  The expectation value of Wilson loop operators in three-dimensional SO(N)
Chern-Simons gauge theory gives a known knot invariant: the Kauffman
polynomial. Here this result is derived, at the first order, via a simple
variational method. With the same procedure the skein relation for Sp(N) are
also obtained. Jones polynomial arises as special cases: Sp(2), SO(-2) and
SL(2,R). These results are confirmed and extended up to the second order, by
means of perturbation theory, which moreover let us establish a duality
relation between SO(+/-N) and Sp(-/+N) invariants. A correspondence between the
firsts orders in perturbation theory of SO(-2), Sp(2) or SU(2) Chern-Simons
quantum holonomies and the partition function of the Q=4 Potts Model is built.


  Video Visual Relation Detection (VidVRD), has received significant attention
of our community over recent years. In this paper, we apply the
state-of-the-art video object tracklet detection pipeline MEGA and deepSORT to
generate tracklet proposals. Then we perform VidVRD in a tracklet-based manner
without any pre-cutting operations. Specifically, we design a tracklet-based
visual Transformer. It contains a temporal-aware decoder which performs feature
interactions between the tracklets and learnable predicate query embeddings,
and finally predicts the relations. Experimental results strongly demonstrate
the superiority of our method, which outperforms other methods by a large
margin on the Video Relation Understanding (VRU) Grand Challenge in ACM
Multimedia 2021. Codes are released at
https://github.com/Dawn-LX/VidVRD-tracklets.


  Particle flows of high particle concentration are important in many fields,
including chemical processing, pharmaceutical processing, energy conversion and
powder transport. However, despite decades of research and industrial
application, the real time behavior of particle flow fields is still not well
understood. One of the main reasons is that experimental data is difficult to
acquire in such harsh, opaque particle flow environments. In this educational
video for the Gallery of Fluid Motion, high speed video acquired with a new,
patented high speed particle imaging velocimetry (high speed PIV) technology is
presented. This technology was developed by the USDOE National Energy
Technology Laboratory (NETL). It is being applied to observe and measure the
real time behavior of individual particle motion inside particle flow fields of
high particle concentration for the first time. The high speed PIV system
records high speed videos of particle motion with excellent spatial and
temporal clarity. The high speed videos are analyzed to measure the
concentration and the two-dimensional motion (velocity and trajectory) of
individual particles. Data sample rates for velocity vectors are in the range
of 0.1 to 3 million vectors per second, thereby providing full resolution of
the temporal domain of particle velocity. To see and measure particle motion
inside the flow fields at high particle concentrations, a custom borescope is
inserted into particle flow fields.
  The particle tracking technique can measure gas and fluid flow if the
particles are small enough (have a low enough Stokes number) to follow the
gas/fluid flow. In areas of rapid mixing, such as turbulent wakes, particle
tracking can provide better spatial resolution that conventional
cross-correlation based PIV analysis.
  Keywords: fluid dynamics video, particle tracking, fluidization


  We derive the equations of motion for the dynamics of a porous media filled
with an incompressible fluid. We use a variational approach with a Lagrangian
written as the sum of terms representing the kinetic and potential energy of
the elastic matrix, and the kinetic energy of the fluid, coupled through the
constraint of incompressibility. As an illustration of the method, the
equations of motion for both the elastic matrix and the fluid are derived in
the spatial (Eulerian) frame. Such an approach is of relevance e.g. for
biological problems, such as sponges in water, where the elastic porous media
is highly flexible and the motion of the fluid has a 'primary' role in the
motion of the whole system. We then analyze the linearized equations of motion
describing the propagation of waves through the media. In particular, we derive
the propagation of S-waves and P-waves in an isotropic media. We also analyze
the stability criteria for the wave equations and show that they are equivalent
to the physicality conditions of the elastic matrix. Finally, we show that the
celebrated Biot's equations for waves in porous media are obtained for certain
values of parameters in our models.


  Given a countably infinite group $G$ acting on some space $X$, an increasing
family of finite subsets $G_n$ and $x\in X$, a natural question to ask is what
asymptotical distribution the sets $G_nx$ form. More formally, we define for a
function $f$ over $X$ the sums $S_n(f,x)=\sum_{g\in G_n}f(gx)$ and ask whether
exists a function $\Psi(n):\mathbb{N}\to\mathbb{R}$ such that the sequence
$\Psi(n)S_n(f,x)$ converges. This is a delicate problem that was studied under
various settings. We first show a full solution when elements are chosen using
a carefully chosen word metric from a specific lattice in $SL(2,\mathbb{Z})$
acting on the circle. In addition, it is proven that the resulting measure is
stationary with respect to a certain random walk and has a tight connection to
a well studied function from the field of Diophantine approximations. We then
proceed to study the asymptotic distribution problem when elements are chosen
using a random walk over $SL(2,\mathbb{R})$ acting on $\mathbb{R}^2$. We offer
a variant of our initial problem which yields some surprising and interesting
results.


  Understanding the complexity of sampling from a strongly log-concave and
log-smooth distribution $\pi$ on $\mathbb{R}^d$ to high accuracy is a
fundamental problem, both from a practical and theoretical standpoint. In
practice, high-accuracy samplers such as the classical Metropolis-adjusted
Langevin algorithm (MALA) remain the de facto gold standard; and in theory, via
the proximal sampler reduction, it is understood that such samplers are key for
sampling even beyond log-concavity (in particular, for distributions satisfying
isoperimetric assumptions).
  In this work, we improve the dimension dependence of this sampling problem to
$\tilde{O}(d^{1/2})$, whereas the previous best result for MALA was
$\tilde{O}(d)$. This closes the long line of work on the complexity of MALA,
and moreover leads to state-of-the-art guarantees for high-accuracy sampling
under strong log-concavity and beyond (thanks to the aforementioned reduction).
  Our starting point is that the complexity of MALA improves to
$\tilde{O}(d^{1/2})$, but only under a warm start (an initialization with
constant R\'enyi divergence w.r.t. $\pi$). Previous algorithms took much longer
to find a warm start than to use it, and closing this gap has remained an
important open problem in the field. Our main technical contribution settles
this problem by establishing the first $\tilde{O}(d^{1/2})$ R\'enyi mixing
rates for the discretized underdamped Langevin diffusion. For this, we develop
new differential-privacy-inspired techniques based on R\'enyi divergences with
Orlicz--Wasserstein shifts, which allow us to sidestep longstanding challenges
for proving fast convergence of hypocoercive differential equations.


  We propose a regular way to construct lattice versions of $W$-algebras, both
for quantum and classical cases. In the classical case we write the algebra
explicitly and derive the lattice analogue of Boussinesq equation from the
Hamiltonian equations of motion. Connection between the lattice
Faddeev-Takhtadjan-Volkov algebra [1] and q-deformed Virasoro is also
discussed.


  We investigate the conditions that are sufficient to make the Ext-algebra of
an object in a (triangulated) category into a Frobenius algebra and compute the
corresponding Nakayama automorphism. As an application, we prove the conjecture
that hdet($\mu_A$) = 1 for any noetherian Artin-Schelter regular (hence skew
Calabi-Yau) algebra A.


  In this note we give a summary of some elementary results in the theory of
super Riemann surfaces (SUSY curves).


  I discuss some connotations of mathematical notion of "truth" in the context
of humanistic discourse


  In addition to astro-meteorological parameters, such as seeing, coherence
time and isoplanatic angle, the vertical profile of the Earth's atmospheric
turbulence strength and velocity is important for instrument design,
performance validation and monitoring, and observation scheduling and
management. Here we compare these astro-meteorological parameters as well as
the vertical profile itself from a forecast model based on a General
Circulation Model from the European Centre for Median range Weather Forecasts
and the stereo-SCIDAR, a high-sensitivity turbulence profiling instrument in
regular operation at Paranal, Chile. The model is fast to process as no spatial
nesting or data manipulation is performed. This speed enables the model to be
reactive based on the most up to date forecasts. We find that the model is
statistically consistent with measurements from stereo-SCIDAR. The correlation
of the median turbulence profile from the model and the measurement is 0.98. We
also find that the distributions of astro-meteorological parameters are
consistent. We compare contemporaneous measurements and show that the free
atmosphere seeing, isoplanatic angle and coherence time have correlation values
of 0.64, 0.40 and 0.63 respectively. We show and compare the profile sequences
from a large number of trial nights. We see that the model is able to forecast
the evolution of dominating features. In addition to smart scheduling, ensuring
that the most sensitive astronomical observations are scheduled for the optimum
time, this model could enable remote site characterisation using a large
archive of weather forecasts and could be used to optimise the performance of
wide-field AO system.


  We consider a cloud of fermionic atoms in an optical lattice described by a
Hubbard model with an additional linear potential. While homogeneous
interacting systems mainly show damped Bloch oscillations and heating, a finite
cloud behaves differently: It expands symmetrically such that gains of
potential energy at the top are compensated by losses at the bottom.
Interactions stabilize the necessary heat currents by inducing gradients of the
inverse temperature 1/T, with T<0 at the bottom of the cloud. An analytic
solution of hydrodynamic equations shows that the width of the cloud increases
with t^(1/3) for long times consistent with results from our Boltzmann
simulations.


  We have performed a first-principles density functional theory investigation
of the penetration of helium atoms through a graphene monolayer with defects.
The relaxation of the graphene layer caused by the incoming helium atoms does
not have a strong influence on the height of the energy barriers for
penetration. For defective graphene layers, the penetration barriers decrease
exponentially with the size of the defects but they are still sufficiently high
that very large defects are needed to make the graphene sheet permeable for
small atoms and molecules. This makes graphene a very promising material for
the construction of nanocages and nanomembranes.


  This paper considers the recovery of group sparse signals over a multi-agent
network, where the measurements are subject to sparse errors. We first
investigate the robust group LASSO model and its centralized algorithm based on
the alternating direction method of multipliers (ADMM), which requires a
central fusion center to compute a global row-support detector. To implement it
in a decentralized network environment, we then adopt dynamic average consensus
strategies that enable dynamic tracking of the global row-support detector.
Numerical experiments demonstrate the effectiveness of the proposed algorithms.


  We analyze a sample of 3 million quantum-correlated D0 D0bar pairs from 818
pb^-1 of e+e- collision data collected with the CLEO-c detector at E_cm = 3.77
GeV, to give an updated measurement of \cos\delta and a first determination of
\sin\delta, where \delta is the relative strong phase between doubly
Cabibbo-suppressed D0 --> K+pi- and Cabibbo-favored D0bar --> K+pi- decay
amplitudes. With no inputs from other experiments, we find \cos\delta = 0.81
+0.22+0.07 -0.18-0.05, \sin\delta = -0.01 +- 0.41 +- 0.04, and |\delta| = 10
+28+13 -53-0 degrees. By including external measurements of mixing parameters,
we find alternative values of \cos\delta = 1.15 +0.19+0.00 -0.17-0.08,
\sin\delta = 0.56 +0.32+0.21 -0.31-0.20, and \delta = (18 +11-17) degrees. Our
results can be used to improve the world average uncertainty on the mixing
parameter y by approximately 10%.


  The challenging High Luminosity LHC (HL-LHC) beam requirements will lead in
the future to unprecedented beam parameters along the LHC injector chain. In
the SPS accelerator these requests translate into about a factor two higher
intensity and brightness than the present design performance. In addition to
the challenge of producing and accelerating such beams, these parameters affect
the resistance of the existing equipment against beam impact. Most of the
protection devices in the SPS ring, its transfer lines and the LHC injection
areas will be put under operational constraints which are beyond their design
specification. The equipment concerned has been reviewed and their resistance
to the HL-LHC beams checked. Theoretical and simulation studies have been
performed for the SPS beam scraping system, the protection devices and the dump
absorbers of the SPS-to-LHC transfer lines, as well as for the LHC injection
protection devices. The first results of these studies are reported, together
with the future prospects.


  The classic Cluster Editing problem (also known as Correlation Clustering)
asks to transform a given graph into a disjoint union of cliques (clusters) by
a small number of edge modifications. When applied to vertex-colored graphs
(the colors representing subgroups), standard algorithms for the NP-hard
Cluster Editing problem may yield solutions that are biased towards subgroups
of data (e.g., demographic groups), measured in the number of modifications
incident to the members of the subgroups. We propose a modification fairness
constraint which ensures that the number of edits incident to each subgroup is
proportional to its size. To start with, we study Modification-Fair Cluster
Editing for graphs with two vertex colors. We show that the problem is NP-hard
even if one may only insert edges within a subgroup; note that in the classic
"non-fair" setting, this case is trivially polynomial-time solvable. However,
in the more general editing form, the modification-fair variant remains
fixed-parameter tractable with respect to the number of edge edits. We
complement these and further theoretical results with an empirical analysis of
our model on real-world social networks where we find that the price of
modification-fairness is surprisingly low, that is, the cost of optimal
modification-fair solutions differs from the cost of optimal "non-fair"
solutions only by a small percentage.


  We report two novel determinations of |Vub| with reduced model dependence,
based on measurements of the mass distribution of the hadronic system in
semileptonic B decays. Events are selected by fully reconstructing the decay of
one B meson and identifying a charged lepton from the decay of the other B
meson from Y(4S) to BBbar events. In one approach, we combine the inclusive B
to X_u l nu rate, integrated up to a maximum hadronic mass m_X < 1.67GeV, with
a measurement of the inclusive B to X_s gamma photon energy spectrum. We obtain
|Vub| = (4.43 +- 0.38stat +- 0.25syst +- 0.29theo)*10^-3. In another approach
we measure the total B to X_u l nu rate over the full phase space and find
|Vub| = (3.84 +- 0.70stat +- 0.30syst +- 0.10theo)*10^-3.


  We investigate escape of cosmic ray (CR) electrons from a supernova remnant
(SNR) to interstellar space. We show that CR electrons escape in order from
high energies to low energies like CR nuclei, while the escape starts later
than the beginning of the Sedov phase at an SNR age of 10^3 - 7*10^3 yrs and
the maximum energy of runaway CR electrons is below the knee about 0.3 - 50 TeV
because unlike CR nuclei, CR electrons lose their energy due to synchrotron
radiation. Highest energy CR electrons will be directly probed by AMS-02,
CALET, CTA and LHAASO experiments, or have been already detected by H.E.S.S.
and MAGIC as a cutoff in the CR electron spectrum. Furthermore, we also
calculate the spatial distribution of runaway CR electrons and their radiation
spectra around SNRs. Contrary to common belief, maximum-energy photons of
synchrotron radiation around 1 keV are emitted by runaway CR electrons which
have been caught up by the shock. Inverse Compton scattering by runaway CR
electrons can dominate the gamma-ray emission from runaway CR nuclei via pion
decay, and both are detectable by CTA and LHAASO as clues to the CR origin and
the amplification of magnetic fluctuations around the SNR. We also discuss
middle-aged and/or old SNRs as unidentified very-high-energy gamma-ray sources.


  We describe a fully-automatic 3D-segmentation technique for brain MR images.
Using Markov random fields the segmentation algorithm captures three important
MR features, i.e. non-parametric distributions of tissue intensities,
neighborhood correlations and signal inhomogeneities. Detailed simulations and
real MR images demonstrate the performance of the segmentation algorithm. The
impact of noise, inhomogeneity, smoothing and structure thickness is analyzed
quantitatively. Even single echo MR images are well classified into gray
matter, white matter, cerebrospinal fluid, scalp-bone and background. A
simulated annealing and an iterated conditional modes implementation are
presented.
  Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fields


  We detect 20 $z=7.0$ Ly$\alpha$ emitter (LAE) candidates to $L({\rm
Ly}\alpha) \geq 2 \times 10^{42}$ erg s$^{-1}$ or $0.3$ $L^*_{z=7}$ and in
$6.1\times 10^5$ Mpc$^3$ volume in the Subaru Deep Field and the
Subaru/XMM-Newton Deep Survey field by 82 and 37 hours of Subaru Suprime-Cam
narrowband NB973 and reddest optical $y$-band imaging. We compare their
Ly$\alpha$ and UV luminosity functions (LFs) and densities and Ly$\alpha$
equivalent widths (EWs) to those of $z=5.7$, 6.6 and 7.3 LAEs from previous
Suprime-Cam surveys. The Ly$\alpha$ LF (density) rapidly declines by a factor
of $\times$1.5 (1.9) in $L({\rm Ly}\alpha)$ at $z=5.7-6.6$ (160 Myr),
$\times$1.5 (1.6) at $z=6.6-7.0$ (60 Myr) at the faint end and $\times$2.0
(3.8) at $z=7.0-7.3$ (40 Myr). Also, in addition to the systematic decrease in
EW at $z=5.7-6.6$ previously found, 2/3 of the $z=7.0$ LAEs detected in the UV
continuum exhibit lower EWs than the $z=6.6$ ones. Moreover, while the UV LF
and density do not evolve at $z=5.7-6.6$, they modestly decline at $z=6.6-7.0$,
implying galaxy evolution contributing to the decline of the Ly$\alpha$ LF.
Comparison of the $z=7.0$ Ly$\alpha$ LF to the one predicted by an LAE
evolution model further reveals that galaxy evolution alone cannot explain all
the decline of Ly$\alpha$ LF. If we attribute the discrepancy to Ly$\alpha$
attenuation by neutral hydrogen, the intergalactic medium transmission of
Ly$\alpha$ photons at $z=7.0$ would be $T_{{\rm Ly}\alpha}^{\rm IGM} \leq
0.6-0.7$. It is lower (higher) than $T_{{\rm Ly}\alpha}^{\rm IGM}$ at $z=6.6$
(7.3) derived by previous studies, suggesting rapid increase in neutral
fraction at $z > 6$.


  Software interfaces today generally fall at either end of a spectrum. On one
end are programmable systems, which allow expert users (i.e. programmers) to
write software artifacts that describe complex abstractions, but programs are
disconnected from their eventual output. On the other end are domain-specific
graphical user interfaces (GUIs), which allow end users (i.e. non-programmers)
to easily create varied content but present insurmountable walls when a desired
feature is not built-in. Both programmatic and direct manipulation have
distinct strengths, but users must typically choose one over the other or use
some ad-hoc combination of systems. Our goal, put simply, is to bridge this
divide.
  We envision novel software systems that tightly couple programmatic and
direct manipulation --- a combination we dub prodirect manipulation --- for a
variety of use cases. This will require advances in a broad range of software
engineering disciplines, from program analysis and program synthesis technology
to user interface design and evaluation. In this extended abstract, we propose
two general strategies --- real-time program synthesis and domain-specific
synthesis of general-purpose programs --- that may prove fruitful for
overcoming the technical challenges. We also discuss metrics that will be
important in evaluating the usability and utility of prodirect manipulation
systems.


  A position-sensitive, high-resolution time-of-flight detector for fission
fragments has been developed. The SPectrometer for Ion DEterminiation in
fission Research (SPIDER) is a $2E-2v$ spectrometer designed to measure the
mass of light fission fragments to a single mass unit. The time pick-off
detector pairs to be used in SPIDER have been tested with $\alpha$-particles
from $^{229}$Th and its decay chain and $\alpha$-particles and spontaneous
fission fragments from $^{252}$Cf. Each detector module is comprised of a thin
electron conversion foil, electrostatic mirror, microchannel plates, and
delay-line anodes. Particle trajectories on the order of 700 mm are determined
accurately to within 0.7 mm. Flight times on the order of 70 ns were measured
with 200 ps resolution FWHM. Computed particle velocities are accurate to
within 0.06 mm/ns corresponding to precision of 0.5%. An ionization chamber
capable of 400 keV energy resolution coupled with the velocity measurements
described here will pave the way for modestly efficient measurements of light
fission fragments with unit mass resolution.


  We talk about the right way to order amplitudes in a wavefunction, and the
right way to interpret operator matrices in quantum computation.


  We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols
which establish distributed ledger-based markets for loanable funds. PLFs are
emerging as one of the main applications within Decentralized Finance (DeFi),
and use smart contract code to facilitate the intermediation of loanable funds.
In doing so, these protocols allow agents to borrow and save programmatically.
Within these protocols, interest rate mechanisms seek to equilibrate the supply
and demand for funds. In this paper, we review the methodologies used to set
interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We
provide an empirical examination of how these interest rate rules have behaved
since their inception in response to differing degrees of liquidity. We then
investigate the market efficiency and inter-connectedness between multiple
protocols, examining first whether Uncovered Interest Parity holds within a
particular protocol and second whether the interest rates for a particular
token market show dependence across protocols, developing a Vector Error
Correction Model for the dynamics.


  We introduce a minimalistic notion of semiclassical quantization and use it
to prove that the convex hull of the semiclassical spectrum of a quantum system
given by a collection of commuting operators converges to the convex hull of
the spectrum of the associated classical system. This gives a quick alternative
solution to the isospectrality problem for quantum toric systems. If the
operators are uniformly bounded, the convergence is uniform. Analogous results
hold for non-commuting operators.


  High-energy nuclear collision provide a unique tool to study the strongly
interacting medium. Recent results from the Relativistic Heavy Ion Collider
(RHIC) on \phi-meson production has revealed the formation of a dense partonic
medium. The medium constituents are found to exhibit collective behaviour
initiated due to partonic interactions in the medium. We present a brief review
of the recent results on \phi production in heavy-ion collisions at RHIC. One
crucial question is where, in the phase diagram, does the transition happen for
the matter changing from hadronic to partonic degrees of freedom. We discuss
how \phi-meson elliptic flow in heavy-ion collisions can be used for the search
of the QCD phase boundary.


  In this paper, we present a latent variable (LV) framework to identify all
the speakers and their keywords given a multi-speaker mixture signal. We
introduce two separate LVs to denote active speakers and the keywords uttered.
The dependency of a spoken keyword on the speaker is modeled through a
conditional probability mass function. The distribution of the mixture signal
is expressed in terms of the LV mass functions and speaker-specific-keyword
models. The proposed framework admits stochastic models, representing the
probability density function of the observation vectors given that a particular
speaker uttered a specific keyword, as speaker-specific-keyword models. The LV
mass functions are estimated in a Maximum Likelihood framework using the
Expectation Maximization (EM) algorithm. The active speakers and their keywords
are detected as modes of the joint distribution of the two LVs. In mixture
signals, containing two speakers uttering the keywords simultaneously, the
proposed framework achieves an accuracy of 82% for detecting both the speakers
and their respective keywords, using Student's-t mixture models as
speaker-specific-keyword models.


  Motivated by the studies done on magnetically induced superconduc- tivity on
QCD vaccum we propose that de Sitter spacetime is a natural superconductor.
This is due to the occurrence of spinor condensation. We provide a framework
for joining the curvature of spacetime with super- conductor processes. We
demonstrate that for a critical value of de Sitter radius unstable tachyon
modes exist showing the existence of a supercon- ducting phase


  The oscillation of neutrino flavors, due to its interferometry nature, is
extremely sensitive to the phase differences developing during the propagation
of neutrinos. In this paper we investigate the effect of the Violation of
Equivalence Principle (VEP) on the flavor oscillation probabilities of
atmospheric and cosmic neutrinos observed at neutrino telescopes such as
IceCube. Assuming a general parameterization of VEP, dubbed extended parameter
space, we show that the synergy between the collected data of high energy
atmospheric and cosmic neutrinos severely constrains the VEP parameters. Also,
the projected sensitivity of IceCube-Gen2 to VEP parameters is discussed.


  An attempt is made to present the influence of the scalar unparticle on some
scattering processes in the Randall - Sundum model. The contribution of the
scalar unparticle on the production of Higgs - radion at high energy colliders
is studied in detail. We evaluate the production cross-sections in the
electron-positron ($e^{+}e^{-}$), photon-photon ($\gamma\gamma$) and
gluon-gluon ($gg$) collisions, which depend strongly on the collision energy
$\sqrt{s}$, the scaling dimension $d_{U}$ of the unparticle operator
$\mathcal{O}_{U}$ and the energy scale $\Lambda_{U}$. Numerical evaluation
shows that the cross - sections for the pair production of scalar particles are
much larger than that of the associated production of the scalar particle with
unparticle in the same condition


  Important observables to reveal the nature of dark energy are the equation of
state $w$ and its time derivative in units of the Hubble time $w'$. Recently,
it is shown that the simplest scalar field models of dark energy (quintessence)
occupy rather narrow regions in the $w-w'$ plane. We extend the $w-w'$ plane to
$w<-1$ and derive bounds on $w'$ as a function of $w$ for tracker phantom dark
energy. We also derive bounds on tracker k-essence.


  We report on our numerical implementation of fully relativistic hydrodynamics
coupled to Einstein's field equations in three spatial dimensions. We briefly
review several steps in our code development, including our recasting of
Einstein's equations and several tests which demonstrate its advantages for
numerical integrations. We outline our implementation of relativistic
hydrodynamics, and present numerical results for the evolution of both stable
and unstable Oppenheimer-Volkov equilibrium stars, which represent a very
promising first test of our code.


  We have analyzed ISO SWS 01 observations for 61 proto-planetary nebulae
candidates and classified their spectra according to their dominant chemistry.
On the basis of our classification and the more general classification of SWS
01 spectra by Kraemer et al. (2002) we discuss the connection between
proto-planetary nebulae candidates and planetary nebulae, with emphasis on
possible precursors of planetary nebulae with [WR] central stars.


  Response functions for spin-density-wave (SDW) and d-wave singlet
superconductivity ($d$SC) in quasi-one-dimensional (Q1D) electron systems are
calculated by a renormalization group technique. It is shown that the response
functions for both SDW, $\chi_\rs$, and $d$SC, $\chi_d$, are enhanced by
interchain hopping, $t_\perp$, i.e., by quasi-one dimensionality. When the
Fermi surface deviates from perfect nesting, $\chi_\rs$ saturates below the
energy scale of imperfectness of the nesting, while $\chi_d$ is hardly
affected. Consequently, the superconducting correlation increases even away
from the SDW phase. This gives a possible interpretation of the recent
experimental results of Q1D organic conductor (TMTTF)$_2$SbF$_6$, where $T_{\rm
c}$ increases even away from the SDW phase.


  Randomized iterative algorithms for solving a factorized linear system,
$\mathbf A\mathbf B\mathbf x=\mathbf b$ with $\mathbf A\in{\mathbb{R}}^{m\times
\ell}$, $\mathbf B\in{\mathbb{R}}^{\ell\times n}$, and $\mathbf
b\in{\mathbb{R}}^m$, have recently been proposed. They take advantage of the
factorized form and avoid forming the matrix $\mathbf C=\mathbf A\mathbf B$
explicitly. However, they can only find the minimum norm (least squares)
solution. In contrast, the regularized randomized Kaczmarz (RRK) algorithm can
find solutions with certain structures from consistent linear systems. In this
work, by combining the randomized Kaczmarz algorithm or the randomized
Gauss--Seidel algorithm with the RRK algorithm, we propose two novel
regularized randomized iterative algorithms to find (least squares) solutions
with certain structures of $\mathbf A\mathbf B\mathbf x=\mathbf b$. We prove
linear convergence of the new algorithms. Computed examples are given to
illustrate that the new algorithms can find sparse (least squares) solutions of
$\mathbf A\mathbf B\mathbf x=\mathbf b$ and can be better than the existing
randomized iterative algorithms for the corresponding full linear system
$\mathbf C\mathbf x=\mathbf b$ with $\mathbf C=\mathbf A\mathbf B$.


  We associate, with every infinite word over a finite alphabet, an increasing
sequence of rooted finite graphs, which provide a discrete approximation of the
famous Sierpi\'nski carpet fractal. Each of these sequences converges, in the
Gromov-Hausdorff topology, to an infinite rooted graph. We give an explicit
description of the metric compactification of each of these limit graphs. In
particular, we are able to classify Busemann and non-Busemann points of the
metric boundary. It turns out that, with respect to the uniform Bernoulli
measure on the set of words indexing the graphs, for almost all the infinite
graphs, the boundary consists of four Busemann points and countably many
non-Busemann points.


  We proposed a new Convolution Neural Network implementation optimized for
sparse 3D data inference. This implementation uses NanoVDB as the data
structure to store the sparse tensor. It leaves a relatively small memory
footprint while maintaining high performance. We demonstrate that this
architecture is around 20 times faster than the state-of-the-art dense CNN
model on a high-resolution 3D object classification network.


  We theoretically study the transmission of few-photon quantum fields through
a strongly nonlinear optical medium. We develop a general approach to
investigate non-equilibrium quantum transport of bosonic fields through a
finite-size nonlinear medium and apply it to a recently demonstrated
experimental system where cold atoms are loaded in a hollow-core optical fiber.
We show that when the interaction between photons is effectively repulsive, the
system acts as a single-photon switch. In the case of attractive interaction,
the system can exhibit either anti-bunching or bunching, associated with the
resonant excitation of bound states of photons by the input field. These
effects can be observed by probing statistics of photons transmitted through
the nonlinear fiber.


  We propose a novel approach that jointly removes reflection or translucent
layer from a scene and estimates scene depth. The input data are captured via
light field imaging. The problem is couched as minimizing the rank of the
transmitted scene layer via Robust Principle Component Analysis (RPCA). We also
impose regularization based on piecewise smoothness, gradient sparsity, and
layer independence to simultaneously recover 3D geometry of the transmitted
layer. Experimental results on synthetic and real data show that our technique
is robust and reliable, and can handle a broad range of layer separation
problems.


  Privacy protection is an important research area, which is especially
critical in this big data era. To a large extent, the privacy of visual
classification data is mainly in the mapping between the image and its
corresponding label, since this relation provides a great amount of information
and can be used in other scenarios. In this paper, we propose the mapping
distortion based protection (MDP) and its augmentation-based extension (AugMDP)
to protect the data privacy by modifying the original dataset. In the modified
dataset generated by MDP, the image and its label are not consistent ($e.g.$, a
cat-like image is labeled as the dog), whereas the DNNs trained on it can still
achieve good performance on benign testing set. As such, this method can
protect privacy when the dataset is leaked. Extensive experiments are
conducted, which verify the effectiveness and feasibility of our method. The
code for reproducing main results is available at
\url{https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion}.


  In the system of several interacting spins, geometric phases have been
researched intensively.However, the studies are mainly focused on the adiabatic
case (Berry phase), so it is necessary for us to study the non-adiabatic
counterpart (Aharonov and Anandan phase). In this paper, we analyze both the
non-degenerate and degenerate geometric phase of Lipkin-Meskov-Glick type
model, which has many application in Bose-Einstein condensates and entanglement
theory. Furthermore, in order to calculate degenerate geometric phases, the
Floquet theorem and decomposition of operator are generalized. And the general
formula is achieved.


  Motivated by the multilevel Monte Carlo method introduced by Giles [5], we
study the asymptotic behavior of the normalized error process
$u_{n,m}(X^n-X^{nm})$ where $X^n$ and $X^{nm}$ are respectively Euler
approximations with time steps $1/n$ and $1/nm$ of a given stochastic
differential equation $X$ driven by a pure jump L\'evy process. In this paper,
we prove that this normalized multilevel error converges to different
non-trivial limiting processes with various sharp rates $u_{n,m}$ depending on
the behavior of the L\'evy measure around zero. Our results are consistent with
those of Jacod [9] obtained for the normalized error $u_n(X^n-X)$, as when
letting $m$ tends to infinity, we recover the same limiting processes. For the
multilevel error, the proofs of the current paper are challenging since unlike
[9] we need to deal with $m$ dependent triangular arrays instead of one.


  We present a triangulated version of the conjectures of Tate and Beilinson on
algebraic cycles over a finite field. This sheds a new light on Lichtenbaum's
Weil-etale cohomology.


  An ensemble of neural networks is known to be more robust and accurate than
an individual network, however usually with linearly-increased cost in both
training and testing. In this work, we propose a two-stage method to learn
Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we
run SG-MCMC with group sparse priors to draw an ensemble of samples from the
posterior distribution of network parameters. In the second stage, we apply
weight-pruning to each sampled network and then perform retraining over the
remained connections. In this way of learning SSEs with SG-MCMC and pruning, we
not only achieve high prediction accuracy since SG-MCMC enhances exploration of
the model-parameter space, but also reduce memory and computation cost
significantly in both training and testing of NN ensembles. This is thoroughly
evaluated in the experiments of learning SSE ensembles of both FNNs and LSTMs.
For example, in LSTM based language modeling (LM), we obtain 21% relative
reduction in LM perplexity by learning a SSE of 4 large LSTM models, which has
only 30% of model parameters and 70% of computations in total, as compared to
the baseline large LSTM LM. To the best of our knowledge, this work represents
the first methodology and empirical study of integrating SG-MCMC, group sparse
prior and network pruning together for learning NN ensembles.


  In classical secretary problems, a sequence of $n$ elements arrive in a
uniformly random order, and we want to choose a single item, or a set of size
$K$. The random order model allows us to escape from the strong lower bounds
for the adversarial order setting, and excellent algorithms are known in this
setting. However, one worrying aspect of these results is that the algorithms
overfit to the model: they are not very robust. Indeed, if a few "outlier"
arrivals are adversarially placed in the arrival sequence, the algorithms
perform poorly. E.g., Dynkin's popular $1/e$-secretary algorithm fails with
even a single adversarial arrival.
  We investigate a robust version of the secretary problem. In the Byzantine
Secretary model, we have two kinds of elements: green (good) and red (rogue).
The values of all elements are chosen by the adversary. The green elements
arrive at times uniformly randomly drawn from $[0,1]$. The red elements,
however, arrive at adversarially chosen times. Naturally, the algorithm does
not see these colors: how well can it solve secretary problems?
  We give algorithms which get value comparable to the value of the optimal
green set minus the largest green item. Specifically, we give an algorithm to
pick $K$ elements that gets within $(1-\varepsilon)$ factor of the above
benchmark, as long as $K \geq \mathrm{poly}(\varepsilon^{-1} \log n)$. We
extend this to the knapsack secretary problem, for large knapsack size $K$.
  For the single-item case, an analogous benchmark is the value of the
second-largest green item. For value-maximization, we give a $\mathrm{poly}
\log^* n$-competitive algorithm, using a multi-layered bucketing scheme that
adaptively refines our estimates of second-max over time. For
probability-maximization, we show the existence of a good randomized algorithm,
using the minimax principle.


  The clathrate compound Ce3Pd20Si6 is a heavy-fermion metal that exhibits
magnetically hidden order at low temperatures. Reputedly, this exotic type of
magnetic ground state, known as "phase II", could be associated with the
ordering of Ce 4f quadrupolar moments. In contrast to conventional (dipolar)
order, it has vanishing Bragg intensity in zero magnetic field and, as a
result, has escaped direct observation by neutron scattering until now. Here we
report the observation of diffuse magnetic neutron scattering induced by an
application of magnetic field along either the [110] or the [001] direction
within phase II. The broad elastic magnetic signal that surrounds the (111)
structural Bragg peak can be attributed to a short-range G-type
antiferromagnetic arrangement of field-induced dipoles modulated by the
underlying multipolar order on the simple-cubic sublattice of Ce ions occupying
the 8c Wyckoff site. In addition, for magnetic fields applied along the [001]
direction, the diffuse magnetic peaks in Ce3Pd20Si6 become incommensurate,
suggesting a more complex modulated structure of the underlying multipolar
order that can be continuously tuned by a magnetic field.


  Quantum computation provides great speedup over its classical counterpart for
certain problems. One of the key challenges for quantum computation is to
realize precise control of the quantum system in the presence of noise. Control
of the spin-qubits in solids with the accuracy required by fault-tolerant
quantum computation under ambient conditions remains elusive. Here, we
quantitatively characterize the source of noise during quantum gate operation
and demonstrate strategies to suppress the effect of these. A universal set of
logic gates in a nitrogen-vacancy centre in diamond are reported with an
average single-qubit gate fidelity of 0.999952 and two-qubit gate fidelity of
0.992. These high control fidelities have been achieved at room temperature in
naturally abundant 13C diamond via composite pulses and an optimized control
method.


  By searching for shared inductive biases across tasks, meta-learning promises
to accelerate learning on novel tasks, but with the cost of solving a complex
bilevel optimization problem. We introduce and rigorously define the trade-off
between accurate modeling and optimization ease in meta-learning. At one end,
classic meta-learning algorithms account for the structure of meta-learning but
solve a complex optimization problem, while at the other end domain randomized
search (otherwise known as joint training) ignores the structure of
meta-learning and solves a single level optimization problem. Taking MAML as
the representative meta-learning algorithm, we theoretically characterize the
trade-off for general non-convex risk functions as well as linear regression,
for which we are able to provide explicit bounds on the errors associated with
modeling and optimization. We also empirically study this trade-off for
meta-reinforcement learning benchmarks.


  Cultural heritage sites in Italy typically attract a large number of tourists
every year. However, the lack of support for i) locating contents of interest;
ii) discovering information on specific contents; and iii) ease of navigation
within the heritage site; hinders the overall experience of the visitor. To
this end, in this work, we present a Digital Object Space Management service
developed as a part of the VASARI project. The service generates a digital twin
(with 3D visualization) of a given cultural heritage site and further provides
support for navigation and localization, thereby providing an immersive
cultural experience to the visitor.


  We present a new perspective on the crystallinity of dust in protoplanetary
disks. The dominant crystallization by thermal annealing happens in the very
early phases of disk formation and evolution. Both the disk properties and the
level of crystallinity are thereby directly linked to the properties of the
molecular cloud core from which the star+disk system was formed. We show that,
under the assumption of single star formation, rapidly rotating clouds produce
disks which, after the main infall phase (i.e. in the optically revealed class
II phase), are rather massive and have a high accretion rate but low
crystallinity. Slowly rotating clouds, on the other hand, produce less massive
disks with lower accretion rate, but high levels of crystallinity. Cloud
fragmentation and the formation of multiple stars complicates the problem and
necessitates further study. The underlying physics of the model is
insufficiently understood to provide the precise relationship between
crystallinity, disk mass and accretion rate. But the fact that with `standard'
input physics the model produces disks which, in comparison to observations,
appear to have either too high levels of crystallinity or too high disk masses,
demonstrates that the comparison of these models to observations can place
strong contraints on the disk physics. The question to ask is not why some
sources are so crystalline, but why some other sources have such a low level of
crystallinity.


  This paper addresses the problem of deriving distance measures between parent
and daughter languages with specific relevance to historical Chinese phonology.
The diachronic relationship between the languages is modelled as a
Probabilistic Finite State Automaton. The Minimum Message Length principle is
then employed to find the complexity of this structure. The idea is that this
measure is representative of the amount of dissimilarity between the two
languages.


  A brief review of the current state of observed deviations of theoretical
predictions from experimental data in semileptonic decays of $B$ and $B_c$
mesons is given. A theoretical analysis of these decays is carried out, taking
into account the effects of new physics, which appear due to the introduction
of new four-fermion operators, which are absent in the basis of the Standard
Model (SM) operators. The necessary form factors are calculated within the
framework of the covariant quark model developed in our papers.


This manuscript has been removed duo to incorrect contents.


  In many strongly correlated electron metals the thermoelectric power has a
non-monotonic temperature dependence and values that are orders of magnitude
larger than for elemental metals. Kelvin proposed a particularly simple
expression for the thermopower in terms of the temperature dependence of the
chemical potential. We consider a Hubbard model on an anisotropic triangular
lattice at half filling, a minimal effective Hamiltonian for several classes of
organic charge transfer salts. The finite temperature Lanczos method is used to
calculate the temperature dependence of the thermopower using the Kelvin
formula. We find that electronic correlations significantly enhance the
magnitude of the thermopower and lead to a non-monotonic temperature
dependence. The latter reflects a crossover with increasing temperature from a
Fermi liquid to a bad metal. Although, the Kelvin formula gives a
semi-quantitative description of some experimental results it cannot describe
the directional dependence of the sign of the thermopower in some materials.


  In the quest for realizations of quantum spin liquids, the exploration of
Kitaev materials - spin-orbit entangled Mott insulators with strong
bond-directional exchanges - has taken center stage. However, in these
materials the local spin-orbital j=1/2 moments typically show long-range
magnetic order at low temperature, thus defying the formation of a spin-liquid
ground state. Using resonant inelastic x-ray scattering (RIXS), we here report
on a proximate spin liquid regime with clear fingerprints of Kitaev physics in
the magnetic excitations of the honeycomb iridates alpha-Li2IrO3 and Na2IrO3.
We observe a broad continuum of magnetic excitations that persists up to at
least 300K, more than an order of magnitude larger than the magnetic ordering
temperatures. We prove the magnetic character of this continuum by an analysis
of the resonance behavior. RIXS measurements of the dynamical structure factor
for energies within the continuum show that dynamical spin-spin correlations
are restricted to nearest neighbors. Notably, these spectroscopic observations
are also present in the magnetically ordered state for excitation energies
above the conventional magnon excitations. Phenomenologically, our data agree
with inelastic neutron scattering results on the related honeycomb compound
RuCl3, establishing a common ground for a proximate Kitaev spin-liquid regime
in these materials.


  With the rapid development of the computer industry and computer software,
the risk of software vulnerabilities being exploited has greatly increased.
However, there are still many shortcomings in the existing mining techniques
for leakage source research, such as high false alarm rate, coarse-grained
detection, and dependence on expert experience. In this paper, we mainly use
the c/c++ source code data of the SARD dataset, process the source code of
CWE476, CWE469, CWE516 and CWE570 vulnerability types, test the Joern
vulnerability scanning function of the cutting-edge tool, and propose a new
cascading deep learning model VMCDL based on source code control flow to
effectively detect vulnerabilities. First, this paper uses joern to locate and
extract sensitive functions and statements to form a sensitive statement
library of vulnerable code. Then, the CFG flow vulnerability code snippets are
generated by bidirectional breadth-first traversal, and then vectorized by
Doc2vec. Finally, the cascade deep learning model based on source code control
flow is used for classification to obtain the classification results. In the
experimental evaluation, we give the test results of Joern on specific
vulnerabilities, and give the confusion matrix and label data of the binary
classification results of the model algorithm on single vulnerability type
source code, and compare and verify the five indicators of FPR, FNR, ACC, P and
F1, respectively reaching 10.30%, 5.20%, 92.50%,85.10% and 85.40%,which shows
that it can effectively reduce the false alarm rate of static analysis.


  Automated Vehicles (AVs) are expected to disrupt the transport sector in the
future. Extensive research efforts have been dedicated to studying its
potential implications. However, the existing literature is yet limited
regarding the long-term impacts. To fill this gap, this paper estimates and
validates a residential location choice model to evaluate the impacts of AVs on
residential location distributions in a context of Japanese regional area.
Activity-based accessibility is used to reflect the changes from AVs in
transport costs. The year 2040 is set as the backdrop for the analyses, where
the effects of the decreased population are reflected in the scenario settings,
along with some other variables to accommodate the uncertainties in the
characteristics of AVs. The simulation results confirm the potential of urban
expansion. The results demonstrate that, compared to Base Scenario, the median
distances between the residences and the closest Dwelling Attraction Areas
expand by 7.2% and 41.6% for two AV scenarios, respectively. Two hypothetical
policy mandates are then applied to alleviate the problem. The results suggest
that providing a 20% subsidy to the land price is effective for the scenario
with relatively conservative AV settings, as the median distance indicator can
be resumed to the level of Base Scenario.


  Given a positive integer $n$ and a planar graph $H$, let $\mathcal{T}_n(H)$
be the family of all plane triangulations $T$ on $n$ vertices such that $T$
contains a subgraph isomorphic to $H$. The planar anti-Ramsey number of $H$,
denoted $ar_{_\mathcal{P}}(n, H)$, is the maximum number of colors in an
edge-coloring of a plane triangulation $T\in \mathcal{T}_n(H)$ such that $T$
contains no rainbow copy of $H$. In this paper we study planar anti-Ramsey
numbers of matchings. For all $t\ge1$, let $M_t$ denote a matching of size $t$.
We prove that for all $t\ge6$ and $n\ge 3t-6$, $2n+3t-15\le
ar_{_{\mathcal{P}}}(n, {M}_t)\le 2n+4t-14$, which significantly improves the
existing lower and upper bounds for $ar_{_\mathcal{P}}(n, M_t)$. It seems that
for each $t\ge6$, the lower bound we obtained is the exact value of
$ar_{_{\mathcal{P}}}(n, {M}_t)$ for sufficiently large $n$. This is indeed the
case for $M_6$. We prove that $ar_{_\mathcal{P}}(n, M_6)=2n+3$ for all
$n\ge30$.


  We derive It\^o-type change of variable formulas for smooth functionals of
irregular paths with non-zero $p-$th variation along a sequence of partitions
where $p \geq 1$ is arbitrary, in terms of fractional derivative operators,
extending the results of the F\"ollmer-Ito calculus to the general case of
paths with 'fractional' regularity. In the case where $p$ is not an integer, we
show that the change of variable formula may sometimes contain a non-zero a
'fractional' It\^o remainder term and provide a representation for this
remainder term. These results are then extended to paths with non-zero
$\phi-$variation and multi-dimensional paths. Finally, we derive an isometry
property for the pathwise F\"ollmer integral in terms of $\phi$ variation.


  The human papillomavirus (HPV) vaccine is the most effective way to prevent
HPV-related cancers. Integrating provider vaccine counseling is crucial to
improving HPV vaccine completion rates. Automating the counseling experience
through a conversational agent could help improve HPV vaccine coverage and
reduce the burden of vaccine counseling for providers. In a previous study, we
tested a simulated conversational agent that provided HPV vaccine counseling
for parents using the Wizard of OZ protocol. In the current study, we assessed
the conversational agent among young college adults (n=24), a population that
may have missed the HPV vaccine during their adolescence when vaccination is
recommended. We also administered surveys for system and voice usability, and
for health beliefs concerning the HPV vaccine. Participants perceived the agent
to have high usability that is slightly better or equivalent to other voice
interactive interfaces, and there is some evidence that the agent impacted
their beliefs concerning the harms, uncertainty, and risk denials for the HPV
vaccine. Overall, this study demonstrates the potential for conversational
agents to be an impactful tool for health promotion endeavors.


  Weak lensing shear estimation typically results in per galaxy statistical
errors significantly larger than the sought after gravitational signal of only
a few percent. These statistical errors are mostly a result of shape-noise --
an estimation error due to the diverse (and a-priori unknown) morphology of
individual background galaxies. These errors are inversely proportional to the
limiting angular resolution at which localized objects, such as galaxy
clusters, can be probed with weak lensing shear. In this work we report on our
initial attempt to reduce statistical errors in weak lensing shear estimation
using a machine learning approach -- training a multi-layered convolutional
neural network to directly estimate the shear given an observed background
galaxy image. We train, calibrate and evaluate the performance and stability of
our estimator using simulated galaxy images designed to mimic the distribution
of HST observations of lensed background sources in the CLASH galaxy cluster
survey. Using the trained estimator, we produce weak lensing shear maps of the
cores of 20 galaxy clusters in the CLASH survey, demonstrating an RMS scatter
reduced by approximately 26% when compared to maps produced with a commonly
used shape estimator. This is equivalent to a survey speed enhancement of
approximately 60%. However, given the non-transparent nature of the machine
learning approach, this result requires further testing and validation. We
provide python code to train and test this estimator on both simulated and real
galaxy cluster observations. We also provide updated weak lensing catalogues
for the 20 CLASH galaxy clusters studied.


  As jurisdictions around the world take their first steps toward regulating
the most powerful AI systems, such as the EU AI Act and the US Executive Order
14110, there is a growing need for effective enforcement mechanisms that can
verify compliance and respond to violations. We argue that compute providers
should have legal obligations and ethical responsibilities associated with AI
development and deployment, both to provide secure infrastructure and to serve
as intermediaries for AI regulation. Compute providers can play an essential
role in a regulatory ecosystem via four key capacities: as securers,
safeguarding AI systems and critical infrastructure; as record keepers,
enhancing visibility for policymakers; as verifiers of customer activities,
ensuring oversight; and as enforcers, taking actions against rule violations.
We analyze the technical feasibility of performing these functions in a
targeted and privacy-conscious manner and present a range of technical
instruments. In particular, we describe how non-confidential information, to
which compute providers largely already have access, can provide two key
governance-relevant properties of a computational workload: its type-e.g.,
large-scale training or inference-and the amount of compute it has consumed.
Using AI Executive Order 14110 as a case study, we outline how the US is
beginning to implement record keeping requirements for compute providers. We
also explore how verification and enforcement roles could be added to establish
a comprehensive AI compute oversight scheme. We argue that internationalization
will be key to effective implementation, and highlight the critical challenge
of balancing confidentiality and privacy with risk mitigation as the role of
compute providers in AI regulation expands.


  Brane model of universe is considered for a particle. Conservation laws
inside the brane are obtained. Equation of motion is derived for a particle
using variation principle from these conservation laws. This equation includes
terms accounting the variation of brane topology. Its solution is obtained at
some approximations and dispersion relation for a particle is derived.


  We present ABEX, a novel and effective generative data augmentation
methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX
is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms
of an input document -- we first convert a document into its concise, abstract
description and then generate new documents based on expanding the resultant
abstraction. To learn the task of expanding abstract descriptions, we first
train BART on a large-scale synthetic dataset with abstract-document pairs.
Next, to generate abstract descriptions for a document, we propose a simple,
controllable, and training-free method based on editing AMR graphs. ABEX brings
the best of both worlds: by expanding from abstract representations, it
preserves the original semantic properties of the documents, like style and
meaning, thereby maintaining alignment with the original label and data
distribution. At the same time, the fundamental process of elaborating on
abstract descriptions facilitates diverse generations. We demonstrate the
effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource
settings. ABEX outperforms all our baselines qualitatively with improvements of
0.04% - 38.8%. Qualitatively, ABEX outperforms all prior methods from
literature in terms of context and length diversity.


  In this short note we use holographic methods to study the response of
quantum critical points with hyperscaling violation to a disturbance caused by
a massive charged particle. We give analytical expressions for the two-point
functions of the fluctuations of the massive probe as a function of arbitrary
(allowed) values of the hyperscaling violation exponent {\theta} and the
dynamical exponent z. We point out the existence of markedly different
behaviors of the two-point functions in the parameter space of {\theta} and z
at late times. In particular, as expected, the late-time dynamics of the probe
becomes independent of its inertial mass in the range z+2{\theta}/d>2.


  For the classical power indices there is a disproportion between power and
relative weights, in general. We introduce two new indices, based on weighted
representations, which are proportional to suitable relative weights and which
also share several important properties of the classical power indices.
Imposing further restrictions on the set of representations may lead to a whole
family of such indices.


  The game tree languages can be viewed as an automata-theoretic counterpart of
parity games on graphs. They witness the strictness of the index hierarchy of
alternating tree automata, as well as the fixed-point hierarchy over binary
trees. We consider a game tree language of the first non-trivial level, where
Eve can force that 0 repeats from some moment on, and its dual, where Adam can
force that 1 repeats from some moment on. Both these sets (which amount to one
up to an obvious renaming) are complete in the class of co-analytic sets. We
show that they cannot be separated by any Borel set, hence {\em a fortiori} by
any weakly definable set of trees. This settles a case left open by
L.Santocanale and A.Arnold, who have thoroughly investigated the separation
property within the $\mu $-calculus and the automata index hierarchies. They
showed that separability fails in general for non-deterministic automata of
type $\Sigma^{\mu}_{n} $, starting from level $n=3$, while our result settles
the missing case $n=2$.


  We demonstrate how micromagnetic simulations can be employed in order to
characterize and analyze the magnetic microstructure of nanocomposites. For the
example of nanocrystalline Nd$-$Fe$-$B, which is a potential material for
future permanent-magnet applications, we have compared three different models
for the micromagnetic analysis of this material class: (i) a description of the
nanocomposite microstructure in terms of Stoner-Wohlfarth particles with and
without the magnetodipolar interaction; (ii) a model based on the core-shell
representation of the nanograins; (iii) the latter model including a
contribution of superparamagnetic clusters. The relevant parameter spaces have
been systematically scanned with the aim to establish which micromagnetic
approach can most adequately describe experimental data for this material.
According to our results, only the last, most sophisticated model is able to
provide an excellent agreement with the measured hysteresis loop. The presented
methodology is generally applicable to multiphase magnetic nanocomposites and
it highligths the complex interrelationship between the microstructure,
magnetic interactions, and the macroscopic magnetic properties.


  Honeycomb iridate Na2IrO3, a Jeff=1/2 magnet, is a potential platform for
realizing the quantum spin liquid. Many experiments have shown that its
magnetic ground state has a zigzag antiferromagnetic (AFM) order. However,
there is still a lack of consensus on the theoretical model explaining such
order, since its second nearest neighbor (NN) and long-range third NN magnetic
interactions are highly unclear. By properly taking into account the orbital
moments, achieved through constraining their directions in the first-principles
calculations, we obtain that the relative angle between orbital and spin
moments is fairly small and in the order of several degrees, which thus
validates the Jeff=1/2 state in Na2IrO3. Surprisingly, we find that the
long-range third NN Heisenberg interactions are sizable whereas the second NN
magnetic interactions are negligible. Using maximally localized Wannier
functions, we show that the sizable long-range third NN Heisenberg interaction
results from the extended nature of the Jeff=1/2 state. Based on our study, we
propose a minimal J1-K1-{\Gamma}_1-J3 model in which the magnetic excitations
have an intensity peak at 5.6 meV, consistent with the inelastic neutron
scattering experiment [Phys. Rev. Lett. 108, 127204 (2012)]. The present work
demonstrates again that constraining orbital moments in the first-principles
calculations is powerful to investigate the intriguing magnetism in the
Jeff=1/2 magnets, and paves the way toward gaining a deep insight into the
novel magnetism discovered in the honeycomb Jeff=1/2 magnets.


  When suggesting Points of Interest (PoIs) to people with autism spectrum
disorders, we must take into account that they have idiosyncratic sensory
aversions to noise, brightness and other features that influence the way they
perceive places. Therefore, recommender systems must deal with these aspects.
However, the retrieval of sensory data about PoIs is a real challenge because
most geographical information servers fail to provide this data. Moreover,
ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical
areas and lack sustainability. Thus, we investigate the extraction of sensory
data about places from the consumer feedback collected by location-based
services, on which people spontaneously post reviews from all over the world.
Specifically, we propose a model for the extraction of sensory data from the
reviews about PoIs, and its integration in recommender systems to predict item
ratings by considering both user preferences and compatibility information. We
tested our approach with autistic and neurotypical people by integrating it
into diverse recommendation algorithms. For the test, we used a dataset built
in a crowdsourcing campaign and another one extracted from TripAdvisor reviews.
The results show that the algorithms obtain the highest accuracy and ranking
capability when using TripAdvisor data. Moreover, by jointly using these two
datasets, the algorithms further improve their performance. These results
encourage the use of consumer feedback as a reliable source of information
about places in the development of inclusive recommender systems.


  Recalling each edge of a graph $H$ has 2 oppositely oriented arcs, each
vertex $v$ of $H$ is identified with the set of arcs, denoted $(v,e)$,
departing from $v$ along the edges $e$ of $H$ incident to $v$. Let $H$ be a
$(\lambda,\mu)$-biregular graph with bipartition $(Y,X)$, where $|Y|=k\mu$ and
$|X|=k\lambda$, ($0<k,\lambda,\mu\in\mathbb{Z}$). We consider the problem, for
each edge $e=yx$ in $H$, of assigning, a color (given by an element) of $Y$,
resp. $X$, to the arc $(y,e)$, resp. $(x,e)$, so that each color is assigned
exactly once in the set of arcs departing from each vertex of $H$. Furthermore,
we set such assignment to fulfill a specific bicolor weight function over a
monotonic subset of $Y\times X$. This problem applies to the Design of
Experiments for Industrial Chemistry, Molecular Biology, Cellular Neuroscience,
etc. An algorithmic construction based on biregulzr graphs with bipartitions
given by cyclic-group pairs is presented, as well as 3 essentially different
solutions to the Great Circle Challenge Puzzle based on a different biregular
graph whose bipartition is formed by the vertices and 5-cycles of the Petersen
graph.


  Super-luminous supernovae that radiate more than 10^44 ergs per second at
their peak luminosity have recently been discovered in faint galaxies at
redshifts of 0.1-4. Some evolve slowly, resembling models of 'pair-instability'
supernovae. Such models involve stars with original masses 140-260 times that
of the Sun that now have carbon-oxygen cores of 65-30 solar masses. In these
stars, the photons that prevent gravitational collapse are converted to
electron-positron pairs, causing rapid contraction and thermonuclear
explosions. Many solar masses of 56Ni are synthesized; this isotope decays to
56Fe via 56Co, powering bright light curves. Such massive progenitors are
expected to have formed from metal-poor gas in the early Universe. Recently,
supernova 2007bi in a galaxy at redshift 0.127 (about 12 billion years after
the Big Bang) with a metallicity one-third that of the Sun was observed to look
like a fading pair-instability supernova. Here we report observations of two
slow-to-fade super-luminous supernovae that show relatively fast rise times and
blue colours, which are incompatible with pair-instability models. Their
late-time light-curve and spectral similarities to supernova 2007bi call the
nature of that event into question. Our early spectra closely resemble typical
fast-declining super-luminous supernovae, which are not powered by
radioactivity. Modelling our observations with 10-16 solar masses of
magnetar-energized ejecta demonstrates the possibility of a common explosion
mechanism. The lack of unambiguous nearby pair-instability events suggests that
their local rate of occurrence is less than 6x10^-6 times that of the
core-collapse rate.


  3D Rashba materials can be a leading player in spin-related novel phenomena,
ranging from the metallic extreme (unconventional superconductivity) to the
transport intermediate (spin Hall effects) to the novel insulating variant (3D
topological insulating states). As the essential backbone for both fundamental
and applied research of such a 3D Rashba material, this study established the
growth of sizeable single crystals of a candidate compound BiTeI with adjusted
carrier concentrations. Three techniques (standard vertical Bridgman, modified
horizontal Bridgman, and vapour transport) were employed, and BiTeI crystals (>
1 * 1 * 0.2 mm3) with fundamentally different electronic states from metallic
to insulating were successfully grown by the chosen techniques. The 3D Rashba
electronic states, including the Fermi surface topology, for the corresponding
carrier concentrations of the obtained BiTeI crystals were revealed by
relativistic first-principles calculations.


  We investigate pathways for the formation of icy super-Earth mass planets
orbiting at 125-250 AU around a 1 solar mass star. An extensive suite of
coagulation calculations demonstrates that swarms of 1 cm to 10 m planetesimals
can form super-Earth mass planets on time scales of 1-3 Gyr. Collisional
damping of 0.01-100 cm particles during oligarchic growth is a highlight of
these simulations. In some situations, damping initiates a second runaway
growth phase where 100-3000 km protoplanets grow to super-Earth sizes. Our
results establish the initial conditions and physical processes required for in
situ formation of super-Earth planets at large distances from the host star.
For nearby dusty disks in HD 107146, HD 202628, and HD 207129, ongoing
super-Earth formation at 80-150 AU could produce gaps and other structures in
the debris. In the solar system, forming a putative planet X at a < 300 AU (a >
1000 AU) requires a modest (very massive) protosolar nebula.


  Three conjectures #1999--8, #1999--9 and #1999--10 which were posed by V.
Arnold [2] and devoted to the statistics of the numerical semigroups are
refuted for the case of semigroups generated by three positive integers
d_1,d_2,d_3 with gcd(d_1,d_2,d_3)=1. Weak asymptotics of conductor
C(d_1,d_2,d_3) of numerical semigroup and fraction p(d_1,d_2,d_3) of a segment
[0;C(d_1,d_2,d_3)-1] occupied by semigroup are found.


  Higher education in the fourth industrial revolution, HE 4.0, is a complex,
dialectical and exciting opportunity which can potentially transform society
for the better. The fourth industrial revolution is powered by artificial
intelligence and it will transform the workplace from tasks based
characteristics to the human centred characteristics. Because of the
convergence of man and machine, it will reduce the subject distance between
humanities and social science as well as science and technology. This will
necessarily require much more interdisciplinary teaching, research and
innovation. This paper explores the impact of HE 4.0 on the mission of a
university which is teaching, research (including innovation) and service.


  Anomaly detection is a widely explored domain in machine learning. Many
models are proposed in the literature, and compared through different metrics
measured on various datasets. The most popular metrics used to compare
performances are F1-score, AUC and AVPR. In this paper, we show that F1-score
and AVPR are highly sensitive to the contamination rate. One consequence is
that it is possible to artificially increase their values by modifying the
train-test split procedure. This leads to misleading comparisons between
algorithms in the literature, especially when the evaluation protocol is not
well detailed. Moreover, we show that the F1-score and the AVPR cannot be used
to compare performances on different datasets as they do not reflect the
intrinsic difficulty of modeling such data. Based on these observations, we
claim that F1-score and AVPR should not be used as metrics for anomaly
detection. We recommend a generic evaluation procedure for unsupervised anomaly
detection, including the use of other metrics such as the AUC, which are more
robust to arbitrary choices in the evaluation protocol.


  Chemical and infra-red sensors generate distinct responses under similar
conditions because of sensor drift, noise or resolution errors. In this work,
we use different time-series data sets obtained by infra-red and E-nose sensors
in order to detect Volatile Organic Compounds (VOCs) and Ammonia vapor leaks.
We process time-series sensor signals using deep neural networks (DNN). Three
neural network algorithms are utilized for this purpose. Additive neural
networks (termed AddNet) are based on a multiplication-devoid operator and
consequently exhibit energy-efficiency compared to regular neural networks. The
second algorithm uses generative adversarial neural networks so as to expose
the classifying neural network to more realistic data points in order to help
the classifier network to deliver improved generalization. Finally, we use
conventional convolutional neural networks as a baseline method and compare
their performance with the two aforementioned deep neural network algorithms in
order to evaluate their effectiveness empirically.


  In this review, I discuss the use of galaxy-galaxy weak lensing measurements
to study the masses of dark matter halos in which galaxies reside. After
summarizing how weak gravitational lensing measurements can be interpreted in
terms of halo mass, I review measurements that were used to derive the
relationship between optical galaxy mass tracers, such as stellar mass or
luminosity, and dark matter halo mass. Measurements of galaxy-galaxy lensing
from the past decade have led to increasingly tight constraints on the
connection between dark matter halo mass and optical mass tracers, including
both the mean relationships between these quantities and the intrinsic scatter
between them. I also review some of the factors that can complicate analysis,
such as the choice of modeling procedure, and choices made when dividing up
samples of lens galaxies.


  We review the current state of knowledge of magnetic fields inside stars,
concentrating on recent developments concerning magnetic fields in stably
stratified (zones of) stars, leaving out convective dynamo theories and
observations of convective envelopes. We include the observational properties
of A, B and O-type main-sequence stars, which have radiative envelopes, and the
fossil field model which is normally invoked to explain the strong fields
sometimes seen in these stars. Observations seem to show that Ap-type stable
fields are excluded in stars with convective envelopes. Most stars contain both
radiative and convective zones, and there are potentially important effects
arising from the interaction of magnetic fields at the boundaries between them.
Related to this, we discuss whether the Sun could harbour a magnetic field in
its core. Recent developments regarding the various convective and radiative
layers near the surfaces of early-type stars and their observational effects
are examined. We look at possible dynamo mechanisms that run on differential
rotation rather than convection. Finally we turn to neutron stars with a
discussion of the possible origins for their magnetic fields.


  Let $\mathbb{F}_{p^m}$ be the finite field of order $p^m$, where $p$ is an
odd prime and $m$ is a positive integer. In this paper, we investigate a class
of subfield codes of linear codes and obtain the weight distribution of
\begin{equation*} \begin{split} \mathcal{C}_k=\left\{\left(\left( {\rm
Tr}_1^m\left(ax^{p^k+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}}, {\rm
Tr}_1^m(a)\right) : \, a,b \in \mathbb{F}_{p^m}, c \in \mathbb{F}_p\right\},
\end{split} \end{equation*}
  where $k$ is a nonnegative integer. Our results generalize the results of the
subfield codes of the conic codes in \cite{Hengar}. Among other results, we
study the punctured code of $\mathcal{C}_k$, which is defined as
$$\mathcal{\bar{C}}_k=\left\{\left( {\rm Tr}_1^m\left(a
x^{{p^k}+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}} : \, a,b \in
\mathbb{F}_{p^m}, \,\,c \in \mathbb{F}_p\right\}.$$ The parameters of these
linear codes are new in some cases. Some of the presented codes are optimal or
almost optimal. Moreover, let $v_2(\cdot)$ denote the 2-adic order function and
$v_2(0)=\infty$, the duals of $\mathcal{C}_k$ and $\mathcal{\bar{C}}_k$ are
optimal with respect to the Sphere Packing bound if $p>3$, and the dual of
$\mathcal{\bar{C}}_k$ is an optimal ternary linear code for the case
$v_2(m)\leq v_2(k)$ if $p=3$ and $m>1$.


  We introduce a screw function corresponding to the Riemann zeta-function and
study its properties from various aspects. Typical results are several
equivalent conditions for the Riemann hypothesis in terms of the screw
function. One of them can be considered an analog of so-called Weil's
positivity or Li's criterion. In addition, we prove a few partial but
unconditional results for such equivalents.


  Accurate estimation of building heights is essential for urban planning,
infrastructure management, and environmental analysis. In this study, we
propose a supervised Multimodal Building Height Regression Network (MBHR-Net)
for estimating building heights at 10m spatial resolution using Sentinel-1 (S1)
and Sentinel-2 (S2) satellite time series. S1 provides Synthetic Aperture Radar
(SAR) data that offers valuable information on building structures, while S2
provides multispectral data that is sensitive to different land cover types,
vegetation phenology, and building shadows. Our MBHR-Net aims to extract
meaningful features from the S1 and S2 images to learn complex spatio-temporal
relationships between image patterns and building heights. The model is trained
and tested in 10 cities in the Netherlands. Root Mean Squared Error (RMSE),
Intersection over Union (IOU), and R-squared (R2) score metrics are used to
evaluate the performance of the model. The preliminary results (3.73m RMSE,
0.95 IoU, 0.61 R2) demonstrate the effectiveness of our deep learning model in
accurately estimating building heights, showcasing its potential for urban
planning, environmental impact analysis, and other related applications.


  This paper proposes a funnel control method under time-varying hard and soft
output constraints. First, an online funnel planning scheme is designed that
generates a constraint consistent funnel, which always respects hard (safety)
constraints, and soft (performance) constraints are met only when they are not
conflicting with the hard constraints. Next, the prescribed performance control
method is employed for designing a robust low-complexity funnel-based
controller for uncertain nonlinear Euler-Lagrangian systems such that the
outputs always remain within the planned constraint consistent funnels.
Finally, the results are verified with a simulation example of a mobile robot
tracking a moving object while staying in a box-constrained safe space.


  We have studied the dependence of the rotation angle and ellipticity on the
sample orientation and incident polarization from metallic nanohole arrays. The
arrays have four-fold symmetry and thus do not possess any intrinsic chirality.
We elucidate the role of surface plasmon polaritons (SPPs) in determining the
extrinsic chirality and we verify the results by using finite-difference
time-domain simulation. Our results have indicated the outgoing reflection
arises from the interference between the nonresonant background, which
preserves the input polarization, and the SPP radiation damping, which is
linearly polarized but carries a different polarization defined by the
vectorial field of SPPs. More importantly, the interference manifests various
polarization states ranging from linear to elliptical across the SPP resonance.
We analytically formulate the outgoing waves based on temporal coupled mode
theory (CMT) and the results agree well with the experiment and simulation.
From CMT, we find the polarization conversion depends on the interplay between
the absorption and radiative decay rates of SPPs and the sample orientation.


  This study aims to assess the properties and classification of 40 variable
stars in Sagittarius, little studied since their discovery and reported in the
Information Bulletin on Variable Stars (IBVS) 985 and update. Using data from
previous studies and several astronomical databases, we performed our analysis
mainly utilizing a period analysis software and comparing the photometric
characteristics of the variables in a Colour-Absolute Magnitude Diagram. For
all stars, the variability is confirmed. We discovered new significant results
for the period and/or type of 15 variables and highlighted incorrect
cross-reference names on astronomical databases for 5 stars. This assessment
also identifies 9 cases for which results from the ASAS-SN Catalog of Variable
Stars are systematically not consistent with the original light curves. A
correct identification of NSV 10522 is provided.


  We present experimental evidence of emergent density wave instability in
single crystalline low dimensional wires of Yittrium based Pyrochlore Iridates.
We demonstrate electric field induced nonlinear hysteretic switching of the
density wave at low temperature, followed by smooth nonlinear conduction at
higher temperature (T > 40 K) in $Y_{2-x}Bi_{x}Ir_{2}O_{7}$ with x = 0 and 0.3.
AC transport measurements reveal the presence of four different collective
relaxation processes which dominate at different temperature scales. There is a
strong coupling of the normal charge carriers with the density wave condensate,
which is reflected in the linear scaling of the dc conductivity with the
collective relaxation rate across a wide range of frequency and temperature
regime. The evidence of density wave in low dimensional single crystals of
Pyrochlore Iridate could be a precursor to the possible experimental
confirmation of the Weyl semimetallic ground state with broken chiral symmetry.


  A thermal Hall effect is observed in an increasing number of insulators often
attributed to phonons, but the underlying mechanism is in most cases unknown. A
coupling of phonons to spins has been invoked and scattering of phonons by
impurities or defects has been proposed, but there is no systematic evidence to
support either scenario. Here we present a study on the effect of adding Rh
impurities to the antiferromagnetic insulator Sr2IrO4, substituting for the
spin-carrying Ir atoms. We find that adding small concentrations of Rh
impurities increases the thermal Hall conductivity dramatically, but adding
enough Rh to suppress the magnetic order eventually decreases the thermal Hall
conductivity until it nearly vanishes. We conclude that the thermal Hall effect
in this material is caused by the scattering of phonons by impurities embedded
within a magnetic environment.


  We report detailed calculations of the profiles of energy and action
densities in the quark-antiquark string in SU(2) lattice gauge theory.


  We investigate architectures of discriminatively trained deep Convolutional
Networks (ConvNets) for action recognition in video. The challenge is to
capture the complementary information on appearance from still frames and
motion between frames. We also aim to generalise the best performing
hand-crafted features within a data-driven learning framework.
  Our contribution is three-fold. First, we propose a two-stream ConvNet
architecture which incorporates spatial and temporal networks. Second, we
demonstrate that a ConvNet trained on multi-frame dense optical flow is able to
achieve very good performance in spite of limited training data. Finally, we
show that multi-task learning, applied to two different action classification
datasets, can be used to increase the amount of training data and improve the
performance on both.
  Our architecture is trained and evaluated on the standard video actions
benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of
the art. It also exceeds by a large margin previous attempts to use deep nets
for video classification.


  We introduce a framework for simultaneously investigating the structure and
luminosity evolution of early-type gravitational lens galaxies. The method is
based on the fundamental plane, which we interpret using the aperture
mass-radius relations derived from lensed image geometries. We apply this
method to our previous sample of 22 lens galaxies with measured redshifts and
excellent photometry. Modeling the population with a single mass profile and
evolutionary history, we find that early-type galaxies are nearly isothermal
(logarithmic density slope n = 2.06 +/- 0.17, 68% C.L.), and that their stars
evolve at a rate of dlog(M/L)_B/dz = -0.50 +/- 0.19 (68% C.L.) in the rest
frame B band. For a Salpeter IMF and a concordance cosmology, this implies a
mean star formation redshift of <z_f> > 1.5 at 95% confidence. While this model
can neatly describe the mean properties of early-type galaxies, it is clear
that the scatter of the lens sample is too large to be explained by
observational uncertainties alone. We therefore consider statistical models in
which the galaxy population is described by a distribution of star formation
redshifts. We find that stars must form over a significant range of redshifts
(Delta z_f > 1.7, 68% C.L.), which can extend as low as z_f = 1 for some
acceptable models. However, the typical galaxy will still have an old stellar
population (<z_f> > 1.5). The lens sample therefore favors early star formation
in field ellipticals -- even if we make no a priori assumption regarding the
shape of the mass distribution in lenses, and include the range of possible
deviations from homology in the uncertainties. Our evolution results call into
question several recent claims that early-type galaxies in low-density
environments have much younger stars than those in rich clusters.


  We have studied the spectral evolution of stellar populations in function of
their heavy element content and star formation, using Geneva and Padova stellar
track sets, both covering a number of metallicities, and Kurucz grid of stellar
atmospheres. The general trend is the progressive growth of absorption and
decline of flux towards shorter, UV-wavelengths, with increasing metallicity,
and increase of infrared flux. Our models help to quantify given theoretically
quite obvious effect. Presented results of integration of flux in different
regions indicate, that with a change of metallicity from Z=0.001 to Z=0.04
total amount of radiation energy emitted in the shortest wavelength region by a
stellar population during its lifetime diminishes typically by a factor ~10,
with the corresponding typical increase in the infrared about 1.5-2 times.
These numbers are quite independent on the actual IMF slope values under the
assumption of identical slopes.


  We present a numerical scheme for solving Einstein's Equations in the
presence of a negative cosmological constant and an event horizon with planar
topology. Our scheme allows for the introduction of a particular metric source
at the conformal boundary. Such a spacetime has a dual holographic description
in terms of a strongly interacting quantum field theory at nonzero temperature.
By introducing a sinusoidal static metric source that breaks translation
invariance, we study momentum relaxation in the field theory. In the long
wavelength limit, our results are consistent with the fluid-gravity
correspondence and relativistic hydrodynamics. In the small amplitude limit,
our results are consistent with the memory function prediction for the momentum
relaxation rate. Our numerical scheme allows us to study momentum relaxation
outside these two limits as well.


  This paper celebrates the remarkable life, science and legacy of Abb\'e
Georges Lema\^itre, the Belgian cleric and professor of physics; he was the
architect of the fireworks model for the origin of the universe. He died half a
century ago, three days after learning that Arno Penzias and Robert Wilson had
discovered the cosmic microwave background. Despite being gravely ill from
leukaemia, Lema\^itre lucidly praised this news, which confirmed the explosive
genesis of our universe.


  We perform a set of 36 nonprecessing black-hole binary simulations with spins
either aligned or counteraligned with the orbital angular momentum in order to
model the final mass, spin, and recoil of the merged black hole as a function
of the individual black hole spin magnitudes and the mass ratio of the
progenitors. We find that the maximum recoil for these configurations is
$V_{max}=526\pm23\,km/s$, which occurs when the progenitor spins are maximal,
the mass ratio is $q_{max}=m_1/m_2=0.623\pm0.038$, the smaller black-hole spin
is aligned with the orbital angular momentum, and the larger black-hole spin is
counteraligned ($\alpha_1=-\alpha_2=1$). This maximum recoil is about
$80\,km/s$ larger than previous estimates, but most importantly, because the
maximum occurs for smaller mass ratios, the probability for a merging binary to
recoil faster than $400\,km/s$ can be as large as $17\%$, while the probability
for recoils faster than $250\, km/s$ can be as large as $45\%$. We provide
explicit phenomenological formulas for the final mass, spin, and recoil as a
function of the individual BH spins and the mass difference between the two
black holes. Here we include terms up through fourth-order in the initial spins
and mass difference, and find excellent agreement (within a few percent) with
independent results available in the literature. The maximum radiated energy is
$E_{\rm rad}/m\approx11.3\%$ and final spin $\alpha_{\rm rem}^{\rm
max}\approx0.952$ for equal mass, aligned maximally spinning binaries.


  In this paper, we introduce a generalization of Liu-Yang's weighted norm to
linear and to nonlinear hyperbolic equations. Extending a result by Hu and
LeFloch for piecewise constant solutions, we establish sharp L1 continuous
dependence estimates for general solutions of bounded variation. Two different
strategies are successfully investigated. On one hand, we justify passing to
the limit in an L1 estimate valid for piecewise constant wave-front tracking
approximations. On the other hand, we use the technique of generalized
characteristics and, following closely an approach by Dafermos, we derive the
sharp L1 estimate directly from the equation.


  We propose a scheme to deal with certain time-dependent non-Hermitian
Hamiltonian operators $H(t)$ that generate a real phase in their
time-evolution. This involves the use of invariant operators $I_{PH}(t)$ that
are pseudo-Hermitian with respect to the time-dependent metric operator, which
implies that the dynamics is governed by unitary time evolution. Furthermore,
$H(t)$ is not generally quasi-Hermitian and does not define an observable of
the system but $I_{PH}(t)$ obeys a quasi-hermiticity transformation as in the
completely time-independent Hamiltonian systems case. The harmonic oscillator
with a time-dependent frequency under the action of a complex time-dependent
linear potential is considered as an illustrative example.


  Traditional art pricing models often lack fine measurements of painting
content. This paper proposes a new content measurement: the Shannon information
quantity measured by the singular value decomposition (SVD) entropy of the
painting image. Using a large sample of artworks' auction records and images,
we show that the SVD entropy positively affects the sales price at 1%
significance level. Compared to the other commonly adopted content variables,
the SVD entropy has advantages in variable significance, sample robustness as
well as model fit. Considering the convenient availability of digital painting
images and the straightforward calculation algorithm of this measurement, we
expect its wide application in future research.


  In this paper we study the dynamics of 1- and 2- dimensional cellular
automata, using a 2-adic representation of the states, we give a simple
graphical technique for finding periodic solutions. We also study the
continuity properties of the associated 2-adic system and show how to compute
the entropy.


  Tidal effects in capped geometries computed in previous literature display no
dynamics along internal (toroidal) directions. However, the dual CFT picture
suggests otherwise. To resolve this tension, we consider a set of infalling
null geodesics in a family of black hole microstate geometries with a smooth
cap at the bottom of a long BTZ-like throat. Using the Penrose limit, we show
that a string following one of these geodesics feels tidal stresses along all
spatial directions, including internal toroidal directions. We find that the
tidal effects along the internal directions are of the same order of magnitude
as those along other, non-internal, directions. Furthermore, these tidal
effects oscillate as a function of the distance from the cap -- as a string
falls down the throat it alternately experiences compression and stretching. We
explain some physical properties of this oscillation and comment on the dual
CFT interpretation.


  Whole Slide Images (WSIs) provide exceptional detail for studying tissue
architecture at the cell level. To study tumour microenvironment (TME) with the
context of various protein biomarkers and cell sub-types, analysis and
registration of features using multi-stained WSIs is often required.
Multi-stained WSI pairs normally suffer from rigid and non-rigid deformities in
addition to slide artefacts and control tissue which present challenges at
precise registration. Traditional registration methods mainly focus on global
rigid/non-rigid registration but struggle with aligning slides with complex
tissue deformations at the nuclei level. However, nuclei level non-rigid
registration is essential for downstream tasks such as cell sub-type analysis
in the context of protein biomarker signatures. This paper focuses on local
level non-rigid registration using a nuclei-location based point set
registration approach for aligning multi-stained WSIs. We exploit the spatial
distribution of nuclei that is prominent and consistent (to a large level)
across different stains to establish a spatial correspondence. We evaluate our
approach using the HYRECO dataset consisting of 54 re-stained images of H\&E
and PHH3 image pairs. The approach can be extended to other IHC and IF stained
WSIs considering a good nuclei detection algorithm is accessible. The
performance of the model is tested against established registration algorithms
and is shown to outperform the model for nuclei level registration.


  It is still a challenging problem to the theoretical physicists to know the
exact nature of the galactic dark matter which causes the galactic rotational
velocity to be more or less a constant. We have proposed that the dark matter
as an effect of f(T) gravity. Assuming the flat rotation curves as input we
have shown that f(T) gravity can explain galactic dynamics. Here, we don' have
to introduce dark matter. Spacetime metric inspired by f(T) gravity describes
the region up to which the tangential velocity of the test particle is
constant. This inherent property appears to be enough to produce stable
circular orbits as well as attractive gravity.


  A measurement of transverse polarisation in pbar-p -> all-neutral final
states would almost certainly determine a complete set of partial wave
amplitudes over the mass range 1910 to 2410 MeV. This should identify all
resonances in this mass range. The experiment is technically straightforward
and cheap by present standards.


  Topological phonon polaritons (TPhPs) are promising optical modes relevant in
long-range radiative heat transfer, information processing and infrared
sensing, whose topological protection is expected to enable their robust
existence and transport. In this work we show that TPhPs can be supported in
one-dimensional (1D) bichromatic silicon carbide nanoparticle (NP) chains, and
demonstrate that they can considerably enhance radiative heat transfer for an
array much longer than the wavelength of radiation. By introducing
incommensurate or commensurate modulations on the interparticle distances, the
NP chain can be regarded as an extension of the off-diagonal
Aubry-Andr\'e-Harper (AAH) model. By calculating the eigenstate spectra with
respect to the modulation phase that creates a synthetic dimension, we
demonstrate that under this type of modulation the chain supports nontrivial
topological modes localized over the boundaries, since the present system
inherits the topological property of two-dimensional integer quantum Hall
systems. In this circumstance the gap-labeling theorem and corresponding Chern
number can be used to characterize the features of band gaps and topological
edge modes. Based on many-body radiative heat transfer theory for a set of
dipoles, we theoretically show the presence of topological gaps and midgap
TPhPs can substantially enhance radiative heat transfer for an array much
longer than the wavelength of radiation. We show how the modulation phase that
acts as the synthetic dimension can tailor the radiative heat transfer rate by
inducing or annihilating topological modes. We also discuss the role of
dissipation in the enhancement of radiative heat transfer. These findings
therefore provide a fascinating route for tailoring near-field radiative heat
transfer based on the concept of topological physics.


  Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide
and play an important role in many urban transportation systems. BSSs alleviate
congestion, reduce pollution and promote physical exercise. It is essential to
explore the spatiotemporal patterns of bike-sharing demand, as well as the
factors that influence these patterns, in order to optimise system operational
efficiency. In this study, an optimised geo-temporal graph is constructed using
trip data from Moby Bikes, a dockless BSS operator. The process of optimising
the graph unveiled prime locations for erecting new stations during future
expansions of the BSS. The Louvain algorithm, a community detection technique,
is employed to uncover usage patterns at different levels of temporal
granularity. The community detection results reveal largely self-contained
sub-networks that exhibit similar usage patterns at their respective levels of
temporal granularity. Overall, this study reinforces that BSSs are
intrinsically spatiotemporal systems, with community presence driven by
spatiotemporal dynamics. These findings may aid operators in improving
redistribution efficiency.