  Given $n$ subspaces of a finite-dimensional vector space over a fixed finite
field $\mathbb F$, we wish to find a "branch-decomposition" of these subspaces
of width at most $k$ that is a subcubic tree $T$ with $n$ leaves mapped
bijectively to the subspaces such that for every edge $e$ of $T$, the sum of
subspaces associated to the leaves in one component of $T-e$ and the sum of
subspaces associated to the leaves in the other component have the intersection
of dimension at most $k$. This problem includes the problems of computing
branch-width of $\mathbb F$-represented matroids, rank-width of graphs,
branch-width of hypergraphs, and carving-width of graphs.
  We present a fixed-parameter algorithm to construct such a
branch-decomposition of width at most $k$, if it exists, for input subspaces of
a finite-dimensional vector space over $\mathbb F$. Our algorithm is analogous
to the algorithm of Bodlaender and Kloks (1996) on tree-width of graphs. To
extend their framework to branch-decompositions of vector spaces, we developed
highly generic tools for branch-decompositions on vector spaces. The only known
previous fixed-parameter algorithm for branch-width of $\mathbb F$-represented
matroids was due to Hlin\v{e}n\'y and Oum (2008) that runs in time $O(n^3)$
where $n$ is the number of elements of the input $\mathbb F$-represented
matroid. But their method is highly indirect. Their algorithm uses the
nontrivial fact by Geelen et al. (2003) that the number of forbidden minors is
finite and uses the algorithm of Hlin\v{e}n\'y (2006) on checking monadic
second-order formulas on $\mathbb F$-represented matroids of small
branch-width. Our result does not depend on such a fact and is completely
self-contained, and yet matches their asymptotic running time for each fixed
$k$.


  The change in the color pattern of the petals of Passiflora incarnata is
studied using the chaos theory in the form of logistic maps and plotted using
the corresponding bifurcation diagram. Based on a colorful inspection of the
beginning of violet-colored dots along the filament of the flower's bud stage
and the emergence of alternating bands of violet and white color in the matured
bloom, it is possible to deduce that a two-degree model for polynomial mapping
can be used to study color oscillations in the flower.


  We study Standard Model four-fermion production in e+ e- annihilation at LEP2
energies and above using a semi-analytical approach. We derive the complete QED
initial state corrections (ISR) to the reactions e+e- -> ZZ ->
$f_1\bar{f_1}f_2\bar{f_2}$ and e+e- -> W+W- -> ${\bar f}_1^u f_1^d f_2^u {\bar
f}_2^d$ with $f_1\neq f_2$~and $f_i \neq e^\pm, \nu_e$. As compared to the
well-known universal s-channel ISR, additional complexity arises due to
non-universal, process-dependent ISR contributions from t- and u-channel
fermion exchanges. The full set of formulae needed to perform numerical
calculations is given together with samples of numerical results.


  Automatic traffic accidents detection has appealed to the machine vision
community due to its implications on the development of autonomous intelligent
transportation systems (ITS) and importance to traffic safety. Most previous
studies on efficient analysis and prediction of traffic accidents, however,
have used small-scale datasets with limited coverage, which limits their effect
and applicability. Existing datasets in traffic accidents are either
small-scale, not from surveillance cameras, not open-sourced, or not built for
freeway scenes. Since accidents happened in freeways tend to cause serious
damage and are too fast to catch the spot. An open-sourced datasets targeting
on freeway traffic accidents collected from surveillance cameras is in great
need and of practical importance. In order to help the vision community address
these shortcomings, we endeavor to collect video data of real traffic accidents
that covered abundant scenes. After integration and annotation by various
dimensions, a large-scale traffic accidents dataset named TAD is proposed in
this work. Various experiments on image classification, object detection, and
video classification tasks, using public mainstream vision algorithms or
frameworks are conducted in this work to demonstrate performance of different
methods. The proposed dataset together with the experimental results are
presented as a new benchmark to improve computer vision research, especially in
ITS.


  In this paper, we obtain the Berry-Esseen bound for multivariate normal
approximation for the Polyak-Ruppert averaged iterates of the linear stochastic
approximation (LSA) algorithm with decreasing step size. Our findings reveal
that the fastest rate of normal approximation is achieved when setting the most
aggressive step size $\alpha_{k} \asymp k^{-1/2}$. Moreover, we prove the
non-asymptotic validity of the confidence intervals for parameter estimation
with LSA based on multiplier bootstrap. This procedure updates the LSA estimate
together with a set of randomly perturbed LSA estimates upon the arrival of
subsequent observations. We illustrate our findings in the setting of temporal
difference learning with linear function approximation.


  The critical nature of vehicular communications requires their extensive
testing and evaluation. Analytical models can represent an attractive and
cost-effective approach for such evaluation if they can adequately model all
underlying effects that impact the performance of vehicular communications.
Several analytical models have been proposed to date to model vehicular
communications based on the IEEE 802.11p (or DSRC) standard. However, existing
models normally model in detail the MAC (Medium Access Control), and generally
simplify the propagation and interference effects. This reduces their value as
an alternative to evaluate the performance of vehicular communications. This
paper addresses this gap, and presents new analytical models that accurately
model the performance of vehicle-to-vehicle communications based on the IEEE
802.11p standard. The models jointly account for a detailed modeling of the
propagation and interference effects, as well as the impact of the hidden
terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a
function of the distance between transmitter and receiver. The paper also
presents new analytical models to quantify the probability of the four
different types of packet errors in IEEE 802.11p. In addition, the paper
presents the first analytical model capable to accurately estimate the Channel
Busy Ratio (CBR) metric even under high channel load levels. All the analytical
models are validated by means of simulation for a wide range of parameters,
including traffic densities, packet transmission frequencies, transmission
power levels, data rates and packet sizes. An implementation of the models is
provided openly to facilitate their use by the community.


  We study the principal subspaces, introduced by B. Feigin and A. Stoyanovsky,
of the level 1 standard modules for $\hat{\goth{sl}(l+1)}$ with $l \geq 2$. In
this paper we construct exact sequences which give us a complete set of
recursions that characterize the graded dimensions of the principal subspaces
of these representations. This problem can be viewed as a continuation of a new
program to obtain Rogers-Ramanujan-type recursions, which was initiated by S.
Capparelli, J. Lepowsky and A. Milas. In order to prove the exactness of the
sequences we use intertwining vertex operators and we supply a proof of the
completeness of a list of relations for the principal subspaces. By solving
these recursions we recover the graded dimensions of the principal subspaces,
previously obtained by G. Georgiev using a different method.


  The current state-of-the-art Scrabble agents are not learning-based but
depend on truncated Monte Carlo simulations and the quality of such agents is
contingent upon the time available for running the simulations. This thesis
takes steps towards building a learning-based Scrabble agent using self-play.
Specifically, we try to find a better function approximation for the static
evaluation function used in Scrabble which determines the move goodness at a
given board configuration. In this work, we experimented with evolutionary
algorithms and Bayesian Optimization to learn the weights for an approximate
feature-based evaluation function. However, these optimization methods were not
quite effective, which lead us to explore the given problem from an Imitation
Learning point of view. We also tried to imitate the ranking of moves produced
by the Quackle simulation agent using supervised learning with a neural network
function approximator which takes the raw representation of the Scrabble board
as the input instead of using only a fixed number of handcrafted features.


  We consider the solution of the problem $$ -\Delta u=f(u) \ \mbox{ and } \
u>0 \ \ \mbox{ in } \ \Omega, \ \ u=0 \ \mbox{ on } \ \Gamma, $$ where $\Omega$
is a bounded domain in $\mathbb{R}^N$ with boundary $\Gamma$ of class
$C^{2,\tau}$, $0<\tau<1$, and $f$ is a locally Lipschitz continuous
non-linearity. Serrin's celebrated symmetry theorem states that, if the normal
derivative $u_\nu$ is constant on $\Gamma$, then $\Omega$ must be a ball.
  In [CMS2], it has been conjectured that Serrin's theorem may be obtained by
stability in the following way: first, for a solution $u$ prove the estimate $$
r_e-r_i\le C_\delta\,[u]_{\Gamma^\delta} $$ for some constant $C_\delta$
depending on $\delta>0$, where $r_e$ and $r_i$ are the radii of a spherical
annulus containing $\Gamma$, $\Gamma^\delta$ is a surface parallel to $\Gamma$
at distance $\delta$ and sufficiently close to $\Gamma$, and
$[u]_{\Gamma^\delta}$ is the Lipschitz semi-norm of $u$ on $\Gamma^\delta$;
secondly, if in addition $u_\nu$ is constant on $\Gamma$, show that $$
[u]_{\Gamma^\delta}=o(C_\delta)\ \mbox{ as } \ \delta\to 0^+. $$ In this paper,
we prove that this strategy is successful.
  As a by-product of this method, for $C^{2,\tau}$-regular domains, we also
obtain a linear stability estimate for Serrin's symmetry result. Our result is
optimal and greatly improves the similar logarithmic-type estimate of [ABR] and
the H\"{o}lder estimate of [CMV] that was restricted to convex domains.


  In this article we will construct a universal moduli space of stable
parabolic vector bundles over the moduli space of marked Deligne-Mumford stable
curves $\overline{M}_{_{g, n}}$. The objects that appear over the boundary of
$\overline{M}_{_{g, n}}$ i.e., over singular curves will remain vector bundles.
The total space and the fibers over $\overline{M}_{_{g, n}}$ will have good
singularities.


  Stellar streams are excellent probes of the underlying gravitational
potential in which they evolve. In this work, we fit dynamical models to five
streams in the Southern Galactic hemisphere, combining observations from the
Southern Stellar Stream Spectroscopic Survey (${S}^5$), Gaia EDR3, and the Dark
Energy Survey (DES), to measure the mass of the Large Magellanic Cloud (LMC).
With an ensemble of streams, we find a mass of the LMC ranging from 14 to $19
\times 10^{10}\ \mathrm{M}_{\odot}$, probed over a range of closest approach
times and distances. With the most constraining stream (Orphan-Chenab), we
measure an LMC mass of $18.8^{+ 3.5}_{- 4.0} \times 10^{10}\
\mathrm{M}_{\odot}$, probed at a closest approach time of 310 Myr and a closest
approach distance of 25.4 kpc. This mass is compatible with previous
measurements, showing that a consistent picture is emerging of the LMC's
influence on structures in the Milky Way. Using this sample of streams, we find
that the LMC's effect depends on the relative orientation of the stream and LMC
at their point of closest approach. To better understand this, we present a
simple model based on the impulse approximation and we show that the LMC's
effect depends both on the magnitude of the velocity kick imparted to the
stream and the direction of this kick.


  The generation of music that adapts dynamically to content and actions has an
important role in building more immersive, memorable and emotive game
experiences. To date, the development of adaptive music systems for video games
is limited by both the nature of algorithms used for real-time music generation
and the limited modelling of player action, game world context and emotion in
current games. We propose that these issues must be addressed in tandem for the
quality and flexibility of adaptive game music to significantly improve.
Cognitive models of knowledge organisation and emotional affect are integrated
with multi-modal, multi-agent composition techniques to produce a novel
Adaptive Music System (AMS). The system is integrated into two stylistically
distinct games. Gamers reported an overall higher immersion and correlation of
music with game-world concepts with the AMS than with the original game
soundtracks in both games.


  A Banach space X has Pelczynski's property (V) if for every Banach space Y
every unconditionally converging operator T: X -> Y is weakly compact. H.
Pfitzner proved that C*-algebras have Pelczynski's property (V). In the
preprint "H. Krulisova: Quantification of Pelczynski's property (V)" the author
explores possible quantifications of the property (V) and shows that C(K)
spaces for a compact Hausdorff space K enjoy a quantitative version of the
property (V). In this paper we generalize this result by quantifying Pfitzner's
theorem. Moreover, we prove that in dual Banach spaces a quantitative version
of the property (V) implies a quantitative version of the Grothendieck
property.


  Risk-adjusted quality measures are used to evaluate healthcare providers
while controlling for factors beyond their control. Existing healthcare
provider profiling approaches typically assume that the risk adjustment is
perfect and the between-provider variation in quality measures is entirely due
to the quality of care. However, in practice, even with very good models for
risk adjustment, some between-provider variation will be due to incomplete risk
adjustment, which should be recognized in assessing and monitoring providers.
Otherwise, conventional methods disproportionately identify larger providers as
outliers, even though their provider effects need not be "extreme.'' Motivated
by efforts to evaluate the quality of care provided by transplant centers, we
develop a composite evaluation score based on a novel individualized empirical
null method, which robustly accounts for overdispersion due to unobserved risk
factors, models the marginal variance of standardized scores as a function of
the effective center size, and only requires the use of publicly-available
center-level statistics. The evaluations of United States kidney transplant
centers based on the proposed composite score are substantially different from
those based on conventional methods. Simulations show that the proposed
empirical null approach more accurately classifies centers in terms of quality
of care, compared to existing methods.


  In this work we present a randomized gossip algorithm for solving the average
consensus problem while at the same time protecting the information about the
initial private values stored at the nodes. We give iteration complexity bounds
for the method and perform extensive numerical experiments.


  With a bottom-up approach, we consider naturalness in the MSSM and NMSSM.
Assuming the light stops, the LHC gluino search implies that the degree of fine
tuning in both models is less than 2.5%. Taking the LHC hints for the SM-like
Higgs boson mass m_h\sim125 GeV seriously, we find that naturalness will favor
the NMSSM. We study the Higgs boson mass for several scenarios in the NMSSM:
(1) A large \lambda and the doublet-singlet Higgs boson mixing effect pushing
upward or pulling downward m_h. The former case can readily give the di-photon
excess of the Higgs boson decay whereas the latter case can not. However, we
point out that the former case has a new large fine-tuning related to strong
\lambda-RGE running effect and vacuum stability. (2) A small \lambda and the
mixing effect pushing m_h upward. Naturalness status becomes worse and no
significant di-photon excess can be obtained. In these scenarios, the lightest
supersymmetric particle (LSP) as a dark matter candidate is strongly disfavored
by the XENON100 experiment. Even if the LSP can be a viable dark matter
candidate, there does exist fine-tuning. The above naturalness evaluation is
based on a high mediation scale for supersymmetry breaking, whereas for a low
mediation scale, fine-tuning can be improved by about one order.


  We consider the impact of electron-electron interactions on the temperature
dependence of the anomalous Hall effect in disordered conductors. The
microscopic analysis is carried out within the diagrammatic approach of the
linear response Kubo-Streda formula with an account of both extrinsic
skew-scattering and side-jump mechanisms of the anomalous Hall effect arising
in the presence of spin-orbit coupling. We demonstrate the importance of
electron interactions in the Cooper channel even for nominally
non-superconducting materials and find that the corresponding low-temperature
dependence of the anomalous Hall conductivity is asymptotically of the form
$\sqrt{T}/\ln(T_0/T)$ in three dimensions and $\ln[\ln(T_0/T)]$ in two
dimensions, where the scale of $T_0$ is parametrically of the order of Fermi
energy. These results, in particular, may provide an explanation for the
recently observed unconventional temperature dependence of the anomalous Hall
effect in HgCr$_2$Se$_4$.


  We delve deeper into the study of semimartingale attractors that we recently
introduced in Allouba and Langa \cite{AL0}. In this article we focus on second
order SPDEs of the Allen-Cahn type. After proving existence, uniqueness, and
detailed regularity results for our SPDEs and a corresponding random PDE of
Allen-Cahn type, we prove the existence of semimartingale global attractors for
these equations. We also give some results on the finite dimensional asymptotic
behavior of the solutions. In particular, we show the finite fractal dimension
of this random attractor and give a result on determining modes, both in the
forward and the pullback sense.


  Single-crystal europium iron garnet (EuIG) thin films epitaxially
strain-grown on gadolinium gallium garnet (GGG)(100) substrates using off-axis
sputtering have strain-induced perpendicular magnetic anisotropy (PMA). By
varying the sputtering conditions, we have tuned the europium/iron (Eu/Fe)
composition ratios in the films to tailor the film strains. The films exhibited
an extremely smooth, particle-free surface with roughness as low as 0.1 nm as
observed using atomic force microscopy. High-resolution x-ray diffraction
analysis and reciprocal space maps showed in-plane epitaxial film growth, very
smooth film/substrate interface, excellent film crystallinity with a small full
width at half maximum of 0.012$^{\circ}$ in the rocking curve scans, and an
in-plane compressive strain without relaxation. In addition, spherical
aberration-corrected scanning transmission electron microscopy showed an
atomically abrupt interface between the EuIG film and GGG. The measured
squarish out-of-plane magnetization-field hysteresis loops by vibrating sample
magnetometry in conjunction with the measurements from angle-dependent x-ray
magnetic dichroism demonstrated the PMA in the films. We have tailored the
magnetic properties of the EuIG thin films, including saturation magnetization
ranging from 71.91 to 124.51 emu/c.c. (increase with the (Eu/Fe) ratios),
coercive field from 27 to 157.64 Oe, and the strength of PMA field ($H_\bot$)
increasing from 4.21 to 18.87 kOe with the in-plane compressive strain from
-0.774 to -1.044%. We have also investigated spin transport in Pt/EuIG bi-layer
structure and evaluated the real part of spin mixing conductance to be
$3.48\times10^{14} {\Omega}^{-1}m^{-2}$. We demonstrated the current-induced
magnetization switching with a low critical switching current density of
$3.5\times10^6 A/cm^2$, showing excellent potential for low-dissipation
spintronic devices.


  We construct models of universe with a generalized equation of state
$p=(\alpha \rho+k\rho^{1+1/n})c^2$ having a linear component and a polytropic
component. In this paper, we consider positive indices $n>0$. In that case, the
polytropic component dominates in the early universe where the density is high.
For $\alpha=1/3$, $n=1$ and $k=-4/(3\rho_P)$, we obtain a model of early
universe describing the transition from a pre-radiation era to the radiation
era. The universe exists at any time in the past and there is no singularity.
However, for $t<0$, its size is less than the Planck length $l_P=1.62 10^{-35}
m$. In this model, the universe undergoes an inflationary expansion with the
Planck density $\rho_P=5.16 10^{99} g/m^3$ that brings it to a size $a_1=2.61
10^{-6} m$ at $t_1=1.25 10^{-42} s$ (about 20 Planck times $t_P$). For
$\alpha=1/3$, $n=1$ and $k=4/(3\rho_P)$, we obtain a model of early universe
with a new form of primordial singularity: The universe starts at t=0 with an
infinite density and a finite radius $a=a_1$. Actually, this universe becomes
physical at a time $t_i=8.32 10^{-45} s$ from which the velocity of sound is
less than the speed of light. When $a\gg a_1$, the universe evolves like in the
standard model. We describe the transition from the pre-radiation era to the
radiation era by analogy with a second order phase transition where the Planck
constant $\hbar$ plays the role of finite size effects (the standard Big Bang
theory is recovered for $\hbar=0$).


  Plant breeding is one of the key strategies for further enhancement of crop
yields; however, effective breeding strategies require phenotypic
characterisation of the available germplasm. This study sought to characterise
the phenotypic expression of fourteen crop traits related to phenology, plant
architecture, and yield in a panel of 40 cultivars selected to represent the
phenotypic variability present in Italian rice germplasm during a two-season
field experiment. The observed range of phenotypic variation was high for many
traits (coefficients of variation ranging from 5.9\% to 45.4\%) including yield
(mean: 6.47 t ha\textsuperscript{-1}; CV: 15.4\%; min: 2.19 t
ha\textsuperscript{-1}; max: 8.95 t ha\textsuperscript{-1}), and multiple
strong associations emerged in all the analysed traits. Cluster analysis
extracted three groups of genotypes characterised by alternative yield
strategies, i.e. "high-tillering", "early maturing", and "increased
source-sink". Our findings highlight that rice yield decrease when one strategy
is overemphasised. In contrast, the highest-yielding genotypes have a balanced
ratio between sink and source organs, along with a proportional duration of
vegetative and reproductive phases. This study depicts the phenotypic
variability in Italian rice cultivars and proposes a novel classification based
on yield-related traits which could be of use in multiple rice breeding
applications.


  Mobile payment such as Alipay has been widely used in our daily lives. To
further promote the mobile payment activities, it is important to run marketing
campaigns under a limited budget by providing incentives such as coupons,
commissions to merchants. As a result, incentive optimization is the key to
maximizing the commercial objective of the marketing campaign. With the
analyses of online experiments, we found that the transaction network can
subtly describe the similarity of merchants' responses to different incentives,
which is of great use in the incentive optimization problem. In this paper, we
present a graph representation learning method atop of transaction networks for
merchant incentive optimization in mobile payment marketing. With limited
samples collected from online experiments, our end-to-end method first learns
merchant representations based on an attributed transaction networks, then
effectively models the correlations between the commercial objectives each
merchant may achieve and the incentives under varying treatments. Thus we are
able to model the sensitivity to incentive for each merchant, and spend the
most budgets on those merchants that show strong sensitivities in the marketing
campaign. Extensive offline and online experimental results at Alipay
demonstrate the effectiveness of our proposed approach.


  We experimentally studied the Josephson supercurrent in Nb/InN-nanowire/Nb
junctions. Large critical currents up to 5.7 $\mu$A have been achieved, which
proves the good coupling of the nanowire to the superconductor. The effect of a
magnetic field perpendicular to the plane of the Josephson junction on the
critical current has been studied. The observed monotonous decrease of the
critical current with magnetic field is explained by the magnetic pair-breaking
effect in planar Josephson junctions of ultra-narrow width [J. C. Cuevas and F.
S. Bergeret, Phys. Rev. Lett. 99, 217002 (2007)]


  Purpose: In addition to playing an important role in creating economic
security and investment development, insurance companies also invest. The
country's insurance industry as one of the country's financial institutions has
a special place in the investment process and special attention to appropriate
investment policies in the field of insurance industry is essential. So that
the efficiency of this industry in allocating the existing budget stimulates
other economic sectors. This study seeks to model investment in the performance
of dynamic networks of insurance companies.
  Methodology: In this paper, a new investment model is designed to examine the
dynamic network performance of insurance companies in Iran. The designed model
is implemented using GAMS software and the outputs of the model are analyzed
based on regression method. The required information has been collected based
on the statistics of insurance companies in Iran between 1393 and 1398.
  Findings: After evaluating these units, out of 15 companies evaluated, 6
companies had unit performance and were introduced as efficient companies. The
average efficiency of insurance companies is 0.78 and the standard deviation is
0.2. The results show that the increase in the value of investments is due to
the large reduction in costs and in terms of capital and net profit of
companies is a large number that has a clear and strong potential for insurance
companies.
  Originality/Value: In this paper, investment modeling is performed to examine
the performance of dynamic networks of insurance companies in Iran.


  Jets and winds are significant channels for energy loss from accreting black
holes. These outflows mechanically heat their surroundings, through shocks as
well as gentler forms of heating. We discuss recent efforts to understand the
nature and distribution of mechanical heating by central AGNs in clusters of
galaxies, using numerical simulations and analytic models. Specifically, we
will discuss whether the relatively gentle `effervescent heating' mechanism can
compensate for radiative losses in the central regions of clusters, and account
for the excess entropy observed at larger radii.


  A recently claimed resolution to the lattice Gribov problem in the context of
chiral lattice gauge theories is examined. Unfortunately, I find that the old
problem remains.


  In recent years, many deep learning techniques for single-channel sound
source separation have been proposed using recurrent, convolutional and
transformer networks. When multiple microphones are available, spatial
diversity between speakers and background noise in addition to spectro-temporal
diversity can be exploited by using multi-channel filters for sound source
separation. Aiming at end-to-end multi-channel source separation, in this paper
we propose a transformer-recurrent-U network (TRUNet), which directly estimates
multi-channel filters from multi-channel input spectra. TRUNet consists of a
spatial processing network with an attention mechanism across microphone
channels aiming at capturing the spatial diversity, and a spectro-temporal
processing network aiming at capturing spectral and temporal diversities. In
addition to multi-channel filters, we also consider estimating single-channel
filters from multi-channel input spectra using TRUNet. We train the network on
a large reverberant dataset using a combined compressed mean-squared error loss
function, which further improves the sound separation performance. We evaluate
the network on a realistic and challenging reverberant dataset, generated from
measured room impulse responses of an actual microphone array. The experimental
results on realistic reverberant sound source separation show that the proposed
TRUNet outperforms state-of-the-art single-channel and multi-channel source
separation methods.


  Major academic publishers need to be able to analyse their vast catalogue of
products and select the best items to be marketed in scientific venues. This is
a complex exercise that requires characterising with a high precision the
topics of thousands of books and matching them with the interests of the
relevant communities. In Springer Nature, this task has been traditionally
handled manually by publishing editors. However, the rapid growth in the number
of scientific publications and the dynamic nature of the Computer Science
landscape has made this solution increasingly inefficient. We have addressed
this issue by creating Smart Book Recommender (SBR), an ontology-based
recommender system developed by The Open University (OU) in collaboration with
Springer Nature, which supports their Computer Science editorial team in
selecting the products to market at specific venues. SBR recommends books,
journals, and conference proceedings relevant to a conference by taking
advantage of a semantically enhanced representation of about 27K editorial
products. This is based on the Computer Science Ontology, a very large-scale,
automatically generated taxonomy of research areas. SBR also allows users to
investigate why a certain publication was suggested by the system. It does so
by means of an interactive graph view that displays the topic taxonomy of the
recommended editorial product and compares it with the topic-centric
characterization of the input conference. An evaluation carried out with seven
Springer Nature editors and seven OU researchers has confirmed the
effectiveness of the solution.


  The way a principle system and its environment interact characterizes the
non-Markovianity of the dynamics. Herein, we investigate the non-Markovian
dynamics of photon polarization in a birefringent crystal. We consider the
so-called `quantity of quantum-mechanical process' first defined by Hsieh et
al. [Sci. Rep. 7, 13588 (2017)]. The non-Markovianity of the photon dynamics is
evaluated by examining the quantity of quantum-mechanical process varying with
time, and the difference between the quantity of quantum-mechanical process in
a complete dynamics and that in a process composed of two subprocesses. We show
that all of the processes identified as Markovian in the seminal study of Liu
et al. [Nat. Phys. 7, 931 (2011)] can actually be identified as non-Markovian.
The presented method enables us to classify non-Markovianity in the dynamical
processes that are classified as Markovian by existing verification criteria.
Overall, the results confirm the feasibility of performing the experimental
characterization of photon dynamics using an all-optical setup and provide a
useful insight into enhancing the accuracy and control of quantum system
designs for quantum engineering applications.


  A set of fully relativistic one-pion-exchange electromagnetic operators is
developed for use in one-particle emission reactions induced by electrons. To
preserve the gauge invariance of the theory additional pionic correlation
operators are required beyond the usual meson-exchange current operators. Of
these, in the present work emphasis is placed on the self-energy current which
is infinite and needs to be renormalized. The renormalized current is expanded
to first order to obtain a genuine one-pion-exchange contribution to the
inclusive responses.


  Recent spectro-polarimetric observations have provided detailed measurements
of magnetic field, velocity and intensity during events of magnetic field
intensification in the solar photosphere. We consider the temporal evolution of
the relevant physical quantities for three cases of magnetic field
intensification in a numerical simulation. We determine the evolution of the
intensity, magnetic flux density and zero-crossing velocity derived from the
synthetic Stokes parameters by taking into account the spectral and spatial
resolution of the spectropolarimeter (SP) on board Hinode. The three events
considered show a similar evolution: advection of magnetic flux to a granular
vertex, development of a strong downflow, evacuation of the magnetic feature,
increase of the field strength and the appearance of the bright point. We find
that synthetic and real observations are qualitatively consistent and, for one
of the cases considered, agree very well also quantitatively. The effect of
finite resolution (spatial smearing) is most pronounced in the case of small
features, for which the synthetic Hinode/SP observations miss the bright point
formation and also the high-velocity downflows during the formation of the
smaller magnetic features.


  Beam energy scan programs in heavy-ion collisions aim to explore the QCD
phase structure at high baryon density. Sensitive observables are applied to
probe the signatures of the QCD phase transition and critical point in
heavy-ion collisions at RHIC and SPS. Intriguing structures, such as dip, peak
and oscillation, have been observed in the energy dependence of various
observables. In this paper, an overview is given and corresponding physics
implications will be discussed for the experimental highlights from the beam
energy scan programs at the STAR, PHENIX and NA61/SHINE experiments.
Furthermore, the beam energy scan phase II at RHIC (2019-2020) and other future
experimental facilities for studying the physics at low energies will be also
discussed.


  We compute the Kauffman bracket polynomial of the numerator and denominator
closures of A + A + ... + A ( A is repeated n times), where A is a 2-tangle
shadow that has at most 4 crossings.


  Optical entropy bounds for metal nanoparticles immersed in nonlinear optical
media and for nonlinear dielectric microdroplets on metal surfaces are
calculated near the frequency of the surface plasmon resonance. Similar to the
Bekenstein-Hawking result for the black hole entropy, the entropy bounds in
nonlinear quantum nanooptics may be expressed as the ratios of the droplet
perimeter (nanoparticle area) to the effective Planck length (effective Planck
length squared).


  Two semi-implicit Euler schemes for differential inclusions are proposed and
analyzed in depth. An error analysis shows that both semi-implicit schemes
inherit favorable stability properties from the differential inclusion. Their
performance is considerably better than that of the implicit Euler scheme,
because instead of implicit inclusions only implicit equations have to be
solved for computing their images. In addition, they are more robust with
respect to spatial discretization than the implicit Euler scheme.


  Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200
countries affecting millions and claiming almost 1 million lives, since its
emergence in late 2019. This highly contagious disease can easily spread, and
if not controlled in a timely fashion, can rapidly incapacitate healthcare
systems. The current standard diagnosis method, the Reverse Transcription
Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low
sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is
readily available and gives immediate results. However, it has notoriously
lower sensitivity than Computed Tomography (CT), which can be used efficiently
to complement other diagnostic methods. This paper introduces a new COVID-19 CT
scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19
cases, but also healthy and subjects infected by Community Acquired Pneumonia
(CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level
and patient-level labels, has the potential to facilitate the COVID-19
research, in particular COVID-CT-MD can assist in development of advanced
Machine Learning (ML) and Deep Neural Network (DNN) based solutions.


  Consider a commutative unital ring $A$ and a unital $A$-algebra $R$. Let $d$
be a positive integer. Chenevier proved that when $(2d)!$ is invertible in $A$,
the map associating to a determinant its trace is a bijection between
$A$-valued $d$-dimensional determinants of $R$ and $A$-valued $d$-dimensional
pseudocharacters of $R$. In this paper, we show that assuming $d!$ is
invertible in $A$ is sufficient. This assumption is already made in the
definition of a $d$-dimensional pseudocharacter.
  Our proof involves establishing a product formula for pseudocharacters, which
might be of independent interest.


  The comparison of current image data with existing 3D model data of a scene
provides an efficient method to keep models up to date. In order to transfer
information between 2D and 3D data, a preliminary co-registration is necessary.
In this paper, we present a concept to automatically co-register aerial imagery
and untextured 3D model data. To refine a given initial camera pose, our
algorithm computes dense correspondence fields using SIFT flow between gradient
representations of the model and camera image, from which 2D-3D correspondences
are obtained. These correspondences are then used in an iterative optimization
scheme to refine the initial camera pose by minimizing the reprojection error.
Since it is assumed that the model does not contain texture information, our
algorithm is built up on an existing method based on Average Shading Gradients
(ASG) to generate gradient images based on raw geometry information only. We
apply our algorithm for the co-registering of aerial photographs to an
untextured, noisy mesh model. We have investigated different magnitudes of
input error and show that the proposed approach can reduce the final
reprojection error to a minimum of 1.27 plus-minus 0.54 pixels, which is less
than 10 % of its initial value. Furthermore, our evaluation shows that our
approach outperforms the accuracy of a standard Iterative Closest Point (ICP)
implementation.


  In safety-critical deep learning applications robustness measurement is a
vital pre-deployment phase. However, existing robustness verification methods
are not sufficiently practical for deploying machine learning systems in the
real world. On the one hand, these methods attempt to claim that no
perturbations can ``fool'' deep neural networks (DNNs), which may be too
stringent in practice. On the other hand, existing works rigorously consider
$L_p$ bounded additive perturbations on the pixel space, although
perturbations, such as colour shifting and geometric transformations, are more
practically and frequently occurring in the real world. Thus, from the
practical standpoint, we present a novel and general {\it probabilistic
robustness assessment method} (PRoA) based on the adaptive concentration, and
it can measure the robustness of deep learning models against functional
perturbations. PRoA can provide statistical guarantees on the probabilistic
robustness of a model, \textit{i.e.}, the probability of failure encountered by
the trained model after deployment. Our experiments demonstrate the
effectiveness and flexibility of PRoA in terms of evaluating the probabilistic
robustness against a broad range of functional perturbations, and PRoA can
scale well to various large-scale deep neural networks compared to existing
state-of-the-art baselines. For the purpose of reproducibility, we release our
tool on GitHub: \url{ https://github.com/TrustAI/PRoA}.


  Even this saying itself is a variant of a similar statement attributed to
Bernard of Chartres in the 12th Century, and inspired the title for a book by
Steven Hawking and an album by Oasis. Creative ideas beget other creative ideas
and, as a result, modifications accumulate, and we see an overall increase in
the complexity of cultural novelty over time, a phenomenon sometimes referred
to as the ratchet effect (Tomasello, Kruger, & Ratner, 1993). Although we may
never meet the people or objects that creatively influence us, by assimilating
what we encounter around us and bringing to bear our own insights and
perspectives, we all contribute in our own way, however small, to a second
evolutionary process -- the evolution of culture. This chapter explores how we
can better understand culture by understanding the creative processes that fuel
it, and better understand creativity by examining it from its cultural context.
First, we look at some theoretical frameworks for how culture evolves and what
these frameworks imply for the role of creativity. Then we will see how
questions about the relationship between creativity and cultural evolution have
been addressed using an agent-based model. We will also discuss studies of how
creative outputs are influenced, in perhaps unexpected ways, by other ideas and
individuals, and how individual creative styles "peek through" cultural outputs
in different domains.


  Self-supervised learning (SSL) has emerged as a promising alternative to
create supervisory signals to real-world problems, avoiding the extensive cost
of manual labeling. SSL is particularly attractive for unsupervised tasks such
as anomaly detection (AD), where labeled anomalies are rare or often
nonexistent. A large catalog of augmentation functions has been used for
SSL-based AD (SSAD) on image data, and recent works have reported that the type
of augmentation has a significant impact on accuracy. Motivated by those, this
work sets out to put image-based SSAD under a larger lens and investigate the
role of data augmentation in SSAD. Through extensive experiments on 3 different
detector models and across 420 AD tasks, we provide comprehensive numerical and
visual evidences that the alignment between data augmentation and
anomaly-generating mechanism is the key to the success of SSAD, and in the lack
thereof, SSL may even impair accuracy. To the best of our knowledge, this is
the first meta-analysis on the role of data augmentation in SSAD.


  Hypersingular integrals of the type $I_{\alpha}(T_n,m,r) = \int_{-1}^{1}
\hpsngAbs \frac{T_n(s)(1-s^2)^{m-{1/2}}}{(s-r)^\alpha}ds |r|<1$ and
$I_{\alpha}(U_n,m,r) = \int_{-1}^{1} \hpsngAbs
\frac{U_n(s)(1-s^2)^{m-{1/2}}}{(s-r)^\alpha}ds |r|<1$ are investigated for
general integers $\alpha$ (positive) and $m$ (non-negative), where $T_n(s)$ and
$U_n(s)$ are the Tchebyshev polynomials of the 1st and 2nd kinds, respectively.
Exact formulas are derived for the cases $\alpha = 1, 2, 3, 4$ and $m = 0, 1,
2, 3$; most of them corresponding to new solutions derived in this paper.
Moreover, a systematic approach for evaluating these integrals when $\alpha >
4$ and $m>3$ is provided. The integrals are also evaluated as $|r|>1$ in order
to calculate stress intensity factors (SIFs). Examples involving crack problems
are given and discussed with emphasis on the linkage between mathematics and
mechanics of fracture. The examples include classical linear elastic fracture
mechanics (LEFM), functionally graded materials (FGM), and gradient elasticity
theory. An appendix, with closed form solutions for a broad class of integrals,
supplements the paper.


  We present Multiple Mirror Telescope (MMT) spectrophotometry and 3.5m Calar
Alto telescope R, I photometry of the western component of the extremely
low-metallicity blue compact galaxy SBS 0335-052. We argue that both
components, separated by 24 kpc, are members of an unique, physically connected
system. It is shown that SBS 0335-052W has the same redshift as SBS 0335-052
and has an extremely low oxygen abundance, 12 + log (O/H) = 7.45+-0.22,
comparable to those in I Zw 18 and SBS 0335--052. The (R-I) color profiles are
very blue in both galaxies due to the combined effect of ionized gas and a
young stellar population. We argue that SBS 0335--052W is likely to be a nearby
young dwarf galaxy with age not exceeding 10^8 yr. The implications of this
conclusion to the question of galaxy formation and to the properties of
primeval dwarf galaxies are discussed.


  FeSe undergoes a transition from a tetragonal to a slightly orthorhombic
phase at 90\,K, and becomes a superconductor below 8\,K. The orthorhombic phase
is sometimes called a nematic phase because quantum oscillation, neutron, and
other measurements detect a significant asymmetry in $x$ and $y$. How
nematicity affects superconductivity has recently become a matter of intense
speculation. Here we employ an advanced \emph{ab-initio} Green's function
description of superconductivity and show that bulk tetragonal FeSe would, in
principle, superconduct with almost the same T$_{c}$ as the nematic phase. The
mechanism driving the observed nematicity is not yet understood. Since the
present theory underestimates nematicity, we simulate the full nematic
asymmetry by artificially enhancing the orthorhombic distortion. For
benchmarking, we compare theoretical spin susceptibilities against
experimentally observed data over all energies and relevant momenta. When the
orthorhombic distortion is adjusted to correlate with observed nematicity in
spin susceptibility, the enhanced nematicity causes spectral weight
redistribution in the Fe-3d$_{xz}$ and d$_{yz}$ orbitals, but it leads to at
most 10-15$\%$ increment in T$_{c}$. This is because the d$_{xy}$ orbital
always remains the most strongly correlated and provides most of the source of
the superconducting glue. Nematicity suppresses the density of states at Fermi
level; nevertheless T$_{c}$ increases, in contradiction to both BCS and BEC
theories. We show how the increase is connected to the structure of the
particle-particle vertex. Our results suggest while nematicity may be intrinsic
property of bulk FeSe, is not the primary force driving the superconducting
pairing.


  While current vision algorithms excel at many challenging tasks, it is
unclear how well they understand the physical dynamics of real-world
environments. Here we introduce Physion, a dataset and benchmark for rigorously
evaluating the ability to predict how physical scenarios will evolve over time.
Our dataset features realistic simulations of a wide range of physical
phenomena, including rigid and soft-body collisions, stable multi-object
configurations, rolling, sliding, and projectile motion, thus providing a more
comprehensive challenge than previous benchmarks. We used Physion to benchmark
a suite of models varying in their architecture, learning objective,
input-output structure, and training data. In parallel, we obtained precise
measurements of human prediction behavior on the same set of scenarios,
allowing us to directly evaluate how well any model could approximate human
behavior. We found that vision algorithms that learn object-centric
representations generally outperform those that do not, yet still fall far
short of human performance. On the other hand, graph neural networks with
direct access to physical state information both perform substantially better
and make predictions that are more similar to those made by humans. These
results suggest that extracting physical representations of scenes is the main
bottleneck to achieving human-level and human-like physical understanding in
vision algorithms. We have publicly released all data and code to facilitate
the use of Physion to benchmark additional models in a fully reproducible
manner, enabling systematic evaluation of progress towards vision algorithms
that understand physical environments as robustly as people do.


  We present a new approach for measuring fiber tip temperature using
low-coherence Brillouin optical correlation-domain reflectometry, which
eliminates the need for an independent reference path and does not entail
specific processing of the fiber tip.


  We extend to higher order a recently published method for calculating the
deflection angle of light in a general static and spherically symmetric metric.
Since the method is convergent we obtain very accurate analytical expressions
that we compare with numerical results.


  We consider scalar quantum electrodynamics in the Higgs phase and in the
presence of Lorentz violation. Spontaneous breaking of the gauge symmetry gives
rise to Lorentz-violating gauge field mass terms. These may cause the
longitudinal mode of the gauge field to propagate superluminally. The theory
may be quantized by the Faddeev-Popov procedure, although the Lagrangian for
the ghost fields also needs to be Lorentz violating.


  Distributed Denial of Service (DDOS) attack is one of the most common network
attacks. DDoS attacks are becoming more and more diverse, which makes it
difficult for some DDoS attack detection methods based on single network flow
characteristics to detect various types of DDoS attacks, while the detection
methods of multi-feature DDoS attacks have a certain lag due to the complexity
of the algorithm. Therefore, it is necessary and urgent to monitor the trend of
traffic change and identify DDoS attacks timely and accurately. In this paper,
a method of DDoS attack detection based on deep belief network feature
extraction and LSTM model is proposed. This method uses deep belief network to
extract the features of IP packets, and identifies DDoS attacks based on LSTM
model. This scheme is suitable for DDoS attack detection technology. The model
can accurately predict the trend of normal network traffic, identify the
anomalies caused by DDoS attacks, and apply to solve more detection methods
about DDoS attacks in the future.


  We study the temperature-dependent quantum correction to conductivity due to
the interplay of spin density fluctuations and weak disorder for a
two-dimensional metal near an antiferromagnetic (AFM) quantum critical point.
AFM spin density fluctuations carry large momenta around the ordering vector
$\mathbf{Q}$ and, at lowest order of the spin-fermion coupling, only scatter
electrons between "hot spots" of the Fermi surface which are connected by
$\mathbf{Q}$. Earlier, it was seen that the quantum interference between AFM
spin density fluctuations and soft diffusive modes of the disordered metal is
suppressed, a consequence of the large-momentum scattering. The suppression of
this interference results in a non-singular temperature dependence of the
corresponding interaction correction to conductivity. However, at higher order
of the spin-fermion coupling, electrons on the entire Fermi surface can be
scattered successively by two spin density fluctuations and, in total, suffer a
small momentum transfer. This higher-order process can be described by
composite modes which carry small momenta. We show that the interference
between formally subleading composite modes and diffusive modes generates
singular interaction corrections which ultimately dominate over the
non-singular first-order correction at low temperatures. We derive an effective
low-energy theory from the spin-fermion model which includes the
above-mentioned higher-order process implicitly and show that for weak
spin-fermion coupling the small-momentum transfer is mediated by a composite
propagator. Employing the conventional diagrammatic approach to impurity
scattering, we find the correction $\delta \sigma \sim +\ln^2 T$ for
temperatures above an exponentially small crossover scale.


  Optimal information and knowledge management is crucial for organizations to
achieve their objectives efficiently. As a rare and valuable resource,
effective knowledge management provides a strategic advantage and has become a
key determinant of organizational success. The study aims to identify critical
success and failure factors for implementing knowledge management systems in
government organizations. This research employs a descriptive survey
methodology, collecting data through random interviews and questionnaires. The
study highlights the critical success factors for knowledge management systems
in government organizations, including cooperation, an open atmosphere, staff
training, creativity and innovation, removal of organizational constraints,
reward policies, role modeling, and focus. Conversely, failure to consider
formality, staff participation, collaboration technologies, network and
hardware infrastructure, complexity, IT staff, and trust can pose significant
obstacles to successful implementation.


  The channel estimation overhead of reconfigurable intelligent surface (RIS)
assisted communication systems can be prohibitive. Prior works have
demonstrated via simulations that grouping neighbouring RIS elements can help
to reduce the pilot overhead and improve achievable rate. In this paper, we
present an analytical study of RIS element grouping. We derive a tight
closed-form upper bound for the achievable rate and then maximize it with
respect to the group size. Our analysis reveals that more coarse-grained
grouping is important-when the channel coherence time is low (high mobility
scenarios) or the transmit power is large. We also demonstrate that optimal
grouping can yield significant performance improvements over simple `On-Off'
RIS element switching schemes that have been recently considered.


  In the absence of dark matter, the dynamical and kinematical interpretations
of the special relativistic spacetime have been and still are the topic of
philosophic debate, which whilst fertile, is by and large of little predictive
power. This changes dramatically if the debate includes a dark matter candidate
in a "non-trivial" extension of the standard model. Here I argue that rods and
clocks made out of dark matter may not reveal the same underlying algebraic
structure as the rods and clocks made out of standard model particles. For the
sake of concreteness I here exemplify the argument by looking at a particular
dark matter candidate called Elko. Inevitably, one is led to the conclusion
that gravity within the dark sector, and at the interface between dark matter
and standard-model matter, may deviate from the canonical general relativistic
predictions. For Elko dark matter such effects will be of second order in the
sense that they will depend only on the angular momentum and spin of the
gravitational environment.


  Archiving and systematic backup of large digital data generates a quick
demand for multi-peta byte scale storage systems. As drive capacities continue
to grow beyond the few terabytes range to address the demands of today's cloud,
the likelihood of having multiple/simultaneous disk failures become a reality.
Among the main factors causing catastrophic system failures, correlated disk
failures and the network bandwidth are reported to be the two common source of
performance degradation. The emerging trend is to use efficient/sophisticated
erasure codes (EC) equipped with multiple parities and efficient repairs in
order to meet the reliability/bandwidth requirements. It is known that mean
time to failure and repair rates reported by the disk manufacturers cannot
capture life cycle patterns of distributed storage systems. In this study, we
develop failure models based on generalized Markov chains that can accurately
capture correlated performance degradations with multi-parity protection
schemes based on modern Maximum Distance Separable (MDS) EC. Furthermore, we
use the proposed model in a distributed storage scenario to quantify two
example use cases: Primarily, the common sense that adding more parity disks
are only meaningful if we have a decent decorrelation between the failure
domains of storage systems and the reliability of generic multiple
single-dimensional EC protected storage systems.


  The Hong-Ou-Mandel (HOM) effect is a striking demonstration of destructive
quantum interference between pairs of indistinguishable bosons, realised so far
only with massless photons. Here we propose an experiment which can realise
this effect in the matter-wave regime using pair-correlated atoms produced via
a collision of two Bose-Einstein condensates and subjected to two laser induced
Bragg pulses. We formulate a novel measurement protocol appropriate for the
multimode matter-wave field, which---unlike the typical two-mode optical
case---bypasses the need for repeated measurements under different displacement
settings of the beam-splitter, thus dramatically reducing the number of
experimental runs required to map out the interference visibility. The protocol
can be utilised in related matter-wave schemes; here we focus on condensate
collisions and by simulating the entire experiment we predict a HOM-dip
visibility of ~69%. By being larger than 50%, such a visibility highlights
strong quantum correlations between the atoms and paves the way for a possible
demonstration of a Bell inequality violation with massive particles in a
related Rarity-Tapster setup.


  Computational models corresponding to supply security in natural gas networks
aim to describe flows and consumption values in the case of component failures
or unforseen pipeline shutdowns. The role of natural gas reservoires in this
process has only been marginally analyzed in such models, and typically only on
the level of countries. In this paper we define a computational framework,
which is capable of interpreting real flow and reservoir data to assign a
quantitative supply security related measure to reservoires, depending on how
much the given reservoir is critical in the process of restoring consumption
outages in the network.


  We show that if $(M,\tensor,I)$ is a monoidal model category then
$\REnd_M(I)$ is a (weak) 2-monoid in $\sSet$. This applies in particular when
$M$ is the category of $A$-bimodules over a simplicial monoid $A$: the derived
endomorphisms of $A$ then form its Hochschild cohomology, which therefore
becomes a simplicial 2-monoid.


  We introduce a novel theoretical framework for Return On Investment (ROI)
maximization in repeated decision-making. Our setting is motivated by the use
case of companies that regularly receive proposals for technological
innovations and want to quickly decide whether they are worth implementing. We
design an algorithm for learning ROI-maximizing decision-making policies over a
sequence of innovation proposals. Our algorithm provably converges to an
optimal policy in class $\Pi$ at a rate of order
$\min\big\{1/(N\Delta^2),N^{-1/3}\}$, where $N$ is the number of innovations
and $\Delta$ is the suboptimality gap in $\Pi$. A significant hurdle of our
formulation, which sets it aside from other online learning problems such as
bandits, is that running a policy does not provide an unbiased estimate of its
performance.


  A deterministic trading strategy by a representative investor on a single
market asset, which generates complex and realistic returns with its first four
moments similar to the empirical values of European stock indices, is used to
simulate the effects of financial regulation that either pricks bubbles, props
up crashes, or both. The results suggest that regulation makes the market
process appear more Gaussian and less complex, with the difference more
pronounced for more frequent intervention, though particular periods can be
worse than the non-regulated version, and that pricking bubbles and propping up
crashes are not symmetrical.


  We measure the C+N+O abundance sum in red giant stars in two Galactic
globular clusters, NGC 1851 and NGC 6752. NGC 1851 has a split subgiant branch
which could be due to different ages or C+N+O content while NGC 6752 is
representative of the least complex globular clusters. For NGC 1851 and NGC
6752, we obtain average values of A(C+N+O) = 8.16 +/- 0.10 (\sigma = 0.34) and
7.62 +/- 0.02 (\sigma = 0.06), respectively. When taking into account the
measurement errors, we find a constant C+N+O abundance sum in NGC 6752. The
C+N+O abundance dispersion is only 0.06 dex, and such a result requires that
the source of the light element abundance variations does not increase the
C+N+O sum in this cluster. For NGC 1851, we confirm a large spread in C+N+O. In
this cluster, the anomalous RGB has a higher C+N+O content than the canonical
RGB by a factor of four (~0.6 dex). This result lends further support to the
idea that the two subgiant branches in NGC 1851 are roughly coeval, but with
different CNO abundances.


  Horava gravity is a proposal for completing general relativity in the
ultraviolet by interactions that violate Lorentz invariance at very high
energies. We focus on (2+1)-dimensional projectable Horava gravity, a theory
which is renormalizable and perturbatively ultraviolet-complete, enjoying an
asymptotically free ultraviolet fixed point. Adding a small cosmological
constant to regulate the long distance behavior of the metric, we search for
all circularly symmetric stationary vacuum solutions with vanishing angular
momentum and approaching the de Sitter metric with a possible angle deficit at
infinity. We find a two-parameter family of such geometries. Apart from the
cosmological de Sitter horizon, these solutions generally contain another
Killing horizon and should therefore be interpreted as black holes from the
viewpoint of the low-energy theory. Contrary to naive expectations, their
central singularity is not resolved by the higher derivative terms present in
the action. It is unknown at present if these solutions form as a result of
gravitational collapse. The only solution regular everywhere is just the de
Sitter metric devoid of any black hole horizon.


  Numerical integrators could be used to form interpolation conditions when
training neural networks to approximate the vector field of an ordinary
differential equation (ODE) from data. When numerical one-step schemes such as
the Runge-Kutta methods are used to approximate the temporal discretization of
an ODE with a known vector field, properties such as symmetry and stability are
much studied. Here, we show that using mono-implicit Runge-Kutta methods of
high order allows for accurate training of Hamiltonian neural networks on small
datasets. This is demonstrated by numerical experiments where the Hamiltonian
of the chaotic double pendulum in addition to the Fermi-Pasta-Ulam-Tsingou
system is learned from data.


  A concise description is presented of the basic features of the formalism of
non-canonical spacetime volume-forms and its application in modified gravity
theories and cosmology. The well known unimodular gravity theory appears as a
very special case. Concerning the hot issues facing cosmology now, we
specifically briefly outline the construction of: (a) unified description of
dark energy and dark matter as manifestations of a single material entity -- a
special scalar field "darkon"; (b) quintessential models of universe evolution
with a gravity-"inflaton"-assisted dynamical Higgs mechanism -- dynamical
suppression/generation of spontaneous electroweak gauge symmetry breaking in
the "early"/"late" universe; (c) unification of dark energy and dark matter
with diffusive interaction among them; (d) mechanism for suppression of 5-th
force without fine-tuning.


  We derive the hydrodynamic equations of motion of solid and supersolid 4He,
that describe the collective modes of these phases. In particular, the usual
hydrodynamics is modified in such a way that it leads to the presence of a
propagating instead of a diffusive defect mode. The former is appropriate for a
quantum crystal and observed in recent experiments. Furthermore, we find that
in supersolid helium there are two additional modes associated with the
superfluid degrees of freedom. The observation of these additional modes is a
clear experimental signature of the supersolid phase.


  We present a method to perform fault-tolerant single-qubit gate operations
using Landau-Zener tunneling. In a single Landau-Zener pulse, the qubit
transition frequency is varied in time so that it passes through the frequency
of the radiation field. We show that a simple three-pulse sequence allows
eliminating errors in the gate up to the third order in errors in the qubit
energies or the radiation frequency.


  We compute deep-sea energy spectra and zenith-angle distributions of the
atmospheric muons, both conventional and prompt. The prompt muon contribution
to the muon flux underwater is calculated taking into consideration predictions
of recent charm production models in which probed is the small-x behavior of
the gluon distribution inside of a nucleon. We argue for a possibility to
discriminate the PQCD models of the charm production differing in the slope of
the gluon distribution, in measurements with neutrino telescopes of the muon
flux at energies 10-100 TeV.


  Post-Translational Modifications (PTMs) are known to play a critical role in
the regulation of the protein functions. Their impact on protein structures,
and their link to disorder regions have already been spotted on the past
decade. Nonetheless, the high diversity of PTMs types, and the multiple schemes
of protein modifications (multiple PTMs, of different types, at different time,
etc) make difficult the direct confrontation of PTM annotations and protein
structures data.We so analyzed the impact of the residue modifications on the
protein structures at local level. Thanks to a dedicated structure database,
namely PTM-SD, a large screen of PTMs have been done and analyze at a local
protein conformation levels using the structural alphabet Protein Blocks (PBs).
We investigated the relation between PTMs and the backbone conformation of
modified residues, of their local environment, and at the level of the complete
protein structure. The two main PTM types (N-glycosylation and phosphorylation)
have been studied in non-redundant datasets, and then, 4 different proteins
were focused, covering 3 types of PTMs: N-glycosylation in renin endopeptidase
and liver carboxylesterase, phosphorylation in cyclin-dependent kinase 2
(CDK2), and methylation in actin. We observed that PTMs could either stabilize
or destabilize the backbone structure, at a local and global scale, and that
these effects depend on the PTM types.


  The behavior of annealed branched polymers near adsorbing surfaces plays a
fundamental role in many biological and industrial processes. Most importantly
single stranded RNA in solution tends to fold up and self-bind to form a highly
branched structure. Using a mean field theory, we both perturbatively and
numerically examine the adsorption of branched polymers on surfaces of several
different geometries in a good solvent. Independent of the geometry of the
wall, we observe that as branching density increases, surface tension
decreases. However, we find a coupling between the branching density and
curvature in that a further lowering of surface tension occurs when the wall
curves towards the polymer, but the amount of lowering of surface tension
decreases when the wall curves away from the polymer. We find that for branched
polymers confined into spherical cavities, most of branch-points are located in
the vicinity of the interior wall and the surface tension is minimized for a
critical cavity radius. For branch polymers next to sinusoidal surfaces, we
find that branch-points accumulate at the valleys while end-points on the
peaks.


  We present the result of the survey of sunspots and auroras in ${\it
Q\bar{\imath}ngsh\check{\imath}ga\check{o}}$, the draft chronicle of ${\it
Q\bar{\imath}ng}$ dynasty, for the period of 1559-1912 CE, as a sequel of the
series of works surveying historical sunspot and aurora records, and providing
online data to the scientific community regarding the attained results. In
total of this ${\it Q\bar{\imath}ngsh\check{\imath}ga\check{o}}$ survey, we
found 111 records of night-sky luminous events with the keywords such as vapor
(${\it q\grave{\imath}}$), cloud (${\it y\acute{u}n}$), and light (${\it
gu\bar{a}ng}$), which may indicate auroras as well as some other phenomena.
Similarly keyword survey for sunspots were done, but no sunspot record was
found. In comparison with the aurora records in the western world, we found 14
of the 111 records have a corresponding record of simultaneous observation in
the western world and hence are very likely to be aurora. In order to
investigate the likeliness of the rest of the record being aurora, we
calculated the lunar age and the phase of a solar cycle for each record. After
these calculations, notable fraction of these records clustered near the full
moon were found statistically doubtful in considerations with atmospheric
optics, meanwhile a few records near the new moon could be more likely
interpreted as auroras including three records during the Maunder minimum.


  We discuss the theoretical bases that underpin the automation of the
computations of tree-level and next-to-leading order cross sections, of their
matching to parton shower simulations, and of the merging of matched samples
that differ by light-parton multiplicities. We present a computer program,
MadGraph5_aMC@NLO, capable of handling all these computations -- parton-level
fixed order, shower-matched, merged -- in a unified framework whose defining
features are flexibility, high level of parallelisation, and human intervention
limited to input physics quantities. We demonstrate the potential of the
program by presenting selected phenomenological applications relevant to the
LHC and to a 1-TeV $e^+e^-$ collider. While next-to-leading order results are
restricted to QCD corrections to SM processes in the first public version, we
show that from the user viewpoint no changes have to be expected in the case of
corrections due to any given renormalisable Lagrangian, and that the
implementation of these are well under way.


  We compute the $L$-characteristic cycle of an $A$-hypergeometric system and
higher Euler-Koszul homology modules of the toric ring. We also prove upper
semicontinuity results about the multiplicities in these cycles and apply our
results to analyze the behavior of Gevrey solution spaces of the system.


  There are several potential advantages for a collider operation with a
lattice with negative momentum compaction factor (alfa). Since the lattice of
the Frascati e+e- Phi-factory DAFNE is flexible enough to provide collider
operation even with alfa < 0, we have exploited this possibility for an
experimental study of the beam dynamics. The negative momentum compaction
lattices have been successfully implemented and stable 1 A currents have been
stored in both electron and positron rings without any problem for RF cavities
and feedback systems operation. First collisions have been tested at low
currents. In this paper we describe the experimental results and compare them
with expectations and numerical simulations. Present limitations to DAFNE
operation with alfa < 0 and ways to overcome them are also discussed.


  Quantum critical (QC) phenomena can be accessed by studying quantum magnets
under an applied magnetic field ($B$). The QC points are located at the
endpoints of magnetization plateaus and separate gapped and gapless phases. In
one dimension, the low-energy excitations of the gapless phase form a Luttinger
liquid (LL), and crossover lines bound insulating (plateau) and LL regimes, as
well as the QC regime. Alternating ferrimagnetic chains have a spontaneous
magnetization at $T=0$ and gapped excitations at zero field. Besides the
plateau at the fully polarized (FP) magnetization; due to the gap, there is
another magnetization plateau at the ferrimagnetic (FRI) magnetization. We
develop spin-wave theories to study the thermal properties of these chains
under an applied magnetic field: one from the FRI classical state, and other
from the FP state, comparing their results with quantum Monte Carlo data. We
deepen the theory from the FP state, obtaining the crossover lines in the $T$
vs. $B$ low-$T$ phase diagram. In particular, from local extreme points in the
susceptibility and magnetization curves, we identify the crossover between an
LL regime formed by excitations from the FRI state to another built from
excitations of the FP state. These two LL regimes are bounded by an asymmetric
dome-like crossover line, as observed in the phase diagram of other quantum
magnets under an applied magnetic field.


  Algorithms for building bimachines from functional transducers found in the
literature in a run of the bimachine imitate one successful path of the input
transducer. Each single bimachine output exactly corresponds to the output of a
single transducer transition. Here we introduce an alternative construction
principle where bimachine steps take alternative parallel transducer paths into
account, maximizing the possible output at each step using a joint view. The
size of both the deterministic left and right automaton of the bimachine is
restricted by $2^{\vert Q\vert}$ where $\vert Q\vert$ is the number of
transducer states. Other bimachine constructions lead to larger subautomata. As
a concrete example we present a class of real-time functional transducers with
$n+2$ states for which the standard bimachine construction generates a
bimachine with at least $\Theta(n!)$ states whereas the construction based on
the equalizer accumulation principle leads to $2^n + n +3$ states. Our
construction can be applied to rational functions from free monoids to "mge
monoids", a large class of monoids including free monoids, groups, and others
that is closed under Cartesian products.


  Mathematical models of biological systems are beginning to be used for
safety-critical applications, where large numbers of repeated model evaluations
are required to perform uncertainty quantification and sensitivity analysis.
Most of these models are nonlinear both in variables and parameters/inputs
which has two consequences. First, analytic solutions are rarely available so
repeated evaluation of these models by numerically solving differential
equations incurs a significant computational burden. Second, many models
undergo bifurcations in behaviour as parameters are varied. As a result,
simulation outputs often contain discontinuities as we change parameter values
and move through parameter/input space.
  Statistical emulators such as Gaussian processes are frequently used to
reduce the computational cost of uncertainty quantification, but
discontinuities render a standard Gaussian process emulation approach
unsuitable as these emulators assume a smooth and continuous response to
changes in parameter values.
  In this article, we propose a novel two-step method for building a Gaussian
Process emulator for models with discontinuous response surfaces. We first use
a Gaussian Process classifier to detect boundaries of discontinuities and then
constrain the Gaussian Process emulation of the response surface within these
boundaries. We introduce a novel `certainty metric' to guide active learning
for a multi-class probabilistic classifier.
  We apply the new classifier to simulations of drug action on a cardiac
electrophysiology model, to propagate our uncertainty in a drug's action
through to predictions of changes to the cardiac action potential. The proposed
two-step active learning method significantly reduces the computational cost of
emulating models that undergo multiple bifurcations.


  We introduce diffusion geometry as a new framework for geometric and
topological data analysis. Diffusion geometry uses the Bakry-Emery
$\Gamma$-calculus of Markov diffusion operators to define objects from
Riemannian geometry on a wide range of probability spaces. We construct
statistical estimators for these objects from a sample of data, and so
introduce a whole family of new methods for geometric data analysis and
computational geometry. This includes vector fields and differential forms on
the data, and many of the important operators in exterior calculus. Unlike
existing methods like persistent homology and local principal component
analysis, diffusion geometry is explicitly related to Riemannian geometry, and
is significantly more robust to noise, significantly faster to compute,
provides a richer topological description (like the cup product on cohomology),
and is naturally vectorised for statistics and machine learning. We find that
diffusion geometry outperforms multiparameter persistent homology as a
biomarker for real and simulated tumour histology data and can robustly measure
the manifold hypothesis by detecting singularities in manifold-like data.


  In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.


  We report on large scale numerical simulations of fracture surfaces using
random fuse networks for two very different disorders. There are some
properties and exponents that are different for the two distributions, but
others, notably the roughness exponents, seem universal. For the universal
roughness exponent we found a value of zeta = 0.62 +/- 0.05. In contrast to
what is observed in two dimensions, this value is lower than that reported in
experimental studies of brittle fractures, and rules out the minimal energy
surface exponent, 0.41 +/- 0.01.


  Rectangular standard Young tableaux with 2 or 3 rows are in bijection with
$U_q(\mathfrak{sl}_2)$-webs and $U_q(\mathfrak{sl}_3)$-webs respectively. When
$W$ is a web with a reflection symmetry, the corresponding tableau $T_W$ has a
rotational symmetry. Folding $T_W$ transforms it into a domino tableau $D_W$.
We study the relationships between these correspondences. For 2-row tableaux,
folding a rotationally symmetric tableau corresponds to "literally folding" the
web along its axis of symmetry. For $3$-row tableaux, we give simple
algorithms, which provide direct bijective maps between symmetrical webs and
domino tableaux (in both directions). These details of these algorithms reflect
the intuitive idea that $D_W$ corresponds to "$W$ modulo symmetry".


  This study demonstrates that a space charge layer is formed on dislocation
during mechanical deformation at elevated temperature. High density of
dislocation lines is generated in bulk single crystalline Y2O3 stabilized ZrO2
(YSZ) by uniaxial compression at elevated temperature. The creation of
dislocation is proven with transmission electron microscopy (TEM). Then,
energy-dispersive X-ray spectroscopy (EDS) and electron energy loss
spectroscopy (EELS) are used to explore the changes in the composition on and
away from the dislocation lines. Also, it is clarified that segregation of
dopant atoms (yttrium) on the dislocation line is induced by high temperature
annealing. Comparing the compositional variations with and without thermal
annealing, we study the space charge layer formed on dislocation lines in a
doped system.


  The azimuthal angular decorrelation of a vector boson and jet is sensitive to
QCD radiation, and can be used to probe the quark-gluon plasma in heavy-ion
collisions. By using a recoil-free jet definition, the sensitivity to
contamination from soft radiation on the measurement is reduced, and the
complication of non-global logarithms is eliminated from our theoretical
calculation. Specifically we will consider the $p_T^n$ recombination scheme, as
well as the $n\to \infty$ limit, known as the winner-take-all scheme. These jet
definitions also significantly simplify the calculation for a track-based
measurement, which is preferred due to its superior angular resolution. We
present a detailed discussion of the factorization in Soft-Collinear Effective
Theory, revealing why the transverse momentum $\vec q_T$ is more complicated
than the azimuthal angle. We show that potential glauber contributions do not
spoil our factorization formalism, at least up to and including order
$\alpha_s^3$. The resummation is carried out using the renormalization group,
and all necessary ingredients are collected or calculated. We conclude with a
detailed phenomenological study, finding an enhanced matching correction for
high jet $p_T$ due to the electroweak collinear enhancement of a boson emission
off di-jets. We also compare with the Pythia event generator, finding that our
observable is very robust to hadronization and the underlying event.


  We report on simulation results that shows optimum photon absorption by
superconducting nanowires can happen at a fill-factor that is much less than
100%. We also present experimental results on high performance of our
superconducting nanowire single photon detectors realized using NbTiN on
oxidized silicon.


  In the decades following Shannon's work, the quest to design codes for the
additive white Gaussian noise (AWGN) channel led to the development of a rich
theory, revealing a number of beautiful connections between information theory
and geometry of numbers. One of the most striking examples is the connection
between classical lattice sphere packing and the capacity of the AWGN channel.
The main result states that any family of lattice codes with linearly growing
Hermite invariant achieves a constant gap to capacity. These classical results
and many more can be found in the comprehensive book by Conway and Sloane
[5].....


  The choice of good distances and similarity measures between objects is
important for many machine learning methods. Therefore, many metric learning
algorithms have been developed in recent years, mainly for Euclidean data in
order to improve performance of classification or clustering methods. However,
due to difficulties in establishing computable, efficient and differentiable
distances between attributed graphs, few metric learning algorithms adapted to
graphs have been developed despite the strong interest of the community. In
this paper, we address this issue by proposing a new Simple Graph Metric
Learning - SGML - model with few trainable parameters based on Simple Graph
Convolutional Neural Networks - SGCN - and elements of Optimal Transport
theory. This model allows us to build an appropriate distance from a database
of labeled (attributed) graphs to improve the performance of simple
classification algorithms such as $k$-NN. This distance can be quickly trained
while maintaining good performances as illustrated by the experimental study
presented in this paper.


  A graph $G$ on $n$ vertices is Hamiltonian if it contains a spanning cycle,
and pancyclic if it contains cycles of all lengths from 3 to $n$. In 1984, Fan
presented a degree condition involving every pair of vertices at distance two
for a 2-connected graph to be Hamiltonian. Motivated by Fan's result, we say
that an induced subgraph $H$ of $G$ is $f_1$-heavy if for every pair of
vertices $u,v\in V(H)$, $d_{H}(u,v)=2$ implies $\max\{d(u),d(v)\}\geq (n+1)/2$.
For a given graph $R$, $G$ is called $R$-$f_1$-heavy if every induced subgraph
of $G$ isomorphic to $R$ is $f_1$-heavy. In this paper we show that for a
connected graph $S$ with $S\neq P_3$ and a 2-connected claw-$f_1$-heavy graph
$G$ which is not a cycle, $G$ being $S$-$f_1$-heavy implies $G$ is pancyclic if
$S=P_4,Z_1$ or $Z_2$, where claw is $K_{1,3}$ and $Z_i$ is the path
$a_1a_2a_3... a_{i+2}a_{i+3}$ plus the edge $a_1a_3$. Our result partially
improves a previous theorem due to Bedrossian on pancyclicity of 2-connected
graphs.


  Segmenting overlapping cytoplasm of cells in cervical smear images is a
clinically essential task, for quantitatively measuring cell-level features in
order to diagnose cervical cancer. This task, however, remains rather
challenging, mainly due to the deficiency of intensity (or color) information
in the overlapping region. Although shape prior-based models that compensate
intensity deficiency by introducing prior shape information (shape priors)
about cytoplasm are firmly established, they often yield visually implausible
results, mainly because they model shape priors only by limited shape
hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and
impose no shape constraint on the resulting shape of the cytoplasm. In this
paper, we present a novel and effective shape prior-based approach, called
constrained multi-shape evolution, that segments all overlapping cytoplasms in
the clump simultaneously by jointly evolving each cytoplasm's shape guided by
the modeled shape priors. We model local shape priors (cytoplasm--level) by an
infinitely large shape hypothesis set which contains all possible shapes of the
cytoplasm. In the shape evolution, we compensate intensity deficiency for the
segmentation by introducing not only the modeled local shape priors but also
global shape priors (clump--level) modeled by considering mutual shape
constraints of cytoplasms in the clump. We also constrain the resulting shape
in each evolution to be in the built shape hypothesis set, for further reducing
implausible segmentation results. We evaluated the proposed method in two
typical cervical smear datasets, and the extensive experimental results show
that the proposed method is effective to segment overlapping cytoplasm,
consistently outperforming the state-of-the-art methods.


  Images of heavily occluded objects in cluttered scenes, such as fruit
clusters in trees, are hard to segment. To further retrieve the 3D size and 6D
pose of each individual object in such cases, bounding boxes are not reliable
from multiple views since only a little portion of the object's geometry is
captured. We introduce the first CNN-based ellipse detector, called Ellipse
R-CNN, to represent and infer occluded objects as ellipses. We first propose a
robust and compact ellipse regression based on the Mask R-CNN architecture for
elliptical object detection. Our method can infer the parameters of multiple
elliptical objects even they are occluded by other neighboring objects. For
better occlusion handling, we exploit refined feature regions for the
regression stage, and integrate the U-Net structure for learning different
occlusion patterns to compute the final detection score. The correctness of
ellipse regression is validated through experiments performed on synthetic data
of clustered ellipses. We further quantitatively and qualitatively demonstrate
that our approach outperforms the state-of-the-art model (i.e., Mask R-CNN
followed by ellipse fitting) and its three variants on both synthetic and real
datasets of occluded and clustered elliptical objects.


  I present and summarize a software package ("LPipe") for completely
automated, end-to-end reduction of both bright and faint sources with the
Low-Resolution Imaging Spectrometer (LRIS) at Keck Observatory. It supports all
gratings, grisms, and dichroics, and also reduces imaging observations,
although it does not include multislit or polarimetric reduction capabilities
at present. It is suitable for on-the-fly quicklook reductions at the
telescope, for large-scale reductions of archival data-sets, and (in many
cases) for science-quality post-run reductions of PI data. To demonstrate its
capabilities the pipeline is run in fully-automated mode on all LRIS longslit
data in the Keck Observatory Archive acquired during the 12-month period
between August 2016 and July 2017. The reduced spectra (of 675 single-object
targets, totaling ~200 hours of on-source integration time in each camera), and
the pipeline itself, are made publicly available to the community.


  Data clones are defined as multiple copies of the same data among datasets.
Presence of data clones between datasets can cause issues such as difficulties
in managing data assets and data license violations when using datasets with
clones to build AI software. However, detecting data clones is not trivial.
Majority of the prior studies in this area rely on structural information to
detect data clones (e.g., font size, column header). However, tabular datasets
used to build AI software are typically stored without any structural
information. In this paper, we propose a novel method called SimClone for data
clone detection in tabular datasets without relying on structural information.
SimClone method utilizes value similarities for data clone detection. We also
propose a visualization approach as a part of our SimClone method to help
locate the exact position of the cloned data between a dataset pair. Our
results show that our SimClone outperforms the current state-of-the-art method
by at least 20\% in terms of both F1-score and AUC. In addition, SimClone's
visualization component helps identify the exact location of the data clone in
a dataset with a Precision@10 value of 0.80 in the top 20 true positive
predictions.


  We reprove and generalize a result of Moh which bounds the minimal number of
generators of an ideal in a power series ring in three variables over a field.
As a consequence, we obtain minimal generating sets for the first prime of Moh,
proving that the minimal number of generators might decrease depending on the
field characteristic. This contradicts an statement of Sally. Finally, we show
that these minimal generating sets are standard basis with the negative degree
reverse lexicographic order.


  We analyze the optically induced Kondo effect in the absorption spectrum for
a quantum dot with an even number of electrons, for which the Kondo effect does
not occur in the ground state. The Kondo exchange couplings generated for
photo-excited states can be either antiferromagnetic or ferromagnetic, which
may result in different types of the edge singularities, depending on the
microscopic parameters of the dot. We discuss critical properties in the
spectrum by using low-energy effective field theory.


  We show that the exact ground state of the Lieb-Mattis Hamiltonian is an
equal-weight superposition of all possible classical N\'{e}el states, and
provide an exact formulation of this superposition in the $z$-spin basis for
both $S=1/2$ and general $S$ using Schwinger bosons. In general, a
superposition of possible rotations on a general initial state is symmetric if
and only if the initial state has a nonzero overlap with a singlet state and is
otherwise made up of states that vanish due to the symmetrization. Most
notably, $|s, m=0 \rangle$ states will vanish if symmetrized, which explains
how a superposition of N\'{e}el states projects onto its singlet component.


  We demonstrate a method of controlled creation of spin domains in spin-1
antiferromagnetic Bose-Einstein condensates. The method exploits the phenomenon
of phase separation of spin components in an external potential. By using an
appropriate time dependent potential, a composition of spin domains can be
created, as we demonstrate in the particular cases of a double well and a
periodic potential. In contrast to other methods, which rely on spatially
inhomogeneous magnetic fields, here the domain structure is completely
determined by the optical fields, which makes the method versatile and
reconfigurable. It allows for creation of domains of various sizes, with the
spatial resolution limited by the spin healing length only.


  We prove that if $0<T_0<T\leq\infty$, $(\mathbf{u},\mathbf{b},p)$ is a
suitable weak solution of the MHD equations in $\mathbb{R}^3\times(0,T)$ and
either $\mathcal{F}_{\gamma}(p_-)\in L^{\infty}(0,T_0;\,
L^{3/2}(\mathbb{R}^3))$ or $\mathcal{F}_{\gamma}((|\mathbf{u}|^2+
|\mathbf{b}|^2+2p)_+)\in L^{\infty}(0,T_0;\, L^{3/2}(\mathbb{R}^3))$ for some
$\gamma>0$, where $\mathcal{F}_{\gamma}(s)=s\, [\ln{}(1+ s)]^{1+\gamma}$ and
the subscripts "$-$" and "$+$" denote the negative and the nonnegative part,
respectively, then the solution $(\mathbf{u},\mathbf{b},p)$ has no singular
points in $\mathbb{R}^3\times(0,T_0]$.


  Progressive compression allows images to start loading as low-resolution
versions, becoming clearer as more data is received. This increases user
experience when, for example, network connections are slow. Today, most
approaches for image compression, both classical and learned ones, are designed
to be non-progressive. This paper introduces ProgDTD, a training method that
transforms learned, non-progressive image compression approaches into
progressive ones. The design of ProgDTD is based on the observation that the
information stored within the bottleneck of a compression model commonly varies
in importance. To create a progressive compression model, ProgDTD modifies the
training steps to enforce the model to store the data in the bottleneck sorted
by priority. We achieve progressive compression by transmitting the data in
order of its sorted index. ProgDTD is designed for CNN-based learned image
compression models, does not need additional parameters, and has a customizable
range of progressiveness. For evaluation, we apply ProgDTDto the hyperprior
model, one of the most common structures in learned image compression. Our
experimental results show that ProgDTD performs comparably to its
non-progressive counterparts and other state-of-the-art progressive models in
terms of MS-SSIM and accuracy.


  Numerical techniques to efficiently model out-of-equilibrium dynamics in
interacting quantum many-body systems are key for advancing our capability to
harness and understand complex quantum matter. Here we propose a new numerical
approach which we refer to as GDTWA. It is based on a discrete semi-classical
phase-space sampling and allows to investigate quantum dynamics in lattice spin
systems with arbitrary $S\geq 1/2$. We show that the GDTWA can accurately
simulate dynamics of large ensembles in arbitrary dimensions. We apply it for
$S>1/2$ spin-models with dipolar long-range interactions, a scenario arising in
recent experiments with magnetic atoms. We show that the method can capture
beyond mean-field effects, not only at short times, but it also correctly
reproduces long time quantum-thermalization dynamics. We benchmark the method
with exact diagonalization in small systems, with perturbation theory for short
times, and with analytical predictions made for closed system which feature
quantum-thermalization at long times. By computing the Renyi entropy, currently
an experimentally accessible quantifier of entanglement, we reveal that large
$S$ systems can feature larger entanglement than corresponding $S=1/2$ systems.
Our analyses demonstrate that the GDTWA can be a powerful tool for modeling
complex spin dynamics in regimes where other state-of-the art numerical methods
fail.


  Adiabatic Rapid Passage (ARP) is a powerful technique for efficient transfer
of population between quantum states. In the lab, the efficiency of ARP is
often limited by noise on either the energies of the states or the frequency of
the driving field. We study ARP in the simple setting of a two-level system
subject to sinusoidal fluctuations on the energy level separation by
numerically solving the optical Bloch equations in the absence of damping. We
investigate the dependence of the efficiency of population transfer on the
frequency and amplitude of the perturbation, and find that it is predominantly
affected by resonant coupling when the detuning matches the frequency of the
noise. We present intuitive principles for when ARP becomes inefficient within
this model, and provide a sufficient condition for the population transfer to
be above an arbitrary threshold.


  Medium modifications of the pion-omega-rho vertex are analyzed in context of
the omega -> pi gamma* and rho -> pi gamma* decays in nuclear matter. A
relativistic hadronic model with mesons, nucleons, and Delta(1232)isobars is
applied. A substantial increase of the widths for the decays omega -> pi gamma*
and rho -> pi gamma* is found for photon virtualities in the range 0.3-0.6 GeV.
This enhancement has a direct importance for the description of dilepton yields
obtained in relativistic heavy-ion collisions.


  In this short note we prove that a class of Artin groups of affine and
complex types are virtually poly-free, answering partially the question if all
Artin groups are virtually poly-free.


  Network softwarization has revolutionized the architecture of cellular
wireless networks. State-of-the-art container based virtual radio access
networks (vRAN) provide enormous flexibility and reduced life cycle management
costs, but they also come with prohibitive energy consumption. We argue that
for future AI-native wireless networks to be flexible and energy efficient,
there is a need for a new abstraction in network softwarization that caters for
neural network type of workloads and allows a large degree of service
composability. In this paper we present the NeuroRAN architecture, which
leverages stateful function as a user facing execution model, and is
complemented with virtualized resources and decentralized resource management.
We show that neural network based implementations of common transceiver
functional blocks fit the proposed architecture, and we discuss key research
challenges related to compilation and code generation, resource management,
reliability and security.


  Agol proved that ribbon concordance forms a partial ordering on the set of
knots in the $3$-sphere. In this paper, we prove that all tight fibered knots
are minimal in this partially ordered set. We also give the table of prime
minimal knots up to $8$-crossings except for $8_{15}$.