  We present a 3D MHD simulation that qualitatively models the coronal mag-
netic field evolution associated with the eruptive flare that occurred on
December 13, 2006 in the emerging {\delta}-sunspot region NOAA 10930 observed
by the Hinode satellite. The simulation is set up where we drive the emergence
of an east-west oriented magnetic flux rope at the lower boundary into a
pre-existing coronal field constructed from the SOHO/MDI full-disk magnetogram
at 20:51:01 UT on December 12, 2006. The resulting coronal flux rope embedded
in the ambi- ent coronal magnetic field first settles into a stage of
quasi-static rise, and then undergoes a dynamic eruption, with the leading edge
of the flux rope cavity accel- erating to a steady speed of about 830 km/s. The
pre-eruption coronal magnetic field shows morphology that is in qualitative
agreement with that seen in the Hinode soft X-ray observation in both the
magnetic connectivity as well as the development of an inverse-S shaped X-ray
sigmoid. We examine the properties of the erupting flux rope and the morphology
of the post-reconnection loops, and compare them with the observations.


  We reconstruct the Standard Model (SM) quark masses and the
Cabibbo-Kobayashi-Maskawa (CKM) matrix from a five-dimensional model, with the
fifth dimension compactified on an S^1/Z_2 orbifold. Fermions are localized
only at the orbifold fixed points and the induced quark mass matrices are
almost democratic. Two specific versions of our model with 15 and 24 parameters
are presented, and for both versions we can reproduce the quark mass spectrum
and CKM matrix correctly to the level they are observed in current experiments.


  The couplings of the electroweak effective theory contain information on the
heavy-mass scales which are no-longer present in the low-energy Lagrangian. We
build a general effective Lagrangian, implementing the electroweak chiral
symmetry breaking $SU(2)_L\otimes SU(2)_R\to SU(2)_{L+R}$, which couples the
known particle fields to heavier states with bosonic quantum numbers
$J^P=0^\pm$ and $1^\pm$. We consider colour-singlet heavy fields that are in
singlet or triplet representations of the electroweak group. Integrating out
these heavy scales, we analyze the pattern of low-energy couplings among the
light fields which are generated by the massive states. We adopt a generic
non-linear realization of the electroweak symmetry breaking with a singlet
Higgs, without making any assumption about its possible doublet structure.
Special attention is given to the different possible descriptions of massive
spin-1 fields and the differences arising from naive implementations of these
formalisms, showing their full equivalence once a proper short-distance
behaviour is required.


  We compute four-point functions with two maximal giant gravitons and two
chiral primary operators at three-loop order in planar $\mathcal{N}=4$
Super-Yang-Mills theory. The Lagrangian insertion method, together with
symmetries of the theory fix the integrand up to a few constants, which can be
determined by lower loop data. The final result can be written in terms of the
known three-loop conformal integrals. From the four-point function, we extract
the OPE coefficients of two giant gravitons and one non-BPS twist-2 operator
with arbitrary spin at three-loops, given in terms of harmonic sums. We observe
an intriguingly simple relation between the giant graviton OPE coefficients and
the OPE coefficients of three single-trace operators.


  Collective rotation channels are a fundamental class of channels in quantum
computing and quantum information theory. The commutant of the noise operators
for such a channel is a C*-algebra which is equal to the set of fixed points
for the channel. Finding the precise spatial structure of the commutant algebra
for a set of noise operators associated with a channel is a core problem in
quantum error prevention. We draw on methods of operator algebras, quantum
mechanics and combinatorics to explicitly determine the structure of the
commutant for the class of collective rotation channels.


  We review the properties of the 22 millisecond radio pulsars currently known
in the globular cluster 47 Tucanae, and their implications for the mass and gas
content of the cluster. Further details can be found in the publications from
this project to date (Camilo et al. 2000; Freire 2000; Freire et al. 2001a,b;
Freire et al. 2003). Throughout the review, we look ahead to the future results
anticipated from this fascinating cluster.


  The Rabinowitz-Floer homology of a Liouville domain W is the Floer homology
of the free period Hamiltonian action functional associated to a Hamiltonian
whose zero energy level is the boundary of W. It has been introduced by K.
Cieliebak and U. Frauenfelder. Together with A. Oancea, the same authors have
recently computed the Rabinowitz-Floer homology of the cotangent disk bundle
D^*M of a closed manifold M, by establishing a long exact sequence. The first
aim of this paper is to present a chain level construction of this exact
sequence. In fact, we show that this sequence is the long homology sequence
induced by a short exact sequence of chain complexes, which involves the Morse
chain complex and the Morse differential complex of the energy functional for
closed geodesics on M. These chain maps are defined by considering spaces of
solutions of the Rabinowitz-Floer equation on half-cylinders, with suitable
boundary conditions which couple them with the negative gradient flow of the
geodesic energy functional. The second aim is to generalize this construction
to the case of a fiberwise uniformly convex compact subset W of T^*M whose
interior part contains a Lagrangian graph. Equivalently, W is the energy
sublevel associated to an arbitrary Tonelli Lagrangian L on TM and to any
energy level which is larger than the strict Mane' critical value of L. In this
case, the energy functional for closed geodesics is replaced by the free period
Lagrangian action functional associated to a suitable calibration of L. An
important issue in our analysis is to extend the uniform estimates for the
solutions of the Rabinowitz-Floer equation to Hamiltonians which have quadratic
growth in the momenta. These uniform estimates are obtained by suitable
versions of the Aleksandrov maximum principle.


  We ask about the simply connected compact smooth 6-manifolds which can
support structures of Calabi-Yau threefolds. In particular, we study the
interesting case of Calabi-Yau threefolds $X$ with second betti number 3. We
have a cup-product cubic form on the second integral cohomology, a linear form
given by the second Chern class, and the integral middle cohomology, and if $X$
is simply connected with torsion free homology this information determines
precisely the diffeomorphism class of the underlying 6-manifold by a result of
Wall. For simplicity, we assume that the cubic form defines a smooth real
elliptic curve whose Hessian is irreducible. Under a further relatively mild
assumption that there are no non-movable surfaces $E$ on $X$ with $1 \le E^3
\le 8$, we prove that the real elliptic curve must have two connected
components rather than one, and that the K\"ahler cone is contained in the open
positive cone on the bounded component; we show moreover that the second Chern
class is also positive on this open cone. Using Wall's result, for any given
third Betti number we therefore have an abundance of examples of smooth compact
oriented 6-manifolds which support no Calabi-Yau structures, both in the cases
when the cubic defines a real elliptic curve with one or two connected
components. Moreover, except possibly if $c_2$ vanishes at a real inflexion
point of the elliptic curve, even when Calabi-Yau structures do occur under the
above conditions, there will be only a bounded family of them which are not
birationally elliptic.


  Galactic orbits have been constructed over long time intervals for ten
globular clusters located near the Galactic center. A model with an axially
symmetric gravitational potential for the Galaxy was initially applied, after
which a non-axially symmetric potential corresponding to the central bar was
added. Variations in the trajectories of all these globular clusters in the XY
plane due to the influence of the bar were detected. These were greatest for
the cluster Terzan 4 in the meridional (RZ) plane. The globular clusters Terzan
1, Terzan 2, Terzan 4, Terzan 9, NGC 6522, and NGC 6558 always remained within
the Galactic bulge, no farther than 4 kpc from the Galactic center.


  Quantum computation offers the potential to solve fundamental yet otherwise
intractable problems across a range of active fields of research. Recently,
universal quantum-logic gate sets - the building blocks for a quantum computer
- have been demonstrated in several physical architectures. A serious obstacle
to a full-scale implementation is the sheer number of these gates required to
implement even small quantum algorithms. Here we present and demonstrate a
general technique that harnesses higher dimensions of quantum systems to
significantly reduce this number, allowing the construction of key quantum
circuits with existing technology. We are thereby able to present the first
implementation of two key quantum circuits: the three-qubit Toffoli and the
two-qubit controlled-unitary. The gates are realised in a linear optical
architecture, which would otherwise be absolutely infeasible with current
technology.


  Recent years have witnessed tremendous interest in understanding and
predicting information spread on social media platforms such as Twitter,
Facebook, etc. Existing diffusion prediction methods primarily exploit the
sequential order of influenced users by projecting diffusion cascades onto
their local social neighborhoods. However, this fails to capture global social
structures that do not explicitly manifest in any of the cascades, resulting in
poor performance for inactive users with limited historical activities.
  In this paper, we present a novel variational autoencoder framework (Inf-VAE)
to jointly embed homophily and influence through proximity-preserving social
and position-encoded temporal latent variables. To model social homophily,
Inf-VAE utilizes powerful graph neural network architectures to learn social
variables that selectively exploit the social connections of users. Given a
sequence of seed user activations, Inf-VAE uses a novel expressive co-attentive
fusion network that jointly attends over their social and temporal variables to
predict the set of all influenced users. Our experimental results on multiple
real-world social network datasets, including Digg, Weibo, and Stack-Exchanges
demonstrate significant gains (22% MAP@10) for Inf-VAE over state-of-the-art
diffusion prediction models; we achieve massive gains for users with sparse
activities, and users who lack direct social neighbors in seed sets.


  We discuss the long range nature of the molecules produced in recent
experiments on molecular Bose-Einstein condensation. The properties of these
molecules depend on the full two-body Hamiltonian and not just on the states of
the system in the absence of interchannel couplings. The very long range nature
of the state is crucial to the efficiency of production in the experiments. Our
many-body treatment of the gas accounts for the full binary physics and
describes properly how these molecular condensates can be directly probed.


  We reformulate Pratt's tableau decision procedure of checking satisfiability
of a set of formulas in PDL. Our formulation is simpler and more direct for
implementation. Extending the method we give the first EXPTIME (optimal)
tableau decision procedure not based on transformation for checking consistency
of an ABox w.r.t. a TBox in PDL (here, PDL is treated as a description logic).
We also prove the new result that the data complexity of the instance checking
problem in PDL is coNP-complete.


  Sequencing errors and biases in metagenomic datasets affect coverage-based
assemblies and are often ignored during analysis. Here, we analyze read
connectivity in metagenomes and identify the presence of problematic and likely
a-biological connectivity within metagenome assembly graphs. Specifically, we
identify highly connected sequences which join a large proportion of reads
within each real metagenome. These sequences show position-specific bias in
shotgun reads, suggestive of sequencing artifacts, and are only minimally
incorporated into contigs by assembly. The removal of these sequences prior to
assembly results in similar assembly content for most metagenomes and enables
the use of graph partitioning to decrease assembly memory and time
requirements.


  For a given snark G and edge e of G, we can form a cubic graph G_e using an
operation we call "edge subtraction". The number of 3-edge-colourings of G_e is
18 * \psi(G,e) for some nonnegative integer \psi(G,e). Given snarks G_1 and
G_2, we can form a new snark G using techniques given by Isaacs and Kochol. In
this note we give relationships between \psi(G_1,e_1), \psi(G_2,e_2), and
\psi(G,e) for particular edges e_1, e_2, and e, in G_1, G_2, and G
(respectively). As a consequence, if g,h,i,j,k,l are each a nonnegative
integer, then there exists a cyclically 5-edge-connected snark G with an edge e
such that \psi(G,e)=5^g * 7^h, and a cyclically 4-edge-connected snark G_0 with
an edge e_0 such that \psi(G_0,e_0)=2^i * 3^j * 5^k * 7^l.


  Arbitrary amplitude nonlinear ion waves is investigated in an extremely
degenerate electron-positron-ion plasma with relativistic electrons/positrons
and dynamic cold ions using Sagdeev pseudo-potential method in framework of
quantum hydrodynamics model. The matching criteria of existence for ion
solitary as well as periodic nonlinear excitations is studied numerically in
terms of the relativistic degeneracy parameter, relative positron concentration
and the relativistic normalized propagation-speed range of these waves are
obtained. It is remarked that the electron relativistic degeneracy and relative
positron concentration in such plasmas have significant effects on nonlinear
wave propagations in relativistically degenerate Fermi-Dirac plasma. Our
results are in good agreement with the previously reported ones obtained using
semiclassical Thomas-Fermi approximation in dense plasmas with non-relativistic
and ultra-relativistic electrons and positrons. Current findings can be
appropriate for the study of astrophysical superdense compact objects such as
white dwarfs.


  Convergence properties of Shannon Entropy are studied. In the differential
setting, it is shown that weak convergence of probability measures, or
convergence in distribution, is not enough for convergence of the associated
differential entropies. A general result for the desired differential entropy
convergence is provided, taking into account both compactly and uncompactly
supported densities. Convergence of differential entropy is also characterized
in terms of the Kullback-Liebler discriminant for densities with fairly general
supports, and it is shown that convergence in variation of probability measures
guarantees such convergence under an appropriate boundedness condition on the
densities involved. Results for the discrete setting are also provided,
allowing for infinitely supported probability measures, by taking advantage of
the equivalence between weak convergence and convergence in variation in this
setting.


  IGMtransmission is a Java graphical user interface that implements Monte
Carlo simulations to compute the corrections to colours of high-redshift
galaxies due to intergalactic attenuation based on current models of the
Intergalactic Medium. The effects of absorption due to neutral hydrogen are
considered, with particular attention to the stochastic effects of Lyman Limit
Systems. Attenuation curves are produced, as well as colours for a wide range
of filter responses and model galaxy spectra. Alternative filter response
curves and spectra may be readily uploaded. The code is freely available from
http://code.google.com/p/igmtransmission. It is licensed under the GNU General
Public License v.3.


  We study the quantum electrodynamics of Luttinger fermions with quadratic
band-crossing dispersion in three dimensions. The model can be viewed as the
low-energy effective theory of a putative $U(1)$ quantum spin liquid with
fermionic Luttinger spinons, or as an extension of the
Luttinger-Abrikosov-Beneslavskii (LAB) model that accounts for transverse gauge
fluctuations with finite photon velocity. Aided by a renormalization group
analysis below four dimensions, we elucidate the presence and stability of
quantum critical phenomena in this model. We find that the non-Fermi liquid LAB
phase is stable against gauge fluctuations, and can thus also be viewed as a
$U(1)$ spin liquid with gapless Luttinger spinons. We discover a multicritical
point with Lifshitz scaling that corresponds to a time-reversal
symmetry-breaking quantum phase transition from the LAB state to a chiral spin
liquid with spinon Landau levels and birefringent emergent photons. This
multicritical point is characterized by a finite fermion-photon coupling in the
infrared and can be viewed as a fermionic analog of the Rokhsar-Kivelson point
in three-dimensional quantum dimer models.


  We investigate the convergence of phase fields for the Willmore problem away
from the support of a limiting measure $\mu$. For this purpose, we introduce a
suitable notion of essentially uniform convergence. This mode of convergence is
a natural generalisation of uniform convergence that precisely describes the
convergence of phase fields in three dimensions.
  More in detail, we show that, in three space dimensions, points close to
which the phase fields stay bounded away from a pure phase lie either in the
support of the limiting mass measure $\mu$ or contribute a positive amount to
the limiting Willmore energy. Thus there can only be finitely many such points.
  As an application, we investigate the Hausdorff limit of level sets of
sequences of phase fields with bounded energy. We also obtain results on
boundedness and $L^p$-convergence of phase fields and convergence from outside
the interval between the wells of a double-well potential. For minimisers of
suitable energy functionals, we deduce uniform convergence of the phase fields
from essentially uniform convergence.


  Let $F:[0,T]\times\R^n\mapsto 2^{\R^n}$ be a continuous multifunction with
compact, not necessarily convex values. In this paper, we prove that, if $F$
satisfies the following Lipschitz Selection Property: \begin{itemize}
\item[{(LSP)}] {\sl For every $t,x$, every $y\in \overline{co} F(t,x)$ and
$\varepsilon>0$, there exists a Lipschitz selection $\phi$ of $\overline{co}F$,
defined on a neighborhood of $(t,x)$, with $|\phi(t,x)-y|<\varepsilon$.}
\end{itemize} then there exists a measurable selection $f$ of $ext F$\ such
that, for every $x_0$, the Cauchy problem $$ \dot x(t)=f(t,x(t)),\qquad\qquad
x(0)=x_0 $$ has a unique Caratheodory solution, depending continuously on
$x_0$. We remark that every Lipschitz multifunction with compact values
satisfies (LSP). Another interesting class, for which (LSP) holds, consists of
those continuous multifunctions $F$ whose values are compact and have convex
closure with nonempty interior.


  Similarity metrics, e.g., signatures as used by anti-virus products, are the
dominant technique to detect if a given binary is malware. The underlying
assumption of this approach is that all instances of a malware (or even malware
family) will be similar to each other.
  Software diversification is a probabilistic technique that uses code and data
randomization and expressiveness in the target instruction set to generate
large amounts of functionally equivalent but different binaries. Malware
diversity builds on software diversity and ensures that any two diversified
instances of the same malware have low similarity (according to a set of
similarity metrics). An LLVM-based prototype implementation diversifies both
code and data of binaries and our evaluation shows that signatures based on
similarity only match one or few instances in a pool of diversified binaries
generated from the same source code.


  Graph Neural Networks (GNNs) are well-suited for learning on homophilous
graphs, i.e., graphs in which edges tend to connect nodes of the same type.
Yet, achievement of consistent GNN performance on heterophilous graphs remains
an open research problem. Recent works have proposed extensions to standard GNN
architectures to improve performance on heterophilous graphs, trading off model
simplicity for prediction accuracy. However, these models fail to capture basic
graph properties, such as neighborhood label distribution, which are
fundamental for learning. In this work, we propose GCN for Heterophily (GCNH),
a simple yet effective GNN architecture applicable to both heterophilous and
homophilous scenarios. GCNH learns and combines separate representations for a
node and its neighbors, using one learned importance coefficient per layer to
balance the contributions of center nodes and neighborhoods. We conduct
extensive experiments on eight real-world graphs and a set of synthetic graphs
with varying degrees of heterophily to demonstrate how the design choices for
GCNH lead to a sizable improvement over a vanilla GCN. Moreover, GCNH
outperforms state-of-the-art models of much higher complexity on four out of
eight benchmarks, while producing comparable results on the remaining datasets.
Finally, we discuss and analyze the lower complexity of GCNH, which results in
fewer trainable parameters and faster training times than other methods, and
show how GCNH mitigates the oversmoothing problem.


  Insufficient or even unavailable training data of emerging classes is a big
challenge of many classification tasks, including text classification.
Recognising text documents of classes that have never been seen in the learning
stage, so-called zero-shot text classification, is therefore difficult and only
limited previous works tackled this problem. In this paper, we propose a
two-phase framework together with data augmentation and feature augmentation to
solve this problem. Four kinds of semantic knowledge (word embeddings, class
descriptions, class hierarchy, and a general knowledge graph) are incorporated
into the proposed framework to deal with instances of unseen classes
effectively. Experimental results show that each and the combination of the two
phases achieve the best overall accuracy compared with baselines and recent
approaches in classifying real-world texts under the zero-shot scenario.


  Chiral magnetic Mn$_x$Fe$_{1-x}$Ge compounds have an antisymmetric exchange
interaction that is tunable with the manganese stoichiometric fraction, $x$.
Although millimeter-scale, polycrystalline bulk samples of this family of
compounds have been produced, thin-film versions of these materials will be
necessary for devices. In this study, we demonstrate the growth of epitaxial
Mn$_x$Fe$_{1-x}$Ge thin films on Si (111) substrates with a pure B20 crystal
structure in the stoichiometric fraction range x from 0 to 0.81. Following
systematic physical and magnetic characterization including microwave
absorption spectroscopy, we quantify the antisymmetric exchange interaction and
helical period as a function of $x$, which ranges from 200 nm to 8 nm. Our
results demonstrate an approach to engineering the size of magnetic skyrmions
in epitaxial films that are grown using scalable techniques.


  A recent study of coherent noise model for the system size independent case
provides an exact relation between the exponent $\tau$ of avalanche size
distribution and the $q$ value of appropriate $q$-Gaussian that fits the return
distribution of the model. This relation is applied to Ehrenfest's historical
dog-flea model by treating the fluctuations around the thermal equilibrium as
avalanches. We provide a clear numerical evidence that the relation between the
exponent $\tau$ of fluctuation length distribution and the $q$ value of
appropriate $q$-Gaussian obeys this exact relation when the system size is
large enough. This allows us to determine the value of $q$-parameter \emph{a
priori} from one of the well known exponents of such dynamical systems.
Furthermore, it is shown that the return distribution in dog-flea model
gradually approaches to $q$-Gaussian as the system size increases and this
tendency can be analyzed by a well defined analytical expression.


  Sound event detection (SED) methods typically rely on either strongly
labelled data or weakly labelled data. As an alternative, sequentially labelled
data (SLD) was proposed. In SLD, the events and the order of events in audio
clips are known, without knowing the occurrence time of events. This paper
proposes a connectionist temporal classification (CTC) based SED system that
uses SLD instead of strongly labelled data, with a novel unsupervised
clustering stage. Experiments on 41 classes of sound events show that the
proposed two-stage method trained on SLD achieves performance comparable to the
previous state-of-the-art SED system trained on strongly labelled data, and is
far better than another state-of-the-art SED system trained on weakly labelled
data, which indicates the effectiveness of the proposed two-stage method
trained on SLD without any onset/offset time of sound events.


  In the objective of building intelligent searching systems for Elibraries or
online bookstores, we have proposed a searching system model based on a
Vietnamese language query processing component. Such document searching systems
based on this model can allow users to use Vietnamese queries that represent
content information as input, instead of entering keywords for searching in
specific fields in database. To simplify the realization process of system
based on this searching system model, we set a target of building a framework
to support the rapid development of Vietnamese language query processing
components. Such framework let the implementation of Vietnamese language query
processing component in similar systems in this domain to be done more easily.


  Within the framework of parameter dependent PDEs, we develop a constructive
approach based on Deep Neural Networks for the efficient approximation of the
parameter-to-solution map. The research is motivated by the limitations and
drawbacks of state-of-the-art algorithms, such as the Reduced Basis method,
when addressing problems that show a slow decay in the Kolmogorov n-width. Our
work is based on the use of deep autoencoders, which we employ for encoding and
decoding a high fidelity approximation of the solution manifold. To provide
guidelines for the design of deep autoencoders, we consider a nonlinear version
of the Kolmogorov n-width over which we base the concept of a minimal latent
dimension. We show that the latter is intimately related to the topological
properties of the solution manifold, and we provide theoretical results with
particular emphasis on second order elliptic PDEs, characterizing the minimal
dimension and the approximation errors of the proposed approach. The theory
presented is further supported by numerical experiments, where we compare the
proposed approach with classical POD-Galerkin reduced order models. In
particular, we consider parametrized advection-diffusion PDEs, and we test the
methodology in the presence of strong transport fields, singular terms and
stochastic coefficients.


  Conformal field theory, describing systems with scaling symmetry, plays a
crucial role throughout physics. We describe a quantum algorithm to simulate
the dynamics of conformal field theories, including the action of local
conformal transformations. A full analysis of the approximation errors suggests
near-term applicability of our algorithm: promising results for conformal field
theories with central charge c=1/2 are obtained already with 128 logical
qubits.


  Women are influential online, especially in image-based social media such as
Twitter and Instagram. However, many in the network environment contain gender
discrimination and aggressive information, which magnify gender stereotypes and
gender inequality. Therefore, the filtering of illegal content such as gender
discrimination is essential to maintain a healthy social network environment.
In this paper, we describe the system developed by our team for SemEval-2022
Task 5: Multimedia Automatic Misogyny Identification. More specifically, we
introduce two novel system to analyze these posts: a multimodal multi-task
learning architecture that combines Bertweet for text encoding with ResNet-18
for image representation, and a single-flow transformer structure which
combines text embeddings from BERT-Embeddings and image embeddings from several
different modules such as EfficientNet and ResNet. In this manner, we show that
the information behind them can be properly revealed. Our approach achieves
good performance on each of the two subtasks of the current competition,
ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706
macro F1-score) while exceeding the official baseline results by high margins.


  We consider billiard ball motion in a convex domain of a constant curvature
surface influenced by the constant magnetic field. We prove that if the
billiard map is totally integrable then the boundary curve is necessarily a
circle. This result is a manifestation of the so-called Hopf rigidity
phenomenon which was recently obtained for classical billiards on constant
curvature surfaces.


  Biological neurons and their in-silico emulations for neuromorphic artificial
intelligence (AI) use extraordinarily energy-efficient mechanisms, such as
spike-based communication and local synaptic plasticity. It remains unclear
whether these neuronal mechanisms only offer efficiency or also underlie the
superiority of biological intelligence. Here, we prove rigorously that, indeed,
the Bayes-optimal prediction and inference of randomly but continuously
transforming environments, a common natural setting, relies on short-term
spike-timing-dependent plasticity, a hallmark of biological synapses. Further,
this dynamic Bayesian inference through plasticity enables circuits of the
cerebral cortex in simulations to recognize previously unseen, highly distorted
dynamic stimuli. Strikingly, this also introduces a biologically-modelled AI,
the first to overcome multiple limitations of deep learning and outperform
artificial neural networks in a visual task. The cortical-like network is
spiking and event-based, trained only with unsupervised and local plasticity,
on a small, narrow, and static training dataset, but achieves recognition of
unseen, transformed, and dynamic data better than deep neural networks with
continuous activations, trained with supervised backpropagation on the
transforming data. These results link short-term plasticity to high-level
cortical function, suggest optimality of natural intelligence for natural
environments, and repurpose neuromorphic AI from mere efficiency to
computational supremacy altogether.


  We consider the quantum dynamics of both open and closed two- dimensional
universes with ``wormholes'' and particles. The wave function is given as a sum
of freely propagating amplitudes, emitted from a network of mapping class
images of the initial state. Interference between these amplitudes gives
non-trivial scattering effects, formally analogous to the optical diffraction
by a multidimensional grating; the ``bright lines'' correspond to the most
probable geometries.


  Silicon-based fast time detectors have been widely used in high energy
physics, nuclear physics, space exploration and other fields in recent years.
However, silicon detectors often require complex low-temperature systems when
operating in irradiation environment, and their detection performance decrease
with the increase of irradiation dose. Compared with silicon, silicon carbide
(SiC) has a wider bandgap, higher atomic displacement energy, saturated
electron drift velocity and thermal conductivity. Simultaneously, the low gain
avalanche detector avoids crosstalk and high noise from high multiplication due
to its moderate gain, and thus can maintain a high detector signal without
increasing noise. Thus, the 4H-SiC particle detector, especially the low gain
avalanche detector has the potential to detect the minimal ionized particles
(MIPs) under extreme irradiation and high temperature environments. In this
work, the emphasis was placed on the design of a 4H-SiC Low Gain Avalanche
Detector (LGAD), especially the epitaxial structure and technical process which
played the main roles. In addition, a simulation tool--RASER(RAdiation
SEmiconductoR) was developed to simulate the performances including the
electrical properties and time resolution of the 4H-SiC LGAD we proposed. The
working voltage and gain effectiveness of the LGAD were verified by the
simulation of electrical performances. The time resolution of the LGAD is (35.0
$\pm$ 0.2) ps under the electrical field of -800 V, which is better than that
of the 4H-SiC PIN detector.


  We present deep ground-based {\it B} and {\it R} observations of 12 fields in
the Small Magellanic Cloud (SMC). The resulting color-magnitude diagrams (CMDs)
reach the oldest main-sequence (MS) turnoff at M$_{R}$$\thicksim$3.5 and reveal
the stellar population differences between the part of the galaxy facing the
Large Magellanic Cloud (LMC) and an area on the opposite side. In the Southern
part of the galaxy, we found that there are still intermediate-age stars as far
as 4 kpc from the SMC center.
  The Chemical Enrichment History (CEH) in one of our SMC fields is also
presented.


  We show that a Bose-Einstein condensate and a portion of its surrounding
thermal cloud can exhibit mutual self-trapping, supported by the attractive
particle interactions, and not by the external confinement. This type of
dynamics is characteristic of composite random-phase solitons.


  We discuss dissipative stochastic wave and diffusion equations resulting from
an interaction of the inflaton with an environment in an external expanding
metric. We show that a diffusion equation well approximates the wave equation
in a strong friction limit. We calculate the long wave power spectrum of the
wave equation under the assumption that the perturbations are slowly varying in
time and the expansion is almost exponential. Under the assumption that the
noise has a form invariant under coordinate transformations we obtain the power
spectrum close to the scale invariant one. In the diffusion approximation we go
beyond the slow variation assumption. We calculate the power spectrum exactly
in models with exponential inflation and polynomial potentials and with
power-law inflation and exponential potentials.


  We study linear power corrections ${\cal O}(\Lambda_{\rm QCD}/Q)$ to certain
collider observables. We present arguments that prove that such corrections
cannot appear in observables that are inclusive with respect to QCD radiation,
such as total cross sections as well as rapidity and transverse momentum
distributions of color-neutral particles. Although our calculations are carried
out in a simplified framework, our arguments and conclusions are applicable,
with some reservations, to processes both at lepton and hadron colliders. We
also show how an improved understanding of the origin of linear power
corrections allows us to simplify their calculation. As an application, we
compute the leading non-perturbative corrections to the $C$-parameter and the
thrust in $e^+e^-$ annihilation in a generic three-jet configuration.


  We consider a model which in a certain limit reduces to the large N ${\cal
N}=1$ supersymmetric SU(N) gauge theory without matter. The gaugino condensate
in this model is controlled by the dynamics of an additional singlet
superfield. Using this model we explicitly construct BPS domain walls arising
due to the chiral symmetry breaking. In particular, in the large N limit we
obtain the exact shapes of the domain walls corresponding to solitons, and also
of the domain walls interpreted as D-branes on which the SQCD string can end,
whose existence was previously argued by Witten in the context of the large N
SQCD. We also discuss various points which appear to support the consistency of
the D-brane interpretation for these domain walls within the SQCD string
context.


  By means of high-resolution spectra we have measured radial velocities of the
companion (hereafter COM J1740-5340) to the eclipsing millisecond pulsar PSR
J1740-5340 in the Galactic globular cluster NGC 6397. The radial-velocity curve
fully confirms that COM J1740-5340 is orbiting the pulsar and enables us to
derive the most accurate mass ratio (M_ PSR/M_COM=5.85+/-0.13) for any
non-relativistic binary system containing a neutron star. Assuming a pulsar
mass in the range 1.3-1.9 Msun, the mass of COM J1740-5340 spans the interval
0.22-0.32 Msun, the inclination of the system is constrained within 56 deg <= i
<= 47 deg and the Roche lobe radius is r_RL ~ 1.5-1.7 Rsun. A preliminary
chemical abundance analysis confirms that COM J1740-5340 has a metallicity
compatible with that measured for other stars in this metal-poor globular, but
the unexpected detection of strong He I absorption lines implies the existence
of regions at T>10,000 K, significantly warmer than the rest of the star. The
intensity of this line correlates with the orbital phase, suggesting the
presence of a region on the companion surface, heated by the millisecond pulsar
flux.


  Actor of its presentation and actor of its online representation, the diarist
draws his diegetic existence by setting up a strategy of automediation. The
Self-representation is a personal creation determined by the interface and the
functionalities of the software. A pragmatic approach of the
Self-representation in the Livejournal Blog and the Touchgraph Livejournal
browser provides a way to observe the play between intimacy and
intersubjectivity. The software leads the user from the lonely space of writing
to the community space of publication.


  We consider the effect of the cosmic microwave background (CMB) frequency
spectral distortions arising due to the Compton scattering of the anisotropic
radiation on Sunyaev-Zel'dovich (SZ) clusters. We derive the correction to the
thermal SZ effect due to the presence of multipoles with $\ell=1,2,3$ in the
anisotropy of the CMB radiation. We show that this effect gives us an
opportunity for an independent evaluation of the CMB dipole, quadrupole and
octupole angular anisotropy in our location using distorted signal from the
nearby galaxy clusters and to distinguish between the Sachs-Wolfe (SW) and the
Integrated Sachs-Wolfe (ISW) effects by combining such signals from distant and
nearby clusters. The future space mission 'Millimetron' will have unprecedented
sensitivity, which will make it possible to observe the spectral distortion we
are considering.


  Nowadays, different types of context information are integrated into mobile
messaging to increase expressiveness and awareness, including mobile device
setting, location, activity, and heart rate. Due to low recognition accuracy,
sometimes users cannot accurately infer others' status through activity
awareness. Recently, activity recognition technology has advanced. However, the
user behaviors of activity awareness with improved technology have not been
studied. In this study, we design ActAware, a mobile instant messaging
application that integrates activity awareness based on improved activity
recognition technology, i.e., improved recognition accuracy and the addition of
activity transition notification. We conduct a field study to explore user
behaviors and found that activity awareness allows users to speculate on the
reasons for chat interruption, plan communication, speculate on whether the
chat partner is departing/arriving, and deepen the understanding of living
patterns. Compared with disclosing other types of context information, users
have fewer privacy concerns about disclosing activity information in ActAware.
Based on these findings, we provide design recommendations for mobile messaging
to better support activity awareness.


  We investigated the negative relationship between mortality and COVID-19
vaccination at ecological level, which has been established through clinical
trials and other investigations at the individual level. We conducted an
exploratory, correlational, country-level analysis of open data centralized by
Our World in Data concerning the cumulative COVID-19 mortality for the winter
wave of the pandemic as function of the vaccination rate in October 2021. In
order to disentangle the protective relationship from confounding processes, we
controlled variables that capture country-level social development and level of
testing. We also deployed three segmentation tactics, distinguishing among
countries based on their level of COVID-19 testing, age structure, and types of
vaccines used. Controlling for confounding factors did not highlight a
statistically significant global relationship between vaccination and
cumulative mortality in the total country sample. As suggested by previous
estimates at country level, a strong, significant, negative relationship
between cumulative mortality and vaccination was highlighted through
segmentation analysis for countries positioned at the higher end of the social
development spectrum. The strongest estimate for vaccine effectiveness at
ecological level was obtained for countries that use Western-only vaccines.
This may partly reflect the higher effectiveness of Western vaccines in
comparison with the average of all vaccines in use; it may also derive from the
lower social heterogeneity of countries included in this segment. COVID-19
testing has a significant and positive relationship with cumulative mortality
for all subsamples. This indicates that testing intensity should be controlled
as a potential confounder in future ecological analyses of COVID-19 mortality.


  In the immersed boundary (IB) approach to fluid-structure interaction
modeling, the coupling between the fluid and structure variables is mediated
using a regularized version of Dirac delta function. In the IB literature, the
regularized delta functions, also referred to IB kernel functions, are either
derived analytically from a set of postulates or computed numerically using the
moving least squares (MLS) approach. Whereas the analytical derivations
typically assume a regular Cartesian grid, the MLS method is a meshless
technique that can be used to generate kernel functions on complex domains and
unstructured meshes. In this note we take a viewpoint that IB kernel
generation, either analytically or via MLS, is a constrained quadratic
minimization problem. The extremization of a constrained quadratic function is
a broader concept than kernel generation, and there are well-established
numerical optimization techniques to solve this problem. For example, we show
that the constrained quadratic minimization technique can be used to generate
one-sided (anisotropic) IB kernels and/or to bound their values.


  We have been developing X-ray SOIPIXs for next-generation satellites for
X-ray astronomy. Their high time resolution ($\sim10~\mu$s) and
event-trigger-output function enable us to read out without pile-ups and to use
anti-coincidence systems. Their performance in imaging spectroscopy is
comparable to that in the CCDs. A problem in our previous model was degradation
of charge-collection efficiency (CCE) at pixel borders. We measured the
response in the sub-pixel scale, using finely collimated X-ray beams at
$10~\mu$m\Phi$ at SPring-8, and investigated the non-uniformity of the CCE
within a pixel. We found that the X-ray detection efficiency and CCE degrade in
the sensor region under the pixel circuitry placed outside the buried p-wells
(BPW). A 2D simulation of the electric fields shows that the isolated
pixel-circuitry outside the BPW creates local minimums in the electric
potentials at the interface between the sensor and buried oxide layers. Thus, a
part of signal charge is trapped there and is not collected to the BPW. Based
on this result, we modified the placement of the in-pixel circuitry so that the
electric fields would converge toward the BPW. We confirmed that the CCE at
pixel borders is successfully improved with the updated model.


  J/psi production has been measured in proton-proton collisions at sqrt(s)=
200 GeV over a wide rapidity and transverse momentum range by the PHENIX
experiment at RHIC. Distributions of the rapidity and transverse momentum,
along with measurements of the mean transverse momentum and total production
cross section are presented and compared to available theoretical calculations.
The total J/psi cross section is 3.99 +/- 0.61(stat) +/- 0.58(sys) +/-
0.40(abs) micro barns. The mean transverse momentum is 1.80 +/- 0.23(stat) +/-
0.16(sys) GeV/c.


  We study the equation of state, fluctuations and static correlators of
electric charge, baryon number and strangeness, by considering a realization of
the Hadron Resonance Gas model in the light flavor sector of QCD. We emphasize
the importance of these observables to study, within this approach, the
possible existence of exotic and missing states in the hadron spectrum. Some
preliminary results for the baryon spectrum have been obtained within a
relativistic quark-diquark model, leading to an overall good agreement with the
spectrum obtained with other quark models. Finally, it is conjectured, within
the Hadron Resonance Gas approach, the existence of a singularity in the
correlators at zero temperature, which turns out to be analogous to the
divergence of the partition function at the Hagedorn temperature.


  We prove an analogue of the Korneichuk--Stechkin lemma for functions with
values in $L$-spaces. As applications, we obtain sharp Ostrowski type
inequalities and solve problems of optimal recovery of identity and
convexifying operators, as well as the problem of integral recovery, on the
classes of $L$-space valued functions with given majorant of modulus of
continuity. The recovery is done based on $n$ mean values of the functions over
some intervals. Moreover, on the classes of functions with given majorant of
modulus of continuity of their Hukuhara type derivative, we solve the problem
of optimal recovery of the function and the Hukuhara type derivative. The
recovery is done based on $n$ values of the function. We also obtain some sharp
Landau type inequalities and solve an analogue of the Stechkin problem about
approximation of unbounded operators by bounded ones and the problem of optimal
recovery of an unbounded operator on a class of elements, known with error.
Consideration of $L$-space valued functions gives a unified approach to
solution of the mentioned above extremal problems for the classes of multi- and
fuzzy-valued functions as well as for the classes of functions with values in
Banach spaces, in particular random processes, and many other classes of
functions.


  Datasets for autonomous cars are essential for the development and
benchmarking of perception systems. However, most existing datasets are
captured with camera and LiDAR sensors in good weather conditions. In this
paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to
facilitate research on object detection, tracking and scene understanding using
radar sensing for safe autonomous driving. RADIATE includes 3 hours of
annotated radar images with more than 200K labelled road actors in total, on
average about 4.6 instances per radar image. It covers 8 different categories
of actors in a variety of weather conditions (e.g., sun, night, rain, fog and
snow) and driving scenarios (e.g., parked, urban, motorway and suburban),
representing different levels of challenge. To the best of our knowledge, this
is the first public radar dataset which provides high-resolution radar images
on public roads with a large amount of road actors labelled. The data collected
in adverse weather, e.g., fog and snowfall, is unique. Some baseline results of
radar based object detection and recognition are given to show that the use of
radar data is promising for automotive applications in bad weather, where
vision and LiDAR can fail. RADIATE also has stereo images, 32-channel LiDAR and
GPS data, directed at other applications such as sensor fusion, localisation
and mapping. The public dataset can be accessed at
http://pro.hw.ac.uk/radiate/.


  We show that the de Haas van Alphen effect can be induced in a two
dimensional atomic gas by the He-McKellar-Wilkens interaction mediated via an
electric dipole moment. Under an appropriate field-dipole configuration, we
show that the neutral atoms subject to a synthetic magnetic field arrange
themselves in Landau levels. An experimental arrangement for observation of the
atomic analog of dHvA oscillations is proposed. In a strong effective magnetic
field regime we present the quantum oscillations in the energy and effective
magnetization of the two dimensional atomic gas. From the dHvA period we
determine the area of the Fermi circle of the atomic cloud.


  A small amount of polymer additives can cause substantial reduction in the
energy dissipation and friction loss of turbulent flow. The problem of
polymer-induced drag reduction has attracted continuous attention over the
seven decades since its discovery. However, changes in research paradigm and
perspectives have triggered a wave of new advancements in the past decade. This
review attempts to bring researchers of all levels, from beginners to experts,
to the forefront of this area. It starts with a comprehensive coverage of
fundamental knowledge and classical findings and theories. It then highlights
several recent developments that bring fresh insights into long-standing
problems. Open questions and ongoing debates are also discussed.


  Let $X$ be a complex quasi-projective algebraic variety. In this paper we
study the mixed Hodge structures of the symmetric products $Sym^{n}X$ when the
cohomology of $X$ is given by exterior products of cohomology classes with odd
degree. We obtain an expression for the equivariant mixed Hodge polynomials
$\mu_{X^{n}}^{S_{n}}\left(t,u,v\right)$, codifying the permutation action of
$S_{n}$ as well as its subgroups. This allows us to deduce formulas for the
mixed Hodge polynomials of its symmetric products
$\mu_{Sym^{n}X}\left(t,u,v\right)$. These formulas are then applied to the case
of linear algebraic groups.


  The main result of this paper is that, if $\Gamma$ is a connected 4-valent
$G$-arc-transitive graph and $v$ is a vertex of $\Gamma$, then either $\Gamma$
is one of a well understood infinite family of graphs, or $|G_v|\leq 2^43^6$ or
$2|G_v|\log_2(|G_v|/2)\leq |\V\Gamma|$ and that this last bound is tight. As a
corollary, we get a similar result for $3$-valent vertex-transitive graphs.


  Following the unified approach of A. Kriegl and P.W. Michor (1997) for a
treatment of global analysis on a class of locally convex spaces known as
convenient, we give a generalization of Rashevsky-Chow's theorem for control
systems in regular connected manifolds modelled on convenient
(infinite-dimensional) locally convex spaces which are not necessarily
normable.


  We investigate the dynamics of linear perturbations in Keplerian flow under
external stochastic force. To abstract from the details of flow structure and
boundary conditions, we consider the problem in the shearing box approximation.
An external force is assumed to have zero mean, even so, induced perturbations
form a steady-state, which provides angular momentum transfer to the periphery
of the flow. The most effective scenario is based on the transient
amplification of induced vortices with the following emission of shearing sound
wave, wherein the maximum of the flux linearly depends on Reynolds number. Thus
such a mechanism is significant for astrophysical flows, for which enormous
Reynolds numbers are typical. At the same time, addressing the problem
analytically, we found that for incompressible fluid in the shearing box
approximation stochastic forcing does not lead to average angular momentum
transfer. Thus the compressibility of the fluid plays an important role here,
and one cannot neglect it.


  Comments to K.M. Pervaiz Fathima, Ren\'e de Borst, Implications of single or
multiple pressure degrees of freedom at fracture in fluid saturated porous
media, Engineering Fracture Mechanics, 213 (2019), 1-20.


  Most work in text classification and Natural Language Processing (NLP)
focuses on English or a handful of other languages that have text corpora of
hundreds of millions of words. This is creating a new version of the digital
divide: the artificial intelligence (AI) divide. Transfer-based approaches,
such as Cross-Lingual Text Classification (CLTC) - the task of categorizing
texts written in different languages into a common taxonomy, are a promising
solution to the emerging AI divide. Recent work on CLTC has focused on
demonstrating the benefits of using bilingual word embeddings as features,
relegating the CLTC problem to a mere benchmark based on a simple averaged
perceptron.
  In this paper, we explore more extensively and systematically two flavors of
the CLTC problem: news topic classification and textual churn intent detection
(TCID) in social media. In particular, we test the hypothesis that embeddings
with context are more effective, by multi-tasking the learning of multilingual
word embeddings and text classification; we explore neural architectures for
CLTC; and we move from bi- to multi-lingual word embeddings. For all
architectures, types of word embeddings and datasets, we notice a consistent
gain trend in favor of multilingual joint training, especially for
low-resourced languages.


  The dynamics of an ultracold dilute gas of bosonic atoms in an optical
lattice can be described by a Bose-Hubbard model where the system parameters
are controlled by laser light. We study the continuous (zero temperature)
quantum phase transition from the superfluid to the Mott insulator phase
induced by varying the depth of the optical potential, where the Mott insulator
phase corresponds to a commensurate filling of the lattice (``optical
crystal''). Examples for formation of Mott structures in optical lattices with
a superimposed harmonic trap, and in optical superlattices are presented.


  It is well known that the generalized Ces\`aro matrices of order one are
hyponormal operators on $\ell^2$, and it has recently been shown that the
Ces\`aro matrix of order two is also hyponormal. Here the relatively new
concept of supraposinormality is used to show that the generalized Ces\`aro
matrices of order two are both posinormal and coposinormal, and that "most" of
them are also hyponormal. A conjecture is propounded that would extend the
hyponormality result.


  The temperature dependence of the sigma meson and pion masses is studied in
the framework of the O(N) model. The Cornwall-Jackiw-Tomboulis formalism is
applied to derive gap equations for the masses in the Hartree and large-N
approximations. Renormalization of the gap equations is carried out within the
cut-off and counter-term renormalization schemes. A consistent renormalization
of the gap equations within the cut-off scheme is found to be possible only in
the large-N approximation and for a finite value of the cut-off. On the other
hand, the counter-term scheme allows for a consistent renormalization of both
the large-N and Hartree approximations. In these approximations, the meson
masses at a given nonzero temperature depend in general on the choice of the
cut-off or renormalization scale. As an application, we also discuss the
in-medium on-shell decay widths for sigma mesons and pions at rest.


  We present a model of the Galactic Habitable Zone (GHZ), described in terms
of the spatial and temporal dimensions of the Galaxy that may favour the
development of complex life. The Milky Way galaxy is modelled using a
computational approach by populating stars and their planetary systems on an
individual basis using Monte-Carlo methods. We begin with well-established
properties of the disk of the Milky Way, such as the stellar number density
distribution, the initial mass function, the star formation history, and the
metallicity gradient as a function of radial position and time. We vary some of
these properties, creating four models to test the sensitivity of our
assumptions. To assess habitability on the Galactic scale, we model supernova
rates, planet formation, and the time required for complex life to evolve. Our
study improves on other literature on the GHZ by populating stars on an
individual basis and by modelling SNII and SNIa sterilizations by selecting
their progenitors from within this preexisting stellar population. Furthermore,
we consider habitability on tidally locked and non-tidally locked planets
separately, and study habitability as a function of height above and below the
Galactic midplane. In the model that most accurately reproduces the properties
of the Galaxy, the results indicate that an individual SNIa is ~5.6 \times more
lethal than an individual SNII on average. In addition, we predict that ~1.2%
of all stars host a planet that may have been capable of supporting complex
life at some point in the history of the Galaxy. Of those stars with a
habitable planet, ~75% of planets are predicted to be in a tidally locked
configuration with their host star. The majority of these planets that may
support complex life are found towards the inner Galaxy, distributed within,
and significantly above and below, the Galactic midplane.


  In this paper, we establish the weighted anisotropic Hardy and Rellich type
inequalities with boundary terms for general (real-valued) vector fields. As
consequences, we derive new as well as many of the fundamental Hardy and
Rellich type inequalities which are known in different settings.


  Binary pulsar systems are superb probes of stellar and binary evolution and
the physics of extreme environments. In a survey with the Arecibo telescope, we
have found PSR J1903+0327, a radio pulsar with a rotational period of 2.15 ms
in a highly eccentric (e = 0.44) 95-day orbit around a solar mass companion.
Infrared observations identify a possible main-sequence companion star.
Conventional binary stellar evolution models predict neither large orbital
eccentricities nor main-sequence companions around millisecond pulsars.
Alternative formation scenarios involve recycling a neutron star in a globular
cluster then ejecting it into the Galactic disk or membership in a hierarchical
triple system. A relativistic analysis of timing observations of the pulsar
finds its mass to be 1.74+/-0.04 Msun, an unusually high value.


  Background: Cluster states in $^{13}$N are extremely difficult to measure due
to the unavailability of $^{9}$B+$\alpha$ elastic scattering data. Purpose:
Using $\beta$-delayed charged-particle spectroscopy of $^{13}$O, clustered
states in $^{13}$N can be populated and measured in the 3$\alpha$+p decay
channel. Method: One-at-a-time implantation/decay of $^{13}$O was performed
with the Texas Active Target Time Projection Chamber (TexAT TPC). 149 $\beta
3\alpha p$ decay events were observed and the excitation function in $^{13}$N
reconstructed. Results: Four previously unknown $\alpha$-decaying excited
states were observed in $^{13}$N at an excitation energy of 11.3 MeV, 12.4 MeV,
13.1 MeV and 13.7 MeV decaying via the 3$\alpha$+p channel. Conclusion: These
states are seen to have a [$^{9}\mathrm{B}(\mathrm{g.s}) \bigotimes \alpha$/
$p+^{12}\mathrm{C}(0_{2}^{+})$], [$^{9}\mathrm{B}(\frac{1}{2}^{+}) \bigotimes
\alpha$], [$^{9}\mathrm{B}(\frac{5}{2}^{+}) \bigotimes \alpha$] and
[$^{9}\mathrm{B}(\frac{5}{2}^{+}) \bigotimes \alpha$] structure respectively. A
previously-seen state at 11.8 MeV was also determined to have a
[$p+^{12}\mathrm{C}(\mathrm{g.s.})$/ $p+^{12}\mathrm{C}(0_{2}^{+})$] structure.
The overall magnitude of the clustering is not able to be extracted however due
to the lack of a total width measurement. Clustered states in $^{13}$N (with
unknown magnitude) seem to persist from the addition of a proton to the highly
$\alpha$-clustered $^{12}$C. Evidence of the $\frac{1}{2}^{+}$ state in $^{9}$B
was also seen to be populated by decays from $^{13}$N$^{\star}$.


  Optimization problem, nowadays, have more application in all major but they
have problem in computation. Calculation of the optimum point in the spaces
with the above dimensions is very time consuming. In this paper, there is
presented a new approach for the optimization of continuous functions with
rotational mutation that is called RM. The proposed algorithm starts from the
point which has best fitness value by elitism mechanism. Then, method of
rotational mutation is used to reach optimal point. In this paper, RM algorithm
is implemented by GA(Briefly RMGA) and is compared with other well- known
algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and
simulation results show that RMGA achieve global optimal point with more
decision by smaller generations.


  Electronic structure of bismuth telluride nanowires with the growth
directions [110] and [015] is studied in the framework of anisotropic effective
mass method using the parabolic band approximation. The components of the
electron and hole effective mass tensor for six valleys are calculated for both
growth directions. For a square nanowire, in the temperature range from 77 K to
500 K, the dependence of the Seebeck coefficient, the electron thermal and
electrical conductivity as well as the figure of merit ZT on the nanowire
thickness and on the excess hole concentration are investigated in the
constant-relaxation-time approximation. The carrier confinement is shown to
play essential role for square nanowires with thickness less than 30 nm. The
confinement decreases both the carrier concentration and the thermal
conductivity but increases the maximum value of Seebeck coefficient in contrast
to the excess holes (impurities). The confinement effect is stronger for the
direction [015] than for the direction [110] due to the carrier mass difference
for these directions. The carrier confinement increases maximum value of ZT and
shifts it towards high temperatures. For the p-type bismuth telluride nanowires
with growth direction [110], the maximum value of the figure of merit is equal
to 1.3, 1.6, and 2.8, correspondingly, at temperatures 310 K, 390 K, 480 K and
the nanowire thicknesses 30 nm, 15 nm, and 7 nm. At the room temperature, the
figure of merit equals 1.2, 1.3, and 1.7, respectively.


  A variable speed of light (VSL) cosmology is described in which the causal
mechanism of generating primordial perturbations is achieved by varying the
speed of light in a primordial epoch. This yields an alternative to inflation
for explaining the formation of the cosmic microwave background (CMB) and the
large scale structure (LSS) of the universe. The initial value horizon and
flatness problems in cosmology are solved. The model predicts primordial scalar
and tensor fluctuation spectral indices $n_s=0.96$ and $n_t=- 0.04$,
respectively. We make use of the $\delta{\cal N}$ formalism to identify
signatures of primordial nonlinear fluctuations, and this allows the VSL model
to be distinguished from inflationary models. In particular, we find that the
parameter $f_{\rm NL}=5$ in the variable speed of light cosmology. The value of
the parameter $g_{\rm NL}$ evolves during the primordial era and shows a
running behavior.


  The Gemini Planet Imager (GPI) is an integral field spectrograph (IFS) and
coronagraph that is one of the few current generation instruments optimized for
high-contrast direct imaging of substellar companions. The instrument is in the
process of being upgraded and moved from its current mount on the Gemini South
Observatory in Cerro Pachon, Chile, to its twin observatory, Gemini North, on
Mauna Kea (a process colloquially dubbed 'GPI 2.0'). We present the designs
that have been developed for the part of GPI 2.0 that pertains to upgrading
various optical components of the GPI coronagraphic system. More specifically,
we present new designs for the apodizer and Lyot stop (LS) that achieve better
raw contrast at the inner working angle of the dark zone as well as improved
core throughput while retaining a similar level of robustness to LS
misalignment. To generate these upgraded designs, we use our own publicly
available software package called APLC-Optimization that combines a commercial
linear solver (Gurobi) with a high contrast imaging simulation package (HCIPy)
in order to iteratively propagate light through a simulated model of an
apodized phase lyot coronagraph (APLC), optimizing for the best coronagraph
performance metrics. The designs have recently finished being lithographically
printed by a commercial manufacturer and will be ready for use when GPI 2.0
goes on-sky in 2023.


  Using archival spectral-imaging data with a total exposure of $\sim144$ ks
obtained by {\it Chandra}, 43 X-ray sources are detected within the half-light
radius of globular cluster M62 (NGC6266). Based on the X-ray colour-luminosity
diagram or the positional coincidences with known sources, we have classified
these sources into different groups of compact binaries including cataclysmic
variable (CV), quiescent low mass X-ray binary (qLMXB), millisecond pulsar
(MSP) and black hole (BH). Candidates of the X-ray counterparts of 12 CVs, 4
qLMXBs, 2 MSPs and 1 BH are identified in our analysis. The data used in our
analysis consist of two frames separated by 12 years, which enable us to search
for the long-term variability as well as the short-term X-ray flux variability
within each observation window. Evidence for the short-term variability and
long-term variability have been found in 7 and 12 sources respectively. For a
number of bright sources with X-ray luminosities $L_{x}\gtrsim 10^{32}$~erg/s,
we have characterized their spectral properties in further details. By
comparing the X-ray population in M62 with those in several other prototypical
globular clusters, we found the proportion of bright sources is larger in M62
which can possibly be a result of their active dynamical formation processes.


  In recent years, Deep Learning (DL) has found great success in domains such
as multimedia understanding. However, the complex nature of multimedia data
makes it difficult to develop DL-based software. The state-of-the art tools,
such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their
applicable domains, are programming libraries with fixed user interface,
internal representation, and execution environment. This makes it difficult to
implement portable and customized DL applications.
  In this paper, we present DeepDSL, a domain specific language (DSL) embedded
in Scala, that compiles deep networks written in DeepDSL to Java source code.
Deep DSL provides (1) intuitive constructs to support compact encoding of deep
networks; (2) symbolic gradient derivation of the networks; (3) static analysis
for memory consumption and error detection; and (4) DSL-level optimization to
improve memory and runtime efficiency.
  DeepDSL programs are compiled into compact, efficient, customizable, and
portable Java source code, which operates the CUDA and CUDNN interfaces running
on Nvidia GPU via a Java Native Interface (JNI) library. We evaluated DeepDSL
with a number of popular DL networks. Our experiments show that the compiled
programs have very competitive runtime performance and memory efficiency
compared to the existing libraries.


  Security aspects of the Index Coding with Side Information (ICSI) problem are
investigated. Building on the results of Bar-Yossef et al. (2006), the
properties of linear index codes are further explored. The notion of weak
security, considered by Bhattad and Narayanan (2005) in the context of network
coding, is generalized to block security. It is shown that the linear index
code based on a matrix $L$, whose column space code $C(L)$ has length $n$,
minimum distance $d$ and dual distance $d^\perp$, is $(d-1-t)$-block secure
(and hence also weakly secure) if the adversary knows in advance $t \leq d-2$
messages, and is completely insecure if the adversary knows in advance more
than $n - d$ messages. Strong security is examined under the conditions that
the adversary: (i) possesses $t$ messages in advance; (ii) eavesdrops at most
$\mu$ transmissions; (iii) corrupts at most $\delta$ transmissions. We prove
that for sufficiently large $q$, an optimal linear index code which is strongly
secure against such an adversary has length $\kappa_q+\mu+2\delta$. Here
$\kappa_q$ is a generalization of the min-rank over $F_q$ of the side
information graph for the ICSI problem in its original formulation in the work
of Bar- Yossef et al.


  This paper addresses the problem of cross-season visual place classification
(VPC) from a novel perspective of long-term map learning. Our goal is to enable
transfer learning efficiently from one season to the next, at a small constant
cost, and without wasting the robot's available long-term-memory by memorizing
very large amounts of training data. To realize a good tradeoff between
generalization and specialization abilities, we employ an ensemble of
convolutional neural network (DCN) classifiers and consider the task of
scheduling (when and which classifiers to retrain), given a previous season's
DCN classifiers as the sole prior knowledge. We present a unified framework for
retraining scheduling and discuss practical implementation strategies.
Furthermore, we address the task of partitioning a robot's workspace into
places to define place classes in an unsupervised manner, rather than using
uniform partitioning, so as to maximize VPC performance. Experiments using the
publicly available NCLT dataset revealed that retraining scheduling of a DCN
classifier ensemble is crucial and performance is significantly increased by
using planned scheduling.


  We address the geometrical critical behavior of the two-dimensional Q-state
Potts model in terms of the spin clusters (i.e., connected domains where the
spin takes a constant value). These clusters are different from the usual
Fortuin-Kasteleyn clusters, and are separated by domain walls that can cross
and branch. We develop a transfer matrix technique enabling the formulation and
numerical study of spin clusters even when Q is not an integer. We further
identify geometrically the crossing events which give rise to conformal
correlation functions. This leads to an infinite series of fundamental critical
exponents h_{l_1-l_2,2 l_1}, valid for 0 </- Q </- 4, that describe the
insertion of l_1 thin and l_2 thick domain walls.


  We discuss the fundamental theoretical framework together with numerous
results obtained by the authors and colleagues over an extended period of
investigation on the Information Geometric Approach to Chaos (IGAC).


  Obtaining estimates of the convergence rate of the regenerating process
$Q(t)$ whose value at time $t$ is equal to the number of claims in the system $
M \backslash G \backslash \infty $ at this moment, to the limit (stationary)
regime.


  The CHORUS experiment was designed to search for $\nu_{\mu}\to \nu_{\tau}$
oscillation by detecting the decay topology of the $\tau$ in charged current
(CC) $\nu_{\tau}$ events. The detector was exposed to the Wide Band Neutrino
Beam of the CERN SPS during the years 1994-1997. About $10^{6} \nu_{\mu}$ CC
events were collected in the nuclear emulsion target. Up to now, about 170,000
$\nu_{\mu}$ events have been located and analysed. The speed of the automated
emulsion scanning systems increases each year. With the present performance of
these systems, it has become possible to perform large volume scanning. All
tracks belonging to an interaction vertex can be recognized and measured
precisely. This technique is not only applied to the search for neutrino
oscillation but can also be used for the recognition of events where charmed
particles are produced. Results obtained from the analysis of a sub-sample of
the data on the production rate in $\nu_\mu$ CC interactions of neutral charmed
mesons ($D^0$) and charmed baryons ($\Lambda_c$) are presented. In addition a
new measurement of the branching ratio for the decays of charmed hadrons into
muons is given. Also measurements of topological branching ratios of $D^0$ and
$\Lambda_c$ are presented. Finally, a search for associated charm production is
discussed.


  In Software Reengineering, one of the central artifacts is the source code of
the legacy system in question. In fact, in most cases it is the only definitive
artifact, because over the time the code has diverged from the original
architecture and design documents. The first task of any reengineering project
is to gather an understanding of the system's architecture. Therefore, a common
approach is to use parsers to translate the source code into a model conforming
to the abstract syntax of the programming language the system is implemented in
which can then be subject to querying. Despite querying, transformations can be
used to generate more abstract views on the system's architecture. This
transformation case deals with the creation of a state machine model out of a
Java syntax graph. It is derived from a task that originates from a real
reengineering project.


  We study how to watermark LLM outputs, i.e. embedding algorithmically
detectable signals into LLM-generated text to track misuse. Unlike the current
mainstream methods that work with a fixed LLM, we expand the watermark design
space by including the LLM tuning stage in the watermark pipeline. While prior
works focus on token-level watermark that embeds signals into the output, we
design a model-level watermark that embeds signals into the LLM weights, and
such signals can be detected by a paired detector. We propose a co-training
framework based on reinforcement learning that iteratively (1) trains a
detector to detect the generated watermarked text and (2) tunes the LLM to
generate text easily detectable by the detector while keeping its normal
utility. We empirically show that our watermarks are more accurate, robust, and
adaptable (to new attacks). It also allows watermarked model open-sourcing. In
addition, if used together with alignment, the extra overhead introduced is low
- only training an extra reward model (i.e. our detector). We hope our work can
bring more effort into studying a broader watermark design that is not limited
to working with a fixed LLM. We open-source the code:
https://github.com/xiaojunxu/learning-to-watermark-llm .


  In communication networks, cooperative strategies are coding schemes where
network nodes work together to improve network performance metrics such as
sum-rate. This work studies encoder cooperation in the setting of a discrete
multiple access channel with two encoders and a single decoder. A node in the
network that is connected to both encoders via rate-limited links, referred to
as the cooperation facilitator (CF), enables the cooperation strategy.
Previously, the authors presented a class of multiple access channels where the
average-error sum-capacity has an infinite derivative in the limit where CF
output link capacities approach zero. The authors also demonstrated that for
some channels, the maximal-error sum-capacity is not continuous at the point
where the output link capacities of the CF equal zero. This work shows that the
the average-error sum-capacity is continuous when CF output link capacities
converge to zero; that is, the infinite derivative of the average-error
sum-capacity is not a result of its discontinuity as in the maximal-error case.


  Using echelle spectroscopy, obtained at Las Campanas Observatory, we present
a detailed study of the internal kinematics of the nebular material in multiple
knots of the blue compact dwarf galaxy Haro 15. A detailed analysis of the
complex emission line profiles show the presence of an underlying broad
component in almost all knots, and the brightest star-forming region shows
unmistakable signs for the presence of two distinct narrow kinematical
components. We also study the information that our analysis provides regarding
the motion of the individual knots in the Haro 15 galaxy potential, confirming
that they follow galactic rotation. Finally, we examine the relation between
their velocity dispersion and luminosity, finding that almost all knots follow
the relation for virialised systems. This holds for the strong narrow
components identified in complex fits and for single profile fits, although the
latter show a flatter slope. In agreement with previous findings, in this paper
we show that the existence of multiple kinematical components among massive
starbursts cannot be overlooked, as it has a noticeable effect on any
subsequent analysis that relies on basic parameters.


  We consider the following natural problem that generalizes min-sum-radii
clustering: Given is $k\in\mathbb{N}$ as well as some metric space $(V,d)$
where $V=F\cup C$ for facilities $F$ and clients $C$. The goal is to find a
clustering given by $k$ facility-radius pairs $(f_1,r_1),\dots,(f_k,r_k)\in
F\times\mathbb{R}_{\geq 0}$ such that $C\subseteq B(f_1,r_1)\cup\dots\cup
B(f_k,r_k)$ and $\sum_{i=1,\dots,k} g(r_i)$ is minimized for some increasing
function $g:\mathbb{R}_{\geq 0}\rightarrow\mathbb{R}_{\geq 0}$. Here, $B(x,r)$
is the radius-$r$ ball centered at $x$. For the case that $(V,d)$ is the
shortest-path metric of some edge-weighted graph of bounded treewidth, we
present a dynamic program that is tailored to this class of problems and
achieves a polynomial running time, establishing that the problem is in
$\mathsf{XP}$ with parameter treewidth.


  We show by adopting Schertz's argument with the Siegel-Ramachandra invariant
that singular values of certain quotients of the $\Delta$-function generate
ring class fields over imaginary quadratic fields.


  Aligning signals from different modalities is an important step in
vision-language representation learning as it affects the performance of later
stages such as cross-modality fusion. Since image and text typically reside in
different regions of the feature space, directly aligning them at instance
level is challenging especially when features are still evolving during
training. In this paper, we propose to align at a higher and more stable level
using cluster representation. Specifically, we treat image and text as two
"views" of the same entity, and encode them into a joint vision-language coding
space spanned by a dictionary of cluster centers (codebook). We contrast
positive and negative samples via their cluster assignments while
simultaneously optimizing the cluster centers. To further smooth out the
learning process, we adopt a teacher-student distillation paradigm, where the
momentum teacher of one view guides the student learning of the other. We
evaluated our approach on common vision language benchmarks and obtain new SoTA
on zero-shot cross modality retrieval while being competitive on various other
transfer tasks.


  The emergent behaviors in thin films of a multiaxial ferroelectric due to an
electrochemical coupling between the rotating polarization and surface ions are
explored within the framework of the 2-4 Landau-Ginzburg-Devonshire (LGD)
thermodynamic potential combined with the Stephenson-Highland (SH) approach.
The combined LGD-SH approach allows to describe the electrochemical switching
and rotation of polarization vector in the multiaxial ferroelectric film
covered by surface ions with a charge density dependent to the relative partial
oxygen pressure. We calculate the phase diagrams and analyze the dependence of
polarization components on the applied voltage, and discuss the peculiarities
of quasi-static ferroelectric, dielectric and piezoelectric hysteresis loops in
thin strained multiaxial ferroelectric films. The nonlinear surface screening
by oxygen ions makes the diagrams very different from the known diagrams of
e.g., strained BaTiO3 films. Quite unexpectedly we predict the appearance of
the ferroelectric reentrant phases. Obtained results point on the possibility
to control the appearance and features of ferroelectric, dielectric and
piezoelectric hysteresis in multiaxial FE films covered by surface ions by
varying their concentration via the partial oxygen pressure. The LGD-SH
description of a multiaxial FE film can be further implemented within the
Bayesian optimization framework, opening the pathway towards predictive
materials optimization.


  Magnetic reconnection is invoked as one of the primary mechanisms to produce
energetic particles. We employ large-scale three-dimensional (3D)
particle-in-cell simulations of reconnection in magnetically-dominated
($\sigma=10$) pair plasmas to study the energization physics of high-energy
particles. We identify a novel acceleration mechanism that only operates in 3D.
For weak guide fields, 3D plasmoids / flux ropes extend along the $z$ direction
of the electric current for a length comparable to their cross-sectional
radius. Unlike in 2D simulations, where particles are buried in plasmoids, in
3D we find that a fraction of particles with $\gamma\gtrsim 3\sigma$ can escape
from plasmoids by moving along $z$, and so they can experience the large-scale
fields in the upstream region. These "free" particles preferentially move in
$z$ along Speiser-like orbits sampling both sides of the layer, and are
accelerated linearly in time -- their Lorentz factor scales as $\gamma\propto
t$, in contrast to $\gamma\propto \sqrt{t}$ in 2D. The energy gain rate
approaches $\sim eE_{\rm rec}c$, where $E_{\rm rec}\simeq 0.1 B_0$ is the
reconnection electric field and $B_0$ the upstream magnetic field. The spectrum
of free particles is hard, $dN_{\rm free}/d\gamma\propto \gamma^{-1.5}$,
contains $\sim 20\%$ of the dissipated magnetic energy independently of domain
size, and extends up to a cutoff energy scaling linearly with box size. Our
results demonstrate that relativistic reconnection in GRB and AGN jets may be a
promising mechanism for generating ultra-high-energy cosmic rays.


  We show that soliton interaction with finite-width inhomogeneities can
activate a great number of soliton internal modes. We obtain the exact
stationary soliton solution in the presence of inhomogeneities and solve
exactly the stability problem. We present a Karhunen-Loeve analysis of the
soliton structure dynamics as a time-dependent force pumps energy into the
traslational mode of the kink. We show the importance of the internal modes of
the soliton as they can generate shape chaos for the soliton as well as cases
in which the first shape mode leads the dynamics.


  The interaction of quantum emitters with one-dimensional photon-like
reservoirs induces strong and long-range dissipative couplings that give rise
to the emergence of so-called Decoherence Free Subspaces (DFS) which are
decoupled from dissipation. When introducing weak perturbations on the
emitters, e.g., driving, the strong collective dissipation enforces an
effective coherent evolution within the DFS. In this work, we show explicitly
how by introducing single-site resolved drivings, we can use the effective
dynamics within the DFS to design a universal set of one and two-qubit gates
within the DFS of two-level atom-like systems. Using Liouvillian perturbation
theory we calculate the scaling with the relevant figures of merit of the
systems, such as the Purcell Factor and imperfect control of the drivings.
Finally, we compare our results with previous proposals using atomic $\Lambda$
systems in leaky cavities.


  Single ion detection has, for many years, been the domain of large devices
such as the Geiger counter, and studies on interactions of ionized gasses with
materials have been limited to large systems. To date, there have been no
reports on single gaseous ion interaction with microelectronic devices, and
single neutral atom detection techniques have shown only small, barely
detectable responses. Here, we report the first observation of single ion
adsorption onto individual carbon nanotubes (CNTs), which, due to the severely
restricted one-dimensional current path, experience discrete, quantized
resistance increases of over two orders of magnitude. Only positive ions cause
changes, by the mechanism of ion potential induced carrier depletion, which is
supported by density functional and Landauer transport theory. Our observations
reveal a new single-ion/CNT heterostructure with novel electronic properties,
and demonstrate that as electronics are ultimately scaled towards the
one-dimensional limit, atomic scale effects become increasingly important.


  Wireless sensor networks are often used for environmental monitoring
applications. In this context sampling and reconstruction of a physical field
is one of the most important problems to solve. We focus on a bandlimited field
and find under which conditions on the network topology the reconstruction of
the field is successful, with a given probability. We review irregular sampling
theory, and analyze the problem using random matrix theory. We show that even a
very irregular spatial distribution of sensors may lead to a successful signal
reconstruction, provided that the number of collected samples is large enough
with respect to the field bandwidth. Furthermore, we give the basis to
analytically determine the probability of successful field reconstruction.


  Given a Hecke symmetry $R$, one can define a matrix bialgebra $E_R$ and a
matrix Hopf algebra $H_R$, which are called function rings on the matrix
quantum semi-group and matrix quantum groups associated to $R$. We show that
for an even Hecke symmetry, the rational representations of the corresponding
quantum group are absolutely reducible and that the fusion coefficients of
simple representations depend only on the rank of the Hecke symmetry. Further
we compute the quantum rank of simple representations. We also show that the
quantum semi-group is ``Zariski'' dense in the quantum group. Finally we give a
formula for the integral.


  Experiments on the ultimatum game have revealed that humans are remarkably
fond of fair play. When asked to share an amount of money, unfair offers are
rare and their acceptance rate small. While empathy and spatiality may lead to
the evolution of fairness, thus far considered continuous strategies have
precluded the observation of solutions that would be driven by pattern
formation. Here we introduce a spatial ultimatum game with discrete strategies,
and we show that this simple alteration opens the gate to fascinatingly rich
dynamical behavior. Besides mixed stationary states, we report the occurrence
of traveling waves and cyclic dominance, where one strategy in the cycle can be
an alliance of two strategies. The highly webbed phase diagram, entailing
continuous and discontinuous phase transitions, reveals hidden complexity in
the pursuit of human fair play.


  Deep neural networks, especially convolutional deep neural networks, are
state-of-the-art methods to classify, segment or even generate images, movies,
or sounds. However, these methods lack of a good semantic understanding of what
happens internally. The question, why a COVID-19 detector has classified a
stack of lung-ct images as positive, is sometimes more interesting than the
overall specificity and sensitivity. Especially when human domain expert
knowledge disagrees with the given output. This way, human domain experts could
also be advised to reconsider their choice, regarding the information pointed
out by the system. In addition, the deep learning model can be controlled, and
a present dataset bias can be found. Currently, most explainable AI methods in
the computer vision domain are purely used on image classification, where the
images are ordinary images in the visible spectrum. As a result, there is no
comparison on how the methods behave with multimodal image data, as well as
most methods have not been investigated on how they behave when used for object
detection. This work tries to close the gaps. Firstly, investigating three
saliency map generator methods on how their maps differ across the different
spectra. This is achieved via accurate and systematic training. Secondly, we
examine how they behave when used for object detection. As a practical problem,
we chose object detection in the infrared and visual spectrum for autonomous
driving. The dataset used in this work is the Multispectral Object Detection
Dataset, where each scene is available in the FIR, MIR and NIR as well as
visual spectrum. The results show that there are differences between the
infrared and visual activation maps. Further, an advanced training with both,
the infrared and visual data not only improves the network's output, it also
leads to more focused spots in the saliency maps.


  The nitrides of group III metals: AlN, GaN and InN are very important
materials due to their applications for short wavelength opto-electronics
(light-emitting diodes and laser diodes). It is essential for the realization
of such novel devices to grow high-quality nitride single crystals. In this
paper, we report the first stages of the InP(1 0 0) surfaces nitridation in
order to grow high-quality nitride films. Indeed, the nitridation process is an
important step in the growth of nitrides [J. Vac. Sci. Technol. A 17 (1999)
2194; Phys. Status Solidi A 176 (1999) 595]. Previous works [Synth. Met. 90
(1997) 2233; Appl. Phys. Lett. 63 (1993) 1957] have shown that in situ Ar+ ions
bombardment is useful on the one hand to clean the surface, and on the other
hand to create droplets of metallic indium in well-controlled quantity. Then
the indium metallic enrichment of the surface, monitoring by elastic peak
electron spectroscopy (EPES) and Auger electron spectroscopy (AES) allows to
prepare the III-V semiconductors surfaces to the nitridation step. The
nitridated process has been performed with a high voltage plasma discharge cell
and has been studied using quantitative Auger electron spectroscopy, elastic
peak electron spectroscopy and electron energy loss spectroscopy (EELS), in
order to optimize the conditions of InN layers formation.


  We propose an efficient neural framework for sentence-level discourse
analysis in accordance with Rhetorical Structure Theory (RST). Our framework
comprises a discourse segmenter to identify the elementary discourse units
(EDU) in a text, and a discourse parser that constructs a discourse tree in a
top-down fashion. Both the segmenter and the parser are based on Pointer
Networks and operate in linear time. Our segmenter yields an $F_1$ score of
95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled
(relation) metric, surpassing previous approaches by a good margin and
approaching human agreement on both tasks (98.3 and 83.0 $F_1$).


  A coordinate system is a foundation for every quantitative science,
engineering, and medicine. Classical physics and statistics are based on the
Cartesian coordinate system. The classical probability and hypothesis testing
theory can only be applied to Euclidean data. However, modern data in the real
world are from natural language processing, mathematical formulas, social
networks, transportation and sensor networks, computer visions, automations,
and biomedical measurements. The Euclidean assumption is not appropriate for
non Euclidean data. This perspective addresses the urgent need to overcome
those fundamental limitations and encourages extensions of classical
probability theory and hypothesis testing , diffusion models and stochastic
differential equations from Euclidean space to non Euclidean space. Artificial
intelligence such as natural language processing, computer vision, graphical
neural networks, manifold regression and inference theory, manifold learning,
graph neural networks, compositional diffusion models for automatically
compositional generations of concepts and demystifying machine learning
systems, has been rapidly developed. Differential manifold theory is the
mathematic foundations of deep learning and data science as well. We urgently
need to shift the paradigm for data analysis from the classical Euclidean data
analysis to both Euclidean and non Euclidean data analysis and develop more and
more innovative methods for describing, estimating and inferring non Euclidean
geometries of modern real datasets. A general framework for integrated analysis
of both Euclidean and non Euclidean data, composite AI, decision intelligence
and edge AI provide powerful innovative ideas and strategies for fundamentally
advancing AI. We are expected to marry statistics with AI, develop a unified
theory of modern statistics and drive next generation of AI and data science.


  Let $G_1=(V_1,E_1)$ and $G_2=(V_2,E_2)$ be two graphs with disjoint vertex
sets $V_1$ and $V_2$. Let $u_1 \in V_1$ and $u_2 \in V_2$. A splice of $G_1$
and $G_2$ by vertices $u_1$ and $u_2$, $\mathcal{S}(G_1,G_2;u_1,u_2)$, is
defined by identifying the vertices $u_1$ and $u_2$ in the union of $G_1$ and
$G_2$. In this paper we calculate the Szeged, edge-Szeged, $PI$, vertex-$PI$
and eccentric connectivity indices of splice graphs.


  In this paper, a symbol-level selective transmission for virtual full-duplex
(FD) relaying networks is proposed, which aims to mitigate error propagation
effects and improve system spectral efficiency. The idea is to allow two
half-duplex relays, mimicked as FD relaying, to alternatively serve as
transmitter and receiver to forward the source's messages. In this case, each
relay predicts the correctly decoded symbols of its received frame, based on
the generalized square deviation method, and discard the erroneously decoded
symbols, resulting in fewer errors being forwarded to the destination. Then, a
modified maximum \textit{a posteriori} receiver at the destination is provided
to eliminate the inter-frame interference and identify the positions of
discarded symbols from the relays. In addition, the diversity-multiplexing
trade-off (DMT) for our proposed scheme is also analysed. It is found that our
proposed scheme outperforms the conventional selective decode-and-forward
(S-DF) relaying schemes, such as cyclic redundancy check based S-DF and
threshold based S-DF, in terms of DMT. Moreover, the bit-error-rate performance
are also simulated to confirm the DMT results.


  The $SU(N)$ Yang-Mills theory in $\mathbb R^4\times S^1$ spacetime is studied
as a simple toy model of Gauge-Higgs unification. The theory is perturbatively
nonrenormalizable but could be formulated as an asymptotically safe theory,
namely a nonperturbatively renormalizable theory. We study the fixed point
structure of the Yang-Mills theory in $\mathbb R^4\times S^1$ by using the
functional renormalization group in the background field approximation. We
derive the functional flow equations for the gauge coupling and the background
gauge-field potential. There exists a nontrivial fixed point for both couplings
at finite compactification radii. At the fixed point, gauge coupling and vacuum
energy are both relevant. The renormalization group flow of the gauge coupling
describes the smooth transition between the ultraviolet asymptotically safe
regime and the strong interacting infrared limit.


  Software architecture decision-making is critical to the success of a
software system as software architecture sets the structure of the system,
determines its qualities, and has far-reaching consequences throughout the
system life cycle. The complex nature of the software development context and
the importance of the problem has led the research community to develop several
techniques, tools, and processes to assist software architects in making better
decisions. Despite these effort, the adoption of such systematic approaches
appears to be quite limited in practice. In addition, the practitioners are
also facing new challenges as different software development methods suggest
different approaches for architecture design. In this paper, we study the
current software architecture decision-making practices in the industry using a
case study conducted among professional software architects in three different
companies in Europe. As a result, we identified different software architecture
decision-making practices followed by the software teams as well as their
reasons for following them, the challenges associated with them, and the
possible improvements from the software architects' point of view. Based on
that, we recognized that improving software architecture knowledge management
can address most of the identified challenges and would result in better
software architecture decision-making.


  We compare the properties of giant molecular clouds (GMCs) in M51 identified
by the Plateau de Bure Interferometer Whirlpool Arcsecond Survey (PAWS) with
GMCs identified in wide-field, high resolution surveys of CO emission in M33
and the Large Magellanic Cloud (LMC). We find that GMCs in M51 are larger,
brighter and have higher velocity dispersions relative to their size than
equivalent structures in M33 and the LMC. These differences imply that there
are genuine variations in the average mass surface density of the different GMC
populations. To explain this, we propose that the pressure in the interstellar
medium surrounding the GMCs plays a role in regulating their density and
velocity dispersion. We find no evidence for a correlation between size and
linewidth in any of M51, M33 or the LMC when the CO emission is decomposed into
GMCs, although moderately robust correlations are apparent when regions of
contiguous CO emission (with no size limitation) are used. Our work
demonstrates that observational bias remains an important obstacle to the
identification and study of extragalactic GMC populations using CO emission,
especially in molecule-rich galactic environments.