studies. The
results also show that a proposed copula score provides very strong distinction
between models with correct and incorrect dependency structure. We close with a
comprehensive discussion on the proposed methodology.


  An introduction to various topics in neutrino astrophysics is given for
students with little prior exposure to this field. We explain neutrino
production and propagation in stars, neutrino oscillations, and experimental
searches for this effect. We also touch upon the cosmological role of
neutrinos. A number of exercises is also included.


  We present the new preliminary MRST parton distributions at NLO and NNLO. The
analysis includes some new data and there is an improvement in the theoretical
treatment at NNLO. Essentially complete NNLO partons are presented for the
first time, together with uncertainties.


  We present a novel optomechanical inertial sensor for low frequency
applications and corresponding acceleration measurements. This sensor has a
resonant frequency of 4.7Hz, a mechanical quality factor of 476k, a test mass
of 2.6 gram, and a projected noise floor of approximately 5E-11 m s-2. per
root-Hz at 1Hz. Such performance, together with its small size, low weight,
reduced power consumption, and low susceptibility to environmental variables
such as magnetic field or drag conditions makes it an attractive technology for
future geodesy missions. In this paper, we present an experimental
demonstration of low-frequency ground seismic noise detection by direct
comparison with a commercial seismometer, anda data analysis algorithms for the
identification, characterization, and correction of several noise sources.


  This paper uses transaction data from a large bank in Scandinavia to estimate
the effect of social distancing laws on consumer spending in the COVID-19
pandemic. The analysis exploits a natural experiment to disentangle the effects
of the virus and the laws aiming to contain it: Denmark and Sweden were
similarly exposed to the pandemic but only Denmark imposed significant
restrictions on social and economic activities. We estimate that aggregate
spending dropped by around 25 percent in Sweden and, as a result of the
shutdown, by 4 additional percentage points in Denmark. This implies that most
of the economic contraction is caused by the virus itself and occurs regardless
of social distancing laws. The age gradient in the estimates suggest that
social distancing reinforces the virus-induced drop in spending for low
health-risk individuals but attenuates it for high-risk individuals by lowering
the overall prevalence of the virus in the society.


  We discuss the exotic properties of the heat-trace asymptotics for a
regular-singular operator with general boundary conditions at the singular end,
as observed by Falomir, Muschietti, Pisani and Seeley as well as by Kirsten,
Loya and Park. We explain how their results alternatively follow from the
general heat kernel construction by Mooers, a natural question that has not
been addressed yet, as the latter work did not elaborate explicitly on the
singular structure of the heat trace expansion beyond the statement of
non-polyhomogeneity of the heat kernel.


  Hawking radiation is the proposed thermal black-body radiation of quantum
nature emitted from a black hole. One common way to give an account of Hawking
radiation is to consider a detector that follows a static trajectory in the
vicinity of a black hole and interacts with the quantum field of the radiation.
In the present work, we study the Hawking radiation perceived by a detector
that follows a quantum superposition of static trajectories in Schwarzschild
spacetime, instead of a unique well-defined trajectory. We analyze the quantum
state of the detector after the interaction with a massless real scalar field.
We find that for certain trajectories and excitation levels, there are
non-vanishing coherences in the final state of the detector. We then examine
the dependence of these coherences on the trajectories followed by the detector
and relate them to the distinguishability of the different possible states in
which the field is left after the excitation of the detector. We interpret our
results in terms of the spatial distribution and propagation of particles of
the quantum field.


  The interpretation of observations of cooling neutron star crusts in
quasi-persistent X-ray transients is affected by predictions of the strength of
neutrino cooling via crust Urca processes. The strength of crust Urca neutrino
cooling depends sensitively on the electron-capture and $\beta$-decay
ground-state to ground-state transition strengths of neutron-rich rare
isotopes. Nuclei with mass number $A=61$ are predicted to be among the most
abundant in accreted crusts, and the last remaining experimentally undetermined
ground-state to ground-state transition strength was the $\beta$-decay of
$^{61}$V. This work reports the first experimental determination of this
transition strength, a ground-state branching of 8.1$^{+2.2}_{-2.0} \%$,
corresponding to a log $ft$ value of 5.5$^{+0.2}_{-0.2}$. This result was
achieved through the measurement of the $\beta$-delayed $\gamma$ rays using the
total absorption spectrometer SuN and the measurement of the $\beta$-delayed
neutron branch using the neutron long counter system NERO at the National
Superconducting Cyclotron Laboratory at Michigan State University. This method
helps to mitigate the impact of the Pandemonium effect in extremely
neutron-rich nuclei on experimental results. The result implies that $A=61$
nuclei do not provide the strongest cooling in accreted neutron star crusts as
expected by some predictions, but that their cooling is still larger compared
to most other mass numbers. Only nuclei with mass numbers 31, 33, and 55 are
predicted to be cooling more strongly. However, the theoretical predictions for
the transition strengths of these nuclei are not consistently accurate enough
to draw conclusions on crust cooling. With the experimental approach developed
in this work all relevant transitions are within reach to be studied in the
future.


  In this study, we address the challenge of analyzing electrophysiological
measurements in neuronal networks. Our computational model, based on the
Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data
obtained from electrophysiological measurements of neuronal cultures. By
reconstructing the network structure on a macroscopic scale, we reveal the
connectivity between neuronal units. Notably, our model outperforms common
methods like Cross-Correlation and Transfer-Entropy in predicting the network's
connectivity map. Furthermore, we experimentally validate its ability to
forecast network responses to specific inputs, including localized optogenetic
stimuli.


  Clustering is a popular unsupervised learning tool often used to discover
groups within a larger population such as customer segments, or patient
subtypes. However, despite its use as a tool for subgroup discovery and
description - few state-of-the-art algorithms provide any rationale or
description behind the clusters found. We propose a novel approach for
interpretable clustering that both clusters data points and constructs
polytopes around the discovered clusters to explain them. Our framework allows
for additional constraints on the polytopes - including ensuring that the
hyperplanes constructing the polytope are axis-parallel or sparse with integer
coefficients. We formulate the problem of constructing clusters via polytopes
as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we
propose a two phase approach where we first initialize clusters and polytopes
using alternating minimization, and then use coordinate descent to boost
clustering performance. We benchmark our approach on a suite of synthetic and
real world clustering problems, where our algorithm outperforms state of the
art interpretable and non-interpretable clustering algorithms.


  It is shown that a class of important integrable nonlinear evolution
equations in (2+1) dimensions can be associated with the motion of space curves
endowed with an extra spatial variable or equivalently, moving surfaces.
Geometrical invariants then define topological conserved quantities. Underlying
evolution equations are shown to be associated with a triad of linear
equations. Our examples include Ishimori equation and Myrzakulov equations
which are shown to be geometrically equivalent to Davey-Stewartson and Zakharov
-Strachan (2+1) dimensional nonlinear Schr\"odinger equations respectively.


  Stereotactic radiotherapy (SRT) is one of the most important treatment for
patients with brain metastases (BM). Conventionally, following SRT patients are
monitored by serial imaging and receive salvage treatments in case of
significant tumor growth. We hypothesized that using radiomics and machine
learning (ML), metastases at high risk for subsequent progression could be
identified during follow-up prior to the onset of significant tumor growth,
enabling personalized follow-up intervals and early selection for salvage
treatment. All experiments are performed on a dataset from clinical routine of
the Radiation Oncology department of the University Hospital Erlangen (UKER).
The classification is realized via the maximum-relevance minimal-redundancy
(MRMR) technique and support vector machines (SVM). The pipeline leads to a
classification with a mean area under the curve (AUC) score of 0.83 in internal
cross-validation and allows a division of the cohort into two subcohorts that
differ significantly in their median time to progression (low-risk metastasis
(LRM): 17.3 months, high-risk metastasis (HRM): 9.6 months, p < 0.01). The
classification performance is especially enhanced by the analysis of medical
images from different points in time (AUC 0.53 -> AUC 0.74). The results
indicate that risk stratification of BM based on radiomics and machine learning
during post-SRT follow-up is possible with good accuracy and should be further
pursued to personalize and improve post-SRT follow-up.


  Einstein Gravity can be formulated as a gauge theory with the tangent space
respecting the Lorentz symmetry. In this paper we show that the dimension of
the tangent space can be larger than the dimension of the manifold and by
requiring the invariance of the theory with respect to 5d Lorentz group (de
Sitter group) Einstein theory is reproduced unambiguously. The other
possibility is to have unitary symmetry on a complex tangent space of the same
dimension as the manifold. In this case the resultant theory is
Einstein-Strauss Hermitian gravity. The tangent group is important for matter
couplings. We show that in the de Sitter case the 4 dimensional space time
vector and scalar are naturally unified by a hidden symmetry being components
of a 5d vector in the tangent space. With a de Sitter tangent group spinors can
exist only when they are made complex or taken in doublets in a way similar to
N=2 supersymmetry.


  In this paper, we propose a transformer based approach for visual grounding.
Unlike previous proposal-and-rank frameworks that rely heavily on pretrained
object detectors or proposal-free frameworks that upgrade an off-the-shelf
one-stage detector by fusing textual embeddings, our approach is built on top
of a transformer encoder-decoder and is independent of any pretrained detectors
or word embedding models. Termed VGTR -- Visual Grounding with TRansformers,
our approach is designed to learn semantic-discriminative visual features under
the guidance of the textual description without harming their location ability.
This information flow enables our VGTR to have a strong capability in capturing
context-level semantics of both vision and language modalities, rendering us to
aggregate accurate visual clues implied by the description to locate the
interested object instance. Experiments show that our method outperforms
state-of-the-art proposal-free approaches by a considerable margin on five
benchmarks while maintaining fast inference speed.


  We compute the contribution of Euclidean D-branes in type IIB string theory
on Calabi-Yau threefolds to the metric on the hypermultiplet moduli space in
the large volume, weak coupling limit. Our results are in perfect agreement
with the predictions based on S-duality, mirror symmetry and supersymmetry.


  This paper studies Fenchel-Young losses, a generic way to construct convex
loss functions from a regularization function. We analyze their properties in
depth, showing that they unify many well-known loss functions and allow to
create useful new ones easily. Fenchel-Young losses constructed from a
generalized entropy, including the Shannon and Tsallis entropies, induce
predictive probability distributions. We formulate conditions for a generalized
entropy to yield losses with a separation margin, and probability distributions
with sparse support. Finally, we derive efficient algorithms, making
Fenchel-Young losses appealing both in theory and practice.


  The correlated motion of electrons in the presence of strong orbital
fluctuations and correlations is investigated with respect to magnetic
couplings and excitations in an orbitally degenerate ferromagnet. Introduction
of the orbital degree of freedom results in a class of diagrams representing
spin-orbital coupling which become particularly important near the orbital
ordering instability. Low-energy staggered orbital fluctuation modes,
particularly with momentum near (\pi/2,\pi/2,0) (corresponding to CE-type
orbital correlations), are shown to generically yield strong intrinsically
non-Heisenberg (1-\cos q)^2 magnon self energy correction, resulting in no spin
stiffness reduction, but strongly suppressed zone-boundary magnon energies in
the Gamma-X direction. The zone-boundary magnon softening is found to be
strongly enhanced with increasing hole doping and for narrow-band materials,
which provides insight into the origin of zone-boundary anomalies observed in
ferromagnetic manganites.


  The rapid experimental progress of ultra-cold dipolar fermions opens up a
whole new opportunity to investigate novel many-body physics of fermions. In
this article, we review theoretical studies of the Fermi liquid theory and
Cooper pairing instabilities of both electric and magnetic dipolar fermionic
systems from the perspective of unconventional symmetries. When the electric
dipole moments are aligned by the external electric field, their interactions
exhibit the explicit $d_{r^2-3z^2}$ anisotropy. The Fermi liquid properties,
including the single-particle spectra, thermodynamic susceptibilities, and
collective excitations, are all affected by this anisotropy. The electric
dipolar interaction provides a mechanism for the unconventional spin triplet
Cooper pairing, which is different from the usual spin-fluctuation mechanism in
solids and the superfluid $^3$He. Furthermore, the competition between pairing
instabilities in the singlet and triplet channels gives rise to a novel
time-reversal symmetry breaking superfluid state. Unlike electric dipole
moments which are induced by electric fields and unquantized, magnetic dipole
moments are intrinsic proportional to the hyperfine-spin operators with a Lande
factor. Its effects even manifest in unpolarized systems exhibiting an
isotropic but spin-orbit coupled nature. The resultant spin-orbit coupled Fermi
liquid theory supports a collective sound mode exhibiting a topologically
non-trivial spin distribution over the Fermi surface. It also leads to a novel
$p$-wave spin triplet Cooper pairing state whose spin and orbital angular
momentum are entangled to the total angular momentum $J=1$ dubbed the
$J$-triplet pairing. This $J$-triplet pairing phase is different from both the
spin-orbit coupled $^3$He-$B$ phase with $J=0$ and the spin-orbit decoupled
$^3$He-$A$ phase.


  The increasing abundance of video data enables users to search for events of
interest, e.g., emergency incidents. Meanwhile, it raises new concerns, such as
the need for preserving privacy. Existing approaches to video search require
either manual inspection or a deep learning model with massive training. We
develop a method that uses recent advances in vision and language models, as
well as formal methods, to search for events of interest in video clips
automatically and efficiently. The method consists of an algorithm to map
text-based event descriptions into linear temporal logic over finite traces
(LTL$_f$) and an algorithm to construct an automaton encoding the video
information. Then, the method formally verifies the automaton representing the
video against the LTL$_f$ specifications and adds the pertinent video clips to
the search result if the automaton satisfies the specifications. We provide
qualitative and quantitative analysis to demonstrate the video-searching
capability of the proposed method. It achieves over 90 percent precision in
searching over privacy-sensitive videos and a state-of-the-art autonomous
driving dataset.


  The flat spectrum radio quasars (FSRQs) are a sub-class of blazars
characterised by prominent optical emission lines and a collimated large-scale
jet along the observer line of sight. An X-ray spectral flattening has been
reported in FSRQs (at relatively high redshifts), attributable to either
absorption from gas along the line of sight or intrinsic jet based radiative
processes. We study a sample of 16 high redshift FSRQs (z of 1.1 -- 4.7; rest
frame energy upto 50 keV) observed with XMM-Newton and Swift satellites
spanning 29 epochs. The X-ray spectra are fit with a power law including free
excess absorption and one multiplied by an exponential roll off to account for
the intrinsic jet based processes. A statistical analysis is used to
distinguish between these models to understand the origin of the spectral
flattening. The model selection is unable to distinguish between them in ten of
the sixteen FSRQs. Intrinsic jet based radiative processes are indicated in
four FSRQs where we infer energy breaks consistent with their expectation from
the external Compton scattering of low energy ambient photons. Two of the FSRQs
indicate mixed results, supportive of either scenario, illustrating the
difficulty in identifying X-ray absorption signatures. A clear detection can be
employed to disentangle the relative contributions from the inter-galactic
medium and the intra-cluster medium, the methodology of which is outlined and
applied to the latter two sources.


  Mean-field backward stochastic Volterra integral equations (MF-BSVIEs, for
short) are introduced and studied. Well-posedness of MF-BSVIEs in the sense of
introduced adapted M-solutions is established. Two duality principles between
linear mean-field (forward) stochastic Volterra integral equations (MF-FSVIEs,
for short) and MF-BSVIEs are obtained. As applications, a multi-dimensional
comparison theorem is proved for adapted M-solutions of MF-BSVIEs and a maximum
principle is established for an optimal control of MF-FSVIEs.


  We discuss the implications of a recently proposed pattern of Lorentz
symmetry violation on very high-energy cross sections. As a consequence of the
breaking of local Lorentz invariance by the introduction of a fundamental
length, $a$ , the kinematics is modified and the properties of final states are
fundamentally different in collider-like (two incoming particles with equal,
opposite momenta with respect to the vacuum rest frame) and fixed-target (one
of the incoming particles at rest with respect to the vacuum rest frame)
situations. In the first case, the properties of the allowed final states are
similar to relativistic kinematics, as long as the relevant wave vectors are
much smaller than the critical wave vector scale $a^{-1}$ . But, if one of the
incoming particles is close to rest in the vacuum rest frame, energy
conservation reduces the final-state phase space at very high energy and can
lead to a sharp fall of cross sections starting at incoming-particle wave
vectors well below the inverse of the fundamental length. Then, the Froissart
bound may cease to be relevant, as total cross sections seem to become much
smaller than it would be allowed by local, Lorentz-invariant, field theory.
Important experimental implications of the new scenario are found for
cosmic-ray astrophysics and for very high-energy cosmic rays reaching the
earth.


  The Advanced Mo-based Rare process Experiment (AMoRE)-Pilot experiment is an
initial phase of the AMoRE search for neutrinoless double beta decay of
$^{100}$Mo, with the purpose of investigating the level and sources of
backgrounds. Searches for neutrinoless double beta decay generally require
ultimately low backgrounds. Surface $\alpha$ decays on the crystals themselves
or nearby materials can deposit a continuum of energies that can be as high as
the $Q$-value of the decay itself and may fall in the region of interest (ROI).
To understand these background events, we studied backgrounds from radioactive
contaminations internal to and on the surface of the crystals or nearby
materials with Geant4-based Monte Carlo simulations. In this study, we report
on the measured $\alpha$ energy spectra fitted with the corresponding simulated
spectra for six crystal detectors, where sources of background contributions
could be identified through high energy $\alpha$ peaks and continuum parts in
the energy spectrum for both internal and surface contaminations. We determine
the low-energy contributions from internal and surface $\alpha$ contaminations
by extrapolating from the $\alpha$ background fitting model.


  Communication is compositional if complex signals can be represented as a
combination of simpler subparts. In this paper, we theoretically show that
inductive biases on both the training framework and the data are needed to
develop a compositional communication. Moreover, we prove that compositionality
spontaneously arises in the signaling games, where agents communicate over a
noisy channel. We experimentally confirm that a range of noise levels, which
depends on the model and the data, indeed promotes compositionality. Finally,
we provide a comprehensive study of this dependence and report results in terms
of recently studied compositionality metrics: topographical similarity,
conflict count, and context independence.


  We investigate the role of clustering on the critical behavior of the contact
process (CP) on small-world networks using the Watts-Strogatz (WS) network
model with an edge rewiring probability p. The critical point is well predicted
by a homogeneous cluster-approximation for the limit of vanishing clustering (p
close to 1). The critical exponents and dimensionless moment ratios of the CP
are in agreement with those predicted by the mean-field theory for any p > 0.
This independence on the network clustering shows that the small-world property
is a sufficient condition for the mean-field theory to correctly predict the
universality of the model. Moreover, we compare the CP dynamics on WS networks
with rewiring probability p = 1 and random regular networks and show that the
weak heterogeneity of the WS network slightly changes the critical point but
does not alter other critical quantities of the model.


  The exact role of quasars during the Epoch of Reionization remains uncertain.
With consensus leaning towards quasars producing a negligible amount of
ionizing photons, we pose an alternate question: Can quasars indirectly
contribute to reionization by allowing ionizing photons from stars to escape
more easily? Using the Semi-Analytic Galaxy Evolution model to evolve a galaxy
population through cosmic time, we construct an idealized scenario in which the
escape fraction of stellar ionizing photons ($f_\mathrm{esc}$) is boosted
following quasar wind events, potentially for several dynamical times. We find
that under this scenario, the mean value of $f_\mathrm{esc}$ as a function of
galaxy stellar mass peaks for intermediate mass galaxies. This mass dependence
will have consequences for the 21cm power spectrum, enhancing power at small
scales and suppressing it at large scales. This hints that whilst quasars may
not directly contribute to the ionizing photon budget, they could influence
reionization indirectly by altering the topology of ionized regions.


  Hydrogen-release by photoexcitation, excited-state- hydrogen-transfer (ESHT),
is one of the important photo- chemical processes that occur in aromatic acids
and is responsible for photoprotection of biomolecules. The mecha- nism is
described by conversion of the initial state to a charge- separated state along
the O(N)-H bond elongation, leading to dissociation. Thus ESHT is not a simple
H-atom transfer in which a proton and a 1s electron move together. Here we show
that the electron-transfer and the proton-motion are decoupled in gas-phase
ESHT. We monitor electron and proton transfer independently by picosecond
time-resolved near-infrared and infrared spectroscopy for isolated
phenol--(ammonia)5, a benchmark molecular cluster. Electron transfer from
phenol to ammonia occurred in less than 3 picoseconds, while the overall H-atom
transfer took 15 picoseconds. The observed electron-proton decoupling will
allow for a deeper understanding and control of of photochemistry in
biomolecules


  A simple model of extra-dimensional radius stabilization in a supersymmetric
Randall-Sundrum model is presented. In our model, we introduce only a bulk
hypermultiplet and source terms on each boundary branes. With an appropriate
choice of model parameters, we find that the radius can be stabilized by
supersymmetric vacuum conditions. Since the radion mass can be much larger than
the gravitino mass and the supersymmetry breaking scale, the radius stability
is ensured even with the supersymmetry breaking. We find a parameter region
where unwanted scalar masses induced by quantum corrections through the bulk
hypermultiplet and a bulk gravity multiplet are suppressed and the anomaly
mediation contributions dominate.


  MGRO J2019+37 is an unidentified source of very high energy gamma-rays
originally reported by the MILAGRO collaboration as the brightest TeV source in
the Cygnus region. Despite the poor angular resolution of MILAGRO, this object
seems to be most likely an extended source or, alternatively, a superposition
of point-like TeV sources.
  In order to contribute to the understanding of this peculiar object, we have
mosaiced it with the Giant Metrewave Radio Telescope (GMRT) in Pune, India, at
the 610 MHz frequency covering a field of view of about 6 square degrees down
to a typical rms noise of a few tenths of mJy. We also observed the central
square degree of this mosaic in the near infrared Ks-band using the 3.5 m
telescope and the OMEGA2000 camera at the Calar Alto observatory (Spain). We
present here a first account of our observations and results, together with a
preliminar cross correlation of the radio and infrared source catalog as well
as a correlation with the available X-ray observations of the region.


  Objects orbiting in the presence of a rotating massive body experience a
gravitomagnetic frame-dragging effect, known as the Lense-Thirring effect, that
has been experimentally confirmed in the weak-field limit. In the strong-field
limit, near the horizon of a rotating black hole, frame dragging becomes so
extreme that all objects must co-rotate with the black hole's angular momentum.
In this work, we perform general relativistic numerical simulations to identify
observable signatures of frame dragging in the strong-field limit that appear
when infalling gas is forced to flip its direction of rotation as it is being
accreted. In total intensity images, infalling streams exhibit "S"-shaped
features due to the switch in the tangential velocity. In linear polarization,
a flip in the handedness of spatially resolved polarization ticks as a function
of radius encodes a transition in the magnetic field geometry that occurs due
to magnetic flux freezing in the dragged plasma. Using a network of telescopes
around the world, the Event Horizon Telescope collaboration has demonstrated
that it is now possible to directly image black holes on event horizon scales.
We show that the phenomena described in this work would be accessible to the
next-generation Event Horizon Telescope (ngEHT) and extensions of the array
into space, which would produce spatially resolved images on event horizon
scales with higher spatial resolution and dynamic range.


  In the context of f(R) theories of gravity, we address the problem of finding
a rotating charged black hole solution in the case of constant curvature. The
new metric is obtained by solving the field equations and we show that the
behavior of it is typical of a rotating charged source. In addition, we analyze
the thermodynamics of the new black hole. The results ensures that the
thermodynamical properties in f(R) gravities are qualitatively similar to those
of standard General Relativity.


  We develop an idempotent version of probabilistic potential theory. The goal
is to describe the set of max-plus harmonic functions, which give the
stationary solutions of deterministic optimal control problems with additive
reward. The analogue of the Martin compactification is seen to be a
generalisation of the compactification of metric spaces using (generalised)
Busemann functions. We define an analogue of the minimal Martin boundary and
show that it can be identified with the set of limits of ``almost-geodesics'',
and also the set of (normalised) harmonic functions that are extremal in the
max-plus sense. Our main result is a max-plus analogue of the Martin
representation theorem, which represents harmonic functions by measures
supported on the minimal Martin boundary. We illustrate it by computing the
eigenvectors of a class of translation invariant Lax-Oleinik semigroups. In
this case, we relate the extremal eigenvectors to the Busemann points of a
normed space.


  In the present paper, we consider a smooth $C^\infty$ symplectic
classification of Lagrangian fibrations near cusp singularities, parabolic
orbits and cuspidal tori. We show that for these singularities as well as for
an arrangement of singularities known as a flap, which arises in the integrable
subcritical Hamiltonian Hopf bifurcation, the action variables form a complete
set of $C^\infty$ symplectic invariants. We also give a symplectic
classification for parabolic orbits in the real-analytic case. Namely, we prove
that a complete symplectic invariant in this case is given by a real-analytic
function germ in two variables. Additionally, we construct several symplectic
normal forms in the $C^\infty$ and/or real-analytic categories, including
real-analytic right and right-left symplectic normal forms for parabolic
orbits.


  We compute the $K$-theory of crossed products of rotation algebras
$\mathcal{A}_\theta$, for any real angle $\theta$, by matrices in
$\mathrm{SL}(2,\mathbb{Z})$ with infinite order. Using techniques of continuous
fields, we show that the canonical inclusion of $\mathcal{A}_\theta$ into the
crossed products is injective at the level of $K_0$-groups. We then give an
explicit set of generators for the $K_0$-groups and compute the tracial ranges
concretely.


  This paper describes results from a project in an undergraduate engineering
physics course that coupled classroom use of interactive computer simulations
with the collection of real-time formative assessment using pen-enabled mobile
technology. Interactive simulations (free or textbook-based) are widely used
across the undergraduate science and engineering curriculia to help actively
engaged students increase their understanding of abstract concepts or phenomena
which are not directly or easily observable. However, there are indications in
the literature that we do not yet know the pedagogical best practices
associated with their use to maximize learning. This project couples student
use of interactive simulations with the gathering of real-time formative
assessment via pen-enabled mobile technology (in this case, Tablet PCs). The
research question addressed in this paper is: are learning gains achieved with
this coupled model greater for certain types of learners in undergraduate STEM
classrooms? To answer this, we correlate learning gains with various learning
styles, as identified using the Index of Learning Styles (ILS) developed by
Felder and Soloman. These insights will be useful for others who use
interactive computer simulations in their instruction and other adopters of
this pedagogical model; the insights may have broader implications about
modification of instruction to address various learning styles.


  Recently, inhomogeneous generalisations of the
Friedmann-Lemaitre-Robertson-Walker cosmological models have gained interest in
the astrophysical community and are more often employed to study cosmological
phenomena. However, in many papers the inhomogeneous cosmological models are
treated as an alternative to the FLRW models. In fact, they are not an
alternative, but an exact perturbation of the latter, and are gradually
becoming a necessity in modern cosmology. The assumption of homogeneity is just
a first approximation introduced to simplify equations. So far this assumption
is commonly believed to have worked well, but future and more precise
observations will not be properly analysed unless inhomogeneities are taken
into account. This paper reviews recent developments in the field and shows the
importance of an inhomogeneous framework in the analysis of cosmological
observations.


  Terahertz (THz) communication is considered to be a promising technology for
future 6G network. To overcome the severe attenuation and relieve the high
power consumption, massive MIMO with hybrid precoding has been widely
considered for THz communication. However, accurate wideband channel estimation
is challenging in THz massive MIMO systems. The existing wideband channel
estimation schemes based on the ideal assumption of common sparse channel
support will suffer from a severe performance loss due to the beam split
effect. In this paper, we propose a beam split pattern detection based channel
estimation scheme to realize reliable wideband channel estimation.
Specifically, a comprehensive analysis on the angle-domain sparse structure of
the wideband channel is provided by considering the beam split effect. Based on
the analysis, we define a series of index sets called as beam split patterns,
which are proved to have a one-to-one match to different physical channel
directions. Inspired by this one-to-one match, we propose to estimate the
physical channel direction by exploiting beam split patterns at first. Then,
the sparse channel supports at different subcarriers can be obtained by
utilizing a support detection window. This support detection window is
generated by expanding the beam split pattern which is determined by the
obtained physical channel direction. The above estimation procedure will be
repeated path by path until all path components are estimated. The proposed
scheme exploits the wideband channel property implied by the beam split effect,
which can significantly improve the channel estimation accuracy. Simulation
results show that the proposed scheme is able to achieve higher accuracy than
existing schemes.


  In this paper, we show that if the tangent bundle of a smooth projective
variety is strictly nef, then it is isomorphic to a projective space; if a
projective variety $X^n$ $(n>4)$ has strictly nef $\Lambda^2 TX$, then it is
isomorphic to $\mathbb{P}^n$ or quadric $\mathbb{Q}^n$. We also prove that on
elliptic curves, strictly nef vector bundles are ample, whereas there exist
Hermitian flat and strictly nef vector bundles on any smooth curve with genus
$g\geq 2$.


  A brief account is given of the problems involved in extended the pinch
technique beyond the one-loop level, and of investigations into one possible
approach to solving them.


  When studying social, economic and biological systems, one has often access
to only limited information about the structure of the underlying networks. An
example of paramount importance is provided by financial systems: information
on the interconnections between financial institutions is privacy-protected,
dramatically reducing the possibility of correctly estimating crucial systemic
properties such as the resilience to the propagation of shocks. The need to
compensate for the scarcity of data, while optimally employing the available
information, has led to the birth of a research field known as network
reconstruction. Since the latter has benefited from the contribution of
researchers working in disciplines as different as mathematics, physics and
economics, the results achieved so far are still scattered across heterogeneous
publications. Most importantly, a systematic comparison of the network
reconstruction methods proposed up to now is currently missing. This review
aims at providing a unifying framework to present all these studies, mainly
focusing on their application to economic and financial networks.


  Developers often refactor source code to improve its quality during software
development. A challenge in refactoring is to determine if it can be applied or
not. To help with this decision-making process, we aim to search for past
refactoring cases that are similar to the current refactoring scenario. We have
designed and implemented a system called RefSearch that enables users to search
for refactoring cases through a user-friendly query language. The system
collects refactoring instances using two refactoring detectors and provides a
web interface for querying and browsing the cases. We used four refactoring
scenarios as test cases to evaluate the expressiveness of the query language
and the search performance of the system. RefSearch is available at
https://github.com/salab/refsearch.


  The equations describing the Kaluza-Klein reduction of conformally flat
spaces are investigated in arbitrary dimensions. Special classes of solution
related to pseudo-Kahler and para-Kahler structures are constructed and
classified according to spacetime dimension, signature and gauge field rank.
Remarkably, rank two solutions include gravitational kinks together with their
centripetal and centrifugal deformations.


  When students make a mistake in an exercise, they can consolidate it by
``similar exercises'' which have the same concepts, purposes and methods.
Commonly, for a certain subject and study stage, the size of the exercise bank
is in the range of millions to even tens of millions, how to find similar
exercises for a given exercise becomes a crucial technical problem. Generally,
we can assign a variety of explicit labels to the exercise, and then query
through the labels, but the label annotation is time-consuming, laborious and
costly, with limited precision and granularity, so it is not feasible. In
practice, we define ``similar exercises'' as a retrieval process of finding a
set of similar exercises based on recall, ranking and re-rank procedures,
called the \textbf{FSE} problem (Finding similar exercises). Furthermore,
comprehensive representation of the semantic information of exercises was
obtained through representation learning. In addition to the reasonable
architecture, we also explore what kind of tasks are more conducive to the
learning of exercise semantic information from pre-training and supervised
learning. It is difficult to annotate similar exercises and the annotation
consistency among experts is low. Therefore this paper also provides solutions
to solve the problem of low-quality annotated data. Compared with other
methods, this paper has obvious advantages in both architecture rationality and
algorithm precision, which now serves the daily teaching of hundreds of
schools.


  We present the results based on the optical $R$-band polarization
observations of 280 stars distributed towards the dark globule LDN\,1225. {\it
Gaia} data release 2 parallaxes along with the polarization data of $\sim$200
stars have been used to (a) constrain the distance of LDN\,1225 as
830$\pm$83~pc, (b) determine the contribution of interstellar polarization
(ISP), and (c) characterize the dust properties and delineate the magnetic
field (B-field) morphology of LDN\,1225. We find that B-fields are more
organized and exhibit a small dispersion of 12$\degr$. Using the $^{12}$CO
molecular line data from the Purple Mountain Observatory (PMO), along with the
column density, dispersion in B-fields, we estimate B-field strength to be
$\sim$56\,$\pm$\,10\,$\mu$G, magnetic to turbulence pressure to be
$\sim$3\,$\pm$\,2, and the mass-to-magnetic flux ratio (in units of critical
value) to be~$<$\,1. These results indicate the dominant role of B-fields in
comparison to turbulence and gravity in rendering the cloud support. B-fields
are aligned parallel to the low-density parts (traced by $^{12}$CO map) of the
cloud, in contrast they are neither parallel nor perpendicular to the
high-density core structures (traced by $^{13}$CO and C$^{18}$O maps).
LDN\,1225 hosts two 70\,$\mu$m sources which seem to be of low-mass Class 0
sources. The total-to-selective extinction derived using optical and
near-infrared photometric data is found to be anomalous ($R_{V}$~$=$~3.4),
suggesting dust grain growth in LDN\,1225. Polarization efficiency of dust
grains follows a power-law index of $-$0.7 inferring that optical polarimetry
traces B-fields in the outer parts of the cloud.


  Document interpretation and dialog understanding are the two major challenges
for conversational machine reading. In this work, we propose Discern, a
discourse-aware entailment reasoning network to strengthen the connection and
enhance the understanding for both document and dialog. Specifically, we split
the document into clause-like elementary discourse units (EDU) using a
pre-trained discourse segmentation model, and we train our model in a
weakly-supervised manner to predict whether each EDU is entailed by the user
feedback in a conversation. Based on the learned EDU and entailment
representations, we either reply to the user our final decision
"yes/no/irrelevant" of the initial question, or generate a follow-up question
to inquiry more information. Our experiments on the ShARC benchmark (blind,
held-out test set) show that Discern achieves state-of-the-art results of 78.3%
macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question
generation. Code and models are released at
https://github.com/Yifan-Gao/Discern.


  The Advanced Encryption Standard (AES) is widely recognized as the most
important block cipher in common use nowadays. This high assurance in AES is
given by its resistance to ten years of extensive cryptanalysis, that has shown
no weakness, not even any deviation from the statistical behaviour expected
from a random permutation. Only reduced versions of the ciphers have been
broken, but they are not usually implemented. In this paper we build a
distinguishing attack on the AES, exploiting the properties of a novel cipher
embedding. With our attack we give some statistical evidence that the set of
AES-$128$ encryptions acts on the message space in a way significantly
different than that of the set of random permutations acting on the same space.
While we feel that more computational experiments by independent third parties
are needed in order to validate our statistical results, we show that the
non-random behaviour is the same as we would predict using the property of our
embedding. Indeed, the embedding lowers the nonlinearity of the AES rounds and
therefore the AES encryptions tend, on average, to keep low the rank of
low-rank matrices constructed in the large space. Our attack needs $2^{23}$
plaintext-ciphertext pairs and costs the equivalent of $2^{48}$ encryptions.
  We expect our attack to work also for AES-$192$ and AES-$256$, as confirmed
by preliminary experiments.


  In the Colored Bin Packing problem a sequence of items of sizes up to $1$
arrives to be packed into bins of unit capacity. Each item has one of $c\geq 2$
colors and an additional constraint is that we cannot pack two items of the
same color next to each other in the same bin. The objective is to minimize the
number of bins.
  In the important special case when all items have size zero, we characterize
the optimal value to be equal to color discrepancy. As our main result, we give
an (asymptotically) 1.5-competitive algorithm which is optimal. In fact, the
algorithm always uses at most $\lceil1.5\cdot OPT\rceil$ bins and we show a
matching lower bound of $\lceil1.5\cdot OPT\rceil$ for any value of $OPT\geq
2$. In particular, the absolute ratio of our algorithm is $5/3$ and this is
optimal.
  For items of unrestricted sizes we give an asymptotically $3.5$-competitive
algorithm. When the items have sizes at most $1/d$ for a real $d \geq 2$ the
asymptotic competitive ratio is $1.5+d/(d-1)$. We also show that classical
algorithms First Fit, Best Fit and Worst Fit are not constant competitive,
which holds already for three colors and small items.
  In the case of two colors---the Black and White Bin Packing problem---we
prove that all Any Fit algorithms have absolute competitive ratio $3$. When the
items have sizes at most $1/d$ for a real $d \geq 2$ we show that the Worst Fit
algorithm is absolutely $(1+d/(d-1))$-competitive.


  We consider the mass splittings and strong decays of members of the
lowest-lying pentaquark multiplet, which we take to be a parity-odd
antidecuplet. We derive useful decompositions of the quark model wave functions
that allow for easy computation of color-flavor-spin matrix elements. We
compute mass splittings within the antidecuplet including spin-color and
spin-isospin interactions between constituents and point out the importance of
hidden strangeness in rendering the nucleon-like states heavier than the S=1
state. Using recent experimental data on a possible S=1 pentaquark state, we
make decay predictions for other members of the antidecuplet.


  We extend known results on the number of solutions to a linear equation in at
least three prime numbers when the primes involved are required to lie in
specified Chebotarev classes. We prove asymptotic results similar to previous
ones only now taking into account corrections coming form the Chebotarev
Density Theorem and Global Class Field Theory. We then apply these results to
find elliptic curves whose discriminants split completely of a given number
field.


  The stability of the stored information in recording media depends on the
anisotropy energy Ea = KeffV of nanoparticles (NPs) of volume V or diameter D.
Therefore, the knowledge of how the effective magnetic anisotropy Keff varies
with D for a given system is important for technological applications. In a
recent paper [Appl. Phys. Lett. 110 (22), 222409 (2017)], the variation of Keff
versus D in NPs of maghemite ({\gamma}-Fe2O3) was best described by the Eq.:
Keff = Kb + (6KS/D) +Ksh{[1-(2d/D)]-3 -1}, where Kb, KS, and Ksh are the
anisotropy constants of spins in the core, surface layer, and a shell of
thickness d, respectively. This core-shell-surface layer (CSSL) model is an
extension of the often used core-surface layer (CSL) model described by Keff =
Kb + (6KS/D) [Phys. Rev. Lett. 72, 282 (1994)]. The additional term involving
Ksh was found to be necessary to fit the data for smaller NPs of {\gamma}-Fe2O3
with D < 5 nm. Here we check the validity of the CSSL model for metallic
magnetic NPs of Co, Ni, Fe and magnetite using Keff vs. D data from published
literature. Care was taken in selecting data only for those NPs for which the
effects of interparticle interactions has been taken into account in
determining Keff. The importance of the new CSSL model is that it describes
well the Keff vs. D variation for all particles sizes whereas the core-surface
layer model often fails for smaller particles with the notable exception of Fe
NPs. The verification of the CSSL model for metallic NPs of Co, Ni, and
magnetite along with NPs of NiO and {\gamma}-Fe2O3 validates its general
applicability.


  We consider the implementation of the split-step method where the linear part
of the nonlinear Schr\"odinger equation is solved using a finite-difference
discretization of the spatial derivative. The von Neumann analysis predicts
that this method is unconditionally stable on the background of a
constant-amplitude plane wave. However, simulations show that the method can
become unstable on the background of a soliton. We present an analysis
explaining this instability. Both this analysis and the instability itself are
substantially different from those of the Fourier split-step method, which
computes the spatial derivative by spectral discretization. We also found that
the modes responsible for the numerical instability are supported by the sides
of the soliton, in contrast to unstable modes of linearized nonlinear wave
equations, which (the modes) are supported by the soliton's core.


  In this paper, we tackle an emerging computer vision task, open-vocabulary
universal image segmentation, that aims to perform semantic/instance/panoptic
segmentation (background semantic labeling + foreground instance segmentation)
for arbitrary categories of text-based descriptions in inference time. We first
build a baseline method by directly adopting pre-trained CLIP models without
finetuning or distillation. We then develop MaskCLIP, a Transformer-based
approach with a MaskCLIP Visual Encoder, which is an encoder-only module that
seamlessly integrates mask tokens with a pre-trained ViT CLIP model for
semantic/instance segmentation and class prediction. MaskCLIP learns to
efficiently and effectively utilize pre-trained partial/dense CLIP features
within the MaskCLIP Visual Encoder that avoids the time-consuming
student-teacher training process. MaskCLIP outperforms previous methods for
semantic/instance/panoptic segmentation on ADE20K and PASCAL datasets. We show
qualitative illustrations for MaskCLIP with online custom categories. Project
website: https://maskclip.github.io.


  We present the recent high-statistics calculations of the nucleon
electromagnetic form factors with fully dynamical domain wall fermions on the
32^3x64 lattices generated by the RBC and UKQCD collaborations, with pion
masses at roughly 297 MeV, 355 MeV and 403 MeV. We study the phenomenological
fits to the momentum transfer dependence of the form factors and investigate
chiral extrapolations for the Dirac radius, Pauli radius and the anomalous
magnetic moment using two variants of chiral effective field theories, the
small scale expansion (SSE) and covariant baryon chiral perturbation theory.


  A subset of vertices of a graph is minimal if, within all subsets of the same
size, its vertex boundary is minimal. We give a complete, geometric
characterization of minimal sets for the planar integer lattice X. Our
characterization elucidates the structure of all minimal sets, and we are able
to use it to obtain several applications. We characterize uniquely minimal sets
of X: those which are congruent to any other minimal set of the same size. We
also classify all efficient sets of X: those that have maximal size amongst all
such sets with a fixed vertex boundary. We define and investigate the graph G
of minimal sets whose vertices are congruence classes of minimal sets of X and
whose edges connect vertices which can be represented by minimal sets that
differ by exactly one vertex. We prove that G has exactly one infinite
component, has infinitely many isolated vertices and has bounded components of
arbitrarily large size. Finally, we show that all minimal sets, except one, are
connected.


  Self-Organizing Network (SON) technology aims at autonomously deploying,
optimizing and repairing the Radio Access Networks (RAN). SON algorithms
typically use Key Performance Indicators (KPIs) from the RAN. It is shown that
in certain cases, it is essential to take into account the impact of the
backhaul state in the design of the SON algorithm. We revisit the Base Station
(BS) load definition taking into account the backhaul state. We provide an
analytical formula for the load along with a simple estimator for both elastic
and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load
estimator in a self-optimized load balancing algorithm. Simulation results for
a backhaul constrained heterogeneous network illustrate how the correct load
definition can guarantee a proper operation of the SON algorithm.


  Multi-head attention empowers the recent success of transformers, the
state-of-the-art models that have achieved remarkable success in sequence
modeling and beyond. These attention mechanisms compute the pairwise dot
products between the queries and keys, which results from the use of
unnormalized Gaussian kernels with the assumption that the queries follow a
mixture of Gaussian distribution. There is no guarantee that this assumption is
valid in practice. In response, we first interpret attention in transformers as
a nonparametric kernel regression. We then propose the FourierFormer, a new
class of transformers in which the dot-product kernels are replaced by the
novel generalized Fourier integral kernels. Different from the dot-product
kernels, where we need to choose a good covariance matrix to capture the
dependency of the features of data, the generalized Fourier integral kernels
can automatically capture such dependency and remove the need to tune the
covariance matrix. We theoretically prove that our proposed Fourier integral
kernels can efficiently approximate any key and query distributions. Compared
to the conventional transformers with dot-product attention, FourierFormers
attain better accuracy and reduce the redundancy between attention heads. We
empirically corroborate the advantages of FourierFormers over the baseline
transformers in a variety of practical applications including language modeling
and image classification.


  Developing intelligent pediatric consultation systems offers promising
prospects for improving diagnostic efficiency, especially in China, where
healthcare resources are scarce. Despite recent advances in Large Language
Models (LLMs) for Chinese medicine, their performance is sub-optimal in
pediatric applications due to inadequate instruction data and vulnerable
training procedures. To address the above issues, this paper builds PedCorpus,
a high-quality dataset of over 300,000 multi-task instructions from pediatric
textbooks, guidelines, and knowledge graph resources to fulfil diverse
diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the
first Chinese pediatric LLM assistant built on a systematic and robust training
pipeline. In the continuous pre-training phase, we introduce a hybrid
instruction pre-training mechanism to mitigate the internal-injected knowledge
inconsistency of LLMs for medical domain adaptation. Immediately, the
full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the
general medical knowledge schema into the models. After that, we devise a
direct following preference optimization to enhance the generation of
pediatrician-like humanistic responses. In the parameter-efficient secondary
SFT phase, a mixture of universal-specific experts strategy is presented to
resolve the competency conflict between medical generalist and pediatric
expertise mastery. Extensive results based on the metrics, GPT-4, and doctor
evaluations on distinct doctor downstream tasks show that PediatricsGPT
consistently outperforms previous Chinese medical LLMs. Our model and dataset
will be open-source for community development.


  We show that U(\infty) symmetry transformations of the noncommutative field
theory in the Moyal space are generated by a combination of two W_{1+\infty}
algebras in the Landau problem. Geometrical meaning of this infinite symmetry
is illustrated by examining the transformations of an invariant subgroup on the
noncommutative solitons, which generate deformations and boosts of solitons. In
particular, we find that boosts of solitons, which was first introduced in
Ref[22], are valid only in the infinite "theta" limit.


  A theory of the circular photogalvanic effect caused by spin splitting in
quantum wells is developed. Direct interband transitions between the hole and
electron size-quantized subbands are considered. It is shown that the
photocurrent value and direction depend strongly on the form of the spin-orbit
interaction. The currents induced by structure-, bulk-, and interface-inversion
asymmetry are investigated. The photocurrent excitation spectra caused by spin
splittings in both conduction and valence bands are calculated.


  The self-force of a point charge moving on a rectilinear trajectory is
obtained, with no need of any explicit removal of infinities, as the negative
of the time rate of change of the momentum of its retarded self-field.


  This paper outlines how modern first-principle calculations can adequately
address the needs for ever higher levels of numerical accuracy and
high-performance in large-scale electronic structure simulations, and pioneer
the fundamental study of quantum many-body effects in a large number of
emerging nanomaterials.


  QCD-like theories provide testing grounds for truncations of functional
equations at non-zero density, since comparisons with lattice results are
possible due to the absence of the sign problem. As a first step towards such a
comparison, we determine for QCD and $G_2$ QCD the chiral and
confinement/deconfinement transitions from the quark propagator Dyson-Schwinger
equation at zero chemical potential by calculating the chiral and dual chiral
condensates, respectively.


  At present, metal hydrides are considered highly promising materials for
phonon-mediated superconductors, that exhibit high values of the critical
temperature. In the present study, the superconducting properties of the
compressed selenium hydride in its simplest form (HSe) are analyzed, toward
quantitative characterization of this phase. By using the state-of-art
Migdal-Eliashberg formalism, it is shown that the critical temperature in this
material is relatively high ($T_{c}$=42.65 K) and surpass the level of
magnesium diboride superconductor, assuming that the Coulomb pseudopotential
takes value of $0.1$. Moreover, the employed theoretical model allows us to
characterize other pivotal thermodynamic properties such as the superconducting
band gap, the free energy, the specific heat and the critical magnetic field.
In what follows, it is shown that the characteristic thermodynamic ratios for
the aforementioned parameters differ from the predictions of the
Bardeen-Cooper-Schrieffer theory. As a result, we argue that strong-coupling
and retardation effects play important role in the discussed superconducting
state, which cannot be described within the weak-coupling regime.


  Cardiovascular Diseases (CVD) and strokes produce immense health and economic
burdens globally. Coronary Artery Disease (CAD) is the most common type of
cardiovascular disease. Coronary Angiography, which is an invasive treatment,
is also the standard procedure for diagnosing CAD. In this work, we illustrate
a Medical Decision Support System for the prediction of Coronary Artery Disease
(CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling
methodology, based on human knowledge, capable of dealing with ambiguity and
uncertainty, and learning how to adapt to the unknown or changing environment.
The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and
Fuzzy Cognitive Maps, with some adjustments to improve the results. The
proposed model, tested on a labelled CAD dataset of 303 patients, obtains an
accuracy of 78.2% outmatching several state-of-the-art classification
algorithms.


  Online learning in a two-sided matching market, with demand side agents
continuously competing to be matched with supply side (arms), abstracts the
complex interactions under partial information on matching platforms (e.g.
UpWork, TaskRabbit). We study the decentralized serial dictatorship setting, a
two-sided matching market where the demand side agents have unknown and
heterogeneous valuation over the supply side (arms), while the arms have known
uniform preference over the demand side (agents). We design the first
decentralized algorithm -- UCB with Decentralized Dominant-arm Deletion
(UCB-D3), for the agents, that does not require any knowledge of reward gaps or
time horizon. UCB-D3 works in phases, where in each phase, agents delete
\emph{dominated arms} -- the arms preferred by higher ranked agents, and play
only from the non-dominated arms according to the UCB. At the end of the phase,
agents broadcast in a decentralized fashion, their estimated preferred arms
through {\em pure exploitation}. We prove both, a new regret lower bound for
the decentralized serial dictatorship model, and that UCB-D3 is order optimal.


  We calculate the interaction vertex of three BFKL states including the bare
triple BFKL Pomeron coupling and discuss its relation with the correlation
functions in two-dimensional conformal field theory. We construct the operator
algebra of the fields interpolating the BFKL states and show that in the
multi-color limit the vertex satisfies the constraints imposed by the conformal
bootstrap on the structure constants of the operator product expansion in
conformal field theory.


  A novel proposal linking the origin of supermassive blackhole formation and
ultralight dark matter. Ultralight bosons of mass $10^{-22}-10^{-24}$ eV which
is one of the front running proposal for dark matter could also linked with the
formation and evolution of supermassive blackholes at the center of all the
galaxies.


  Multimodal learning typically relies on the assumption that all modalities
are fully available during both the training and inference phases. However, in
real-world scenarios, consistently acquiring complete multimodal data presents
significant challenges due to various factors. This often leads to the issue of
missing modalities, where data for certain modalities are absent, posing
considerable obstacles not only for the availability of multimodal pretrained
models but also for their fine-tuning and the preservation of robustness in
downstream tasks. To address these challenges, we propose a novel framework
integrating parameter-efficient fine-tuning of unimodal pretrained models with
a self-supervised joint-embedding learning method. This framework enables the
model to predict the embedding of a missing modality in the representation
space during inference. Our method effectively predicts the missing embedding
through prompt tuning, leveraging information from available modalities. We
evaluate our approach on several multimodal benchmark datasets and demonstrate
its effectiveness and robustness across various scenarios of missing
modalities.


  The Dirac equation requires a treatment of the step potential that differs
fundamentally from the traditional treatment, because the Dirac plane waves,
besides momentum and spin, are characterized by a quantum number with the
physical meaning of sign of charge. Since the Hermitean operator corresponding
to this quantum number does not commute with the step potential, the time
displacement parameter used in the ansatz of the stationary state does not have
the physical meaning of energy. Therefore there are no paradoxal values of the
energy. The new solution of the Dirac equation with a step potential is
obtained. This solution, again, allows for phenomena of the Klein paradox type,
but in addition it contains a positron amplitude localized at the threshold
point of the step potential.


  We examine the use of phenomenological form factors in tree level amplitudes
for meson photoproduction. Two common recipes are shown to be fundamentally
incorrect. An alternate form consistent with gauge invariance and crossing
symmetry is proposed.


  We introduce a new paradigm for generative modeling built on Continuous
Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale.
Specifically, we present the notion of Flow Matching (FM), a simulation-free
approach for training CNFs based on regressing vector fields of fixed
conditional probability paths. Flow Matching is compatible with a general
family of Gaussian probability paths for transforming between noise and data
samples -- which subsumes existing diffusion paths as specific instances.
Interestingly, we find that employing FM with diffusion paths results in a more
robust and stable alternative for training diffusion models. Furthermore, Flow
Matching opens the door to training CNFs with other, non-diffusion probability
paths. An instance of particular interest is using Optimal Transport (OT)
displacement interpolation to define the conditional probability paths. These
paths are more efficient than diffusion paths, provide faster training and
sampling, and result in better generalization. Training CNFs using Flow
Matching on ImageNet leads to consistently better performance than alternative
diffusion-based methods in terms of both likelihood and sample quality, and
allows fast and reliable sample generation using off-the-shelf numerical ODE
solvers.


  We consider "double-winding" Wilson loops in SU(2) gauge theory. These are
contours which wind once around a loop $C_1$ and once around a loop $C_2$,
where the two co-planar loops share one point in common, and where $C_1$ lies
entirely in (or is displaced slightly from) the minimal area of $C_2$. We
discuss the expectation value of such double-winding loops in abelian
confinement pictures, where the spatial distribution of confining abelian
fields is controlled by either a monopole Coulomb gas, a caloron ensemble, or a
dual abelian Higgs model, and argue that in such models an exponential falloff
in the sum of areas $A_1+A_2$ is expected. In contrast, in a center vortex
model of confinement, the behavior is an exponential falloff in the difference
of areas $A_2-A_1$. We compute such double-winding loops by lattice Monte Carlo
simulation, and find that the area law falloff follows a difference-in-areas
law. The conclusion is that even if confining gluonic field fluctuations are,
in some gauge, mainly abelian in character, the spatial distribution of those
abelian fields cannot be the distribution predicted by the simple monopole gas,
caloron ensemble, or dual abelian Higgs actions, which have been used in the
past to explain the area law falloff of Wilson loops.


  Although unsupervised person re-identification (Re-ID) has drawn increasing
research attention recently, it remains challenging to learn discriminative
features without annotations across disjoint camera views. In this paper, we
address the unsupervised person Re-ID with a conceptually novel yet simple
framework, termed as Multi-label Learning guided self-paced Clustering (MLC).
MLC mainly learns discriminative features with three crucial modules, namely a
multi-scale network, a multi-label learning module, and a self-paced clustering
module. Specifically, the multi-scale network generates multi-granularity
person features in both global and local views. The multi-label learning module
leverages a memory feature bank and assigns each image with a multi-label
vector based on the similarities between the image and feature bank. After
multi-label training for several epochs, the self-paced clustering joins in
training and assigns a pseudo label for each image. The benefits of our MLC
come from three aspects: i) the multi-scale person features for better
similarity measurement, ii) the multi-label assignment based on the whole
dataset ensures that every image can be trained, and iii) the self-paced
clustering removes some noisy samples for better feature learning. Extensive
experiments on three popular large-scale Re-ID benchmarks demonstrate that our
MLC outperforms previous state-of-the-art methods and significantly improves
the performance of unsupervised person Re-ID.


  The cryogenic performance of GaN-based HEMTs (high-electron-mobility
transistors) is systematically investigated by the direct current (DC) and
low-frequency noise (LFN) characteristics within the temperature (T) range from
300 K to 4.2 K. The important electrical merits of the device, including drain
saturation current (IDsat), on-resistance (RON), transductance, subthreshold
swing (SS), gate leakage current, and Schottky barrier height, are
comprehensively characterized and their temperature-dependent behavior was
statistically analyzed. In addition, the LFN of the device shows an evident
behavior of 1/f noise from 10 Hz to 10 kHz in the measured temperature range
and can be significantly reduced at cryogenic temperature. These results are of
great importance to motivate further studies into the GaN-based cryo-devices
and systems.


  We consider the rate at which energy is emitted by neutrinos from the dense
interior of neutron stars containing a Bose condensate of pions or kaons. The
rates obtained are larger, by a factor of 2, than those found earlier, and are
consistent with those found for the direct Urca processes.


  In this work, we investigate the presence of occupational gender stereotypes
in sentiment analysis models. Such a task has implications for reducing
implicit biases in these models, which are being applied to an increasingly
wide variety of downstream tasks. We release a new gender-balanced dataset of
800 sentences pertaining to specific professions and propose a methodology for
using it as a test bench to evaluate sentiment analysis models. We evaluate the
presence of occupational gender stereotypes in 3 different models using our
approach, and explore their relationship with societal perceptions of
occupations.


  Massive and massless Dirac equations with Lorentz-covariant cubic
nonlinearities are considered in spatial dimension $d=2,3$. Global
well-posedness of the Cauchy problem for small initial data in scale-invariant
Sobolev spaces and scattering of solutions is proved by a new approach which
uses bilinear Fourier restriction estimates and atomic function spaces.
Furthermore, global uniform convergence results, both in the massless and in
the non-relativistic limit, are proved at optimal regularity. In both regimes,
these are the first results which imply convergence of scattering states and
wave operators.


  We present a minimalist social robot that relies on long timeseries of low
resolution data such as mechanical vibration, temperature, lighting, sounds and
collisions. Our goal is to develop an experimental system for growing socially
situated robotic agents whose behavioral repertoire is subsumed by the social
order of the space. To get there we are designing robots that use their simple
sensors and motion feedback routines to recognize different classes of human
activity and then associate to each class a range of appropriate behaviors. We
use the Katie Family of robots, built on the iRobot Create platform, an Arduino
Uno, and a Raspberry Pi. We describe its sensor abilities and exploratory tests
that allow us to develop hypotheses about what objects (sensor data) correspond
to something known and observable by a human subject. We use machine learning
methods to classify three social scenarios from over a hundred experiments,
demonstrating that it is possible to detect social situations with high
accuracy, using the low-resolution sensors from our minimalist robot.


  Tight $2 s$-designs are the $2 s$-$(v, k, \lambda)$ designs whose sizes
achieve the Fisher type lower bound ${v \choose s}$. Symmetric $2$-designs, the
Witt $4$-$(23, 7, 1)$ design and the Witt $4$-$(23, 16, 52)$ design are tight
designs. It has been widely conjectured since 1970s that there are no other
nontrivial tight designs. In this paper, we give a proof of this conjecture. In
the proof, an upper bound $v \ll s$ is shown by analyzing the parameters of the
designs and the coefficients of the Wilson polynomials, and a lower bound $v
\gg s (\ln s)^2$ is shown by using estimates on prime gaps.


  The free-boundary compressible 1-D Euler equations with moving physical
vacuum boundary are a system of hyperbolic conservation laws which are both
characteristic and degenerate. The physical vacuum singularity (or
rate-of-degeneracy) requires the sound speed $c= \gamma \rho^{\gamma -1}$ to
scale as the square-root of the distance to the vacuum boundary, and has
attracted a great deal of attention in recent years. We establish the existence
of unique solutions to this system on a short time-interval, which are smooth
(in Sobolev spaces) all the way to the moving boundary. The proof is founded on
a new higher-order Hardy-type inequality in conjunction with an approximation
of the Euler equations consisting of a particular degenerate parabolic
regularization. Our regular solutions can be viewed as degenerate viscosity
solutions.


  Exposure to beams of low energy electrons (2 to 30 keV) in a scanning
electron microscope locally induces formation of NV-centers without thermal
annealing in diamonds that have been implanted with nitrogen ions. We find that
non-thermal, electron beam induced NV-formation is about four times less
efficient than thermal annealing. But NV-center formation in a consecutive
thermal annealing step (800C) following exposure to low energy electrons
increases by a factor of up to 1.8 compared to thermal annealing alone. These
observations point to reconstruction of nitrogen-vacancy complexes induced by
electronic excitations from low energy electrons as an NV-center formation
mechanism and identify local electronic excitations as a means for spatially
controlled room-temperature NV-center formation.


  Ground-based gamma ray observations of the Galactic Center region have
revealed a high energy gamma ray source spatially coincident with the
gravitational centroid of our Galaxy. The pointlike emission detected by
H.E.S.S. exhibits an energy cut-off at about 10 TeV. We identify the parameters
of the best fit of the exponential and the super-exponential cutoff models to
the spectrum of the pointlike source and find that super-exponential one
provides a similar quality of the fit to the spectrum of the pointlike source
as the best-fit exponential cutoff model, while a dark matter interpretation
does not provide as good a fit in the whole energy range of the signal. Based
on the magnitude of the flux we derive constraints in the plane of the slope of
the density profile $\gamma$ and the critical radius, below which the density
is assumed to be constant, $r_c$. Motivated by recent results on the spectrum
and morphology from H.E.S.S. and by the possible observation of a
super-exponential cutoff, we forecast the observations of super-exponential
versus exponential cut-offs by the upcoming Cherenkov Telescope Array (CTA). We
derive a formula for $J$-factor in the small angle approximation and propose
approximate morphological constraints on the central source.


  We introduce a doubly stochastic marked point process model for supervised
classification problems. Regardless of the number of classes or the dimension
of the feature space, the model requires only 2--3 parameters for the
covariance function. The classification criterion involves a permanental ratio
for which an approximation using a polynomial-time cyclic expansion is
proposed. The approximation is effective even if the feature region occupied by
one class is a patchwork interlaced with regions occupied by other classes. An
application to DNA microarray analysis indicates that the cyclic approximation
is effective even for high-dimensional data. It can employ feature variables in
an efficient way to reduce the prediction error significantly. This is critical
when the true classification relies on non-reducible high-dimensional features.


  In this follow-up of our earlier two works D(11.1) (arXiv:1406.0929
[math.DG]) and D(11.2) (arXiv:1412.0771 [hep-th]) in the D-project, we study
further the notion of a `differentiable map from an Azumaya/matrix manifold to
a real manifold'. A conjecture is made that the notion of differentiable maps
from Azumaya/matrix manifolds as defined in D(11.1) is equivalent to one
defined through the contravariant ring-homomorphisms alone. A proof of this
conjecture for the smooth (i.e. $C^{\infty}$) case is given in this note. Thus,
at least in the smooth case, our setting for D-branes in the realm of
differential geometry is completely parallel to that in the realm of algebraic
geometry, cf.\ arXiv:0709.1515 [math.AG] and arXiv:0809.2121 [math.AG]. A
related conjecture on such maps to ${\Bbb R}^n$, as a $C^k$-manifold, and its
proof in the $C^{\infty}$ case is also given. As a by-product, a conjecture on
a division lemma in the finitely differentiable case that generalizes the
division lemma in the smooth case from Malgrange is given in the end, as well
as other comments on the conjectures in the general $C^k$ case. We remark that
there are similar conjectures in general and theorems in the smooth case for
the fermionic/super generalization of the notion.


  The Kalman filter (KF) and the extended Kalman filter (EKF) are well
established techniques for state estimation. However, the choice of the filter
tuning parameters still poses a major challenge for the engineers [1]. In the
present work, two new costs have been proposed for determining the filter
tuning parameters on the basis of the innovation covariance. This provides a
cost function based method for the selection of suitable combination(s) of
filter tuning parameters in order to ensure the design of a KF or an EKF having
an optimally balanced RMSE performance. Index Terms-Kalman filter, tuning
parameters, innovation covariance, cost function


  Let (M,g) be a smooth compact, n dimensional Riemannian manifold, n=3,4 with
smooth n-1 dimensional boundary. We search the positive solutions of the
singularly perturbed Klein Gordon Maxwell Proca system with homogeneous Neumann
boundary conditions or for the singularly perturbed Klein Gordon Maxwell system
with mixed Dirichlet Neumann homogeneous boundary conditions. We prove that
stable critical points of the mean curvature of the boundary generates
solutions when the perturbation parameter is sufficiently small.


  Given a derived equivalence of orbifolds associated to projective varieties
with (not necessarily Gorenstein) quotient singularities, we deduce
consequences related to the behavior of orbifold Hodge numbers and the Picard
variety, extending what is known in the smooth case.


  Uncontrolled hypertension is a global problem that needs to be addressed.
Despite the many mHealth solutions in the market, the nonadherence relative to
intended use jeopardizes treatment success. Although investigating user
experience is one of the most important mechanisms for understanding mHealth
discontinuance, surprisingly, the core determinants of overall user experience
(i.e., positive and negative) about mHealth apps for hypertension are unknown.
To address the mentioned gap in knowledge, this study adopts the computational
grounded theory methodological framework and employs advanced deep learning
algorithms to predict core quality criteria that affect overall user experience
of hypertension apps published in the Apple App Store. This study contributes
to theory and practice of designing evidence-based interventions for
hypertension in the form of propositions and provide valuable managerial
implications and recommendations for manufacturers.


  In this paper, we address the limitations of the DETR-based semi-supervised
object detection (SSOD) framework, particularly focusing on the challenges
posed by the quality of object queries. In DETR-based SSOD, the one-to-one
assignment strategy provides inaccurate pseudo-labels, while the one-to-many
assignments strategy leads to overlapping predictions. These issues compromise
training efficiency and degrade model performance, especially in detecting
small or occluded objects. We introduce Sparse Semi-DETR, a novel
transformer-based, end-to-end semi-supervised object detection solution to
overcome these challenges. Sparse Semi-DETR incorporates a Query Refinement
Module to enhance the quality of object queries, significantly improving
detection capabilities for small and partially obscured objects. Additionally,
we integrate a Reliable Pseudo-Label Filtering Module that selectively filters
high-quality pseudo-labels, thereby enhancing detection accuracy and
consistency. On the MS-COCO and Pascal VOC object detection benchmarks, Sparse
Semi-DETR achieves a significant improvement over current state-of-the-art
methods that highlight Sparse Semi-DETR's effectiveness in semi-supervised
object detection, particularly in challenging scenarios involving small or
partially obscured objects.


  This study employs the annual unbalanced panel data of 42 listed banks in
China from 2006 to 2021, adopts the non-radial and non-oriented
super-efficiency Data envelopment analysis (Super-SBM-UND-VRS based DEA) model
considering NPL as undesired output. Our results show that the profitability
super-efficiency of State-owned banks and Rural/City Commercial Banks is better
than that of Joint-stock Banks. In terms of intermediary efficiency(deposit and
loan), state-owned banks have advantage on other two type of banks. The
determinants analysis shows that all type of banks significantly benefits from
the decrease of ownership concentration which support reformation and IPO.
Regional commercial banks significantly benefit from the decrease of customer
concentration and the increase of reserves. On the other hand, State-owned
banks should increase its loan to deposit ratio while joint-stock banks should
do the opposite.


  We consider Exclusive Double Diffractive Events (EDDE) as a powerfull tool to
study the picture of the $pp$ interaction. Calculations of the cross-sections
for the process $p+p\to p+M+p$ are presented in the convenient form for further
experimental applications. We propose measurements of t-distributions in the
joint CMS-TOTEM experiment. It is shown that important information on the
interaction region could be extracted from the diffractive pattern.


  A novel approach to investigation of correlation effects in the electronic
structure of magnetic crystals which takes into account a frequency dependence
of the self energy (so called ``LDA++ approach'') is developed. The fluctuation
exchange approximation is generalized to the spin-polarized multi-band case and
its local version is proposed. As an example, we calculate the electronic
quasiparticle spectrum of ferromagnetic iron. It is shown that the Fermi liquid
description of the bands near the Fermi level is reasonable, while the
quasiparticle states beyond approximately 1 eV range are strongly damped, in
agreement with photoemission data. The result of the spin-polarized
thermoemission experiment is explained satisfactory. The problem of satellite
structure is discussed.


  This paper provides some useful tests for fitting a parametric single-index
regression model when covariates are measured with error and validation data is
available. We propose two tests whose consistency rates do not depend on the
dimension of the covariate vector when an adaptive-to-model strategy is
applied. One of these tests has a bias term that becomes arbitrarily large with
increasing sample size but its asymptotic variance is smaller, and the other is
asymptotically unbiased with larger asymptotic variance. Compared with the
existing local smoothing tests, the new tests behave like a classical local
smoothing test with only one covariate, and still are omnibus against general
alternatives. This avoids the difficulty associated with the curse of
dimensionality. Further, a systematic study is conducted to give an insight on
the effect of the values of the ratio between the sample size and the size of
validation data on the asymptotic behavior of these tests. Simulations are
conducted to examine the performance in several finite sample scenarios.


  Let $\psi$ and $F$ be positive definite forms with integral coefficients of
equal degree. Using the circle method, we establish an asymptotic formula for
the number of identical representations of $\psi$ by $F$, provided $\psi$ is
everywhere locally representable and the number of variables of $F$ is large
enough. In the quadratic case this supersedes a recent result due to Dietmann
and Harvey. Another application addresses the number of primitive linear spaces
contained in a hypersurface.


  Sleep plays a vital role in human health, both mental and physical. Sleep
disorders like sleep apnea are increasing in prevalence, with the rapid
increase in factors like obesity. Sleep apnea is most commonly treated with
Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is
no mechanism to monitor a patient's progress with CPAP. Accurate detection of
sleep stages from CPAP flow signal is crucial for such a mechanism. We propose,
for the first time, an automated sleep staging model based only on the flow
signal. Deep neural networks have recently shown high accuracy on sleep staging
by eliminating handcrafted features. However, these methods focus exclusively
on extracting informative features from the input signal, without paying much
attention to the dynamics of sleep stages in the output sequence. We propose an
end-to-end framework that uses a combination of deep convolution and recurrent
neural networks to extract high-level features from raw flow signal with a
structured output layer based on a conditional random field to model the
temporal transition structure of the sleep stages. We improve upon the previous
methods by 10% using our model, that can be augmented to the previous sleep
staging deep learning methods. We also show that our method can be used to
accurately track sleep metrics like sleep efficiency calculated from sleep
stages that can be deployed for monitoring the response of CPAP therapy on
sleep apnea patients. Apart from the technical contributions, we expect this
study to motivate new research questions in sleep science.


  In this paper we prove that one-sided Duo rings are (two-sided) McCoy. By
doing so, we are then able to explicitly describe some of these ring element
annihilators of polynomials in McCoy rings. We conclude the paper by showing
the place of these results in the literature by way of an extension of a
convenient diagram from a paper by Camillo and Nielsen.


  High-quality shadow anti-aliasing is a challenging problem in shadow mapping.
Revectorization-based shadow mapping (RBSM) minimizes shadow aliasing by
revectorizing the jagged shadow edges generated with shadow mapping, keeping
low memory footprint and real-time performance for the shadow computation.
However, the current implementation of RBSM is not so well optimized because
its visibility functions are composed of a set of 43 cases, each one of them
handling a specific revectorization scenario and being implemented as a
specific branch in the shader. Here, we take advantage of the shadow shape
patterns to reformulate the RBSM visibility functions, simplifying the
implementation of the technique and further providing an optimized version of
the RBSM. Our results indicate that our implementation runs faster than the
original implementation of RBSM, while keeping its same visual quality and
memory consumption. Furthermore, we show GLSL source codes to ease the
implementation of our technique, provide a comparison between the optimized
RBSM and related work, and discuss the limitations of the shadow
revectorization.


  Based on a variation of the BIP operational semantics using the offer
predicate introduced in our previous work, we extend the algebras used to model
glue operators in BIP to encompass priorities. This extension uses the Algebra
of Causal Interaction Trees, T(P), as a pivot: existing transformations
automatically provide the extensions for the Algebra of Connectors. We then
extend the axiomatisation of T(P), since the equivalence induced by the new
operational semantics is weaker than that induced by the interaction semantics.
This extension leads to canonical normal forms for all structures and to a
simplification of the algorithm for the synthesis of connectors from Boolean
coordination constraints.


  Given a smooth curve of genus 2 embedded in P^(d-2) with a complete linear
system of degree d>=6, we list all types of rational normal scrolls arising
from linear systems g^1_2 and g^1_3 on C. Furthermore, we give a description of
the ideal of such a curve of genus 2 embedded in P^(d-2) as a sum of the ideal
of the two-dimensional scroll defined by the unique g^1_2 on C and the ideal of
a three-dimensional scroll arising from a g^1_3 on C and not containing the
scroll defined by the g^1_2 on C.


  In [M. K. Vemuri, Realizations of the canonical representation], it was shown
that the spectral synthesis problem for the Alpha transform is closely related
to the problem of classifying realizations of the canonical representation (of
the Heisenberg group). In this paper, we show that discrete sets are sets of
spectral synthesis for the Alpha transform.


  The increasing use of hardware processing accelerators tailored for specific
applications, such as the Vision Processing Unit (VPU) for image recognition,
further increases developers' configuration, development, and management
overhead. Developers have successfully used fully automated elastic cloud
services such as serverless computing to counter these additional efforts and
shorten development cycles for applications running on CPUs. Unfortunately,
current cloud solutions do not yet provide these simplifications for
applications that require hardware acceleration. However, as the development of
specialized hardware acceleration continues to provide performance and cost
improvements, it will become increasingly important to enable ease of use in
the cloud.
  In this paper, we present an initial design and implementation of Hardless,
an extensible and generalized serverless computing architecture that can
support workloads for arbitrary hardware accelerators. We show how Hardless can
scale across different commodity hardware accelerators and support a variety of
workloads using the same execution and programming model common in serverless
computing today.


  We have theoretically studied the supercurrent profiles in three-dimensional
normal metal and ferromagnetic Josephson configurations, where the magnitude of
the superconducting gaps in the superconducting leads are unequal, i.e.,
$\Delta_1\neq \Delta_2$, creating asymmetric $S_1NS_2$ and $S_1FS_2$ systems.
Our results reveal that by increasing the ratio of the superconducting gaps
$\Delta_2/\Delta_1$, the critical supercurrent in a ballistic $S_1NS_2$ system
can be enhanced by more than $100\%$, and reaches a saturation point, or decays
away, depending on the junction thickness, magnetization strength, and chemical
potential. The total critical current in a diffusive $S_1NS_2$ system was found
to be enhanced by more than $50\%$ parabolically, and reaches saturation by
increasing one of the superconducting gaps. In a uniform ferromagnetic
junction, the supercurrent undergoes reversal by increasing
$\Delta_2/\Delta_1>1$. Through decomposing the total supercurrent into its
supergap and subgap components, our results illustrate their crucial relative
contributions to the Josephson current flow. It was found that the competition
of subgap and supergap currents in a $S_1FS_2$ junction results in the
emergence of second harmonics in the current-phase relation. In contrast to a
diffusive asymmetric Josephson configuration, the behavior of the supercurrent
in a ballistic system with $\Delta_2/\Delta_1=1$ can be properly described by
the subgap current component only, in a wide range of parameter sets, including
Fermi level mismatch, magnetization strength, and junction thickness.
Interestingly, when $\Delta_2/\Delta_1>1$, our results have found multiple
parameter sets where the total supercurrent is driven by the supergap
component. Therefore, our comprehensive study highlights the importance of
subgap and supergap supercurrent components in both the ballistic and diffusive
regimes.


  This paper addresses two major challenges in terahertz (THz) channel
estimation: the beam-split phenomenon, i.e., beam misalignment because of
frequency-independent analog beamformers, and computational complexity because
of the usage of ultra-massive number of antennas to compensate propagation
losses. Data-driven techniques are known to mitigate the complexity of this
problem but usually require the transmission of the datasets from the users to
a central server entailing huge communication overhead. In this work, we
introduce a federated multi-task learning (FMTL), wherein the users transmit
only the model parameters instead of the whole dataset, for THz channel and
user direction-of-arrival (DoA) estimation to improve the
communications-efficiency. We first propose a novel beamspace support alignment
technique for channel estimation with beam-split correction. Then, the channel
and DoA information are used as labels to train an FMTL model. By exploiting
the sparsity of the THz channel, the proposed approach is implemented with
fewer pilot signals than the traditional techniques. Compared to the previous
works, our FMTL approach provides higher channel estimation accuracy as well as
approximately 25 (32) times lower model (channel) training overhead,
respectively.


  The process of heating and reionization of the Universe at high redshift
links small scale structures/galaxy formation and large scale inter-galactic
medium properties. Even if the first one is difficult to observe, an
observation window is opening on the second one, with the promising development
of current and future radio telescopes. They will permit to observe the 21cm
brightness temperature global signal and fluctuations. The need of large scale
simulations is therefore strong to understand the properties of the IGM that
will be observed. But at the same time the urge to resolve the structures
responsible of those process is important. We introduce in this study, a
coupled hydro-radiative transfer simulations of the Cosmic Dawn and
Reionization with a simple sub-grid star formation process developed and
calibrated on the state of the art simulation CoDaII. This scheme permits to
follow consistently dark matter, hydrodynamics and radiative transfer
evolution's on large scales, while the sub-grid models bridges to the galaxy
formation scale. We process the simulation to produce 21cm signal as close as
possible to the observations.