  Previous work on symmetric group equivariant neural networks generally only
considered the case where the group acts by permuting the elements of a single
vector. In this paper we derive formulae for general permutation equivariant
layers, including the case where the layer acts on matrices by permuting their
rows and columns simultaneously. This case arises naturally in graph learning
and relation learning applications. As a specific case of higher order
permutation equivariant networks, we present a second order graph variational
encoder, and show that the latent distribution of equivariant generative models
must be exchangeable. We demonstrate the efficacy of this architecture on the
tasks of link prediction in citation graphs and molecular graph generation.


  Recent $N_f=2+1$ lattice data for meson-meson scattering in $p$-wave and
isospin $I=1$ are analyzed using a unitarized model inspired by Chiral
Perturbation Theory in the inverse-amplitude formulation for two and three
flavors. Chiral extrapolations are performed that postdict phase shifts
extracted from experiment quite well. In addition, the low-energy constants are
compared to the ones from a recent analysis of $N_f=2$ lattice QCD simulations
to check for the consistency of the hadronic model used here. Some
inconsistencies are detected in the fits to $N_f=2+1$ data, in contrast to the
previous analysis of $N_f=2$ data.


  Interest in organic solar cells (OSCs) is constantly rising in the field of
photovoltaic devices. The device performance relies on the bulk heterojunction
(BHJ) nanomorphology, which develops during the drying process and additional
post-treatment. This work studies the effect of thermal annealing (TA) on an
all-small molecule DRCN5T: PC71 BM blend with phase field simulations. The
objective is to determine the physical phenomena driving the evolution of the
BHJ morphology for a better understanding of the posttreatment/morphology
relationship. Phase-field simulation results are used to investigate the impact
on the final BHJ morphology of the DRCN5T crystallization-related mechanisms,
including nucleation, growth, crystal stability, impingement, grain coarsening,
and Ostwald ripening, of the amorphous-amorphous phase separation (AAPS), and
of diffusion limitations. The comparison of simulation results with
experimental data shows that the morphological evolution of the BHJ under TA is
dominated by dissolution of the smallest, unstable DRCN5T crystals and
anisotropic growth of the largest crystals.


  Recent advancements in the integration of artificial intelligence (AI) and
machine learning (ML) with physical sciences have led to significant progress
in addressing complex phenomena governed by nonlinear partial differential
equations (PDE). This paper explores the application of novel operator learning
methodologies to unravel the intricate dynamics of flame instability,
particularly focusing on hybrid instabilities arising from the coexistence of
Darrieus-Landau (DL) and Diffusive-Thermal (DT) mechanisms. Training datasets
encompass a wide range of parameter configurations, enabling the learning of
parametric solution advancement operators using techniques such as parametric
Fourier Neural Operator (pFNO), and parametric convolutional neural networks
(pCNN). Results demonstrate the efficacy of these methods in accurately
predicting short-term and long-term flame evolution across diverse parameter
regimes, capturing the characteristic behaviors of pure and blended
instabilities. Comparative analyses reveal pFNO as the most accurate model for
learning short-term solutions, while all models exhibit robust performance in
capturing the nuanced dynamics of flame evolution. This research contributes to
the development of robust modeling frameworks for understanding and controlling
complex physical processes governed by nonlinear PDE.


  The Penrose limits of a D5-brane wrapped on the sphere of AdS_5 x S^5 and
connected to the boundary by M fundamental strings, which is dual to the baryon
vertex of the N=4 SU(M) super Yang-Mills theory, are investigated. It is shown
that, for null geodesics that lead to the maximally supersymmetric Hpp-wave
background, the resulting D5-brane is a 1/2-supersymmetric null brane. For an
appropriate choice of radial geodesic, however, the limiting configuration is
1/4-supersymmetric and closely related to the Penrose limit of a flat space
BIon.


  Training deep neural networks on large and sparse datasets is still
challenging and can require large amounts of computation and memory. In this
work, we address the task of performing semantic segmentation on large
volumetric data sets, such as CT scans. Our contribution is threefold: 1) We
propose a boosted sampling scheme that uses a-posterior error maps, generated
throughout training, to focus sampling on difficult regions, resulting in a
more informative loss. This results in a significant training speed up and
improves learning performance for image segmentation. 2) We propose a novel
algorithm for boosting the SGD learning rate schedule by adaptively increasing
and lowering the learning rate, avoiding the need for extensive hyperparameter
tuning. 3) We show that our method is able to attain new state-of-the-art
results on the VISCERAL Anatomy benchmark.


  Detection of gravitational waves (GW) from highly eccentric binary black hole
(BBH) systems can provide insight into their dynamics and formation. To date,
all of the LIGO-Virgo BBH detections have been made using quasi-circular
templates in their initial discovery. However, recent studies have found some
of these systems to be compatible with high eccentricity in the LIGO band,
$e_{10 \textrm{Hz}} > 0.1$, possibly pointing to a population of sources that
are challenging to detect. Current low-latency search methods used with
ground-based GW detector data are not well equipped to detect highly eccentric
sources. Template-based, matched-filter searches require accurate eccentric
waveform models that are computational expensive. Unmodeled burst searches are
designed to detected localized excess power and are unable to identify multiple
isolated bursts, as would originate from a single highly eccentric BBH.
Therefore, we propose a signal-based prior that can be incorporated into an
existing GW burst search to target highly eccentric BBHs. Our eccentric burst
prior is based on the Newtonian burst model described by Loutrel & Yunes
(2017). As a proof of concept, we test our method on simulated data and find
that for intermediate SNR $\sim3-6$ signals using the eccentric burst prior
more effectively localizes GW bursts when compared to a uniform prior.


  Purpose: To evaluate safety of MRI in patients with fragmented retained leads
(FRLs) through numerical simulation and phantom experiments.
  Methods: Electromagnetic and thermal simulations were performed to determine
the worst-case RF heating of 10 patient-derived FRL models during MRI at 1.5 T
and 3 T and at imaging landmarks corresponding to head, chest and abdomen. RF
heating measurements were performed in phantoms implanted with reconstructed
FRL models that produced highest heating in numerical simulations. The
potential for unintended tissue stimulation was assessed through a conservative
estimation of the electric field induced in the tissue due to gradient-induced
voltages developed along the length of FRLs.
  Results: In simulations under conservative approach, RF exposure at B1+<2 uT
generated CEM43<40 at all imaging landmarks at both 1.5 T and 3 T indicating no
thermal damage for acquisition times (TA) <10 minutes. In experiments, the
maximum temperature rise when FRLs were positioned at the location of maximum
electric field exposure was measured to be 2.4 degrees Celsius at 3 T and 2.1
degrees Celsius at 1.5 T. Electric fields induced in the tissue due to
gradient-induced voltages remained below the threshold for cardiac tissue
stimulation in all cases.
  Conclusions: Simulation and experimental results indicate that patients with
FRLs can be scanned safely at both 1.5 T and 3 T with majority of clinical
pulse sequences.


  We investigate the time evolution of the entanglement entropy of coupled
single-mode Bose-Einstein condensates in a double well potential at $T=0$
temperature, by combining numerical results with analytical approximations. We
find that the coherent oscillations of the condensates result in entropy
oscillations on the top of a linear entropy generation at short time scales.
Due to dephasing, the entropy eventually saturates to a stationary value, in
spite of the lack of equilibration. We show that this long time limit of the
entropy reflects the semiclassical dynamics of the system, revealing the
self-trapping phase transition of the condensates at large interaction strength
by a sudden entropy jump. We compare the stationary limit of the entropy to the
prediction of a classical microcanonical ensemble, and find surprisingly good
agreement in spite of the non-equilibrium state of the system. Our predictions
should be experimentally observable on a Bose-Einstein condensate in a double
well potential or on a two-component condensate with inter-state coupling.


  We give a solution to the inverse moment problem for a certain class of
Hessenberg and symmetric matrices related to integrable lattices of Toda type.


  We give a complete solution of the problem of constructing a scattering
potential v(x) that possesses scattering properties of one's choice at an
arbitrary prescribed wavenumber. Our solution involves expressing v(x) as the
sum of at most six unidirectionally invisible finite-range potentials for which
we give explicit formulas. Our results can be employed for designing optical
potentials. We discuss its application in modeling threshold lasers, coherent
perfect absorbers, and bidirectionally and unidirectionally reflectionless
absorbers, amplifiers, and phase shifters.


  Sentiment analysis is one of the most widely studied applications in NLP, but
most work focuses on languages with large amounts of data. We introduce the
first large-scale human-annotated Twitter sentiment dataset for the four most
widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and
Yor\`ub\'a ) consisting of around 30,000 annotated tweets per language (and
14,000 for Nigerian-Pidgin), including a significant fraction of code-mixed
tweets. We propose text collection, filtering, processing and labeling methods
that enable us to create datasets for these low-resource languages. We evaluate
a rangeof pre-trained models and transfer strategies on the dataset. We find
that language-specific models and language-adaptivefine-tuning generally
perform best. We release the datasets, trained models, sentiment lexicons, and
code to incentivizeresearch on sentiment analysis in under-represented
languages.


  We propose a superconducting diode device comprising a central
superconducting film flanked by two wires carrying an applied DC bias, suitably
chosen so as to generate different asymmetric field profiles. Through numerical
simulations of the coupled Ginzburg-Landau and heat-diffusion equations, we
show that this design is capable of efficiently breaking the reciprocity of the
critical current in the central superconductor, thus promoting the diode effect
in response to an applied AC current. By adjusting the DC bias in the wires, we
find the optimum inhomogeneous field profile that facilitates the entrance of
vortices and antivortices in a given polarity of the applied AC current and
impede their entrance in the other polarity. This way, the system behaves as an
ideal superconducting half-wave rectifier, with diode efficiencies surpassing
70%. Furthermore, we detail the behavior and diode efficiency of the system
under different experimental conditions, such as the substrate heat transfer
coefficient and the sweep rate of the external current.


  Recently, the CMS Collaboration observed the hint of a resonance decaying to
two photons at about 96 GeV with a local significance of $2.8\sigma$. While it
is too early to say whether this will stand the test of time, such a resonance
can easily be accommodated in many extensions of the Standard Model (SM). The
more challenging part is to tune such an extension so that the required number
of diphoton events is reproduced. Assuming that the new resonance is a scalar,
we propose that the signal may come either from an ultraviolet complete model
with vectorial quarks, or a model involving gluon-scalar and photon-scalar
effective operators. We then incorporate this portal to several extensions of
the SM that include one or more cold dark matter candidates, and try to
investigate how the existence of such a scalar resonance affects the parameter
space of such models. As expected, we find that with such a scalar, the
parameter space gets more constrained and hence, more tractable. We show how
significant constraints can be placed on the parameter space, not only from
direct dark matter searches or LHC data but also from theoretical
considerations like scattering unitarity or stability of the potential, and
discuss some novel features of the allowed parameter space.


  The magnetic dissipative droplet is a strongly nonlinear wave structure that
can be stabilized in a thin film ferromagnet exhibiting perpendicular magnetic
anisotropy by use of spin transfer torque. These structures have been observed
experimentally at room temperature, showcasing their robustness against noise.
Here, we quantify the effects of thermal noise by deriving the stochastic
equations of motion for a droplet based on soliton perturbation theory. First,
it is found that deterministic droplets are linearly unstable at large bias
currents, subject to a drift instability. When the droplet is linearly stable,
our framework allows us to analytically compute the droplet's generation
linewidth and center variance. Additionally, we study the influence of
non-local and Oersted fields with micromagnetic simulations, providing insight
into their effect on the generation linewidth. These results motivate detailed
experiments on the current and temperature-dependent linewidth as well as drift
instability statistics of droplets, which are important figures-of-merit in the
prospect of droplet-based applications.


  The cannonball (CB) model of gamma ray bursts (GRBs) predicts that the
asymptotic behaviour of the spectral energy density of the X-ray afterglow of
GRBs is a power-law in time and in frequency where the difference between the
temporal and spectral power-law indexes, $\alpha_X-\beta_X$, is restricted to
the values 0, 1/2 and 1. Here we report the distributions of the values
$\alpha_X$, $\beta_X$ and their difference for a sample of 315 Swift GRBs. This
sample includes all Swift GRBs that were detected before August 1, 2012, whose
X-ray afterglow extended well beyond 1 day and the estimated error in
$\alpha_X-\beta_X$ was $\leq 0.25$. The values of $\alpha_X$ were extracted
from the CB model fits to the entire light curves of their X-ray afterglow
while the spectral index was extracted by the Swift team from the time
integrated X-ray afterglow of these GRBs. We found that the distribution of the
difference $\alpha_X-\beta_X$ for these 315 Swift GRBs has three narrow peaks
around 0, 1/2 and 1 whose widths are consistent with being due to the
measurement errors, in agreement with the CB model prediction.


  Histopathology can help clinicians make accurate diagnoses, determine disease
prognosis, and plan appropriate treatment strategies. As deep learning
techniques prove successful in the medical domain, the primary challenges
become limited data availability and concerns about data sharing and privacy.
Federated learning has addressed this challenge by training models locally and
updating parameters on a server. However, issues, such as domain shift and
bias, persist and impact overall performance. Dataset distillation presents an
alternative approach to overcoming these challenges. It involves creating a
small synthetic dataset that encapsulates essential information, which can be
shared without constraints. At present, this paradigm is not practicable as
current distillation approaches only generate non human readable
representations and exhibit insufficient performance for downstream learning
tasks. We train a latent diffusion model and construct a new distilled
synthetic dataset with a small number of human readable synthetic images.
Selection of maximally informative synthetic images is done via graph community
analysis of the representation space. We compare downstream classification
models trained on our synthetic distillation data to models trained on real
data and reach performances suitable for practical application.


  Ultra-high-Q resonators are fundamentally important to optics and microwave
photonics. Up to now, it is still very challenging to boost the Q factor while
maintaining a compact size for a resonator. Herein, we proposed and
demonstrated an ultra-high-Q silicon-nitride (Si3N4) racetrack resonator with
uniform multi-mode Si3N4 photonic waveguides. It consists of two multi-mode
straight waveguides connected by two multi-mode waveguide bends (MWBs). In
particular, the MWBs are based on modified Euler curves, and a multi-mode
straight waveguide directional coupler is used for the fundamental mode
coupling and avoid exciting higher-order modes in the racetrack. In this way,
the fundamental mode is excited and propagates in the multi-mode racetrack
resonator with ultra-low loss. Meanwhile, it helps to achieve a compact
180{\deg} bend to reduce the chip footprint. In this paper, the propagation
loss of the fundamental mode is significantly reduced with standard fabrication
process by broadening the waveguides width to as wide as 3{\mu}m. Results show
that an ultra-high-Q resonator with an intrinsic Q of TE mode is 4.57*10^7, and
the corresponding propagation loss of the waveguide is only 1.80 dB/m. To the
best of our knowledge, this is the highest Q value of the ring resonator with
only 2.226 mm ring length reported so far. The proposed ultra-high-Q Si3N4
resonator can be used to microwave photonic filters and optoelectronic
oscillators with large operation bandwidth.


  The presence of scalar fields with non-minimal gravitational interactions of
the form $\xi |\phi|^2 R$ may have important implications for the physics of
the early universe. While many studies solve the dynamics of non-minimally
coupled scalars in the Einstein frame, where gravity is simply described by the
Einstein-Hilbert action, we instead propose a procedure to solve the dynamics
directly in the original Jordan frame where the non-minimal couplings are
maintained explicitly. Our algorithm can be applied to scenarios that include
minimally coupled fields and an arbitrary number of non-minimally coupled
scalars, with the expansion of the universe sourced by all fields present. This
includes situations when the dynamics become fully inhomogeneous, fully
non-linear (due to e.g.~backreaction or mode rescattering effects), and/or when
the expansion of the universe is dominated by non-minimally coupled species. As
an example, we study geometric preheating with a non-minimally coupled scalar
spectator field when the inflaton oscillates following the end of inflation. In
the future, our technique may be used to shed light on aspects of the
equivalence of the Jordan and Einstein frames at the quantum level.


  Shared mobility on demand (MoD) services are receiving increased attention as
many high volume ride-hailing companies are offering shared services (e.g.
UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous
vehicles (AVs) promises further operational opportunities to benefit from these
developments as AVs enable a centrally operated and fully connected fleet.
There are two fundamental tasks for a shared MoD service: ride-matching and
vehicle rebalancing. Traditionally, these two functions are performed
sequentially and independently. In this paper, we propose and formulate an
integrated ride-matching problem which aims to integrate ride-matching and
rebalancing into a single formulation. The integrated problem benefits from
interactions between these two tasks. We also propose a methodology to solve
the integrated shared ride-matching problem by using supply level information
based on a grid representation of the city network. We demonstrate the
effectiveness of the proposed methodology through a comparative case study
using a benchmark sequential approach and an open source data set. Our results
show that the integrated model is able to serve at least the same amount of
passengers with significant gains in terms of level of service and
sustainability metrics.


  It has been shown that inclusion of higher order curvature invariant terms in
the Robertson-Walker minisuperspace model of the Einstein-Hilbert action leads
to Schrodinger like equation, whose corresponding effective action is
hermitian. Thus, it is possible to write the continuity equation in a straight
forward manner, which reveals a quantum mechanical probability interpretation
of the theory.


  In this paper, we give a small data blow-up result for the one-dimensional
semilinear wave equation with damping depending on time and space variables. We
show that if the damping term can be regarded as perturbation, that is,
non-effective damping in a certain sense, then the solution blows up in finite
time for any power of nonlinearity. This gives an affirmative answer for the
conjecture that the critical exponent agrees with that of the wave equation
when the damping is non-effective in one space dimension.


  In the field of Prognostics and Health Management (PHM), recent years have
witnessed a significant surge in the application of machine learning (ML).
Despite this growth, the field grapples with a lack of unified guidelines and
systematic approaches for effectively implementing these ML techniques and
comprehensive analysis regarding industrial open-source data across varied
scenarios. To address these gaps, this paper provides a comprehensive review of
machine learning approaches for diagnostics and prognostics of industrial
systems using open-source datasets from PHM Data Challenge Competitions held
between 2018 and 2023 by PHM Society and IEEE Reliability Society and
summarizes a unified ML framework. This review systematically categorizes and
scrutinizes the problems, challenges, methodologies, and advancements
demonstrated in these competitions, highlighting the evolving role of both
conventional machine learning and deep learning in tackling complex industrial
tasks related to detection, diagnosis, assessment, and prognosis. Moreover,
this paper delves into the common challenges in PHM data challenge competitions
by emphasizing both data-related and model-related issues and summarizes the
solutions that have been employed to address these challenges. Finally, we
identify key themes and potential directions for future research, providing
opportunities and prospects for ML further development in PHM.


  Existing video understanding datasets mostly focus on human interactions,
with little attention being paid to the "in the wild" settings, where the
videos are recorded outdoors. We propose WILDQA, a video understanding dataset
of videos recorded in outside settings. In addition to video question answering
(Video QA), we also introduce the new task of identifying visual support for a
given question and answer (Video Evidence Selection). Through evaluations using
a wide range of baseline models, we show that WILDQA poses new challenges to
the vision and language research communities. The dataset is available at
https://lit.eecs.umich.edu/wildqa/.


  Electromagnetic duality of Maxwell theory is a symmetry of equations but not
of the action. The usual application of the `complexity=action' conjecture
would thus loose this duality. It was recently proposed in arxiv:1901.00014
that the duality can be restored by adding some appropriate boundary term, at
the price of introducing the mixed boundary condition in the variation
principle. We present universal such a term in both first-order and
second-order formalism for a general theory of a minimally-coupled Maxwell
field. The first-order formalism has the advantage that the variation principle
involves only the Dirichlet boundary condition. Including this term, we compute
the on-shell actions in the Wheeler-De Witt patch and find that the duality
persists in these actions for a variety of theories, including
Einstein-Maxwell, Einstein-Maxwell-Dilaton, Einstein-Born-Infeld and
Einstein-Horndeski-Maxwell theories.


  Recent studies of azo-dye doped liquid crystal elastomers show a strong
photomechanical response. We report on models that predict experimental results
that suggest photothermal heating is the dominant mechanism in a planar
constrained geometry. We compare our models with experiments to determine key
material parameters, which are used to predict the dynamical response as a
function of intensity. We show that a local strain from photothermal heating
and a nonlocal strain from thermal diffusion is responsible for the observed
length changes over time. This work both elucidates the fundamental mechanisms
and provides input for the design of photomechanical optical devices, which
have been shown to have the appropriate properties for making smart materials.


  The production of Kshort and Lambda hadrons is studied in inelastic pp
collisions at sqrt(s) = 0.9 and 7 TeV collected with the ATLAS detector at the
LHC using a minimum-bias trigger. The observed distributions of transverse
momentum, rapidity, and multiplicity are corrected to hadron level in a
model-independent way within well defined phase-space regions. The distribution
of the production ratio of Lambdabar to Lambda baryons is also measured. The
results are compared with various Monte Carlo simulation models. Although most
of these models agree with data to within 15% in the Kshort distributions,
substantial disagreements with data are found in the Lambda distributions of
transverse momentum.


  In this paper, we establish global $C^{1+\alpha,\frac{1+\alpha}{2}}$
estimates for solutions of the linearized parabolic Monge-Amp$\grave{e}$re
equation $$\mathcal{L}_\phi
u(x,t):=-u_t\,\mathrm{det}D^2\phi(x)+\mathrm{tr}[\Phi(x) D^2 u]=f(x,t)$$ under
appropriate conditions on the domain, Monge-Amp$\grave{e}$re measures, boundary
data and $f$, where $\Phi:=\mathrm{det}(D^2\phi)(D^2\phi)^{-1}$ is the cofactor
of the Hessian of $D^2\phi$.


  The present-day orbit of 2017 UV43 resembles the average one of the fragments
of comet Shoemaker-Levy 9. Here, we present numerical evidence suggesting that
2017 UV43 might be related to Shoemaker-Levy 9. With our exploratory results we
intend to encourage a search for precovery images of 2017 UV43 and perhaps even
follow-up observations that may help in improving its poorly determined orbit
so its true dynamical nature can be understood.


  We introduce a finite off-shell hypermultiplet with no off-shell central
charge. This requires 192+192 degrees of freedom, all but 8+8 of which are
auxiliary or gauge. In the absence of supergravity, the model has a
saddle-point vacuum instability implying ghost-like propagators. These are
cured by realizing the model superconformally, such that the erstwhile ghosts
are realized as compensators. Gauge fixing these links the physical
hypermultiplets to supergravity. This evokes the prospect of realizing ${\cal
N}=4$ Super Yang-Mills theory off-shell.


  We compare different methods for performing analytical continuation of
spectral data from the imaginary time or frequency axis to the real frequency
axis for the optical conductivity sigma(omega). We compare the maximum entropy
(MaxEnt), singular value decomposition (SVD), sampling and Pade methods for
analytical continuation. We also study two direct methods for obtaining
sigma(0). For the MaxEnt approach we focus on a recent modification. The data
are split up in batches, a separate MaxEnt calculation is done for each batch
and the results are averaged. For the problems studied here, we find that
typically the SVD, sampling and modified MaxEnt methods give comparable
accuracy, while the Pade approximation is usually less reliable.


  As low-threshold dark matter detectors advance in development, they will
become sensitive to recoils from solar neutrinos which opens up the possibility
to explore neutrino properties. We predict the enhancement of the event rate of
solar neutrino scattering from Beyond the Standard Model interactions in
low-threshold DM detectors, with a focus on silicon, germanium, gallium
arsenide, xenon, and argon-based detectors. We consider a set of general
neutrino interactions, which fall into five categories: the neutrino magnetic
moment as well as interactions mediated by four types of mediators (scalar,
pseudoscalar, vector, and axial vector), and consider coupling these mediators
to either quarks or electrons. Using these predictions, we place constraints on
the mass and couplings of each mediator and the neutrino magnetic moment from
current low-threshold detectors like SENSEI, Edelweiss, and SuperCDMS, as well
as projections relevant for future experiments such as DAMIC-M, Oscura, Darwin,
and ARGO. We find that such low-threshold detectors can improve current
constraints by up to two orders of magnitude for vector mediators and one order
of magnitude for scalar mediators.


  Road networks in cities are massive and is a critical component of mobility.
Fast response to defects, that can occur not only due to regular wear and tear
but also because of extreme events like storms, is essential. Hence there is a
need for an automated system that is quick, scalable and cost-effective for
gathering information about defects. We propose a system for city-scale road
audit, using some of the most recent developments in deep learning and semantic
segmentation. For building and benchmarking the system, we curated a dataset
which has annotations required for road defects. However, many of the labels
required for road audit have high ambiguity which we overcome by proposing a
label hierarchy. We also propose a multi-step deep learning model that segments
the road, subdivide the road further into defects, tags the frame for each
defect and finally localizes the defects on a map gathered using GPS. We
analyze and evaluate the models on image tagging as well as segmentation at
different levels of the label hierarchy.


  When Cold Dark Matter (CDM) power spectrum normalized by COBE results, its
amplitude at smaller scales can be parametrized by $\Gamma\sim\Omega_0h$. The
expected variations of Hubble flows in two samples, the sample of 36 clusters
in the Mark III catalogue, and the sample of 20 Type Ia supernovae (SNe), are
calculated for the power spectrum in critical-density CDM models (including
tilted CDM models and vacuum-dominated CDM models). The comparison between the
expectations and the real variations in the data offers a bias-free way to
constrain the power spectrum. The cluster sample yields $\Gamma\le
0.30-0.88(n_{ps}-1)+1.9 (n_{ps}-1)^2$ at 95% C.L., with best fits $\Gamma =
0.15-0.39(n_{ps}-1)+0.37(n_{ps}-1)^2$, where $n_{ps}$ is the spectral index of
the power spectrum. The Type Ia SN sample yields $\Gamma\le
0.25-0.80(n_{ps}-1)+1.6 (n_{ps}-1)^2$ at 95% C.L., strongly favoring lower
$\Gamma$'s. The results are inconsistent with a critical-density
matter-dominated universe with $n_{ps}\ga 0.8$ and $H_0\ga 50$ km/sec/Mpc.


  The NA38 and NA50 experiments at the CERN SPS have measured charmonium
production in different colliding systems with the aim of observing a phase
transition from ordinary hadronic matter towards a state in which quarks and
gluons are deconfined (Quark Gluon Plasma, QGP). In fact it was predicted that
the J/psi yield should be suppressed in deconfined matter. The analysis of the
data collected by the NA50 experiment with Pb-Pb collisions at 158 GeV/c per
nucleon shows that the J/psi is anomalously suppressed in central collisions
and the observed pattern can be considered as a strong indication for QGP
production.


  We cross-correlate the third-year WMAP data with galaxy samples extracted
from the SDSS DR4 (SDSS4) covering 13% of the sky, increasing by a factor of
3.7 the volume sampled in previous analyses. The new measurements confirm a
positive cross-correlation with higher significance (total signal-to-noise of
about 4.7). The correlation as a function of angular scale is well fitted by
the integrated Sachs-Wolfe (ISW) effect for LCDM flat FRW models with a
cosmological constant. The combined analysis of different samples gives
Omega_L=0.80-0.85$ (68% Confidence Level, CL) or $0.77-0.86$ (95% CL). We find
similar best fit values for Omega_L for different galaxy samples with median
redshifts of z ~0.3 and z ~0.5, indicating that the data scale with redshift as
predicted by the LCDM cosmology (with equation of state parameter w=-1). This
agreement is not trivial, but can not yet be used to break the degeneracy
constraints in the w versus Omega_L plane using only the ISW data.


  An unrefinable chain of a finite group $G$ is a chain of subgroups $G = G_0 >
G_1 > \cdots > G_t = 1$, where each $G_i$ is a maximal subgroup of $G_{i-1}$.
The length (respectively, depth) of $G$ is the maximal (respectively, minimal)
length of such a chain. We studied the depth of finite simple groups in a
previous paper, which included a classification of the simple groups of depth
$3$. Here we go much further by determining the finite groups of depth $3$ and
$4$. We also obtain several new results on the lengths of finite groups. For
example, we classify the simple groups of length at most $9$, which extends
earlier work of Janko and Harada from the 1960s, and we use this to describe
the structure of arbitrary finite groups of small length. We also present a
number-theoretic result of Heath-Brown, which implies that there are infinitely
many non-abelian simple groups of length at most $9$.
  Finally we study the chain difference of $G$ (namely the length minus the
depth). We obtain results on groups with chain difference $1$ and $2$,
including a complete classification of the simple groups with chain difference
$2$, extending earlier work of Brewster et al. We also derive a best possible
lower bound on the chain ratio (the length divided by the depth) of simple
groups, which yields an explicit linear bound on the length of $G/R(G)$ in
terms of the chain difference of $G$, where $R(G)$ is the soluble radical of
$G$.


  It has recently been suggested that observed galaxy rotation curves can be
accounted for by general relativity without recourse to dark-matter halos. A
number of objections have been raised, which have been addressed by the
authors. Here, the calculation of tangential velocity is questioned.


  Designs conditions for marine structures are typically informed by
threshold-based extreme value analyses of oceanographic variables, in which
excesses of a high threshold are modelled by a generalized Pareto (GP)
distribution. Too low a threshold leads to bias from model mis-specification;
raising the threshold increases the variance of estimators: a bias-variance
trade-off. Many existing threshold selection methods do not address this
trade-off directly, but rather aim to select the lowest threshold above which
the GP model is judged to hold approximately. In this paper Bayesian
cross-validation is used to address the trade-off by comparing thresholds based
on predictive ability at extreme levels. Extremal inferences can be sensitive
to the choice of a single threshold. We use Bayesian model-averaging to combine
inferences from many thresholds, thereby reducing sensitivity to the choice of
a single threshold. The methodology is applied to significant wave height
datasets from the northern North Sea and the Gulf of Mexico.


  In this paper we study generalized Gorenstein Arf rings; a class of
one-dimensional Cohen-Macaulay local Arf rings that is strictly contained in
the class of Gorenstein rings. We obtain new characterizations and examples of
Arf rings, and give applications of our argument to numerical semigroup rings
and certain idealizations. In particular, we generalize a beautiful result of
Barucci and Fr\"oberg concerning Arf numerical semigroup rings.


  Policy Brief on "Workforce Development in Astronomy and Astroinformatics",
distilled from the corresponding panel that was part of the discussions during
S20 Policy Webinar on Astroinformatics for Sustainable Development held on 6-7
July 2023.
  The discipline of astronomy and astroinformatics is dynamically evolving
thereby creating a compelling opportunity to foster a more inclusive, diverse,
and proficient workforce. This is crucial for addressing multifaceted
challenges that emerge as we progress and harness the potential therein. To
realize this goal, it's imperative to cultivate strategies that promote
inclusive practices in STEM education, encourage participation from
historically excluded groups, provide training and mentorship, as well as
provide active champions, especially for students and early career
professionals from (historically) excluded groups. We provide an overview of
the current status, resources available, and possible steps especially keeping
in mind large international projects.
  The policy webinar took place during the G20 presidency in India (2023). A
summary based on the seven panels can be found here: arxiv:2401.04623.


  Vortex matter phase transitions in the high-temperature superconductor
Bi2Sr2CaCu2O8 were studied using local magnetization measurements combined with
a vortex 'shaking' technique. The measurements revealed thermodynamic evidence
of a first-order transition along the second magnetization peak line, at
temperatures below the apparent critical point Tcp. We found that the
first-order transition line does not terminate at Tcp, but continues down to at
least 30 K. This observation suggests that the ordered vortex lattice phase is
destroyed through a unified first-order transition that changes its character
from thermally induced melting at high temperatures to a disorder-induced
transition at low temperatures. At intermediate temperatures the transition
line shows an upturn, which implies that the vortex matter displays 'inverse'
melting behavior.


  Inspired by the Bloch-Beilinson conjectures, Voisin has formulated a
conjecture concerning the behaviour of 0-cycles on self-products of varieties
of geometric genus one. This note presents some new examples of surfaces for
which Voisin's conjecture is verified.


  We propose using spontaneous Raman scattering from an optically driven
Bose-Einstein condensate as a source of atom-photon pairs whose internal states
are maximally entangled. Generating entanglement between a particle which is
easily transmitted (the photon) and one which is easily trapped and coherently
manipulated (an ultracold atom) will prove useful for a variety of
quantum-information related applications. We analyze the type of entangled
states generated by spontaneous Raman scattering and construct a geometry which
results in maximum entanglement.


  We study the problem of characterizing the expected hitting times for a
robust generalization of continuous-time Markov chains. This generalization is
based on the theory of imprecise probabilities, and the models with which we
work essentially constitute sets of stochastic processes. Their inferences are
tight lower- and upper bounds with respect to variation within these sets.
  We consider three distinct types of these models, corresponding to different
levels of generality and structural independence assumptions on the constituent
processes.
  Our main results are twofold; first, we demonstrate that the hitting times
for all three types are equivalent. Moreover, we show that these inferences are
described by a straightforward generalization of a well-known linear system of
equations that characterizes expected hitting times for traditional
time-homogeneous continuous-time Markov chains.


  The modular representation theory of the queer Lie superalgebra q(n) over
characteristic p>2 is developed. We obtain a criterion for the irreducibility
of baby Verma modules with semisimple p-characters and a criterion for the
semisimplicity of the corresponding reduced enveloping algebras. A (2p)-power
divisibility of dimensions of q(n)-modules with nilpotent p-characters is
established. The representation theory of q(2) is treated in detail. We
formulate a Morita super-equivalence conjecture for q(n) with general
p-characters which is verified for n=2.


  Let M be a connected sum of finitely many lens spaces, and let N be a
connected sum of finitely many copies of S^1xS^2. We show that there is a
uniruled algebraic variety X such that the connected sum M#N of M and N is
diffeomorphic to a connected component of the set of real points X(R) of X. In
particular, any finite connected sum of lens spaces is diffeomorphic to a real
component of a uniruled algebraic variety.


  The union of topology and non-Hermiticity has led to the unveiling of many
intriguing phenomena. We introduce a synthetic spin-engineered model belonging
to symmetry class AI, which is a rare occurrence, and demonstrate the emergence
of a multi-fold spectral topology. As an example of our proposal, we engineer
non-Hermiticity in the paradigmatic Su-Schrieffer-Heeger (SSH) model by
introducing a generalized synthetic spin, leading to an emergent two-fold
spectral topology that governs the decoupled behaviour of the corresponding
non-Hermitian skin effect. As a consequence of the spin choice, our model
exhibits a rich phase diagram consisting of distinct topological phases, which
we characterize by introducing the notion of paired winding numbers, which, in
turn, predict the direction of skin localization under open boundaries. We
demonstrate that the choice of spin parameters enables control over the
directionality of the skin effect, allowing for it to be unilateral or
bilateral. Furthermore, we discover non-dispersive flat bands emerging within
the inherent SSH model framework, arising from the spin-engineering approach.
We also introduce a simplified toy model to capture the underlying physics of
the emergent flat bands and direction-selective skin effect. As an illustration
of experimental feasibility, we present a topoelectric circuit that faithfully
emulates the underlying spin-engineered Hamiltonian, providing a viable
platform for realizing our predicted effects. Our findings pave way for the
exploration of unconventional spectral topology through spin-designed models.


  In this work, it is shown that a non-zero vacuum energy density (the Casimir
energy) for a scalar field appears in a continuous elastic solid due to the
presence of a topological defect, the screw dislocation. An exact expression is
obtained for this energy density in terms of the Burgers vector describing the
defect, for zero and finite temperature.


  We prove versions of the Suslin and Gabber rigidity theorems in the setting
of equivariant pseudo pretheories of smooth schemes over a field with an action
of a finite group. Examples include equivariant algebraic $K$-theory,
presheaves with equivariant transfers, equivariant Suslin homology, and Bredon
motivic cohomology.


  We present a study of the magnetoresistivity of thin film MnSi in high
magnetic fields. We establish that the magnetoresistivity can be understood in
terms of spin fluctuation theory, allowing us to compare our data to studies of
bulk material. Despite of a close qualitative resemblance of bulk and thin film
data, there are clear quantitative differences. We propose that these reflect a
difference of the spin fluctuation spectra in thin film and bulk material MnSi.


  Along with the development of large language models (LLMs), e.g., ChatGPT,
many existing approaches and tools for software security are changing. It is,
therefore, essential to understand how security-aware these models are and how
these models impact software security practices and education. In exercises of
a software security course at our university, we ask students to identify and
fix vulnerabilities we insert in a web application using state-of-the-art
tools. After ChatGPT, especially the GPT-4 version of the model, we want to
know how the students can possibly use ChatGPT to complete the exercise tasks.
We input the vulnerable code to ChatGPT and measure its accuracy in
vulnerability identification and fixing. In addition, we investigated whether
ChatGPT can provide a proper source of information to support its outputs.
Results show that ChatGPT can identify 20 of the 28 vulnerabilities we inserted
in the web application in a white-box setting, reported three false positives,
and found four extra vulnerabilities beyond the ones we inserted. ChatGPT makes
nine satisfactory penetration testing and fixing recommendations for the ten
vulnerabilities we want students to fix and can often point to related sources
of information.


  Cold Rydberg atoms are known to display dipole-dipole interaction allowed
resonances, also called F\"orster resonances, which lead to an efficient energy
transfer when the proper electric field is used. This electric field also
enables resonances which do not respect the dipole-dipole selection rules under
zero field. A few of these quasi-forbidden resonances have been observed but
they are often overlooked. Here we show that in cold $^{133}$Cs atoms there is
a large number of these resonances that display a significant transfer
efficiency due to their strong interactions, even at low electric field. We
also develop a graphical method enabling to find all possible resonances
simultaneously. The resulting dramatic increase in the total number of
addressable resonant energy transfers at different electric fields could have
implications in the search for few-body interactions or macro-molecules built
from Rydberg atoms.


  Fast radio bursts (FRBs) are millisecond radio transients observed at
cosmological distances. The nature of their progenitors is still a matter of
debate, although magnetars are invoked by most models. The proposed
FRB-magnetar connection was strengthened by the discovery of an FRB-like event
from the Galactic magnetar SGR J1935+2154. In this work, we aim to investigate
how prevalent magnetars such as SGR J1935+2154 are within FRB progenitors. We
carried out an FRB search in a sample of seven nearby (< 12 Mpc) galaxies with
the Northern Cross radio telescope for a total of 692 h. We detected one 1.8 ms
burst in the direction of M101 with a fluence of $58 \pm 5$ Jy ms. Its
dispersion measure of 303 pc cm$^{-3}$ places it most-likely beyond M101.
Considering that no significant detection comes indisputably from the selected
galaxies, we place a 38 yr$^{-1}$ upper limit on the total burst rate (i.e.
including the whole sample) at the 95\% confidence level. This upper limit
constrains the event rate per magnetar $\lambda_{\rm mag} < 0.42$
magnetar$^{-1}$ yr$^{-1}$ or, if combined with literature observations of a
similar sample of nearby galaxies, it yields a joint constraint of
$\lambda_{\rm mag} < 0.25$ magnetar$^{-1}$ yr$^{-1}$. We also provide the first
constraints on the expected rate of FRBs hypothetically originating from
ultraluminous X-ray (ULX) sources, since some of the galaxies observed during
our observational campaign host confirmed ULXs. We obtain $< 13$ yr$^{-1}$ per
ULX for the total sample of galaxies observed. Our results indicate that bursts
with energies $E>10^{34}$ erg from magnetars like SGR J1935+2154 appear more
rarely compared to previous observations and further disfavour them as unique
progenitors for the cosmological FRB population, leaving more space open to the
contribution from a population of more exotic magnetars, not born via
core-collapsed supernovae.


  We classify tube domains in $\mathbb C^{n+1}$ ($n\ge 1$) with affinely
homogeneous base of their boundary and a.) with positive definite Levi form and
b.) with Lorentzian type Levi form and affine isotropy of dimension at least
$\frac{(n-2)(n-3)}2$.


  We compute the quantum circuit complexity of the evolution of scalar
curvature perturbations on expanding backgrounds, using the language of
squeezed vacuum states. In particular, we construct a simple cosmological model
consisting of an early-time period of de Sitter expansion followed by a
radiation-dominated era and track the evolution of complexity throughout this
history. During early-time de Sitter expansion the complexity grows linearly
with the number of e-folds for modes outside the horizon. The evolution of
complexity also suggests that the Universe behaves like a chaotic system during
this era, for which we propose a scrambling time and Lyapunov exponent. During
the radiation-dominated era, however, the complexity decreases until it
"freezes in" after horizon re-entry, leading to a "de-complexification" of the
Universe.


  In this work, we provide a non-perturbative description of the phenomenon of
dynamical mass generation in the case of quantum electrodynamics in $2+1$
dimensions. We will use the Kugo-Ojima-Nakanishi formalism to conclude that the
physical Hilbert space of the asymptotic photon field is the same as that of
the Maxwell-Chern-Simons.


  We study the minimax optimal rate for estimating the Wasserstein-$1$ metric
between two unknown probability measures based on $n$ i.i.d. empirical samples
from them. We show that estimating the Wasserstein metric itself between
probability measures, is not significantly easier than estimating the
probability measures under the Wasserstein metric. We prove that the minimax
optimal rates for these two problems are multiplicatively equivalent, up to a
$\log \log (n)/\log (n)$ factor.


  The role of data libraries as a collaborative tool across Monte Carlo codes
is discussed. Some new contributions in this domain are presented; they concern
a data library of proton and alpha ionization cross sections, the development
in progress of a data library of electron ionization cross sections and
proposed improvements to the EADL (Evaluated Atomic Data Library), the latter
resulting from an extensive data validation process.


  We study Read and Green's mean-field model of the spinless $p_x+ip_y$
superconductor [N.Read and D.Green, Phys. Rev. B 61, 10267 (2000)] at a special
set of parameters where we find the analytic expressions for the topologically
degenerate ground states and the Majorana modes, including in finite systems
with edges and in the presence of an arbitrary number of vortices. The
wavefunctions of these ground states are similar (but not always identical) to
the Moore-Read Pfaffian states proposed for the $\nu=5/2$ fractional quantum
Hall system, which are interpreted as the p-wave superconducting states of
composite fermions. The similarity in the long-wavelength universal properties
is expected from previous work, but at the special point studied herein the
wavefunctions are exact even for short-range, non-universal properties. As an
application of these results, we show how to obtain the non-Abelian statistics
of the vortex Majorana modes by explicitly calculating the analytic
continuation of the ground state wavefunctions when vortices are adiabatically
exchanged, an approach different from the previous one based on universal
arguments. Our results are also useful for constructing particle
number-conserving (and interacting) Hamiltonians with exact projected
mean-field states.


  In the nearby universe jets from AGN are observed to have a dramatic impact
on their surrounding extragalactic environment. Their effect at the `cosmic
noon' (z>1.5), the epoch when star formation and AGN activity peak, is instead
much less constrained. Here we present a study of the giant (750 kpc) radio
galaxy 103025+052430 located at the centre of a protocluster at redshift z=1.7,
with a focus on its interaction with the external medium. We present new LOFAR
observations at 144 MHz, which we combine with VLA 1.4 GHz and 0.5-7 keV
Chandra archival data. The new map at 144 MHz confirms that the source has a
complex morphology, possibly consistent with the `hybrid morphology'
classification. The large size of the source gave us the possibility to perform
a resolved radio spectral index analysis, a very unique opportunity for a
source at such high redshift. This reveals a tentative flattening of the radio
spectral index at the edge of the backflow in the Western lobe, which might be
indicating plasma compression. The spatial coincidence between this region and
the thermal X-ray bubble C suggests a causal connection between the two.
Contrary to previous estimates for the bright X-ray component A, we find that
inverse Compton scattering between the radio-emitting plasma of the Eastern
lobe and the CMB photons can account for a large fraction (~45%-80%) of its
total 0.5-7 keV measured flux. Finally, the X-ray bubble C, which is consistent
with a thermal origin, is found to be significantly overpressurised with
respect to the ambient medium. This suggests that it will tend to expand and
release its energy in the surroundings, contributing to the overall
intracluster medium heating. Overall, 103025+052430 gives us the chance to
investigate the interaction between AGN jets and the surrounding gas in a
system that is likely the predecessor of the rich galaxy clusters we all well
know at z=0.


  We deduce using the Ringel-Hall algebra approach explicit formulas for the
cardinalities of some Grassmannians over a finite field associated to the
Kronecker quiver. We realize in this way a quantification of the formulas
obtained by Caldero and Zelevinsky for the Euler characteristics of these
Grassmannians. We also present a recursive algorithm for computing the
cardinality of every Kronecker quiver Grassmannian over a finite field.


  We measure the large-scale cross-correlation of quasars with the Lyman
\alpha\ forest absorption in redshift space, using ~ 60000 quasar spectra from
Data Release 9 (DR9) of the Baryon Oscillation Spectroscopic Survey (BOSS). The
cross-correlation is detected over a wide range of scales, up to comoving
separations r of 80 Mpc/h. For r > 15 Mpc/h, we show that the cross-correlation
is well fitted by the linear theory prediction for the mean overdensity around
a quasar host halo in the standard \Lambda CDM model, with the redshift
distortions indicative of gravitational evolution detected at high confidence.
Using previous determinations of the Lyman \alpha\ forest bias factor obtained
from the Lyman \alpha\ autocorrelation, we infer the quasar bias factor to be
b_q = 3.64^+0.13_-0.15 at a mean redshift z=2.38, in agreement with previous
measurements from the quasar auto-correlation. We also obtain a new estimate of
the Lyman \alpha\ forest redshift distortion factor, \beta_F = 1.1 +/- 0.15,
slightly larger than but consistent with the previous measurement from the
Lyman \alpha\ forest autocorrelation. The simple linear model we use fails at
separations r < 15 Mpc/h, and we show that this may reasonably be due to the
enhanced ionization due to radiation from the quasars. We also provide the
expected correction that the mass overdensity around the quasar implies for
measurements of the ionizing radiation background from the line-of-sight
proximity effect.


  We extend results of Kass--Wickelgren to define an Euler class for a
non-orientable (or non-relatively orientable) vector bundle on a smooth scheme,
valued in the Grothendieck--Witt group of the ground field. We use a root stack
construction to produce this Euler class and discuss its relation to other
versions of an Euler class in $\mathbb{A}^{1}$-homotopy theory. This allows one
to apply Kass--Wickelgren's technique for arithmetic enrichments of enumerative
geometry to a larger class of problems; as an example, we use our construction
to give an arithmetic count of the number of lines meeting $6$ planes in
$\mathbb{P}^4$.


  An artificial lateral line (ALL) is a bioinspired flow sensing system of an
underwater robot that consists of distributed flow sensors. The ALL has
achieved great success in sensing the motion states of bioinspired underwater
robots, e.g., robotic fish, that are driven by body undulation and/or tail
flapping. However, the ALL has not been systematically tested and studied in
the sensing of underwater robots driven by rotating propellers due to the
highly dynamic and complex flow field therein. This paper makes a bold
hypothesis that the distributed flow measurements sampled from the propeller
wake flow, although infeasible to represent the entire flow dynamics, provides
sufficient information for estimating the lateral motion states of the leader
underwater robot. An experimental testbed is constructed to investigate the
feasibility of such a state estimator which comprises a cylindrical ALL sensory
system, a rotating leader propeller, and a water tank with a planar sliding
guide. Specifically, a hybrid network that consists of a one-dimensional
convolution network (1DCNN) and a bidirectional long short-term memory network
(BiLSTM) is designed to extract the spatiotemporal features of the time series
of distributed pressure measurements. A multi-output deep learning network is
adopted to estimate the lateral motion states of the leader propeller. In
addition, the state estimator is optimized using the whale optimization
algorithm (WOA) considering the comprehensive estimation performance. Extensive
experiments are conducted the results of which validate the proposed
data-driven algorithm in estimating the motion states of the leader underwater
robot by propeller wake sensing.


  The abundance spectrum of Na^-_{n~7-140} anions formed by low energy electron
attachment to free nanoclusters is measured to be strongly and nontrivially
restructured with respect to the neutral precursor beam. This restructuring is
explained in quantitative detail by a general framework of evaporative
attachment: an electron is captured by the long-range polarization potential,
its energy is transferred into thermal vibrations, and dissipated by
evaporative cooling. The data also affirm a formulated relation between the
binding energies of cationic, neutral, and anionic clusters, and an adjustment
to the prior values of dimer evaporation energies.


  In this paper we extend the investigation of Adami and Ver Steeg [Class.
Quantum Grav. \textbf{31}, 075015 (2014)] to treat the process of black hole
particle emission effectively as the analogous quantum optical process of
parametric down conversion (PDC) with a dynamical (depleted vs. non-depleted)
`pump' source mode which models the evaporating black hole (BH) energy degree
of freedom. We investigate both the short time (non-depleted pump) and long
time (depleted pump) regimes of the quantum state and its impact on the Holevo
channel capacity for communicating information from the far past to the far
future in the presence of Hawking radiation. The new feature introduced in this
work is the coupling of the emitted Hawking radiation modes through the common
black hole `source pump' mode which phenomenologically represents a quantized
energy degree of freedom of the gravitational field. This (zero-dimensional)
model serves as a simplified arena to explore BH particle
production/evaporation and back-action effects under an explicitly unitary
evolution which enforces quantized energy/particle conservation. Within our
analogous quantum optical model we examine the entanglement between two emitted
particle/anti-particle and anti-particle/particle pairs coupled via the black
hole (BH) evaporating `pump' source. We also analytically and dynamically
verify the `Page information time' for our model which refers to the
conventionally held belief that the information in the BH radiation becomes
significant after the black hole has evaporated half its initial energy into
the outgoing radiation. Lastly, we investigate the effect of BH particle
production/evaporation on two modes in the exterior region of the BH event
horizon that are initially maximally entangled, when one mode falls inward and
interacts with the black hole, and the other remains forever outside and
non-interacting.


  We show that each limiting semiclassical measure obtained from a sequence of
eigenfunctions of the Laplacian on a compact hyperbolic surface is supported on
the entire cosphere bundle. The key new ingredient for the proof is the fractal
uncertainty principle, first formulated in [arXiv:1504.06589] and proved for
porous sets in [arXiv:1612.09040].


  This is a copy of the article by the same authors published in Duke Math. J.
(1994).


  We present the design of a quantum carry-lookahead adder using
measurement-based quantum computation. The quantum carry-lookahead adder (QCLA)
is faster than a quantum ripple-carry adder; QCLA has logarithmic depth while
ripple adders have linear depth. Our design is evaluated in terms of number of
time steps and the total number of qubits used.


  When a mixture of two kinds of grains differing in size and shape is poured
in a vertical two-dimensional cell, the mixture spontaneously stratifies in
alternating layers of small and large grains, whenever the large grains are
more faceted than the small grains. Otherwise, the mixture spontaneously
segregates in different regions of the cell when the large grains are more
rounded than the small grains. We address the question of the origin of the
instability mechanism leading to stratification using a recently proposed set
of equations for surface flow of granular mixtures. We show that the stable
solution of the system is a segregation solution due to size (large grains tend
to segregate downhill near the substrate and small grains tend to segregate
uphill) and shape (rounded grains tend to segregate downhill and more faceted
grains tend to segregate uphill). As a result, the segregation solution of the
system is realized for mixtures of large-rounded grains and small-cubic grains
with the large-rounded grains segregating near the bottom of the pile.
Stability analysis reveals the instability mechanism driving the system to
stratification as a competition between size-segregation and shape-segregation
taking place for mixtures of large-cubic grains and small-rounded grains. The
large-cubic grains tend to size-segregate at the bottom of the pile, while at
the same time, they tend to shape-segregate near the pouring point. Thus, the
segregation solution becomes unstable, and the system evolves spontaneously to
stratification.


  Quandle 2-cocycles define invariants of classical and virtual knots, and
extensions of quandles. We show that the quandle 2-cocycle invariant with
respect to a non-trivial $2$-cocycle is constant, or takes some other
restricted form, for classical knots when the corresponding extensions satisfy
certain algebraic conditions. In particular, if an abelian extension is a
conjugation quandle, then the corresponding cocycle invariant is constant.
Specific examples are presented from the list of connected quandles of order
less than 48. Relations among various quandle epimorphisms involved are also
examined.


  Hybrid ECAL is a cost-conscious option of electromagnetic calorimeter (ECAL)
for particle flow calorimetry to be used in a detector of International Linear
Collider (ILC). It is a combination of silicon-tungsten ECAL, which realizes
high granularity and robust measurement of electromagnetic shower, and
scintillator-tungsten ECAL, which gives affordable cost with similar
performance to silicon. Optimization and a data acquisition trial in a test
bench for the hybrid ECAL are described in this article.


  We present a first calculation of the heavy flavor contribution to the
longitudinally polarized DIS structure function $g_1$, differential in the
transverse momentum or the rapidity of the observed heavy antiquark
$\overline{Q}$. All results are obtained at next-to-leading order accuracy with
a newly developed parton-level Monte Carlo generator that also allows one to
study observables associated with the heavy quark pair such as its invariant
mass distribution or its correlation in azimuthal angle. First phenomenological
studies are presented in a kinematic regime relevant for a future Electron-Ion
Collider with a particular emphasis on the sensitivity to the helicity gluon
distribution. Finally, we also provide first NLO results for the full
neutral-current sector of polarized DIS, i.e., including contributions from
Z-boson exchange.


  This paper deals with a situation of some importance for the analysis of
experimental data via Neural Network (NN) or similar devices: Let $N$ data be
given, such that $N=N_s+N_b$, where $N_s$ is the number of signals, $N_b$ the
number of background events, both unknown. Assume that a NN has been trained,
such that it will tag signals with efficiency $F_s$, $(0<F_s<1)$ and background
data with $F_b$, $(0<F_b<1)$. Applying the NN yields $N^Y$ tagged events. We
demonstrate that the knowledge of $N^Y$ is sufficient to calculate confidence
bounds for the signal likelihood, which have the same statistical
interpretation as the Clopper-Pearson bounds for the well-studied case of
direct signal observation. Subsequently, we discuss rigorous bounds for the
a-posteriori distribution function of the signal probability, as well as for
the (closely related) likelihood that there are $N_s$ signals in the data. We
compare them with results obtained by starting off with a maximum entropy type
assumption for the a-priori likelihood that there are $N_s$ signals in the data
and applying the Bayesian theorem. Difficulties are encountered with the latter
method.


  We report on experiments in which magnetically driven radiatively cooled
plasma jets were produced by a 1 MA, 250 ns current pulse on the MAGPIE pulsed
power facility. The jets were driven by the pressure of a toroidal magnetic
field in a ''magnetic tower'' jet configuration. This scenario is characterized
by the formation of a magnetically collimated plasma jet on the axis of a
magnetic ''bubble'', confined by the ambient medium. The use of a radial
metallic foil instead of the radial wire arrays employed in our previous work
allows for the generation of episodic magnetic tower outflows which emerge
periodically on timescales of ~30 ns. The subsequent magnetic bubbles propagate
with velocities reaching ~300 km/s and interact with previous eruptions leading
to the formation of shocks.


  We study the interaction of focused radially-polarized light with metal
nanospheres. By expanding the electromagnetic field in terms of multipoles, we
gain insight on the excitation of localized surface plasmon-polariton
resonances in the nanoparticle. We show that focused radially-polarized beams
offer more opportunities than a focused plane wave or a Gaussian beam for
tuning the near- and far-field system response. These results find applications
in nano-optics, optical tweezers, and optical data storage.


  We propose and implement a novel scheme for dissipatively pumping two qubits
into a singlet Bell state. The method relies on a process of collective optical
pumping to an excited level, to which all states apart from the singlet are
coupled. We apply the method to deterministically entangle two trapped
${}^{40}\text{Ca}^+$ ions with a fidelity of $93(1)\%$. We theoretically
analyze the performance and error susceptibility of the scheme and find it to
be insensitive to a large class of experimentally relevant noise sources.


  For any Lie supergroup whose underlying Lie group is reductive, we prove an
extension of the Casselman-Wallach globalisation theorem: There is an
equivalence between the category of Harish-Chandra modules and the category of
SF-representations (smooth Fr\'echet representations of moderate growth) whose
module of finite vectors is Harish-Chandra. As an application, we extend to Lie
supergroups a general general form of the Gel'fand-Kazhdan criterion due to
Sun-Zhu.


  This paper proposes averaging estimation methods to improve the finite-sample
efficiency of the instrumental variables quantile regression (IVQR) estimation.
First, I apply Cheng, Liao, Shi's (2019) averaging GMM framework to the IVQR
model. I propose using the usual quantile regression moments for averaging to
take advantage of cases when endogeneity is not too strong. I also propose
using two-stage least squares slope moments to take advantage of cases when
heterogeneity is not too strong. The empirical optimal weight formula of Cheng
et al. (2019) helps optimize the bias-variance tradeoff, ensuring uniformly
better (asymptotic) risk of the averaging estimator over the standard IVQR
estimator under certain conditions. My implementation involves many
computational considerations and builds on recent developments in the quantile
literature. Second, I propose a bootstrap method that directly averages among
IVQR, quantile regression, and two-stage least squares estimators. More
specifically, I find the optimal weights in the bootstrap world and then apply
the bootstrap-optimal weights to the original sample. The bootstrap method is
simpler to compute and generally performs better in simulations, but it lacks
the formal uniform dominance results of Cheng et al. (2019). Simulation results
demonstrate that in the multiple-regressors/instruments case, both the GMM
averaging and bootstrap estimators have uniformly smaller risk than the IVQR
estimator across data-generating processes (DGPs) with all kinds of
combinations of different endogeneity levels and heterogeneity levels. In DGPs
with a single endogenous regressor and instrument, where averaging estimation
is known to have least opportunity for improvement, the proposed averaging
estimators outperform the IVQR estimator in some cases but not others.


  This work studies improving large language model (LLM) generations at
inference time by mitigating fact-conflicting hallucinations. Particularly, we
propose a self-endorsement framework that leverages the fine-grained fact-level
comparisons across multiple sampled responses. Compared with prior ensemble
methods (Wang et al., 2022;Chen et al., 2023)) that perform response-level
selection, our approach can better alleviate hallucinations, especially for
longform generation tasks. Our approach can broadly benefit smaller and
open-source LLMs as it mainly conducts simple content-based comparisons.
Experiments on Biographies show that our method can effectively improve the
factuality of generations with simple and intuitive prompts across different
scales of LLMs. Besides, comprehensive analyses on TriviaQA and GSM8K
demonstrate the potential of self-endorsement for broader application.


  Cosmological inflation is discussed in the framework of $F(R,{\cal G})$
gravity where $F$ is a generic function of the curvature scalar $R$ and the
Gauss-Bonnet topological invariant $\cal G$. The main feature that emerges in
this analysis is the fact that this kind of theory can exhaust all the
curvature budget related to curvature invariants without considering
derivatives of $R,$ $R_{\mu\nu}$, $R^{\lambda}_{\sigma\mu\nu}$ etc. in the
action. Cosmological dynamics results driven by two effective masses (lenghts)
related to the $R$ scalaron and the $\cal G$ scalaron working respectively at
early and very early epochs of cosmic evolution. In this sense, a double
inflationary scenario naturally emerges.


  Fine-grained recognition distinguishes among categories with subtle visual
differences. In order to differentiate between these challenging visual
categories, it is helpful to leverage additional information. Geolocation is a
rich source of additional information that can be used to improve fine-grained
classification accuracy, but has been understudied. Our contributions to this
field are twofold. First, to the best of our knowledge, this is the first paper
which systematically examined various ways of incorporating geolocation
information into fine-grained image classification through the use of
geolocation priors, post-processing or feature modulation. Secondly, to
overcome the situation where no fine-grained dataset has complete geolocation
information, we release two fine-grained datasets with geolocation by providing
complementary information to existing popular datasets - iNaturalist and
YFCC100M. By leveraging geolocation information we improve top-1 accuracy in
iNaturalist from 70.1% to 79.0% for a strong baseline image-only model.
Comparing several models, we found that best performance was achieved by a
post-processing model that consumed the output of the image-only baseline
alongside geolocation. However, for a resource-constrained model (MobileNetV2),
performance was better with a feature modulation model that trains jointly over
pixels and geolocation: accuracy increased from 59.6% to 72.2%. Our work makes
a strong case for incorporating geolocation information in fine-grained
recognition models for both server and on-device.


  We numerically study spin turbulence in a two-dimensional trapped spin-1
spinor Bose--Einstein condensate, focusing on the energy spectrum. The spin
turbulence in the trapped system is generated by instability of the helical
structure of the spin density vector in the initial state. Our numerical
calculation finds that in the trapped system the spectrum of the spin-dependent
interaction energy in the ferromagnetic case exhibits a -7/3 power law, which
was confirmed in a uniform system by our previous study. The relation between
the -7/3 power law and the motion of the spin density vector is discussed by
investigating the orbits of dynamical variables in the spin space.


  Keeping fit has been increasingly important for people nowadays. However,
people may not get expected exercise results without following professional
guidance while hiring personal trainers is expensive. In this paper, an
effective real-time system called Fitness Done Right (FDR) is proposed for
helping people exercise correctly on their own. The system includes detecting
human body parts, recognizing exercise pose and detecting errors for test poses
as well as giving correction advice. Generally, two branch multi-stage CNN is
used for training data sets in order to learn human body parts and
associations. Then, considering two poses, which are plank and squat in our
model, we design a detection algorithm, combining Euclidean and angle
distances, to determine the pose in the image. Finally, key values for key
features of the two poses are computed correspondingly in the pose error
detection part, which helps give correction advice. We conduct our system in
real-time situation with error rate down to $1.2\%$, and the screenshots of
experimental results are also presented.


  We explore the reflection-transmission quantum Yang-Baxter equations, arising
in factorized scattering theory of integrable models with impurities. The
physical origin of these equations is clarified and three general families of
solutions are described in detail. Explicit representatives of each family are
also displayed. These results allow to establish a direct relationship with the
different previous works on the subject and make evident the advantages of the
reflection-transmission algebra as an universal approach to integrable systems
with impurities.


  While motion planning approaches for automated driving often focus on safety
and mathematical optimality with respect to technical parameters, they barely
consider convenience, perceived safety for the passenger and comprehensibility
for other traffic participants. For automated driving in mixed traffic,
however, this is key to reach public acceptance. In this paper, we revise the
problem statement of motion planning in mixed traffic: Instead of largely
simplifying the motion planning problem to a convex optimization problem, we
keep a more complex probabilistic multi agent model and strive for a near
optimal solution. We assume cooperation of other traffic participants, yet
being aware of violations of this assumption. This approach yields solutions
that are provably safe in all situations, and convenient and comprehensible in
situations that are also unambiguous for humans. Thus, it outperforms existing
approaches in mixed traffic scenarios, as we show in simulation.


  In online communities, recent studies have strongly improved our knowledge
about the different types or profiles of contributors, from casual to very
involved ones, through focused people. However they do so by using very complex
methodologies (qualitative-quantitative mix, with a high workload to manually
codify/characterize the edits), making their replication for the practitioners
limited. These studies are on the English Wikipedia only. The objective of this
paper is to highlight different profiles of contributors with clustering
techniques. The originality is to show how using only the edits, and their
distribution over time, allows to build these contributors profiles with a good
accuracy and stability amongst languages. The methodology is validated with
both Romanian and Danish wikis. The highlighted profiles are identifiable early
in the history of involvement, suggesting that light monitoring of newcomers
may be sufficient to adapt the interaction with them and increase the retention
rate.


  We prove that all tree-level $n$-point supergluon (scalar) amplitudes in
AdS$_5$ can be recursively constructed, using factorization and flat-space
limit. Our method is greatly facilitated by a natural R-symmetry basis for
planar color-ordered amplitudes, which reduces the latter to "partial
amplitudes" with simpler pole structures and factorization properties. Given
the $n$-point scalar amplitude, we first extract spinning amplitudes with
$n{-}2$ scalars and one gluon by imposing "gauge invariance", and then use a
special "no-gluon kinematics" to determine the $(n{+}1)$-point scalar amplitude
completely (which in turn contains the $n$-point single-gluon amplitude).
Explicit results of up to 8-point scalar amplitudes and up to 6-point
single-gluon amplitudes are included as supplemental materials.


  We found that a network-organized metapopulation of cooperators, defectors
and destructive agents playing the public goods game with mutations, can
collectively reach global synchronization or chimera states. Global
synchronization is accompanied by a collective periodic burst of cooperation,
whereas chimera states reflect the tendency of the networked metapopulation to
be fragmented in clusters of synchronous and incoherent bursts of cooperation.
Numerical simulations have shown that the system's dynamics alternates between
these two steady states through a first order transition. Depending on the
parameters determining the dynamical and topological properties, chimera states
with different numbers of coherent and incoherent clusters are observed. Our
results present the first systematic study of chimera states and their
characterization in the context of evolutionary game theory. This provides a
valuable insight into the details of their occurrence, extending the relevance
of such states to natural and social systems.


  In a mesoscopic metal in proximity with a superconductor, the electronic
conductance is enhanced in a very energy-sensitive way. In this paper, we
discuss the spectral conductance of a proximity superconductor from both the
theoretical and experimental point of view. The dependence of the spectral
conductance on the phase-breaking length, gap of the superconductor and
interface transparency is theoretically investigated. We present experimental
data on the re-entrance of the normal-state conductance at very low temperature
and bias voltage. A complete description of the experimental data needs taking
into account heating of the reservoirs by the bias current. In addition, we
show that the energy sensitivity of the proximity effect enables one to access
the energy distribution of the conduction electrons inside a mesoscopic sample.


  With the recent surge of interest in introducing autonomous vehicles to the
everyday lives of people, developing accurate and generalizable algorithms for
predicting human behavior becomes highly crucial. Moreover, many of these
emerging applications occur in a safety-critical context, making it even more
urgent to develop good prediction models for human-operated vehicles. This is
fundamentally a challenging task as humans are often noisy in their decision
processes. Hamilton-Jacobi (HJ) reachability is a useful tool in control theory
that provides safety guarantees for collision avoidance. In this paper, we
first demonstrate how to incorporate information derived from HJ reachability
into a machine learning problem which predicts human behavior in a simulated
collision avoidance context, and show that this yields a higher prediction
accuracy than learning without this information. Then we propose a framework to
generate stochastic forward reachable sets that flexibly provides different
safety probabilities and generalizes to novel scenarios. We demonstrate that we
can construct stochastic reachable sets that can capture the trajectories with
probability from 0.75 to 1.


  The emergence of ChatGPT and other large language models (LLMs) has greatly
increased interest in utilizing LLMs as therapists to support individuals
struggling with mental health challenges. However, due to the lack of
systematic studies, our understanding of how LLM therapists behave, i.e., ways
in which they respond to clients, is significantly limited. Understanding their
behavior across a wide range of clients and situations is crucial to accurately
assess their capabilities and limitations in the high-risk setting of mental
health, where undesirable behaviors can lead to severe consequences. In this
paper, we propose BOLT, a novel computational framework to study the
conversational behavior of LLMs when employed as therapists. We develop an
in-context learning method to quantitatively measure the behavior of LLMs based
on 13 different psychotherapy techniques including reflections, questions,
solutions, normalizing, and psychoeducation. Subsequently, we compare the
behavior of LLM therapists against that of high- and low-quality human therapy,
and study how their behavior can be modulated to better reflect behaviors
observed in high-quality therapy. Our analysis of GPT and Llama-variants
reveals that these LLMs often resemble behaviors more commonly exhibited in
low-quality therapy rather than high-quality therapy, such as offering a higher
degree of problem-solving advice when clients share emotions, which is against
typical recommendations. At the same time, unlike low-quality therapy, LLMs
reflect significantly more upon clients' needs and strengths. Our analysis
framework suggests that despite the ability of LLMs to generate anecdotal
examples that appear similar to human therapists, LLM therapists are currently
not fully consistent with high-quality care, and thus require additional
research to ensure quality care.


  We present an analysis of the Hubble Space Telescope/Space Telescope Imaging
Spectrograph (HST/STIS) far ultraviolet snapshot spectrum of the dwarf nova BZ
UMa taken during quiescence. We find that the flux continuum is consistent with
that of a white dwarf atmosphere with a temperature of 15,000K, and a distance
of about 156pc when assuming a white dwarf gravity of log(g)=7.5. We also show
that the hydrogen quasi-molecular satellite lines opacity affects the spectrum
around 1300-1400 A and has to be included to fine-tune the spectral modeling,
this is self-consistent with the low stellar temperature we find.


  Superconducting circuits coupled to acoustic waveguides have extended the
range of phenomena that can be experimentally studied using tools from quantum
optics. In particular giant artificial atoms permit the investigation of
systems in which the electric dipole approximation breaks down and pronounced
non-Markovian effects become important. While previous studies of giant atoms
focused on the realm of the rotating-wave approximation, we go beyond this and
perform a numerically exact analysis of giant atoms strongly coupled to their
environment, in regimes where counterrotating terms cannot be neglected. To
achieve this, we use a Lanczos transformation to cast the field Hamiltonian
into the form of a one-dimensional chain and employ matrix-product state
simulations. This approach yields access to a wide range of system-bath
observables and to previously unexplored parameter regimes.


  We report a precise $^{59}$Co nuclear quadrupolar resonance (NQR) measurement
on the recently discovered cobalt oxyhydrate Na$_{0.35}$CoO$_{2}$$\cdot
y$H$_{2}$O superconductor from $T$=40 K down to 0.2 K. We find that in the
normal state the spin-lattice relaxation rate $1/T_1$ follows a Curie-Weiss
type temperature ($T$) variation, $1/T_1T=C/(T-\theta)$, with $\theta$=-42 K,
suggesting two-dimensional antiferromagnetic spin correlations. Below $T_c$=3.9
K, $1/T_1$ decreases with no coherence peak and follows a $T^n$ dependence with
$n\simeq$2.2 down to $\sim$2.0 K but crosses over to a $1/T_1\propto T$
variation below $T$=1.4 K, which suggests non s-wave superconductivity. The
data in the superconducting state are most consistent with the existence of
line nodes in the gap function.


  We present a chemical abundance analysis of four additional confirmed member
stars of Tucana III, a Milky Way satellite galaxy candidate in the process of
being tidally disrupted as it is accreted by the Galaxy. Two of these stars are
centrally located in the core of the galaxy while the other two stars are
located in the eastern and western tidal tails. The four stars have chemical
abundance patterns consistent with the one previously studied star in Tucana
III: they are moderately enhanced in $r$-process elements, i.e. they have
$<$[Eu/Fe]$> \approx +$0.4 dex. The non-neutron-capture elements generally
follow trends seen in other dwarf galaxies, including a metallicity range of
0.44 dex and the expected trend in $\alpha$-elements, i.e., the lower
metallicity stars have higher Ca and Ti abundance. Overall, the chemical
abundance patterns of these stars suggest that Tucana III was an ultra-faint
dwarf galaxy, and not a globular cluster, before being tidally disturbed. As is
the case for the one other galaxy dominated by $r$-process enhanced stars,
Reticulum II, Tucana III's stellar chemical abundances are consistent with
pollution from ejecta produced by a binary neutron star merger, although a
different $r$-process element or dilution gas mass is required to explain the
abundances in these two galaxies if a neutron star merger is the sole source of
$r$-process enhancement.


  We study the bias-dependent spin-transfer torque in magnetic tunnel junctions
in the Stoner model by scattering theory. We show that the in-plane
(Slonczewski type) torque vanishes and subsequently reverses its direction when
the bias voltage becomes larger or the barrier wider than material and
device-dependent critical values. We are able to reproduce the magnitude and
the bias dependence of measured in-plane and out-of-plane torques using
realistic parameters. The condition for the vanishing torque is summarized by a
phase diagram depending on the applied bias and barrier width, which is
explained in terms of an interface spin polarization and the electron focusing
by the barrier. Quantum size effects in the spin-transfer torque are predicted
as a function of the thickness of a normal metal layer inserted between the
ferromagnet and tunnel barrier.


  The implementation of QED initial-state radiative corrections in the process
of four-fermion production at LEP200 and higher-energy e+e- colliders is
discussed. Because of the presence of charged-current processes, this is a
nontrivial problem, and we compare our approach with other existing treatments.
We describe the Monte Carlo algorithm used for the generation of 4-fermion
events with photon bremsstrahlung. Comparison between our event generator and
semi-numerical calculations are presented, as well as predictions for W and Z
pair related cross sections.


  We investigate the competition between anisotropic excluded-volume and
repulsive electrostatic interactions in suspensions of thin charged colloidal
discs, by means of Monte-Carlo simulations and dynamical characterization of
the structures found. We show that the original intrinsic anisotropy of the
electrostatic potential between charged platelets, obtained within the
non-linear Poisson-Boltzmann formalism, not only rationalizes the generic
features of the complex phase diagram of charged colloidal platelets such as
Gibbsite and Beidellite clays, but also predicts the existence of novel
structures. In addition, we find evidences of a strong slowing down of the
dynamics upon increasing density.


  Detecting abrupt changes in the mean of a time series, so-called
changepoints, is important for many applications. However, many procedures rely
on the estimation of nuisance parameters (like long-run variance). Under the
alternative (a change in mean), estimators might be biased and data-adaptive
rules for the choice of tuning parameters might not work as expected. If the
data is not stationary, but heteroscedastic, this becomes more challenging. The
aim of this paper is to present and investigate two changepoint tests, which
involve neither nuisance nor tuning parameters. This is achieved by combing
self-normalization and wild bootstrap. We study the asymptotic behavior and
show the consistency of the bootstrap under the hypothesis as well as under the
alternative, assuming mild conditions on the weak dependence of the time series
and allowing the variance to change over time. As a by-product of the proposed
tests, a changepoint estimator is introduced and its consistency is proved. The
results are illustrated through a simulation study, which demonstrates
computational efficiency of the developed methods. The new tests will also be
applied to real data examples from finance and hydrology.


  We study the degree distribution of a randomly chosen vertex in a
duplication--divergence graph, under a variety of different generalizations of
the basic model of Bhan, Galas and Dewey (2002) and V\'azquez, Flammini,
Maritan and Vespignani (2003). We pay particular attention to what happens when
a non-trivial proportion of the vertices have large degrees, establishing a
central limit theorem for the logarithm of the degree distribution. Our
approach, as in Jordan (2018) and Hermann and Pfaffelhuber (2021), relies
heavily on the analysis of related birth--catastrophe processes, and couplings
are used to show that a number of different formulations of the process have
asymptotically similar expected degree distributions.


  Large-scale quantum information processors must be able to transport and
maintain quantum information, and repeatedly perform logical operations. Here
we demonstrate a combination of all the fundamental elements required to
perform scalable quantum computing using qubits stored in the internal states
of trapped atomic ions. We quantify the repeatability of a multi-qubit
operation, observing no loss of performance despite qubit transport over
macroscopic distances. Key to these results is the use of different pairs of
beryllium ion hyperfine states for robust qubit storage, readout and gates, and
simultaneous trapping of magnesium re-cooling ions along with the qubit ions.


  In this paper, we state two combination theorems for relatively quasiconvex
subgroups in a relatively hyperbolic group. Applications are given to the
separability of double cosets of certain relatively quasiconvex subgroups and
the existence of closed surface subgroups in relatively hyperbolic groups.


  After recalling the Dirichlet problem at infinity on a Cartan-Hadamard
manifold, we discuss what is known and the difference between the
two-dimensional and higher-dimensional cases. Turning our attention to the
two-dimensional case, we prove that the Dirichlet problem at infinity on a
two-dimensional Cartan-Hadamard manifold is solvable under the curvature
condition $K\leq (1+\epsilon)/(r^2 \log r)$, outside of a compact set, for some
$\epsilon>0$ in polar coordinates around some pole. This condition on the
curvature is sharp, and improves upon the previously known case of quadratic
curvature decay. Finally, we briefly discuss the issues which arise in trying
to extend this method to higher dimensions.


  We establish an analogue of the Grothendieck inequality where the rectangular
matrix is replaced by a symmetric/Hermitian matrix and the bilinear form by a
quadratic form. We call this the symmetric Grothendieck inequality; despite its
name, it is a generalization -- the original Grothendieck inequality is a
special case. While there are other proposals for such an inequality, ours
differs in two important ways: (i) we have no additional requirement like
positive semidefiniteness; (ii) our symmetric Grothendieck constant is
universal, i.e., independent of the matrix and its dimensions. A consequence of
our symmetric Grothendieck inequality is a "conic Grothendieck inequality" for
any family of cones of symmetric matrices: The original Grothendieck inequality
is a special case; as is the Nesterov $\pi/2$-Theorem, which corresponds to the
cones of positive semidefinite matrices; as well as the Goemans-Williamson
inequality, which corresponds to the cones of Laplacians. For yet other cones,
e.g., of diagonally dominant matrices, we get new Grothendieck-like
inequalities. A slight extension leads to a unified framework that treats any
Grothendieck-like inequality as an inequality between two norms within a family
of "Grothendieck norms" restricted to a family of cones. This allows us to
place on equal footing the Goemans-Williamson inequality, Nesterov
$\pi/2$-Theorem, Ben-Tal-Nemirovski-Roos $4/\pi$-Theorem, generalized
Grothendieck inequality, order-$p$ Grothendieck inequality, rank-constrained
positive semidefinite Grothendieck inequality; and in turn allows us to
simplify proofs, extend results from real to complex, obtain new bounds or
establish sharpness of existing ones. The symmetric Grothendieck inequality may
also be applied to obtain polynomial-time approximation bounds for NP-hard
combinatorial, integer, and nonconvex optimization problems.


  Credit card fraud detection based on machine learning has recently attracted
considerable interest from the research community. One of the most important
tasks in this area is the ability of classifiers to handle the imbalance in
credit card data. In this scenario, classifiers tend to yield poor accuracy on
the fraud class (minority class) despite realizing high overall accuracy. This
is due to the influence of the majority class on traditional training criteria.
In this paper, we aim to apply genetic programming to address this issue by
adapting existing fitness functions. We examine two fitness functions from
previous studies and develop two new fitness functions to evolve GP classifier
with superior accuracy on the minority class and overall. Two UCI credit card
datasets are used to evaluate the effectiveness of the proposed fitness
functions. The results demonstrate that the proposed fitness functions augment
GP classifiers, encouraging fitter solutions on both the minority and the
majority classes.


  Many common document formats on the Internet are text-only such as email
(MIME) and the Web (HTML, JavaScript, JSON and XML). To include images or
executable code in these documents, we first encode them as text using base64.
Standard base64 encoding uses 64~ASCII characters: both lower and upper case
Latin letters, digits and two other symbols. We show how we can encode and
decode base64 data at nearly the speed of a memory copy (memcpy) on recent
Intel processors, as long as the data does not fit in the first-level (L1)
cache. We use the SIMD (Single Instruction Multiple Data) instruction set
AVX-512 available on commodity processors. Our implementation generates several
times fewer instructions than previous SIMD-accelerated base64 codecs. It is
also more versatile, as it can be adapted---even at runtime---to any base64
variant by only changing constants.


  Scaling behavior is analyzed for the two-flavor QCD lattice gauge theory
chiral transition. Leading scaling behavior and correction to leading scaling
from lattice spacing effects are examined for the quark condensate. Scaling
predictions under the assumption of quark mass dominance are tested for the
longitudinal correlation length. Second order scaling behavior is consistent
with present data.


  In this article, we prove that if $\mathcal{D}$ is a $2$-design with $k=7$
admitting flag-transitive almost simple automorphism group with socle an
alternating group, then $\mathcal{D}$ is $PG_{2}(3,2)$ with parameter set
$(15,7,3)$ and $G=A_7$, or $\mathcal{D}$ is the $2$-design with parameter set
$(55, 7, 1680)$ and $G=A_{11}$ or $S_{11}$.


  The rectification efficiency of an underdamped ratchet operated in the
adiabatic regime increases according to a scaling current-amplitude curve as
the damping constant approaches a critical threshold; below threshold the
rectified signal becomes extremely irregular and eventually its time average
drops to zero. Periodic (locked) and diffusive (fully chaotic) trajectories
coexist on fine tuning the amplitude of the input signal. The transition from
regular to chaotic transport in noiseless ratchets is studied numerically.


  In this paper we prove the existence of a periodic highest, cusped, traveling
wave solution for the Burgers-Hilbert equation $f_{t} + f f_{x} = H[f]$ and
give its asymptotic behaviour at $0$. The proof combines careful asymptotic
analysis and a computer-assisted approach.


  We report on experimental studies of magnetoresistance oscillations that
originate from the resonant interaction of two-dimensional electrons with
thermal transverse-acoustic phonons in very high-mobility GaAs/AlGaAs quantum
wells. We find that the oscillation maxima consistently occur when a frequency
of a phonon with twice the Fermi momentum exceeds an integer multiple of the
cyclotron frequency. This observation is in contrast to to all previous
experiments associating resistance maxima with magnetophonon resonance and its
harmonics. Our experimentally obtained resonant condition is in excellent
quantitative agreement with recent theoretical proposals.