  We present a color-magnitude diagram (CMD) for a field in the giant tidal
stream of the Andromeda galaxy (M31). These observations, taken with the
Advanced Camera for Surveys on the Hubble Space Telescope, are 50% complete at
V~30 mag, reaching 1 mag below the oldest main-sequence turnoff. Striking
similarities between the stream and a previous spheroid CMD imply they have
very similar age and metallicity distributions, but present something of an
enigma; we speculate on possible interpretations of this result, but note that
none are without problems. Distinct multiple turnoffs, as might be expected
from pulses of star formation caused by interaction with Andromeda, are not
apparent in the stream CMD. Star formation in both fields lasted about 6
billion years, building up to relatively high metallicities and being largely
complete 6 billion years ago. The close similarity of the spheroid and stream
suggests that both may have derived from the same event; it would be worth
exploring to what extent stars in these structures are the remnants of a disk
galaxy that interacted with M31, or even were disrupted from the M31 disk
itself by the interaction.


  A central task in the study of molecular sequence data from present-day
species is the reconstruction of the ancestral relationships. The most
established approach to tree reconstruction is the maximum likelihood (ML)
method. In this method, evolution is described in terms of a discrete-state
continuous-time Markov process on a phylogenetic tree. The substitution rate
matrix, that determines the Markov process, can be estimated using the
expectation maximization (EM) algorithm. Unfortunately, an exhaustive search
for the ML phylogenetic tree is computationally prohibitive for large data
sets. In such situations, the neighbor-joining (NJ) method is frequently used
because of its computational speed. The NJ method reconstructs trees by
clustering neighboring sequences recursively, based on pairwise comparisons
between the sequences. The NJ method can be generalized such that
reconstruction is based on comparisons of subtrees rather than pairwise
distances. In this paper, we present an algorithm for simultaneous substitution
rate estimation and phylogenetic tree reconstruction. The algorithm iterates
between the EM algorithm for estimating substitution rates and the generalized
NJ method for tree reconstruction. Preliminary results of the approach are
encouraging.


  Inflation of cosmic gauge and global strings is investigated by numerically
solving the combined Einstein and field equations. Above some critical
symmetry-breaking scales, the strings undergo inflation along the radial
direction as well as the axial direction at the core. The nonsingular nature of
the spacetimes around supercritical gauge and global strings is discussed and
contrasted to the singular static solutions that have been discussed in the
literature.


  Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in
real-time over several respiratory cycles, forming a comprehensive picture of
tumor movement. We developed and validated a method which uses these
projections to determine the trajectory of and dose to highly mobile tumors
during each fraction of treatment.
  Methods: CBCT images of a respiration phantom were acquired, the trajectory
of which mimicked a lung tumor with high amplitude (up to 2.5 cm) and
hysteresis. A template-matching algorithm was used to identify the location of
a steel BB in each CBCT projection, and a Gaussian probability density function
for the absolute BB position was calculated which best fit the observed
trajectory of the BB in the imager geometry. Two modifications of the
trajectory reconstruction were investigated: first, using respiratory phase
information to refine the trajectory estimation (Phase), and second, using the
Monte Carlo (MC) method to sample the estimated Gaussian tumor position
distribution.
  Results: With all methods, the mean position of the BB was determined with
accuracy better than 0.1 mm, and trajectory errors averaged 3.8$\pm$1.1% of the
marker amplitude. Dosimetric calculations using Phase methods were more
accurate, with mean absolute error less than 0.5%, and with error less than 1%
in the highest-noise trajectory. MC-based trajectories prevent the
over-estimation of dose, but when viewed in an absolute sense, add a small
amount of dosimetric error (<0.1%).
  Conclusions: Marker trajectory and target dose-of-the-day were accurately
calculated using CBCT projections. This technique provides a method to evaluate
highly-mobile tumors using ordinary CBCT data, and could facilitate better
strategies to mitigate or compensate for motion during SBRT.


  We consider the problem of minimizing the sum of two convex functions: one is
differentiable and relatively smooth with respect to a reference convex
function, and the other can be nondifferentiable but simple to optimize. We
investigate a triangle scaling property of the Bregman distance generated by
the reference convex function and present accelerated Bregman proximal gradient
(ABPG) methods that attain an $O(k^{-\gamma})$ convergence rate, where
$\gamma\in(0,2]$ is the triangle scaling exponent (TSE) of the Bregman
distance. For the Euclidean distance, we have $\gamma=2$ and recover the
convergence rate of Nesterov's accelerated gradient methods. For non-Euclidean
Bregman distances, the TSE can be much smaller (say $\gamma\leq 1$), but we
show that a relaxed definition of intrinsic TSE is always equal to 2. We
exploit the intrinsic TSE to develop adaptive ABPG methods that converge much
faster in practice. Although theoretical guarantees on a fast convergence rate
seem to be out of reach in general, our methods obtain empirical $O(k^{-2})$
rates in numerical experiments on several applications and provide posterior
numerical certificates for the fast rates.


  Cryptic crosswords are puzzles that rely not only on general knowledge but
also on the solver's ability to manipulate language on different levels and
deal with various types of wordplay. Previous research suggests that solving
such puzzles is a challenge even for modern NLP models. However, the abilities
of large language models (LLMs) have not yet been tested on this task. In this
paper, we establish the benchmark results for three popular LLMs -- LLaMA2,
Mistral, and ChatGPT -- showing that their performance on this task is still
far from that of humans.


  The over-damped motion of a Brownian particle in an asymmetric, bistable,
fluctuating potential shows noise induced stability: For intermediate
fluctuation rates the mean occupancy of minima with an energy above the
absolute minimum is enhanced. The model works as a detector for potential
fluctuations being not too fast and not too slow. This effect occurs due to the
different time scales in the problem. We present a detailed analysis of this
effect using the exact solution of the Fokker-Planck equation for a simple
model. Further we show that for not too fast fluctuations the system can be
well described by effective rate equations. The results of the rate equations
agree quantitatively with the exact results.


  We discuss Beauville groups whose corresponding Beauville surfaces are either
always strongly real or never strongly real producing several infinite families
of examples.


  The validity of the DFT models implemented by FIREBALL for CNT electronic
device modeling is assessed. The effective masses, band gaps, and transmission
coefficients of semi-conducting, zigzag, (n,0) carbon nanotubes (CNTs)
resulting from the ab initio tight-binding density functional theory (DFT) code
FIREBALL and the empirical, nearest-neighbor pi-bond model are compared for all
semiconducting n values 5 <(=) n <(=) 35. The DFT values for the effective
masses differ from the pi-bond values by +(-) 9% over the range of n values, 17
<(=) n <(=) 29, most important for electronic device applications. Over the
range 13 <(=) n <(=) 35, the DFT bandgaps are less than the empirical bandgaps
by 20-180 meV depending on the functional and the n value. The pi-bond model
gives results that differ signifcantly from the DFT results when the CNT
diameter goes below 1 nm due to the large curvature of the CNT. The pi-bond
model quickly becomes inaccurate away from the bandedges for a (10, 0) CNT, and
it is completely inaccurate for n <(=) 8.


  A lattice calculation is presented for the electrical conductivity of the QCD
plasma with 2+1 dynamical flavours at nonzero temperature. We employ the
conserved lattice current on anisotropic lattices using a tadpole-improved
clover action and study the behaviour of the conductivity over a wide range of
temperatures, both below and above the deconfining transition. The conductivity
is extracted from a spectral-function analysis using the Maximal Entropy Method
and a discussion of its systematics is provided.


  Exoplanets smaller than Neptune are common around red dwarf stars (M dwarfs),
with those that transit their host star constituting the bulk of known
temperate worlds amenable for atmospheric characterization. We analyze the
masses and radii of all known small transiting planets around M dwarfs,
identifying three populations: rocky, water-rich, and gas-rich. Our results are
inconsistent with the previously known bimodal radius distribution arising from
atmospheric loss of a hydrogen/helium envelope. Instead, we propose that a
density gap separates rocky from water-rich exoplanets. Formation models that
include orbital migration can explain the observations: Rocky planets form
within the snow line, whereas water-rich worlds form outside it and later
migrate inward.


  A panchromatic investigation of morphology for the early-type spiral galaxy
M81 is presented in this paper. We perform bulge-disk decomposition in M81
images at a total of 20 wavebands from FUV to NIR obtained with GALEX, Swift,
SDSS, WIYN, 2MASS, WISE, and Spitzer. Morphological parameters such as Sersic
index, effective radius, position angle, and axis ratio for the bulge and the
disk are thus derived at all of the wavebands, which enables quantifying the
morphological K-correction for M81 and makes it possible to reproduce images
for the bulge and the disk in the galaxy at any waveband. The morphology as a
function of wavelength appears as a variable-slope trend of the Sersic index
and the effective radius, in which the variations are steep at UV--optical and
shallow at optical--NIR bands; the position angle and the axis ratio keep
invariable at least at optical--NIR bands. It is worth noting that the Sersic
index for the bulge reaches about 4--5 at optical and NIR bands, but drops to
about 1 at UV bands. This difference brings forward a caveat that a classical
bulge is likely misidentified for a pseudo-bulge or no bulge at high redshifts
where galaxies are observed through rest-frame UV channels with optical
telescopes. The next work of this series is planned to study spatially resolved
SEDs for the bulge and the disk, respectively, and thereby explore stellar
population properties and star formation/quenching history for the the galaxy
composed of the subsystems.


  Mitigating the disparate impact of statistical machine learning methods is
crucial for ensuring fairness. While extensive research aims to reduce
disparity, the effect of using a \emph{finite dataset} -- as opposed to the
entire population -- remains unclear. This paper explores the statistical
foundations of fair binary classification with two protected groups, focusing
on controlling demographic disparity, defined as the difference in acceptance
rates between the groups. Although fairness may come at the cost of accuracy
even with infinite data, we show that using a finite sample incurs additional
costs due to the need to estimate group-specific acceptance thresholds. We
study the minimax optimal classification error while constraining demographic
disparity to a user-specified threshold. To quantify the impact of fairness
constraints, we introduce a novel measure called \emph{fairness-aware excess
risk} and derive a minimax lower bound on this measure that all classifiers
must satisfy. Furthermore, we propose FairBayes-DDP+, a group-wise thresholding
method with an offset that we show attains the minimax lower bound. Our lower
bound proofs involve several innovations. Experiments support that
FairBayes-DDP+ controls disparity at the user-specified level, while being
faster and having a more favorable fairness-accuracy tradeoff than several
baselines.


  We provide a summary of results, obtained from a multiwavelength (TeV
gamma-ray, X-ray, UV, optical, and radio) campaign of observations of AE Aqr
conducted in 2005 August 28-September 2, on the nature and correlation of the
flux variations in the various wavebands, the white dwarf spin evolution, the
properties of the X-ray emission region, and the very low upper limits on the
TeV gamma-ray flux.


  In this paper, we propose an efficient downlink channel reconstruction scheme
for a frequency-division-duplex multi-antenna system by utilizing uplink
channel state information combined with limited feedback. Based on the spatial
reciprocity in a wireless channel, the downlink channel is reconstructed by
using frequency-independent parameters. We first estimate the gains, delays,
and angles during uplink sounding. The gains are then refined through downlink
training and sent back to the base station (BS). With limited overhead, the
refinement can substantially improve the accuracy of the downlink channel
reconstruction. The BS can then reconstruct the downlink channel with the
uplink-estimated delays and angles and the downlink-refined gains. We also
introduce and extend the Newtonized orthogonal matching pursuit (NOMP)
algorithm to detect the delays and gains in a multi-antenna multi-subcarrier
condition. The results of our analysis show that the extended NOMP algorithm
achieves high estimation accuracy. Simulations and over-the-air tests are
performed to assess the performance of the efficient downlink channel
reconstruction scheme. The results show that the reconstructed channel is close
to the practical channel and that the accuracy is enhanced when the number of
BS antennas increases, thereby highlighting that the promising application of
the proposed scheme in large-scale antenna array systems.


  Accurate and consistent construction of point clouds from LiDAR scanning data
is fundamental for 3D modeling applications. Current solutions, such as
multiview point cloud registration and LiDAR bundle adjustment, predominantly
depend on the local plane assumption, which may be inadequate in complex
environments lacking of planar geometries or substantial initial pose errors.
To mitigate this problem, this paper presents a LiDAR bundle adjustment with
progressive spatial smoothing, which is suitable for complex environments and
exhibits improved convergence capabilities. The proposed method consists of a
spatial smoothing module and a pose adjustment module, which combines the
benefits of local consistency and global accuracy. With the spatial smoothing
module, we can obtain robust and rich surface constraints employing smoothing
kernels across various scales. Then the pose adjustment module corrects all
poses utilizing the novel surface constraints. Ultimately, the proposed method
simultaneously achieves fine poses and parametric surfaces that can be directly
employed for high-quality point cloud reconstruction. The effectiveness and
robustness of our proposed approach have been validated on both simulation and
real-world datasets. The experimental results demonstrate that the proposed
method outperforms the existing methods and achieves better accuracy in complex
environments with low planar structures.


  We establish Lieb-Thirring power bounds on discrete eigenvalues of Jacobi
operators for Schatten class perturbations under very general assumptions. Our
results apply, in particular, to perturbations of reflectionless Jacobi
operators with finite gap and Cantor-type essential spectrum.


  We study vortex lattices (VLs) in superconducting weak-pinning platelet-like
crystals of $\beta$-Bi$_{2}$Pd in tilted fields with a Scanning Tunneling
Microscope. We show that vortices exit the sample perpendicular to the surface
and are thus bent beneath the surface. The structure and orientation of tilted
VL in the bulk are, for large tilt angles, strongly affected by Coulomb-type
intervortex repulsion at the surface due to stray fields.


  Stellar ages are a crucial component to studying the evolution of the Milky
Way. Using Gaia DR2 distance estimates, it is now possible to estimate stellar
ages for a larger volume of evolved stars through isochrone matching. This work
presents [M/H]-age and [$\alpha$/M]-age relations derived for different spatial
locations in the Milky Way disc. These relations are derived by hierarchically
modelling the star formation history of stars within a given chemical abundance
bin. For the first time, we directly observe that significant variation is
apparent in the [M/H]-age relation as a function of both Galactocentric radius
and distance from the disc mid-plane. The [M/H]-age relations support claims
that radial migration has a significant effect in the plane of the disc. Using
the [M/H] bin with the youngest mean age at each radial zone in the plane of
the disc, the present-day metallicity gradient is measured to be $-0.059 \pm
0.010$ dex kpc$^{-1}$, in agreement with Cepheids and young field stars. We
find a vertically flared distribution of young stars in the outer disc,
confirming predictions of models and previous observations. The mean age of the
[M/H]-[$\alpha$/M] distribution of the solar neighborhood suggests that the
high-[M/H] stars are not an evolutionary extension of the low-$\alpha$
sequence. Our observational results are important constraints to Galactic
simulations and models of chemical evolution.


  In this paper, we propose a novel framework for multi-image co-segmentation
using class agnostic meta-learning strategy by generalizing to new classes
given only a small number of training samples for each new class. We have
developed a novel encoder-decoder network termed as DVICE (Directed Variational
Inference Cross Encoder), which learns a continuous embedding space to ensure
better similarity learning. We employ a combination of the proposed DVICE
network and a novel few-shot learning approach to tackle the small sample size
problem encountered in co-segmentation with small datasets like iCoseg and
MSRC. Furthermore, the proposed framework does not use any semantic class
labels and is entirely class agnostic. Through exhaustive experimentation over
multiple datasets using only a small volume of training data, we have
demonstrated that our approach outperforms all existing state-of-the-art
techniques.


  The $q$-Painlev\'e equation of type $E^{(1)}_6$ is obtained by Pad\'e method.
Special solutions in determinant formula to the $q$-Painlev\'e equation is
presented. A relation between Pad\'e method and B\"acklund transformation of
type $E^{(1)}_6$ is given.


  Our dictionary-based lemmatizer for the Bulgarian language presented here is
distributed as free software, publicly available to download and use under the
GPL v3 license. The presented software is written entirely in Java and is
distributed as a GATE plugin. To our best knowledge, at the time of writing
this article, there are not any other free lemmatization tools specifically
targeting the Bulgarian language. The presented lemmatizer is a work in
progress and currently yields an accuracy of about 95% in comparison to the
manually annotated corpus BulTreeBank-Morph, which contains 273933 tokens.


  We consider probabilistic topic models and more recent word embedding
techniques from a perspective of learning hidden semantic representations.
Inspired by a striking similarity of the two approaches, we merge them and
learn probabilistic embeddings with online EM-algorithm on word co-occurrence
data. The resulting embeddings perform on par with Skip-Gram Negative Sampling
(SGNS) on word similarity tasks and benefit in the interpretability of the
components. Next, we learn probabilistic document embeddings that outperform
paragraph2vec on a document similarity task and require less memory and time
for training. Finally, we employ multimodal Additive Regularization of Topic
Models (ARTM) to obtain a high sparsity and learn embeddings for other
modalities, such as timestamps and categories. We observe further improvement
of word similarity performance and meaningful inter-modality similarities.


  The gravitational waves emitted by neutron stars carry unique information
about their structure and composition. Direct detection of these gravitational
waves, however, is a formidable technical challenge. In a recent study we
quantified the hurdles facing searches for gravitational waves from the known
accreting neutron stars, given the level of uncertainty that exists regarding
spin and orbital parameters. In this paper we reflect on our conclusions, and
issue an open challenge to the theoretical community to consider how searches
should be designed to yield the most astrophysically interesting upper limits.
With this in mind we examine some more optimistic emission scenarios involving
spin-down, and show that there are technically feasible searches, particularly
for the accreting millisecond pulsars, that might place meaningful constraints
on torque mechanisms. We finish with a brief discussion of prospects for
indirect detection.


  The DIRC, a novel type of Cherenkov ring imaging device, is the primary
hadronic particle identification system for the BABAR detector at the
asymmetric B-factory, PEP-II at SLAC. BABAR began taking data with colliding
beams mode in late spring 1999. This paper describes the performance of the
DIRC during the first 16 months of operation.


  The Wright-Fisher model and the Moran model are both widely used in
population genetics. They describe the time evolution of the frequency of an
allele in a well-mixed population with fixed size. We propose a simple and
tractable model which bridges the Wright-Fisher and the Moran descriptions. We
assume that a fixed fraction of the population is updated at each discrete time
step. In this model, we determine the fixation probability of a mutant in the
diffusion approximation, as well as the effective population size. We
generalize our model, first by taking into account fluctuating updated
fractions or individual lifetimes, and then by incorporating selection on the
lifetime as well as on the reproductive fitness.


  Efficient sub-Doppler laser cooling and optical trapping of YO molecules
offer new opportunities to study collisional dynamics in the quantum regime.
Confined in a crossed optical dipole trap, we achieve the highest phase-space
density of $2.5 \times 10^{-5}$ for a bulk laser-cooled molecular sample. This
sets the stage to study YO-YO collisions in the microkelvin temperature regime,
and reveal state-dependent, single-partial-wave two-body collisional loss
rates. We determine the partial-wave contributions to specific rotational
states (first excited $N=1$ and ground $N=0$) following two strategies. First,
we measure the change of the collision rate in a spin mixture of $N=1$ by
tuning the kinetic energy with respect to the p- and d-wave centrifugal
barriers. Second, we compare loss rates between a spin mixture and a
spin-polarized state in $N=0$. Using quantum defect theory with a partially
absorbing boundary condition at short range, we show that the dependence on
temperature for $N=1$ can be reproduced in the presence of a d-wave or f-wave
resonance, and the dependence on a spin mixture for $N=0$ with a p-wave
resonance.


  This paper presents recent results obtained by the authors (partly in
collaboration with A. Dabrowska) concerning expansions of zonal functions on
Euclidean spheres into spherical harmonics and some applications of such
expansions for problems involving Fourier transforms of functions with
rotational symmetry. The method used to derive the expansion formula is based
entirely on differential methods and completely avoids the use of various
integral identities commonly used in this context. Some new identities for the
Fourier transform are derived and as a byproduct seemingly new recurrence
relations for the classical Bessel functions are obtained.


  We have examined a solution to the FRW model of the Einstein and de Sitter
Universe, often termed the standard model of cosmology, using wide values for
the normalized cosmological constant Omega_L and spacetime curvature Omega_k
with proposed values of normalized matter density. These solutions were
evaluated using a combination of the third type of elliptical equations and
were found to display critical points for redshift z, between 1 and 3, when
Omega_L is positive. These critical points occur at values for normalized
cosmological constant higher than those currently thought important, though we
find this solution interesting because the Omega_L term may increase in
dominance as the Universe evolves bringing this discontinuity into importance.
We also find positive Omega_L tends towards attractive at values of z which are
commonly observed for distant galaxies.


  As part of our program to study stellar photospheric and chromospheric
activity, we have examined several young solar type stars with activity levels
intermediate between the Sun and the very active RS CVn binaries. We have
analysed contemporaneous spectroscopic data obtained at Catania Observatory
(Serra La Nave station, Mt. Etna) and photometric data acquired in the
Stromgren bands with an automatic photometric telescope (APT) at Fairborn
Observatory (Arizona, USA). Surface inhomogeneities have been detected from the
rotational modulation of stellar brightness as well as from the modulation of
several photospheric line-depth ratios (LDRs). The presence of chromospheric
plage-like regions has been inferred from the rotational modulation of the
Halpha line equivalent width (EW_Halpha) evaluated with the spectral synthesis
method. The most relevant results are the strong correlation between the
brightness and temperature curves derived respectively from photometry and the
LDRs as well as the striking anti-correlation between brightness and Halpha
emission. This suggests a close spatial association of spots and plages, as
frequently observed for the largest sunspot groups (e.g., Catalano et al.
(1998)) and for some very active RS CVn systems (Catalano et al. 2002).
Moreover, a simple spot/plage model applied to the observed flux curves allows
a rough reconstruction of photospheric and chromospheric features of young main
sequence stars.


  Neuroimaging datasets are rapidly growing in size as a result of advancements
in image acquisition methods, open-science and data sharing. However, the
adoption of Big Data processing strategies by neuroimaging processing engines
remains limited. Here, we evaluate three Big Data processing strategies
(in-memory computing, data locality and lazy evaluation) on typical
neuroimaging use cases, represented by the BigBrain dataset. We contrast these
various strategies using Apache Spark and Nipype as our representative Big Data
and neuroimaging processing engines, on Dell EMC's Top-500 cluster. Big Data
thresholds were modelled by comparing the data-write rate of the application to
the filesystem bandwidth and number of concurrent processes. This model
acknowledges the fact that page caching provided by the Linux kernel is
critical to the performance of Big Data applications. Results show that
in-memory computing alone speeds-up executions by a factor of up to 1.6,
whereas when combined with data locality, this factor reaches 5.3. Lazy
evaluation strategies were found to increase the likelihood of cache hits,
further improving processing time. Such important speed-up values are likely to
be observed on typical image processing operations performed on images of size
larger than 75GB. A ballpark speculation from our model showed that in-memory
computing alone will not speed-up current functional MRI analyses unless
coupled with data locality and processing around 280 subjects concurrently.
Furthermore, we observe that emulating in-memory computing using in-memory file
systems (tmpfs) does not reach the performance of an in-memory engine,
presumably due to swapping to disk and the lack of data cleanup. We conclude
that Big Data processing strategies are worth developing for neuroimaging
applications.


  Statistical distance measures have found wide applicability in information
retrieval tasks that typically involve high dimensional datasets. In order to
reduce the storage space and ensure efficient performance of queries,
dimensionality reduction while preserving the inter-point similarity is highly
desirable. In this paper, we investigate various statistical distance measures
from the point of view of discovering low distortion embeddings into
low-dimensional spaces. More specifically, we consider the Mahalanobis distance
measure, the Bhattacharyya class of divergences and the Kullback-Leibler
divergence. We present a dimensionality reduction method based on the
Johnson-Lindenstrauss Lemma for the Mahalanobis measure that achieves
arbitrarily low distortion. By using the Johnson-Lindenstrauss Lemma again, we
further demonstrate that the Bhattacharyya distance admits dimensionality
reduction with arbitrarily low additive error. We also examine the question of
embeddability into metric spaces for these distance measures due to the
availability of efficient indexing schemes on metric spaces. We provide
explicit constructions of point sets under the Bhattacharyya and the
Kullback-Leibler divergences whose embeddings into any metric space incur
arbitrarily large distortions. We show that the lower bound presented for
Bhattacharyya distance is nearly tight by providing an embedding that
approaches the lower bound for relatively small dimensional datasets.


  As a fundamental problem in computer vision, 3D point cloud registration
(PCR) aims to seek the optimal pose to align a point cloud pair. In this paper,
we present a 3D registration method with maximal cliques (MAC). The key insight
is to loosen the previous maximum clique constraint, and mine more local
consensus information in a graph for accurate pose hypotheses generation: 1) A
compatibility graph is constructed to render the affinity relationship between
initial correspondences. 2) We search for maximal cliques in the graph, each of
which represents a consensus set. We perform node-guided clique selection then,
where each node corresponds to the maximal clique with the greatest graph
weight. 3) Transformation hypotheses are computed for the selected cliques by
the SVD algorithm and the best hypothesis is used to perform registration.
Extensive experiments on U3M, 3DMatch, 3DLoMatch and KITTI demonstrate that MAC
effectively increases registration accuracy, outperforms various
state-of-the-art methods and boosts the performance of deep-learned methods.
MAC combined with deep-learned methods achieves state-of-the-art registration
recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch.


  Particle acceleration is one of the most significant features that are
ubiquitous among space and cosmic plasmas. It is most prominent during flares
in the case of the Sun, with which huge amount of electromagnetic radiation and
high-energy particles are expelled into the interplanetary space through
acceleration of plasma particles in the corona. Though it has been well
understood that energies of flares are supplied by the mechanism called
magnetic reconnection based on the observations in X-rays and EUV with space
telescopes, where and how in the flaring magnetic field plasmas are accelerated
has remained unknown due to the low plasma density in the flaring corona. We
here report the first observational identification of the energetic non-thermal
electrons around the point of the ongoing magnetic reconnection (X-point); with
the location of the X-point identified by soft X-ray imagery and the localized
presence of non-thermal electrons identified from imaging-spectroscopic data at
two microwave frequencies. Considering the existence of the reconnection
outflows that carries both plasma particles and magnetic fields out from the
X-point, our identified non-thermal microwave emissions around the X-point
indicate that the electrons are accelerated around the reconnection X-point.
Additionally, the plasma around the X-point was also thermally heated up to 10
MK. The estimated reconnection rate of this event is ~0.017.


  We propose a new method for measuring CP violation in neutrino oscillation
experiments. The idea is to isolate the term due to the CP-violating phase out
of the oscillation probability by taking difference between yields of two (or
three) detectors at path-lengths $L = 250 (\frac{E}{1.35 {GeV}}) (\frac{\Delta
m^2}{10^{-2}{eV}^2})^{-1} {km}$ and at $L/3$ (and also at $2L/3$ in the case of
three detectors). We use possible hierarchies in neutrino masses suggested by
the astrophysical and the cosmological observations to motivate the idea and to
examine how the method works.


  A closed expression is obtained for the cross-section for Coulomb excitation
of levels of the giant dipole resonance of given angular momentum and phonon
number. Applications are made to the Goldhaber-Teller and Steinwedel-Jensen
descriptions of the resonance, at non-relativistic and relativistic bombarding
energies.


  In this paper, we investigate the P\'olya-Knopp type inequality for Sugeno
integrals in two cases. In the first case, we suppose that the inner integral
is the standard Riemann integral and the remaining two integrals are of Sugeno
type. In the second case, all involved integrals are Sugeno integral. We
present several examples illustrating the validity of our theorems. Finally, we
prove a Hardy-Knopp type inequality for Sugeno integral.


  Identifying a low-dimensional informed parameter subspace offers a viable
path to alleviating the dimensionality challenge in the sampled-based solution
to large-scale Bayesian inverse problems. This paper introduces a novel
gradient-based dimension reduction method in which the informed subspace does
not depend on the data. This permits an online-offline computational strategy
where the expensive low-dimensional structure of the problem is detected in an
offline phase, meaning before observing the data. This strategy is particularly
relevant for multiple inversion problems as the same informed subspace can be
reused. The proposed approach allows controlling the approximation error (in
expectation over the data) of the posterior distribution. We also present
sampling strategies that exploit the informed subspace to draw efficiently
samples from the exact posterior distribution. The method is successfully
illustrated on two numerical examples: a PDE-based inverse problem with a
Gaussian process prior and a tomography problem with Poisson data and a
Besov-$\mathcal{B}^2_{11}$ prior.


  Human teams can be exceptionally efficient at adapting and collaborating
during manipulation tasks using shared mental models. However, the same shared
mental models that can be used by humans to perform robust low-level force and
motion control during collaborative manipulation tasks are non-existent for
robots. For robots to perform collaborative tasks with people naturally and
efficiently, understanding and predicting human intent is necessary. However,
humans are difficult to predict and model. We have completed an exploratory
study recording motion and force for 20 human dyads moving an object in tandem
in order to better understand how they move and how their movement can be
predicted. In this paper, we show how past motion data can be used to predict
human intent. In order to predict human intent, which we equate with the human
team's velocity for a short time horizon, we used a neural network. Using the
previous 150 time steps at a rate of 200 Hz, human intent can be predicted for
the next 50 time steps with a mean squared error of 0.02 (m/s)^2. We also show
that human intent can be estimated in a human-robot dyad. This work is an
important first step in enabling future work of integrating human intent
estimation on a robot controller to execute a short-term collaborative
trajectory.


  Verlinde's theory of Emergent Gravity (EG) describes gravity as an emergent
phenomenon rather than a fundamental force. Applying this reasoning in de
Sitter space leads to gravity behaving differently on galaxy and galaxy cluster
scales; this excess gravity might offer an alternative to dark matter. Here we
test these ideas using the data from the Coma cluster and from 58 stacked
galaxy clusters. The X-ray surface brightness measurements of the clusters at
$0.1 < z < 1.2$ along with the weak lensing data are used to test the theory.
We find that the simultaneous EG fits of the X-ray and weak lensing datasets
are significantly worse than those provided by General Relativity (with cold
dark matter). For the Coma cluster, the predictions from Emergent Gravity and
General Relativity agree in the range of 250 - 700 kpc, while at around 1 Mpc
scales, EG total mass predictions are larger by a factor of 2. For the cluster
stack the predictions are only in good agreement at around the 1 - 2 Mpc
scales, while for $r \gtrsim 10$ Mpc EG is in strong tension with the data.
According to the Bayesian information criterion analysis, GR is preferred in
all tested datasets; however, we also discuss possible modifications of EG that
greatly relax the tension with the data.


  Phase-space techniques are generalized to nonlinear quantum electrodynamics
beyond the rotating wave approximation, resulting in an essentially classical
picture of radiation dynamics.


  We present a unified framework for studying Coulomb interactions in arbitrary
environments using macroscopic quantum electrodynamics on the basis of the
electromagnetic Green's function. Our theory can be used to derive the Coulomb
potential of a single charged particle as well as that between two charges in
the presence of media, bodies and interfaces of arbitrary shapes. To
demonstrate this, we reproduce the well-known screened Coulomb force, account
for local-field effects and consider new cases such as a dielectric cavity and
a conducting plate with a hole.


  Vyper has been proposed as a new high-level language for Ethereum smart
contract development due to numerous security vulnerabilities and attacks
witnessed on contracts written in Solidity since the system's inception. Vyper
aims to address these vulnerabilities by providing a language that focuses on
simplicity, auditability and security. We present a survey where we study how
well-known and commonly-encountered vulnerabilities in Solidity feature in
Vyper's development environment. We analyze all such vulnerabilities
individually and classify them into five groups based on their status in Vyper.
To the best of our knowledge, our survey is the first attempt to study security
vulnerabilities in Vyper.


  The one-loop nuclear structure corrections of order (Z alpha)^5 to the Lamb
shift and hyperfine splitting of the deuterium are calculated. The contribution
of the deuteron structure effects to the isotope shift (ep)-(ed), (mu p)-(mu d)
in the interval (1S - 2S) is obtained on the basis of modern experimental data
on the deuteron electromagnetic form factors. The comparison with the similar
contributions to the Lamb shift for electronic and muonic hydrogen shows, that
the relative contribution due to the nucleus structure increases when passing
from the hydrogen to the deuterium.


  We constuct a family of hemisystems of the parabolic quadric $\mathcal{Q}(2d,
q)$, for all ranks $d \ge 2$ and all odd prime powers $q$, that admit
$\Omega_3(q) \cong \mathrm{PSL}_2(q)$. This yields the first known construction
for $d \ge 4$.


  The performance of various superconducting devices operating at ultra-low
temperatures is impaired by the presence of non-equilibrium quasiparticles.
Inelastic quasiparticle (QP) tunneling across Josephson junctions in
superconducting qubits results in decoherence and spurious excitations and,
notably, can trigger correlated errors that severely impede quantum error
correction. In this work, we use "gap engineering" to suppress the tunneling of
low-energy quasiparticles in Al-based transmon qubits, a leading building block
for superconducting quantum processors. By implementing potential barriers for
QP, we strongly suppress QP tunneling across the junction and preserve charge
parity for over $10^3$ seconds. The suppression of QP tunneling also results in
a reduction in the qubit energy relaxation rates. The demonstrated approach to
gap engineering can be easily implemented in all Al-based circuits with
Josephson junctions.


  This paper looks at the increasing popularity of massive open and online
courses (MOOCs) and open educational resources (OERs) offered in Singapore.
Despite being a relatively new phenomenon, the Singapore government has
collaborated with different organizations to improve the quality and
accessibility of MOOCs, and many institutions of higher learning (IHLs) are
spearheading efforts to improve OERs to facilitate greater public access to
educational resources. It will also explore the benefits and potential problems
that MOOCs and OERs face. For example, both MOOCs and OERs are able to lower
the costs of university-level education and increase public access to such
courses. They also provide skills and job training for members of the public as
well as encourage lifelong learning. However, both MOOCs and OERs may not be
sustainable in the long run, as the financial gains of both may not be able to
cover the costs of mounting them. Each system also has its own set of problems.
For example, formal structures to guarantee the quality of MOOCs offered remain
lacking. MOOCs also tend to have low completion rates and there have been
issues regarding plagiarism with the use of MOOCs as learning platforms. OERs
pose challenges to traditional copyright policies while lack of sustainable
funding prevents them from being adopted more widely. Even though both systems
may potentially transform the traditional education system, a deeper
understanding of MOOCs and OERs as well as their implications on learning will
be useful.


  Using a nominally symmetric annular combustor, we present experimental
evidence of a predicted spontaneous symmetry breaking and an unexpected
explicit symmetry breaking in the neighborhood of the Hopf bifurcation, which
separates linearly-stable azimuthal thermoacoustic modes from self-oscillating
modes. We derive and solve a multidimensional Fokker-Planck equation to unravel
a unified picture of the phase space topology. We demonstrate that symmetric
probability density functions of the thermoacoustic state vector are elusive,
because the effect of asymmetries, even imperceptible ones, is magnified close
to the bifurcation.


  Reliable evaluations of geotechnical hazards like landslides and debris flow
require accurate simulation of granular flow dynamics. Traditional numerical
methods can simulate the complex behaviors of such flows that involve
solid-like to fluid-like transitions, but they are computationally intractable
when simulating large-scale systems. Surrogate models based on statistical or
machine learning methods are a viable alternative, but they are typically
empirical and rely on a confined set of parameters in evaluating associated
risks. Due to their permutation-dependent learning, conventional machine
learning models require an unreasonably large amount of training data for
building generalizable surrogate models. We employ a graph neural network
(GNN), a novel deep learning technique, to develop a GNN-based simulator (GNS)
for granular flows to address these issues. Graphs represent the state of
granular flows and interactions, like the exchange of energy and momentum
between grains, and GNN learns the local interaction law. GNS takes the current
state of the granular flow and estimates the next state using Euler explicit
integration. We train GNS on a limited set of granular flow trajectories and
evaluate its performance in a three-dimensional granular column collapse
domain. GNS successfully reproduces the overall behaviors of column collapses
with various aspect ratios that were not encountered during training. The
computation speed of GNS outperforms high-fidelity numerical simulators by 300
times.


  Epitaxial semiconducting NiYBi thin films were directly prepared on MgO(100)
substrates by magnetron sputtering. The intensity ratio of the (200) and (400)
diffraction peaks, I(200)/I(400) = 2.93, was close to the theoretical value
(3.03). The electronic structure of NiYBi was calculated using WIEN2k and a
narrow indirect band gap of width 210 meV was found. The valence band spectra
of the films obtained by linear dichroism in hard X-ray photoelectron
spectroscopy exhibit clear structures that are in good agreement with the
calculated band structure of NiYBi.


  Traffic Engineering (TE) is critical for improving network performance and
reliability. A key challenge in TE is the management of sudden traffic bursts.
Existing TE schemes often struggle to accurately determine the extent of focus
required for these surges, thereby facing difficulties in achieving a balance
between performance under normal and peak traffic conditions. To address this
issue, we introduce FIGRET, a Fine-Grained Robustness-Enhanced TE Scheme.
FIGRET offers a novel approach to TE by providing varying levels of robustness
enhancements, customized according to the distinct traffic characteristics of
various source-destination pairs. By leveraging a sophisticated loss function
and advanced deep learning techniques, FIGRET is capable of generating
high-quality TE solutions efficiently. Our evaluations of real-world production
networks, including Wide Area Networks and data centers, demonstrate that
FIGRET significantly outperforms existing TE schemes. Compared to the TE scheme
currently deployed in the Jupiter network of Google, FIGRET achieves a 9\%-34\%
reduction in average Maximum Link Utilization and improves solution speed by
$35\times$-$1800 \times$. Against DOTE, a state-of-the-art deep learning-based
TE method, FIGRET substantially lowers the occurrence of significant congestion
events triggered by traffic bursts by 41\%-53.9\% in topologies characterized
by high traffic dynamics.


  We study the dynamics induced by an $m$-linear operator. We answer a question
of B\`es and Conejero showing an example of an $m$-linear hypercyclic operator
acting on a Banach space. Moreover we prove the existence of $m$-linear
hypercyclic operators on arbitrary infinite dimensional separable Banach
spaces. We also prove an existence result about symmetric bihypercyclic
bilinear operators, answering a question by Grosse-Erdman and Kim.


  Recent analyses of the gamma-ray spectrum from the ultra-luminous infrared
galaxy Arp 220 have revealed a discrepancy in the cosmic ray energy injection
rates derived from the gamma-rays versus the radio emission. While the observed
radio emission is consistent with the star formation rate inferred from
infrared observations, a significantly higher cosmic ray population is
necessary to accurately model the measured gamma-ray flux. To resolve this
discrepancy between the radio and gamma-ray observations, we find that we must
increase the cosmic ray energy injection rate and account for an infrared
optical depth greater than unity. Raising the energy injection rate naturally
raises the total gamma-ray flux but also raises the radio flux unless there is
also an increase in the energy loss rate for cosmic ray leptons. A optically
thick medium results in an increase in energy losses via inverse Compton for
cosmic ray leptons and preserves agreement with submillimeter, millimeter, and
infrared wavelength observations.


  This work studies the uplink of grant-free low data-rate massive
machine-to-machine communications (mMTC) where devices are only active
sporadically, which requires a joint activity and data detection at the
receiver. We develop an adaptive decision feedback detector along with an
$l_0$-norm regularized activity-aware recursive least-squares algorithm that
only require pilot symbols. An iterative detection and decoding scheme based on
low-density parity-check (LDPC) is also devised for signal detection in mMTC.
Simulations show the performance of the proposed approaches against existing
schemes.


  The planned luminosity increase at the Large Hadron Collider in the coming
years has triggered interest in the use of the particles' time of arrival as
additional information in specialized detectors to mitigate the impact of
pile-up. The required time resolution is of the order of tens of picoseconds,
with a spatial granularity of the order of 1 mm. A time measurement at this
precision level will also be of interest beyond the LHC and beyond high energy
particle physics. We present in this paper the first developments towards a
radiation hard Depleted Monolithic Active Pixel Sensor (DMAPS), with
high-resolution time measurement capability. The technology chosen is a
standard high voltage CMOS process, in conjunction with a high resistivity
detector material, which has already proven to efficiently detect particles in
tracking applications after several hundred of Mrad of irradiation.


  Adopting the omni-Lie algebroid approach to Dirac-Jacobi structures, we
propose and investigate a notion of weak dual pairs in Dirac-Jacobi geometry.
Their main motivating examples arise from the theory of multiplicative
precontact structures on Lie groupoids. Among other properties of weak dual
pairs, we prove two main results. 1) We show that the property of fitting in a
weak dual pair defines an equivalence relation for Dirac-Jacobi manifolds. So,
in particular, we get the existence of self-dual pairs and this immediately
leads to an alternative proof of the normal form theorem around Dirac-Jacobi
transversals. 2) We prove the characteristic leaf correspondence theorem for
weak dual pairs paralleling and extending analogous results for symplectic and
contact dual pairs. Moreover, the same ideas of this proof apply to get a
presymplectic leaf correspondence for weak dual pairs in Dirac geometry (not
yet present in literature).


  We further investigate the relations between the large degree asymptotics of
the number of real zeros of random trigonometric polynomials with dependent
coefficients and the underlying correlation function. We consider trigonometric
polynomials of the form \[ f_n(t):= \frac{1}{\sqrt{n}}\sum_{k=1}^{n}a_k
\cos(kt)+b_k\sin(kt), ~x\in [0,2\pi], \] where the sequences $(a_k)_{k\geq 1}$
and $(b_k)_{k\geq 1}$ are two independent copies of a stationary Gaussian
process centered with variance one and correlation function $\rho$ with
associated spectral measure $\mu_{\rho}$. We focus here on the case where
$\mu_{\rho}$ is not purely singular and we denote by $\psi_{\rho}$ its density
component with respect to the Lebesgue measure $\lambda$. Quite surprisingly,
we show that the asymptotics of the number of real zeros
$\mathcal{N}(f_n,[0,2\pi])$ of $f_n$ in $[0,2\pi]$ is not related to the decay
of the correlation function $\rho$ but instead to the Lebesgue measure of the
vanishing locus of $\psi_{\rho}$. Namely, assuming that $\psi_{\rho}$ is
$\mathcal{C}^1$ with H\"older derivative on an open set of full measure, one
establishes that \[ \lim_{n \to +\infty} \frac{\mathbb
E\left[\mathcal{N}(f_n,[0,2\pi])\right]}{n}=
\frac{\lambda(\{\psi_{\rho}=0\})}{\pi \sqrt{2}} + \frac{2\pi -
\lambda(\{\psi_{\rho}=0\})}{\pi\sqrt{3}}. \] On the other hand, assuming a sole
log-integrability condition on $\psi_{\rho}$, which implies that it is positive
almost everywhere, we recover the asymptotics of the independent case, i.e. the
limit is $\frac{2}{\sqrt{3}}$. Besides, with further assumptions of regularity
and existence of negative moment for $\psi_{\rho}$, we moreover show that the
above convergence in expectation can be strengthened to an almost sure
convergence.


  Recently, the method of off-resonant modulated driving (ORMD) with a special
category of synthetic analytical pulses has improved the experimental
performance of two- and multi-qubit gates and aroused many interests for
further investigations. It particularly offers a helpful tool to the cold atom
qubit platform and works well with the Rydberg dipole-dipole interaction. In
order to explore more possibilities and wider ranges of options in constructing
fast-speed and high-fidelity quantum logic gates, we design and analyze the
entangling quantum gates via the almost-resonant modulated driving (ARMD)
method. Apart from the apparent distinctions in resonance conditions, the ARMD
gate protocols also have its different mechanisms in quantum physics compared
with ORMD gate protocols. ARMD gates usually have abrupt phase changes and at
certain points during the time evolution. In other words, whilst the modulation
forms the key concept of high-fidelity Rydberg blockade gates, the on-off
resonance condition can lead to nontrivial nuances in the styles of dynamics.
From a more fundamental point of view, the ORMD and the ARMD methods all
together belong to the unitary operation family of fast modulated driving with
respect to precisely characterized inter-qubit interactions, which usually
allows the quantum logic gate to concludes within one continuous pulse.


  Traditional approaches to urban income segregation focus on static
residential patterns, often failing to capture the dynamic nature of social
mixing at the neighborhood level. Leveraging high-resolution location-based
data from mobile phones, we capture the interplay of three different income
groups (high, medium, low) based on their daily routines. We propose a
three-dimensional space to analyze social mixing, which is embedded in the
temporal dynamics of urban activities. This framework offers a more detailed
perspective on social interactions, closely linked to the geographical features
of each neighborhood. While residential areas fail to encourage social mixing
in the nighttime, the working hours foster inclusion, with the city center
showing a heightened level of interaction. As evening sets in, leisure areas
emerge as potential facilitators for social interactions, depending on urban
features such as public transport and a variety of Points Of Interest. These
characteristics significantly modulate the magnitude and type of social
stratification involved in social mixing, also underscoring the significance of
urban design in either bridging or widening socio-economic divides.


  Convolutional neural networks have been shown to develop internal
representations, which correspond closely to semantically meaningful objects
and parts, although trained solely on class labels. Class Activation Mapping
(CAM) is a recent method that makes it possible to easily highlight the image
regions contributing to a network's classification decision. We build upon
these two developments to enable a network to re-examine informative image
regions, which we term introspection. We propose a weakly-supervised iterative
scheme, which shifts its center of attention to increasingly discriminative
regions as it progresses, by alternating stages of classification and
introspection. We evaluate our method and show its effectiveness over a range
of several datasets, where we obtain competitive or state-of-the-art results:
on Stanford-40 Actions, we set a new state-of the art of 81.74%. On
FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements
over baselines, some of which include significantly more supervision.


  In this paper, we generalize the completely monotone conjecture from Shannon
entropy to the R\'enyi entropy. We confirm this conjecture for the order of
derivative up to $3$, when the order of R\'enyi entropy is in certain regimes.
We also investigate concavity of R\'enyi entropy power and the completely
monotone conjecture for Tsallis entropy. We observe that the completely
monotone conjecture is true for Tsallis entropy of order $2$. Our proofs in
this paper are based on the techniques of integration-by-parts and
sum-of-squares.


  The social recommender system that supports the creation of new relations
between users in the multimedia sharing system is presented in the paper. To
generate suggestions the new concept of the multirelational social network was
introduced. It covers both direct as well as object-based relationships that
reflect social and semantic links between users. The main goal of the new
method is to create the personalized suggestions that are continuously adapted
to users' needs depending on the personal weights assigned to each layer from
the social network. The conducted experiments confirmed the usefulness of the
proposed model.


  The potential habitability of a terrestrial planet is usually defined by the
possible existence of liquid water on its surface. The potential presence of
liquid water depends on many factors such as, most importantly, surface
temperatures. The properties of the planetary atmosphere and its interaction
with the radiative energy provided by the planet's host star are thereby of
decisive importance. In this study we investigate the influence of different
main-sequence stars upon the climate of Earth-like extrasolar planets and their
potential habitability by applying a 3D Earth climate model accounting for
local and dynamical processes. The calculations have been performed for planets
with Earth-like atmospheres at orbital distances where the total amount of
energy received from the various host stars equals the solar constant. In
contrast to previous 3D modeling studies, we include the effect of ozone
radiative heating upon the vertical temperature structure of the atmospheres.
The global orbital mean results obtained have been compared to those of a 1D
radiative convective climate model. The different stellar spectral energy
distributions lead to different surface temperatures and due to ozone heating
to very different vertical temperature structures. As previous 1D studies we
find higher surface temperatures for the Earth-like planet around the K-type
star, and lower temperatures for the planet around the F-type star compared to
an Earth-like planet around the Sun. However, this effect is more pronounced in
the 3D model results than in the 1D model because the 3D model accounts for
feedback processes such as the ice-albedo and the water vapor feedback. Whether
the 1D model may approximate the global mean of the 3D model results strongly
depends on the choice of the relative humidity profile in the 1D model, which
is used to determine the water vapor profile.


  Our current understanding of the structure of nuclei with up to A=8,
including energy spectra, electromagnetic form factors, and weak transitions,
is reviewed within the context of a realistic approach to nuclear dynamics
based on two- and three- nucleon interactions and associated electro-weak
currents. Low-energy radiative and weak capture reactions of astrophysical
relevance involving these light systems are also discussed.


  WTe$_2$ is a material with rich topological properties: it is a 2D
topological insulator as a monolayer and a Weyl-semimetal and higher-order
topological insulator (HOTI) in the bulk form. Inducing superconductivity in
topological materials is a way to obtain topological superconductivity, which
lays at the foundation for many proposals of fault tolerant quantum computing.
Here, we demonstrate the emergence of superconductivity at the interface
between WTe$_2$ and the normal metal palladium. The superconductivity has a
critical temperature of about 1.2 K. By studying the superconductivity in
perpendicular magnetic field, we obtain the coherence length and the London
penetration depth. These parameters correspond to a low Fermi velocity and a
high density of states at the Fermi level. This hints to a possible origin of
superconductivity due to the formation of flat bands. Furthermore, the critical
in-plane magnetic field exceeds the Pauli limit, suggesting a non-trivial
nature of the superconducting state.


  Exact conditions on the clock parameters corresponding to the minimal
uncertainty in distance measurement are derived in uniform manner for any
number of space time dimensions. The result espouses the holography principle
no matter what the number of space time dimensions is. In this context the ADD
braneworld model is considered. Some remarks are made on deviation of
holography as well as of special relativity at the scales provided by the
cosmological constant. We also comment on the potential influence of the
background radiation on the uncertainty in length measurement. The presence of
unavoidable quantum uncertainty in length measurement results in fluctuations
of the black hole thermodynamics that can be interested to address the
information loss problem. The quantum corrections to the black hole entropy
obtained in various scenarios are imperceptible because of these fluctuations.
At the Planck scale the fluctuations destroy the thermodynamic picture of the
black hole.


  We propose an original quantum memory protocol. It belongs to the class of
rephasing processes and is closely related to two-pulse photon echo. It is
known that the strong population inversion produced by the rephasing pulse
prevents the plain two-pulse photon echo from serving as a quantum memory
scheme. Indeed gain and spontaneous emission generate prohibitive noise. A
second $\pi$-pulse can be used to simultaneously reverse the atomic phase and
bring the atoms back into the ground state. Then a secondary echo is radiated
from a non-inverted medium, avoiding contamination by gain and spontaneous
emission noise. However, one must kill the primary echo, in order to preserve
all the information for the secondary signal. In the present work, spatial
phase mismatching is used to silence the standard two-pulse echo. An
experimental demonstration is presented.


  With the advent of large scale surveys (i.e., Legacy Surveys) it is now
possible to start looking beyond the galaxy luminosity function (LF) to more
detailed statistical representations of the galaxy population, i.e.,
multivariate distributions. In this review I first summarise the current
state-of-play of the B-band global and cluster LFs and then briefly present two
promising bivariate distributions: the luminosity-surface brightness plane
(LSP); and the colour-luminosity plane (CLP). In both planes galaxy bulges and
galaxy disks form marginally overlapping but distinct distributions, indicating
two key formation/evolutionary processes (presumably merger and accretion).
Forward progress in this subject now requires the routine application of
reliable bulge-disk decomposition codes to allow independent investigation of
these two key components.


  In the paper [P. Achar, "On the equivariant $K$-theory of the nilpotent cone
in the general linear group," Represent. Theory 8 (2004), 180-211], the author
gave a combinatorial algorithm for computing the Lusztig-Vogan bijection for
$GL(n,\mathbb{C})$. However, that paper failed to mention one easy case that
may sometimes arise, making the description of the algorithm incomplete. This
note fills in that gap.


  We analyse the physics of nonlinear gravitational processes inside a
spherical charged black hole perturbed by a self-gravitating massless scalar
field. For this purpose we created an appropriate numerical code. Throughout
the paper, in addition to investigation of the properties of the mathematical
singularities where some curvature scalars are equal to infinity, we analyse
the properties of the physical singularities where the Kretschmann curvature
scalar is equal to the planckian value. Using a homogeneous approximation we
analyse the properties of the spacetime near a spacelike singularity in
spacetimes influenced by different matter contents namely a scalar field,
pressureless dust and matter with ultrarelativistic isotropic pressure. We also
carry out full nonlinear analyses of the scalar field and geometry of spacetime
inside black holes by means of an appropriate numerical code with adaptive mesh
refinement capabilities. We use this code to investigate the nonlinear effects
of gravitational focusing, mass inflation, matter squeeze, and these effects
dependence on the initial boundary conditions. It is demonstrated that the
position of the physical singularity inside a black hole is quite different
from the positions of the mathematical singularities. In the case of the
existence of a strong outgoing flux of the scalar field inside a black hole it
is possible to have the existence of two null singularities and one central
$r=0$ singularity simultaneously.


  One of the three X-ray detectors of the CAST experiment searching for solar
axions is a Time Projection Chamber (TPC) with a multi-wire proportional
counter (MWPC) as a readout structure. Its design has been optimized to provide
high sensitivity to the detection of the low intensity X-ray signal expected in
the CAST experiment. A low hardware threshold of 0.8 keV is safely set during
normal data taking periods, and the overall efficiency for the detection of
photons coming from conversion of solar axions is 62 %. Shielding has been
installed around the detector, lowering the background level to 4.10 x 10^-5
counts/cm^2/s/keV between 1 and 10 keV. During phase I of the CAST experiment
the TPC has provided robust and stable operation, thus contributing with a
competitive result to the overall CAST limit on axion-photon coupling and mass.


  Polyhydrides are a novel class of superconducting materials with extremely
high critical parameters, which is very promising for applications. On the
other hand, complete experimental study of the magnetic phase diagram for the
best so far known superconductor, lanthanum decahydride LaH$_{10}$, encounters
a serious complication because of the large upper critical magnetic field
$\textit{H}$$_{C2}$(0), exceeding 120-160 T. Partial replacement of La atoms by
magnetic Nd atoms results in a decrease of the upper critical field, which
makes it attainable for existing pulse magnets. We found that addition of
neodymium leads to significant suppression of superconductivity in LaH$_{10}$:
each atomic % of Nd causes decrease in $\textit{T}$$_{C}$ by 10-11 K. Using
strong pulsed magnetic fields up to 68 T, we constructed the magnetic phase
diagram of the ternary (La,Nd)H$_{10}$ superhydride, which appears to be
surprisingly linear with $\textit{H}$$_{C2}$ $\propto$ |$\textit{T}$ -
$\textit{T}$$_C$|. The pronounced suppression of superconductivity in
LaH$_{10}$ by magnetic Nd atoms and the robustness of $\textit{T}$$_C$ with
respect to nonmagnetic impurities (e.g., Y, Al, C) under Anderson's theorem
indicate the isotropic ($\textit{s}$-wave) character of conventional
electron-phonon pairing in the synthesized superhydrides.


  We present a method to construct a continuous extension (otherwise known as
dense output) for a numerical routine in the special case of the numerical
solution being a scalar-valued function exhibiting rapid oscillations. Such
cases call for numerical routines that make use of the known global behaviour
of the solution, one example being methods using asymptotic expansions to
forecast the solution at each step of the independent variable. An example is
oscode, numerical routine which uses the Wentzel-Kramers-Brillouin (WKB)
approximation when the solution oscillates rapidly and otherwise behaves as a
Runge-Kutta (RK) solver. Polynomial interpolation is not suitable for producing
the solution at an arbitrary point mid-step, since efficient numerical methods
based on the WKB approximation will step through multiple oscillations in a
single step. Instead we construct the continuous solution by extending the
numerical quadrature used in computing a WKB approximation of the solution with
no additional evaluations of the differential equation or terms within, and
provide an error estimate on this dense output. Finally, we draw attention to
previous work on the continuous extension of Runge-Kutta formulae, and
construct an extension to a RK method based on Gauss--Lobatto quadrature nodes,
thus describing how to generate dense output from each of the methods
underlying oscode.


  The algorithm is proposed which by combination of the files with the events
from linac and with the final data events from the observation of solar
neutrinos enables to check correctness of the data treatment used in
SuperKamiokande experiment.


  Automated structural damage diagnosis after earthquakes is important for
improving the efficiency of disaster response and rehabilitation. In
conventional data-driven frameworks which use machine learning or statistical
models, structural damage diagnosis models are often constructed using
supervised learning. The supervised learning requires historical structural
response data and corresponding damage states (i.e., labels) for each building
to learn the building-specific damage diagnosis model. However, in
post-earthquake scenarios, historical data with labels are often not available
for many buildings in the affected area. This makes it difficult to construct a
damage diagnosis model. Further, directly using the historical data from other
buildings to construct a damage diagnosis model for the target building would
lead to inaccurate results. This is because each building has unique physical
properties and thus unique data distribution. To this end, we introduce a new
framework to transfer the model learned from other buildings to diagnose
structural damage states in the target building without any labels. This
framework is based on an adversarial domain adaptation approach that extracts
domain-invariant feature representations of data from different buildings. The
feature extraction function is trained in an adversarial way, which ensures
that the extracted feature distributions are robust to changes in structures
while being predictive of the damage states. With the extracted
domain-invariant feature representations, the data distributions become
consistent across different buildings. We evaluate our framework on both
numerical simulation and field data collected from multiple building
structures, which outperforms the state-of-the-art benchmark methods.


  Song et al. [Phys. Rev. C 102, 065208 (2020)] presented results for the
$\Lambda_c N$ interaction based on an extrapolation of lattice simulations by
the HAL QCD Collaboration at unphysical quark masses to the physical point via
covariant chiral effective field theory. We point out that their predictions
for the $^3D_1$ partial wave disagree with available lattice results. We
discuss the origin of that disagreement and present a comparison with
predictions from conventional (non-relativistic) chiral effective field theory.


  One-atom thick crystalline layers and their vertical heterostructures carry
the promise of designer electronic materials that are unattainable by standard
growth techniques. In order to realize their potential it is necessary to
isolate them from environmental disturbances in particular those introduced by
the substrate. But finding and characterizing suitable substrates, and
minimizing the random potential fluctuations they introduce, has been a
persistent challenge in this emerging field. Here we show that Landau-level
(LL) spectroscopy is exquisitely sensitive to potential fluctuations on both
local and global length scales. Harnessing this technique we demonstrate that
the insertion of an intermediate graphene layer provides superior screening of
substrate induced disturbances, more than doubling the electronic mean free
path. Furthermore, we find that the proximity of hBN acts as a nano-scale
vacuum cleaner, dramatically suppressing the global potential fluctuations.
This makes it possible to fabricate high quality devices on standard SiO2
substrates.


  Charge density wave (CDW) correlations have recently been shown to
universally exist in cuprate superconductors. However, their nature at high
fields inferred from nuclear magnetic resonance is distinct from that measured
by x-ray scattering at zero and low fields. Here we combine a pulsed magnet
with an x-ray free electron laser to characterize the CDW in YBa2Cu3O6.67 via
x-ray scattering in fields up to 28 Tesla. While the zero-field CDW order,
which develops below T ~ 150 K, is essentially two-dimensional, at lower
temperature and beyond 15 Tesla, another three-dimensionally ordered CDW
emerges. The field-induced CDW onsets around the zero-field superconducting
transition temperature, yet the incommensurate in-plane ordering vector is
field-independent. This implies that the two forms of CDW and high-temperature
superconductivity are intimately linked.


  We prove that if a system has superpolynomial (faster than any power law)
decay of correlations (with respect to Lipschitz observables) then the time
$\tau (x,S_{r})$ needed for a typical point $x$ to enter for the first time a
set $S_{r}=\{x:f(x)\leq r\}$ which is a sublevel of a Lipschitz funcion $f$
scales as $\frac{1}{\mu (S_{r})}$ i.e. \begin{equation*} \underset{r\to 0}{\lim
}\frac{\log \tau (x,S_{r})}{-\log r}=\underset{r\to 0}{\lim}\frac{\log \mu
(S_{r})}{\log (r)}. \end{equation*} This generalizes a previous result obtained
for balls. We will also consider relations with the return time distributions,
an application to observed systems and to the geodesic flow of negatively
curved manifolds.


  Given a probability measure $\mu$ on the unit circle $\mathbb{T}$, consider
the reproducing kernel $k_{\mu,n}(z_1, z_2)$ in the space of polynomials of
degree at most $n-1$ with the $L^2(\mu)$-inner product. Let $u, v \in
\mathbb{C}$. It is known that under mild assumptions on $\mu$ near $\zeta \in
\mathbb{T}$, the ratio $k_{\mu,n}(\zeta e^{u/n}, \zeta
e^{v/n})/k_{\mu,n}(\zeta, \zeta)$ converges to a universal limit $S(u, v)$ as
$n \to \infty$. We give an estimate for the rate of this convergence for
measures $\mu$ with finite logarithmic integral.


  A massless Dirac particle is considered, moving along the x-axis while
Pauli-coupled by its anomalous magnetic moment to a piecewise constant magnetic
field along the same axis, with stochastically varying sign. The motion is
approximated as a quantum walk with unitary noise, for which the evolution can
be found exactly. Initially ballistic, the motion approaches a classical
diffusion on a time-scale determined by the speed of light, the size of the
magnetic moment, the strength of the field and the time interval between
changes in its direction. It is suggested that a process of this type could
occur in the Sun's corona, significantly affecting the solar fluxes of one or
more neutrino types.


  Hopf algebroid structures on the Weyl algebra (phase space) are presented. We
define the coproduct for the Weyl generators from Leibniz rule. The codomain of
the coproduct is modified in order to obtain an algebra structure. We use the
dual base to construct the target map and antipode. The notion of twist is
analyzed for $\kappa$-deformed phase space in Hopf algebroid setting. It is
outlined how the twist in the Hopf algebroid setting reproduces the full Hopf
algebra structure of $\kappa$-Poincar\'e algebra. Several examples of
realizations are worked out in details.


  In this paper we present the Braid Monodromy Type (BMT) of curves and
surfaces; past, present and future. The BMT is an invariant that can
distinguish between non-isotopic curves; between different families of surfaces
of general type; between connected components of moduli space of surfaces and
between non symplectmorphic 4-manifolds. BMT is a finer invariant than the
Sieberg-Witten invariants. Consider 2 simply connected surfaces of general type
with the same Chern classes. It is known that if they are in the same
deformation class, they are diffeomorphic to each other. Are there computable
invariants distinguishing between these 2 classes? The new invariant, proposed
here, is located between the 2 classes. In this paper we shall introduce the
new invariant, state the current results and pose an open question.


  In this paper we establish eigenvector delocalization and bulk universality
for L\'{e}vy matrices, which are real, symmetric, $N \times N$ random matrices
$\textbf{H}$ whose upper triangular entries are independent, identically
distributed $\alpha$-stable laws. First, if $\alpha \in (1, 2)$ and $E \in
\mathbb{R}$ is any energy bounded away from $0$, we show that every eigenvector
of $\textbf{H}$ corresponding to an eigenvalue near $E$ is completely
delocalized and that the local spectral statistics of $\textbf{H}$ around $E$
converge to those of the Gaussian Orthogonal Ensemble (GOE) as $N$ tends to
$\infty$. Second, we show for almost all $\alpha \in (0, 2)$, there exists a
constant $c(\alpha) > 0$ such that the same statements hold if $|E| < c
(\alpha)$.


  Although the magnetic order is suppressed by a strong magnetic frustration,
it is maintained but appears in complex order forms such as a cycloid or spin
density wave in weakly frustrated systems. Herein, we report a weakly
magnetic-frustrated two-dimensional van der Waals material CrPSe$_3$.
Polycrystalline CrPSe$_3$ was synthesized at an optimized temperature of
700$^\circ$C to avoid the formation of any secondary phases (e.g.,
Cr$_2$Se$_3$). The antiferromagnetic transition appeared at $T_N\sim 126$ K
with a large Curie-Weiss temperature $T_{\rm CW} \sim -371$ via magnetic
susceptibility measurements, indicating weak frustration in CrPSe$_3$ with a
frustration factor $f (|T_{\rm CW}|/T_N) \sim 3$. Evidently, the formation of
long-range incommensurate spin-density wave antiferromagnetic order with the
propagation vector $k = (0, 0.04, 0)$ was revealed by neutron diffraction
measurements at low temperatures (below 120K). The monoclinic crystal structure
of C2/m symmetry is preserved over the studied temperature range down to 20K,
as confirmed by Raman spectroscopy measurements. Our findings on the spin
density wave antiferromagnetic order in two-dimensional (2D) magnetic
materials, not previously observed in the MPX$_3$ family, are expected to
enrich the physics of magnetism at the 2D limit, thereby opening opportunities
for their practical applications in spintronics and quantum devices.


  We study two-dimensional Ising spins, evolving through reinforcement learning
using their state, action, and reward. The state of a spin is defined as
whether it is in the majority or minority with its nearest neighbours. The spin
updates its state using an {\epsilon}-greedy algorithm. The parameter
{\epsilon} plays the role equivalent to the temperature in the Ising model. We
find a phase transition from long-ranged ordered to a disordered state as we
tune {\epsilon} from small to large values. In analogy with the phase
transition in the Ising model, we calculate the critical {\epsilon} and the
three critical exponents {\beta}, {\gamma}, {\nu} of magnetization,
susceptibility, and correlation length, respectively. A hyper-scaling relation
d{\nu} = 2{\beta} + {\gamma} is obtained between the three exponents. The
system is studied for different learning rates. The exponents approach the
exact values for two-dimensional Ising model for lower learning rates.


  We investigate the quantum corrections of the anomalous magnetic moment (AMM)
for fermions in the presence of a strong magnetic field using the Ritus's
approach. At strong fields the particles get different AMM's depending on the
LL's. This result is different from what is obtained with the Schwinger's
approximation at weak field where the AMM is independent of the LL. We analyze
the significance of the AMM contribution to the Equation of State (EoS) of the
magnetized system, in the weak and strong field approximations.


  The two-body photodisintegration of 3He has been investigated using tagged
photons with energies from 14 -- 31 MeV at MAX-lab in Lund, Sweden. The
two-body breakup channel was unambiguously identified by the (nonsimultaneous)
detection of both protons and deuterons. This approach was made feasible by the
over-determined kinematic situation afforded by the tagged-photon technique.
Proton- and deuteron-energy spectra were measured using four silicon
surface-barrier detector telescopes located at a laboratory angle of 90deg with
respect to the incident photon-beam direction. Average statistical and
systematic uncertainties of 5.7% and 6.6% in the differential cross section
were obtained for 11 photon-energy bins with an average width of 1.2 MeV. The
results are compared to previous experimental data measured at comparable
photon energies as well as to the results of two recent Faddeev calculations
which employ realistic potential models and take into account three-nucleon
forces and final-state interactions. Both the accuracy and precision of the
present data are improved over the previous measurements. The data are in good
agreement with most of the previous results, and favor the inclusion of
three-nucleon forces in the calculations.


  We show that a (complex) $C_\sigma$-space is separably injective if and only
if it is linearly isometric to the Banach space $C_0(\Omega)$ of complex
continuous functions vanishing at infinity on a substonean locally compact
Hausdorff space $\Omega$.


  Charge-transfer excitations are of paramount importance for understanding the
electronic structure of copper-oxide based high-temperature superconductors. In
this study, we investigate the response of a
Bi$_2$Sr$_2$CaCu$_2$O$_{\mathrm{8}+ \delta}$ crystal to the charge
redistribution induced by an infrared ultrashort pulse. Element-selective
time-resolved core-level photoelectron spectroscopy with a high energy
resolution allows disentangling the dynamics of oxygen ions with different
coordination and bonds thanks to their different chemical shifts. Our
experiment shows that the O\,$1s$ component arising from the Cu-O planes is
significantly perturbed by the infrared light pulse. Conversely, the apical
oxygen, also coordinated with Sr ions in the Sr-O planes, remains unaffected.
This result highlights the peculiar behavior of the electronic structure of the
Cu-O planes. It also unlocks the way to study the out-of-equilibrium electronic
structure of copper-oxide-based high-temperature superconductors by identifying
the O\,$1s$ core-level emission originating from the oxygen ions in the Cu-O
planes. This ability could be critical to gain information about the
strongly-correlated electron ultrafast dynamical mechanisms in the Cu-O plane
in the normal and superconducting phases.


  We prove that for any $n\geq 4$ there are infinitely many real homotopy types
of $2n$-dimensional nilmanifolds admitting generalized complex structures of
every type $k$, for $0 \leq k \leq n$. This is in deep contrast to the
$6$-dimensional case.


  Unsupervised visual representation learning has gained much attention from
the computer vision community because of the recent achievement of contrastive
learning. Most of the existing contrastive learning frameworks adopt the
instance discrimination as the pretext task, which treating every single
instance as a different class. However, such method will inevitably cause class
collision problems, which hurts the quality of the learned representation.
Motivated by this observation, we introduced a weakly supervised contrastive
learning framework (WCL) to tackle this issue. Specifically, our proposed
framework is based on two projection heads, one of which will perform the
regular instance discrimination task. The other head will use a graph-based
method to explore similar samples and generate a weak label, then perform a
supervised contrastive learning task based on the weak label to pull the
similar images closer. We further introduced a K-Nearest Neighbor based
multi-crop strategy to expand the number of positive samples. Extensive
experimental results demonstrate WCL improves the quality of self-supervised
representations across different datasets. Notably, we get a new
state-of-the-art result for semi-supervised learning. With only 1\% and 10\%
labeled examples, WCL achieves 65\% and 72\% ImageNet Top-1 Accuracy using
ResNet50, which is even higher than SimCLRv2 with ResNet101.


  We study finite graphs embedded in oriented surfaces by associating a
polynomial to it. The tools used in developing a theory of such graph
polynomials are algebraic topological while the polynomial itself is inspired
from ideas arising in physics. We also analyze a variant of these polynomials
for colored embedded graphs. This is used to describe the change in the
polynomial under basic graph theoretic operations. We conclude with several
applications of this polynomial including detection of certain classes of
graphs and the connection of this polynomial with topological entanglement
entropy.


  Diffusion models have achieved significant success in image and video
generation. This motivates a growing interest in video editing tasks, where
videos are edited according to provided text descriptions. However, most
existing approaches only focus on video editing for short clips and rely on
time-consuming tuning or inference. We are the first to propose Video
Instruction Diffusion (VIDiff), a unified foundation model designed for a wide
range of video tasks. These tasks encompass both understanding tasks (such as
language-guided video object segmentation) and generative tasks (video editing
and enhancement). Our model can edit and translate the desired results within
seconds based on user instructions. Moreover, we design an iterative
auto-regressive method to ensure consistency in editing and enhancing long
videos. We provide convincing generative results for diverse input videos and
written instructions, both qualitatively and quantitatively. More examples can
be found at our website https://ChenHsing.github.io/VIDiff.


  We study various dynamical aspects of systems possessing a first order phase
transition in their phase diagram. We isolate three qualitatively distinct
types of theories depending on the structure of instabilities and the nature of
the low temperature phase. The non-equilibrium dynamics is modeled by a dual
gravitational theory in 3+1 dimension which is coupled to massive scalar field
with self interacting potential. By numerically solving the Einstein-matter
equations of motion with various initial configurations, we investigate the
structure of the final state arising through coalescence of phase domains. We
find that static phase domains, even quite narrow are very long lived and we
find a phenomenological equation for their lifetime. Within our framework we
also analyze moving phase domains and their collision as well as the effects of
spinodal instability and dynamical instability on an expanding boost invariant
plasma.


  In the (special) smoothing spline problem one considers a variational problem
with a quadratic data fidelity penalty and Laplacian regularisation. Higher
order regularity can be obtained via replacing the Laplacian regulariser with a
poly-Laplacian regulariser. The methodology is readily adapted to graphs and
here we consider graph poly-Laplacian regularisation in a fully supervised,
non-parametric, noise corrupted, regression problem. In particular, given a
dataset $\{x_i\}_{i=1}^n$ and a set of noisy labels
$\{y_i\}_{i=1}^n\subset\mathbb{R}$ we let $u_n:\{x_i\}_{i=1}^n\to\mathbb{R}$ be
the minimiser of an energy which consists of a data fidelity term and an
appropriately scaled graph poly-Laplacian term. When $y_i = g(x_i)+\xi_i$, for
iid noise $\xi_i$, and using the geometric random graph, we identify (with high
probability) the rate of convergence of $u_n$ to $g$ in the large data limit
$n\to\infty$. Furthermore, our rate, up to logarithms, coincides with the known
rate of convergence in the usual smoothing spline model.


  We develop a framework of calculating entanglement entropy for non-conformal
field theories with the use of the dilaton effective action. To illustrate it,
we locate a theory on a cylinder $\mathbb{R} \times \mathbb{S}^{2}$ and compute
entanglement entropy of a cap-like region perturbatively with respect to the
mass for a free massive scalar field. A renormalized entanglement entropy (REE)
is proposed to regularize the ultraviolet divergence on the cylinder. We find
that the REE decreases monotonically both in the small and large mass regions
as the mass increases. We confirm all of these behaviors by the numerical
calculations, which further shows the monotonic decrease of the REE in the
entire renormalization group flow.


  We examine one-hidden-layer neural networks with random weights. It is
well-known that in the limit of infinitely many neurons they simplify to
Gaussian processes. For networks with a polynomial activation, we demonstrate
that the rate of this convergence in 2-Wasserstein metric is
$O(n^{-\frac{1}{2}})$, where $n$ is the number of hidden neurons. We suspect
this rate is asymptotically sharp. We improve the known convergence rate for
other activations, to power-law in $n$ for ReLU and inverse-square-root up to
logarithmic factors for erf. We explore the interplay between spherical
harmonics, Stein kernels and optimal transport in the non-isotropic setting.


  Continuous-wave (CW) squeezed light is used in generation of various optical
quantum states thus is a fundamental resource of fault-tolerant universal
quantum computation using optical continuous variables. To realize a practical
quantum computer, a waveguide optical parametric amplifier (OPA) is an
attractive CW squeezed light source in terms of its THz-order bandwidth and
suitability for modularization. The usages of a waveguide OPA in quantum
applications thus far, however, are limited due to the difficulty of the
generation of the squeezed light with a high purity. In this paper, we report
the first observation of Wigner negativity of the states generated by a
heralding method using a waveguide OPA. We generate Schr\"{o}dinger cat states
at the wavelength of 1545 nm with Wigner negativity using a quasi-single-mode
ZnO-doped periodically poled ${\rm LiNbO_3}$ waveguide module we developed.
Wigner negativity is regarded as an important indicator of the usefulness of
the quantum states as it is essential in the fault-tolerant universal quantum
computation. Our result shows that our waveguide OPA can be used in wide range
of quantum applications leading to a THz-clock optical quantum computer.


  This paper describes an interactive tool that facilitates following
define-use chains in large codes. The motivation for the work is to support
relative debugging, where it is necessary to iteratively refine a set of
asser-tions between different versions of a program. DUCT is novel because it
exploits the Microsoft Intermediate Language (MSIL) that underpins the .NET
Framework. Accordingly, it works on a wide range of programming languages
without any modification. The paper describes the design and implementation of
DUCT, and then illustrates its use with a small case study.


  We present a novel and comprehensive microscopic study of Luther-Emery-paired
phases in a strongly interacting atomic Fermi gas inside a parabolic trap and a
one-dimensional (1D) optical lattice. Our work is based on a lattice version of
density-functional theory, which uses as reference system the 1D homogeneous
Hubbard model. We test our approach for repulsive interactions against Quantum
Monte Carlo data and show that, for sufficiently strong attractions, an
atomic-density wave (ADW) in the central portion of the trap breaks the
discrete translational symmetry of the underlying lattice. We demonstrate that
the emergence of an ADW has a dramatic impact on experimental observables such
as the Fraunhofer diffraction pattern and the momentum distribution.


  State-of-the-art sequence-to-sequence models often require autoregressive
decoding, which can be highly expensive. However, for some downstream tasks
such as out-of-distribution (OOD) detection and resource allocation, the actual
decoding output is not needed just a scalar attribute of this sequence. In
these scenarios, where for example knowing the quality of a system's output to
predict poor performance prevails over knowing the output itself, is it
possible to bypass the autoregressive decoding? We propose Non-Autoregressive
Proxy (NAP) models that can efficiently predict general scalar-valued
sequence-level attributes. Importantly, NAPs predict these metrics directly
from the encodings, avoiding the expensive autoregressive decoding stage. We
consider two sequence-to-sequence task: Machine Translation (MT); and Automatic
Speech Recognition (ASR). In OOD for MT, NAPs outperform a deep ensemble while
being significantly faster. NAPs are also shown to be able to predict
performance metrics such as BERTScore (MT) or word error rate (ASR). For
downstream tasks, such as data filtering and resource optimization, NAPs
generate performance predictions that outperform predictive uncertainty while
being highly inference efficient.


  Deuterated methanol is one of the most robust windows astrochemists have on
the individual chemical reactions forming deuterium-bearing molecules and the
physicochemical history of the regions where they reside. The first-time
detection of mono- and di-deuterated methanol in a cometary coma is presented
for comet 67P/Churyumov-Gerasimenko using Rosetta-ROSINA data. D-methanol
(CH3OD and CH2DOH combined) and D2-methanol (CH2DOD and CHD2OH combined) have
an abundance of 5.5+/-0.46 and 0.00069+/-0.00014 per cent relative to normal
methanol. The data span a methanol deuteration fraction (D/H ratio) in the
0.71-6.6 per cent range, accounting for statistical corrections for the
location of D in the molecule and including statistical error propagation in
the ROSINA measurements. It is argued that cometary CH2DOH forms from CO
hydrogenation to CH3OH and subsequent H-D substitution reactions in CH3-R.
CHD2OH is likely produced from deuterated formaldehyde. Meanwhile, CH3OD and
CH2DOD, could form via H-D exchange reactions in OH-R in the presence of
deuterated water ice. Methanol formation and deuteration is argued to occur at
the same epoch as D2O formation from HDO, with formation of mono-deuterated
water, hydrogen sulfide, and ammonia occurring prior to that. The cometary
D-methanol/methanol ratio is demonstrated to agree most closely with that in
prestellar cores and low-mass protostellar regions. The results suggest that
cometary methanol stems from the innate cold (10-20 K) prestellar core that
birthed our Solar System. Cometary volatiles individually reflect the
evolutionary phases of star formation from cloud to core to protostar.


  We propose a method for calculating the isothermal critical exponent $\delta$
in Ising systems undergoing a second-order phase transition. It is based on the
calculation of the mean magnetization time series within a small connected
domain of a lattice after equilibrium is reached. At the pseudocritical point,
the magnetization time series attains intermittent characteristics and the
probability density for consecutive values of mean magnetization within a
region around zero becomes a power law. Typically the size of this region is of
the order of the standard deviation of the magnetization. The emerging
power-law exponent is directly related to the isothermal critical exponent
$\delta$ through a simple analytical expression. We employ this method to
calculate with remarkable accuracy the exponent $\delta$ for the square-lattice
Ising model where traditional approaches, like the constrained effective
potential, typically fail to provide accurate results.


  Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.


  Polarized inelastic neutron scattering experiments recently identified the
amplitude (Higgs) mode in C$_9$H$_{18}$N$_2$CuBr$_4$, a two-dimensional
near-quantum-critical spin-1/2 two-leg ladder compound, which exhibits a weak
easy-axis exchange anisotropy. Here, we theoretically examine the dynamic spin
structure factor of such planar coupled spin-ladder systems using large-scale
quantum Monte Carlo simulations. This allows us to provide a quantitative
account of the experimental neutron scattering data within a consistent quantum
spin model. Moreover, we trance the details of the continuous evolution of the
amplitude mode from a two-particle bound state of coupled ladders in the
classical Ising limit all the way to the quantum spin-1/2 Heisenberg limit with
fully restored SU(2) symmetry, where it gets overdamped by the two-magnon
continuum in neutron scattering.


  The recent theory of graph limits gives a powerful framework for
understanding the properties of suitable (convergent) sequences $(G_n)$ of
graphs in terms of a limiting object which may be represented by a symmetric
function $W$ on $[0,1]$, i.e., a kernel or graphon. In this context it is
natural to wish to relate specific properties of the sequence to specific
properties of the kernel. Here we show that the kernel is monotone (i.e.,
increasing in both variables) if and only if the sequence satisfies a
`quasi-monotonicity' property defined by a certain functional tending to zero.
As a tool we prove an inequality relating the cut and $L^1$ norms of kernels of
the form $W_1-W_2$ with $W_1$ and $W_2$ monotone that may be of interest in its
own right; no such inequality holds for general kernels.