  The feed-forward architectures of recently proposed deep super-resolution
networks learn representations of low-resolution inputs, and the non-linear
mapping from those to high-resolution output. However, this approach does not
fully address the mutual dependencies of low- and high-resolution images. We
propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and
down-sampling layers, providing an error feedback mechanism for projection
errors at each stage. We construct mutually-connected up- and down-sampling
stages each of which represents different types of image degradation and
high-resolution components. We show that extending this idea to allow
concatenation of features across up- and down-sampling stages (Dense DBPN)
allows us to reconstruct further improve super-resolution, yielding superior
results and in particular establishing new state of the art results for large
scaling factors such as 8x across multiple data sets.


  We find a three-parameter family of solutions to IIB supergravity that
corresponds to N=1 supersymmetric holographic RG flows of N=4 supersymmetric
Yang Mills theory. This family of solutions allows one to give a mass to a
single chiral superfield, and to probe a two-dimensional subspace of the
Coulomb branch. In particular, we examine part of the Coulomb branch of the
Leigh-Strassler fixed point. We look at the infra-red asymptotics of these
flows from the ten-dimensional perspective. We also make general conjectures
for the lifting Ansatz of five-dimensional scalar configurations to
ten-dimensional tensor gauge fields. Our solution provides a highly non-trivial
test of these conjectures.


  The ordering of block copolymers into lamellar microstructures is an
attractive route for creating nanopatterns on scales too small to be
constructed by current photolithography techniques. This utilises a technique
known as graphoepitaxy where topography is used to define the alignment of the
pattern for precise placement of pattern features. One problem with this
approach is the failure of lamellae to maintain continuity around corners, due
to geometrical frustration. We report simulation results using the extended
Cahn-Hilliard equation which suggest that this problem could be solved by using
rounded corners.


  We experimentally determine the quantum discord present in two-mode squeezed
vacuum generated by a four-wave mixing process in hot rubidium vapor. The
frequency spectra of the discord, as well as the quantum and classical mutual
information are also measured. In addition, the effects of symmetric
attenuation introduced into both modes of the squeezed vacuum on the discord,
the quantum mutual information and the classical correlations are examined
experimentally. Finally, we show that due to the multi-spatial-mode nature of
the four-wave mixing process, the quantum discord may exhibit sub- or
superadditivity depending on which spatial channels are selected.


  In this paper, we study the representations of integral quadratic
polynomials. Particularly, it is shown that there are only finitely many
equivalence classes of positive ternary universal integral quadratic
polynomials, and that there are only finitely many regular ternary triangular
forms. A more general discussion of integral quadratic polynomials over a
Dedekind domain inside a global field is also given.


  We establish a link between the Gerstenhaber bracket in the Hochshild
cohomology and the behaviour of cycles in the module category of a monomial
quadratic algebra $A$.


  We initiate the study of the spectrum $Vspec(\kappa)$ of sets that can be
realized as the vanishing levels $V(T)$ of a normal $\kappa$-tree $T$. The
latter is an invariant in the sense that if $T$ and $T'$ are club-isomorphic,
then the symmetric difference of $V(T)$ and $V(T')$ is nonstationary.
Additional features of this invariant imply that $Vspec(\kappa)$ is closed
under finite unions and intersections.
  The set $V(T)$ must be stationary for an homogeneous normal
$\kappa$-Aronszajn tree $T$, and if there exists a special $\kappa$-Aronszajn
tree, then there exists one $T$ that is homogeneous and satisfies $V(T)=\kappa$
(modulo clubs). It is consistent (from large cardinals) that there is an
$\aleph_2$-Souslin tree, and yet $V(T)$ is co-stationary for every
$\aleph_2$-tree $\mathbf T$. Both $V(T)=\emptyset$ and $V(T)=\kappa$ (modulo
clubs) are shown to be feasible using $\kappa$-Souslin trees even at some large
cardinal close to a weakly compact. It is also possible to have a family of
$2^\kappa$ many $\kappa$-Souslin trees for which the corresponding family of
vanishing levels forms an antichain modulo clubs.


  The main aim of the present paper is to establish the existence of a phase
transition for the quantum Ising model with competing XY interactions within
the quantum Markov chain (QMC) scheme. In this scheme, we employ the
$C^*$-algebraic approach to the phase transition problem. Note that these kinde
of models do not have one-dimensional analogues, i.e. the considered model
persists only on trees. It turns out that if the Ising part interactions vanish
then the model with only competing XY -interactions on the Cayley tree of order
two does not have a phase transition. By phase transition we mean the existence
of two distinct QMC which are not quasi-equivalent and their supports do not
overlap. Moreover, it is also shown that the QMC associated with the model have
clustering property which implies that the von Neumann algebras corresponding
to the states are factors.


  Consider the following problem: given a few demonstrations of a task across a
few different objects, how can a robot learn to perform that same task on new,
previously unseen objects? This is challenging because the large variety of
objects within a class makes it difficult to infer the task-relevant
relationship between the new objects and the objects in the demonstrations. We
address this by formulating imitation learning as a conditional alignment
problem between graph representations of objects. Consequently, we show that
this conditioning allows for in-context learning, where a robot can perform a
task on a set of new objects immediately after the demonstrations, without any
prior knowledge about the object class or any further training. In our
experiments, we explore and validate our design choices, and we show that our
method is highly effective for few-shot learning of several real-world,
everyday tasks, whilst outperforming baselines. Videos are available on our
project webpage at https://www.robot-learning.uk/implicit-graph-alignment.


  We studied the supersymmetric and non-supersymmetric perturbations to
cascading gauge theory. In particular we use KT background and the back
reaction of the generic linearized perturbation make the dilaton to run and the
$T^{(1,1)}$ gets squashed which in turn make the supersymmetry to be broken.
But if we make a special linearized perturbation in such way that the
$T^{(1,1)}$ is not squashed then the corresponding perturbation preserve
supersymmetry.


  This paper presents an overview of some techniques and concepts coming from
dynamical system theory and used for the analysis of dynamical neural networks
models. In a first section, we describe the dynamics of the neuron, starting
from the Hodgkin-Huxley description, which is somehow the canonical description
for the ``biological neuron''. We discuss some models reducing the
Hodgkin-Huxley model to a two dimensional dynamical system, keeping one of the
main feature of the neuron: its excitability. We present then examples of phase
diagram and bifurcation analysis for the Hodgin-Huxley equations. Finally, we
end this section by a dynamical system analysis for the nervous flux
propagation along the axon. We then consider neuron couplings, with a brief
description of synapses, synaptic plasticiy and learning, in a second section.
We also briefly discuss the delicate issue of causal action from one neuron to
another when complex feedback effects and non linear dynamics are involved. The
third section presents the limit of weak coupling and the use of normal forms
technics to handle this situation. We consider then several examples of
recurrent models with different type of synaptic interactions (symmetric,
cooperative, random). We introduce various techniques coming from statistical
physics and dynamical systems theory. A last section is devoted to a detailed
example of recurrent model where we go in deep in the analysis of the dynamics
and discuss the effect of learning on the neuron dynamics. We also present
recent methods allowing the analysis of the non linear effects of the neural
dynamics on signal propagation and causal action. An appendix, presenting the
main notions of dynamical systems theory useful for the comprehension of the
chapter, has been added for the convenience of the reader.


  We study the effects of a fixed de Sitter geometry background in scenarios of
false vacuum decay. It is currently understood that bubble nucleation processes
associated with first order phase transitions are particularly important in
cosmology. The geometry of spacetime complicates the interpretation of the
decay rate of a metastable vacuum. However, the effects of curvature can still
be studied in the particular case where backreaction is neglected. We compute
the imaginary part of the action in de Sitter space, including the one-loop and
the gradient corrections. We use two independent methodologies and quantify the
size of the corrections without any assumptions on the thickness of the wall of
the scalar background configuration.


  The Nernst effect is known to be a sensitive probe of the scattering
mechanisms. Large Nernst effects, a decade ago a topic mainly in vortex
dynamics in superconductors, have recently attracted interest in correlated
systems like cuprate metals and heavy fermion systems. However, it has been
found that these ``giant" Nernst signals stay within one order of magnitude of
the classical Sondheimer formula for a Fermi liquid. It therefore seems that a
large Nernst effect neither relies on nor is sensitive to strong correlations.
This might, however, be different in some recently studied supposedly
correlated systems like FeSB_2. We do not address these systems specifically,
but take them only as one inspiration for considering a theoretical scenario
where correlations could actually sizably modify the Sondheimer formula. We
point out that an enhanced Nernst signal would follow from the phenomenological
Boltzmann approach introduced by Coleman, Schofield and Tsvelik proposed to
capture electronic correlations by imposing different relaxation times on the
charge conjugation even and odd Majorana components of the electron.


  In this work we handle with categorical (ordinal) variables and we focus on
the (in)dependence relationship under the marginal, conditional and
context-specific perspective. If the first two are well known, the last one
concerns independencies holding only in a subspace of the outcome space. We
take advantage from the Hierarchical Multinomial Marginal models and provide
several original results about the representation of context-specific
independencies through these models. By considering the graphical aspect, we
take advantage from the chain graphical models. The resultant graphical model
is a so-called "stratified" chain graphical model with labelled arcs. New
Markov properties are provided. Furthermore, we consider the graphical models
under the regression poit of view. Here we provide simplification of the
regression parameters due to the context-specific independencies. Finally, an
application about the innovation degree of the Italian enterprises is provided.


  We present optical and near-IR observations of the young eruptive variable
star V1647 Orionis which illuminates McNeil's Nebula. In late 2003, V1647 Ori
was observed to brightened by around 5 mag to r'=17.7. In early 2006 the star
faded back to its quiescent brightness of r'~23, however, in mid-2008 it
brightened yet again by ~5 mag. Our new observations, taken in early 2011, show
V1647 Ori to be in an elevated photometric state with an optical brightness
similar to the value found at the start of the 2003 and 2008 outbursts. Optical
images taken between 2008 and 2011 suggest that the star has remained in
outburst from mid 2008 to the present. H-alpha and the far-red CaII triplet
lines remain in emission with H-alpha possessing a significant P Cygni profile.
A self-consistent study of the accretion luminosity and rate using data taken
in 2004, 2007, 2008, and 2011, indicates that when bright, V1647 Ori has values
of 16+-2 Lsun and 4+-2x10^-6 Msun/yr, respectively. We support the premise that
the accretion luminosity and rate both declined by a factor 2-3 during the 5mag
fading in 2007. However, a significant parts of the fading was due to either
variable extinction or dust reformation. We discuss these new observations in
relation to previous published data and the classification schemes for young
eruptive variables.


  We study the problem of ordered stabbing of $n$ balls (of arbitrary and
possibly different radii, no ball contained in another) in $\mathbb{R}^d$, $d
\geq 3$, with either a directed line segment or a (directed) polygonal curve.
Here, the line segment, respectively polygonal curve, shall visit (intersect)
the given sequence of balls in the order of the sequence. We present a
deterministic algorithm that decides whether there exists a line segment
stabbing the given sequence of balls in order, in time $O(n^{4d-2} \log n)$.
Due to the descriptional complexity of the region containing these line
segments, we can not extend this algorithm to actually compute one. We
circumvent this hurdle by devising a randomized algorithm for a relaxed variant
of the ordered line segment stabbing problem, which is built upon the central
insights from the aforementioned decision algorithm. We further show that this
algorithm can be plugged into an algorithmic scheme by Guibas et al., yielding
an algorithm for a relaxed variant of the minimum-link ordered stabbing path
problem that achieves approximation factor 2 with respect to the number of
links. We conclude with experimental evaluations of the latter two algorithms,
showing practical applicability.


  Formation control deals with the design of decentralized control laws that
stabilize agents at prescribed distances from each other. We call any
configuration that satisfies the inter-agent distance conditions a target
configuration. It is well known that when the distance conditions are defined
via a rigid graph, there is a finite number of target configurations modulo
rotations and translations. We can thus recast the objective of formation
control as stabilizing one or many of the target configurations. A major issue
is that such control laws will also have equilibria corresponding to
configurations which do not meet the desired inter-agent distance conditions;
we refer to these as undesired equilibria. The undesired equilibria become
problematic if they are also stable. Designing decentralized control laws whose
stable equilibria are all target configurations in the case of a general rigid
graph is still an open problem. We propose here a partial solution to this
problem by exhibiting a class of rigid graphs and control laws for which all
stable equilibria are target configurations.


  Data workers use various scripting languages for data transformation, such as
SAS, R, and Python. However, understanding intricate code pieces requires
advanced programming skills, which hinders data workers from grasping the idea
of data transformation at ease. Program visualization is beneficial for
debugging and education and has the potential to illustrate transformations
intuitively and interactively. In this paper, we explore visualization design
for demonstrating the semantics of code pieces in the context of data
transformation. First, to depict individual data transformations, we structure
a design space by two primary dimensions, i.e., key parameters to encode and
possible visual channels to be mapped. Then, we derive a collection of 23
glyphs that visualize the semantics of transformations. Next, we design a
pipeline, named Somnus, that provides an overview of the creation and evolution
of data tables using a provenance graph. At the same time, it allows detailed
investigation of individual transformations. User feedback on Somnus is
positive. Our study participants achieved better accuracy with less time using
Somnus, and preferred it over carefully-crafted textual description. Further,
we provide two example applications to demonstrate the utility and versatility
of Somnus.


  This paper considers an approximation usually used when implementing
Ramaswami's recursion for the stationary distribution of the M/G/1-type Markov
chain. The approximation is called the level-increment-truncation approximation
because it truncates level increment at a given threshold. The main
contribution of this paper is to present a geometric convergence formula of the
level-wise difference between the respective stationary distributions of the
original M/G/1-type Markov chain and its LI truncation approximation under the
assumption that the level-increment distribution is light-tailed.


  To improve the theoretical prediction of the anomalous dipole moments of the
$\tau$-neutrino, we have carried out a study through the process $\gamma e^-
\to \tau \bar\nu_\tau \nu_e$, which represents an excellent and useful option
in determination of these anomalous parameters. To study the potential of the
process $\gamma e^- \to \tau \bar\nu_\tau \nu_e$, we apply a future high-energy
and high-luminosity linear electron-positron collider, such as the CLIC, with
$\sqrt{s}=380, 1500, 3000\hspace{0.8mm}GeV$ and ${\cal L}=10, 50, 100, 200,
500, 1000, 1500, 2000, 3000\hspace{0.8mm}fb^{-1}$, and we consider systematic
uncertainties of $\delta_{sys}=0, 5, 10\%$. With these elements, we present a
comprehensive and detailed sensitivity study on the total cross-section of the
process $\gamma e^- \to \tau \bar\nu_\tau \nu_e$, as well as on the dipole
moments $\mu_{\nu_\tau}$ and $d_{\nu_\tau}$ at the $95\%$ C.L., showing the
feasibility of such process at the CLIC at the $\gamma e^-$ mode with
unpolarized and polarized electron beams.


  In this letter we study the problem of scalar particle production in external
electric field in de Sitter geometry. The total probability is calculated using
the previously obtained result in Ref.\cite{6} for transition amplitude in
external electric field on de Sitter space. Then we make a graphical study of
the total probability in terms of the ratio mass of the particle/expansion
factor. Our results show that the probability depend of the direction in which
the particles are emitted and that the probability becomes maxim when particles
are emitted on the direction of the electric field. In the Minkowski limit we
obtain that the probability is vanishing.


  Neural networks are vulnerable to adversarially-constructed perturbations of
their inputs. Most research so far has considered perturbations of a fixed
magnitude under some $l_p$ norm. Although studying these attacks is valuable,
there has been increasing interest in the construction of (and robustness to)
unrestricted attacks, which are not constrained to a small and rather
artificial subset of all possible adversarial inputs. We introduce a novel
algorithm for generating such unrestricted adversarial inputs which, unlike
prior work, is adaptive: it is able to tune its attacks to the classifier being
targeted. It also offers a 400-2,000x speedup over the existing state of the
art. We demonstrate our approach by generating unrestricted adversarial inputs
that fool classifiers robust to perturbation-based attacks. We also show that,
by virtue of being adaptive and unrestricted, our attack is able to defeat
adversarial training against it.


  We consider central simple $K$-algebras which happen to bedifferential graded
$K$-algebras. Two such algebras $A$ and $B$are considered equivalent if there
are bounded complexes of finite dimensional$K$-vector spaces $C_A$ and $C_B$
such that the differential graded algebras $A\otimes_K {\rm
End}_K^\bullet(C_A)$ and $B\otimes_K {\rm End}_K^\bullet(C_B)$ are
isomorphic.Equivalence classes form an abelian group, which we call thedg
Brauer group.We prove that this group is isomorphic to the ordinary Brauer
group of the field $K$.


  The recently discovered universal thermodynamic behaviour of dilute, strongly
interacting Fermi gases also implies a universal structure in the many-body
pair-correlation function at short distances, as quantified by the contact
${\cal I}$. This quantity is an excellent indicator of the presence of strong
correlations in these systems, which provide a highly accessible physical model
for other strongly correlated quantum fluids. Here we theoretically calculate
the temperature dependence of this universal contact for a Fermi gas in free
space and in a harmonic trap. At high temperatures above the Fermi degeneracy
temperature, $T\gtrsim T_{F}$, we obtain a reliable non-perturbative quantum
virial expansion up to third order. At low temperatures we compare different
approximate strong coupling theories. These make different predictions, which
need to be tested either by future experiments or advanced quantum Monte Carlo
simulations. We conjecture that in the universal unitarity limit, the contact
or correlation decreases monotonically with increasing temperature, unless the
temperature is significantly lower than the critical temperature, $T\ll
T_{c}\sim0.2T_{F}$. We also discuss briefly how to measure the universal
contact either in homogeneous or harmonically trapped Fermi gases.


  Quantum computing using two optical coherent states as qubit basis states has
been suggested as an interesting alternative to single photon optical quantum
computing with lower physical resource overheads. These proposals have been
questioned as a practical way of performing quantum computing in the short term
due to the requirement of generating fragile diagonal states with large
coherent amplitudes. Here we show that by using a fault-tolerant error
correction scheme, one need only use relatively small coherent state amplitudes
($\alpha > 1.2$) to achieve universal quantum computing. We study the effects
of small coherent state amplitude and photon loss on fault tolerance within the
error correction scheme using a Monte Carlo simulation and show the quantity of
resources used for the first level of encoding is orders of magnitude lower
than the best known single photon scheme. %We study this reigem using a Monte
Carlo simulation and incorporate %the effects of photon loss in this
simulation.


  We present here some basic properties around the Poincar\'e exponent of a
discrete group of isometries in pinched negatived curvature. We state some
important results and present the main tools which are used in this domain.


  The asymptotics of n-point Green's function at large external momenta is
obtained in the exponentiated form using the Fock-Feynman-Schwinger
representation for propagators in the external field. The method is applied to
gauge theories such as QCD and QED, and the Sudakov form-factor is calculated
as an example in QED and meson form-factor in QCD. Nonperturbative
contributions can be conveniently included, as it is demonstrated in the
example of the confinement correction to the form-factor.


  This paper proposes a computational framework for the design optimization of
stable structures under large deformations by incorporating nonlinear buckling
constraints. A novel strategy for suppressing spurious buckling modes related
to low-density elements is proposed. The strategy depends on constructing a
pseudo-mass matrix that assigns small pseudo masses for DOFs surrounded by only
low-density elements and degenerates to an identity matrix for the solid
region. A novel optimization procedure is developed that can handle both simple
and multiple eigenvalues wherein consistent sensitivities of simple eigenvalues
and directional derivatives of multiple eigenvalues are derived and utilized in
a gradient-based optimization algorithm - the method of moving asymptotes. An
adaptive linear energy interpolation method is also incorporated in nonlinear
analyses to handle the low-density elements distortion under large
deformations. The numerical results demonstrate that, for systems with either
low or high symmetries, the nonlinear stability constraints can ensure
structural stability at the target load under large deformations. Post-analysis
on the B-spline fitted designs shows that the safety margin, i.e., the gap
between the target load and the 1st critical load, of the optimized structures
can be well controlled by selecting different stability constraint values.
Interesting structural behaviors such as mode switching and multiple
bifurcations are also demonstrated.


  A novel method of determining the mass of the W boson in the W+W- -> lnu lnu
channel is presented and applied to 667pb^-1 of data recorded at the center-of-
mass energies in the range 183-207 GeV with the OPAL detector at LEP. The
Measured energies of charged leptons and the results of a new procedure based
on an approximate kinematic reconstruction of the events are combined to give:
Mw=80.41+-0.41+-0.13 GeV, when the first error is statistical and the second is
systematic. The systematic error is dominated by the uncertainty on the lepton
energy, which is calibrated using data, and the parameterization of the
variables used in the fitting, which is obtained using Monte Carlo events. Both
of these are limited by statistics.


  This paper explores formalizing Geometric (or Clifford) algebras into the
Lean 3 theorem prover, building upon the substantial body of work that is the
Lean mathematics library, mathlib. As we use Lean source code to demonstrate
many of our ideas, we include a brief introduction to the Lean language
targeted at a reader with no prior experience with Lean or theorem provers in
general.
  We formalize the multivectors as the quotient of the tensor algebra by a
suitable relation, which provides the ring structure automatically, then go on
to establish the universal property of the Clifford algebra. We show that this
is quite different to the approach taken by existing formalizations of
Geometric algebra in other theorem provers; most notably, our approach does not
require a choice of basis.
  We go on to show how operations and structure such as involutions, versors,
and the $\mathbb{Z}_2$-grading can be defined using the universal property
alone, and how to recover an induction principle from the universal property
suitable for proving statements about these definitions. We outline the steps
needed to formalize the wedge product and $\mathbb{N}$-grading, and some of the
gaps in mathlib that currently make this challenging.


  The signature of a path is an essential object in the theory of rough paths.
The signature representation of the data stream can recover standard
statistics, e.g. the moments of the data stream. The classification of random
walks indicates the advantages of using the signature of a stream as the
feature set for machine learning.


  We consider a gravitational model in a Weyl-Cartan space-time, in which the
Weitzenb\"{o}ck condition of the vanishing of the sum of the curvature and
torsion scalar is also imposed. Moreover, a kinetic term for the torsion is
also included in the gravitational action. The field equations of the model are
obtained from a Hilbert-Einstein type variational principle, and they lead to a
complete description of the gravitational field in terms of two fields, the
Weyl vector and the torsion, respectively, defined in a curved background. The
cosmological applications of the model are investigated for a particular choice
of the free parameters in which the torsion vector is proportional to the Weyl
vector. Depending on the numerical values of the parameters of the cosmological
model, a large variety of dynamic evolutions can be obtained, ranging from
inflationary/accelerated expansions to non-inflationary behaviors. In
particular we show that a de Sitter type late time evolution can be naturally
obtained from the field equations of the model. Therefore the present model
leads to the possibility of a purely geometrical description of the dark
energy, in which the late time acceleration of the Universe is determined by
the intrinsic geometry of the space-time.


  The performance of instance search depends heavily on the ability to locate
and describe a wide variety of object instances in a video/image collection.
Due to the lack of proper mechanism in locating instances and deriving feature
representation, instance search is generally only effective for retrieving
instances of known object categories. In this paper, a simple but effective
instance-level feature representation is presented. Different from other
approaches, the issues in class-agnostic instance localization and distinctive
feature representation are considered. The former is achieved by detecting
salient instance regions from an image by a layer-wise back-propagation
process. The back-propagation starts from the last convolution layer of a
pre-trained CNN that is originally used for classification. The
back-propagation proceeds layer-by-layer until it reaches the input layer. This
allows the salient instance regions in the input image from both known and
unknown categories to be activated. Each activated salient region covers the
full or more usually a major range of an instance. The distinctive feature
representation is produced by average-pooling on the feature map of certain
layer with the detected instance region. Experiments show that such kind of
feature representation demonstrates considerably better performance over most
of the existing approaches. In addition, we show that the proposed feature
descriptor is also suitable for content-based image search.


  Thin-film ferromagnetic disks present a vortex spin structure whose dynamics,
added to the small size (~10 nm) of their core, earned them intensive study.
Here we use a scanning nitrogen-vacancy (NV) center microscope to
quantitatively map the stray magnetic field above a 1 micron-diameter disk of
permalloy, unambiguously revealing the vortex core. Analysis of both
probe-to-sample distance and tip motion effects through stroboscopic
measurements, allows us to compare directly our quantitative images to
micromagnetic simulations of an ideal structure. Slight perturbations with
respect to the perfect vortex structure are clearly detected either due to an
applied in-plane magnetic field or imperfections of the magnetic structures.
This work demonstrates the potential of scanning NV microscopy to map tiny
stray field variations from nanostructures, providing a nanoscale,
non-perturbative detection of their magnetic texture.


  WhatsApp has become a pivotal communication tool in India, transcending
cultural boundaries and deeply integrating into the nation's digital landscape.
Meta's introduction of WhatsApp for Business aligns seamlessly with the
platform's popularity, offering businesses a crucial tool. However, the
monetization plans pose challenges, particularly for smaller businesses, in
balancing revenue goals with accessibility. This study, employing discourse
analysis, examines Meta's infrastructuring of WhatsApp in India, emphasizing
the dynamic interplay of technological, social, and cultural dimensions.
Consequently, it highlights potential power differences caused by the
deployment of WhatsApp for Business followed by its gradual but significant
modifications, encouraging scholars to investigate the implications and ethics
of rapid technological changes, particularly for marginalized users.


  The dressing and vertex operator formalism is emploied to study the soliton
solutions of the N=1 super mKdV and sinh-Gordon models. Explicit two and four
vertex solutions are constructed. The relation between the soliton solutions of
both models is verified.


  Vector-like quarks (VLQs) that are partners of the heavy top and bottom
quarks are predicted in many extensions of the Standard Model (SM). We explore
the possibility that these states could explain not only the longstanding
anomaly in the forward-backward asymmetry in $b$-quark production at LEP,
$A_{\rm FB}^b $, but also the more recent $\sim 2\sigma$ deviation of the cross
section for the associated Higgs production with top quark pairs at the LHC,
$\sigma(pp\to t\bar t H)$. Introducing three illustrative models for VLQs with
different representations under the SM gauge group, we show that the two
anomalies can be resolved while satisfying all other theoretical and
experimental constraints. In this case, the three different models predict VLQ
states in the $1-2$ TeV mass range that can be soon probed at the LHC. In a
second step, we discuss the sensitivity on the VLQ masses and couplings that
could be obtained by means of a percent level accuracy in the measurement of
ratios of partial Higgs decay widths, in particular $\Gamma(H \! \to\!
\gamma\gamma)/\Gamma(H \! \to\! ZZ^*)$ and $\Gamma(H \! \to \! b\bar
b)/\Gamma(H \! \to \! WW^*)$. We show that top and bottom VL partners with
masses up to $\sim 5$ TeV and exotic VLQs with masses in the $10$ TeV range can
be probed at the high-luminosity LHC.


  In this paper, we shall give a way to construct a graded Lie algebra
$L(\mathfrak{g},\rho,V,{\cal V},B_0)$ from a standard pentad
$(\mathfrak{g},\rho,V,{\cal V},B_0)$ which consists of a Lie algebra
$\mathfrak{g}$ which has a non-degenerate invariant bilinear form $B_0$ and
$\mathfrak{g}$-modules $(\rho, V)$ and ${\cal V}\subset \mathrm {Hom
}(V,\mathfrak{k})$ all defined over a field $\mathfrak{k}$. In general, we do
not assume that these objects are finite-dimensional. We can embed the objects
$\mathfrak{g},\rho,V,{\cal V}$ into $L(\mathfrak{g},\rho,V,{\cal V},B_0)$.
Moreover, we construct specific positively and negatively graded modules of
$L(\mathfrak{g},\rho,V,{\cal V},B_0)$. Finally, we give a chain rule on the
embedding rules of standard pentads.


  This paper presents in detail the generalized pignistic transformation (GPT)
succinctly developed in the Dezert-Smarandache Theory (DSmT) framework as a
tool for decision process. The GPT allows to provide a subjective probability
measure from any generalized basic belief assignment given by any corpus of
evidence. We mainly focus our presentation on the 3D case and provide the
complete result obtained by the GPT and its validation drawn from the
probability theory.


  We report on a protocol to achieve full control of the polarization in a
nanofiber. The protocol relies on studying the light scattered out from a
nanofiber by means of two optical systems with $45^\circ$ camera angle
difference. By studying the disturbance of the nanofiber on the radiation of
embedded scatterers, we propose an explanation for the observed reduced
scattering contrast of the nanofiber. Thanks to this approach, we demonstrate
an accuracy of the polarization control larger than 95\%.


  We have searched for evidence of soft X-ray absorption by hot metals in the
vicinity of the Galaxy in the spectra of a small sample of fifteen Type I AGN
observed with the high resolution X-ray gratings on board Chandra. This is an
extension of our previous survey of hot OVII and OVIII absorbing gas in the
vicinity of the Galaxy. The strongest absorption signatures within a few
hundred km/s of their rest-frame energies are most likely due to warm absorbing
outflows from the nearest AGN, which are back-lighting the local hot gas. We
emphasize that absorption signatures in the spectra of some distant AGN that
are kinematically consistent with the recessional velocity of the AGN are most
likely to be due to hot local gas. Along the sightline towards PG 1211+143, PDS
456 and MCG-6-30-15 there is a very large absorbing Fe column density which is
kinematically consistent with absorption by hot, local Fe. The sightlines to
these three AGN pass through the limb of the Northern Polar Spur (NPS), a local
bubble formed from several supernovae which, if rich in Fe, may account for a
large local Fe column.
  We obtain limits on the column density of local, highly ionized N, Ne, Mg, Si
along all of the sightlines in our sample. We correlate the column density
limits with those of highly ionized O along the same sightlines. Assuming the
hot local gas is in collisionally ionized equilibrium, we obtain limits on the
temperature and relative abundances of the metals in the hot local gas. Our
limits on the ionic column densities in the local hot gas seem to be consistent
with those observed in the hot halo gas of edge-on normal spiral galaxies.


  Molecular dynamics simulation is applied to Poiseuille flow of liquid methane
in planar graphite channels, covering channel diameters between 3 and 135 nm.
On this length scale, a transition is found between the regime where local
ordering induced by the wall dominates the entire system and larger channel
diameters where the influence of boundary slip is still present, but of a more
limited extent. The validity of Darcy's law for pressure-driven flow through
porous media is not affected by the transition between these regimes.


  The recent observation of the mass difference in $B_s$ system seems to be not
in complete agreement with the corresponding standard model value. We consider
the model with an extra vector like down quark to explain this discrepancy and
obtain the constraints on the new physics parameters. Thereafter, we show that
with these new constraints this model can successfully explain other observed
deviations associated with $b \to s$ transitions, namely, $B_s \to \psi \phi$,
$B\to K \pi$ and $B\to \phi K_s$.


  Numerous circumbinary planets have been discovered in surveys of transiting
planets. Often, these planets are found to orbit near to the zone of dynamical
instability, close to the central binary. The existence of these planets has
been explained by hydrodynamical simulations that show that migrating
circumbinary planets, embedded in circumbinary discs, halt at the central
cavity that is formed by the central binary. Transit surveys are naturally most
sensitive to finding circumbinary planets with the shortest orbital periods.
The future promise of detecting longer period systems using radial-velocity
searches, combined with the anticipated detection of numerous circumbinary
planets by ESA's PLATO mission, points to the need to model and understand the
formation and evolution of circumbinary planets in a more general sense than
has been considered before. With this goal in mind, we present a newly
developed global model of circumbinary planet formation that is based on the
mercury6 symplectic N-body integrator, combined with a model for the
circumbinary disc and prescriptions for a range of processes involved in planet
formation such as pebble accretion, gas envelope accretion and migration. Our
results show that under reasonable assumptions, the pebble accretion scenario
can produce circumbinary systems that are similar to those observed, and in
particular is able to produce planets akin to Kepler-16b and Kepler-34b.
Comparing our results to other systems, we find that our models also adequately
reproduce such systems, including multi-planet systems. Resonances between
neighbouring planets are frequently obtained, whilst ejections of planets by
the central binary acts as an effective source of free floating planets.


  In this paper, general logic-systems and a necessary and sufficient algorithm
are used to substantiate significant consequence operator properties. It is
shown, among other results, that, in certain cases, (1) if the number of steps
in a deduction is restricted, then such deduction does not yield a consequence
operator. (2) In general, for any non-organized infinite language L, there is a
special class of finite consequence operators that is not meet-complete. (3)
For classical deduction, three different examples of modified propositional
deduction yield collections of finite consequence operators that are not
meet-complete. Other general logic-system examples are given. In a final
section, the notion of potentially finite is investigated.


  Fundamental understanding of the effect of microscopic parameters on the
dynamics of probe particles in different complex environments has wide
implications. Examples include diffusion of proteins in the biological
hydrogels, porous media, polymer matrix, etc. Here, we use extensive molecular
dynamics simulations to investigate the dynamics of the probe particle in a
polymer network on a diamond lattice which provides substantial crowding to
mimic the cellular environment. Our simulations show that the dynamics of the
probe increasingly becomes restricted, non-Gaussian and subdiffusive on
increasing the network rigidity, binding affinity and the probe size. In
addition, the velocity autocorrelation functions show negative dips owing to
the viscoelasticity and caging due to surrounding network. These observations
go with general experimental findings. Surprisingly for a probe particle of
size comparable to the mesh size, unrestricted motion engulfing large length
scales has been observed. This happens with the more flexible polymer network,
which is easily pushed by the bigger probe. Our study gives a general
qualitative picture of transport of probes in gel like medium, as encountered
in different contexts.


  Contemporary practices in instruction tuning often hinge on enlarging data
scaling without a clear strategy for ensuring data quality, inadvertently
introducing noise that may compromise model performance. To address this
challenge, we introduce \textsc{Nuggets}, a novel and efficient methodology
that leverages one-shot learning to discern and select high-quality instruction
data from extensive datasets. \textsc{Nuggets} assesses the potential of
individual instruction examples to act as effective one-shot learning
instances, thereby identifying those that can significantly improve performance
across diverse tasks. \textsc{Nuggets} utilizes a scoring system based on the
impact of candidate examples on the perplexity of a diverse anchor set,
facilitating the selection of the most advantageous data for instruction
tuning. Through comprehensive evaluations on two benchmarks, including MT-Bench
and Alpaca-Eval, we show that instruction tuning with the top 1\% of examples
curated by \textsc{Nuggets} substantially outperforms conventional methods
employing the entire dataset.


  We prove the existence of periodic solutions of the N=(n+1)-body problem
starting with n bodies whose reduced motion is close to a non-degenerate
central configuration and replacing one of them by the center of mass of a pair
of bodies rotating uniformly. When the motion takes place in the standard
Euclidean plane, these solutions are a special type of braid solutions obtained
numerically by C. Moore. The proof uses blow-up techniques to separate the
problem into the n-body problem, the Kepler problem, and a coupling which is
small if the distance of the pair is small. The formulation is variational and
the result is obtained by applying a Lyapunov-Schmidt reduction and by using
the equivariant Lyusternik-Schnirelmann category.


  This work combines Convolutional Neural Networks (CNNs), clustering via
Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks
of Convolutional Self-Organizing Neural Networks (CSNNs), which learn
representations in an unsupervised and Backpropagation-free manner. Our
approach replaces the learning of traditional convolutional layers from CNNs
with the competitive learning procedure of SOMs and simultaneously learns local
masks between those layers with separate Hebbian-like learning rules to
overcome the problem of disentangling factors of variation when filters are
learned through clustering. We investigate the learned representation by
designing two simple models with our building blocks, achieving comparable
performance to many methods which use Backpropagation, while we reach
comparable performance on Cifar10 and give baseline performances on Cifar100,
Tiny ImageNet and a small subset of ImageNet for Backpropagation-free methods.


  We construct $N$-soliton solution for the non-autonomous discrete-time Toda
lattice equation, which is a generalization of the discrete-time Toda equation
such that the lattice interval with respect to time is an arbitrary function in
time.


  SAR (VV and VH polarization) and optical data are widely used in image fusion
to use the complimentary information of each other and to obtain the
better-quality image (in terms of spatial and spectral features) for the
improved classification results. This paper uses anisotropic diffusion with PCA
for the fusion of SAR and optical data and patch-based SVM Classification with
LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH
polarization using considered fusion method. For classification, the
performance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with
SVM classifier (without patch) and PSVM classifier (with patch), respectively.
Classification results suggests that the LBP-PSVM classifier is more effective
in comparison to SVM and PSVM classifiers for considered data.


  We discuss Hironaka's theorem on resolution of singularities in
charactetistic 0 as well as more recent progress, both on simplifying and
improving Hironaka's method of proof and on new results and directions on
families of varieties, leading to joint work on toroidal orbifolds with Michael
Temkin and Jaros{\l}aw W{\l}odarczyk.


  Thanks to the availability of large online data sets, it has become possible
to quantify success in different fields of human endeavour. The study presented
here contributes to this literature in evaluating the effect of social media
activity, as a means of 'self-branding', to increase the chances of models
being elected for the Playboy Magazine's Playmate of the Year award. We
hypothesise that candidates who actively manage their Instagram accounts can
increase their likelihood to win the award: they use social media to gain more
followers, who then might vote for them in the award polls. The findings
indicate that social media activity actually has predictive capacity to
estimate the outcome of the award. We find evidence that candidates who manage
their social media accounts more actively than other candidates have a higher
probability to become Playmate of the Year. The findings underline the benefits
of social media


  The intermittency of turbulent superfluid helium is explored systematically
in a steady wake flow from 1.28 K up to T>2.18K using a local anemometer. This
temperature range spans relative densities of superfluid from 96% down to 0%,
allowing to test numerical predictions of enhancement or depletion of
intermittency at intermediate superfluid fractions. Using the so-called
extended self-similarity method, scaling exponents of structure functions have
been calculated. No evidence of temperature dependence is found on these
scaling exponents in the upper part of the inertial cascade, where turbulence
is well developed and fully resolved by the probe. This result supports the
picture of a profound analogy between classical and quantum turbulence in their
inertial range, including the violation of self-similarities associated with
inertial-range intermittency.


  KIC 8462852 stood out among more than 100,000 stars in the Kepler catalogue
because of the strange features of its light curve: a wide, asymmetric dimming
taking up to 15 per cent of the light at D793 and a period of multiple, narrow
dimmings happening approximately 700 days later. Several models have been
proposed to account for this abnormal behaviour, most of which require either
unlikely causes or a finely-tuned timing. We aim at offering a relatively
natural solution, invoking only phenomena that have been previously observed,
although perhaps in larger or more massive versions. We model the system using
a large, ringed body whose transit produces the first dimming and a swarm of
Trojan objects sharing its orbit that causes the second period of multiple
dimmings. The resulting orbital period is $T\approx12$ years, with a semi-major
axis $a\approx6$ au. Our model allows us to make two straightforward
predictions: we expect the passage of a new swarm of Trojans in front of the
star starting during the early months of 2021, and a new transit of the main
object during the first half of 2023.


  PT symmetric Aubry-Andre model describes an array of N coupled optical
waveguides with position dependent gain and loss. We show that the reality of
the spectrum depends sensitively on the degree of disorder for small number of
lattice sites. We obtain the Hofstadter Butterfly spectrum and discuss the
existence of the phase transition from extended to localized states. We show
that rapidly changing periodical gain/loss materials almost conserves the total
intensity.


  The classical Debye-H\"{u}ckel screening effect of the electrostatic field
generated by isolated charged particles immersed in a plasma is reviewed. The
validity of the underlying mathematical model, and particularly of the
weak-field approximation, are analyzed. It is shown that the presence of the
plasma sheath around test particles and the resulting effect of charge
screening are essential for the description of plasmas which are strongly
coupled.


  Discussion of the designation of multiple-star components leads to a
conclusion that, apart from components, we need to designate systems and
centers-of-mass. The hierarchy is coded then by simple links to parent. This
system is adopted in the multiple star catalogue, now available on-line. A
short review of multiple-star statistics is given: the frequency of different
multiplicities in the field, periods of spectroscopic sub-systems, relative
orbit orientation, empirical stability criterion, and period-period diagram
with its possible connection to formation of multiple stars.


  We investigate strong mechanical feedback for the single-electron tunneling
device coupled to an underdamped harmonic oscillator in the high-frequency
case, when the mechanical energy of the oscillator exceeds the tunnel rate, and
for weak coupling. In the strong feedback regime, the mechanical oscillations
oscillated by the telegraph signal from the SET in their turn modify the
electric current. In contrast to the earlier results for the low frequencies,
the current noise in not enhanced above the Poisson value.


  The nested algebraic Bethe ansatz is presented for the anisotropic
supersymmetric $U$ model maintaining quantum supersymmetry. The Bethe ansatz
equations of the model are obtained on a one-dimensional closed lattice and an
expression for the energy is given.


  Fluctuation relations for the entropy production in non equilibrium
stationary states of Ising models are investigated by Monte Carlo simulations.
Systems in contact with heat baths at two different temperatures or subject to
external driving will be studied. In the first case, by considering different
kinetic rules and couplings with the baths, the behavior of the probability
distributions of the heat exchanged in a time $\tau$ with the thermostats, both
in the disordered and in the low temperature phase, are discussed. The
fluctuation relation is always verified in the large $\tau $ limit and
deviations from linear response theory are observed. Finite-$\tau$ corrections
are shown to obey a scaling behavior. In the other case the system is in
contact with a single heat bath but work is done by shearing it. Also for this
system the statistics collected for the mechanical work shows the validity of
the fluctuation relation and preasymptotic corrections behave analogously to
the case with two baths.


  In the field of social networking services, finding similar users based on
profile data is common practice. Smartphones harbor sensor and personal context
data that can be used for user profiling. Yet, one vast source of personal
data, that is text messaging data, has hardly been studied for user profiling.
We see three reasons for this: First, private text messaging data is not shared
due to their intimate character. Second, the definition of an appropriate
privacy-preserving similarity measure is non-trivial. Third, assessing the
quality of a similarity measure on text messaging data representing a
potentially infinite set of topics is non-trivial. In order to overcome these
obstacles we propose affinity, a system that assesses the similarity between
text messaging histories of users reliably and efficiently in a
privacy-preserving manner. Private texting data stays on user devices and data
for comparison is compared in a latent format that neither allows to
reconstruct the comparison words nor any original private plain text. We
evaluate our approach by calculating similarities between Twitter histories of
60 US senators. The resulting similarity network reaches an average 85.0%
accuracy on a political party classification task.


  We present an efficient, accurate, and robust method for simulation of dense
suspensions of deformable and rigid particles immersed in Stokesian fluid in
two dimensions. We use a well-established boundary integral formulation for the
problem as the foundation of our approach. This type of formulations, with a
high-order spatial discretization and an implicit and adaptive time
discretization, have been shown to be able to handle complex interactions
between particles with high accuracy. Yet, for dense suspensions, very small
time-steps or expensive implicit solves as well as a large number of
discretization points are required to avoid non-physical contact and
intersections between particles, leading to infinite forces and numerical
instability.
  Our method maintains the accuracy of previous methods at a significantly
lower cost for dense suspensions. The key idea is to ensure interference-free
configuration by introducing explicit contact constraints into the system.
While such constraints are unnecessary in the formulation, in the discrete form
of the problem, they make it possible to eliminate catastrophic loss of
accuracy by preventing contact explicitly.
  Introducing contact constraints results in a significant increase in stable
time-step size for explicit time-stepping, and a reduction in the number of
points adequate for stability.


  The ALICE experiment has measured the inclusive J/$\psi$ production in Pb-Pb
collisions at $\sqrt{s_{\mathrm{NN}} } = 2.76$ TeV down to zero transverse
momentum in the rapidity range $2.5 < y < 4$. A suppression of the inclusive
J/$\psi$ yield in Pb-Pb is observed with respect to the one measured in pp
collisions scaled by the number of binary nucleon-nucleon collisions. The
nuclear modification factor, integrated over the 0-80% most central collisions,
is $0.545 \pm 0.032 \rm{(stat.)} \pm 0.083 \rm{(syst.)}$ and does not exhibit a
significant dependence on the collision centrality. These features appear
significantly different from measurements at lower collision energies. Models
including J/$\psi$ production from charm quarks in a deconfined partonic phase
can describe our data.


  Specialized Monte Carlo methods are nowadays routinely employed, in
combination with thermodynamic integration (TI), to locate phase boundaries of
classical many-particle systems. This is especially useful for the fluid-solid
transition, where a critical point does not exist and both phases may
notoriously go deeply metastable. Using the Lennard-Jones model for
demonstration, we hereby investigate on the alternate possibility of tracing
reasonably accurate transition lines directly by integrating the pressure
equation of state computed in a canonical-ensemble simulation with local moves.
The recourse to this method would become a necessity when the stable crystal
structure is not known. We show that, rather counterintuitively, metastability
problems can be alleviated by reducing (rather than increasing) the size of the
system. In particular, the location of liquid-vapor coexistence can exactly be
predicted by just TI. On the contrary, TI badly fails in the solid-liquid
region, where a better assessment (to within 10\% accuracy) of the coexistence
pressure can be made by following the expansion, until melting, of the
defective solid which has previously emerged from the decay of the metastable
liquid.


  Recent advances in self-supervised speech models have shown significant
improvement in many downstream tasks. However, these models predominantly
centered on frame-level training objectives, which can fall short in spoken
language understanding tasks that require semantic comprehension. Existing
works often rely on additional speech-text data as intermediate targets, which
is costly in the real-world setting. To address this challenge, we propose
Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level
targets into the training process, where the targets are derived from a
visually-ground speech model, notably eliminating the need for speech-text
paired data. Our experimental results on four spoken language understanding
(SLU) benchmarks suggest the superiority of our model in capturing semantic
information.


  This note demonstrates how both the concept of distance and the concept of
holonomy can be constructed from a suitable network with directed edges (and no
lengths). The number of different edge types depends on the signature of the
metric and the dimension of the holonomy group. If the holonomy group is of
dimension one and the metric is positive-definite, a single type of directed
edges is needed.


  We investigate basic features of Bianchi's B\"acklund transformation of
quadrics to see if it can be obtained under weaker assumptions and if it can be
generalized to deformations of other surfaces.


  It is well known that in models with time-homogeneous local volatility
functions and constant interest and dividend rates, the European Put prices are
transformed into European Call prices by the simultaneous exchanges of the
interest and dividend rates and of the strike and spot price of the underlying.
This paper investigates such a Call Put duality for perpetual American options.
It turns out that the perpetual American Put price is equal to the perpetual
American Call price in a model where, in addition to the previous exchanges
between the spot price and the strike and between the interest and dividend
rates, the local volatility function is modified. We prove that equality of the
dual volatility functions only holds in the standard Black-Scholes model with
constant volatility. Thanks to these duality results, we design a theoretical
calibration procedure of the local volatility function from the perpetual Call
and Put prices for a fixed spot price $x_0$. The knowledge of the Put (resp.
Call) prices for all strikes enables to recover the local volatility function
on the interval $(0,x_0)$ (resp. $(x_0,+\infty)$).


  We investigate the feasibility of probing the neutrino mass hierarchy and the
mixing angle $\phi_{13}$ with the neutrino burst from a future supernova. An
inverse power-law density $\rho \sim r^{n} $ with varying $n$ is adopted in the
analysis as the density profile of a typical core-collapse supernova. The
survival probabilities of $\nu_{e}$ and $\bar{\nu}_{e}$ are shown to reduce to
two-dimensional functions of $n$ and $\phi_{13}$. It is found that in the
$n-\sin^{2} \phi_{13}$ parameter space, the 3D plots of the probability
functions exhibit highly non-trivial structures that are sensitive to the mass
hierarchy, the mixing angle $\phi_{13}$, and the value of $n$. The conditions
that lead to observable differences in the 3D plots are established. With the
uncertainty of $n$ considered, a qualitative analysis of the Earth matter
effect is also included.


  We study ballistic interfacial thermal transport across atomic junctions.
Exact expressions for phonon transmission coefficients are derived for thermal
transport in one-junction and two-junction chains, and verified by numerical
calculation based on a nonequilibrium Green's function method. For a
single-junction case, we find that the phonon transmission coefficient
typically decreases monotonically with increasing freqency. However, in the
range between equal frequency spectrum and equal acoustic impedance, it
increases first then decreases, which explains why the Kapitza resistance
calculated from the acoustic mismatch model is far larger than the experimental
values at low temperatures. The junction thermal conductance reaches a maximum
when the interfacial coupling equals the harmonic average of the spring
constants of the two semi-infinite chains. For three-dimensional junctions, in
the weak coupling limit, we find that the conductance is proportional to the
square of the interfacial coupling, while for intermediate coupling strength
the conductance is approximately proportional to the interfacial coupling
strength. For two-junction chains, the transmission coefficient oscillates with
the frequency due to interference effects. The oscillations between the two
envelop lines can be understood analytically, thus providing guidelines in
designing phonon frequency filters.


  The excellent performance of convolutional low-density parity-check codes is
the result of the spatial coupling of individual underlying codes across a
window of growing size, but much smaller than the length of the individual
codes. Remarkably, the belief-propagation threshold of the coupled ensemble is
boosted to the maximum-a-posteriori one of the individual system. We
investigate the generality of this phenomenon beyond coding theory: we couple
general graphical models into a one-dimensional chain of large individual
systems. For the later we take the Curie-Weiss, random field Curie-Weiss,
$K$-satisfiability, and $Q$-coloring models. We always find, based on
analytical as well as numerical calculations, that the message passing
thresholds of the coupled systems come very close to the static ones of the
individual models. The remarkable properties of convolutional low-density
parity-check codes are a manifestation of this very general phenomenon.


  In this paper we consider a distributed convex optimization problem over
time-varying networks. We propose a dual method that converges R-linearly to
the optimal point given that the agents' objective functions are strongly
convex and have Lipschitz continuous gradients. The proposed method requires
half the amount of variable exchanges per iterate than methods based on DIGing,
and yields improved practical performance as empirically demonstrated.


  In this paper, we prove existence, uniqueness and regularity for a class of
stochastic partial differential equations with a fractional Laplacian driven by
a space-time white noise in dimension one. The equation we consider may also
include a reaction term.


  We analyze the De Donder-Weyl covariant field equations for the topologically
massive Yang-Mills theory. These equations are obtained through the
Poisson-Gerstenhaber bracket described within the polysymplectic framework.
Even though the Lagrangian defining the system of our interest is singular, we
show that by appropriately choosing the polymomenta one may obtain an
equivalent regular Lagrangian, thus avoiding the standard analysis of
constraints. Further, our simple treatment allows us to only consider the
privileged $(n-1)$-forms in order to obtain the correct field equations, in
opposition to certain examples found in the literature.


  Autonomous deployment of unmanned aerial vehicles (UAVs) supporting
next-generation communication networks requires efficient trajectory planning
methods. We propose a new end-to-end reinforcement learning (RL) approach to
UAV-enabled data collection from Internet of Things (IoT) devices in an urban
environment. An autonomous drone is tasked with gathering data from distributed
sensor nodes subject to limited flying time and obstacle avoidance. While
previous approaches, learning and non-learning based, must perform expensive
recomputations or relearn a behavior when important scenario parameters such as
the number of sensors, sensor positions, or maximum flying time, change, we
train a double deep Q-network (DDQN) with combined experience replay to learn a
UAV control policy that generalizes over changing scenario parameters. By
exploiting a multi-layer map of the environment fed through convolutional
network layers to the agent, we show that our proposed network architecture
enables the agent to make movement decisions for a variety of scenario
parameters that balance the data collection goal with flight time efficiency
and safety constraints. Considerable advantages in learning efficiency from
using a map centered on the UAV's position over a non-centered map are also
illustrated.


  It has always been an important yet challenging problem to control language
models to avoid generating texts with undesirable attributes, such as toxic
language and unnatural repetition. We introduce Click for controllable text
generation, which needs no modification to the model architecture and
facilitates out-of-the-box use of trained models. It employs a contrastive loss
on sequence likelihood, which fundamentally decreases the generation
probability of negative samples (i.e., generations with undesirable
attributes). It also adopts a novel likelihood ranking-based strategy to
construct contrastive samples from model generations. On the tasks of language
detoxification, sentiment steering, and repetition reduction, we show that
Click outperforms strong baselines of controllable text generation and
demonstrate the superiority of Click's sample construction strategy.


  This paper studies billiard models with a generalized law of reflection, the
so-called projective billiards. They unify various laws, including the
classical one in a Euclidean, pseudo-Euclidean or Riemannian metric. They were
introduced and studied by Sergey Tabachnikov. The paper studies these models in
dimension at least 3 and focuses on the existece and properties of caustics,
i.e. hypersurfaces to which any light trajectory remains tangent to after
successive reflections. It restricts then the study to the particular case of
Riemannian billiards whose geodesics are supported by lines, and of
pseudo-Euclidean billiards. More precisely, this article 1) describes the class
of projective billiards whose only possible caustics are quadrics; 2)
translates this result to Riemannian billiards; 3) applies it to show that if a
pseudo-Euclidean billiard has a caustic, then both are pseudo-confocal
quadrics.


  The haunt of high redshift BL Lacerate objects is day by day more compelling,
to firmly understand their intrinsic nature and evolution. SDSS
J004054.65-0915268 is, at the moment, one of the most distant BL Lac candidate
at z \sim 5 (Plotkin et al 2010). We present a new optical-near IR spectrum
obtained with ALFOSC-NOT with a new, custom designed dispersive grating aimed
to detect broad emission lines that could disprove this classification. In the
obtained spectra we do not detect any emission features and we provide an upper
limit to the luminosity of the C IV broad emission line. Therefore, the nature
of the object is then discussed, building the overall spectral energy
distribution and fitting it with three different models. Our fits, based on the
SED modeling with different possible scenarios, cannot rule out the possibility
that this source is indeed a BL Lac object although, the absence of optical
variability and lack of strong radio flux, they seems to suggest that the
observed optical emission originate from a thermalized accretion disk.


The pure $O$-sequences of the form $(1,a,a,\ldots)$ are classified.


  Recent advances in technology coupled with the progress of observational
radio astronomy methods resulted in achieving a major milestone of astrophysics
- a direct image of the shadow of a supermassive black hole, taken by the
Earth-based Event Horizon Telescope (EHT). The EHT was able to achieve a
resolution of $\sim$20 $\mu$as, enabling it to resolve the shadows of the black
holes in the centres of two celestial objects: the supergiant elliptical galaxy
M87 and the Milky Way Galaxy. The EHT results mark the start of a new round of
development of next generation Very Long Baseline Interferometers (VLBI) which
will be able to operate at millimetre and sub-millimetre wavelengths. The
inclusion of baselines exceeding the diameter of the Earth and observation at
as short a wavelength as possible is imperative for further development of high
resolution astronomical observations. This can be achieved by a spaceborne VLBI
system. We consider the preliminary mission design of such a system,
specifically focused on the detection and analysis of photon rings, an
intrinsic feature of supermassive black holes. Optimised Earth, Sun-Earth L2
and Earth-Moon L2 orbit configurations for the space interferometer system are
presented, all of which provide an order of magnitude improvement in resolution
compared to the EHT. Such a space-borne interferometer would be able to conduct
a comprehensive survey of supermassive black holes in active galactic nuclei
and enable uniquely robust and accurate tests of strong gravity, through
detection of the photon ring features.


  This paper addresses the influence of III-V nucleation routines on Ge
substrates for the growth of high efficiency multijunction solar cells. Three
exemplary nucleation routines with differences in thickness and temperature
were evaluated. The resulting open circuit voltage of triple-junction solar
cells with these designs is significantly affected (up to 50 mV for the best
optimization routine), whereas minimal differences in short circuit current are
observed. Electroluminescence measurements show that both the Ge bottom cell
and the Ga(In)As middle cell present a VOC gain of 25 mV each. This result
indicates that the first stages of the growth not only affect the Ge subcell
itself but also to subsequent subcells. This study highlights the impact of the
nucleation routine design in the performance of high efficiency multijunction
solar cell based on Ge substrates.


  A polarization control experiment by utilizing a pair of crossed undulators
has been proposed for the Shanghai deep ultraviolet free electron laser test
facility. Numerical simulations indicate that, with the electromagnetic
phase-shifter located between the two crossed planar undulators, fully coherent
radiation with 100 nJ order pulse energy, 5 picoseconds pulse length and
circular polarization degree above 90% could be generated. The physical design
study and the preparation status of the experiment are presented in the paper.


  In conversational question answering (CQA), the task of question
rewriting~(QR) in context aims to rewrite a context-dependent question into an
equivalent self-contained question that gives the same answer. In this paper,
we are interested in the robustness of a QR system to questions varying in
rewriting hardness or difficulty. Since there is a lack of questions classified
based on their rewriting hardness, we first propose a heuristic method to
automatically classify questions into subsets of varying hardness, by measuring
the discrepancy between a question and its rewrite. To find out what makes
questions hard or easy for rewriting, we then conduct a human evaluation to
annotate the rewriting hardness of questions. Finally, to enhance the
robustness of QR systems to questions of varying hardness, we propose a novel
learning framework for QR that first trains a QR model independently on each
subset of questions of a certain level of hardness, then combines these QR
models as one joint model for inference. Experimental results on two datasets
show that our framework improves the overall performance compared to the
baselines.


  Here, we show that the conductivity of conductor-insulator composites in
which electrons can tunnel from each conducting particle to all others may
display both percolation and tunneling (i.e. hopping) regimes depending on few
characteristics of the composite. Specifically, we find that the relevant
parameters that give rise to one regime or the other are $D/\xi$ (where $D$ is
the size of the conducting particles and $\xi$ is the tunneling length) and the
specific composite microstructure. For large values of $D/\xi$, percolation
arises when the composite microstructure can be modeled as a regular lattice
that is fractionally occupied by conducting particle, while the tunneling
regime is always obtained for equilibrium distributions of conducting particles
in a continuum insulating matrix. As $D/\xi$ decreases the percolating behavior
of the conductivity of lattice-like composites gradually crosses over to the
tunneling-like regime characterizing particle dispersions in the continuum. For
$D/\xi$ values lower than $D/\xi\simeq 5$ the conductivity has tunneling-like
behavior independent of the specific microstructure of the composite.


  Previous work has shown that the one-dimensional (1D) inviscid compressible
flow (Euler) equations admit a wide variety of scale-invariant solutions
(including the famous Noh, Sedov, and Guderley shock solutions) when the
included equation of state (EOS) closure model assumes a certain
scale-invariant form. However, this scale-invariant EOS class does not include
even simple models used for shock compression of crystalline solids, including
many broadly applicable representations of Mie-Gr\"uneisen EOS. Intuitively,
this incompatibility naturally arises from the presence of multiple dimensional
scales in the Mie-Gr\"uneisen EOS, which are otherwise absent from
scale-invariant models that feature only dimensionless parameters (such as the
adiabatic index in the ideal gas EOS). The current work extends previous
efforts intended to rectify this inconsistency, by using a scale-invariant EOS
model to approximate a Mie- Gr\"uneisen EOS form. To this end, the adiabatic
bulk modulus for the Mie-Gr\"uneisen EOS is constructed, and its key features
are used to motivate the selection of a scale-invariant approximation form. The
remaining surrogate model parameters are selected through enforcement of the
Rankine-Hugoniot jump conditions for an infinitely strong shock in a
Mie-Gr\"uneisen material. Finally, the approximate EOS is used in conjunction
with the 1D inviscid Euler equations to calculate a semi-analytical,
Guderley-like imploding shock solution in a metal sphere, and to determine if
and when the solution may be valid for the underlying Mie-Gr\"uneisen EOS.


  The physics-informed neural networks (PINNs) can be used to deep learn the
nonlinear partial differential equations and other types of physical models. In
this paper, we use the multi-layer PINN deep learning method to study the
data-driven rogue wave solutions of the defocusing nonlinear Schr\"odinger
(NLS) equation with the time-dependent potential by considering several initial
conditions such as the rogue wave, Jacobi elliptic cosine function,
two-Gaussian function, or three-hyperbolic-secant function, and periodic
boundary conditions. Moreover, the multi-layer PINN algorithm can also be used
to learn the parameter in the defocusing NLS equation with the time-dependent
potential under the sense of the rogue wave solution. These results will be
useful to further discuss the rogue wave solutions of the defocusing NLS
equation with a potential in the study of deep learning neural networks.


  Contrary to the case of solids and gases, where Debye theory and kinetic
theory offer a good description for most of the physical properties, a complete
theoretical understanding of the vibrational and thermodynamic properties of
liquids is still missing. Liquids exhibit a vibrational density of states
(VDOS) which does not obey Debye law, and a heat capacity which decreases
monotonically with temperature, rather than growing as in solids. Despite many
attempts, a simple, complete and widely accepted theoretical framework able to
formally derive the aforementioned properties has not been found yet. Here, we
revisit one of the theoretical proposals, and in particular we re-analyze the
properties of liquids within the soft-potential model, originally formulated
for glasses. We confirm that, at least at a qualitative level, many
characteristic properties of liquids can be rationalized within this model. We
discuss the validity of several phenomenological expressions proposed in the
literature for the density of unstable modes, and in particular for its
temperature and frequency dependence. We discuss the role of negative curvature
regions and unstable modes as fundamental ingredients to have a linear in
frequency VDOS. Finally, we compute the heat capacity within the soft potential
model for liquids and we show that it decreases with temperature, in agreement
with experimental and simulation data.


  We study the Susceptible-Infected-Susceptible model of epidemic spreading on
two layers of networks interconnected by adaptive links, which are rewired at
random to avoid contacts between infected and susceptible nodes at the
interlayer. We find that the rewiring reduces the effective connectivity for
the transmission of the disease between layers, and may even totally decouple
the networks. Weak endemic states, in which the epidemics spreads only if the
two layers are interconnected, show a transition from the endemic to the
healthy phase when the rewiring overcomes a threshold value that depends on the
infection rate, the strength of the coupling and the mean connectivity of the
networks. In the strong endemic scenario, in which the epidemics is able to
spread on each separate network, the prevalence in each layer decreases when
increasing the rewiring, arriving to single network values only in the limit of
infinitely fast rewiring. We also find that finite-size effects are amplified
by the rewiring, as there is a finite probability that the epidemics stays
confined in only one network during its lifetime.


  In this paper we consider closed orientable surfaces $S$ of positive genus
and $C^r$-diffeomorphisms $f:S\rightarrow S$ isotopic to the identity ($r\geq
1)$. The main objective is to study periodic open topological disks which are
homotopically unbounded (i.e. which lift to unbounded connected sets in the
universal covering). We show that these disks are not uncommon, and are related
to important dynamical phenomena. We also study the dynamics on these disks
under certain generic conditions. Our first main result implies that for the
torus (or for arbitrary surfaces, with an additional condition) if the rotation
set of a map has nonempty interior and is not locally constant, then the map is
$C^r$-accumulated by diffeomorphisms exhibiting periodic homotopically
unbounded disks. Our second result shows that $C^r$-generically, if the
rotation set has nonempty interior (plus an additional hypothesis if the genus
of $S$ is greater than $1$) a maximal periodic disk which is unbounded and has
a rational prime ends rotation number must be the basin of some compact
attractor or repeller contained in the disk. As a byproduct we obtain results
describing certain periodic components of the complement of the closure of
stable or unstable manifolds of a periodic orbit in the $C^r$-generic setting.


  Shot noise cross-correlations across single layer graphene structures are
calculated with insulators separating a superconducting region. A new feature
of specular crossed Andreev reflection comes into play due to the unique band
structure of graphene. This gives rise to a rich structure in the states of the
electric current flowing across the graphene sheet. We identified a parametric
regime where {\em positive} shot noise cross-correlations of the current appear
signifying entanglement. In contrast to previous proposals the sign of the
cross-correlations can be easily tuned by the application of a gate voltage.


  We formulate a Stackelberg game between an attacker and a defender of a power
system. The attacker attempts to alter the load setpoints of the power system
covertly and intelligently, so that the voltage stability margin of the grid is
reduced, driving the entire system towards a voltage collapse. The defender, or
the system operator, aims to compensate for this reduction by retuning the
reactive power injection to the grid by switching on control devices, such as a
bank of shunt capacitors. A modified Backward Induction method is proposed to
find a cost-based Stackelberg equilibrium (CBSE) of the game, which saves the
players' costs while providing the optimal allocation of both players'
investment resources under budget and covertness constraints. We analyze the
proposed game extensively for the IEEE 9-bus power system model and present an
example of its performance for the IEEE 39-bus power system model. It is
demonstrated that the defender is able to maintain system stability unless its
security budget is much lower than the attacker's budget.


  We show that an action of a group on a set $X$ is locally finite if and only
if $X$ is not equidecomposable with a proper subset of itself. As a
consequence, a group is locally finite if and only if its uniform Roe algebra
is finite.


  Given a hypergraph $\mathcal{H}$, the $\mathcal{H}$-bootstrap process starts
with an initial set of infected vertices of $\mathcal{H}$ and, at each step, a
healthy vertex $v$ becomes infected if there exists a hyperedge of
$\mathcal{H}$ in which $v$ is the unique healthy vertex. We say that the set of
initially infected vertices percolates if every vertex of $\mathcal{H}$ is
eventually infected. We show that this process exhibits a sharp threshold when
$\mathcal{H}$ is a hypergraph obtained by randomly sampling hyperedges from an
approximately $d$-regular $r$-uniform hypergraph satisfying some mild degree
and codegree conditions; this confirms a conjecture of Morris. As a corollary,
we obtain a sharp threshold for a variant of the graph bootstrap process for
strictly $2$-balanced graphs which generalises a result of Kor\'{a}ndi, Peled
and Sudakov. Our approach involves an application of the differential equations
method.


  Federated learning learns a neural network model by aggregating the knowledge
from a group of distributed clients under the privacy-preserving constraint. In
this work, we show that this paradigm might inherit the adversarial
vulnerability of the centralized neural network, i.e., it has deteriorated
performance on adversarial examples when the model is deployed. This is even
more alarming when federated learning paradigm is designed to approximate the
updating behavior of a centralized neural network. To solve this problem, we
propose an adversarially robust federated learning framework, named Fed_BVA,
with improved server and client update mechanisms. This is motivated by our
observation that the generalization error in federated learning can be
naturally decomposed into the bias and variance triggered by multiple clients'
predictions. Thus, we propose to generate the adversarial examples via
maximizing the bias and variance during server update, and learn the
adversarially robust model updates with those examples during client update. As
a result, an adversarially robust neural network can be aggregated from these
improved local clients' model updates. The experiments are conducted on
multiple benchmark data sets using several prevalent neural network models, and
the empirical results show that our framework is robust against white-box and
black-box adversarial corruptions under both IID and non-IID settings.


  In this paper we construct a family of exceptional Stewart-Gough mechanisms,
whose motion curves are algebraic curves of genus 7. Up to translations,
rotations and dilatations this family of mechanisms is 13-dimensional.


  We numerically simulated quantum turbulence in superfluid $^4$He to
investigate the emission of vortex rings from a localized vortex tangle.
Turbulence is characterized by some universal statistical laws. Although there
are a lot of studies on statistical laws in bulk quantum turbulence, studies in
inhomogeneous or localized turbulence is scarce. We first investigate the
statistical laws of localized quantum turbulence, referring to two statistical
laws deduced from the vibrating wire experiments in [Yano $et$ $al.$, J. Low
Temp. Phys. $\bf{196}$, $184\ (2019)$]. The first law is the Poisson process
for the detection of vortex rings; the vortex tangle emits vortex rings with
frequencies depending on their sizes. The second law is the power law between
the frequency and the size of the emitted vortex rings, showing the
self-similarity of the tangle. To study these statistical laws numerically, we
developed a system similar to experiments. First, we generate a localized
statistically steady vortex tangle by injecting vortex rings from two opposite
sides and causing collisions. We investigated the conditions that aid the
formation of the tangles and the anisotropy of the emission of vortex rings
from the tangle. Second, from the data on emitted rings, we reconstruct the two
statistical laws. Results from our numerical investigations are consistent with
the known self-similarity of emitted vortex rings and localized tangles.


  Increasing ellipticity usually suppresses the recollision probability
drastically. In contrast, we report on a recollision channel with large return
energy and a substantial probability, regardless of the ellipticity. The laser
envelope plays a dominant role in the energy gained by the electron, and in the
conditions under which the electron comes back to the core. We show that this
recollision channel eciently triggers multiple ionization with an elliptically
polarized pulse.


  The Cherenkov Telescope Array (CTA) is an international project for a
next-generation ground-based gamma-ray observatory. CTA, conceived as an array
of tens of imaging atmospheric Cherenkov telescopes, comprising small, medium
and large-size telescopes, is aiming to improve on the sensitivity of
current-generation experiments by an order of magnitude and provide energy
coverage from 20 GeV to more than 300 TeV. The Schwarzschild-Couder (SC)
medium-size candidate telescope model features a novel aplanatic two-mirror
optical design capable of a wide field-of-view with significantly improved
imaging resolution as compared to the traditional Davis-Cotton optics design.
Achieving this imaging resolution imposes strict alignment requirements to be
accomplished by a dedicated alignment system. In this contribution we present
the status of the development of the SC optical alignment system, soon to be
materialized in a full-scale prototype SC medium-size telescope at the Fred
Lawrence Whipple Observatory in southern Arizona.


  If Gamma-Ray Bursts (GRBs) are cosmologically distributed standard candles
and are associated with the luminous galaxies, then the observed angular
distribution of all GRBs is altered due to weak gravitational lensing of bursts
by density inhomogeneities. The amplitude of the effect is generally small. For
example, if the current catalogs extend to $z_{max}\sim 1$ and we live in a
flat $\Omega=1$ Universe, the angular auto-correlation function of GRBs will be
enhanced by $\sim 8\%$ due to lensing, on all angular scales. For an extreme
case of $z_{max}= 1.5$ and ($\Omega$, $\Lambda$)=(0.2, 0.8), an enhancement of
$\sim 33\%$ is predicted. If the observed distribution of GRBs is used in the
future to derive power spectra of mass density fluctuations on large angular
scales, the effect of weak lensing should probably be taken into account.


  Typo-squatting domains are a common cyber-attack technique. It involves
utilising domain names, that exploit possible typographical errors of commonly
visited domains, to carry out malicious activities such as phishing, malware
installation, etc. Current approaches typically revolve around string
comparison algorithms like the Demaru-Levenschtein Distance (DLD) algorithm.
Such techniques do not take into account keyboard distance, which researchers
find to have a strong correlation with typical typographical errors and are
trying to take account of. In this paper, we present the TypoSwype framework
which converts strings to images that take into account keyboard location
innately. We also show how modern state of the art image recognition techniques
involving Convolutional Neural Networks, trained via either Triplet Loss or
NT-Xent Loss, can be applied to learn a mapping to a lower dimensional space
where distances correspond to image, and equivalently, textual similarity.
Finally, we also demonstrate our method's ability to improve typo-squatting
detection over the widely used DLD algorithm, while maintaining the
classification accuracy as to which domain the input domain was attempting to
typo-squat.


  Tunneling from a two-dimensional contact into quantum-Hall edges is
considered theoretically for a case where the barrier is extended, uniform, and
parallel to the edge. In contrast to previously realized tunneling geometries,
details of the microscopic edge structure are exhibited directly in the voltage
and magnetic-field dependence of the differential tunneling conductance. In
particular, it is possible to measure the dispersion of the edge-magnetoplasmon
mode, and the existence of additional, sometimes counterpropagating,
edge-excitation branches could be detected.


  Automatic traffic classification is increasingly important in networking due
to the current trend of encrypting transport information (e.g., behind HTTP
encrypted tunnels) which prevents intermediate nodes to access end-to-end
transport headers. This paper proposes an architecture for supporting Quality
of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated
traffic classification. Traffic profiles are constructed by machine-learning
(ML) algorithms using the series of packet sizes and arrival times of QUIC
connections. Thus, the proposed QoS method does not require an explicit setup
of a path (i.e. it provides soft QoS), but employs agents within the network to
verify that flows conform to a given traffic profile. Results over a range of
ML models encourage integrating ML technology in SATCOM equipment. The
availability of higher computation power at a low cost creates fertile ground
for the implementation of these techniques.


  The mixed fractional Brownian motion ($mfBm$) has become quite popular in
finance, since it allows one to model long-range dependence and self-similarity
while remaining, for certain values of the Hurst parameter, arbitrage-free. In
the present paper, we propose approximate closed-form solutions for pricing
arithmetic Asian options on an underlying described by the $mfBm$.
Specifically, we consider both arithmetic Asian options and arithmetic Asian
power options, and we obtain analytical formulas for pricing them based on a
convenient approximation of the strike price. Both the standard $mfBm$ and the
$mfBm$ with Poisson log-normally distributed jumps are taken into account.


  Quantum error mitigation (QEM) is a class of promising techniques capable of
reducing the computational error of variational quantum algorithms tailored for
current noisy intermediate-scale quantum computers. The recently proposed
permutation-based methods are practically attractive, since they do not rely on
any a priori information concerning the quantum channels. In this treatise, we
propose a general framework termed as permutation filters, which includes the
existing permutation-based methods as special cases. In particular, we show
that the proposed filter design algorithm always converge to the global
optimum, and that the optimal filters can provide substantial improvements over
the existing permutation-based methods in the presence of narrowband quantum
noise, corresponding to large-depth, high-error-rate quantum circuits.


  We investigate the three-body Coulomb breakup of a two-neutron halo nucleus
$^{11}$Li. We use the coupled-channel $^9$Li + $n$ + $n$ three-body model,
which includes the coupling between last neutron states and the various
$2p$-$2h$ configurations in $^9$Li due to the tensor and pairing correlations.
The three-body scattering states of $^{11}$Li are described by using the
combined methods of the complex scaling and the Lippmann-Schwinger equation.
The calculated breakup cross section successfully reproduces the experiments.
The large mixing of the s-state in the halo ground state of $^{11}$Li is shown
to play an important role in explanation of shape and strength of the breakup
cross section. In addition, we predict the invariant mass spectra for binary
subsystems of $^{11}$Li. It is found that the two kinds of virtual s-states of
$^9$Li-$n$ and $n$-$n$ systems in the final three-body states of $^{11}$Li
largely contribute to make low-lying peaks in the invariant mass spectra. On
the other hand, in the present analysis, it is suggested that the contributions
of the p-wave resonances of $^{10}$Li is hardly confirmed in the spectra.


  We propose a novel method for effective retrieval of multi-agent
spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers
several unique challenges compared to conventional text-based retrieval
settings. Most notably, the data is fine-grained meaning that the specific
location of agents is important in describing behavior. Additionally, the data
often contains tracks of multiple agents (e.g., multiple players in a sports
game), which generally leads to a permutational alignment problem when
performing relevance estimation. Due to the frequent position swap of agents,
it is difficult to maintain the correspondence of agents, and such issues make
the pairwise comparison problematic for multi-agent spatiotemporal data. To
address this issue, we propose a tree-based method to estimate the relevance
between multi-agent spatiotemporal tracks. It uses a hierarchical structure to
perform multi-agent data alignment and partitioning in a coarse-to-fine
fashion. We validate our approach via user studies with domain experts. Our
results show that our method boosts performance in retrieving similar sports
plays -- especially in interactive situations where the user selects a subset
of trajectories compared to current state-of-the-art methods.


  Quantum anomalies in Weyl semimetal (for either ${\bf E}\cdot{\bf B} \neq 0$
or ${\bf \nabla}T\cdot{\bf B} \neq 0$) leads to chiral charge and energy
pumping between the opposite chirality nodes. This results in chiral charge and
energy imbalance between the Weyl nodes which manifests in several intriguing
magneto-transport phenomena. Here, we investigate the role of electrical-,
thermal-, and gravitational chiral anomaly on magneto-transport in Weyl
semimetals. We predict the planar Ettinghausen and Righi-Leduc effect to be a
distinct signature of these quantum anomalies. We also demonstrate a
significant enhancement in the thermo-electric conductivity, Seebeck effect,
Nernst effect and thermal conductivity with increasing temperature.
Interestingly, this anomaly induced transport violates the Wiedemann-Franz law
and Mott relation.


  We explore in this paper ways to qualify and quantify the quantum
entanglement $E(M)$ between two macroscopic objects by way of model studies.
Knowing that a macroscopic object is a composite, how does one determine $E(M)$
in terms of the entanglements between its micro-constituents $E(\mu)$? We
assert that the notion of `levels of structure', the coupling strength between
constituents in different levels, and the use of collective variables in each
level are all pertinent factors. We consider two types of coupling, each
constituent particle is coupled to only one other particle (1-to-1) versus it
coupled to all particles (1-to-all). In the 1-1 case with pairwise interactions
of equal strength, the entanglement is independent of the number of constituent
particles $N$ in the macroscopic object. In the 1-to-all case the relative
coordinates are decoupled and the center of mass (CoM) coupling scales with
$N$. We provide a proof of the conditions whereby the CoM variable decouples, a
cause for the special role the CoM variable plays in the entanglement between
the two such macroscopic objects. This qualitative behavior is largely not
affected by fluctuations in the interaction strength. We also analyzed the
entanglement pattern of 4 coupled oscillators in two pairs, representing the
two objects A and B (or two adjacent levels of structure), each with two
constituents. By assigning different coupling strengths we can investigate the
interplay of inter-level entanglement with intra- level interactions. From the
entanglement dynamics of the 4-oscillator system with varying coupling strength
we see the entanglement between constituents meeting sudden death while the CoM
variables may sustain over longer times. This offers another way to determine
under what conditions quantum entanglement between macroscopic objects can
persist.


  We study the charged non-relativistic Bose gas interacting with a constant
magnetic field but which is otherwise free. The notion of Bose-Einstein
condensation for the three dimensional case is clarified, and we show that
although there is no condensation in the sense of a phase transition, there is
still a maximum in the specific heat which can be used to define a critical
temperature. Although the absence of a phase transition persists for all values
of the magnetic field, we show how as the magnetic field is reduced the curves
for the specific heat approach the free field curve. For large values of the
magnetic field we show that the gas undergoes a "dimensional reduction" and
behaves effectively as a one-dimensional gas except at very high temperatures.
These general features persist for other spatial dimensions D and we show
results for D=5. Finally we examine the magnetization and the
Meissner-Ochsenfeld effect.


  We studied a method of measuring upper critical field (H$_{c2}$) of a
superconductor based on the width of $\Delta$H = $\Delta$B region, which
appears in the superconductor that volume defects are many and dominant. Here
we present the basic concept and details of the method. Although H$_{c2}$ of a
superconductor is fixed according to kind of the superconductor, it is
difficult to measure H$_{c2}$ experimentally, and the results are different
depending on the experimental conditions. H$_{c2}$ was calculated from the
theory that pinned fluxes at volume defects are picked out and move into an
inside of the superconductor when their arrangement is the same as that of
H$_{c2}$ state of the superconductor. H$_{c2}$ of MgB$_2$ obtained by the
method was 65.4 Tesla at 0 K. The reason that H$_{c2}$ obtained by the method
is closer to ultimate H$_{c2}$ is based on that
$\Delta$F$_{pinning}$/$\Delta$F$_{pickout}$ is more than 4 when pinned fluxes
at volume defects of 163 nm radius are picked out. The method will help to find
the ultimate H$_{c2}$ of volume defect-dominating superconductors.