  We propose contextual convolution (CoConv) for visual recognition. CoConv is
a direct replacement of the standard convolution, which is the core component
of convolutional neural networks. CoConv is implicitly equipped with the
capability of incorporating contextual information while maintaining a similar
number of parameters and computational cost compared to the standard
convolution. CoConv is inspired by neuroscience studies indicating that (i)
neurons, even from the primary visual cortex (V1 area), are involved in
detection of contextual cues and that (ii) the activity of a visual neuron can
be influenced by the stimuli placed entirely outside of its theoretical
receptive field. On the one hand, we integrate CoConv in the widely-used
residual networks and show improved recognition performance over baselines on
the core tasks and benchmarks for visual recognition, namely image
classification on the ImageNet data set and object detection on the MS COCO
data set. On the other hand, we introduce CoConv in the generator of a
state-of-the-art Generative Adversarial Network, showing improved generative
results on CIFAR-10 and CelebA. Our code is available at
https://github.com/iduta/coconv.


  In this report, we present a study of eight corpora of online hate speech, by
demonstrating the NLP techniques that we used to collect and analyze the
jihadist, extremist, racist, and sexist content. Analysis of the multilingual
corpora shows that the different contexts share certain characteristics in
their hateful rhetoric. To expose the main features, we have focused on text
classification, text profiling, keyword and collocation extraction, along with
manual annotation and qualitative study.


  The structure of the $^9$Be low-lying spectrum is studied within the cluster
model $\alpha+\alpha+n$. In the model the total orbital momentum is fixed for
each energy level. Thus each level is determined as a member of the spin-flip
doublet corresponding to the total orbital momentum ($L^\pi=0^+, 2^+,4^+, 1^-,
2^-,3^-, 4^-$) of the system. The Ali-Bodmer potential (model E) is applied for
the $\alpha\alpha$ interaction. We employ a local $\alpha n$ potential which
was constructed to reproduce the $\alpha-n$ scattering data. The Pauli blocking
is simulated by the repulsive core of the $s$-wave components of these
potentials. Configuration space Faddeev equations are used to calculate the
energy of the bound state ($E_{cal.}$=-1.493 MeV v.s. $E_{exp.}$=-1.5735 MeV)
and resonances. A variant of the method of analytical continuation in the
coupling constant is applied to calculate the energies of low-lying levels.
Available $^9$Be spectral data are satisfactorily reproduced by the proposed
model.


  The release process for the fabrication of freestanding oxide microstructures
relies on appropriate, controllable and repeatable wet etching procedures.
SrTiO3 is among the most employed substrates for oxide thin films growth and
can be decomposed in HF:water solution. Such process is strongly anisotropic
and is affected by local defects and substrate cut-plane. We analyze the
etching behavior of SrTiO3 substrates having (100), (110), and (111) cut-planes
during immersion in a 5% HF:water solution. The etching process over the three
substrates is compared in terms of pitting, anisotropy, macroscopic etch rate
and underetching effects around HF-resistant (La,Sr)MnO3 thin film
micropatterns. The release of targeted structures, such as the reported
(La,Sr)MnO3 freestanding microbridges, depends on the substrate
crystallographic symmetry and on the in-plane orientation of the structures
themselves along the planar directions. By comparing the etching evolution at
two different length scales, we distinguish two regimes for the propagation of
the etching front: an intrinsic one, owning to a specific lattice direction,
and a macroscopic one, resulting from the mixing of different etching fronts.
We report the morphologies of the etched SrTiO3 surfaces and the geometries of
the underetched regions as well as of the microbridge clamping zones. The
reported analysis will enable the design of complex MEMS devices by allowing to
model the evolution of the etching process required for the release of
arbitrary structures made of oxide thin films deposited on top of STO.


  Despite having the somewhat successful description of accelerated cosmology,
the early evolution of the universe always challenges mankind. Our promising
approach lies in a new class of symmetric teleparallel theory of gravity named
$f(Q)$, where the non-metricity scalar $Q$ is responsible for the gravitational
interaction, which may resolve some of the issues. To study the early evolution
of the universe, we presume an anisotropic locally rotationally symmetric (LRS)
Bianchi-I spacetime and derive the motion equations. We discuss the profiles of
energy density, equation of state and skewness parameter and observe that our
models archive anisotropic spatial geometry in the early phase of the universe
with a possible presence of anisotropic fluid and as time goes on, even in the
presence of an anisotropic fluid, the universe could approach isotropy due to
inflation and the anisotropy of the fluid fades away at the same time.


  We propose a method of quantum machine learning called quantum mutual
information neural estimation (QMINE) for estimating von Neumann entropy and
quantum mutual information, which are fundamental properties in quantum
information theory. The QMINE proposed here basically utilizes a technique of
quantum neural networks (QNNs), to minimize a loss function that determines the
von Neumann entropy, and thus quantum mutual information, which is believed
more powerful to process quantum datasets than conventional neural networks due
to quantum superposition and entanglement. To create a precise loss function,
we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum
analog of the classical Donsker-Varadhan representation. By exploiting a
parameter shift rule on parameterized quantum circuits, we can efficiently
implement and optimize the QNN and estimate the quantum entropies using the
QMINE technique. Furthermore, numerical observations support our predictions of
QDVR and demonstrate the good performance of QMINE.


  In this work, we investigate the violation of Lorentz symmetry through the
Casimir effect. The Casimir effect is one of the most intriguing aspects of
modern physics, representing a macroscopic quantum-origin force between two
neutral conducting surfaces, and it stands as a triumph of Quantum Field
Theory. Here, we examine the Casimir effects associated with a massive
fermionic quantum field confined in the region between two large and parallel
plates within the Horava-Lifshitz framework of Lorentz symmetry violation. To
calculate the Casimir energy and consequently the Casimir pressure, we impose a
MIT bag boundary condition on two plates, compatible with the higher-order
derivative term in the modified Dirac equation. Our results indicate a strong
influence of Lorentz violation on the Casimir effect. We observe that the
Casimir energy is affected, both in intensity and sign, potentially exhibiting
repulsive or attractive force between the plates, depending on the critical
exponent associated with the Horava-Lifshitz formalism.


  Given semantic descriptions of object classes, zero-shot learning aims to
accurately recognize objects of the unseen classes, from which no examples are
available at the training stage, by associating them to the seen classes, from
which labeled examples are provided. We propose to tackle this problem from the
perspective of manifold learning. Our main idea is to align the semantic space
that is derived from external information to the model space that concerns
itself with recognizing visual features. To this end, we introduce a set of
"phantom" object classes whose coordinates live in both the semantic space and
the model space. Serving as bases in a dictionary, they can be optimized from
labeled data such that the synthesized real object classifiers achieve optimal
discriminative performance. We demonstrate superior accuracy of our approach
over the state of the art on four benchmark datasets for zero-shot learning,
including the full ImageNet Fall 2011 dataset with more than 20,000 unseen
classes.


  In this letter, we analyse the properties of a maximum likelihood channel
estimator based on the syndrome of a linear code. For the two examples of a
binary symmetric channel and a binary input additive white Gaussian noise
channel, we derive expressions for the bias and the mean squared error and
compare them to the Cram\'er-Rao bound. The analytical expressions show the
relationship between the estimator properties and the parameters of the linear
code, i.e., the number of check nodes and the check node degree.


  The study of protoplanetary disc evolution and planet formation has mainly
concentrated on solar (and low) mass stars since they host the majority of the
confirmed exoplanets. Nevertheless, the numerous planets found orbiting stars
up to $\sim3M_\odot$ has sparked interest in understanding how they form and
how their hosting discs evolve. Our goal is to improve our knowledge on the gas
disc evolution around intermediate mass stars for future planet formation
studies. We study the long-term evolution of protoplanetary discs affected by
viscous accretion, X-ray and FUV photoevaporation from the central star around
stars between $1 - 3M_\odot$ considering the effects of stellar evolution. We
explore different values of the viscosity parameter and the initial mass of the
disc. We find that the evolutionary pathway of disc dispersal depends on the
stellar mass. Our simulations reveal four distinct evolutionary pathways for
the gas disc not reported before that are a consequence of stellar evolution,
and which will likely impact dust evolution and planet formation. As the
stellar mass grows from 1 to $\sim2M_\odot$, the disc evolution changes from
the conventional inside-out clearing to a homogeneous disc evolution scenario
where both inner and outer discs, formed after photoevaporation opened a gap,
vanish over a similar timescale. As the stellar mass continues to increase,
reaching $\sim 3M_\odot$, we have identified a distinct pathway that we refer
to as revenant disc evolution, where the inner and outer discs reconnect after
the gap opened. For the largest masses, we observe outside-in disc dispersal,
in which the outer disc dissipates first due to the strong FUV
photoevaporation. Revenant disc evolution stands out as it is capable of
extending the disc lifespan. Otherwise, the disc dispersal time scale decreases
with increasing stellar mass except for low viscosity discs.


  The coexistence of semiconducting (2H) and metallic (1T) phases of MoS$_{2}$
monolayers have further pushed their strong potential for applications in the
next generation of electronic devices based on the two-dimensional lateral
heterojunctions. Structural defects have considerable effects on the properties
of these 2D devices. In particular, the interfaces of two phases are often
imperfect and may contain numerous vacancies created by phase engineering
techniques, e.g. under the electron beam. Here, the transport behaviors of the
heterojunctions in the existence of point defects are explored by means of
first-principles calculations and non-equilibrium Green's function approach.
While vacancies in semiconducting MoS$_{2}$ act as scattering centers, their
presence at the interface improves the flow of the charge carriers. In the case
of $V_{Mo}$, the current has been increased by two orders of magnitude in
comparison to the perfect device. The enhancement of transmission was explained
by changes in the electronic densities at the T-H interface, which open new
transport channels for electron conduction.


  In this letter we study the effects of a noncommutative minisuperspace,
including matter degrees of freedom on a FRW universe with cosmological
constant. In this setting the vacuum energy density can be calculated to be of
the same order as the observed energy density of the universe.


  The recent success of generative adversarial networks and variational
learning suggests training a classifier network may work well in addressing the
classical two-sample problem. Network-based tests have the computational
advantage that the algorithm scales to large samples. This paper proposes a
two-sample statistic which is the difference of the logit function, provided by
a trained classification neural network, evaluated on the testing set split of
the two datasets. Theoretically, we prove the testing power to differentiate
two sub-exponential densities given that the network is sufficiently
parametrized. When the two densities lie on or near to low-dimensional
manifolds embedded in possibly high-dimensional space, the needed network
complexity is reduced to only scale with the intrinsic dimensionality. Both the
approximation and estimation error analysis are based on a new result of
near-manifold integral approximation. In experiments, the proposed method
demonstrates better performance than previous network-based tests using
classification accuracy as the two-sample statistic, and compares favorably to
certain kernel maximum mean discrepancy tests on synthetic datasets and
hand-written digit datasets.


  We present a vision for the next decade of hadron physics in which the
central question being addressed is how one might win new physical insight into
the way hadronic systems work. The topics addressed include the relevance of
model building, the role of spontaneously broken chiral symmetry, spectroscopy,
form factors and physics in the deep inelastic regime.


  We introduce a discrete binary tree for pricing contingent claims with the
underlying security prices exhibiting history dependence characteristic of that
induced by market microstructure phenomena. Example dependencies considered
include moving average or autoregressive behavior. Our model is
market-complete, arbitrage-free, and preserves all of the parameters governing
the historical (natural world) price dynamics when passing to an equivalent
martingale (risk-neutral) measure. Specifically, this includes the
instantaneous mean and variance of the asset return and the instantaneous
probabilities for the direction of asset price movement. We believe this is the
first paper to demonstrate the ability to include market microstructure effects
in dynamic asset/option pricing in a market-complete, no-arbitrage, format.


  We provide an explicit algorithm to calculate invariant tensors for the
adjoint representation of the simple Lie algebra $sl(n)$, as well as arbitrary
representation in terms of roots. We also obtain explicit formulae for the
adjoint representations of the orthogonal and symplectic Lie algebras $so(n)$
and $sp(n)$.


  In this paper we introduce the iterations of the Jacob's ladder and the new
type of integral containing certain product of the factors $|\zeta|^2$
corresponding to the components of some disconnected set of the critical line.
Next, we obtain an asymptotic formula for this integral, its factorization and,
for example, the essential generalization of two Selberg's formulae (1946).


  It is found that the 2-index potential in nonabelian theories does not behave
geometrically as a connection but that, considered as an element of the second
de Rham cohomology group twisted by a flat connection, it fits well with all
the properties assigned to it in various physical contexts. We also prove some
results on the Euler characteristic of the twisted de Rham complex. Finally,
provided that some conditions are satisfied, we propose a non-Abelian
generalisation of S-duality.


  Considering the rapidly increasing number of academic papers, searching for
and citing appropriate references has become a non-trial task during the wiring
of papers. Recommending a handful of candidate papers to a manuscript before
publication could ease the burden of the authors, and help the reviewers to
check the completeness of the cited resources. Conventional approaches on
citation recommendation generally consider recommending one ground-truth
citation for a query context from an input manuscript, but lack of
consideration on co-citation recommendations. However, a piece of context often
needs to be supported by two or more co-citation pairs. Here, we propose a
novel scientific paper modeling for citation recommendations, namely
Multi-Positive BERT Model for Citation Recommendation (MP-BERT4CR), complied
with a series of Multi-Positive Triplet objectives to recommend multiple
positive citations for a query context. The proposed approach has the following
advantages: First, the proposed multi-positive objectives are effective to
recommend multiple positive candidates. Second, we adopt noise distributions
which are built based on the historical co-citation frequencies, so that
MP-BERT4CR is not only effective on recommending high-frequent co-citation
pairs; but also the performances on retrieving the low-frequent ones are
significantly improved. Third, we propose a dynamic context sampling strategy
which captures the ``macro-scoped'' citing intents from a manuscript and
empowers the citation embeddings to be content-dependent, which allow the
algorithm to further improve the performances. Single and multiple positive
recommendation experiments testified that MP-BERT4CR delivered significant
improvements. In addition, MP-BERT4CR are also effective in retrieving the full
list of co-citations, and historically low-frequent co-citation pairs compared
with the prior works.


  The fast (ms) radio bursts reported by Thornton, {\it et al.} have extremely
high brightness temperatures if at the inferred cosmological distances. This
implies coherent emission by "bunches" of charges. We model the emission region
as a screen of dipole radiators resonant at the frequency of observation and
calculate the necessary charge bunching. From this we infer the minimum
electron energy required to overcome electrostatic repulsion. The FRB, like the
giant pulses of the Crab pulsar, display banded spectra that may be harmonics
of plasma frequency emission by plasma turbulence. If FRB are the counterparts
of comparatively frequent Galactic events, these may be detected by radio
telescopes in their far sidelobes or by small arrays of dipoles.


  Recent works identified resolution limits for the distance between incoherent
point sources. However, it remains unclear how to choose suitable observables
and estimators to reach these limits in practical situations. Here, we show how
estimators saturating the Cram\'er-Rao bound for the distance between two
thermal point sources can be constructed using an optimally designed observable
in the presence of practical imperfections, such as misalignment, crosstalk and
detector noise.


  We prove that the extremal stratification of an Alexandrov space by
Perelman-Petrunin is a CS stratification in the sense of Siebenmann. We also
show that every space of directions of an Alexandrov space without proper
extremal subsets is homeomorphic to a sphere. In the appendix we give an
example of a primitive extremal subset of codimension 2 that is not an
Alexandrov space with respect to the induced intrinsic metric.


  We revisit the computation of the 1-loop string correction to the "latitude"
minimal surface in $AdS_5 \times S^5$ representing 1/4 BPS Wilson loop in
planar $\cal N$=4 SYM theory previously addressed in arXiv:1512.00841 and
arXiv:1601.04708. We resolve the problem of matching with the subleading term
in the strong coupling expansion of the exact gauge theory result (derived
previously from localization) using a different method to compute determinants
of 2d string fluctuation operators. We apply perturbation theory in a small
parameter (angle of the latitude) corresponding to an expansion near the
$AdS_2$ minimal surface representing 1/2 BPS circular Wilson loop. This allows
us to compute the corrections to the heat kernels and zeta-functions of the
operators in terms of the known heat kernels on $AdS_2$. We apply the same
method also to two other examples of Wilson loop surfaces: generalized cusp and
$k$-wound circle.


  We present models of giant planet migration in evolving protoplanetary discs.
We show that disc clearing by EUV photoevaporation can have a strong effect on
the distribution of giant planet semi-major axes. During disc clearing planet
migration is slowed or accelerated in the region where photoevaporation opens a
gap in the disc, resulting in "deserts" where few giant planets are found and
corresponding "pile-ups" at smaller and larger radii. However, the precise
locations and sizes of these features are strong functions of the efficiency of
planetary accretion, and therefore also strongly dependent on planet mass. We
suggest that photoevaporative disc clearing may be responsible for the pile-up
of ~Jupiter-mass planets at ~1AU seen in exoplanet surveys, and show that
observations of the distribution of exoplanet semi-major axes can be used to
test models of both planet migration and disc clearing.


  In this paper, we present and analyse a class of "filtered" numerical schemes
for second order Hamilton-Jacobi-Bellman equations. Our approach follows the
ideas introduced in B.D. Froese and A.M. Oberman, Convergent filtered schemes
for the Monge-Amp\`ere partial differential equation, SIAM J. Numer. Anal.,
51(1):423--444, 2013, and more recently applied by other authors to stationary
or time-dependent first order Hamilton-Jacobi equations. For high order
approximation schemes (where "high" stands for greater than one), the
inevitable loss of monotonicity prevents the use of the classical theoretical
results for convergence to viscosity solutions. The work introduces a suitable
local modification of these schemes by "filtering" them with a monotone scheme,
such that they can be proven convergent and still show an overall high order
behaviour for smooth enough solutions. We give theoretical proofs of these
claims and illustrate the behaviour with numerical tests from mathematical
finance, focussing also on the use of backward difference formulae (BDF) for
constructing the high order schemes.


  The Belle~II Collaboration presents four new analyses: The measurement of
$V_{ub}$ from $B \to \pi e \nu_e$ decays with a fit to the differential $q^2$
spectrum, the determination of $V_{cb}$ using a fit to the differential $w$
distribution from $B^0 \to D^{\ast -} \ell^+ \nu_\ell$ decays, and the
measurement of $q^2$ moments in inclusive $B \to X_c \ell \nu_\ell$ decays. In
all these analyses tag-side $B$ meson is reconstructed in a fully hadronic
decay chain. The last result are branching fraction measurement of $B \to
K^\ast \ell^+ \ell^-$ decays using an untagged approach.


  Content generation that is both relevant and up to date with the current
threats of the target audience is a critical element in the success of any
Cyber Security Exercise (CSE). Through this work, we explore the results of
applying machine learning techniques to unstructured information sources to
generate structured CSE content. The corpus of our work is a large dataset of
publicly available cyber security articles that have been used to predict
future threats and to form the skeleton for new exercise scenarios. Machine
learning techniques, like named entity recognition (NER) and topic extraction,
have been utilised to structure the information based on a novel ontology we
developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used
clustering with outliers to classify the generated extracted data into objects
of our ontology. Graph comparison methodologies were used to match generated
scenario fragments to known threat actors' tactics and help enrich the proposed
scenario accordingly with the help of synthetic text generators. CESO has also
been chosen as the prominent way to express both fragments and the final
proposed scenario content by our AI-assisted Cyber Exercise Framework (AiCEF).
Our methodology was put to test by providing a set of generated scenarios for
evaluation to a group of experts to be used as part of a real-world awareness
tabletop exercise.


  Controlling bias in training datasets is vital for ensuring equal treatment,
or parity, between different groups in downstream applications. A naive
solution is to transform the data so that it is statistically independent of
group membership, but this may throw away too much information when a
reasonable compromise between fairness and accuracy is desired. Another common
approach is to limit the ability of a particular adversary who seeks to
maximize parity. Unfortunately, representations produced by adversarial
approaches may still retain biases as their efficacy is tied to the complexity
of the adversary used during training. To this end, we theoretically establish
that by limiting the mutual information between representations and protected
attributes, we can assuredly control the parity of any downstream classifier.
We demonstrate an effective method for controlling parity through mutual
information based on contrastive information estimators and show that they
outperform approaches that rely on variational bounds based on complex
generative models. We test our approach on UCI Adult and Heritage Health
datasets and demonstrate that our approach provides more informative
representations across a range of desired parity thresholds while providing
strong theoretical guarantees on the parity of any downstream algorithm.


  Maximal Orders over an algebra are a generalization of the concept of a
Dedekind domain. The definition given in Maximal Orders by Reiner, assumes that
the field over which the algebra is defined is in the center of the order.
Since we want to define maximal orders over a Crystalline Graded Ring (defined
in Nauwelaerts, E.; Van Oystaeyen, F., Introducing crystalline graded algebras,
Algebras and Representation Theory vol 11(2008), no. 2, 133--148.), this
concept needs to be generalized. In this paper, we will weaken the condition
that the field needs to be in the center, and still retain many of the desired
properties of a maximal order.


  Let $N$ be the number of copies of a small subgraph $H$ in an
Erd\H{o}s-R\'enyi graph $G \sim \mathcal{G}(n, p_n)$ where $p_n \to 0$ is
chosen so that $\mathbb{E} N = c$, a constant. Results of Bollob\'as show that
for regular graphs $H$, the count $N$ weakly converges to a Poisson random
variable. For large but finite $n$, and for the specific case of the triangle,
investigations of the upper tail $\mathbb{P}(N \geq k_n)$ by Ganguly, Hiesmayr
and Nam (2022) revealed that there is a phase transition in the tail behavior
and the associated mechanism. Smaller values of $k_n$ correspond to disjoint
occurrences of $H$, leading to Poisson tails, with a different behavior
emerging when $k_n$ is large, guided by the appearance of an almost clique. We
show that a similar phase transition also occurs when $H$ is any regular graph,
at the point where $k_n^{1 -2/q}\log k_n = \log n$ ($q$ is the number of
vertices in $H$). This establishes universality of this transition, previously
known only for the case of the triangle.


  An explicit construction of surfaces with flat normal bundle in the Euclidean
space (unit hypersphere) in terms of solutions of certain linear system is
proposed. In the case of 3-space our formulae can be viewed as the direct Lie
sphere analog of the generalized Weierstrass representation of surfaces in
conformal geometry or the Lelieuvre representation of surfaces in the affine
space.
  An explicit parametrization of Ribaucour congruences of spheres by three
solutions of the linear system is obtained. In view of the classical Lie
correspondence between Ribaucour congruences and surfaces with flat normal
bundle in the Lie quadric this gives an explicit representation of surfaces
with flat normal bundle in the 4-dimensional space form of the Lorentzian
signature. Direct projective analog of this construction is the known
parametrization of W-congruences by three solutions of the Moutard equation.
Under the Pl\"ucker embedding W-congruences give rise to surfaces with flat
normal bundle in the Pl\"ucker quadric.
  Integrable evolutions of surfaces with flat normal bundle and parallels with
the theory of nonlocal Hamiltonian operators of hydrodynamic type are discussed
in the conclusion.


  In recent years, the US has experienced an opioid epidemic with an
unprecedented number of drugs overdose deaths. Research finds such overdose
deaths are linked to neighborhood-level traits, thus providing opportunity to
identify effective interventions. Typically, techniques such as Ordinary Least
Squares (OLS) or Maximum Likelihood Estimation (MLE) are used to document
neighborhood-level factors significant in explaining such adverse outcomes.
These techniques are, however, less equipped to ascertain non-linear
relationships between confounding factors. Hence, in this study we apply
machine learning based techniques to identify opioid risks of neighborhoods in
Delaware and explore the correlation of these factors using Shapley Additive
explanations (SHAP). We discovered that the factors related to neighborhoods
environment, followed by education and then crime, were highly correlated with
higher opioid risk. We also explored the change in these correlations over the
years to understand the changing dynamics of the epidemic. Furthermore, we
discovered that, as the epidemic has shifted from legal (i.e., prescription
opioids) to illegal (e.g.,heroin and fentanyl) drugs in recent years, the
correlation of environment, crime and health related variables with the opioid
risk has increased significantly while the correlation of economic and
socio-demographic variables has decreased. The correlation of education related
factors has been higher from the start and has increased slightly in recent
years suggesting a need for increased awareness about the opioid epidemic.


  According to the flow narrative commonly applied to high-energy nuclear
collisions a 1D cylindrical-quadrupole component of 2D angular correlations
conventionally denoted by quantity $v_2$ is interpreted to represent elliptic
flow: azimuth modulation of transverse or radial flow in noncentral
nucleus-nucleus (A-A) collisions. The nonjet (NJ) quadrupole component exhibits
various properties inconsistent with a flow or hydro interpretation, including
the observation that NJ-quadrupole centrality variation in $A$-$A$ collisions
has no relation to strongly-varying jet modification ("jet quenching") in those
collisions commonly attributed to jet interaction with a dense flowing medium.
In the present study I report isolation of quadrupole spectra from
$p_t$-differential $v_2(p_t)$ data obtained at the relativistic heavy ion
collider (RHIC) and large hadron collider (LHCr). I demonstrate that NJ
quadrupole spectra have characteristics very different from the single-particle
spectra for most hadrons, that quadrupole spectra indicate a common boosted
hadron source for a small minority of hadrons that "carry" the quadrupole
structure, that the narrow source-boost distribution is characteristic of an
expanding thin cylindrical shell (also strongly contradicting a hydro
interpretation), and that in the boost frame a single universal quadrupole
spectrum (L\'evy distribution) on transverse mass $m_t$ accurately describes
data for several hadron species scaled according to their statistical-model
abundances. The quadrupole spectrum shape changes very little from RHIC to LHC
energies. Taken in combination those characteristics strongly suggest a unique
{\em nonflow} (and nonjet) QCD mechanism for the NJ quadrupole conventionally
represented by $v_2$.


  Camera model identification refers to the problem of linking a picture to the
camera model used to shoot it. As this might be an enabling factor in different
forensic applications to single out possible suspects (e.g., detecting the
author of child abuse or terrorist propaganda material), many accurate camera
model attribution methods have been developed in the literature. One of their
main drawbacks, however, is the typical closed-set assumption of the problem.
This means that an investigated photograph is always assigned to one camera
model within a set of known ones present during investigation, i.e., training
time, and the fact that the picture can come from a completely unrelated camera
model during actual testing is usually ignored. Under realistic conditions, it
is not possible to assume that every picture under analysis belongs to one of
the available camera models. To deal with this issue, in this paper, we present
the first in-depth study on the possibility of solving the camera model
identification problem in open-set scenarios. Given a photograph, we aim at
detecting whether it comes from one of the known camera models of interest or
from an unknown one. We compare different feature extraction algorithms and
classifiers specially targeting open-set recognition. We also evaluate possible
open-set training protocols that can be applied along with any open-set
classifier, observing that a simple of those alternatives obtains best results.
Thorough testing on independent datasets shows that it is possible to leverage
a recently proposed convolutional neural network as feature extractor paired
with a properly trained open-set classifier aiming at solving the open-set
camera model attribution problem even to small-scale image patches, improving
over state-of-the-art available solutions.


  We present a study of the dust, stars and atomic gas (HI) in an HI-selected
sample of local galaxies (z<0.035) in the Herschel Astrophysical Terahertz
Large Area Survey (H-ATLAS) fields. This HI-selected sample reveals a
population of very high gas fraction (>80 per cent), low stellar mass sources
that appear to be in the earliest stages of their evolution. We compare this
sample with dust and stellar mass selected samples to study the dust and gas
scaling relations over a wide range of gas fraction (proxy for evolutionary
state of a galaxy). The most robust scaling relations for gas and dust are
those linked to NUV-r (SSFR) and gas fraction, these do not depend on sample
selection or environment. At the highest gas fractions, our additional sample
shows the dust content is well below expectations from extrapolating scaling
relations for more evolved sources, and dust is not a good tracer of the gas
content. The specific dust mass for local galaxies peaks at a gas fraction of
~75 per cent. The atomic gas depletion time is also longer for high gas
fraction galaxies, opposite to the trend found for molecular gas depletion
timescale. We link this trend to the changing efficiency of conversion of HI to
H2 as galaxies increase in stellar mass surface density as they evolve.
Finally, we show that galaxies start out barely obscured and increase in
obscuration as they evolve, yet there is no clear and simple link between
obscuration and global galaxy properties.


  This study considers the extent to which users with the same query agree as
to what is relevant, and how what is considered relevant may translate into a
retrieval algorithm and results display. To combine user perceptions of
relevance with algorithm rank and to present results, we created a prototype
digital library of scholarly literature. We confine studies to one population
of scientists (paleontologists), one domain of scholarly scientific articles
(paleo-related), and a prototype system (PaleoLit) that we built for the
purpose. Based on the principle that users do not pre-suppose answers to a
given query but that they will recognize what they want when they see it, our
system uses a rules-based algorithm to cluster results into fuzzy categories
with three relevance levels. Our system matches at least 1/3 of our
participants' relevancy ratings 87% of the time. Our subsequent usability study
found that participants trusted our uncertainty labels but did not value our
color-coded horizontal results layout above a standard retrieval list. We posit
that users make such judgments in limited time, and that time optimization per
task might help explain some of our findings.


  We construct integrable Hamiltonian systems on $G/K$, where $G$ is a
quasitriangular Poisson Lie group and $K$ is a Lie subgroup arising as the
fixed point set of a group automorphism $\sigma$ of $G$ satisfying the
classical reflection equation. In the case that $G$ is factorizable, we show
that the time evolution of these systems is described by a Lax equation, and
present its solution in terms of a factorization problem in $G$. Our
construction is closely related to the semiclassical limit of Sklyanin's
integrable quantum spin chains with reflecting boundaries.


  Determining the plausibility of causal relations between clauses is a
commonsense reasoning task that requires complex inference ability. The general
approach to this task is to train a large pretrained language model on a
specific dataset. However, the available training data for the task is often
scarce, which leads to instability of model training or reliance on the shallow
features of the dataset. This paper presents a number of techniques for making
models more robust in the domain of causal reasoning. Firstly, we perform
adversarial training by generating perturbed inputs through synonym
substitution. Secondly, based on a linguistic theory of discourse connectives,
we perform data augmentation using a discourse parser for detecting causally
linked clauses in large text, and a generative language model for generating
distractors. Both methods boost model performance on the Choice of Plausible
Alternatives (COPA) dataset, as well as on a Balanced COPA dataset, which is a
modified version of the original data that has been developed to avoid
superficial cues, leading to a more challenging benchmark. We show a
statistically significant improvement in performance and robustness on both
datasets, even with only a small number of additionally generated data points.


  We study commutative ring structures on the integral span of rooted trees and
$n$-dimensional skew shapes. The multiplication in these rings arises from the
smash product operation on monoid representations in pointed sets. We interpret
these as Grothendieck rings of indecomposable monoid representations over
$\fun$ - the "field" of one element. We also study the base-change homomorphism
from $\mt$-modules to $k[t]$-modules for a field $k$ containing all roots of
unity, and interpret the result in terms of Jordan decompositions of adjacency
matrices of certain graphs.


  We report on the direct observation of the thermoelectric transport in a
nondegenerate correlated electron system formed on the surface of liquid
helium. We find that the microwave-induced excitation of the vertical
transitions of electrons between the surface-bound states leads to their
lateral flow, which we were able to detect by employing a segmented electrode
configuration. We show that this flow of electrons arises due to the Seebeck
effect, thus our method provides a new tool to study thermoelectricity in a
disorder-free correlated electron system. Our experimental results are in good
agreement with the theoretical calculations based on kinetic equations, with
proper account of fast electron-electron collisions.


  We consider the problem of estimating the spectral density of the normalized
adjacency matrix of an $n$-node undirected graph. We provide a randomized
algorithm that, with $O(n\epsilon^{-2})$ queries to a degree and neighbor
oracle and in $O(n\epsilon^{-3})$ time, estimates the spectrum up to $\epsilon$
accuracy in the Wasserstein-1 metric. This improves on previous
state-of-the-art methods, including an $O(n\epsilon^{-7})$ time algorithm from
[Braverman et al., STOC 2022] and, for sufficiently small $\epsilon$, a
$2^{O(\epsilon^{-1})}$ time method from [Cohen-Steiner et al., KDD 2018]. To
achieve this result, we introduce a new notion of graph sparsification, which
we call nuclear sparsification. We provide an $O(n\epsilon^{-2})$-query and
$O(n\epsilon^{-2})$-time algorithm for computing $O(n\epsilon^{-2})$-sparse
nuclear sparsifiers. We show that this bound is optimal in both its sparsity
and query complexity, and we separate our results from the related notion of
additive spectral sparsification. Of independent interest, we show that our
sparsification method also yields the first deterministic algorithm for
spectral density estimation that scales linearly with $n$ (sublinear in the
representation size of the graph).


  We calculate the processes e+ e- -> 4 pions and tau -> nu_tau + 4 pions to
O(p^4) in the low-energy expansion of the standard model. The chiral amplitudes
of O(p^4) can be extended via resonance exchange to energies around 1 GeV.
Higher-order effects have been included in the form of omega, a_1 and double
rho exchange and by performing a resummation of the pion form factor. The
predicted cross sections and the branching ratios BR(rho -> 4 pions) are in
good agreement with available data.


  Radio interferometry invariably suffers from an incomplete coverage of the
spatial Fourier space, which leads to imaging artifacts. The current
state-of-the-art technique is to create an image by Fourier-transforming the
incomplete visibility data and to clean the systematic effects originating from
incomplete data in Fourier space. Previously, we have shown how
super-resolution methods based on convolutional neural networks can reconstruct
sparse visibility data. Our previous work has suffered from a low realism of
the training data. The aim of this work is to build a whole simulation chain
for realistic radio sources that then leads to a vastly improved neural net for
the reconstruction of missing visibilities. This method offers considerable
improvements in terms of speed, automatization and reproducibility over the
standard techniques. Here we generate large amounts of training data by
creating images of radio galaxies with a generative adversarial network (GAN)
that has been trained on radio survey data. Then, we applied the Radio
Interferometer Measurement Equation (RIME) in order to simulate the measurement
process of a radio interferometer. We show that our neural network can
reconstruct faithfully images of realistic radio galaxies. The reconstructed
images agree well with the original images in terms of the source area,
integrated flux density, peak flux density, and the multi-scale structural
similarity index. Finally, we show how the neural net can be adapted to
estimate the uncertainties in the imaging process.


  Defect centers are promising candidates for waveguide-integrated silicon
light sources. We demonstrate microresonator- and waveguide-coupled
photoluminescence from silicon W~centers. Microphotoluminescence measurements
indicate wavelengths on-resonance with resonator modes are preferentially
coupled to an adjacent waveguide. Quality factors of at least 5,300 are
measured, and free spectral ranges closely match expectation. The W~center
phonon sideband can be used as a spectral diagnostic for a broader range
waveguide-based devices on cryogenic silicon photonic platforms.


  The positive energy theorem precludes the possibility of Minkowski flat space
decaying by any mechanism. In certain circumstances, however, large quantum
fluctuations of the gravitational field could arise---not only at the Planck
scale, but also at larger scales. This is because there exists a set of
localised weak field configurations which satisfy the condition int d4x
sqrt{g}R = 0 and thus give a null contribution to the Einstein action. Such
configurations can be constructed by solving Einstein field equations with
unphysical dipolar sources. We discuss this mechanism and its modification in
the presence of a cosmological term and/or an external field.


  Partially class-disjoint data (PCDD), a common yet under-explored data
formation where each client contributes a part of classes (instead of all
classes) of samples, severely challenges the performance of federated
algorithms. Without full classes, the local objective will contradict the
global objective, yielding the angle collapse problem for locally missing
classes and the space waste problem for locally existing classes. As far as we
know, none of the existing methods can intrinsically mitigate PCDD challenges
to achieve holistic improvement in the bilateral views (both global view and
local view) of federated learning. To address this dilemma, we are inspired by
the strong generalization of simplex Equiangular Tight Frame~(ETF) on the
imbalanced data, and propose a novel approach called FedGELA where the
classifier is globally fixed as a simplex ETF while locally adapted to the
personal distributions. Globally, FedGELA provides fair and equal
discrimination for all classes and avoids inaccurate updates of the classifier,
while locally it utilizes the space of locally missing classes for locally
existing classes. We conduct extensive experiments on a range of datasets to
demonstrate that our FedGELA achieves promising performance~(averaged
improvement of 3.9% to FedAvg and 1.5% to best baselines) and provide both
local and global convergence guarantees. Source code is available
at:https://github.com/MediaBrain-SJTU/FedGELA.git.


  In this article we apply a recently invented analytical real-space
renormalization group formulation which is based on numerical concepts of the
density matrix renormalization group. Within a rigorous mathematical framework
we construct non-perturbative renormalization group transformations for the
spin-1/2 XXX Heisenberg model in the finite temperature regime. The developed
renormalization group scheme allows for calculating the renormalization group
flow behaviour in the temperature dependent coupling constant. The constructed
renormalization group transformations are applied within the ferromagnetic and
the anti-ferromagnetic regime of the Heisenberg chain. The ferromagnetic fixed
point is computed and compared to results derived by other techniques.


  In this paper we look at the knot complement problem for L-space
$\mathbb{Z}$-homology spheres. We show that an L-space $\mathbb{Z}$-homology
sphere $Y$ cannot be obtained as a non-trivial surgery along a knot $K\subset
Y$. As a consequence, we prove that knots in an L-space $\mathbb{Z}$-homology
sphere are determined by their complements.


  Directional data require specialized probability models because of the
non-Euclidean and periodic nature of their domain. When a directional variable
is observed jointly with linear variables, modeling their dependence adds an
additional layer of complexity. This paper introduces a novel Bayesian
nonparametric approach for directional-linear data based on the Dirichlet
process. We first extend the projected normal distribution to model the joint
distribution of linear variables and a directional variable with arbitrary
dimension as a projection of a higher-dimensional augmented multivariate normal
distribution (MVN). We call the new distribution the semi-projected normal
distribution (SPN); it possesses properties similar to the MVN. The SPN is then
used as the mixture distribution in a Dirichlet process model to obtain a more
flexible class of models for directional-linear data. We propose a normal
conditional inverse-Wishart distribution as part of the prior distribution to
address an identifiability issue inherited from the projected normal and
preserve conjugacy with the SPN distribution. A Gibbs sampling algorithm is
provided for posterior inference. Experiments on synthetic data and the
Berkeley image database show superior performance of the Dirichlet process SPN
mixture model (DPSPN) in clustering compared to other directional-linear
models. We also build a hierarchical Dirichlet process model with the SPN to
develop a likelihood ratio approach to bloodstain pattern analysis using the
DPSPN model for density estimation to estimate the likelihood of a given
pattern from a set of training data.


  We present a microscopic theory of single molecule magnets. From our exact
single-ion spin matrix elements for four arbitrary spins, we study the
single-ion anisotropy of equal spins exhibiting $T_d$, $D_{2d}$, or $C_{4v}$
molecular group symmetry. Each group generates site-dependent single-ion
anisotropy. For weak anisotropy, accurate Hartree expressions for the
magnetization, specific heat, electron paramagnetic resonance (EPR) absorption,
and inelastic neutron scattering cross-section are given. For $D_{2d}$,
azimuthal single-ion anisotropy leads to the observed Ni$_4$ EPR splittings.


  We demonstrate the generation of narrowband biphotons with
polarization-frequency coupled hy- perentanglement from spontaneous four-wave
mixing in cold atoms. The coupling between polariza- tion and frequency is
realized through a frequency shifter and linear optics. When the polarization-
frequency degrees of freedom are decoupled, it is robust to create polarization
and frequency Bell states, confirmed by the polarization quantum-state
tomography and the two-photon temporal quan- tum beating. Making use of the
polarization-frequency coupling to transfer polarization phase retard to the
entangled frequency modes, we produce a frequency Bell state with tunable phase
difference between its two bases.


  I describe a colour scheme that is appropriate for the screen display of
intensity images. This -- unlike many currently available schemes -- is
designed to be monotonically increasing in terms of its perceived brightness.
Also, when printed on a black and white postscript printer, the scheme results
in a greyscale with monotonically increasing brightness. This scheme has
recently been incorporated into the radio astronomical analysis packages CASA
and AIPS.


  An accurate language identification tool is an absolute necessity for
building complex NLP systems to be used on code-mixed data. Lot of work has
been recently done on the same, but there's still room for improvement.
Inspired from the recent advancements in neural network architectures for
computer vision tasks, we have implemented multichannel neural networks
combining CNN and LSTM for word level language identification of code-mixed
data. Combining this with a Bi-LSTM-CRF context capture module, accuracies of
93.28% and 93.32% is achieved on our two testing sets.


  Deep learning-based underwater object detection (UOD) remains a major
challenge due to the degraded visibility and difficulty to obtain sufficient
underwater object images captured from various perspectives for training. To
address these issues, this paper presents a high-precision UOD based on joint
self-supervised deblurring and improved spatial transformer network. A
self-supervised deblurring subnetwork is introduced into the designed
multi-task learning aided object detection architecture to force the shared
feature extraction module to output clean features for detection subnetwork.
Aiming at alleviating the limitation of insufficient photos from different
perspectives, an improved spatial transformer network is designed based on
perspective transformation, adaptively enriching image features within the
network. The experimental results show that the proposed UOD approach achieved
47.9 mAP in URPC2017 and 70.3 mAP in URPC2018, outperforming many
state-of-the-art UOD methods and indicating the designed method is more
suitable for UOD.


  We study the behavior of Dirac fermions in the presence of electron
correlation in a nonsymmorphic Kondo lattice system, CeAgSb2 employing
high-resolution angle-resolved photoemission spectroscopy and first-principles
calculations. Experiments reveal crossings of highly dispersive linear bands at
the Brillouin zone boundary due to non-symmorphic symmetry. In addition,
anisotropic Dirac cones are observed constituted by the squarenet Sb 5p states
forming a diamond-shaped nodal line. The Dirac bands are linear in a wide
energy range with a unusually high slope and exhibit distinct Dirac point in
this highly spin-orbit coupled system. Interestingly, the linearity of the
bands are preserved even after the hybridization of these states with the local
Ce 4f states, which leads to a small reduction of slope via formation of a
'kink'. These results seed the emergence of an area of robust topological
fermions even in presence of strong correlation.


  We study the clustering of the lowest non negative eigenvalue of the Dirac
operator on a general Dirac bundle when the metric structure is varied. In the
classical case we show that any closed spin manifold of dimension greater than
or equal to four has a Riemannian metric admitting non trivial harmonic
spinors.


  We calculate, for the first time, three-point correlation functions involving
"heavy" operators in the Schrodinger/null-dipole CFT correspondence at strong
coupling. In particular, we focus on the three-point functions of the dilaton
modes and two "heavy" operators. The heavy states are dual to the single spin
and dyonic magnon, the single spin and dyonic spike solutions or to two novel
string solutions which do not have an undeformed counterpart. Our results
provide the leading term of the correlators in the large $\lambda$ expansion
and are in perfect agreement with the form of the correlator dictated by
non-relativistic conformal invariance. We also specify the scaling function
which can not be fixed by using conformal invariance.


  The processes that govern the formation of intermediate-mass black holes
(IMBHs) in dense stellar clusters are still unclear. Here, we discuss the role
of stellar mergers, star-BH interactions and accretion, as well as BH binary
(BBH) mergers in seeding and growing IMBHs in the \textsc{Dragon-II} simulation
database, a suite of 19 direct $N$-body models representing dense clusters with
up to $10^6$ stars. \textsc{Dragon-II} IMBHs have typical masses of $m_{\rm
IMBH} = (100-380)$ M$_\odot$ and relatively large spins $\chi_{\rm IMBH} >
0.6$. We find a link between the IMBH formation mechanism and the cluster
structure. In clusters denser than $3\times 10^5$ M$_\odot$ pc$^{-3}$, the
collapse of massive star collision products represents the dominant IMBH
formation process, leading to the formation of heavy IMBHs ($m_{\rm IMBH} >
200$ M$_\odot$), possibly slowly rotating, that form over times $<5$ Myr and
grow further via stellar accretion and mergers in just $<30$ Myr. BBH mergers
are the dominant IMBH formation channel in less dense clusters, for which we
find that the looser the cluster, the longer the formation time ($10-300$ Myr)
and the larger the IMBH mass, although remaining within $200$ M$_\odot$. Strong
dynamical scatterings and relativistic recoil efficiently eject all IMBHs in
\textsc{Dragon-II} clusters, suggesting that IMBHs in this type of cluster are
unlikely to grow beyond a few $10^2$ M$_\odot$.


  I review recent lattice calculations of hyperon physics, including hyperon
spectroscopy, axial coupling constants, form factors and semileptonic decays.


  We study outer Lipschitz geometry of real semialgebraic or, more general,
definable in a polynomially bounded o-minimal structure over the reals, surface
germs. In particular, any definable H\"older triangle is either Lipschitz
normally embedded or contains some "abnormal" arcs. We show that abnormal arcs
constitute finitely many "abnormal zones" in the space of all arcs, and
investigate geometric and combinatorial properties of abnormal surface germs.
We establish a strong relation between geometry and combinatorics of abnormal
H\"older triangles.


  Gallium oxide (Ga2O3) can be crystallized in several polymorphs exhibiting
different physical properties. In this work, polymorphic structures consisting
of the cubic defective spinel (gamma) film on the top of the monoclinic (beta)
substrate were fabricated by disorder-induced ordering, known to be a practical
way to stack these polymorphs together. Such bilayer structures were annealed
to investigate the optical properties and phase transformations. Specifically,
photoluminescence and diffuse reflectance spectroscopies were combined with
transmission electron microscopy, Rutherford backscattering/channeling
spectrometry and x-ray diffraction to monitor the evolutions. As a result we
observe a two-stage annealing kinetics in gamma/beta Ga2O3 bilayers associated
with the epitaxial gamma-to-beta regrowth at the interface at temperatures
below 700 {\deg}C and a non-planar gamma-to-beta phase transformation starting
at higher temperatures. Thus, the present data enhance understanding of the
polymorphism in Ga2O3, interconnecting the phase transformation kinetics with
the evolution of the optical properties.


  Retinal projection is required for xR applications that can deliver immersive
visual experience throughout the day. If general-purpose retinal projection
methods can be realized at a low cost, not only could the image be displayed on
the retina using less energy, but there is also the possibility of cutting off
the weight of projection unit itself from the AR goggles. Several retinal
projection methods have been previously proposed; however, as the lenses and
iris of the eyeball are in front of the retina, which is a limitation of the
eyeball, the proposal of retinal projection is generally fraught with narrow
viewing angles and small eyebox problems. In this short technical report, we
introduce ideas and samples of an optical system for solving the common
problems of retinal projection by using the metamaterial mirror (plane
symmetric transfer optical system). Using this projection method, the designing
of retinal projection can becomes easy, and if appropriate optics are
available, it would be possible to construct an optical system that allows the
quick follow-up of retinal projection hardware.


  The role of implicit assumptions in current decoherence theory is pointed out
and clarified.


  We consider i.i.d. random variables {\omega (b):b \in E_d} parameterized by
the family of bonds in Z^d, d>1. The random variable \omega(b) is thought of as
the conductance of bond b and it ranges in a finite interval [0,c_0]. Assuming
the probability m of the event {\omega(b)>0} to be supercritical and denoting
by C(\omega) the unique infinite cluster associated to the bonds with positive
conductance, we study the zero range process on C(\omega) with
\omega(b)-proportional probability rate of jumps along bond b. For almost all
realizations of the environment we prove that the hydrodynamic behavior of the
zero range process is governed by the nonlinear heat equation $\partial_t \rho=
m \nabla \cdot (D \nabla\phi(\rho/m))$, where the matrix D and the function
\phi are \omega--independent. We do not require any ellipticity condition.


  We exhibit, for any positive integer parameter $s$, an involution on the set
of integer partitions of $n$. These involutions show the joint symmetry of the
distributions of the following two statistics. The first counts the number of
parts of a partition divisible by $s$, whereas the second counts the number of
cells in the Ferrers diagram of a partition whose leg length is zero and whose
arm length has remainder $s-1$ when dividing by $s$. In particular, for $s=1$
this involution is just conjugation. Additionally, we provide explicit
expressions for the bivariate generating functions.
  Our primary motivation to construct these involutions is that we know only of
two other "natural" bijections on integer partitions of a given size, one of
which is the Glaisher-Franklin bijection sending the set of parts divisible by
$s$, each divided by $s$, to the set of parts occurring at least $s$ times.


  We consider a non-local spin valve in a Van der Pauw cross geometry with four
ferromagnetic electrodes. Two antiparallel ferromagnets are used as (charge)
source and drain while the detector circuit involves measuring the voltage
between two collinear ferromagnets with parallel or antiparallel
magnetizations. We find a potentially large increase of the non-local spin
voltage. The setup displays several additional interesting properties: (i)
infinite GMR for the non-local resistance (if a symmetry requirement for the
device is met); (ii) ON-OFF switch effect, when the injector electrodes are
parallel instead of antiparallel; (iii) insensitivity to offset voltages. The
device can additionally be used as a Direct Spin Hall Effect probe and as a
reprogrammable magneto-logic gate implementing basic operations (NOR, NAND,
inverter, AND, OR,...).


  The use of a Kerr nonlinearity to generate squeezed light is a well-known way
to surpass the quantum noise limit along a given field quadrature.
Nevertheless, in the most common regime of weak nonlinearity, a single Kerr
resonator is unable to provide the proper interrelation between the field
amplitude and squeezing required to induce a sizable deviation from Poissonian
statistics. We demonstrate experimentally that weakly coupled bosonic modes
allow exploration of the interplay between squeezing and displacement, which
can give rise to strong deviations from the Poissonian statistics. In
particular, we report on the periodic bunching in a Josephson junction formed
by two coupled exciton-polariton modes. Quantum modeling traces the bunching
back to the presence of quadrature squeezing. Our results, linking the light
statistics to squeezing, are a precursor to the study of nonclassical features
in semiconductor microcavities and other weakly nonlinear bosonic systems.


  In this paper we continue the study of compact-like operators in lattice
normed spaces started recently by Aydin, Emelyanov, Erkur\c{s}un \"Ozcand and
Marabeh. We show among others, that every p-compact operator between lattice
normed spaces is p-bounded. The paper contains answers of almost all questions
asked by these authors.


  We examine the UV emission from luminous early-type galaxies as a function of
redshift. We perform a stacking analysis using Galaxy Evolution Explorer
(GALEX) images of galaxies in the NOAO Deep Wide Field Survey (NDWFS) Bo\"otes
field and examine the evolution in the UV colors of the average galaxy. Our
sample, selected to have minimal ongoing star formation based on the optical to
mid-IR SEDs of the galaxies, includes 1843 galaxies spanning the redshift range
$0.05\leq z\leq0.65$. We find evidence that the strength of the UV excess
decreases, on average, with redshift, and our measurements also show moderate
disagreement with previous models of the UV excess. Our results show little
evolution in the shape of the UV continuum with redshift, consistent either
with the binary model for the formation of Extreme Horizontal Branch (EHB)
stars or with no evolution in EHB morphology with look-back time. However, the
binary formation model predicts that the strength of the UV excess should also
be relatively constant, in contradiction with our measured results. Finally, we
see no significant influence of a galaxy's environment on the strength of its
UV excess.


  The limitation of Fermi liquid theory to very low energies and temperatures
poses a fundamental problem for describing the temperature evolution of the
Kondo peak. Here Fermi liquid theory for the single impurity Anderson model is
extended beyond the low-energy and low-temperature regime by means of an ansatz
for the impurity self-energy based on the accurate description of the Kondo
peak by the Frota function, the similarity between energy and temperature in
the second-order self-energy, and by exploiting Fermi liquid conditions.
Analytic expressions for the temperature dependence of the Kondo peak height
and width derived from this ansatz are in excellent agreement with numerical
renormalization group data for temperatures beyond the Kondo temperature. The
derived expression thus allows to unambiguously determine the intrinsic Kondo
peak width and Kondo temperature from finite temperature measurements of the
Kondo resonance.


  The last few decades of warp drive research have focused on analytic methods
to explore warp solutions to Einstein's field equations. These analytic
solutions tend to favor simple metric forms which are easier to analyze but
limit the space of exploration. In addition, all solutions to date have
involved unphysical qualities, such as negative energy, violation of energy
conditions, or enormous energy requirements. In an effort to explore the space
of physically meaningful warp drives, the Advanced Propulsion Laboratory (APL)
at Applied Physics has developed Warp Factory, a toolkit written in MATLAB for
numerically analyzing and optimizing warp drive geometries. Warp Factory
consists of a series of three primary modules: the solver, the analyzer, and
the optimizer. Together, these modules allow users to solve the Einstein field
equations, compute energy conditions and scalars, and perturbatively optimize
general metrics. Finally, the toolkit offers insightful 2D and 3D
visualizations of general metrics and stress-energy tensors. The methods used
in Warp Factory, along with their application in evaluating and optimizing
common metrics, are discussed. With Warp Factory, APL hopes to accelerate warp
research and bring us one step closer to physical and realizable warp drives.


  Adversarial robustness is one of the essential safety criteria for
guaranteeing the reliability of machine learning models. While various
adversarial robustness testing approaches were introduced in the last decade,
we note that most of them are incompatible with non-differentiable models such
as tree ensembles. Since tree ensembles are widely used in industry, this
reveals a crucial gap between adversarial robustness research and practical
applications. This paper proposes a novel whitebox adversarial robustness
testing approach for tree ensemble models. Concretely, the proposed approach
smooths the tree ensembles through temperature controlled sigmoid functions,
which enables gradient descent-based adversarial attacks. By leveraging
sampling and the log-derivative trick, the proposed approach can scale up to
testing tasks that were previously unmanageable. We compare the approach
against both random perturbations and blackbox approaches on multiple public
datasets (and corresponding models). Our results show that the proposed method
can 1) successfully reveal the adversarial vulnerability of tree ensemble
models without causing computational pressure for testing and 2) flexibly
balance the search performance and time complexity to meet various testing
criteria.


  Let $p$ be a prime for which the congruence group $\Gamma_0(p)^*$ is of genus
zero, and $j_p^*$ be the corresponding Hauptmodul. Let $f$ be a nearly
holomorphic modular form of weight 1/2 on $\Gamma_0(4p)$ which satisfies some
congruence condition on its Fourier coefficients. We interpret $f$ as a vector
valued modular form. Applying Borcherds lifting of vector valued modular forms
we construct infinite products associated to $j_p^*$ and extend Zagier's trace
formula for singular values of $j_p^*$. Further we investigate the twisted
traces of sigular values of $j_p^*$ and construct Borcherds products related to
them.


  Algorithmic risk assessments are used to inform decisions in a wide variety
of high-stakes settings. Often multiple predictive models deliver similar
overall performance but differ markedly in their predictions for individual
cases, an empirical phenomenon known as the "Rashomon Effect." These models may
have different properties over various groups, and therefore have different
predictive fairness properties. We develop a framework for characterizing
predictive fairness properties over the set of models that deliver similar
overall performance, or "the set of good models." Our framework addresses the
empirically relevant challenge of selectively labelled data in the setting
where the selection decision and outcome are unconfounded given the observed
data features. Our framework can be used to 1) replace an existing model with
one that has better fairness properties; or 2) audit for predictive bias. We
illustrate these uses cases on a real-world credit-scoring task and a
recidivism prediction task.


  Many electromagnetic properties of graphene can be described by the Hubbard
model on a honeycomb lattice. However, this system suffers strongly from the
sign problem if a chemical potential is included. Tensor network methods are
not affected by this problem. We use the imaginary time evolution of a
fermionic projected entangled pair state, which allows to simulate both parity
sectors independently. Incorporating the fermionic nature on the level of the
tensor network allows to fix the particle number to be either even or odd. This
way we can access the states at half filling and with one additional electron.
We calculate the energy and other observables of both states, which was not
possible before with Monte Carlo methods.


  Despite the basic premise that next-generation wireless networks (e.g., 6G)
will be artificial intelligence (AI)-native, to date, most existing efforts
remain either qualitative or incremental extensions to existing "AI for
wireless" paradigms. Indeed, creating AI-native wireless networks faces
significant technical challenges due to the limitations of data-driven,
training-intensive AI. These limitations include the black-box nature of the AI
models, their curve-fitting nature, which can limit their ability to reason and
adapt, their reliance on large amounts of training data, and the energy
inefficiency of large neural networks. In response to these limitations, this
article presents a comprehensive, forward-looking vision that addresses these
shortcomings by introducing a novel framework for building AI-native wireless
networks; grounded in the emerging field of causal reasoning. Causal reasoning,
founded on causal discovery, causal representation learning, and causal
inference, can help build explainable, reasoning-aware, and sustainable
wireless networks. Towards fulfilling this vision, we first highlight several
wireless networking challenges that can be addressed by causal discovery and
representation, including ultra-reliable beamforming for terahertz (THz)
systems, near-accurate physical twin modeling for digital twins, training data
augmentation, and semantic communication. We showcase how incorporating causal
discovery can assist in achieving dynamic adaptability, resilience, and
cognition in addressing these challenges. Furthermore, we outline potential
frameworks that leverage causal inference to achieve the overarching objectives
of future-generation networks, including intent management, dynamic
adaptability, human-level cognition, reasoning, and the critical element of
time sensitivity.


  In ZM theory the direction of time has a non-zero projection onto space and
this projection corresponds to the local velocity relative to the observer.
Classical trajectories can be obtained by following the local direction of
time. The relationship of time to space enables the change in momentum over
time to be related to the spatial change in energy and momentum. Previously
Hamilton's equations-of-motion were derived by considering trajectories in one
space and one time dimensions. Here we consider three space and one time
dimension. Without any other assumptions we derive the Lorentz force law of
electromagnetism with relevant definitions of the scalar and vector potentials.


  We study the design of storage-efficient algorithms for emulating atomic
shared memory over an asynchronous, distributed message-passing system. Our
first algorithm is an atomic single-writer multi-reader algorithm based on a
novel erasure-coding technique, termed \emph{multi-version code}. Next, we
propose an extension of our single-writer algorithm to a multi-writer
multi-reader environment. Our second algorithm combines replication and
multi-version code, and is suitable in situations where we expect a large
number of concurrent writes. Moreover, when the number of concurrent writes is
bounded, we propose a simplified variant of the second algorithm that has a
simple structure similar to the single-writer algorithm.
  Let $N$ be the number of servers, and the shared memory variable be of size 1
unit. Our algorithms have the following properties:
  (i) The write operation terminates if the number of server failures is
bounded by a parameter $f$. The algorithms also guarantee the termination of
the read as long as the number of writes concurrent with the read is smaller
than a design parameter $\nu$, and the number of server failures is bounded by
$f$.
  (ii) The overall storage size for the first algorithm, and the steady-state
storage size for the second algorithm, are all $N/\lceil \frac{N-2f}{\nu}
\rceil$ units. Moreover, our simplified variant of the second algorithm
achieves the worst-case storage cost of $N/\lceil \frac{N-2f}{\nu} \rceil$,
asymptotically matching a lower bound by Cadambe et al. for $N \gg f, \nu \le
f+1$.
  (iii) The write and read operations only consist of a small number (2 to 3)
of communication rounds.
  (iv) For all algorithms, the server maintains a simple data structure. A
server only needs to store the information associated with the latest value it
observes, similar to replication-based algorithms.


  We investigate Refined Algebraic Quantization (RAQ) with group averaging in a
constrained Hamiltonian system with unreduced phase space T^*R^4 and gauge
group SL(2,R). The reduced phase space M is connected and contains four
mutually disconnected `regular' sectors with topology R x S^1, but these
sectors are connected to each other through an exceptional set where M is not a
manifold and where M has non-Hausdorff topology. The RAQ physical Hilbert space
H_{phys} decomposes as H_{phys} = (direct sum of) H_i, where the four subspaces
H_i naturally correspond to the four regular sectors of M. The RAQ observable
algebra A_{obs}, represented on H_{phys}, contains natural subalgebras
represented on each H_i. The group averaging takes place in the oscillator
representation of SL(2,R) on L^2(R^{2,2}), and ensuring convergence requires a
subtle choice for the test state space: the classical analogue of this choice
is to excise from M the exceptional set while nevertheless retaining
information about the connections between the regular sectors. A quantum theory
with the Hilbert space H_{phys} and a finitely-generated observable subalgebra
of A_{obs} is recovered through both Ashtekar's Algebraic Quantization and
Isham's group theoretic quantization.


  This paper presents an explainable AI (XAI) system that provides explanations
for its predictions. The system consists of two key components -- namely, the
prediction And-Or graph (AOG) model for recognizing and localizing concepts of
interest in input data, and the XAI model for providing explanations to the
user about the AOG's predictions. In this work, we focus on the XAI model
specified to interact with the user in natural language, whereas the AOG's
predictions are considered given and represented by the corresponding parse
graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides
answers to the user's questions using the following types of reasoning: direct
evidence (e.g., detection scores), part-based inference (e.g., detected parts
provide evidence for the concept asked), and other evidences from
spatio-temporal context (e.g., constraints from the spatio-temporal surround).
We identify several correlations between user's questions and the XAI answers
using Youtube Action dataset.


  We calculate an electron-phonon scattering and intrinsic transport properties
of black phosphorus monolayer using tight-binding and Boltzmann treatments as a
function of temperature, carrier density, and electric field. The low-field
mobility shows weak dependence on density and, at room temperature, falls in
the range of 300 - 1000 cm^2/Vs in the armchair direction and 50 - 120 cm^2/Vs
in the zig-zag direction with the anisotropy due to an effective mass
difference. At high fields, drift velocity is linear with electric field up to
1 - 2 V/micron reaching values of 10^7 cm/s in the armchair direction, unless
self-heating effects are included.


  We propose the Self Returning Excluded Volume (SR-EV) model for the structure
of chromatin based on stochastic rules and physical interactions. The SR-EV
rules of return generate conformationally-defined domains observed by single
cell imaging techniques. From nucleosome to chromosome scales, the model
captures the overall chromatin organization as a corrugated system, with dense
and dilute regions alternating in a manner that resembles the mixing of two
disordered bi-continuous phases. This particular organizational topology is a
consequence of the multiplicity of interactions and processes occurring in the
nuclei, and mimicked by the proposed return rules. Single configuration
properties and ensemble averages show a robust agreement between theoretical
and experimental results including chromatin volume concentration, contact
probability, packing domain identification and size characterization, and
packing scaling behavior. Model and experimental results suggest that there is
an inherent chromatin organization regardless of the cell character and
resistant to an external forcing such as Rad21 degradation.


  We study the structure and perform some explicit calculations for the power
ultraviolet divergences and their renormalization in the Chiral Pertubation
Theory with lattice regularization in 1 and 2 loops.


  We compute -in the saddle point approximation- the partition function for the
2+1 black hole using the Gibbons-Hawking approach. Some issues concerning the
definition of thermodynamical ensembles are clarified. It is pointed out that
the right action in covariant form is exactly equal to the Chern-Simons action
with no added boundary terms. This action is finite, yields the right canonical
free energy and has an extremum when the temperature and angular velocity are
fixed. The correspondence with a 1+1 $CFT$ is indicated.


  We obtain the asymptotic expansion for the Gauss hypergeometric function
\[F(a-\lambda,b+\lambda;c+i\alpha\lambda;z)\] for $\lambda\rightarrow+\infty$
with $a$, $b$ and $c$ finite parameters by application of the method of
steepest descents. The quantity $\alpha$ is real, so that the denominatorial
parameter is complex and $z$ is a finite complex variable restricted to lie in
the sector $|\arg (1-z)|<\pi$. We concentrate on the particular case $a=0$,
$b=c=1$, which is associated with the Legendre functions of real degree and
imaginary order. The resulting expansions are of Poincar\'e type and hold in
restricted domains of the $z$-plane. An expansion is given at the coalescence
of two saddle points. Numerical results illustrating the accuracy of the
different expansions are given.


  We establish expansion properties for suitably generic polynomials of degree
$d$ in $d+1$ variables over finite fields. In particular, we show that if
$P\in\mathbb{F}_q[x_1,\ldots,x_{d+1}]$ is a polynomial of degree $d$ coming
from an explicit, Zariski dense set, and
$X_1,\ldots,X_{d+1}\subseteq\mathbb{F}_q$ are suitably large, then
$|P(X_1,\ldots,X_{d+1})|=q-O(1)$. Our methods rely on a higher-degree extension
of a result of Vinh on point--line incidences over a finite field.


  Halide perovskites are highly promising light-harvesting materials with
strong ionic character, enabling in principle the combination of a solar cell
and a Li-ion battery in one integrated photo-battery device. Here, we
investigate Li ions inside crystals of CsPbI$_3$, as a prototype compound, by
means of density-functional-theory calculations. Our findings demonstrate that
the interstitial location and migration of Li ions depend strongly on the
dynamic nature of the crystal structure of the perovskite compound. We consider
two limiting cases for Li in CsPbI$_{3}$,(i) the cubic-symmetry structure as a
model for the limit of fast ion motion and (ii) a distorted cubic structure as
a model for the limit of slow ion motion. For both limiting cases we obtain
moderate energy barriers for migrating Li ions, which highlight the potential
of halide perovskites like CsPbI$_3$ for applications in photo-battery devices.


  Multiarm interferometers can enhance measurement precision and provide
multiparameter capability to the measurement. Their realisation requires
multiport beam splitters, which has been a long-standing challenge in
free-space and integrated optics. Here, we propose a new type of multiport
interferometers suitable for implementation on optical chips. Their prospective
advantages over the standard directional-coupler architectures: an arbitrary
number of arms, planar architecture and two orders of magnitude reduction in
footprint, are achieved by the layout based exclusively on the finite modulated
photonic lattices. The inverse design of photonic lattices is facilitated by
restricting the light propagation to periodic patterns. The corresponding
interferometer model predicts the maximum $1/\sqrt{N}$ sensitivity scaling with
the number of arms $N$. While the presented design solutions are ubiquitous to
all implementations, the advantages are discussed in both the low and high
refractive-index contrast platforms. Finally, we provide an outlook on the
possibilities for the interferometer applications in distributed classical and
quantum measurements.


  We explore toroidal analogues of the Maxwell-Cremona correspondence. Erickson
and Lin [arXiv:2003.10057] showed the following correspondence for geodesic
torus graphs $G$: a positive equilibrium stress for $G$, an orthogonal
embedding of its dual graph $G^*$, and vertex weights such that $G$ is the
intrinsic weighted Delaunay graph of its vertices. We extend their results to
equilibrium stresses that are not necessarily positive, which correspond to
orthogonal drawings of $G^*$ that are not necessarily embeddings. We also give
a correspondence between equilibrium stresses and parallel drawings of the
dual.


  Computational fluid dynamics (CFD) simulation provides valuable information
on blood flow from the vascular geometry. However, it requires extracting
precise models of arteries from low-resolution medical images, which remains
challenging. Centerline-based representation is widely used to model large
vascular networks with small vessels, as it encodes both the geometric and
topological information and facilitates manual editing. In this work, we
propose an automatic method to generate a structured hexahedral mesh suitable
for CFD directly from centerlines. We addressed both the modeling and meshing
tasks. We proposed a vessel model based on penalized splines to overcome the
limitations inherent to the centerline representation, such as noise and
sparsity. The bifurcations are reconstructed using a parametric model based on
the anatomy that we extended to planar n-furcations. Finally, we developed a
method to produce a volume mesh with structured, hexahedral, and flow-oriented
cells from the proposed vascular network model. The proposed method offers
better robustness to the common defects of centerlines and increases the mesh
quality compared to state-of-the-art methods. As it relies on centerlines
alone, it can be applied to edit the vascular model effortlessly to study the
impact of vascular geometry and topology on hemodynamics. We demonstrate the
efficiency of our method by entirely meshing a dataset of 60 cerebral vascular
networks. 92% of the vessels and 83% of the bifurcations were meshed without
defects needing manual intervention, despite the challenging aspect of the
input data. The source code is released publicly.


  A properly colored cycle (path) in an edge-colored graph is a cycle (path)
with consecutive edges assigned distinct colors. A monochromatic triangle is a
cycle of length $3$ with the edges assigned a same color. It is known that
every edge-colored complete graph without containing monochromatic triangles
always contains a properly colored Hamilton path. In this paper, we investigate
the existence of properly colored cycles in edge-colored complete graphs when
monochromatic triangles are forbidden. We obtain a vertex-pancyclic analogous
result combined with a characterization of all the exceptions.


  This work is a PhD thesis. First we provide some general context on wonderful
varieties and moduli spaces of rational curves. Working over complex numbers we
prove that the moduli space of rational curves with no marked points on the
wonderful compactification of a symmetric space is not irreducible in general.
Lastly we show that in the case of wonderful group compactifications the set of
rational curves with no marked points and irreducible source is irreducible.


  The electron current tensor for the scattering of heavy photon on
longitudinally polarized electron accompanied with two hard real photonsis
considered. The contribution of collinear and semicollinear kinematics is
computed. The obtained result allows to calculate the corresponding
contribution into second order radiative correction to DIS or electron-positron
annihilation cross-sections with the next-to-leading accuracy.


  The nuclear recoil correction to the g factor of boronlike ions is evaluated
within the lowest-order relativistic (Breit) approximation. The
interelectronic-interaction effects are taken into account to the first order
of the perturbation theory in 1/Z. Higher orders in 1/Z are partly accounted
for by means of the effective screening potential. The most accurate up-to-date
values of this contribution are presented for the ions in the range Z=10-20.


  Given a set $S \subseteq \mathbb{R}^2$, define the \emph{Helly number of
$S$}, denoted by $H(S)$, as the smallest positive integer $N$, if it exists,
for which the following statement is true: for any finite family $\mathcal{F}$
of convex sets in~$\mathbb{R}^2$ such that the intersection of any $N$ or fewer
members of~$\mathcal{F}$ contains at least one point of $S$, there is a point
of $S$ common to all members of $\mathcal{F}$.
  We prove that the Helly numbers of \emph{exponential lattices} $\{\alpha^n
\colon n \in \mathbb{N}_0\}^2$ are finite for every $\alpha>1$ and we determine
their exact values in some instances. In particular, we obtain $H(\{2^n \colon
n \in \mathbb{N}_0\}^2)=5$, solving a problem posed by Dillon (2021).
  For real numbers $\alpha, \beta > 1$, we also fully characterize exponential
lattices $L(\alpha,\beta) = \{\alpha^n \colon n \in \mathbb{N}_0\} \times
\{\beta^n \colon n \in \mathbb{N}_0\}$ with finite Helly numbers by showing
that $H(L(\alpha,\beta))$ is finite if and only if $\log_\alpha(\beta)$ is
rational.


  The techniques most extensively used to retrieve interesting data from
data-sets are the Skyline and the Top-k queries. Sadly, they are not enough for
facing modern problems, so the needing of something more usable and reliable
has come. In this survey we are going to explore Flexible Skylines which are
proposed to overcame the old fashion techniques' problems by extending the
concept of dominance. After, we are going to compare this approach with the old
and new ones evaluating pros and cons. Finally, we will see some interesting
applications.


  We construct a new class of complete constant mean curvature surfaces in R^3.
These are geometrically different than the surfaces constructed by Kapouleas'
gluing technique. These are obtained by piecing together half-Delaunay surfaces
to the truncations of minimal k-noids. The gluing techniques are new: the
surfaces are obtained by matching Cauchy data from the infinite dimensional
family of exact solutions of the problem on each of the component pieces. These
surfaces are also proved to be nondegenerate in the CMC moduli space.


  The modified gravitational equations to describe a four-dimensional
braneworld in the case with the Lorentz invariant violation in a bulk spacetime
is presented. It contains a trace part of the brane energy-momentum tensor and
the coefficients of all terms describe the Lorentz violation effects from the
bulk spacetime. As an application, we apply this formalism to study cosmology.
In respect to standard effective Friedmann equations on the brane, Lorentz
invariance violation in the bulk causes a modification of this equations that
can lead to significant physical consequences. In particular, the effective
Friedmann equation on the brane explicitly depends on the equation of state of
the brane matter and the Lorentz violating parameters. We show that the
components of five-dimensional Weyl curvature are related to the matter on
brane even at low energies. We also find that the constraints on the theory
parameters are depend on the equation of state of the energy components of the
brane matter. Finally, the stability of the model depend on the specific
choices of initial conditions and the parameters $\beta_i$.


  The 360-degree video allows users to enjoy the whole scene by interactively
switching viewports. However, the huge data volume of the 360-degree video
limits its remote applications via network. To provide high quality of
experience (QoE) for remote web users, this paper presents a tile-based
adaptive streaming method for 360-degree videos. First, we propose a simple yet
effective rate adaptation algorithm to determine the requested bitrate for
downloading the current video segment by considering the balance between the
buffer length and video quality. Then, we propose to use a Gaussian model to
predict the field of view at the beginning of each requested video segment. To
deal with the circumstance that the view angle is switched during the display
of a video segment, we propose to download all the tiles in the 360-degree
video with different priorities based on a Zipf model. Finally, in order to
allocate bitrates for all the tiles, a two-stage optimization algorithm is
proposed to preserve the quality of tiles in FoV and guarantee the spatial and
temporal smoothness. Experimental results demonstrate the effectiveness and
advantage of the proposed method compared with the state-of-the-art methods.
That is, our method preserves both the quality and the smoothness of tiles in
FoV, thus providing the best QoE for users.


  Purpose: To develop an open-source and easy-to-use segmentation model that
can automatically and robustly segment most major anatomical structures in MR
images independently of the MR sequence.
  Materials and Methods: In this study we extended the capabilities of
TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to
segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3
tissue types) relevant for use cases such as organ volumetry, disease
characterization, and surgical planning. The MR and CT images were randomly
sampled from routine clinical studies and thus represent a real-world dataset
(different ages, pathologies, scanners, body parts, sequences, contrasts, echo
times, repetition times, field strengths, slice thicknesses and sites). We
trained an nnU-Net segmentation algorithm on this dataset and calculated Dice
similarity coefficients (Dice) to evaluate the model's performance.
  Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the
test set, which included a wide range of clinical data with major pathologies.
The model significantly outperformed two other publicly available segmentation
models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542;
p<0.001). On the CT image test set of the original TotalSegmentator paper it
almost matches the performance of the original TotalSegmentator (Dice score,
0.960 versus 0.970; p<0.001).
  Conclusion: Our proposed model extends the capabilities of TotalSegmentator
to MR images. The annotated dataset
(https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit
(https://www.github.com/wasserth/TotalSegmentator) are publicly available.


  We propose iterative projection methods for solving square or rectangular
consistent linear systems Ax = b. Existing projection methods use sketching
matrices (possibly randomized) to generate a sequence of small projected
subproblems, but even the smaller systems can be costly. We develop a process
that appends one column to the sketching matrix each iteration and converges in
a finite number of iterations whether the sketch is random or deterministic. In
general, our process generates orthogonal updates to the approximate solution
xk. By choosing the sketch to be the set of all previous residuals, we obtain a
simple recursive update and convergence in at most rank(A) iterations (in exact
arithmetic). By choosing a sequence of identity columns for the sketch, we
develop a generalization of the Kaczmarz method. In experiments on large sparse
systems, our method (PLSS) with residual sketches is competitive with LSQR and
LSMR, and with residual and identity sketches compares favorably with
state-of-the-art randomized methods.


  We introduce the "total s-energy" of a multiagent system with time-dependent
links. This provides a new analytical lens on bidirectional agreement dynamics,
which we use to bound the convergence rates of dynamical systems for
synchronization, flocking, opinion dynamics, and social epistemology.


  Dimerized antiferromagnetic spin-1/2 ladders are known to exhibit a quantum
critical phase transition in the ground state, the existence or absence of
which is dependent on the dimerization pattern of the ladder. The gapped phases
cannot be distinguished by the conventional Landau long-range order parameter.
However, they possess a non-local (hidden) string order parameter, which is
non-zero in one phase and vanishes in the other. We use an exact
diagonalization technique to calculate ground state energies, energy gaps and
string order parameters of dimerized two- and three-leg Heisenberg ladders, as
well as a critical scaling analysis to yield estimates of the critical
exponents nu and beta.


  Consider the characteristic initial value problem for the Einstein vacuum
equations without any symmetry assumptions. Impose a sequence of data on two
intersecting null hypersurfaces, each of which is foliated by spacelike
$2$-spheres. Assume that the sequence of data is such that the derivatives of
the metrics along null directions are only uniformly bounded in $L^2$ but the
derivatives of the metrics along the directions tangential to the $2$-spheres
obey higher regularity bounds uniformly. By the results in [J. Luk and I.
Rodnianski, Nonlinear interaction of impulsive gravitational waves for the
vacuum Einstein equations, Camb. J. Math. 5(4), 2017], it follows that the
sequence of characteristic initial value problems gives rise to a sequence of
vacuum spacetimes $(\mathcal M, g_n)$ in a fixed double-null domain $\mathcal
M$. Since the existence theorem requires only very low regularity, the sequence
of solutions may exhibit both oscillations and concentrations, and the limit
need not be vacuum. We prove nonetheless that, after passing to a subsequence,
the metrics converge in $C^0$ and weakly in $W^{1,2}$ to a solution of the
Einstein-null dust system with two families of (potentially measure-valued)
null dust.
  We show moreover that all sufficiently regular solutions to the Einstein-null
dust system (with potentially measure-valued null dust) adapted to a double
null coordinate system arise locally as weak limits of solutions to the
Einstein vacuum system in the manner described above. As a consequence, we also
give the first general local existence and uniqueness result for solutions to
the Einstein-null dust system for which the null dusts are only measures. This
in particular includes as a special case solutions featuring propagating and
interacting shells of null dust.


  Classification of social media posts in emergency response is an important
practical problem: accurate classification can help automate processing of such
messages and help other responders and the public react to emergencies in a
timely fashion. This research focused on classifying Facebook messages of US
police departments. Randomly selected 5,000 messages were used to train
classifiers that distinguished between four categories of messages: emergency
preparedness, response and recovery, as well as general engagement messages.
Features were represented with bag-of-words and word2vec, and models were
constructed using support vector machines (SVMs) and convolutional (CNNs) and
recurrent neural networks (RNNs). The best performing classifier was an RNN
with a custom-trained word2vec model to represent features, which achieved the
F1 measure of 0.839.


  Engineering light-matter interactions at the quantum level has been central
to the pursuit of quantum optics for decades. Traditionally, this has been done
by coupling emitters, typically natural atoms and ions, to quantized
electromagnetic fields in optical and microwave cavities. In these systems, the
emitter is approximated as an idealized dipole, as its physical size is orders
of magnitude smaller than the wavelength of light. Recently, artificial atoms
made from superconducting circuits have enabled new frontiers in light-matter
coupling, including the study of "giant" atoms which cannot be approximated as
simple dipoles. Here, we explore a new implementation of a giant artificial
atom, formed from a transmon qubit coupled to propagating microwaves at
multiple points along an open transmission line. The nature of this coupling
allows the qubit radiation field to interfere with itself leading to some
striking giant-atom effects. For instance, we observe strong
frequency-dependent couplings of the qubit energy levels to the electromagnetic
modes of the transmission line. Combined with the ability to in situ tune the
qubit energy levels, we show that we can modify the relative coupling rates of
multiple qubit transitions by more than an order of magnitude. By doing so, we
engineer a metastable excited state, allowing us to operate the giant transmon
as an effective lambda system where we clearly demonstrate electromagnetically
induced transparency.


  Since its debut in 2016, Federated Learning (FL) has been tied to the inner
workings of Deep Neural Networks (DNNs). On the one hand, this allowed its
development and widespread use as DNNs proliferated. On the other hand, it
neglected all those scenarios in which using DNNs is not possible or
advantageous. The fact that most current FL frameworks only allow training DNNs
reinforces this problem. To address the lack of FL solutions for non-DNN-based
use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a
model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL
framework: Intel OpenFL. MAFL is the first FL system not tied to any specific
type of machine learning model, allowing exploration of FL scenarios beyond
DNNs and trees. We test MAFL from multiple points of view, assessing its
correctness, flexibility and scaling properties up to 64 nodes. We optimised
the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is
compatible with x86-64, ARM-v8, Power and RISC-V.