  Canonical quantization of quantum field theory models is inherently related
to the Lorentz invariant partition of classical fields into the positive and
the negative frequency parts $u(x) = u^+(x) + u^-(x),$ performed with the help
of Fourier transform in Minkowski space. That is the commutation relations are
being established between non localized solutions of field equations. At the
same time the construction of divergence free physical theory requires the
separation of the contributions of different space-time scales. In present
paper, using the light-cone variables, we propose a quantization procedure
which is compatible with separation of scales using continuous wavelet
transform, as described in our previous paper Phys.Rev D 88(2013)025015


  The winds of massive stars are important for their direct impact on the
interstellar medium, and for their influence on the final state of a star prior
to it exploding as a supernova. However, the dynamics of these winds is
understood primarily via their illumination from a single central source. The
Doppler shift seen in resonance lines is a useful tool for inferring these
dynamics, but the mapping from that Doppler shift to the radial distance from
the source is ambiguous. Binary systems can reduce this ambiguity by providing
a second light source at a known radius in the wind, seen from orbitally
modulated directions. From the nature of the collision between the winds, a
massive companion also provides unique additional information about wind
momentum fluxes. Since massive stars are strong ultraviolet (UV) sources, and
UV resonance line opacity in the wind is strong, UV instruments with a high
resolution spectroscopic capability are essential for extracting this dynamical
information. Polarimetric capability also helps to further resolve ambiguities
in aspects of the wind geometry that are not axisymmetric about the line of
sight, because of its unique access to scattering direction information. We
review how the proposed MIDEX-scale mission Polstar can use UV
spectropolarimetric observations to critically constrain the physics of
colliding winds, and hence radiatively-driven winds in general. We propose a
sample of 20 binary targets, capitalizing on this unique combination of
illumination by companion starlight, and collision with a companion wind, to
probe wind attributes over a range in wind strengths. Of particular interest is
the hypothesis that the radial distribution of the wind acceleration is altered
significantly, when the radiative transfer within the winds becomes optically
thick to resonance scattering in multiple overlapping UV lines.


  We study a two-component Fermi system with attractive interactions and
different populations of the two species in a cubic lattice. For an
intermediate coupling we find a uniformly polarized superfluid which is stable
down to very low temperatures. The momentum distribution of this phase closely
resembles that of the Sarma phase, characterized by two Fermi surfaces. This
phase is shown to be stabilized by a potential energy gain, as in a BCS
superfluid, in contrast to the unpolarized BEC which is stabilized by kinetic
energy. We present general arguments suggesting that preformed pairs in the
unpolarized superfluid favor the stabilization of a polarized superfluid phase.


  A (1+1)D unitary bosonic rational conformal field theory (RCFT) may be
organized according to its genus, a tuple $(c,\mathscr{C})$ consisting of its
central charge $c$ and a unitary modular tensor category $\mathscr{C}$ which
describes the (2+1)D topological quantum field theory (TQFT) for which its
maximally extended chiral algebra forms a holomorphic boundary condition. We
establish a number of results pertaining to RCFTs in "small" genera, by which
we informally mean genera with the central charge $c$ and the number of primary
operators $\mathrm{rank}(\mathscr{C})$ both not too large.
  We start by completely solving the modular bootstrap problem for theories
with at most four primary operators. In particular, we characterize, and
provide an algorithm which efficiently computes, the function spaces to which
the partition function of any bosonic RCFT with $\mathrm{rank}(\mathscr{C})\leq
4$ must belong. Using this result, and leveraging relationships between RCFTs
and holomorphic vertex operator algebras which come from "gluing" and cosets,
we rigorously enumerate all bosonic theories in $95$ of the $105$ genera
$(c,\mathscr{C})$ with $c\leq 24$ and $\mathrm{rank}(\mathscr{C})\leq 4$. This
includes as (new) special cases the classification of chiral algebras with
three primaries and $c<\frac{120}7\sim 17.14$, and the classification of chiral
algebras with four primaries and $c<\frac{62}3\sim 20.67$.
  We then study two applications of our classification. First, by making use of
chiral versions of bosonization and fermionization, we obtain the complete list
of purely left-moving fermionic RCFTs with $c< 23$ as a corollary of the
results of the previous paragraph. Second, using a (conjectural) concept which
we call "symmetry/subalgebra duality," we precisely relate our bosonic
classification to the problem of determining certain generalized global
symmetries of chiral CFTs.


  We present the problematic of controlling the discreteness effects in
cosmological N-body simulations. We describe a perturbative treatment which
gives an approximation describing the evolution under self-gravity of a lattice
perturbed from its equilibrium, which allows to trace the evolution of the
fully discrete distribution until the time when particles approach one another
("shell-crossing"). Perturbed lattices are typical initial conditions for
cosmological N-body simulations and thus we can describe precisely the early
time evolution of these simulations. A quantitative comparison with fluid
Lagrangian theory permits to study discreteness effects in the linear regime of
the simulations. We show finally some work in progress about quantifying
discreteness effects in the non-perturbative (highly non-linear) regime of
cosmological N-body simulations by evolving different discretizations of the
same continuous density field.


  Lung cancer follow-up is a complex, error prone, and time consuming task for
clinical radiologists. Several lung CT scan images taken at different time
points of a given patient need to be individually inspected, looking for
possible cancerogenous nodules. Radiologists mainly focus their attention in
nodule size, density, and growth to assess the existence of malignancy. In this
study, we present a novel method based on a 3D siamese neural network, for the
re-identification of nodules in a pair of CT scans of the same patient without
the need for image registration. The network was integrated into a two-stage
automatic pipeline to detect, match, and predict nodule growth given pairs of
CT scans. Results on an independent test set reported a nodule detection
sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a
sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.


  Is preferred tokenization for humans also preferred for machine-learning (ML)
models? This study examines the relations between preferred tokenization for
humans (appropriateness and readability) and one for ML models (performance on
an NLP task). The question texts of the Japanese commonsense question-answering
dataset are tokenized with six different tokenizers, and the performances of
human annotators and ML models were compared. Furthermore, we analyze relations
among performance of answers by human and ML model, the appropriateness of
tokenization for human, and response time to questions by human. This study
provides a quantitative investigation result that shows that preferred
tokenizations for humans and ML models are not necessarily always the same. The
result also implies that existing methods using language models for
tokenization could be a good compromise both for human and ML models.


  We present a scheme for quantum randomness certification based on quantum
steering. The protocol is one-sided device independent, providing high
security, but requires only states and measurements that are simple to realise
on quantum optics platforms - entangled squeezed vacuum states and homodyne
detection. This ease of implementation is demonstrated by certifying randomness
in existing experimental data and implies that giga-hertz random bit rates
should be attainable with current technology. Furthermore, the steering-based
setting represents the closest to full device independence that can be achieved
using purely Gaussian states and measurements.


  We present a first-principles study of the effect of 3$d$ transition metal
intercalation on the magnetic properties of the 2H-NbS$_2$ system, using
spin-resolved density functional theory calculations to investigate the
electronic structure of $N_{1/3}$NbS$_2$ ($N$ = Ti, V, Cr, Mn, Fe, Co, Ni).
  We are able to accurately determine the magnetic moments and crystal field
splitting, and find that the magnetic properties of the materials are
determined by a mechanism based on filling rigid bands with electrons from the
intercalant.
  We predict the dominant magnetic interaction of these materials by
considering Fermi surface nesting, finding agreement with experiment where data
are available.


  We address the problem of predicting the leading political ideology, i.e.,
left-center-right bias, for YouTube channels of news media. Previous work on
the problem has focused exclusively on text and on analysis of the language
used, topics discussed, sentiment, and the like. In contrast, here we study
videos, which yields an interesting multimodal setup. Starting with gold
annotations about the leading political ideology of major world news media from
Media Bias/Fact Check, we searched on YouTube to find their corresponding
channels, and we downloaded a recent sample of videos from each channel. We
crawled more than 1,000 YouTube hours along with the corresponding subtitles
and metadata, thus producing a new multimodal dataset. We further developed a
multimodal deep-learning architecture for the task. Our analysis shows that the
use of acoustic signal helped to improve bias detection by more than 6%
absolute over using text and metadata only. We release the dataset to the
research community, hoping to help advance the field of multi-modal political
bias detection.


  We discuss a basic thermodynamic properties of systems with multifractal
structure. This is possible by extending the notion of Gibbs-Shannon's entropy
into more general framework - Renyi's information entropy. We show a connection
of Renyi's parameter q with the multifractal singularity spectrum f(\alpha) and
clarify a relationship with the Tsallis-Havrda-Charvat entropy. Finally, we
generalize Hagedorn's statistical theory and apply it to high-energy particle
collisions.


  Protein gamma-turn prediction is useful in protein function studies and
experimental design. Several methods for gamma-turn prediction have been
developed, but the results were unsatisfactory with Matthew correlation
coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy
is the limited capacity of the methods; in particular, the traditional
machine-learning methods like SVM may not extract high-level features well to
distinguish between turn or non-turn. Hence, it is worthwhile exploring new
machine-learning methods for the prediction. A cutting-edge deep neural
network, named Capsule Network (CapsuleNet), provides a new opportunity for
gamma-turn prediction. Even when the number of input samples is relatively
small, the capsules from CapsuleNet are very effective to extract high-level
features for classification tasks. Here, we propose a deep inception capsule
network for gamma-turn prediction. Its performance on the gamma-turn benchmark
GT320 achieved an MCC of 0.45, which significantly outperformed the previous
best method with an MCC of 0.38. This is the first gamma-turn prediction method
utilizing deep neural networks. Also, to our knowledge, it is the first
published bioinformatics application utilizing capsule network, which will
provides a useful example for the community.


  The exotic states $X_{0,1}(2900)$ with the quark flavor of $cs\bar{u}\bar{d}$
are recently observed in the mass spectrum of $D^+K^-$ in $B^-\to D^-D^+K^-$ by
the LHCb collaboration. To explore the nature of $X_{0,1}(2900)$, except for
analyzing their masses and decay widths as usually did in literatures, the
study of their production mechanism in $B$-meson weak decays would provide
another important information. The amplitude of $B^-\to D^- X_{0,1}$ is
non-factorizable. We consider the final-state-interaction effects and calculate
them via the rescattering mechanism. The measured branching fractions of
$B^-\to D^- X_{0,1}$ are revealed. It is manifested by ${B}^-\to
\Lambda_c^-\Xi_c^{(\prime)0}$ and $\Lambda_b^0\to P_c^+K^-$ that the
rescattering mechanism can result in the relatively large branching fractions.
The similar processes of $B^-\to \pi^-X_{0,1}$ are also analyzed. The isospins
of $X_{0,1}$ can be investigated by $B\to DX_{0,1}^{\pm,0}$ decays.


  A function that is analytic on a domain of $\mathbb{C}^n$ is holonomic if it
is the solution to a holonomic system of linear homogeneous differential
equations with polynomial coefficients. We define and study the Bernstein-Sato
polynomial of a holonomic function on a smooth algebraic variety. We analyze
the structure of certain sheaves of holonomic functions, such as the algebraic
functions along a hypersurface, determining their direct sum decompositions
into indecomposables, that further respect decompositions of Bernstein-Sato
polynomials. When the space is endowed with the action of a linear algebraic
group $G$, we study the class of $G$-finite analytic functions, i.e. functions
that under the action of the Lie algebra of $G$ generate a finite dimensional
rational $G$-module. These are automatically algebraic functions on a variety
with a dense orbit. When $G$ is reductive, we give several
representation-theoretic techniques toward the determination of Bernstein-Sato
polynomials of $G$-finite functions. We classify the $G$-finite functions on
all but one of the irreducible reduced prehomogeneous vector spaces, and
compute the Bernstein-Sato polynomials for distinguished $G$-finite functions.
The results can be used to construct explicitly equivariant
$\mathcal{D}$-modules.


  Heterogeneous ensembles built from the predictions of a wide variety and
large number of diverse base predictors represent a potent approach to building
predictive models for problems where the ideal base/individual predictor may
not be obvious. Ensemble selection is an especially promising approach here,
not only for improving prediction performance, but also because of its ability
to select a collectively predictive subset, often a relatively small one, of
the base predictors. In this paper, we present a set of algorithms that
explicitly incorporate ensemble diversity, a known factor influencing
predictive performance of ensembles, into a reinforcement learning framework
for ensemble selection. We rigorously tested these approaches on several
challenging problems and associated data sets, yielding that several of them
produced more accurate ensembles than those that don't explicitly consider
diversity. More importantly, these diversity-incorporating ensembles were much
smaller in size, i.e., more parsimonious, than the latter types of ensembles.
This can eventually aid the interpretation or reverse engineering of predictive
models assimilated into the resultant ensemble(s).


  The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) has
the potential to measure parallaxes for thousands of nearby ultracool dwarfs,
enabling improved measurements of the brown dwarf luminosity function. We
develop a simple model to estimate the number of L dwarfs and T dwarfs with
parallaxes with signal-to-noise ratio greater than ten in the baseline LSST
survey. High quality astrometry imposes scheduling constraints. We assess
different possible observing strategies using quantitative metrics and make
recommendations as part of the LSST community input process. We find that the
new substellar parallax sample will represent a nearly order-of-magnitude
increase on existing samples, with ~50-100 objects per spectral type bin for
late-L to mid-T dwarfs. The sample size is robust (+/- 5%) against most survey
strategy changes under consideration, although we do identify areas of tension
with other uses of twilight time that could have larger impact.


  We study the photoinduced ultrafast dynamics in relativistic Mott insulators,
i.e., Mott insulators with strong spin-orbit coupling. For this purpose, we
consider a minimal one-band Hubbard model on lattices with square and
triangular symmetries, as relevant for layered transition metal compounds such
as Sr$_2$IrO$_4$. Depending on the lattice and the spin-orbit coupling, the
systems have canted antiferromagnetic or $120^\circ$ order. They are excited by
simulating a short laser pulse, and the dynamics is solved using nonequilibrium
dynamical mean-field theory. The pulse generates hot carriers, which
subsequently perturb the magnetic order due to the coupling between the
collective order and photocarriers. We find that this dynamics, which is known
form regular antiferromagnets, depends sensitively on the spatial structure of
the spin-orbit coupling. On the triangular lattice, in particular, relaxation
times are influenced by the spin-orbit coupling for the chiral $120^\circ$
order, while on the square lattice with canted antiferromagnetic order the
spin-orbit induced canting angle remains unchanged after the excitation. Our
study opens up new possibilities of controlling magnetism and exotic spin
states on the ultrafast timescales.


  The recently discovered fully charmed tetraquark candidate $X(6900)$ is
analyzed within the frameworks of effective-range expansion, compositeness
relation and width saturation, and a coupled multichannel dynamical study. By
taking into account constraints from heavy-quark spin symmetry, the
coupled-channel amplitude including the $J/\psi J/\psi,~ \chi_{c0}\chi_{c0}$
and $\chi_{c1}\chi_{c1}$ is constructed to fit the experimental di-$J/\psi$
event distributions around the energy region near $6.9$ GeV. Another dynamical
two-coupled-channel amplitude with the $J/\psi J/\psi$ and $\psi(3770) J/\psi$
is also considered to describe the same datasets. The three different
theoretical approaches lead to similar conclusions that the two-meson
components do not play dominant roles in the $X(6900)$. Our determinations of
the resonance poles in the complex energy plane from the refined
coupled-channel study are found to be consistent with the experimental
analyses. The coupled-channel amplitudes also have another pole corresponding
to a narrow resonance $X(6825)$ that we predict sitting below the
$\chi_{c0}\chi_{c0}$ threshold and of molecular origin. We give predictions to
the line shapes of the $\chi_{c0}\chi_{c0}$ and $\chi_{c1}\chi_{c1}$ channels,
which could provide a useful guide for future experimental measurements.


  Machine-Type Communication (MTC) is a rapidly growing technology which covers
a broad range of automated applications and propels the world into a fully
connected society. Two new use cases of MTC are mMTC and URLLC, where mMTC
support a large number of devices with high reliability and low rate
connectivity while URLLC refers to excessively low outage probability under
very stringent latency constraint. Herein, we examine the URLLC through three
cooperative schemes, namely dual-hop DF, SC and MRC, and compare to direct
transmission under Rayleigh fading. We compare the performance of studied
cooperative protocols under two distinct power constraints with respect to
latency and energy efficiency. Moreover, we illustrate the impact of coding
rate on the probability of successful transmission in ultra-reliable region in
addition to the effect of power allocation on the outage probability. We also
provide the performance analysis of cooperative schemes in terms of energy
efficiency and latency requirements.


  The simulation of calorimeter showers presents a significant computational
challenge, impacting the efficiency and accuracy of particle physics
experiments. While generative ML models have been effective in enhancing and
accelerating the conventional physics simulation processes, their application
has predominantly been constrained to fixed detector readout geometries. With
CaloPointFlow we have presented one of the first models that can generate a
calorimeter shower as a point cloud. This study describes CaloPointFlow II,
which exhibits several significant improvements compared to its predecessor.
This includes a novel dequantization technique, referred to as
CDF-Dequantization, and a normalizing flow architecture, referred to as
DeepSet- Flow. The new model was evaluated with the fast Calorimeter Simulation
Challenge (CaloChallenge) Dataset II and III.


  The M31 globular cluster candidate 037-B327 has long been known to be an
extremely red, non-stellar object. The first published spectrum of this object
is used to confirm that it is a globular cluster belonging to M31, with rather
typical values of v_r = -338 +/- 12 km/s and [Fe/H]= -1.07 +/- 0.20 dex. Using
the spectroscopic metallicity to predict the intrinsic colours, we derive a
reddening value of E(B-V)=1.30 +/- 0.04 in good agreement with the value
obtained using reddening-free parameters. The extinction-corrected magnitude of
037-B327 is V_0=12.73 (absolute magnitude M_V = -11.74), which makes it the
most luminous globular cluster in M31. We examine van den Bergh's (1968)
argument regarding the brightest and most-reddened globular cluster in in M31;
we find that the brightest clusters are more heavily-reddened than average, but
this can be explained by selection effects rather than a different R_V in M31.


  The problem of color confinement in lattice Landau gauge QCD is briefly
reviewed in the light of Kugo-Ojima criterion and Gribov-Zwanziger theory. The
infrared properties of the Landau gauge QCD is studied by the measurement of
the gluon propagator, ghost propagator, Kugo-Ojima parameter and the running
coupling via $\beta=6.4,32^4,48^4$ lattice simulation. The data are analyzed in
the principle of minimum sensitivity(PMS) in the $\widetilde{MOM}$ scheme. We
observe the running coupling $\alpha_s(q)$ has a maximum about 1 and decreases
near q=0. The Kugo-Ojima parameter, which is expected to be 1 was about 0.8.


  We report on our analysis of 300 ks of Chandra observations of the neutron
star soft X-ray transient 1H1905+000 in quiescence. We do not detect the source
down to a 95% confidence unabsorbed flux upper limit of 2E-16 erg cm-2 s-1 in
the 0.5-10 keV energy range for an assumed Gamma=2 power law spectral model. A
limit of 1.4E-16 erg cm-2 s-1 is derived if we assume that the spectrum of
1H1905+000 in quiescence is described well with a black body of temperature of
0.2 keV. For the upper limit to the source distance of 10 kpc this yields a
0.5-10 keV luminosity limit of 2.4E30 / 1.7E30 erg/s for the abovementioned
power law or black body spectrum, respectively. This luminosity limit is lower
than the luminosity of A0620-00, the weakest black hole soft X-ray transient in
quiescence reported so far. Together with the uncertainties in relating the
mass transfer and mass accretion rates we come to the conclusion that the claim
that there is evidence for the presence of a black hole event horizon on the
basis of a lower quiescent luminosity for black holes than for neutron stars is
unproven. We also briefly discuss the implications of the low quiescent
luminosity of 1H1905+000 for the neutron star equation of state. Using deep
Magellan images of the field of 1H1905+000 obtained at excellent observing
conditions we do not detect the quiescent counterpart at the position of the
outburst optical counterpart down to a magnitude limit of i'>25.3. This can be
converted to a limit on the absolute magnitude of the counterpart of I>9.6
which implies that the counterpart can only be a brown or a white dwarf.


  In [2] it was proven that the Cass algorithm is a polynomial-time algorithm
for constructing level<=2 networks from clusters. Here we demonstrate, for each
k>=0, a polynomial-time algorithm for constructing level-k phylogenetic
networks from clusters. Unlike Cass the algorithm scheme given here is only of
theoretical interest. It does, however, strengthen the hope that efficient
polynomial-time algorithms (and perhaps fixed parameter tractable algorithms)
exist for this problem.


  Clustering Transactions in sequence, temporal and time series databases is
achieving an important attention from the database researchers and software
industry. Significant research is carried out towards defining and validating
the suitability of new similarity measures for sequence, temporal, time series
databases which can accurately and efficiently find the similarity between user
transactions in the given database to predict the user behavior. The
distribution of items present in the transactions contributes to a great extent
in finding the degree of similarity between them. This forms the key idea of
the proposed similarity measure. The main objective of the research is to first
design the efficient similarity measure which essentially considers the
distribution of the items in the item set over the entire transaction data set
and also considers the commonality of items present in the transactions, which
is the major drawback in the Jaccard, Cosine, Euclidean similarity measures. We
then carry out the analysis for worst case, the average case and best case
situations. The Similarity measure designed is Gaussian based and preserves the
properties of Gaussian function. The proposed similarity measure may be used to
both cluster and classify the user transactions and predict the user behaviors.


  Radar-based contactless cardiac monitoring has become a popular research
direction recently, but the fine-grained electrocardiogram (ECG) signal is
still hard to reconstruct from millimeter-wave radar signal. The key obstacle
is to decouple the cardiac activities in the electrical domain (i.e., ECG) from
that in the mechanical domain (i.e., heartbeat), and most existing research
only uses pure data-driven methods to map such domain transformation as a black
box. Therefore, this work first proposes a signal model for domain
transformation, and then a novel deep learning framework called radarODE is
designed to fuse the temporal and morphological features extracted from radar
signals and generate ECG. In addition, ordinary differential equations are
embedded in radarODE as a decoder to provide morphological prior, helping the
convergence of the model training and improving the robustness under body
movements. After being validated on the dataset, the proposed radarODE achieves
better performance compared with the benchmark in terms of missed detection
rate, root mean square error, Pearson correlation coefficient with the
improvement of 9%, 16% and 19%, respectively. The validation results imply that
radarODE is capable of recovering ECG signals from radar signals with high
fidelity and can be potentially implemented in real-life scenarios.


  In this Comment it is shown that it cannot be argued that in the magnetic AB
effect there is no force acting on the particle, i.e., that the observed phase
shift is entirely due to nonzero vector potential. In stationary resistive
conductors carrying constant currents there are quasistatic surface charges,
which generate not only the electric field inside the wire driving the current,
but also a static electric field outside it. These external static electric
fields have nothing to do with Boyer's force picture and with his result for
the existence of a time delay.


  We present an effective field theory approach to the Fracton phases. The
approach is based the notion of a multipole algebra. It is an extension of
space(-time) symmetries of a charge-conserving matter that includes global
symmetries responsible for the conservation of various components of the
multipole moments of the charge density. We explain how to construct field
theories invariant under the action of the algebra. These field theories
generally break rotational invariance and exhibit anisotropic scaling. We
further explain how to partially gauge the multipole algebra. Such gauging
makes the symmetries responsible for the conservation of multipole moments
local, while keeping rotation and translations symmetries global. It is shown
that upon such gauging one finds the symmetric tensor gauge theories, as well
as the generalized gauge theories discussed recently in the literature. The
outcome of the gauging procedure depends on the choice of the multipole
algebra. In particular, we show how to construct an effective theory for the
$U(1)$ version of the Haah code based on the principles of symmetry and provide
a two dimensional example with operators supported on a Sierpinski triangle. We
show that upon condensation of charged excitations Fracton phases of both types
as well as various SPTs emerge. Finally, the relation between the present
approach and the formalism based on polynomials over finite fields is
discussed.


  We study a one-dimensional $p$-wave superconductor subject to non-Hermitian
quasiperiodic potentials. Although the existence of the non-Hermiticity, the
Majorana zero mode is still robust against the disorder perturbation. The
analytic topological phase boundary is verified by calculating the energy gap
closing point and the topological invariant. Furthermore, we investigate the
localized properties of this model, revealing that the topological phase
transition is accompanied with the Anderson localization phase transition, and
a wide critical phase emerges with amplitude increments of the non-Hermitian
quasiperiodic potentials. Finally, we numerically uncover a non-conventional
real-complex transition of the energy spectrum, which is different from the
conventional $\mathcal{PT}$ symmetric transition.


  We show that the spontaneous compactification of the Abelian and non-Abelian
two-form gauge field theories from $D=4+1$ to $D=3+1$ leads to the same
theories plus the Maxwell and Yang-Mills ones, respectively. The vector
potential comes from the zero mode of the fifth component of the tensor gauge
field in D=5. Concerning to the non-Abelian case, it is necessary to make a
more refined definition of the three-form stress tensor in order to be
compatible, after the compactification, with the two-form stress tensor of the
Yang-Mills theory.


  In the semi-supervised learning field, Graph Convolution Network (GCN), as a
variant model of GNN, has achieved promising results for non-Euclidean data by
introducing convolution into GNN. However, GCN and its variant models fail to
safely use the information of risk unlabeled data, which will degrade the
performance of semi-supervised learning. Therefore, we propose a Safe GCN
framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we
design an iterative process to label the unlabeled data. In each iteration, a
GCN and its supervised version(S-GCN) are learned to find the unlabeled data
with high confidence. The high-confidence unlabeled data and their pseudo
labels are then added to the label set. Finally, both added unlabeled data and
labeled ones are used to train a S-GCN which can achieve the safe exploration
of the risk unlabeled data and enable safe use of large numbers of unlabeled
data. The performance of Safe-GCN is evaluated on three well-known citation
network datasets and the obtained results demonstrate the effectiveness of the
proposed framework over several graph-based semi-supervised learning methods.


  The energies and widths of gamma-ray lines emitted by ambient nuclei excited
by flare-accelerated protons and alpha particles provide information on the
ions directionality and spectra, and on the characteristics of the interaction
region. We have measured the energies and widths of strong lines from
de-excitations of 12C, 16O, and 20Ne in solar flares as a function of
heliocentric angle. The line energies from all three nuclei exhibit ~1%
redshifts for flares at small heliocentric angles, but are not shifted near the
limb. The lines have widths of ~3% FWHM. We compare the 12C line measurements
for flares at five different heliocentric angles with calculations for
different interacting-particle distributions. A downward isotropic distribution
(or one with a small upward component) provides a good fit to the line
measurements. An angular distribution derived for particles that undergo
significant pitch angle scattering by MHD turbulence in coronal magnetic loops
provides comparably good fits.


  The conductance of a contact, having a radius smaller than the Fermi wave
length, on the surface of a thin metal film is investigated theoretically. It
is shown that quantization of the electron energy spectrum in the film leads to
a step-like dependence of differential conductance G(V) as a function of
applied bias eV. The distance between neighboring steps in eV equals the energy
level spacing due to size quantization. We demonstrate that a study of G(V) for
both signs of the voltage maps the spectrum of energy levels above and below
Fermi surface in scanning tunneling experiments.


  In order to gain a mechanistic understanding of how tinnitus emerges in the
brain, we must build biologically plausible computational models that mimic
both tinnitus development and perception, and test the tentative models with
brain and behavioral experiments. With a special focus on tinnitus research, we
review recent work at the intersection of artificial intelligence, psychology
and neuroscience, indicating a new research agenda that follows the idea that
experiments will yield theoretical insight only when employed to test
brain-computational models. This view challenges the popular belief, that
tinnitus research is primarily data limited, and that producing large,
multi-modal, and complex datasets, analyzed with advanced data analysis
algorithms, will finally lead to fundamental insights into how tinnitus
emerges. However, there is converging evidence that although modern
technologies allow assessing neural activity in unprecedentedly rich ways in
both, animals and humans, empirical testing one verbally defined hypothesis
about tinnitus after another, will never lead to a mechanistic understanding.
Instead, hypothesis testing needs to be complemented with the construction of
computational models that generate verifiable predictions. We argue, that even
though, contemporary artificial intelligence and machine learning approaches
largely lack biological plausibility, the models to be constructed will have to
draw on concepts from these fields, since they have already proven to do well
in modeling brain function. Nevertheless, biological fidelity will have to be
increased successively, leading to ever better and fine-grained models,
allowing at the end for even testing possible treatment strategies in silico,
before application in animal or patient studies.


  This paper shows that causal model discovery is not an NP-hard problem, in
the sense that for sparse graphs bounded by node degree k the sound and
complete causal model can be obtained in worst case order N^{2(k+2)}
independence tests, even when latent variables and selection bias may be
present. We present a modification of the well-known FCI algorithm that
implements the method for an independence oracle, and suggest improvements for
sample/real-world data versions. It does not contradict any known hardness
results, and does not solve an NP-hard problem: it just proves that sparse
causal discovery is perhaps more complicated, but not as hard as learning
minimal Bayesian networks.


  Invariant coordinate selection (ICS) and projection pursuit (PP) are two
methods that can be used to detect clustering directions in multivariate data
by optimizing criteria sensitive to non-normality. In particular, ICS finds
clustering directions using a relative eigen-decomposition of two scatter
matrices with different levels of robustness; PP is a one-dimensional variant
of ICS. Each of the two scatter matrices includes an implicit or explicit
choice of location. However, when different measures of location are used, ICS
and PP can behave counter-intuitively. In this paper we explore this behavior
in a variety of examples and propose a simple and natural solution: use the
same measure of location for both scatter matrices.


  We compute the spectrum of the classical and quantum mechanical
coarse-grained propagators for a piecewise linear discontinuous map. We analyze
the quantum - classical correspondence and the evolution of the spectrum with
increasing resolution. Our results are compared to the ones obtained for a
mixed system.


  The gravitational Higgs mechanism proposed by 't Hooft in arXiv:0708.3184
involves the spacetime metric g_{mu nu} as well as the induced metric
\bar{g}_{mu nu} proportional to \eta_{a b} \partial_{mu} \phi^a \partial_{nu}
\phi^b where \phi^{a} (a=0,...,3), as we call it, break all four
diffeomorphisms spontaneously via the vacuum expectation values < \phi^a >
proportional to x^a. In this framework, we construct and analyze the most
general action density in terms of various invariants involving the curvature
tensors, connexion coefficients, and the contractions and the determinants of
the two metric fields. We show that this action admits a consistent expansion
about the flat background such that the resulting Lagrangian possesses several
novel features not found in the linearized Einstein-Hilbert Lagrangian with
Fierz-Pauli mass term (LELHL-FP): (i) its kinetic part generalizes that of
LELHL-FP by weighing the corresponding structures with certain coefficients
generated by invariants, (ii) the entire Lagrangian is ghost-- and
tachyon--free for mass terms not necessarily in the Fierz-Pauli form, and,
(iii) a consistent mass term is generated with no apparent need to higher
derivative couplings.


  $\textit{Magic: The Gathering}$ is a popular and famously complicated trading
card game about magical combat. In this paper we show that optimal play in
real-world $\textit{Magic}$ is at least as hard as the Halting Problem, solving
a problem that has been open for a decade. To do this, we present a methodology
for embedding an arbitrary Turing machine into a game of $\textit{Magic}$ such
that the first player is guaranteed to win the game if and only if the Turing
machine halts. Our result applies to how real $\textit{Magic}$ is played, can
be achieved using standard-size tournament-legal decks, and does not rely on
stochasticity or hidden information. Our result is also highly unusual in that
all moves of both players are forced in the construction. This shows that even
recognising who will win a game in which neither player has a non-trivial
decision to make for the rest of the game is undecidable. We conclude with a
discussion of the implications for a unified computational theory of games and
remarks about the playability of such a board in a tournament setting.


  The SU(4) algebraic model of high-temperature superconductivity is studied
employing the boson mapping techniques. The bosonization of the model enables
us to get exact numerical solution of the model. The order parameters are
discussed. The situation close to the SO(5) dynamical symmetry limit is
interpreted as a modelling case for the behaviour of $D$-wave superconducting
and antiferromagnetic phases in cuprates.


  Linking two network flows that have the same source is essential in intrusion
detection or in tracing anonymous connections. To improve the performance of
this process, the flow can be modified (fingerprinted) to make it more
distinguishable. However, an adversary located in the middle can modify the
flow to impair the correlation by delaying the packets or introducing dummy
traffic.
  We introduce a game-theoretic framework for this problem, that is used to
derive the Nash Equilibrium. As obtaining the optimal adversary delays
distribution is intractable, some approximations are done. We study the
concrete example where these delays follow a truncated Gaussian distribution.
We also compare the optimal strategies with other fingerprinting schemes. The
results are useful for understanding the limits of flow correlation based on
packet timings under an active attacker.


  It is emphasized that the $E_{\nu}$ (neutrino energy) spectrum of the $\nu_e$
survival probability in the Resonant Spin-Flavor Precession (RSFP) scenario has
very desirable shape to reconcile all existing solar neutrino data. As the
result, the RSFP scenario is shown to have rather broad allowed region to
reconcile the data in the ($B,\ \Delta m^2$) plane, with $B$ being the magnetic
field strength inside the Sun. The sensitivity of the allowed region on the
different choices of the $B$ profile, and the time variations of the solar
neutrino event rates in the RSFP scenario are also discussed in some detail.


  A pyrochlore lattice is considered where the average electron number of
electrons per site is half--integer, concentrating on the case of exactly half
an electron per site. Strong on-site repulsions are assumed, so that all sites
are either empty or singly occupied. Where there are in addition strong
nearest--neighbour repulsions, a tetrahedron rule comes into effect, as
previously suggested for magnetite. We show that in this case, there exist
excitations with fractional charge (+/-) e/2. These are intimately connected
with the high degeneracy of the ground state in the absence of kinetic energy
terms. When an additional electron is inserted into the system, it decays into
two point like excitations with charge -e/2, connected by a Heisenberg spin
chain which carries the electron's spin.


  This article presents a series of opposed flow flame spread experiments,
conducted using cast cylindrical PMMA (acrylic) rods, 80 mm long and of
diameters 1 mm and 0.5 mm, in normal gravity and microgravity environments. The
experiments are primarily conducted for molar oxygen levels of 21%, 23% and 40%
at 1 atmosphere pressure and opposed flow speed ranging from 0 cm/s to 25 cm/s.
Experiments are also conducted in normal gravity for oxygen levels 21% to 60%
to study the effect of oxygen level. At near ambient oxygen levels, the flame
shape in microgravity resembles a mushroom and there are fluctuations at the
leading edge due to sporadic fuel jets emanating from bursting bubbles at the
fuel surface. The flame spreads faster in microgravity, which is determined to
be due to increased preheat length. Preheat length is measured for flame
spreading over 1 mm diameter fuel rod under no flow condition using fine
thermocouples and is found to be 0.98 cm in microgravity and 0.34 cm in normal
gravity. It is found that for scaling analysis, including Stefan flow velocity
in the definition of reference velocity results in a reasonable estimate of the
preheat length of a flame spreading in quiescent microgravity environment. At
high oxygen levels (> 35%) the flame becomes turbulent and spreads at nearly
the same rate in normal gravity and microgravity environments and the flame
spread rates are not affected by external flow speed.


  We propose a self-supervised method to solve Pronoun Disambiguation and
Winograd Schema Challenge problems. Our approach exploits the characteristic
structure of training corpora related to so-called "trigger" words, which are
responsible for flipping the answer in pronoun disambiguation. We achieve such
commonsense reasoning by constructing pair-wise contrastive auxiliary
predictions. To this end, we leverage a mutual exclusive loss regularized by a
contrastive margin. Our architecture is based on the recently introduced
transformer networks, BERT, that exhibits strong performance on many NLP
benchmarks. Empirical results show that our method alleviates the limitation of
current supervised approaches for commonsense reasoning. This study opens up
avenues for exploiting inexpensive self-supervision to achieve performance gain
in commonsense reasoning tasks.


  In this article we consider resummed expressions for the heat-kernel's trace
of a Laplace operator, the latter including a potential and imposing Dirichlet
semitransparent boundary conditions on a surface of codimension one in flat
space. We obtain resummed expressions that correspond to the first and second
order expansion of the heat-kernel in powers of the potential. We show how to
apply these results to obtain the bulk and surface form factors of a scalar
quantum field theory in $d=4$ with a Yukawa coupling to a background. A
characterization of the form factors in terms of pseudo-differential operators
is given. Additionally, we discuss a connection between heat-kernels for
Dirichlet semitransparent, Dirichlet and Robin boundary conditions.


  Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR)
images from the corresponding Low-Resolution (LR) counterparts. Although recent
progress in ISR has been remarkable. However, they are way too computationally
intensive to be deployed on edge devices, since most of the recent approaches
are deep learning-based. Besides, these methods always fail in real-world
scenes, since most of them adopt a simple fixed "ideal" bicubic downsampling
kernel from high-quality images to construct LR/HR training pairs which may
lose track of frequency-related details. In this work, an approach for
real-time ISR on mobile devices is presented, which is able to deal with a wide
range of degradations in real-world scenarios. Extensive experiments on
traditional super-resolution datasets (Set5, Set14, BSD100, Urban100, Manga109,
DIV2K) and real-world images with a variety of degradations demonstrate that
our method outperforms the state-of-art methods, resulting in higher PSNR and
SSIM, lower noise and better visual quality. Most importantly, our method
achieves real-time performance on mobile or edge devices.


  Despite their impressive capabilities, large language models (LLMs) have been
observed to generate responses that include inaccurate or fabricated
information, a phenomenon commonly known as ``hallucination''. In this work, we
propose a simple \textit{Induce-then-Contrast} Decoding (ICD) strategy to
alleviate hallucinations. We first construct a factually weak LLM by inducing
hallucinations from the original LLMs. Then, we penalize these induced
hallucinations during decoding to enhance the factuality of the generated
content. Concretely, we determine the final next-token predictions by
amplifying the predictions from the original model and downplaying the induced
untruthful predictions via contrastive decoding. Experimental results on both
discrimination-based and generation-based hallucination evaluation benchmarks,
such as TruthfulQA and \textsc{FActScore}, demonstrate that our proposed ICD
methods can effectively enhance the factuality of LLMs across various model
sizes and families. For example, when equipped with ICD, Llama2-7B-Chat and
Mistral-7B-Instruct achieve performance comparable to ChatGPT and GPT4 on
TruthfulQA, respectively.


  All 280 of the statistically-complete Palomar sample of nearby (<120 Mpc)
galaxies dec > 20 degrees have been observed at 1.5 GHz as part of the LeMMINGs
e-MERLIN legacy survey. Here, we present Chandra X-ray observations of the
nuclei of 213 of these galaxies, including a statistically-complete sub-set of
113 galaxies in the declination range 40 degrees to 65 degrees. We observed
galaxies of all optical spectral types, including 'active' galaxies (e.g.,
LINERs and Seyferts) and 'inactive' galaxies like HII galaxies and absorption
line galaxies (ALG). The X-ray flux limit of our survey is
1.65$\times$10$^{-14}$~erg s$^{-1}$ cm$^{-2}$ (0.3$-$10 keV). We detect X-ray
emission coincident within 2-arcsec of the nucleus in 150/213 galaxies,
including 13/14 Seyferts, 68/77 LINERs, 13/22 ALGs and 56/100 HII galaxies, but
cannot completely rule out contamination from non-AGN processes in sources with
nuclear luminosities <10$^{39}$ erg s$^{-1}$. We construct an X-ray Luminosity
function (XLF) and find that the local galaxy XLF, when including all AGN
types, can be represented as a single power-law of slope $-0.54 \pm 0.06$. The
Eddington ratio of the Seyferts is usually 2-4 decades higher than that of the
LINERs, ALGs and HII galaxies, which are mostly detected with Eddington ratios
<10$^{-3}$. Using [O III] line measurements and BH masses from the literature,
we show that LINERs, HII galaxies and ALGs follow similar correlations to low
luminosities, suggesting that some 'inactive' galaxies may harbour AGN.


  In Gaia DR2, the unprecedented high-precision level reached in sub-mas for
astrometry and mmag for photometry. Using cluster members identified with these
astrometry and photometry in Gaia DR2, we can obtain a reliable determination
of cluster properties. However, because of the shortcoming of Gaia
spectroscopic observation in dealing with densely crowded cluster region, the
number of radial velocity and metallicity for cluster member stars from Gaia
DR2 is still lacking. In this study, we aim to improve the cluster properties
by combining the LAMOST spectra. In particular, we provide the list of cluster
members with spectroscopic parameters as an add-value catalog in LAMOST DR5,
which can be used to perform detailed study for a better understanding on the
stellar properties, by using their spectra and fundamental properties from the
host cluster. We cross-matched the spectroscopic catalog in LAMOST DR5 with the
identified cluster members in Cantat-Gaudin et al.2018 and then used members
with spectroscopic parameters to derive statistical properties of open
clusters. We obtained a list of 8811 members with spectroscopic parameters and
a catalog of 295 cluster properties. In addition, we study the radial and
vertical metallicity gradient and age-metallicity relation with the compiled
open clusters as tracers, finding slopes of -0.053$\pm$0.004 dex kpc$^{-1}$,
-0.252$\pm$0.039 dex kpc$^{-1}$ and 0.022$\pm$0.008 dex Gyr$^{-1}$,
respectively. Both slopes of metallicity distribution relation for young
clusters (0.1 Gyr < Age < 2 Gyr) and the age-metallicity relation for clusters
within 6 Gyr are consistent with literature results. In order to fully study
the chemical evolution history in the disk, more spectroscopic observations for
old and distant open clusters are needed for further investigation.


  While a variety of ensemble methods for multilabel classification have been
proposed in the literature, the question of how to aggregate the predictions of
the individual members of the ensemble has received little attention so far. In
this paper, we introduce a formal framework of ensemble multilabel
classification, in which we distinguish two principal approaches: "predict then
combine" (PTC), where the ensemble members first make loss minimizing
predictions which are subsequently combined, and "combine then predict" (CTP),
which first aggregates information such as marginal label probabilities from
the individual ensemble members, and then derives a prediction from this
aggregation. While both approaches generalize voting techniques commonly used
for multilabel ensembles, they allow to explicitly take the target performance
measure into account. Therefore, concrete instantiations of CTP and PTC can be
tailored to concrete loss functions. Experimentally, we show that standard
voting techniques are indeed outperformed by suitable instantiations of CTP and
PTC, and provide some evidence that CTP performs well for decomposable loss
functions, whereas PTC is the better choice for non-decomposable losses.


  Artificially induced asymmetric tribological properties of ferroelectrics
offer an alternative route to visualize and control ferroelectric domains.
Here, we observe the switchable friction and wear behavior of ferroelectrics
using a nanoscale scanning probe where down domains having lower friction
coefficient than up domains can be used as smart masks as they show slower wear
rate than up domains. This asymmetry is enabled by flexoelectrically coupled
polarization in the up and down domains under a sufficiently high contact
force. Moreover, we determine that this polarization-sensitive tribological
asymmetry is universal across ferroelectrics with different chemical
composition and crystalline symmetry. Finally, using this switchable tribology
and multi-pass patterning with a domain-based dynamic smart mask, we
demonstrate three-dimensional nanostructuring exploiting the asymmetric wear
rates of up and down domains, which can, furthermore, be scaled up to
technologically relevant (mm-cm) size. These findings establish that
ferroelectrics are electrically tunable tribological materials at the nanoscale
for versatile applications.


  The use of carbon nanotube (CNT) field-effect transistors (FETs) in microwave
circuit design requires an appropriate, immediate and efficient description of
their performance. This work describes a technique to extract the parameters of
an electrical equivalent circuit for CNTFETs. The equivalent circuit is used to
model the dynamic and noise performance at low- and high-frequency of different
CNTFET technologies, considering extrinsic and intrinsic device parameters as
well as the contact resistance. The estimation of the contact resistance at the
metal/CNTs interfaces is obtained from a Y-function based extraction method.
The noise model includes four noise sources: thermal noise, thermal channel
noise, shot channel noise and flicker noise. The proposed model is compared
with a compact model calibrated to hysteresis-free experimental data from a
high-frequency multi-tube (MT)-CNTFET technology. Additionally, it has been
applied to experimental data from another fabricated MT-CNTFET technology. The
comparison in both cases shows a good agreement between reference data
(simulation and experimental) and results from the proposed model. Low- and
high-frequency noise projections of the fabricated reference device are further
studied. Noise results from both studied technologies show that shot noise
mainly contributes to the total noise due to the presence of Schottky barriers
at contacts and along the channel.


  To overcome topological constraints and improve the expressiveness of
normalizing flow architectures, Wu, K\"ohler and No\'e introduced stochastic
normalizing flows which combine deterministic, learnable flow transformations
with stochastic sampling methods. In this paper, we consider stochastic
normalizing flows from a Markov chain point of view. In particular, we replace
transition densities by general Markov kernels and establish proofs via
Radon-Nikodym derivatives which allows to incorporate distributions without
densities in a sound way. Further, we generalize the results for sampling from
posterior distributions as required in inverse problems. The performance of the
proposed conditional stochastic normalizing flow is demonstrated by numerical
examples.


  We present new static spherically-symmetric solutions of Einstein equations
with the quintessence-like matter surrounding a global monopole. These new
solutions of the coupling scalar-Einstein equations are more complicated, which
depend on the parameter of equation of state $-1 < w_{q} <-{1/3}$. A
gravitating global monopole produces a gravitational field of de Sitter kind
outside the core in addition to a solid angular deficit. In the $w_{q} =
-{1/3}$ case, we have proved that the solution cannot exist since the density
of quintessence-like tends to zero if $w_{q} \to -{1/3}$. As a new feature,
these monopoles have the outer horizon depending on both Goldstone field and
quintessence-like. Since current observations constrain $-1.14 < w_{q} <
-0.93$, new global monopoles have interesting astrophysical applications.


  We obtain some Bailey pairs associated with indefinite quadratic forms with
the $\beta_n$ connected to a finite sum. A new general identity is given, which
provides identities for $q$-hypergeometric series, including mock theta
functions.


  An alphabetic binary tree formulation applies to problems in which an outcome
needs to be determined via alphabetically ordered search prior to the
termination of some window of opportunity. Rather than finding a decision tree
minimizing $\sum_{i=1}^n w(i) l(i)$, this variant involves minimizing $\log_a
\sum_{i=1}^n w(i) a^{l(i)}$ for a given $a \in (0,1)$. This note introduces a
dynamic programming algorithm that finds the optimal solution in polynomial
time and space, and shows that methods traditionally used to improve the speed
of optimizations in related problems, such as the Hu-Tucker procedure, fail for
this problem. This note thus also introduces two approximation algorithms which
can find a suboptimal solution in linear time (for one) or $\order(n \log n)$
time (for the other), with associated coding redundancy bounds.


  Let $K $ be a nonic number field generated by a complex root $\th$ of a monic
irreducible trinomial $ F(x)= x^9+ax+b \in \Z[x]$, where $ab \neq 0$. Let
$i(K)$ be the index of $K$. A rational prime $p$ dividing $ i(K)$ is called a
prime common index divisor of $K$. In this paper, for every rational prime $p$,
we give necessary and sufficient conditions depending only $a$ and $b$ for
which $p$ is a common index divisor of $K$. As application of our results we
identify infinite parametric families of non-monogenic nonic numbers fields
defined by such trinomials. At the end, some numerical examples illustrating
our theoretical results are given.


  Minimum total energy calculations, which account for both electron--lattice
and electron--electron interactions in conjugated polymers are performed for
chains with up to eight carbon atoms. These calculations are motivated in part
by recent experimental results on the spectroscopy of polyenes and conjugated
polymers and shed light on the longstanding question of the relative importance
of electron--lattice vs. electron--electron interactions in determining the
properties of these systems.


  Language generation models' democratization benefits many domains, from
answering health-related questions to enhancing education by providing
AI-driven tutoring services. However, language generation models'
democratization also makes it easier to generate human-like text at-scale for
nefarious activities, from spreading misinformation to targeting specific
groups with hate speech. Thus, it is essential to understand how people
interact with bots and develop methods to detect bot-generated text. This paper
shows that bot-generated text detection methods are more robust across datasets
and models if we use information about how people respond to it rather than
using the bot's text directly. We also analyze linguistic alignment, providing
insight into differences between human-human and human-bot conversations.


  Key to the effectiveness of crowdsourcing approaches for software engineering
is workflow design, describing how complex work is organized into small,
relatively independent microtasks. In this paper, we introduce a
Behavior-Driven Development (BDD) workflow for accomplishing programming work
through self-contained microtasks, implemented as a preconfigured environment
called Crowd Microservices. In our approach, a client, acting on behalf of a
software team, describes a microservice as a set of endpoints with paths,
requests, and responses. A crowd then implements the endpoints, identifying
individual endpoint behaviors which they test, implement, and debug, creating
new functions and interacting with persistence APIs as needed. To evaluate our
approach, we conducted a feasibility study in which a small crowd worked to
implement a small ToDo microservice. The crowd created an implementation with
only four defects, completing 350 microtasks and implementing 13 functions. We
discuss the implications of these findings for incorporating crowdsourced
programming contributions into traditional software projects.


  The versatility of recent machine learning approaches makes them ideal for
improvement of next generation video compression solutions. Unfortunately,
these approaches typically bring significant increases in computational
complexity and are difficult to interpret into explainable models, affecting
their potential for implementation within practical video coding applications.
This paper introduces a novel explainable neural network-based inter-prediction
scheme, to improve the interpolation of reference samples needed for fractional
precision motion compensation. The approach requires a single neural network to
be trained from which a full quarter-pixel interpolation filter set is derived,
as the network is easily interpretable due to its linear structure. A novel
training framework enables each network branch to resemble a specific
fractional shift. This practical solution makes it very efficient to use
alongside conventional video coding schemes. When implemented in the context of
the state-of-the-art Versatile Video Coding (VVC) test model, 0.77%, 1.27% and
2.25% BD-rate savings can be achieved on average for lower resolution sequences
under the random access, low-delay B and low-delay P configurations,
respectively, while the complexity of the learned interpolation schemes is
significantly reduced compared to the interpolation with full CNNs.


  In this paper we present the first on-sky results with the fibered aperture
masking instrument FIRST. Its principle relies on the combination of spatial
filtering and aperture masking using single-mode fibers, a novel technique that
is aimed at high dynamic range imaging with high angular resolution. The
prototype has been tested with the Shane 3-m telescope at Lick Observatory. The
entrance pupil is divided into subpupils feeding single-mode fibers. The flux
injection into the fibers is optimized by a segmented mirror. The beams are
spectrally dispersed and recombined in a non-redundant exit configuration in
order to retrieve all contrasts and phases independently. The instrument works
at visible wavelengths between 600 nm and 760 nm and currently uses nine of the
30 43 cm subapertures constituting the full pupil. First fringes were obtained
on Vega and Deneb. Stable closure phases were measured with standard deviations
on the order of 1 degree. Closure phase precision can be further improved by
addressing some of the remaining sources of systematic errors. While the number
of fibers used in the experiment was too small to reliably estimate visibility
amplitudes, we have measured closure amplitudes with a precision of 10 % in the
best case. These first promising results obtained under real observing
conditions validate the concept of the fibered aperture masking instrument and
open the way for a new type of ground-based instrument working in the visible.
The next steps of the development will be to improve the stability and the
sensitivity of the instrument in order to achieve more accurate closure phase
and visibility measurements, and to increase the number of sub-pupils to reach
full pupil coverage.


  Understanding animal movements and modelling the routes they travel can be
essential in studies of pathogen transmission dynamics. Pathogen biology is
also of crucial importance, defining the manner in which infectious agents are
transmitted. In this article we investigate animal movement with relevance to
pathogen transmission by physical rather than airborne contact, using the
domestic chicken and its protozoan parasite Eimeria as an example. We have
obtained a configuration for the maximum possible distance that a chicken can
walk through straight and non-overlapping paths (defined in this paper) on
square grid graphs. We have obtained preliminary results for such walks which
can be practically adopted and tested as a foundation to improve understanding
of non-airborne pathogen transmission. Linking individual non-overlapping walks
within a grid-delineated area can be used to support modeling of the frequently
repetitive, overlapping walks characteristic of the domestic chicken, providing
a framework to model faecal deposition and subsequent parasite dissemination by
faecal/host contact.We also pose an open problem on multiple walks on finite
grid graphs. These results grew from biological insights and have potential
applications. Keywords: Spread of bird diseases, Eimeria, Maximum walks,
longest paths, NP-Complete. MSC: 92A17, 68Q17


  Annotating a large number of training images is very time-consuming. In this
background, this paper focuses on learning from easy-to-acquire web data and
utilizes the learned model for fine-grained image classification in labeled
datasets. Currently, the performance gain from training with web data is
incremental, like a common saying "better than nothing, but not by much".
Conventionally, the community looks to correcting the noisy web labels to
select informative samples. In this work, we first systematically study the
built-in gap between the web and standard datasets, i.e. different data
distributions between the two kinds of data. Then, in addition to using web
labels, we present an unsupervised object localization method, which provides
critical insights into the object density and scale in web images.
Specifically, we design two constraints on web data to substantially reduce the
difference of data distributions for the web and standard datasets. First, we
present a method to control the scale, localization and number of objects in
the detected region. Second, we propose to select the regions containing
objects that are consistent with the web tag. Based on the two constraints, we
are able to process web images to reduce the gap, and the processed web data is
used to better assist the standard dataset to train CNNs. Experiments on
several fine-grained image classification datasets confirm that our method
performs favorably against the state-of-the-art methods.


  This report is an overview of the gamma-gamma physics capabilities of LEP2,
and covers the following topics: structure functions, equivalent photon
approximation, tagging conditions etc, soft and semihard physics, large-$p_t$
processes, heavy-quark physics, and exclusive channels.


  We construct a three dimensional toy systems with two types of fermions
forming a composite boson. They are hold in a harmonic potential. The basis
functions are constructed from an internal and an external Gauss function. All
integrals have analytical solutions. The high symmetry reduces the number of
integrals to be calculated for the symmetrized wave functions. With the
internal Gauss function the composite bosons can be tuned from fermionic
unbound behavior to bosonic bound behavior.


  The colossal evolution of wireless communication technologies over the past
few years has driven increased interest in its integration in a variety of
less-explored environments, such as the underwater medium. In this magazine
paper, we present a comprehensive discussion on a novel concept of routing
protocol known as cross-media routing, incorporating the marine and aerial
interfaces. In this regard, we discuss the limitation of single-media routing
and advocate the need for cross-media routing along with the current status of
research development in this direction. To this end, we also propose a novel
cross-media routing protocol known as bubble routing for autonomous marine
systems where different sets of AUVs, USVs, and airborne nodes are considered
for the routing problem. We evaluate the performance of the proposed routing
protocol by using the two key performance metrics, i.e., packet delivery ratio
(PDR) and end-to-end delay. Moreover, we delve into the challenges encountered
in cross-media routing, unveiling exciting opportunities for future research
and innovation. As wireless communication expands its horizons to encompass the
underwater and aerial domains, understanding and addressing these challenges
will pave the way for enhanced cross-media communication and exploration.


  Semantic patterns of fine-grained objects are determined by subtle appearance
difference of local parts, which thus inspires a number of part-based methods.
However, due to uncontrollable object poses in images, distinctive details
carried by local regions can be spatially distributed or even self-occluded,
leading to a large variation on object representation. For discounting pose
variations, this paper proposes to learn a novel graph based object
representation to reveal a global configuration of local parts for
self-supervised pose alignment across classes, which is employed as an
auxiliary feature regularization on a deep representation learning
network.Moreover, a coarse-to-fine supervision together with the proposed
pose-insensitive constraint on shallow-to-deep sub-networks encourages
discriminative features in a curriculum learning manner. We evaluate our method
on three popular fine-grained object classification benchmarks, consistently
achieving the state-of-the-art performance. Source codes are available at
https://github.com/yangxh11/P2P-Net.


  We consider world-sheet instanton effects in N=1 string orientifolds of
noncompact toric Calabi-Yau threefolds. We show that unoriented closed string
topological amplitudes can be exactly computed using localization techniques
for holomorphic maps with involution. Our results are in precise agreement with
mirror symmetry and large N duality predictions.


  Technology developed for the T2K electromagnetic calorimeter has been adapted
to make a small footprint, reliable, segmented detector to characterise
anti-neutrinos emitted by nuclear reactors. The device has been developed and
demonstrated by the University of Liverpool and underwent field tests at the
Wylfa Magnox Reactor on Anglesey, UK. It was situated in a 20\,ft ISO shipping
container, above ground, roughly 60\,m from the 1.5\,\GWt\ reactor core. Based
on the design of the T2K Near Detector ECal, the device detects anti-neutrinos
through the distinctive delayed coincidence signal of inverse $\beta$-decay
interactions using extruded plastic scintillator and Hamamatsu Multi-Pixel
Photon Counters.


  In this article, we study topological properties of Voisin's punctual Hilbert
schemes of an almost-complex fourfold $X$. In this setting, we compute their
Betti numbers and construct Nakajima operators. We also define tautological
bundles associated with any complex bundle on $X$, which are shown to be
canonical in $K$-theory.


  It is a well-established fact that intranuclear-cascade models generally fail
to consistently reproduce the cross sections for one-proton and one-neutron
removal from stable nuclei by a high-energy proton beam. We use simple
shell-model calculations to investigate the reasons of this deficiency. We find
that a refined description of the neutron skin and of the energy density in the
nuclear surface is crucial for the aforementioned observables, and that neither
ingredient is sufficient if taken separately. As a by-product, the predictions
for removal of several nucleons are also improved by the refined treatment.


  Let $\mm=(m_0,...,m_n)$ be an arithmetic sequence, i.e., a sequence of
integers $m_0<...<m_n$ with no common factor that minimally generate the
numerical semigroup $\sum_{i=0}^{n}m_i\N$ and such that
$m_i-m_{i-1}=m_{i+1}-m_i$ for all $i\in\{1,...,n-1\}$. The homogeneous
coordinate ring $\Gamma_\mm$ of the affine monomial curve parametrically
defined by $X_0=t^{m_0},...,X_n=t^{m_n}$ is a graded $R$-module where $R$ is
the polynomial ring $k[X_0,...,X_n]$ with the grading obtained by setting
$\deg{X_i}:=m_i$. In this paper, we construct an explicit minimal graded free
resolution for $\Gamma_\mm$ and show that its Betti numbers depend only on the
value of $m_0$ modulo $n$. As a consequence, we prove a conjecture of Herzog
and Srinivasan on the eventual periodicity of the Betti numbers of semigroup
rings under translation for the monomial curves defined by an arithmetic
sequence.


  The submodular Santa Claus problem was introduced in a seminal work by
Goemans, Harvey, Iwata, and Mirrokni (SODA'09) as an application of their
structural result. In the mentioned problem $n$ unsplittable resources have to
be assigned to $m$ players, each with a monotone submodular utility function
$f_i$. The goal is to maximize $\min_i f_i(S_i)$ where $S_1,\dotsc,S_m$ is a
partition of the resources. The result by Goemans et al. implies a polynomial
time $O(n^{1/2 +\varepsilon})$-approximation algorithm. Since then progress on
this problem was limited to the linear case, that is, all $f_i$ are linear
functions. In particular, a line of research has shown that there is a
polynomial time constant approximation algorithm for linear valuation functions
in the restricted assignment case. This is the special case where each player
is given a set of desired resources $\Gamma_i$ and the individual valuation
functions are defined as $f_i(S) = f(S \cap \Gamma_i)$ for a global linear
function $f$. This can also be interpreted as maximizing $\min_i f(S_i)$ with
additional assignment restrictions, i.e., resources can only be assigned to
certain players. In this paper we make comparable progress for the submodular
variant. Namely, if $f$ is a monotone submodular function, we can in polynomial
time compute an $O(\log\log(n))$-approximate solution.


  Exceptional point (EP) denotes the non-Hermitian degeneracy, in which both
eigenvalues and eigenstates become identical. By the conventional local
Markovian master equation, EP can be constructed by parity-time (PT) or anti-PT
symmetry in a system composed of coupled subsystems. However, the coupling
between two systems makes the conventional local Markovian master equation
become inconsistent. By using the self-consistent Markovian master equation, we
show that there is no EP in the system composed of two bosonic subsystems. We
further prove that the conventional local master equation can be valid only
when the coupling strength is much smaller than the difference in resonance
frequency between the two subsystems, rather than the resonance frequencies. In
a system composed of three bosonic subsystems, EP can be obtained by
adiabatically eliminating one of the three subsystems.


  We consider a random walk on a random graph $(V,E)$, where $V$ is the set of
open sites under i.i.d. Bernoulli site percolation on the multi-dimensional
integer set $\mathbf{Z}^d$, and the transition probabilities of the walk are
generated by i.i.d. random conductances (positive numbers) assigned to the
edges in $E$. This random walk in random environments has long range jumps and
is reversible. We prove the quenched invariance principle for this walk when
the random conductances are unbounded from above but uniformly bounded from
zero by taking the corrector approach. To this end, we prove a metric
comparison between the graph metric and the Euclidean metric on the graph $(V,
E)$, an estimation of a first-passage percolation and an almost surely weighted
Poincar{\'{e}} inequality on $(V,E)$, which are used to prove the quenched heat
kernel estimations for the random walk.


  For over 25 years, common belief has been widely viewed as necessary for
joint behavior. But this is not quite correct. We show by example that what can
naturally be thought of as joint behavior can occur without common belief. We
then present two variants of common belief that can lead to joint behavior,
even without standard common belief ever being achieved, and show that one of
them, action-stamped common belief, is in a sense necessary and sufficient for
joint behavior. These observations are significant because, as is well known,
common belief is quite difficult to achieve in practice, whereas these variants
are more easily achievable.


  Laser beams can be made to form bright and dark intensity helices of light.
Such helices have a pitch length on the order of a wavelength and may have
applications in lithography and the manipulation of particles through optical
forces. The formation of bright helices is more strongly constrained by optical
resolution limits than that of dark helices, corresponding scaling laws are
derived and their relevance for photo-lithography pointed out. It is shown how
to arrange dark helices on a grid in massively parallel fashion in order to
create handed materials using photo-lithographic techniques.


  Our study is a follow-up of the SACY project, an extended survey in the
Southern Hemisphere targeted to search for young nearby associations. Nine
associations have either been newly identified, or had their member list better
defined. These associations, with ages between about 6 Myr and 70 Myr, form an
excellent sample to study the Li depletion in the pre-main sequence (PMS)
evolution. We investigate the use of Li abundances as an independent clock to
constrain the PMS evolution. We have calculated the LTE Li abundances for 376
members of different young associations. In addition we considered the effects
of their projected stellar rotation. We present the Li depletion as function of
age in the first hundred million years for the first time for the most extended
sample of Li abundances in young stellar associations. A clear Li depletion can
be measured in the temperature range from 5000K to 3500K for the age span
covered by these nine associations. The age sequence based on the Li-clock
agrees well with the isochronal ages, $\epsilon$Cha association being the only
possible exception. The lithium depletion patterns for those associations
resemble those of the young open clusters, strengthening the notion that the
members proposed for these loose young associations have indeed a common
physical origin. The observed scatter in the Li abundances hampers the use of
Li to determine reliable ages for individual stars. Rotation velocities above
20 km s$^{-1}$ seem to inhibit the Li depletion.


  In this paper we study a non strictly systems of conservation law by
stochastic perturbation. We show the existence and uniqueness of the solution.
We do not assume that $BV$-regularity for the initial conditions. The proofs
are based on the concept of entropy solution and in the characteristics method
(in the influence of noise). This is the first result on the regularization by
noise in hyperbolic systems of conservation law.


  For a totally real field F of degree d>1 and a quadratic space V of signature
(m,2)^{d_+} x (m+2,0)^{d-d_+} with associated Shimura variety Sh(V), we
consider the subring of cohomology generated by the classes of weighted special
cycles. We assume that d_+<d. We take the quotient SC(V) of this ring by the
radical of the restriction of the intersection pairing to it. We show that the
inner products of classes in SC(V) are determined by Fourier coefficients of
pullbacks of Hilbert-Siegel Eisenstein series of genus m to products of smaller
Siegel spaces and that the products of classes in SC(V) are determined by
Fourier coefficients of pullbacks to triple products of smaller Siegel spaces.
As a consequence, we show that, for quadratic spaces V and V' over F that are
isomorphic at all finite places, but with no restriction on d_+(V) and d_+(V')
other than the necessary condition that they have the same parity, the special
cycles rings SC(V) and SC(V') are isometrically isomorphic. This is a
consequence of the Siegel-Weil formula and the matching principle. Finally, we
give a combinatorial construction of a ring SC(V_+) associated to a totally
positive definite quadratic space V_+ of dimension m+2 over F and show that the
comparison isomorphism extends to this case.


  It was shown in arXiv:2309.10786 that the leading non-perturbative
contribution to the large $N$ expansion of superconformal index of (2,0) 6d
theory (which describes low-energy dynamics of $N$ coincident M5 branes) is
reproduced by the semiclassical partition function of quantum M2 brane wrapped
on $S^{1}\times S^{2}$ in a twisted version of AdS$_{7}\times S^{4}$
background. Here we demonstrate an analogous relation for the leading
non-perturbative contribution to the large $N$ expansion of the superconformal
index of the ${n}=8$ supersymmetric level-one $U(N) \times U(N)$ ABJM theory
(which describes low-energy dynamics of $N$ coincident M2 branes). The roles of
M2 and M5 branes get effectively interchanged. Namely, the large $N$ correction
to the ABJM index is found to be given by the semiclassical partition function
of quantum M5 brane wrapped on $S^1 \times S^5$ in a twisted version of
AdS$_{4}\times S^{7}$ background. This effectively confirms the suggestion for
the "M5 brane index" made in arXiv:2007.05213 on the basis of indirect
superconformal algebra considerations.


  The upgraded Giant Metrewave Radio Telescope (GMRT) has been used to image
the head-tail radio galaxy NGC 4869 in the Coma cluster with an angular
resolution of 6.26 arcsec at 250-500 MHz and 2.18 arcsec at the 1050-1450 MHz
bands. The archival legacy GMRT data have also been used to image the source
with angular resolutions from 4.9 to 21.8 arcsec at 610 MHz, 325 MHz, 240 MHz,
and 150 MHz. We find that the ~200 kpc scale radio morphology consists of five
distinct regions with the clear presence of a pinch at ~1.4 arcmin (= 38.8 kpc)
and a ridge at ~3.4 arcmin (= 94.2 kpc) from the head. The sharp bend by ~70
deg at ~3.5 arcmin (= 97 kpc) from the head is possibly due to projection
effects. The radio spectra show progressive spectral steepening as a function
of distance from the head and there is possibly re-acceleration of the
synchrotron electrons and perhaps also magnetic field re-generation in the
6-208 arcsec (= 2.8-96.1 kpc) region of the jet. We report a steep spectrum
sheath layer enveloping a flat spectrum spine, hinting at a transverse velocity
structure with a fast-moving spine surrounded by a slow-moving sheath layer. We
also derive the lifetimes of the radiating electrons and equipartition
parameters. A plausible explanation for the characteristic feature, a ridge of
emission perpendicular to the direction of tail is the flaring of a straight,
collimated radio jet as it crosses a surface brightness edge due to
Kelvin-Helmholtz instabilities.


  Calculations of the $^{6,8}$He + $^{28}$Si total reaction cross sections at
intermediate energies are performed on the basis of the Glauber-Sitenko
microscopic optical-limit model. The target-nucleus density distribution is
taken from the electron-nucleus scattering data, and the $^{6,8}$He densities
are used as they are derived in different models. The results of the
calculations are compared with the existing experimental data. The effects of
the density tails of the projectile nuclei as well as the role of shell
admixtures and short-range correlations are analyzed.


  This paper uses extended-magnetohydrodynamics (MHD) simulations to explore an
extreme magnetized plasma regime realisable by cylindrical implosions on the
OMEGA laser facility. This regime is characterized by highly compressed
magnetic fields (greater than 10~kT across the fuel), which contain a
significant proportion of the implosion energy and induce large electrical
currents in the plasma. Parameters governing the different magnetization
processes such as Ohmic dissipation and suppression of instabilities by
magnetic tension are presented, allowing for optimization of experiments to
study specific phenomena. For instance, a dopant added to the target gas-fill
can enhance magnetic flux compression while enabling spectroscopic diagnosis of
the imploding core. In particular, the use of Ar K-shell spectroscopy is
investigated by performing detailed non-LTE atomic kinetics and radiative
transfer calculations on the MHD data. Direct measurement of the core electron
density and temperature would be possible, allowing for both the impact of
magnetization on the final temperature and thermal pressure to be obtained. By
assuming the magnetic field is frozen into the plasma motion, which is shown to
be a good approximation for highly magnetized implosions, spectroscopic
diagnosis could be used to estimate which magnetization processes are ruling
the implosion dynamics; for example, a relation is given for inferring whether
thermally-driven or current-driven transport is dominating.


  Gaia is a space mission currently measuring the five astrometric parameters
as well as spectrophotometry of at least 1 billion stars to G = 20.7 mag with
unprecedented precision. The sixth parameter in phase space (radial velocity)
is also measured thanks to medium-resolution spectroscopy being obtained for
the 150 million brightest stars. During the commissioning phase, two fields,
one around each ecliptic pole, have been repeatedly observed to assess and to
improve the overall satellite performances as well as the associated reduction
and analysis software. A ground-based photometric and spectroscopic survey was
therefore initiated in 2007, and is still running in order to gather as much
information as possible about the stars in these fields. This work is of
particular interest to the validation of the Radial Velocity Spectrometer (RVS)
outputs. The paper presents the radial velocity measurements performed for the
Southern targets in the 12 - 17 R magnitude range on high- to mid-resolution
spectra obtained with the GIRAFFE and UVES spectrographs.


  We investigate the nature of dissipative instability appearing in a
prominence planar thread filled with partially ionised plasma in the
incompressible limit. The importance of partial ionisation is investigated in
terms of the ionisation factor and wavelength of waves propagating in the slab.
To highlight the role of partial ionisation, we have constructed models
describing various situations we can meet in solar prominence fine structure.
Matching the solutions for the transversal component of the velocity and total
pressure at the interfaces between the prominence slab and surrounding plasmas,
we derived a dispersion relation whose imaginary part describes the evolution
of the instability. Results are obtained in the limit of weak dissipation. We
have investigated the appearance of instabilities in prominence dark plumes
using single and two-fluid approximations. We show that dissipative
instabilities appear for flow speeds that are less than the Kelvin-Helmholtz
instability threshold. The onset of instability is determined by the
equilibrium flow strength, the ionisation factor of the plasma, the wavelength
of waves and the ion-neutral collisional rate. For a given wavelength and for
ionisation degrees closer to a neutral gas, the propagating waves become
unstable for a narrow band of flow speeds, meaning that neutrals have a
stabilising effect. Our results show that the partially ionised plasma
describing prominence dark plumes becomes unstable only in a two-fluid (charged
particles-neutrals) model, that is for periods that are smaller than the
ion-neutral collision time. The present study improves our understanding of
stability of solar prominences and the role of partial ionisation in
destabilising the plasma. We show the necessity of two-fluid approximation when
discussing the nature of instabilities: waves in a single fluid approximation
show a great deal of stability.


  Electronic properties of graphene oxides enriched by the strong chemical
bondings are investigated using first-principle calculations. They are very
sensitive to the changes in the number of graphene layer, stacking
configuration, and distribution of oxygen. The feature-rich electronic
structures exhibit the destruction or distortion of Dirac cone, opening of band
gap, anisotropic energy dispersions, O- and (C,O)-dominated energy dispersions,
and extra critical points. All the few-layer graphene oxides are semi-metals
except for the semiconducting monolayer ones. For the former, the distorted
Dirac-cone structures and the O-dominated energy bands near the Fermi level are
revealed simultaneously. The orbital-projected density of states (DOS) have
many special structures mainly coming from a composite energy band, the
parabolic and partially flat ones. The DOS and spatial charge distributions
clearly indicate the critical bondings in O-O, C-O and C-C bonds, being
responsible for the diversified properties.


  In this note we study $T^2$-invariant pluriclosed metrics on the
Kodaira-Thurston surface. We obtain a characterization of $T^2$-invariant
Vaisman metrics, and notice that the Kodaira-Thurston surface admits Vaisman
metrics with non-constant scalar curvature. Then we study the behaviour of the
Vaisman condition in relation to the pluriclosed flow. As a consequence, we
show that if the initial metric on the Kodaira-Thurston surface is a
$T^2$-invariant Vaisman metric, then the pluriclosed flow preserves the Vaisman
condition, extending to the non-constant scalar curvature case the previous
results in [6].


  Narrated instructional videos often show and describe manipulations of
similar objects, e.g., repairing a particular model of a car or laptop. In this
work we aim to reconstruct such objects and to localize associated narrations
in 3D. Contrary to the standard scenario of instance-level 3D reconstruction,
where identical objects or scenes are present in all views, objects in
different instructional videos may have large appearance variations given
varying conditions and versions of the same product. Narrations may also have
large variation in natural language expressions. We address these challenges by
three contributions. First, we propose an approach for correspondence
estimation combining learnt local features and dense flow. Second, we design a
two-step divide and conquer reconstruction approach where the initial 3D
reconstructions of individual videos are combined into a 3D alignment graph.
Finally, we propose an unsupervised approach to ground natural language in
obtained 3D reconstructions. We demonstrate the effectiveness of our approach
for the domain of car maintenance. Given raw instructional videos and no manual
supervision, our method successfully reconstructs engines of different car
models and associates textual descriptions with corresponding objects in 3D.


  We consider soliton formation in thermal nonlinear media bounded by
rectangular cross-sections and uncover a new class of nonlinear stationary
topological state. Specifically, we find that stationary higher-order vortex
states in standard shapes do not exist, but rather they take the form of
multiple, spatially separated single-charge singularities nested in an
elliptical beam. Double-charge states are found to be remarkably robust despite
their shape asymmetry and the phase-singularity splitting. States with higher
topological charges are found to be unstable.


  In part I of this analysis, the striking similarities of the declining oil
production in the North Sea, Indonesia and Mexico were used to model the future
maximum possible oil production per annum in all larger countries and regions
of the planet from 2015 to 2050.
  In part II, the oil export and oil consumption patterns, that were
established in recent decades, are combined with the consequences of the
forecast declines in regional oil production that were developed in part I of
this analysis. The results are quantitative predictions of the maximum possible
region-by-region oil consumption during the next 20 years.
  The predictions indicate that several of the larger oil consuming and
importing countries and regions will be confronted with the economic
consequences of the onset of the world's final oil supply crisis as early as
2020. In particular, during the next few years a reduction of the average per
capita oil consumption of about 5%/year is predicted for most OECD countries in
Western Europe, and slightly smaller reductions, about 2-3%/year, is predicted
for all other oil importing countries and regions. The consequences of the
predicted oil supply crisis are thoroughly at odds with business-as-usual,
never-ending-global-growth predictions of oil production and consumption.


  Although eye-tracking technology is being integrated into more VR and MR
headsets, the true potential of eye tracking in enhancing user interactions
within XR settings remains relatively untapped. Presently, one of the most
prevalent gaze applications in XR is input control; for example, using gaze to
control a cursor for pointing. However, our eyes evolved primarily for sensory
input and understanding of the world around us, and yet few XR applications
have leveraged natural gaze behavior to infer and support users' intent and
cognitive states. Systems that can represent a user's context and interaction
intent can better support the user by generating contextually relevant content,
by making the user interface easier to use, by highlighting potential errors,
and more. This mode of application is not fully taken advantage of in current
commercially available XR systems and yet it is likely where we'll find
paradigm-shifting use cases for eye tracking. In this paper, we elucidate the
state-of-the-art applications for eye tracking and propose new research
directions to harness its potential fully.


  It has been proposed that primordial gas in early dark matter halos, with
virial temperatures above 10^4 K, can avoid fragmentation and undergo rapid
collapse, possibly resulting in a supermassive black hole (SMBH). This requires
the gas to avoid cooling and to remain at temperatures near T=10^4 K. We show
that this condition can be satisfied in the presence of a sufficiently strong
primordial magnetic field, which heats the collapsing gas via ambipolar
diffusion. If the field has a strength above B = 3.6 (comoving) nG, the
collapsing gas is kept warm (T=10^4K) until it reaches the critical density
n_crit=10^3 cm^{-3} at which the roto-vibrational states of H_2 approach local
thermodynamic equilibrium. H_2-cooling then remains inefficient, and the gas
temperature stays near 10^4K, even as it continues to collapse to higher
densities. The critical magnetic field strength required to permanently
suppress H_2-cooling is somewhat higher than upper limit of approx. 2 nG from
the cosmic microwave background (CMB). However, it can be realized in the rare
(2-3)-sigma regions of the spatially fluctuating B-field; these regions contain
a sufficient number of halos to account for the z=6 quasar BHs.


  The question of whether one can systematically identify (previously unknown)
ferroelectric phases of a given material is addressed, taking hafnia (HfO$_2$)
as an example. Low free energy phases at various pressures and temperatures are
identified using a first-principles based structure search algorithm.
Ferroelectric phases are then recognized by exploiting group theoretical
principles for the symmetry-allowed displacive transitions between non-polar
and polar phases. Two orthorhombic polar phases occurring in space groups
$Pca2_1$ and $Pmn2_1$ are singled out as the most viable ferroelectric phases
of hafnia, as they display low free energies (relative to known non-polar
phases), and substantial switchable spontaneous electric polarization. These
results provide an explanation for the recently observed surprising
ferroelectric behavior of hafnia, and reveal pathways for stabilizing
ferroelectric phases of hafnia as well as other compounds.


  Process-Aware Information Systems (PAIS) support organizations in managing
and automating their processes. A full automation of processes is in particular
industries, such as service-oriented markets, not practicable. The integration
of humans in PAIS is necessary to manage and perform processes that require
human capabilities, judgments and decisions. A challenge of interdisciplinary
PAIS research is to provide concepts and solutions that support human
integration in PAIS and human orientation of PAIS in a way that provably
increase the PAIS users' satisfaction and motivation with working with the
Human-Centric Process Aware Information System (HC-PAIS) and consequently
influence users' performance of tasks. This work is an initial step of research
that aims at providing a definition of Human-Centric Process Aware Information
Systems (HC-PAIS) and future research challenges of HC-PAIS. Results of focus
group research are presented.


  We present a generalized version of classical geometric invariant theory \`a
la Mumford where we consider an affine algebraic group $G$ acting on a specific
affine algebraic variety $X$. We define the notions of linearly reductive and
of unipotent action in terms of the $G$ fixed point functor in the category of
$(G,\Bbbk [X])$--modules. In the case that $X=\{\star\}$ we recuperate the
concept of lineraly reductive and of unipotent group. We prove in our
"relative" context some of the classical results of GIT such as: existence of
quotients, finite generation of invariants, Kostant--Rosenlicht's theorem and
Matsushima's criterion. We also present a partial description of the geometry
of such linearly reductive actions.


  We analyze the rest-optical emission-line ratios of z~1.5 galaxies drawn from
the MOSFIRE Deep Evolution Field (MOSDEF) survey. Using composite spectra we
investigate the mass-metallicity relation (MZR) at z~1.5 and measure its
evolution to z=0. When using gas-phase metallicities based on the N2 line
ratio, we find that the MZR evolution from z~1.5 to z=0 depends on stellar
mass, evolving by $\Delta\rm log(\rm O/H)\sim0.25$ dex at
$M_*<10^{9.75}M_{\odot}$ down to $\Delta\rm log(\rm O/H)\sim0.05$ at
$M_*>10^{10.5}M_{\odot}$. In contrast, the O3N2-based MZR shows a constant
offset of $\Delta\rm log(\rm O/H)\sim0.30$ across all masses, consistent with
previous MOSDEF results based on independent metallicity indicators, and
suggesting that O3N2 provides a more robust metallicity calibration for our
z~1.5 sample. We investigated the secondary dependence of the MZR on SFR by
measuring correlated scatter about the mean $M_*$-specific SFR and
$M_*-\log(\rm O3N2)$ relations. We find an anti-correlation between $\log(\rm
O/H)$ and sSFR offsets, indicating the presence of a $M_*$-SFR-Z relation,
though with limited significance. Additionally, we find that our z~1.5 stacks
lie along the z=0 metallicity sequence at fixed
$\mu=\log(M_*/M_{\odot})-0.6\times\log(\rm SFR / M_{\odot} yr^{-1})$ suggesting
that the z~1.5 stacks can be described by the z=0 fundamental metallicity
relation (FMR). However, using different calibrations can shift the calculated
metallicities off of the local FMR, indicating that appropriate calibrations
are essential for understanding metallicity evolution with redshift. Finally,
understanding how [NII]/H$\alpha$ scales with galaxy properties is crucial to
accurately describe the effects of blended [NII] and H$\alpha$ on redshift and
H$\alpha$ flux measurements in future large surveys utilizing low-resolution
spectra such as with Euclid and the Roman Space Telescope.