  Critical transitions occur in a variety of dynamical systems. Here, we employ
quantifiers of chaos to identify changes in the dynamical structure of complex
systems preceding critical transitions. As suitable indicator variables for
critical transitions, we consider changes in growth rates and directions of
covariant Lyapunov vectors. Studying critical transitions in several models of
fast-slow systems, i.e., a network of coupled FitzHugh-Nagumo oscillators,
models for Josephson junctions and the Hindmarsh-Rose model, we find that
tangencies between covariant Lyapunov vectors are a common and maybe generic
feature during critical transitions. We further demonstrate that this deviation
from hyperbolic dynamics is linked to the occurrence of critical transitions by
using it as an indicator variable and evaluating the prediction success through
receiver operating characteristic curves. In the presence of noise, we find the
alignment of covariant Lyapunov vectors and changes in finite-time Lyapunov
exponents to be more successful in announcing critical transitions than common
indicator variables as, e.g., finite-time estimates of the variance.
Additionally, we propose a new method for estimating approximations of
covariant Lyapunov vectors without knowledge of the future trajectory of the
system. We find that these approximated covariant Lyapunov vectors can also be
applied to predict critical transitions.


  Topological defects are increasingly being identified in various biological
systems, where their characteristic flow fields and stress patterns are
associated with continuous active stress generation by biological entities.
Here, using numerical simulations of continuum fluctuating nematohydrodynamics
we show that even in the absence of any specific form of active stresses
associated with self-propulsion, mesoscopic fluctuations in either
orientational alignment or hydrodynamics can independently result in flow
patterns around topological defects that resemble the ones observed in active
systems. Our simulations further show the possibility of extensile- and
contractile-like motion of fluctuation-induced positive half-integer
topological defects. Remarkably, isotropic stress fields also reproduce the
experimentally measured stress patterns around topological defects in
epithelia. Our findings further reveal that extensile- or contractile-like flow
and stress patterns around fluctuation-induced defects are governed by passive
elastic stresses and flow-aligning behavior of the nematics.


  (TMTTF)2AsF6 undergoes two phase transitions upon cooling from 300 K. At
Tco=103 K a charge-ordering (CO) occurs, and at Tsp(B=9 T)=11 K the material
undergoes a spin-Peierls (SP) transition. Within the intermediate, CO phase,
the charge disproportionation ratio is found to be at least 3:1 from carbon-13
NMR 1/T1 measurements on spin-labeled samples. Above Tsp, up to about 3Tsp,
1/T1 is independent of temperature, indicative of low-dimensional magnetic
correlations. With the application of about 0.15 GPa pressure, Tsp increases
substantially, while Tco is rapidly suppressed, demonstrating that the two
orders are competing. The experiments are compared to results obtained from
calculations on the 1D extended Peierls-Hubbard model.


  The Near Infrared Camera (NIRCam) on the James Webb Space Telescope (JWST)
will be an incredibly powerful instrument for studying red supergiants (RSGs).
The high luminosities and red peak wavelengths of these stars make them ideal
targets for JWST/NIRCam. With effective photometric diagnostics in place,
imaging RSG populations in multiple filters will make it possible to determine
these stars' physical properties and, in cases where JWST pre-explosion imaging
is available, to identify RSG supernova progenitors. This paper uses observed
and model spectra of Galactic RSGs to simulate JWST/NIRCam near-IR photometry
and colors, quantify and test potential diagnostics of effective temperature
and bolometric magnitude, and present photometric techniques for separating
background RSG and foreground dwarf populations. While results are presented
for the full suite of near-IR filters, this work shows that (F070W-F200W) is
the JWST/NIRCam color index most sensitive to effective temperature, F090W is
the best band for determining bolometric magnitude, and the (F070W-F090W) vs.
(F090W-F200W) color-color diagram can be used to separate foreground dwarf and
background RSG samples. The combination of these three filters is recommended
as the best suite of photometric observations to use when studying RSGs with
JWST.


  The world is facing a huge health crisis due to the rapid transmission of
coronavirus (COVID-19). Several guidelines were issued by the World Health
Organization (WHO) for protection against the spread of coronavirus. According
to WHO, the most effective preventive measure against COVID-19 is wearing a
mask in public places and crowded areas. It is very difficult to monitor people
manually in these areas. In this paper, a transfer learning model is proposed
to automate the process of identifying the people who are not wearing mask. The
proposed model is built by fine-tuning the pre-trained state-of-the-art deep
learning model, InceptionV3. The proposed model is trained and tested on the
Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted
to address the limited availability of data for better training and testing of
the model. The model outperformed the other recently proposed approaches by
achieving an accuracy of 99.9% during training and 100% during testing.


  Oxygen vacancies in two-dimensional metal-oxide structures garner much
attention due to unique conductive, magnetic and even superconductive
functionalities they induce. Ferroelectric domain walls have been a prominent
recent example because they serve as a hub for topological defects that enable
unusual symmetries and are relevant for low-energy switching technologies.
However, owing to the light weight of oxygen atoms and localized effects of
their vacancies, the atomic-scale electrical and mechanical influence of oxygen
vacancies has remained elusive. Here, stable individual oxygen vacancies were
found and engineered in situ at domain walls of seminal titanate perovskite
ferroics. The atomic-scale strain, electric-field, charge and dipole-moment
distribution around these vacancies were characterized by combining advanced
transmission electron microscopy and first-principle methodologies. 3-5 %
tensile strain was observed at the immediate surrounding unit cells of the
vacancies. The dipole-moment distribution around the vacancy was found to be an
alternating head-to-head $-$ tail-to-tail $-$ head-to-head structure, giving
rise to a quasi-linear quadrupole topology. Reduction of the nearby Ti ion as
well as enhanced charging and electric-field concentration near the vacancy
confirmed the quadrupole structure and illustrated its local effects on the
electrical and structural properties. Significant intra-band states were found
in the unit cell of the vacancies, proposing a meaningful domain-wall
conductivity. Oxygen-vacancy engineering and controllable quadrupoles that
enable pre-determining both atomic-scale and global functional properties offer
a promising platform of electro-mechanical topological solitons and device
miniaturization in metal oxides.


  We extend the construction of the BFV-complex of a coisotropic submanifold
from the Poisson setting to the Jacobi setting. In particular, our construction
applies in the contact and l.c.s. settings. The BFV-complex of a coisotropic
submanifold $S$ controls the coisotropic deformation problem of $S$ under both
Hamiltonian and Jacobi equivalence.


  We study the geometry of a general class of vacuum asymptotically Anti-de
Sitter spacetimes near the conformal boundary. In particular, the spacetime is
only assumed to have finite regularity, and it is allowed to have arbitrary
boundary topology and geometry. For the main results, we derive limits at the
conformal boundary of various geometric quantities, and we use these limits to
construct partial Fefferman--Graham expansions from the boundary. The results
of this article will be applied, in upcoming papers, toward proving symmetry
extension and gravity--boundary correspondence theorems for vacuum
asymptotically Anti-de Sitter spacetimes.


  In dynamic positron emission tomography (PET) reconstruction, the importance
of leveraging the temporal dependence of the data has been well appreciated.
Current deep-learning solutions can be categorized in two groups in the way the
temporal dynamics is modeled: data-driven approaches use spatiotemporal neural
networks to learn the temporal dynamics of tracer kinetics from data, which
relies heavily on data supervision; physics-based approaches leverage \textit{a
priori} tracer kinetic models to focus on inferring their parameters, which
relies heavily on the accuracy of the prior kinetic model. In this paper, we
marry the strengths of these two approaches in a hybrid kinetics embedding
(HyKE-Net) framework for dynamic PET reconstruction. We first introduce a novel
\textit{hybrid} model of tracer kinetics consisting of a physics-based function
augmented by a neural component to account for its gap to data-generating
tracer kinetics, both identifiable from data. We then embed this hybrid model
at the latent space of an encoding-decoding framework to enable both supervised
and unsupervised identification of the hybrid kinetics and thereby dynamic PET
reconstruction. Through both phantom and real-data experiments, we demonstrate
the benefits of HyKE-Net -- especially in unsupervised reconstructions -- over
existing physics-based and data-driven baselines as well as its ablated
formulations where the embedded tracer kinetics are purely physics-based,
purely neural, or hybrid but with a non-adaptable neural component.


  Context. Images of spatially resolved astrophysical objects contain a wealth
of morphological and dynamical information, and effective extraction of this
information is of paramount importance for understanding the physics and
evolution of these objects. Algorithms and methods employed presently for this
purpose (such as, for instance, Gaussian model fitting) often use simplified
approaches for describing the structure of resolved objects. Aims. Automated
(unsupervised) methods for structure decomposition and tracking of structural
patterns are needed for this purpose, in order to be able to deal with the
complexity of structure and large amount of data involved. Methods. A new
Wavelet-based Image Segmentation and Evaluation (WISE) method is developed for
multiscale decomposition, segmentation, and tracking of structural patterns in
astronomical images. Results. The method is tested against simulated images of
relativistic jets and applied to data from long-term monitoring of parsec-
scale radio jets in 3C 273 and 3C 120. Working at its coarsest resolution, WISE
reproduces exceptionally well the previous results of model fitting evaluation
of the structure and kinematics in these jets. Extending the WISE structure
analysis to the finer scales provides the first robust measurements of
two-dimensional velocity fields in these jets and indicates that the velocity
fields are likely to reflect the evolution of Kelvin-Hemlholtz instability
developing the flow.


  The linear representation $T_n^*(\mathcal{K})$ of a point set $\mathcal{K}$
in a hyperplane of $\mathrm{PG}(n+1,q)$ is a point-line geometry embedded in
this projective space. In this paper, we will determine the isomorphisms
between two linear representations $T_n^*(\mathcal{K})$ and
$T_n^*(\mathcal{K}')$, under a few conditions on $\mathcal{K}$ and
$\mathcal{K}'$. First, we prove that an isomorphism between
$T_n^*(\mathcal{K})$ and $T_n^*(\mathcal{K}')$ is induced by an isomorphism
between the two linear representations $T_n^*(\overline{\mathcal{K}})$ and
$T_n^*(\overline{\mathcal{K}'})$ of their closures $\overline {\mathcal{K}}$
and $\overline{\mathcal{K}'}$.
  This allows us to focus on the automorphism group of a linear representation
$T_n^*(\mathcal{S})$ of a subgeometry $\mathcal{S}\cong\mathrm{PG}(n,q)$
embedded in a hyperplane of the projective space $\mathrm{PG}(n+1,q^t)$. To
this end we introduce a geometry $X(n,t,q)$ and determine its automorphism
group. The geometry $X(n,t,q)$ is a straightforward generalization of
$H_{q}^{n+2}$ which is known to be isomorphic to the linear representation of a
Baer subgeometry. By providing an elegant algebraic description of $X(n,t,q)$
as a coset geometry we extend this result and prove that $X(n,t,q)$ and
$T_n^*(\mathcal{S})$ are isomorphic.
  Finally, we compare the full automorphism group of $T^*_n(\mathcal{S})$ with
the "natural" group of automorphisms that is induced by the collineation group
of its ambient space.


  LHCb is the experiment at the Large Hadron Collider devoted to studies of new
phenomena in CP violation and in rare decays. This review summarizes the status
of the experiment in the imminence of the data taking, the prospects for the
first measurements and highlights of its full physics program.


  Neurosymbolic background knowledge and the expressivity required of its logic
can break Machine Learning assumptions about data Independence and Identical
Distribution. In this position paper we propose to analyze IID relaxation in a
hierarchy of logics that fit different use case requirements. We discuss the
benefits of exploiting known data dependencies and distribution constraints for
Neurosymbolic use cases and argue that the expressivity required for this
knowledge has implications for the design of underlying ML routines. This opens
a new research agenda with general questions about Neurosymbolic background
knowledge and the expressivity required of its logic.


  A family of concurrent data predictors is derived from the decision tree
classifier by removing the limitation of sequentially evaluating attributes. By
evaluating attributes concurrently, the decision tree collapses into a flat
structure. Experiments indicate improvements of the prediction accuracy.


  In collaboration with the Liaoning CDC, China, we propose a prediction system
to predict the subsequent hospitalization of children with adverse reactions
based on data on adverse events following immunization. We extracted multiple
features from the data, and selected "hospitalization or not" as the target for
classification. Since the data are imbalanced, we used various class-imbalance
learning methods for training and improved the RUSBoost algorithm. Experimental
results show that the improved RUSBoost has the highest Area Under the ROC
Curve on the target among these algorithms. Additionally, we compared these
class-imbalance learning methods with some common machine learning algorithms.
We combined the improved RUSBoost with dynamic web resource development
techniques to build an evaluation system with information entry and vaccination
response prediction capabilities for relevant medical practitioners.


  In March of this year, COVID-19 was declared a pandemic and it continues to
threaten public health. This global health crisis imposes limitations on daily
movements, which have deteriorated every sector in our society. Understanding
public reactions to the virus and the non-pharmaceutical interventions should
be of great help to fight COVID-19 in a strategic way. We aim to provide
tangible evidence of the human mobility trends by comparing the day-by-day
variations across the U.S. Large-scale public mobility at an aggregated level
is observed by leveraging mobile device location data and the measures related
to social distancing. Our study captures spatial and temporal heterogeneity as
well as the sociodemographic variations regarding the pandemic propagation and
the non-pharmaceutical interventions. All mobility metrics adapted capture
decreased public movements after the national emergency declaration. The
population staying home has increased in all states and becomes more stable
after the stay-at-home order with a smaller range of fluctuation. There exists
overall mobility heterogeneity between the income or population density groups.
The public had been taking active responses, voluntarily staying home more, to
the in-state confirmed cases while the stay-at-home orders stabilize the
variations. The study suggests that the public mobility trends conform with the
government message urging to stay home. We anticipate our data-driven analysis
offers integrated perspectives and serves as evidence to raise public awareness
and, consequently, reinforce the importance of social distancing while
assisting policymakers.


  Perturbative coefficients grow factorially with the order and one needs a
prescription to truncate the series in order to obtain a finite result. A
common prescription consists in dropping the smallest contribution at a given
coupling and all the higher orders terms. We discuss the error associated with
this procedure. We advocate a more systematic approach which consists in
controlling the large field configurations in the functional integral. We
summarize our best understanding of these issues for lattice QCD in the
quenched approximation and their connection with convergence problems found in
the continuum.


  We study the viscous Burgers equation with a family of initial data having
infinite mass. After rescaling, the solution converges toward a bounded
discontinuous profile in the long-time limit. Moreover, by changing the scale
near the discontinuity point again, we find a new profile that is also
discontinuous. This process can be repeated an arbitrary number of times.


  We discuss the modified gravity which includes negative and positive powers
of the curvature and which provides the gravitational dark energy. It is shown
that in GR plus the term containing negative power of the curvature the cosmic
speed-up may be achieved, while the effective phantom phase (with $w$ less than
-1) follows when such term contains the fractional positive power of the
curvature. The minimal coupling with matter makes the situation more
interesting: even 1/R theory coupled with the usual ideal fliud may describe
the (effective phantom) dark energy. The account of $R^2$ term (consistent
modified gravity) may help to escape of cosmic doomsday.


  This document presents TLS and how to make it secure enough as of 2014
Spring. Of course all the information given here will rot with time. Protocols
known as secure will be cracked and will be replaced with better versions.
Fortunately we will see that there are ways to assess the current security of
your setup, but this explains why you may have to read further from this
document to get the up to date knowledge on TLS security.
  We will first introduce the TLS protocol and its underlying components: X.509
certificates, ciphers, and protocol versions. Next we will have a look at TLS
hardening for web servers, and how to plug various vulnerabilities: CRIME,
BREACH, BEAST, session renegotiation, Heartbleed, and others. We will finally
see how the know-how acquired on hardening web servers can be used for other
protocols and tools such as Dovecot, Sendmail, SquirrelMail, RoundCube, and
OpenVPN.
  We assume you already maintain services that use TLS, and have basic TCP/IP
network knowledge. Some information will also be useful for the application
developer.


  Least absolute deviation regression is applied using a fixed number of points
for all values of the index to estimate the index and scale parameter of the
stable distribution using regression methods based on the empirical
characteristic function. The recognized fixed number of points estimation
procedure uses ten points in the interval zero to one, and least squares
estimation. It is shown that using the more robust least absolute regression
based on iteratively re-weighted least squares outperforms the least squares
procedure with respect to bias and also mean square error in smaller samples.


  We consider the following game that has been used as a way of testing claims
of extrasensory perception (ESP). One is given a deck of $mn$ cards comprised
of $n$ distinct types each of which appears exactly $m$ times: this deck is
shuffled and then cards are discarded from the deck one at a time from top to
bottom. At each step, a player (whose psychic powers are being tested) tries to
guess the type of the card currently on top, which is then revealed to the
player before being discarded. We study the expected number $S_{n,m}$ of
correct predictions a player can make: one could always guess the exact same
type of card which shows that one can achieve $S_{n,m}>m$. We prove that the
optimal (non-psychic) strategy is just slightly better than that and find the
first order correction when $n, m$ grows at suitable rates. This is very
different from the case where $m$ is fixed and $n$ is large (He & Ottolini) and
similar to the case of fixed $n$ and $m$ is large (Graham & Diaconis). The case
$m=n$ answers a question of Diaconis.


  We propose a Multi-Layer Network based on the Bayesian framework of the
Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional
lattice. The Latent Variable Model (LVM) is the basic building block of a
quadtree hierarchy built on top of a bottom layer of random variables that
represent pixels of an image, a feature map, or more generally a collection of
spatially distributed discrete variables. The multi-layer architecture
implements a hierarchical data representation that, via belief propagation, can
be used for learning and inference. Typical uses are pattern completion,
correction and classification. The FGrn paradigm provides great flexibility and
modularity and appears as a promising candidate for building deep networks: the
system can be easily extended by introducing new and different (in cardinality
and in type) variables. Prior knowledge, or supervised information, can be
introduced at different scales. The FGrn paradigm provides a handy way for
building all kinds of architectures by interconnecting only three types of
units: Single Input Single Output (SISO) blocks, Sources and Replicators. The
network is designed like a circuit diagram and the belief messages flow
bidirectionally in the whole system. The learning algorithms operate only
locally within each block. The framework is demonstrated in this paper in a
three-layer structure applied to images extracted from a standard data set.


  Sequential recommendation (SR) learns users' preferences by capturing the
sequential patterns from users' behaviors evolution. As discussed in many
works, user-item interactions of SR generally present the intrinsic power-law
distribution, which can be ascended to hierarchy-like structures. Previous
methods usually handle such hierarchical information by making user-item
sectionalization empirically under Euclidean space, which may cause distortion
of user-item representation in real online scenarios. In this paper, we propose
a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the
sequential pattern information as well as hierarchical information contained in
the data of SR scenarios simultaneously. Specifically, for the purpose of
explicitly capturing the hierarchical information, we first construct a
weighted user-item heterogeneous graph by aliening all the user-item
interactions to improve the perception domain of each user from a global view.
Then the output of the global representation would be used to complement the
local directed item-item homogeneous graph convolution. By defining a novel
hyperbolic inner product operator, the global and local graph representation
learning are directly conducted in Poincar\'{e} ball instead of commonly used
projection operation between Poincar\'{e} ball and Euclidean space, which could
alleviate the cumulative error issue of general bidirectional translation
process. Moreover, for the purpose of explicitly capturing the sequential
dependency information, we design two types of temporal attention operations
under Poincar\'{e} ball space. Empirical evaluations on datasets from the
public and financial industry show that PHGR outperforms several comparison
methods.


  Improving the best known examples, two planar straight-line graphs which
cause the non-termination of Ruppert's algorithm for a minimum angle threshold
as low as 29.06 degrees are given.


  Using a lattice model of polymers in a tube, we define one way to
characterise different configurations of a given knot as either "local" or
"non-local" and, for several ring polymer models, we provide both theoretical
and numerical evidence that, at equilibrium, the non-local configurations are
more likely than the local ones. These characterisations are based on a
standard approach for measuring the "size" of a knot within a knotted polymer
chain. The method involves associating knot-types to subarcs of the chain, and
then identifying a knotted subarc with minimal arclength; this arclength is
then the knot-size. If the resulting knot-size is small relative to the whole
length of the chain, then the knot is considered to be localised or "local". If
on the other hand the knot-size is comparable to the length of the chain, then
the knot is considered to be "non-local".
  Using this definition, we establish that all but exponentially few
sufficiently long self-avoiding polygons (closed chains) in a tubular
sublattice of the simple cubic lattice are "non-locally" knotted. This is shown
to also hold for the case when the same polygons are subject to an external
tensile force, as well as in the extreme case when they are as compact as
possible (no empty lattice sites). We also provide numerical evidence for small
tube sizes that at equilibrium non-local knotting is more likely than local
knotting, regardless of the strength of the stretching or compressing force. We
note however that because of the tube confinement, the occurrence of non-local
knotting in walks (open chains) is significantly different than for polygons.
The relevance of these results to recent experiments involving DNA knots in
solid-state nanopores is also discussed.


  We describe curved-space BPS dyon solutions, the ADM mass of which saturates
the gravitational version of the Bogomol'nyi bound. This generalizes
self-gravitating BPS monopole solutions of Gibbons et al. when there is no
dilaton.


  We present the first review of the current state of the literature on
electronic properties and phase transitions in TlX and TlMX2 (M = Ga, In; X =
Se, S, Te) compounds. These chalcogenides belong to a family of the
low-dimensional semiconductors possessing chain or layered structure. They are
of significant interest because of their highly anisotropic properties, semi-
and photoconductivity, non-linear effects in their I-V characteristics
(including a region of negative differential resistance), switching and memory
effects, second harmonic optical generation, relaxor behavior and potential
applications for optoelectronic devices. We review the crystal structure of TlX
and TlMX2 compounds, their transport properties under ambient conditions,
experimental and theoretical studies of the electronic structure, transport
properties and semiconductor-metal phase transitions under high pressure, and
sequences of temperature-induced structural phase transitions with intermediate
incommensurate states. Electronic nature of the ferroelectric phase transitions
in the above-mentioned compounds, as well as relaxor behavior, nanodomains and
possible occurrence of quantum dots in doped and irradiated crystals is
discussed.


  T violation has previously been shown to induce destructive interference
between different paths that the universe can take through time and leads to a
new quantum equation of motion called bievolution. Here we examine further
details of the interference and clarify the conditions needed for the
bievolution equation.


  We present the analysis of an optical data cube of the central region of NGC
1448, obtained with the Multi Unit Spectroscopic Explorer (MUSE). Chandra X-ray
data indicate that the active galactic nucleus (AGN) is not located at the
apparent stellar nucleus of the galaxy, but at a projected distance of $1.75$
$\pm$ $0.22$ arcsec ($139 \pm 17$ pc). This is probably caused by the high
interstellar extinction in the surroundings of the AGN, which corresponds to
the true nucleus of the galaxy, as also proposed by previous studies. The
morphology and classification of the optical line-emitting regions indicate two
ionization cones, around an axis with a position angle of $PA_{cones}$ =
-50{\deg} $\pm$ 7{\deg}, with emission-line spectra characteristic of Seyfert
galaxies. The stellar and gas kinematics are consistent with a stellar and gas
rotating disc around the nucleus, with a velocity amplitude of 125 km s$^{-1}$.
Two probable outflows from the AGN were detected along the region of the two
ionization cones. The AGN position does not coincide with the brightest
line-emitting region at the centre of NGC 1448. That may be a consequence of
the high obscuration from the AGN towards the observer (the AGN is actually
Compton thick), mostly caused by a nearly edge-on torus. An additional
hypothesis is that the AGN reduced its luminosity, during the last 440 yr, to
nearly half of the value in the past. In this case, the brightest line-emitting
region corresponds to a "light echo" or a "fossil" of the AGN in the past.


  For control systems in discrete time, this paper discusses measure-theoretic
invariance entropy for a subset Q of the state space with respect to a
quasi-stationary measure obtained by endowing the control range with a
probability measure. The main results show that this entropy is invariant under
measurable transformations and that it is already determined by certain subsets
of Q which are characterized by controllability properties.


  We propose a methodology that adapts graph embedding techniques (DeepWalk
(Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as
cross-lingual vector space mapping approaches (Least Squares and Canonical
Correlation Analysis) in order to merge the corpus and ontological sources of
lexical knowledge. We also perform comparative analysis of the used algorithms
in order to identify the best combination for the proposed system. We then
apply this to the task of enhancing the coverage of an existing word
embedding's vocabulary with rare and unseen words. We show that our technique
can provide considerable extra coverage (over 99%), leading to consistent
performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.\S
3.3) on the Rare Word Similarity dataset.


  A diffusion taking value in probability measures on a graph with a vertex set
$V$, $\sum_{i\in V}x_i\delta_i$, is studied. The masses on each vertices
satisfy the stochastic differential equation of the form $dx_i=\sum_{j\in
N(i)}\sqrt{x_ix_j}dB_{ij}$ on the simplex, where $\{B_{ij}\}$ are independent
standard Brownian motions with skew symmetry and $N(i)$ is the neighbour of the
vertex $i$. A dual Markov chain on integer partitions to the Markov semigroup
associated with the diffusion is used to show that the support of an extremal
stationary state of the adjoint semigroup is an independent set of the graph.
We also investigate the diffusion with a linear drift, which gives a killing of
the dual Markov chain on a finite integer lattice. The Markov chain is used to
study the unique stationary state of the diffusion, which generalizes the
Dirichlet distribution. Two applications of the diffusions are discussed:
analysis of an algorithm to find an independent set of a graph, and a Bayesian
graph selection based on computation of probability of a sample by using
coupling from the past.


  The evolution of the logarithmic scaled energy spectra with energy scale, in
the framework of the modified leading logarithmic approximation (MLLA), is
investigated. The behaviour of the higher order moments of HERA deep inelastic
scattering data are compared with the analytic predictions of MLLA.


  In this article, we study a class of contractive factors of
$m$-hypercontractions for $m \in \mathbb{N}$. We find a characterization of
such factors and this is achieved by finding explicit dilation of these factors
on some weighted Bergman spaces. This is a generalization of the work done in
[14].


  We study Polyakov loop correlations and spatial Wilson loop at finite
Temperature in two-flavor QCD simulations with the RG-improved gluon action and
the clover-improved Wilson quark action on a $ 16^3 \times 4$ lattice. From the
line of constant physics at $m_{\rm PS}/m_{\rm V}=0.65$ and 0.80, we extract
the heavy-quark free energies, the effective running coupling $g_{\rm eff}(T)$
and the Debye screening mass $m_D(T)$ for various color channels of heavy
quark--quark and quark--anti-quark pairs above the critical temperature. The
free energies are well approximated by the screened Coulomb form with the
appropriate Casimir factors at high temperature. The magnitude and the
temperature dependence of the Debye mass are compared to those of the
next-to-leading order thermal perturbation theory and to a phenomenological
formula in terms of $g_{\rm eff}(T)$. We make a comparison between our results
with the Wilson quark action and the previous results with the staggered quark
action. The spatial string tension is also studied in the high temperature
phase and is compared to the next-to-next-leading order prediction in an
effective theory with dimensional reduction.


  We reformulate the self-dual Einstein equation as a trio of differential form
equations for simple two-forms. Using them, we can quickly show the equivalence
of the theory and 2D sigma models valued in an infinite-dimensional group,
which was shown by Park and Husain earlier. We also derive other field theories
including the 2D Higgs bundle equation. This formulation elucidates the
relation among those field theories.


  In this study the influence of stratification on surface tidal elevations in
a two-layer analytical model is examined. The model assumes linearized,
non-rotating, shallow-water dynamics in one dimension with astronomical forcing
and allows for arbitrary topography. Using a natural modal separation, both
large scale (barotropic) and small scale (baroclinic) components of the surface
tidal elevation are shown to be comparably affected by stratification. It is
also shown that the topography and basin boundaries affect the sensitivity of
the barotropic surface tide to stratification significantly. This paper,
therefore, provides a framework to understand how the presence of
stratification impacts barotropic as well as baroclinic tides, and how climatic
perturbations to oceanic stratification contribute to secular variations in
tides. Results from a realistic-domain global numerical two-layer tide model
are briefly examined and found to be qualitatively consistent with the
analytical model results.


  Face recognition is known to be vulnerable to adversarial face images.
Existing works craft face adversarial images by indiscriminately changing a
single attribute without being aware of the intrinsic attributes of the images.
To this end, we propose a new Semantic Adversarial Attack called SAA-StarGAN
that tampers with the significant facial attributes for each image. We predict
the most significant attributes by applying the cosine similarity or
probability score. The probability score method is based on training a Face
Verification model for an attribute prediction task to obtain a class
probability score for each attribute. The prediction process will help craft
adversarial face images more easily and efficiently, as well as improve the
adversarial transferability. Then, we change the most significant facial
attributes, with either one or more of the facial attributes for impersonation
and dodging attacks in white-box and black-box settings. Experimental results
show that our method could generate diverse and realistic adversarial face
images meanwhile avoid affecting human perception of the face recognition.
SAA-StarGAN achieves an 80.5% attack success rate against black-box models,
outperforming existing methods by 35.5% under the impersonation attack.
Concerning the black-box setting, SAA-StarGAN achieves high attack success
rates on various models. The experiments confirm that predicting the most
important attributes significantly affects the success of adversarial attacks
in both white-box and black-box settings and could enhance the transferability
of the crafted adversarial examples.


  It is argued that there is no low-energy resonance associated with the
pi-operators introduced by Demler and Zhang. This implies that the Hubbard
model does not possess an approximate SO(5) symmetry generated by these
operators. Recent finite-size studies are re-interpreted accordingly.


  Markov Chain Monte Carlo (MCMC) requires to evaluate the full data likelihood
at different parameter values iteratively and is often computationally
infeasible for large data sets. In this paper, we propose to approximate the
log-likelihood with subsamples taken according to nonuniform subsampling
probabilities, and derive the most likely optimal (MLO) subsampling
probabilities for better approximation. Compared with existing subsampled MCMC
algorithm with equal subsampling probabilities, our MLO subsampled MCMC has a
higher estimation efficiency with the same subsampling ratio. We also derive a
formula using the asymptotic distribution of the subsampled log-likelihood to
determine the required subsample size in each MCMC iteration for a given level
of precision. This formula is used to develop an adaptive version of the MLO
subsampled MCMC algorithm. Numerical experiments demonstrate that the proposed
method outperforms the uniform subsampled MCMC.


  Nanoparticles can influence the properties of polymer materials by a variety
of mechanisms. With fullerene, carbon nanotube, and clay or graphene sheet
nanocomposites in mind, we investigate how particle shape influences the melt
shear viscosity $\eta$ and the tensile strength $\tau$, which we determine via
molecular dynamics simulations. Our simulations of compact (icosahedral), tube
or rod-like, and sheet-like model nanoparticles indicate an order of magnitude
increase in the viscosity $\eta$ relative to the pure melt. This finding
evidently can not be explained by continuum hydrodynamics and we provide
evidence that the $\eta$ increase in our model nanocomposites has its origin in
chain bridging between the nanoparticles. We find that this increase is the
largest for the rod-like nanoparticles and least for the sheet-like
nanoparticles. Curiously, the enhancements of $\eta$ and $\tau$ exhibit {\it
opposite trends} with increasing chain length $N$ and with particle shape
anisotropy. Evidently, the concept of bridging chains alone cannot account for
the increase in $\tau$ and we suggest that the deformability or flexibility of
the sheet nanoparticles contributes to nanocomposite strength and toughness by
reducing the relative value of the Poisson ratio of the composite. Our
simulations point to a substantial contribution of nanoparticle shape to both
mechanical and processing properties of polymer nanocomposites.


  We discuss the interplay between the piece-line regular and vertex-angle
singular boundary effects, related to integrability and chaotic features in
rational polygonal billiards. The approach to controversial issue of regular
and irregular motion in polygons is taken within the alternative deterministic
and stochastic frameworks. The analysis is developed in terms of the
billiard-wall collision distribution and the particle survival probability,
simulated in closed and weakly open polygons, respectively. In the multi-vertex
polygons, the late-time wall-collision events result in the circular-like
regular periodic trajectories (sliding orbits), which, in the open billiard
case are likely transformed into the surviving collective excitations
(vortices). Having no topological analogy with the regular orbits in the
geometrically corresponding circular billiard, sliding orbits and vortices are
well distinguished in the weakly open polygons via the universal and
non-universal relaxation dynamics.


  Zero mean curvature surfaces in the simply isotropic 3-space $\mathbb{I}^3$
naturally appear as intermediate geometry between geometry of minimal surfaces
in $\mathbb{E}^3$ and that of maximal surfaces in $\mathbb{L}^3$. In this
paper, we investigate reflection principles for zero mean curvature surfaces in
$\mathbb{I}^3$ as with the above surfaces in $\mathbb{E}^3$ and $\mathbb{L}^3$.
In particular, we show a reflection principle for isotropic line segments on
such zero mean curvature surfaces in $\mathbb{I}^3$, along which the induced
metrics become singular.


  The Event Horizon Telescope (EHT), a global very long baseline
interferometric array observing at a wavelength of 1.3 mm, detected the first
image of the M87 supermassive black hole (SMBH). M87 is a giant elliptical
galaxy at the center of Virgo cluster, which is expected to have formed through
merging of cluster galaxies. Consequently M87* hosted mergers of black holes
through dynamical friction and could have one or multiple binary companions
with a low mass ratio at large separations. We show that a long-term monitoring
of the M87 SMBH image over $\sim$1 year with absolute positional accuracy of
1$\approx\mu as$ could detect such binary companions and exclude a large
parameter space in semi major axis ($a_0$) and mass ratio ($q$), which is
currently not constrained. Moreover, the presence of the accretion disk around
M87* excludes a binary companion with $a_0\approx$ of order a mili parsec, as
otherwise the accretion disk would have been tidally disrupted.


  Due to a well-known, but curious, minus sign in the Gibbons-Hawking first law
for the static patch of de Sitter space, the entropy of the cosmological
horizon is reduced by the addition of Killing energy. This minus sign raises
the puzzling question how the thermodynamics of the static patch should be
understood. We argue the confusion arises because of a mistaken interpretation
of the matter Killing energy as the total internal energy, and resolve the
puzzle by introducing a system boundary at which a proper thermodynamic
ensemble can be specified. When this boundary shrinks to zero size the total
internal energy of the ensemble (the Brown-York energy) vanishes, as does its
variation. Part of this vanishing variation is thermalized, captured by the
horizon entropy variation, and part is the matter contribution, which may or
may not be thermalized. If the matter is in global equilibrium at the de Sitter
temperature, the first law becomes the statement that the generalized entropy
is stationary.


  In this paper, the Cherenkov radiation process is investigated in the frame
of the Quantum Field Theory, both for the spinless and spin-1/2 charged
particles. In the latter case, the polarization density matrix technique was
used to account for the spin flip. If compared with the Classical
Electrodynamics results, in both cases several quantum corrections arise,
coinciding up to the photon's energy linear term. It is shown that, while the
absolute value of these corrections significantly relies on the refractive
index, the relative quantum correction stays independent of the medium in the
case of the ultrarelativistic particles. It the case of the spin-1/2 particles
it turns out that the spin flip practically never occurs in this process. For
both cases, spinless and spin-1/2, the outgoing photon polarization vector lies
always in the plane formed by the initial particle's momentum vector and the
final photon's wave vector.


  We study the problem of finding connected components in the Adaptive
Massively Parallel Computation (AMPC) model. We show that when we require the
total space to be linear in the size of the input graph the problem can be
solved in $O(\log^* n)$ rounds in forests (with high probability) and
$2^{O(\log^* n)}$ expected rounds in general graphs. This improves upon an
existing $O(\log \log_{m/n} n)$ round algorithm. For the case when the desired
number of rounds is constant we show that both problems can be solved using
$\Theta(m + n \log^{(k)} n)$ total space in expectation (in each round), where
$k$ is an arbitrarily large constant and $\log^{(k)}$ is the $k$-th iterate of
the $\log_2$ function. This improves upon existing algorithms requiring
$\Omega(m + n \log n)$ total space.


  We investigate non-degenerate bound state solitons systematically in
multi-component Bose-Einstein condensates, through developing Darboux
transformation method to derive exact soliton solutions analytically. In
particular, we show that bright solitons with nodes correspond to the excited
bound eigen-states in the self-induced effective quantum wells, in sharp
contrast to the bright soliton and dark soliton reported before (which usually
correspond to ground state and free eigen-state respectively). We further
demonstrate that the bound state solitons with nodes are induced by incoherent
interactions between solitons in different components. Moreover, we reveal that
the interactions between these bound state solitons are usually inelastic,
caused by the incoherent interactions between solitons in different components
and the coherent interactions between solitons in same component. The bound
state solitons can be used to discuss many different physical problems, such as
beating dynamics, spin-orbital coupling effects, quantum fluctuations, and even
quantum entanglement states.


  We investigate the generalization of the spin-boson model to arbitrary spin
size. The Born-Markov approximation is employed to derive a master equation in
the regime of small coupling strengths to the environment. For spin one half,
the master equation transforms into a set of Bloch equations, the solution of
which is in good agreement with results of the spin-boson model for weak ohmic
dissipation. For larger spins, we find a superradiance-like behavior known from
the Dicke model. The influence of the nonresonant bosons of the dissipative
environment can lead to the formation of a beat pattern in the dynamics of the
$z$-component of the spin. The beat frequency is approximately proportional to
the cutoff $\omega_c$ of the spectral function.


  As artificial intelligence (AI) is integrated into various services and
systems in society, many companies and organizations have proposed AI
principles, policies, and made the related commitments. Conversely, some have
proposed the need for independent audits, arguing that the voluntary principles
adopted by the developers and providers of AI services and systems
insufficiently address risk. This policy recommendation summarizes the issues
related to the auditing of AI services and systems and presents three
recommendations for promoting AI auditing that contribute to sound AI
governance. Recommendation1.Development of institutional design for AI audits.
Recommendation2.Training human resources for AI audits. Recommendation3.
Updating AI audits in accordance with technological progress.
  In this policy recommendation, AI is assumed to be that which recognizes and
predicts data with the last chapter outlining how generative AI should be
audited.


  The inference of minimum spanning arborescences within a set of objects is a
general problem which translates into numerous application-specific
unsupervised learning tasks. We introduce a unified and generic structure
called edit arborescence that relies on edit paths between data in a
collection, as well as the Min Edit Arborescence Problem, which asks for an
edit arborescence that minimizes the sum of costs of its inner edit paths.
Through the use of suitable cost functions, this generic framework allows to
model a variety of problems. In particular, we show that by introducing
encoding size preserving edit costs, it can be used as an efficient method for
compressing collections of labeled graphs. Experiments on various graph
datasets, with comparisons to standard compression tools, show the potential of
our method.


  In this paper we define and study for a finite partially ordered set P a
class of simplicial complexes on the set P_r of r-element multichains from P.
The simplicial complexes depend on a strictly monotone function from [r] to
[2r]. We show that there exactly 2^r such functions which yield subdivisions of
the order complex of P of which 2^{r-1} are pairwise different. Within this
class are for example the order complexes of the interval and the zig-zag poset
of P and the rth edgewise subdivision of the order complex of P. We also
exhibit a large subclass for which our simplicial complexes are order complexes
and homotopy equivalent to the order complex of P.


  Language-capable interactive robots participating in dialogues with human
interlocutors must be able to naturally and efficiently communicate about the
entities in their environment. A key aspect of such communication is the use of
anaphoric language. The linguistic theory of the Givenness Hierarchy(GH)
suggests that humans use anaphora based on the cognitive statuses their
referents have in the minds of their interlocutors. In previous work,
researchers presented GH-theoretic approaches to robot anaphora understanding.
In this paper we describe how the GH might need to be used quite differently to
facilitate robot anaphora generation.


  For two supersingular elliptic curves $E$ and $E'$ defined over
$\mathbb{F}_{p^2}$, let $[E \times E']$ be the superspecial abelian surface
with the principal polarization $\{0\} \times E' + E \times \{0\}$. We
determine local structure of the vertices $[E \times E']$ in the $(\ell,
\ell)$-isogeny graph of principally polarized superspecial abelian surfaces
where either $E$ or $E'$ is defined over $\mathbb{F}_p$. We also present a
simple new proof of the main theorem in \cite{LOX20}.


  Hand hygiene is one of the most significant factors in preventing hospital
acquired infections (HAI) which often be transmitted by medical staffs in
contact with patients in the operating room (OR). Hand hygiene monitoring could
be important to investigate and reduce the outbreak of infections within the
OR. However, an effective monitoring tool for hand hygiene compliance is
difficult to develop due to the visual complexity of the OR scene. Recent
progress in video understanding with convolutional neural net (CNN) has
increased the application of recognition and detection of human actions.
Leveraging this progress, we proposed a fully automated hand hygiene monitoring
tool of the alcohol-based hand rubbing action of anesthesiologists on OR video
using spatio-temporal features with 3D CNN. First, the region of interest (ROI)
of anesthesiologists' upper body were detected and cropped. A temporal
smoothing filter was applied to the ROIs. Then, the ROIs were given to a 3D CNN
and classified into two classes: rubbing hands or other actions. We observed
that a transfer learning from Kinetics-400 is beneficial and the optical flow
stream was not helpful in our dataset. The final accuracy, precision, recall
and F1 score in testing is 0.76, 0.85, 0.65 and 0.74, respectively.


  The simulation of vehicular wireless channels using geometry-based radio
channel models is computationally intensive when the number of scatterers is
significantly high. In this paper, we propose a new geometry-based stochastic
channel model to simulate and analyze the aforementioned channels based on a
framework developed from the theory of ambit processes. Under reasonable
assumptions, the underlying mathematical structure of the proposed channel
model enables the characterization of high mobility channels in terms of fading
statistics, spatio-temporal channel correlation, and Doppler spectrum, besides
ensuring tractable analysis. The developed algorithm facilitates fast
simulation of high mobility channels and accounts for key features of vehicular
channels including appearance and disappearance of multi-path components,
spatial consistency, and captures the correlation between time-evolving delay
and Doppler associated with multi-path components. Finally, we carry out
simulations to obtain crucial insights about the characteristics of typical
vehicle-to-infrastructure channels based on the proposed channel model.


  We prove a sharp bound for the remainder term of the number of lattice points
inside a ball, when averaging over a compact set of (not necessarily
unimodular) lattices, in dimensions two and three. We also prove that such a
bound cannot hold if one averages over the space of all lattices.


  A simple and highly reproducible single electron transistor (SET) has been
fabricated using gated silicon nanowires. The structure is a
metal-oxide-semiconductor field-effect transistor made on silicon-on-insulator
thin films. The channel of the transistor is the Coulomb island at low
temperature. Two silicon nitride spacers deposited on each side of the gate
create a modulation of doping along the nanowire that creates tunnel barriers.
Such barriers are fixed and controlled, like in metallic SETs. The period of
the Coulomb oscillations is set by the gate capacitance of the transistor and
therefore controlled by lithography. The source and drain capacitances have
also been characterized. This design could be used to build more complex SET
devices.


  We present RBC heavy-light meson spectroscopy with quenched DBW2 gauge
configurations at lattice cutoff of about 3 GeV. Both heavy and light quarks
are described by domain-wall fermions (DWF). The heavy quark mass ranges
between 0.1 and 0.4 lattice units, covering charm. The light quark mass ranges
between 0.008 and 0.04, covering strange. In particular, we discuss charmed (D
and D*) and charm-strange (Ds and DsJ) mesons with spin-parity JP= 0+/- and
1+/-. The preliminary results indicate that DWF describe charm on the quenched
DBW2 ensemble at this cutoff. The masses of the JP=0+/- and 1+/- D, D*, Ds and
DsJ meson states are well reproduced to within a few %; their parity splitting,
DeltaJ, are better reproduced than previous works, with only 10-20 % over
estimations; the experimental observation that the splitting for non-strange
states is bigger than that for strange states is reproduced as well; but the
hyperfine splittings are only 60-65 % reproduced. Regarding the depenence on
heavy quark mass, J=0 and J=1 parity splittings are degenerate for heavy quark
mass heavier than 0.2-0.3 lattice units a; the J=0 parity splitting increases
as the heavy quark mass decreases further while the J=1 splitting does not.


  Volume conserving surface (VCS) models without deposition and evaporation, as
well as ideal molecular-beam epitaxy models, are prototypes to study the
symmetries of conserved dynamics. In this work we study two similar VCS models
with conserved noise, which differ from each other by the axial symmetry of
their dynamic hopping rules. We use a coarse-grained approach to analyze the
models and show how to determine the coefficients of their corresponding
continuous stochastic differential equation (SDE) within the same universality
class. The employed method makes use of small translations in a test space
which contains the stationary probability density function (SPDF). In case of
the symmetric model we calculate all the coarse-grained coefficients of the
related conserved Kardar-Parisi-Zhang (KPZ) equation. With respect to the
symmetric model, the asymmetric model adds new terms which have to be analyzed,
first of all the diffusion term, whose coarse-grained coefficient can be
determined by the same method. In contrast to other methods, the used formalism
allows to calculate all coefficients of the SDE theoretically and within limits
numerically. Above all, the used approach connects the coefficients of the SDE
with the SPDF and hence gives them a precise physical meaning.


  We extend three related results from the analysis of influences of Boolean
functions to the quantum setting, namely the KKL Theorem, Friedgut's Junta
Theorem and Talagrand's variance inequality for geometric influences. Our
results are derived by a joint use of recently studied hypercontractivity and
gradient estimates. These generic tools also allow us to derive generalizations
of these results in a general von Neumann algebraic setting beyond the case of
the quantum hypercube, including examples in infinite dimensions relevant to
quantum information theory such as continuous variables quantum systems.
Finally, we comment on the implications of our results as regards to
noncommutative extensions of isoperimetric type inequalities, quantum circuit
complexity lower bounds and the learnability of quantum observables.


  In a representation of a graph $G$ as an edge intersection graph of paths on
a grid (EPG) every vertex of $G$ is represented by a path on a grid and two
paths share a grid edge iff the corresponding vertices are adjacent. In a
monotonic EPG representation every path on the grid is ascending in both rows
and columns. In a (monotonic) $B_k$-EPG representation every path on the grid
has at most $k$ bends. The (monotonic) bend number $b(G)$ ($b^m(G)$) of a graph
$G$ is the smallest natural number $k$ for which there exists a (monotonic)
$B_k$-EPG representation of $G$.
  In this paper we deal with the monotonic bend number of outerplanar graphs
and show that $b^m(G)\leqslant 2$ holds for every outerplanar graph $G$.
Moreover, we characterize the maximal outerplanar graphs and the cacti with
(monotonic) bend number equal to $0$, $1$ and $2$ in terms of forbidden induced
subgraphs. As a byproduct we obtain low-degree polynomial time algorithms to
construct (monotonic) EPG representations with the smallest possible number of
bends for maximal outerplanar graphs and cacti.


  Cross-domain sentiment analysis is currently a hot topic in the research and
engineering areas. One of the most popular frameworks in this field is the
domain-invariant representation learning (DIRL) paradigm, which aims to learn a
distribution-invariant feature representation across domains. However, in this
work, we find out that applying DIRL may harm domain adaptation when the label
distribution $\rm{P}(\rm{Y})$ changes across domains. To address this problem,
we propose a modification to DIRL, obtaining a novel weighted domain-invariant
representation learning (WDIRL) framework. We show that it is easy to transfer
existing SOTA DIRL models to WDIRL. Empirical studies on extensive cross-domain
sentiment analysis tasks verified our statements and showed the effectiveness
of our proposed solution.


  A minimal system of equations is introduced and applied to study the drift
motion of ferromagnetic particles suspended in a viscous fluid and subjected to
a time-periodic driving force and a nonuniformly rotating magnetic field. It is
demonstrated that the synchronized translational and rotational oscillations of
these particles are accompanied by their drift in a preferred direction, which
occurs under the action of the Magnus force. We calculate both analytically and
numerically the drift velocity of particles characterized by single-domain
cores and nonmagnetic shells and show that there are two types of drift,
unidirectional and bidirectional, which can be realized in suspensions composed
of particles with different core-shell ratios. The possibility of using the
effect of bidirectional drift for the separation of core-shell particles in
suspensions is also discussed.


  In this paper we present a robust parsing algorithm based on the link grammar
formalism for parsing natural languages. Our algorithm is a natural extension
of the original dynamic programming recognition algorithm which recursively
counts the number of linkages between two words in the input sentence. The
modified algorithm uses the notion of a null link in order to allow a
connection between any pair of adjacent words, regardless of their dictionary
definitions. The algorithm proceeds by making three dynamic programming passes.
In the first pass, the input is parsed using the original algorithm which
enforces the constraints on links to ensure grammaticality. In the second pass,
the total cost of each substring of words is computed, where cost is determined
by the number of null links necessary to parse the substring. The final pass
counts the total number of parses with minimal cost. All of the original
pruning techniques have natural counterparts in the robust algorithm. When used
together with memoization, these techniques enable the algorithm to run
efficiently with cubic worst-case complexity. We have implemented these ideas
and tested them by parsing the Switchboard corpus of conversational English.
This corpus is comprised of approximately three million words of text,
corresponding to more than 150 hours of transcribed speech collected from
telephone conversations restricted to 70 different topics. Although only a
small fraction of the sentences in this corpus are "grammatical" by standard
criteria, the robust link grammar parser is able to extract relevant structure
for a large portion of the sentences. We present the results of our experiments
using this system, including the analyses of selected and random sentences from
the corpus.


  We use the Melnikov integral method to prove that the Hamiltonian flow on the
zero-energy manifold for the Kepler problem perturbed by a quadrupole moment is
chaotic, irrespective of the perturbation being of prolate or oblate type. This
result helps to elucidate some recent conflicting works in the physical
literature based on numerical simulations.


  This work is a technical approach to modeling false information nature,
design, belief impact and containment in multi-agent networks. We present a
Bayesian mathematical model for source information and viewer's belief, and how
the former impacts the latter in a media (network) of broadcasters and viewers.
Given the proposed model, we study how a particular information (true or false)
can be optimally designed into a report, so that on average it conveys the most
amount of the original intended information to the viewers of the network.
Consequently, the model allows us to study susceptibility of a particular group
of viewers to false information, as a function of statistical metrics of the
their prior beliefs (e.g. bias, hesitation, open-mindedness, credibility
assessment etc.). In addition, based on the same model we can study false
information "containment" strategies imposed by network administrators.
Specifically, we study a credibility assessment strategy, where every
disseminated report must be within a certain distance of the truth. We study
the trade-off between false and true information-belief convergence using this
scheme which leads to ways for optimally deciding how truth sensitive an
information dissemination network should operate.


  Forests are vital for the wellbeing of our planet. Large and small scale
deforestation across the globe is threatening the stability of our climate,
forest biodiversity, and therefore the preservation of fragile ecosystems and
our natural habitat as a whole. With increasing public interest in climate
change issues and forest preservation, a large demand for carbon offsetting,
carbon footprint ratings, and environmental impact assessments is emerging.
Most often, deforestation maps are created from optical data such as Landsat
and MODIS. These maps are not typically available at less than annual intervals
due to persistent cloud cover in many parts of the world, especially the
tropics where most of the world's forest biomass is concentrated. Synthetic
Aperture Radar (SAR) can fill this gap as it penetrates clouds. We propose and
evaluate a novel method for deforestation detection in the absence of reliable
reference data which often constitutes the largest practical hurdle. This
method achieves a change detection sensitivity (producer's accuracy) of 96.5%
in the study area, although false positives lead to a lower user's accuracy of
about 75.7%, with a total balanced accuracy of 90.4%. The change detection
accuracy is maintained when adding up to 20% noise to the reference labels.
While further work is required to reduce the false positive rate, improve
detection delay, and validate this method in additional circumstances, the
results show that Sentinel-1 data have the potential to advance the timeliness
of global deforestation monitoring.


  This paper focuses on the temporal discretization of the Langevin dynamics,
and on different resulting numerical integration schemes. Using a method based
on the exponentiation of time dependent operators, we carefully derive a
numerical scheme for the Langevin dynamics, that we found equivalent to the
proposal of Ermak, and not simply to the stochastic version of the
velocity-Verlet algorithm. However, we checked on numerical simulations that
both algorithms give similar results, and share the same ``weak order two''
accuracy. We then apply the same strategy to derive and test two numerical
schemes for the dissipative particle dynamics (DPD). The first one of them was
found to compare well, in terms of speed and accuracy, with the best currently
available algorithms.


  We explore identical R\"ossler systems organized into two equally-sized
groups, among which differing positive and negative in- and out-coupling
strengths are allowed. Patterns of distinctly synchronized phase dynamics are
observed, which coexist with chaotically evolving amplitudes. In particular, we
report the emergence of traveling phase waves, i.e. states in which the
oscillators settle on a new rhythm different from their own. We further
elucidate our findings through phase-coupled R\"ossler systems, establishing a
connection with the Kuramoto model. Together with the study of noise effects,
our results suggest a promising new avenue towards the coexistence of chaotic,
noisy and regular collective dynamics.


  Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular
paradigm for capturing human intent to alleviate the challenges of
hand-crafting the reward values. Despite the increasing interest in RLHF, most
works learn black box reward functions that while expressive are difficult to
interpret and often require running the whole costly process of RL before we
can even decipher if these frameworks are actually aligned with human
preferences. We propose and evaluate a novel approach for learning expressive
and interpretable reward functions from preferences using Differentiable
Decision Trees (DDTs). Our experiments across several domains, including
CartPole, Visual Gridworld environments and Atari games, provide evidence that
the tree structure of our learned reward function is useful in determining the
extent to which the reward function is aligned with human preferences. We also
provide experimental evidence that not only shows that reward DDTs can often
achieve competitive RL performance when compared with larger capacity deep
neural network reward functions but also demonstrates the diagnostic utility of
our framework in checking alignment of learned reward functions. We also
observe that the choice between soft and hard (argmax) output of reward DDT
reveals a tension between wanting highly shaped rewards to ensure good RL
performance, while also wanting simpler, more interpretable rewards. Videos and
code, are available at: https://sites.google.com/view/ddt-rlhf


  This paper addresses the problem of seeking a common fixed point for a
collection of nonexpansive operators over time-varying multi-agent networks in
real Hilbert spaces, where each operator is only privately and approximately
known to each individual agent, and all agents need to cooperate to solve this
problem by propagating their own information to their neighbors through local
communications over time-varying networks. To handle this problem, inspired by
the centralized inexact Krasnosel'ski\u{\i}-Mann (IKM) iteration, we propose a
distributed algorithm, called distributed inexact Krasnosel'ski\u{\i}-Mann
(D-IKM) iteration. It is shown that the D-IKM iteration can converge weakly to
a common fixed point of the family of nonexpansive operators. Moreover, under
the assumption that all operators and their own fixed point sets are
(boundedly) linearly regular, it is proved that the D-IKM iteration converges
with a rate $O(1/k^{\ln(1/\xi)})$ for some constant $\xi\in(0,1)$, where $k$ is
the iteration number. To reduce computational complexity and burden of storage
and transmission, a scenario, where only a random part of coordinates for each
agent is updated at each iteration, is further considered, and a corresponding
algorithm, named distributed inexact block-coordinate Krasnosel'ski\u{\i}-Mann
(D-IBKM) iteration, is developed. The algorithm is proved to be weakly
convergent to a common fixed point of the group of considered operators, and,
with the extra assumption of (bounded) linear regularity, it is convergent with
a rate $O(1/k^{\ln(1/\xi)})$. Furthermore, it is shown that the convergence
rate $O(1/k^{\ln(1/\xi)})$ can still be guaranteed under a more relaxed
(bounded) power regularity condition.


  We show that the binary coin set minimizes the number of coins needed to
guarantee the ability to make change in any one transaction and its asymptotic
uniform average cost is no worse than that of any completely greedy coin set.


  Recent progress in pretraining language models on large textual corpora led
to a surge of improvements for downstream NLP tasks. Whilst learning linguistic
knowledge, these models may also be storing relational knowledge present in the
training data, and may be able to answer queries structured as
"fill-in-the-blank" cloze statements. Language models have many advantages over
structured knowledge bases: they require no schema engineering, allow
practitioners to query about an open class of relations, are easy to extend to
more data, and require no human supervision to train. We present an in-depth
analysis of the relational knowledge already present (without fine-tuning) in a
wide range of state-of-the-art pretrained language models. We find that (i)
without fine-tuning, BERT contains relational knowledge competitive with
traditional NLP methods that have some access to oracle knowledge, (ii) BERT
also does remarkably well on open-domain question answering against a
supervised baseline, and (iii) certain types of factual knowledge are learned
much more readily than others by standard language model pretraining
approaches. The surprisingly strong ability of these models to recall factual
knowledge without any fine-tuning demonstrates their potential as unsupervised
open-domain QA systems. The code to reproduce our analysis is available at
https://github.com/facebookresearch/LAMA.


  LiDAR is one of the most crucial sensors for autonomous vehicle perception.
However, current LiDAR-based point cloud perception algorithms lack
comprehensive and rigorous LiDAR quality assessment methods, leading to
uncertainty in detection performance. Additionally, existing point cloud
quality assessment algorithms are predominantly designed for indoor
environments or single-object scenarios. In this paper, we introduce a novel
image-guided point cloud quality assessment algorithm for outdoor autonomous
driving environments, named the Image-Guided Outdoor Point Cloud Quality
Assessment (IGO-PQA) algorithm. Our proposed algorithm comprises two main
components. The first component is the IGO-PQA generation algorithm, which
leverages point cloud data, corresponding RGB surrounding view images, and
agent objects' ground truth annotations to generate an overall quality score
for a single-frame LiDAR-based point cloud. The second component is a
transformer-based IGO-PQA regression algorithm for no-reference outdoor point
cloud quality assessment. This regression algorithm allows for the direct
prediction of IGO-PQA scores in an online manner, without requiring image data
and object ground truth annotations. We evaluate our proposed algorithm using
the nuScenes and Waymo open datasets. The IGO-PQA generation algorithm provides
consistent and reasonable perception quality indices. Furthermore, our proposed
IGO-PQA regression algorithm achieves a Pearson Linear Correlation Coefficient
(PLCC) of 0.86 on the nuScenes dataset and 0.97 on the Waymo dataset.


  The exact Quantum Kinetic Equations for a two-flavour active-sterile neutrino
system are used to provide a systematic derivation of approximate evolution
equations for the relic neutrino asymmetry. An extension of the adiabatic
approximation for matter-affected neutrino oscillations is developed which
incorporates decoherence due to collisions. Exact and approximate expressions
for the decoherence and repopulation functions are discussed. A first pass is
made over the exact treatment of multi-flavour partially incoherent
oscillations.


  The double electron capture half-lives of 156Dy, 162Er and 168Yb are
evaluated using the pseudo SU(3) model, which describes ground and excited
bands as well as their B(E2) and B(M1) transition strengths in remarkable
agreement with experiment. The best candidate for experimental detection is the
decay 156Dy -> 156Gd, with tau{1/2} (0+{gs} --> 0+{gs}) = 2.74 E22 yrs and
tau{1/2} (0+{gs} --> 0+{1}) = 8.31 E24 yrs.


  Tractable yet expressive density estimators are a key building block of
probabilistic machine learning. While sum-product networks (SPNs) offer
attractive inference capabilities, obtaining structures large enough to fit
complex, high-dimensional data has proven challenging. In this paper, we
present random sum-product forests (RSPFs), an ensemble approach for mixing
multiple randomly generated SPNs. We also introduce residual links, which
reference specialized substructures of other component SPNs in order to
leverage the context-specific knowledge encoded within them. Our empirical
evidence demonstrates that RSPFs provide better performance than their
individual components. Adding residual links improves the models further,
allowing the resulting ResSPNs to be competitive with commonly used structure
learning methods.


  We contribute an extension of large-deviation results obtained in [N.J.B.
Aza, J.-B. Bru, W. de Siqueira Pedra, A. Ratsimanetrimanana, J. Math. Pures
Appl. 125 (2019) 209] on conductivity theory at atomic scale of free lattice
fermions in disordered media. Disorder is modeled by (i) a random external
potential, like in the celebrated Anderson model, and (ii) a nearest-neighbor
hopping term with random complex-valued amplitudes. In accordance with
experimental observations, via the large deviation formalism, our previous
paper showed in this case that quantum uncertainty of microscopic electric
current densities around their (classical) macroscopic value is suppressed,
exponentially fast with respect to the volume of the region of the lattice
where an external electric field is applied. Here, the quantum fluctuations of
linear response currents are shown to exist in the thermodynamic limit and we
mathematically prove that they are related to the rate function of the large
deviation principle associated with current densities. We also demonstrate
that, in general, they do not vanish (in the thermodynamic limit) and the
quantum uncertainty around the macroscopic current density disappears
exponentially fast with an exponential rate proportional to the squared
deviation of the current from its macroscopic value and the inverse current
fluctuation, with respect to growing space (volume) scales.


  Semantic communications have shown promising advancements by optimizing
source and channel coding jointly. However, the dynamics of these systems
remain understudied, limiting research and performance gains. Inspired by the
robustness of Vision Transformers (ViTs) in handling image nuisances, we
propose a ViT-based model for semantic communications. Our approach achieves a
peak signal-to-noise ratio (PSNR) gain of +0.5 dB over convolutional neural
network variants. We introduce novel measures, average cosine similarity and
Fourier analysis, to analyze the inner workings of semantic communications and
optimize the system's performance. We also validate our approach through a real
wireless channel prototype using software-defined radio (SDR). To the best of
our knowledge, this is the first investigation of the fundamental workings of a
semantic communications system, accompanied by the pioneering hardware
implementation. To facilitate reproducibility and encourage further research,
we provide open-source code, including neural network implementations and
LabVIEW codes for SDR-based wireless transmission systems.


  Learning a reward function from demonstrations suffers from low
sample-efficiency. Even with abundant data, current inverse reinforcement
learning methods that focus on learning from a single environment can fail to
handle slight changes in the environment dynamics. We tackle these challenges
through adaptive environment design. In our framework, the learner repeatedly
interacts with the expert, with the former selecting environments to identify
the reward function as quickly as possible from the expert's demonstrations in
said environments. This results in improvements in both sample-efficiency and
robustness, as we show experimentally, for both exact and approximate
inference.


  In this paper we will discuss the optimal risk transfer problems when risk
measures are generated by G-expectations, and we present the relationship
between inf-convolution of G-expectations and the inf-convolution of drivers G.


  This paper presents asymptotic properties of the maximum pseudo-likelihood
estimator of a vector $\Vect{\theta}$ parameterizing a stationary Gibbs point
process. Sufficient conditions, expressed in terms of the local energy function
defining a Gibbs point process, to establish strong consistency and asymptotic
normality results of this estimator depending on a single realization, are
presented.These results are general enough to no longer require the local
stability and the linearity in terms of the parameters of the local energy
function. We consider characteristic examples of such models, the Lennard-Jones
and the finite range Lennard-Jones models. We show that the different
assumptions ensuring the consistency are satisfied for both models whereas the
assumptions ensuring the asymptotic normality are fulfilled only for the finite
range Lennard-Jones model.


  With the growing size and complexity of turbulent flow models, data
compression approaches are of the utmost importance to analyze, visualize, or
restart the simulations. Recently, in-situ autoencoder-based compression
approaches have been proposed and shown to be effective at producing reduced
representations of turbulent flow data. However, these approaches focus solely
on training the model using point-wise sample reconstruction losses that do not
take advantage of the physical properties of turbulent flows. In this paper, we
show that training autoencoders with additional physics-informed
regularizations, e.g., enforcing incompressibility and preserving enstrophy,
improves the compression model in three ways: (i) the compressed data better
conform to known physics for homogeneous isotropic turbulence without
negatively impacting point-wise reconstruction quality, (ii) inspection of the
gradients of the trained model uncovers changes to the learned compression
mapping that can facilitate the use of explainability techniques, and (iii) as
a performance byproduct, training losses are shown to converge up to 12x faster
than the baseline model.


  In 2008, M. Kaneko made several interesting observations about the values of
the modular j invariant at real quadratic irrationalities. The values of
modular functions at real quadratics are defined in terms of their cycle
integrals along the associated geodesics. In this paper we prove some of the
conjectures of M. Kaneko for a general modular function.


  We present the Spectroscopic Classification of Astronomical Transients (SCAT)
survey, which is dedicated to spectrophotometric observations of transient
objects such as supernovae and tidal disruption events. SCAT uses the SuperNova
Integral-Field Spectrograph (SNIFS) on the University of Hawai'i 2.2-meter
(UH2.2m) telescope. SNIFS was designed specifically for accurate transient
spectrophotometry, including absolute flux calibration and host-galaxy removal.
We describe the data reduction and calibration pipeline including spectral
extraction, telluric correction, atmospheric characterization, nightly
photometricity, and spectrophotometric precision. We achieve $\lesssim 5\%$
spectrophotometry across the full optical wavelength range ($3500-9000~\r{A}$)
under photometric conditions. The inclusion of photometry from the SNIFS
multi-filter mosaic imager allows for decent spectrophotometric calibration
($10-20\%$) even under unfavorable weather/atmospheric conditions. SCAT
obtained $\approx 640$ spectra of transients over the first 3 years of
operations, including supernovae of all types, active galactic nuclei,
cataclysmic variables, and rare transients such as superluminous supernovae and
tidal disruption events. These observations will provide the community with
benchmark spectrophotometry to constrain the next generation of hydrodynamic
and radiative transfer models.


  A two-dimensional homomorphic logistic map that preserves features of the
Lotka-Volterra equations was proposed. To examine chaos, iteration plots of the
population, Lyapunov exponents calculated from Jacobian eigenvalues of the $2$D
logistic mapping, and from time series algorithms of Rosenstein and Eckmann et
al. were calculated. Bifurcation diagrams may be divided into four categories
depending on topological shapes. Our model not only recovered the $1$D logistic
map, which exhibits flip bifurcation, for the prey when there is a nonzero
initial predator population, but it can also simulate normal competition
between two species with equal initial populations. Despite the possibility for
two species to go into chaos simultaneously, where the Neimark-Sacker
bifurcation was observed, it is also possible that with the same interspecies
parameters as normal but with a predator population $10$ times more than that
of the prey, the latter becomes chaotic, while the former dramatically reduces
to zero with only a few iterations, indicating total annihilation of the
predator species. Interpreting humans as predators and natural resources as
preys in the ecological system, the above-mentioned conclusion may imply that
not only excessive consumption of natural resources, but its chaotic state
triggered by an overpopulation of humans may backfire in a manner of total
extinction of the human species. Fortunately, there is little chance for the
survival of the human race, as isolated fixed points in the bifurcation diagram
of the predator reveal. Finally, two possible applications of the phenomenon of
chaotic extinction are proposed: one is to inhibit viruses or pests by
initiating the chaotic states of the prey on which the viruses or pests rely
for existence, and the other is to achieve the superconducting state with the
chaotic state of the applied magnetic field.


  Large language models (LLMs) have emerged as a widely-used tool for
information seeking, but their generated outputs are prone to hallucination. In
this work, our aim is to allow LLMs to generate text with citations, improving
their factual correctness and verifiability. Existing work mainly relies on
commercial search engines and human evaluation, making it challenging to
reproduce and compare different modeling approaches. We propose ALCE, the first
benchmark for Automatic LLMs' Citation Evaluation. ALCE collects a diverse set
of questions and retrieval corpora and requires building end-to-end systems to
retrieve supporting evidence and generate answers with citations. We develop
automatic metrics along three dimensions -- fluency, correctness, and citation
quality -- and demonstrate their strong correlation with human judgements. Our
experiments with state-of-the-art LLMs and novel prompting strategies show that
current systems have considerable room for improvement -- For example, on the
ELI5 dataset, even the best models lack complete citation support 50% of the
time. Our analyses further highlight promising future directions, including
developing better retrievers, advancing long-context LLMs, and improving the
ability to synthesize information from multiple sources.


  We report about recent results on the Quantum Field Theory of neutrino mixing
and oscillations. A discussion of the relevant observables for flavor fields is
given, leading to oscillation formulas which exhibit corrections with respect
to the usual ones.


  We present the first physical-continuum limit $x$-dependent nucleon gluon
distribution from lattice QCD using the pseudo-PDF approach, on lattice
ensembles with $2+1+1$ flavors of highly improved staggered quarks (HISQ),
generated by MILC Collaboration. We use clover fermions for the valence action
on three lattice spacings $a \approx 0.9$, 0.12 and 0.15~fm and three pion
masses $M_\pi \approx 220$, 310 and 690~MeV, with nucleon two-point
measurements numbering up to $O(10^6)$ and nucleon boost momenta up to 3~GeV.
We study the lattice-spacing and pion-mass dependence of the reduced pseudo-ITD
matrix elements obtained from the lattice calculation, then extrapolate them to
the continuum-physical limit before extracting $xg(x)/\langle x \rangle_g$. We
use the gluon momentum fraction $\langle x \rangle_g$ calculated from the same
ensembles to determine the nucleon gluon unpolarized PDF $xg(x)$ for the first
time entirely through lattice-QCD simulation. We compare our results with
previous single-ensemble lattice calculations, as well as selected global fits.


  We introduce the cosmological HYPER code based on an innovative
hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas
and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui
(1998) to expand the scope of its application from the lower-density
intergalactic medium (IGM) to the higher-density intracluster medium (ICM).
While the original algorithm tracks only one effective particle species, the
updated version separately tracks the gas and dark matter particles as they do
not exactly trace each other on small scales. For the approximate hydrodynamics
solver, the pressure term in the gas equations of motion is calculated using
robust physical models. In particular, we use a dark matter halo model, ICM
pressure profile, and IGM temperature-density relation, all of which can be
systematically varied for parameter-space studies. We show that the HYPER
simulation results are in good agreement with the halo model expectations for
the density, temperature, and pressure radial profiles. Simulated galaxy
cluster scaling relations for Sunyaev-Zel'dovich (SZ) and X-ray observables are
also in good agreement with mean predictions, with scatter comparable to that
found in hydrodynamic simulations. HYPER also produces lightcone catalogs of
dark matter halos and full-sky tomographic maps of the lensing convergence, SZ
effect, and X-ray emission. These simulation products are useful for testing
data analysis pipelines, generating training data for machine learning,
understanding selection and systematic effects, and for interpreting
astrophysical and cosmological constraints.


  We find the allowed complex numbers associated with the inner product of N
equally separated pure quantum states. The allowed areas on the unitary complex
plane have the form of petals. A point inside the petal-shape represents a set
of N linearly independent (LI) pure states, and a point on the edge of that
area represents a set of N linearly dependent (LD) pure states. For each one of
those LI sets we study the complete discrimination of its N equi-separated
states combining sequentially the two known strategies: first the unambiguous
identification protocol for LI states, followed, if necessary, by the
error-minimizing measurement scheme for LD states. We find that the
probabilities of success for both unambiguous and ambiguous discrimination
procedures depend on both the module and the phase of the involved inner
product complex number. We show that, with respect to the phase-parameter, the
maximal probability of discriminating unambiguously the N non-orthogonal pure
states holds just when there no longer be probability of obtaining ambiguously
information about the prepared state by applying the second protocol if the
first one was not successful.


  Residential consumers can use the demand response program (DRP) if they can
utilize the home energy management system (HEMS), which reduces consumer costs
by automatically adjusting air conditioning (AC) setpoints and shifting some
appliances to off-peak hours. If HEMS knows occupancy status, consumers can
gain more economic benefits and thermal comfort. However, for the building
occupancy status, direct sensing is costly, inaccurate, and intrusive for
residents. So, forecasting algorithms could serve as an effective alternative.
The goal of this study is to present a non-intrusive, accurate, and
cost-effective approach, to develop a multi-objective simulation model for the
application of DRPs in a smart residential house, where (a) electrical load
demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints,
and (c) , worst cases scenario approach is very conservative. Because that is
unlikely all uncertain parameters take their worst values at all times. So, the
flexible robust counterpart optimization along with uncertainty budgets is
developed to consider uncertainty realistically. Simulated results indicate
that considering uncertainty increases the costs by 36 percent and decreases
the AC temperature setpoints. Besides, using DRPs reduces demand by shifting
some appliance operations to off-peak hours and lowers costs by 13.2 percent.


  The gas motions in the intracluster medium (ICM) are governed by stratified
turbulence. Stratified turbulence is fundamentally different from Kolmogorov
(isotropic, homogeneous) turbulence; kinetic energy not only cascades from
large to small scales, but it is also converted into buoyancy potential energy.
To understand the density and velocity fluctuations in the ICM, we conduct
high-resolution ($1024^2\times 1536$ grid points) hydrodynamical simulations of
subsonic turbulence (with rms Mach number $\mathcal{M}\approx 0.25$) and
different levels of stratification, quantified by the Richardson number
$\mathrm{Ri}$, from $\mathrm{Ri}=0$ (no stratification) to $\mathrm{Ri}=13$
(strong stratification). We quantify the density, pressure and velocity fields
for varying stratification because observational studies often use surface
brightness fluctuations to infer the turbulent gas velocities of the ICM. We
find that the standard deviation of the logarithmic density fluctuations
($\sigma_s$), where $s=\ln(\rho/\left<\rho(z)\right>)$, increases with
$\mathrm{Ri}$. For weakly stratified subsonic turbulence
($\mathrm{Ri}\lesssim10$, $\mathcal{M}<1$), we derive a new
$\sigma_s$--$\mathcal{M}$--$\mathrm{Ri}$ relation,
$\sigma_s^2=\ln(1+b^2\mathcal{M}^4+0.09\mathcal{M}^2\mathrm{Ri}H_P/H_S)$, where
$b=1/3$--$1$ is the turbulence driving parameter, and $H_P$ and $H_S$ are the
pressure and entropy scale heights respectively. We further find that the power
spectrum of density fluctuations, $P(\rho_k/\left<\rho\right>)$, increases in
magnitude with increasing $\mathrm{Ri}$, whereas the velocity power spectrum is
invariant. Thus, the ratio between density and velocity power spectra strongly
depends on $\mathrm{Ri}$. Pressure fluctuations, on the other hand, are
independent of stratification and only depend on $\mathcal{M}$.


  Different geometrical and topological properties have been shown for two
kinds of extreme Reissner-Nordstr$\ddot{o}$m-anti-de Sitter black holes. The
relationship between the geometrical properties and the intrinsic
thermodynamical properties has been made explicit.


  We investigate the 1/3 fractional quantum Hall state with one and two
quasiparticle excitations. It is shown that the quasiparticle excitations are
best described as excited composite fermions occupying higher composite-fermion
quasi-Landau levels. In particular, the composite-fermion wave function for a
single quasiparticle has 15% lower energy than the trial wave function
suggested by Laughlin, and for two quasiparticles, the composite fermion theory
also gives new qualitative structures.


  Emergent language is unique among fields within the discipline of machine
learning for its open-endedness, not obviously presenting well-defined problems
to be solved. As a result, the current research in the field has largely been
exploratory: focusing on establishing new problems, techniques, and phenomena.
Yet after these problems have been established, subsequent progress requires
research which can measurably demonstrate how it improves on prior approaches.
This type of research is what we call systematic research; in this paper, we
illustrate this mode of research specifically for emergent language. We first
identify the overarching goals of emergent language research, categorizing them
as either science or engineering. Using this distinction, we present core
methodological elements of science and engineering, analyze their role in
current emergent language research, and recommend how to apply these elements.


  The group of matrices $P_1$ of Pauli is a finite 2-group of order 16 and
plays a fundamental role in quantum information theory, since it is related to
the quantum information on the 1-qubit. Here we show that both $P_1$ and the
Pauli 2-group $P_2$ of order 64 on 2-qubits, other than in quantum computing,
can also appear in dynamical systems which are described by non self-adjoint
Hamiltonians. This will allow us to represent $P_1$ and $P_2$ in terms of
pseudofermionic operators.


  Externally applied electromagnetic fields in general have an influence on the
width of atomic spectral lines. The decay rates of atomic states can also be
affected by the geometry of an applied field configuration giving rise to an
imaginary geometric phase. A specific chiral electromagnetic field
configuration is presented which geometrically modifies the lifetimes of
metastable states of hydrogen. We propose to extract the relevant observables
in a realistic longitudinal atomic beam spin-echo apparatus which allows the
initial and final fluxes of the metastable atoms to be compared with each other
interferometrically. A geometry-induced change in lifetimes at the 5%-level is
found, an effect large enough to be observed in an available experiment.


  Calibration of devices with different modalities is a key problem in robotic
vision. Regular spatial objects, such as planes, are frequently used for this
task. This paper deals with the automatic detection of ellipses in camera
images, as well as to estimate the 3D position of the spheres corresponding to
the detected 2D ellipses. We propose two novel methods to (i) detect an ellipse
in camera images and (ii) estimate the spatial location of the corresponding
sphere if its size is known. The algorithms are tested both quantitatively and
qualitatively. They are applied for calibrating the sensor system of autonomous
cars equipped with digital cameras, depth sensors and LiDAR devices.


  In this paper we consider stochastic Fokker-Planck Partial Differential
Equations (PDEs), obtained as the mean-field limit of weakly interacting
particle systems subjected to both independent (or idiosyncratic) and common
Brownian noises. We provide sufficient conditions under which the deterministic
counterpart of the Fokker-Planck equation, which corresponds to particle
systems that are just subjected to independent noises, has several invariant
measures, but for which the stochastic version admits a unique invariant
measure under the presence of the additive common noise. The very difficulty
comes from the fact that the common noise is just of finite dimension while the
state variable, which should be seen as the conditional marginal law of the
system given the common noise, lives in a space of infinite dimension. In this
context, our result holds true if, in addition to standard confining
properties, the mean field interaction term forces the system to be attracted
by its conditional mean given the common noise and the intensity of the
idiosyncratic noise is small.


  The Hoyle state and other resonances in the continuum above the 3 alpha
threshold in 12C are studied in a microscopic cluster model. Whereas the Hoyle
state is a very sharp resonance and can be treated reasonably well in bound
state approximation, the other higher lying states require a proper treatment
of the continuum. The model space consists of an internal region with 3 alpha
particles on a triangular grid and an external region consisting of the 8Be
ground state and excited (pseudo)-states of 8Be with an additional alpha. The
microscopic R-matrix method is used to match the many-body wave function to the
asymptotic Coulomb behavior of bound states, Gamow states and scattering
states. 8Be-alpha phase shifts are analyzed and resonance properties like radii
and transition strengths are investigated.


  Green Pea galaxies are remarkable for their intense star formation and serve
as a window into the early universe. In our study, we used integral field
spectroscopy to examine 24 of these galaxies in the optical spectrum. We
focused on the interaction between their ionized interstellar medium and the
star formation processes within them. Our research generated spatial maps of
emission lines and other properties like ionization structures and chemical
conditions. These maps showed that areas with higher levels of excitation are
usually located where starbursts are occurring. Continuum maps displayed more
intricate structures than emission line maps and hinted at low brightness
ionized gas in the galaxies' outer regions. We also analyzed integrated spectra
from selected areas within these galaxies to derive physical properties like
electron densities and temperatures. In some galaxies, we were able to
determine metallicity levels. Our observations revealed the presence of
high-ionizing lines in three galaxies, two of which had extremely high rates of
star formation. Our findings provide valuable insights into the properties and
star-forming processes in Green Pea galaxies, contributing to our broader
understanding of galactic evolution in the early universe.