  We study squeeze flow of two different fluids (castor oil and ethylene
glycol) between a pair of glass plates and a pair of perspex plates, under an
applied load. The film thickness is found to vary with time as a power-law,
where the exponent increases with load. After a certain time interval the area
of fluid-solid contact saturates to a constant value. This saturation area,
increases with load at different rates for different fluid-solid combinations.


  A definition of quantum mechanics on a manifold $ M $ is proposed and a
method to realize the definition is presented. This scheme is applicable to a
homogeneous space $ M = G / H $. The realization is a unitary representation of
the transformation group $ G $ on the space of vector bundle-valued functions.
When $ H \ne \{ e \} $, there exist a number of inequivalent realizations. As
examples, quantum mechanics on a sphere $ S^n $, a torus $ T^n $ and a
projective space $ \RP $ are studied. In any case, it is shown that there are
an infinite number of inequivalent realizations.


  In this study, we demonstrate the dependence of atmospheric dust size on the
near-infrared spectra of ten L dwarfs, and constrain the sizes of dust grains
in each L dwarf atmosphere. In previous studies, by comparing observed and
modeled spectra, it was suggested that the deviations of their spectral shapes
from theoretical prediction are general characteristics. Here, we focus on the
dust size in brown dwarf atmospheres to understand the observed spectra. We
confirm that changing the dust size changes the temperature-pressure structure
of the atmosphere, with the shape of the spectrum changing accordingly. At the
wavelength at which dust is the main absorber of radiation (the dust-dominated
regime), a large dust opacity combined with a medium grain size, e.g., 0.1
$\mu$m, results in a low photospheric temperature, and thus a small flux.
Conversely, for the wavelength at which gas absorption is dominant (the
gas-dominated regime), a large dust opacity modifies the temperature-pressure
structure, resulting in a high photospheric temperature, which corresponds to
large flux emissions. Taking into account the size effect, we compare the model
spectral fluxes in the wavelength range 1-5 $\mu$m with the observational ones
to constrain the main dust size in the atmosphere of each of the ten L dwarfs
observed with AKARI and SpeX or CGS4. Ultimately, we reveal that the observed
data are reproduced with higher fidelity by models based on a medium dust size
of 0.1-3.0 $\mu$m for six of these L dwarfs; therefore, we suggest that such
atmospheric dust sizes apply to the majority of L dwarfs.


  Within thermodynamic models of gravity, where the universe is considered as a
finite ensemble of quantum particles, cosmological constant in the Einstein's
equations appears as a constant of integration. Then it can be bounded using
Karolyhazy uncertainty relation applied for horizon distances, as the amount of
information in principle accessible to an external observer.


  Using the full potential linearised augmented planewave method we study the
electronic and magnetic properties of the (001) and (111) surfaces of the
half-metallic Heusler alloy NiMnSb from first-principles. We take into account
all possible surface terminations including relaxations of these surfaces.
Special attention is paid to the spin-polarization at the Fermi level which
governs the spin-injection from such a metal into a semiconductor. In general,
these surfaces lose the half-metallic character of the bulk NiMnSb, but for the
(111) surfaces this loss is more pronounced. Although structural optimization
does not change these features qualitatively, specifically for the (111)
surfaces relaxations can compensate much of the spin-polarization at the Fermi
surface that has been lost upon formation of the surface.


  The generalization of machine learning models has a complex dependence on the
data, model and learning algorithm. We study train and test performance, as
well as the generalization gap given by the mean of their difference over
different data set samples to understand their ``typical" behavior. We derive
an expression for the gap as a function of the covariance between the model
parameter distribution and the train loss, and another expression for the
average test performance, showing test generalization only depends on
data-averaged parameter distribution and the data-averaged loss. We show that
for a large class of model parameter distributions a modified generalization
gap is always non-negative. By specializing further to parameter distributions
produced by stochastic gradient descent (SGD), along with a few approximations
and modeling considerations, we are able to predict some aspects about how the
generalization gap and model train and test performance vary as a function of
SGD noise. We evaluate these predictions empirically on the Cifar10
classification task based on a ResNet architecture.


  Following the detection of high-energy astrophysical neutrinos in 2013, their
origin is still unknown. Aiming for the identification of an electromagnetic
counterpart of a rapidly fading source, we have implemented a realtime analysis
framework for the IceCube neutrino observatory. Several analyses selecting
neutrinos of astrophysical origin are now operating in realtime at the detector
site in Antarctica and are producing alerts to the community to enable rapid
follow-up observations. The goal of these observations is to locate the
astrophysical objects responsible for these neutrino signals. This paper
highlights the infrastructure in place both at the South Pole detector site and
at IceCube facilities in the north that have enabled this fast follow-up
program to be developed. Additionally, this paper presents the first realtime
analyses to be activated within this framework, highlights their sensitivities
to astrophysical neutrinos and background event rates, and presents an outlook
for future discoveries.


  In this paper, we propose a modified nonlinear conjugate gradient (NCG)
method for functions with a non-Lipschitz continuous gradient. First, we
present a new formula for the conjugate coefficient \beta_k in NCG, conducting
a search direction that provides an adequate function decrease. We can derive
that our NCG algorithm guarantees strongly convergent for continuous
differential functions without Lipschitz continuous gradient. Second, we
present a simple interpolation approach that could automatically achieve
shrinkage, generating a step length satisfying the standard Wolfe conditions in
each step. Our framework considerably broadens the applicability of NCG and
preserves the superior numerical performance of the PRP-type methods.


  In this paper, we propose a scheme to eliminate the influence of noises on
system dynamics, by means of a sequential unsharp measurements and unitary
feedback operations. The unsharp measurements are carried out periodically
during system evolution, while the feedback operations are well designed based
on the eigenstates of the density matrices of the exact (noiseless) dynamical
states and its corresponding post-measurement states. For illustrative
examples, we show that the dynamical trajectory errors caused by both static
and non-static noises are successfully eliminated in typical two-level and
multi-level systems, i.e., the high-fidelity quantum dynamics can be
maintained. Furthermore, we discuss the influence of noise strength and
measurement strength on the degree of precise quantum control. Crucially, the
measurement-feedback scheme is quite universal in that it can be applied to
precise quantum control for any dimension systems. Thus, it naturally finds
extensive applications in quantum information processing.


  The advanced versions of the LIGO and Virgo ground-based gravitational-wave
detectors are expected to operate from three sites: Hanford, Livingston, and
Cascina. Recent proposals have been made to place a fourth site in Australia or
India; and there is the possibility of using the Large Cryogenic Gravitational
Wave Telescope in Japan to further extend the network. Using Bayesian
parameter-estimation analyses of simulated gravitational-wave signals from a
range of coalescing-binary locations and orientations at fixed distance or
signal-to-noise ratio, we study the improvement in parameter estimation for the
proposed networks. We find that a fourth detector site can break degeneracies
in several parameters; in particular, the localization of the source on the sky
is improved by a factor of ~ 3--4 for an Australian site, or ~ 2.5--3.5 for an
Indian site, with more modest improvements in distance and binary inclination
estimates. This enhanced ability to localize sources on the sky will be crucial
in any search for electromagnetic counterparts to detected gravitational-wave
signals.


  We address a possibility of creating soliton states in oblate
binary-fermionic clouds in the framework of the density-functional theory,
which includes the spin-orbit coupling (SOC) and nonlinear attraction between
spin-up and down-polarized components of the spinor wave function. In the limit
when the inter-component attraction is much stronger than the effective
intra-component Pauli repulsion, the resulting model also represents a system
of Gross-Pitaevskii equations for a binary Bose-Einstein condensate including
the SOC effect. We show that the model gives rise to two-dimensional quiescent
composite solitons in free space. A stability region is identified for solitons
of the mixed-mode type (which feature mixtures of zero-vorticity and vortical
terms in both components), while solitons of the other type, semi-vortices
(with the vorticity carried by one component) are unstable. Due to breaking of
the Galilean invariance by SOC, the system supports moving solitons with
velocities up to a specific critical value. Collisions between moving solitons
are briefly considered too. The collisions lead, in particular, to a
quasi-elastic rebound, or an inelastic outcome, which features partial merger
of the solitons.


  We study the emergence of synchronization in the Kuramoto model on a digraph
in the presence of time delays. Assuming the digraph is strongly connected, we
first establish a uniform bound on the phase diameter and subsequently prove
the asymptotic frequency synchronization of the oscillators under suitable
assumptions on the initial configurations. In the case of an all-to-all
connection, we obtain an exponential synchronization estimate. Additionally, we
present numerical simulations, providing further insights into the
synchronization and oscillatory behaviors of the oscillator frequencies
depending on the network structure and the magnitude of the time delay.


  The quadratic cycle cover problem is the problem of finding a set of
node-disjoint cycles visiting all the nodes such that the total sum of
interaction costs between consecutive arcs is minimized. In this paper we study
the linearization problem for the quadratic cycle cover problem and related
lower bounds.
  In particular, we derive various sufficient conditions for the quadratic cost
matrix to be linearizable, and use these conditions to compute bounds. We also
show how to use a sufficient condition for linearizability within an iterative
bounding procedure. In each step, our algorithm computes the best equivalent
representation of the quadratic cost matrix and its optimal linearizable matrix
with respect to the given sufficient condition for linearizability. Further, we
show that the classical Gilmore-Lawler type bound belongs to the family of
linearization based bounds, and therefore apply the above mentioned iterative
reformulation technique. We also prove that the linearization vectors resulting
from this iterative approach satisfy the constant value property.
  The best among here introduced bounds outperform existing lower bounds when
taking both quality and efficiency into account.


  Television is an ever-evolving multi billion dollar industry. The success of
a television show in an increasingly technological society is a vast
multi-variable formula. The art of success is not just something that happens,
but is studied, replicated, and applied. Hollywood can be unpredictable
regarding success, as many movies and sitcoms that are hyped up and promise to
be a hit end up being box office failures and complete disappointments. In
current studies, linguistic exploration is being performed on the relationship
between Television series and target community of viewers. Having a decision
support system that can display sound and predictable results would be needed
to build confidence in the investment of a new TV series. The models presented
in this study use data to study and determine what makes a sitcom successful.
In this paper, we use descriptive and predictive modeling techniques to assess
the continuing success of television comedies: The Office, Big Bang Theory,
Arrested Development, Scrubs, and South Park. The factors that are tested for
statistical significance on episode ratings are character presence, director,
and writer. These statistics show that while characters are indeed crucial to
the shows themselves, the creation and direction of the shows pose implication
upon the ratings and therefore the success of the shows. We use machine
learning based forecasting models to accurately predict the success of shows.
The models represent a baseline to understanding the success of a television
show and how producers can increase the success of current television shows or
utilize this data in the creation of future shows. Due to the many factors that
go into a series, the empirical analysis in this work shows that there is no
one-fits-all model to forecast the rating or success of a television show.


  Following a long tradition of physicists who have noticed that the Ising
model provides a general background to build realistic models of social
interactions, we study a model of financial price dynamics resulting from the
collective aggregate decisions of agents. This model incorporates imitation,
the impact of external news and private information. It has the structure of a
dynamical Ising model in which agents have two opinions (buy or sell) with
coupling coefficients which evolve in time with a memory of how past news have
explained realized market returns. We study two versions of the model, which
differ on how the agents interpret the predictive power of news. We show that
the stylized facts of financial markets are reproduced only when agents are
over-confident and mis-attribute the success of news to predict return to
herding effects, thereby providing positive feedbacks leading to the model
functioning close to the critical point. Our model exhibits a rich multifractal
structure characterized by a continuous spectrum of exponents of the power law
relaxation of endogenous bursts of volatility, in good agreement with previous
analytical predictions obtained with the multifractal random walk model and
with empirical facts.


  Surrogate safety measures can provide fast and pro-active safety analysis and
give insights on the pre-crash process and crash failure mechanism by studying
near misses. However, validating surrogate safety measures by connecting them
to crashes is still an open question. This paper proposed a method to connect
surrogate safety measures to crash probability using probabilistic time series
prediction. The method used sequences of speed, acceleration and
time-to-collision to estimate the probability density functions of those
variables with transformer masked autoregressive flow (transformer-MAF). The
autoregressive structure mimicked the causal relationship between condition,
action and crash outcome and the probability density functions are used to
calculate the conditional action probability, crash probability and conditional
crash probability. The predicted sequence is accurate and the estimated
probability is reasonable under both traffic conflict context and normal
interaction context and the conditional crash probability shows the
effectiveness of evasive action to avoid crashes in a counterfactual
experiment.


  After a brief historical introduction, we summarize current efforts and
accomplishments in the study of supermassive black holes.


  One of the many number theoretic topics investigated by the ancient Greeks
was perfect numbers, which are positive integers equal to the sum of their
proper positive integral divisors. Mathematicians from Euclid to Euler
investigated these mysterious numbers. We present results on perfect numbers in
the ring of Eisenstein integers.


  Thomas-Fermi theory is developed to evaluate nuclear matrix elements averaged
on the energy shell, on the basis of independent particle Hamiltonians. One-
and two-body matrix elements are compared with the quantal results and it is
demonstrated that the semiclassical matrix elements, as function of energy,
well pass through the average of the scattered quantum values. For the one-body
matrix elements it is shown how the Thomas-Fermi approach can be projected on
good parity and also on good angular momentum. For the two-body case the
pairing matrix elements are considered explicitly.


  We introduce a new approach for finite element simulations of the
time-dependent Ginzburg-Landau equations (TDGL) in a general curved polygon,
possibly with reentrant corners. Specifically, we reformulate the TDGL into an
equivalent system of equations by decomposing the magnetic potential to the sum
of its divergence-free and curl-free parts, respectively. Numerical simulations
of vortex dynamics show that, in a domain with reentrant corners, the new
approach is much more stable and accurate than the old approaches of solving
the TDGL directly (under either the temporal gauge or the Lorentz gauge); in a
convex domain, the new approach gives comparably accurate solutions as the old
approaches.


  WhatsApp is a popular messaging app used by over a billion users around the
globe. Due to this popularity, understanding misbehavior on WhatsApp is an
important issue. The sending of unwanted junk messages by unknown contacts via
WhatsApp remains understudied by researchers, in part because of the end-to-end
encryption offered by the platform. We address this gap by studying junk
messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp
groups in India. We characterise both junk content and senders. We find that
nearly 1 in 10 messages is unwanted content sent by junk senders, and a number
of unique strategies are employed to reflect challenges faced on WhatsApp,
e.g., the need to change phone numbers regularly. We finally experiment with
on-device classification to automate the detection of junk, whilst respecting
end-to-end encryption.


  The action of the discrete symmetries on the scalar mode functions of the de
Sitter spacetime is studied. The invariance with respect to a combination of
discrete symmetries is put forward as a criterion to select a certain vacuum
out of a family of vacua. The implications of the choices for eigenfunctions of
various common sets of commuting operators are explored, and the results are
compared to the different original choices from the literature that have been
utilized in order to exhibit thermal effects.


  First-principles simulations have been performed to investigate the phase
stability of tantalum metal under high pressure and high temperature (HPHT). We
searched its low-energy structures globally using our developed multi-algorithm
collaborative (MAC) crystal structure prediction technique. The body-centred
cubic (bcc) was found to be stable at pressure up to 300 GPa. The previously
reported $\omega$ and \textit{A}15 structures were also reproduced
successfully. More interestingly, we observed another phase (space group:
\textit{Pnma}, 62) that is more stable than $\omega$ and \textit{A}15. Its
stability is confirmed by its phonon spectra and elastic constants. For
$\omega$-Ta, the calculated elastic constants and high-temperature phonon
spectra both imply that it is neither mechanically nor dynamically stable.
Thus, $\omega$ is not the structure to which bcc-Ta transits before melting. On
the contrary, the good agreement of \textit{Pnma}-Ta shear sound velocities
with experiment suggests \textit{Pnma} is the new structure of Ta implied by
the discontinuation of shear sound velocities in recent shock experiment [J.
Appl. Phys. \textbf{111}, 033511 (2012)].


  Let $X$ be a convex polyomino such that its vertex set is a sublattice of
$\mathbb{N}^2$. Let $\Bbbk[X]$ be the toric ring (over a field $\Bbbk$)
associated to $X$ in the sense of Qureshi, \emph{J. Algebra}, 2012. Write the
Hilbert series of $\Bbbk[X]$ as $(1 + h_1 t + h_2 t^2 + \cdots
)/(1-t)^{\dim(\Bbbk[X])}$. For $k \in \mathbb{N}$, let $r_k$ be the number of
configurations in $X$ with $k$ pairwise non-attacking rooks. We show that $h_2
< r_2$ if $X$ is not a thin polyomino. This partially confirms a conjectured
characterization of thin polyominoes by Rinaldo and Romeo, \emph{J. Algebraic
Combin.}, 2021.


  Research in the field of Materials Science and Engineering focuses on the
design, synthesis, properties, and performance of materials. An important class
of materials that is widely investigated are crystalline materials, including
metals and semiconductors. Crystalline material typically contains a distinct
type of defect called "dislocation". This defect significantly affects various
material properties, including strength, fracture toughness, and ductility.
Researchers have devoted a significant effort in recent years to understanding
dislocation behavior through experimental characterization techniques and
simulations, e.g., dislocation dynamics simulations. This paper presents how
data from dislocation dynamics simulations can be modeled using semantic web
technologies through annotating data with ontologies. We extend the already
existing Dislocation Ontology by adding missing concepts and aligning it with
two other domain-related ontologies (i.e., the Elementary Multi-perspective
Material Ontology and the Materials Design Ontology) allowing for representing
the dislocation simulation data efficiently. Moreover, we show a real-world use
case by representing the discrete dislocation dynamics data as a knowledge
graph (DisLocKG) that illustrates the relationship between them. We also
developed a SPARQL endpoint that brings extensive flexibility to query
DisLocKG.


  We present the current status of the apeNEXT project. Aim of this project is
the development of the next generation of APE machines which will provide
multi-teraflop computing power. Like previous machines, apeNEXT is based on a
custom designed processor, which is specifically optimized for simulating QCD.
We discuss the machine design, report on benchmarks, and give an overview on
the status of the software development.


  Let $(\mathcal{C},\mathbb{E},\mathfrak{s})$ be an extriangulated category
with a proper class $\xi$ of $\mathbb{E}$-triangles. In this paper, we first
introduce the $\xi$-Gorenstein cohomology in terms of
$\xi$-$\mathcal{G}$projective resolutions and $\xi$-$\mathcal{G}$injective
coresolutions, respectively, and then we get the balance of $\xi$-Gorenstein
cohomology. Moreover, we study the interplay among $\xi$-cohomology,
$\xi$-Gorenstein cohomology and $\xi$-complete cohomology, and obtain the
Avramov-Martsinkovsky type exact sequences in this setting.


  Formal verse poetry imposes strict constraints on the meter and rhyme scheme
of poems. Most prior work on generating this type of poetry uses existing poems
for supervision, which are difficult to obtain for most languages and poetic
forms. In this work, we propose an unsupervised approach to generate poems
following any given meter and rhyme scheme, without requiring any poetic text
for training. Our method works by splitting a regular, non-poetic corpus into
phrases, prepending control codes that describe the length and end rhyme of
each phrase, and training a transformer language model in the augmented corpus.
During inference, we build control codes for the desired meter and rhyme
scheme, and condition our language model on them to generate formal verse
poetry. Experiments in Spanish and Basque show that our approach is able to
generate valid poems, which are often comparable in quality to those written by
humans.


  The quasiclassical distribution function for photon density in the phase
space is obtained from solution of the kinetic equation. This equation
describes propagation of paraxial laser beams in the Earth atmosphere where
"collision integral" embodies the influence of turbulence. The anisotropy of
photon distribution is shown and explained. For long propagation path, the
explicit expression for fourth-order moment of light is obtained as a sum of
linear and quadratic forms of the average distribution function. This moment
describes a spatial correlation of four light waves, giving the information
about the photon distribution in the cross-section of the beam. The fourth
moment can be measured using two small detectors outside the central part of
the beam. The linear form describes the shot noise (quantum fluctuations) of
photons. The range where the shot noise exceeds the classical noise is found
analytically. Derived photon fluctuations are the valuable source of
information about statistical properties and local structure of the laser
radiation that can be used for applications.


  We evaluate the effect of screening by bound electron in (7Be,e) + p -->
(8B,e) + photon transition in the framework of the adiabatic representation of
the three particle problem. Comparison with two approaches (united nucleus and
static) is presented. We discuss possible applications of this effect both for
Solar Neutrinos and low energy fusion experiments.


  October 1957, and the successful launch of Sputnik 1 into Earth orbit, marked
the dawn of the Space Age. The first of the 'fellow travellers' - humanity's
first artificial satellite - orbited for a mere three months before re-entering
the Earth's atmosphere, though its mission paved the way for an era of
exploration that has endured to the present day. For many, a world without
satellites would be a difficult one to imagine. As a society, we have become
reliant on them for a vast array of services and applications. With a divine
view of large swathes of the Earth's surface, and the ability to relay signals
around its curvature, satellites have enabled the fast transfer of data on a
global scale, bypassing the challenges associated with ground-based
broadcasting, long-distance wiring, and so on. Positioning, Navigation and
Timing (PNT) satellites have revolutionised transportation by land, air, and
sea, while weather satellites enable scientists to monitor and warn of
large-scale phenomena as they develop in near real-time. Satellites have
extended the frontiers of observation: looking outwards, astronomers are able
to circumvent the Earth's atmosphere to look deeper into the cosmos than ever
before; looking inwards, patterns and processes that feed into general
circulation models can be monitored on a range of timescales, improving our
understanding of climate change. Satellites, and the services they provide, are
not going to disappear any time soon. That said, threats to satellite safety do
exist, and it is important that they be addressed as soon as possible to avoid
long-lasting damage to operations in the space domain. Nearly sixty-five years
on from the advent of human activity in space, I chart the evolution of the
orbital debris environment and review latest efforts to make space operations
more sustainable.


  We report on the structural phase transitions in the S doped FeSe
superconductor by powder synchrotron X ray diffraction at high pressures up to
18.5 GPa under compression and decompression modes. In order to create high
quasi hydrostatic pressures, diamond anvil cells filled with helium as a
pressure transmitting medium were used. It was found that at ambient pressure
and room temperature, S doped FeSe has a tetragonal structure. Under
compression, in the region of 10 GPa, a phase transition from the tetragonal
into the orthorhombic structure is observed, which persists up to 18.5 GPa. Our
results strongly suggest that, at decompression, as the applied pressure
decreases to 6 GPa and then is completely removed, most of the sample
recrystallizes into the hexagonal phase of the structural type NiAs. However,
the other part of the sample remains in the high pressure orthorhombic phase,
while the tetragonal phase is not restored. These observations illustrate a
strong hysteresis of the structural properties of S doped FeSe during a phase
transition under pressure.


  In the context of mathematical cosmology, the study of necessary and
sufficient conditions for a semi-Riemannian manifold to be a (generalised)
Robertson-Walker space-time is important. In particular, it is a requirement
for the development of initial data to reproduce or approximate the standard
cosmological model. Usually these conditions involve the Einstein field
equations, which change if one considers alternative theories of gravity or if
the coupling matter fields change. Therefore, the derivation of conditions
which do not depend on the field equations is an advantage. In this work we
present a geometric derivation of such a condition. We require the existence of
a unit vector field to distinguish at each point of space two (non-equal)
sectional curvatures. This is equivalent for the Riemann tensor to adopt a
specific form. Our geometrical approach yields a local isometry between the
space and a Robertson-Walker space of the same dimension, curvature and metric
tensor sign (the dimension of the largest subspace on which the metric tensor
is negative definite). Remarkably, if the space is simply-connected, the
isometry is global. Our result generalize to a class of spaces of non-constant
curvature the theorem that spaces of the same constant curvature, dimension and
metric tensor sign must be locally isometric. Because we do not make any
assumptions regarding field equations, matter fields or metric tensor sign, one
can readily use this result to study cosmological models within alternative
theories of gravity or with different matter fields.


  We construct a new reduced nuclear reaction network able to reproduce the
energy production due to the photo-disintegration of heavy elements such as Ru,
which are believed to occur during superbursts in mixed H/He accreting systems.
We use this network to simulate a detonation propagation, inside a mixture of
C/Ru. As our reference, we use a full nuclear reaction network, including 14758
reactions on 1381 nuclides. Until the reduced and full networks converge to a
good level of accuracy in the energy production rate, we iterate between the
hydrodynamical simulation, with a given reduced network, and the readjustment
of a new reduced network, on the basis of previously derived hydrodynamical
profiles. We obtain the thermodynamic state of the material after the passage
of the detonation, and the final products of the combustion. Interestingly, we
find that all reaction lengths can be resolved in the same simulation. This
will enable C/Ru detonations to be more easily studied in future
multi-dimensional simulations, than pure carbon ones. We underline the
dependence of the combustion products on the initial mass fraction of Ru. In
some cases, a large fraction of heavy nuclei, such as Mo, remains after the
passage of the detonation front. In other cases, the ashes are principally
composed of iron group elements.


  The use of semiclassical models based on the Strong Field Approximation (SFA)
is ubiquitous in strong field multi-photon ionization and underlies many key
developments in attosecond science, including the description of High Harmonic
Generation (HHG). However, such models are notably lacking in streaking
experiments, which use an attosecond pulse to initiate single-photon ionization
and a lower frequency infrared pulse to provide timing information. Here, we
introduce a classical Wigner propagation (CWP) method, which analogously to
semiclassical models in strong field ionization, treats the ionization step
quantum mechanically, followed subsequently by classical propagation (with
initial conditions obtained from the Wigner function) in the infrared probe
field. As we demonstrate, this method compares well with experimental data and
full two-electron quantum simulations available for helium, includes
multi-electron effects, and can be applied to molecules, where the full
solution of the time-dependent Schrodinger equation is not feasible. Applying
the CWP method to a many-atom molecule, like 2,3,3-trimethyl-butyl-2-iodide, we
find a relatively significant directional dependence of streaking delays,
indicating the importance of orientation-resolved measurements in molecules.


  Continuing the description of rf vacuum arcs from an earlier paper, we
describe some aspects of the interaction of vacuum arcs that involve the
surface. This paper describes aspects of plasma materials interactions that
affect the arc and models measurement of the surface field using the
Tonks-Frenkel and the spinodal electrohydrodynamic instabilities, a realistic
model for the generation and evaluation of high field enhancements, unipolar
arcs, creep and other effects.


  Whenever we do not have an informationally complete set of measurements, the
estimate of a quantum state can not be uniquely determined. In this case, among
the density matrices compatible with the available data, it is commonly
preferred that one which is the most uncommitted with the missing information.
This is the purpose of the Maximum Entropy estimation (MaxEnt) and the
Variational Quantum Tomography (VQT). Here, we propose a variant of Variational
Quantum Tomography and show its relationship with Maximum Entropy methods in
quantum tomographies with incomplete set of measurements. We prove their
equivalence in case of eigenbasis measurements, and through numerical
simulations we stress their similar behavior. Hence, in the modified VQT
formulation we have an estimate of a quantum state as unbiased as in MaxEnt and
with the benefit that VQT can be more efficiently solved by means of linear
semidefinite programs.


  The Sine-Gordon equation is integrable in (1+1)-dimensional Minkowski and in
2-dimensional Euclidean spaces. In each case, it has a Lax pair, and a Hirota
algorithm generates its N soliton solutions for all N greater than or equal to
1. The (1+2)-dimensional equation does not pass known integrability tests and
does not have a Lax pair. Still, the Hirota algorithm generates N soliton
solutions of that equation for all N greater than or equal to 1. Each
multi-soliton solution propagates rigidly at a constant velocity, v. The
solutions are divided into two unconnected subspaces: Solutions with v greater
than or equal to c =1, and v smaller than c. Each subspace is connected by an
invertible transformation (rotation plus dilation) to the space of soliton
solutions of an integrable Sine-Gordon equation in two dimensions. The
faster-than-light solutions are connected to the solutions in (1+1)-dimensional
Minkowski space. The slower-than-light solutions are connected to the solutions
in 2-dimensional Euclidean space by this transformation and also by Lorentz
transformations. The Sine-Gordon equation in (1+3)-dimensional Minkowski space
has a richer variety of solutions. Its slower-than-light solutions are
connected to the solutions of the integrable equation in 2-dimensional
Euclidean space. However, only a subset of its faster-than-light solutions is
connected to the solutions of the integrable equation in (1+1)-dimensional
Minkowski space.


  The growth factor of linear fluctuations is probably one of the least known
quantity in observational cosmology. Here we discuss the constraints that
baryon oscillations in galaxy power spectra from future surveys can put on a
conveniently parametrized growth factor. We find that spectroscopic surveys of
$5000 deg^2$ extending to $z \approx 3$ could estimate the growth index
$\gamma$ within 0.06; a similar photometric survey would give
$\Delta\gamma\approx 0.15$. This test provides an important consistency check
for standard cosmological model and could constrain modified gravity models. We
discuss the errors and the figure of merit for various combinations of redshift
errors and survey size.


  We study frequentist risk properties of predictive density estimators for
mean mixtures of multivariate normal distributions, involving an unknown
location parameter $\theta \in \mathbb{R}^d$, and which include multivariate
skew normal distributions. We provide explicit representations for Bayesian
posterior and predictive densities, including the benchmark minimum risk
equivariant (MRE) density, which is minimax and generalized Bayes with respect
to an improper uniform density for $\theta$. For four dimensions or more, we
obtain Bayesian densities that improve uniformly on the MRE density under
Kullback-Leibler loss. We also provide plug-in type improvements, investigate
implications for certain type of parametric restrictions on $\theta$, and
illustrate and comment the findings based on numerical evaluations.


  The self-consistent random phase approximation (RPA) approach with the
residual interaction derived from a relativistic point-coupling energy
functional is applied to evaluate the isospin symmetry-breaking corrections
{\delta}c for the 0+\to0+ superallowed Fermi transitions. With these {\delta}c
values, together with the available experimental ft values and the improved
radiative corrections, the unitarity of the Cabibbo-Kobayashi-Maskawa (CKM)
matrix is examined. Even with the consideration of uncertainty, the sum of
squared top-row elements has been shown to deviate from the unitarity condition
by 0.1% for all the employed relativistic energy functionals.


  Millimetre-wave (mmWave) radar has emerged as an attractive and
cost-effective alternative for human activity sensing compared to traditional
camera-based systems. mmWave radars are also non-intrusive, providing better
protection for user privacy. However, as a Radio Frequency (RF) based
technology, mmWave radars rely on capturing reflected signals from objects,
making them more prone to noise compared to cameras. This raises an intriguing
question for the deep learning community: Can we develop more effective point
set-based deep learning methods for such attractive sensors?
  To answer this question, our work, termed MiliPoint, delves into this idea by
providing a large-scale, open dataset for the community to explore how mmWave
radars can be utilised for human activity recognition. Moreover, MiliPoint
stands out as it is larger in size than existing datasets, has more diverse
human actions represented, and encompasses all three key tasks in human
activity recognition. We have also established a range of point-based deep
neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint,
which can serve to set the ground baseline for further development.


  Large Language Models (LLMs) have made significant progress in assisting
users to query databases in natural language. While LLM-based techniques
provide state-of-the-art results on many standard benchmarks, their performance
significantly drops when applied to large enterprise databases. The reason is
that these databases have a large number of tables with complex relationships
that are challenging for LLMs to reason about. We analyze challenges that LLMs
face in these settings and propose a new solution that combines the power of
LLMs in understanding questions with automated reasoning techniques to handle
complex database constraints. Based on these ideas, we have developed a new
framework that outperforms state-of-the-art techniques in zero-shot text-to-SQL
on complex benchmarks


  A goal of forthcoming imaging surveys is to use weak gravitational lensing
shear measurements to constrain dark energy. We quantify the importance of
uncalibrated photometric redshift outliers to the dark energy goals of
forthcoming imaging surveys in a manner that does not assume any particular
photometric redshift technique or template. In so doing, we provide an
approximate blueprint for computing the influence of specific outlier
populations on dark energy constraints. We find that outliers whose photo-z
distributions are tightly localized about a significantly biased redshift must
be controlled to a per-galaxy rate of <~ a few times 10^-3 to insure that
systematic errors on dark energy parameters are rendered negligible. In the
complementary limit, a subset of imaged galaxies with uncalibrated photometric
redshifts distributed over a broad range must be limited to fewer than a
per-galaxy error rate of <~ a few times 10^-4. Additionally, we explore the
relative importance of calibrating the photo-z's of a core set of relatively
well-understood galaxies as compared to the need to identify potential
catastrophic photo-z outliers. We discuss the degradation of the statistical
constraints on dark energy parameters induced by excising source galaxies at
high- and low-photometric redshifts, concluding that removing galaxies with
z_phot >~ 2.4 and z_phot <~ 0.3 may mitigate damaging catastrophic redshift
outliers at a relatively small (~ 20%) cost in statistical error. In an
appendix, we show that forecasts for the degradation in dark energy parameter
constraints due to uncertain photometric redshifts depend sensitively on the
treatment of the nonlinear matter power spectrum. Previous work using PD96 may
have overestimated the photo-z calibration requirements of future surveys.


  Advanced thermostats for molecular dynamics are proposed on the base of the
rigorous Langevin dynamics. Because the latter accounts for the subsystem-bath
interactions in details, the bath anisotropy and nonuniformity are described
via the relevant friction tensor. The developed model reflects properly the
relativistic dynamics of the subsystem evolution as well as the nonlinear
friction, which can occur for fast particles with large momenta at elevated
temperature.


  In this paper, we empirically evaluate algorithms for learning four types of
Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN
augmented Naive-Bayes and general BNs, where the latter two are learned using
two variants of a conditional-independence (CI) based BN-learning algorithm.
Experimental results show the obtained classifiers, learned using the CI based
algorithms, are competitive with (or superior to) the best known classifiers,
based on both Bayesian networks and other formalisms; and that the
computational time for learning and using these classifiers is relatively
small. Moreover, these results also suggest a way to learn yet more effective
classifiers; we demonstrate empirically that this new algorithm does work as
expected. Collectively, these results argue that BN classifiers deserve more
attention in machine learning and data mining communities.


  Precipitation in Mg-Zn alloys was analyzed by means of first principles
calculations. Formation energies of symmetrically distinct hcp Mg1-xZnx (0 < x
< 1) configurations were determined and potential candidates for
Guinier-Preston zones coherent with the matrix were identified from the convex
hull. The most likely structures were ranked depending on the interface energy
along the basal plane. In addition, the formation energy and vibrational
entropic contributions of several phases reported experimentally (Mg4Zn7, MgZn2
cubic, MgZn2 hexagonal, Mg21Zn25 and Mg2Zn11) were calculated. The formation
energies of Mg4Zn7, MgZn2 cubic, and MgZn2 hexagonal Laves phases were very
close because they were formed by different arrangements of rhombohedral and
hexagonal lattice units. It was concluded that \beta_1^' precipitates were
formed by a mixture of all of them. Nevertheless, the differences in the
geometrical arrangements led to variations in the entropic energy contributions
which determined the high temperature stability. It was found that the MgZn2
hexagonal Laves phase is the most stable phase at high temperature and, thus,
\beta_1^' precipitates tend to transform into the \beta_2^' (MgZn2 hexagonal)
precipitates with higher aging temperature or longer aging times. Finally, the
equilibrium \beta phase (Mg21Zn25) was found to be a long-range order that
precipitates the last one on account of the kinetic processes necessary to
trigger the transformation from a short-range order phase \beta_2^' to \beta .


  We analyze theoretically and experimentally vortex configurations in
mesoscopic superconducting squares. Our theoretical approach is based on the
analytical solution of the London equation using Green's-function method. The
potential-energy landscape found for each vortex configuration is then used in
Langevin-type molecular-dynamics simulations to obtain stable vortex
configurations. Metastable states and transitions between them and the ground
state are analyzed. We present our results of the first direct visualization of
vortex patterns in micrometer-sized Nb squares, using the Bitter decoration
technique. We show that the filling rules for vortices in squares with
increasing applied magnetic field can be formulated, although in a different
manner than in disks, in terms of formation of vortex "shells".


  We present the result of lattice QCD calculation of the scalar, vector and
tensor form factors for the $B\to K\ell^+\ell^-$ decay, across the full
physical range of momentum transfer. We use the highly improved staggered quark
(HISQ) formalism for all valence quarks on eight ensembles of gluon field
configurations generated by the MILC collaboration. These include four flavours
of HISQ quarks in the sea, with three ensembles having the light $u/d$ quarks
at physical masses. In the first fully relativistic calculation of these form
factors, we use the heavy-HISQ method. This allows us to determine the form
factors as a function of heavy quark mass from the $c$ to the $b$, and so we
also obtain new results for the $D\to K$ tensor form factor. The advantage of
the relativistic formalism is that we can match the lattice weak currents to
their continuum counterparts much more accurately than in previous
calculations; our scalar and vector currents are renormalised fully
nonperturbatively and we use a well-matched intermediate momentum-subtraction
scheme for our tensor current. Our scalar and vector $B\to K$ form factors have
uncertainties of less than 4% across the entire physical $q^2$ range and the
uncertainty in our tensor form factor is less than 7%. Our heavy-HISQ method
allows us to map out the dependence on heavy-quark mass of the form factors and
we can also see the impact of changing spectator quark mass by comparing to
earlier HPQCD results for the same quark weak transition but for heavier
mesons.


  In this work we consider periodic spherically symmetric metrics of constant
positive scalar curvature on the n-dimensional cylinder called pseudo-cylindric
metrics. These metrics belong to the conformal class $[g_0]$ of the Riemannian
product $S^1\times S^{n-1}$ : a circle of length $T$ crossed with the
(n-1)-dimensional standard sphere. Such metrics have a harmonic Riemannian
curvature and a non parallel Ricci tensor, except for the cylindric one. Thus,
it appears a natural link between them and the Derdzinski metrics which are
warped product and classify a family of Riemannian manifolds. These two
families actually differ by conformal transformations. Moreover, we are
interested in the multiplicity problem of the pseudo-cylindric metrics in
$[g_0]$. We also study the existence problem and the number of the Derdzinski
metrics. Furthermore, we prove that the pseudo-cylindric metrics may be
expressed in terms of elliptic functions for the dimension $n = 3,4$ and 6
only, and in terms of automorphic functions for any other dimension. This fact
allows us to give new bounds for global estimates. Finally, we examine the
curvature of the asymptotic pseudo-cylindric metrics which are complete
singular Yamabe metrics on the standard sphere punctured of $k$ points. We show
that any of such (non trivial) metric has a non parallel Ricci tensor.


  We provide a space domain oriented separation of magnetic fields into parts
generated by sources in the exterior and sources in the interior of a given
sphere. The separation itself is well-known in geomagnetic modeling, usually in
terms of a spherical harmonic analysis or a wavelet analysis that is spherical
harmonic based. In contrast to these frequency oriented methods, we use a more
spatially oriented approach in this paper. We derive integral representations
with explicitly known convolution kernels. Regularizing these singular kernels
allows a multiscale representation of the internal and external contributions
to the magnetic field with locally supported wavelets. This representation is
applied to a set of CHAMP data for crustal field modeling.


  We present results on Mrk 573 obtained as part of the CHandra survey of
Extended Emission-line Regions in nearby Seyfert galaxies (CHEERS). Previous
studies showed that this source features a biconical emission in the soft X-ray
band closely related with the Narrow Line Region as mapped by the [O iii]
emission line and the radio emission, though on a smaller scale; we investigate
the properties of soft X-ray emission from this source with new deep Chandra
observations. Making use of the subpixel resolution of the Chandra/ACIS image
and PSF-deconvolution, we resolve and study substructures in each ionizing
cone. The two cone spectra are fitted with photoionization model, showing a
mildly photoionized phase diffused over the bicone. Thermal collisional gas at
about ~ 1.1 keV and ~ 0.8 keV appears to be located between the nucleus and the
"knots" resolved in radio observations, and between the "arcs" resolved in the
optical images, respectively; this can be interpreted in terms of shock
interaction with the host galactic plane. The nucleus shows a significant flux
decrease across the observations indicating variability of the AGN, with the
nuclear region featuring higher ionization parameter with respect to the bicone
region. The long exposure allows us to find extended emission up to ~ 7 kpc
from the nucleus along the bicone axis. Significant emission is also detected
in the direction perpendicular to the ionizing cones, disagreeing with the
fully obscuring torus prescribed in the AGN unified model, and suggesting
instead the presence of a clumpy structure.


  In this paper, we review the study of the distribution of the zeros of
certain approximations for the Ramanujan $\Xi-$function given by Haseo Ki, and
we provide a new proof of his results. Our approach is motivated by the ideas
of Vel\'asquez in the study of the zeros of certain sums of entire functions
with some condition of stability related to the Hermite-Biehler theorem.


  We present a tutorial on the properties of the new ideal circuit element, a
memristor. By definition, a memristor M relates the charge q and the magnetic
flux $\phi$ in a circuit, and complements a resistor R, a capacitor C, and an
inductor L as an ingredient of ideal electrical circuits. The properties of
these three elements and their circuits are a part of the standard curricula.
The existence of the memristor as the fourth ideal circuit element was
predicted in 1971 based on symmetry arguments, but was clearly experimentally
demonstrated just this year. We present the properties of a single memristor,
memristors in series and parallel, as well as ideal memristor-capacitor (MC),
memristor-inductor (ML), and memristor-capacitor-inductor (MCL) circuits. We
find that the memristor has hysteretic current-voltage characteristics. We show
that the ideal MC (ML) circuit undergoes non-exponential charge (current) decay
with two time-scales, and that by switching the polarity of the capacitor, an
ideal MCL circuit can be tuned from overdamped to underdamped. We present
simple models which show that these unusual properties are closely related to
the memristor's internal dynamics. This tutorial complements the pedagogy of
ideal circuit elements (R,C, and L) and the properties of their circuits.


  With the rapid development of various services in wireless communications,
spectrum resource has become increasingly valuable. Faster than Nyquist (FTN)
signaling, proposed in the 1970s, is a promising paradigm for improving
spectrum utilization. This paper proposes intelligent variable-packing-ratio
(VPR)-based transmissions for high spectrum efficiency (SE) and security,
respectively. Aided by deep learning (DL)-based estimation, the proposed scheme
for high SE can achieve a higher capacity with negligible modification to
existing communication paradigms (e.g., spectrum allocation or frame
structure). Also, for VPR-based secure transmission, a dynamic generation
scheme is proposed to produce randomly distributed positions to switch the
packing ratio, which can effectively avoid detections and attacks. In addition,
we propose a simplified DL-based packing ratio estimation for both of these two
scenarios so that the receiver can estimate the packing ratio without any
in-band or out-band control messages. Simulation results show that the proposed
simplified estimation achieves nearly the same accuracy and convergence speed
as the original multi-branch fully-connected structure with a complexity
reduction of 20 folds. Finally, we derive the closed-form SE of the proposed
VPR transmission under different channels. The numerical results validate the
correctness of the derivation and demonstrate the SE gains of the VPR scheme
beyond conventional Nyquist transmission.


  Variational Physics-Informed Neural Networks (VPINNs) utilize a variational
loss function to solve partial differential equations, mirroring Finite Element
Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency
problems, are computationally intensive and scale poorly with increasing
element counts, limiting their use in complex geometries. This work introduces
FastVPINNs, a tensor-based advancement that significantly reduces computational
overhead and improves scalability. Using optimized tensor operations,
FastVPINNs achieve a 100-fold reduction in the median training time per epoch
compared to traditional hp-VPINNs. With proper choice of hyperparameters,
FastVPINNs surpass conventional PINNs in both speed and accuracy, especially in
problems with high-frequency solutions. Demonstrated effectiveness in solving
inverse problems on complex domains underscores FastVPINNs' potential for
widespread application in scientific and engineering challenges, opening new
avenues for practical implementations in scientific machine learning.


  We consider Anderson model $H^{\omega}=-\Delta+V^{\omega}$ on
$\ell^2(\mathbb{Z}^d)$ with decaying random potential. We study the point
process $\xi^{\omega}_{L,\lambda}$ associated with eigenvalues of
$H^{\omega}_{\Lambda_L}$, the retriction of $H^{\omega}$ to the finite cube
$\Lambda_L$. Our result is that the weak limit points of
$\{\xi^{\omega}_{L,\lambda}\}$ are poisson point processes as $L\to\infty$.


  We present our recent results on the hadronic light-by-light
pseudoscalar-exchange contribution to the anomalous magnetic moment of the muon
using rational approximants. Our work provides a generalization of Pad\'e
approximants to the bivariate case to describe the most general doubly-virtual
transition form factor, which is required in the calculation. This method
provides a powerful tool to systematically implement the known experimental
data on transition form factors in the space-like region and low energies in a
model-independent way. Given the lack of experimental data on the
doubly-virtual transition form factor, we make use of the pseduoscalar decays
into lepton pairs. We find an interesting and puzzling situation which calls
for new experimental measurements to clarify the present state.


  The effect of an electric field on the conductance of ultrathin films of
metals deposited on substrates coated with a thin layer of amorphous Ge was
investigated. A contribution to the conductance modulation symmetric with
respect to the polarity of the applied electric field was found in regimes in
which there was no sign of glassy behavior. For films with thicknesses that put
them on the insulating side of the superconductor-insulator transition, the
conductance increased with electric field, whereas for films that were becoming
superconducting it decreased. Application of magnetic fields to the latter,
which reduce the transition temperature and ultimately quench
superconductivity, changed the sign of the reponse of the conductance to
electric field back to that found for insulators. We propose that this
symmetric response to capacitive charging is a consequence of changes in the
conductance of the a-Ge layer, and is not a fundamental property of the physics
of the superconductor-insulator transition as previously suggested.


  We investigate the formation of non-ground-state Bose-Einstein condensates
within the mean-field description represented by the Gross-Pitaevskii equation
(GPE). The objective is to form excited states of a condensate known as
nonlinear topological modes, which are stationary solutions of the GPE.
Nonlinear modes can be generated by modulating either the trapping potential or
the atomic scattering length. We show that it is possible to coherently control
the transitions to excited nonlinear modes by manipulating the relative phase
of the modulations. In addition, we show that the use of both modulations can
modify the speed of the transitions. In our analysis, we employ approximate
analytical techniques, including a perturbative treatment, and numerical
calculations for the GPE. Our study evidences that the coherent control of the
GPE presents novel possibilities which are not accessible for the Schr\"odinger
equation.


  We show that the Riccati--Pad\'{e} method is suitable for the calculation of
the complex eigenvalues of the Schr\"{o}dinger equation with a repulsive
exponential potential. The accuracy of the results is remarkable for realistic
potential parameters.


  We study polymer layers formed by irreversible adsorption from a polymer
melt. Our theory describes an experiment which is a ``slow'' version of that
proposed by Guiselin [Europhys. Lett., v. 17 (1992) p. 225] who considered
instantaneously irreversibly adsorbing chains and predicted a universal density
profile of the layer after swelling with solvent to produce the ``Guiselin
brush.'' Here we ask what happens when adsorption is not instantaneous. The
classic example is chemisorption. In this case the brush is formed slowly and
the final structure depends on the experiment's duration, $t_{final}$. We find
the swollen layer consists of an inner region of thickness $z^* \sim
t_{final}^{-5/3}$ with approximately constant density and an outer region
extending up to height $h \sim N^{5/6}$ which has the same density decay $\sim
z^{-2/5}$ as for the Guiselin case.


  It is important to understand whether $\alpha$-clustering structures can
leave traces in ultra-relativistic heavy ion collisions. Using the modified
AMPT model, we simulate three $\alpha$ + core configurations of $^{44}$Ti in
$^{44}$Ti+$^{44}$Ti collisions at $\sqrt{s_{NN}}=5.02$ TeV as well as other
systems with Woods-Saxon structures. One of these configurations has no
additional constraint, but the other two have the Mott density edge
$r_{\mathrm{Mott}}$ set as either a lower or upper bound on the cluster
position $r_{\alpha}$ to check the influence of $\alpha$ dissolution. This is
the first time that the initial stage of the geometric properties in heavy-ion
collisions has been configured using the traditional treatment of the nuclear
structure. We compare the radial nucleon density, multiplicity distribution,
transverse momentum spectra, eccentricity, triangularity, elliptic flow and
triangular flow of these six systems. $\alpha$ + core structures can alter all
these observations especially in the most-central collisions, among which
elliptic flow is the most hopeful as a probe of such structures.


  Multicomplex numbers of order n have an associated trigonometry (multisine
functions with (n-1) parameters) leading to a natural extension of the
sine-Gordon model. The parameters are constrained from the requirement of local
current conservation. In two dimensions for n < 6 known integrable models
(deformed Toda and non-linear sigma, pure affine Toda...) with dual
counterparts are obtained in this way from the multicomplex space MC itself and
from the natural embedding $\MC_n \subset \MMC_m, n < m$. For $ n \ge 6$ a
generic constraint on the space of parametersis obtained from current
conservation at first order in the interaction Lagragien.


  We introduce a minimal model for the evolution of functional
protein-interaction networks using a sequence-based mutational algorithm, and
apply the model to study neutral drift in networks that yield oscillatory
dynamics. Starting with a functional core module, random evolutionary drift
increases network complexity even in the absence of specific selective
pressures. Surprisingly, we uncover a hidden order in sequence space that gives
rise to long-term evolutionary memory, implying strong constraints on network
evolution due to the topology of accessible sequence space.


  We present here the first result on the three-loop gluon jet function in
perturbative QCD. Using the three-loop coefficient functions [1,2] for
deep-inelastic scattering via the exchange of a virtual photon that couples to
quarks or a scalar that couples to gluons and employing the KG equation,
renormalization group invariance and factorization theorem, we obtain both the
quark and the gluon jet functions up to the three-loop level. The former agrees
with the recent result [3]. These jet functions being universal ingredients for
many collider and decay processes, will play an important role in the
phenomenological studies at the Large Hadron Collider.


  In this paper, we investigate the heavy quark potential in some holographic
QCD models. The calculation relies on a modified renormalization scheme
mentioned in a previous work of Albacete et al. After studying the heavy quark
potential in Pirner-Galow model and Andreev-Zakharov model, we extend the
discussion to a general deformed $AdS_5$ case. It is shown that the obtained
potential is negative definite for all quark-antiquark separations, differs
from that using the usual renormalization scheme.


  Despite the large body of work on fairness-aware learning for individual
modalities like tabular data, images, and text, less work has been done on
multimodal data, which fuses various modalities for a comprehensive analysis.
In this work, we investigate the fairness and bias implications of multimodal
fusion techniques in the context of multimodal AI-based recruitment systems
using the FairCVdb dataset. Our results show that early-fusion closely matches
the ground truth for both demographics, achieving the lowest MAEs by
integrating each modality's unique characteristics. In contrast, late-fusion
leads to highly generalized mean scores and higher MAEs. Our findings emphasise
the significant potential of early-fusion for accurate and fair applications,
even in the presence of demographic biases, compared to late-fusion. Future
research could explore alternative fusion strategies and incorporate
modality-related fairness constraints to improve fairness. For code and
additional insights, visit:
https://github.com/Swati17293/Multimodal-AI-Based-Recruitment-FairCVdb


  In this paper we deal with the problem of axiomatizing the preference
relations modelled through Choquet integral with respect to a $k$-additive
capacity, i.e. whose M\"obius transform vanishes for subsets of more than $k$
elements. Thus, $k$-additive capacities range from probability measures ($k=1$)
to general capacities ($k=n$). The axiomatization is done in several steps,
starting from symmetric 2-additive capacities, a case related to the Gini
index, and finishing with general $k$-additive capacities. We put an emphasis
on 2-additive capacities. Our axiomatization is done in the framework of social
welfare, and complete previous results of Weymark, Gilboa and Ben Porath, and
Gajdos.


  Coherent transport of atoms trapped in an optical lattice can be controlled
by microwave-induced spin flips that correlate with site-to-site hopping. We
study the controllability of homogeneous one-dimensional systems of
noninteracting atoms in the absence of site addressability. Given these
restrictions, we construct a deterministic protocol to map an initially
localized Wannier state to a wave packet that that is coherently distributed
over n sites. This is extended to analytic solutions for arbitrary unitary maps
given homogenous systems and in the presence of time-dependent uniform forces.
Such control is important for applications in quantum information processing
such as quantum computing and quantum simulations of condensed matter
phenomena.


  I give a short and completely elementary proof of Takagi's 1921 theorem on
the zeros of a composite polynomial $f(d/dz) \, g(z)$.


  Theoretically, bound binaries of massive black holes are expected as the
natural outcome of mergers of massive galaxies. From the observational side,
however, massive black hole binaries remain elusive. Velocity shifts between
narrow and broad emission lines in quasar spectra are considered a promising
observational tool to search for spatially unresolved, dynamically bound
binaries. In this series of papers we investigate the nature of such candidates
through analyses of their spectra, images and multi-wavelength spectral energy
distributions. Here we investigate the properties of the optical spectra,
including the evolution of the broad line profiles, of all the sources
identified in our previous study. We find a diverse phenomenology of broad and
narrow line luminosities, widths, shapes, ionization conditions and time
variability, which we can broadly ascribe to 4 classes based on the shape of
the broad line profiles: 1) Objects with bell-shaped broad lines with big
velocity shifts (>1000 km/s) compared to their narrow lines show a variety of
broad line widths and luminosities, modest flux variations over a few years,
and no significant change in the broad line peak wavelength. 2) Objects with
double-peaked broad emission lines tend to show very luminous and broadened
lines, and little time variability. 3) Objects with asymmetric broad emission
lines show a broad range of broad line luminosities and significant variability
of the line profiles. 4) The remaining sources tend to show moderate to low
broad line luminosities, and can be ascribed to diverse phenomena. We discuss
the implications of our findings in the context of massive black hole binary
searches.


  We find an exact series solution for the steady-state probability
distribution of a harmonically trapped active Brownian particle in two
dimensions, in the presence of translational diffusion. This series solution
allows us to efficiently explore the behavior of the system in different
parameter regimes. Identifying "active" and "passive" regimes, we predict a
surprising re-entrant active-to-passive transition with increasing trap
stiffness. Our numerical simulations validate this finding. We discuss various
interesting limiting cases wherein closed form expressions for the
distributions can be obtained.


  In a quest towards an intelligent decision-making machine, the ability to
make plausible predictions is the central pillar of its intelligence. A
predicting algorithm's central idea is to understand the governing physical
rules and make plausible and apt predictions based on the same governing laws.
Extending the study towards the astrophysical phenomenon puts the model's
ability to test since the model has to understand various parameters that
govern the dynamics of the event and understand the spatial and temporal
evolution by applying the plausible laws. This work presents a deep learning
model to predict plausible future events that maintain spatial and temporal
coherence. We have trained over two broad classes, the evolution of Sa, Sb, S0,
and Sd galaxy mergers and evolution of gravitational lenses with a higher
redshift of the foreground galaxy having $15M_{\odot}$. We extended our work
towards developing a direct measure of the performance metric for any
prediction algorithm. We thereby introduce a novel metric, Correctness Factor
(CF), which directly outputs how accurate a prediction is.


  We introduce a class of thin combings and prove that it has a nice behaviour
in free constructions. Among corollaries, we deduce the relative metabolicity,
in the sense of Gersten, of Sela's limit groups.


  The spectral window, containing Fraunhofer lines formed in the solar
photosphere, around the magnetically sensitive Cr I lines at 5780.9, 5781.1,
5781.7, 5783.0, and 5783.8 \r{A}, with Land\'e g-factors between 1.6 and 2.5,
is explored. The goal is to analyze simultaneously 15 spectral lines, which
comprise Cr I, Cu I, Fe I, Mn I, and Si I lines, without polarimetry to infer
the thermodynamic and magnetic properties in strongly magnetized plasmas using
an inversion code. The study is based on a new setup at the Vacuum Tower
Telescope (VTT, Tenerife) which includes fast spectroscopic scans in the
wavelength range around the Cr I 5781.75 \r{A} line. The snapshot 385 of the
Enhanced Network simulation from the Bifrost code serves to synthesize all the
lines, which are in turn inverted simultaneously with SIR to establish the best
inversion strategy. This strategy is then applied to VTT observations of a
sunspot belonging to NOAA 12723 on 2018 September 30 and the results are
compared to full-disk vector-field data obtained with the Helioseismic and
Magnetic Imager (HMI). The 15 simultaneously inverted intensity profiles
(Stokes I) delivered accurate temperatures and Doppler velocities when compared
against the simulations. The derived magnetic fields and inclinations are most
accurate when the fields are oriented along the line-of-sight (LOS) and less
accurate when the fields are transverse to the LOS. In general, the results
appear similar to the HMI vector-field data, although some discrepancies exist.
The analyzed spectral range has the potential to deliver thermal, dynamic, and
magnetic information in strongly magnetized features on the Sun, such as pores
and sunspots, even without polarimetry. The highest sensitivity of the lines is
found in the lower photosphere, on average around $\log \tau = -1$. The
multiple-line inversions provide smooth results across the whole field-of-view.


  Matching 2D keypoints in an image to a sparse 3D point cloud of the scene
without requiring visual descriptors has garnered increased interest due to its
low memory requirements, inherent privacy preservation, and reduced need for
expensive 3D model maintenance compared to visual descriptor-based methods.
However, existing algorithms often compromise on performance, resulting in a
significant deterioration compared to their descriptor-based counterparts. In
this paper, we introduce DGC-GNN, a novel algorithm that employs a
global-to-local Graph Neural Network (GNN) that progressively exploits
geometric and color cues to represent keypoints, thereby improving matching
accuracy. Our procedure encodes both Euclidean and angular relations at a
coarse level, forming the geometric embedding to guide the point matching. We
evaluate DGC-GNN on both indoor and outdoor datasets, demonstrating that it not
only doubles the accuracy of the state-of-the-art visual descriptor-free
algorithm but also substantially narrows the performance gap between
descriptor-based and descriptor-free methods.


  SDSS J083942.11+380526.3 ($z=2.315$) is a FeLoBAL quasar that exhibits
visible Balmer absorption lines (H$\alpha$), implying a significant $n=2$
population. The quasar also shows an array of absorption lines, including \oi,
\niii, \feii, \mgii, \aliii\, to \civ\ and \nv. The high-ionization absorption
lines such as \civ\ and \siiv\ are revealed by slightly blueshifted BAL
troughs. The resonance doublets such as \mgii\ and \aliii\ are saturated but
did not reached zero intensity which indicates that the BLR is partially
covered. Overall, however, the absorption is predominantly from low-ionization
\feii\ lines, emitted from ground and excited states up to at least 3.814 eV.
This implies that the absorbing gas spans the hydrogen ionization front and
extends into the partially ionized zone where neutral hydrogen is certainly
present. Notably, the hydrogen line spectrum of the quasar shows no signature
of expected Ly$\alpha$ absorption. Instead, the line spectrum shows an unusual
Ly$\alpha$ emission characterized by a fully filled emission line spectrum
which is a composite of a strong narrow core superposed on a weak broad base.
Taking into account the effect of partial covering to BLR, we have extracted a
strong DLA trough in Ly$\alpha$ emission region. To fit the spectrum, we
performed photoionized model calculations and compared them to the
observations. We found that photoionization modeling using CLOUDY can
successfully reproduce the main characteristics of the quasar spectrum, and the
predicted neutral hydrogen column density arising from the clouds responsible
for the low-ionization absorption provides a good match to the extracted DLA
trough. This indicates that both the DLA and the low-ionization absorption
arise from the same medium that is roughly collocated with the dusty torus.


  This paper addresses asymptotic properties of general penalized spline
estimators with an arbitrary B-spline degree and an arbitrary order difference
penalty. The estimator is approximated by a solution of a linear differential
equation subject to suitable boundary conditions. It is shown that, in certain
sense, the penalized smoothing corresponds approximately to smoothing by the
kernel method. The equivalent kernels for both inner points and boundary points
are obtained with the help of Green's functions of the differential equation.
Further, the asymptotic normality is established for the estimator at interior
points. It is shown that the convergence rate is independent of the degree of
the splines, and the number of knots does not affect the asymptotic
distribution, provided that it tends to infinity fast enough.


  Thomas' partial likelihood estimator of regression parameters is widely used
in the analysis of nested case-control data with Cox's model. This paper
proposes a new estimator of the regression parameters, which is consistent and
asymptotically normal. Its asymptotic variance is smaller than that of Thomas'
estimator away from the null. Unlike some other existing estimators, the
proposed estimator does not rely on any more data than strictly necessary for
Thomas' estimator and is easily computable from a closed form estimating
equation with a unique solution. The variance estimation is obtained as minus
the inverse of the derivative of the estimating function and therefore the
inference is easily available. A numerical example is provided in support of
the theory.


  Traditional Recurrent Neural Network (RNN) architectures, such as LSTM and
GRU, have historically held prominence in time series tasks. However, they have
recently seen a decline in their dominant position across various time series
tasks. As a result, recent advancements in time series forecasting have seen a
notable shift away from RNNs towards alternative architectures such as
Transformers, MLPs, and CNNs. To go beyond the limitations of traditional RNNs,
we design an efficient RNN-based model for time series tasks, named RWKV-TS,
with three distinctive features: (i) A novel RNN architecture characterized by
$O(L)$ time complexity and memory usage. (ii) An enhanced ability to capture
long-term sequence information compared to traditional RNNs. (iii) High
computational efficiency coupled with the capacity to scale up effectively.
Through extensive experimentation, our proposed RWKV-TS model demonstrates
competitive performance when compared to state-of-the-art Transformer-based or
CNN-based models. Notably, RWKV-TS exhibits not only comparable performance but
also demonstrates reduced latency and memory utilization. The success of
RWKV-TS encourages further exploration and innovation in leveraging RNN-based
approaches within the domain of Time Series. The combination of competitive
performance, low latency, and efficient memory usage positions RWKV-TS as a
promising avenue for future research in time series tasks. Code is available
at:\href{https://github.com/howard-hou/RWKV-TS}{
https://github.com/howard-hou/RWKV-TS}


  In the United States the preferred method of obtaining dietary intake data is
the 24-hour dietary recall, yet the measure of most interest is usual or
long-term average daily intake, which is impossible to measure. Thus, usual
dietary intake is assessed with considerable measurement error. Also, diet
represents numerous foods, nutrients and other components, each of which have
distinctive attributes. Sometimes, it is useful to examine intake of these
components separately, but increasingly nutritionists are interested in
exploring them collectively to capture overall dietary patterns. Consumption of
these components varies widely: some are consumed daily by almost everyone on
every day, while others are episodically consumed so that 24-hour recall data
are zero-inflated. In addition, they are often correlated with each other.
Finally, it is often preferable to analyze the amount of a dietary component
relative to the amount of energy (calories) in a diet because dietary
recommendations often vary with energy level. The quest to understand overall
dietary patterns of usual intake has to this point reached a standstill. There
are no statistical methods or models available to model such complex
multivariate data with its measurement error and zero inflation. This paper
proposes the first such model, and it proposes the first workable solution to
fit such a model. After describing the model, we use survey-weighted MCMC
computations to fit the model, with uncertainty estimation coming from balanced
repeated replication.


  We show that the spin pumping from ferromagnetic insulator into the adjacent
metallic spin sink can be strongly stimulated by the superconducting
correlations.
  The key physical mechanism responsible for this effect is the presence of
quasiparticle surface states at the ferromagnetic insulator/superconductor
interface. We consider the minimal model when these states appear because of
the suppressed pairing constant within the interfacial normal layer. For thin
normal layers we obtain a strongly peaked temperature dependence of the Gilbert
damping coefficient which has been recently observed in such systems. For
thicker normal layers the Gilbert damping monotonically increases down to the
temperatures much smaller than the critical one. The suggested model paves the
way to controlling the temperature dependence of the spin pumping by
fabricating hybrid normal metal/superconductor spin sinks.


  We investigate the statistical properties of cosmic baryon fluid in the
nonlinear regime, which is crucial for understanding the large-scale structure
formation of the universe. With the hydrodynamic simulation sample of the
Universe in the cold dark matter model with a cosmological constant, we show
that the intermittency of the velocity field of cosmic baryon fluid at redshift
z=0 in the scale range from the Jeans length to about 16 Mpc/h can be extremely
well described by She-Leveque's universal scaling formula. The baryon fluid
also possesses the following features: (1) for volume weight statistics, the
dissipative structures are dominated by sheets, and (2) the relation between
the intensities of fluctuations is hierarchical. These results imply that the
evolution of highly evolved cosmic baryon fluid is similar to a fully developed
turbulence.


  Exploring and navigating in extreme environments, such as caves, oceans, and
planetary bodies, are often too hazardous for humans, and as such, robots are
possible surrogates. These robots are met with significant locomotion
challenges that require traversing a wide range of surface roughnesses and
topologies. Previous locomotion strategies, involving wheels or ambulatory
motion, such as snake platforms, have success on specific surfaces but fail in
others which could be detrimental in exploration and navigation missions. In
this paper, we present a novel approach that combines snake-like robots with an
Archimedean screw locomotion mechanism to provide multiple, effective mobility
strategies in a large range of environments, including those that are difficult
to traverse for wheeled and ambulatory robots. This work develops a robotic
system called ARCSnake to demonstrate this locomotion principle and tested it
in a variety of different terrains and environments in order to prove its
controllable, multi-domain, navigation capabilities. These tests show a wide
breadth of scenarios that ARCSnake can handle, hence demonstrating its ability
to traverse through extreme terrains.


  The Fedosov deformation quantization of the symplectic manifold is determined
by a 1-form differential r. We identify a class of r for which the $\star$
product becomes the Moyal product by taking appropriate Darboux coordinates,
but invariant by canonically transforming the coordinates. This respect of the
$\star$ product is explained by studying the fuzzy algebrae of the Kaehler
coset space.


  The phase transition of confined fluids in mesoporous materials deviates from
that of bulk fluids due to the interactions with the surrounding heterogeneous
structure. For example, adsorbed fluids in metal-organic-frameworks (MOFs) have
atypical phase characteristics such as capillary condensation and higher-order
phase transitions due to a strong heterogeneous field. Considering a many-body
problem in the presence of a nonuniform external field, we model the host-guest
and guest-guest interactions in MOFs. To solve the three-dimensional Ising
model, we use the mean-field theory to approximate the guest-guest interactions
and Mayer's f-functions to describe the host-guest interactions in a unit cell.
Later, using Hill's theory of nanothermodynamics, we define differential
thermodynamic functions to understand the distribution of intensive properties
and integral thermodynamic functions to explain the phase transition in
confined fluids. The investigation reveals a distinct behavior where fluids
confined in larger pores undergo a discontinuous (first-order) phase
transition, whereas those confined in smaller pores experience a continuous
(higher-order) phase transition. Furthermore, the results indicate that the
free-energy barrier for phase transitions is lower in confined fluids than in
bulk fluids giving rise to a lower condensation pressure relative to the bulk
saturation pressure. Finally, the integral thermodynamic functions are
succinctly presented in the form of a phase diagram, marking an initial step
toward a more practical approach for understanding the phase behavior of
confined fluids.


  Using very general arguments, we prove that any entanglement measures based
on distance must be maximal on pure states. Furthermore, we show that Bures
measure of entanglement and geometric measure of entanglement satisfy the
monogamy inequality on all pure multiqubit states. Finally, using the power of
Bures measure of entanglement and geometric measure of entanglement, we present
a class of tight monogamy relations for pure states of multiqubit systems.


  Two prerequisites for robotic multiagent systems are mobility and
communication. Fast multipole networks (FMNs) enable both ends within a unified
framework. FMNs can be organized very efficiently in a distributed way from
local information and are ideally suited for motion planning using artificial
potentials. We compare FMNs to conventional communication topologies, and find
that FMNs offer competitive communication performance (including higher network
efficiency per edge at marginal energy cost) in addition to advantages for
mobility.


  We consider the induced subgraph of the corresponding Grassmann graph formed
by $q$-ary simplex codes of dimension $2$, $q\ge 5$. This graph contains
precisely two types of maximal cliques. If $q=5$, then for any two maximal
cliques of the same type there is a monomial linear automorphism transferring
one of them to the other. Examples concerning the cases $q=7,11$ finish the
note.


  We give an overview of the theory of framed correspondences in motivic
homotopy theory. Motivic spaces with framed transfers are the analogue in
motivic homotopy theory of $E_{\infty}$-spaces in classical homotopy theory,
and in particular they provide an algebraic description of infinite
$\mathbb{P}^1$-loop spaces. We will discuss the foundations of the theory
(following Voevodsky, Garkusha, Panin, Ananyevskiy, and Neshitov), some
applications such as the computations of the infinite loop spaces of the
motivic sphere and of algebraic cobordism (following Elmanto, Hoyois, Khan,
Sosnilo, and Yakerson), and some open problems.


  We consider the fusion of two aerodynamic data sets originating from
differing fidelity physical or computer experiments. We specifically address
the fusion of: 1) noisy and in-complete fields from wind tunnel measurements
and 2) deterministic but biased fields from numerical simulations. These two
data sources are fused in order to estimate the \emph{true} field that best
matches measured quantities that serves as the ground truth. For example, two
sources of pressure fields about an aircraft are fused based on measured forces
and moments from a wind-tunnel experiment. A fundamental challenge in this
problem is that the true field is unknown and can not be estimated with 100\%
certainty. We employ a Bayesian framework to infer the true fields conditioned
on measured quantities of interest; essentially we perform a \emph{statistical
correction} to the data. The fused data may then be used to construct more
accurate surrogate models suitable for early stages of aerospace design. We
also introduce an extension of the Proper Orthogonal Decomposition with
constraints to solve the same problem. Both methods are demonstrated on fusing
the pressure distributions for flow past the RAE2822 airfoil and the Common
Research Model wing at transonic conditions. Comparison of both methods reveal
that the Bayesian method is more robust when data is scarce while capable of
also accounting for uncertainties in the data. Furthermore, given adequate
data, the POD based and Bayesian approaches lead to \emph{similar} results.


  By high temperature series expansion and exact complete diagonalization the
magnetic susceptibility chi(T) and the specific heat C(T) of a two-leg S=1/2
ladder with cyclic (4-spin) exchange are computed. Both methods yield
convincing results for not too small temperatures. We find that a small amount
of cyclic exchange influences the thermodynamical properties significantly. Our
results can serve as reliable basis for an efficient analysis of experimental
data


  This paper presents a parameter-lite transfer learning approach of pretrained
language models (LM) for knowledge graph (KG) completion. Instead of
finetuning, which modifies all LM parameters, we only tune a few new parameters
while keeping the original LM parameters fixed. We establish this via
reformulating KG completion as a "fill-in-the-blank" task, and introducing a
parameter-lite encoder on top of the original LMs. We show that, by tuning far
fewer parameters than finetuning, LMs transfer non-trivially to most tasks and
reach competitiveness with prior state-of-the-art approaches. For instance, we
outperform the fully finetuning approaches on a KG completion benchmark by
tuning only 1% of the parameters. The code and datasets are available at
\url{https://github.com/yuanyehome/PALT}.


  Ranking is used for a wide array of problems, most notably information
retrieval (search). There are a number of popular approaches to the evaluation
of ranking such as Kendall's $\tau$, Average Precision, and nDCG. When dealing
with problems such as user ranking or recommendation systems, all these
measures suffer from various problems, including an inability to deal with
elements of the same rank, inconsistent and ambiguous lower bound scores, and
an inappropriate cost function. We propose a new measure, rankDCG, that
addresses these problems. This is a modification of the popular nDCG algorithm.
We provide a number of criteria for any effective ranking algorithm and show
that only rankDCG satisfies all of them. Results are presented on constructed
and real data sets. We release a publicly available rankDCG evaluation package.


  We investigate numerically the time dependence of the multiple quantum
coherences and entanglement in linear chains up to nine nuclear spins of 1/2
coupled by the dipole-dipole interactions. Two models are considered: (1) a
spin chain with nearest-neighbor dipole -dipole interactions; (2) a more
realistic model with interactions between all spins. It is shown that the
entangled states appear between remote particles which do not interact directly
(model 1), while the interaction between all spins (model 2) not always results
in entanglement between remote spins.


  In this work, we propose ReStoCNet, a residual stochastic multilayer
convolutional Spiking Neural Network (SNN) composed of binary kernels, to
reduce the synaptic memory footprint and enhance the computational efficiency
of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input
layer followed by stacked convolutional layers for hierarchical input feature
extraction, pooling layers for dimensionality reduction, and fully-connected
layer for inference. In addition, we introduce residual connections between the
stacked convolutional layers to improve the hierarchical feature learning
capability of deep SNNs. We propose Spike Timing Dependent Plasticity (STDP)
based probabilistic learning algorithm, referred to as Hybrid-STDP (HB-STDP),
incorporating Hebbian and anti-Hebbian learning mechanisms, to train the binary
kernels forming ReStoCNet in a layer-wise unsupervised manner. We demonstrate
the efficacy of ReStoCNet and the presented HB-STDP based unsupervised training
methodology on the MNIST and CIFAR-10 datasets. We show that residual
connections enable the deeper convolutional layers to self-learn useful
high-level input features and mitigate the accuracy loss observed in deep SNNs
devoid of residual connections. The proposed ReStoCNet offers >20x kernel
memory compression compared to full-precision (32-bit) SNN while yielding high
enough classification accuracy on the chosen pattern recognition tasks.


  An interpretation of AGASA (Akeno Giant Air Shower Array) data by comparing
the experimental results with the simulated ones by CORSIKA (COsmic Ray
SImulation for KASCADE) has been made. General features of the electromagnetic
component and low energy muons observed by AGASA can be well reproduced by
CORSIKA. The form of the lateral distribution of charged particles agrees well
with the experimental one between a few hundred metres and 2000 m from the
core, irrespective of the hadronic interaction model studied and the primary
composition (proton or iron). It does not depend on the primary energy between
10^17.5 and 10^20 eV as the experiment shows. If we evaluate the particle
density measured by scintillators of 5 cm thickness at 600 m from the core
(S_0(600), suffix 0 denotes the vertically incident shower) by taking into
account the similar conditions as in the experiment, the conversion relation
from S_0(600) to the primary energy is expressed as E [eV] = 2.15 x 10^17 x
S_0(600)^1.015, within 10% uncertainty among the models and composition used,
which suggests the present AGASA conversion factor is the lower limit. Though
the form of the muon lateral distribution fits well to the experiment within
1000 m from the core, the absolute values change with hadronic interaction
model and primary composition. The slope of the rho_mu(600) (muon density above
1 GeV at 600 m from the core) vs. S_0(600) relation in experiment is flatter
than that in simulation of any hadronic model and primary composition. Since
the experimental slope is constant from 10^15 eV to 10^19 eV, we need to study
this relation in a wide primary energy range to infer the rate of change of
chemical composition with energy. keywords: cosmic ray, extensive air shower,
simulation, primary energy estimation PACS number ; 96.40.De, 96.40.Pq


  In recent years, the growing ubiquity of Internet memes on social media
platforms, such as Facebook, Instagram, and Twitter, has become a topic of
immense interest. However, the classification and recognition of memes is much
more complicated than that of social text since it involves visual cues and
language understanding. To address this issue, this paper proposed a
parallel-channel model to process the textual and visual information in memes
and then analyze the sentiment polarity of memes. In the shared task of
identifying and categorizing memes, we preprocess the dataset according to the
language behaviors on social media. Then, we adapt and fine-tune the
Bidirectional Encoder Representations from Transformers (BERT), and two types
of convolutional neural network models (CNNs) were used to extract the features
from the pictures. We applied an ensemble model that combined the BiLSTM,
BIGRU, and Attention models to perform cross domain suggestion mining. The
officially released results show that our system performs better than the
baseline algorithm. Our team won nineteenth place in subtask A (Sentiment
Classification). The code of this paper is availabled at :
https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis.


  Complementarity is one of the main features of quantum physics that radically
departs from classical notions. Here we consider the limitations that this
principle imposes due to the unpredictability of measurement outcomes of
incompatible observables. For two-level systems, it is shown that any
preparation violating complementarity enables the preparation of a
non-signalling box violating Tsirelson's bound. Moreover, these
"beyond-quantum" objects could be used to distinguish a plethora of
non-orthogonal quantum states and hence enable improved cloning protocols. For
higher-dimensional systems the main ideas are sketched.


  In this article, I discuss how the AI community views concerns about the
emergence of superintelligent AI and related philosophical issues.


  We compute the Hausdorff dimension of any random statistically self-affine
Sierpinski sponge $K\subset \mathbb{R}^k$ ($k\ge 2$) obtained by using some
percolation process in $[0,1]^k$. To do so, we first exhibit a Ledrappier-Young
type formula for the Hausdorff dimensions of statistically self-affine measures
supported on $K$. This formula presents a new feature compared to its
deterministic or random dynamical version. Then, we establish a variational
principle expressing $\dim_H K$ as the supremum of the Hausdorff dimensions of
statistically self-affine measures supported on $K$, and show that the supremum
is uniquely attained. The value of $\dim_H K$ is also expressed in terms of the
weighted pressure function of some deterministic potential. As a by-product,
when $k=2$, we give an alternative approach to the Hausdorff dimension of $K$,
which was first obtained by Gatzouras and Lalley \cite{GL94}. The value of the
box counting dimension of $K$ and its equality with $\dim_H K$ are also
studied. We also obtain a variational formula for the Hausdorff dimensions of
some orthogonal projections of $K$, and for statistically self-affine measures
supported on~$K$, we establish a dimension conservation property through these
projections.


  While the sample of confirmed exoplanets continues to increase, the
population of transiting exoplanets around early-type stars is still limited.
These planets allow us to investigate the planet properties and formation
pathways over a wide range of stellar masses and study the impact of high
irradiation on hot Jupiters orbiting such stars. We report the discovery of
TOI-615b, TOI-622b, and TOI-2641b, three Saturn-mass planets transiting main
sequence, F-type stars. The planets were identified by the Transiting Exoplanet
Survey Satellite (TESS) and confirmed with complementary ground-based and
radial velocity observations. TOI-615b is a highly irradiated ($\sim$1277
$F_{\oplus}$) and bloated Saturn-mass planet (1.69$^{+0.05}_{-0.06}$$R_{Jup}$
and 0.43$^{+0.09}_{-0.08}$$M_{Jup}$) in a 4.66 day orbit transiting a 6850 K
star. TOI-622b has a radius of 0.82$^{+0.03}_{-0.03}$$R_{Jup}$ and a mass of
0.30$^{+0.07}_{-0.08}$~$M_{Jup}$ in a 6.40 day orbit. Despite its high
insolation flux ($\sim$600 $F_{\oplus}$), TOI-622b does not show any evidence
of radius inflation. TOI-2641b is a 0.39$^{+0.02}_{-0.04}$$M_{Jup}$ planet in a
4.88 day orbit with a grazing transit (b = 1.04$^{+0.05}_{-0.06 }$) that
results in a poorly constrained radius of 1.61$^{+0.46}_{-0.64}$$R_{Jup}$.
Additionally, TOI-615b is considered attractive for atmospheric studies via
transmission spectroscopy with ground-based spectrographs and $\textit{JWST}$.
Future atmospheric and spin-orbit alignment observations are essential since
they can provide information on the atmospheric composition, formation and
migration of exoplanets across various stellar types.


  This paper develops new theory and algorithms for 1D general mode
decompositions. First, we introduce the 1D synchrosqueezed wave packet
transform and prove that it is able to estimate the instantaneous information
of well-separated modes from their superposition accurately. The
synchrosqueezed wave packet transform has a better resolution than the
synchrosqueezed wavelet transform in the time-frequency domain for separating
high frequency modes. Second, we present a new approach based on
diffeomorphisms for the spectral analysis of general shape functions. These two
methods lead to a framework for general mode decompositions under a weak
well-separation condition and a well different condition. Numerical examples of
synthetic and real data are provided to demonstrate the fruitful applications
of these methods.