  Today's ride-hailing systems experienced significant growth and ride-pooling
promises to allow for efficient and sustainable on-demand transportation.
However, efficient ride-pooling requires a large pool of participating
customers. To increase the customers' willingness for participation, we study a
novel customer-centered pooling (CCP) mechanism, accounting for individual
customers' pooling benefits. We study the benefit of this mechanism from a
customer, fleet operator, and system perspective, and compare it to existing
provider-centered pooling (PCP) mechanisms. We prove that it is individually
rational and weakly dominant for a customer to participate in CCP, but not for
PCP. We substantiate this analysis with complementary numerical studies,
implementing a simulation environment based on real-world data that allows us
to assess both mechanisms' benefit. To this end, we present results for both
pooling mechanisms and show that pooling can benefit all stakeholders in
on-demand transportation. Moreover, we analyze in which cases a CCP mechanism
Pareto dominates a PCP mechanism and show that a mobility service operator
would prefer CCP mechanisms over PCP mechanisms for all price segments.
Simultaneously, CCP mechanisms reduce the overall distance driven in the system
up to 32% compared to not pooling customers. Our results provide decision
support for mobility service operators that want to implement and improve
pooling mechanisms as they allow us to analyze the impact of a CCP and a PCP
mechanism from a holistic perspective. Among others, we show that CCP
mechanisms can lead to a win-win situation for operators and customers while
simultaneously improving system performance and reducing emissions.


  We study the holographic duality between the M x M matrix extension of
Vasiliev higher spin theories on AdS3 and the large N limit of SU(N+M)/SU(N) x
U(1) type cosets. We present a simplified proof for the agreement of the
spectra and clarify the relation between this duality and the version in which
the cosets are replaced by Kazama-Suzuki models of Grassmannian type.


  Triangle counting is a key algorithm for large graph analysis. The Graphulo
library provides a framework for implementing graph algorithms on the Apache
Accumulo distributed database. In this work we adapt two algorithms for
counting triangles, one that uses the adjacency matrix and another that also
uses the incidence matrix, to the Graphulo library for server-side processing
inside Accumulo. Cloud-based experiments show a similar performance profile for
these different approaches on the family of power law Graph500 graphs, for
which data skew increasingly bottlenecks. These results motivate the design of
skew-aware hybrid algorithms that we propose for future work.


  We report a coherence-preserving photon frequency down-conversion experiment
based on difference-frequency generation in a periodically poled Lithium
niobate waveguide, at the single-photon level. The coherence of the process has
been demonstrated by measuring the phase coherence of pseudo single-photon
time-bin qubits after frequency conversion with an interference visibility of >
96 %. This interface could be of interest for quantum repeater based hybrid
networks.


  We present an efficient method for adapting a monolingual Large Language
Model (LLM) to another language, addressing challenges of catastrophic
forgetting and tokenizer limitations. We focus this study on adapting Llama 2
to Arabic. Our two-stage approach begins with expanding the vocabulary and
training only the embeddings matrix, followed by full model continual
pre-training on a bilingual corpus. By continually pre-training on a mix of
Arabic and English corpora, the model retains its proficiency in English while
acquiring capabilities in Arabic. Our approach results in significant
improvements in Arabic and slight enhancements in English, demonstrating
cost-effective cross-lingual transfer. We perform ablations on embedding
initialization techniques, data mix ratios, and learning rates and release a
detailed training recipe. To demonstrate generalizability of this approach we
also adapted Llama 3 8B to Arabic and Llama 2 13B to Hindi.


  This lecture covers the fundamental aspects of the measurement of beam losses
including their use for beam diagnostic and safety issues. The detailed
functionality and detection principle of various common beam loss monitors are
also presented, with a focus on their intrinsic sensitivity.


  State-of-the-art speaker verification models are based on deep learning
techniques, which heavily depend on the handdesigned neural architectures from
experts or engineers. We borrow the idea of neural architecture search(NAS) for
the textindependent speaker verification task. As NAS can learn deep network
structures automatically, we introduce the NAS conception into the well-known
x-vector network. Furthermore, this paper proposes an evolutionary algorithm
enhanced neural architecture search method called Auto-Vector to automatically
discover promising networks for the speaker verification task. The experimental
results demonstrate our NAS-based model outperforms state-of-the-art speaker
verification models.


  In this article we present recent developments in numerical methods for
performing a Large Eddy Simulation (LES) of the formation and evolution of a
wingtip vortex. The development of these vortices in the near wake, in
combination with the large Reynolds numbers present in these cases, make these
types of test cases particularly challenging to investigate numerically. We
first give an overview of the Spectral Vanishing Viscosity--implicit LES
(SVV-iLES) solver that is used to perform the simulations, and highlight
techniques that have been adopted to solve various numerical issues that arise
when studying such cases. To demonstrate the method's viability, we present
results from numerical simulations of flow over a NACA 0012 profile wingtip at
$Re_c = 1.2\cdot 10^6$ and compare them against experimental data, which is to
date the highest Reynolds number achieved for a LES that has been correlated
with experiments for this test case. Our model correlates favorably with
experiment, both for the characteristic jetting in the primary vortex and
pressure distribution on the wing surface. The proposed method is of general
interest for the modeling of transitioning vortex dominated flows over complex
geometries.


  The goal of much research in relativity is to understand gravitational waves
generated by a strong-field dynamical spacetime. Quantities of particular
interest for many calculations are the Weyl scalar $\psi_4$, which is simply
related to the flux of gravitational waves far from the source, and the flux of
energy carried to distant observers, $\dot E$. Conservation laws guarantee
that, in asympotically flat spacetimes, $\psi_4 \propto 1/r$ and $\dot E
\propto 1/r^2$ as $r \to \infty$. Most calculations extract these quantities at
some finite extraction radius. An understanding of finite radius corrections to
$\psi_4$ and $\dot E$ allows us to more accurately infer their asymptotic
values from a computation. In this paper, we show that, if the final state of
the system is a black hole, then the leading correction to $\psi_4$ is ${\cal
O}(1/r^3)$, and that to the energy flux is ${\cal O}(1/r^4)$ --- not ${\cal
O}(1/r^2)$ and ${\cal O}(1/r^3)$ as one might naively guess. Our argument only
relies on the behavior of the curvature scalars for black hole spacetimes.
Using black hole perturbation theory, we calculate the corrections to the
leading falloff, showing that it is quite easy to correct for finite extraction
radius effects.


  VAE requires the standard Gaussian distribution as a prior in the latent
space. Since all codes tend to follow the same prior, it often suffers the
so-called "posterior collapse". To avoid this, this paper introduces the class
specific distribution for the latent code. But different from CVAE, we present
a method for disentangling the latent space into the label relevant and
irrelevant dimensions, $\bm{\mathrm{z}}_s$ and $\bm{\mathrm{z}}_u$, for a
single input. We apply two separated encoders to map the input into
$\bm{\mathrm{z}}_s$ and $\bm{\mathrm{z}}_u$ respectively, and then give the
concatenated code to the decoder to reconstruct the input. The label irrelevant
code $\bm{\mathrm{z}}_u$ represent the common characteristics of all inputs,
hence they are constrained by the standard Gaussian, and their encoder is
trained in amortized variational inference way, like VAE. While
$\bm{\mathrm{z}}_s$ is assumed to follow the Gaussian mixture distribution in
which each component corresponds to a particular class. The parameters for the
Gaussian components in $\bm{\mathrm{z}}_s$ encoder are optimized by the label
supervision in a global stochastic way. In theory, we show that our method is
actually equivalent to adding a KL divergence term on the joint distribution of
$\bm{\mathrm{z}}_s$ and the class label $c$, and it can directly increase the
mutual information between $\bm{\mathrm{z}}_s$ and the label $c$. Our model can
also be extended to GAN by adding a discriminator in the pixel domain so that
it produces high quality and diverse images.


  The IceCube experiment has recently reported the observation of 28
high-energy (> 30 TeV) neutrino events, separated into 21 showers and 7 muon
tracks, consistent with an extraterrestrial origin. In this letter we compute
the compatibility of such an observation with possible combinations of neutrino
flavors with relative proportion (alpha_e:alpha_mu:alpha_tau). Although the
7:21 track-to-shower ratio is naively favored for the canonical (1:1:1) at
Earth, this is not true once the atmospheric muon and neutrino backgrounds are
properly accounted for. We find that, for an astrophysical neutrino E^(-2)
energy spectrum, (1:1:1) at Earth is disfavored at 81% C.L. If this proportion
does not change, 6 more years of data would be needed to exclude (1:1:1) at
Earth at 3 sigma C.L. Indeed, with the recently-released 3-year data, that
flavor composition is excluded at 92% C.L. The best-fit is obtained for (1:0:0)
at Earth, which cannot be achieved from any flavor ratio at sources with
averaged oscillations during propagation. If confirmed, this result would
suggest either a misunderstanding of the expected background events, or a
misidentification of tracks as showers, or even more compellingly, some exotic
physics which deviates from the standard scenario.


  In recent years, many variants of percolation have been used to study network
structure and the behavior of processes spreading on networks. These include
bond percolation, site percolation, $k$-core percolation, bootstrap
percolation, the generalized epidemic process, and the Watts Threshold Model
(WTM). We show that --- except for bond percolation --- each of these processes
arises as a special case of the WTM and bond percolation arises from a small
modification. In fact "heterogeneous $k$-core percolation", a corresponding
"heterogeneous bootstrap percolation" model, and the generalized epidemic
process are completely equivalent to one another and the WTM. We further show
that a natural generalization of the WTM in which individuals "transmit" or
"send a message" to their neighbors with some probability less than $1$ can be
reformulated in terms of the WTM, and so this apparent generalization is in
fact not more general. Finally, we show that in bond percolation, finding the
set of nodes in the component containing a given node is equivalent to finding
the set of nodes activated if that node is initially activated and the node
thresholds are chosen from the appropriate distribution. A consequence of these
results is that mathematical techniques developed for the WTM apply to these
other models as well, and techniques that were developed for some particular
case may in fact apply much more generally.


  Average stellar radii in open clusters can be estimated from rotation periods
and projected rotational velocities under the assumption of random orientation
of the spin axis. Such estimates are independent of distance, interstellar
absorption, and models, but their validity can be limited by missing data
(truncation) or data that only represent upper/lower limits (censoring). We
present a new statistical analysis method to estimate average stellar radii in
the presence of censoring and truncation. We use theoretical distribution
functions of the projected stellar radius $R \sin i$ to define a likelihood
function in the presence of censoring and truncation. Average stellar radii in
magnitude bins are then obtained by a maximum likelihood parametric estimation
procedure. This method is capable of recovering the average stellar radius
within a few percent with as few as $\approx$ 10 measurements. Here it is
applied for the first time to the dataset available for the Pleiades. We find
an agreement better than $\approx$ 10 percent between the observed $R$ vs $M_K$
relationship and current standard stellar models for 1.2 $\ge M/M_{\odot} \ge$
0.85 with no evident bias. Evidence of a systematic deviation at $2\sigma$
level are found for stars with 0.8 $\ge M/M_{\odot} \ge$ 0.6 approaching the
slow-rotator sequence. Fast-rotators ($P$ < 2 d) agree with standard models
within 15 percent with no systematic deviations in the whole 1.2 $\ge
M/M_{\odot} \ge$ 0.5 range. The evidence found of a possible radius inflation
just below the lower mass limit of the slow-rotator sequence indicates a
possible connection with the transition from the fast to the slow-rotator
sequence.


  The space missions designed to visit small bodies of the Solar System boosted
the study of the dynamics around non-spherical bodies. In this vein, we study
the dynamics around a class of objects classified by us as Non-Spherical
Symmetric Bodies, including contact binaries, triaxial ellipsoids, spherical
bodies with a mass anomaly, among others. In the current work, we address the
results for a body with a mass anomaly. We apply the pendulum model to obtain
the width of the spin-orbit resonances raised by non-asymmetric gravitational
terms of the central object. The Poincare surface of section technique is
adopted to confront our analytical results and to study the system's dynamics
by varying the parameters of the central object. We verify the existence of two
distinct regions around an object with a mass anomaly: a chaotic inner region
that extends beyond the corotation radius and a stable outer region. In the
latter, we identify structures remarkably similar to those of the classical
restrict and planar 3-body problem in the Poincare surface of sections,
including asymmetric periodic orbits associated with 1:1+p resonances. We apply
our results to a Chariklo with a mass anomaly, obtaining that Chariklo rings
are probably related to first kind periodic orbits and not with 1:3 spin-orbit
resonance, as proposed in the literature. We believe that our work presents the
first tools for studying mass anomaly systems.


  Based on $e^{+}e^{-}$ collision data collected at center-of-mass energies
from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a
partial wave analysis is performed for the process $e^{+}e^{-}\to K_{S}^{0}
K_{L}^{0} \pi^{0}$. The results allow the Born cross sections of the process
$e^{+}e^{-}\to K_{S}^{0} K_{L}^{0} \pi^{0}$, as well as its subprocesses
$e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ and $K^{*}_{2}(1430)^{0}\bar{K}^{0}$
to be measured. The Born cross sections for $e^{+}e^{-}\to
K_{S}^{0}K_{L}^{0}\pi^{0}$ are consistent with previous measurements by BaBar,
but with substantially improved precision. The Born cross section lineshape of
the process $e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ is consistent with a
vector meson state around 2.2 GeV with a significance of 3.2$\sigma$. A
Breit-Wigner fit determines its mass as
$M_Y=(2164.7\pm9.1\pm3.1)~{\rm{MeV}}/c^{2}$ and its width as
$\Gamma_{Y}=(32.4\pm21.0\pm1.8)~\rm{MeV}$.


  Long quantum codes using projective Reed-Muller codes are constructed.
Projective Reed-Muller are evaluation codes obtained by evaluating homogeneous
polynomials at the projective space. We obtain asymmetric and symmetric quantum
codes by using the CSS construction and the Hermitian construction,
respectively. We provide entanglement-assisted quantum error-correcting codes
from projective Reed-Muller codes with flexible amounts of entanglement by
considering equivalent codes. Moreover, we also construct quantum codes from
subfield subcodes of projective Reed-Muller codes.


  We note that data on Si/SiGe heterostructures, reported in the Comment of
Ismail et al. (cond-mat/9707061) on our recent Letter (cond-mat/9608101), are
insufficient to establish the claim for a transition to a metallic phase.


  In this paper we provide a general condition for the reducibility of the
Reshetikhin-Turaev quantum representations of the mapping class groups. Namely,
for any modular tensor category with a special symmetric Frobenius algebra with
a non-trivial genus one partition function, we prove that the quantum
representations of all the mapping class groups built from the modular tensor
category are reducible. In particular for SU(N) we get reducibility for certain
levels and ranks. For the quantum SU(2) Reshetikhin-Turaev theory we construct
a decomposition for all even levels. We conjecture this decomposition is a
complete decomposition into irreducible representations for high enough levels.


  The remarkable performance of recent stereo depth estimation models benefits
from the successful use of convolutional neural networks to regress dense
disparity. Akin to most tasks, this needs gathering training data that covers a
number of heterogeneous scenes at deployment time. However, training samples
are typically acquired continuously in practical applications, making the
capability to learn new scenes continually even more crucial. For this purpose,
we propose to perform continual stereo matching where a model is tasked to 1)
continually learn new scenes, 2) overcome forgetting previously learned scenes,
and 3) continuously predict disparities at inference. We achieve this goal by
introducing a Reusable Architecture Growth (RAG) framework. RAG leverages
task-specific neural unit search and architecture growth to learn new scenes
continually in both supervised and self-supervised manners. It can maintain
high reusability during growth by reusing previous units while obtaining good
performance. Additionally, we present a Scene Router module to adaptively
select the scene-specific architecture path at inference. Comprehensive
experiments on numerous datasets show that our framework performs impressively
in various weather, road, and city circumstances and surpasses the
state-of-the-art methods in more challenging cross-dataset settings. Further
experiments also demonstrate the adaptability of our method to unseen scenes,
which can facilitate end-to-end stereo architecture learning and practical
deployment.


  When impacted by a rigid object, a thin elastic membrane with negligible
bending rigidity floating on a liquid pool deforms. Two axisymmetric waves
radiating from the impact point propagate. In the first place, a longitudinal
wave front -- associated with in-plane deformation of the membrane and
traveling at constant speed -- separates an outward stress free domain with a
stretched but flat domain. Then, in the stretched domain a dispersive
transverse wave travels at a wave speed that depends on the local stretching
rate. We study the dynamics of this fluid-body system and we show that the wave
dynamics is similar to the capillary waves that propagate at a liquid-gas
interface but with a surface tension coefficient that depends on impact speed.
We emphasize the role of the stretching in the membrane in the wave dynamics
but also in the development of a buckling instability that give rise to radial
wrinkles.


  Acquiring data for spectral classification of heavily reddened stars using
traditional criteria in the blue-violet region of the spectrum can be
prohibitively time consuming using small to medium sized telescopes. One such
star is the Vatican Observatory emission-line star VES 735, which we have found
excites the HII region KR140. In order to classify VES 735, we have constructed
an atlas of stellar spectra of O stars in the yellow-green (4800 - 5420 A). We
calibrate spectral type versus the line ratio HeI 4922:HeII 5411, showing that
this ratio should be useful for the classification of heavily reddened O stars
associated with HII regions.
  Application to VES 735 shows that the spectral type is O8.5. The absolute
magnitude suggests luminosity class V. Comparison of the rateof emission of
ionizing photons and the bolometric luminosity of VES 735, inferred from radio
and infrared measurements of the KR 140 region, to recent stellar models gives
consistent evidence for a main sequence star of mass 25 M_sun and age less than
a few million years with a covering factor 0.4 - 0.5 by the nebular material.
  Spectra taken in the red (6500 - 6700 A) show that the stellar H-alpha
emission is double-peaked about the systemic velocity and slightly variable.
H-beta is in absorption, so that the emission-line classification is ``(e)''.
However, unlike the case of the more well known O(e) star Zeta Oph, the
emission from VES 735 appears to be long-lived rather than episodic.


  Direct detection experiments searching for weakly interacting massive
particle (WIMP) dark matter typically use a simplified model of the Galactic
halo to derive parameter constraints. However, there is strong evidence that
this Standard Halo Model is not a good approximation to our Galaxy. We discuss
previous attempts to extract the WIMP mass, cross-section and speed
distribution from direct detection data and show that these lead to significant
biases in the reconstructed parameter values. We develop and test an
alternative model-independent method based on parametrising the momentum
distribution of the WIMPs. This allows us to limit the analysis only to those
regions of momentum space to which the experiments are sensitive. The method
can be applied to a single experiment to extract information about the
distribution function, as well as information on the degenerate WIMP mass and
interaction cross-section combined in a single parameter. This degeneracy can
be broken by including data from additional experiments, meaning that the WIMP
mass and speed distribution can be recovered. We test the momentum
parametrisation method using mock datasets from proposed ton-scale direct
detection experiments, showing that it exhibits improved coverage properties
over previous methods, as well as significantly reduced bias. We are also able
to accurately reconstruct the shape of the WIMP speed distribution but
distinguishing between different underlying distributions remains difficult.


  A standard approach in pattern classification is to estimate the
distributions of the label classes, and then to apply the Bayes classifier to
the estimates of the distributions in order to classify unlabeled examples. As
one might expect, the better our estimates of the label class distributions,
the better the resulting classifier will be. In this paper we make this
observation precise by identifying risk bounds of a classifier in terms of the
quality of the estimates of the label class distributions. We show how PAC
learnability relates to estimates of the distributions that have a PAC
guarantee on their $L_1$ distance from the true distribution, and we bound the
increase in negative log likelihood risk in terms of PAC bounds on the
KL-divergence. We give an inefficient but general-purpose smoothing method for
converting an estimated distribution that is good under the $L_1$ metric into a
distribution that is good under the KL-divergence.


  Some basic concepts are discussed to derive renormalisation factors of local
lattice operators relevant to deep inelastic structure functions and to other
measurable quantities. These $Z$ factors can be used to relate matrix elements
measured by lattice techniques to their continuum counterparts. We discuss the
$O(a)$ improvement of point and one-link lattice quark operators. Suitable
bases of improved operators are derived. Tadpole improvement is applied to get
more reliable perturbative results.


  In the early days of quantum mechanics, Schr\"odinger noticed that
oscillations of a wave packet in a one-dimensional harmonic potential well are
periodic and, in contrast to those in anharmonic potential wells, do not
experience distortion over time. This original idea did not find applications
up to now since an exact one-dimensional harmonic resonator does not exist in
nature and has not been created artificially. However, an optical pulse
propagating in a bottle microresonator (a dielectric cylinder with a
nanoscale-high bump of the effective radius) can exactly imitate a quantum wave
packet in the harmonic potential. Here, we propose a tuneable microresonator
that can trap an optical pulse completely, hold it as long as the material
losses permit, and release it without distortion. This result suggests the
solution of the long standing problem of creating a microscopic optical buffer,
the key element of the future optical signal processing devices.


  We present our spectropolarimetric observations of SN 2017egm, a Type I
superluminous supernova (SLSN-I) in a nearby galaxy NGC 3191, with Subaru
telescope at 185.0 days after the g-band maximum light. This is the first
spectropolarimetric observation for SLSNe at late phases. We find that the
degree of the polarization in the late phase significantly changes from that
measured at the earlier phase. The spectrum at the late phase shows a strong Ca
emission line and therefore we reliably estimate the interstellar polarization
component assuming that the emission line is intrinsically unpolarized. By
subtracting the estimated interstellar polarization, we find that the intrinsic
polarization at the early phase is only ~ 0.2 %, which indicates an almost
spherical photosphere, with an axial ratio ~ 1.05. The intrinsic polarization
at the late phase increases to ~ 0.8 %, which corresponds to the photosphere
with an axial ratio 1.2. A nearly constant position angle of the polarization
suggests the inner ejecta are almost axisymmetric. By these observations, we
conclude that the inner ejecta are more aspherical than the outer ejecta. It
may suggest the presence of a central energy source producing aspherical inner
ejecta.


  Quantum mechanics sets severe limits on the sensitivity and required
circulating energy in traditional free-mass gravitational-wave antennas. One
possible way to avoid these restrictions is the use of intracavity QND
measurements. We analyze a new QND observable, which possesses a number of
features that make it a promising candidate for such measurements and propose a
practical scheme for the realization of this measurement. In combination with
an advanced coordinate meter, this scheme makes it possible to lower
substantially the requirements on the circulating power.


  The subjective quality of transmitted speech is traditionally assessed in a
controlled laboratory environment according to ITU-T Rec. P.800. In turn, with
crowdsourcing, crowdworkers participate in a subjective online experiment using
their own listening device, and in their own working environment. Despite such
less controllable conditions, the increased use of crowdsourcing micro-task
platforms for quality assessment tasks has pushed a high demand for
standardized methods, resulting in ITU-T Rec. P.808. This work investigates the
impact of the number of judgments on the reliability and the validity of
quality ratings collected through crowdsourcing-based speech quality
assessments, as an input to ITU-T Rec. P.808 . Three crowdsourcing experiments
on different platforms were conducted to evaluate the overall quality of three
different speech datasets, using the Absolute Category Rating procedure. For
each dataset, the Mean Opinion Scores (MOS) are calculated using differing
numbers of crowdsourcing judgements. Then the results are compared to MOS
values collected in a standard laboratory experiment, to assess the validity of
crowdsourcing approach as a function of number of votes. In addition, the
reliability of the average scores is analyzed by checking inter-rater
reliability, gain in certainty, and the confidence of the MOS. The results
provide a suggestion on the required number of votes per condition, and allow
to model its impact on validity and reliability.


  In recent years we have witnessed a renewed interest in machine learning
methodologies, especially for deep representation learning, that could overcome
basic i.i.d. assumptions and tackle non-stationary environments subject to
various distributional shifts or sample selection biases. Within this context,
several computational approaches based on architectural priors, regularizers
and replay policies have been proposed with different degrees of success
depending on the specific scenario in which they were developed and assessed.
However, designing comprehensive hybrid solutions that can flexibly and
generally be applied with tunable efficiency-effectiveness trade-offs still
seems a distant goal. In this paper, we propose "Architect, Regularize and
Replay" (ARR), an hybrid generalization of the renowned AR1 algorithm and its
variants, that can achieve state-of-the-art results in classic scenarios (e.g.
class-incremental learning) but also generalize to arbitrary data streams
generated from real-world datasets such as CIFAR-100, CORe50 and ImageNet-1000.


  Causal discovery with latent confounders is an important but challenging task
in many scientific areas. Despite the success of some overcomplete independent
component analysis (OICA) based methods in certain domains, they are
computationally expensive and can easily get stuck into local optima. We notice
that interestingly, by making use of higher-order cumulants, there exists a
closed-form solution to OICA in specific cases, e.g., when the mixing procedure
follows the One-Latent-Component structure. In light of the power of the
closed-form solution to OICA corresponding to the One-Latent-Component
structure, we formulate a way to estimate the mixing matrix using the
higher-order cumulants, and further propose the testable One-Latent-Component
condition to identify the latent variables and determine causal orders. By
iteratively removing the share identified latent components, we successfully
extend the results on the One-Latent-Component structure to the
Multi-Latent-Component structure and finally provide a practical and
asymptotically correct algorithm to learn the causal structure with latent
variables. Experimental results illustrate the asymptotic correctness and
effectiveness of the proposed method.


  We are motivated by problems that arise in a number of applications such as
Online Marketing and explosives detection, where the observations are usually
modeled using Poisson statistics. We model each observation as a Poisson random
variable whose mean is a sparse linear superposition of known patterns. Unlike
many conventional problems observations here are not identically distributed
since they are associated with different sensing modalities. We analyze the
performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting
involves a non-linear optimization but yet is computationally tractable. We
derive fundamental sample complexity bounds for sparse recovery when the
measurements are contaminated with Poisson noise. In contrast to the
least-squares linear regression setting with Gaussian noise, we observe that in
addition to sparsity, the scale of the parameters also fundamentally impacts
sample complexity. We introduce a novel notion of Restricted Likelihood
Perturbation (RLP), to jointly account for scale and sparsity. We derive sample
complexity bounds for $\ell_1$ regularized ML estimators in terms of RLP and
further specialize these results for deterministic and random sensing matrix
designs.


  This article is the first in a series of papers on the analysis of a basic
model for fluid vesicle dynamics. There are two variants of this model, a
parabolic one decribing purely relaxational dynamics and a non-parabolic one
containing the full dynamics. At the heart of both variants lies a linear
elliptic system of Stokes-type. Understanding the mapping properties of this
Stokes-type system is crucial for all further analysis. In this article we give
a basic exposition of the dynamical model and a thorough $L_2$-analysis of the
Stokes-type system that takes into account geometric variations of the fluid
vesicle.


  The relationship between the complexity classes P and NP is an unsolved
question in the field of theoretical computer science. In this paper, we look
at the link between the P - NP question and the "Deterministic" versus "Non
Deterministic" nature of a problem, and more specifically at the temporal
nature of the complexity within the NP class of problems. Let us remind that
the NP class is called the class of "Non Deterministic Polynomial" languages.
Using the meta argument that results in Mathematics should be "time
independent" as they are reproducible, the paper shows that the P!=NP assertion
is impossible to prove in the a-temporal framework of Mathematics. In a
previous version of the report, we use a similar argument based on randomness
to show that the P = NP assertion was also impossible to prove, but this part
of the paper was shown to be incorrect. So, this version deletes it. In fact,
this paper highlights the time dependence of the complexity for any NP problem,
linked to some pseudo-randomness in its heart.


  The strange quark star is investigated within the enlarged SU(3)
Nambu-Jona-Lasinio (NJL). The stable quark star exists till maximal
configutation with rho_m=3.1, 10^15, g/cm^3 with M_m=1.61, M_Sun and R_m=8.74,
km is reached. Strange quarks appear for density above rho_c=9.84 g/cm^3 for
the quark star with radius R_c=8.003 km and M_c=0.77, M_Sun. The comparison of
a quark star properties obtained in the Quark Mean Field (QMF) approach to a
neutron star model constructed within the Relativistic Mean Field (RMF) theory
is presented.


  The formation and dissociation of bosonic molecules in an optical lattice,
formed by spin-1/2 fermionic atoms, is considered in the presence of an
attractive nearest-neighbor interaction. A mean-field approximation reveals
three different phases at zero temperature: an empty, a condensate and a
Mott-insulating phase. The density of fermionic atoms and the density of
bosonic molecules indicate a characteristic behavior with respect to the
interaction strength that distinguishes between a dilute and a dense regime. In
particular, the attractive interaction favors the formation of molecules in the
dilute regime and the dissociation of atoms in the dense regime.


  Wolfgang S.M. Werner, Florian Simperl, Felix Bloedorn, Julian Brunner,
Johannes Kero, Alessandra Bellissimo and Olga Ridzel
  Spectroscopy of correlated electron pairs was employed to investigate the
energy dissipation process as well as the transport and the emission of low
energy electrons on a polymethylmetracylate (PMMA) surface, providing secondary
electron (SE) spectra causally related to the energy loss of the primary
electron. Two groups of electrons are identified in the cascade of slow
electrons, corresponding to different stages in the energy dissipation process.
For both groups, the characteristic lengths for attenuation due to collective
excitations and momentum relaxation are quantified and are found to be
distinctly different: l1=(12.0+/-2) Angstroem and l2=(61.5+/-11) Angstroem. The
results strongly contradict the commonly employed model of exponential
attenuation with the electron inelastic mean free path (IMFP) as characteristic
length, but essentially agree with a theory used for decades in astrophysics
and neutron transport, albeit with characteristic lengths expressed in units of
Angstroems rather than lightyears.


  The cumulative distribution and quantile functions for the one-sided one
sample Kolmogorov-Smirnov probability distributions are used for
goodness-of-fit testing. While the Smirnov-Birnbaum-Tingey formula for the CDF
appears straight forward, its numerical evaluation generates intermediate
results spanning many hundreds of orders of magnitude and at times requires
very precise accurate representations. Computing the quantile function for any
specific probability may require evaluating both the CDF and its derivative,
both of which are computationally expensive. To work around avoid these issues,
different algorithms can be used across different parts of the domain, and
approximations can be used to reduce the computational requirements. We show
here that straight forward implementation incurs accuracy loss for sample sizes
of well under 1000. Further the approximations in use inside the open source
SciPy python software often result in increased computation, not just reduced
accuracy, and at times suffer catastrophic loss of accuracy for any sample
size. Then we provide alternate algorithms which restore accuracy and
efficiency across the whole domain.


  This survey is on forward-looking, emerging security concerns in post-quantum
era, i.e., the implementation attacks for 2022 winners of NIST post-quantum
cryptography (PQC) competition and thus the visions, insights, and discussions
can be used as a step forward towards scrutinizing the new standards for
applications ranging from Metaverse, Web 3.0 to deeply-embedded systems. The
rapid advances in quantum computing have brought immense opportunities for
scientific discovery and technological progress; however, it poses a major risk
to today's security since advanced quantum computers are believed to break all
traditional public-key cryptographic algorithms. This has led to active
research on PQC algorithms that are believed to be secure against classical and
powerful quantum computers. However, algorithmic security is unfortunately
insufficient, and many cryptographic algorithms are vulnerable to side-channel
attacks (SCA), where an attacker passively or actively gets side-channel data
to compromise the security properties that are assumed to be safe
theoretically. In this survey, we explore such imminent threats and their
countermeasures with respect to PQC. We provide the respective, latest
advancements in PQC research, as well as assessments and providing visions on
the different types of SCAs.


  This is a continuation of developing mutation theory in exact WKB analysis
using the framework of cluster algebras. Here we study the Schrodinger equation
on a compact Riemann surface with turning points of simple-pole type. We show
that the orbifold triangulations by Felikson, Shapiro, and Tumarkin provide a
natural framework of describing the mutation of Stokes graphs, where simple
poles correspond to orbifold points. We then show that under the mutation of
Stokes graphs around simple poles the Voros symbols mutate as the variables of
generalized cluster algebras introduced by Chekhov and Shapiro.


  The gravitational radiation generated by a particle in a close unbounded
orbit around a neutron star is computed as a means to study the importance of
the $w$ modes of the neutron star. For simplicity, attention is restricted to
odd parity (``axial'') modes which do not couple to the neutron star's fluid
modes. We find that for realistic neutron star models, particles in unbounded
orbits only weakly excite the $w$ modes; we conjecture that this is also the
case for astrophysically interesting sources of neutron star perturbations. We
also find that for cases in which there is significant excitation of quadrupole
$w$ modes, there is comparable excitation of higher multipole modes.


  Long-term slow-slip events (LSSEs) usually occur on the deep, shallow parts
of subducting plates and have substantial relation with adjacent megathrust
fault motion. Conventional techniques of quantifying slow earthquake frictional
features show that these features may be indicative of predictive seismic
motion; however, quantifying high-accuracy uncertainty of the frictional fields
has not yet been achieved. We therefore propose a method of uncertainty
quantification for spatially inhomogeneous frictional features from slip motion
on an LSSE fault--megathrust fault complex in southwestern Japan. By combining
a fault motion model that mimics slow-slip motion and a variational data
assimilation (DA) technique using a second-order adjoint method, we have
succeeded in quantifying the spatial distribution of the uncertainty of the
frictional features. Further, evaluation of the spatial distribution in
high-resolution reveals the correlation between the dynamics of the slow-slip
motion and the important components of the frictional features, which is
valuable information for observational DA design. Findings from this study are
expected to advance the theoretical foundation of applied seismic motion
prediction techniques using slow-slip frictional features as stress meters for
megaquakes, as well as improve understanding of the relationship between the
slow-slip motion and frictional parameters of a fault.


  We propose to measure azimuthal correlations of heavy-flavor hadrons to
address the status of thermalization at the partonic stage of light quarks and
gluons in high-energy nuclear collisions. In particular, we show that hadronic
interactions at the late stage cannot significantly disturb the initial
back-to-back azimuthal correlations of DDbar pairs. Thus, a decrease or the
complete absence of these initial correlations does indicate frequent
interactions of heavy-flavor quarks and also light partons in the partonic
stage, which are essential for the early thermalization of light partons.


  If there is explicit violation of baryon plus lepton number at some energy
scale, then the electroweak theory depends upon a {\theta}-angle. Due to a
singular integration over small scale size instantons, this {\theta}-dependence
is sensitive to very high momentum scales. Assuming that there is no new
physics between the electroweak and Planck scales, for an electroweak axion the
energy difference between the vacuum at {\theta} /= 0, and that at {\theta} =
0, is of the correct order of magnitude to be the dark energy observed in the
present epoch.


  Non-Hermitian Hamiltonians and Lindblad operators are some of the most
important generators of dynamics for describing quantum systems interacting
with different kinds of environments. The first type differs from conservative
evolution by an anti-Hermitian term that causes particle decay, while the
second type differs by a dissipation operator in Lindblad form that allows
energy exchange with a bath. However, although under some conditions the two
types of maps can be used to describe the same observable, they form a disjoint
set. In this work, we propose a generalized generator of dynamics of the form
$L_\text{mixed}(z,\rho_S) = -i[H,\rho_S] + \sum_i
\left(\frac{\Gamma_{c,i}}{z+\Gamma_{c,i}}F_i\rho_S F_i^{\dagger} -\frac{1}{2}
\{F_i^{\dagger} F_i,\rho_S \}_+\right)$ that depends on a general energy $z$,
and has a tunable parameter $\Gamma_c$ that determines the degree of particle
density lost. It has as its limits non-Hermitian ($\Gamma_c \to 0$) and
Lindbladian dynamics ($\Gamma_c \to \infty$). The intermediate regime evolves
density matrices such that $0 \leq \text{Tr} (\rho_S) \leq 1$. We derive our
generator with the help of an ancillary continuum manifold acting as a sink for
particle density. The evolution describes a system that can exchange both
particle density and energy with its environment. We illustrate its features
for a two level system and a five $M$ level system with a coherent population
trapping point.


  A class of the symmetrically frustrated SU(N) models is constructed for
quantum magnets based on the generators of SU(N) group. The total Hamiltonian
lacks SU(N) symmtry. A mean field theory in the quasi-particle representation
is developed for spin liquid states. Numerical solutions in two dimension
indicate that the ground states are gapless and the quasi-particles are Dirac
particles. The mechanism may be helpful in exploring the spin liquid phases in
the spin-1 bilinear-biquadratic model and the spin-orbital model in higher
dimensions.


  Ahlswede and Khachatrian's diametric theorem is a weighted version of their
complete intersection theorem, itself an extension of the $t$-intersecting
Erd\H{o}s-Ko-Rado theorem. Their intersection theorem says that the maximum
size of a family of subsets of $[n] = \{1, \dots, n\}$, every pair of which
intersects in at least $t$ elements, is the size of certain trivially
intersecting families proposed by Frankl. We address a cross intersecting
version of their diametric theorem.
  Two families $\mathcal{A}$ and $\mathcal{B}$ of subsets of $[n]$ are {\em
cross $t$-intersecting} if for every $A \in \mathcal{A}$ and $B \in
\mathcal{B}$, $A$ and $B$ intersect in at least $t$ elements. The $p$-weight of
a $k$ element subset $A$ of $[n]$ is $p^{k}(1-p)^{n-k}$, and the weight of a
family $\mathcal{A}$ is the sum of the weights of its sets. The weight of a
pair of families is the product of the weights of the families.
  The maximum $p$-weight of a $t$-intersecting family depends on the value of
$p$. Ahlswede and Khachatrian showed that for $p$ in the range $[\frac{r}{t +
2r - 1}, \frac{r+1}{t + 2r + 1}]$, the maximum $p$-weight of a $t$-intersecting
family is that of the family $\mathcal{F}^t_r$ consisting of all subsets of
$[n]$ containing at least $t+r$ elements of the set $[t+2r]$.
  In a previous paper we showed a cross $t$-intersecting version of this for
large $t$ in the case that $r = 0$. In this paper, we do the same in the case
that $r = 1$. We show that for $p$ in the range $[\frac{1}{t + 1}, \frac{2}{t +
3}]$ the maximum $p$-weight of a cross $t$-intersecting pair of families, for
$t \geq 200$, is achieved when both families are $\mathcal{F}^t_1$. Further, we
show that except at the endpoints of this range, this is, up to isomorphism,
the only pair of $t$-intersecting families achieving this weight.


  The environment, especially water, gets polluted due to industrialization and
urbanization. Pollution due to industrialization and urbanization has harmful
effects on both the environment and the lives on Earth. This polluted water can
cause food poisoning, diarrhea, short-term gastrointestinal problems,
respiratory diseases, skin problems, and other serious health complications. In
a developing country like Bangladesh, where ready-made garments sector is one
of the major sources of the total Gross Domestic Product (GDP), most of the
wastes released from the garment factories are dumped into the nearest rivers
or canals. Hence, the quality of the water of these bodies become very
incompatible for the living beings, and so, it has become one of the major
threats to the environment and human health. In addition, the amount of fish in
the rivers and canals in Bangladesh is decreasing day by day as a result of
water pollution. Therefore, to save fish and other water animals and the
environment, we need to monitor the quality of the water and find out the
reasons for the pollution. Real-time monitoring of the quality of water is
vital for controlling water pollution. Most of the approaches for controlling
water pollution are mainly biological and lab-based, which takes a lot of time
and resources. To address this issue, we developed an Internet of Things
(IoT)-based real-time water quality monitoring system, integrated with a mobile
application. The proposed system in this research measures some of the most
important indexes of water, including the potential of hydrogen (pH), total
dissolved solids (TDS), and turbidity, and temperature of water. The proposed
system results will be very helpful in saving the environment, and thus,
improving the health of living creatures on Earth.


  Simulation speed matters for neuroscientific research: this includes not only
how quickly the simulated model time of a large-scale spiking neuronal network
progresses, but also how long it takes to instantiate the network model in
computer memory. On the hardware side, acceleration via highly parallel GPUs is
being increasingly utilized. On the software side, code generation approaches
ensure highly optimized code, at the expense of repeated code regeneration and
recompilation after modifications to the network model. Aiming for a greater
flexibility with respect to iterative model changes, here we propose a new
method for creating network connections interactively, dynamically, and
directly in GPU memory through a set of commonly used high-level connection
rules. We validate the simulation performance with both consumer and data
center GPUs on two neuroscientifically relevant models: a cortical microcircuit
of about 77,000 leaky-integrate-and-fire neuron models and 300 million static
synapses, and a two-population network recurrently connected using a variety of
connection rules. With our proposed ad hoc network instantiation, both network
construction and simulation times are comparable or shorter than those obtained
with other state-of-the-art simulation technologies, while still meeting the
flexibility demands of explorative network modeling.


  Correlation of interacting particles is studied in their dynamics and
localization in ideal and disordered lattice systems with the help of numerical
tools. Both 1D and 2D systems are considered. In 1D lattices with long-range
hopping, differences between dynamics of attractively and repulsively
interacting particles are noted. For calculations of 2D systems, a recursion
based numerical algorithm for two-particle Greens functions is implemented
which provides accurate results. Using this algorithm, spectral properties of
2D Hubbard and Hofstadter models is computed, along with localization
parameters of 2D disordered systems.


  The solution of a sparse system of linear equations is ubiquitous in
scientific applications. Iterative methods, such as the Preconditioned
Conjugate Gradient method (PCG), are normally chosen over direct methods due to
memory and computational complexity constraints. However, the efficiency of
these methods depends on the preconditioner utilized. The development of the
preconditioner normally requires some insight into the sparse linear system and
the desired trade-off of generating the preconditioner and the reduction in the
number of iterations. Incomplete factorization methods tend to be black box
methods to generate these preconditioners but may fail for a number of reasons.
These reasons include numerical issues that require searching for adequate
scaling, shifting, and fill-in while utilizing a difficult to parallelize
algorithm. With a move towards heterogeneous computing, many sparse
applications find GPUs that are optimized for dense tensor applications like
training neural networks being underutilized. In this work, we demonstrate that
a simple artificial neural network trained either at compile time or in
parallel to the running application on a GPU can provide an incomplete sparse
Cholesky factorization that can be used as a preconditioner. This generated
preconditioner is as good or better in terms of reduction of iterations than
the one found using multiple preconditioning techniques such as scaling and
shifting. Moreover, the generated method also works and never fails to produce
a preconditioner that does not reduce the iteration count.


  We demonstrate active embedded microfluidic cooling in $\beta$-Ga$_2$O$_3$.
We employ a cost-effective infra-red laser etch setup to achieve controlled
etching of micro-channels in 500 um thick $\beta$-Ga$_2$O$_3$ substrate. The
micro-channels are about 210 um deep and 340 um wide. Resistive heating is used
as proof-of-concept. At a water flow rate of 50 ml/min, a 50% reduction in
surface temperature from ~140$^\circ$C to ~72$^\circ$C is achieved for 3.5 W of
input power. The experimental observations are backed by thermal simulation.
This work is expected to lead to a new paradigm in thermal management in
emerging $\beta$-Ga$_2$O$_3$ devices.


  Motivated by the reported peculiar dynamics of a red blood cell in shear
flow, we develop an analytical theory for the motion of a nearly--spherical
fluid particle enclosed by a visco--elastic incompressible interface in linear
flows. The analysis explains the effect of particle deformability on the
transition from tumbling to swinging as the shear rate increases. Near the
transition, intermittent behavior is predicted only if the particle has a fixed
shape; the intermittency disappears for a deformable particle. Comparison with
available phenomenological models based on the fixed shape assumption
highlights their physical foundations and limitations.


  These informal notes concern some basic themes of harmonic analysis related
to representations of groups.


  We investigate the relation of the stellar initial mass function (IMF) and
the dense core mass function (CMF), using stellar masses and positions in 14
well-studied young groups. Initial column density maps are computed by
replacing each star with a model initial core having the same star formation
efficiency (SFE). For each group the SFE, core model, and observational
resolution are varied to produce a realistic range of initial maps. A
clumpfinding algorithm parses each initial map into derived cores, derived core
masses, and a derived CMF. The main result is that projected blending of
initial cores causes derived cores to be too few and too massive. The number of
derived cores is fewer than the number of initial cores by a mean factor 1.4 in
sparse groups and 5 in crowded groups. The mass at the peak of the derived CMF
exceeds the mass at the peak of the initial CMF by a mean factor 1.0 in sparse
groups and 12.1 in crowded groups. These results imply that in crowded young
groups and clusters, the mass distribution of observed cores may not reliably
predict the mass distribution of protostars which will form in those cores.


  A feasibility demonstration of three-dimensional (3D) muon tomography was
performed for infrastructure equivalent targets using the proposed portable
muography detector. For the target, we used two sets of lead blocks placed at
different heights. The detector consists of two muon position-sensitive
detectors, made of plastic scintillating fibers (PSFs) and multi-pixel photon
counters (MPPCs) with an angular resolution of 8 msr. The maximum
likelihood-expectation maximization (ML-EM) method was used for the 3D imaging
reconstruction of the muography simulation and measurement. For both simulation
and experiment, the reconstructed positions of the blocks produce consistent
results with prior knowledge of the blocks' arrangement. This result
demonstrates the potential of the 3D tomographic imaging of infrastructure by
using eleven detection positions for portable muography detectors to image
infrastructure scale targets.


  The decoherence of a localized electron spin in a lattice of nuclear spins is
an important problem for potential solid-state implementations of a quantum
computer. We demonstrate that even at high fields, virtual electron spin-flip
processes due solely to the hyperfine interaction can lead to complex nuclear
spin dynamics. These dynamics, in turn, can lead to single electron spin phase
fluctuation and decoherence. We show here that remarkably, a spin echo pulse
sequence can almost completely reverse these nuclear dynamics except for a
small visibility loss, thereby suppressing contribution of the hyperfine
interaction to T_2 processes. For small systems, we present numerical evidence
which demonstrates a universal scaling of the magnitude of visibility loss that
depends only on the inhomogeneous line width of the system and the magnetic
field.


  Large multi-tenant production clusters often have to handle a variety of jobs
and applications with a variety of complex resource usage characteristics. It
is non-trivial and non-optimal to manually create placement rules for
scheduling that would decide which applications should co-locate. In this
paper, we present DeepPlace, a scheduler that learns to exploits various
temporal resource usage patterns of applications using Deep Reinforcement
Learning (Deep RL) to reduce resource competition across jobs running in the
same machine while at the same time optimizing for overall cluster utilization.


  In this paper, we present a result similar to the shift-coupling result of
Thorisson (1996) in the context of random graphs and networks. The result is
that a given random rooted network can be obtained by changing the root of
another given one if and only if the distributions of the two agree on the
invariant sigma-field. Several applications of the result are presented for the
case of unimodular networks. In particular, it is shown that the distribution
of a unimodular network is uniquely determined by its restriction to the
invariant sigma-filed. Also, the theorem is applied to the existence of an
invariant transport kernel that balances between two given (discrete) measures
on the vertices. An application is the existence of a so called extra head
scheme for the Bernoulli process on an infinite unimodular graph. Moreover, a
construction is presented for balancing transport kernels that is a
generalization of the Gale-Shapley stable matching algorithm in bipartite
graphs. Another application is on a general method that covers the situations
where some vertices and edges are added to a unimodular network and then, to
make it unimodular, the probability measure is biased and then a new root is
selected. It is proved that this method provides all possible
unimodularizations in these situations. Finally, analogous existing results for
stationary point processes and unimodular networks are discussed in detail.


  We identify the dessin partition function with the partition function of the
Laguerre unitary ensemble (LUE). Combined with the result due to Cunden et al
on the relationship between the LUE correlators and strictly monotone Hurwitz
numbers introduced by Goulden et al, we then establish connection of dessin
counting to strictly monotone Hurwitz numbers. We also introduce a correction
factor for the dessin/LUE partition function, which plays an important role in
showing that the corrected dessin/LUE partition function is a tau-function of
the Toda lattice hierarchy. As an application, we use the approach of Dubrovin
and Zhang for the computation of the dessin correlators. In physicists'
terminology, we establish dualities among dessin counting, generalized Penner
model, and $\mathbb{P}^1$-topological sigma model.


  The present work is devoted to characterize the family of symmetric
undirected Cayley graphs over finite Abelian groups for degrees 4 and 6.


  Style transfer for human face has been widely researched in recent years.
Majority of the existing approaches work in 2D image domain and have 3D
inconsistency issue when applied on different viewpoints of the same face. In
this paper, we tackle the problem of 3D face style transfer which aims at
generating stylized novel views of a 3D human face with multi-view consistency.
We propose to use a neural radiance field (NeRF) to represent 3D human face and
combine it with 2D style transfer to stylize the 3D face. We find that directly
training a NeRF on stylized images from 2D style transfer brings in 3D
inconsistency issue and causes blurriness. On the other hand, training a NeRF
jointly with 2D style transfer objectives shows poor convergence due to the
identity and head pose gap between style image and content image. It also poses
challenge in training time and memory due to the need of volume rendering for
full image to apply style transfer loss functions. We therefore propose a
hybrid framework of NeRF and mesh rasterization to combine the benefits of high
fidelity geometry reconstruction of NeRF and fast rendering speed of mesh. Our
framework consists of three stages: 1. Training a NeRF model on input face
images to learn the 3D geometry; 2. Extracting a mesh from the trained NeRF
model and optimizing it with style transfer objectives via differentiable
rasterization; 3. Training a new color network in NeRF conditioned on a style
embedding to enable arbitrary style transfer to the 3D face. Experiment results
show that our approach generates high quality face style transfer with great 3D
consistency, while also enabling a flexible style control.


  This article discusses some trends and concepts in developing new generation
of future Artificial General Intelligence (AGI) systems which relate to complex
facets and different types of human intelligence, especially social, emotional,
attentional and ethical intelligence. We describe various aspects of multiple
human intelligences and learning styles, which may impact on a variety of AI
problem domains. Using the concept of 'multiple intelligences' rather than a
single type of intelligence, we categorize and provide working definitions of
various AGI depending on their cognitive skills or capacities. Future AI
systems will be able not only to communicate with human users and each other,
but also to efficiently exchange knowledge and wisdom with abilities of
cooperation, collaboration and even co-creating something new and valuable and
have meta-learning capacities. Multi-agent systems such as these can be used to
solve problems that would be difficult to solve by any individual intelligent
agent.
  Key words: Artificial General Intelligence (AGI), multiple intelligences,
learning styles, physical intelligence, emotional intelligence, social
intelligence, attentional intelligence, moral-ethical intelligence, responsible
decision making, creative-innovative intelligence, cognitive functions,
meta-learning of AI systems.


  We study the solutions of the interacting Fermionic cellular automaton
introduced in Ref. [Phys Rev A 97, 032132 (2018)]. The automaton is the
analogue of the Thirring model with both space and time discrete. We present a
derivation of the two-particles solutions of the automaton, which exploits the
symmetries of the evolution operator. In the two-particles sector, the
evolution operator is given by the sequence of two steps, the first one
corresponding to a unitary interaction activated by two-particle excitation at
the same site, and the second one to two independent one-dimensional Dirac
quantum walks. The interaction step can be regarded as the discrete-time
version of the interacting term of some Hamiltonian integrable system, such as
the Hubbard or the Thirring model. The present automaton exhibits scattering
solutions with nontrivial momentum transfer, jumping between different regions
of the Brillouin zone that can be interpreted as Fermion-doubled particles, in
stark contrast with the customary momentum-exchange of the one dimensional
Hamiltonian systems. A further difference compared to the Hamiltonian model is
that there exist bound states for every value of the total momentum, and even
for vanishing coupling constant. As a complement to the analytical derivations
we show numerical simulations of the interacting evolution.


  Circadian rhythms synchronize a variety of physiological processes ranging
from neural activity and hormone secretion to sleep cycles and feeding habits.
Despite significant diurnal variation, time-of-day (TOD) is rarely recorded or
analyzed in human brain research. Moreover, sleep-wake patterns, diurnal
preferences, and daytime alertness vary across individuals, known as sleep
chronotypes. Here, we performed graph-theory network analysis on resting-state
functional MRI (rs-fMRI) data to explore topological differences in whole-brain
functional networks between morning and evening sessions (TOD effect), and
between extreme morning-type and evening-type chronotypes. To that end, 62
individuals (31 extreme morning, 31 evening-type) underwent two fMRI sessions:
about 1 hour after the wake-up time (morning), and 10 hours thereafter,
scheduled in accord with their declared habitual sleep-wake pattern. TOD
significantly altered functional connectivity (FC) patterns, but there was no
significant difference in chronotypic categories. Compared to the morning
session, we found relatively increased small-worldness, modularity,
assortativity, and synchronization in the evening session, indicating more
efficient functional topology. Local measures were changed during the day
predominantly across the areas involved in somatomotor, ventral attention, as
well as default mode networks. Also, connectivity and hub analyses showed that
the somatomotor, ventral attention, and visual networks are the most
densely-connected brain areas in both sessions, respectively, with the first
being more active in the evening session and the two latter in the morning
session. Collectively, these findings suggest TOD can impact classic analyses
used in human neuroimaging such as functional connectivity, and should be
recorded and included in the analysis of functional neuroimaging data.


  We consider the problem of finding paths of shortest transit time between two
points (popularly known as Brachistochrone) for cylinders with off-centered
center of mass, rolling down without slip, subject solely to the force of
gravity. This problem is set up using principles of classical rigid body
dynamics and the desired path function is solved for numerically using the
method of discrete calculus of variations. We discover a distinct array of
brachistochrone trajectories for off-centered cylinders, demonstrate a critical
dependence of such paths on the initial location and orientation of cylinders'
centers of mass and bring new insights into the family of brachistochrone
problems and solutions.


  We review the consistent histories formulations of quantum mechanics
developed by Griffiths, Omn\`es and Gell-Mann and Hartle, and describe the
classification of consistent sets. We illustrate some general features of
consistent sets by a few simple lemmas and examples. We consider various
interpretations of the formalism, and examine the new problems which arise in
reconstructing the past and predicting the future. It is shown that Omn\`es'
characterisation of true statements --- statements which can be deduced
unconditionally in his interpretation --- is incorrect. We examine critically
Gell-Mann and Hartle's interpretation of the formalism, and in particular their
discussions of communication, prediction and retrodiction, and conclude that
their explanation of the apparent persistence of quasiclassicality relies on
assumptions about an as yet unknown theory of experience. Our overall
conclusion is that the consistent histories approach illustrates the need to
supplement quantum mechanics by some selection principle in order to produce a
fundamental theory capable of unconditional predictions.


  Question and answer (Q&A) forums contain valuable information regarding
software reuse, but they can be challenging to analyse due to their
unstructured free text. Here we introduce a new approach (LANLAN), using word
embeddings and machine learning, to harness information available in
StackOverflow. Specifically, we consider two different kinds of user
communication describing difficulties encountered in software reuse: 'problem
reports' point to potential defects, while 'support requests' ask for
clarification on software usage. Word embeddings were trained on 1.6 billion
tokens from StackOverflow and applied to identify which Q&A forum messages
(from two large open source projects: Eclipse and Bioconductor) correspond to
problem reports or support requests. LANLAN achieved an area under the receiver
operator curve (AUROC) of over 0.9; it can be used to explore the relationship
between software reusability metrics and difficulties encountered by users, as
well as predict the number of difficulties users will face in the future. Q&A
forum data can help improve understanding of software reuse, and may be
harnessed as an additional resource to evaluate software reusability metrics.


  The 3C 220.3 system is a rare case of a foreground narrow-line radio galaxy
("galaxy A," $z_A = 0.6850$) lensing a background submillimeter galaxy
($z_{{\rm SMG1}} = 2.221$). New spectra from MMT/Binospec confirm that the
companion galaxy ("galaxy B") is part of the lensing system with $z_B =
0.6835$. New three-color HST data reveal a full Einstein ring and allow a more
precise lens model. The new HST images also reveal extended emission around
galaxy A, and the spectra show extended [OII] emission with irregular
morphology and complex velocity structure. All indications are that the two
lensing galaxies are a gravitationally interacting pair. Strong [OII] emission
from both galaxies A and B suggests current star formation, which could be a
consequence of the interaction. This would indicate a younger stellar
population than previously assumed and imply smaller stellar masses for the
same luminosity. The improved lens model and expanded spectral energy
distributions have enabled better stellar-mass estimates for the foreground
galaxies. The resulting dark matter fractions are ~0.8, higher than previously
calculated. Deeper Chandra imaging shows extended X-ray emission but no
evidence for a point X-ray source associated with either galaxy. The detection
of X-rays from the radio lobes of 3C 220.3 allows an estimate of ~3 nT for the
magnetic fields in the lobes, a factor of ~3 below the equipartition fields, as
typical for radio galaxies.


  The failure of adhesive bondlines has been studied at the microscopic level
via tensile tests. Stable crack propagation could be generated by means of
samples with improved geometry, which made in-situ observations possible. The
interaction of cracks with adhesive bondlines under various angles to the crack
propagation was the focus of this study as well as the respective loading
situations for the adhesives UF, PUR, and PVAc, which have distinctly different
mechanical behaviors. It is shown how adhesive properties influence the
occurrence of certain failure mechanisms and determine their appearance and
order of magnitude. With the observed failure mechanisms, it becomes possible
to predict the propagation path of a crack through the specimen.


  Sketch-based streaming algorithms allow efficient processing of big data.
These algorithms use small fixed-size storage to store a summary ("sketch") of
the input data, and use probabilistic algorithms to estimate the desired
quantity. However, in many real-world applications it is impractical to collect
and process the entire data stream, the common practice is thus to sample and
process only a small part of it. While sampling is crucial for handling massive
data sets, it may reduce accuracy. In this paper we present a new framework
that can accurately estimate the cardinality of any set expression between any
number of streams using only a small sample of each stream. The proposed
framework consists of a new sketch, called Maximal-Term with Subsample (MTS),
and a family of algorithms that use this sketch. An example of a possible query
that can be efficiently answered using the proposed sketch is, How many
distinct tuples appear in tables $T_1$ and $T_2$, but not in $T_3$? The
algorithms presented in this paper answer such queries accurately, processing
only a small sample of the tuples in each table and using a constant amount of
memory. Such estimations are useful for the optimization of queries over very
large database systems. We show that all our algorithms are unbiased, and we
analyze their asymptotic variance.


  Natural language processing (NLP) models may leak private information in
different ways, including membership inference, reconstruction or attribute
inference attacks. Sensitive information may not be explicit in the text, but
hidden in underlying writing characteristics. Methods to protect privacy can
involve using representations inside models that are demonstrated not to detect
sensitive attributes or -- for instance, in cases where users might not trust a
model, the sort of scenario of interest here -- changing the raw text before
models can have access to it. The goal is to rewrite text to prevent someone
from inferring a sensitive attribute (e.g. the gender of the author, or their
location by the writing style) whilst keeping the text useful for its original
intention (e.g. the sentiment of a product review). The few works tackling this
have focused on generative techniques. However, these often create extensively
different texts from the original ones or face problems such as mode collapse.
This paper explores a novel adaptation of adversarial attack techniques to
manipulate a text to deceive a classifier w.r.t one task (privacy) whilst
keeping the predictions of another classifier trained for another task
(utility) unchanged. We propose IDT, a method that analyses predictions made by
auxiliary and interpretable models to identify which tokens are important to
change for the privacy task, and which ones should be kept for the utility
task. We evaluate different datasets for NLP suitable for different tasks.
Automatic and human evaluations show that IDT retains the utility of text,
while also outperforming existing methods when deceiving a classifier w.r.t
privacy task.


  We present a non-abelian cousin of the model presented in [1] which induces
cosmological anisotropies on top of standard FLRW geometry. This is in some
sense doing a cosmological mean field approximation, where the mean field
cosmological model under consideration would be the standard FLRW, and the
induced anisotropies are small perturbative corrections on top of it. Here we
mostly focus on the non-abelian $SU(2)$ gauge fields coupled to the gravity to
generate the anisotropies, which can be a viable model for the axion-like
particle (ALP) dark sector. The induced anisotropies are consequences of the
non-trivial back-reaction of the gauge fields on the gravity sector, and by a
clever choice of the parametrization, one can generate the Bianchi model we
have studied in this note. We also show that the anisotropies influence the
Sachs-Wolfe effect and we discuss the implications.


  We have studied the potential barriers for the penetration of atomic
beryllium or boron inside the C60 fullerene by performing ab initio density
functional theory (DFT) calculations with three variants for the exchange and
correlation: B3LYP (hybrid functional), PW91 and PBE. Four principal
trajectories to the inner part of C60 for the penetrating atom have been
considered: through the center of six-member-carbon ring (hexagon),
five-member-carbon ring (pentagon), and also through the center of the double
C-C bond (D-bond) and the center of the single C-C bond (S-bond). Averaging
over the three DFT variants yields the following barriers for beryllium
penetrating inside a deformable fullerene: 3.2 eV (hexagon), 4.8 eV (S-bond),
5.3 eV (D-bond), 5.9~eV (pentagon). These barriers correspond to the slow and
adiabatic penetration of Be, in contrast to the fast (non-adiabatic)
penetration through the rigid cage of C60 resulting in 5.6 eV (hexagon), 16.3
eV (pentagon), 81.8 eV (S-bond) and 93.4 eV (D-bond). The potential barriers
for the boron penetrating inside deformable/rigid C60 are: 3.7/105.4 eV
(D-bond), 4.0/86.8 eV (S-bond), 4.7/7.8 eV (hexagon), 6.8/14.0 eV (pentagon).
The potential barriers for Be and B escaping from the inner part of C$_{60}$
are higher by the value of 0.84 eV for Be and 0.81 eV for B. The considerable
reduction of the potential barriers for the deformable fullerene is ascribed to
the formation of the Be-C and B-C bonds. We discuss the difference between Be
and B, compare three variants of DFT, and analyze the role of the dispersion
interaction.


  Inspired by proposals for continuous-variable quantum approximate
optimization (CV-QAOA), we investigate the utility of continuous-variable
neural network quantum states (CV-NQS) for performing continuous optimization,
focusing on the ground state optimization of the classical antiferromagnetic
rotor model. Numerical experiments conducted using variational Monte Carlo with
CV-NQS indicate that although the non-local algorithm succeeds in finding
ground states competitive with the local gradient search methods, the proposal
suffers from unfavorable scaling. A number of proposed extensions are put
forward which may help alleviate the scaling difficulty.


  The Artin stack $\mathcal M_n$ of $0$-dimensional sheaves of length $n$ on
$\mathbb A^3$ carries two natural d-critical structures in the sense of Joyce.
One comes from its description as a quotient stack
$[\textrm{crit}(f_n)/\textrm{GL}_n]$, another comes from derived deformation
theory of sheaves. We show that these d-critical structures agree. We use this
result to prove the analogous statement for the Quot scheme of points
$\textrm{Quot}_{\mathbb A^3}(\mathscr O^{\oplus r},n) =
\textrm{crit}(f_{r,n})$, which is a global critical locus for every $r>0$, and
also carries a derived-in-flavour d-critical structure besides the one induced
by the potential $f_{r,n}$. Again, we show these two d-critical structures
agree. Moreover, we prove that they locally model the d-critical structure on
$\textrm{Quot}_X(F,n)$, where $F$ is a locally free sheaf of rank $r$ on a
projective Calabi-Yau $3$-fold $X$.
  Finally, we prove that the perfect obstruction theory on
$\textrm{Hilb}^n\mathbb A^3=\textrm{crit}(f_{1,n})$ induced by the Atiyah class
of the universal ideal agrees with the critical obstruction theory induced by
the Hessian of the potential $f_{1,n}$.


  The main goals of the present work are (i) to study the substructure content
of a representative, X-ray selected sample of 31 galaxy clusters, as traced by
the spatial distribution of their red-sequence galaxies, and (ii) to compare it
to that observed in the intracluster medium distribution. Our substructure
indicators are the asymmetry test, $\beta$, the residuals of the galaxy surface
density map, $\Delta_\Sigma$, and the Fourier elongation, $FE$. We probe the
clusters core with secondary tests: the offset between the central brightest
cluster galaxy and the X-ray emission peak, $\Delta r_{\mathrm{BCG-X}}$, the
magnitude offset between the first and second brightest galaxies, $\Delta
m_{12}$, and their radial offset, $\Delta r_{12}$. The main indicators exhibit
continuous distributions, making a discrete classification difficult. A
partition based on $\beta$ and $\Delta_\Sigma$ gives a fraction $\sim35\%$ of
disturbed systems; $\sim65\%$ of the clusters are disturbed according to at
least one of these two quantities. The main indicators poorly correlate with
the secondary quantities, likely due to substructures observed prior core
crossing. Nine of the 12 X-ray disturbed clusters are also flagged as such by
either $\beta$ or $\Delta_\Sigma$. Cool cores are hosted by systems with higher
optical concentrations, smaller $\Delta r_{\mathrm{BCG-X}}$, and larger $\Delta
r_{12}$; however, both populations do not differ significantly in terms of
their overall morphology probed by the main indicators. Our results show the
necessity of using a variety of independent tests and data sets to obtain a
clear picture of a cluster's morphology. Furthermore, we find that a cluster's
dynamical state, consequence of its recent merger history, is not necessarily
representative of a future mass assembly via accretion of substructures.


  We study how a Reinforcement Learning (RL) system can remain sample-efficient
when learning from an imperfect model of the environment. This is particularly
challenging when the learning system is resource-constrained and in continual
settings, where the environment dynamics change. To address these challenges,
our paper introduces an online, meta-gradient algorithm that tunes a
probability with which states are queried during Dyna-style planning. Our study
compares the aggregate, empirical performance of this meta-gradient method to
baselines that employ conventional sampling strategies. Results indicate that
our method improves efficiency of the planning process, which, as a
consequence, improves the sample-efficiency of the overall learning process. On
the whole, we observe that our meta-learned solutions avoid several pathologies
of conventional planning approaches, such as sampling inaccurate transitions
and those that stall credit assignment. We believe these findings could prove
useful, in future work, for designing model-based RL systems at scale.


  Learning quantum mechanics is challenging, even for upper-level undergraduate
and graduate students. Interactive tutorials which build on students' prior
knowledge can be useful tools to enhance student learning. We have been
investigating student difficulties with the quantum mechanics pertaining to the
double-slit experiment in various situations. Here we discuss the development
and evaluation of a Quantum Interactive Learning Tutorial (QuILT) which makes
use of an interactive simulation to improve student understanding. We summarize
common difficulties and discuss the extent to which the QuILT is effective in
addressing them in two types of courses.


  For a linear algebraic group $G$ over a field $k$, we define an equivariant
version of the Voevodsky's motivic cobordism $MGL$. We show that this is an
oriented cohomology theory with localization sequence on the category of smooth
$G$-schemes and there is a natural transformation from this functor to the
functor of equivariant motivic cohomology. We give several applications. In
particular, we use this equivariant motivic cobordism to study the cobordism
ring of the classifying spaces and the cycle class maps from the algebraic to
the singular cohomology of such spaces. This theory of motivic cobordism allows
us to define the theory of motivic cobordism on the category of all smooth
quotient stacks.


  The Kamioka Liquid scintillator Anti-Neutrino Detector (KamLAND) is used in a
search for single neutron or two neutron intra-nuclear disappearance that would
produce holes in the $\it{s}$-shell energy level of $^{12}$C nuclei. Such holes
could be created as a result of nucleon decay into invisible modes ($inv$),
e.g. $n \to 3\nu$ or $nn \to 2\nu$. The de-excitation of the corresponding
daughter nucleus results in a sequence of space and time correlated events
observable in the liquid scintillator detector. We report on new limits for
one- and two-neutron disappearance: $\tau(n\to inv)> 5.8\times 10^{29}$ years
and $\tau (nn \to inv)> 1.4 \times 10^{30}$ years at 90% CL. These results
represent an improvement of factors of $\sim$3 and $>10^4$ over previous
experiments.


  No woman of science has lived a more controversial life nor possessed a most
contrasting character than Gabrielle Emilie Le Tonnelier, Marquise du Chatelet.
One one hand, she was a woman of great intelligence, a philosopher of science,
a student of mathematics, and she was an ardent supporter of Newton and his new
laws of physics. At the same time, Emilie du Chatelet was an aristocrat society
woman who gambled, enjoyed parties, and had several extramarital affairs,
provoking numerous scandals in her native Paris. She was a passionate woman who
was at ease conversing with the nobles at the court and with the most renowned
scholars of her time. Emilie du Chatelet did not develop theorems and she did
not discover new scientific principles. However, she studied mathematics with
Maupertuis and Clairaut to better understand the geometrical language in
Newton's Principia. In this article, we review some important aspects of this
controversial woman of science, exploring her relationship with the greatest
scholars of her time.


  In the present paper we prove that every 2-local inner derivation on the
Jordan ring of self-adjoint matrices over a commutative involutive ring is a
derivation. We also apply our technique to various Jordan algebras of infinite
dimensional self-adjoint matrix-valued maps on a set and prove that every
2-local spatial derivation on such algebras is a spatial derivation. It is also
proved that every local spatial derivation on the same Jordan algebras is a
derivation.


  A toric vector bundle $\mathcal{E}$ is a torus equivariant vector bundle on a
toric variety. We give a valuation theoretic and tropical point of view on
toric vector bundles. We present three (equivalent) classifications of toric
vector bundles, which should be regarded as repackagings of the Klyachko data
of compatible $\mathbb{Z}$-filtrations of a toric vector bundle: (1) as
piecewise linear maps to space of $\mathbb{Z}$-valued valuations, (2) as
valuations with values in the semifield of piecewise linear functions, and (3)
as points in tropical linear ideals over the semifield of piecewise linear
functions. Moreover, we interpret the known criteria for ampleness and global
generation of $\mathcal{E}$ as convexity conditions on its piecewise linear map
in (1). Finally, using (2) we associate to $\mathcal{E}$ a collection of
polytopes indexed by elements of a certain (representable) matroid encoding the
dimensions of weight spaces of global sections of $\mathcal{E}$. This recovers
and extends the Di Rocco-Jabbusch-Smith matriod and parliament of polytopes of
$\mathcal{E}$. This is a follow up paper to arXiv:1806.05613.


  In December 2023, the European Parliament provisionally agreed on the EU AI
Act. This unprecedented regulatory framework for AI systems lays out guidelines
to ensure the safety, legality, and trustworthiness of AI products. This paper
presents a methodology for interpreting the EU AI Act requirements for
high-risk AI systems by leveraging product quality models. We first propose an
extended product quality model for AI systems, incorporating attributes
relevant to the Act not covered by current quality models. We map the Act
requirements to relevant quality attributes with the goal of refining them into
measurable characteristics. We then propose a contract-based approach to derive
technical requirements at the stakeholder level. This facilitates the
development and assessment of AI systems that not only adhere to established
quality standards, but also comply with the regulatory requirements outlined in
the Act for high-risk (including safety-critical) AI systems. We demonstrate
the applicability of this methodology on an exemplary automotive supply chain
use case, where several stakeholders interact to achieve EU AI Act compliance.


  Accounting for the anharmonicity of the real interatomic potentials in a
model in 1+1 dimensions shows that coherent 3D islands can be formed on the
wetting layer in a Stranski-Krastanov growth mode predominantly in compressed
overlayers. Coherent 3D islanding in expanded overlayers could be expected as
an exception rather than as a rule. The thermodynamic driving force of
formation of coherent 3D islands on the wetting layer of the same material is
the weaker adhesion of the atoms near the islands edges. The average adhesion
gets weaker with increasing island's thickness but reaches a saturation after
several monolayers. A misfit greater than a critical value is a necessary
condition for coherent 3D islanding. Monolayer height islands with a critical
size appear as necessary precursors of the 3D islands. The 2D-3D transformation
from monolayer-high islands to three-dimensional pyramids takes place through a
series of stable intermediate 3D islands with discretely increasing thickness.


  Multibody dynamics simulators are an important tool in many fields, including
learning and control for robotics. However, many existing dynamics simulators
suffer from inaccuracies when dealing with constrained mechanical systems due
to unsuitable integrators with bad energy behavior and problematic constraint
violations, for example for contact interactions. Variational integrators are
numerical discretization methods that can reduce physical inaccuracies when
simulating mechanical systems, and formulating the dynamics in maximal
coordinates allows for easy and numerically robust incorporation of constraints
such as kinematic loops or contacts. Therefore, this article derives a
variational integrator for mechanical systems with equality and inequality
constraints in maximal coordinates. Additionally, efficient graph-based
sparsity-exploiting algorithms for solving the integrator are provided and
implemented as an open-source simulator. The evaluation of the simulator shows
improved physical accuracy due to the variational integrator and the advantages
of the sparse solvers. Comparisons to minimal-coordinate algorithms show
improved numerical robustness and application examples of a walking robot and
an exoskeleton with explicit constraints demonstrate the necessity and
capabilities of maximal coordinates.


  Necessary and sufficient conditions for the third order Kolmogorov universal
scaling flux laws are derived for the stochastically forced incompressible
Navier-Stokes equations on the torus in 2d and 3d. This paper rigorously
generalizes the result of \cite{bedrossian2019sufficient} to functions which
are heavy-tailed in Fourier space or have local finite time singularities in
the inviscid limit. In other words we have rigorously derived the well known
physical relationship the direct cascade is a local process and is non-trivial
if and only if energy moves toward the small scales or singularities have
occurred. Similarly, an inverse cascade occurs if and only if energy moves
towards the $k = 0$ Fourier mode in the invisicid limit.


  Quantum annealing is a contender to solve combinatorial optimization problems
based on quantum dynamics. While significant efforts have been undertaken to
investigate the quality of the solutions and the required runtimes, much less
attention has been paid to understanding the dynamics of quantum annealing and
the process leading to the solution during the sweep itself. In this
comprehensive study, we investigate various aspects of the quantum annealing
dynamics using different approaches. We perform quantum annealing, simulated
quantum annealing, and classical annealing on several hundred instances of the
Sherrington-Kirkpatrick model with intermediate system sizes up to 22 spins
using numerical simulations. We observe qualitative differences between the
quantum and classical methods, in particular at intermediate times, where a
peak in the fidelity, also known as diabatic bump, appears for hard instances.
Furthermore, we investigate the two-point correlation functions, which feature
differences at intermediate times as well. At short times, however, the methods
are similar again, which can be explained by relating the short-time expansion
of quantum annealing to a high-temperature expansion, thus allowing in
principle to find the classical solution already at short times, albeit at
prohibitive sampling cost.


  Nonlocal Hamiltonian-type operators, like e.g. fractional and
quasirelativistic, seem to be instrumental for a conceptual broadening of
current quantum paradigms. However physically relevant properties of related
quantum systems have not yet received due (and scientifically undisputable)
coverage in the literature. In the present paper we address
Schr\"{o}dinger-type eigenvalue problems for $H=T+V$, where a kinetic term
$T=T_m$ is a quasirelativistic energy operator $T_m = \sqrt{-\hbar ^2c^2 \Delta
+ m^2c^4} - mc^2$ of mass $m\in (0,\infty)$ particle. A potential $V$ we assume
to refer to the harmonic confinement or finite well of an arbitrary depth. We
analyze spectral solutions of the pertinent nonlocal quantum systems with a
focus on their $m$-dependence. Extremal mass $m$ regimes for eigenvalues and
eigenfunctions of $H$ are investigated: (i) $m\ll 1$ spectral affinity
("closeness") with the Cauchy-eigenvalue problem ($T_m \sim T_0=\hbar c |\nabla
|$) and (ii) $m \gg 1$ spectral affinity with the nonrelativistic eigenvalue
problem ($T_m \sim -\hbar ^2 \Delta /2m $). To this end we generalize to
nonlocal operators an efficient computer-assisted method to solve
Schr\"{o}dinger eigenvalue problems, widely used in quantum physics and quantum
chemistry. A resultant spectrum-generating algorithm allows to carry out all
computations directly in the configuration space of the nonlocal quantum
system. This allows for a proper assessment of the spatial nonlocality impact
on simulation outcomes. Although the nonlocality of $H$ might seem to stay in
conflict with various numerics-enforced cutoffs, this potentially serious
obstacle is kept under control and effectively tamed.


  We study graph products of groups from the viewpoint of measured group
theory. We first establish a full measure equivalence classification of graph
products of countably infinite groups over finite simple graphs with no
transvection and no partial conjugation. This finds applications to their
classification up to commensurability, and up to isomorphism, and to the study
of their automorphism groups. We also derive structural properties of von
Neumann algebras associated to probability measure-preserving actions of graph
products. Variations of the measure equivalence classification statement are
given with fewer assumptions on the defining graphs. We also provide a
quantified version of our measure equivalence classification theorem, that
keeps track of the integrability of associated cocycles. As an application, we
solve an inverse problem in quantitative orbit equivalence for a large family
of right-angled Artin groups. We then establish several rigidity theorems.
First, in the spirit of work of Monod-Shalom, we achieve rigidity in orbit
equivalence for probability measure-preserving actions of graph products, upon
imposing extra ergodicity assumptions. Second, we establish a sufficient
condition on the defining graph and on the vertex groups ensuring that a graph
product G is rigid in measure equivalence among torsion-free groups (in the
sense that every torsion-free countable group H which is measure equivalent to
G, is in fact isomorphic to G). Using variations over the Higman groups as the
vertex groups, we construct the first example of a group which is rigid in
measure equivalence, but not in quasi-isometry, among torsion-free groups.


  We prove that the presence of a diagonal assortative degree correlation, even
if small, has the effect of dramatically lowering the epidemic threshold of
large scale-free networks. The correlation matrix considered is
$P(h|k)=(1-r)P^U_{hk}+r\delta_{hk}$, where $P^U$ is uncorrelated and $r$ (the
Newman assortativity coefficient) can be very small. The effect is uniform in
the scale exponent $\gamma$, if the network size is measured by the largest
degree $n$. We also prove that it is possible to construct, via the Porto-Weber
method, correlation matrices which have the same $k_{nn}$ as the $P(h|k)$
above, but very different elements and spectrum, and thus lead to different
epidemic diffusion and threshold. Moreover, we study a subset of the admissible
transformations of the form $P(h|k) \to P(h|k)+\Phi(h,k)$ with $\Phi(h,k)$
depending on a parameter which leave $k_{nn}$ invariant. Such transformations
affect in general the epidemic threshold. We find however that this does not
happen when they act between networks with constant $k_{nn}$, i.e. networks in
which the average neighbor degree is independent from the degree itself (a
wider class than that of strictly uncorrelated networks).


  UAV enabled communications and networking can enhance wireless connectivity
and support emerging services. However, this would require system-level
understanding to modify and extend the existing terrestrial network
infrastructure. In this paper, we integrate UAVs both as user equipment and
base stations into existing LTE-Advanced heterogeneous network (HetNet) and
provide system-level insights of this three-tier LTE-Advanced air-ground HetNet
(AG-HetNet). This AG-HetNet leverages cell range expansion (CRE), ICIC, 3D
beamforming, and enhanced support for UAVs. Using system-level understanding
and through brute-force technique and heuristics algorithms, we evaluate the
performance of AG-HetNet in terms of fifth percentile spectral efficiency
(5pSE) and coverage probability. We compare 5pSE and coverage probability, when
aerial base-stations (UABS) are deployed on a fixed hexagonal grid and when
their locations are optimized using genetic algorithm (GA) and elitist harmony
search algorithm based on genetic algorithm (eHSGA). Our simulation results
show the heuristic algorithms outperform the brute-force technique and achieve
better peak values of coverage probability and 5pSE. Simulation results also
show that trade-off exists between peak values and computation time when using
heuristic algorithms. Furthermore, the three-tier hierarchical structuring of
FeICIC provides considerably better 5pSE and coverage probability than eICIC.


  Magnitude is a numerical invariant of metric spaces introduced by Leinster,
motivated by considerations from category theory. This paper extends the
original definition for finite spaces to compact spaces, in an equivalent but
more natural and direct manner than in previous works by Leinster, Willerton,
and the author. The new definition uncovers a previously unknown relationship
between magnitude and capacities of sets. Exploiting this relationship, it is
shown that for a compact subset of Euclidean space, the magnitude dimension
considered by Leinster and Willerton is equal to the Minkowski dimension.


  In the present work, control of time-optimal trajectory for a Dubins airplane
in presence of moving and fixed obstacles is obtained.
  We show that for a Dubins airplane with an initial position, the control
variable can be obtained using the exact penalty function method so that the
airplane reaches the end position in the shortest time in the presence of
obstacles.


  Security Enhanced Linux (SELinux) is a security architecture for Linux
implementing mandatory access control. It has been used in numerous
security-critical contexts ranging from servers to mobile devices. But this is
challenging as SELinux security policies are difficult to write, understand,
and maintain. Recently, the intermediate language CIL was introduced to foster
the development of high-level policy languages and to write structured
configurations. However, CIL lacks mechanisms for ensuring that the resulting
configurations obey desired information flow policies. To remedy this, we
propose IFCIL, a backward compatible extension of CIL for specifying
fine-grained information flow requirements for CIL configurations. Using IFCIL,
administrators can express, e.g., confidentiality, integrity, and
non-interference properties. We also provide a tool to statically verify these
requirements.