  We consider 1-complemented subspaces (ranges of contractive projections) of
vector-valued spaces $\ell_p(X)$, where $X$ is a Banach space with a
1-unconditional basis and $p \in (1,2)\cup (2,\infty)$. If the norm of $X$ is
twice continuously differentiable and satisfies certain conditions connecting
the norm and the notion of disjointness with respect to the basis, then we
prove that every 1-complemented subspace of $\ell_p(X)$ admits a basis of
mutually disjoint elements. Moreover, we show that every contractive projection
is then an averaging operator. We apply our results to the space
$\ell_p(\ell_q)$ with $p,q\in (1,2)\cup (2,\infty)$ and obtain a complete
characterization of its 1-complemented subspaces.


  We reconsider the problem of the spontaneous emission of light by an excited
atomic state. We scrutinize the survival probability of this excited state for
very short times, in the so-called Zeno regime, for which we show that the
dynamics is dictated by a coherent --in phase -- response of the on-shell and
off-shell vacuum modes. We also develop a perturbative approach in order to
interpolate between different temporal regimes: the Zeno, golden rule (linear)
and Wigner-Weisskopf (exponential) regimes. We compare results obtained with
the $\hat{\mathbf{E}}\cdot\hat{\mathbf{x}}$ and
$\hat{\mathbf{A}}\cdot\hat{\mathbf{p}}$ interaction Hamiltonian,s, using
successively the dipole approximation and the exact coupling.


  Differential privacy protects an individual's privacy by perturbing data on
an aggregated level (DP) or individual level (LDP). We report four online
human-subject experiments investigating the effects of using different
approaches to communicate differential privacy techniques to laypersons in a
health app data collection setting. Experiments 1 and 2 investigated
participants' data disclosure decisions for low-sensitive and high-sensitive
personal information when given different DP or LDP descriptions. Experiments 3
and 4 uncovered reasons behind participants' data sharing decisions, and
examined participants' subjective and objective comprehensions of these DP or
LDP descriptions. When shown descriptions that explain the implications instead
of the definition/processes of DP or LDP technique, participants demonstrated
better comprehension and showed more willingness to share information with LDP
than with DP, indicating their understanding of LDP's stronger privacy
guarantee compared with DP.


  Manually grading the Response to Text Assessment (RTA) is labor intensive.
Therefore, an automatic method is being developed for scoring analytical
writing when the RTA is administered in large numbers of classrooms. Our
long-term goal is to also use this scoring method to provide formative feedback
to students and teachers about students' writing quality. As a first step
towards this goal, interpretable features for automatically scoring the
evidence rubric of the RTA have been developed. In this paper, we present a
simple but promising method for improving evidence scoring by employing the
word embedding model. We evaluate our method on corpora of responses written by
upper elementary students.


  We present refined values for the physical parameters of transiting
exoplanets, based on a self-consistent and uniform analysis of transit light
curves and the observable properties of the host stars. Previously it has been
difficult to interpret the ensemble properties of transiting exoplanets,
because of the widely different methodologies that have been applied in
individual cases. Furthermore, previous studies often ignored an important
constraint on the mean stellar density that can be derived directly from the
light curve. The main contributions of this work are 1) a critical compilation
and error assessment of all reported values for the effective temperature and
metallicity of the host stars; 2) the application of a consistent methodology
and treatment of errors in modeling the transit light curves; and 3) more
accurate estimates of the stellar mass and radius based on stellar evolution
models, incorporating the photometric constraint on the stellar density. We use
our results to revisit some previously proposed patterns and correlations
within the ensemble. We confirm the mass-period correlation, and we find
evidence for a new pattern within the scatter about this correlation: planets
around metal-poor stars are more massive than those around metal-rich stars at
a given orbital period. Likewise, we confirm the proposed dichotomy of planets
according to their Safronov number, and we find evidence that the systems with
small Safronov numbers are more metal-rich on average. Finally, we confirm the
trend that led to the suggestion that higher-metallicity stars harbor planets
with a greater heavy-element content.


  We emphasize the fact the evolution of quantum states in the inverted
oscillator (IO) is reduced to classical equations of motion, stressing that the
corresponding tunnelling and reflexion coefficients addressed in the literature
are calculated by considering only classically trajectories. The Wigner
function formalism is employed to describe the IO classical dynamics,
subsequently leading to the introduction of the Ambiguity function lying in the
so-called Reciprocal phase space. Our findings, show that the Ambiguity
function behavior, subjected to the IO, allude a classical propagation with an
associated integral of motion, and complex conjugated doubly degenerate energy
states.


  To investigate the possibility of measuring the intermolecular and
intramolecular anharmonic coupling of balk water, we calculate third-order
two-dimensional (2D) infrared (IR) spectra and fifth-order 2D IR-IR-Raman-Raman
spectra expressed in terms of four-body correlation functions of optical
observables. For this purpose, a multimode Brownian oscillator model of four
interacting anharmonic oscillators strongly coupled to their respective heat
baths is employed. The nonlinearity of the system-bath interactions is
considered to describe thermal relaxation and vibrational dephasing. The linear
and nonlinear spectra are then computed in a non-Markovian and nonperturbative
regime in a rigorous manner using the discretized hierarchical equations of
motion in mixed Liouville-Wigner space (DHEOM-MLWS). The calculated 2D spectra
for stretching-bending, bending-librational, stretching-librational, and
stretching-translational modes consist of various positive and negative peaks
exhibiting essential details of the intermolecular and intramolecular mode-mode
interactions under thermal relaxation and dephasing at finite temperature.


  The baryon acoustic oscillation (BAO) method for constraining the expansion
history is adversely affected by non-linear structure formation, which washes
out the correlation function peak created at decoupling. To increase the
constraining power of low z BAO experiments, it has been proposed that one use
the observed distribution of galaxies to "reconstruct'' the acoustic peak.
Recently Padmanabhan, White and Cohn provided an analytic formalism for
understanding how reconstruction works within the context of Lagrangian
perturbation theory. We extend that formalism to include the case of biased
tracers of the mass and, because the quantitative validity of LPT is
questionable, we investigate reconstruction in N-body simulations. We find that
LPT does a good job of explaining the trends seen in simulations for both the
mass and for biased tracers and comment upon the implications this has for
reconstruction.


  We investigate the appearance of the square of a Hamilton cycle in the model
of randomly perturbed graphs, which is, for a given $\alpha \in (0,1)$, the
union of any $n$-vertex graph with minimum degree $\alpha n$ and the binomial
random graph $G(n,p)$. This is known when $\alpha > 1/2$, and we determine the
exact perturbed threshold probability in all the remaining cases, i.e., for
each $\alpha \le 1/2$. We demonstrate that, as $\alpha$ ranges over the
interval $(0,1)$, the threshold performs a countably infinite number of
`jumps'. Our result has implications on the perturbed threshold for
$2$-universality, where we also fully address all open cases.


  We propose a physics and chemistry-based constitutive framework to predict
the stress responses of thermo-chemically aged elastomers and capture their
brittle failure using the phase-field approach. High-temperature aging in the
presence of oxygen causes the macromolecular network of elastomers to undergo
complex chemical reactions inducing two main mechanisms: chain-scission and
crosslinking. Chemical crosslinking contributes to the stiffening behavior
characterizing the brittle response of aged elastomers. In this work, we first
modify the Helmholtz free energy to incorporate the effect of
thermo-chemically-driven crosslinking processes. Then, we equip the
constitutive description with phase-field to capture the induced brittle
failure via a strain-based criterion for fracture. We show that our proposed
framework is self-contained and requires only four main material properties
whose evolution due to thermo-chemical aging is characterized entirely by the
change of the crosslink density obtained based on chemical characterization
experiments. The developed constitutive framework is first solved analytically
for the case of uniaxial tension in a homogeneous bar to highlight the
interconnection between all four material properties. Then, the framework is
numerically implemented within a finite element (FE) context via a user-element
subroutine (UEL) in the commercial FE software Abaqus to simulate more
complicated geometries and loading states. The framework is finally validated
with respect to a set of experimental results available in the literature. The
comparison confirms that the proposed constitutive framework can accurately
predict the mechanical response of thermo-chemically aged elastomers. Further
numerical examples are provided to demonstrate the effects of evolving material
properties on the response of specimens containing pre-existing cracks.


  We present algebraic and geometric classifications of the $4$-dimensional
complex nilpotent right alternative algebras. Specifically, we find that, up to
isomorphism, there are only $9$ non-isomorphic nontrivial nilpotent right
alternative algebras. The corresponding geometric variety has dimension $13$
and it is determined by the Zariski closure of $4$ rigid algebras and one
one-parametric family of algebras.


  An MBM class on a hyperkahler manifold M is a second cohomology class such
that its orthogonal complement in H^2(M) contains a maximal dimensional face of
the boundary of the Kahler cone for some hyperkahler deformation of M. An MBM
curve is a rational curve in an MBM class and such that its local deformation
space has minimal possible dimension 2n-2, where 2n is the complex dimension of
M. We study the MBM loci, defined as the subvarieties covered by deformations
of an MBM curve within M. When M is projective, MBM loci are centers of
birational contractions. For each MBM class z, we consider the Teichmuller
space $Teich^{min}_z$ of all deformations of M such that $z^{\bot}$ contains a
face of the Kahler cone. We prove that for all $I,J\in Teich^{min}_z$, the MBM
loci of (M, I) and (M,J) are homeomorphic under a homeomorphism preserving the
MBM curves, unless possibly the Picard number of I or J is maximal.


  We have developed a steady state theory of complex transport networks used to
model the flow of commodity, information, viruses, opinions, or traffic. Our
approach is based on the use of the Markov chains defined on the graph
representations of transport networks allowing for the effective network
design, network performance evaluation, embedding, partitioning, and network
fault tolerance analysis. Random walks embed graphs into Euclidean space in
which distances and angles acquire a clear statistical interpretation. Being
defined on the dual graph representations of transport networks random walks
describe the equilibrium configurations of not random commodity flows on
primary graphs. This theory unifies many network concepts into one framework
and can also be elegantly extended to describe networks represented by directed
graphs and multiple interacting networks.


  The in-plane thermal conductivity K(T) of Zn-doped YBCO single crystals
(YBCO:Zn) has been investigated in the temperature range from 5 to 250 K and in
magnetic fields up to 30 kOe. The thermal conductivity decreases substantially
with increase of the Zn concentration providing reduction in the thermal
conductivity peak and its disappearance at a high content of the dopant. At
temperatures below 15 K the K(T) dependencies are functionally similar for all
the samples. The magnetic field effect on the thermal conductivity is strongly
suppressed by doping even in the crystal with the lowest Zn content. For
YBCO:Zn with x=0.003 at temperatures below 25 K the magnitude of remnant
magneto-thermal resistivity is about one tenth of that for pure YBCO. Analysing
experimental data we use the theory of thermal conductivity in high-Tc
superconductors and speculations about the modification of quasiparticle
excitation spectrum due to the Cooper pair breaking.


  In each round of the Namer-Claimer game, Namer names a distance d, then
Claimer claims a subset of [n] that does not contain two points that differ by
d. Claimer wins once they have claimed sets covering [n]. I show that the
length of this game is of order log log n with optimal play from each side.


  Manipulation of sudden death of entanglement (ESD) of two qubits interacting
with statistically uncorrelated thermal reservoirs is investigated. It is shown
that for initially prepared X-states of the two qubits a simple (necessary and
sufficient) criterion for ESD can be derived with the help of the
Peres-Horodecki criterion. This criterion implies that, in contrast to the
zero-temperature case, at finite temperature of at least one of the reservoirs
all initially prepared two-qubit X-states exhibit ESD. General conditions are
derived under which ESD can be hastened, delayed, or averted.


  The rapidly-developing intersection of machine learning (ML) with high-energy
physics (HEP) presents both opportunities and challenges to our community. Far
beyond applications of standard ML tools to HEP problems, genuinely new and
potentially revolutionary approaches are being developed by a generation of
talent literate in both fields. There is an urgent need to support the needs of
the interdisciplinary community driving these developments, including funding
dedicated research at the intersection of the two fields, investing in
high-performance computing at universities and tailoring allocation policies to
support this work, developing of community tools and standards, and providing
education and career paths for young researchers attracted by the intellectual
vitality of machine learning for high energy physics.


  The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence
measure that has applications in various aspects of machine learning.
Conveniently, the objectives of different dimensionality reduction applications
using HSIC often reduce to the same optimization problem. However, the
nonconvexity of the objective function arising from non-linear kernels poses a
serious challenge to optimization efficiency and limits the potential of
HSIC-based formulations. As a result, only linear kernels have been
computationally tractable in practice. This paper proposes a spectral-based
optimization algorithm that extends beyond the linear kernel. The algorithm
identifies a family of suitable kernels and provides the first and second-order
local guarantees when a fixed point is reached. Furthermore, we propose a
principled initialization strategy, thereby removing the need to repeat the
algorithm at random initialization points. Compared to state-of-the-art
optimization algorithms, our empirical results on real data show a run-time
improvement by as much as a factor of $10^5$ while consistently achieving lower
cost and classification/clustering errors. The implementation source code is
publicly available on https://github.com/endsley.


  We compute the master integrals required for the calculation of the
double-real emission contributions to the matching coefficients of 0-jettiness
beam functions at next-to-next-to-next-to-leading order in perturbative QCD. As
an application, we combine these integrals and derive the double-real gluon
emission contribution to the matching coefficient $I_{qq}(t,z)$ of the quark
beam function.


  The zodiacal dust complex, a population of dust and small particles that
pervades the Solar System, provides important insight into the formation and
dynamics of planets, comets, asteroids, and other bodies. Here we present a new
set of data obtained using a novel technique: direct measurements of momentum
transfer to a spacecraft from individual particle impacts. This technique is
made possible by the extreme precision of the instruments flown on the LISA
Pathfinder spacecraft, a technology demonstrator for a future space-based
gravitational wave observatory that operated near the first Sun-Earth Lagrange
point from early 2016 through Summer of 2017. Using a simple model of the
impacts and knowledge of the control system, we show that it is possible to
detect impacts and measure properties such as the transferred momentum (related
to the particle's mass and velocity), direction of travel, and location of
impact on the spacecraft. In this paper, we present the results of a systematic
search for impacts during 4348 hours of Pathfinder data. We report a total of
54 candidates with momenta ranging from 0.2$\,\mu\textrm{Ns}$ to
230$\,\mu\textrm{Ns}$. We furthermore make a comparison of these candidates
with models of micrometeoroid populations in the inner solar system including
those resulting from Jupiter-family comets, Oort-cloud comets, Hailey-type
comets, and Asteroids. We find that our measured population is consistent with
a population dominated by Jupiter-family comets with some evidence for a
smaller contribution from Hailey-type comets. This is in agreement with
consensus models of the zodiacal dust complex in the momentum range sampled by
LISA Pathfinder.


  We study the task of gesture recognition from electromyography (EMG), with
the goal of enabling expressive human-computer interaction at high accuracy,
while minimizing the time required for new subjects to provide calibration
data. To fulfill these goals, we define combination gestures consisting of a
direction component and a modifier component. New subjects only demonstrate the
single component gestures and we seek to extrapolate from these to all possible
single or combination gestures. We extrapolate to unseen combination gestures
by combining the feature vectors of real single gestures to produce synthetic
training data. This strategy allows us to provide a large and flexible gesture
vocabulary, while not requiring new subjects to demonstrate combinatorially
many example gestures. We pre-train an encoder and a combination operator using
self-supervision, so that we can produce useful synthetic training data for
unseen test subjects. To evaluate the proposed method, we collect a real-world
EMG dataset, and measure the effect of augmented supervision against two
baselines: a partially-supervised model trained with only single gesture data
from the unseen subject, and a fully-supervised model trained with real single
and real combination gesture data from the unseen subject. We find that the
proposed method provides a dramatic improvement over the partially-supervised
model, and achieves a useful classification accuracy that in some cases
approaches the performance of the fully-supervised model.


  One of the most important predictions of any gap model for pulsar
magnetospheres is the predicted $\gamma$-ray spectra. In the outer gap model,
the properties of the synchro-curvature radiation are sensitive to many
parameters, whose realistic ranges have been studied in detail in an
accompanying paper. There we demonstrated that the uncertainty in the radius of
curvature, the magnetic field geometry, and the X-ray surface flux may affect
by orders of magnitude the predicted flux and spectral peak in the $\gamma$-ray
regime. Here, we present a systematic, numerical study of the impact of the
different parameters on the particle dynamics along the gap and calculate the
emitted synchro-curvature radiation along the trajectory. By integrating the
emitted radiation along the gap and convolving it with a parametrized particle
distribution, we discuss how the comparison with the wealth of {\em Fermi}-LAT
data can be used to constrain the applicability of the model. The resulting
spectra show very different energy peaks, fluxes and shapes, qualitatively
matching the great variety of the observed {\em Fermi}-LAT pulsars. In
particular, if we see a large fraction of photons emitted from the initial part
of the trajectory, we show that the spectra will be flatter at the low-energy
{\it Fermi}-LAT regime (100 MeV -- 1 GeV). This provides a solution for such
observed flat spectra, while still maintain synchro-curvature radiation as the
origin of these photons.


  The Zwicky Transient Facility has begun to discover binary systems with
orbital periods that are less than 1\,hr. Combined with dedicated follow-up
systems, which allow for high-cadence photometry of these sources, systematic
confirmation and characterization of these sources are now possible. Here, we
report the discovery of ZTF J190125.42+530929.5, a 40.6\,min orbital period,
eclipsing double white-dwarf binary. Both photometric and spectroscopic
modeling confirm its nature, yielding an estimated inclination of $i =
86.2^{+0.6}_{-0.2}\,\rm degrees$ and primary and secondary effective
temperatures of $\textrm{T}_\textrm{eff} = 28,000^{+500}_{-500}\,K$ and
$\textrm{T}_\textrm{eff} = 17,600^{+400}_{-400}\,K$ respectively. This system
adds to a growing list of sources for future gravitational-wave detectors and
contributes to the demographic analysis of double degenerates.


  We present results from particle-in-cell simulations of driven turbulence in
magnetized, collisionless, and relativistic pair plasma. We find that
fluctuations are consistent with the classical $k_\perp^{-5/3}$ magnetic energy
spectrum at fluid scales and a steeper $k_\perp^{-4}$ spectrum at sub-Larmor
scales, where $k_\perp$ is the wavevector perpendicular to the mean field. We
demonstrate the development of a non-thermal, power-law particle energy
distribution, $f(E) \sim E^{-\alpha}$, with index $\alpha$ that decreases with
increasing magnetization and increases with increasing system size (relative to
the characteristic Larmor radius). Our simulations indicate that turbulence can
be a viable source of energetic particles in high-energy astrophysical systems,
such as pulsar wind nebulae, if scalings asymptotically become insensitive to
the system size.


  We study the statistical properties of learning to defer (L2D) to multiple
experts. In particular, we address the open problems of deriving a consistent
surrogate loss, confidence calibration, and principled ensembling of experts.
Firstly, we derive two consistent surrogates -- one based on a softmax
parameterization, the other on a one-vs-all (OvA) parameterization -- that are
analogous to the single expert losses proposed by Mozannar and Sontag (2020)
and Verma and Nalisnick (2022), respectively. We then study the frameworks'
ability to estimate P( m_j = y | x ), the probability that the jth expert will
correctly predict the label for x. Theory shows the softmax-based loss causes
mis-calibration to propagate between the estimates while the OvA-based loss
does not (though in practice, we find there are trade offs). Lastly, we propose
a conformal inference technique that chooses a subset of experts to query when
the system defers. We perform empirical validation on tasks for galaxy, skin
lesion, and hate speech classification.


  Recently, materials exhibiting colossal dielectric constant ($CDC$) have
attracted significant attention because of their high dielectric constant and
potential applications in electronic devices, such as high dielectric
capacitors, capacitor sensors, random access memories and so on.


  We study a non-local hydrodynamic system with control. First we characterize
the control dynamics as a sub-optimal approximation to the optimal control
problem constrained to the evolution of the pressureless Euler alignment
system. We then discuss the critical thresholds that leading to global
regularity or finite-time blow-up of strong solutions in one and two
dimensions. Finally we propose a finite volume scheme for numerical solutions
of the controlled system. Several numerical simulations are shown to validate
the theoretical and computational results of the paper.


  Labelled Transition Systems (LTSs) are a fundamental semantic model in many
areas of informatics, especially concurrency theory. Yet, reasoning on LTSs and
relations between their states can be difficult and elusive: very simple
process algebra terms can give rise to a large (possibly infinite) number of
intricate transitions and interactions. To ease this kind of study, we present
LTSwb, a flexible and extensible LTS toolbox: this tutorial paper discusses its
design and functionalities.


  A 0-Hecke algebra is a deformation of the group algebra of a Coxeter group.
Based on work of Norton and Krob--Thibon, we introduce a tableau approach to
the representation theory of 0-Hecke algebras of type A, which resembles the
classic approach to the representation theory of symmetric groups by Young
tableaux and tabloids. We extend this approach to type B and D, and obtain a
correspondence between the representation theory of 0-Hecke algebras of type B
and D and quasisymmetric functions and noncommutative symmetric functions of
type B and D. Other applications are also provided.


  Advances in experimental neuroscience have transformed our ability to explore
the structure and function of neural circuits. At the same time, advances in
machine learning have unleashed the remarkable computational power of
artificial neural networks (ANNs). While these two fields have different tools
and applications, they present a similar challenge: namely, understanding how
information is embedded and processed through high-dimensional representations
to solve complex tasks. One approach to addressing this challenge is to utilize
mathematical and computational tools to analyze the geometry of these
high-dimensional representations, i.e., neural population geometry. We review
examples of geometrical approaches providing insight into the function of
biological and artificial neural networks: representation untangling in
perception, a geometric theory of classification capacity, disentanglement and
abstraction in cognitive systems, topological representations underlying
cognitive maps, dynamic untangling in motor systems, and a dynamical approach
to cognition. Together, these findings illustrate an exciting trend at the
intersection of machine learning, neuroscience, and geometry, in which neural
population geometry provides a useful population-level mechanistic descriptor
underlying task implementation. Importantly, geometric descriptions are
applicable across sensory modalities, brain regions, network architectures and
timescales. Thus, neural population geometry has the potential to unify our
understanding of structure and function in biological and artificial neural
networks, bridging the gap between single neurons, populations and behavior.


  Receptor cells with electrically coupled axons can improve both their input
sensitivity and dynamical range due to collective non-linear wave properties.
This mechanism is illustrated by a network of axons modeled by excitable maps
subjected to a Poison signal process with rate r. We find that, in a network of
N cells, the amplification factor A (number of cells excited by a single signal
event) decreases smoothly from A= O(N) to A=1 as r increases, preventing
saturation in a self-organized way and leading to a Weber-Fechner law behavior.
This self-limited amplification mechanism is generic for excitable media and
could be implemented in other biological contexts and artificial sensor
devices.


  Granular media are frequently found in nature and in industry and their
transport by a fluid flow is of great importance to human activities. One case
of particular interest is the transport of sand in open-channel and river
flows. In many instances, the shear stresses exerted by the fluid flow are
bounded to certain limits and some grains are entrained as bed-load: a mobile
layer which stays in contact with the fixed part of the granular bed. Under
these conditions, an initially flat granular bed may be unstable, generating
ripples and dunes such as those observed on the bed of rivers. In free-surface
water flows, dunes are bedforms that scale with the flow depth, while ripples
do not scale with it. This article presents a model for the formation of
ripples and dunes based on the proposition that ripples are primary linear
instabilities and that dunes are secondary instabilities formed from the
competition between the coalescence of ripples and free surface effects.
Although simple, the model is able to explain the growth of ripples, their
saturation (not explained in previous models) and the evolution from ripples to
dunes, presenting a complete picture for the formation of dunes.


  The family of symmetric one sided subshifts in two symbols given by a
sequence $a$ is studied. We analyse some of their topological properties such
as transitivity, the specification property and intrinsic ergodicity. It is
shown that almost every member of this family admits only one measure of
maximal entropy.


  Let $G$ be a simple algebraic group of type $B_2$ over an algebraically
closed field of odd characteristic. We prove that the flag variety $G/B$ is
D-affine. This extends an earlier result of H.H.Andersen and M.Kaneda.


  We provide an explicit formula on the growth rate of ample heights of
rational points under iteration of endomorphisms on smooth projective varieties
over number fields. As an application, we give a positive answer to a problem
of Dynamical Mordell-Lang type for pairs of \'etale endomorphisms, which is a
variant of the original one stated by Bell, Ghioca, and Tucker in their
monograph.


  Synthesizing face images from monochrome sketches is one of the most
fundamental tasks in the field of image-to-image translation. However, it is
still challenging to (1)~make models learn the high-dimensional face features
such as geometry and color, and (2)~take into account the characteristics of
input sketches. Existing methods often use sketches as indirect inputs (or as
auxiliary inputs) to guide the models, resulting in the loss of sketch features
or the alteration of geometry information. In this paper, we introduce a
Sketch-Guided Latent Diffusion Model (SGLDM), an LDM-based network architect
trained on the paired sketch-face dataset. We apply a Multi-Auto-Encoder (AE)
to encode the different input sketches from different regions of a face from
pixel space to a feature map in latent space, which enables us to reduce the
dimension of the sketch input while preserving the geometry-related information
of local face details. We build a sketch-face paired dataset based on the
existing method that extracts the edge map from an image. We then introduce a
Stochastic Region Abstraction (SRA), an approach to augment our dataset to
improve the robustness of SGLDM to handle sketch input with arbitrary
abstraction. The evaluation study shows that SGLDM can synthesize high-quality
face images with different expressions, facial accessories, and hairstyles from
various sketches with different abstraction levels.


  The learning of interpretable representations from raw data presents
significant challenges for time series data like speech. In this work, we
propose a relevance weighting scheme that allows the interpretation of the
speech representations during the forward propagation of the model itself. The
relevance weighting is achieved using a sub-network approach that performs the
task of feature selection. A relevance sub-network, applied on the output of
first layer of a convolutional neural network model operating on raw speech
signals, acts as an acoustic filterbank (FB) layer with relevance weighting. A
similar relevance sub-network applied on the second convolutional layer
performs modulation filterbank learning with relevance weighting. The full
acoustic model consisting of relevance sub-networks, convolutional layers and
feed-forward layers is trained for a speech recognition task on noisy and
reverberant speech in the Aurora-4, CHiME-3 and VOiCES datasets. The proposed
representation learning framework is also applied for the task of sound
classification in the UrbanSound8K dataset. A detailed analysis of the
relevance weights learned by the model reveals that the relevance weights
capture information regarding the underlying speech/audio content. In addition,
speech recognition and sound classification experiments reveal that the
incorporation of relevance weighting in the neural network architecture
improves the performance significantly.


  We give a trigonometric proof of the Steiner-Lehmus Theorem in hyperbolic
geometry. Precisely we show that if two internal bisectors of a triangle on the
hyperbolic plane are equal, then the triangle is isosceles.


  We consider the linear perturbations for the single scalar field inflation
model interacting with an additional triad of scalar fields. The background
solutions of the three additional scalar fields depend on spatial coordinates
with a constant gradient $\alpha$ and the ensuing evolution preserves the
homogeneity of the cosmological principle. After we discuss the properties of
background evolution including an exact solution for the exponential-type
potential, we investigate the linear perturbations of the scalar and tensor
modes in the background of the slow-roll inflation. In our model with small
$\alpha$, the comoving wavenumber has {\it a lower bound} $\sim \alpha M_{\rm
P}$ to have well-defined initial quantum states. We find that cosmological
quantities, for instance, the power spectrums and spectral indices of the
comoving curvature and isocurvature perturbations, and the running of the
spectral indices have small corrections depending on {\it the lower bound}.
Similar behaviors happen for the tensor perturbation with the same lower bound.


  We use empirical molecular dynamics technique to study the low-energy
vibrations in a large 4096 atom model for pure amorphous silicon and a set of
models with voids of different size based on it. Numerical vibrational
eigenvalues and eigenvectors for our models are obtained by exact
diagonalization of their dynamical matrices. Our calculations show that
localized low-energy vibrational excitations of rather complex structure are
present in amorphous silicon models with voids. According to their spatial
localization patterns we make an attempt to classify these excitations as modes
associated with the void and "mixed" modes associated with the interaction of
the void with strained regions of silicon network.


  We observed the nearby early-type spiral galaxy NGC 4594 (M 104, Sombrero
galaxy) with the Very Large Array at 4.86 GHz, with the Effelsberg 100-m
telescope at 8.35 GHz as well as with the Heinrich Hertz Telescope at 345 GHz
in radio continuum. The 4.86 and 8.35 GHz data contain polarization information
and hence information about the magnetic fields: we detected a large-scale
magnetic field which is to our knowledge the first detection of a large-scale
magnetic field in an Sa galaxy in the radio range. The magnetic field
orientation in M104 is predominantly parallel to the disk but has also vertical
components at larger z-distances from the disk. This field configuration is
typical for normal edge-on spiral galaxies. The 345 GHz data pertain to the
cold dust content of the galaxy. Despite the optical appearance of the object
with the huge dust lane, its dust content is smaller than that of more
late-type spirals.


  The scattering of a fermion in the background of a sign potential is
considered with a general mixing of vector and scalar Lorentz structures with
the scalar coupling stronger than or equal to the vector coupling under the
Sturm-Liouville perspective. When the vector coupling and the scalar coupling
have different magnitudes, an isolated solution shows that the fermion under a
strong potential can be trapped in a highly localized region without
manifestation of Klein's paradox. It is also shown that the lonely bound-state
solution disappears asymptotically as one approaches the conditions for the
realization of spin and pseudospin symmetries.


  The term Delay/Disruption-Tolerant Networks (DTN) invented to describe and
cover all types of long-delay, disconnected, intermittently connected networks,
where mobility and outages or scheduled contacts may be experienced. This
environment is characterized by frequent network partitioning, intermittent
connectivity, large or variable delay, asymmetric data rate, and low
transmission reliability. There have been routing protocols developed in DTN.
However, those routing algorithms are design based upon specific assumptions.
The assumption makes existing algorithms suitable for specific environment
scenarios. Different routing algorithm uses different relay node selection
criteria to select the replication node. Too Frequently forwarding messages can
result in excessive packet loss and large buffer and network overhead. On the
other hand, less frequent transmission leads to a lower delivery ratio. In DTN
there is a trade-off off between delivery ratio and overhead. In this study, we
proposed context-adaptive reinforcement learning based routing(CARL-DTN)
protocol to determine optimal replicas of the message based on the real-time
density. Our routing protocol jointly uses a real-time physical context,
social-tie strength, and real-time message context using fuzzy logic in the
routing decision. Multi-hop forwarding probability is also considered for the
relay node selection by employing Q-Learning algorithm to estimate the
encounter probability between nodes and to learn about nodes available in the
neighbor by discounting reward. The performance of the proposed protocol is
evaluated based on various simulation scenarios. The result shows that the
proposed protocol has better performance in terms of message delivery ratio and
overhead.


  In the literature on deep neural networks, there is considerable interest in
developing activation functions that can enhance neural network performance. In
recent years, there has been renewed scientific interest in proposing
activation functions that can be trained throughout the learning process, as
they appear to improve network performance, especially by reducing overfitting.
In this paper, we propose a trainable activation function whose parameters need
to be estimated. A fully Bayesian model is developed to automatically estimate
from the learning data both the model weights and activation function
parameters. An MCMC-based optimization scheme is developed to build the
inference. The proposed method aims to solve the aforementioned problems and
improve convergence time by using an efficient sampling scheme that guarantees
convergence to the global maximum. The proposed scheme is tested on three
datasets with three different CNNs. Promising results demonstrate the
usefulness of our proposed approach in improving model accuracy due to the
proposed activation function and Bayesian estimation of the parameters.


  I discuss recent theoretical results for inclusive decays of quarkonia into a
photon plus hadrons, and summarize the status of perturbative calculations.


  We develop an algorithm for systematic design of a large artificial neural
network using a progression property. We find that some non-linear functions,
such as the rectifier linear unit and its derivatives, hold the property. The
systematic design addresses the choice of network size and regularization of
parameters. The number of nodes and layers in network increases in progression
with the objective of consistently reducing an appropriate cost. Each layer is
optimized at a time, where appropriate parameters are learned using convex
optimization. Regularization parameters for convex optimization do not need a
significant manual effort for tuning. We also use random instances for some
weight matrices, and that helps to reduce the number of parameters we learn.
The developed network is expected to show good generalization power due to
appropriate regularization and use of random weights in the layers. This
expectation is verified by extensive experiments for classification and
regression problems, using standard databases.


  This work evaluates six state-of-the-art deep neural network (DNN)
architectures applied to the problem of enhancing camera-captured document
images. The results from each network were evaluated both qualitatively and
quantitatively using Image Quality Assessment (IQA) metrics, and also compared
with an existing approach based on traditional computer vision techniques. The
best performing architectures generally produced good enhancement compared to
the existing algorithm, showing that it is possible to use DNNs for document
image enhancement. Furthermore, the best performing architectures could work as
a baseline for future investigations on document enhancement using deep
learning techniques. The main contributions of this paper are: a baseline of
deep learning techniques that can be further improved to provide better
results, and a evaluation methodology using IQA metrics for quantitatively
comparing the produced images from the neural networks to a ground truth.


  This paper is the third of a series on Hamiltonian stationary Lagrangian
surfaces. We present here the most general theory, valid for any Hermitian
symmetric target space. Using well-chosen moving frame formalism, we show that
the equations are equivalent to an integrable system, generalizing the C^2
subcase analyzed in the first article (arXiv:math.DG/0009202). This system
shares many features with the harmonic map equation of surfaces into symmetric
spaces, allowing us to develop a theory close to Dorfmeister, Pedit and Wu's,
including for instance a Weierstrass-type representation. Notice that this
article encompasses the article mentioned above, although much fewer details
will be given on that particular flat case.


  We have studied the Josephson coupling between a conventional s-wave
superconductor (In) and Sr2RuO4 and found that the coupling is allowed in the
in-plane direction, but not along the c axis. This selection rule indicates
that the symmetry of superconducting order parameter of Sr2RuO4 is either p- or
alternatively, purely d-wave. If Sr2RuO4 is a p-wave superconductor, as
strongly favored by other experimental evidence, our result suggests that the
pairing state of Sr2RuO4 is Gamma5 with d(k) = z(kx +/- iky), a nodeless state
in which the spins of the superconducting electrons lie in the RuO2 planes.


  The Large Observatory For X-ray Timing (LOFT), currently in an assessment
phase in the framework the ESA M3 Cosmic Vision programme, is an innovative
medium-class mission specifically designed to answer fundamental questions
about the behaviour of matter, in the very strong gravitational and magnetic
fields around compact objects and in supranuclear density conditions. Having an
effective area of ~10 m^2 at 8 keV, LOFT will be able to measure with high
sensitivity very fast variability in the X-ray fluxes and spectra. A good
knowledge of the in-orbit background environment is essential to assess the
scientific performance of the mission and optimize the design of its main
instrument, the Large Area Detector (LAD). In this paper the results of an
extensive Geant-4 simulation of the instrument will be discussed, showing the
main contributions to the background and the design solutions for its reduction
and control. Our results show that the current LOFT/LAD design is expected to
meet its scientific requirement of a background rate equivalent to 10 mCrab in
2-30 keV, achieving about 5 mCrab in the most important 2-10 keV energy band.
Moreover, simulations show an anticipated modulation of the background rate as
small as 10% over the orbital timescale. The intrinsic photonic origin of the
largest background component also allows for an efficient modelling, supported
by an in-flight active monitoring, allowing to predict systematic residuals
significantly better than the requirement of 1%, and actually meeting the 0.25%
science goal.


  It is shown that, the wavelet regression detrended fluctuations of the
monthly sunspot number for 1749-2009 years exhibit strong periodicity with a
period approximately equal to 3.7 years. The wavelet regression method detrends
the data from the approximately 11-years period. Therefore, it is suggested
that the one-third subharmonic resonance can be considered as a background for
the 11-years solar cycle. It is also shown that the broad-band part of the
wavelet regression detrended fluctuations spectrum exhibits an exponential
decay that, together with the positive largest Lyapunov exponent, are the
hallmarks of chaos. Using a complex-time analytic approach the rate of the
exponential decay of the broad-band part of the spectrum has been theoretically
related to the Carrington solar rotation period. Relation of the driving period
of the subharmonic resonance (3.7-years) to the active longitude flip-flop
phenomenon, in which the dominant part of the sunspot activity changes the
longitude every 3.7 years on average, has been briefly discussed.


  We present the analysis of the spin vector orientation of 5$\,$987 SDSS
galaxies having negative redshift from $-$87.6 to $-$0.3 km$\,$s$^{-1}$. Two
dimensional observed parameters are used to compute three dimensional galaxy
rotation axes by applying `position angle--inclination' method. We aim to
examine the non-random effects in the spatial orientation of blue-shifted
galaxies. We generate 5$\times$10$^6$ virtual galaxies to find expected
isotropic distributions by performing numerical simulations. We have written
MATLAB program to facilitate the simulation process and eliminate the manual
errors in the process. Chi-square, auto-correlation, and the Fourier tests are
used to examine non-random effects in the polar and azimuthal angle
distributions of the galaxy rotation axes. In general, blue-shifted galaxies
show no preferred alignments of galaxy rotation axes. Our results support
Hierarchy model, which suggests a random orientation of angular momentum
vectors of galaxies. However, local effects are noted suggesting gravitational
tidal interaction between neighboring galaxies.


  In type IIB light-cone superstring field theory, the cubic interaction has
two pieces: a delta-functional overlap and an operator inserted at the
interaction point. In this paper we extend our earlier work hep-th/0204146 by
computing the matrix elements of this operator in the oscillator basis of
pp-wave string theory for all mu p^+ alpha'. By evaluating these matrix
elements for large mu p^+ alpha', we check a recent conjecture relating matrix
elements of the light-cone string field theory Hamiltonian (with prefactor) to
certain three-point functions of BMN operators in the gauge theory. We also
make several explicit predictions for gauge theory.


  Over the last few years natural language interfaces (NLI) for databases have
gained significant traction both in academia and industry. These systems use
very different approaches as described in recent survey papers. However, these
systems have not been systematically compared against a set of benchmark
questions in order to rigorously evaluate their functionalities and expressive
power.
  In this paper, we give an overview over 24 recently developed NLIs for
databases. Each of the systems is evaluated using a curated list of ten sample
questions to show their strengths and weaknesses. We categorize the NLIs into
four groups based on the methodology they are using: keyword-, pattern-,
parsing-, and grammar-based NLI. Overall, we learned that keyword-based systems
are enough to answer simple questions. To solve more complex questions
involving subqueries, the system needs to apply some sort of parsing to
identify structural dependencies. Grammar-based systems are overall the most
powerful ones, but are highly dependent on their manually designed rules. In
addition to providing a systematic analysis of the major systems, we derive
lessons learned that are vital for designing NLIs that can answer a wide range
of user questions.


  We address the challenge of online convex optimization where the objective
function's gradient exhibits sparsity, indicating that only a small number of
dimensions possess non-zero gradients. Our aim is to leverage this sparsity to
obtain useful estimates of the objective function's gradient even when the only
information available is a limited number of function samples. Our motivation
stems from distributed queueing systems like microservices-based applications,
characterized by request-response workloads. Here, each request type proceeds
through a sequence of microservices to produce a response, and the resource
allocation across the collection of microservices is controlled to balance
end-to-end latency with resource costs. While the number of microservices is
substantial, the latency function primarily reacts to resource changes in a
few, rendering the gradient sparse. Our proposed method, CONGO (Compressive
Online Gradient Optimization), combines simultaneous perturbation with
compressive sensing to estimate gradients. We establish analytical bounds on
the requisite number of compressive sensing samples per iteration to maintain
bounded bias of gradient estimates, ensuring sub-linear regret. By exploiting
sparsity, we reduce the samples required per iteration to match the gradient's
sparsity, rather than the problem's original dimensionality. Numerical
experiments and real-world microservices benchmarks demonstrate CONGO's
superiority over multiple stochastic gradient descent approaches, as it quickly
converges to performance comparable to policies pre-trained with workload
awareness.


  Many chalcogenide glasses undergo a breakdown in electronic resistance above
a critical field strength. Known as threshold switching, this mechanism enables
field-induced crystallization in emerging phase-change memory. Purely
electronic as well as crystal nucleation assisted models have been employed to
explain the electronic breakdown. Here, picosecond electric pulses are used to
excite amorphous Ag$_4$In$_3$Sb$_{67}$Te$_{26}$. Field-dependent reversible
changes in conductivity and pulse-driven crystallization are observed. The
present results show that threshold switching can take place within the
electric pulse on sub-picosecond time-scales - faster than crystals can
nucleate. This supports purely electronic models of threshold switching and
reveals potential applications as an ultrafast electronic switch.


  Although the Industrial Internet of Things has increased the number of
sensors permanently installed in industrial plants, there will be gaps in
coverage due to broken sensors or sparse density in very large plants, such as
in the petrochemical industry. Modern emergency response operations are
beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to
drop sensor robots to precise locations. sUAS can provide longer-term
persistent monitoring that aerial drones are unable to provide. Despite the
relatively low cost of these assets, the choice of which robotic sensing
systems to deploy to which part of an industrial process in a complex plant
environment during emergency response remains challenging.
  This paper describes a framework for optimizing the deployment of emergency
sensors as a preliminary step towards realizing the responsiveness of robots in
disaster circumstances. AI techniques (Long short-term memory, 1-dimensional
convolutional neural network, logistic regression, and random forest) identify
regions where sensors would be most valued without requiring humans to enter
the potentially dangerous area. In the case study described, the cost function
for optimization considers costs of false-positive and false-negative errors.
Decisions on mitigation include implementing repairs or shutting down the
plant. The Expected Value of Information (EVI) is used to identify the most
valuable type and location of physical sensors to be deployed to increase the
decision-analytic value of a sensor network. This method is applied to a case
study using the Tennessee Eastman process data set of a chemical plant, and we
discuss implications of our findings for operation, distribution, and
decision-making of sensors in plant emergency and resilience scenarios.


  We report on the calculation of the cross section for Higgs boson production
in association with three jets via gluon fusion, at next-to-leading-order (NLO)
accuracy in QCD, in the infinite top-mass approximation. After including the
complete NLO QCD corrections, we observe a strong reduction in the scale
dependence of the result, and an increased steepness in the transverse momentum
distributions of both the Higgs and the leading jets. The results are obtained
with the combined use of GoSam, Sherpa, and the MadDipole/MadEvent framework.


  Observations of gravitational radiation from compact binary systems provide
an unprecedented opportunity to test General Relativity in the strong field
dynamical regime. In this paper, we investigate how future observations of
gravitational radiation from binary neutron star mergers might provide
constraints on finite-range forces from a universally coupled massive scalar
field. Such scalar degrees of freedom are a characteristic feature of many
extensions of General Relativity. For concreteness, we work in the context of
metric $f(R)$ gravity, which is equivalent to General Relativity and a
universally coupled scalar field with a non-linear potential whose form is
fixed by the choice of $f(R)$. In theories where neutron stars (or other
compact objects) obtain a significant scalar charge, the resulting attractive
finite-range scalar force has implications for both the inspiral and merger
phases of binary systems. We first present an analysis of the inspiral dynamics
in Newtonian limit, and forecast the constraints on the mass of the scalar and
charge of the compact objects for the Advanced LIGO gravitational wave
observatory. We then perform a comparative study of binary neutron star mergers
in General Relativity with those of a one-parameter model of $f(R)$ gravity
using fully relativistic hydrodynamical simulations. These simulations
elucidate the effects of the scalar on the merger and post-merger dynamics. We
comment on the utility of the full waveform (inspiral, merger, post-merger) to
probe different regions of parameter space for both the particular model of
$f(R)$ gravity studied here and for finite-range scalar forces more generally.


  In this report, which is an extended version of that appearing in the
Proceedings of GR16, I will give a summary of the main topics covered in
Session A.3. on mathematical relativity at GR16, Durban. The summary is mainly
based on extended abstracts submitted by the speakers. I would like to thank
all participants for their contributions and help with this summary.


  In networks of identical linear oscillators (e.g. pendulums undergoing small
vibrations) coupled through both dissipative connectors (e.g. dampers) and
restorative connectors (e.g. springs) the relation between asymptotic
synchronization and coupling structure is studied. Conditions on the
interconnection under which synchronization can be achieved for some selection
of coupling strengths are established. How to strengthen those conditions so
that synchronization is guaranteed for all admissible parameter values is also
presented.


  The development of powerful 3D scanning hardware and reconstruction
algorithms has strongly promoted the generation of 3D surface reconstructions
in different domains. An area of special interest for such 3D reconstructions
is the cultural heritage domain, where surface reconstructions are generated to
digitally preserve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation, matching,
and classification) of reconstructed 3D data is still an open topic. In this
paper, we target the automatic and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.


  In these proceedings results are presented from the measurement of open
heavy-flavour production as a function of charged-particle multiplicity in pp
collisions at $\sqrt {s}$ = 7 TeV and p-Pb collisions at $\sqrt {s_{\rm
NN}}$=5.02 TeV recorded with the ALICE detector in 2010 and 2013, respectively.
$\rm D^{0}$, $\rm D^{+}$ and $\rm D^{*+}$ mesons are reconstructed from their
hadronic decay channels in the central rapidity region, and their production
yields are measured in various multiplicity and $p_{\rm T}$ intervals. The
per-event yields of $\rm D$ mesons in the various multiplicity intervals,
normalized to their multiplicity-integrated value, and their evolution with
$p_{\rm T}$ are measured for pp and p-Pb collisions to study the contribution
of Multi-Parton Interactions (MPIs) to open charm production in the two
systems. The nuclear modification factor of $\rm D$ mesons in p-Pb collisions,
defined as the ratio of the $\rm D$-meson yields in p-Pb and pp collisions
scaled by the average number of binary collisions $< N_{\rm coll}>$, is
discussed in terms of its dependence on the event activity. Several
experimental estimators of the event activity are used in order to assess the
role of kinematic biases.


  Energies for excited light baryons are computed in quenched QCD with a pion
mass of 490 MeV. Operators used in the simulations include local operators and
the simplest nonlocal operators that have nontrivial orbital structures. All
operators are designed with the use of Clebsch-Gordan coefficients of the
octahedral group so that they transform irreducibly under the group rotations.
Matrices of correlation functions are computed for each irreducible
representation, and then the variational method is applied to separate mass
eigenstates. We obtained 17 states for isospin 1/2 and 11 states for isospin
3/2 in various spin-parity channels including $J^P=5/2^\pm$. The pattern of the
lowest-lying energies from each irrep is discussed. We use anisotropic lattices
of volume $24^3\times 64$ with temporal lattice spacing $a_t^{-1}=6.05$ GeV
with renormalized anisotropy $\xi=3.0$.


  We present a study of the 1173 sources brighter than $S_{1.4\,\rm GHz}=
120\,\mu$Jy detected over an area of $\simeq 1.4\,\hbox{deg}^{2}$ in the
Lockman Hole field. Exploiting the multi-band information available in this
field for $\sim$79% of the sample, sources have been classified into radio loud
(RL) active galactic nuclei (AGNs), star forming galaxies (SFGs) and radio
quiet (RQ) AGNs, using a variety of diagnostics available in the literature.
Exploiting the observed tight anti-correlations between IRAC band 1 or band 2
and the source redshift we could assign a redshift to 177 sources missing a
spectroscopic measurement or a reliable photometric estimate. A Monte Carlo
approach was used to take into account the spread around the mean relation. The
derived differential number counts and luminosity functions at several
redshifts of each population show a good consistency with models and with
earlier estimates made using data from different surveys and applying different
approaches. Our results confirm that below $\sim300\,\mu$Jy SFGs$+$RQ AGNs
overtake RL AGNs that dominate at brighter flux densities. We also confirm
earlier indications of a similar evolution of RQ AGNs and SFGs. Finally, we
discuss the angular correlation function of our sources and highlight its
sensitivity to the criteria used for the classification.


  We find bases for naturally defined lattices over certain rings of integers
in the SU(2)-TQFT-theory modules of surfaces. We consider the TQFT where the
Kauffman's A variable is a root of unity of order four times an odd prime. As
an application, we show that the Frohman Kania-Bartoszynska ideal invariant for
3-manifolds with boundary using the SU(2)-TQFT-theory is equal to the product
of the ideals using the 2^{'}-theory and the SO(3)-TQFT-theory under a certain
change of coefficients.


  The goal of teleportation is to transfer the state of one particle to another
particle. In coined quantum walks, conditional shift operators can introduce
entanglement between position space and coin space. This entanglement resource
can be used as a quantum channel for teleportation, as proposed by Wang, Shang
and Xue [Quantum Inf. Process. 16, 221 (2017)]. Here, we demonstrate the
implementation of quantum teleportation using quantum walks on a five-qubit
quantum computer and a 32-qubit simulator provided by IBM quantum experience
beta platform. We show the teleportation of single-qubit, two-qubit and
three-qubit quantum states with circuit implementation on the quantum devices.
The teleportation of Bell, W and GHZ states has also been demonstrated as
special cases of the above states.


  Motivated by the growing importance of strong system-bath coupling in several
branches of quantum information and related technological applications, we
analyze and compare two strategies currently used to obtain (approximately)
steady states in strong coupling. The first strategy is based on perturbative
expansions while the second one uses reaction coordinate mapping. Focusing on
the widely used spin-boson model, we show that, as expected and hoped, the
predictions of these two strategies coincide for some parameter regions. This
confirms and strengthens the relevance of both techniques. Beyond that, it is
also crucial to know precisely their respective range of validity. In that
perspective, thanks to their different limitations, we use one to benchmark the
other. We introduce and successfully test some very simple validity criteria
for both strategies, bringing some answers to the question of the validity
range.


  We study corotational wave maps from $(1+3)$-dimensional Minkowski space into
the three-sphere. We establish the asymptotic stability of an explicitly known
self-similar wave map under perturbations that are small in the critical
Sobolev space. This is accomplished by proving Strichartz estimates for a
radial wave equation with a potential in similarity coordinates. Compared to
earlier work, the main novelty lies with the fact that the critical Sobolev
space is of fractional order.


  The parametric families of integrable boundary affine Toda theories are
considered. We calculate boundary one-point functions and propose boundary
S-matrices in these theories. We use boundary one-point functions and S-matrix
amplitudes to derive boundary ground state energies and exact solutions
describing classical vacuum configurations.


  We provide new results on the vanishing of local cohomology modules supported
at ideals of minors of matrices over arbitrary commutative Noetherian rings. In
the process, we compute the local cohomology of rings of polynomials with
integer coefficients---supported at generic determinantal ideals---and also
obtain results on F-modules and D-modules that are likely to be of independent
interest.


  In this paper we study the model of heat transfer in a porous medium with a
critical diffusion. We obtain global existence and uniqueness of solutions to
the equations of heat transfer of incompressible fluid in Besov spaces $\dot
B^{3/p}_{p,1}(\mathbb{R}^3)$ with $1\le p\le\infty$ by the method of modulus of
continuity and Fourier localization technique.


  Using high-resolution, two-dimensional hydrodynamic simulations, we
investigate nonlinear gravitational responses of gas to, and the resulting drag
force on, a very massive perturber M_p moving at velocity V_p through a uniform
gaseous medium of adiabatic sound speed a_0. We model the perturber as a
Plummer potential with softening radius r_s, and run various models with
differing A=GM_p/(a_0^2 r_s) and M=V_p/a_0 by imposing cylindrical symmetry
with respect to the line of perturber motion. For supersonic cases, a massive
perturber quickly develops nonlinear flows that produce a detached bow shock
and a vortex ring, which is unlike in the linear cases where Mach cones are
bounded by low-amplitude Mach waves. The flows behind the shock are initially
non-steady, displaying quasi-periodic, overstable oscillations of the vortex
ring and the shock. The vortex ring is eventually shed downstream and the flows
evolve toward a quasi-steady state where the density wake near the perturber is
in near hydrostatic equilibrium. We find that the detached shock distance
$\delta$ and the nonlinear drag force F depend solely on \eta=A/(M^2-1) such
that \delta/r_s=\eta and F/F_{lin}=(\eta/2)^{-0.45} for \eta>2, where F_{lin}
is the linear drag force of Ostriker (1999). The reduction of F compared with
F_{lin} is caused by front-back symmetry in the nonlinear density wakes. In
subsonic cases, the flows without involving a shock do not readily reach a
steady state. Nevertheless, the subsonic density wake near a perturber is close
to being hydrostatic, resulting in the drag force similar to the linear case.
Our results suggest that dynamical friction of a very massive object as in a
merger of black holes near a galaxy center will take considerably longer than
the linear prediction.


  The existence of quintessential dark energy around a black hole has
considerable consequences on its spacetime geometry. Hence, in this article, we
explore its effect on horizons and the silhouette generated by a Kerr-Newman
black hole in quintessential dark energy. Moreover, to analyze the deflection
angle of light, we utilize the Gauss-Bonnet theorem. The obtained result
demonstrates that, due to the dragging effect, the black hole spin elongates
its shadow in the direction of the rotational axis, while increases the
deflection angle. On the other hand, the black hole charge diminishing its
shadow, as well as the angle of lights deflection. Besides, both spin and
charge significantly increase the distortion effect in the black hole's shadow.
The quintessence parameter \gamma, increases the shadow radius, while decreases
the distortion effect at higher values of charge and spin parameters.


  Since the inception of Deep Reinforcement Learning (DRL) algorithms, there
has been a growing interest in both research and industrial communities in the
promising potentials of this paradigm. The list of current and envisioned
applications of deep RL ranges from autonomous navigation and robotics to
control applications in the critical infrastructure, air traffic control,
defense technologies, and cybersecurity. While the landscape of opportunities
and the advantages of deep RL algorithms are justifiably vast, the security
risks and issues in such algorithms remain largely unexplored. To facilitate
and motivate further research on these critical challenges, this paper presents
a foundational treatment of the security problem in DRL. We formulate the
security requirements of DRL, and provide a high-level threat model through the
classification and identification of vulnerabilities, attack vectors, and
adversarial capabilities. Furthermore, we present a review of current
literature on security of deep RL from both offensive and defensive
perspectives. Lastly, we enumerate critical research venues and open problems
in mitigation and prevention of intentional attacks against deep RL as a
roadmap for further research in this area.


  The two parameters Weibull function is used to describe the charged particle
multiplicity distribution in $e^{+}e^{-}$ collisions at the highest available
energy measured by TASSO and ALEPH experiments. The Weibull distribution has
wide applications in naturally evolving processes based on fragmentation and
sequential branching. The Weibull model describes the multiplicity distribution
very well, as particle production processes involve QCD parton fragmentation.
The effective energy model of particle production was verified using Weibull
parameters and the same was used to predict the multiplicity distribution in
$e^{+}e^{-}$ collisions at future collider energies.


  As the number of confirmed exoplanets continues to grow, there is an
increased push to spectrally characterize them to determine their atmospheric
composition, formation paths, rotation rates, and habitability. However, there
is a large population of known exoplanets that either do not transit their star
or have been detected via the radial velocity (RV) method at very small angular
separations such that they are inaccessible to traditional coronagraph systems.
Vortex Fiber Nulling (VFN) is a new single-aperture interferometric technique
that uses the entire telescope pupil to bridge the gap between traditional
coronagraphy and RV or Transit methods by enabling the direct observation and
spectral characterization of targets at and within the diffraction limit. By
combining a vortex mask with a single mode fiber, the on-axis starlight is
rejected while the off-axis planet light is coupled and efficiently routed to a
radiometer or spectrograph for analysis. We have demonstrated VFN in the lab
monochromatically in the past. In this paper we present a polychromatic
validation of VFN with nulls of $<10^{-4}$ across 15% bandwidth light. We also
provide an update on deployment plans and predicted yield estimates for the VFN
mode of the Keck Planet Imager and Characterizer (KPIC) instrument. Using
PSISIM, a simulation package developed in cooperation with several groups, we
assess KPIC VFN's ability to detect and characterize different types of targets
including planet candidates around promising young-moving-group stars as well
as known exoplanets detected via the RV method. The KPIC VFN on-sky
demonstration will pave the road to deployment on future instruments such as
Keck-HISPEC and TMT-MODHIS where it could provide high-resolution spectra of
sub-Jupiter mass planets down to 5 milliarcseconds from their star.


  We study low momentum excitations of a Bose-Einstein condensate using a novel
matter-wave interference technique. In time-of-flight expansion images we
observe strong matter-wave fringe patterns. The fringe contrast is a sensitive
spectroscopic probe of in-trap phonons and is explained by use of a Bogoliubov
excitation projection method applied to the rescaled order parameter of the
expanding condensate. Gross-Pitaevskii simulations agree with the experimental
data and confirm the validity of the theoretical interpretation. We show that
the high sensitivity of this detection scheme gives access to the quantized
quasiparticle regime.


  We prove scattering below the ground state threshold for an energy-critical
inhomogeneous nonlinear Schr\"odinger equation in three space dimensions. In
particular, we extend results of Cho, Hong, and Lee from the radial to the
non-radial setting.


  It has been suggested that single and double jets observed emanating from
certain astrophysical objects may have a purely gravitational origin. We
discuss new classes of pulsed gravitational wave solutions to the equation for
perturbations of Ricci-flat spacetimes around Minkowski metrics, as models for
the genesis of such phenomena. We discuss how these solutions are motivated by
the analytic structure of spatially compact finite energy pulse solutions of
the source-free Maxwell equations generated from complex chiral eigenmodes of a
chirality operator. Complex gravitational pulse solutions to the linearised
source-free Einstein equations are classified in terms of their chirality and
generate a family of non-stationary real spacetime metrics. Particular members
of these families are used as backgrounds in analysing time-like solutions to
the geodesic equation for test particles. They are found numerically to exhibit
both single and double jet-like features with dimensionless aspect ratios
suggesting that it may be profitable to include such backgrounds in simulations
of astrophysical jet dynamics from rotating accretion discs involving
electromagnetic fields.


  We prove that if $\mathcal{A}$ is an infinite Boolean algebra in the ground
model $V$ and $\mathbb{P}$ is a notion of forcing adding any of the following
reals: a Cohen real, an unsplit real, or a random real, then, in any
$\mathbb{P}$-generic extension $V[G]$, $\mathcal{A}$ has neither the Nikodym
property nor the Grothendieck property. A similar result is also proved for a
dominating real and the Nikodym property.


  To elucidate the origin of nematic order in Fe-based superconductors, we
report a Raman scattering study of lattice dynamics, which quantify the extent
of $C_4$-symmetry breaking, in BaFe$_2$As$_2$ and FeSe. FeSe possesses a
nematic ordering temperature $T_\mathrm{s}$ and orbital-related band-energy
split below $T_\mathrm{s}$ that are similar to those in BaFe$_2$As$_2$, but
unlike BaFe$_2$As$_2$ it has no long-range magnetic order. We find that the
$E_g$ phonon-energy split in FeSe sets in only well below $T_\mathrm{s}$, and
its saturated value is substantially smaller than that in BaFe$_2$As$_2$.
Together with reported results for the Ba(Fe$_{1-x}$Co$_{x}$)$_2$As$_2$ family,
the data suggest that magnetism exerts a major influence on the lattice.


  The observability of primordial cosmic fluctuations does not require a
geometric horizon $H^{-1}$, which is exceeded temporarily by the wavelength of
fluctuations. The primordial information can be protected against later thermal
washout even if all relevant wavelengths remain smaller than $H^{-1}$. This is
demonstrated by formulating the equations governing the cosmic fluctuations in
a form that is manifestly invariant under conformal field transformations of
the metric. Beyond the field equations this holds for the defining equation for
the correlation function, as expressed by the inverse of the second functional
derivative of the quantum effective action. An observable almost scale
invariant spectrum does not need an expanding geometry. For a variable Planck
mass it can even arise in flat Minkowski space.


  Convolutional network techniques have recently achieved great success in
vision based detection tasks. This paper introduces the recent development of
our research on transplanting the fully convolutional network technique to the
detection tasks on 3D range scan data. Specifically, the scenario is set as the
vehicle detection task from the range data of Velodyne 64E lidar. We proposes
to present the data in a 2D point map and use a single 2D end-to-end fully
convolutional network to predict the objectness confidence and the bounding
boxes simultaneously. By carefully design the bounding box encoding, it is able
to predict full 3D bounding boxes even using a 2D convolutional network.
Experiments on the KITTI dataset shows the state-of-the-art performance of the
proposed method.


  We formulate and prove a finite version of Vinogradov's bilinear sum
inequality. We use it together with Ratner's joinings theorems to prove that
the Mobius function is disjoint from discrete horocycle flows on $\Gamma
\backslash SL_2(\mathbb{R}).


  Scientific applications that run on leadership computing facilities often
face the challenge of being unable to fit leading science cases onto
accelerator devices due to memory constraints (memory-bound applications). In
this work, the authors studied one such US Department of Energy
mission-critical condensed matter physics application, Dynamical Cluster
Approximation (DCA++), and this paper discusses how device memory-bound
challenges were successfully reduced by proposing an effective "all-to-all"
communication method -- a ring communication algorithm. This implementation
takes advantage of acceleration on GPUs and remote direct memory access (RDMA)
for fast data exchange between GPUs.
  Additionally, the ring algorithm was optimized with sub-ring communicators
and multi-threaded support to further reduce communication overhead and expose
more concurrency, respectively. The computation and communication were also
analyzed by using the Autonomic Performance Environment for Exascale (APEX)
profiling tool, and this paper further discusses the performance trade-off for
the ring algorithm implementation. The memory analysis on the ring algorithm
shows that the allocation size for the authors' most memory-intensive data
structure per GPU is now reduced to 1/p of the original size, where p is the
number of GPUs in the ring communicator. The communication analysis suggests
that the distributed Quantum Monte Carlo execution time grows linearly as
sub-ring size increases, and the cost of messages passing through the network
interface connector could be a limiting factor.


  The THGEMs based on ceramic substrate were developed successfully for neutron
and single photon detection. The influences on thermal neutron scattering and
the internal radioactivity of both ceramic and FR-4 substrates were studied and
compared. The ceramic THGEMs are homemade of 200 um hole diameter, 600 um
pitch, 200 um thickness, 80 um rim, and 50 mm*50 mm sensitive area. The FR-4
THGEMs with the same geometry were used for the reference. The gas gain, energy
resolution and gain stability were measured in different gas mixtures by using
the 5.9 keV X-rays. The maximum gain of single layer ceramic THGEM reaches
6*104 and 1.5*104 at Ne+CH4 = 95:5 and Ar+i-C4H10 = 97:3, respectively. The
energy resolution is better than 24%. The good gain stability was obtained
during more than 100 hour continuous test at Ar+CO2 = 80:20. By using the 239Pu
source, the alpha deposited energy spectrum and gain curve of ceramic THGEM
were measured.


  An important step of seismic data processing is removing noise, including
interference due to simultaneous and blended sources, from the recorded data.
Traditional methods are time-consuming to apply as they often require manual
choosing of parameters to obtain good results. We use deep learning, with a
U-net model incorporating a ResNet architecture pretrained on ImageNet and
further trained on synthetic seismic data, to perform this task. The method is
applied to common offset gathers, with adjacent offset gathers of the gather
being denoised provided as additional input channels. Here we show that this
approach leads to a method that removes noise from several datasets recorded in
different parts of the world with moderate success. We find that providing
three adjacent offset gathers on either side of the gather being denoised is
most effective. As this method does not require parameters to be chosen, it is
more automated than traditional methods.


  Most of the recent neural source separation systems rely on a masking-based
pipeline where a set of multiplicative masks are estimated from and applied to
a signal representation of the input mixture. The estimation of such masks, in
almost all network architectures, is done by a single layer followed by an
optional nonlinear activation function. However, recent literatures have
investigated the use of a deep mask estimation module and observed performance
improvement compared to a shallow mask estimation module. In this paper, we
analyze the role of such deeper mask estimation module by connecting it to a
recently proposed unsupervised source separation method, and empirically show
that the deep mask estimation module is an efficient approximation of the
so-called overseparation-grouping paradigm with the conventional shallow mask
estimation layers.


  The Social Internet of Things (SIoT), integration of the Internet of Things
and Social Networks paradigms, has been introduced to build a network of smart
nodes that are capable of establishing social links. In order to deal with
misbehaving service provider nodes, service requestor nodes must evaluate their
trustworthiness levels. In this paper, we propose a novel trust management
mechanism in the SIoT to predict the most reliable service providers for each
service requestor, which leads to reduce the risk of being exposed to malicious
nodes. We model the SIoT with a flexible bipartite graph (containing two sets
of nodes: service providers and service requestors), then build a social
network among the service requestor nodes, using the Hellinger distance.
Afterward, we develop a social trust model using nodes' centrality and
similarity measures to extract trust behaviors among the social network nodes.
Finally, a matrix factorization technique is designed to extract latent
features of SIoT nodes, find trustworthy nodes, and mitigate the data sparsity
and cold start problems. We analyze the effect of parameters in the proposed
trust prediction mechanism on prediction accuracy. The results indicate that
feedbacks from the neighboring nodes of a specific service requestor with high
Hellinger similarity in our mechanism outperforms the best existing methods. We
also show that utilizing the social trust model, which only considers a
similarity measure, significantly improves the accuracy of the prediction
mechanism. Furthermore, we evaluate the effectiveness of the proposed trust
management system through a real-world SIoT use case. Our results demonstrate
that the proposed mechanism is resilient to different types of network attacks,
and it can accurately find the most proper and trustworthy service provider.


  The ideas here are a continuation of a previous article. Some of the
applications of the main ideas in the previous article are explained, along
with some limitations of the general ideas. There are situations where
additional hypotheses allow the use of the main ideas in problems that are not
immediately accessible.


  We are concerned with singular elliptic problems of the form $-\Delta u\pm
p(d(x))g(u)=\la f(x,u)+\mu |\nabla u|^a$ in $\Omega,$ where $\Omega$ is a
smooth bounded domain in $\RR^N$, $d(x)={\rm dist}(x,\partial\Omega),$ $\la>0,$
$\mu\in\RR$, $0<a\leq 2$, and $f,k$ are nonnegative and nondecreasing
functions. We assume that $p(d(x))$ is a positive weight with possible singular
behavior on the boundary of $\Omega$ and that the nonlinearity $g$ is unbounded
around the origin. Taking into account the competition between the anisotropic
potential $p(d(x))$, the convection term $|\nabla u|^a$, and the singular
nonlinearity $g$, we establish various existence and nonexistence results.


  We consider algebras defined over a complete, local and noetherian ground
ring. They are gentle algebras in case the ground ring is a field. The
unbounded homotopy category of complexes of projective modules is considered.
Complexes with finitely-generated homogeneous components are shown to be
isomorphic to direct sums of indecomposable string and band complexes. The
corresponding isoclasses are described, and the Krull-Remak-Schmidt-Azumaya
property is verified. This classification problem is solved using the idea of
functorial filtrations.


  In previous work we found that many of the spectral properties of x-ray
binaries, including both galactic black hole candiates (GBHC) and neutron
stars, were consistent with the existence of intrinsically magnetized central
objects. Here we review and extend the observational evidence for the existence
of intrinsically magnetized GBHC and show that their existence is consistent
with a new class of solutions of the Einstein field equations of General
Relativity. These solutions are based on a strict adherence to the Principle of
Equivalence, which prevents the time-like geodesics of physical matter from
becoming null on trapped surfaces of infinite red shift. The new solutions
emerge from the fact that the structure and radiation transfer properties of
the energy-momentum tensor on the right hand side of the Einstein field
equations must have a form that is consistent with this Principle of
Equivalence requirement. In this context, we show that the Einstein field
equations allow the existence of highly red shifted, magnetospheric, eternally
collapsing objects (MECO) which do not have trapped surfaces which lead to
event horizons. Since MECO lifetimes are many orders of magnitude greater than
a Hubble time, they provide an elegant and unified framework for understanding
the broad range of observations associated with GBHC and active galactic
nuclei.


  We investigate the transport properties of pristine zigzag-edged borophene
nanoribbons (ZBNRs) of different widths, using the fist-principles
calculations. We choose ZBNRs with widths of 5 and 6 as odd and even widths.
The differences of the quantum transport properties are found, where even-N
BNRs and odd-N BNRs have different current-voltage relationships. Moreover, the
negative differential resistance (NDR) can be observed within certain bias
range in 5-ZBNR, while 6-ZBNR behaves as metal whose current rises with the
increase of the voltage. The spin filter effect of 36% can be revealed when the
two electrodes have opposite magnetization direction. Furthermore, the
magnetoresistance effect appears to be in even-N ZBNRs, and the maximum value
can reach 70%.


  On-the-fly syntax highlighting is the task of rapidly associating visual
secondary notation values with each character of a language derivation.
Research in this domain is driven by the prevalence of online software
development tools, which frequently display source code on screen and heavily
rely on syntax highlighting mechanisms. In this context, three contrasting
demands confront resolvers in this space: speed, accuracy, and development
costs. Speed constraints are essential to ensure tool usability, manifesting as
responsiveness for end users accessing online source code and minimising system
overhead. Simultaneously, achieving precise highlighting is critical for
enhancing code comprehensibility. Nevertheless, obtaining accurate results
necessitates the capacity to perform grammatical analysis on the code under
consideration, even in cases of varying grammatical correctness. Furthermore,
addressing the development costs of such resolvers is imperative, given the
multitude of programming language versions. The current state-of-the-art
approach in this field leverages the original lexer and parser of programming
languages to create syntax highlighting oracles, subsequently used for training
base Recurrent Neural Network models. As the question of the generalisation of
such a solution persists, this paper addresses this aspect by extending the
original work to three additional mainstream programming languages and
conducting a comprehensive review of the outcomes. Moreover, the original
limitations in evaluation performance and training costs are mitigated through
the introduction of a novel Convolutional based Neural Network model. This
study examines the performance gains of running models on GPUs, finding that
the new CNN implementation is much faster than previous methods while
maintaining high accuracy.


  We analyze the time evolution of a one-dimensional quantum system with an
attractive delta function potential whose strength is subjected to a time
periodic (zero mean) parametric variation $\eta(t)$. We show that for generic
$\eta(t)$, which includes the sum of any finite number of harmonics, the
system, started in a bound state will get fully ionized as $t\to\infty$. This
is irrespective of the magnitude or frequency (resonant or not) of $\eta(t)$.
There are however exceptional, very non-generic $\eta(t)$, that do not lead to
full ionization, which include rather simple explicit periodic functions. For
these $\eta(t)$ the system evolves to a nontrivial localized stationary state
which is related to eigenfunctions of the Floquet operator.


  We present the direct imaging detection of a faint tertiary companion to the
single-lined spectroscopic binary HD 8375 AB. Initially noticed as an 53 m/s/yr
Doppler acceleration by Bowler et al. 2010, we have obtained high-contrast
adaptive optics observations at Keck using NIRC2 that spatially resolve HD 8375
C from its host(s). Astrometric measurements demonstrate that the companion
shares a common proper-motion. We detect orbital motion in a clockwise
direction. Multiband relative photometry measurements are consistent with a
spectral-type of M1V. Our combined Doppler and imaging observations place a
lower-limit of m>0.297Msun on its dynamical mass. We also provide a refined
orbit for the inner pair using recent RV measurements obtained with HIRES. HD
8375 is one of many triple-star systems that are apparently missing in the
solar neighborhood.


  Stochastic approximation with multiple coupled sequences (MSA) has found
broad applications in machine learning as it encompasses a rich class of
problems including bilevel optimization (BLO), multi-level compositional
optimization (MCO), and reinforcement learning (specifically, actor-critic
methods). However, designing provably-efficient federated algorithms for MSA
has been an elusive question even for the special case of double sequence
approximation (DSA). Towards this goal, we develop FedMSA which is the first
federated algorithm for MSA, and establish its near-optimal communication
complexity. As core novelties, (i) FedMSA enables the provable estimation of
hypergradients in BLO and MCO via local client updates, which has been a
notable bottleneck in prior theory, and (ii) our convergence guarantees are
sensitive to the heterogeneity-level of the problem. We also incorporate
momentum and variance reduction techniques to achieve further acceleration
leading to near-optimal rates. Finally, we provide experiments that support our
theory and demonstrate the empirical benefits of FedMSA. As an example, FedMSA
enables order-of-magnitude savings in communication rounds compared to prior
federated BLO schemes.


  Computing optimal transport (OT) distances such as the earth mover's distance
is a fundamental problem in machine learning, statistics, and computer vision.
In this paper, we study the problem of approximating the general OT distance
between two discrete distributions of size $n$. Given the cost matrix
$C=AA^\top$ where $A \in \mathbb{R}^{n \times d}$, we proposed a faster
Sinkhorn's Algorithm to approximate the OT distance when matrix $A$ has
treewidth $\tau$. To approximate the OT distance, our algorithm improves the
state-of-the-art results [Dvurechensky, Gasnikov, and Kroshnin ICML 2018] from
$\widetilde{O}(\epsilon^{-2} n^2)$ time to $\widetilde{O}(\epsilon^{-2} n
\tau)$ time.


  We present a representation for linguistic structure that we call a
Fock-space representation, which allows us to embed problems in language
processing into small quantum devices. We further develop a formalism for
understanding both classical as well as quantum linguistic problems and phrase
them both as a Harmony optimization problem that can be solved on a quantum
computer which we show is related to classifying vectors using quantum
Boltzmann machines. We further provide a new training method for learning
quantum Harmony operators that describe a language. This also provides a new
algorithm for training quantum Boltzmann machines that requires no
approximations and works in the presence of hidden units. We additionally show
that quantum language processing is BQP-complete, meaning that it is
polynomially equivalent to the circuit model of quantum computing which implies
that quantum language models are richer than classical models unless BPP=BQP.
It also implies that, under certain circumstances, quantum Boltzmann machines
are more expressive than classical Boltzmann machines. Finally, we examine the
performance of our approach numerically for the case of classical harmonic
grammars and find that the method is capable of rapidly parsing even
non-trivial grammars. This suggests that the work may have value as a quantum
inspired algorithm beyond its initial motivation as a new quantum algorithm.


  Let M be a closed symplectic manifold of volume V. We say that M admits an
unobstructed symplectic packing by balls if any collection of symplectic balls
(of possibly different radii) of total volume less than V admits a symplectic
embedding to M. In 1994 McDuff and Polterovich proved that symplectic packings
of Kahler manifolds can be characterized in terms of the Kahler cones of their
blow-ups. When M is a Kahler manifold which is not a union of its proper
subvarieties (such a manifold is called Campana simple) these Kahler cones can
be described explicitly using the Demailly and Paun structure theorem. We prove
that any Campana simple Kahler manifold, as well as any manifold which is a
limit of Campana simple manifolds in a smooth deformation, admits an
unobstructed symplectic packing by balls. This is used to show that all
even-dimensional tori equipped with Kahler symplectic forms and all hyperkahler
manifolds of maximal holonomy admit unobstructed symplectic packings by balls.
This generalizes a previous result by Latschev-McDuff-Schlenk. We also consider
symplectic packings by other shapes and show, using Ratner's orbit closure
theorem, that any even-dimensional torus equipped with a Kahler form whose
cohomology class is not proportional to a rational one admits a full symplectic
packing by any number of equal polydisks (and, in particular, by any number of
equal cubes).


  Recent research on Simultaneous Localization and Mapping (SLAM) based on
implicit representation has shown promising results in indoor environments.
However, there are still some challenges: the limited scene representation
capability of implicit encodings, the uncertainty in the rendering process from
implicit representations, and the disruption of consistency by dynamic objects.
To address these challenges, we propose a real-time dynamic visual SLAM system
based on local-global fusion neural implicit representation, named DVN-SLAM. To
improve the scene representation capability, we introduce a local-global fusion
neural implicit representation that enables the construction of an implicit map
while considering both global structure and local details. To tackle
uncertainties arising from the rendering process, we design an information
concentration loss for optimization, aiming to concentrate scene information on
object surfaces. The proposed DVN-SLAM achieves competitive performance in
localization and mapping across multiple datasets. More importantly, DVN-SLAM
demonstrates robustness in dynamic scenes, a trait that sets it apart from
other NeRF-based methods.


  Magnetic fluctuations and electrons couple in intriguing ways in the vicinity
of zero temperature phase transitions - quantum critical points - in conducting
materials. Quantum criticality is implicated in non-Fermi liquid behavior of
diverse materials, and in the formation of unconventional superconductors. Here
we uncover an entirely new type of quantum critical point describing the onset
of antiferromagnetism in a nodal semimetal engendered by the combination of
strong spin-orbit coupling and electron correlations, and which is predicted to
occur in the iridium oxide pyrochlores. We formulate and solve a field theory
for this quantum critical point by renormalization group techniques, show that
electrons and antiferromagnetic fluctuations are strongly coupled, and that
both these excitations are modified in an essential way. This quantum critical
point has many novel features, including strong emergent spatial anisotropy, a
vital role for Coulomb interactions, and highly unconventional critical
exponents. Our theory motivates and informs experiments on pyrochlore iridates,
and constitutes a singular realistic example of a non-trivial quantum critical
point with gapless fermions in three dimensions.


  We present expressions for the coefficients which arise in asymptotic
expansions of multiple integrals of Laplace type (the first term of which is
known as Laplace's approximation) in terms of asymptotic series of the
functions in the integrand. Our most general result assumes no smoothness of
the functions of the integrand, but the expressions we obtain contain integrals
which may be difficult to evaluate in practice. We then make additional
assumptions which are sufficient to simplify these integrals, in some cases
obtaining explicit formulae for the coefficients in the asymptotic expansions.


  Posture estimation using a single depth camera has become a useful tool for
analyzing movements in rehabilitation. Recent advances in posture estimation in
computer vision research have been possible due to the availability of
large-scale pose datasets. However, the complex postures involved in
rehabilitation exercises are not represented in the existing benchmark depth
datasets. To address this limitation, we propose two rehabilitation-specific
pose datasets containing depth images and 2D pose information of patients, both
adult and children, performing rehab exercises. We use a state-of-the-art
marker-less posture estimation model which is trained on a non-rehab benchmark
dataset. We evaluate it on our rehab datasets, and observe that the performance
degrades significantly from non-rehab to rehab, highlighting the need for these
datasets. We show that our dataset can be used to train pose models to detect
rehab-specific complex postures. The datasets will be released for the benefit
of the research community.


  The iron arsenide Eu3Fe2O5Fe2As2 was synthesized at 1173-1373 K in a
resistance furnace and characterized by X-ray powder diffraction with Rietveld
analysis: Sr3Fe2O5Cu2S2-type, I4/mmm, a = 406.40(1) pm, c = 2646.9(1) pm.
Layers of edge-sharing FeAs4/4 tetrahedra are separated by perovskite-like
oxide blocks. No structural transition occurs in the temperature range from 10
to 300 K. Magnetic measurements have revealed Curie-Weiss behavior with an
effective magnetic moment of 7.79 muB per europium atom in agreement with the
theoretical value of 7.94 muB for Eu2+. A drop in the magnetic susceptibility
at 5 K indicates possible antiferromagnetic ordering. 151Eu and 57Fe
M\"ossbauer spectroscopic measurements have confirmed a beginning cooperative
magnetic phenomenon by showing significantly broadened spectra at 4.8 K
compared to those at 78 K.