  Using TESS photometry and Rozhen spectra of the Be/gamma-ray binaries MWC 148
and MWC 656, we estimate the projected rotational velocity ($ {v} \sin i$), the
rotational period (P$_{\rm rot}$), radius (R$_{\rm 1}$), and inclination ($i$)
of the mass donor. For MWC 148 we derive P$_{\rm rot} = 1.10 \pm 0.03$~d,
R$_{\rm 1}= 9.2 \pm 0.5$~R$_\odot$, $i = 40^\circ \pm 2^\circ$, and $ {v} \sin
i =272 \pm 5$~km~s$^{-1}$. For MWC 656 we obtain P$_{\rm rot} = 1.12 \pm
0.03$~d, R$_{\rm 1}= 8.8 \pm 0.5$~R$_\odot$, $i = 52^\circ \pm 3^\circ$, and $
{v} \sin i =313 \pm 3$~km~s$^{-1}$. For MWC 656 we also find that the rotation
of the mass donor is coplanar with the orbital plane.


  Electron-phonon coupling in magic-angle twisted bilayer graphene is an
important but difficult topic. We propose a scheme to simplify and understand
this problem. Weighted by the coupling strength with the low-energy heavy
electrons ($f$ orbitals), several moir\'{e} optical phonons are singled out
which strongly couple to the flat bands. These modes have localized envelopes
in the moir\'{e} scale, while in the atomic scale they inherit the monolayer
oscillations like the Kekul\'{e} pattern. They flip the flavor of $f$ orbitals,
helping stabilize some symmetry-breaking orders. Such electron-phonon couplings
are incorporated into an effective extended Holstein model, where both phonons
and electrons are written as moir\'{e} scale basis. We hope this model will
inspire some insights guiding further studies about the superconductivity and
other correlated effects in this system.


  On the projective plane there is a unique cubic root of the canonical bundle
and this root is acyclic. On fake projective planes such root exists and is
unique if there are no 3-torsion divisors (and usually exists, but not unique,
otherwise). Earlier we conjectured that any such cubic root must be acyclic. In
the present note we give two short proofs of this statement and show acyclicity
of some other line bundles on the fake projective planes with at least $9$
automorphisms. Similarly to our earlier work we employ simple representation
theory for non-abelian finite groups. The first proof is based on the
observation that if some line bundle is non-linearizable with respect to a
finite abelian group, then it should be linearized by a finite,
\emph{non-abelian}, Heisenberg group. For the second proof, we also demonstrate
vanishing of odd Betti numbers for a class of abelian covers, and use
linearization of an auxiliary line bundle as well.


  We analyse the energy cost of building or demolishing a wall for a massless
Dirac field in (1+1)-dimensional Minkowski spacetime and the response of an
Unruh-DeWitt particle detector to the generated radiation. For any
smoothly-evolving wall, both the field's energy density and the detector's
response are finite. In the limit of rapid wall creation or demolition, the
energy density displays a delta function squared divergence. By contrast, the
response of an Unruh-DeWitt detector, evaluated within first-order perturbation
theory, diverges only logarithmically in the duration of the wall evolution.
The results add to the evidence that a localised matter system may not be as
sensitive to the rapid wall creation as the local expectation values of field
observables. This disparity has potential interest for quantum information
preservation scenarios.


  Unrestricted DFT methods are typically expected to describe the homolytic
dissociation of nonpolar single bonds in neutral species with qualitative
accuracy, due to the lack of significant delocalization error. We however find
that many widely used density functional approximations fail to describe
features along the dissociation curve of the simple H$_2$ molecule. This is not
an universal failure of DFT in the sense that many classic functionals like PBE
and B3LYP give very reasonable results, as do some more modern methods like
MS2. However, some other widely used functionals like B97-D (empirically
fitted) and TPSS (non-empirically constrained) predict qualitatively wrong
static polarizabilities, force constants and some even introduce an artificial
barrier against association of independent H atoms to form H$_2$. The
polarizability and force constant prediction failures appear to stem from
incomplete spin localization into individual H atoms beyond the Coulson-Fisher
point, resulting in `fractionally bonded' species where the ionic contributions
to the Slater determinant are not completely eliminated, unlike the case of
unrestricted Hartree-Fock. These errors therefore appear to be a consequence of
poor self-consistent density prediction by the problematic functional. The same
reasons could potentially lead to spurious barriers towards H atom association,
indirectly also leading to incorrect forces. These unphysicalities suggest that
the use of problematic functionals is probably unwise in \textit{ab initio}
dynamics calculations, especially if strong electrostatic interactions are
possible.


  Recently, there has been much attention devoted to resolving the quantum
corrections to the Bekenstein-Hawking (black hole) entropy, which relates the
entropy to the cross-sectional area of the black hole horizon. Using
generalized uncertainty principle (GUP), corrections to the geometric entropy
and thermodynamics of black hole will be introduced. The impact of GUP on the
entropy near the horizon of three types of black holes; Schwarzschild,
Garfinkle-Horowitz-Strominger and Reissner-Nordstr\"om is determined. It is
found that the logarithmic divergence in the entropy-area relation turns to be
positive. The entropy $S$, which is assumed to be related to horizon's
two-dimensional area, gets an additional terms, for instance $2\, \sqrt{\pi}\,
\alpha\, \sqrt{S}$, where $\alpha$ is the GUP parameter.


  Granato et al(2004) have elaborated a physically grounded model exploiting
the mutual feedback between star-forming spheroidal galaxies and the active
nuclei growing in their cores to overcome, within the hierarchical clustering
scenario for galaxy formation, one of the main challenges facing such scenario,
the fact that massive spheroidal galaxies appear to have formed earlier and
faster than predicted by previous models. Adopting the choice by Granato et al
for the parameters governing the history of the SF,of chemical abundances and
of the gas and dust content of galaxies, we are left with only two parameters
affecting the time and mass dependent SED of spheroidal galaxies. After
complementing the model with a simple description of evolutionary properties of
starburst, normal late-type galaxies and AGNs we have successfully compared the
model with a broad variety of observational data, deep K-band, ISO, IRAS,
SCUBA, radio counts, the corresponding redshift distributions, the IR
background spectrum, and also with data for EROs. We also present detailed
predictions for the GOODS and SWIRE surveys with the Spitzer Space Telescope.
We find that the GOODS deep survey at 24$\mu$m and the SWIRE surveys at 70 and
160$\mu$m are likely to be severely confusion limited. The GOODS surveys in the
IRAC channels are expected to resolve most of the background, to explore the
full passive evolution phase of spheroidal galaxies and most of their active
star-forming phase, detecting galaxies up to z\simeq 4. A substantial number of
high z star-forming spheroidal galaxies should also be detected by the 24\mum
SWIRE and GOODS surveys, while the 70 and 160\mum will be particularly useful
to study the evolution of such galaxies in the range 1 \lsim z \lsim
2.[abridged]


  Face parsing assigns pixel-wise semantic labels as the face representation
for computers, which is the fundamental part of many advanced face
technologies. Compared with 2D face parsing, 3D face parsing shows more
potential to achieve better performance and further application, but it is
still challenging due to 3D mesh data computation. Recent works introduced
different methods for 3D surface segmentation, while the performance is still
limited. In this paper, we propose a method based on the "3D-2D-3D" strategy to
accomplish 3D face parsing. The topological disk-like 2D face image containing
spatial and textural information is transformed from the sampled 3D face data
through the face parameterization algorithm, and a specific 2D network called
CPFNet is proposed to achieve the semantic segmentation of the 2D parameterized
face data with multi-scale technologies and feature aggregation. The 2D
semantic result is then inversely re-mapped to 3D face data, which finally
achieves the 3D face parsing. Experimental results show that both CPFNet and
the "3D-2D-3D" strategy accomplish high-quality 3D face parsing and outperform
state-of-the-art 2D networks as well as 3D methods in both qualitative and
quantitative comparisons.


  The imaginary-time evolution of quantum states is integral to various fields,
ranging from natural sciences to classical optimization or machine learning.
Since simulating quantum imaginary-time evolution generally requires storing an
exponentially large wave function, quantum computers are emerging as a
promising platform for this task. However, variational approaches, suitable for
near-term quantum computers, struggle with a prohibitive number of measurements
and impractical runtimes for relevant system sizes. Here, we suggest a
stochastic approach to variational quantum imaginary-time evolution, which
allows a significant reduction in runtimes. Our approach allows trading off
invested resources and accuracy, which makes it also suitable for ground state
preparation, where simulating the exact dynamics is not required. We
demonstrate the efficiency of our algorithm in simulations and show a hardware
experiment performing the imaginary-time evolution of the transverse field
Ising model on 27 qubits.


  We find the Courant-sharp Neumann eigenvalues of the Laplacian on some
2-rep-tile domains. In $\R^{2}$ the domains we consider are the isosceles right
triangle and the rectangle with edge ratio $\sqrt{2}$ (also known as the A4
paper). In $\R^{n}$ the domains are boxes which generalize the mentioned planar
rectangle. The symmetries of those domains reveal a special structure of their
eigenfunctions, which we call folding\textbackslash{}unfolding. This structure
affects the nodal set of the eigenfunctions, which in turn allows to derive
necessary conditions for Courant-sharpness. In addition, the eigenvalues of
these domains are arranged as a lattice which allows for a comparison between
the nodal count and the spectral position. The Courant-sharpness of most
eigenvalues is ruled out using those methods. In addition, this analysis allows
to estimate the nodal deficiency - the difference between the spectral position
and the nodal count.


  In this paper, we study the existence of weak solutions to a steady system
that describes the motion of a micropolar electrorheological fluid. The
constitutive relations for the stress tensors belong to the class of
generalized Newtonian fluids. The analysis of this particular problem leads
naturally to weighted variable exponent Sobolev spaces. We establish the
existence of solutions for a material function $\hat p$ that is
$\log$--H\"older continuous and an electric field $\mathbf{E}$ for that
$\vert\mathbf{E}\vert^2$ is bounded and smooth. Note that these conditions do
not imply that the variable shear exponent $p=\hat p\circ\vert
\mathbf{E}\vert^2$ is globally $\log$--H\"older continuous.


  Globular clusters provide a unique probe of galaxy formation and evolution.
Here I briefly summarize the known observational properties of globular cluster
systems. One re-occurring theme is that the globular cluster systems of spirals
and ellipticals are remarkably similar. Photometry, and the limited spectra
available, are consistent with metal-poor clusters forming before the main
spheroid component is established and the metal-rich ones forming at the same
time as the spheroid in a burst of star formation. These observations are
compared to a model for globular cluster formation in a LCDM hierarchical
universe. One model result reported here is that S_N is determined at early
times and little affected by late epoch mergers.


  The dynamics of a propagating magnetic island interacting with a resistive
wall or a static external magnetic perturbation is investigated using
two-fluid, drift-MHD theory in slab geometry. In both cases, the island
equation of motion is found to take exactly the same form as that predicted by
single-fluid MHD theory. Three separate ion polarization terms are found in the
Rutherford island width evolution equation. The first is the drift-MHD
polarization term for an isolated island, and is completely unaffected by
interaction with a wall or magnetic perturbation. Next, there is the
polarization term due to interaction with a wall or magnetic perturbation which
is predicted by single-fluid MHD theory. Finally, there is a hybrid of the
other two polarization terms. The sign of this term depends on many factors.
However, under normal conditions, it is stabilizing if the unperturbed island
propagates in the ion diamagnetic direction (in the lab. frame), and
destabilizing if it propagates in the electron diamagnetic direction.


  We have carried out boundary-layer-resolved, unstructured fully-compressible
Navier--Stokes simulations of an ultrasonic standing-wave thermoacoustic engine
(TAE) model. The model is constructed as a quarter-wavelength engine,
approximately 4 mm by 4 mm in size and operating at 25 kHz, and comprises a
thermoacoustic stack and a coin-shaped cavity, a design inspired by Flitcroft
and Symko (2013). Thermal and viscous boundary layers (order of 10
$\mathrm{\mu}$m) are resolved. Vibrational and rotational molecular relaxation
are modeled with an effective bulk viscosity coefficient modifying the viscous
stress tensor. The effective bulk viscosity coefficient is estimated from the
difference between theoretical and semi-empirical attenuation curves.
Contributions to the effective bulk viscosity coefficient can be identified as
from vibrational and rotational molecular relaxation. The inclusion of the
coefficient captures acoustic absorption from infrasonic ($\sim$10 Hz) to
ultrasonic ($\sim$100 kHz) frequencies. The value of bulk viscosity depends on
pressure, temperature, and frequency, as well as the relative humidity of the
working fluid. Simulations of the TAE are carried out to the limit cycle, with
growth rates and limit-cycle amplitudes varying non-monotonically with the
magnitude of bulk viscosity, reaching a maximum for a relative humidity level
of 5%. A corresponding linear model with minor losses was developed; the linear
model overpredicts transient growth rate but gives an accurate estimate of
limit cycle behavior. An improved understanding of thermoacoustic energy
conversion in the ultrasonic regime based on a high-fidelity computational
framework will help to further improve the power density advantages of
small-scale thermoacoustic engines.


  Early detection of COVID-19 is vital to control its spread. Deep learning
methods have been presented to detect suggestive signs of COVID-19 from chest
CT images. However, due to the novelty of the disease, annotated volumetric
data are scarce. Here we propose a method that does not require either large
annotated datasets or backpropagation to estimate the filters of a
convolutional neural network (CNN). For a few CT images, the user draws markers
at representative normal and abnormal regions. The method generates a feature
extractor composed of a sequence of convolutional layers, whose kernels are
specialized in enhancing regions similar to the marked ones, and the decision
layer of our CNN is a support vector machine. As we have no control over the CT
image acquisition, we also propose an intensity standardization approach. Our
method can achieve mean accuracy and kappa values of $0.97$ and $0.93$,
respectively, on a dataset with 117 CT images extracted from different sites,
surpassing its counterpart in all scenarios.


  Which $4$-manifolds admit a flag-no-square (fns) triangulation? We introduce
the "star-connected-sum" operation on such triangulations, which preserves the
fns property, from which we derive new constructions of fns $4$-manifolds. In
particular, we show the following: (i) there exist non-aspherical fns
$4$-manifolds, answering in the negative a question by Przytycki and
Swiatkowski; (ii) for every large enough integer $k$ there exists a fns
$4$-manifold $M_{2k}$ of Euler characteristic $2k$, and further, (iii) $M_{2k}$
admits a super-exponential number (in $k$) of fns triangulations - at least
$2^{\Omega(k \log k)}$ and at most $2^{O(k^{1.5} \log k)}$.


  Our aim is to study the backward problem, i.e. recover the initial data from
the terminal observation, of the subdiffusion with time dependent coefficients.
First of all, by using the smoothing property of solution operators and a
perturbation argument of freezing the diffusion coefficients, we show a
stability estimate in Sobolev spaces, under some smallness/largeness condition
on the terminal time. Moreover, in case of noisy observation, we apply a
quasi-boundary value method to regularize the problem and then show the
convergence of the regularization scheme. Finally, to numerically reconstruct
the initial data, we propose a completely discrete scheme by applying the
finite element method in space and backward Euler convolution quadrature in
time. An \textsl{a priori} error estimate is then established. The proof is
heavily built on a perturbation argument dealing with time dependent
coefficients and some nonstandard error estimates for the direct problem. The
error estimate gives a useful guide for balancing discretization parameters,
regularization parameter and noise level. Some numerical experiments are
presented to illustrate our theoretical results.


  We consider a stochastic financial incomplete market where the price
processes are described by a vector-valued semimartingale that is possibly
nonlocally bounded. We face the classical problem of utility maximization from
terminal wealth, with utility functions that are finite-valued over
$(a,\infty)$, $a\in\lbrack-\infty,\infty)$, and satisfy weak regularity
assumptions. We adopt a class of trading strategies that allows for stochastic
integrals that are not necessarily bounded from below. The embedding of the
utility maximization problem in Orlicz spaces permits us to formulate the
problem in a unified way for both the cases $a\in\mathbb{R}$ and $a=-\infty$.
By duality methods, we prove the existence of solutions to the primal and dual
problems and show that a singular component in the pricing functionals may also
occur with utility functions finite on the entire real line.


  Imaging polarimetry of the 0.85mm continuum emission in the NGC 7538 region,
obtained with the SCUBA Polarimeter, is presented. The polarization map is
interpreted in terms of thermal radiation by magnetically aligned dust grains.
Two prominent cores associated with IRS 1 and IRS 11, IRS 1(SMM) and IRS
11(SMM), are found in the surface brightness map. Although these cores look
similar in surface brightness, their polarization shows striking differences.
In IRS 11(SMM), the polarization vectors are extremely well ordered, and the
degrees of polarization are quite high with an average of ~3.9 %. In IRS
1(SMM), on the other hand, the directions of polarization vectors are locally
disturbed, and the degrees of polarization are much lower than those of IRS
11(SMM). These differences suggest that small scale fluctuations of the
magnetic field are more prominent in IRS 1(SMM). This can be interpreted in
terms of the difference in evolutionary stage of the cores. Inside IRS 1(SMM),
which seems to be at a later evolutionary stage than IRS 11(SMM), substructures
such as subclumps or a cluster of infrared sources have already formed. Small
scale fluctuations in the magnetic field could have developed during the
formation of these substructures. The distribution of magnetic field directions
derived from our polarization map agrees well with those of molecular outflows
associated with IRS 1(SMM) and IRS 11(SMM). Comparisons of energy densities
between the magnetic field and the outflows show that the magnetic field
probably plays an important role in guiding the directions of the outflows.


  By generalizing a construction of Garling, for each $1\leqslant p<\infty$ and
each normalized, nonincreasing sequence of positive numbers $w\in
c_0\setminus\ell_1$ we exhibit an $\ell_p$-saturated, complementably
homogeneous Banach space $g(w,p)$ related to the Lorentz sequence space
$d(w,p)$. Using methods originally developed for studying $d(w,p)$, we show
that $g(w,p)$ admits a unique (up to equivalence) subsymmetric basis, although
when $w=(n^{-\theta})_{n=1}^\infty$ for some $0<\theta<1$, it does not admit a
symmetric basis. We then discuss some additional properties of $g(w,p)$ related
to uniform convexity and superreflexivity.


  In order to identify the rapidly-fading, optical transient counterparts of
gravitational wave (GW) sources, an efficient follow-up strategy is required.
Since most ground-based optical observatories aimed at following-up GW sources
have a telescope with a small field-of-view (FOV) as compared to the GW sky
error region, we focus on a search strategy that involves dividing the GW patch
into tiles of the same area as the telescope FOV to strategically image the
entire patch. We present an improvement over the optimal telescope-scheduling
algorithm outlined in Rana et al. (2016), by combining the tiling and
galaxy-targeted search strategies, and factoring the effects of the source
airmass and telescope slew, along with setting constraints, into the scheduling
algorithm in order to increase the chances of identifying the GW counterpart.
We propose two separate algorithms: the airmass-weighted algorithm, a specific
solution to the Hungarian algorithm that maximizes probability acquired, while
minimizing the image airmass, and the slew-optimization algorithm that
minimizes the overall slew angle covered between images for the given
probability acquired by the optimal telescope-scheduling algorithm in Rana et
al. (2016). Using the observatory site of the GROWTH-India telescope as an
example, we generate 100s of skymaps to test the performance of our algorithms.
Our results indicate that slew-optimization can reduce the cumulative slew
angle in the observing schedule by 100s of degrees, saving several of minutes
of observing time without the loss of tiles and probability. Further, we
demonstrate that as compared to the greedy algorithm, the airmass-weighted
algorithm can acquire up to 20 % more probability and 30 sq. deg. more in areal
coverage for skymaps of all sizes and configurations.


  Let $u_k$ be a solution of the Helmholtz equation with the wave number $k$,
$\Delta u_k+k^2 u_k=0$, on a small ball in either $\mathbb{R}^n$,
$\mathbb{S}^n$, or $\mathbb{H}^n$. For a fixed point $p$, we define
$M_{u_k}(r)=\max_{d(x,p)\le r}|u_k(x)|.$ The following three ball inequality
$M_{u_k}(2r)\le C(k,r,\alpha)M_{u_k}(r)^{\alpha}M_{u_k}(4r)^{1-\alpha}$ is well
known, it holds for some $\alpha\in (0,1)$ and $C(k,r,\alpha)>0$ independent of
$u_k$. We show that the constant $C(k,r,\alpha)$ grows exponentially in $k$
(when $r$ is fixed and small). We also compare our result with the increased
stability for solutions of the Cauchy problem for the Helmholtz equation on
Riemannian manifolds.


  We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_\circ$ need to be convex but not smooth. If $f$ is smooth, the
proposed Block-Simultaneous Direction Method of Multipliers (bSDMM) can be
interpreted as a proximal analog to inexact coordinate descent methods under
constraints. Unlike alternative approaches for joint solvers of
multiple-constraint problems, we do not require linear operators $L$ of a
constraint function $g(L\ \cdot)$ to be invertible or linked between each
other. bSDMM is well-suited for a range of optimization problems, in particular
for data analysis, where $f$ is the likelihood function of a model and $L$
could be a transformation matrix describing e.g. finite differences or basis
transforms. We apply bSDMM to the Non-negative Matrix Factorization task of a
hyperspectral unmixing problem and demonstrate convergence and effectiveness of
multiple constraints on both matrix factors. The algorithms are implemented in
python and released as an open-source package.


  Deep learning (DL) defines a new data-driven programming paradigm where the
internal system logic is largely shaped by the training data. The standard way
of evaluating DL models is to examine their performance on a test dataset. The
quality of the test dataset is of great importance to gain confidence of the
trained models. Using an inadequate test dataset, DL models that have achieved
high test accuracy may still lack generality and robustness. In traditional
software testing, mutation testing is a well-established technique for quality
evaluation of test suites, which analyzes to what extent a test suite detects
the injected faults. However, due to the fundamental difference between
traditional software and deep learning-based software, traditional mutation
testing techniques cannot be directly applied to DL systems. In this paper, we
propose a mutation testing framework specialized for DL systems to measure the
quality of test data. To do this, by sharing the same spirit of mutation
testing in traditional software, we first define a set of source-level mutation
operators to inject faults to the source of DL (i.e., training data and
training programs). Then we design a set of model-level mutation operators that
directly inject faults into DL models without a training process. Eventually,
the quality of test data could be evaluated from the analysis on to what extent
the injected faults could be detected. The usefulness of the proposed mutation
testing techniques is demonstrated on two public datasets, namely MNIST and
CIFAR-10, with three DL models.


  We use methods inspired from complex Tauberian theorems to make progress in
understanding the asymptotic behavior of the magnitude of heavy-light-heavy
three point coefficients rigorously. The conditions and the precise sense of
averaging, which can lead to exponential suppression of such coefficients are
investigated. We derive various bounds for the typical average value of the
magnitude of heavy-light-heavy three point coefficients and verify them
numerically.


  In this paper, we argue that machine learning techniques are not ready for
malware detection in the wild. Given the current trend in malware development
and the increase of unconventional malware attacks, we expect that dynamic
malware analysis is the future for antimalware detection and prevention
systems. A comprehensive review of machine learning for malware detection is
presented. Then, we discuss how malware detection in the wild present unique
challenges for the current state-of-the-art machine learning techniques. We
defined three critical problems that limit the success of malware detectors
powered by machine learning in the wild. Next, we discuss possible solutions to
these challenges and present the requirements of next-generation malware
detection. Finally, we outline potential research directions in machine
learning for malware detection.


  Dragonfly interconnect is a crucial network technology for supercomputers. To
support exascale systems, network resources are shared such that links and
routers are not dedicated to any node pair. While link utilization is
increased, workload performance is often offset by network contention.
Recently, intelligent routing built on reinforcement learning demonstrates
higher network throughput with lower packet latency. However, its effectiveness
in reducing workload interference is unknown. In this work, we present
extensive network simulations to study multi-workload contention under
different routing mechanisms, intelligent routing and adaptive routing, on a
large-scale Dragonfly system. We develop an enhanced network simulation
toolkit, along with a suite of workloads with distinctive communication
patterns. We also present two metrics to characterize application communication
intensity. Our analysis focuses on examining how different workloads interfere
with each other under different routing mechanisms by inspecting both
application-level and network-level metrics. Several key insights are made from
the analysis.


  Computing moments of various parameter estimators related to an
autoregressive model of Statistics, one needs to evaluate several expressions
of the type mentioned in the title of this article. We proceed to derive the
corresponding formulas.


  During its early evolution the Universe provided a laboratory to probe
fundamental physics at high energies. Relics from those early epochs, such as
the light elements synthesized during primordial nucleosynthesis when the
Universe was only a few minutes old, and the cosmic background photons, last
scattered when the protons (and alphas) and electrons (re)combined some 400
thousand years later, may be used to probe the standard models of cosmology and
of particle physics. The internal consistency of primordial nucleosynthesis is
tested by comparing the predicted and observed abundances of the light
elements, and the consistency of the standard models is explored by comparing
the values of the cosmological parameters inferred from primordial
nucleosynthesis with those determined by studying the cosmic background
radiation.


  We construct a generalization of the Hasse invariant for any Shimura variety
of PEL type A over a prime of good reduction, whose vanishing locus is the open
and dense \mu-ordinary locus.


  The graphene-like two-dimensional (2D) silicon carbide or siligraphene has
attracted remarkable attentions, owing to its fascinating physical properties.
Nevertheless, the first high-quality siligraphene, i.e., monolayer Si9C15 was
synthesised very recently, which exhibits an excellent semiconducting
behaviour. In this work, we investigate the mechanical properties of Si9C15
siligraphene by using atomistic simulations including density functional theory
(DFT) calculations and molecular dynamics (MD) simulations. Both methods
confirm the existence of intrinsic negative Poisson's ratios in Si9C15
siligraphene, which, as illustrated by MD simulations, is attributed to the
tension-induced de-wrinkling behaviours of its intrinsic rippled configuration.
Different de-wrinkling behaviours are observed in different directions of
Si9C15 siligraphene, which result in the anisotropy of its auxetic property.
The fracture property of Si9C15 siligraphene is similarly anisotropic, but
relatively large fracture strains are observed in different orientations,
indicating the stretchability of Si9C15 siligraphene. The stretchability
together with the strain-sensitive bandgap of Si9C15 siligraphene observed in
DFT calculations indicates the effectiveness of strain engineering in
modulating its electronic property. The combination of unique auxetic property,
excellent mechanical property and tunable electronic property may render Si9C15
siligraphene a novel 2D material with multifunctional applications.


  We propose a block-online algorithm of guided source separation (GSS). GSS is
a speech separation method that uses diarization information to update
parameters of the generative model of observation signals. Previous studies
have shown that GSS performs well in multi-talker scenarios. However, it
requires a large amount of calculation time, which is an obstacle to the
deployment of online applications. It is also a problem that the offline GSS is
an utterance-wise algorithm so that it produces latency according to the length
of the utterance. With the proposed algorithm, block-wise input samples and
corresponding time annotations are concatenated with those in the preceding
context and used to update the parameters. Using the context enables the
algorithm to estimate time-frequency masks accurately only from one iteration
of optimization for each block, and its latency does not depend on the
utterance length but predetermined block length. It also reduces calculation
cost by updating only the parameters of active speakers in each block and its
context. Evaluation on the CHiME-6 corpus and a meeting corpus showed that the
proposed algorithm achieved almost the same performance as the conventional
offline GSS algorithm but with 32x faster calculation, which is sufficient for
real-time applications.


  Traveler's dilemma (TD) is one of social dilemmas which has been well studied
in the economics community, but it is attracted little attention in the physics
community. The TD game is a two-person game. Each player can select an integer
value between $R$ and $M$ ($R < M$) as a pure strategy. If both of them select
the same value, the payoff to them will be that value. If the players select
different values, say $i$ and $j$ ($R \le i < j \le M$), then the payoff to the
player who chooses the small value will be $i+R$ and the payoff to the other
player will be $i-R$. We term the player who selects a large value as the
cooperator, and the one who chooses a small value as the defector. The reason
is that if both of them select large values, it will result in a large total
payoff. The Nash equilibrium of the TD game is to choose the smallest value
$R$. However, in previous behavioral studies, players in TD game typically
select values that are much larger than $R$, and the average selected value
exhibits an inverse relationship with $R$. To explain such anomalous behavior,
in this paper, we study the evolution of cooperation in spatial traveler's
dilemma game where the players are located on a square lattice and each player
plays TD games with his neighbors. Players in our model can adopt their
neighbors' strategies following two standard models of spatial game dynamics.
Monte-Carlo simulation is applied to our model, and the results show that the
cooperation level of the system, which is proportional to the average value of
the strategies, decreases with increasing $R$ until $R$ is greater than the
threshold where cooperation vanishes. Our findings indicate that spatial
reciprocity promotes the evolution of cooperation in TD game and the spatial TD
game model can interpret the anomalous behavior observed in previous behavioral
experiments.


  Text style transfer aims to paraphrase a sentence in one style into another
style while preserving content. Due to lack of parallel training data,
state-of-art methods are unsupervised and rely on large datasets that share
content. Furthermore, existing methods have been applied on very limited
categories of styles such as positive/negative and formal/informal. In this
work, we develop a meta-learning framework to transfer between any kind of text
styles, including personal writing styles that are more fine-grained, share
less content and have much smaller training data. While state-of-art models
fail in the few-shot style transfer task, our framework effectively utilizes
information from other styles to improve both language fluency and style
transfer accuracy.


  In this paper we provide a straightforward proof that if a pair of amicable
numbers with different parity exists (one number odd and the other one even),
then the odd amicable number must be a perfect square, while the even amicable
number has to be equal to the product of a power of 2 and an odd perfect
square.


  Existing approaches to system identification (estimating the physical
parameters of an object) from videos assume known object geometries. This
precludes their applicability in a vast majority of scenes where object
geometries are complex or unknown. In this work, we aim to identify parameters
characterizing a physical system from a set of multi-view videos without any
assumption on object geometry or topology. To this end, we propose "Physics
Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the
unknown geometry and physical parameters of highly dynamic objects from
multi-view videos. We design PAC-NeRF to only ever produce physically plausible
states by enforcing the neural radiance field to follow the conservation laws
of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian
representation of the neural radiance field, i.e., we use the Eulerian grid
representation for NeRF density and color fields, while advecting the neural
radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian
representation seamlessly blends efficient neural rendering with the material
point method (MPM) for robust differentiable physics simulation. We validate
the effectiveness of our proposed framework on geometry and physical parameter
estimation over a vast range of materials, including elastic bodies,
plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate
significant performance gain on most tasks.


  High-definition (HD) map provides abundant and precise static environmental
information of the driving scene, serving as a fundamental and indispensable
component for planning in autonomous driving system. In this paper, we present
\textbf{Map} \textbf{TR}ansformer, an end-to-end framework for online
vectorized HD map construction. We propose a unified permutation-equivalent
modeling approach, \ie, modeling map element as a point set with a group of
equivalent permutations, which accurately describes the shape of map element
and stabilizes the learning process. We design a hierarchical query embedding
scheme to flexibly encode structured map information and perform hierarchical
bipartite matching for map element learning. To speed up convergence, we
further introduce auxiliary one-to-many matching and dense supervision. The
proposed method well copes with various map elements with arbitrary shapes. It
runs at real-time inference speed and achieves state-of-the-art performance on
both nuScenes and Argoverse2 datasets. Abundant qualitative results show stable
and robust map construction quality in complex and various driving scenes. Code
and more demos are available at \url{https://github.com/hustvl/MapTR} for
facilitating further studies and applications.


  This paper is devoted to the analysis of the long-time behavior of a
phenotypic-structured model where phenotypic changes do not occur. We give a
mathematical description of the process in which the best adapted trait is
selected in a given environment created by the total population. It is
exhibited that the long-time limit of the unique solution to the nonlocal
equation is given by a Dirac mass centered at the peak of the fitness within or
at the boundary of the region where the initial data is positive. Specially, If
the peak of the fitness can't be in the support of the solution, then the
infinite time blow-up of the solution occurs near the boundary of the region
where the solution is positive. Moreover, our numerical results facilitate a
deeper understanding of identifying the position of the centers.


  (Abridged) We present the results of variable star searches of the M31 dwarf
spheroidal companions Andromeda I and Andromeda III using the Hubble Space
Telescope. A total of 100 variable stars were found in Andromeda I, while 56
were found in Andromeda III. One variable found in Andromeda I and another in
Andromeda III may be Population II Cepheids. In addition to this variable in
Andromeda III, another four variables are anomalous Cepheids. We found 72
fundamental mode RR Lyrae stars and 26 first-overtone mode RR Lyrae stars in
Andromdea I giving mean periods of 0.575 day and 0.388 day, respectively. One
likely RR Lyrae was not classified. For Andromeda III, 39 RR Lyrae stars are
pulsating in the fundamental mode with a mean period of 0.657 day and 12 are in
the first-overtone mode with a mean period of 0.402 day. While Andromeda I has
a mean period that is consistent with its metallicity, the mean period of the
fundamental mode RR Lyrae in Andromeda III is larger than expected. In
Andromeda I, we found two RR Lyrae stars which are noticeably fainter than the
horizontal branch. We discuss the possibility that these stars are associated
with the recently discovered stellar stream in the halo of M31. We find
<V_RR>=25.14+/-0.04 mag for Andromeda I resulting in a distance of 765+/-25
kpc. For Andromeda III, <V_RR>=25.01+/-0.04 mag giving a distance of 740+/-20
kpc. The relation between specific frequency of anomalous Cepheids and mean
metallicity of the galaxy in the M31 dwarf spheroidal galaxies follows the same
relation as the Galactic dwarf spheroidal galaxies.


  We apply weighted Mourre commutator theory to prove the limiting absorption
principle for the discrete Schr{\"o}dinger operator perturbed by the sum of a
Wigner-von Neumann and long-range type potential. In particular, this implies a
new result concerning the absolutely continuous spectrum for these operators
even for the one-dimensional operator. We show that methods of classical Mourre
theory based on differential inequalities and on the generator of dilation
cannot apply to the mentionned Schr{\"o}dinger operators.


  We investigate the potential of a minimal Scotogenic model with two
additional scalar doublets and a single heavy Majorana fermion to explain
neutrino masses, dark matter, and the baryon asymmetry of the Universe. In this
minimal setup, Leptogenesis is purely flavored, and a second Majorana neutrino
is not necessary because the Yukawa couplings of the extra doublets yield the
necessary $CP$-odd phases. The mechanism we employ can also be applied to a
wide range of scenarios with at least one singlet and two gauge multiplets.
Despite stringent limits from the dark matter abundance, direct detection
experiments, and the baryon asymmetry of the Universe, we find a parametric
region consistent with all bounds which could resolve the above shortcomings of
the Standard Model of particle physics. Methodically, we improve on the
calculation of correlations between the mixing scalar fields given their finite
width. We also present an argument to justify the kinetic equilibrium
approximation for out-of-equilibrium distribution functions often used in
calculations of Baryogenesis and Leptogenesis.


  We report the first measurements of anisotropy in the cosmic microwave
background (CMB) radiation with the Arcminute Cosmology Bolometer Array
Receiver (ACBAR). The instrument was installed on the 2.1m Viper telescope at
the South Pole in January 2001; the data presented here are the product of
observations up to and including July 2002. The two deep fields presented here,
have had offsets removed by subtracting lead and trail observations and cover
approximately 24 deg^2 of sky selected for low dust contrast. These results
represent the highest signal to noise observations of CMB anisotropy to date;
in the deepest 150GHz band map, we reached an RMS of 8.0\mu K per 5' beam. The
3 degree extent of the maps, and small beamsize of the experiment allow the
measurement of the CMB anisotropy power spectrum over the range \ell = 150-3000
with resolution of \Delta \ell=150. The contributions of galactic dust and
radio sources to the observed anisotropy are negligible and are removed in the
analysis. The resulting power spectrum is found to be consistent with the
primary anisotropy expected in a concordance \Lambda CDM Universe.


  Preliminary results obtained by the CDF and D0 Collaborations on the Standard
Model Higgs searches at the Tevatron Run II are discussed. The data that
correspond to an integrated luminosity of > 150/pb are compared to theoretical
expectations. Various final states involving the W and Z bosons production are
examined, and new limits are set on the sigma[W(->enu/munu)H(->bb)] and
sigma[H(->WW(*))] cross sections.


  While stellar processes are believed to be the main source of feedback in
dwarf galaxies, the accumulating discoveries of AGN in dwarf galaxies over
recent years arouse the interest to also consider AGN feedback in them. Fast,
AGN-driven outflows, a major mechanism of AGN feedback, have indeed been
discovered in dwarf galaxies and may be powerful enough to provide feedback to
their dwarf hosts. In this paper, we search for outflows traced by the
blueshifted ultraviolet absorption features in three dwarf galaxies with AGN
from the sample examined in our previous ground-based study. We confirm
outflows traced by blueshifted absorption features in two objects and
tentatively detect an outflow in the third object. In one object where the
outflow is clearly detected in multiple species, photoionization modeling
suggests that this outflow is located $\sim$0.5 kpc from the AGN, implying a
galactic-scale impact. This outflow is much faster and possesses higher kinetic
energy outflow rate than starburst-driven outflows in sources with similar star
formation rates, and is likely energetic enough to provide negative feedback to
its host galaxy as predicted by simulations. Much broader ($\sim$4000 km
s$^{-1}$) absorption features are also discovered in this object which may have
the same origin as that of broad absorption lines in quasars. Additionally,
strong He II $\lambda$1640 emission is detected in both objects where the
transition falls in the wavelength coverage, and is consistent with an AGN
origin. In one of these two objects, blueshifted He II emission line is clearly
detected, likely tracing a highly-ionized AGN wind.


  The Common Vulnerabilities and Exposures (CVE) represent standard means for
sharing publicly known information security vulnerabilities. One or more CVEs
are grouped into the Common Weakness Enumeration (CWE) classes for the purpose
of understanding the software or configuration flaws and potential impacts
enabled by these vulnerabilities and identifying means to detect or prevent
exploitation. As the CVE-to-CWE classification is mostly performed manually by
domain experts, thousands of critical and new CVEs remain unclassified, yet
they are unpatchable. This significantly limits the utility of CVEs and slows
down proactive threat mitigation. This paper presents the first automatic tool
to classify CVEs to CWEs. ThreatZoom uses a novel learning algorithm that
employs an adaptive hierarchical neural network which adjusts its weights based
on text analytic scores and classification errors. It automatically estimates
the CWE classes corresponding to a CVE instance using both statistical and
semantic features extracted from the description of a CVE. This tool is
rigorously tested by various datasets provided by MITRE and the National
Vulnerability Database (NVD). The accuracy of classifying CVE instances to
their correct CWE classes are 92% (fine-grain) and 94% (coarse-grain) for NVD
dataset, and 75% (fine-grain) and 90% (coarse-grain) for MITRE dataset, despite
the small corpus.


  In the cuprate superconductor $YBa_2Cu_3O_{6+x}$, hole doping in the $CuO_2$
layers is controlled by both oxygen content and the degree of oxygen-ordering.
At the composition $\rm YBa_2Cu_3O_{6.35}$, the ordering can occur at room
temperature, thereby tuning the hole doping so that the superconducting
critical temperature gradually rises from zero to 20 K. Here we exploit this to
study the c-axis penetration depth as a function of temperature and doping. The
temperature dependence shows the d-wave superconductor surviving to very low
doping, with no sign of another ordered phase interfering with the nodal
quasiparticles. The only apparent doping dependence is a smooth decline of
superfluid density as Tc decreases.


  Prompts for pre-trained language models (PLMs) have shown remarkable
performance by bridging the gap between pre-training tasks and various
downstream tasks. Among these methods, prompt tuning, which freezes PLMs and
only tunes soft prompts, provides an efficient and effective solution for
adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to
be fully explored. In our pilot experiments, we find that prompt tuning
performs comparably with conventional full-model fine-tuning when downstream
data are sufficient, whereas it performs much worse under few-shot learning
settings, which may hinder the application of prompt tuning in practice. We
attribute this low performance to the manner of initializing soft prompts.
Therefore, in this work, we propose to pre-train prompts by adding soft prompts
into the pre-training stage to obtain a better initialization. We name this
Pre-trained Prompt Tuning framework "PPT". To ensure the generalization of PPT,
we formulate similar classification tasks into a unified task form and
pre-train soft prompts for this unified task. Extensive experiments show that
tuning pre-trained prompts for downstream tasks can reach or even outperform
full-model fine-tuning under both full-data and few-shot settings. Our approach
is effective and efficient for using large-scale PLMs in practice.


  Mathematical and numerical modeling is an increasingly important, yet often
neglected, topic for biology students. We have found Glowscript to facilitate
teaching and introducing computer simulations to students. In particular, the
built-in the graphics and graphing capabilities can provide students with
immediate feedback. Glowscript is a web-based form of visual Python that runs
in a standard browser, and students can easily embed their simulations in
external websites. Here we show various examples of how Glowscript is
implemented in an undergraduate computational biology course. We hope these
examples inspire others to adopt Glowscript in their science classrooms.


  We show that the spectrum of the SYK model can be interpreted as that of a 3D
scalar coupled to gravity. The scalar has a mass which is at the
Breitenholer-Freedman bound of AdS$_2$, and subject to a delta function
potential at the center of the interval along the third direction. This,
through Kaluza-Klein procedure on AdS$_2 \times (S^1)/Z_2$, generates the
spectrum reproducing the bi-local propagator at strong coupling. Furthermore,
the leading $1/J$ correction calculated in this picture reproduces the known
correction to the poles of the SYK propagator, providing credence to a
conjecture that the bulk dual of this model can be interpreted as a three
dimensional theory.


  A novel efficient method to calculate the scattering matrix (SM) of arbitrary
tight-binding Hamiltonians is proposed, including cases with multiterminal
structures. In particular, the SM of two kind of fundamental structures are
given, which can be used to obtain the SM of bigger systems iteratively. Also,
a procedure to obtain the SM of layer-composed periodic leads is described.
This method allows renormalization approaches, which permits computations over
macroscopic length systems without introducing additional approximations.
Finally, the transmission coefficient of a ring-shaped multiterminal system and
the transmission function of a square-lattice nanoribbon with a reduced width
region are calculated.


  Combined ASCA and SIGMA data of 1E1740.7-2942 during the standard state
(September 1993 and 1994) were fitted with two-phase models (ISMBB/Poutanen at
al. and ADAF/Narayan et al.). In ISMBB's, the radius of the spherical hot (Te =
150 - 200 KeV) corona lies between 200 - 250 km where it joins the classical
inner disc. The disc radiates 40 per cent of the total luminosity with 1.7 per
cent of the Eddington accretion rate of 10 solar mass black hole. ADAF's need
an extra component to reproduce the soft part of the spectrum. However, the
origin of the soft excess remains somewhat uncertain, although special care was
taken in the background elimination.


  This paper presents a new semantic frame parsing model, based on Berkeley
FrameNet, adapted to process spoken documents in order to perform information
extraction from broadcast contents. Building upon previous work that had shown
the effectiveness of adversarial learning for domain generalization in the
context of semantic parsing of encyclopedic written documents, we propose to
extend this approach to elocutionary style generalization. The underlying
question throughout this study is whether adversarial learning can be used to
combine data from different sources and train models on a higher level of
abstraction in order to increase their robustness to lexical and stylistic
variations as well as automatic speech recognition errors. The proposed
strategy is evaluated on a French corpus of encyclopedic written documents and
a smaller corpus of radio podcast transcriptions, both annotated with a
FrameNet paradigm. We show that adversarial learning increases all models
generalization capabilities both on manual and automatic speech transcription
as well as on encyclopedic data.


  We use the more intuitive approach due to Kolmogorov (and subsequently,
Landau in his text on fluid dynamics) to calculate some third-order structure
functions for quasi-geostrophic turbulence for the forward cascade of
pseudo-potential enstrophy and the inverse energy cascade in quasi-geostrophic
turbulence.


  Femtosecond spectroscopy is an important tool for tracking rapid photoinduced
processes in a variety of materials. To spatially map the processes in a sample
would substantially expand the capabilities of the method. This is, however,
difficult to achieve due to the necessity to use low-noise detection and to
maintain feasible data acquisition time. Here we demonstrate realization of an
imaging pump-probe setup, featuring sub-100 fs temporal resolution, by a
straightforward modification of a standard pump-probe technique, using a
randomly structured probe beam. The structured beam, made by a diffuser,
enabled us to computationally reconstruct the maps of transient absorption
dynamics based on the concept of compressed sensing. We demonstrate the
functionality of the setup in two proof-of-principle experiments, were we
achieve spatial resolution of 20 \mu m. The presented concept provides a
feasible route to imaging, using the pump-probe technique and ultrafast
spectroscopy in general.


  To characterize atypical brain dynamics under diseases, prevalent studies
investigate functional magnetic resonance imaging (fMRI). However, most of the
existing analyses compress rich spatial-temporal information as the brain
functional networks (BFNs) and directly investigate the whole-brain network
without neurological priors about functional subnetworks. We thus propose a
novel graph learning framework to mine fMRI signals with topological priors
from brain parcellation for disease diagnosis. Specifically, we 1) detect
diagnosis-related temporal features using a "Transformer" for a higher-level
BFN construction, and process it with a following graph convolutional network,
and 2) apply an attention-based multiple instance learning strategy to
emphasize the disease-affected subnetworks to further enhance the diagnosis
performance and interpretability. Experiments demonstrate higher effectiveness
of our method than compared methods in the diagnosis of early mild cognitive
impairment. More importantly, our method is capable of localizing crucial brain
subnetworks during the diagnosis, providing insights into the pathogenic source
of mild cognitive impairment.


  The quark and lepton mass patterns upset their naive unification. In this
paper, a new approach to solve this problem is proposed. Model-independently,
we find that a successful unification can be achieved. A mechanism is
identified by which the large top quark mass renders its third-generation
leptonic partner very light. This state is thus identified with the electron.
We then provide a generic dynamical implementation of this mechanism, using
tree-level exchanges of vector leptons to relate the quark and lepton flavor
structures. In a supersymmetric context, this same mechanism splits the squark
masses, and third generation squarks end up much lighter than the others.
Finally, the implementation of this mechanism in SU(5) GUT permits to avoid
introducing any flavor structure beyond the two minimal Yukawa couplings,
ensuring the absence of unknown mixing matrices and their potentially large
impact on FCNC.


  The set $GU_f$ of possible effective elastic tensors of composites built from
two materials with positive definite elasticity tensors $\BC_1$ and
$\BC_2=\Gd\BC_0$ comprising the set $U=\{\BC_1,\Gd\BC_0\}$ and mixed in
proportions $f$ and $1-f$ is partly characterized in the limit $\Gd\to\infty$.
The material with tensor $\BC_2$ corresponds to a material which (for technical
reasons) is almost rigid in the limit $\Gd\to \infty$. The paper, and the
underlying microgeometries, have many aspects in common with the companion
paper "On the possible effective elasticity tensors of 2-dimensional printed
materials". The chief difference is that one has a different algebraic problem
to solve: determining the subspaces of stress fields for which the thin walled
structures can be rigid, rather than determining, as in the companion paper,
the subspaces of strain fields for which the thin walled structure is
compliant. Recalling that $GU_f$ is completely characterized through minimums
of sums of energies, involving a set of applied strains, and complementary
energies, involving a set of applied stresses, we provide descriptions of
microgeometries that in appropriate limits achieve the minimums in many cases.
In these cases the calculation of the minimum is reduced to a finite
dimensional minimization problem that can be done numerically. Each
microgeometry consists of a union of walls in appropriate directions, where the
material in the wall is an appropriate $p$-mode material, that is almost rigid
to $6-p\leq 5$ independent applied stresses, yet is compliant to any strain in
the orthogonal space. Thus the walls, by themselves, can support stress with
almost no deformation. The region outside the walls contains "Avellaneda
material" that is a hierarchical laminate which minimizes an appropriate sum of
elastic energies.


  Packet trimming is a primitive that has been proposed for datacenter
networks: to minimize latency, switches run small queues; when the queue
overflows, rather than dropping packets the switch trims off the packet payload
and either forwards the header to the destination or back to the source. In
this way a low latency network that is largely lossless for metadata can be
built. Ideally, trimming would be implemented as a primitive in switch ASICs,
but hardware development cycles are slow, costly, and require demonstrated
customer demand. In this paper we investigate how trimming can be implemented
in existing programmable switches which were not designed with trimming in
mind, with a particular focus on a P4 implementation on the Tofino switch ASIC.
We show that it is indeed possible to closely approximate idealized trimming
and demonstrate that trimming can be integrated into a production-grade
datacenter switch software stack.


  In computer-aided design (CAD) community, the point cloud data is pervasively
applied in reverse engineering, where the point cloud analysis plays an
important role. While a large number of supervised learning methods have been
proposed to handle the unordered point clouds and demonstrated their remarkable
success, their performance and applicability are limited to the costly data
annotation. In this work, we propose a novel self-supervised pretraining model
for point cloud learning without human annotations, which relies solely on
upsampling operation to perform feature learning of point cloud in an effective
manner. The key premise of our approach is that upsampling operation encourages
the network to capture both high-level semantic information and low-level
geometric information of the point cloud, thus the downstream tasks such as
classification and segmentation will benefit from the pre-trained model.
Specifically, our method first conducts the random subsampling from the input
point cloud at a low proportion e.g., 12.5%. Then, we feed them into an
encoder-decoder architecture, where an encoder is devised to operate only on
the subsampled points, along with a upsampling decoder is adopted to
reconstruct the original point cloud based on the learned features. Finally, we
design a novel joint loss function which enforces the upsampled points to be
similar with the original point cloud and uniformly distributed on the
underlying shape surface. By adopting the pre-trained encoder weights as
initialisation of models for downstream tasks, we find that our UAE outperforms
previous state-of-the-art methods in shape classification, part segmentation
and point cloud upsampling tasks. Code will be made publicly available upon
acceptance.


  Generating inferential texts about an event in different perspectives
requires reasoning over different contexts that the event occurs. Existing
works usually ignore the context that is not explicitly provided, resulting in
a context-independent semantic representation that struggles to support the
generation. To address this, we propose an approach that automatically finds
evidence for an event from a large text corpus, and leverages the evidence to
guide the generation of inferential texts. Our approach works in an
encoder-decoder manner and is equipped with a Vector Quantised-Variational
Autoencoder, where the encoder outputs representations from a distribution over
discrete variables. Such discrete representations enable automatically
selecting relevant evidence, which not only facilitates evidence-aware
generation, but also provides a natural way to uncover rationales behind the
generation. Our approach provides state-of-the-art performance on both
Event2Mind and ATOMIC datasets. More importantly, we find that with discrete
representations, our model selectively uses evidence to generate different
inferential texts.


  Thermalization and scrambling are the subject of much recent study from the
perspective of many-body quantum systems with locally bounded Hilbert spaces
(`spin chains'), quantum field theory and holography. We tackle this problem in
1D spin-chains evolving under random local unitary circuits and prove a number
of exact results on the behavior of out-of-time-ordered commutators (OTOCs),
and entanglement growth in this setting. These results follow from the
observation that the spreading of operators in random circuits is described by
a `hydrodynamical' equation of motion, despite the fact that random unitary
circuits do not have locally conserved quantities (e.g., no conserved energy).
In this hydrodynamic picture quantum information travels in a front with a
`butterfly velocity' $v_{\text{B}}$ that is smaller than the light cone
velocity of the system, while the front itself broadens diffusively in time.
The OTOC increases sharply after the arrival of the light cone, but we do
\emph{not} observe a prolonged exponential regime of the form $\sim
e^{\lambda_\text{L}(t-x/v)}$ for a fixed Lyapunov exponent $\lambda_\text{L}$.
We find that the diffusive broadening of the front has important consequences
for entanglement growth, leading to an entanglement velocity that can be
significantly smaller than the butterfly velocity. We conjecture that the
hydrodynamical description applies to more generic ergodic systems and support
this by verifying numerically that the diffusive broadening of the operator
wavefront also holds in a more traditional non-random Floquet spin-chain. We
also compare our results to Clifford circuits, which have less rich
hydrodynamics and consequently trivial OTOC behavior, but which can
nevertheless exhibit linear entanglement growth and thermalization.


  By use of the recurrent relation approach (RRA) we study the microscopic
dynamics of liquid aluminium at T=973 K and develop a theoretical model which
satisfies all the corresponding sum rules. The investigation covers the
inelastic features as well as the crossover of our theory into the
hydrodynamical and the free-particle regimes. A comparison between our
theoretical results with those following from a generalized hydrodynamical
approach is also presented. In addition to this we report the results of our
molecular dynamics simulations for liquid aluminium, which are also discussed
and compared to experimental data. The received results reveal that (i) the
microscopical dynamics of density fluctuations is defined mainly by the first
four even frequency moments of the dynamic structure factor, and (ii) the
inherent relation of the high-frequency collective excitations observed in
experimental spectra of dynamic structure factor $S(k,\omega)$ with the two-,
three- and four-particle correlations.


  We analyze the potential of the Fermilab Tevatron and CERN Large Hadron
Collider (LHC) to study anomalous quartic vector--boson interactions (photon
photon Z Z) and (photon photon W+ W-). Working in the framework of SU(2)_L X
U(1)_Y chiral Lagrangians, we study the production of photons pairs accompanied
by (e+e-), (e nu), and jet pairs to impose bounds on these new couplings,
taking into account the unitarity constraints. We compare our findings with the
indirect limits coming from precision electroweak measurements as well as with
presently available direct searches at LEPII. We show that the Tevatron Run II
can provide limits on these quartic limits which are of the same order of
magnitude as the existing bounds from LEPII searches. LHC will be able to
tighten considerably the direct constraints on these possible new interactions,
leading to more stringent limits than the presently available indirect ones.


  Deriving the total masses of galaxy clusters from observations of the
intracluster medium (ICM) generally requires some prior information, in
addition to the assumptions of hydrostatic equilibrium and spherical symmetry.
Often, this information takes the form of particular parametrized functions
used to describe the cluster gas density and temperature profiles. In this
paper, we investigate the implicit priors on hydrostatic masses that result
from this fully parametric approach, and the implications of such priors for
scaling relations formed from those masses. We show that the application of
such fully parametric models of the ICM naturally imposes a prior on the slopes
of the derived scaling relations, favoring the self-similar model, and argue
that this prior may be influential in practice. In contrast, this bias does not
exist for techniques which adopt an explicit prior on the form of the mass
profile but describe the ICM non-parametrically. Constraints on the slope of
the cluster mass--temperature relation in the literature show a separation
based the approach employed, with the results from fully parametric ICM
modeling clustering nearer the self-similar value. Given that a primary goal of
scaling relation analyses is to test the self-similar model, the application of
methods subject to strong, implicit priors should be avoided. Alternative
methods and best practices are discussed.


  We consider the parametric family of sextic Thue equations \[
x^6-2mx^5y-5(m+3)x^4y^2-20x^3y^3+5mx^2y^4+2(m+3)xy^5+y^6=\lambda \] where
$m\in\mathbb{Z}$ is an integer and $\lambda$ is a divisor of $27(m^2+3m+9)$. We
show that the only solutions to the equations are the trivial ones with
$xy(x+y)(x-y)(x+2y)(2x+y)=0$.


  Shannon entropy is the most crucial foundation of Information Theory, which
has been proven to be effective in many fields such as communications. Renyi
entropy and Chernoff information are other two popular measures of information
with wide applications. The mutual information is effective to measure the
channel information for the fact that it reflects the relation between output
variables and input variables. In this paper, we reexamine these channel
information measures in big data viewpoint by means of ACE algorithm. The
simulated results show us that decomposition results of Shannon and Chernoff
mutual information with respect to channel parametersare almost the same. In
this sense, Shannon shakes hands with Chernoff since they are different
measures of the same information quantity. We also propose a conjecture that
there is nature of channel information which is only decided by the channel
parameters.


  Biological lasers have attracted vast attention because of their potential
medical application prospects, especially the low threshold biological laser,
which can be used for ultrasensitive biological detection while ensuring that
its luminous gain medium is not damaged by the high-energy pump light. By
coupling the low concentration green fluorescent protein (GFP) solution with a
high Q whispering gallery mode microbubble resonator, we managed to fabricate a
miniature GFP laser with ultralow lasing threshold of 500 nJ/mm^2. The energy
used to excite the GFP can be reduced to 380 fJ, two orders of magnitude lower
than that of the lowest excitation energy GFP laser known. The Q value of the
optical cavity in this biological laser is 5.3 x 10^7, the highest among GFP
lasers at present. We further confirmed the long-term stability of the working
characteristics of GFP laser for the first time and found that its optical
characteristics can be maintained for at least 23 days. Finally, we measured
the effects of different concentrations of fluorescent protein on the laser
threshold. The data show that this biological laser can be used for a highly
sensitive detection of GFP concentration.


  John N. Bahcall championed solar neutrino physics for many years. Thanks to
his pioneering and long-lasting contributions, this field of research has not
only reached maturity, but has also opened a new window on physics beyond the
standard electroweak model through the phenomenon of neutrino flavor
oscillations. We briefly outline some recent accomplishments in the field, and
also discuss a couple of issues that do not seem to fit in the ``standard
picture,'' namely, the chemical controversy at the solar surface, and possible
implications of recent gallium radioactive source experiments.


  We discriminate gluonic hadrons from conventional $q\bar q$ states by
surveying radial and orbital excitations of all I=0 and I=1 $n\bar n$ systems
anticipated up to 2.1 GeV. We give detailed predictions of their quasi-two-body
branching fractions and identify characteristic decay modes that can isolate
quarkonia. Several of the ``missing mesons'' with L$_{q\bar q}=2$ and L$_{q\bar
q}=3$ are predicted to decay dominantly into certain S+P and S+D modes, and
should appear in experimental searches for hybrids in the same mass region. We
also consider the topical issues of whether some of the recently discovered or
controversial meson resonances, including glueball and hybrid candidates, can
be accommodated as quarkonia.


  Machine learning from training data with a skewed distribution of examples
per class can lead to models that favor performance on common classes at the
expense of performance on rare ones. AudioSet has a very wide range of priors
over its 527 sound event classes. Classification performance on AudioSet is
usually evaluated by a simple average over per-class metrics, meaning that
performance on rare classes is equal in importance to the performance on common
ones. Several recent papers have used dataset balancing techniques to improve
performance on AudioSet. We find, however, that while balancing improves
performance on the public AudioSet evaluation data it simultaneously hurts
performance on an unpublished evaluation set collected under the same
conditions. By varying the degree of balancing, we show that its benefits are
fragile and depend on the evaluation set. We also do not find evidence
indicating that balancing improves rare class performance relative to common
classes. We therefore caution against blind application of balancing, as well
as against paying too much attention to small improvements on a public
evaluation set.


  The effect of the pulse-shape on pump-probe spectroscopies is examined for
the simplest model of noninteracting fermions on an infinite-dimensional
hypercubic lattice. The probe-modified density of states follows the time
evolution of the pump and displays narrowing and Floquet-like sidebands at the
pump maximum, whereas the photoelectron spectra are also strongly affected by
the nonequilibrium occupation of the single-particle states due to the
excitation from the pump. The nonequilibrium Raman cross section is derived,
and the nonresonant one in both the $A_{1\mathrm{g}}$ and $B_{1\mathrm{g}}$
symmetries contains a number of peaks at the pump maximum, which can be
attributed to an interference effect or Brillouin scattering off the time
variations of the stress tensor. Both the "measured" occupation of
single-particle states and the ratio of Stokes to anti-Stokes peaks are
strongly modified by the probe-pulse width, which must be included in the
interpretation of experimental results.


  A generalized cross-validation approach to estimate the reconstruction filter
bandwidth in two-dimensional Filtered Backprojection is presented. The method
writes the reconstruction equation in equivalent backprojected filtering form,
derives results on eigendecomposition of symmetric two-dimensional circulant
matrices and applies them to make bandwidth estimation a computationally
efficient operation within the context of standard backprojected filtering
reconstruction. Performance evaluations on a wide range of simulated emission
tomography experiments give promising results. The superior performance holds
at both low and high total expected counts, pointing to the method's
applicability even in weaker signal-noise situations. The approach also applies
to the more general class of elliptically symmetric filters, with
reconstruction performance often better than even that obtained with the true
optimal radially symmetric filter.


  We present the first Ga(4f) NMR study of the Cr susceptibility in the
archetype of Kagome based frustrated antiferromagnets,
SrCr$_{8}$Ga$_{4}$O$_{19}$. Our major finding is that the susceptibility of the
frustrated lattice goes through a maximum around 50 K. Our data also supports
the existence of paramagnetic ``clusters'' of spins, responsible for the Curie
behavior observed in the macroscopic susceptibility at low T. These results set
novel features for the constantly debated physics of geometrically frustrated
magnets.


  We use color-magnitude diagram synthesis together with theoretical relations
from non-linear pulsation models to approach the long-standing problem of the
Oosterhoff dichotomy related to the distribution of the mean periods of
fundamental RR Lyrae variables in globular clusters. By adopting the chemical
composition determined from spectroscopic observations and a criterion to
account for the hysteresis mechanism, we tuned age and mass-loss to
simultaneously reproduce the morphology of both the turn-off and the Horizontal
Branch of a sample of 17 globular clusters of the Milky Way and of nearby dwarf
galaxies in the crucial metallicity range (-1.9<[Fe/H]<-1.4) where the
Oostheroff transition is apparent. We find that the Oosterhoff dichotomy among
Galactic globular clusters is naturally reproduced by models. The analysis of
the relative impact of the various involved parameters indicates that the main
responsibles of the dichotomy are the peculiar distribution of clusters in the
age-metallicity plane and the hysteresis. In particular, there is a clear
connection between the two main branches of the age-metallicity relation for
Galactic globular clusters and the Oosterhoff groups. The properties of
clusters' RR Lyrae belonging to other Oostheroff groups (OoInt and OoIII) are
instead not well reproduced. While for OoIII clusters a larger helium abundance
for a fraction of the cluster's stars can reconcile the model prediction with
observations, some other parameter affecting both the Horizontal Branch
morphology and the RR Lyrae periods is required to reproduce the behavior of
OoInt clusters.


  Nonthermal X-rays and very high-energy (VHE) $\gamma$-rays have been detected
from the supernova remnant (SNR) RX J1713.7-3946, and especially the recent
observations with the \textit{Suzaku} satellite clearly reveal a spectral
cutoff in the X-ray spectrum, which directly relates to the cutoff of the
energy spectrum of the parent electrons. However, whether the origin of the VHE
$\gamma$-rays from the SNR is hadronic or leptonic is still in debate. We
studied the multi-band nonthermal emission from RX J1713.7-3946 based on a
semi-analytical approach to the nonlinear shock acceleration process by
including the contribution of the accelerated electrons to the nonthermal
radiation. The results show that the multi-band observations on RX J1713.7-3946
can be well explained in the model with appropriate parameters and the TeV
$\gamma$-rays have hadronic origin, i.e., they are produced via proton-proton
(p-p) interactions as the relativistic protons accelerated at the shock collide
with the ambient matter.


  We report on searches for the Standard Model Higgs boson decaying to $b
\bar{b}$ in $pp$ collisions at the ATLAS and CMS experiments. These rely on
signatures where the Higgs boson is produced in association with a vector boson
or in association with a top-quark pair. Results are presented, based on
$5.0~\mathrm{fb}^{-1}$ of $pp$ collision data at $\sqrt{s} = 7$~TeV and up to
$13.0~\mathrm{fb}^{-1}$ of data at $\sqrt{s} = 8$~TeV collected by the LHC
accelerator. These are expressed in terms of 95\% confidence level upper limits
on the production cross section of the Standard Model Higgs boson times the
branching ratio to decay to $b$-quark pair.


  The existence of equilibrium points and the effect of radiation pressure have
been discussed numerically. The problem is generalized by considering bigger
primary as a source of radiation and small primary as an oblate spheroid. We
have also discussed the Poynting-Robertson(P-R) effect which is caused due to
radiation pressure. It is found that the collinear points $L_1,L_2,L_3$ deviate
from the axis joining the two primaries, while the triangular points $L_4,L_5$
are not symmetrical due to radiation pressure. We have seen that $L_1,L_2,L_3$
are linearly unstable while $L_4,L_5$ are conditionally stable in the sense of
Lyapunov when P-R effect is not considered. We have found that the effect of
radiation pressure reduces the linear stability zones while P-R effect induces
an instability in the sense of Lyapunov.


  We apply a new highly efficient method of solving Faddeev-Merkuriev equations
to multi-channel scattering calculations of the antihydrogen formation cross
section for antiproton scattering off the ground and excited states of the
positronium. Our results demonstrate good agreement with the known data on
total and partial cross sections for all the reaction channels. Using moderate
computational resources we have achieved a supreme energy resolution.


  We consider a queue with renewal arrivals and n exponential servers in the
Halfin-Whitt heavy traffic regime, where n and the arrival rate increase
without bound, so that a critical loading condition holds. Server k serves at
rate $\mu_k $, and the empirical distribution of the $\mu_k $ is assumed to
converge weakly. We show that very little information on the service rates is
required for a routing mechanism to perform well. More precisely, we construct
a routing mechanism that has access to a single sample from the service time
distribution of each of $n$ to the power of $1/2 + \epsilon $ randomly selected
servers, but not to the actual values of the service rates, the performance of
which is asymptotically as good as the best among mechanisms that have the
complete information on $ \mu_k $.


  In this paper we consider a class of hypoelliptic second-order partial
differential operators $\mathcal{L}$ in divergence form on $\mathbb{R}^N$,
arising from CR geometry and Lie group theory, and we prove the Strong and Weak
Maximum Principles and the Harnack Inequality for $\mathcal{L}$. The involved
operators are not assumed to belong to the H\"ormander hypoellipticity class,
nor to satisfy subelliptic estimates, nor Muckenhoupt-type estimates on the
degeneracy of the second order part; indeed our results hold true in the
infinitely-degenerate case and for operators which are not necessarily sums of
squares. We use a Control Theory result on hypoellipticity in order to recover
a meaningful geometric information on connectivity and maxima propagation, yet
in the absence of any H\"ormander condition. For operators $\mathcal{L}$ with
$C^\omega$ coefficients, this control-theoretic result will also imply a Unique
Continuation property for the $\mathcal{L}$-harmonic functions. The (Strong)
Harnack Inequality is obtained via the Weak Harnack Inequality by means of a
Potential Theory argument, and by a crucial use of the Strong Maximum Principle
and the solvability of the Dirichlet problem for $\mathcal{L}$ on a basis of
the Euclidean topology.


  Decision Transformer (DT) is a recently proposed architecture for
Reinforcement Learning that frames the decision-making process as an
auto-regressive sequence modeling problem and uses a Transformer model to
predict the next action in a sequence of states, actions, and rewards. In this
paper, we analyze how crucial the Transformer model is in the complete DT
architecture on continuous control tasks. Namely, we replace the Transformer by
an LSTM model while keeping the other parts unchanged to obtain what we call a
Decision LSTM model. We compare it to DT on continuous control tasks, including
pendulum swing-up and stabilization, in simulation and on physical hardware.
Our experiments show that DT struggles with continuous control problems, such
as inverted pendulum and Furuta pendulum stabilization. On the other hand, the
proposed Decision LSTM is able to achieve expert-level performance on these
tasks, in addition to learning a swing-up controller on the real system. These
results suggest that the strength of the Decision Transformer for continuous
control tasks may lie in the overall sequential modeling architecture and not
in the Transformer per se.


  The high penetration of converter-based renewable energy sources has brought
challenges to the power system frequency control. It is essential to consider
the frequency security constraints and frequency control reserve requirements
in unit commitment (UC). Considering that the risk of frequency insecurity
varies under the changeable operational condition, we propose to optimize the
PFC droop gains and reserve capacities in the UC model to provide diverse
control efforts in different risk levels adaptively. Copula theory is used to
establish the joint distribution model among frequency control performance,
secondary frequency control (SFC) reserve capacities, and power fluctuations.
Then the distributionally robust optimization technique is utilized in the SFC
reserve requirement determination to handle the possible error in the
probability model. The UC simulation is conducted on IEEE 118-bus system to
test the proposed optimal PFC droop gain strategy and SFC reserve requirement
quantification method. Simulation results show that the proposed optimal PFC
droop gain strategy is better than the traditional fixed PFC droop gain setting
on economic efficiency and operational flexibility. Besides, the SFC reserve
requirement calculated by the proposed method is more appropriate than the
actual SFC reserve capacity in the historical operation.


  The perturbation expansion of the MSbar coupling in the SU(N) gauge theory in
powers of the bare lattice coupling is extended to two-loop order. A discussion
of our results has already been published elsewhere. We here describe the
calculation in some detail. It relies on the background field technique and a
recently introduced coordinate space method to evaluate lattice Feynman
integrals.


  Recently, video scene text detection has received increasing attention due to
its comprehensive applications. However, the lack of annotated scene text video
datasets has become one of the most important problems, which hinders the
development of video scene text detection. The existing scene text video
datasets are not large-scale due to the expensive cost caused by manual
labeling. In addition, the text instances in these datasets are too clear to be
a challenge. To address the above issues, we propose a tracking based
semi-automatic labeling strategy for scene text videos in this paper. We get
semi-automatic scene text annotation by labeling manually for the first frame
and tracking automatically for the subsequent frames, which avoid the huge cost
of manual labeling. Moreover, a paired low-quality scene text video dataset
named Text-RBL is proposed, consisting of raw videos, blurry videos, and
low-resolution videos, labeled by the proposed convenient semi-automatic
labeling strategy. Through an averaging operation and bicubic down-sampling
operation over the raw videos, we can efficiently obtain blurry videos and
low-resolution videos paired with raw videos separately. To verify the
effectiveness of Text-RBL, we propose a baseline model combined with the text
detector and tracker for video scene text detection. Moreover, a failure
detection scheme is designed to alleviate the baseline model drift issue caused
by complex scenes. Extensive experiments demonstrate that Text-RBL with paired
low-quality videos labeled by the semi-automatic method can significantly
improve the performance of the text detector in low-quality scenes.


  The thermodynamic quantities (spin-spin correlation functions <{\bf S}_0{\bf
S}_n>, correlation length {\xi}, spin susceptibility {\chi}, and specific heat
C_V) of the frustrated one-dimensional J1-J2 Heisenberg ferromagnet with
arbitrary spin quantum number S below the quantum critical point, i.e. for J2<
|J1|/4, are calculated using a rotation-invariant Green-function formalism and
full diagonalization as well as a finite-temperature Lanczos technique for
finite chains of up to N=18 sites. The low-temperature behavior of the
susceptibility {\chi} and the correlation length {\xi} is well described by
\chi = (2/3)S^4 (|J1|-4J2) T^{-2} + A S^{5/2} (|J1|-4J2)^{1/2} T^{-3/2} and \xi
= S^2 (|J1|-4J2) T^{-1} + B S^{1/2} (|J1|-4J2)^{1/2} T^{-1/2} with A \approx
1.1 ... 1.2 and B \approx 0.84 ... 0.89. The vanishing of the factors in front
of the temperature at J2=|J1|/4 indicates a change of the critical behavior of
{\chi} and {\xi} at T \to 0. The specific heat may exhibit an additional
frustration-induced low-temperature maximum when approaching the quantum
critical point. This maximum appears for S=1/2 and S=1, but was not found for
S>1.


  This article introduces an enhancement to the Grover search algorithm to
speed up computing the probability of finding good states. It suggests
incorporating a rotation phase angle determined mathematically from the
derivative of the model during the initial iteration. At each iteration, a new
phase angle is computed and used in a rotation gate around y+z axis in the
diffusion operator. The computed phase angles are optimized through an adaptive
adjustment based on the estimated increasing ratio of the consecutive
amplitudes. The findings indicate an average decrease of 28% in the required
number of iterations resulting in a faster overall process and fewer number of
quantum gates. For large search space, this improvement rises to 29.58%. Given
the computational capabilities of the computer utilized for the simulation, the
approach is applied to instances with up to 12 qubits or 4096 possible
combination of search entries.


  We consider the full 3D dynamics of a thin falling liquid film on a flat
plate inclined at some non-zero angle to the horizontal. In addition to
gravitational effects, the flow is driven by an electric field, which is normal
to the substrate far from the flow. We study both the cases of overlying and
hanging films, where the liquid rests above and below the substrate
respectively. Starting with the Navier-Stokes equations coupled with
electrostatics, a fully nonlinear 2D Benney equation for the interfacial
dynamics is derived valid for waves that are long compared to the film
thickness. The weakly nonlinear evolution is governed by a Kuramoto-Sivashinsky
equation with a non-local term due to the electric field effect. The electric
field term is linearly destabilising and produces growth rates proportional to
the cube of the size of the wavenumber vector of the perturbations. It is found
that transverse gravitational instabilities are always present for hanging
films and lead to unboundedness of solutions even in the absence of electric
fields. For overlying films and a restriction on the strength of the electric
field, the equation possesses bounded solutions. This 2D equation is studied
numerically for the case of periodic boundary conditions in order to assess the
effects of inertia, electric field strength the dimensions of the periodic
domain. Rich dynamical behaviours are observed and classified in various
parameter windows. For subcritical Reynolds number flows, a sufficiently strong
electric field can promote non-trivial dynamics for some choices of domain
dimensions, leading to fully 2D evolutions for the interface. These dynamics
are also found to produce spatiotemporal chaos on sufficiently large domains.
For supercritical flows, such 2D chaotic dynamics emerge in the absence of a
field, and its presence enhances the amplitude of the fluctuations and broadens
their spectrum.


  Using accurate dynamic polarizabilities of Li, Na, K and, Rb atoms, we
scrutinize the thermal Casimir-Polder interactions of these atoms with a single
layered graphene. Considering the modified Lifshitz theory for material
interactions, we reanalyze the dispersion coefficients ($C_3$s) of the above
atoms with graphene as functions of separation distance, gap parameter and
temperature among which some of them were earlier studied by estimating dynamic
polarizabilities of the above atoms from the single oscillator model
approximation. All these $C_3$ coefficients have been evaluated in the
framework of the Dirac model. The interactions are described for a wide range
of distances and temperatures to demonstrate the changes in behavior with the
varying conditions of the system and also sensitivities in the interactions are
analyzed by calculating them for different values of the gap parameter. From
these analyses, we find a suitable value of the gap parameter for which the
true nature of the interactions in graphene can be surmised more accurately.


  A committee's decisions on more than two alternatives much depend on the
adopted voting method, and so does the distribution of power among the
committee members. We investigate how different aggregation methods such as
plurality runoff, Borda count, or Copeland rule map asymmetric numbers of
seats, shares, voting weights, etc. to influence on outcomes when preferences
vary. A generalization of the Penrose-Banzhaf power index is proposed and
applied to the IMF Executive Board's election of a Managing Director, extending
a priori voting power analysis from binary simple voting games to choice in
weighted committees.


  Let $C$ be a nonsingular irreducible projective curve of genus $g\ge2$
defined over the complex numbers. Suppose that $1\le n'\le n-1$ and
$n'd-nd'=n'(n-n')(g-1)$. It is known that, for the general vector bundle $E$ of
rank $n$ and degree $d$, the maximal degree of a subbundle of $E$ of rank $n'$
is $d'$ and that there are finitely many such subbundles. We obtain a formula
for the number of these maximal subbundles when $(n',d')=1$. For $g=2$, $n'=2$,
we evaluate this formula explicitly. The numbers computed here are
Gromov-Witten invariants in the sense of a recent paper of Ch. Okonek and A.
Teleman (to appear in Commun. Math. Phys.) and our results answer a question
raised in that paper. In this revised version some references are added.


  Our Universe is described jointly by Einstein's relativity principle and the
quantum principle; there the existence of the smallest units of matter, such as
electrons, neutrinos, quarks, and photons, is well established and the smallest
units of matter are described by the Standard Model. Based on these, we have a
clear picture for the propagation of ultra high energy cosmic rays (UHECR's),
for energies greater than $E > 10^{13}\, eV$ but less than $E < 10^{26}\, eV$,
in the cosmic medium of the Cosmic Microwave Background (CMB) and of the cosmic
background neutrinos (CB$\nu$'s). We find that the CMB plays a pivot role in
this energy range, so that the observed "knee(s)" and the "ankle" could be
understood in reasonable terms. What we might observe at the energy near
$10^{25}\,eV$', the so-called "$W^\pm$ bursts" or "$Z^0$ bursts", is briefly
discussed. Meanwhile, in the early Universe, the CB$\nu$'s, in view of the tiny
neutrino mass, gave rise to the clustering phenomenon, resulting in a neutrino
halo attached to each visual ordinary-matter object, such as the Earth, the
Sun, and stars - with the $PeV$ neutrinos near the surface of the Fermi-Dirac
sphere of the neutrino halo of the Earth playing the most fundamental role.


  Most algorithms that rely on deep learning-based approaches to generate 3D
point sets can only produce clouds containing fixed number of points.
Furthermore, they typically require large networks parameterized by many
weights, which makes them hard to train. In this paper, we propose an
auto-encoder architecture that can both encode and decode clouds of arbitrary
size and demonstrate its effectiveness at upsampling sparse point clouds.
Interestingly, we can do so using less than half as many parameters as
state-of-the-art architectures while still delivering better performance. We
will make our code base fully available.


  We report on the discovery of variable circular polarization in the SW Sex
star LS Pegasi. The observed modulation has an amplitude of ~0.3 % and a period
of 29.6 minutes, which we assume as the spin period of the magnetic white
dwarf. We also detected periodic flaring in the blue wing of Hbeta, with a
period of 33.5 minutes. The difference between both frequencies is just the
orbital frequency, so we relate the 33.5-min modulation to the beat between the
orbital and spin period. We propose a new accretion scenario in SW Sex stars,
based on the shock of the disk-overflown gas stream against the white dwarf's
magnetosphere, which extends to the corotation radius. From this geometry, we
estimate a magnetic field strength of B(1) ~ 5-15 MG. Our results indicate that
magnetic accretion plays an important role in SW Sex stars and we suggest that
these systems are probably Intermediate Polars with the highest mass accretion
rates.


  We consider, for a class of functions $\varphi : \mathbb{R}^{2} \setminus \{
{\bf 0} \} \to \mathbb{R}^{2}$ satisfying a nonisotropic homogeneity condition,
the Fourier transform $\hat{\mu}$ of the Borel measure on $\mathbb{R}^{4}$
defined by \[ \mu(E) = \int_{U} \chi_{E}(x, \varphi(x)) \, dx \] where $E$ is a
Borel set of $\mathbb{R}^{4}$ and $U = \{ (t^{\alpha_1}, t^{\alpha_2}s) : c < s
< d, \, 0 < t < 1 \}$. The aim of this article is to give a decay estimate for
$\hat{\mu}$, for the case where the set of nonelliptic points of $\varphi$ is a
curve in $\bar{U} \setminus \{ {\bf 0} \}$. From this estimate we obtain a
restriction theorem for the usual Fourier transform to the graph of
$\varphi_{U} : U \to \mathbb{R}^{2}$. We also give $L^{p}$-improving properties
for the convolution operator $T_{\mu} f = \mu \ast f$.


  The generative autoencoders, such as the variational autoencoders or the
adversarial autoencoders, have achieved great success in lots of real-world
applications, including image generation, and signal communication.
  However, little concern has been devoted to their robustness during practical
deployment.
  Due to the probabilistic latent structure, variational autoencoders (VAEs)
may confront problems such as a mismatch between the posterior distribution of
the latent and real data manifold, or discontinuity in the posterior
distribution of the latent.
  This leaves a back door for malicious attackers to collapse VAEs from the
latent space, especially in scenarios where the encoder and decoder are used
separately, such as communication and compressed sensing.
  In this work, we provide the first study on the adversarial robustness of
generative autoencoders in the latent space.
  Specifically, we empirically demonstrate the latent vulnerability of popular
generative autoencoders through attacks in the latent space.
  We also evaluate the difference between variational autoencoders and their
deterministic variants and observe that the latter performs better in latent
robustness.
  Meanwhile, we identify a potential trade-off between the adversarial
robustness and the degree of the disentanglement of the latent codes.
  Additionally, we also verify the feasibility of improvement for the latent
robustness of VAEs through adversarial training.
  In summary, we suggest concerning the adversarial latent robustness of the
generative autoencoders, analyze several robustness-relative issues, and give
some insights into a series of key challenges.


  This paper examines model parameter estimation in dynamic power systems whose
governing electro-mechanical equations are ill-conditioned or singular. This
ill-conditioning is because of converter-interfaced power systems generators'
zero or small inertia contribution. Consequently, the overall system inertia
decreases, resulting in low-inertia power systems. We show that the standard
state-space model based on least squares or subspace estimators fails to exist
for these models. We overcome this challenge by considering a least-squares
estimator directly on the coupled swing-equation model but not on its
transformed first-order state-space form. We specifically focus on estimating
inertia (mechanical and virtual) and damping constants, although our method is
general enough for estimating other parameters. Our theoretical analysis
highlights the role of network topology on the parameter estimates of an
individual generator. For generators with greater connectivity, estimation of
the associated parameters is more susceptible to variations in other generator
states. Furthermore, we numerically show that estimating the parameters by
ignoring their ill-conditioning aspects yields highly unreliable results.