  The sunspot drawings of Johann Staudacher of 1749--1799 were used to
determine the solar differential rotation in that period. These drawings of the
full disk lack any indication of their orientation. We used a Bayesian
estimator to obtain the position angles of the drawings, the corresponding
heliographic spot positions, a time offset between the drawings and the
differential rotation parameter \delta\Omega, assuming the equatorial rotation
period is the same as today. The drawings are grouped in pairs, and the
resulting marginal distributions for \delta\Omega were multiplied. We obtain
\delta\Omega=-0.048 \pm 0.025 d^-1 (-2.75^o/d) for the entire period. There is
no significant difference to the value of the present Sun. We find an
(insignificant) indication for a change of \delta\Omega throughout the
observing period from strong differential rotation, \delta\Omega\approx -0.07
d^-1, to weaker differential rotation, \delta\Omega\approx-0.04 d^-1.


  A long spin relaxation time (tausf) is the key for the applications of
graphene to spintronics but the experimental values of tausf have been
generally much shorter than expected. We show that the usual determination by
the Hanle method underestimates tausf if proper account of the spin absorption
by contacts is lacking. By revisiting series of experimental results, we find
that the corrected tausf are longer and less dispersed, which leads to a more
unified picture of tausf derived from experiments. We also discuss how the
correction depends on the parameters of the graphene and contacts.


  Despite the recent advance of automated program verification, reasoning about
recursive data structures remains as a challenge for verification tools and
their backends such as SMT and CHC solvers. To address the challenge, we
introduce the notion of symbolic automatic relations (SARs), which combines
symbolic automata and automatic relations, and inherits their good properties
such as the closure under Boolean operations. We consider the satisfiability
problem for SARs, and show that it is undecidable in general, but that we can
construct a sound (but incomplete) and automated satisfiability checker by a
reduction to CHC solving. We discuss applications to SMT and CHC solving on
data structures, and show the effectiveness of our approach through
experiments.


  Enabling robotic agents to perform complex long-horizon tasks has been a
long-standing goal in robotics and artificial intelligence (AI). Despite the
potential shown by large language models (LLMs), their planning capabilities
remain limited to short-horizon tasks and they are unable to replace the
symbolic planning approach. Symbolic planners, on the other hand, may encounter
execution errors due to their common assumption of complete domain knowledge
which is hard to manually prepare for an open-world setting. In this paper, we
introduce a Language-Augmented Symbolic Planner (LASP) that integrates
pre-trained LLMs to enable conventional symbolic planners to operate in an
open-world environment where only incomplete knowledge of action preconditions,
objects, and properties is initially available. In case of execution errors,
LASP can utilize the LLM to diagnose the cause of the error based on the
observation and interact with the environment to incrementally build up its
knowledge base necessary for accomplishing the given tasks. Experiments
demonstrate that LASP is proficient in solving planning problems in the
open-world setting, performing well even in situations where there are multiple
gaps in the knowledge.


  Silicon carbide (SiC) is one of the major cosmic dust components in
carbon-rich environments. However, the formation of SiC dust is not well
understood. In particular, the initial stages of the SiC condensation (i.e. the
SiC nucleation) remain unclear, as the basic building blocks (i.e. molecular
clusters) exhibit atomic segregation at the (sub-)nanoscale. We report vertical
and adiabatic ionization energies of small silicon carbide clusters, (SiC)$_n$
, n=2-12, ranging from 6.6-10.0 eV, which are lower than for the SiC molecule
($\sim$ 10.6 eV). The most favorable structures of the singly ionized
(SiC)$_n^+$, n=5-12, cations resemble their neutral counterparts. However, for
sizes n=2-4, these structural analogues are metastable and different cation
geometries are favored. Moreover, we find that the (SiC)$_5^+$ cation is likely
to be a transition state. Therefore, we place constraints on the stability
limit for small, neutral (SiC)$_n$ clusters to persist ionization through
(inter)-stellar radiation fields or high temperatures.


  We study a two-parameter generalization of the Catalan numbers: $C_{d,p}(n)$
is the number of ways to subdivide the $d$-dimensional hypercube into $n$
rectangular blocks using orthogonal partitions of fixed arity $p$. Bremner \&
Dotsenko introduced $C_{d,p}(n)$ in their work on Boardman--Vogt tensor
products of operads; they used homological algebra to prove a recursive formula
and a functional equation. We express $C_{d,p}(n)$ as simple finite sums, and
determine their growth rate and asymptotic behaviour. We give an elementary
proof of the functional equation, using a bijection between hypercube
decompositions and a family of full $p$-ary trees. Our results generalize the
well-known correspondence between Catalan numbers and full binary trees.


  Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches.


  Correlations of an environment are crucial for the dynamics of non-Markovian
quantum systems, which may not be known in advance. In this paper, we propose a
gradient algorithm for identifying the correlations in terms of time-varying
damping rate functions in a time-convolution-less master equation for spin
chains. By measuring time trace observables of the system, the identification
procedure can be formulated as an optimization problem. The gradient algorithm
is designed based on a calculation of the derivative of an objective function
with respect to the damping rate functions, whose effectiveness is shown in a
comparison to a differential approach for a two-qubit spin chain.


  Following the recent proposal by Weeks et al., which suggested that indium
(or thallium) adatoms deposited on the surface of graphene should turn the
latter into a quantum spin Hall (QSH) insulator characterized by a sizeable
gap, we perform a systematic study of the transport properties of this system
as a function of the density of randomly distributed adatoms. While the samples
are, by construction, very disordered, we find that they exhibit an extremely
stable QSH phase with no signature of the spatial inhomogeneities of the adatom
configuration. We find that a simple rescaling of the spin-orbit coupling
parameter allows us to account for the behaviour of the inhomogeneous system
using a homogeneous model. This robustness opens the route to a much easier
experimental realization of this topological insulator. We additionally find
this material to be a very promising candidate for thermopower generation with
a target temperature tunable from 1 to 80K and an efficiency ZT close to 1.


  In many mobile visual analysis applications, compressed video is transmitted
over a communication network and analyzed by a server. Typical processing steps
performed at the server include keypoint detection, descriptor calculation, and
feature matching. Video compression has been shown to have an adverse effect on
feature-matching performance. The negative impact of compression can be reduced
by using the keypoints extracted from the uncompressed video to calculate
descriptors from the compressed video. Based on this observation, we propose to
provide these keypoints to the server as side information and to extract only
the descriptors from the compressed video. First, we introduce four different
frame types for keypoint encoding to address different types of changes in
video content. These frame types represent a new scene, the same scene, a
slowly changing scene, or a rapidly moving scene and are determined by
comparing features between successive video frames. Then, we propose Intra,
Skip and Inter modes of encoding the keypoints for different frame types. For
example, keypoints for new scenes are encoded using the Intra mode, and
keypoints for unchanged scenes are skipped. As a result, the bitrate of the
side information related to keypoint encoding is significantly reduced.
Finally, we present pairwise matching and image retrieval experiments conducted
to evaluate the performance of the proposed approach using the Stanford mobile
augmented reality dataset and 720p format videos. The results show that the
proposed approach offers significantly improved feature matching and image
retrieval performance at a given bitrate.


  The prevalent interpretation of G\"odel's Second Theorem states that a
sufficiently adequate and consistent theory does not prove its consistency. It
is however not entirely clear how to justify this informal reading, as the
formulation of the underlying mathematical theorem depends on several arbitrary
formalisation choices. In this paper I examine the theorem's dependency
regarding G\"odel numberings. I introduce deviant numberings, yielding
provability predicates satisfying L\"ob's conditions, which result in provable
consistency sentences. According to the main result of this paper however,
these "counterexamples" do not refute the theorem's prevalent interpretation,
since once a natural class of admissible numberings is singled out, invariance
is maintained.


  We show that there does not exist a generalised polynomial which vanishes
precisely on the set of powers of two. In fact, if $k \geq 2$ is and integer
and $g \colon \mathbb{N} \to \mathbb{R}$ is a generalised polynomial such that
$g(k^n) = 0$ for all $n \geq 0$ then there exists infinitely many $m \in
\mathbb{N}$, not divisible by $k$, such that $g(mk^n) = 0$ for some $n \geq 0$.
As a consequence, we obtain a complete characterisation of sequences which are
simultaneously automatic and generalised polynomial.


  A new type of bolometer arrays sensitive in the Far Infrared and
Submillimeter range has been developped and manufactured by CEA/LETI/SLIR and
will be integrated in the PACS instrument onboard the Herschel Space
Observatory (launch date 2008). The main innovations are their collective
manufacturing technique (production of 3-side buttable 16x16 arrays) and their
high mapping efficiency (large format detector and instantaneous Nyquist
sampling). Their measured NEP is 2x10^-16 W/Hz^-0.5 and their thermometric
passband about 4-5 Hz. In this paper we describe CEA bolometers and present the
results obtained during the last test campaign.


  Contemporary robots in precision agriculture focus primarily on automated
harvesting or remote sensing to monitor crop health. Comparatively less work
has been performed with respect to collecting physical leaf samples in the
field and retaining them for further analysis. Typically, orchard growers
manually collect sample leaves and utilize them for stem water potential
measurements to analyze tree health and determine irrigation routines. While
this technique benefits orchard management, the process of collecting,
assessing, and interpreting measurements requires significant human labor and
often leads to infrequent sampling. Automated sampling can provide highly
accurate and timely information to growers. The first step in such automated
in-situ leaf analysis is identifying and cutting a leaf from a tree. This
retrieval process requires new methods for actuation and perception. We present
a technique for detecting and localizing candidate leaves using point cloud
data from a depth camera. This technique is tested on both indoor and outdoor
point clouds from avocado trees. We then use a custom-built leaf-cutting
end-effector on a 6-DOF robotic arm to test the proposed detection and
localization technique by cutting leaves from an avocado tree. Experimental
testing with a real avocado tree demonstrates our proposed approach can enable
our mobile manipulator and custom end-effector system to successfully detect,
localize, and cut leaves.


  We prove the following propositions. Theorem 1: Let $M$ be a subfield of a
fixed algebraic closure $\tilde \Q$ of $\Q$ whose existential elementary theory
is decidable (resp. primitively decidable). Then, M is conjugate to a recursive
(resp. primitive recursive) subfield $L \subset \tilde \Q$.
  Theorem 2: For each positive integer $e$ there are infinitely many $e$-tuples
$\boldsymbol \sigma \in \Gal(\Q)^e$ such that the field $\tilde \Q(
{\boldsymbol \sigma})$ -- the fixed field of $\boldsymbol \sigma$, is recursive
in $\tilde\Q$ and its elementary theory is decidable. Moreover, $\tilde
\Q(\boldsymbol \sigma)$ is PAC and $\Gal(\tilde\Q(\boldsymbol \sigma))$ is
isomorphic to the free profinite group on $e$ generators.


  We report the statistical properties of stars, brown dwarfs and multiple
systems obtained from the largest radiation hydrodynamical simulation of star
cluster formation to date that resolves masses down to the opacity limit for
fragmentation (a few Jupiter masses). The initial conditions are identical to
those of previous barotropic calculations published by Bate, but this time the
calculation is performed using a realistic equation of state and radiation
hydrodynamics. The calculation uses sink particles to model 183 stars and brown
dwarfs, including 28 binaries and 12 higher-order multiple systems, the
properties of which are compared the results from observational surveys. We
find that the radiation hydrodynamical/sink particle simulation reproduces many
observed stellar properties very well. In particular, whereas using a
barotropic equation of state produces more brown dwarfs than stars, the
inclusion of radiative feedback results in a stellar mass function and a ratio
of brown dwarfs to stars in good agreement with observations of Galactic
star-forming regions. In addition, many of the other statistical properties of
the stars and brown dwarfs are in reasonable agreement with observations,
including multiplicity as a function of primary mass, the frequency of
very-low-mass binaries, and general trends for the mass ratio and separation
distributions of binaries. We also examine the velocity dispersion of the
stars, the distributions of disc truncation radii due to dynamical
interactions, and coplanarity of orbits and sink particle spins in multiple
systems. Overall, the calculation produces a cluster of stars whose statistical
properties are difficult to distinguish from observed systems, implying that
gravity, hydrodynamics, and radiative feedback are the primary ingredients for
determining the origin of the statistical properties of low-mass stars.


  We prove a complexity dichotomy theorem for counting planar graph
homomorphisms of domain size 3. Given any 3 by 3 real valued symmetric matrix
$H$ defining a graph homomorphism from all planar graphs $G \mapsto Z_H(G)$, we
completely classify the computational complexity of this problem according to
the matrix $H$. We show that for every $H$, the problem is either polynomial
time computable or \#P-hard. The P-time computable cases consist of precisely
those that are P-time computable for general graphs (a complete classification
is known) or computable by Valiant's holographic algorithm via matchgates. We
also prove several results about planar graph homomorphisms for general domain
size $q$. The proof uses mainly analytic arguments.


  We develop a phenomenological theory to predict the characteristic features
of the momentum-dependent scattering amplitude in resonant inelastic x-ray
scattering (RIXS) at the energy scale of the superconducting gap in iron-based
superconductors. Taking into account all relevant orbital states as well as
their specific content along the Fermi surface we evaluate the charge and spin
dynamical structure factors for the compounds LaOFeAs and LiFeAs, based on
tight-binding models which are fully consistent with recent angle-resolved
photoemission spectroscopy (ARPES) data. We find a characteristic intensity
redistribution between charge and spin dynamical structure factors which
discriminates between sign-reversing and sign-preserving quasiparticle
excitations. Consequently, our results show that RIXS spectra can distinguish
between $s_\pm$ and $s_{++}$ wave gap functions in the singlet pairing case. In
addition, we find that an analogous intensity redistribution at small momenta
can reveal the presence of a chiral $p$-wave triplet pairing.


  Intracavity second harmonic generation is one of the simplest of the quantum
optical processes and is well within the expertise of most optical
laboratories. It is well understood and characterised, both theoretically and
experimentally. We show that it can be a source of continuous variable
asymmetric Gaussian harmonic steering with fields which have a coherent
excitation, hence combining the important effects of harmonic entanglement and
asymmetric steering in one easily controllable device, adjustable by the simple
means of tuning the cavity loss rates at the fundamental and harmonic
frequencies. We find that whether quantum steering is available via the
standard measurements of the Einstein-Podolsky-Rosen correlations can depend on
which quadrature measurements are inferred from output spectral measurements of
the fundamental and the harmonic. Altering the ratios of the cavity loss rates
can be used to tune the regions where symmetric steering is available, with the
results becoming asymmetric over all frequencies as the cavity damping at the
fundamental frequency becomes significantly greater than at the harmonic.
  This asymmetry and its functional dependence on frequency is a potential new
tool for experimental quantum information science, with possible utility for
quantum key distribution. Although we show the effect here for Gaussian
measurements of the quadratures, and cannot rule out a return of the steering
symmetry for some class of non-Gaussian measurements, we note here that the
system obeys Gaussian statistics in the operating regime investigated and
Gaussian inference is at least as accurate as any other method for calculating
the necessary correlations. Perhaps most importantly, this system is simpler
than any other methods we are aware of which have been used or proposed to
create asymmetric steering.


  Using a twisted nematic liquid crystal system exhibiting planar Ising model
dynamics, we have measured the scaling exponent $\theta$ which characterizes
the time evolution, $p(t) \sim t^{-\theta}$, of the probability p(t) that the
local order parameter has not switched its state by the time t. For 0.4 seconds
to 200 seconds following the phase quench, the system exhibits scaling behavior
and, measured over this interval, $\theta = 0.19 \pm 0.031$, in good agreement
with theoretical analysis and numerical simulations.


  Given a social network with diffusion probabilities as edge weights and an
integer k, which k nodes should be chosen for initial injection of information
to maximize influence in the network? This problem is known as Target Set
Selection in a social network (TSS Problem) and more popularly, Social
Influence Maximization Problem (SIM Problem). This is an active area of
research in computational social network analysis domain since one and half
decades or so. Due to its practical importance in various domains, such as
viral marketing, target advertisement, personalized recommendation, the problem
has been studied in different variants, and different solution methodologies
have been proposed over the years. Hence, there is a need for an organized and
comprehensive review on this topic. This paper presents a survey on the
progress in and around TSS Problem. At last, it discusses current research
trends and future research directions as well.


  INT8 quantization has become one of the standard techniques for deploying
convolutional neural networks (CNNs) on edge devices to reduce the memory and
computational resource usages. By analyzing quantized performances of existing
mobile-target network architectures, we can raise an issue regarding the
importance of network architecture for optimal INT8 quantization. In this
paper, we present a new network architecture search (NAS) procedure to find a
network that guarantees both full-precision (FLOAT32) and quantized (INT8)
performances. We first propose critical but straightforward optimization method
which enables quantization-aware training (QAT) : floating-point statistic
assisting (StatAssist) and stochastic gradient boosting (GradBoost). By
integrating the gradient-based NAS with StatAssist and GradBoost, we discovered
a quantization-efficient network building block, Frost bottleneck. Furthermore,
we used Frost bottleneck as the building block for hardware-aware NAS to obtain
quantization-efficient networks, FrostNets, which show improved quantization
performances compared to other mobile-target networks while maintaining
competitive FLOAT32 performance. Our FrostNets achieve higher recognition
accuracy than existing CNNs with comparable latency when quantized, due to
higher latency reduction rate (average 65%).


  The success of recurrent neural networks (RNNs) has been demonstrated in many
applications related to turbulence, including flow control, optimization,
turbulent features reproduction as well as turbulence prediction and modeling.
With this study we aim to assess the capability of these networks to reproduce
the temporal evolution of a minimal turbulent channel flow. We first obtain a
data-driven model based on a modal decomposition in the Fourier domain (which
we denote as FFT-POD) of the time series sampled from the flow. This particular
case of turbulent flow allows us to accurately simulate the most relevant
coherent structures close to the wall. Long-short-term-memory (LSTM) networks
and a Koopman-based framework (KNF) are trained to predict the temporal
dynamics of the minimal-channel-flow modes. Tests with different configurations
highlight the limits of the KNF method compared to the LSTM, given the
complexity of the flow under study. Long-term prediction for LSTM show
excellent agreement from the statistical point of view, with errors below 2%
for the best models with respect to the reference. Furthermore, the analysis of
the chaotic behaviour through the use of the Lyapunov exponents and of the
dynamic behaviour through Poincar\'e maps emphasizes the ability of the LSTM to
reproduce the temporal dynamics of turbulence. Alternative reduced-order models
(ROMs), based on the identification of different turbulent structures, are
explored and they continue to show a good potential in predicting the temporal
dynamics of the minimal channel.


  Far-infrared (FIR)--radio correlation is a well-established empirical
connection between continuum radio and dust emission of star-forming galaxies,
often used as a tool in determining star-formation rates. Here we expand the
point made by Murphy (2013) that in the case of some interacting star-forming
galaxies there is a non-thermal emission from the gas bridge in between them,
which might cause a dispersion in this correlation. Galactic interactions and
mergers have been known to give rise to tidal shocks and disrupt morphologies
especially in the smaller of the interacting components. Here we point out that
these shocks can also heat the gas and dust and will inevitably accelerate
particles and result in a tidal cosmic-ray population in addition to standard
galactic cosmic rays in the galaxy itself. This would result in a non-thermal
emission not only from the gas bridges of interacting systems, but from
interacting galaxies as a whole in general. Thus both tidal heating and
additional non-thermal radiation will obviously affect the FIR-radio
correlation of these systems, the only question is how much. In this scenario
the FIR-radio correlation is not stable in interacting galaxies, but rather
evolves as the interaction/merger progresses. To test this hypothesis and probe
the possible impact of tidal cosmic ray population we have analyzed a sample of
43 infrared bright star-forming interacting galaxies at different merger
stages. We have found that their FIR-radio correlation parameter and radio
emission spectral index vary noticeably over different merger stages and behave
as it would be expected from our tidal-shock scenario. Important implications
of departure of interacting galaxies from the FIR-radio correlation are
discussed.


  We review studies on spin-dependent tunneling phenomena in systems containing
ferromagnetic nanoparticles. We discuss preparation methods of assembling
nanoparticles as well as the mechanisms and results of spin-dependent transport
properties. The emphasis of this review is on characteristic spin-dependent
tunneling phenomena such as enhanced tunnel magnetoresistance (TMR) due to
co-tunneling in the Coulomb blockade regime and sign changes of the TMR due to
spin accumulation in nanoparticles.


  In a recent contribution, we introduced and applied a detector-independent
method to uncover nonclassicality. Here, we extend those techniques and give
more details on the performed analysis. We derive a general theory of the
positive-operator-valued measure that describes multiplexing layouts with
arbitrary detectors. From the resulting quantum version of a multinomial
statistics, we infer nonclassicality probes based on a matrix of normally
ordered moments. We discuss these criteria and apply the theory to our data
which are measured with superconducting transition-edge sensors. Our experiment
produces heralded multi-photon states from a parametric down-conversion light
source. We show that the known notions of sub-Poisson and sub-binomial light
can be deduced from our general approach, and we establish the concept of
sub-multinomial light, which is shown to outperform the former two concepts of
nonclassicality for our data.


  We fix a finitely presented group $Q$ and consider short exact sequences
$1\to N\to G\to Q\to 1$ with $G$ finitely generated. The inclusion $N\to G$
induces a morphism of profinite completions $\hat N\to \hat G$. We prove that
this is an isomorphism for all $N$ and $G$ if and only if $Q$ is super-perfect
and has no proper subgroups of finite index.
  We prove that there is no algorithm that, given a finitely presented,
residually finite group $G$ and a finitely presentable subgroup $P\subset G$,
can determine whether or not $\hat P\to\hat G$ is an isomorphism.


  Let $\Omega$ be a class of ${\rm C^*}$-algebras. In this paper, we study a
class of not necessarily unital generalized tracial approximation ${\rm
C^*}$-algebras, and the class of simple ${\rm C^*}$-algebras which can be
generally tracially approximated by ${\rm C^*}$-algebras in $\Omega$, denoted
by ${\rm gTA}\Omega$. Let $\Omega$ be a class of unital ${\rm C^*}$-algebras
and let $A$ be a simple unital ${\rm C^*}$-algebra. Then $A\in {\rm
gTA}\Omega$, if, and only if, $A\in {\rm WTA}\Omega$ (where ${\rm TA}\Omega$ is
the class of weakly tracially approximable unital ${\rm C^*}$-algebras
introduced by Elliott, Fan, and Fang).Consider the class of ${\rm
C^*}$-algebras which are tracially $\mathcal{Z}$-absorbing (or are of tracial
nuclear dimension at most $n$, or are $m$-almost divisible, or have the
property $\rm SP$). Then $A$ is tracially $\mathcal{Z}$-absorbing
(respectively, has tracial nuclear dimension at most $n$, is weakly ($n,
m$)-almost divisible, has the property $\rm SP$) for any simple ${\rm
C^*}$-algebra $A$ in the corresponding class of generalized tracial
approximation ${\rm C^*}$-algebras.


  We reconsider the well-known issue of string corrections to Supergravity
theory. Our treatment is carried out to second order in the string slope
parameter. We establish a procedure for solving the Bianchi identities in the
non minimal case, and we solve a long standing problem in the perturbative
expansion of D=10, N=1 string corrected Supergravity, obtaining the H sector
tensors, torsions and curvatures.


  This paper studies the time-symmetry problem in quantum gravity. The issue
depends critically on the choice of the quantum state and has been considered
in this paper by restricting to the case of quantum wormholes. It is seen that
pure states represented by a wave functional are time symmetric. However, a
maximal analytic extension of the wormhole manifold is found that corresponds
to a mixed state describable by a nondegenerate density-matrix functional that
involves an extra quantum uncertainty for the three-metric, and is free from
the divergences encountered so far in statistical states formulated in quantum
gravity. It is then argued that, relative to one asymptotic region, the
statistical quantum state of single Euclidean wormholes in semiclassical
approximation is time-asymmetric and gives rise to a {\it topological} arrow of
time which will reflect in the set of all quantum fields at low energies of the
asymptotic flat region (To appear in Int. J. Mod. Phys. D)


  This paper characterizes the optimal type-II error exponent for a distributed
hypothesis testing-against-independence problem when the \emph{expected} rate
of the sensor-detector link is constrained. Unlike for the well-known
Ahlswede-Csiszar result that holds under a \emph{maximum} rate constraint and
where a strong converse holds, here the optimal exponent depends on the allowed
type-I error exponent. Specifically, if the type-I error probability is limited
by $\epsilon$, then the optimal type-II error exponent under an \emph{expected}
rate constraint $R$ coincides with the optimal type-II error exponent under a
\emph{maximum} rate constraint of $(1-\epsilon)R$.


  The study of the development of structures on multiple scales in the cold
interstellar medium has experienced rapid expansion in the past decade, on both
the observational and the theoretical front. Spectral line studies at
(sub-)millimeter wavelengths over a wide range of physical scales have provided
unique probes of the kinematics of dense gas in star-forming regions, and have
been complemented by extensive, high dynamic range dust continuum surveys of
the column density structure of molecular cloud complexes, while dust
polarization maps have highlighted the role of magnetic fields. This has been
accompanied by increasingly sophisticated numerical simulations including new
physics (e.g., supernova driving, cosmic rays, non-ideal magneto-hydrodynamics,
radiation pressure) and new techniques such as zoom-in simulations allowing
multi-scale studies. Taken together, these new data have emphasized the
anisotropic growth of dense structures on all scales, from giant ISM bubbles
driven by stellar feedback on $\sim$50-100 pc scales through parsec-scale
molecular filaments down to $<$0.1 pc dense cores and $<$1000 au protostellar
disks. Combining observations and theory, we present a coherent picture for the
formation and evolution of these structures and synthesize a comprehensive
physical scenario for the initial conditions and early stages of star and disk
formation.


  We consider a dynamical generalization of the Eshelby problem: the strain
profile due to an inclusion or "defect" in an isotropic elastic medium. We show
that the higher the oscillation frequency of the defect, the more localized is
the strain field around the defect. We then demonstrate that the qualitative
nature of the interaction between two defects is strongly dependent on
separation, frequency and direction, changing from "ferromagnetic" to
"antiferromagnetic" like behavior. We generalize to a finite density of defects
and show that the interactions in assemblies of defects can be mapped to XY
spin-like models, and describe implications for frustration and
frequency-driven pattern transitions.


  The experimental results of Takaki et al. [1] on the stage I resistivity
recovery of electron irradiated iron are analyzed using the analytical theory
of diffusion annealing formulated by Simpson & Sossin [2] and Schroeder [3]
taking into account the recent first-principles calculations of Fu et al. [4]
regarding the mobility of interstitials. Excellent agreement between theory and
experiment is obtained by a minimal set of adjustable parameters. The results
show that the diffusion annealing equations can be successfully employed for
the analysis of recovery experiments in iron.


  We report new follow-up spectroscopy of i-dropout galaxies with an NB921-band
depression found in the Subaru Deep Field. The NB921-depressed i-dropout
selection method is expected to select galaxies with large equivalent width Ly
alpha emission over a wide redshift range, 6.0<z<6.5. Two of four observed
targets show a strong emission line with a clear asymmetric profile, identified
as Ly alpha emitters at z=6.11 and 6.00. Their rest-frame equivalent widths are
153A and 114A, which are lower limits on the intrinsic equivalent widths.
Through our spectroscopic observations (including previous ones) of
NB921-depressed i-dropout galaxies, we identified 5 galaxies in total with a
rest-frame equivalent width larger than 100A at 6.0<z<6.5 out of 8 photometric
candidates, which suggests that the NB921-depressed i-dropout selection method
is possibly an efficient way to search for Ly alpha emitters with a large Ly
alpha equivalent width, in a wider redshift range than usual narrow-band excess
techniques. By combining these findings with our previous observational
results, we infer that the fraction of broad-band selected galaxies having a
rest-frame equivalent width larger than 100A is significantly higher at z~6
(the cosmic age of ~1 Gyr) than that at z~3 (~2 Gyr), being consistent with the
idea that the typical stellar population of galaxies is significantly younger
at z~6 than that at z~3. The NB921-depressed i-dropout galaxies may be
interesting candidates for hosts of massive, zero-metallicity Population III
stars.


  It is shown that the R\'enyi and Tsallis entropies and the q-expectation
values, are continuous and stable if $q>1$ and are not continuous and instable
for uniform finite distributions if $q<1$.


  The fine structure of hydrogen energy was calculated by using the usual
momentum-wavefunction relation directly, rather than establishing the
well-known Dirac wave equation. As the results, the energy levels are
completely the same as that of the Dirac wave equation, while the wavefunction
is single component that is quite different from Dirac's four component
wavefunction, the most important thing is that the present calculation brings
out electronic spin in a new way which has never been reported and indicates
that the electronic spin is a kind of orbital motion.


  Objective: The purpose of this study was to assess and compare the seed
germination response of six Acacia species under different NaCl concentrations
in order to explore opportunities for selection and breeding salt tolerant
genotypes. Methodology: Germination of seeds was evaluated under salt stresses
using 5 treatment levels: 0, 100, 200, 300 and 400 mM of NaCl. Corrected
germination rate (GC), germination rate index (GRI) and mean germination time
(MGT) were recorded during 10 days. Results: The results indicated that
germination was significantly reduced in all species with the increase in NaCl
concentrations. However, significant interspecific variation for salt tolerance
was observed. The greatest variability in tolerance was observed at moderate
salt stress (200 mM of NaCl) and the decrease in germination appeared to be
more accentuated in A. cyanophylla and A. cyclops. Although, A. raddiana,
remains the most interesting, it preserved the highest percentage (GC = 80%)
and velocity of germination in all species studied in this study, even in the
high salt levels. This species exhibited a particular adaptability to salt
environment, at least at this stage in the life cycle and could be recommended
for plantation establishment in salt affected areas. On the other hand, when
ungerminated seeds were transferred from NaCl treatments to distilled water,
they recovered largely their germination without a lag period and with high
speed. This indicated that the germination inhibition was related to a
reversible osmotic stress that induced dormancy rather than specific ion
toxicity. Conclusion: This ability to germinate after exposure to higher
concentrations of NaCl suggests that studied species, especially the most
tolerant could be able to germinate under the salt affected soils and could be
utilized for the rehabilitation of damaged arid zones.


  Multi-document summarization is the process of automatically generating a
concise summary of multiple documents related to the same topic. This summary
can help users quickly understand the key information from a large collection
of documents. Multi-document summarization systems are more complex than
single-document summarization systems due to the need to identify and combine
information from multiple sources. In this paper, we have developed a machine
learning model that generates a concise summary of a topic from multiple news
documents. The model is designed to be unbiased by sampling its input equally
from all the different aspects of the topic, even if the majority of the news
sources lean one way.


  The desire for privacy significantly impacts various aspects of social
behavior as illustrated by people's tendency to seek out the most secluded spot
when multiple options are available. In particular, this can be seen at rows of
payphones, where people tend to occupy an available payphone that is most
distant from those already occupied. Assuming that there are n payphones in a
row and that n people occupy payphones one after another as privately as
possible, the resulting assignment of people to payphones defines a
permutation, which we will refer to as a payphone permutation. In the present
study, we consider different variations of payphone permutations and enumerate
them.


  The advent of autonomous vehicles has heralded a transformative era in
transportation, reshaping the landscape of mobility through cutting-edge
technologies. Central to this evolution is the integration of Artificial
Intelligence (AI) and learning algorithms, propelling vehicles into realms of
unprecedented autonomy. This paper provides a comprehensive exploration of the
evolutionary trajectory of AI within autonomous vehicles, tracing the journey
from foundational principles to the most recent advancements. Commencing with a
current landscape overview, the paper delves into the fundamental role of AI in
shaping the autonomous decision-making capabilities of vehicles. It elucidates
the steps involved in the AI-powered development life cycle in vehicles,
addressing ethical considerations and bias in AI-driven software development
for autonomous vehicles. The study presents statistical insights into the usage
and types of AI/learning algorithms over the years, showcasing the evolving
research landscape within the automotive industry. Furthermore, the paper
highlights the pivotal role of parameters in refining algorithms for both
trucks and cars, facilitating vehicles to adapt, learn, and improve performance
over time. It concludes by outlining different levels of autonomy, elucidating
the nuanced usage of AI and learning algorithms, and automating key tasks at
each level. Additionally, the document discusses the variation in software
package sizes across different autonomy levels


  Spectral-based subspace clustering methods have proved successful in many
challenging applications such as gene sequencing, image recognition, and motion
segmentation. In this work, we first propose a novel spectral-based subspace
clustering algorithm that seeks to represent each point as a sparse convex
combination of a few nearby points. We then extend the algorithm to constrained
clustering and active learning settings. Our motivation for developing such a
framework stems from the fact that typically either a small amount of labelled
data is available in advance; or it is possible to label some points at a cost.
The latter scenario is typically encountered in the process of validating a
cluster assignment. Extensive experiments on simulated and real data sets show
that the proposed approach is effective and competitive with state-of-the-art
methods.


  We explain the observed specific heat anomaly (and hence entropy change) in
the colossal magnetoresistive manganite Sm0.55Sr0.45MnO3, by introducing phase
separation into the current carrier density collapse theory via the notion of
the ferromagnetic volume fraction. Within the same framework, we have also been
able to explain the observed electrical resistivity of Sm0.55Sr0.45MnO3 by
using appropriate expressions governing the scattering mechanisms far away from
the transition. Fitting specific heat and resistivity results has allowed us to
separate the hopping activation energy of polaronic carriers and the bipolaron
binding energy contributions to the exponential behaviour of resistivity in the
paramagnetic phase.


  We analyze the mean-field yrast spectrum of a two-component Bose gas in the
ring geometry with arbitrary interaction asymmetry. Of particular interest is
the possibility that the yrast spectrum develops local minima at which
persistent superfluid flow can occur. By analyzing the mean-field energy
functional, we show that local minima can be found at plane-wave states and
arise when the system parameters satisfy certain inequalities. We then go on to
show that these plane-wave states can be yrast states even when the yrast
spectrum no longer exhibits a local minimum. Finally, we obtain conditions
which establish when the plane-wave states cease to be yrast states. Specific
examples illustrating the roles played by the various interaction asymmetries
are presented.


  Recently, the Fermilab heavy-quark action was extended to include
dimension-six and -seven operators in order to reduce the discretization
errors. In this talk, we present results of the first numerical simulations
with this action (the OK action), where we study the masses of the quarkonium
and heavy-light systems. We calculate combinations of masses designed to test
improvement and compare results obtained with the OK action to their
counterparts obtained with the clover action. Our preliminary results show a
clear improvement.


  Head-related transfer function (HRTF) is an essential component to create an
immersive listening experience over headphones for virtual reality (VR) and
augmented reality (AR) applications. Metaverse combines VR and AR to create
immersive digital experiences, and users are very likely to interact with
virtual objects in the near-field (NF). The HRTFs of such objects are highly
individualized and dependent on directions and distances. Hence, a significant
number of HRTF measurements at different distances in the NF would be needed.
Using conventional static stop-and-go HRTF measurement methods to acquire these
measurements would be time-consuming and tedious for human listeners. In this
paper, we propose a continuous measurement system targeted for the NF, and
efficiently capturing HRTFs in the horizontal plane within 45 secs. Comparative
experiments are performed on head and torso simulator (HATS) and human
listeners to evaluate system consistency and robustness.


  The hysteretic behavior of many-particle systems with non-convex free energy
can be modeled by nonlocal Fokker-Planck equations that involve two small
parameters and are driven by a time- dependent constraint. In this paper we
consider the fast reaction regime related to Kramers-type phase transitions and
show that the dynamics in the small-parameter limit can be described by a
rate-independent evolution equation with hysteresis. For the proof we first
derive mass-dissipation estimates by means of Muckenhoupt constants, formulate
conditional stability estimates, and char- acterize the mass flux between the
different phases in terms of moment estimates that encode large deviation
results. Afterwards we combine all these partial results and establish the
dynamical sta- bility of localized peaks as well as sufficiently strong
compactness results for the basic macroscopic quantities.


  In this paper, we study batch parallel algorithms for the dynamic
connectivity problem, a fundamental problem that has received considerable
attention in the sequential setting. The most well known sequential algorithm
for dynamic connectivity is the elegant level-set algorithm of Holm, de
Lichtenberg and Thorup (HDT), which achieves $O(\log^2 n)$ amortized time per
edge insertion or deletion, and $O(\log n / \log\log n)$ time per query. We
design a parallel batch-dynamic connectivity algorithm that is work-efficient
with respect to the HDT algorithm for small batch sizes, and is asymptotically
faster when the average batch size is sufficiently large. Given a sequence of
batched updates, where $\Delta$ is the average batch size of all deletions, our
algorithm achieves $O(\log n \log(1 + n / \Delta))$ expected amortized work per
edge insertion and deletion and $O(\log^3 n)$ depth w.h.p. Our algorithm
answers a batch of $k$ connectivity queries in $O(k \log(1 + n/k))$ expected
work and $O(\log n)$ depth w.h.p. To the best of our knowledge, our algorithm
is the first parallel batch-dynamic algorithm for connectivity.


  Lutz-Kelker bias corrected absolute magnitude calibrations for the detached
binary systems with main-sequence components are presented. The absolute
magnitudes of the calibrator stars were derived at intrinsic colours of
Johnson-Cousins and 2MASS (Two Micron All Sky Survey) photometric systems. As
for the calibrator stars, 44 detached binaries were selected from the Hipparcos
catalogue, which have relative observed parallax errors smaller than 15%
($\sigma_{\pi}/\pi\leq0.15$). The calibration equations which provide the
corrected absolute magnitude for optical and near-infrared pass bands are valid
for wide ranges of colours and absolute magnitudes: $-0.18<(B-V)_{0}<0.91$,
$-1.6<M_{V}<5.5$ and $-0.15<(J-H)_{0}<0.50$, $-0.02<(H-K_{s})_{0}<0.13$,
$0<M_{J}<4$, respectively. The distances computed using the luminosity-colours
(LCs) relation with optical (BV) and near-infrared ($JHK_{s}$) observations
were compared to the distances found from various other methods. The results
show that new absolute magnitude calibrations of this study can be used as a
convenient statistical tool to estimate the true distances of detached binaries
out of Hipparcos' distance limit.


  In this paper we describe and continue the study begun by the author, Jones,
and Segal, of the homotopy theory that underlies Floer theory. In that paper
the authors addressed the question of realizing a Floer complex as the celluar
chain complex of a CW -spectrum or pro-spectrum, where the attaching maps are
determined by the compactified moduli spaces of connecting orbits. The basic
obstructions to the existence of this realization are the smoothness of these
moduli spaces, and the existence of compatible collections of framings of their
stable tangent bundles. In this note we describe a generalization of this, to
show that when these moduli spaces are smooth, and are oriented with respect to
a generalized cohomology theory E^*, then a Floer E_* -homology theory can be
defined. In doing this we describe a functorial viewpoint on how chain
complexes can be realized by E -module spectra, generalizing the stable
homotopy realization criteria given earlier by the author, Jones, and Segal.
Since these moduli spaces, if smooth, will be manifolds with corners, we give a
discussion about the appropriate notion of orientations of manifolds with
corners.


  We consider a class of 5-D brane-world solutions with a power-law warp factor
$a(y)\propto y^{q}$, and bulk dilaton with profile $\phi \propto \ln y$, where
$y$ is the proper distance in the extra dimension. This class includes the
Heterotic M-theory brane-world of Refs. \cite{ovrut,ekpy} and the
Randall-Sundrum (RS) model as a limiting case. In general, there are two moduli
fields $y_{\pm}$, corresponding to the "positions" of two branes (which live at
the fixed points of an orbifold compactification). Classically, the moduli are
massless, due to a scaling symmetry of the action. However, in the absence of
supersymmetry, they develop an effective potential at one loop. Local terms
proportional to $K_\pm^4$, where $K_\pm=q/y_\pm$ is the local curvature scale
at the location of the corresponding brane, are needed in order to remove the
divergences in the effective potential. Such terms break the scaling symmetry
and hence they may act as stabilizers for the moduli. When the branes are very
close to each other, the effective potential induced by massless bulk fields
behaves like $V\sim d^{-4}$, where $d$ is the separation between branes. When
the branes are widely separated, the potentials for each one of the moduli
generically develop a "Coleman-Weinberg"-type behaviour of the form
$a^4(y_\pm)K_\pm^4 \ln(K_\pm/\mu_\pm)$, where $\mu_\pm$ are renormalization
scales. In the RS case, the bulk geometry is $AdS$ and $K_\pm$ are equal to a
constant, independent of the position of the branes, so these terms do not
contribute to the mass of the moduli. However, for generic warp factor, they
provide a simple stabilization mechanism. For $q\gtrsim 10$, the observed
hierarchy can be naturally generated by this potential, giving the lightest
modulus a mass of order $m_- \lesssim TeV$.


  We present results on a free-space atom interferometer operating on the first
order magnetically insensitive |F=1,mF=0> -> |F=2,mF=0> transition of
Bose-condensed 87Rb atoms. A pulsed atom laser is output-coupled from a
Bose-Einstein condensate and propagates through a sequence of two internal
state beam splitters, realized via coherent Raman transitions between the two
interfering states. We observe Ramsey fringes with a visibility close to 100%
and determine the current and the potentially achievable interferometric phase
sensitivity. This system is well suited to testing recent proposals for
generating and detecting squeezed atomic states.


  We introduce a method to evaluate untyped lambda terms by combining the
theory of traversals, a term-tree traversing technique inspired from Game
Semantics, with judicious use of the eta-conversion rule of the lambda
calculus.
  The traversal theory of the simply-typed lambda calculus relies on the
eta-long transform to ensure that when traversing an application, there is a
subterm representing every possible operator's argument. In the untyped
setting, we instead exhibit the missing operand via ad-hoc instantiation of the
eta-expansion rule, which allows the traversal to proceed as if the operand
existed in the original term. This gives rise to a more generic concept of
traversals for lambda terms. A notable improvement, in addition to handling
untyped terms, is that no preliminary transformation is required: the original
unaltered lambda term is traversed.
  We show that by bounding the non-determinism of the traversal rule for free
variables, one can effectively compute a set of traversals characterizing the
paths in the tree representation of the beta-normal form, when it exists. This
yields an evaluation algorithm for untyped lambda-terms. We prove correctness
by showing that traversals implement leftmost linear reduction, a
generalization of the head linear reduction of Danos et. al.


  We look for ways to destabilise the vacuum. We describe how dense matter
environments source a contribution to moduli potentials and analyse the
conditions required to initiate either decompactification or a local shift in
moduli vevs. We consider astrophysical objects such as neutron stars as well as
cosmological and black hole singularities. Regrettably neutron stars cannot
destabilise realistic Planck coupled moduli, which would require objects many
orders of magnitude denser. However gravitational collapse, either in
matter-dominated universes or in black hole formation, inevitably leads to a
destabilisation of the compact volume causing a super-inflationary expansion of
the extra dimensions.


  It is demonstrated that the reason of SERS on dielectric and semiconductor
substrates is the enhancement of the electric field in the regions of the tops
of the surface roughness with very small radius, or a very large curvature. The
enhancement depends on the dielectric constant of the substrate and is stronger
for a larger dielectric constant. It is indicated that the enhancement on
dielectrics and semiconductors is weaker than on metals with the same modulus
of the dielectric constant. The result obtained is confirmed by experimental
data on the enhancement coefficients obtained for various semiconductor and
dielectric substrates.


  The Karin cluster is a young asteroid family thought to have formed only
$\simeq 5.75$~My ago. The young age can be demonstrated by numerically
integrating the orbits of Karin cluster members backward in time and showing
the convergence of the perihelion and nodal longitudes (as well as other
orbital elements). Previous work has pointed out that the convergence is not
ideal if the backward integration only accounts for the gravitational
perturbations from the Solar System planets. It improves when the thermal
radiation force known as the Yarkovsky effect it is accounted for. This
argument can be used to estimate the spin obliquities of the Karin cluster
members. Here we take advantage of the fast growing membership of the Karin
cluster and show that the obliquity distribution of diameter $D\simeq 1-2$ km
Karin asteroids is bimodal, as expected if the YORP effect acted to move
obliquities toward the extreme values ($0^\circ$ or $180^\circ$). The measured
magnitude of the effect is consistent with the standard YORP model. The surface
thermal conductivity is inferred to be $0.07$-0.2 W m$^{-1}$ K$^{-1}$ (thermal
inertia $\simeq 300-500$ J m$^{-2}$ K$^{-1}$s$^{-1/2}$). We find that the
strength of the YORP effect is roughly $\simeq 0.7$ of the nominal strength
obtained for a collection of random Gaussian spheroids. These results are
consistent with a surface composed of rough, rocky regolith. The obliquity
values predicted here for 480 members of the Karin cluster can be validated by
the light-curve inversion method.


  As control-flow protection gets widely deployed, it is difficult for
attackers to corrupt control-data and achieve control-flow hijacking. Instead,
data-oriented attacks, which manipulate non-control data, have been
demonstrated to be feasible and powerful. In data-oriented attacks, a
fundamental step is to identify non-control, security-critical data. However,
critical data identification processes are not scalable in previous works,
because they mainly rely on tedious human efforts to identify critical data. To
address this issue, we propose a novel approach that combines traditional
program analysis with deep learning. At a higher level, by examining how
analysts identify critical data, we first propose dynamic analysis algorithms
to identify the program semantics (and features) that are correlated with the
impact of a critical data. Then, motivated by the unique challenges in the
critical data identification task, we formalize the distinguishing features and
use customized program dependence graphs (PDG) to embed the features. Different
from previous works using deep learning to learn basic program semantics, this
paper adopts a special neural network architecture that can capture the long
dependency paths (in the PDG), through which a critical variable propagates its
impact. We have implemented a fully-automatic toolchain and conducted
comprehensive evaluations. According to the evaluations, our model can achieve
90% accuracy. The toolchain uncovers 80 potential critical variables in Google
FuzzBench. In addition, we demonstrate the harmfulness of the exploits using
the identified critical variables by simulating 7 data-oriented attacks through
GDB.


  Light-pulse atom interferometers are highly sensitive to inertial and
gravitational effects. As such they are promising candidates for tests of
gravitational physics. In this article the state-of-the-art and proposals for
fundamental tests of gravity are reviewed. They include the measurement of the
gravitational constant $G$, tests of the weak equivalence principle, direct
searches of dark energy and gravitational-wave detection. Particular emphasis
is put on long-time interferometry in microgravity environments accompanied by
an enormous increase of sensitivity. In addition, advantages as well as
disadvantages of Bose-Einstein condensates as atom sources are discussed.


  Multivariate time series forecasting plays a crucial role in various fields
such as finance, traffic management, energy, and healthcare. Recent studies
have highlighted the advantages of channel independence to resist distribution
drift but neglect channel correlations, limiting further enhancements. Several
methods utilize mechanisms like attention or mixer to address this by capturing
channel correlations, but they either introduce excessive complexity or rely
too heavily on the correlation to achieve satisfactory results under
distribution drifts, particularly with a large number of channels. Addressing
this gap, this paper presents an efficient MLP-based model, the Series-cOre
Fused Time Series forecaster (SOFTS), which incorporates a novel STar
Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage
channel interactions through distributed structures, \textit{e.g.}, attention,
STAR employs a centralized strategy to improve efficiency and reduce reliance
on the quality of each channel. It aggregates all series to form a global core
representation, which is then dispatched and fused with individual series
representations to facilitate channel interactions effectively.SOFTS achieves
superior performance over existing state-of-the-art methods with only linear
complexity. The broad applicability of the STAR module across different
forecasting models is also demonstrated empirically. For further research and
development, we have made our code publicly available at
https://github.com/Secilia-Cxy/SOFTS.


  In this paper, a new variant to fractional signal processing is proposed
known as the Reduced Order Fractional Fourier Transform. Various properties
satisfied by its transformation kernel is derived. The properties associated
with the proposed Reduced Order Fractional Fourier Transform like shift,
modulation, time-frequency shift property are also derived and it is shown
mathematically that when the rotation angle of Reduced Order Fractional Fourier
Transform approaches 90 degrees, the proposed Reduced Order Fractional Fourier
Transform reduces to the conventional Fourier transform. Also, the Reduced
Order Fractional Fourier Transform of various kinds of signals is also derived
and it is shown that the obtained analytical expressions of different Reduced
Order Fractional Fourier Transform are a reduced form of the conventional
fractional Fourier transform. It is also shown that proposed definition of
Fractional Fourier Transform is easier to be handled analytically. Finally, the
convolution theorem associated with the proposed Reduced Order Fractional
Fourier Transform is derived with its various properties like shift
convolution, modulation convolution, and time-frequency shift modulation
properties. It has been shown that with this proposed new definition of
fractional Fourier transform, the convolution theorem has been reduced to
multiplication in the fractional frequency domain.


  We present the results of a lattice study of light-cone distribution
amplitudes (DAs) of the nucleon and negative parity nucleon resonances using
two flavors of dynamical (clover) fermions on lattices of different volumes and
pion masses down to m_pi = 150 MeV. We find that the three valence quarks in
the proton share their momentum in the proportion 37% : 31% : 31%, where the
larger fraction corresponds to the u-quark that carries proton helicity, and
determine the value of the wave function at the origin in position space, which
turns out to be small compared to the existing estimates based on QCD sum
rules. Higher-order moments are constrained by our data and are all compatible
with zero within our uncertainties. We also calculate the normalization
constants of the higher-twist DAs that are related to the distribution of quark
angular momentum. Furthermore, we use the variational method and customized
parity projection operators to study the states with negative parity. In this
way we are able to separate the contributions of the two lowest states that, as
we argue, possibly correspond to N*(1535) and a mixture of N*(1650) and the
pion-nucleon continuum, respectively. It turns out that the state that we
identify with N*(1535) has a very different DA as compared to both the second
observed negative parity state and the nucleon, which may explain the
difference in the decay patterns of N*(1535) and N*(1650) observed in
experiment.


  In this paper, we review some of the extant literature on the study of
interstellar objects (ISOs). With the forthcoming Vera C. Rubin Telescope and
Legacy Survey of Space and Time (LSST), we find that $0.38 - 84$ `Oumuamua-like
interstellar objects are expected to be detected in the next 10 years, with
95\% confidence. The feasibility of a rendezvous trajectory has been
demonstrated in previous work. In this paper, we investigate the requirements
for a rendezvous mission with the primary objective of producing a resolved
image of an interstellar object. We outline the rendezvous distances necessary
as a function of resolution elements and object size. We expand upon current
population synthesis models to account for the size dependency on the detection
rates for reachable interstellar objects. We assess the trade-off between
object diameter and occurrence rate, and conclude that objects with the size
range between a third of the size and the size of `Oumuamua will be optimal
targets for an imaging rendezvous. We also discuss expectations for surface
properties and spectral features of interstellar objects, as well as the
benefits of various spacecraft storage locations.


  We present the distance priors that we have derived from the 2015 Planck
data, and use these in combination with the latest observational data from Type
Ia Supernovae (SNe Ia) and galaxy clustering, to explore the systematic
uncertainties in dark energy constraints. We use the Joint Lightcurve Analysis
(JLA) set of 740 SNe Ia, galaxy clustering measurements of H(z)s and D_A(z)/s
(where s is the sound horizon at the drag epoch) from the Sloan Digital Sky
Survey (SDSS) at z=0.35 and z=0.57 (BOSS DR12). We find that the combined dark
energy constraints are insensitive to the assumptions made in the galaxy
clustering measurements (whether they are for BAO only or marginalized over
RSD), which indicates that as the analysis of galaxy clustering data becomes
more accurate and robust, the systematic uncertainties are reduced. On the
other hand, we find that flux-averaging SNe Ia at z>= 0.5 significantly
tightens the dark energy constraints, and excludes a flat universe with a
cosmological constant at 68% confidence level, assuming a dark energy equation
of state linear in the cosmic scale factor. Flux-averaging has the most
significant effect when we allow dark energy density function X(z) to be a free
functions given by the cubic spline of its value at z=0, 1/3, 2/3, 1; the
measured X(z) deviates from a cosmological constant at more than 95% confidence
level for 0.4 < z < 0.7. Since flux-averaging reduces the bias in the SN
distance measurements, this may be an indication that we have arrived in the
era when the SN distance measurements are limited by systematic uncertainties.


  We analyze a class of spatial random spanning trees built on a realization of
a homogeneous Poisson point process of the plane. This tree has a simple radial
structure with the origin as its root. We first use stochastic geometry
arguments to analyze local functionals of the random tree such as the
distribution of the length of the edges or the mean degree of the vertices. Far
away from the origin, these local properties are shown to be close to those of
a variant of the directed spanning tree introduced by Bhatt and Roy. We then
use the theory of continuous state space Markov chains to analyze some nonlocal
properties of the tree, such as the shape and structure of its semi-infinite
paths or the shape of the set of its vertices less than $k$ generations away
from the origin. This class of spanning trees has applications in many fields
and, in particular, in communications.


  We develop algorithms to turn quotients of rings of rings of integers into
effective Euclidean rings by giving polynomial algorithms for all fundamental
ring operations. In addition, we study normal forms for modules over such rings
and their behavior under certain quotients. We illustrate the power of our
ideas in a new modular normal form algorithm for modules over rings of
integers, vastly outperforming classical algorithms.


  The graph invariant examined in this paper is the largest eigenvalue of the
adjacency matrix of a graph. Previous work demonstrates the tight relationship
between this invariant, the birth and death rate of a contagion spreading on
the graph, and the trajectory of the contagion over time. We begin by
conducting a simulation confirming this and explore bounds on the birth and
death rate in terms of well-known graph invariants. As a result, the change in
the largest eigenvalue resulting from removal of a vertex in the network is the
best measure of effectiveness of interventions that slow the spread of a
contagion. We define the spread centrality of a vertex $v$ in a graph $G$ as
the difference between the largest eigenvalues of $G$ and $G-v$. While the
spread centrality is a distinct centrality measure and serves as another graph
invariant for distinguishing graphs, we found experimental evidence that
vertices ranked by the spread centrality and those ranked by eigenvector
centrality are strongly correlated. Since eigenvector centrality is easier to
compute than the spread centrality, this justifies using eigenvector centrality
as a measure of spread, especially in large networks with unknown portions. We
also examine two strategies for selecting members of a population to vaccinate.


  TThe problem is to identify a probability associated with a set of natural
numbers, given an infinite data sequence of elements from the set. If the given
sequence is drawn i.i.d. and the probability mass function involved (the
target) belongs to a computably enumerable (c.e.) or co-computably enumerable
(co-c.e.) set of computable probability mass functions, then there is an
algorithm to almost surely identify the target in the limit. The technical tool
is the strong law of large numbers. If the set is finite and the elements of
the sequence are dependent while the sequence is typical in the sense of
Martin-L\"of for at least one measure belonging to a c.e. or co-c.e. set of
computable measures, then there is an algorithm to identify in the limit a
computable measure for which the sequence is typical (there may be more than
one such measure). The technical tool is the theory of Kolmogorov complexity.
We give the algorithms and consider the associated predictions.


  Prompted by recent progress in the study of N=4 super Yang-Mills amplitudes,
and evidence that similar approaches might be relevant to N=8 supergravity, we
investigate possible iterative structures and applications of Wilson loop
techniques in maximal supergravity. We first consider the two-loop, four-point
MHV scattering amplitude in N=8 supergravity, confirming that the infrared
divergent parts exponentiate, and we give the explicit expression which
represents the failure for this to occur for the finite part. We observe that
each term in the expansion of the one- and two-loop amplitudes in the
dimensional regularisation parameter epsilon has a uniform degree of
transcendentality. We then turn to consider Wilson loops in supergravity,
showing that a natural definition of the loop, involving the Christoffel
connection, fails to reproduce the one-loop amplitude. An alternative
expression, which involves the metric explicitly, is shown to have a close
relationship with the physical amplitude. We find that in a gauge in which the
cusp diagrams vanish, the remaining diagrams for this Wilson loop correctly
generate the full one-loop, four-point N=8 supergravity amplitude.


  Hot Jupiter atmospheres exhibit fast, weakly-ionized winds. The interaction
of these winds with the planetary magnetic field generates drag on the winds
and leads to ohmic dissipation of the induced electric currents. We study the
magnitude of ohmic dissipation in representative, three-dimensional atmospheric
circulation models of the hot Jupiter HD 209458b. We find that ohmic
dissipation can reach or exceed 1% of the stellar insolation power in the
deepest atmospheric layers, in models with and without dragged winds. Such
power, dissipated in the deep atmosphere, appears sufficient to slow down
planetary contraction and explain the typically inflated radii of hot Jupiters.
This atmospheric scenario does not require a top insulating layer or radial
currents that penetrate deep in the planetary interior. Circulation in the
deepest atmospheric layers may actually be driven by spatially non-uniform
ohmic dissipation. A consistent treatment of magnetic drag and ohmic
dissipation is required to further elucidate the consequences of magnetic
effects for the atmospheres and the contracting interiors of hot Jupiters.


  sPHENIX is a new collaboration and future detector project at Brookhaven
National Laboratory's Relativistic Heavy Ion Collider (RHIC). It seeks to
answer fundamental questions on the nature of the quark gluon plasma (QGP),
including its temperature dependence and coupling strength, by using a suite of
precision jet and upsilon measurements that probe different length scales of
the QGP. This will be achieved with large acceptance, $|\eta| < 1$ and
$0$-$2\pi$ in $\phi$, electromagentic and hadronic calorimeters and precision
tracking enabled by a $1.5$ T superconducting magnet. With the increased
luminosity afforded by accelerator upgrades, sPHENIX will perform high
statistics measurements extending the kinematic reach at RHIC to overlap the
LHC's. This overlap with the LHC will facilitate better understanding of the
role of temperature, density and parton virtuality in QGP dynamics and for jet
quenching in particular. This talk will focus on key future measurements and
the current state of the sPHENIX project.


  We have searched for lepton flavor violating decays of a $\tau$ to a
lighter-mass charged lepton and an $\omega$ vector mesonusing 384.1 $fb^{-}$ of
$e^+e^- $ annihilation data collected with the BABAR detector at the Stanford
Linear Accelerator Center PEP-II storage ring. No signal is found, and the
upper limits on the branching ratios are determined to be ${\cal B}(\tau^{\pm}
\to \mathrm{e}^{\pm} \omega) < 1.1\times10^{-7}$ and ${\cal B}(\tau^{\pm} \to
\mu^{\pm} \omega) < 1.0\times10^{-7}$ at 90% confidence level.


  We estimate from below the expected Betti numbers of real hypersurfaces taken
at random in a smooth real projective n-dimensional manifold. These random
hypersurfaces are chosen in the linear system of a large d-th power of a real
ample line bundle equipped with a Hermitian metric of positive curvature. As
for the upper bounds that we recently established, these lower bounds read as a
product of a constant which only depends on the dimension n of the manifold
with the K\"ahlerian volume of its real locus RX and d^{n/2}. Actually, any
closed affine real algebraic hypersurface appears with positive probability as
part of such random real hypersurfaces in any ball of RX of radius O(d^{-1/2}).


  Averting the effects of anthropogenic climate change requires a transition
from fossil fuels to low-carbon technology. A way to achieve this is to
decarbonize the electricity grid. However, further efforts must be made in
other fields such as transport and heating for full decarbonization. This would
reduce carbon emissions due to electricity generation, and also help to
decarbonize other sources such as automotive and heating by enabling a
low-carbon alternative. Carbon taxes have been shown to be an efficient way to
aid in this transition. In this paper, we demonstrate how to to find optimal
carbon tax policies through a genetic algorithm approach, using the electricity
market agent-based model ElecSim. To achieve this, we use the NSGA-II genetic
algorithm to minimize average electricity price and relative carbon intensity
of the electricity mix. We demonstrate that it is possible to find a range of
carbon taxes to suit differing objectives. Our results show that we are able to
minimize electricity cost to below \textsterling10/MWh as well as carbon
intensity to zero in every case. In terms of the optimal carbon tax strategy,
we found that an increasing strategy between 2020 and 2035 was preferable. Each
of the Pareto-front optimal tax strategies are at least above
\textsterling81/tCO2 for every year. The mean carbon tax strategy was
\textsterling240/tCO2.


  This work revisits the ChaLearn First Impressions database, annotated for
personality perception using pairwise comparisons via crowdsourcing. We analyse
for the first time the original pairwise annotations, and reveal existing
person perception biases associated to perceived attributes like gender,
ethnicity, age and face attractiveness. We show how person perception bias can
influence data labelling of a subjective task, which has received little
attention from the computer vision and machine learning communities by now. We
further show that the mechanism used to convert pairwise annotations to
continuous values may magnify the biases if no special treatment is considered.
The findings of this study are relevant for the computer vision community that
is still creating new datasets on subjective tasks, and using them for
practical applications, ignoring these perceptual biases.


  In order to ask for future concepts of relativity, one has to build upon the
original concepts instead of the nowadays common formalism only, and as such
recall and reconsider some of its roots in geometry. So in order to discuss
3-space and dynamics, we recall briefly Minkowski's approach in 1910
implementing the nowadays commonly used 4-vector calculus and related tensorial
representations as well as Klein's 1910 paper on the geometry of the Lorentz
group. To include microscopic representations, we discuss few aspects of
Wigner's and Weinberg's 'boost' approach to describe 'any spin' with respect to
its reductive Lie algebra and coset theory, and we relate the physical
identification to objects in $P^{5}$ based on the case $(1,0)\oplus(0,1)$ of
the electromagnetic field. So instead of following this -- in some aspects --
special and misleading 'old' representation theory, based on 4-vector calculus
and tensors, we provide and use an alternative representation based on line
geometry which -- besides comprising known representation theory -- is capable
of both describing (classical) projective geometry of 3-space as well as it
yields spin matrices and the classical Lie transfer. In addition, this geometry
is capable of providing a more general route to known Lie symmetries,
especially of the su(2)$\oplus$i~su(2) Lie algebra of special relativity, as
well as it comprises gauge theories and affine geometry. Thus it serves as
foundation for a future understanding of more general representation theory of
relativity based, however, on roots known from classical projective geometry
and $P^{5}$. As an application, we discuss Lorentz transformations in point
space in terms of line and Complex geometry, where we can identify them as...


  As an important biomarker for human identification, human gait can be
collected at a distance by passive sensors without subject cooperation, which
plays an essential role in crime prevention, security detection and other human
identification applications. At present, most research works are based on
cameras and computer vision techniques to perform gait recognition. However,
vision-based methods are not reliable when confronting poor illuminations,
leading to degrading performances. In this paper, we propose a novel multimodal
gait recognition method, namely GaitFi, which leverages WiFi signals and videos
for human identification. In GaitFi, Channel State Information (CSI) that
reflects the multi-path propagation of WiFi is collected to capture human
gaits, while videos are captured by cameras. To learn robust gait information,
we propose a Lightweight Residual Convolution Network (LRCN) as the backbone
network, and further propose the two-stream GaitFi by integrating WiFi and
vision features for the gait retrieval task. The GaitFi is trained by the
triplet loss and classification loss on different levels of features. Extensive
experiments are conducted in the real world, which demonstrates that the GaitFi
outperforms state-of-the-art gait recognition methods based on single WiFi or
camera, achieving 94.2% for human identification tasks of 12 subjects.


  The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use
probabilistic segmentation methods. By combining contrast terms with prior
terms, it provides accurate segmentations of medical images in a fully
automated manner. However, one of the main drawbacks of using the RW algorithm
is that its parameters have to be hand-tuned. we propose a novel discriminative
learning framework that estimates the parameters using a training dataset. The
main challenge we face is that the training samples are not fully supervised.
Speci cally, they provide a hard segmentation of the images, instead of a
proba- bilistic segmentation. We overcome this challenge by treating the opti-
mal probabilistic segmentation that is compatible with the given hard
segmentation as a latent variable. This allows us to employ the latent support
vector machine formulation for parameter estimation. We show that our approach
signi cantly outperforms the baseline methods on a challenging dataset
consisting of real clinical 3D MRI volumes of skeletal muscles.


  Atacama Pathfinder Experiment (APEX) mapping observations in CO (9-8) and
(4-3) toward a high-mass star-forming region, NGC 6334 I, are presented. The CO
(9-8) map has a 6.4 arcsec resolution, revealing a ~0.5 pc, jet-like, and
bipolar outflow. This is the first map of a molecular outflow in a THz line.
The CO (9-8) and (4-3) lines arising from the outflow lobes both show extremely
high-velocity line wings, and their ratios indicate a gas temperature greater
than 100 K and a density higher than 10^4 cm^{-3}. The spatial-velocity
structure of the CO (9-8) data is typical of a bow-shock-driven flow, which is
consistent with the association between the bipolar outflow and the infrared
bow-shaped tips. In short, the observations unveil a highly-excited and
collimated component in a bipolar outflow that is powered by a high-mass
protostar, and provide insights into the driving mechanism of the outflow.
Meanwhile, the observations demonstrate that high-quality mapping observations
can be performed with the new THz receiver on APEX.


  The notion of a quantum family of maps has been introduced in the framework
of C*-algebras. As in the classical case, one may consider a quantum family of
maps preserving additional structures (e.g. quantum family of maps preserving a
state). In this paper we define a quantum family of homomorphisms of locally
compact quantum groups. Roughly speaking, we show that such a family is
classical. The purely algebraic counterpart of the discussed notion, i.e. a
quantum family of homomorphisms of Hopf algebras, is introduced and the
algebraic counterpart of the aforementioned result is proved. Moreover, we show
that a quantum family of homomorphisms of Hopf algebras is consistent with the
counits and coinverses of the given Hopf algebras. We compare our concept with
weak coactions introduced by Andruskiewitsch and we apply it to the analysis of
adjoint coaction.


  We show that the Fourier transform on the Jacobian of a curve interchanges
"$\delta$ functions" at the curve and the theta divisor. The Torelli theorem is
an immediate consequence.


  Employing simplified models in computer simulation is on the one hand often
enforced by computer time limitations but on the other hand it offers insights
into the molecular properties determining a given physical phenomenon. We
employ this strategy to the determination of the phase behaviour of quadrupolar
fluids, where we study the influence of omitting angular degrees of freedom of
molecules via an effective spherically symmetric potential obtained from a
perturbative expansion. Comparing the liquid-vapor coexistence curve, vapor
pressure at coexistence, interfacial tension between the coexisting phases,
etc., as obtained from both the models with the full quadrupolar interactions
and the (approximate) isotropic interactions, we find discrepancies in the
critical region to be typically (such as in the case of carbon dioxide) of the
order of 4%. However, when the Lennard-Jones parameters are rescaled such that
critical temperatures and critical densities of both models coincide with the
experimental results, almost perfect agreement between the above-mentioned
properties of both models is obtained. This result justifies the use of
isotropic quadrupolar potentials. We present also a detailed comparison of our
simulations with a combined integral equation/density functional approach and
show that the latter provides an accurate description except for the vicinity
of the critical point.


  In this work, we consider a noncommutative (NC) massless scalar field coupled
to the classical nonrotational BTZ geometry. In a manner of the theories where
the gravity emerges from the underlying scalar field theory, we study the
effective action and the entropy derived from this noncommutative model. In
particular, the entropy is calculated by making use of the two different
approaches, the brick wall method and the heat kernel method designed for
spaces with conical singularity. We show that the UV divergent structures of
the entropy, obtained through these two different methods, agree with each
other. It is also shown that the same renormalization condition that removes
the infinities from the effective action can also be used to renormalize the
entanglement entropy for the same system. Besides, the interesting feature of
the NC model considered here is that it allows an interpretation in terms of an
equivalent system comprising of a commutative massive scalar field, but in a
modified geometry; that of the rotational BTZ black hole, the result that hints
at a duality between the commutative and noncommutative systems in the
background of a BTZ black hole.


  Memory dumps that are acquired while the system is running often contain
inconsistencies like page smearing which hamper the analysis. One possibility
to avoid inconsistencies is to pause the system during the acquisition and take
an instantaneous memory dump. While this is possible for virtual machines, most
systems cannot be frozen and thus the ideal dump can only be
quasi-instantaneous, i.e., consistent despite the system running. In this
article, we introduce a method allowing us to measure quasi-instantaneous
consistency and show both, theoretically, and practically, that our method is
valid but that in reality, dumps can be but usually are not
quasi-instantaneously consistent. For the assessment, we run a pivot program
enabling the evaluation of quasi-instantaneous consistency for its heap and
allowing us to pinpoint where exactly inconsistencies occurred.


  Noise-induced phase transitions are common in various complex systems, from
physics to biology. In this article, we investigate the emergence of crucial
events in noise-induced phase transition processes and their potential
significance for understanding complexity in such systems. We utilize the
first-passage time technique and coordinate transformations to study the
dynamics of the system and identify crucial events. Furthermore, we employ
Diffusion Entropy Analysis, a powerful statistical tool, to characterize the
complexity of the system and quantify the information content of the identified
events. Our results show that the emergence of crucial events is closely
related to the complexity of the system and can provide insight into its
behavior. This approach may have applications in diverse fields, such as
climate modeling, financial markets, and biological systems, where
understanding the emergence of crucial events is of great importance.


  We have recently proposed two pile loading controllers that learn from human
demonstrations: a neural network (NNet) [1] and a random forest (RF) controller
[2]. In the field experiments the RF controller obtained clearly better success
rates. In this work, the previous findings are drastically revised by
experimenting summer time trained controllers in winter conditions. The winter
experiments revealed a need for additional sensors, more training data, and a
controller that can take advantage of these. Therefore, we propose a revised
neural controller (NNetV2) which has a more expressive structure and uses a
neural attention mechanism to focus on important parts of the sensor and
control signals. Using the same data and sensors to train and test the three
controllers, NNetV2 achieves better robustness against drastically changing
conditions and superior success rate. To the best of our knowledge, this is the
first work testing a learning-based controller for a heavy-duty machine in
drastically varying outdoor conditions and delivering high success rate in
winter, being trained in summer.


  The aim of this paper is to emphasize various concepts of dichotomies for
evolution equations in Banach spaces, due to the important role they play in
the approach of stable, instable and central manifolds. The asymptotic
properties of the solutions of the evolution equations are studied by means of
the asymptotic behaviors for skew-evolution semiflows.


  We present 36 spectroscopically confirmed intrinsically UV-faint Ly$\alpha$
emitting galaxies from follow-up observations with Keck/DEIMOS of
gravitationally lensed high-redshift candidates. Candidates were selected to be
between $5\lesssim z \lesssim 7$ from photometric data using \textit{HST} and
\textit{Spitzer} imaging surveys. We used photometric redshift information to
perform an integrated photometric redshift probability cut $>1\%$ between
$5<z<7$ to construct a sample of 198 high-redshift objects. Our high-redshift
sample spans intrinsic UV luminosities from a few $L^*$ down to $0.001L^*$. We
identified 19 high-confidence detections of Ly$\alpha$ and an additional 17
likely detections. We divided our sample into lower-redshift ($z\sim5.5$) and
higher-redshift ($z\sim6.5$) bins and ran Monte Carlo trials, incorporating the
strength of the Ly$\alpha$ emission and the photometric redshift of the
non-detections. Considering only objects where Ly$\alpha$ could be detected at
EW(Ly$\alpha$)$>$25{\AA} at $3\sigma$ at the fiducial depth of our survey, and
only those galaxies with EW(Ly$\alpha$)$>$25{\AA} as true LAEs, and finally,
only objects with $m_{AB}<26.8$, we found the LAE fraction to be flat, or
modestly increase from 0.26$\pm0.04$ to 0.30$\pm0.04$. These values relative to
those for lower-redshift samples are consistent with a rising LAE fraction with
redshift out to $z\sim6$, but at $z\sim6.5$ there is some tension between our
results and results from surveys at intrinsically brighter luminosities. We
conclude intrinsically fainter galaxies have Ly$\alpha$ emission, and there is
a steep drop in the LAE fraction from our high-redshift sample at $z\sim6.5$
and from similar galaxies at $z\sim7.5$. This likely indicates we are
witnessing the tail end of the epoch of reionization, as such a drop is not
expected due to changes of intrinsic galaxy properties between these redshifts.


  We present a highly parameter efficient approach for Question Answering that
significantly reduces the need for extended BERT fine-tuning. Our method uses
information from the hidden state activations of each BERT transformer layer,
which is discarded during typical BERT inference. Our best model achieves
maximal BERT performance at a fraction of the training time and GPU or TPU
expense. Performance is further improved by ensembling our model with BERTs
predictions. Furthermore, we find that near optimal performance can be achieved
for QA span annotation using less training data. Our experiments show that this
approach works well not only for span annotation, but also for classification,
suggesting that it may be extensible to a wider range of tasks.


  Prediction without justification has limited utility. Much of the success of
neural models can be attributed to their ability to learn rich, dense and
expressive representations. While these representations capture the underlying
complexity and latent trends in the data, they are far from being
interpretable. We propose a novel variant of denoising k-sparse autoencoders
that generates highly efficient and interpretable distributed word
representations (word embeddings), beginning with existing word representations
from state-of-the-art methods like GloVe and word2vec. Through large scale
human evaluation, we report that our resulting word embedddings are much more
interpretable than the original GloVe and word2vec embeddings. Moreover, our
embeddings outperform existing popular word embeddings on a diverse suite of
benchmark downstream tasks.


  In this paper, a meshless Hermite-HDMR finite difference method is proposed
to solve high-dimensional Dirichlet problems. The approach is based on the
local Hermite-HDMR expansion with an additional smoothing technique. First, we
introduce the HDMR decomposition combined with the multiple Hermite series to
construct a class of Hermite-HDMR approximations, and the relevant error
estimate is theoretically built in a class of Hermite spaces. It can not only
provide high order convergence but also retain good scaling with increasing
dimensions. Then the Hermite-HDMR based finite difference method is
particularly proposed for solving high-dimensional Dirichlet problems. By
applying a smoothing process to the Hermite-HDMR approximations, numerical
stability can be guaranteed even with a small number of nodes. Numerical
experiments in dimensions up to $30$ show that resulting approximations are of
very high quality.


  Explicit expressions for magnetoresistance $R$ of planar and layered strongly
inhomogeneous two-phase systems are obtained, using exact dual transformation,
connecting effective conductivities of in-plane isotropic two-phase systems
with and without magnetic field. These expressions allow to describe the
magnetoresistance of various inhomogeneous media at arbitrary concentrations
$x$ and magnetic fields $H$. All expressions show large linear
magnetoresistance effect with different dependencies on the phase
concentrations. The corresponding plots of the $x$- and $H$-dependencies of
$R(x,H)$ are represented for various values, respectively, of magnetic field
and concentrations at some values of inhomogeneity parameter. The obtained
results show a remarkable similarity with the existing experimental data on
linear magnetoresistance in silver chalcogenides $Ag_{2+\delta}Se.$ A possible
physical explanation of this similarity is proposed. It is shown that the
random, stripe type, structures of inhomogeneities are the most suitable for a
fabrication of magnetic sensors and a storage of information at room
temperatures.


  The conic bundle implementation of the spectral bundle method for large scale
semidefinite programming solves in each iteration a semidefinite quadratic
subproblem by an interior point approach. For larger cutting model sizes the
limiting operation is collecting and factorizing a Schur complement of the
primal-dual KKT system. We explore possibilities to improve on this by an
iterative approach that exploits structural low rank properties. Two
preconditioning approaches are proposed and analyzed. Both might be of interest
for rank structured positive definite systems in general. The first employs
projections onto random subspaces, the second projects onto a subspace that is
chosen deterministically based on structural interior point properties. For
both approaches theoretic bounds are derived for the associated condition
number. In the instances tested the deterministic preconditioner provides
surprisingly efficient control on the actual condition number. The results
suggest that for large scale instances the iterative solver is usually the
better choice if precision requirements are moderate or if the size of the
Schur complemented system clearly exceeds the active dimension within the
subspace giving rise to the cutting model of the bundle method.


  Isolated hydrogen atoms absorbed on graphene are predicted to induce magnetic
moments. Here we demonstrate that the adsorption of a single hydrogen atom on
graphene induces a magnetic moment characterized by a ~20 meV spin-split state
at the Fermi energy. Our scanning tunneling microscopy (STM) experiments,
complemented by first-principles calculations, show that such a spin-polarized
state is essentially localized on the carbon sublattice complementary to the
one where the H atom is chemisorbed. This atomically modulated spin-texture,
which extends several nanometers away from the H atom, drives the direct
coupling between the magnetic moments at unusually long distances. Using the
STM tip to manipulate H atoms with atomic precision, we demonstrate the
possibility to tailor the magnetism of selected graphene regions.


  We developed an experimental platform for studying magnetic reconnection in
an external magnetic field with simultaneous measurements of plasma imaging,
flow velocity, and magnetic-field variation. Here, we investigate the
stagnation and acceleration in counter-streaming plasmas generated by
high-power laser beams. A plasma flow perpendicular to the initial flow
directions is measured with laser Thomson scattering. The flow is,
interestingly, accelerated toward the high-density region, which is opposite to
the direction of the acceleration by pressure gradients. This acceleration is
possibly interpreted by the interaction of two magnetic field loops initially
generated by Biermann battery effect, resulting in a magnetic reconnection
forming a single field loop and additional acceleration by a magnetic tension
force.


  Tracking the players and the ball in team sports is key to analyse the
performance or to enhance the game watching experience with augmented reality.
When the only sources for this data are broadcast videos, sports-field
registration systems are required to estimate the homography and re-project the
ball or the players from the image space to the field space. This paper
describes a new basketball court registration framework in the context of the
MMSports 2022 camera calibration challenge. The method is based on the
estimation by an encoder-decoder network of the positions of keypoints sampled
with perspective-aware constraints. The regression of the basket positions and
heavy data augmentation techniques make the model robust to different arenas.
Ablation studies show the positive effects of our contributions on the
challenge test set. Our method divides the mean squared error by 4.7 compared
to the challenge baseline.


  In scanning electron microscopy, the achievable image quality is often
limited by a maximum feasible acquisition time per dataset. Particularly with
regard to three-dimensional or large field-of-view imaging, a compromise must
be found between a high amount of shot noise, which leads to a low
signal-to-noise ratio, and excessive acquisition times. Assuming a fixed
acquisition time per frame, we compared three different strategies for
algorithm-assisted image acquisition in scanning electron microscopy. We
evaluated (1) raster scanning with a reduced dwell time per pixel followed by a
state-of-the-art Denoising algorithm, (2) raster scanning with a decreased
resolution in conjunction with a state-of-the-art Super Resolution algorithm,
and (3) a sparse scanning approach where a fixed percentage of pixels is
visited by the beam in combination with state-of-the-art inpainting algorithms.
Additionally, we considered increased beam currents for each of the strategies.
The experiments showed that sparse scanning using an appropriate reconstruction
technique was superior to the other strategies.